DO FINANCIAL SERVICES BUILD
DISASTER RESILIENCE?
Examining the Determinants of Recovery from Typhoon Yolanda
in the Philippines
Mercy Corps Working Paper | Dan Hudner and Jon Kurtz
ABSTRACT
In November 2013, the Philippines was devastated by Typhoon Yolanda. In response, Mercy Corps initiated
programming aimed at speeding affected households’ economic recovery and promoting their resilience to
future natural disasters. The interventions were designed with the assumption that access to formal financial
products, such as savings accounts, as well as the provision of cash assistance would increase households’
resilience to future shocks. This study analyzed survey data from households in Western Leyte, Philippines
to test this and other common assumptions regarding what characteristics contribute to disaster resilience.
Specifically, it tests the extent to which formal financial products, diversity of income sources, social capital,
and other sources of support are linked to households’ recovery from effects of the typhoon and their
perceived ability to cope with similar disasters in the future.
The analysis found that use of savings among households is positively associated with greater recovery from
Yolanda, while both savings and loans are positively related to families’ perceived ability to manage future
shocks. The results suggest that informal financial tools are as effective as formal ones in supporting disaster
resilience. Both informal assistance from neighbors and formal government aid both are also positively linked
to households’ recovery. Livelihood diversification is not shown to be linked to greater resilience or recovery,
but this may reflect that diversification often occurs due to economic necessity rather than as intentional
risk management in preparation for disasters. The findings shed new light on the potential effectiveness of
increasing access to formal financial services, supporting livelihood diversification, and enhancing bonding
social capital in contributing to household-level resilience to natural disasters.
Do Financial Services Build Disaster Resilience? | MERCY CORPS i
TABLE OF CONTENTS
ABSTRACT
i
1. INTRODUCTION
1
2. RESEARCH QUESTIONS
2
Financial Services
2
Livelihood Diversification and Independence 2
Social Capital and other Forms of Assistance
2
3. METHODOLOGY
3
Sample Selection
3
Dependent Variables: Resilience Outcomes
4
Independent Variables
4
Analytical Methods
5
4.DESCRIPTIVE STATISTICS 5
Dependent Variables
5
Independent Variables 7
5.REGRESSION RESULTS 9
Determinants of Financial Instrument Use
9
Determinants of Recovery and Resilience 11
Financial Services
12
Income Diversification 13
Social Support and Other Sources of Aid
13
Other Factors 14
6.CONCLUSIONS 15
Implications for Programming 15
Sources of Bias 15
Study Limitations 16
Future Research 16
Do Financial Services Build Disaster Resilience? | MERCY CORPS ii
APPENDIX
APPENDIX A Formulation of the Perceived Economic Resilience Index 18
18
APPENDIX B
Formulation of the Coping Strategies Index (CSI) 19
19
APPENDIX C Definitions of Formal and Informal Financial Instruments 20
20
APPENDIX D
Full Regression Models 21
21
LIST OF FIGURES
Figure 1: Conceptual Framework
3
Figure 2: Self-reported Recovery from Yolanda 5
Figure 3: Self-reported Ability to Cope with Future Disasters 5
Figure 4: Perceived Economic Resilience Index 6
Figure 5: Coping Strategies Index 6
Figure 6: Use of Financial Instruments Prior to Yolanda 7
Figure 7: Sources of Income by Economic Activity 7
Figure 8: Sources of Relief and Recovery Support Post-Yolanda 8
Figure 9: Type of Aid Received Post-Yolanda by Source 8
Figure 10: Severity of Storm Damage by Municipality 9
Figure 11: Logistic Regressions Results on Predictors of Use of Financial Instruments 10
Figure 12: Regression Results on Relationships between Households Characteristics and Resilience Outcomes
11
Do Financial Services Build Disaster Resilience? | MERCY CORPS iii
1. INTRODUCTION
Located on the typhoon belt of the Pacific Ocean, the Philippines is struck by approximately 20 typhoons
every year. As of 2012, more than 30% of the population was engaged in the agriculture sector, making
their livelihoods particularly vulnerable to the effects of these frequent typhoons12. In Eastern Visayas, the
region where Leyte is located, and where this study was conducted, 70% of cities and municipalities had no
commercial banking presence at the end of 2011.3 However, mobile penetration is extensive throughout the
country, with an estimated 100 million SIM cards for the population of 105 million.4
Within this context, in November 2013 Typhoon Yolanda (also known as Typhoon Haiyan locally) struck the
Visayas region of the Philippines. The deadliest recorded storm in Philippine history, Yolanda killed 6,500 and
affected 14 million, displacing more than 4 million. Farmers in the Leyte region were struck especially hard.
In response, Mercy Corps initiated programming aimed at speeding affected households’ economic recovery
and promoting their resilience to future natural disasters. For the purposes of the program and this study,
resilience is defined as “the capacity that ensures adverse stressors and shocks do not have long-lasting
adverse development consequences.”5 Using this definition, resilient households may suffer property damage
and other losses to natural disasters, but in the aftermath will be able to meet their needs of food and shelter
without sacrificing investments in productive assets, education, or their own health. As a result, they will be able
to recover assets and restore their livelihoods more quickly, without extensive humanitarian intervention.
As part of its recovery programming, Mercy Corps has leveraged its partnership with BPI Globe BanKO, the
only mobile-based microfinance bank in the Philippines to deliver initial unconditional electronic cash transfers
and access to formal savings accounts and other financial services to the most severely affected families.
BanKO also has plans to offer a suite of other financial services, including loans and insurance products, to this
population in 2015.
This research is intended to examine the main assumptions underlying Mercy Corps’ programs – and similar
interventions conducted by actors – aimed at strengthening households’ and communities’ resilience to recurrent
natural disasters. The analysis uses household data collected during the program baseline to estimate the
relationships between resilience and use of financial instruments, livelihood diversity, social capital, and sources
of aid. The study takes advantage of pre-existing variation in these factors prior to the beginning of Mercy
Corps’ program activities. Resilience is demonstrated by recovery from the effects of Yolanda and perceived
ability to handle future shocks and disasters. The findings shed new light on the potential effectiveness of
increasing access to financial services, supporting livelihood diversification, and enhancing bonding social
capital in contributing to household-level resilience to natural disasters. This initial analysis will be augmented
through additional studies, including a randomized control trial evaluating the effects of Mercy Corps’ program
on households’ recovery.
1
2
3
4
5
Philippine Statistics Authority, 2012
Bangko Sentral Ng Pilipinas, “Report on the State of Financial Inclusion in the Philippines”, 2011
Mobile & Online, http://mobileandonline.com/mobile-marketing/smartphone-users-in-the-philippines/
UNOCHA, 2014
Constas et al., Resilience Measurement Principles: Toward an Agenda for Measurement Design, 2014
Do Financial Services Build Disaster Resilience? | MERCY CORPS 1
II. RESEARCH QUESTIONS
Financial Services
The connection between access to vehicles for savings and credit and household income has been wellestablished.6 Studies have also shown that increased rates of saving allow small business-owners to
maintain their asset stocks and consumption levels in the face of small-scale shocks such as illness.7
Intuitively, it seems clear that access to cash, whether savings or credit, would help a household quickly
repair or replace their belongings damaged during a natural disaster. However, the source of funds may
matter – predatory moneylenders may exploit their borrowers, and informal savings may not be as secure as
bank accounts. The disorder in the period following a disaster would exacerbate these hazards, impacting
families’ ability to meet their basic needs during the aftermath. Alternatively, in situations when a whole
community is affected, the terms of informal credit may be loosened to provide respite for families which
suffered the most harm.8
This study examines the relationship between households’ use of financial products and resilience to or recovery
from shocks, focusing on the roles of savings, loans, and insurance. The driving questions are:
• Are households with greater financial capability more likely to use formal financial services?
• Does use of financial services bolster household resilience to natural disasters?
• Which financial products and services are linked to more successful recovery? Is the benefit
imited to formal services?
Livelihood Diversification and Independence
Among poor and vulnerable populations, it is common to engage in several income-generating activities
rather than specializing in a single trade. This practice is often a result of limited working capital and local
market constraints, and tends to be associated with lower levels of income. However, in the case of severe
natural catastrophes and other major shocks, diversification may protect households’ livelihoods if one of the
income sources is impaired or lost. In particular, households with more independent income sources – which
draw income from different economic sectors, such as both salaried work and farming – may be most likely
to have at least one source which remains stable in the wake of a disaster. This study seeks to test this
assumption by examining the question:
• Does diversifying sources of income across economic sectors protect livelihoods from
severe natural disasters?
Social Capital and other Forms of Assistance
In the wake of Yolanda, affected areas received relief and recovery assistance from the Government of
the Philippines, foreign governments, and international non-governmental organizations (INGOs). Many
households also received material support from their neighbors, friends, and families – an important form
of bonding social capital. The contribution of social capital – the level of cohesion and mutual assistance
among a group or community – to disaster recovery has been well-documented. In many disaster situations,
neighbors and peers are faster to respond than emergency services9, and continue to provide support during
the recovery period. However, the exact nature and effect of this informal support is not fully understood.
6
Dupas and Robinson, Savings Constraints and Microenterprise Development: Evidence from a Field Experiment in Kenya, January 2009
7
Burgess and Pande, Do Rural Banks Matter? Evidence from the Indian Social Banking Experiment, June 2005
8Udry, Risk and Insurance in a Rural Credit Market: An Empirical Investigation in Northern Nigeria, July 1994
9
Aldrich and Meyer, Social Capital and Community Resilience, October 2014
Do Financial Services Build Disaster Resilience? | MERCY CORPS 2
To address this knowledge gap, this study seeks to address the questions:
•
•
How does bonding social capital contribute to resilience to and recovery from
natural disasters?
How do informal sources of assistance compare to formal aid in supporting
disaster recovery?
FINANCIAL
SERVICES
• Use of savings
(formal and informal)
• Use of loans
(formal and informal)
• Use of insurance
FIGURE 1 presents the conceptual framework
of the factors examined in this study and their
presumed effects on mitigating the impact of the
typhoon on affected households.
• Financial literacy
LIVELIHOOD
DIVERSITY
• Number of
income sources
• Independence of
economic activities
SHOCK
EXPOSURE
• Severity of
storm damage
OUTCOMES
• Food security
• Self-reported recovery
from shock
• Preceived ability
to cope with
future shocks
SOCIAL SUPPORT
AND OTHER AID
Reciept of assistance from:
• Other households
• Govermnent of Philippines
• Foreign sources
(including INGOs)
III.METHODOLOGY
Sample Selection
Data used in this study were collected via a household survey administered to randomly selected households
targeted by Mercy Corps’ economic recovery program. The survey was conducted during between May-June
2014, immediately after the registration process and before project implementation began, covering 3,184
households in Northern Cebu, Eastern Leyte, and Western Leyte. A more comprehensive version of the survey
was used in West Leyte, and only data from that region are examined in this study. The sample size for the
baseline survey in Western Leyte is 1,751.
Do Financial Services Build Disaster Resilience? | MERCY CORPS 3
The key criterion for inclusion in the Mercy Corps program was the extent of damage a household suffered
from Typhoon Yolanda. Only households whose homes were considered to have been “totally damaged” by the
Department of Social Welfare and Development (DSWD) were eligible to enroll in the program. As such, the data
reflect the population most heavily affected by the typhoon. Within this group, there was still variation in their
exposure to the effects of the storm, which was included in the analysis. The estimates of storm severity used were
obtained from www.mapaction.org, which drew on “a multivariate formula incorporating physical factors, (estimated
storm surge, proximity to storm path, etc.) reported affected population stats and baseline vulnerability indicators.”10
Dependent Variables: Resilience Outcomes
The survey included questions capturing data on households’ levels of resilience, to be included in the study
as dependent variables. As resilience is a multi-faceted concept, the study employed multiple measures to
estimate households’ resilience. The outcomes measures for resilience fall into two categories: predictive and
demonstrated. Predictive resilience is based upon the respondents’ anticipated ability to cope with shocks in the
future.11 Demonstrated resilience reflects households’ actual and perceived recovery from Yolanda.
The first measure of predictive resilience is an index consisting of questions on how well households feel they
could cope with a range of future household level shocks, such as illness or death of the primary incomeearner. The index ranges from 10 to 50 with higher values indicating more expected ability to cope without
compromising economic status or living conditions. A full list of the questions included in the perceived economic
resilience index is found in Appendix A. The second predictive indicator is a straightforward inquiry on whether
respondents believed they could successfully cope with a future natural disaster similar to Yolanda. Only 1%
of households believed they could cope without any difficulty, so this study focuses on the difference between
households who reported that they could cope with some changes to economic status and living conditions,
versus those who said they could not cope at all with a natural disaster of the magnitude of Yolanda.
The first indicator for demonstrated resilience is a standard measure of food insecurity, the Coping Strategies
Index12 (CSI). CSI is a weighted measurement of food consumption-related strategies used by the household to
respond to income and assets lost to the typhoon, such as skipping meals, reducing portions, and scavenging.
For this study, the CSI was scaled from 0, indicating daily use of many distressful coping strategies, to 108,
indicating no use of distressful strategies. A full list of the questions used to calculate the CSI, including their
weights, is found in Appendix B. The second measure for demonstrated resilience is subjective. Respondents
were asked the extent to which they had recovered from the effects of Yolanda. As very few households
reported full recovery, the analysis compares households which reporting having recovered somewhat or totally
against those who said they had not recovered at all.
Independent Variables
To assess levels of access to financial services, respondents were asked about their use of financial instruments
prior to Typhoon Yolanda, including informal and formal savings, loans, insurance, and bank accounts. A fourquestion quiz of financial knowledge was used to construct a financial capability score for each respondent.
Other questions addressed respondent household’s primary income-earning activities prior to Yolanda, and
the sources and types of assistance they received after the storm. The survey also included questions on
demographic characteristics of households, to be used as controls. The independent variables are described in
more detail Descriptive Statistics section below.
10http://www.mapaction.org/?option=com_mapcat&view=mapdetail&id=3166
11 The authors recognize that other studies have treated these factors as explanatory variables rather than outcomes. For example, Smith, Frankenberger, et al.
Baseline Results Report of the Resilience Impact Evaluation of the Pastoralist Areas Resilience Improvement through Market Expansion (PRIME) Project.
USAID Feed the Future FEEDBACK, Washington, DC., 2014
12 Maxwell, et al, The Coping Strategies Index: Field Methods Manual, Second Edition,2008
Do Financial Services Build Disaster Resilience? | MERCY CORPS 4
Analytical Methods
To identify important determinants of resilience to Typhoon Yolanda, the explanatory factors were combined
into a single model. This model controlled for the strength of the typhoon by municipality. as well as household
demographics such as family size, income, and education. This was essential, as households who were better off
or less severely hit by the storm would otherwise appear more resilient. Holding these variables constant in the
model allowed the analysis to estimate the unique contribution to resilience of the use of financial services and
the other factors examined among households that were similar prior to the typhoon in terms of poverty status
and other socio-economic factors.
A second model allowed the effects of each factor to vary between more and less severely-struck areas, in the
belief that some characteristics might be more beneficial in areas which suffered more damage. For example, a
household which was not heavily affected by Typhoon Yolanda may only benefit slightly from property insurance,
but if it were in the most severely affected areas insurance may greatly contribute to their recovery.
For the binary (1/0) outcome variables, logistic regression models were used. For the continuous indices, the
logs of indices were taken to normalize the distribution, since the CSI was positively skewed and the index of
economic resilience was negatively skewed. Robust OLS regressions were then used to assess correlation
between the independent variables and the log-indices.
Additionally, propensity score matching (PSM) was used to estimate the causal impact of financial instruments
on a household’s resilience. This method predicted the probability of using each financial instrument – savings,
loans, and insurance – based on financial literacy, education, and other household characteristics. A comparison
was made between households which did and did not use the instruments, but which were equally likely to have
used them based on the prediction. The difference between the two on the resilience outcomes is therefore
considered attributable to the use of the financial tool.
IV.DESCRIPTIVE STATISTICS
Dependent Variables
The data (Figure 2) paints a picture of communities which are on the road to recovery, but still suffering from the
effects of Yolanda. Most respondents reported having recovered to some extent, while just over a third had not
yet recovered at all. However, less than 4% reported having recovered to pre-typhoon levels.
FIGURE 2 shows the variation in self-reported,
recovery from Yolanda
FIGURE 3 shows the variation in self-reported
ability to cope with future disasters
1.1%
2.5%
2.3%
NO
RECOVERY
34.4%
UNABLE TO
COPE
50.5%
SOME
RECOVERY
Percentage of
Households
RECOVERED
TO PREYOLANDA
LEVEL
61.9%
RECOVERED
AND IMPROVED
Percentage of
Households
46.7%
Do Financial Services Build Disaster Resilience? | MERCY CORPS 5
ABLE TO
COPE
WITHOUT
DIFFICULTY
ABLE TO
COPE WITH
CHANGES TO
INCOME AND
FOOD
Just over half of households (Figure 3) felt that they could cope with future disasters with some changes to their
income and food sources, while under half felt that they would be unable to cope with future disasters. Just over
1% believed they could cope without any difficulty. For the analysis, this small minority was combined with those
who felt able to cope with some changes.
% of Households with Score
25%
FIGURE 4 shows the percentage of respondents
who scored at each value on the index of perceived
economic resilience
20%
15%
10%
5%
0%
10
15
20
25
30
35
40
45
50
Score on Perceived Economic Resilience Index
The index of perceived economic resilience (Figure 4) is a continuous variable, with a range from 10 to 50 and
a mean of 19.8. As lower values indicate less perceived economic resilience, this downward skew shows that
most respondents predicted that their economic status and living conditions would suffer significantly from future
household-level shocks, such as the death or illness of the primary income earner. This is unsurprising, but the
variation in responses is interesting. About 20% of households felt that they would suffer substantially from all of
the listed shocks, but a few with scores of 50 felt that they would not suffer at all from any of the shocks.
The Coping Strategies Index (Figure 5) is also a continuous variable, with a range among the sample from 27 to 108
and a mean of 95.0. The scores are skewed towards higher possible values, indicating that most households were
fairly food secure at the time of the survey. One in eight households had a full score of 108, meaning that they did not
make use of any distressful coping strategies such as begging or skipping meals in the weeks preceding the survey.
% of Households with Score
14%
12%
FIGURE 5 provides the percentage of respondents who
scored at each value on the Coping Strategies Index
10%
8%
6%
4%
2%
0%
27
50
55
60
65
70
75
80
85
90
Score on Coping Strategies Index
Do Financial Services Build Disaster Resilience? | MERCY CORPS 6
95
100
105
108
Independent Variables
% of Households That Used Instruments
The survey (Figure 6) reveals some use of informal financial tools, but a low level of engagement with the formal
financial sector. The criteria for determining formal and informal tools are given in Appendix C. This lack of naturallyoccurring variation may cause difficulty in assessing the effect of formal services on resilience and recovery.
However, sizeable minorities of the affected population did make use of both informal loans and insurance.
60
FIGURE 6 displays the percentage of households
which made use of various financial instruments
prior to Typhoon Yolanda
50
40
30
20
10
0
Insurance
Loans
(Informal)
Savings
(Informal)
Loans
(Formal)
Bank
Account
Savings
(Formal)
Over a quarter of households had diversified income sources (Figure 7), meaning they earned income through
engagement in two or more independent economic sectors. Engaging in multiple income-generating activities
within the same sector was not considered to be diverse, as all activities within the sector would likely be affected in
similar ways by the typhoon. More than half of all households earned income through agriculture, the most common
income-generating activity. One in three earned income through daily labor, while other sectors were less prevalent.
60
FIGURE 7 shows the percentage of households
earning income from different economic activities
% of Population
50
40
30
20
10
0
Agriculture
Labor
Commerce
Salary
Driver
Do Financial Services Build Disaster Resilience? | MERCY CORPS 7
Remittances
% of Households Receiving Aid
The nature of aid received differed substantially between sources (Figure 9). Assistance
from neighbors and the local community
consisting largely of meals, temporary
lodging, and rebuilding. Households which
received aid from the national government
were very likely to receive food, and some
were also given non-food items (NFIs) such
as tarps and blankets. Assistance from
foreign sources also primarily included food
and NFIs, but fewer households received
foreign assistance than received help from
the national government.
% of Households That Received Support
Almost 40% of households received informal social support from their neighbors –
including shelter, food, and cash – (Figure 8),
while more than 90% received aid from the
Government of the Philippines and over half
received aid from a foreign source such as
international NGOs and other governments.
FIGURE 8 gives the proportion
of households which received
relief and recovery assistance from
different sources in the 6 months
after Typhoon Yolanda
100
80
60
40
20
0
Government
Aid
Foreign
Aid
Social
Support
FIGURE 9 shows the type of aid
received by households from each source
100
80
SOCIAL
SUPPORT
60
NATIONAL
AID
40
FOREIGN
AID
20
0
Foods/
Meals
Housing/
Hosting
Non-Food
Items
Rebuilding
Money
Severity was divided into ten categories, indicated by the colors on the map. (Figure 10). This study collapsed
the categories into a binary variable, with municipalities in the four most heavily affected of the ten groups
considered to have suffered severe damage. 52.59% of all respondent households lived within severely
damaged municipalities.
Do Financial Services Build Disaster Resilience? | MERCY CORPS 8
(
!
FIGURE 10 shows severity of storm damage by municipality, as calculated by www.mapaction.org
(
!
(
!
JiabongMotiong Paranas
Catbalogan
City
(
!
Hinabangan
San
Sebastian
Zumarraga
Maripipi
Esperanza
Pio V.
Corpuz
Kawayan
Almeria
Philippines:
Typhoon Yolanda Severity and Operational
Presence,
Eastern Visayas
(as at 30 Nov 2013)
Severity score was generated from a
multivariate formula incorporating
physical factors (est storm surge,
proximity to storm path, etc) reported
affected population stats and baseline
vulnerability indicators. Data originally
compiled by Miguel Antonio Garcia
from the Barcelona Graduate School
of Economics and then georeferenced by UN-OCHA, MSF-UK,
and WFP. The operational presence
overlay uses the proxy of number of
organistions reported as active,
through the 3W matrix as at 30
November.
(
!
(
!
(
!
Caibiran
Talalora
Santa
(
!
Maydolong
!
( Rita
!
(
Barugo
Capoocan Carigara Tunga
Daanbantayan
!
(
!
(
(
!
Alangalang
(
!
!
(
!
( Medellin
!
(
Tabogon
!
Isabel (
!
( Merida
!
(
Ormoc
City
Dagami !
(
(
!
Balangiga
!
(
Lawaan
!
(
(
!
Quinapondan
Giporlos
Salcedo
!
(
(
!
Palo Tanauan
(
!
!
(
( Bogo
!
City
Storm track
Pastrana
!
(
Palompon
Sub OSOCC
Jaro
!
(
!
(
!
(
(
!
Kananga
Marabut
Tacloban
City
!
(
(
!
Matag-Ob
!
(
Llorente
General Hernani
Macarthur
!
(
!
(
Villaba
Balangkayan
Basey
!
(
San
(
!
Miguel
(
!
Leyte
!
(
(
!
!
(
Babatngon
(
!
Philippine
Sea
(
!
Borongan
City
(
!
Pinabacdao
(
!
Villareal
(
!
Tabango
´
San
(
!
Julian
Calbiga
Daram
Biliran
Cabucgayan
Calubian
!
(
San
Isidro
Santa
Fe
!
(
OSOCC
Culaba
Naval
MA1003_V01
Taft
Sulat
Mercedes
(
!
!
(
!
(
!
(
Tabontabon
Julita Tolosa
(
!
(
!
Burauen
Albuera
!
(
Borbon
Data for Guiuan includes
island of Homonhon
Dulag
Mayorga
!
(
(
!
La Paz
(
!
Macarthur
!
(
!
(
Pilar
0
5
10
15
20
25
(
!
(
!
(
!
Javier
kilometres
Guiuan
(
!
Danao
Lake
San !
(
Francisco
Data sources
Severity scores: see above
3W data: Clusters/UNOCHA
Boundaries: GADM
Catmon
Carmen
Created
02 Dec 2013 / 16:00 UTC+08:00
Map Document
MA1003_Severity+3WEVisayas
Projection / Datum
WGS 1984 UTM Zone 51N
Glide Number
TC-2013-000139-PHL
The depiction and use of boundaries,
names and associated data shown
here do not imply endorsement or
acceptance by MapAction.
(
!
Produced by
MapAction
www.mapaction.org
Supported by
Poro
(
!
Abuyog
(
!
Tudela
(
!
!
(
Baybay
City
!
(
Mahaplag
Danao
City
Inopacan
Silago
(
!
Compostela
Hinunangan
Sogod
(
!
Hindang
Hilongos
Bontoc
Bato !(
(
!
Cordoba
Jetafe
Talibon
Bien
Unido
Pres.
Carlos P.
Garcia
Matalom
(
!
Tomas
(
!
Oppus
Maasin
City Malitbog
Number of orgs
(3W matrix 30 Nov)
Most severe
(
!
Consolacion
Lapu-Lapu
City
Ranked
Severity
Libagon
Saint Hinundayan
Bernard
(
!
Anahawan
San Juan
(
!
Liloan
Loreto
Least severe
Tubajon
Not ranked
!
(
!
(
10
100
Libjo
V. REGRESSION RESULTS
Determinants of Financial Instrument Use
The table below (Figure 11) shows the effects of financial capability and other household characteristics, in the
use of financial instruments. Only relationships which are statistically significant are reported. The coefficients
are in the form of odds-ratios, meaning that a one-unit change in the independent variable is associated with
an “X times” likelihood of using the given financial instrument. These coefficients can also be expressed as a
percentage change in the likelihood of using each instrument. For example, a respondent who scored one point
higher on the financial capability test was 1.46 times as likely, or 46% more likely, to use formal savings compared
to a similar respondent who scored one point lower. Coefficients less than 1 indicate that the respondent was
less likely to use the financial instrument, meaning the relationship is negative. Male financial decision makers,
for example were only 0.51 times as likely to use informal savings as female financial decision makers, holding
all else equal. Dark blue cells show positive relationships, while light orange cells show negative relationships.
Do Financial Services Build Disaster Resilience? | MERCY CORPS 9
FIGURE 11 Logistic Regressions Results on Predictors of Use of Financial Instruments
Use of Savings
Formal
Informal
Higher Financial Capability
1.46
Higher Income (Log)
1.65
Increased Poverty Likelihood
0.12
More Productive Assets
1.01
1.01
Additional Family Member
1.14
0.88
1.31
Use of Loans
Formal
Informal
1.23
1.14
1.24
1.11
Have Bank
Account
Have
Insurance
1.33
1.20
0.08
0.41
0.24
1.01
1.15
1.16
1.06
1.17
1.13
0.51
Male Financial Decision Maker
More Years of Education
0.0003
0.18
0.01
0.11
0.0007
0.08
Sample Size
1566
1566
1580
1580
1585
1580
Pseudo R-Squared
0.16
0.05
0.08
0.01
0.15
0.04
Constant
Financial capability was measured through a series of questions on arithmetic, calculating interest earned, and
business practices. When the household financial decision maker scored one point higher on these questions
(out of four points), they were 1.46 times as likely to have used formal savings, 1.23 times as likely to have
taken loans from formal sources, and 1.14 times as likely to have taken loans from informal sources. However,
such households were no more likely to have opened a bank account, purchased insurance, or used informal
sources to save.
Other personal and household characteristics were also related to higher use of financial tools. The strongest
relationship was between income and formal savings: a 100% increase in a household’s income was associated
with a 65% increase in the likelihood of using formal savings, a 31% increase in the chance of using informal
savings, and 33% increase in the chance of having a bank account. The magnitude of the relationship was
smaller for use of formal loans (24%), informal loans (11%), and insurance (20%). These findings likely reflect
that households with higher incomes will also have more money available to save, and more incentive to insure
their savings are protected in formal financial institutions. More productive assets were also associated with
the use of savings and insurance. Poorer households were less likely to use formal instruments, but were not
significantly less likely to have informal savings or loans.
Interestingly, larger families were more likely to use formal instruments, but were less likely to save informally.
Each additional family member was associated with a higher probability that the household used formal savings
(1.14 times), formal loans (1.15 times), a bank account (1.16 times) and insurance (1.13 times), but also a 12%
decrease in the probability of having informal savings. An additional year of education on the part of the financial
decision maker was surprisingly only associated with a 6% higher chance to have taken formal loans and a
17% higher chance to have a bank account, but was not related to use of savings or insurance. Male financial
decision makers were 49% less likely to have used informal savings than female decision makers, suggesting
significantly different saving and spending patterns between men and women.
Overall, the results from this model show support for the premise that building financial capability may be a
viable way of increasing the use of formal financial instruments. However, wealth and income appear to be
greater determinants than financial literacy, so this may not hold true for the poorest segments of the population.
Do Financial Services Build Disaster Resilience? | MERCY CORPS 10
Determinants of Recovery and Resilience
Figure 12 below shows the results of correlations between resilience and household characteristics including use
of financial instruments, income diversification, sources of aid, and demographics. Only relationships which are
statistically significant are reported here; the full results of the model can be found in Appendix D. The first two
columns address predictive resilience, with Column 1 displaying the determinants’ effect on the index of perceived
economic resilience, and Column 2 showing their effect on households’ perceived ability to cope with future
natural disasters. The latter two columns cover demonstrated resilience, with Column 3 showing the determinants’
effect on the Coping Strategy Index (CSI) and Column 4 showing the effect on households’ reported recovery
from Yolanda. Dark blue cells show positive correlations, meaning the factor in column A is correlated with greater
resilience or recovery; light orange cells show negative correlations, indicating the opposite.
FIGURE 12 Regression Results on Relationships between Households Characteristics and Resilience Outcomes
Predictive Resilience
Perceived
Economic
Resilience Index
(Log)
Perceived
Ability to Cope
with a Major
Natural Disaster
Demonstrated Resilience
Coping
Strategies
Index
(Log)
Reported
Recovery
from
Yolanda
3.3%P*
Savings, Formal
Savings, Informal
8.1%P
3.13P
Loans, Formal
7.4%
1.70
Loans, Informal
9.3%P
1.9%P
2.12P
1.31P*
Bank Account
-4.9%
Insurance
0.73P
-1.1%
Financial Capability Score
-9.5%
Diverse Income Sources
-2.4%
2.08
Relied on Community Support
-3.8%
13.6%
3.9%
Total 2013 Income (Log)
2.2%
1.9%
Productive Assets
0.1%
Aid from Philippine Government
0.86
Aid from Foreign Source
Storm Severity
1.18
-0.6%
Family Members
Male Financial Decision Maker
(FDM)
6.8%
1.05
FDM Years of Education
0.3%
1.05
FDM Literate
0.67
Household Owns Land
-3.2%
Poverty Likelihood
P
Constant
2.60
0.41
4.37
0.40
Sample Size
1194
1353
1286
1359
Adjusted R-Squared
0.160
0.079
0.091
0.058
Indicates that the results are supported by the propensity-score matching estimate, which was only used to estimate the effect of financial instruments.
*This result was significant using propensity-score matching, but not using the OLS regression.
Do Financial Services Build Disaster Resilience? | MERCY CORPS 11
The coefficients for the log indices, Columns 1 and 3, are the percentage change in the index associated with a
one-unit change in the independent variable. For example, households with informal savings scored, holding all
else equal, 8.1% higher on the index of perceived economic resilience. The coefficients for the binary outcomes,
Columns 2 and 4, are odds-ratios. Following the example used for Figure 11 above, households which used
informal savings were 3.13 times as likely to say that they felt able to cope with future natural disasters than
households without informal savings.
Financial Services
Use of certain financial services prior to Yolanda is positively linked with both predictive and demonstrated
indicators of resilience. Households that saved informally – such as in their home – scored 8.1% higher on
the index of perceived resilience to small-scale shocks compared to households without informal savings, and
were 3.13 times as likely to feel that they could cope with natural disasters. Additionally, these households
scored slightly higher (1.9%) on the index of food security, and were 2.12 times as likely report some recovery
since the typhoon. The analysis controlled for pre-Yolanda poverty status, meaning that these results were not
simply because the households were better-off prior to the storm. These effects are confirmed through the
PSM analysis, and are intuitive: a store of readily available funds allows families to smooth over the damage of
natural disasters and other shocks. These positive relationships also suggest that, in general, informal savings
were resistant to the physical damage wrought by Yolanda, and households were still able to make use of them
after the storm passed.
More surprisingly, households with formal savings in banks or credit unions did not have higher predictive or
demonstrated resilience. This is most likely due to the relatively small proportion of families which made use
of these types of savings, only about 5% of the sampled population. It may also reflect difficulty withdrawing
money in the chaotic aftermath of the storm, or other pitfalls of relying on the formal banking sector. Affected
households reported that long distances to commercial bank branches were cited as one of the major obstacles
to their use of formal financial services.
Use of any loans was positively correlated with some measures of resilience, although the relationship was not
as clear as that of informal savings. Families that took out loans from formal sources, like financial institutions,
prior to Yolanda were 1.7 times as likely to feel able to cope with future disasters, and scored 7.4% higher on the
index of perceived ability to handle household-level shocks. However, they were not significantly more likely to
be more food secure or report greater recovery from Yolanda. Households with informal loans from sources such
as employers, shops, and local moneylenders also had greater predictive resilience. These households scored
9.3% higher on the index of predicted ability to cope with shocks. The positive connection between informal
loans and predicted coping was confirmed by the PSM analysis, and PSM showed a significant correlation with
reported recovery from Yolanda as well.
The results on the use of loans are also intuitive: households with access to credit of any type felt better
prepared to meet their emergency needs after a disaster, and felt more security knowing that they could obtain
funds if needed.
Overall, having savings and loans through formal means, including bank accounts, did not appear superior to
informal means in terms of supporting households’ resilience to Yolanda. In particular, there was no evidence of
a benefit from formal savings accounts, whereas savings through informal means had strong beneficial effects
on both predictive and demonstrated resilience.
Having insurance prior to Yolanda had mixed effects on households’ recovery and resilience. Households
that reporting having any type of insurance scored 4.9% lower on the index measuring perceived ability
to coping with household shocks. They were also 27% less likely to feel able to cope with future natural
Do Financial Services Build Disaster Resilience? | MERCY CORPS 12
disasters than households without any form of insurance. This finding was confirmed by the PSM analysis.
However, in the most severely affected areas families which had insurance scored no lower on the perceived
economic resilience index, and had slightly higher food security than families which were comparably
affected and did not have insurance.
These mixed results may reflect the fact that in the Philippines, the sale of insurance is targeted to particularly
vulnerable households who are likely to suffer the most from disasters and other shocks. These individual
vulnerabilities, such as physical impairments, lack of family network, and personal health status, may not be
accounted for within the survey data. The existing insurance market may also be limited to catastrophic loss,
which would mean that households which suffered extensive damages benefit but those which did not lose
many assets or property would gain little from insurance.
One puzzling result is the negative relationship between financial capability and demonstrated resilience.
Holding use of financial instruments constant, a higher score on the financial literacy test is correlated with less
likelihood to report some recovery since Yolanda and worse food security status. This bears further exploration
in future studies. It may be that respondents with more financial knowledge are also better able to assess their
status before and after Yolanda, and are more acutely aware of their assets lost in the storm.
Income Diversification
Households were considered to have diverse income sources if they earned money through activities in
more than one economic sector, such as both agriculture and commerce. Households with diverse income
sources appear to have been less resilient to Typhoon Yolanda than those which only drew income from a
single sector. They scored 9.5% lower on the perceived economic resilience index, meaning they felt more
susceptible to household-level shocks. They were also less food secure than other households, scoring 2.4%
lower on the CSI.
These counterintuitive results may reflect that income diversification often occurs due to economic necessity
rather than as intentional risk management in preparation for disasters. Poorer, more vulnerable households
are known to diversify their income when the returns from each individual source are insufficient to provide
for their needs, and often require all their sources to be intact in order to make ends meet. Even if one of the
income sources is left intact after the typhoon, the household may be unable to meet their living costs with
the reduced money. It is also possible Yolanda was so devastating that all economic sectors were severely
affected, from direct damage to fields and fishing boats to reduced demand for services. An alternate model
which tested the effect of salaried income sources – which should have remained stable following the
typhoon – against all other forms of livelihoods found no difference between the two groups, reinforcing the
theory that the effects of the typhoon rippled throughout the economy.
Social Support and Other Sources of Aid
Relief and recovery assistance provided after Yolanda was divided into three categories: support from other
community members, aid provided by the Government of the Philippines, and aid through foreign sources
such as international NGOs and other governments. Households which relied on others in their community for
assistance were considered to have greater bonding social capital, and were 2.08 times as likely to feel able to
cope with future disasters than households that did not receive this type of social support. However, these same
households also scored 3.8% lower on the index of food security – meaning that they were more likely to use
distressful strategies to obtain or preserve their food supplies following Yolanda.
These results suggest that community support increases households’ confidence in their ability to manage large
shocks, but does not necessarily increase their prospects for actual recovery, at least in the short term. Based
Do Financial Services Build Disaster Resilience? | MERCY CORPS 13
on Aldrich’s research,1413help from other community members may have addressed the most urgent needs, such
as rescue and shelter in the immediate aftermath of the typhoon. Respondents who benefited from this type of
assistance would therefore feel less concerned about the worst consequences of a storm, including physical
danger, starvation, and exposure. However, by the same token, these respondents may have been the hardest
hit within a community, thus making them less food-secure than their neighbors. The analysis attempted to
control for the severity of damage to households, which would reduce this bias. However, the measures used for
this may not have been sufficiently sensitive to pick up on the variance within broad categories such as having
“completely damaged” houses.
Households that received aid from the Philippines government were more food secure, scoring 3.9% higher on
the index of food security than comparable families. This follows the finding that a large portion of government
support was in the form of food aid. They also felt more confident in their ability to cope with small shocks,
scoring 13.6% higher on the index of perceived economic resilience. This may be because their experience
seeing the government support system in action increased their confidence in their chances of receiving
emergency assistance in the future.
Unlike for aid from the Philippine government, having received assistance from international sources was not
associated with greater household recovery or resilience. Speculatively, it may be that international assistance
was not delivered in a timely manner or distributed effectively. The type of aid may also be relevant: Households
mainly received non-food items from international sources, which may not have been as important for recipients’
recovery as other forms of assistance.
The correlations between sources of aid and resilience are expectedly mixed, given that aid is targeted at
the families which have suffered most from the typhoon and are considered least able to recover on their
own. Receiving aid would therefore be beneficial, but would only bring these severely-affected households
up to the level of those around them. An accurate estimate of the results of each type of aid would require
disentangling types of assistance, selection of beneficiaries, and speed of delivery. A cross-sectional analysis,
like the one used in this study, cannot effectively break apart these different possibilities. Further research
is needed before drawing stronger conclusions regarding the relative contributions of different sources of
support to households’ recovery.
Other Factors
Certain socio-economic characteristics of households are clearly linked with resilience and recovery. Most
notably, each additional year of education for the family’s financial decision maker is associated with 5% increase
in the chance of feeling able to cope with future natural disasters, and an additional 5% chance to report some
recovery from Yolanda. Each year of schooling is also correlated with a 0.3% increase on the CSI. The exact
mechanism through which education affects resilience is not well understood, but it may reflect factors such as
planning for the future, knowledge of disaster risk, and greater ability to access assistance.
Unsurprisingly, income is also a contributor to demonstrated resilience. Households which earned more
were also more likely to have partially recovered from Yolanda, be more food-secure, and be more confident
in their ability to handle future household-level shocks. Intuitively, more wealth helps a family to meet
their food needs and rebuild their lost assets. For this reason, the analysis controlled for wealth status,
education levels, and other socio-economic factors in the model. This enabled the results to pinpoint the
unique contributions of use of financial products and services, livelihood diversity, and sources of aid on
households’ recovery and resilience.
14
Aldrich and Meyer, Social Capital and Community Resilience, October 2014
Do Financial Services Build Disaster Resilience? | MERCY CORPS 14
VI.CONCLUSIONS
Implications for Programming
The findings of this study reinforce the importance of financial instruments as mechanisms by which households
can build their resilience to natural disasters. In particular, families with informal savings had higher predictive
and demonstrated resilience than others, and those which had taken out loans before Yolanda were both more
likely to feel that they could cope with a similar disaster in the future. However, there is not clear evidence for the
benefits of formal financial tools over informal ones; informal savings appear more effective than formal, while
the advantage of formal over informal credit is ambiguous. Development actors engaging in programming aimed
at enhancing resilience should use the expansion of formal financial services as a means of increasing financial
inclusion, but would be wise not to view informal financial outlets as inherently inferior. Programming aimed at
boosting financial capability also appears to have high efficacy in encouraging a diverse range of formal and
informal financial services.
Livelihood diversification has not been proven to be an effective means of building resilience to the effects of
natural disaster as severe as Yolanda. However, since the study addresses only naturally occurring diversity, it
may be that planned and intentional expansion of income sources for the purposes of spreading risk may be more
effective. Based on the current evidence, development programs designed to support livelihood diversification
should be cautious about assuming this will result in contribute to greater resilience to major natural disasters.
Greater social capital – as demonstrated by a households’ ability to rely on neighbors or others in their community
during a time of need – has been shown to be positively correlated with predictive resilience, meaning households
have increased confidence in their coping abilities. Agencies and INGO’s should consider programs which build
the capacity of community support networks and social capital among vulnerable groups. These appear to serve
valuable roles in protecting families from the most damaging effects of disasters, particularly in the short run.
Humanitarian aid in the immediate aftermath of a major disaster might also take into account existing informal
support systems and seek to complement them or use them as a vehicle to deliver assistance. For example,
networks within the community may hold more information on the damage suffered, the immediate priorities
in response and rebuilding, and the families which do not possess the resources to manage on their own.
These networks could be well-placed to distribute aid or to direct government and international aid to the most
vulnerable recipients. Community-based targeting has also been shown to be more cost-effective, and lead to
greater satisfaction among beneficiary communities compared to more traditional targeting methodologies15. 14
Sources of Bias
This study has drawn upon data collected at the baseline of the Mercy Corps’ economic recovery project, and
reflects pre-existing factors which may influence household resilience. As the data is cross-sectional, the results
of the analysis show correlation rather than causation. One primary source of bias in this type of analysis is
risk of reverse-causality, meaning the outcome variable may actually be causing a change in the explanatory
variable. However, the survey did collect information on key socio-demographic factors prior to Yolanda, such
as use of savings and loans before the storm, and time-invariant characteristics like gender and education. The
flow of time from pre-storm status to Yolanda to post-storm recovery limits concerns about reverse causality,
meaning the level of recovery almost certainly did not directly affect households’ actions before Yolanda.
15
Atlas, V, et al. Targeting the poor: evidence from a field experiment in Indonesia, 3ie Impact Evaluation Report 12. New Delhi: International Initiative for Impact
Evaluation (3ie), 2014.
Do Financial Services Build Disaster Resilience? | MERCY CORPS 15
Omitted variable bias is another concern. This comes about when unobserved characteristics affect both the
explanatory and the outcome variables. When unaccounted for in the analysis, omitted variables create the
illusion of a relationship where none exists. In the context of this study, many personal characteristics such as
risk preferences, optimism, and foresight, were not captured. All of these may affect households’ decisions to
protect themselves before the storm by saving or purchasing insurance, and could also influence their speed of
recovery after Yolanda. Similarly, community-level factors such as a well-functioning local government or market
might allow households living in the vicinity to achieve faster recovery after a disaster.
Study Limitations
Some characteristics of interest, particularly use of formal financial tools, were not prevalent in the population
studied, and their effects may be understated as a result. The marginal benefits could vary when promoted at
a large scale through development interventions. This analysis should therefore be combined with program
impact evaluations and qualitative research to fully understand how changes in use of financial services affects
communities’ abilities to cope with shocks and stressors.
The timing of the baseline survey several months after Typhoon Yolanda presents both benefits and limitations
to the measurement of recovery resilience. By being conducted 6 months after the typhoon struck, it was
possible to assess patterns and determinants of recovery. However, it was potentially too long after the height
of the disaster to capture the immediate effects. For example, the Coping Strategy Index was used to indicate
recovery after the storm, but it is most sensitive in the days and weeks immediately after a disaster. By the time
of the survey, the daily behavior of many households had begun to return to normal, and many had repaired the
damage done to their homes and physical property. Since the data is cross-sectional, long-term indicators of
recovery such as recuperation of assets and income are not yet available. As a result, the predictive markers of
resilience are presumed to be more informative than the demonstrated indicators of resilience, i.e. households’
feelings about coping with future shocks better reflect their resilience than their current progress recovering
from Yolanda. In spite of this limitation, the recent experience of Yolanda means that households were wellpositioned give accurate estimates of their predictive resilience, being able to anticipate how well they would
respond to similar shocks in the future.
As discussed in Section IV, the sample of respondents is drawn from beneficiaries of Mercy Corps’ program in
Western Leyte. The results are those of a population heavily-affected by Yolanda living within a heavily-affected
region, and may not be indicative of households which suffered only minor losses in the typhoon or other regions
of the country.
Future Research
Future studies, particularly those associated with Mercy Corps-supported projects in the Philippines, should
further explore whether the formal and informal financial products and services have different effects on
resilience. Concerns among communities regarding formal services will be particularly important for programs
aimed at promoting their use – for example, formal financial institutions may be viewed as overly rigid or
inaccessible. The results also challenge the assumption that informal financial services, such as those provided
by moneylenders, are exploitative. A more thorough understanding of the operations and possible benefits of
informal financial tools would be of value to future program design.
Given that the data do not support livelihood diversification as a means of building resilience, both researchers
and program implementers should seek examples of households which were more resilient through specific
livelihood choices, and carefully assess why most families did not benefit from diversification. Such examination
may help reveal the specific forms of livelihood diversification hold the greatest – and least – potential to
strengthen resilience.
Do Financial Services Build Disaster Resilience? | MERCY CORPS 16
Additionally, social capital appears to be a crucial element for households coping with the aftermath of a disaster.
Studies may seek to delve further into systems of support, and clarify the types of social connections which
determine who benefits from this support. Future project design could tap into these networks as a means of
bolstering community resilience, but there may be marginalized groups which do not benefit from the support
of their neighbors. If so, identifying them would help direct humanitarian aid and development assistance to the
most at-risk groups.
Mercy Corps and its partners are engaged in additional research around these and other outstanding questions.
At the time of writing, Mercy Corps is conducting an impact evaluation of its economic recovery program in
Western Leyte, Philippines. This evaluation will provide credible evidence on the causal effects of providing
households cash transfers, financial literacy, and access to formal bank accounts on their recovery from Yolanda.
Further data collection and analysis in Western Leyte is also planned in early 2015 to more robustly test if and
how the factors examined in this study are important for households during the later recovery period.
Do Financial Services Build Disaster Resilience? | MERCY CORPS 17
APPENDIX A:
Formulation of the Perceived Economic Resilience Index
The index of perceived economic resilience is created by combining responses on how severely a negative
shock would affect the family’s financial status and living conditions. The shocks are below:
Shock
Illness of primary income earner
Death of primary income earner
Business/livelihood/crop performs poorly
Loss of formal/informal employment
Food price inflation
Loss of remittance income
Loss of/damage to primary productive asset
(fishing boat, vehicle, sewing machine, livestock, etc.)
Loss of/damage to household assets
(appliances, furniture, etc.)
Loss of/damage to dwelling
Natural disaster
Respondents chose from the same set of responses for each shock, which were scored from 1 to 5, with
1 indicating the most severe impact and 5 the least severe:
Score
1
2
3
4
Level of Impact
Severe (very large) impact
Large impact
Moderate impact
Small impact
5
Little to no impact
Do Financial Services Build Disaster Resilience? | MERCY CORPS 18
APPENDIX B:
Formulation of the Coping Strategies Index (CSI)
The CSI is created through combining responses to the following questions regarding respondents’ actions
taken to access food, which are weighted to reflect the level of distress associated with them:
In the past 2 weeks, how frequently did your household use one or more of the following strategies in order
to have access to food?:
Activity
Rely on less expensive or less preferred foods
Limit/reduce meal portion sizes subsequent
Reduce number of meals eaten per day
Skip entire day without eating
Reduce adult consumption so children can eat more
Borrow food or rely on help from friends or relatives
Rely on begging for food
Gather unusual types or amounts of wild food/hunt
Consume seed stock to be saved for next season
Take children out of school to work
Weight
1
1
2
4
2
1
2
2
2
4
Respondents chose from the same set of responses for each activity, which were scored from 0 to 4,
with 0 indicating most and 4 indicating least distressful coping:
Score
0
1
2
3
Response
Daily
3 or more times per week
1-2 times per week
Less than once per week
4
Never
Do Financial Services Build Disaster Resilience? | MERCY CORPS 19
APPENDIX C:
Definitions of Formal and Informal Financial Instruments
Savings tools are classified according to the following criteria:
Savings Instrument
Formal
Bank
Credit Union
Formal Savings Association
Informal
Community Welfare Scheme
Informal Savings Club
Inside Home
Sources of loans are classified according to the following criteria:
Loan Source
Formal
Formal Institution
Microfinance Institution
Informal
Employer
Pawnshop
Daily Bank
Community Welfare
Neighborhood Community
Store Credit
Local Moneylender
Do Financial Services Build Disaster Resilience? | MERCY CORPS 20
APPENDIX D:
Full Regression Models
The table below contains the full regression outputs for the relationships between household characteristics
and resilience. For each measure of resilience, Column (1) displays the results of the basic model, and
Column (2) displays the results of the model which allowed for different effects of characteristics between
more and less affected areas. T-statistics are given in parentheses below each coefficient, and significance
is indicated by * for P<0.05, ** for P<0.01, and *** for P<0.001.
Predictive Resilience
Perceived
Economic Resilience
Index (Log)
Savings, Formal
(1)
(2)
0.0208
0.0162
1.277
(0.50)
(0.24)
(0.77)
Severe X Formal Savings
Savings, Informal
0.0808***
(3.79)
Severe X Informal Savings
(1)
(2.17)
Severe X Formal Loans
(4.94)
Severe X Informal Loans
Bank Account
Financial Capability Score
Diverse Income Sources
1.085
0.330
0.0138
1.316
1.189
(0.14)
(1.89)
(0.48)
(0.73)
(0.30)
0.0281
1.189
(0.77)
(0.22)
0.0418
3.128***
3.116***
0.0192*
(1.57)
(6.94)
(5.44)
(2.23)
0.993
0.0192*
2.118***
2,382***
(1.67)
(4.07)
(3.64)
-0.000393
(-0.02)
0.737
(-0.02)
0.0524
1.703*
2.289*
-0.300
(1.05)
(2.45)
(2.29)
(-1.93)
0.619
(-0.82)
-0.0162
1.530***
(-0.60)
(1.72)
-0.0216
(-1.06)
1.072
1.136
-0.00424
(3.35)
(0.53)
(0.68)
(-0.54)
(2.67)
0.313*
(-0.66)
0.0806***
3.144***
(-2.22)
0.00527
1.309
1.727**
(.47)
(1.85)
(2.67)
0.0223
0.926
-0.0153
0.604
(0.60)
(-0.29)
(-0.96)
(-1.75)
-0.0984
1.458
2.274
0.0121
0.0257
1.175
1.033
(-1.37)
(1.21)
(1.43)
(0.68)
(0.98)
(0.46)
(0.06)
-0.0493*
0.214*
0.444
-0.0195
1.289
(2.55)
(-1.19)
(-0.55)
(0.36)
-0.0222
0.728*
0.662*
0.00243
-0.0283*
1.109
0.855
(-0.88)
(-2.46)
(-2.14)
(0.30)
(-2.43)
(0.73)
(-0.78)
-0.0507
1.197
0.0564***
1.644
(-1.33)
(0.69)
(3.51)
(1.76)
-0.00481
-0.00744
1.001
1.017
-0.0113**
-0.0115**
0.859*
0.866*
(-0.51)
(-0.78)
(0.01)
(0.27)
(-2.88)
(-2.95)
(-2.28)
(-2.14)
-0.0946***
-0.0862**
0.982
0.680
-0.0243**
-0.0181
1.011
0.771
(-4.62)
(-3.13)
(-0.14)
(-1.95)
(-2.87)
(-1.42)
(0.07)
(-1.21)
Severe X Diverse Income
Community Support
(2)
(0.26)
(-2.54)
Severe X Insurance
(1)
0.0109
Severe X Bank Account
Insurance
(2)
1.203
(0.57)
0.0930***
(1)
(0.27)
0.0382
Loans, Informal
Reported
Recovery
from Yolanda
(0.46)
(2.12)
0.0736*
(2)
Coping Strategies Index
(Log)
0.0397
0.0913*
Loans, Formal
Demonstrated Resilience
Perceived Ability to
Cope with a Major
Natural Disaster
0.0189
1.992**
-0.00932
1.822*
(-0.47)
(2.59)
(-0.55)
(1.97)
-0.0321
-0.0150
(-1.21)
(-0.43)
2.082***
1.650*
-0.0382***
-0.0498***
0.824
0.525**
(4.94)
(2.36)
(-3.83)
(-3.62)
(-1.21)
(-2.86)
Do Financial Services Build Disaster Resilience? | MERCY CORPS 21
Severe X Community
Support
Aid from Philippine
Gov’t
0.136***
(4.00)
Severe X Government Aid
Aid from Foreign Source
-0.0287
1.644
0.0203
2.534**
(-0.55)
(1.66)
(1.04)
(2.86)
0.186***
0.676
0.546*
0.0390*
(4.44)
(-1.64)
(-2.11)
(2.33)
(1.21)
(0.02)
(-0.39)
0.832
0.663*
(-2.22)
Family Members
(0.22)
(-2.36)
(-1.44)
Productive Assets
(0.23)
0.794
(0.34)
Total 2013 Income (Log)
(2.11)
0.000819
0.00882
-0.000688
1.071
1.938
(1.28)
Severe
1.061
-0.168*
0.0238
Severe X Foreign Aid
0.0389*
-0.00702
(-0.86)
-0.000179
1.012
1.013
(-0.02)
(0.08)
(0.07)
0.0295
1.588
-0.0142
1.004
(0.80)
(1.79)
(-0.87)
(0.02)
0.120
1.036
0.336
(-0.02)
(1.52)
(0.16)
(-1.75)
(0.04)
(-0.25)
(0.71)
(0.12)
0.0221**
0.0217**
1.043
1.060
0.0190***
0.0184***
1.180**
1.176**
(2.75)
(2.70)
(0.83)
(1.13)
(6.05)
(5.67)
(3.21)
(3.02)
0.00122*
0.00118*
1.002
1.002
-0.000320
-0.000292
1.004
1.004
(2.24)
(2.08)
(0.36)
(0.45)
(-1.16)
(-1.04)
(0.73)
(0.74)
-0.00899
-0.00781
1.021
1.020
-0.00648***
-0.00632**
1.010
1.019
(-1.62)
(-1.42)
(0.59)
(0.56)
(-3.34)
(-3.26)
(0.27)
(0.51)
0.0680***
0.0713***
0.956
0.945
0.00545
0.00536
1.060
1.058
(3.72)
(3.85)
(-0.37)
(-0.46)
(0.72)
(0.70)
(0.44)
(0.41)
FDM Years of Education
-0.00476
-0.00416
1.048*
1.047*
0.00319*
0.00323*
1.048*
1.043
(-1.54)
(-1.35)
(2.18)
(2.13)
(2.31)
(2.36)
(2.00)
(1.78)
FDM Literate
0.0389
0.0371
1.250
1.237
-0.00574
-0.00676
0.822
0.806
(1.84)
(1.75)
(1.55)
(1.47)
(-0.59)
(-0.69)
(-1.25)
(-1.35)
Household Owns Land
0.0380
0.0344
0.866
0.853
-0.00915
-0.00798
0.665*
0.655*
(1.58)
(1.45)
(-0.93)
(-1.02)
(-0.91)
(-0.80)
(-2.46)
(-2.54)
Poverty Likely
0.0592
0.0543
1.448
1.443
-0.0323*
-0.0320*
1.086
1.016
(1.67)
(1.53)
(1.60)
(1.57)
(-2.29)
(-2.26)
(0.33)
(0.06)
0
0
1
1
0
0
1
1
(.)
(.)
(.)
(.)
(.)
(.)
(.)
(.)
Municipality Constant (8)
-0.193***
-0.205***
1.038
0.996
0.00748
0.00407
1.131
1.047
(-4.43)
(-4.54)
(0.12)
(-0.01)
(0.36)
(0.19)
(0.41)
(0.15)
Municipality Constant (9)
0.0130
0.0152
1.334
1.311
0.0407***
0.0441***
0.838
0.857
(0.59)
(1.50)
(1.38)
(3.84)
(4.16)
(-0.87)
(-0.73)
-0.128***
-0.113***
1.699*
1.734*
0.0253
0.0247
2.009*
2.106*
(-3.87)
(-3.34)
(2.22)
(2.24)
(1.71)
(1.62)
(2.40)
(2.55)
FDM Male
Municipality Constant (7)
(0.51)
Municipality Constant (10)
Municipality Constant (11)
Municipality Constant (12)
Constant
0.000553
-0.0109
1.198
1.085
0
0
1
1
0
0
1
1
(.)
(.)
(.)
(.)
(.)
(.)
(.)
(.)
-0.168***
-0.159***
1.259
1.320
0.00990
0.0133
0.824
0.879
(-5.29)
(-4.74)
(1.02)
(1.19)
(0.67)
(0.88)
(-0.75)
(-0.50)
2.600***
2.575***
0.4061426
0.5326863
4.365***
4.375***
0.401972
0.4788146
(28.37)
(27.73)
(-1.58)
(-1.08)
(115.90)
(115.55)
(-1.51)
(-1.16)
N
1194
1194
1353
1353
1286
1286
1359
1359
adj. R-sq
0.160
0.168
0.079
0.088
0.091
0.095
0.058
0.072
Do Financial Services Build Disaster Resilience? | MERCY CORPS 22
ABOUT MERCY CORPS
Mercy Corps is a leading global humanitarian agency
saving and improving lives in the world’s toughest places.
Poverty. Conflict. Disaster. In more than 40 countries, we partner
with local people to put bold ideas into action, help them overcome
adversity and build stronger communities. Now, and for the future.
45 SW Ankeny Street
Portland, Oregon 97204
888.842.0842
mercycorps.org
EUROPEAN HEADQUARTERS
40 Sciennes
Edinburgh, EH9 1NJ, UK
44 131 662 5160
mercycorps.org
CONTACT
JON KURTZ
Mercy Corps
Director of Research and Learning
[email protected]
ERYNN CARTER
Mercy Corps Philippines
Country Representative
[email protected]
Do Financial Services Build Disaster Resilience? | MERCY CORPS