Using Vulnerability mapping to measure medium
term impact of relocation and resettlement by
Special Economic Zones (SEZ): Thilawa SEZ,
Yangon Region, Myanmar
Dr. Mike Griffiths, Research Consultant, Social Policy & Poverty Research Group
6th February 2018

Contents
Abstract/Executive Summary........................................................................................................................ 2
Background ................................................................................................................................................... 2
Methodology and methods .......................................................................................................................... 4
Sampling and demographics ......................................................................................................................... 8
Findings ......................................................................................................................................................... 8
Vulnerability (overall) ............................................................................................................................... 8
Dependency .............................................................................................................................................. 9
Debt......................................................................................................................................................... 10
Income/expenditure ............................................................................................................................... 10
Livelihood Diversity ................................................................................................................................. 11
Food security........................................................................................................................................... 11
Water & sanitation.................................................................................................................................. 12
Health ...................................................................................................................................................... 12
Assets ...................................................................................................................................................... 13
Social participation and decision making................................................................................................ 14
Conclusions and recommendations ............................................................................................................ 15
Appendix 1: Methodology Notes ................................................................................................................ 18
Indicators used and definitions ........................................................................................................... 18
References .................................................................................................................................................. 221

Abstract/Executive Summary
Comparative analysis of households which had been relocated by the Thilawa SEZ, and those which
remained in the original communities demonstrated significant differences in the vulnerability
profiles of the household socio-economic status. Although overall vulnerability rates did not
significantly differ, relocated households had significantly higher rates of vulnerability related toeconomic dependency, debt and lack of livelihood/income diversity. Detailed analysis further
demonstrates that relocated households not only have lower levels of income, but are more likely tohave expenditure in excess of annual income, and higher rates of debt and debt interest repayment
rates. Due to a greater reliance on food purchases, and income insufficiency, relocated households
reported higher rates of food insecurity, and nearly one-third reported taking loans to meet foodshortages in the past year. Overall, the pattern of coping amongst relocated households demonstrates
significantly lower levels of resilience than non-relocated households, which results in a rapid
erosion of economic capital, and subsequently a decline in future coping capacity.
These findings challenge the assumptions around capital-based compensation approaches torelocation programmes, and argue instead that the process of relocation is best compared to a rapidtransition to an urban state, where the household economy is more precarious. This precarity
demands a high level of adaptive capacity, which in turn requires not only skills but access toresources, markets and welfare mechanisms. This requires a more multi-faceted and nuanced
approach to relocation, which is based on a detailed knowledge of the economies and ecologies ofcommunities to be relocated, rather than a simplistic assumption that like-for-like compensation andprovision of waged labour will suffice. Thus, when considering whether the provisions made byinvestors and developers to compensate relocated households is sufficient, the assessment criteria
should be based on measures of outcome and impact, not on the size or nature of the compensation
per se. Outcome based approaches require systematic study, and the application of research evidence
in planning interventions which are known to be likely to contribute to sustainable outcomes.
Periodic monitoring and measurement further enables planners to adjust relocation procedures toimprove outcomes, and to be aware if outcomes are not being met. This moves away from a more
simple, transactional model of compensation, and instead encourages and evidence-based dialogue
process with relocated communities.

Background
Special Economic Zones (SEZ)s are a significant feature of industrialization in East Asia, and inparticular the Mekong sub-region (Rigg, 2015; Shrestha & Chongvilaivan, 2013), and are also thesite of contested narratives of development (Moberg, 2015; Walsh, 2015a), particularly around landissues and forced or semi-voluntary relocation. Whilst the macro-economic benefits of SEZ‟s are
frequently highlighted (Farole, 2011; Wang, 2013) the wider social and environmental impacts, interms of human development indicate that the benefits of development are not necessarily equallyshared (Aggarwal, 2007). Numerous processes have been established to try to anticipate and mitigate
negative impacts, such as various forms of impact assessment (Gramling & Freudenburg, 1992;
WALLSTRÖM, KYAW, CORNISH, CHAN, & ALLAN, 2016) or post-operational processes suchas grievance mechanisms (Kaufman & McDONNELL, 2016). However, narratives surrounding
SEZ‟s and similar large -scale development projects involving relocation of rural population are
increasingly negative (Baird & Shoemaker, 2007). Much of the research on the effects of involuntary2resettlement has been done in the context of slum clearances and rehousing (Agbola & Jinadu, 1997;
Kleinhans, 2003), with evidence that even well-managed programmes can exert significantly
negative psychological and social effects on those relocated (Brooks, Zugazaga, Wolk, & Adams,
2005; Shamai & Lev, 1999). What is significantly overlooked are the communitarian dimensions of
the social life of those relocated, where networks of interdependent social relations play an integral
part in the wider socio-economic well-being of the community, despite, in many cases, significantly
high levels of material poverty (Clampet‐Lundquist, 2010; Ekström, 1994). Compensation packages
frequently aim to provide the resources for equivalent, or at times better levels of housing and publicamenities as before
Writing on measures to prevent impoverishment resulting from relocation, Michael Cernea identifies
eight key processes which interplay to affect impoverishment (Cernea, 1996, p. 251). These are,firstly landlessness, with the removal of „the main foundation upon which people's productive
systems, commercial activities and livelihoods are constructed.‟(p. 251) Secondly, joblessness,thirdly homelessness, which can also manifest as „placelessness, loss of a group's cultural space andidentity, or cultural impoverishment‟ (p. 251); marginalization-particularly towards lesseconomically viable livelihoods; increased morbidity through changes in disease exposure and helpseeking patterns; food insecurity; loss of access to common property where loss of „ common
property assets belonging to communities that are relocated (forested lands, water bodies, grazing
lands, etc.) represents a cause of income and livelihood deterioration that is systematicallyoverlooked and typically uncompensated in government schemes‟ (p. 252) and finally, what he terms„social disarticulation‟ where „the dismantling of communities' social organization structures, thedispersion of informal and formal networks, associations, local societies, etc., is an expensive yet
unquantified loss of social capital [which] undermine[s] livelihoods in ways uncounted andunrecognized by planners, and are among the most pervasive causes of enduring impoverishment anddisempowerment.‟(p. 252) It is perhaps these more intangible losses which exert the most influenceon outcomes, where more tangible measures of inputs, such as housing quality and access to services
or utilities may be similar or even improved compared to the previous living context. This challenges
simplistic, input-based compensation approaches to resettlement. Michael Cernea again:
In real life, however, compensation reveals itself to be both impotent and misleading: it isunable to perform the restorative miracles with which it is officially and rhetorically credited.
Compensation is flawed and reconstruction is under-financed. The revealing fact is thatnumerous projects that do pay compensation fail to restore livelihoods and leave people
worse off. (Cernea, 2008, p. 90)
The emergence of Special Economic Zones in Myanmar has been a significant part of both thepolitical rhetoric of development (Isono & Kumagai, 2013) and a site for geo-political rivalries toplay out, where SEZs are frequently an integral component of strategies to attract foreign investment(Hong, 2014; Slodkowski, 2012). Whilst small-scale SEZs have been established in and aroundmajor metropolis, it is the larger-scale projects in places like Dawei and Thilawa which haveattracted both significant investment and critical attention (Isono & Kumagai, 2013; Sekine, 2016; D.
Tang & Kelly, 2015; Walsh, 2015b). The Thilawa SEZ, in an area 23km southeast of Yangon, has along and at times contested history. Although only recently established as an SEZ, there are reports3

of forced relocation and land seizures as early as 1996 (Earthrights, 2016), after which, in 2013, a
more systematic approach was used to appropriate the land earmarked for SEZ development(Earthrights, 2016). The first phase of development, some 400 hectares mainly for industrial use, 84
households were relocated. In terms of compensation,
Compensation for the displaced residents‟ crops was provided under the 2012 Farmland Act.
Compensation was also provided for livestock, but not for the confiscated land. However[ ],there was a wide discrepancy in the amounts residents received for comparable houses, otherstructures, crops and livestock lost due to relocation. Each of 68 households residing in the
Phase I area was provided a 25 x 50 foot (approximately 116 square meters) plot of land for
housing and given the choice of a small house or compensation to build a house on this plot.
They were also promised provision of clean drinking water and vocational training‟(Earthrights, 2016, p. 3)
Numerous studies have attempted to evaluate the resulting socio-economic and health status of
relocated households (Physicians for Human Rights, 2014), both in an attempt highlight perceived
injustices and to open dialogue for more effective pre-location measures for the remaining 800+
households who will be affected in the second, much larger phase of development, covering a further
2,000 hectares. However, much research to date has used small samples, and more qualitative
approaches, which have had little impact on policy makers (Earthrights, 2016). Cernea (1996)
recommends an approach which also studies
successful adaptive strategies, the rebuilding of production systems, and the creation of newsocial organization patterns [..] longitudinal studies, comparative studies, short-term impact
research stand to discover important responses to many unanswered questions about 'whatworks' and what does not when dismantled societies, kinship systems, and local cultures tend
to reassemble, change, and function within new encapsulating environments.(Cernea, 1996,
p. 262)
Based in this, a study was commissioned by Paung Ku, in partnership with several agencies
concerned with relocation issues in both Thilawa and Dawei, to conduct a more comparative,
quantitative and long-term study of the impact of relocation on the socio-economic situation of
relocated households, as a way of both informing a more outcome-based set of measures andstandards for relocation compensation and post-relocation support, and a baseline to measure longerterm impacts well beyond the initial relocation.

Methodology and methods
Initial interviews were conducted with key informants in the relocated communities, and with NGO
workers and activists associated with the relocation issue in the main community of relocated houses
in Myaing Thar Yar village, near to Thilawa Special Industrial Zone, on 1st November 2017. After
discussion, it was decided to focus on a quantitative approach to measuring household vulnerability,
using models and methods applied in wider studies of rural livelihoods in Myanmar by theGovernment, UN Agencies and INGOs (Griffiths, 2012b, 2015, 2017)
4

Field research in Myanmar, based on both qualitative and quantitative analysis, has identified key
variables which are considered by rural communities to be significant contributors to household
poverty, vulnerability and resilience (Griffiths 2012). These include income, assets, livelihood
diversity, debt, landlessness, dependency, health, education, water access and “ethics and morals”.
The measurement of capacities is captured using the „umbrella‟ model, developed initially by theLivelihood and Food Security Trust Fund (LIFT) to measure vulnerability in Myanmar. This model
collects data on ten indicators (dependency, debt, expenditure, livelihood diversity, food security,
water & sanitation, health, social capital and decision making) and calculates relative vulnerability
for each of the ten factors based on standard deviation from the population mean. Overallvulnerability at household level is based on having three or more of the ten factors classified as„vulnerable‟ – which is defined as having a score less than one standard deviation below the
population average for that factor/indicator. It is called the umbrella model because it utilizes a userfriendly umbrella style radar plot to illustrate the relative degree of „protection‟ which a household
has against shocks and hazards, as well as to provide a localized „shock/hazard‟ module by capturing
information on common threats such as food insecurity and ill health. The tool draws on Moser‟s„Asset Vulnerability Framework,‟ which measures household economic vulnerability according toten factors (indebtedness, productive income, livelihood diversity, dependency ratio, asset profile,water and sanitation, food security, health, social capital and decision making power). The model isprimarily capacity focused, and does not directly measure exposure to a wide range of shocks, suchas flooding or crop failure; what it does is look at the relative capacity of households to respond toshocks. However, certain shocks, such as health and food insecurity, are also contributors tovulnerability-and so are included in the model.
The model thus allows a comparative analysis of the coping capacity of different households exposed
to similar threats: for example, we can compare the outcome of flood exposure to households A andB, who have different vulnerabilities. The full list of factors and linked indicators is included inTable 2. The detailed definitions used for each indicator are included in Appendix 1.

5

Table 1: vulnerability indicators
FactorContribution to vulnerability

Indicator

Indebtedness

High levels of non-productive debt put livelihood assets at risk (collateral); repayments may
reduce essential expenditure; high levels of existing debt can reduce ability to access
additional credit
Low or negative income: expenditure ratio can lead to reduction in essential spending,increase risk of debt or negative coping responses. High proportion of income spent on nonproductive items can lead to under-investment in livelihood, leading to higher risk
Ownership of livelihood assets, convertible assets or crucially, land (in the form of usage
right) can provide short term protection against shocks.
Current and prior experience of food insecurity is strongly linked with increased
vulnerability to future food insecurity. Likewise, food insecurity leading to malnutrition canaffect human capital, and put livelihoods at risk.
Income derived from a single source is more vulnerable to shocks. Multiple sources, or the
potential to diversify, can increase protection against shocks affected main/key livelihoods

Debt repayment as proportion ofincome Repayment: income ratio>30% is usually risky
Proportion of income expended on
non-productive items (food, health,rent, fines)
Moser‟s asset vulnerability
Framework, adapted for survey
Consumption index

IncomeAssetsFood Security

Livelihood
diversity
HealthDependents
SocialParticipation

Chronic or frequent illness in primary earner OR one requiring care threatens livelihood
security and reduces income, as well as increasing health expenditure; unplanned healthexpenditure is a common cause of negative coping (e.g. conversion of livelihood assets to
cash)
Water is an essential for health and many livelihoods; more time taken to draw water
reduces time for other activities; unsafe water sources increase risk of ill health which
reduce livelihood effectiveness; unreliable water supplies increase resource expenditure
Household members not engaged in livelihoods
Persons with higher levels of social participation build up social capital, which can increase
the likelihood of relief and assistance in times of difficulty

DecisionmakingPersons with more influence in decision making can have stronger negotiating position forlivelihood related factors such as fair pricing, land and asset use

Water and
Sanitation

Source/validation
World Bank
19971, adapted
World Bank 1997,
adaptedMoser (1998)2
UNDP3, modified

Livelihood diversity index=
number of income generating
activities at HH)
Income generating household
member days per year lost workthrough illness

DHS (2006)
modified

Average time to collect water

DHS (2006)4

Household Dependency scale
Participation in village eventsTLMI5 adapted
TLMI, adaptedfrom p-scale
(KIT)
SPPRG

Participation index

UNDP modified

1 World Bank, 1997. Survey of living conditions: Uttar Pradesh and Bihar. Household Questionnaire, December 1997–March 1998.
2 Moser C (1998) Reassessing urban poverty reduction strategies: The asset vulnerability framework. World Development 26, No 1, pp 1-19
3 UNDP (2006) Integrated Household Living Conditions Analysis. Yangon: UNDP
4 DHS (Demographic Health Survey), 2006. Measure DHS: model questionnaire with commentary. Basic Documentation, Number 2.
5 Griffiths M (2007) Economic Vulnerability Score: applications for Community Based Rehabilitation. Internal.

6Factors are measured using standardized indicators, which were then converted by mathematicalformulas to a scale from 0-1 to allow input into the vulnerability model. The indicators can becollected at a household level or at a community level. Provided that there is a consistent method toconvert to a scale, different and even multiple indicators can be used to measure the different factors.
This is essential as different indicators, or different calibrations, may be required for different
populations or geographical areas. Scores are plotted on a 10-point radar plot, either as a single
household plot, a village aggregate, a township or even State level aggregate. This model looks
primarily the capacity to cope with shocks and hazards rather than relative exposure. Hence, it is best
applied to determine which households are more vulnerable within a given population, rather than for
absolute comparison between regions or countries. Vulnerability was defined in relative terms, bymeasuring the relative deviation of a particular household score from the overall population mean. If
the household score for each factor (for example, health) was more than one standard deviation
below the overall population score average, then that factor was classified as „vulnerable‟. Overall, ahousehold was classified as „vulnerable‟ if three or more of the ten factors scored over 1 standard
deviation lower than the population mean for those factors.
There are several significant features of this model which need further explanation before we canconsider the application of the model. Firstly, the model classifies vulnerability at a household, rather
than individual level, thus moving beyond fixed demographic characteristics to more dynamic socioeconomic characteristics. However, this may mean that some individual vulnerabilities are masked(such as the vulnerability of older persons within a household). However, in measuring the resilienceof a given household, we make the assumption that resources are distributed according to need within
a household, thus imputing the overall household vulnerability onto its members. Secondly, as
mentioned above, the model relies on measurement against the population average to determine
vulnerability. Hence, if a household is classified as vulnerable, it has at least three factors whichscore significantly lower than the overall population average. In essence, a household is judged
according to its neighbors.
Following this, the use of a statistical approach to measure vulnerability (one standard deviation
below the average) does mean that vulnerability is dependent on how equally scores are distributed.
If some scores were very widely distributed, this would lead to a wider range and a larger standard
deviation, meaning that only those with very low scores would be classified as vulnerable. Likewise,
if scores are bunched close together, with very little difference between households, then very small
differences could lead to being classified as vulnerable. One solution could be to take the average ofthe scores for all the factors and use that as the basis for classifying vulnerability. However, thiswould require that each indicator have the same sensitivity and range, in order to contribute equallyto the overall score. As this is very difficult to do, the „three and above‟ rule (three or more factors
more than one standard deviation below the mean) was used. This allows for some errors inhouseholds where there may be one or two scores which are low, but the household itself isreasonably secure.
Validation of the model, and links to resilience, are described further in Appendix 1 (Methodology
notes).
7

Sampling and demographics
The sample was designed to collect data from all of the already relocated households (n=100 ) andfrom at least 90% of the households earmarked for relocation (n=1100), using sampling to build acase-control comparison population.
Data was collected by training volunteers from the villages concerned, and training was provided
over a 2 day period 6-7th December 2017, with data collection taking place over 10 days from 8th-19th
December, supervised by trained enumeration supervisors from New Survey Research Team. Data
entry was completed by 20th January 2017, and analysis was conducted on the data to constructvulnerability models.
The resulting final sample included 97 households who had already been relocated, and 924
households from areas earmarked for future relocation (total 1,021 households). The relocatedhouseholds included those relocated in successive phases of relocation, with 71 who had relocatedbetween 2012 and 2014, and 26 who had relocated in or after 2017. Operational reports describe
differences in relocation processes relating to the time of relocation, and the majority of the relocatedsample are formed from „early‟ relocaters. However, due to the small number of „late‟ relocaters, it
was not possible to demonstrate to statistical significance differences between early and late
relocaters for most indicators. For statistical analysis, a 95% confidence level has been utilized unlessotherwise stated.

Findings
A few demographic differences emerged when analyzing the household composition of households
which had, and had not relocated. First of all, the average household size was larger in relocatedhouseholds (average 4.6 members vs. 3.8 members for non-relocated households, p<0.001), and ahigher proportion of households in non-relocated areas being female-headed (14.4% vs.
12.4%),although that difference is not statistically significant. On average, the education level ofhousehold heads was slightly higher amongst non-relocated households (p<0.1), as measured by the
proportion who had achieved middle school education or higher.

Vulnerability (overall)Using cutoff values derived from national household surveys using the same indicators, theproportion of households classified as vulnerable, and vulnerability in each of the ten categories
could be measured. Overall, the proportion of households classified as vulnerable is similar tonational figures.
Table 2: Vulnerability levels (overall) of relocated and non -relocated, compared to othersimilar rural surveys

Overall
Yangon Region
Relocated

Thilawa (2017)2015 Poverty survey

24.3%

24.25%
31.1%

2016 Mya Sein Yaung Village
project baseline
28.3%
26.6%

25.8%
8

Yet to relocate

24.1%

There is a small, statistically non-significant difference in vulnerability rates amongst relocatedhouseholds; however, the significant differences between the communities are found in the patternsof vulnerability, as shown in figure 3.
Here, the contrast between relocated and non-relocated households is evident in four main sections:debt, dependency, livelihood diversity and decision making, with small differences in food security
also evident. Overall, the picture is of vulnerability in the relocated households being driven by alack of livelihood diversity, lower employment rates, and subsequent higher rates of bothindebtedness, and higher risk of debt.
Figure 3: vulnerability profiles of relocated and non -relocated households

Vulnerability Profiles of relocated and non-relocatedhouseholds
Not relocatedDecision

SocialDependency
50%
40%
30%
20%
10%
0%

AssetsRelocated

Debt

Prod exp

LDI

HealthFood sec
WatsanDependency
Higher levels of economic dependency were found amongst relocated households, specificallyamongst working aged adults, where employment rates were lower both for working aged male andfemale household members, as measured by indication of engagement in household income
generation activities by working aged household members not identified as full-time students.
Table 4: employment status, relocated and non -relocated households

Not relocatedRelocated
Total

Employment (working age male)

Employment (working age female)

93.7%
87.9%
93.1%

76.4%
69.4%
75.6%
9

Debt
Debt-related vulnerability is primarily measured by a complex formula which takes into account two
main factors to measure debt risk: firstly, the debt: income ratio, measured as the ratio of the totalsum of household debt against annual household income, and the riskiness of the debt as measured
by the nature of the main creditors. In most studies, higher risk creditors include village money
lenders and local merchants, with banks, some government and commercial lenders being medium
risk, and NGO and family creditors generally being lower risk. Creditor risk is represented in three
ways: typical interest amount, risk of repossession of assets upon default, and repayment schedules.
Overall, 61% of all debt of relocated households was owned by money-lenders, compared with 49%
of all debt of non-relocated households (p<0.05), and consequently, mean interest rates and interest
burdens differ greatly-over double in relocated households compared to non-relocated ones. The rates
of borrowing to meet food shortages was high in both communities, but again significantly higher in
relocated households, resulting in much higher debt: income ratios. Over 15% of relocatedhouseholds had a debt burden greater than their annual income, compared to less than 5% of nonrelocated households.
Table 5: debt status, relocated and non-relocated households

Not relocatedRelocated
Total

Debt to income
ratio
26.9%
59.6%
30.0%

Interest Burden
84,855
177,288
93,637% taking loans for food inpast 12 months
65.8%
80.4%
67.2%

Total amount
1,022,062
1,699,691
1,086,440

Income/expenditure
Overall, relocated households had lower reported annual income than non-relocated households, andalthough they also had lower levels of expenditure, nearly half of all relocated households reported
annual deficits, with the average income: expenditure ration amongst relocated households at 1.42.
When considered on a per-capita basis, per-capita income for non-relocated households is 1.7 times
higher than for relocated households.
Table 6: income status, relocated and non-relocated households
Annual income
Not relocatedRelocated
Total

6,292,996
4,439,072
6,116,864

AnnualExpenditure
5,608,395
4,678,454
5,520,046

Expenditure: income
ratio (average)
1.24
1.42
1.26

% with higher
expenditure than income
31.1%
45.4%
32.4%

Expenditure patterns differed between groups; although core expenditure on food represented 38% of
all expenditure amongst both groups of households, relocated households had significantly higher
proportions of expenditure on debt (12% vs. 10%) and education (9% vs. 7%), with both groups also
spending nearly 14% of all income on health expenses.
10Livelihood Diversity
One of the key underlying differences between the relocated and non-relocated households is theaccess to livelihoods, with diversification being both quantitatively and qualitatively lower amongst
relocated households. This appears to have occurred despite some efforts to provide vocational
training and access to employment for relocated households by the SEZ programme. This is evident
in the higher proportions of households reporting full time waged employment as the main income
source amongst relocated households, but the higher levels of reliance on a single income, often from
more precarious work (such as selling, day wages and remittances) result in less secure anddiversified household income profiles amongst rural households. Underlying this is the displacementof agriculture as a key livelihood source, with only 6% of relocated households reporting any income
from agriculture.
Table 7: Main livelihood (by reported major income source, per household)
Agriculture

Livestock

Selling/store

Daily
wage

Full time waged
employment

RemittanceTechnical
work

Nonrelocated

22.3%

2.3%

14.3%

43.1%

26.5%

1.2%

1.7%

Relocated

2.1%

2.1%

21.6%

45.4%

37.1%

2.1%

2.1%

Total

20.4%

2.3%

15.0%

43.3%

27.5%

1.3%

1.8%

The proportion of households in relocated areas who have more than one livelihood source is 44%,compared to more than 50% of those in non-relocated areas. The proportions of households reliant on
waged labour was higher than national averages in both groups.

Food security
Food security related vulnerability rates were higher amongst relocated households, mainly related tolower rates of consumption of protein and fresh produce by relocated households. In general,
relocated households were more likely to buy staple and supplementary food, rather than using
home-grown produce. In particular, relocate households were10% more likely to consume purchased
rice, rather than rice grown in their own paddy area. The reliance on bought food is also a likely
cause of the higher frequency of loans for food insecurity amongst relocated household, where nearly
one third of relocated households reported loans for food insecurity in the previous year.
Table 8: food insecurity reported in previous year

Non-relocatedRelocated
Total

Percentage reporting foodinsecurity in previous year
23%
44%
25%

Percentage taking loan for foodinsecurity in previous year
17%
32%
19%

11

Water & sanitation
Overall, the water and sanitation indicators were better in the relocated communities, in terms oftime and resource consumption to meet water needs, with fewer reported having to buy water
regularly. However, this may not take into account regular water meter charges amongst relocatedhouseholds, or the electricity costs of running electric water pumps.
Table 9: water & sanitation status, relocated and non -relocated households

Non-relocatedRelocated
Total

Time to get water (Rainy)
20
13
19

Time to get water (Dry)
25
16
24

Buy Water
35%
27%
35%

HealthOverall, compared to national averages, health-related vulnerability rates were low, mainly due to themethod used to measure this, which derives from an indicator measuring the impact of illness on
livelihood participation. However, more detailed analysis does show that relocated communities on
average experience twice the level of illness-related time loss, both for days lost by illness ofhousehold members, and days lost due to caring for sick household members.
Table 10: health indicators, relocated and non-relocated households
Own illness

Days lost caring forothersAverage

Average per Household
members

1.4
2.8
1.5

0.5
1.7
0.6

1.9
4.6
2.1

0.5
1.0
0.6

Non-relocatedRelocated
Total

There were no significant differences in health expenditure or health related debt between the twogroups, with health-related expenditure accounting for 14% of all expenditure in both groups, similar
to findings from other surveys. However, a higher proportion of relocated households reported
experiencing at least one health emergency in the previous year, and over one third of relocatedhouseholds reported taking out loans to meet emergency healthcare costs.
Table 11: health emergency and healthcare emergency related loans in previous 12
months

Non-relocatedRelocated
Total

Reporting health emergency
39%
57%
41%

Loans to deal with health emergency
25%
36%
26%

12

Assets
Asset profiles showed significant variation between relocated and non-relocated groups, despite little
differences in the proportions recorded as vulnerable in terms of assets. The main reason is that,compared to national samples, households in both communities were relatively asset-rich, withhigher proportions owning some livelihood related assets, and generally having better quality
housing, both of which tended to increase the overall asset value score to above the cutoff level forvulnerability in most cases. However, relocated households were less likely to own larger animals
such as cows or buffalos, and poultry, although more likely to own pigs and goats. Generally,ownership of tools and implements was higher in non-relocated households, and there was a slightly
higher rate of motorcycle ownership amongst relocated households. In terms of asset value, nonrelocated households had net asset worth nearly twice that of relocated households, based on ascoring system used in rural surveys. The overall picture again points to lower rates of livelihoodrelated asset diversity amongst relocated households, despite the value of assets recorded being
similar.
Household assets
Table 12: households owning one or more of different household assets
Generator
25%
12%
23%

Non-relocatedRelocated
Total

TV
63%
81%
65%

Telephone
87%
88%
87%

Radio
19%
3%
17%

Other
15%
24%
16%

Household asset ownership potentially reflects newly acquired assets, such as TVs, with radio
ownership lower. Also, generator ownership reflects lower rates of need amongst relocatedhouseholds who had greater access to grid electricity than relocated households (76% vs. 5%). House
construction amongst relocated households was generally better, with nearly all (93%) having
tin/zinc roofing, compared with just over half (53%) in non-relocated communities.
Livelihood assets (animals)
In terms of animals and livestock, not only were relocated households less likely to own larger
livestock and poultry, but generally, apart from pigs and goats, the average numbers owned were alsomuch lower, probably due to more restricted land areas for animal husbandry. In general, nonrelocated households which owned animals owned twice as many as relocated households.
Table 13: households owning one or more of different animal/livestock assets

Non-relocatedRelocated
Total

Draught animal

Buffalo/Cow

Pig

Chicken

Goat

Duck

14%
5%
13%

13%
9%
13%

8%
12%
9%

54%
35%
53%

12%
14%
12%

16%
8%
15%

13

Table 14: number of animals owned by households which own one or more of different
animals
If large animal how
many
1.5
0.8
1.5

Non-relocatedRelocated
Total

If poultry
how many
19.7
6.6
18.5

If pig or goat how
many
1.6
2.3
1.7

if any how
many
22.9
9.8
21.6

Livelihood assets (tools/implements)
A similar pattern emerges for livelihood implements, where the percentage of households owning
any livelihood relate tools, implements or machinery in relocated households is much lower, with the
exception of a small, non-significant difference in sewing machine ownership rates. The lower
tools/implement ownership rates are likely to reflect livelihood type, with agricultural livelihoods
being more or less absent from households in relocated communities, and hence little or no need for
agricultural tools and implements. However, the absence of tools and animals for livelihoods
indicates a lack of opportunity, or capital, for developing other livelihoods which are not dependent
on waged labour.
Table 15: households owning one or more of different livelihood assets

Non-relocatedRelocated
Total

Hand tools

Machine

5%
2%
4%

7%
1%
6%

Sewingmachine/Loom
4%
5%
4%

Fishingequipment
2%
1%
2%

Transport assets
Table 16: households owning one or more of different transport assets

Non-relocatedRelocated
Total

BicycleMotorcycle

Car

Trawalwgyi

Tricycle

Animaldrawn cart

44%
45%
44%

58%
64%
58%

2%
1%
2%

2%
2%
2%

1%
2%
1%

8%
1%
7%

Social participation and decision making
Social and political capital in general showed more positive trends in relocated communities, withlower proportions of households reporting non or occasional participation in village social events,
and a small proportion of female respondents who reported non or only occasional participation in14

meetings, discussions and decision making. The activities of the various NGOs, CSOs andorganizations for the welfare of relocated households, combined with availability of meeting spaceand capacity building would appear to have contributed to the overall higher rates of social capital.

Influences decision
making (women)

Participates in
discussions(women)attends meetings(women)

63%

36%

97%

87%

66%

21%

21%

65%

82%

58%

32%

97%

75%

51%

attends meetings

86%

Participates in
discussions

60%

Household events

21%

Weddings/
funerals/ festivals

33%

Village meetings
NonrelocatedRelocated

Influences decision
makingTable 17: households reporting never or occasional participation in the following events

Additionally, when looking at trends for assistance for common crisis such as food insecurity, healthemergencies and educational costs, households in relocated communities no more likely to received
assistance from community organizations, with 5% of households in each group reporting receiving
any assistance from community organizations for a social/economic crisis in the previous year.
Households in relocate communities were most likely to report relatives, neighbours or other sources(such as village money lenders) as the main source of assistance.

Conclusions and recommendations
Whilst numerous studies have documented the impact of relocation using ethnographic or otherqualitative methodologies, this study, by utilizing a quantitative approach, enables a multidimensional analysis of both the impact of relocation and the potential contributors to certain
negative outcomes of relocation. Whilst the proportion of households classified as vulnerable using
standardized cutoff values did not differ significantly between relocated and non-relocatedhouseholds, an analysis of the pattern of vulnerability suggests three main conclusions from thisstudy.
Firstly, the lack of opportunities for livelihood development and diversification, manifested by a highdegree of dependency on waged labour, a low proportion of households with diversified income, thedisappearance of agricultural livelihoods from relocated communities, and lower employment rates
amongst working aged adults in relocated communities, is itself likely to be the underlying cause of
ballooning debt and rising food insecurity. Whilst SEZ‟s are generally proposed on the basis of being
providers of employment (Aggarwal, 2007) most studies have focused on conditions directly linked
to SEZ‟s, and not the wider economic geography. Whilst employment may be remunerative, withhigher wages than outside an SEZ, the provision of waged labour in the absence of opportunities todevelop or maintain a diversified household income has resulted a more precarious state. Althoughcurrent definitions of precarity are largely based on the apparent decline of the European labour15

welfare consensus (Standing, 2013) where declining job and income security are growing in the faceof a rolling-back of workers rights and social security, the evidence from this study suggests that asimilar process is taking place, with a decline in the income security provided by a diversified
agricultural base, the near-absence of State-led welfare, and a weakening of communitarian welfare
engendered by the relocation of households from different villages into one place. Critically, perhaps
underlying this is a change in relationship to land: where in the rural context, traditionally owned
land essentially functioned as a place for shelter and livelihood, relocated households use the land
primarily as a living space only, with livelihoods taking place elsewhere. Despite being located in arural setting, the mode of living is essentially urbanized, with a dislocation of work and living. Thesefindings challenge the assumption that the provision of employment n SEZ‟s to members of relocatedhouseholds is a sufficient mechanism to maintain the household economy, and that attention needs tobe paid to developing and maintaining a diversified household economy which retains elements of an
agricultural economy.
Secondly, the evidence of spiraling debt burden, driven by a mixture of insufficient income,
illustrates the short and medium term costs of relocation. This is consistent with other studies such as
Hwang et al (2007), who found that despite better housing conditions post-relocation, involuntary
migrants displaced by China‟s Three Gorges Dam project experienced higher rates of unemployment,household debt and relative loss of earnings. In this study, households which relocated in 2013 or2014 had double the debt burden of non-relocated households, and those relocated in 2017 alreadyhad debt and interest burdens 30% higher than non-relocated households, illustrating the rapid rise in
debt year on year after relocation. Of note, interest burdens also appear to disproportionately rise,indicating not only higher levels of borrowing, but less favourable borrowing terms. This in turn islinked to an erosion of capital, such that assets such as houses and even land may be sold off to repay
debt. This again challenges any concept that asset-based compensation provides sufficient economic
protection for relocated households: whilst the houses, land and utilities provided may be ofequivalent or higher monetary value, the costs of re-establishing a household, coupled with theimmediate decline in income sufficiency, quickly threaten or erode the capital assets.
Thirdly, the increased rates of reported food insecurity, and borrowing for food shortages highlights
the already described precarious household economy. Whilst occasional food insufficiency is notuncommon, one in five relocated household reported three or more occasions of food insecurity inthe previous year. Again, this is commonly noted in other studies (Cernea, 1998), and is linked tothree factors: lower rates of cultivation (and hence higher rates of reliance on food purchases); lower
incomes and joblessness, and the cumulative effects of negative income: expenditure and increasing
debt interest burdens further eroding capital and coping capacity. When considering resilience, using
newly developed resilience indicator tools (Griffiths, 2017), one-third of relocated households had anegative resilience profile-meaning that their erosive coping behaviours (such as borrowing and asset
liquidation and sale to meet acute needs) outweighed any constructive and positive coping, such as
investment in livelihoods and savings. This was more than twice as high as non-relocatedhouseholds. This lack of resilience is indicative of a lack of capacity to cope in non-erosive ways,
which leads to an undermining of coping capacity for the future. Although linked to the wider
provision of resources and opportunities for livelihoods, short and medium term crises require more16

accessible and comprehensive safety nets in the form of social protection and social security
provisions, to prevent smaller crises from undermining coping capacity and thus further amplifying
the impact of subsequent crises.
In terms of recommendations, the findings from this study underline much of the earlier work by
Cernea (1996), Hwang (2007) and others on the need to challenge the assumptions that capital-based
compensation and provision of waged labour to relocated communities are sufficient to maintainhousehold economies. Firstly, detailed analysis of the economy of communities prior to relocation
should be undertaken to ensure that the disruption to economic practices is minimal: if seen throughthe lens of forced urbanization, we should also consider that most rural households do not have theadaptive capacity to become urbanized overnight; hence, sufficient resources in terms of land, accessto markets and investment credit should be made available to allow for agricultural based livelihoods
to be continued.
The short and medium term consequences and costs of relocation should be considered, and thus
compensation should focus not only on the provision of basic services, but also on social protection
and welfare mechanisms, potentially provided through community-based mechanisms. The
widespread presence of community social organizations in Myanmar presents a readily available
model for sustainable welfare provision (Griffiths, 2016a, 2016b; McCarthy, 2016), and investments
here, allied with processes to strengthen access to government welfare, should be a mandatory part ofrelocation process.
This also points attention to the debt cycle, and the need for relocation processes to be alert to thepotential for capital compensation to be rapidly eroded by unsustainable debt. Whilst improved
livelihood bases and access to welfare may alleviate some of the need for emergency, high-cost andhigh-risk loans, the provision of low-interest credit as part of relocation programmes, or facilitating
greater access to such credit, may also provide crucial assistance in the first few years of transition.
The final point is perhaps again reflecting earlier work by Cernea and others; that assessments of therequirements of relocating programmes should be outcome and impact based, not based on inputs
only (Gramling & Freudenburg, 1992; B.-s. Tang, Wong, & Lau, 2008). Hence, when considering
whether the provisions made by investors and developers to compensate relocated households is
sufficient, the assessment criteria should be based on measures of outcome and impact, not on thesize or nature of the compensation per se. Whilst this may appear somewhat counter-intuitive toplanners who wish to know beforehand how much to budget for relocation compensation, carefulresearch may indicate procedures by which compensation measures may be planned and budgetedbased on what interventions and measures are known to be associated with more sustainable socioeconomic outcomes. Periodic monitoring of relocated communities, using a mix of qualitative andquantitative measures, can also provide ongoing information on the extent to which objectives andobligations have been met. This contrasts with the assumptions made by proposals of capital-based
compensation, and provides a useful framework for ongoing dialogue with relocated communities toensure that their economic platform is sustainable, and not precarious.

17

Appendix 1: Methodology Notes
This model has been applied in six large studies in Myanmar, including the REVEAL project(Griffiths, 2012a; LIFT, 2014) where it was used as a baseline and endline measurement; as abaseline and endline measurement approach for a livelihoods project for persons with disabilities
implemented by the Leprosy Mission; an analysis of rural household vulnerability conducted in the
Dry Zone by ActionAid in 2012 (Griffiths, 2012b); a large rural household survey by the Departmentof Rural Development conducted in all States and Regions in 2015 (Griffiths, 2015); a baseline
survey conducted by the Department of Rural Development of the Mya-Sein Yaung project in 2016
(Griffiths, 2016c) and a follow up survey of Mya Sein Yaung project villages in Yangon Region in2017. Thus, the model has been tested in various contexts to assess its suitability in determining
vulnerability and in assisting beneficiary selection. When compared with standard demographic
profiling (which would identify as „vulnerable‟ any household which is either landless, female
headed, has a person with disability, or an older person), the umbrella model has higher specificity
and a strongly positive f-test, indicating a high degree of effectiveness in identifying households who
would be considered poor or vulnerable by other means.
Indicators used and definitions
The indicators used in this study are based on data available from the 2016 Baseline survey, which
was adapted in some places to include specific questions relevant to the construction of the umbrellamodel for vulnerability. Here, key indicators are described, together with a summary of how thevulnerability indicator was calculated for that particular domain
Assets: the questionnaire recorded total numbers of different types of assets in five categories:
household goods (e.g. generator, telephone); livelihood assets (animals, tools, nets, boats) transport
assets (bicycles, trawlawgi, boats etc.; household valuables such as gold and housing quality. Landwas not included in the asset list, as issues of ownership are often complex to describe. Land use andownership was recorded separately. Given the difficulty and inconsistency in calculating monetary
value of assets, and in particular the regional variation in monetary value, an alternative scoring
system was used to calculate asset value. The total score for asset value was calculated using
assigned values for different types of asset. To assess vulnerability, the total scores for assets in eachcategory were capped at a maximum level, as vulnerability reflects risk as well as overall value. For
example-a household may have 1,000 chickens-but if that represents the sum total of their assets, it
represents a risky profile, as the entire asset value could be lost by an outbreak of bird flu.
Asset poverty: asset poverty is measured by calculating the asset value of the lowest quintile andthen classifying as „asset poor‟ those who fall below that level.
Asset vulnerability: asset vulnerability is measured by calculating the weighted score for assets inthe five categories, and if that score is lower than one standard deviation below the population mean,that households is considered „asset vulnerable‟
Debt: the measurement of debt was undertaken not on the total monetary value of the debt, but on theextent to which the degree and nature of indebtedness posed a risk to the household. Hence, debt wasmeasured by 2 factors: the proportion of total household income which was expended on debt
servicing and repayment on a monthly basis, and the identity of the major creditors for thathousehold‟s debt. Whilst there are inevitable variations in practice, qualitative research undertaken in
Myanmar has demonstrated that rural households perceive debt from family members or relatives
18

and NGOs to be low risk, with typically lower interest rates, as compared to loans from community
money lenders, banks and „bosses‟. Hence, it is a reasonable assumption that a household whose debt
is mostly owned by village money lenders is likely to be paying higher interest rates, and to be at
higher risk of negative consequences if they default, than a household whose debt is primarily fromfamily members. Likewise, households who spend 30% or more of their income on debt servicing are
likely to be more vulnerable than those whose debt servicing consumes a lower proportion of theirincome. Households firstly were asked to describe what proportion of their income was spent on
what type of expenditure, using the ten seeds method (see expenditure, below). The number of seeds
allocated to each category was then converted into a percentage (1 seed = 10%). Next, households
were asked to again use the ten seeds method to indicate what proportion of their debt was owed towhich type of creditor. A formula was devised to assign risk weighting to the type of creditor. Thiswas combined with the percentage score for proportion of income consumed by debt repayments tocalculate an overall „debt‟ score.
Debt vulnerability: the overall debt score was inverted (lower score = higher risk) and having
calculated an overall debt score, households whose score was more than 1 standard deviation below
the mean are considered vulnerable in the debt category.
Decision making: part of the overall measure of poverty and vulnerability takes into account power
differentials and participation in decision making. Earlier research by SPPRG has demonstrated astrong correlation between degrees of equality in participation in village decision making and overall
poverty rates at village level. Here, decision making was measured in two ways: firstly, an index
cataloguing the degree of participation of the household head in village decision making process. The
indicator measured the degree of participation at three levels: attending meetings (how often)
participating in discussions (how frequently) and influencing decisions (to what extent). A formula
was devised to allocate scores to the degree of participation, with higher scores allocated to the„influencing decision‟ category. The same questions were then asked about the participation of thewomen in that household in the village decision making processes. These two scores were combined,
and as with the other main indicators was converted to a scale from 0-1 for the purposes of theumbrella model.
Decision making related vulnerability: the overall score was inverted (lower score = higher risk)
and having calculated an overall score, households whose score was more than 1 standard deviation
below the mean are considered vulnerable in the decision making category.
Dependency: the initial part of the survey catalogued details of each household member, including
the way in which they participated in, or contributed to, the household income generation. This
allowed for broad categories such as family business, waged employment, daily labourer (casual)
student and „own work‟/‟own business‟ and of course, „other‟. Based on this, household members
could be defined as economically dependent or not. This category is primarily measuring economic
dependency, whereby household members who are active, and perhaps engaged in domestic
activities such as child care or care for elderly, are nonetheless not included as economically active
unless specified by the respondents. A dependency ratio is then determined by calculating the
proportion of household members who are economically dependent. This excludes school aged
children who are listed as students, but school age children who are listed as being economically
active are included.
19

Dependency vulnerability: the overall score was inverted (lower score = higher risk) and having
calculated an overall score, households whose score was more than 1 standard deviation below themean are considered vulnerable in the dependency category.
Disability: the national disability survey conducted by DSW and TLMI in 2009-2010 used a hybrid
approach to measure disability, with a national prevalence of 2.32%. A more functional based
approach was used by the national census, which yielded a prevalence of 4.6%, with the difference
almost entirely due to higher prevalence of age-related functional decline. Surveys in the Delta andthe Dry Zone using a self-designation approach have typically yielded prevalence rates between 3
and 4%. For the purposes of this survey, self-designation was used, whereby household members
were asked whether they had household members who were considered disabled. A short text andaccompanying pictures were used to illustrate types of disability for households who were notfamiliar with the concept. According to the census and DSW criteria, the main types of disability
recorded were physical, hearing, seeing and intellectual/mental.
Expenditure: measuring household income is challenging, particularly in rural contexts where
income is often seasonal and consumption is potentially reliant on acquired goods as well asmonetary income. Likewise, assigning monetary value to income can be problematic, especiallywhere purchasing power of cash varies from region to region. This means that the absolute monetary
value of household income does not necessarily correlate with income security. However, measuring
expenditure profiles can contribute to the estimation of a reasonable proxy for relative income
security. Households who spend the majority of their income on essentials such as food are more
likely to be experience food poverty. However, prior research in Myanmar categorized the maintypes of household expenditure in rural households as follows: Food, Health, Debt repayments andservicing, Education, Livelihoods (including purchase of tools, fertilizers, repair of Equipment etc.),
Travel, savings and „Official and social‟ which includes various voluntary and non-voluntary
contributions such as official and unofficial taxes, donations and contributions. Households wereasked to describe what proportion of their income was spent on what type of expenditure, using theten seeds method. The number of seeds allocated to each category was then converted into apercentage (1 seed = 10%) for each category. Members could allocate half a seed to a category.
Expenditure related vulnerability: expenditure profile was calculated by measuring the proportionof expenditure in three „essential‟ categories: food, debt repayment and health. The overall score wasinverted (lower score = higher risk) and having calculated an overall score, households whose score
was more than 1 standard deviation below the mean are considered vulnerable in the expenditurecategory.
Food insecurity: the data collected in the Household survey asked questions in section 9 on “Monthsof adequate household food provisioning”. An indicator was derived from 9.1 “Were there months in
the past 12 months in which your household did not have enough food to meet your household‟sneeds?”, 9.2 (months where food was insufficient) and 10.16 “ Overall, how would you compare
your household‟s food availability from all sources in the past 12 months with the previous year?”Food security related vulnerability: the consumption score was converted into a 0-1 scale for the
purposes of the vulnerability model. The overall score was inverted (lower score = higher risk) andhaving calculated an overall score, households whose score was more than 1 standard deviation
below the mean are considered vulnerable in the food security category.
20

Health: indicators for health were measured in two ways. Firstly, the proportion of household
expenditure consumed by health costs was calculated. Secondly, the impact on livelihoods of ill
health was measured. This was measured in two ways. In the initial section of the questionnaire,questions were asked of each household member as to how many productive working days had been
lost to ill health in the previous year, firstly through the ill health of that household member, andsecondly, the days lost by that household member in caring for another household member who was
sick. In the final analysis, data was cross-matched with recorded data on whether or not thathousehold member was economically active or not, to accurately capture the extent to which illhealth in that household had reduced the number of economically productive days. This can be
expressed in several ways: firstly, as the average number of days lost by economically active
household members to ill health or to being a carer; secondly, the total number of economically
productive days lost by that household; and thirdly, the average number of days lost relative to thenumber of income generating members in that household.
Health vulnerability: health vulnerability was estimated using the a formula to calculate the average
number of days lost relative to the number of income generating members in that household, which
was converted into a 0-1 scale for the purposes of the vulnerability model. The overall score wasinverted (lower score = higher risk) and having calculated an overall score, households whose score
was more than 1 standard deviation below the mean are considered vulnerable in the health category.
Household head: household head was recorded in the household profile section, according to the
response of the respondent.
Livelihood diversity: one the key elements of the survey are to measure livelihood diversity athousehold level. Livelihood diversity is measured in three ways: firstly, by the number of different
types of source from which the household derives its income. Secondly, the proportion of income
which is derived from different income source, indicating the degree of dependency on a particularsource of income thirdly, whether those different sources are regular or seasonal, which further
indicates the degree to which the household has regular or irregular income flow. The questionnaire
asked each household to use the ten seeds method to indicate what proportion of their income wasderived from which source. The main categories for rural livelihoods were derived the household
survey. After allocating seeds according to the proportion of income derived from each source,household members indicated whether those source were regular or seasonal. From this, the numberof income sources for that household can be measured, as well as the extent to which the household
has a well-diversified livelihood portfolio.
Livelihood diversity related vulnerability: the livelihood diversity index utilizes existing formulae
to calculate the number of livelihood sources in relation to the household size, further adjusted by theextent to which the household is reliant on more, or fewer income sources, and whether these sources
are regular or not. A household with few members with two main income sources, one of which isregular, may be less vulnerable than a larger household with three sources, but which receives 80%
of its income from one, irregular source. This does not calculate the monetary value of the derived
income, but the extent to which the livelihood portfolio is diversified to ensure that if one sourcedries up, there is still other potential income streams which can supply family income. The overall
score was inverted (lower score = higher risk) and having calculated an overall score, households
whose score was more than 1 standard deviation below the mean are considered vulnerable in the
livelihood diversity category.
21

Social capital: the links between social capital and poverty are well established ; less universallyacknowledged are methods to measure social capital. Where social capital can be constructed innegative and positive forms , the measurement of social capital needs to be done using contextually
relevant factors. The underlying assumption is that households with members who play an active rolein community events or activities are more likely to have positive social capital, which can in turnresult in increased likelihood of receiving assistance from fellow villagers in times of crisis. Field
testing demonstrates this to be the case: most respondents in the pilot testing affirmed that, although
households were not intentionally excluded from receiving assistance if they were less involved incommunity activities, that „active‟ households were perceived more favourably as those who had
contributed to the community‟s well-being and so were more likely to received assistance. In thisstudy, households were asked to indicate the frequency of participation in three types of community
events: household events such as anniversaries, birthdays, to which near-neighbours would be
invited, but not the whole village. Second tier events would be ones where the whole village would
be expected to be invited, such as weddings, funerals and religious festivals. Third tier events are
official village meetings, such as ones held for planning, information giving etc. This overlaps
slightly with the meetings measured in the „Decision Making‟ category, but measure frequency ofattendance only. The score was derived by multiplying the frequency category („Always, „Often‟„Sometimes‟ and „Never‟ by the value of the activity, with third-tier activities being more „valuable‟in terms of building social capital.
Social capital related vulnerability: social capital related vulnerability was estimated using aformula to calculate the overall score for social capital for members in that household, which wasconverted into a 0-1 scale for the purposes of the vulnerability model. Households whose score wasmore than 1 standard deviation below the mean are considered vulnerable in the health category.
Water/Sanitation: water and sanitation was measured with specific reference to livelihood related
vulnerability. There is a link between water scarcity, the time/resources consumed to meet householdwater requirements, and livelihoods , whereby time and resources consumed for water acquisition aretaken from productive economic activity. Hence, this study measured water and sanitation based on
three factors: time taken to acquire household water in the dry season, time taken to acquirehousehold water in the rainy season, and whether the household regularly bought water with cash.
These were combined to calculate an overall water and sanitation index.
Water/Sanitation related vulnerability: vulnerability was estimated using a formula to calculate
the overall score for water and sanitation based on the average time taken to get water, withadditional scoring if water was regularly purchased with cash. This was then inverted and wasconverted into a 0-1 scale for the purposes of the vulnerability model, so that a lower core constituted
higher risk. Households whose score was more than 1 standard deviation below the mean wereconsidered vulnerable in the water and sanitation category.

References
Agbola, T., & Jinadu, A. (1997). Forced eviction and forced relocation in Nigeria: The experience of thoseevicted from Maroko in 1990. Environment and urbanization, 9(2), 271-288.
Aggarwal, A. (2007). Impact of special economic zones on employment, poverty, and humandevelopment: Indian Council for Research on International Economic Relations New Delhi.
22

Baird, I. G., & Shoemaker, B. (2007). Unsettling experiences: Internal resettlement and international aid
agencies in Laos. Development and Change, 38(5), 865-888.
Brooks, F., Zugazaga, C., Wolk, J., & Adams, M. A. (2005). Resident perceptions of housing,neighborhood, and economic conditions after relocation from public housing undergoing HOPE
VI redevelopment. Research on Social Work Practice, 15(6), 481-490.
Cernea, M. M. (1996). Understanding and preventing impoverishment from displacement.
Understanding impoverishment: the consequences of developmentinduced displacement.
Berghahn Books, Oxford, 13-32.
Cernea, M. M. (1998). Impoverishtnent or social justice? A model for planning resettlement. Oxford,
New Delhi.
Cernea, M. M. (2008). Compensation and benefit sharing: Why resettlement policies and practices must
be reformed. Water Science and Engineering, 1(1), 89-120.
Clampet‐Lundquist, S. (2010). “Everyone had your back”: Social ties, perceived safety, and public
housing relocation. City & Community, 9(1), 87-108.
Earthrights. (2016). Analysis of the Affected Communities’ Rights and Remedies Under Myanmar Law
and JICA’s Guidelines. Retrieved from Chiang Mai: https://earthrights.org/wpcontent/uploads/thilawa_briefer_-_earthrights_international_0.pdf
Ekström, M. (1994). Elderly people's experiences of housing renewal and forced relocation: Social
theories and contextual analysis in explanations of emotional experiences. Housing Studies, 9(3),369-391.
Farole, T. (2011). Special economic zones in Africa: comparing performance and learning from global
experiences: World Bank Publications.
Gramling, R., & Freudenburg, W. R. (1992). Opportunity‐threat, development, and adaptation: Toward acomprehensive framework for social impact assessment. Rural sociology, 57(2), 216-234.
Griffiths, M. (2012a). Gender and Age related impact of Disability on Household Economic
Vulnerability:analysis from the REVEAL study in Myanmar. . Paper presented at the WorldDisability Report, Sydney University. http://www.sydney.edu.au/health-sciences/disabilitysymposium/.../MGriffiths.pptx
Griffiths, M. (2012b). Using the Umbrella Model to measure Household Vulnerability: application in
Myanmar to assess disability related vulnerability Paper presented at the 1st Global CBR
Congress, Agra, India.
Griffiths, M. (2015). Dimensions of poverty, vulnerability and social protection in rural communities in
Myanmar. Departmet of Rural Development, Government of Myanmar.
Griffiths, M. (2016a). Community Based Social Protection and Maternal and Child Cash Transfers.
Bulletin of the Social Policy & Poverty Research Group, 1(10).
Griffiths, M. (2016b). Community Social Protection Organizations in Rural Myanmar. Bulletin of theSocial Policy & Poverty Research Group, 2(3).
Griffiths, M. (2016c). Poverty Reduction through Rural Development: the Evergreen Village Project.
Retrieved from Nay Pyi Taw:
Griffiths, M. (2017). Resilience and Vulnerability in Rural Communities in Myanmar. Retrieved from
Yangon, Myanmar:
Hong, Z. (2014). Japan and China: Competing for Good Relations with Myanmar. The Journal of East
Asian Affairs, 28(2), 1.
Hwang, S.-S., Xi, J., Cao, Y., Feng, X., & Qiao, X. (2007). Anticipation of migration and psychological stressand the Three Gorges Dam project, China. Social science & medicine, 65(5), 1012-1024.
Isono, I., & Kumagai, S. (2013). Dawei Revisited: A Reaffirmation of the Importance of the Project in the
Era of Reforms in Myanmar. ERIA Policy Brief, 1.
23

Kaufman, J., & McDONNELL, K. (2016). Community-Driven Operational Grievance Mechanisms. Businessand Human Rights Journal, 1(01), 127-132.
Kleinhans, R. (2003). Displaced but still moving upwards in the housing career? Implications of forced
residential relocation in the Netherlands. Housing Studies, 18(4), 473-499.
LIFT. (2014). Reducing Economic Vulnerability Through An Equitable/Inclusive Approach To Livelihoods(REVEAL) from Livelihood and Food Security Trust Fund http://www.liftfund.org/project/reducing-economic-vulnerability-through-equitableinclusive-approachlivelihoods-reveal
McCarthy, G. (2016). Building on what’s there: Insights on social protection and public goods provisionfrom central-east Myanmar. Retrieved from
Moberg, L. (2015). The political economy of special economic zones. Journal of Institutional Economics,
11(1), 167-190.
Physicians for Human Rights. (2014). A Foreseeable Disaster in Burma: Forced Displacement in the
Thilawa Special
Economic Zone Retrieved from https://s3.amazonaws.com/PHR_Reports/Burma-Thilawa-EnglishReport-Nov2014.pdf
Rigg, J. (2015). Challenging Southeast Asian Development: The Shadows of Success: Routledge.
Sekine, Y. (2016). Land Confiscations and Collective Action in Myanmar’s Dawei Special Economic Zone
Area: Implications for Rural Democratization.
Shamai, M., & Lev, R. (1999). Marital quality among couples living under the threat of forced relocation:
The case of families in the Golan Heights. Journal of marital and family therapy, 25(2), 237-252.
Shrestha, O. L., & Chongvilaivan, A. (2013). Greater Mekong Subregion: From Geographical to SocioEconomic Integration: Institute of Southeast Asian Studies.
Slodkowski, A. (2012). Japan Inc Steals a March in Myanmar. Reuters Special Report, 3, 1-7.
Standing, G. (2013). Defining the precariat: A class in the making. Eurozine.
Tang, B.-s., Wong, S.-w., & Lau, M. C.-h. (2008). Social impact assessment and public participation in
China: A case study of land requisition in Guangzhou. Environmental Impact Assessment Review,
28(1), 57-72.
Tang, D., & Kelly, A. (2015). Dawei SEZ: The Emerging Economic Landscapes of Myanmar. Paper
presented at the International Conference on Burma/Myanmar Studies.
WALLSTRÖM, A., KYAW, D., CORNISH, G., CHAN, D., & ALLAN, D. (2016). GENDER PARTICIPATION IN THE
EIA PROCESS IN MYANMAR.
Walsh, J. (2015a). The special economic zones of the Greater Mekong Subregion: Land ownership andsocial transformation. Paper presented at the An international academic conference.
Walsh, J. (2015b). Thilawa special economic zone and the single window. International Journal ofServices Technology and Management, 21(1-3), 27-39.
Wang, J. (2013). The economic impact of special economic zones: Evidence from Chinese municipalities.
Journal of development Economics, 101, 133-147.

24