Chronic Poverty in Rural Cambodia: Quality of Growth for Whom?
Ippei Tsuruga1

Summary. – With the post-2015 era approaching, debates surrounding poverty have started
seriously considering what makes for quality growth in order to eliminate extreme poverty,rather than just reduce it. Zero poverty cannot be realised without tackling chronic poverty.
However, poverty reduction policies hardly consider particular situations and characteristicsof the chronically poor due to lack of data and evidence. In order to fill such research gaps,
this paper examines the trend and characteristics of chronic poverty in rural Cambodia
between 2004 and 2010. Applying a blend of nationally representative qualitative(participatory poverty assessment) and quantitative sources (household survey), I primarily
estimate chronic poverty headcount rates, based on criteria defined by the poor. Surprisingly,
despite the excellent progress in economic development, the chronic poverty headcount ratio
barely improved from 11 percent. The result implies that rapid economic growth has
successfully raised the consumption of chronically poor households but done little to help
them accumulate productive assets and human capital to break a vicious cycle of poverty.
Structural constraints are identified in their demographic and socioeconomic characteristics,
including: limited asset ownership, low human development, female-heads of household,high child dependency, fewer economically active members, small household size and many
young members. From a policy perspective, one striking finding is that consumption
measurements based on the current national poverty line cannot be used to identify a majorityof the chronic poor. This is not merely a matter of different measurement applications
because the chronically poor identified in this study are just as deprived as the consumptionbased poor in some other attributes like human development. The evidence suggests thatpoverty reduction programmes should take into consideration the multidimensional criteria
identified here to avoid leaving the chronically poor behind in the country’s development.
This policy implication is particularly important for targeting mechanisms of social
protection instruments implemented under the National Social Protection Strategy, which are
key measures in ending poverty in Cambodia.
Keywords – Chronic Poverty, Combining Methods, Multidimensional Poverty, Social
Protection, Targeting, Cambodia

1

Representative, JICA U.S.A Office and former Deputy Assistant Chief, JICA Research Institute (Tsuruga.Ippei@jica.go.jp).
This paper was originally prepared for a research project entitled ‘Quality of Growth’ in collaboration with the Institute ofDevelopment Studies (IDS), French Development Agency (AFD), and JICA Research Institute. I would like to thankLawrence Haddad, an anonymous reviewer, and all the project members for their comments.

11. Introduction
For the past decade, policy makers and researchers have paid great attention to pro-poor
growth, focusing in particular on what types of growth would decrease poverty. With thepost-2015 era approaching and with smaller poverty headcounts compared to the past,debates surrounding poverty have started seriously considering the quality of growth toeliminate extreme poverty in the coming decades, rather than just its decrease. Zero poverty
cannot be realised without tackling structured poverty. The Chronic Poverty Report(Shepherd et al. 2014) calls for the implementation of a comprehensive set of protective andpreventative measures for those living in chronic poverty, or those moving in and out ofpoverty over time due to limited capacities. The paradigm shift from reduction to elimination
requires future growth to be aware of whose poverty counts, or quality of growth for whom.
High growth and consumption increases are likely to benefit many of the poor, but what of
the chronic poor who structurally remain in long-term poverty. One of the critical questions
concerns the effects of past growth, particularly in terms of structured poverty, andimplications for the quality of growth in the new era.
Taking the case of Cambodia, this paper aims to assess the effects of past growth on thechronic poor by estimating the remaining population in chronic poverty, and analysing thestructural characteristics that keep them in poverty indefinitely. Cambodia still faces
significant challenges in its poverty reduction policy. After the devastating destruction ofphysical, social and human capital throughout the Pol Pot regime and the following period of
unstable recovery, the country is finally enjoying steady development. With a favourable
macroeconomic environment, the country achieved a dramatic improvement in consumption
by the poor between 2004 and 2010. With more people emerging from poverty, there are
more people concentrated near or just above the poverty line. The latest poverty assessment
has emphasised the need to prevent these people from falling back into poverty (World Bank
2013a). Despite experiencing an excellent pro-poor growth period, households with certain
attributes still live in poverty. What is missing in the poverty discussion in Cambodia, mainly
due to lack of data and analysis, is the critically important focus on chronic poverty as well as
the transient poor.
This paper attempts to make two major contributions: one is to fill the research gap onchronic poverty. Recent works by the Cambodia Development Resource Institute (CDRI) are
the only widely published studies that have assessed persistent poverty (Tong 2012a, 2012b,
2012c). The second is to provide an estimated chronic poverty headcount with locallymeaningful multidimensional criteria. The research by Tong provides a better understandingof chronic poverty through econometric analysis using panel data but has limited data
coverage. In the absence of nationwide panel data, I estimate a nationally representative
figure. The findings of this study could potentially be of benefit in identifying and targeting
programmes for the chronic poor.

22. Growth and poverty in Cambodia(1) Pro-Poor Growth and Distribution
This section assesses the extent to which pro-poor growth has been achieved from thefollowing perspectives. The definition of pro-poor growth in this study is simply “growthwith poverty reduction,” as widely adopted in the development community (DFID 2004).
Pro-poor growth can be achieved in several ways, such as income growth (Dollar and Kraay
2001, Kraay 2006), distribution change (White and Anderson 2001; Ravallion 2004) and thefavourable sectoral pattern of growth (Eastwood and Lipton 2000).
Quantitative data for poverty analysis has been derived from a series of Cambodia SocioEconomic Surveys (CSES). Following the Socio-Economic Surveys of Cambodia (SESC)
conducted in 1993 and 1996, CSES was initiated in 1997, and data is available from 1997,
1999, 2004, and every year from 2007 onwards. Since 2004, the questionnaire has been
improved to provide more information. The sample size was 12,000 households in 2004 and2009, and 3,600 households in the other years. National poverty indicators have been
calculated using CSES. In April 2013, the Cambodian government modified the official
calculation method of consumption aggregation and redefined national poverty lines based on
CSES 2009 (Cambodia. Ministry of Planning 2013). The food poverty line is now calculated
based on an equivalent food consumption of 2,200 K-calories per person per day. The total
poverty line is calculated by adding an allowance for non-food items to the food poverty line.
Taking price differences into consideration, separate poverty lines are defined in threegeographic areas: Phnom Penh (KHR 6,347), other urban (KHR 4,352), and rural (KHR
3,503) areas.
Applying the new method and adjusting poverty lines for inflation, Table 1 shows estimatedpoverty indicators. All the poverty indicators present a very positive improvement between
2004 and 2010. The poverty headcount ratio dropped significantly from 62.82 percent to20.02 percent at the poverty line and 31.67 percent to 3.38 percent at the food poverty line. In
terms of depth and severity of poverty, poverty indicators show a large improvement acrossthe nation as well. The poverty gap dropped from 22.38 points to 4.17 points, whilst thesquared poverty gap followed the same trend from 10.34 points to 1.32 points.

3

Table 1: Poverty Estimation
Indicator

RegionFood Poverty Line

Poverty Line

Obs.

2004

2010

2004

2010

2004

2010

Other Urban

22.89

2.70

53.50

15.90

8,685

2,938

Rural

35.50

3.83

66.61

21.54

45,25810,011

Phnom Penh

6.88

0.38

39.09

11.93

5,909

3,561

Cambodia

31.67

3.38

62.82

20.02

59,852

16,510

Other Urban
Rural
Phnom Penh

5.96
8.98
1.79

0.45
0.62
0.05

19.02
23.94
12.05

3.36
4.47
2.63

8,685
45,258
5,909

2,938
10,011
3,561

Cambodia

8.03

0.55

22.38

4.17

59,852

16,510

Other Urban

2.30

0.10

9.03

1.12

8,685

2,938

Rural

3.27

0.14

11.05

1.40

45,25810,011

Phnom Penh

0.75

0.01

5.34

0.82

5,909

3,561

Cambodia

2.95

0.12

10.34

1.32

59,852

16,510Updated

Other Urban

1,774

2,694

2,962

4,498

-

-

poverty

Rural

1,565

2,377

2,384

3,620

-

-

lines (riels)

Phnom Penh

2,124

3,226

4,319

6,559

-

-

p0

p1

p2

Headcount ratio (%)

Regional Share (%)

2004

2010

2004

2010

2004

2010

Mekong Plain

63.92

18.02

54.95

43.57

26,5485,485

Tonle Sap

69.29

16.66

26.8

21

11,384

2,659

Coastal

58.22

17.99

5.64

5.58

2,840

665

North and Northeast Mountain

79.83

45.63

12.61

29.85

4,486

1,202

Cambodia

66.61

21.54

100

100

45,25810,011

Region (Rural Only)

Obs.

Source: Author’s calculations based on CSES
Note: Poverty lines have been adjusted by consumer price index (CPI) in Phnom Penh equally across the regional poverty
lines because the regional CPI breakdown is not available. Fixing the averaged CPI October/December 2009 as 100 points,
CPI for the other reference years was 0.68 for 2004 and 1.03 for 2010 (Carpenter 2012, National Institute of Statistics). All
presented indicators have been assigned population weights provided by each survey, calculated based on General
Population Census 1998 and 2008 respectively for CSES 2004 and 2010. Poverty indicators are calculated based on the
Foster-Greer-Thorbecke method (1984).

This trend is likely a result of the favourable macroeconomic environment and growth pattern.
Between 2004 and 2010, even during the global financial crisis, the country enjoyed 6.17
percent annual growth in GDP per capita. Sectoral growth took place almost evenly inagriculture, which most poor people rely on, at 6.81 percent, as well as manufacturing andservices, at about 8 percent (Appendix 1). The consumption growth of the poor was higher
than that of growth rate in mean and at median in all regions, increasing annually by 9.11
percent in Phnom Penh, 11.66 percent in other urban and 10.34 percent in rural areas(Appendix 2). The growth incidence curve clearly indicates that growth and distribution
patterns were pro-poor in the rural settings. Moreover, human development has improved
substantially. The net enrolment ratio presents an upward trend at all levels, primary, lower

4secondary and upper secondary whilst school attendance and adult literacy follow similar
trends. Child mortality has also improved in terms of neonatal, infant and under-5 age groups.
Overall, the quantitative data both on macro and micro confirm that the country has achieved
pro-poor growth.
Looking closely into the socioeconomic data in 2010, geographic distribution and economic
activities illustrate poverty characteristics further. All poverty indicators are noticeably higher
in rural regions than urban settings. Rural poverty was 21.54 percent whilst urban poverty
was even lower, 11.93 percent in Phnom Penh and 15.9 percent in other urban areas.
Although the difference in poverty rates between rural and urban areas might be seen as small,
it is significantly different in absolute terms. In spite of increasing urbanisation, rural areas
are still home to 80 percent of the population and over 86 percent or 2.3 million of the poor.
This geographic distribution correlates with the trends in economic activities: 72 percent ofpoor people depend on agriculture2 for their livelihoods in Cambodia, and the rate increases
to 78 percent in rural settings. Of the poor agrarians, crop farmers account for 80 percent.
Although agricultural labourers, livestock farmers, forestry workers, fishery workers andhunters constitute a minority (12 percent), these groups contribute a higher share to poverty(18 percent) than their population share. Poverty rates are relatively higher among them too.
In rural areas, 33 percent of fishery workers and 40 percent of agricultural labourers live
below the poverty line. On the other hand, urban poverty shows quite different patterns ineconomic activities, with 95 percent of the poor in Phnom Penh and 52 percent in other urban
areas working in secondary or tertiary sectors.
To sum up, Cambodia seems to have achieved pro-poor growth between 2004 and 2010. Bothmacroeconomic environment and human development progress were steadily positive,
consumption growth of the poor increased more than that of other socio-economic groups,
and poverty indicators dramatically improved.
(2) Existing Literature on Chronic Poverty
Quantitative regional studies provide fruitful insights on chronic poverty. Tong (2012a)
conducted a dynamic poverty study using CDRI panel data of 793 households, collected in
2001, 2004/05, 2008 and 2011, from nine villages in seven provinces in four geographic
regions, including Tonle Sap, Mekong Plain, Plateau and Coastal areas. The data include
information on household demographics, consumption, asset ownership and economic
activities. The study applies principal component analysis to construct a wealth index frommixed asset ownership for ranking households (Filmer and Pritchett 2001). Households
always below the set poverty line (40th percentile or 60th percentile) are regarded as thechronic poor. The study finds that most poverty was transient during the period. It also finds
the following characteristics of the chronic poor households. Compared to non-poorhouseholds, the chronic poor households are likely smaller and have more children under-six
2Main economic activities of households are defined by time that household members spend on particular sectors. A detailed
definition is discussed later.

5

years, fewer adults aged 15-64, and the household heads tend to be younger, less educated,female and single. They likely lack agricultural land, non-land assets, livestock, andconnection with their community than other households.
Using the same dataset, Tong (2012b, 2012c) reassesses chronic poverty with consumption
measurements to compare results. It confirms all the major findings above except forhousehold size and age of household heads. While the asset approach finds that the chronicpoor tend to be in smaller households and their heads are likely young, the consumption
approach finds larger households and no significant trends in head’s age among them. Thereis no further analysis of these particular contradictions.
These findings provide a valuable foundation for understanding chronic poverty in Cambodia.
In relation to the previous studies by Tong, this paper potentially makes an important
contribution by confirming some of his major findings from a different approach and at thenational level. There are certain differences in methodologies and scopes. For example, while
Tong’s studies cover households with all the occupations in the selected areas, this paper
limits its scope of analysis to agrarians in rural areas across the nation. While Tong’s study
uses consumption poverty and a wealth index, this study defines chronic poverty by local
perspectives through PPA as discussed later.
3. Methodology for identifying chronic poverty
The methods for measuring the persistence of poverty have been disputed. In
contradistinction to transient poverty, chronic poverty is commonly defined by poverty overlong durations and regarded as intergenerational transmission of poverty through transferred
capital or assets (Hulme and Shepherd 2003). For the identification of chronic poverty inpractice, one of the most common approaches is quantitative assessment using a set of panel
data (McKay and Lawson 2002; Haddad and Ahmed 2003; Wadugodapitiya and Baulch2011). Comparing income or consumption of the same households over time, it provides
informative analysis with figures. On the other hand, Hulme, Moore and Shepherd . (2001,34) argued that monetary measurements cannot fully reflect the complexity of chronic
poverty; therefore such analyses need to take into account the multidimensional
characteristics of chronic poverty and can benefit from qualitative or subjective assessment
by poor people themselves. White (2002) also argued that productive synergy can beestablished between them in poverty analyses.
Building upon these ideas, Howe and McKay (2007) developed an innovative framework for
identifying chronic poverty by combining both quantitative and qualitative approaches. They
pointed out that panel data analysis provides a narrow understanding of chronic poverty
within the capacity of data availability or questionnaires, although it provides the numeric
results that policy makers prefer to have for decision making. It can also assess relatively
short spans and is usually sensitive to measurement errors. They also acknowledge the pros
and cons of a qualitative approach. It provides narrative information based on rich local
knowledge and experience that is usually missed by purely numerical methods. Data can be6ambiguous and therefore difficult for policy makers to use the results, as there are fewer
objective figures and macro perspectives. To overcome the limitations and maximise theadvantages, they innovated by combining the methodologies. The major value of the resulting
methodology is that it does not require panel data – instead, it uses cross-sectionalsocioeconomic data at a single point in time and qualitative information collected throughparticipatory poverty assessments (PPA). They proposed undertaking several steps ofanalysis: firstly, selecting criteria of chronic poverty defined by the poor people themselves in
the PPA, translating those qualitative criteria onto nationally representative household data,
and finally checking the robustness and sensitivity of the estimation.
As Howe and McKay (2007) acknowledge, the sustainable livelihood approach (SLA) is a
useful framework to understand qualitative information in a vicious poverty cycle at the stageof criteria selection (Ellis 2000, 2006). In SLA, livelihood is conceived as a cycle of threemain components: assets, activities and outcomes. Assets consist of five or more types suchas human capital (skills, education, health), physical capital (goods), financial capital(savings, access to loans), natural capital (land, water, forest), and social capital (kinship,friendship). Households are considered to mobilise those assets to produce outcomes throughdifferent types of economic activities, and invest the outcomes to accumulate assets again. In
terms of vulnerability in SLA, households are considered to utilise assets to practise risk
management and coping strategies. If they manage the sequence successfully, they are able tobuild up assets. If unsuccessful, they deplete assets. Institutions and policies are involved in
the framework to reduce vulnerability. In relation to chronic poverty, destitute households
may live in a vicious cycle of asset-activity-outcome.
In this study, I adopt the approach of Howe and McKay (2007) to identify chronic poverty.
The approach is relevant for this study in two reasons. The first reason is data availability.
Cambodia does not have nationwide panel data but socioeconomic survey data and the resultsof PPA are available. Secondly, the methodology allows estimating the chronic poverty
headcount at two points in time, enabling analysis of the extent to which pro-poor growth
benefits the chronic poor over the period. As Howe and McKay admit, this is not the mostrigorous way to estimate chronic poverty and it has a tendency to underestimate thepopulation but it is still considerably useful in assessing chronic poverty given the absence of
panel data.
4. Qualitative insights into chronic poverty in Cambodia
This section reviews qualitative information to identify the characteristics of chronic poverty.
The qualitative information is derived from the PPA conducted across the nation by the AsianDevelopment Bank between October and December 2000 (ADB 2001). The PPA compiled
local voices through focus group discussions (FGDs), formulated in geographically targetedpoor regions based on quantitative surveys (National Population Census and CSES) andselection by local authorities, community or village members and nongovernmentalorganisations. Locally selected poor people participated in 169 FGDs in 154 villages in 70
communes in all 24 provinces and in additional 15 urban areas. The regional share of FGDs
7

was 47 percent in the Mekong Plain, 29 percent in Tonle Sap, 12 percent in the North andNortheast Mountain regions, and 12 percent in coastal areas. The participants included avariety of vulnerable groups: women, children, rural farmers, fisher folk, ethnic minority
groups, female-headed households, demobilised soldiers, orphans, street children, sex
workers, plantation workers, garment workers, garbage collectors and cyclo-drivers. Females
and ethnic minorities accounted for over 50 percent and 13 percent respectively. The PPA
paid particular attention to the process to have real voices from those socially weaker groups.
For instance, the team members conducted separate discussions with women in situations
where they could not openly explore gender issues in the FGD where men were present.
As designed, the PPA provides deep insights about the livelihoods and demographic
characteristics of poor households. It found that food insecurity is a primary concern for all
poor households regardless of region and ethnic group. Poverty means they spend a large
amount of time looking for food, potentially causing loss of other opportunities such as
participation in village activities. It also found that food foraging activities are often
undertaken by women and children; therefore children, particularly girls, in poor families
potentially have a higher risk of missing educational opportunities. Most PPA participants
generate their livelihoods through agriculture. Rice farming was listed as the most importanteconomic activity by 83 percent, with market gardening second at 16 percent, and raising
livestock third at one percent. Some households certainly mixed those activities but very few
households had other supplemental activities for livelihood. Moreover, the PPA found a
vicious poverty cycle in relation to asset deprivation. In rural Cambodia, the poorest families
tend to sell assets to cope with major shocks like natural disasters, sickness or death ofhousehold members, resulting in low levels of asset ownership. As ownership of productive
land was listed as very important for their lives, the coping strategy of selling land is certainly
not an easy choice for the poor.

8

Table 2: Household Characteristics Identified in Participatory Poverty Assessment
Category
PoorestKror bamphot:
Extremely poor
Toal: People who haveno way out of theirpresent situation

Poor
Kror: Literally poor
Kror thomada: Typicalpoverty
Lower medium income
Kror imom: Reasonably
poor
Kandal: Medium
Middle income
Mathyum: Average
Kandal: Medium

Least poor (Non-Poor)
Throuthear: Fully selfsufficient without any
debts;
Neak leu: Living above
poverty

Household Characteristics
1. Little or no land (0.8-1.2 ha)
2. Perhaps one draft animal but no farming implements
3. Housing made of thatch in very poor condition
4. Few household utensils
5. Live on hand-to-mouth basis (food shortages for up to eight months)
6. Much reliance on natural resources to meet subsistence needs
7. Accumulated debts and inability to repay or borrow additional amounts
8. No kinship support
9. Large young families with 5-12 children
1. Less than 2 ha of land in unfavorable locations
2. Usually have at least a pair of draft animals and some farm implements
3. Houses made of thatch sometimes with tile roof and bamboo walls
4. Limited number of household utensils
5. Food shortages for 3-6 months
6. Able to borrow money for rice farming
1. Less than 3 ha of land
2. Draft animals and farm implements
3. Houses made of wood or bamboo, thatched roof and walls and tile roof
4. Limited number of household utensils
5. Food shortages for 3-4 months
6. Able to borrow money for rice farming
1. Land holdings of up to 6 ha
2. 2-4 draft animals, some livestock and all farm implements
3. Houses made of wood with either bamboo or wooden floors and tile roof
4. Reasonable number of household utensils
5. No food shortages except when major crisis or ritual occurs
6. Limited cash savings
7. Small-scale business
8. Old motorbike or boat
1. More than one hectare of very productive agricultural land
2. At least two draft animals and many other livestock and farm implements
3. Houses made of permanent building materials, including corrugated iron andtiles
4. Full food security with limited surplus for lending, sale or labor exchange
5. Well-furnished households, often with television
6. Able and willing to lend money to other villagers

Source: Summarised by the author based on the PPA (ADB 2001)

The PPA identified five broad livelihood ranks and those characteristics (Table 2), enabling achronic poverty threshold to be defined. The first category clearly implies chronic poverty.
The literal description in local language is Toal, people who have no way out of their present
situation, and Kror Bamphot, extremely poor. The identified characteristics also confirm asignificant deprivation – namely, lack of food security most of the time (eight or moremonths per year), relying on subsistent livelihoods (living on a hand-to-mouth basis), no
productive assets or kinship support. Spending most months hungry and unable to escape thesituation, those households in this category can be clearly identified as being in chronic
poverty. The second and third categories are too ambiguous to be regarded as chronic poverty.

9Households in these two categories have land at unfavourable locations and limited farming
implements with relatively long term food shortages. The PPA describes their marginalised
situation but does not provide a clear definition or characteristics of poverty persistence. In
order to avoid ambiguity and subjectivity for selection criteria, this study regards only the
first category as chronic poverty.
Reflecting the limitations in coverage of the PPA, the above criteria potentially underestimate
chronic poverty in particular groups. Firstly, the PPA provides little information about urban
chronic poverty. Although there are some related descriptions such as lack of in-house toilet,
mobile phone, car or motorcycle, or child’s education, it does not link to those characteristics
to poverty persistence. Due to this lack of clear definition, this study is unable to estimate
chronic urban poverty and therefore the following analysis focuses on rural areas. Secondly,
the PPA does not provide sufficient information about characteristics of chronic poverty insecondary and tertiary sectors; therefore, the study’s scope is limited to chronic poverty inagriculture in terms of livelihood. Given the lack of information, it also potentially
underestimates the chronic poor who rely on non-farming agricultural subsectors. For
instance, as resource-based livelihoods are reported to have very different characteristics
from farming (Ballard et al. 2007), households relying on fishery and forestry would not be
rigorously identified in chronic poverty by the single set of criteria. Similarly, chronic
poverty among ethnic minorities cannot be easily identified by the single selection criteria
because each tribe has a variety of perceptions of poverty. For example, whilst the Stieng and
the Tumpoun recognise loss of cultural identity as a characteristic of poverty, the dominant
lowland Khmer, the Cham or the Vietnamese do not have such perceptions. Stieng
participants stated that they do not even have a term to describe poverty and would notcompare life with others in their culture. A tribal elder of the Tumpoun in Ratanakiri defines
poverty based on situations in which they would be unable to protect and hand over their land
to the next generation, and they would not be rich even if they had enough money. Such
differences in values cannot be taken into account in this study.
Lastly, on a possible critique for using the PPA conducted over a decade ago, I would argue
that the validity can be reasonably confirmed, because Tong’s work (Tong 2012a, 2012b,
2012c), conducted throughout the decade after the PPA, also found similar demographic
characteristics of chronically poor households, including lack of agricultural land, non-landassets, livestock, and networks with their community.
In summary, this study defines chronic poverty by the first category of Table 2 and focusessolely on rural areas. With limited information, this study potentially underestimates chronic
poverty among households who make their livelihood through non-farming activities, theurban poor and ethnic minorities. Further studies could explore such categories. Nevertheless,this study is still of value because the proportion of the population in urban settings, forestry,
fishery or ethnic minority groups is relatively small and the vast majority of rural populations
are covered in the following estimation.

10

5. Chronic poverty estimation
I will now combine the quantitative information and the qualitative data to estimate chronic
poverty. In order to identify the chronic poor, the general principle of criteria selection here isto translate as many local definitions as possible to household survey data. One critique of
this combining method is that selected criteria are loosely associated with PPA results(Shaffer 2013, 49). Therefore, it is crucial for this study to test the robustness and sensitivityof the estimation result. This section reviews the descriptive statistics of each dimension that
the PPA identifies, followed by an estimated chronic poverty headcount and finally, arobustness and sensitivity analysis.
(1) Descriptive Statistics and Discussion on Selection Criteria
Concerning economic activities, 77.34 percent of rural people relied on agriculture in 2004
and 72.87 percent in 2010 (Table 3). The data allows further breakdown into agriculturalsubsectors, but the categorisation is not fully comparable over the two datasets at thesubsector level. Among agrarians in 2010, most of them lived on crop farming (87.53
percent), while others relied on agricultural labour (7.5 percent), livestock raising (2.37
percent), fishery (1.35 percent) and forestry (0.26 percent).
In terms of asset ownership among agrarians in each survey year respectively, 58.81 percent
and 57.34 percent owned one hectare or less of land for any agricultural activities such as
vegetable gardening, crop cultivation, livestock raising or private forestry; 42.19 percent and46.48 percent owned one or no draft animals, which included cattle, buffaloes, horses andponies but excluded other types of livestock like pigs, sheep, goats, chickens, ducks or quails;
82.35 percent and 75.44 percent owned no high value farming implements, such as tractors,
bulldozers, threshing machines, hand tractors, rice mills, or water pumps; 58.49 percent and57.06 percent lived in houses where the walls or roof are made from bamboo or thatch.
Some particular dimensions need further discussion in order to determine selection criteria.
Firstly, the PPA found that the chronic poor tend to have 0.8 hectare to 1.2 hectares ofagricultural land, as shown in Table 4, while also indicating that owning productive croplandis one criterion for non-poverty. The major difference between these two descriptions isquality of land. Unfortunately, there is no translatable quantitative data available todistinguish the quality of land. Therefore, defining chronic poverty by taking land ownershipof between one hectare and 1.2 hectares potentially includes households with productive land
who are not poor according to the PPA. To avoid the inclusion error, this study takes one
hectare as a threshold.
Secondly, the farming implement criterion is disputable. Selecting households who have ‘nofarming implements’, as the PPA indicates, identifies only 4.61 percent and 0.51 percent inrespective years. It may cause significant underestimation of chronic poverty. On the other
hand, selecting households with ‘a few farming implements’ potentially identifies those withproductive agricultural machines. As the PPA implies that the poor rely on low productive
activities, they are unlikely to own such modern farming tools. In order to minimise both11

inclusion and exclusion errors, this study adopts ownership of low productive farming
implements including an animal cart, plough, harrow, rake, hoe, spade, axe or none as acriterion. In other words, households with high productive farming implements are excluded
from this category. The number of owned implements is not considered here because the PPA
does not specify the extent of ownership.
Thirdly, the PPA identifies that housing in chronically poor households is likely made ofthatch in very poor condition. Although it does not specify what types of housing materials
are indicated, roofs and walls are repeatedly mentioned in the other categories. As thehousehold survey data do not allow division of bamboo and thatch and those qualities, those
two materials, in any conditions, are treated as a criterion in this study. Finally, the otherlisted characteristics in the first category are not precisely translatable due to either limitationof the survey data or the PPA description. They mostly provide rich understanding of chronic
poverty but are insufficient as identification criteria. A wide range of utensil variables is
actually available in the survey data but the PPA provides little indication of what types ofdurables the participants meant. Although utensil ranking and assigned weights can probably
be inferred through statistical techniques like principal component analysis (Filmer andPritchett 2001), the result would not reflect the self-rated characteristics of the poor people inthis study.
Similarly, the narrative description of “live on hand-to-mouth basis” or “food shortages” isnot directly translatable into the survey data, but alternatively, consumption data are available.
Variables for debt accumulation and kinship support are not available in the household
survey data. Lastly, the dimension of “large young families with 5-12 children” is too
ambiguous to be taken as a criterion and partially conflicts with the survey data. The survey
data show no families with more than nine children and very few of them, 1.44 percent in2010, have five children or more. This contradiction is probably because children in the PPA
period have grown up and the household size norms have changed. However, this assumption
cannot be verified with the available information. Some of these indicators will be used totest estimation robustness later.

12

Table 3: Main Economic Activities and Asset Ownership in Rural Cambodia
2004

2010

Main Household Economic Activities (%)
Crop farmers
Livestock farmers
Forestry workers
Fishery workers or hunters
Agricultural labourer
Mixed agriculture
Non-agricultural activities
None
Obs.

22.05
0.60
45,258

63.50
1.73
0.19
0.99
5.47
0.99
26.73
0.40
10,011

44.91
13.90
41.19
34,786

45.09
12.25
42.66
7,317

32.56
9.63
57.81
34,786

37.2
9.28
53.52
7,317

1.55
16.10
77.74
4.61
34,786

0.78
23.77
74.93
0.51
7,317

16.50
41.99
41.50
34,772

13.84
43.22
42.94
7,317

77.34

Land (among agrarian) (%)
0.8 ha or less0.8< & <=1 ha
1 ha <
Obs.
Draft Animal (among agrarian) (%)
None
One
Two or above
Obs.
Farming Implement (among agrarian) (%)
High farm implements only
Both high and low value farm implements
Low value farm implements only
None
Obs.
Housing Material (among agrarian) (%)
Wall and Roof made of Bamboo or Thatch
Wall or Roof made of Bamboo or Thatch
Others (Tiles, Fibrous cement, Concrete etc.)
Obs.

Source: Author’s calculations based on CSES
Note: Main economic activity is defined by share of time that household members spend on each activity. The sum ofmonths that household members spend in agriculture is divided by the total sum of months in all occupations to obtain theshare of agricultural activity for each household. Then, main economic activity is identified in agriculture if the share is 50
percent or above. All the presented data are population-weighted.

(2) Selection Criteria and Estimation Result
As discussed above, this study adopts the following criteria for identifying chronic poverty,
and regards households that meet all of these criteria as clearly chronically poor according to
the local definition. The identified households would not have sizable-enough land to harvest
sufficient food for household subsistence needs. They have very limited farming assets toincrease the productivity and efficiency of farming activities, although they invest most timeand labour in agriculture throughout the year. The vicious poverty cycle can be observed in13the SLA framework as well. The criteria take into consideration physical capital (draft
animals and farming implements), natural capital (agricultural land) and human capital(labourers). This provides a convincing enough picture of the negative spiral of poverty in thehousehold: Main household economic activity is agriculture, Household owns agricultural land of one hectare or less, Household owns one draft animal or none, Household owns no high value farming implement, and Household walls or roof is bamboo or thatch.
Of the total rural population, these criteria identify chronic poverty rates of 11.53 percent and11.34 percent in the reference years (Table 4). Although there are a few variations acrossdifferent regions, it is notable that the chronic poverty headcount almost levelled off over thefavourable period for economic growth and reduction in consumption poverty.
Table 4: Estimated Chronic Poverty Headcount in Rural Cambodia
Headcount ratio (%)

Regional Share (%)

2004

2010

2004

2010

2004

2010

Mekong Plain

11.04

12.24

54.8

56.19

26,5485,485

Tonle Sap

13.63

10.25

30.45

24.53

11,384

2,659

Coastal

7.39

9.56

4.13

5.63

2,840

665

North and Northeast Mountain

11.64

10.98

10.62

13.64

4,486

1,202

Cambodia

11.53

11.34

100

100

45,25810,011

Region (Rural Only)

Obs.

Source: Author’s calculation based on CSES

(3) Robustness Analysis
In order to see the robustness of estimation, I compared the other indicators of poverty andhuman development, specified by PPA participants, between two groups: the chronic poorand non-chronic poor in the same economic activity at the 95 percent confidence level. The
PPA claims that ill health and education access are major determinants of poverty and foodshortages are important factors in defining chronic poverty. In relation to these descriptions,
there are rich quantitative data available to create indicators of human development andconsumption.
In general, the result shows that the estimation is robust across different indicators (Table 5).
The education indicators of the chronic poor, including school enrolment, attendance andadult literacy, are significantly lower than those of the others in the same economic activity,
except for primary net enrolment ratio in 2010 (for which the difference is statistically notsignificant). Looking over time, although the primary net enrolment ratio has improved
equally and is even slightly higher among the chronic poor, the gap becomes much moreevident in secondary education. Whilst both lower and upper secondary net enrolment ratio

14

improved remarkably among the non-chronic poor, the results were different for the chronic
poor. Only 8.93 percent of chronic poor children in the relevant ages go to lower secondary
school, compared to 27.19 percent in non-chronic poverty in the same activity, and 35.53
percent for the non-agrarians. Moreover, the share of household members who ever attended
school and the proportion of adult members who are able to read and write show considerable
deprivation among the chronic poor.
Similarly, prevalence of illness or injury tends to be slightly higher among the chronic poor.
The share of people who seek advice or care from health practitioners is not very different
between the groups. That is probably because access to health care services improved equallyand most people now seek health care services when they become ill. In terms of
consumption, the estimation is also robust. The result presents a large proportion of chronicpoor identified in the bottom consumption quintile, 34.32 percent and 32.83 percent, and few
in the highest quintile, 5.99 percent and 3.16 percent, respectively; and most of the otherchronic poor are concentrated in second and third lowest quintiles. The comparison of foodconsumption also follows the same distribution pattern. Overall, almost all indicators
demonstrate a significant difference between the chronic poor and the others both in 2004 and2010. It may provide an indication of the robustness for the estimation.

15

Table 5: Comparison of Socio-Economic Indicators
2004
Indicators (%)(1)
Chronic
Poor
(CP)

(2)
Non-CP
in sameactivity

All other

Total(2) - (1)Diff.

t-value

Net enrolment ratio (ages 6‐11)

65.60

74.84

79.72

74.70

9.24

5.34

Net enrolment ratio (ages 12-14)

4.84

10.26

21.34

12.17

5.42

4.54

Net enrolment ratio (ages 15-17)

2.55

3.64

8.75

4.67

1.10

1.12

Ever attended school (ages 5+)

61.52

72.59

81.39

73.39

11.08

13.92

Adult literacy (ages 15+)

50.55

64.50

77.39

66.07

13.95

13.91

Prevalence of illness or injury

20.51

18.20

16.99

18.19

-2.31

-3.75

Seek care during the survey period

13.64

11.70

10.97

11.76

-1.94

-3.68

Seek care when ill or injured

66.87

64.36

64.70

64.76

-2.51

-1.56

1st quintile, Food consumption

27.37

24.63

14.85

22.73

-2.74

-3.98

5th quintile, Food consumption

8.86

11.21

24.07

13.86

2.35

5.38

1st quintile, Total consumption

34.32

24.58

13.61

23.22

-9.74

-13.43

5th quintile, Total consumption

5.99

9.41

25.40

12.64

3.42

9.22

Food consumption

1,351

1,459

1,903

1,547

108

9.88

Total consumption

1,920

2,270

3,761

2,568

350

12.84

2010
Indicators (%)(1)
Chronic
Poor
(CP)

(2)
Non-CP
in sameactivity

All other

Total(2) - (1)Diff.

t-value

Net enrolment ratio (ages 6‐11)

87.51

81.95

90.48

84.77

-5.57

-1.95

Net enrolment ratio (ages 12-14)

8.93

27.19

35.53

27.43

18.26

4.67

Net enrolment ratio (ages 15-17)

2.74

11.72

23.20

13.94

8.98

3.69

Ever attended school (ages 5+)

68.30

78.05

85.94

79.14

9.75

6.06

Adult literacy (ages 15+)

56.50

71.43

81.31

72.65

14.93

7.37

Prevalence of illness or injury

23.36

18.60

21.46

19.91

-4.76

-3.47

Seek care during the survey period

19.97

16.21

18.96

17.38

-3.76

-2.92

Seek care when ill or injured

85.51

87.18

88.36

87.30

1.68

0.65

1st quintile, Food consumption

32.83

23.51

21.19

23.94

-9.31

-6.17

5th quintile, Food consumption

5.54

9.12

16.64

10.75

3.58

4.63

1st quintile, Total consumption

38.46

23.20

19.44

23.91

-15.26-9.77

5th quintile, Total consumption

3.16

8.78

18.81

10.86

5.62

9.04

Food consumption (constant 2004)

1,937

2,132

2,398

2,182

195

7.39

Total consumption (constant 2004)

2,978

3,780

4,570

3,904

802

13.15

Source: Author’s calculation based on CSES

(4) Characteristics of Chronic Poverty, Regional Distribution and Key Factors
The previous sections have shown some key characteristics of the chronic poor – they have
limited assets and relatively lower human development. Table 6 shows additional
demographic characteristics. Chronically poor households are more likely to be headed by16

females with 31.96 percent, compared to 17.72 percent for the non-chronic poor in the same
economic activities. The proportion of either elderly- or ethnic-minority-headed households
is not statistically significant. Notably, households in chronic poverty tend to have higher
dependency, in particular child dependency, mainly due to fewer working aged members, andhousehold size is significantly smaller compared to other groups. The chronic poor also tend
to be younger. The average age of household heads and members is about two years younger
than the national average.
These results support some of the key findings of the previous studies. As Howe and McKay(2007) in Rwanda and Tong (2012a, 2012b, 2012c) in Cambodia found, chronically poor
households are likely female-headed and smaller in rural Cambodia. As Tong also found, thechronic poor are liable to have fewer adults, younger members and less educated household
heads. From a different approach, this paper confirms that chronically poor households seemto have structural challenges to accumulate human capital and make a living with fewer
economically active members and high child dependency.
Table 6: Demographic Characteristics of Chronic Poor in 2010

HH head: female (%)(1)
Chronic
Poor
(CP)
32.01

(2)
Non-CP
in sameactivity
17.53

HH head: elderly 65+ (%)

10.27

HH head: ethnic minority (%)

All other

Total(2) - (1)Diff.

t-value

24.79

21.35

-14.48

-4.82

11.13

11.29

11.06

0.86

0.43

4.19

4.95

2.65

4.24

0.76

0.59

HH head: age

43.66

45.81

46.67

45.76

2.15

2.28

HH member: age

24.55

26.82

26.98

26.60

2.26

3.60

HH head: school attainment (year)

3.03

3.99

5.29

4.22

0.96

4.79

Average HH size

3.98

4.65

4.62

4.55

0.67

5.65

Average number of working age

2.27

2.92

2.99

2.85

0.64

8.15

Average number of children 0-14

1.54

1.51

1.41

1.49

-0.03

-0.31

Average number of elderly 65+

0.17

0.22

0.22

0.21

0.05

1.86

Dependency ratio (%)

85.13

73.45

67.18

73.30

-11.68-2.24

Child dependency ratio 0-14 (%)

75.91

62.21

59.02

63.12

-13.70-2.83

Aged dependency ratio 65+ (%)

9.22

11.24

8.16

10.17

2.02

1.02

Indicators

Source: Own calculations based on CSES

Looking at regional distribution, the criteria seem to capture the chronic poor better in someregions than in others. Most notably, the criteria possibly underestimate chronic poverty in
the North and Northeast Mountain region. The consumption poverty headcount ratio was
45.63 percent in the region in 2010, compared to 16.66 percent to 18.02 percent in the otherregions. This regional disparity does not appear on the estimated distribution of chronic
poverty, which is almost at the same level across the four regions. This estimation gap
between the two measurements was also observed in 2004. One possible reason for theunderestimation is diversity within the region. As discussed later, estimation in the region isrelatively more sensitive to housing and land ownership criteria than the other regions. It
17possibly reflects the diversity of ethnicity, livelihood and concept of value, which
standardised criteria cannot capture. To overcome this potential underestimation, more
information both from quantitative and qualitative sides is necessary.
Regarding key factors, ownership of high value farming implements is the most influential
variable among the four criteria (Appendix 5). With the highest contribution rate, lack of ahigh value farming implement explains chronic poverty most, followed by non-ownership ofland, lack of draft animals and type of housing materials. In other words, owning high value
farming implements has the largest impact on decreasing chronic poverty among the four
variables; and worsening housing materials to thatch, and loss of draft animals to zeropotentially increase chronic poverty most. Moreover, in the North and Northeast Mountainregion, the ranking is clearly different from the others: farming implements, housing, landand draft animals in order. This implies again that particular attention needs to be paid to theunique and diverse characteristics of the Mountainous region in identifying chronic poverty.
(5) Chronic Poverty and Consumption Poverty
In practice, poverty targeting commonly focuses on consumption poverty; it is useful to knowwhether consumption poverty can provide a good proxy for chronic poverty. Comparison
between consumption poverty and chronic poverty shows that measurement based on thenational poverty line cannot capture a large proportion of the chronic poor identified in thisstudy (Table 7). The total poverty line identifies only 36.32 percent of the chronically poor.
Nevertheless, in terms of human development, the rest of the chronic poor (chronic poor but
not consumption poor) are also greatly deprived. Their education indicators are as low as theconsumption poor in lower and upper secondary enrolment, school attendance and adultliteracy, except for the primary enrolment ratio. Health indicators show that the chronicallypoor become ill slightly more frequently than the consumption poor but access health careservices almost equally. One interesting question for further studies at this point is how muchthe chronic poor spend on health services. They might be trapped in a vicious cycle of longterm poverty because of high prevalence of illness and health expenditure. In addition, somecounterintuitive differences in demographic characteristics are worth noting. Unlike in otherlow-income countries, female-headed households are not a particular phenomena among theconsumption poor in Cambodia but more evident amongst the chronic poor. The chronic poor
tend to have smaller families while the consumption poor have larger ones, although bothgroups face higher dependency in common compared to the average.
Inconsistencies between the two measurements likely become greater when the population
below the poverty line is smaller. In fact, the consumption poor overlapped 79.44 percent of
the chronic poor in 2004, compared to only 36.32 percent in 2010. It works even better tolook at consumption poverty in relative terms – by adjusting consumption quintiles, chronic
poverty can be identified more successfully. Looking cumulatively from the bottom, there are
38.46 percent in the lowest quintile, 65.41 percent in the second and 87.41 percent in the third
bottom quintile in 2010. The result was almost identical in 2004, with 80.35 percent in the18third bottom quintile. The national poverty line fails to identify the majority of the chronicpoor but most of them are identified in the third cumulative consumption quintile.
The challenge of consumption poverty measurement is that it potentially underestimates thechronic poverty identified in this study, and that the applied criteria here overestimate it byincluding better-off people in consumption term. There is no doubt that consumption poverty
measurement is useful for chronic poverty identification because households with the mostvulnerable demographic characteristics appear in a group identified both in consumption
poverty and chronic poverty. This study therefore suggests that consumption based targeting
programmes should apply the criteria to identify chronic poverty in a mutuallycomplementary manner. For example, this method can be used to identify potentiallychronically poor households above the consumption poverty line, and to divide theconsumption poor into the persistent poor and the others.

19

Table 7: Comparison between Consumption Poverty and Chronic Poverty among Rural Agrarians in 2010
(A)

(B)

(D)
NonConsumption
Povertyand
Non-CP(A) + (B)(A) + (C)Consumption
Povertybut
Non-CP(C)
NonConsumption
Povertybut
CPConsumption
PovertyandCP

Consumption
Poverty

Chronic
PovertyTotal

Net enrolment ratio (ages 6‐11)

86.28

71.32

88.64

86.45

75.04

87.51

82.90

Net enrolment ratio (ages 12-14)

2.33

14.75

14.90

30.65

11.47

8.93

24.62

Net enrolment ratio (ages 15-17)

2.99

3.95

2.57

13.62

3.74

2.74

10.63

Ever attended school (ages 5+)

69.31

68.87

67.73

80.33

68.97

68.30

76.59

Adult literacy (ages 15+)

54.63

60.29

57.37

73.77

58.96

56.50

69.28

Prevalence of illness or injury

21.09

13.59

24.65

19.91

15.42

23.36

19.34

Seek care when ill or injured

88.17

80.81

84.21

88.32

83.26

85.51

86.87

HH head: female (%)

20.67

16.76

36.18

17.68

17.78

32.01

20.10

HH head: elderly 65+ (%)

2.30

8.79

13.20

11.59

7.10

10.27

10.98

HH head: ethnic minority (%)

2.19

8.77

4.93

4.21

7.05

4.19

4.82

HH head: age

40.50

43.51

44.82

46.25

42.72

43.66

45.42

HH member: age

21.33

22.47

26.40

27.95

22.19

24.55

26.46

HH head: school attainment (year)

3.06

3.00

3.02

4.19

3.02

3.03

3.82

Average HH size

5.37

5.89

3.46

4.40

5.76

3.98

4.53

Average number of working age

2.77

3.13

2.09

2.88

3.03

2.27

2.80

Average number of children 0-14

2.49

2.56

1.19

1.31

2.54

1.54

1.52

Average number of elderly 65+

0.12

0.21

0.19

0.22

0.18

0.17

0.21

Dependency ratio (%)

122.75109.10

70.64

66.42

112.68

85.13

75.48

Child dependency ratio 0-14 (%)

115.22

99.96

60.77

54.76

103.96

75.91

64.59

Aged dependency ratio 65+ (%)

7.53

9.14

9.88

11.66

8.72

9.22

10.89

Food consumption (constant 2004)

1,308

1,297

2,295

2,351

1,299

1,937

2,102

Total consumption (constant 2004)

1,858

1,879

3,617

4,278

1,874

2,978

3,655

Obs. (individual)

412

1,230

740

4,935

1,642

1,152

7,317

Indicators (%)

Source: Author’s calculation based on CSES
Note: (C) is a group categorised as not being in consumption poverty at the national poverty line but identified as being in chronic poverty by the definition of this study.

20

(6) Sensitivity Analysis
There are three types of sensitivity analysis to be considered, including sensitivity to selectionof different criteria, level of identified criteria, and combination of identified criteria. Testing
sensitivity to criteria selection is irrelevant here because chronic poverty is defined bysatisfying all the criteria that the PPA specifies, so there are no unused criteria left. The othertwo sensitivity analyses are tested below.
Firstly, sensitivity to level of identified criteria can be tested through the comparison of thechronic poverty headcount ratio when changing the level of dimensions. There are 16
possible combinations generated by the abovementioned four variables, which have twoalternative levels for each (Appendix 4). The alternative levels are associated with ambiguity
that the PPA leaves as it defines the dimensions with ranges. The result shows that thehovering trend changes little at the national level no matter which levels of dimensions are
adopted: the chronic poverty headcount almost stagnates between 2004 and 2010. It also
indicates that the estimation is most sensitive to the ownership of farming implements
followed by housing materials. Applying a criterion of no farming implements, the estimation
comes up closer to zero for any combinations. Although the PPA recognises that the chronicpoor tend not to have any farming implements, the household survey result shows that there
are very few farmers who meet this criterion. Adopting farming implement ownership as acriterion, future research or targeting policy would need to be careful about the level, which
may have strong effects on project outcomes. Excluding the farming implement variable,
which hides the effects of other criteria, looking by regions reveals interesting tendencies. In
the Mekong Plain, the estimation is sensitive to housing criteria. The Coastal and the Tonle
Sap are not sensitive to the level of any of these selection criteria. In the North and Northeast
Mountain region, the estimation is sensitive to both housing materials and land size.
Secondly, sensitivity to combining method can be tested using the counting method to
examine whether the combination of identified criteria affect the trend (Appendix 5). In thismethod, cutoffs are defined for each dimension, and one point is assigned for each person
below the cutoff. The process is repeated for other variables. It is then aggregated to obtainthe total value of deprivation points (A). Then, the headcount ratio (H), which is the share ofpeople below a set cutoff (k), is calculated. The adjusted headcount ratio (M0) is thencalculated by the formula H times A divided by the number of deprivation criteria. In general,
choice of different methods has little effect on the trend over the period. At almost all cutoff
levels from 1 to 4 in every region, particularly at the higher or stricter cutoffs, little
improvement in headcount ratio is observed (Figure 1).

21

Figure 1: Adjusted Headcount Ratio with Different Cutoffs in Rural Cambodia

Source: Author’s calculation based on CSES

Furthermore, over the different combining methods, the relevance of the intersection
selection may be justified for two reasons: the comparison with consumption poverty andconceptual framework. Firstly, only cutoff 4 shows a lower chronic poverty headcount ratio
than consumption poverty. The headcount rates between cutoff 1 and 3 are even higher thanthe consumption poverty headcount both in 2004 and 2010. The estimation at cutoff 4 is also
close to the estimation of Tong (2012a), 6 to 10 percent. From this point, the intersection
method is likely to be a more suitable option among four alternative cutoffs. Secondly, in
relation to the SLA framework of a vicious poverty cycle, the intersection method may be
more appropriate than applying other cutoff levels. The main question mark for applying theother cutoff levels is on the determination of selected criteria and assigned weight. In thisstudy, I attempt to draw locally meaningful definitions and criteria as strictly as possible, sochosen dimensions must be drawn from the PPA and be able to delineate a vicious cycle ofpoverty with the chosen criteria. From this point, with the PPA information, there are no
reasons to justify the application of other cutoffs and weightings. Hence, the intersection
method may be the most relevant combining method with given information availability.
6. Conclusions and Policy Implications
Despite the achievement of pro-poor consumption growth, this study concludes that chronic
poverty in rural Cambodia, based on criteria defined by the poor, barely improved between
2004 and 2010. The result implies that rapid economic growth has certainly raised theconsumption of chronically poor households by 43 percent in food and 55 percent in total, but
done little to help them accumulate productive assets and human capital to break structural
constraints of persistent poverty.

22

Regarding policy implications, one of the major findings is that consumption measurement
based on the national poverty line cannot identify a majority of the chronic poor. In otherwords, targeting programmes or poverty analysis based on the poverty line would potentiallyignore the chronic poor, which may result in them being left behind in the country’s
development process. More concretely, when the government attempts to implement social
assistance, social insurance and public works to reduce poverty and vulnerability under theumbrella of the National Social Protection Strategy (Cambodia. Royal Government of
Cambodia. 2011), its targeting mechanisms largely rely on consumption measurement. The
application of defined criteria in this study may help the programmes related to the strategy toidentify the chronic poor. Furthermore, the findings show that the consumption poor and thechronic poor have a lot of similar characteristics but some differences, such as household size
and the sex of the household head. As the social protection strategy is expected to play a keyrole in ending poverty in Cambodia, these features of chronic poverty should be understoodin order to implement programmes more effectively.

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Ravallion, M. and S. Chen. 2003. "Measuring Pro-Poor Growth." Economics Letters 78 (1):93–99.
Shaffer, P. 2013. “Q-Squared – Combining Qualitative and Quantitative Approaches in
Poverty Analysis.” Oxford: Oxford University Press.
Shepherd, A., Lucy Scott, Chiara Mariotti, Flora Kessy, Raghav Gaiha, Lucia da Corta,
Katharina Hanifnia, Nidhi Kaicker, Amanda Lenhardt, Charles Lwanga-Ntale, Binayak
Sen, Bandita Sijapati, Tim Strawson, Ganesh Thapa, Helen Underhill and Leni Wild.
2014. The Chronic Poverty Report 2014-2015: The Road to Zero Extreme Poverty.
London: Chronic Poverty Advisory Network, Overseas Development Institute.
Tong, K. 2012a. “Analysing Chronic Poverty in Rural Cambodia: Evidence from Panel
Data.” Working Paper Series 66. Phnom Penh: Cambodia Development ResearchInstitute.
———. 2012b. “Transient and Chronic Poverty in Nine Villages of Cambodia: Panel Data
Evidence – Consumption Approach.” In Understanding Poverty Dynamics: Evidence
from Nine Villages in Cambodia. Working Paper Series No. 69, 43-49. Phnom Penh:
Cambodia Development Research Institute.
———. 2012c. “Transient and Chronic Poverty in Nine Villages of Cambodia: Panel Data
Evidence – Asset Approach.” In Understanding Poverty Dynamics: Evidence from Nine
Villages in Cambodia. Working Paper Series No. 69, 51-59. Phnom Penh: Cambodia
Development Research Institute.

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Wadugodapitiya, D. and B. Baulch, 2011. The Value of Panel Data in Chronic Poverty
Analysis. CPRC Policy Brief 23. Manchester: Institute of Development Policy andManagement University of Manchester.
White, H. 2002. “Combining Quantitative and Qualitative Approaches in Poverty Analysis.”
World Development 30 (3): 511-522.
White, H. and E. Anderson. 2001. “Growth Versus Distribution: Does the Pattern of Growth
Matter?” Development Policy Review 19 (3): 267-289.
World Bank. 2013. Where Have All the Poor Gone? Cambodia Poverty Assessment 2013.
———. 2013. World Development Indicators. Accessed December 2, 2013.
http://data.worldbank.org/data-catalog/world-development-indicators

25

Appendix 1: Macroeconomic and human development indicators
2004-2010

Macroeconomic Indicators
Agriculture, ave. annual growth (%)

6.81

Manufacturing, ave. annual growth (%)

7.96

Industry, ave. annual growth (%)

7.52

Services, ave. annual growth (%)

7.93

GDP, ave. annual growth (%)

7.74

GDP per capita, ave. annual growth (%)

6.17

Inflation, ave. annual change (%)

7.80

Human Development Indicators

2004

2010

Net enrollment ratio (ages 6‐11) (%)*

75.98

85.60

Net enrollment ratio (ages 12-14) (%)*

16.37

30.80

Net enrollment ratio (ages 15-17) (%)*

8.53

17.50

Ever attended school (ages 5+) (%)*

75.92

81.70

Adult literacy (ages 15+) (%)*

69.78

76.28

Mortality rate, neonatal (per 1,000 live births)

27.2

19.7

Mortality rate, infant (per 1,000 live births)

56.6

37.3

Mortality rate, under-5 (per 1,000 live births)

70.3

43.8

Source: Author’s calculation based on WDI (World Bank 2013b) and CSES
Note: Education indicators (*) are calculated based on CSES.

Appendix 2: Consumption growth between 2004 and 2010
Other Urban

Rural

Phnom Penh

Growth rate in mean (%)

1.05

7.23

6.31

Growth rate at median (%)

11.05

9.81

7.86

Mean percentile growth rate (%)

9.52

9.27

7.37

Consumption growth of the poor (%)

11.66

10.34

9.11

Corresponding poverty rate (%)

53.50

66.61

39.09

Indicators

Source: Author’s calculation based on CSES
Note: The calculation is based on the method of Ravallion and Chen (2003). The growth rate is calculated in real terms.
Aggregate consumption has been adjusted by Phnom Penh consumer price index. Growth incidence curves elaborate howmuch the actual consumption by the poor grew over time (Appendix 3).

26

Appendix 3: Growth incidence curve in Cambodia

-30

-20

-10

0

10

20

Growth Incidence Curve - Other Urban 2004-2010

0

20

40

60

80

100

Percentiles
GIC
95% Confidence Interval

Growth rate in mean

-5

0

5

10

15

Growth Incidence Curve - Rural 2004-2010

0

20

40

60

80

100

Percentiles
GIC
95% Confidence Interval

Growth rate in mean

-5

0

5

10

15

20

Growth Incidence Curve - Phnom Penh 2004-2010

0

20

40

60

80

Percentiles
GIC
95% Confidence Interval

Source: Author’s calculation based on CSES

27

Growth rate in mean

100Appendix 4: Sensitivity to level of selected criteria
Possible combination
Land

Draft
Animal

Farm
Tool

House

1 ha

One

Low

OR

1 ha

One

Low

AND

1 ha

One

None

OR

1 ha

One

None

AND

1 ha

None

Low

OR

1 ha

None

Low

AND

1 ha

None

None

OR

1 ha

None

None

AND

0.8 ha

One

Low

OR

Region(Rural Only)
Cambodia
Mekong Plain
Tonle Sap
CoastalN. & NE. Mountain
Cambodia
Mekong Plain
Tonle Sap
CoastalN. & NE. Mountain
Cambodia
Mekong Plain
Tonle Sap
CoastalN. & NE. Mountain
Cambodia
Mekong Plain
Tonle Sap
CoastalN. & NE. Mountain
Cambodia
Mekong Plain
Tonle Sap
CoastalN. & NE. Mountain
Cambodia
Mekong Plain
Tonle Sap
CoastalN. & NE. Mountain
Cambodia
Mekong Plain
Tonle Sap
CoastalN. & NE. Mountain
Cambodia
Mekong Plain
Tonle Sap
CoastalN. & NE. Mountain
Cambodia
Mekong Plain
Tonle Sap
CoastalN. & NE. Mountain28

CP HeadcountRegional Share

2004

2010

2004

2010

11.53
11.04
13.63
7.39
11.64
5.66
5.15
6.74
2.71
7.63
1.31
1.39
1.19
1.04
1.39
0.76
0.76
0.75
0.41
1.02
9.32
8.57
11.7
5.37
10
4.67
4.1
5.83
1.88
6.67
1.13
1.2
1.02
0.75
1.28
0.66
0.68
0.63
0.24
0.91
9.53
9.81
10.51
6.17
7.69

11.34
12.24
10.25
9.56
10.98
4.38
4.36
4.53
6.05
3.37
0.15
0.12
0.32
0
0
0.08
0.02
0.24
0
0
8.95
9.29
8.74
7.65
8.73
3.61
3.55
4.01
4.5
2.62
0.15
0.12
0.32
0
0
0.08
0.02
0.24
0
0
10.39
11.23
9.43
8.65
9.96

100.00
54.80
30.45
4.13
10.62
100.00
52.09
30.65
3.09
14.18
100.00
60.45
23.28
5.12
11.16
100.00
56.99
25.40
3.49
14.12
100.00
52.65
32.34
3.72
11.30
100.00
50.27
32.11
2.59
15.03
100.00
60.69
23.16
4.28
11.88
100.00
58.86
24.39
2.33
14.42
100.00
58.93
28.40
4.18
8.49

100.00
56.19
24.53
5.63
13.64
100.00
51.85
28.07
9.23
10.85
100.00
43.00
57.00
0.00
0.00
100.00
14.09
85.91
0.00
0.00
100.00
54.06
26.49
5.71
13.74
100.00
51.24
30.17
8.34
10.24
100.00
43.00
57.00
0.00
0.00
100.00
14.09
85.91
0.00
0.00
100.00
56.29
24.64
5.56
13.51

0.8 ha

One

Low

AND

0.8 ha

One

None

OR

0.8 ha

One

None

AND

0.8 ha

None

Low

OR

0.8 ha

None

Low

AND

0.8 ha

None

None

OR

0.8 ha

None

None

AND

Cambodia
Mekong Plain
Tonle Sap
CoastalN. & NE. Mountain
Cambodia
Mekong Plain
Tonle Sap
CoastalN. & NE. Mountain
Cambodia
Mekong Plain
Tonle Sap
CoastalN. & NE. Mountain
Cambodia
Mekong Plain
Tonle Sap
CoastalN. & NE. Mountain
Cambodia
Mekong Plain
Tonle Sap
CoastalN. & NE. Mountain
Cambodia
Mekong Plain
Tonle Sap
CoastalN. & NE. Mountain
Cambodia
Mekong Plain
Tonle Sap
CoastalN. & NE. Mountain

4.61
4.56
5.17
2.06
5.06
1.11
1.24
0.81
1.04
1.2
0.61
0.64
0.47
0.41
0.91
7.75
7.74
8.98
4.82
6.64
3.84
3.72
4.45
1.59
4.4
1
1.1
0.74
0.75
1.2
0.57
0.6
0.45
0.24
0.91

4.01
4.1
4.11
5.14
2.95
0.15
0.12
0.32
0
0
0.08
0.02
0.24
0
0
8.4
8.66
8.13
7.65
8.31
3.39
3.35
3.8
4.5
2.2
0.15
0.12
0.32
0
0
0.08
0.02
0.24
0
0

100.00
56.63
28.93
2.88
11.55
100.00
63.89
18.74
6.05
11.32
100.00
60.10
19.96
4.34
15.61
100.00
57.14
29.83
4.02
9.01
100.00
55.42
29.84
2.67
12.07
100.00
63.36
19.14
4.87
12.63
100.00
60.30
20.30
2.70
16.70

100.00
53.26
27.80
8.56
10.38
100.00
43.00
57.00
0.00
0.00
100.00
14.09
85.91
0.00
0.00
100.00
53.69
26.28
6.08
13.95
100.00
51.48
30.46
8.89
9.17
100.00
43.00
57.00
0.00
0.00
100.00
14.09
85.91
0.00
0.00

Source: Author’s calculation based on CSES
Note: The land ownership criterion varies between 0.8 hectares and one hectare; the draft animal criterion is one or none; thefarming implements criterion is low productive implements or none; and the housing material criterion is walls or roof, or
walls and roof.

29

Appendix 5: Sensitivity to selection method
2004
cutoff (k)Headcount(H)

Adjusted
Headcount(M0)

Deprivation(A)

Contribution (%)
Land

Draft
AnimalHousing

Farm
Tool

Cambodia (Rural Only)
1

72.85

43.57

2.39

26.10

18.72

18.63

36.55

2

56.62

39.51

2.79

27.14

19.73

19.71

33.42

3

33.26

27.83

3.35

27.09

23.46

20.29

29.16

4

11.53

11.53

4.00

25.00

25.00

25.00

25.00

1

71.18

43.02

2.42

29.07

18.94

17.39

34.59

2

56.79

39.42

2.78

29.36

19.80

17.94

32.90

3

33.08

27.57

3.33

28.09

23.77

19.06

29.09

4

11.04

11.04

4.00

25.00

25.00

25.00

25.00

1

71.73

43.14

2.41

21.57

20.85

20.03

37.55

2

53.15

38.50

2.90

23.53

21.84

21.69

32.95

3

34.06

28.95

3.40

25.37

24.32

21.43

28.88

4

13.63

13.63

4.00

25.00

25.00

25.00

25.00

1

80.07

44.43

2.22

28.37

17.68

11.36

42.59

2

58.95

39.15

2.66

30.99

19.80

12.89

36.31

3

31.30

25.32

3.24

27.18

25.36

16.87

30.60

4

7.39

7.39

4.00

25.00

25.00

25.00

25.00

Mekong Plain

Tonle Sap

CoastalNorth and Northeast Mountain
1

80.20

47.04

2.35

20.16

13.47

25.84

40.53

2

62.80

42.68

2.72

21.76

14.66

28.13

35.46

3

33.50

28.04

3.35

26.03

18.61

25.90

29.47

4

11.64

11.64

4.00

25.00

25.00

25.00

25.00

30

2010
cutoff (k)Headcount(H)

Adjusted
Headcount(M0)

Deprivation(A)

Contribution (%)
Land

Draft
AnimalHousing

Farm
Tool

Cambodia (Rural Only)
1

66.68

40.53

2.43

25.77

20.89

19.43

33.91

2

52.06

36.87

2.83

26.38

21.43

20.38

31.81

3

32.02

26.85

3.35

26.72

22.71

21.88

28.70

4

11.34

11.34

4.00

25.00

25.00

25.00

25.00

1

65.22

40.05

2.46

28.40

19.72

19.80

33.11

2

50.69

36.42

2.87

28.87

20.80

20.55

31.22

3

32.05

27.10

3.38

28.43

22.00

21.82

28.57

4

12.24

12.24

4.00

25.00

25.00

25.00

25.00

1

66.20

39.02

2.36

22.89

24.27

16.94

32.89

2

50.22

35.03

2.79

24.59

23.93

18.52

31.45

3

29.43

24.63

3.35

26.38

23.85

21.33

28.27

4

10.25

10.25

4.00

25.00

25.00

25.00

25.00

1

70.24

42.10

2.40

27.53

19.40

13.76

37.93

2

56.86

38.76

2.73

27.53

19.80

14.59

34.32

3

31.75

26.20

3.30

25.00

24.12

19.83

29.47

4

9.56

9.56

4.00

25.00

25.00

25.00

25.00

Mekong Plain

Tonle Sap

CoastalNorth and Northeast Mountain
1

71.32

44.43

2.49

21.21

19.50

25.26

36.56

2

58.37

41.20

2.82

20.84

20.09

25.63

33.10

3

37.07

30.55

3.30

22.47

22.63

23.72

29.45

4

10.98

10.98

4.00

25.00

25.00

25.00

25.00

Source: Author’s calculation based on CSES

31