2. • Introduction
• Research problems
• Research objectives
• Rationale for the study
• Data and methodology
• Conceptual framework
• Variable selection
• Analytical techniques
• Key findings
• Conclusion
2
3. Alleviate Poverty : One of the focal themes in global
development agendas
The SDG goal for poverty is;
“End poverty in all its forms everywhere”
A number of direct and indirect programs have been
launched to reduce poverty in Sri Lanka
Significance performance in alleviating poverty at national
level. 3
4. In Sri Lankan’s context;
Narrow definition of poverty
A few attempts to capture poverty broadly
Issues
• Coverage
• Methodological issues
Hypothetical thresholds
Arbitrarily assigning weightings
• Conceptual
Limited literature on multidimensional poverty
4
5. • To measure poverty in multidimensional aspects based on Sen’s
Capability Approach in Sri Lankan context in terms of material
deprivation
• To make comparison of real achievement of non-monetary measures
of poverty with monetary measures of poverty on consumption based
to understand poverty in Sri Lanka
• To what extent does poverty exist in terms of material deprivation?
• What are the main indicators that contribute to material deprivation?
5
6. Why Uva province?
• A Suitable laboratory to investigate poverty in multidimensional approach in Sri
Lanka
• An economically backward province throughout the past. However, it has shown
some progress recently in combating against poverty
• Consists different geographical areas
• Representation of multi ethnic and multi religious backgrounds
• Represents three residential sectors (Urban, Rural, Estate)
Why a new survey?
• The main micro data sources are used to measure poverty in Sri Lanka are;
• Household Income and Expenditure Survey (HIES)
• Demography and Health Survey (DHS)
• Consumption poverty is important but it is incomplete
• Need to collect data on qualitative and quantitative aspects 6
7. Sample selection procedure
First stage :Selection of primary sampling units (Census blocks) using PPS/Systematic sampling
method
(Size measure is no. of housing units in the census blocks)
Second stage :Selection of secondary sampling units (SSU)
SSUs are the housing units in the selected primary sampling units (census block)
From each selected primary sampling units, 10 housing units are selected by using
systematic sampling
• Sample size is 1200 housing units
• The unit of analysis was the respondent (an adult person) above eighteen years old
Sample design
Two stage Stratified Sampling Method
• Primary sampling Units (PSU’s) are the census blocks
• Secondary Sampling Units are the housing units
7
9. Variable selection
• Data redundancy : Dichotomous
• Pearson Correlation test: Continues
• Point Biserial Correlation : Dichotomous and Continuous
1 Housing Facilities
1.1 Principal materials of construction of wall
1.2 Principal materials of construction of floor
1.3 Principal source of lighting
1.4 Principal source of drinking water
1.5 Type of toilet
1.6 Number of bed rooms
1.7 Type of structure
1.8 Total floor area (Sq. feet)
1.9 Tenure
1.10 Toilet facilities
1.11 Satisfaction about the quality and facilities of household
1.12 The adequacy of the facilities of the household for family members
2 Consumer durables
2.1 Television
2.2 Mobile phone
3 Basic lifestyle
3.1 Meal with fish, meat, dried fish or eggs
3.2 Meal with green leafy vegetables
3.3 Eat fruit
3.4 Satisfaction of clothing
3.5 Adequate clothes to wear
3.6 Buying new clothes
Selected Variables
9
10. Two main steps
i) Identification of deprived people
For identification of multidimensionally poor individuals use the
Fuzzy membership function introduced by Cerioli and Zani (1990)
ii) Aggregation of deprivations
The steps and the methods used to aggregate the fuzzy deprivation
score follows the methods introduced by Alkire et al. (2015)
Synthesis Method : This method is a combination of Fuzzy set and Alkire_Foster counting
methods to identify the individual deprivation in well-being 10
11. Totally Fuzzy (TF) method calculates the degree of deprivation for each indicator in terms of fuzzy membership for each
individual ; Cerioli and Zani (1990)
Denote each individual a grade of membership in the sub set poor(𝜇 𝐴𝑖) ;
If 𝜇 𝐴𝑖 = 0 ; ith individual is not definitely belong to poor
If 𝜇 𝐴𝑖 = 1; ith individual is completely poor
If 0 < 𝜇 𝐴𝑖 < 1 then ith individual is partially belong to poor sub set
Identification of deprived people
The value of the membership function is given by the following equation.
Consider 𝑞 𝑗𝑖 is the value of ith individual in jth indicator where (i=1,2……n) and (j=1,2……k) in the poor set 𝜇 𝐴.
Then the membership faction for each individual is;
𝜇 𝐴𝑖 𝑗 = 1 if 0 ≤ 𝑞𝑖𝑗 < 𝑗 𝑚𝑖𝑛
𝜇 𝐴𝑖 𝑗 =
𝑞 𝑗,𝑚𝑎𝑥−𝑞 𝑖𝑗
𝑞 𝑗,𝑚𝑎𝑥− 𝑞 𝑗,𝑚𝑖𝑚
if 𝑗 𝑚𝑖𝑛< 𝑞𝑖𝑗 < 𝑗 𝑚𝑎𝑥 (3.4)
𝜇 𝐴𝑖 𝑗 = 0 if 𝑞𝑖𝑗 ≥ 𝑗 𝑚𝑎𝑥
11
12. • Compute the weighted deprivation score for each indicator for all individuals and create sum of weighted deprivation
score for each individual in all dimensions.
𝜔𝑗 =
𝑙𝑛
1
𝑓𝑗
𝑗=1
𝑘
𝑙𝑛
1
𝑓𝑗
𝜔𝑗 : Weight for jth indicator
𝑓𝑗 :Individuals who are completely deprived in jth indicator
• Weighted fuzzy deprivation was calculated using following equation:
𝜇 𝐴𝑖 =
𝑗=1
𝑘
𝜔𝑗 × 𝜇 𝐴𝑖 𝑗
𝑗=1
𝑘
𝜔𝑗
.
• Determine the deprivation cut-off (z) based on Kendall rank correlation (tau_b) coefficients .
• A person considered to be multidimensionally poor or not with respect to the selected cut-off
and aggregated weighted deprivation score.
Cont.….
12
13. Aggregation of deprivations
Counting approach
Aggregate the fuzzy deprivation score follows the methods introduced by Alkire et al. (2015)
that is an extension of Foster-Greer-Thorbeck (1984).
Five poverty indices are produced using the fuzzy deprivation scores of individuals;
i) Fuzzy Headcount Index (FHI)
ii) Fussy Intensity (FI) ,
iii) Adjusted Fuzzy Deprivation Index (FM0),
iv) Normalized Deprivation Gap Index (FM1)
v) Squared Normalized Deprivation Gap Index (FM2)
13
14. 28.0
6.5
0
5
10
15
20
25
30
Fuzzy deprivation
score
Poverty Headcount
index
Percentage(%)
Type of poverty analysis
Deprivation in well being in Uva province by
type of poverty analysis On average 28.0% of
population in Uva province is
propensity to material
deprivation on Fuzzy
membership measures but
according to the official
statistics the percentage of
people live in consumption
poverty is 6.5 %
14
15. Consumption Poverty
Poverty indices in Uva province
• 20 per cent of the population living in Uva province is materially deprived.
• The average deprivation score (Fuzzy intensity (FI)) in which poor people are deprived is 51%.
• The adjusted fussy headcount ratio indicates that the materially deprived population of Uva province
experience is 10 per cent, as a share of all possible deprivations that would be experienced if all people have
been deprived in all the dimensions.
• Official poverty line indicates that 6.5 per cent are living in consumption poverty in Uva province.
6.5
0.7 0.1
50.9
20.2
10.3 5.4 2.6
0
10
20
30
40
50
60
Poverty
Headcount
Index (PHI)
Poverty Gap
Index (PGI)
Squred
Poverty Gap
Index (SPGI)
Fuzzy
Intensity(FI)
Fuzzy
Headcount
ratio (FH)
Adjusted
Fuzzy
Headcount
ratio (FH0)
Normalized
Deprivation
Gap ratio
(FM1)
Squared
Normalized
Deprivation
Gap ratio
(FM2)
Percentage(%)
Poverty Indices
Multidimensional poverty indices on material deprivation
15
16. Multidimensional Fuzzy Headcount Index on Material deprivation in Uva
47.4
16.6
0 10 20 30 40 50
Plantation area
Non-Plantation area
Percentage(5)
By Plantation and non-plantation area
55
34.4
17.2
7.3
1.9
0 10 20 30 40 50 60
No Schooling
Up to grade 5
Grade 6 to 10
GCE(O/L)
GCE (A/L)
Percentage (%)
By Level of Education
31.5
15.7
11.8
15.3
0 10 20 30 40
Above 60 yrs.
40 to 59
25 to 39
Below 24 yrs.
Percentage (%)
By Age group
• The people living in plantation areas are
more deprived than that of non-plantation
areas.
• Deprivation decreases with the level of
education from lower to higher level.
• The individual who are above 60 years old
are more deprived than the other people.
16
17. Housing
Facilities,
58%
Consumer
Durables,
13, 13%
Basic
Lifestyle,
29, 29%
• The highest contribution to fuzzy poverty to material deprivation is contributed by
housing facilities.
• Poor people are deprived in basic lifestyle than the deprivation of durable goods.
Percentage contribution of dimensions to total adjusted Fuzzy Poverty
17
18. • The individuals are more deprived in well-being in material deprivation than the consumption poverty
especially in the area of housing facilities and clothing.
• The deprivation of nutrition is relatively low in Uva.
• The material deprivation is higher in plantation areas than non-plantation areas.
• The Synthesis method used in this research for measuring deprivation in well being is more preferable and
robust than the traditional approaches which are being employed to measure poverty for policy implication
to reduce poverty.
• Present study has only focused on material deprivation. To understand the idea of deprivation in well-being
in-depth in a more broader manner, it is suggested to consider taking more dimensions into account for
the analysis.
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