TECHNICAL Empirical Assessment of a Proxy for Poverty Measurement in Sri Lanka

1. TECHNICAL Empirical Assessment of a
Proxy for Poverty Measurement
Nulakshi Dissanayake, Varanaga Ratwatte
and Hemesiri Kotagama
Centre for Poverty Analysis

2. Introduction to Poverty Measurement
https://www.facebook.com/watch/?v=839706100917448&extid=NS-UNK-UNK-
UNK-AN_GK0T-GK1C&mibextid=2Rb1fB&ref=sharing

3. Why is poverty measurement needed?
•Identify the poor
•To identify causes of poverty to seek
solutions/policy
•To monitor change and impact of policy on
poverty

4. What is a poverty measure
•Ideally a quantified, single (composite) indicator of
degree of poverty
• Quick, easy, cheap, verifiable, transparent, adjustable
• Social indicators must be socially understood and
accepted
•2 requirements
•Population distribution of poverty related
indicator
•A cut-off distinguishing poor vs rich

5. Measure of Poverty: Distribution and Cut-off

6. Poverty Measures and cut-offs
• Food security
• Energy requirement for survival (2030 kcal/day/person)
• Monetary
• Monetary value for basic food and other livelihood requirements
• Sri Lankan Poverty Line in 2019 was 24381 Rs/M/HH (Pre Economic Crisis)
• Sri Lankan Poverty line in 2022 was 48219 Rs/M/HH (Post Economic Crisis)
• Capabilities (Sen’s)
• No applications ????
• Multidimensional Indicators (measure ranging 0-1 and 1 highly poor)
• HIES cut off is above 0.33
• Aswasuma
• Available money allocation!!!
• Household Electricity Cost
• To be decided

7. Poverty Trends in Sri Lanka

8. Poverty trends in Sri Lanka

9. Comparison of poverty rates:
Amazing Sri Lanka
47.3
20.5
8.2 6.7 8.2
17.4
4.4 4.1
10.5
0
5
10
15
20
25
30
35
40
45
50
Afghanistan Bangladesh Bhutan India Maldives Nepal Pakistan Sri Lanka USA
Percentage
Country

10. What brought down poverty?
• Social-welfare policy
• Universal access to education and health
• Numerous other welfare support (35 programs)
• Dual economic theory (growth of industry and services due to gradual
growth in the agricultural sector…)
• Government jobs
• Foreign remittance (not commodity export growth! It’s export of
labor)
• Economic crisis reversed gains overnight!!!
• Back to square one!

11. Post Economic Crisis Poverty in Sri Lanka

12. Poverty Care (Safety Nets) in Sri Lanka

13. Various poverty care programs
• Universal free access to health and education
• Subsidized transportation, agriculture … etc
• Over years
• Free food (universal) කූපන් පපොත
• Free rice and grains ඇට අට
• Food stamps ආහොර මුද්දර
• Janasaviya ජනසවිය
• Samurdhi සමුරදී
• Divi Naguma දිවි නැගුම
• Asswesuma අස්වැසුම
• More than 30 other programs

14. Samurdhi Since 1995: Comprehensive
Program
• Subsidies/ month: For one person or two people Rs.1500.00, For three people Rs.2500.00, For more than four people Rs.3500.00
• Compulsory Savings
• Further subsidies: I. More than 70 years of age; II. Having a particular disease or having to spend money on such a disease. (cancer,
kidney, and heart diseases, other surgeries); III. A child doing higher studies.
• Providing necessary grants for livelihood projects.
• Housing Lottery Programme: Each Divisional Secretariat gets a winning chance monthly; Value of Rs.200000.00
• Samurdhi Social Security Programme: I. Pay Rs.7500.00 each for three childbirths in a beneficiary family; II. Rs.7500.00 each for two
marriages in a family; III. Rs.1500.00 each for a death in a beneficiary family: IV. For hospitalization in a beneficiary family maximum
amount of Rs.7500.00 is paid as Rs.250.00 per day for maximum number of 30 days per year; V. For a twin birth Rs.5000.00 each until the
child completes one tear (12 months): VI. For a three-childbirth or more Rs.10000.00 per month for 12 months.
• ‘Sipdora’scholarship: Each child who follows Advanced Level in any beneficiary family is given Rsw.1500.00 per year for the two years
they follow Advanced Level.
• Samurdhi livelihood: Treasury grants 60% and beneficiary contribution. Maximum of the treasury is Rs.4500.00 (Differs accordingly):
Encouraging the beneficiary through group projects and individuals based on age; Equipment, animal husbandry, and crops are given as
vocational training material.
• Entrepreneur development; Marketing promotions; Samurdhi Social Development Programme
• Religious programs, International Day programs; Counseling programs, library promotion programs

15. Critique of Samurdhi and initiation of Aswesuma

16. Critique of Samurdhi:
Inadequate national budgetary allocation
Samurdhi Rs 58 Biilion/Yr) (0.3% of
GDP) in 2020
Aswesuma Rs. 187 billion (0.6% of
GDP) in 2023
• Beyond 2023, the authorities
plan to maintain SSN spending at
least at around 0.6-0.7 percent
of GDP.
• IMF condition and World Bank
Loan

17. Critique of Samurdhi:
Inadequate national coverage of the poor
Samurdhi Aswesuma
In 2006, over 50 percent of the poorest quintile
received Samurdhi transfers, whereas only 38
percent of the poorest quintile received
Samurdhi transfers in 2019 (World Bank, 2022).

18. Critique of Samurdhi:
Inadequacy of allowance to households
Samurdhi Aswesuma

19. Critique of Samurdhi:
Inclusion and Exclusion Errors

20. Change of method of poverty measurement:
හිසේ අමාරුවට ස ෝට්සට් මාරු කිරීම
Samurdhi
• Monetary
• Later multidimensional
• Rural formal collective
institution
• Politicized
Aswesuma
• Multidimensional index
• Centralized data collection and
decision making
• Poor data collection
• Method not transparent and understood
socially (like Z score)
• Inappropriate
• Indicators and number of indicators
• Weights
• Cut-off point not known: Budget and
politicized
• Selecting poor among poor
• Large exclusions compared to Samurdhi

21. Aswesuma in Chaos

22. Could HH Electricity Consumption be an
acceptable proxy for poverty measurement?
•One reason for the Aswesuma chaos
could be poor measurement of
poverty.

23. HH electricity cost as a proxy to measure
poverty
• Gunewardena and Siyambalapitiya (2023) states that:
Household electricity consumption can be utilized as a
measure to identify poor to receive welfare benefits.
• For the poorest 10% of the population, targeting the
electricity use criterion captures 81% of households.
• The Sri Lankan government, through a gazette notification in
No. 2302/23 - Thursday, October 20, 2022 has identified HH
electricity cost as an indicator of poverty.
• HH using less than 60 kWh is poor.

24. Justification to use HH electricity cost as a
poverty measure
• Per studies
• It can be measured quickly, easily, and cheaply as 99% of households are connected to the Ceylon
Electricity Board (CEB)
• Households could be moved in or out of the program as their levels of electricity use change !!!
• Politicization and corruption can be eliminated as the eligibility is measured by an objective
criterion
• Measuring HH income is the best measure of poverty but measurement is:
• Difficult!!!
• Unreliable (many types of earnings that have shady earnings)
• Objective verification not possible
• Dishonest reporting

25. Empirical Assessment of Technical Appropriateness of HH
Electricity Consumption as a Proxy Measure of Poverty

26. Assessment criteria of Technical
Appropriateness of a Proxy Measure
• In science, it is sometimes necessary to study a
variable which cannot be measured directly.
• This can be done by "proxy measures," in which a
variable that correlates with the variable of interest is
measured, and then used to infer the value of the
variable of interest.
• Hypothesis: HH Electricity Cost correlates with other
poverty measures (monetary and MPI)

27. Methodology
• HIES 2019 data: Kilinochchi (Poorest district and a sufficient statistical
variation) N=375
• Analytical steps
• Data sorting
• Scatter plots
• Descriptive statistics
• Estimation of MPEEE (Predictive Model)
• Estimation of poverty levels and validation
• Spearmen’s correlation among poverty measures
• HH Consumption expenditure (ConP), MPI, HH Cost of Electricity (ElecP)
• Estimation of predictive regression model: Elc f Cex … Xn
• Purpose to estimate the monetary poverty equivalent of electricity use

28. Descriptive statistics of sample:
Kilinochchi (N=374)
No of HH
members
HH Expenses
(Rs/M)
HH Food
Expenses (Rs/M)
HH Income
(Rs/M)
HH
Electricity
Cost
(Rs/M)
Mean 4 36647 19176 42876 703
Minimum 1 3689 2079 1070 0
Maximum 9 140512 53036 237456 3500
Count 374 374 374 374 377
=2%

29. Scatter plot: Electricity cost and HH Expense:
Hetroskedasticity
0
50000
100000
150000
200000
250000
0 1000 2000 3000 4000 5000 6000 7000 8000
HH
Electricity
Cost/M/Rs
HH Cosumption Expenditure/M/Rs
Scatter Plot - Electricity Cost and HH Expenditure

30. The Predictive Model
• HEE f HCE + HN + HF + HA +e …(Equation 1)
• Where:
• HEE is Expenditure on electricity per month per house hold (Rs/M/HH);
• HCE is Expenditure on consumption per month per household (Rs/M/HH);
• HN is nature of house, where floor, walls and roof built with semi-permanent
material is considered 1 and if not 0;
• HF is nature of cooking fuel used by the household, where use of firewood, other
biomass or kerosene is considered 1 and if not 0;
• HA is the nature of assets owned, where ownership of household electrical
equipment or agriculture and fisheries use based equipment is considered 1 and if
not 0.

31. Results
Regression estimates of Model 1(Comprehensive)
Regression diagnostic R-Square-0.19 F=21.94 (p=0.00) RMSE= 758
Dependent Variable HEE
Coefficients t value p value
Constant 384.4 1.27 0.204
Independent Variables
HCE 0.0091 4.94 0.000
HN -185.8 -1.59 0.112
HF -582.94 -6.06 0.000
HA 500.4 1.72 0.086
Breusch-Pagan / Cook-Weisberg (B-P/C-W) test for heteroskedasticity: Ho: Constant variance: chi2 = 76.92 (p=0.00)

32. Regression estimates of Model 2
(Stepwise Regression)
Regression diagnostic R-Square-0.17 F=40.93 (p=0.00) RMSE= 761
Dependent Variable HEE
Coefficients t value p value
Constant 817.03 6.85 0
Independent Variables
HCE 0.0096 5.26 0.000
HF -563.85 -5.86 0.000
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity: Ho: Constant
variance: chi2 = 75.59 (p=0.00)

33. Regression estimates of Model 3
(Weighted Least Square: Treating Hetroskedasticity)
Regression diagnostic R-Square-0.98 F=179 (p=0.00) RMSE= 42
Dependent Variable HEE
Coefficent t value p value
Constant 284.31 14.65 0
Independent Variables
HCE 0.0121 82.35 0.000
HF 0.875 0.05 0.958
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity: Ho: Constant
variance: chi2 = 8.51(p=0.004)

34. Regression estimates of Model 4
(Final Model)
Regression diagnostic R-Square-0.98 F=360 (p=0.00) RMSE= 42
Dependent Variable HEE
Coefficients t value p value
Constant 285.3 189.77 0
Independent Variables
HCE 0.0121 189.7 0.000
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity: Ho: Constant
variance: chi2 = 8.51(p=0.004)

35. Predictions and validation: Basis CEB and
Aswesuma law
• According to Aswesuma law a household consuming less that 60 units
of electricity a month is poor.

36. Validation
Category of users by
HH expenses
Mean HH Expenses
(Rs/Month)
Predicted HH Expense
on Electricity
(Rs/Month)
Predicted electricity
use (kWh/month/HH)
Strata identified by
CEB for billing
(kWh/HH)
Poverty line (2019)
(2019) 24381
580
62 up to 60
Poverty line (2022)
(2022 December)
48219
868
91
Poorest 10% 20442 532 57 up to 60
0-20% quintile 29601 587 63 up to 60
21-40% quintile 41016 748 79 61-90
41-60% quintile 53482 892 93 91-120
61-80% quintile 72673 1098 114 121-180
81-100% quintile 177067 1917 196 180 more

37. Predicted MPEE
•Pre-Economic Crisis (2019)
•For 2019 Monetary Poverty (24381 Rs/M/HH)
MPEE is 62 kWh/M/HH
•Post-Economic Crisis (2022)
•For 2022 Monetary Poverty (48219 Rs/M/HH)
MPEE is 91 kWh/M/HH

38. Assessment criteria on MPEE as a proxy
measure of poverty
• Pearson’s correlation test between HH consumption expenditure and HH
electricity expenditure.
• 0.34 Correlation between Electricity use and Consumption expenditure
• Welch’s t-test on difference of means between HH consumption expenditure
between those identified as poor using the monetary indicator and MPEE
indicator. Welch’s t-test was used as the sample sizes were different and variance
unequal.
• H0: Means of HEE = HCE and HA Means of HEE ≠ HCE
• H0 Rejected
• Count if analysis in identifying the poor as HH consumption expenditure and HH
electricity expenditure and comparing the inclusion and exclusion errors.
• Inclusion error is including monetarily rich HH when using MPEE to identify poor. (36%)
• Exclusion error is excluding monetarily poor HH when using MPEE to identify poor (5%)
• Qualitative assessment based on good characteristic of a scientific measurement

39. Inclusion and Exclusion Error
Exclusion Error
(Poor excluded)

40. Qualitative Assessment of Measures
Characteristics Monetary
Income
Electricity Cost
1. Accuracy
(How close a measurement is to the true value or the accepted standard.)
Strong! Strong
2. Precision
(The level of detail and consistency in the measurements.)
Moderate Strong
3. Validity
(The measurement is measuring what it is intended to measure, and it is relevant to the research question or
hypothesis.)
Strong Strong
4. Reliability
(The consistency of measurements when repeated under similar conditions.)
Weak Strong
5. Objectivity
(The measurement is independent of the observer's bias or interpretation, minimizing the subjective influence
on the results.)
Weak Strong
6. Sensitivity
(The measurement's ability to detect small changes or variations in the quantity being measured.)
Strong Strong
7. Standardization
(Using recognized and consistent units of measurement, ensuring comparability across different studies and
locations.)
Weak Strong
8. Reproducibility
(The measurement can be independently verified by other researchers.)
Weak Strong

41. Conclusion
• HH Electricity consumption is a TECHNICALLY good PROXY to measure
of poverty.
•SoCiaL aCepTanCe?
•Income in theory and in real, the best proxy
measure of welfare (few may refute).
• Can’t we overcome the deficiencies in income
measurement and use it?
• Can a country survive without being able to measure
income of a HH?

42. Thank You