Transcript Child Well-Being and Equity in Sri Lanka Preliminary Overview Presentation outline • Sri Lankan Child’s profile • Study Operational Structure • Issues, Options and Challenges • Brief overview of.

Child Well-Being
and
Equity
in
Sri Lanka
Preliminary Overview
1
Presentation outline
• Sri Lankan Child’s profile
• Study Operational Structure
• Issues, Options and Challenges
• Brief overview of Policies Affecting Poverty
• Some Methodological concepts for child poverty
analysis
2
Country Overview of Child Indicators
•
•
•
•
Child Population( below 19 yrs.): approx. 7.2 mn (36.3%)
Child Population 0-5 years: approx. 1.7 mn.
Children attending pre-schools: around 80%
Net enrolment and primary completion ratio in primary schools: over 95%
(both boys and girls)
• Secondary enrolement rates is 82.6
• Disabled children 0 -19 yrs – 55,500 (2001)
Infant mortality rate 11.7 per 1000 live-births
• Under 5 mortality rate 13 per 1000 live-births
• Neonatal mortality rates 8.4 per 1000 live-births
• Undernutrition of children varies from 19- 29% for wasting stunting and
underweight
• Immunization rate 96.5
• Maternal mortality rate – 18 per 100,000 live-births
• Internally displaced children living with parents with friends & family
(43,689 families) and in temporary shelter (7,800 families) (UNHCR 2008)
 Children in voluntary homes (22,000 children)
 Child domestic labour– Approx. 25,500 children between 5-14 years
engaged in economic activity (ad hoc studies)
 Street Children about 10,000
The challenge of coverage is nearly achieved but the challenge is to improve
3
quality of services towards enhanced well-being and equity,
Total Population and Child Population
25000
20000
15000
10000
5000
0
1971
Total
1981
2001
% Children < 18 years
2011
2016
Trend (< 18)
Source: Department of Census & Statistics, Census of population & Housing/ Statistical Abstract, 2000
Trend (Pop)
4
5
Population Distribution by Province and District - 2001
Jaf fna
Killinochchi
Majority of population remain
as Rural 14.5 mill, urban 2.8
mill and estate 1 mill.
Mullativu
Mannar
Vavuniya
Trincomalee
Anuradhapura
(plantation of over 20 hecates
and more than 10 resident
labour)
Polonnaruw a
Batticaloa
Puttalam
Matale
Kurunegala
Kandy
Kegalle
Gampaha
Ampara
Nuw ara Eliya
Badulla
Colombo
Monaragala
Kalutara
Ratnapura
Galle
Hambantota
Source: Department of Census & Statistics
Census of Population & Housing 2001
Matara
6
Percentage Distribution of Under 18 Years Population by District
50
1981
2001
45
(%)
40
35
30
Kegalle
Ratnapura
Moneragala
Badulla
Polonnaruwa
Puttalam
Kurunegala
Anuradhapura
Source: Department of Census & Statistics, Census of Population & Housing 2001
Ampara
Hambantota
Matara
Galle
Nuwara Eliya
Matale
Kandy
Kalutara
Gampaha
Colombo
25
7
Child growth - Underweight
40
35
30
25
% 20
15
10
5
0
38.1
38.6
37.6
34.6
32.6
30.7
26.7
26.7
Total
Male
26.6
Female
1987 1993 2000
Source: Department of Census and Statistics, DHS 1987,1993, 2000
8
Immunization Coverage
102
100
100
99.8
99.3 99.3
98
98.2
98
95.5
96
95
94.2
94
93.5
92
90
1993
BCG
DT 3 or 4
2000
Polio 3 or 4
Measles
Full coverage
9
%
Distribution of age of marriage
48.1
50
40
30
33.9
34.6
42.5
42.2
32.5
34.8
27.9
25
20
1.1
Sabara'wa
2.6
1.4
Uva
1.2
north cent
15-19 years
5.9
eastern
1.7
northern
southern
0.3
north west
0-14 years
1.4
central
0
0.9
western
10
10
%
Water supply and Sanitation Coverage by Province - 2006
100
90
80
70
60
50
40
30
20
10
0
Sanitation
Water
All island
Sabaragamuwa
Uva
north central
north western
eastern
southern
central
western
Special MDG survey, Dept. Of Census and Statistics
11
Poverty Profile: Percent population and poor by Province
Country average 15.2% Poor below poverty line (2006/7)
35
30
25
20
15
10
5
0
population %
% Poor
Sabaragamuwa
Uva
north central
north western
eastern
northern
southern
central
western
12
Mean per capita income per income receiver and per
person
16000
14000
12000
10000
8000
6000
4000
2000
0
per
person
All island
Sabaragamuwa
Uva
north central
north western
eastern
northern
southern
central
western
per
income
receiver
13
%
Tuition for students in formal education
90
80
70
60
50
40
30
20
10
0
Primary
secondary
post secondary
All island
Sabaragamuwa
Uva
north central
north western
eastern
northern
southern
central
western
Consumer finance survey 2003/2004, Central Bank of Sri Lanka
14
%
15-18
19-24
Average
Unemployment by age by province
50
45
40
35
30
25
20
15
10
5
0
western
central
southern
northern
eastern
north
west
north cent
Uva
Sabara'wa
All island
15
Migration, persons per 1000 households
per 1000 households
120
100
80
Internal
60
External
40
20
Consumer finance survey 2003/2004, Central Bank of Sri Lanka
All island
Sabaragamuwa
Uva
north central
north western
eastern
northern
southern
central
western
0
16
%
Distribution of internal and external migration by gender
80
70
60
50
40
30
20
10
0
75.6
63.5
58.5
41.5
46.7
53.3
36.5
Internal
24.4
External
1996/97
1996/97
2003/04
2003/04
Females
Males
Females
Males
Consumer finance survey 2003/2004, Central Bank of Sri Lanka
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Study Procedure
• Work Process
TOR developed with Ministry of Finance and Institution identified to
conduct study
Working Group appointed chaired MOF to guide study and
advocate for incorporation of outcomes to relevant
programmes/projects.
Members are Dept. of National Planning, MOF, Dept. of Census and
Statistics and Institute of Policy Studies and Unicef.
Concept
Model B: Child poverty = poverty of HH with children
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Policy and Statistics templates
• Review initiated on policy template
Work initiated, some information on expenditure by specific activities such
as primary health care – costs on breast feeding, counselling, family
based nutrition services, antenatal care, neonatal care, birth registration
is unlikely as these are service provided through MCH clinic package
through the same service provider.
Statistical Templates
• Information from National population census and routine data collection
from administrative records etc. are used to fill some of the earlier
templates. For the latter national survey information is needed.
• Sri Lanka has two main sources of national primary data generators –
Central Bank of Sri Lanka and Dept of Census and Statistics including
periodic Consumer Finance survey by the former and income and
expenditure and DHS surveys by the latter.
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Statistical templates: Data Collection and processing
 No primary data collection planned as main survey data were
to be available.
• Data planned to be used from two main recent surveys of DCS
(DHS appx. 17,000 HH and HIES- 18,500 HH 2006/7) - 20 of 25
districts covered with district level estimates for poverty,
health, nutrition related indicators. The four districts in the
north were not covered.
• No access to data yet due to delays in completing surveys –
With the delay in release of data from the DHS and HIES and
adhering to current time limit of the child poverty study likely
to be an issue.
• Option to use 2003/4 CFS- 11,700 HH (provincial) or 2005 HIES
(district) -5,380 HH (including tsunami HH) – but dated, no
health and nutrition information. Since more recent data are
available - policy prescriptive validity.
• National survey data from Northern district is not available for
nutrition since 2001(special DHS for N/E) and
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income/expenditure since 2003/04 (partial coverage of north)
Data Analysis
• Analysis expected to cover the five areas of income, education,
health, protection and nutrition – within the proposed analysis
protection as in study guide is a challenge (child labour
orphanhood, social insurance and correlates) due to nonavailability of data in these surveys.
• In instances such as protection, where survey information is
not available will be supplemented by ad hoc studies, to
provide a overview. However, will not be representative of
regional level – but as proxies of current status.
• Combining health, nutrition and income data not possible as
data covers different HH – hence correlates as given will not
possible
• Income groups versus composite wealth ranking/indicator as
a compromise to overcome non-availability of income
information from DHS- but is not possible with HIES since HH
chracteristics that could be used for wealth ranking are not
collected
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Policy Advocacy and Programming
•
Analysis can feed in to 2008 budgetary process but unlikely that
current study will provide adequate depth- however, outcome of
study can guide current allocations and identify need for further
study such as social budgeting for greater refinement towards
policy prescriptions.
• Findings will be utilised in GOSL_UNICEF CPAP to re-examine
current interventions to assess if changing needs are addressed.
• The findings are expected to feed in to the next National Plan of
Action for Children 2009-2012, coordinated by the MOF
• The findings will indicate the extent to which cross-sectoral
behaviour change communication can be used as an effective
intervention.
• A workshop is proposed to disseminate findings and to internalise
recommendations as interventions with a monitoring mechanism,
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primarily, within different government agencies and development
agency agenda to enhance child well-being
Challenges
• Completing some of the standard tables where income information
and correlates of health and nutrition is given – will limit some of the
quantitative analysis envisaged in the guide.
• Individual dimensions of child deprivation within a HH cannot be
addressed within available secondary data but will be drawn from ad
hoc studies- more as case studies.
• Public expenditure as listed with separation of costs is unlikely to be
ascertain where it is are part of a whole service package through
same service providers - costs on Juvenile justice, upper secondary
education etc.
• Information gaps in surveys - such as street children, IDP children
(especially within areas of transition), who will not be adequately
represented in HH surveys.
• A few districts in the north where DHS and HIES were not conducted
do not have national level income information. Only CFS of 2003/2004
has some estimates at provincial level but not at districts.
• National child nutrition information is only through DHS 2006/0723
as
the earlier survey in 2000 dates – limited options
Characteristics of the Poor
•
Primary Education Only (38% poor only primary cf 10% & 4% poor
with O & A’s)
•
No specialised skills or training so in casual/seasonal labour – low
employment security
•
•
Low Health (morbidity) / Nutrition Status & low productivity
Employed but In low waged employment (25% of employed are
poor) – high dependency ratio
•
•
•
Loss of livelihood (conflict affected areas)
Low savings / high indebtedness and inability to withstand
economic shocks
No retirement benefits- informal sector
•
Inability to be employed due to disability and sickness
24
Some Findings and Policy Implications
•
Heavy investments in social welfare (over 50 years) with a view to improve
basic needs, human capital & earning capacity has reduced poverty levels
in the country – however, with significantly less benefit to some districts –
regional disparities exists. Although government service sector coverage is
high, sustaining adequate quality of services is a challenge leading to
current outcomes in education and health, in particular.
•
Even with relatively high economic growth in the past few years, Regional
disparities exit and widens for example, Western province generating
nearly half of GDP growth.
•
Inequity in HH income distribution has not reducing with still the highest
income decile accruing nearly 40% total HH income whilst the bottom 10%
accrue only 1.7% which had not changed over the last three decades.
•
Agriculture still employs nearly one third of the labour force but is the lowest
contribution to GDP indicating low productivity hence reduced gain from
employment. High employment in low productive activities limit gains to HH
and exposes HH to high risk of economic shocks.
•
Overseas employment provided a major outlet in the last decade.
Placements have risen significantly however, 76% of placements were
unskilled labour & 68% were Women to middle eastern destinations. Social
impact on children and family are known to be negative.
25
•
Actual and intended transfers to the poor:
(i) Are not accrued to the poor
(ii) When accrued, the intervention too small for a real gain (eg. Value of
transfer eroded by inflation, quality of services inadequate.
•
Resources allocated to the poor spread too thin to create a
significant change. Resources allocated needs to be large enough to
be of some benefit.
•
Inability to identify the poor, leads to inefficient allocation of scarce
resources. But criteria for more accurate identification not available
– targeting vs univerlism and cost benefit aspects.
•
Design of Interventions for the poor similar despite variation in
resources and capabilities within the poor - which have not taken
into account into account.
•
Rationalisation of National/Provincial/District expenditure required –
duplication of work leads to additional costs in administrative
tasks.
•
Despite decentralisation etc., wide variation in both resource
allocation and efficiency of utilisation within districts – poorer
districts are less benefited.
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• Cross subsidize services to the poor through different mechanisms
has to be considered especially in health and education since
government is unlikely to able to increase expenditure in these
sectors and demographic changes will create a different demand on
the same resources.
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Construction of Composite Indicator of Multidimensional Poverty
K.A.P Siddhisena and Ruwan Jayathilaka (2006) Identification of the Poor in Sri Lanka: Development of Composite
Indicator and Regional Poverty Lines
Paper archived at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=891782
Based on the ultimate composite poverty indicator, all
districts are ranked in order to understand the poverty
status in Sri Lanka
Composite Indicator
SLIS Rank
CFS
Rank
Districts
Gampaha
Kalutara
Matale
Nuwara Eliya
Matara
Hambantota
Kurunegala
Puttalam
Polonnaruwa
Badulla
Moneragala
Ratnapura
Kegalle
0.407
0.424
0.143
0.158
-0.315
-0.116
0.117
0.124
-0.154
-0.051
-0.038
-0.366
-0.421
-0.033
-0.545
-0.152
-0.164
16
17
14
15
4
8
12
13
6
9
10
3
2
11
1
7
5
0.992
1.110
0.829
0.557
0.026
0.185
1.015
0.917
0.270
0.464
0.522
-0.096
0.214
0.332
-0.221
0.051
0.382
15
17
13
12
3
5
16
14
7
10
11
2
6
8
1
4
9
Source: K.A.P Siddhisena and Ruwan Jayathilaka (2006)
“……study analysed several other socio-economic
dimensions including income in the identification of poor
districts using two data sets. The number of variables
such as nutrition, water, sanitation, housing
facilities – type of wall, type of floor, source of
drinking water, source of lighting and source of
cooking—minimum level of calorie consumption,
food expenditure, level of education and per capita
total household monthly income are initially used and
significant factors are taken into account using the
Principal Component based Factor Analysis.........”
(K.A.P Siddhisena and Ruwan Jayathilaka (2006))
28
Proposed Data Analysis
Children
Classification of the Children
Female
Male
Poor Male
Children
Age
Group 1
Age
Group 3
Non Poor Male
Children
Age
Group 1
Age
Group 2
Age
Group 3
Age
Group 2
Poor Female
Children
Age
Group 1
Age
Group 3
Age
Group 2
Non Poor
Female Children
Age
Group 1
Age
Group 3
Age
Group 2
Sample Distribution and Background Characteristics of Households
Characteristics of the Child poor households
Demographic, Economic and social Characteristics
•
•
•
•
•
•
•
•
•
•
•
Age distribution
Age-sex distribution
Marital status
Ethnicity/Religion
Education level
Labour force participation
Occupational distribution by poverty
Unemployment and dependency
Expenditure on healthcare, education and food.
Headship,
Alcohol
29
Figure 1: Distribution of the Poor and Non Poor FHHs and MHHs
Non Poor Households
Poor Households
Kegalle
Colombo
100
Ratnapura
80
Gampaha
Kalutara
60
Monaragala
40
Kandy
20
Badulla
Matale
0
Polonnaruw a
Anuradhapura
Puttalam
Kurunegala
Poor FHHs
Colombo
Kegalle 100
80
Ratnapura
Gampaha
Kalutara
60
Monaragala
40
Badulla
20
0
Kandy
Matale
Nuw ara Eliya Polonnaruw a
Galle
Matara
Hambantota
Poor MHHs
Anuradhapura
Puttalam
Kurunegala
Non-Poor FHHs
Figure 2: Regional Disparities of Proportion
of poor and Non Poor households-2002
Nuw ara Eliya
Galle
Matara
Hambantota
Non-Poor MHHs
Source: Ruwan Jayathilaka (2007)
Ruwan Jayathilaka (2007), Alcohol and Poverty: Are they Related? Empirical
Study from Sri Lanka, Sri Lanka Economic Journal, Vol 8 (1), 25-59.
Source: Ruwan Jayathilaka (2007)
30
Source: Ruwan Jayathilaka (2007)
Source: Ruwan Jayathilaka (2007)
31
Proposed Methodology
Logit model / Probit model
Logit Model
 P 
Li  log i   x0  1 x1   2 x2  ...........   n xnn  ui
 1  Pi 
Where
Pi is the probability the ith case experiences the event of interest
Probit Model
Pi 
1
2
zi
e
 s 2 / 2 dt

Pi= the probability that the dummy variable Di=1
Zi= -1(Pi)=ß0+ ß1X1i+ ß2X2i+……..+ ßnXni
s= a standardized normal variable
32
Proposed Regression model
Based on the study of
Ruwan Jayathilaka (2007), Alcohol and Poverty: Are they Related? Empirical
Study from Sri Lanka, Sri Lanka Economic Journal, Vol 8 (1), 25-59.
Y  0  1 X 1  2 X 2  ................... n Xn  
Y = 1 if the child is poor, 0 otherwise
X1 = Age
X2 = Family size
X3 = Per capita household income
X4 = Education (highest level of education in the family)
X6 = proportion of dependency of the household
X7 = unemployment rate
X8 = proportion of expenditure used for healthcare
X9 = proportion of expenditure used for education
X10 = proportion of expenditure used for food
X11 = Gender (1 for male and 0 for Female)
X11 = Headship (1 for MHHs and 0 for FHHs)
X12 = Alcohol (1 for alcohol consumed households, 0 otherwise)
variable X1, X2 and X3 are in natural logarithm
Colinearity problem
33
Model 1: comprised all households that consumed alcohol
Model 2: Non alcohol consumed households.
Model 3: includes the variable of alcohol legality among the alcohol consuming households.
P( Poor  1)  Zi (0  1Headship .............  7ty _ al)  
Source: Ruwan Jayathilaka (2007)
Our study will try to estimate 22 models
3 Sectors
17 Districts
Poorest 40 percent group
Richest 20 percent group
34
Regional Disparities of Probability of Poverty
Regional Disparities of Probability of Child Poverty
?
35
Regional Disparities of Probability of Poverty by Headship in 1995/96
Regional Disparities of Probability of Child Poverty by Sex
Regional Disparities of Probability of Child Poverty by Age groups
Regional Disparities of Probability of Child Poverty by Headship
?
?
?
36
Thank You
37