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1 Child Well-Being and Equity in Sri Lanka Preliminary Overview.

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Presentation on theme: "1 Child Well-Being and Equity in Sri Lanka Preliminary Overview."— Presentation transcript:

1 1 Child Well-Being and Equity in Sri Lanka Preliminary Overview

2 2 Presentation outline Sri Lankan Childs profile Study Operational Structure Issues, Options and Challenges Brief overview of Policies Affecting Poverty Some Methodological concepts for child poverty analysis

3 3 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 quality of services towards enhanced well-being and equity,

4 4 Total Population and Child Population Source: Department of Census & Statistics, Census of population & Housing/ Statistical Abstract, 2000

5 5

6 6 Population Distribution by Province and District - 2001 Source: Department of Census & Statistics Census of Population & Housing 2001 Majority of population remain as Rural 14.5 mill, urban 2.8 mill and estate 1 mill. (plantation of over 20 hecates and more than 10 resident labour)

7 7 Source: Department of Census & Statistics, Census of Population & Housing 2001

8 8 Child growth - Underweight Source: Department of Census and Statistics, DHS 1987,1993, 2000

9 9 Immunization Coverage

10 10

11 11 Water supply and Sanitation Coverage by Province - 2006 Special MDG survey, Dept. Of Census and Statistics

12 12 Poverty Profile: Percent population and poor by Province Country average 15.2% Poor below poverty line (2006/7)

13 13 Mean per capita income per income receiver and per person

14 14 Tuition for students in formal education Consumer finance survey 2003/2004, Central Bank of Sri Lanka

15 15

16 16 Consumer finance survey 2003/2004, Central Bank of Sri Lanka

17 17 Consumer finance survey 2003/2004, Central Bank of Sri Lanka

18 18 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

19 19 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.

20 20 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 income/expenditure since 2003/04 (partial coverage of north)

21 21 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 non- availability 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

22 22 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, primarily, within different government agencies and development agency agenda to enhance child well-being

23 23 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 2006/07 as the earlier survey in 2000 dates – limited options

24 24 Characteristics of the Poor Primary Education Only (38% poor only primary cf 10% & 4% poor with O & As) 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

25 25 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.

26 26 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.

27 27 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.

28 28 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: Composite Indicator SLISRankCFSRank Districts 0.407160.99215 Gampaha0.424171.11017 Kalutara0.143140.82913 0.158150.55712 Matale-0.31540.0263 Nuwara Eliya-0.11680.1855 0.117121.01516 Matara0.124130.91714 Hambantota-0.15460.2707 Kurunegala-0.05190.46410 Puttalam-0.038100.52211 -0.3663-0.0962 Polonnaruwa-0.42120.2146 Badulla-0.033110.3328 Moneragala-0.5451-0.2211 Ratnapura-0.15270.0514 Kegalle-0.16450.3829 Source: K.A.P Siddhisena and Ruwan Jayathilaka (2006) Based on the ultimate composite poverty indicator, all districts are ranked in order to understand the poverty status in Sri Lanka ……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 cookingminimum 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))

29 29 Proposed Data Analysis Classification of the Children Poor Male Children Non Poor Male Children Poor Female Children Non Poor Female Children Age Group 1 Age Group 3 Age Group 2 Age Group 1 Age Group 3 Age Group 2 Age Group 1 Age Group 3 Age Group 2 Age Group 1 Age Group 3 Age Group 2 Male Female Children 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

30 30 Source: Ruwan Jayathilaka (2007) Figure 1: Distribution of the Poor and Non Poor FHHs and MHHs Figure 2: Regional Disparities of Proportion of poor and Non Poor households-2002 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.

31 31 Source: Ruwan Jayathilaka (2007)

32 32 Proposed Methodology Logit model / Probit model Logit Model Where P i is the probability the i th case experiences the event of interest Probit Model Pi= the probability that the dummy variable D i =1 Z i = -1 (P i )=ß 0 + ß 1 X 1 i+ ß 2 X 2i +……..+ ß n X ni s= a standardized normal variable

33 33 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 = 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

34 34 Source: Ruwan Jayathilaka (2007) 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. Our study will try to estimate 22 models 3 Sectors 17 Districts Poorest 40 percent group Richest 20 percent group

35 35 Regional Disparities of Probability of Poverty Regional Disparities of Probability of Child Poverty ?

36 36 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 ?

37 37 Thank You

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