2. South Asia Regional Child Poverty Meeting Kathmandu 7-9 May 2008 ‘Study on Child Poverty and Disparities’ Country Progress Nepal

3. Concepts and methodology –Opportunities Disaggregated data by geographic, demographic, gender as well as ethnic dimensions; First comprehensive study on Child Poverty in Nepal; Reshape national policies at a watershed period in Nepal’s history; Support the Constituent Assembly and the new government of Nepal to develop policies for children; –Challenges Adapt the study to the Nepali context Lack of data to address some of the key research questions Developing a child-focused approach to analyzing socio- economic policies in Nepal

4. Conceptual Framework Fulfillment of Child Rights affected by: –Impact of conflict on children (IDPs; migration; child labour; access to and quality of social services; school drop-out; nutrition…) –Widespread poverty and chronic inequalities and child outcomes: multiple dimensions (geography; community/household/individual; gender; ethnicity; socio-economic status…)

5. Hypothesis: the impact of conflict on children has been extensive including material and non-material deprivations. Social exclusion and inequality, root causes of and aggravated by conflict, determine divergent outcomes for health, education, housing, access to water and sanitation, and other areas, depending on a child’s gender, caste and ethnicity.

6. 12.5 million children and young people (45%of population) 13% of population are under age 5 years old

7. Children poverty and disparity Some examples: Infant mortality rates among Dalit children is 1.4 times higher than in the general population Dalit children are 1.6 times less likely to be attending primary school than the general population 1.8 million child laborers and some 5000 boys and girls living on the streets

10. Impact of policies on Children’s Rights: some areas for further analysis Decentralization policy (DACAW) Budgeting for children (not done yet) Migration and remittances Social Protection (lacking) Government’s New Health Policy Impact of current food crisis on children Nutrition policies and practices

11. Challenges with the statistical and policy analysis methodology Some data gap or insufficient sample for a credible analysis (birth registration; disability; access to free health services and health insurance schemes…) Correlations on some tables due to the requirement of using two different sources. Some tables cannot be completed due to difference in the unit of analysis (eg. deprivation tables).

12. Data collection and processing –Using various sources of data to derive one core child outcome tables and relevant contextual information; –Available information was used to comply with global definitions.

13. Challenges with the sources of data Main Sources: NLSS and NDHS available for at least two points of time. Census information have also been used whenever appropriate. Many circumstances where the available information in the data do not meet the data need. In some instances, although information is available in the data, limited number of cases in the category of interest have produced questionable outcomes to draw conclusions.

14. Policy advocacy Based on preliminary assessment key areas to be prioritized include: Social Protection Nutrition policies and practices Strengthening the Child Protection System starting with birth registration The study will be an important tool for advocating with key decision makers in these areas

15. Questions What should be done to those tables that can not provide as suggested by the statistical template. Example: table 1.1.2. In this case different data sets have to be used according to the information available in the data set. How the issue of the different units of analysis will be approached that deviates as understood from the given statistical tables? There are certain variables for which information is not available. Will these be dropped out? The “sex and age” variable under the “Individual dimension” as understood in the given template was found to be difficult in fitting information in some tables. Any suggestion on what to do in such cases.