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Possibility of Synchronizing Multiple Data for Monitoring One Goal

Possibility of Synchronizing Multiple Data for Monitoring One Goal. By Amara Satharasinge Deputy Director Department of Census and Statistics Sri Lanka.

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Possibility of Synchronizing Multiple Data for Monitoring One Goal

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  1. Possibility of Synchronizing Multiple Data for Monitoring One Goal By Amara Satharasinge Deputy Director Department of Census and Statistics Sri Lanka

  2. Sri Lanka is demarcated into an administrative hierarchy of 9 provinces, 25 districts, 325 Divisional Secretariat (DS) divisions and 14,009 Grama Niladhari (GN) divisions.

  3. Sri Lanka is potentially on tract on most of the available MDG indicators. Among the key achievements are access to safe drinking water, equitable primary education, literacy, child and maternal health. A significant achievement is that there is no gender disparity in these achievements. However, there remain considerable challenges. The critical challenge is that 23% of Sri Lanka’s population is still living below the national poverty line.

  4. Status of indicators at a glance Available from regular stat. activities of DCS (15) New survey for MDG indicators (12) Expected from the Data Producers’ Working Group (13) Not possible to prepare (03) Not applicable (09)

  5. DCS sources related to MDG indicators Regular Surveys Household Income & Expenditure Survey (HIES) Labour Force Survey (LFS) Demographic and Health Survey (DHS) Computer Literacy Survey (CLS)New Survey Survey to capture selected MDG indicators (MDGIS)Administrative RecordsCivil Registration System (CRS)

  6. Data Gaps and Remedial Action • Data from outside sources • Data not available • Data available with limitations

  7. Data not available Three indicators had to be abandoned as it is not possible to collect the required data from surveys or administrative records. Indicator No. and Name 18. HIV prevalence among pregnant women aged 15-24 yrs. 19a. Condom use at last high-risk sex 20. Ratio of school attendance of orphans(due to HIV) to school attendance of non-orphans 10-14 yrs.

  8. Tools for monitoring poverty reduction programmes • Maps at DS division level – 2002 (An application of Small Area Estimation Technique) • Maps at GN division level (compiled based on the analysis measurable characteristics reflecting poverty) • Reports based on the analysis of HIES data

  9. a)Head count ratio (Goal 1) b)Share of poorest quintile in national consumption (Goal 1) c)Literacy rate of 15 – 24 year-olds (Goal 2) d)Net enrolment ratio in primary education (Goal 2) e)Share of women in wage employment in the non-agricultural sector (Goal 3) f)Ratio of girls to boys in education (Goal 3) g)Infant mortality rate (Goal 4) h)Under five mortality rate (Goal 4) i)Maternal mortality (Goal 5) j)Proportion of the population using solid fuels (Goal 7) k)Proportion of households with sustainable access to safe drinking water (Goal 7) l)Proportion of population with access to improved sanitation (Goal 7) Classification of districts by overall status of selected MDG indicators

  10. A tool to facilitate monitoring progress in achieving MDG’s Findings of an exploratory study • An index reflecting the overall situation with respect to a set of selected MDG indicators at district level was computed • 12 indicators representing all goals but goal 6 were selected for the analysis. • By applying Principal Component Analysis 12 indicators were reduced to an index retaining 80% of the total variation • Districts were classified into 5 groups based on the values of this index using Natural Break method • Districts shaded in red are the most backward in terms of achieving the MDGs • The most deprived districts (most unsatisfactory class) are Rathnapura, Polonnaruwa, Badulla and Anuradhapura • This map will be updated as data become available • Comments/suggestions on this methodology are welcome

  11. DCS has carried out several activities to disseminate available MDG indicators for Sri Lanka and to bridge data gaps. Some activities are • Developing tools for monitoring impacts of poverty eradication projects • Preparation of two publications • Preparation of a database using DevInfo software • Preparation of the web version of the MDGInfo database, which will be launched shortly • Preparation of GN division boundary maps for data presentation • Conducting two surveys to bridge some data gaps

  12. Preparation of publications In 2005, DCS prepared a publication presenting available indicators for monitoring MDG goals.

  13. MDGInfo Sri Lanka Millennium Development Goals in Sri Lanka – A Statistical Review Preparation of publications In 2006, DCS prepared a publication presenting the trends and patterns of the available indicators and a user friendly database for monitoring MDG goals.

  14. Preparation of publications Trends and patterns and whether the targets are likely to be achieved is briefly reviewed in the publication titled “ Millanium Development Goals in Sri Lanka – A Statistical Review”

  15. MDGInfo Sri Lanka Preparation of databases A database titled “MDGINFO – Sri Lanka” was released in 2006. This was prepared using DevInfo software. User friendly tools for storing, retrieving and presenting retrieved data by tables, charts and maps are available in this database.

  16. Preperation of “MDGInfo” Online Database for Monitoring Millennium Development Goals

  17. Provision of information for local level planning poverty eradication projets

  18. Classification of GN Divisions by Unsatisfied Basic Needs

  19. Communalities Initial Extraction wallper 1.000 .553 floorper 1.000 .851 roofper 1.000 .822 toityper 1.000 .452 edualper 1.000 .940 gcealper 1.000 .895 non_kero 1.000 .776 non_firw 1.000 .662 emp52pec 1.000 .867 Extraction Method: Principal Component Analysis. Factor extraction: communalities Extraction communalities are estimates of the variance in each variable accounted for by the factors (or components) in the factor solution.

  20. Factor extraction: Total variance explained The "Total" column gives the amount of variance in the observed variables accounted for by each component or factor.

  21. a Rotated Component Matrix Component 1 2 3 floorper .836 .320 roofper .762 -.467 non_kero .759 .349 wallper .703 toityper .647 edualper .953 gcealper .922 non_firw .326 .686 SMEAN(emp52pec) .880 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 4 iterations. This table reports the factor loadings for each variable on the components or factors after rotation. Each number represents the partial correlation between the item and the rotated factor. These correlations can help you formulate an interpretation of the factors or components.

  22. Unsatisfied Basic Needs Index compute f1 = fac1_1*.65 + fac2_1*.19 + fac3_1*.16. Natural breaks Classes are based on natural groupings of data values. ArcMap identifies break points by looking for groupings and patterns inherent in the data. The features are divided into classes whose boundaries are set where there are relatively big jumps in the data values.

  23. Badulla district

  24. Gampaha district

  25. Galle district

  26. Thank you

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