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Statistics on Children in the Philippines

 

Data Limitations/Assumptions

Data limitations do pose constraints in the development of this statistical handbook. The proceeding discussions elaborate the data sources and data limitations in the generation of statistical tables.

A. Survey Data

  1. National Nutrition Survey
  2. Data on nutrition indicators, e.g., stunting, wasting, and underweight were sourced from the 2003 National Nutrition Survey of the Food and Nutrition Research Institute. The reference age of children is 0 to 17 years old and hence, this should be reflected in statistical tables pertaining to all children. However, the initial data sets provided to NSCB only covers children 0 to 10 years old. Thus, this limitation should be considered when comparing nutrition indicators with other indicators on children, e.g., education.

  3. Multiple Indicators Cluster Survey
  4. The 2007 Sub-Regional Multiple Indicators Cluster Survey (SR-MICS) conducted by the National Statistics Office only covered 24 provinces and cities under the Country Program for Children (CPC)-6 of the United Nations Children’s Fund. The provinces/cities include Agusan del Sur, Antique, Aurora, Bukidnon, Camarines Norte, Capiz, Cebu City, Davao City, Eastern Samar, Guimaras, Isabela, Maguindanao, Manila, Masbate, Mountain Province, Negros Oriental, North Cotabato, Northern Samar, Pasay City, Quezon City, Sarangani, Sultan Kudarat, Sulu, and Zamboanga del Sur. There are variables in the statistical template labeled as MICS indicators, but were not covered/available in the final 2007 SR-MICS data sets/report as well as in the 2003 National Demographic and Health Survey (NDHS) and other surveys, which also provide similar information. Following are the identified MICS indicator with no data:

    Table 1.1.2 of the statistical template

    • Child with disability

    • Young children attending preschool

    • No security of tenure in urban areas

    • Boys/girls aged 5 to 14, who are orphans and vulnerable children

  5. National Demographic and Health Survey
  6. The wealth index quintiles, sourced from the 2003 NDHS of the NSO were modified to conform with the published data, i.e., instead of reflecting Q1 (poorest wealth quintile), Q2, Q3, Q4 and Q5, lowest, second, middle, fourth and highest, respectively, were considered. For the NDHS, the official published data are in proportions and sample size. Thus, these were reflected in the statistical handbook.

  7. Labor Force Survey
  8. Some data on the profile of children by geographic and household dimension (Table 1.1.2) were culled from the Labor Force Survey. While this information should be derived from official data source, i.e., NDHS, the NSCB Technical Staff, considering the timeline for generating statistical tables, made use of readily available data sets from the LFS.

B. Other Processed Data

  1. Use of Official Poverty Statistics for statistical tables on poverty
  2. For information on children in poverty, the official poverty statistics was used instead of those prescribed in the statistical template, e.g., poverty headcount by national poverty line, poverty gap ratio at $1 a day (PPP). Also, corresponding coefficients of variation (CVs) were attached to each of the poverty statistics to guide users on the reliability of the data.

  3. Use of Official Population Projections
  4. For consistency of data sources in Table 1.1.1, the 2000 Census-based Population Projections produced by the Technical Committee on Population and Housing Statistics were used. The base year was revised to 2000 instead of 1995 for comparability of estimates.

  5. Dividing complex statistical tables into simpler tables
  6. Some large statistical tables in the statistical template, e.g., Tables 1.1.2, 1.1.4, 1.1.5, were split into small tables according to data sources for easier reference among users of the handbook.

 


As children are our nation’s future, the importance of statistics on children including other non-income indicators on children in poverty cannot be overemphasized. In fact, the latest data on Philippine education indicate a definitive deterioration in the quality of human capital of the country.

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