1. Utilizing CBMS in Monitoring and Targeting the Poor: The case of Barangay Kemdeng, San Vicente, Palawan A CBMS Philippines Research Paper prepared for the PEP Network Meeting June 18, 2004

2. Outline of presentation Part 1: –Objectives –Physical Characteristic of Kemdeng –Results of the 2000 and 2002 CBMS Survey Part 2: –Use of Scores in Ranking Households –Uses of CBMS at the local level –Conclusions and Recommendations

3. General Objectives Monitor the welfare conditions of local people particularly the poor –Examine the changes over time of CBMS core indicators using the survey result in 2000 and 2002 Provide an aggregate measure of poverty to be able to target the poor - Use of composite index

4. Barangay Kemdeng: Physical Characteristic Maunlad Maningning Viscua Nagkakaisa Ugnayan Mahayahay

5. Barangay Kemdeng: Demography In 2000, there were 135 hhlds with a population of 713 persons and hhld size of 5.3 These figures declined in 2002 with 127 hhlds, 612 persons and hhld size of 4.8 The main reason for the decline in population count is migration –due to closure of small-scale silica mining operation in Purok Maunlad in 2000 –other family members who study or work in other locations in Puerto Princesa city or in Manila

6. Barangay Kemdeng: Demography The number of IP households or Tagbanuas increased from 6 hhlds or 24 persons in 2000 to 10 hhlds or 44 persons in 2002. All IP hhlds in the barangay live in Purok Ugnayan There are more males than females with sex ratio of 110.9 in 2000 and 114.7 in 2002.

7. Barangay Kemdeng: Health and Nutrition In 2000, no infant deaths were reported in the survey There were 3 infant deaths according to the 2002 survey –Due to poor health conditions of infants when born –Due to lack of access to safe water and poor sanitation

8. Barangay Kemdeng: Health and Nutrition In 2000, 7 ( or 2 males and 5 females) severely malnourished children were recorded among 137 children No malnourished children were found among the IP hhlds. Due to the conduct of feeding programs by barangay officials, only one child remained severely malnourished in 2002 from among the 7 children who were reported malnourished in 2000

9. Barangay Kemdeng: Health and Nutrition In 2002, 6 ( 2 males and 4 females) case of severely malnourished children were recorded. Two out of the five new cases are member of IP hhlds. The explanation for the malnutrition prevalence are: –Busyness of some mothers in their work –Poor health status of some of the mothers –Lack of money of some households to be able to complete the six month nutrition regime –Due to the poor access to safe water and sanitation in the barangay

10. Barangay Kemdeng: Access to Basic Services In 2000, only 44 hhlds or 32.6% have access to safe drinking water. 2002 survey results show a drastic decline with only 22 hhlds or 17.3% have access to safe water. In both surveys reveal that all IP households do not have access to safe water –The decline was due to the fact that 3 existing deep wells have been damaged and now inoperative, affecting at least 20 hhlds.

11. Barangay Kemdeng: Access to Basic Services Most of these hhlds now tap unsafe water sources such as dug wells, rivers and undeveloped springs for their water needs.

12. Barangay Kemdeng: Access to Basic Services In 2000, 80 hhlds or 59.3% have access to sanitary toilet facility The 2002 survey reveal a drastic decline with only 31 hhlds or 24.4% of hhlds with access The reason for these are: –Toilet bowls distributed in previous sanitation program were not durable –Decline in access to safe water supply, particularly deep wells. Some hhlds opted to build and use closed or open pits which do not need water to clean and maintain.

13. Barangay Kemdeng: Access to Basic Services –The number of hhlds with access to safe water and sanitary toilet drastically declined from 77.3% in 2000 to only 27.3% in 2002. In both survey years, IP hhlds do not have access to water-sealed toilets. Some use closed/open pits while others do not have any kind of sanitation facility.

14. Barangay Kemdeng: Access to Basic Services Only 8 households (5.9%) have access to electricity in 2000 while only 11 hhlds or 8.7% have or avail of these services in 2002. –Due to limited coverage and cost of electric power

15. Barangay Kemdeng: Education

16. Teachers provide free lunch for students, especially to IP students Reasons for decline: –School has only 3 classrooms –Families have difficulty in meeting other school expenses –Some students are already working in the fields, especially IP children

17. Barangay Kemdeng: Education

18. Reasons for low participation in secondary school: –Distant location of the nearest secondary school in Poblacion (by boat or 12 km walk) –Some students are already working to help earn income for their households

19. Barangay Kemdeng: Education

20. There are children 6-11 years old who are still attending daycare, kindergarten or preparatory level There are children 12-15 who are still at the elementary level

21. Barangay Kemdeng: Employment

22. Agriculture employs 72.6% of the employed in the barangay. Fishing and forestry are also among the common occupations of those employed Share of the employed in the industry sector declined from 7.5% in 2000 to 3.8% in 2002 Most male workers are in the agriculture sector while females are in the services sector Most employed IP are in the agriculture sector, undertaking farming activities in the upland and clearing of timberland

23. Barangay Kemdeng: Enabling PovertySubsistence

24. Barangay Kemdeng: Enabling –More accurate income estimates were taken from the 2002 CBMS survey –In 2000 CBMS, only income from wages/salaries and entrepreneurial activities were considered. –To get a more accurate information on income, other income from other sources and other receipts were included in the 2002 questionnaire

25. Barangay Kemdeng: Summary of results Based on the CBMS results, the barangay has not been performing well. Areas to prioritize: –Access to safe water –Access to sanitary toilet –Access to electricity –Health and nutrition –Employment –Elementary and secondary participation

26. Barangay Kemdeng: Summary of results Gender concerns: –Males are more vulnerable in the areas of health while females are not performing well in nutrition –Females slightly performs better when it comes to elementary participation while males are performing well in secondary school participation rate –Males are dominantly employed in agriculture while females are more employed in the services sector

27. Barangay Kemdeng: Summary of results IPs are more marginalized in areas of education, literacy and access to basic services The indicators are interrelated and explains some of the trends

28. Barangay Kemdeng: Benefit Incidence Analysis Proportion and distribution of households with children 6-16 years old who are attending public school, by per capita income decile Per Capita Income Decile Total Househol ds with childre 6- 11 years old Households with children attending elementary school MagnitudeProportion (row)Proportion (column) Kemdeng706491.4100 1st7685.79.4 2nd7685.79.4 3rd7685.79.4 4th7710010.9 5th7710010.9 6th7710010.9 7th7685.79.4 8th7685.79.4 9th7685.79.4 10th7710010.9

29. Barangay Kemdeng: Benefit Incidence Analysis

31. The heads of these households have low educational attainment, only reaching at least the elementary level No access to basic services Two out of the six households are IP households Proximity of the household to the location of the school

32. Barangay Kemdeng: Benefit Incidence Analysis

33. Use of Scores in Ranking the Households Comparison between Simple and Categorically Weighted Composite Indicator

34. Rationale of Composite Indicator Richer concept of multidimensional poverty Identifying the poorest households Discriminating between geopolitical and sub-geopolitical units Resource allocation Impact assessment

35. Composite Indicator: Brief Description Ideally, must summarize the characteristics of a particular household drawn from a set of indicators A function of the set of indicators Categories could be equally or differentially weighted Eventually, must draw household ranking and poverty rates

36. Definition/Denotation of Terms Target or Population Units – 127 households of Brgy. Kemdeng, San Vicente, Palawan Poverty Attributes – CBMS core socio-economic indicators Poverty Measure – quantifier or criterion in classifying a population unit Poverty Indicator – transformation or realization of the poverty measure Poverty Rate – relative magnitude of poor households using the poverty indicator Composite Poverty Measure, Poverty Indicator and Poverty Rate – multidimensional function of the set of univariate Poverty Indicators.

37. Methods Utilized: At a Glance Simple Scoring -Function of binary scores of the welfare indicators -Intuitively, the interest is the number of welfare indicators successfully attained -Poverty attributes are weighted equally -Poverty attributes and population units are treated independently Categorical Weighting -Function of varying weights of categories within welfare indicators -Does not imply that the concern is the number of indicators successfully attained -Poverty thresholds are derived -Relative weights are used, therefore, poverty attributes and population units are not treated independently

38. The CBMS Core Household Indicators Health -With child death -With malnourished children 0-5 years old Education and literacy -With members 6-16 years old not attending school -With illiterate members Housing -Tenure status of house and lot -Construction of the house

39. The CBMS Core Household Indicators Access to basic services -Source of drinking water -Toilet facility -Electricity Enabling -Subsistence status -Poverty status -With at least one employed member -With underemployed member Peace and order -With victims of crime

40. Composite Indicator using Simple Scoring Construction, Ranking and Poverty Rates

41. Simple Scoring: Construction Poverty indicators are transformed with a uniform direction One (1) is assigned to the positive category and zero (0) to the negative ones to form the profiles of the households Percent of scores in each household is derived

42. Simple Scoring: Poorest Households Bottom households that attained less than 50 percent of the indicators

43. Simple Scoring: Well-off Households Top households that attained more than 80 percent of the indicators

44. Simple Scoring: Poverty Rates Brackets of Simple Scores

45. Simple Scoring: Summary of Characteristics Relatively easy and apparently doable Weights are equally and arbitrarily set Profile and composite poverty measure of each household remains the same even when population units are increased/decreased Weights remain the same no matter how many indicators are included/excluded Composite poverty rate may vary greatly depending on the number of indicators that must be attained

46. Simple Scoring: Validation Bottom Households -Nelson Yayen, although in the bottom, has considerably adequate income -Manuel Binggon, Ricardo Padilla and Baltazar Padilla are heads of IP households Top Households -Kenny Dejosco is the barangay Captain -Pedro Dulgeme is a tourist caretaker -Rodencion Labrador is formerly a Municipal Council candidate

47. Categorically Weighted Composite Indicator Construction, Ranking and Poverty Rates

48. Categorically Weighted Composite Indicator: Construction Poverty indicators are transformed or retained in raw (as is) categories possessing some ordinal characteristics A dimension reduction technique (Multiple Correspondence Analysis) is applied on the set of population units and their poverty attributes Elimination of poverty attributes is done during the dimension reduction process A set of category weights within indicators is derived to form the profiles and composite poverty measures Poverty thresholds are defined to compute poverty rates

49. Introduction to Multiple Correspondence Analysis (MCA) An exploratory technique designed to analyze multi-way tables containing some measure of correspondence between the rows and columns The goal is to depict the characteristics of a set of variables in a low dimensional way Variables must be either in categorical or ordinal scale Dimensions extracted are arranged in terms of amount of variation explained

50. Categorically Weighted Composite Indicator: First MCA Measure of Dispersion of each Indicator, Preliminary

51. Categorically Weighted Composite Indicator: First MCA Category Quantifications of two of the least dispersed indicators

52. Categorically Weighted Composite Indicator: First MCA The first set of indicators drew a very low measure of information of 0.1895 in the first axis and 0.1572 in the second axis Toilet facility is the most dispersed indicator given the first MCA ‘Child death,’ ‘at least one employed member’ and ‘makeshift housing’ are the indicators that have the lowest discriminations All of the indicators follow ordering consistency with respect to the first axis except the two of the least dispersed indicators

53. Categorically Weighted Composite Indicator: Final MCA Measure of Dispersion of each Indicator, Final

54. Categorically Weighted Composite Indicator: Final MCA The measure of information has increased given the final set of indicators Toilet facility is still the most dispersed indicator given the final MCA Now, all of the indicators follow ordering consistency with respect to the first axis and ordinal direction of poverty

55. Categorically Weighted Composite Indicator: Final MCA Category Quantifications, Final Note that quantifications to the right of the first axis are categories attributed to well- off characteristics The first axis can be called the poverty axis also because the first axis explains the most variation

56. Categorically Weighted Composite Indicator: Poverty Weights and Thresholds The category quantifications in the first axis are adjusted in scale preserving the nature of ordering and distances to reflect a set of positive weights Household profiles are based on the category weights Composite poverty measure in each household is the averaged profile Poverty thresholds in each indicator is defined Overall poverty threshold is the average of the individual poverty thresholds

57. Categorically Weighted Composite Indicator: Poorest Households Bottom ten households according to the Weighted Composite Poverty Measure

58. Categorically Weighted Composite Indicator: Well-off Households Top ten households according to the Weighted Composite Poverty Measure

59. Categorically Weighted Composite Indicator: Validation Exercise Bottom Households -Two bottom households of Manuel Binggon and Ricardo Padilla are IP households -Prodencio Trepit and Jaime Delos Reyes are also heads of IP households -Nelson Yayen has considerably adequate income -Romeo Yayen Jr. only gets their livelihood from fishing Top Households -Kenny Dejosco is the barangay Captain -Pedro Dulgeme is a tourist property caretaker -Rodencion Labrador is formerly a Municipal Council Candidate

60. Categorically Weighted Composite Indicator: Poverty Rate and Correlations Composite Poverty Threshold=2284.94 Composite poverty measure increases as per capita income or educational attainment increases Composite poverty measure decreases as household size decreases

61. Categorically Weighted Composite Indicator: gender

62. Categorically Weighted Composite Indicator: Sector

63. Categorically Weighted Composite Indicator: Meaning of the Second Axis The second axis could be viewed as a grouping scale of the puroks of the barangay The northwestern barangays cluster together, as well as southwestern barangays Ugnayan, the IP community separates from the rest of the puroks

64. Categorically Weighted Composite Indicator: Meaning of the Second Axis

65. Comparison between the Two Methods Advantages, Disadvantages and Utilization

66. Simple Scoring and Categorical Weighting: Comparison Simple Scoring -Simple scoring is easier to adopt than the categorically weighted composite indicator as far as LGU’s are concerned -However, simple scoring does not utilize underlying discriminating nature of the indicators -Simple scoring has an arbitrary set of weights and, hence, poverty rates -Simple weights do not change no matter how many indicators or population units are used -Ties are among households are more probable Categorically Weighted -Categorically weighted composite indicator using MCA utilizes the discriminating nature and associations of the indicators -Has a constructive way of deriving weights and poverty thresholds -Ties are among households are less probable -Maybe difficult to be adopted -Relative weights are derived, thus, weights change depending on the dataset

67. Barangay Kemdeng: Uses of CBMS Provide crucial information to support planning and project implementation at the local levels The comparison and analysis of the two survey periods provides clear understanding of the development of the barangay –Allow the assessment, fine-tune or change programs and project to yield more desirable results Data are very useful to target beneficiaries of programs They are helpful to identify the poor

68. Barangay Kemdeng: Conclusions and Recommendations The comparative analysis of data from two CBMS surveys is a useful procedure in impact assessment of programs Concern for careful planning of projects at the local level, most specially at the barangay level Composite indices are good tools for ranking households, however, further work is needed to be done in this area