Special Problem

MEASURING POVERTY IN THE PHILIPPINES USING EQUIVALISED HOUSEHOLD INCOME

WERNER BENSON LAZARTE MOLANO

1st Semester, SY 2009-2010

Institute of Statistics

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College of Arts and Sciences, University of the Philippines Los Baños

INSTAT Catalog no. ____________________

Requests and inquiries concerning and reproduction and rights in this publication should be addressed to

The DirectorInstitute of StatisticsCollege of Arts and SciencesUniversity of the Philippines Los Baños

By mail or telephone (049-536-2381) or email (stat_190@uplb.edu.ph)

Views expressed in this paper are those of the author, and do not necessarily represent those of the Institute. Were quoted, they should be attributed clearly to the author.

Produced by the Institute of Statistics, College of the Arts and SciencesUniversity of the Philippines Los Baños

CONTENTS

ABSTRACT……………………………………………………………………………….4

1. INTRODUCTION…………………………………………………………………….52. REVIEW OF RELATED LITERATURE…………………………………………….73. THEORETICAL FRAMEWORK…………………………………………………….124. METHODOLOGY…………………………………………………….........................16 Data Sets used in the Study…………………………………………………................16 Data Analysis……………………………………………………………………….....175. RESULTS AND DISCUSSION…………………………………………………….....18 Characteristics of Filipinos in terms of their age and Employment Status……………18

Equivalised Household Income…………………………………………………...…...19Measuring Poverty based on Different Measures of Annual Household Income……..22

6. SUMMARY AND CONCLUSION ……………………………………………….....23

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The special problem attached hereto entitled

MEASURING POVERTY IN THE PHILIPPINES USING EQUIVALISED HOUSEHOLD INCOME

prepared and submitted by

WERNER BENSON LAZARTE MOLANO

in partial fulfillment of the requirements for the degree of BACHELOR OF SCIENCE in STATISTICS is hereby accepted.

__________________________Zita VJ ALBACEA, Ph.D.

Adviser

___________Date Signed

Accepted as partial fulfillment of the requirements for the degree of BACHELOR of SCIENCE in STATISTICS.

________________________ZITA VJ. ALBACEA, Ph.D.

Director, INSTAT

___________Date Signed

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MEASURING POVERTY IN THE PHILIPPINES USING EQUIVALISED HOUSEHOLD INCOME1

WERNER BENSON LAZARTE MOLANO

ABSTRACT

Four different methods of measuring income to be used in poverty measure were developed using the combined data of 2006 Family Income Expenditure Survey and Labor Force Survey. The first method used the OECD (Organization for Economic Co-operation and Development) scale which was already developed by other countries. The second method used the age classification while the third method used the employment status of household members as an equivalising scale. The last method studied considered the combined age class and employment status as an equivalising scale. To come up with a scale, estimated population distribution by age class, employment status and combined age class and employment status were obtained. Results showed that almost 46 out of 100 Filipinos aged from 22 to 65 years. It was also observed that majority or around 59% of Filipinos are not in the labor force. The income obtained using the four scales were compared to the per capita income which is the income measure used in official methodology of poverty measure. It was found that the equivalising scales affect the estimation of poverty measure in the country. Using the combined age class and employment status as a scale for computing equivalized household income resulted to a poverty incidence of only 3 percent compared to the official estimate of the National Statistical Coordination Board (NSCB) in 2006 which is 27.5 percent or almost 28 out of every 100 Filipino families are poor. Lastly, using the OECD, age class and employment status as an equivalizing scale resulted to a more equally distributed household income while per capita income has a more dispersed distribution of household income.

Keywords: equivalising scales, poverty measure, poverty incidence

1 Special Problem under the supervision of Zita VJ Albacea, Ph.D. submitted as partial fulfillment of the requirements in STAT 190, First Semester, School Year 2009-2010.

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I. INTRODUCTION

Income refers to the sum of all the wages, salaries, profits, interest payments, rents and

other forms of earnings received in a given period of time. Philippines uses income as an

indicator of poverty.

Despite the economic growth and industrial development in the Philippines, the National

Statistical Coordination Board (NSCB) estimated in 2006 that the poverty incidence in the

country is equal to 32.9 percent. In other words, almost 33 out of 100 Filipinos are considered

poor. The increase in poverty incidence is reported to be caused by scarcity of job opportunities,

social and economic exclusion, poor economic policies and weak governance. The effects of

poverty incidence have dimensions on educational attainment, health and crime rate. These

effects drive the need to fight poverty. The Philippines as a member of the United Nations (UN)

together with 148 other countries formulated the Millennium Development Goals (MDG) and

one of these goals is to eradicate extreme poverty.

For the purpose of monitoring the progress towards eradicating poverty, there is a need to

identify who is poor that will allow the estimation of its incidence. The Social Weather Station

(SWS) poverty indicator is the proportion of household heads who rate their own families as

mahirap, which is Tagalog for ‘poor’. This measure of poverty is subjective from the viewpoint

of the family, not the researcher, and is thus capable of being validated by independent surveys

using the same approach (Mangahas, 2004). In comparison to the SWS, the government through

the NSCB has an objective approach in identifying the poor. The NSCB defined the poor as to

individuals and families whose incomes fall below the official poverty threshold defined by the

government or those who cannot afford to provide in sustained manner for their minimum basic

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needs for food, health, education housing, and other social amenities of life. Thus, an ideal

indicator of poverty is one that focuses on whether people are getting enough income to meet

their needs.

Different measures of poverty are being used by the different government agencies. In the

official methodology followed by the NSCB for poverty assessment, the family per capita

income from the Family Income and Expenditures Survey (FIES) conducted by the National

Statistics Office (NSO) is compared with the poverty line. When one’s income is below the

poverty line, the family is considered poor (Virola, 2005). Some issues arise in the

implementation of the existing methodologies. Although the total number of household

members, size N, is a logical candidate for the divisor in computing per capita income, an

underestimation of the real per capita income might occur. Consider household A that is

composed only of two members both have a job and household B with 4 members and only two

of them have a job. If the two households have the same monthly income, the per capita income

of household B might be underestimated since its income will be divided into four even though

only two of them have a job. The underestimation can be corrected and one way to do this is by

developing a scale that will allow a comparison of household income across household types and

can also be used to equivalize annual household income with respect to household composition.

Thus, the general objective of the study is to model the Philippine income distribution

using equivalised income. Specifically, this study aims to:

1. develop an equivalising scale for the family income of Filipinos;

2. construct the Philippine income distribution using per capita income and equivalised

income;

3. compare the characteristics of the constructed income distributions; and

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4. use equivalised income to compute poverty incidence.

Accurate measure of the income distribution in the Philippines will result to an accurate

measure of the state of poverty in the country. The methodology would prove to be most helpful

to the government and other Non-Government Organizations (NGOs) whose primary concern is

extending aid to the less fortunate since the computed estimates will be more accurate and

reliable. The government and NGO’s would have information on which areas of the country

need to be prioritized in terms of livelihood support, employment and other forms of community

development or where the government’s resources must go first. Estimates about the poor

households and how the numbers change through the years can also help the policy makers in

making the right decisions.

Equivalised income will also tackle the problem of underestimation. Compared to per

capita income which only uses the arithmetic mean to compute for the income of each household

member, the equivalised income will adjust the household income to account for the fact that

there are economies of scale in larger households and that adults usually have greater needs than

children.

II. REVIEW OF LITERATURE

Between 1997 and 2000, the distribution of the poor across provinces slightly became

more in accordance with the distribution of the population as the Adjusted Geographic

Concentration Index (AGCI) went down from 0.2295 to 0.2223. Major contributors to the

concentration are the rich regions of NCR, Region III, and Region IV, which have much lower

shares of the poor than of the population and the poor region of Bicol, which has a much higher

share of the poor than of the population. Against a national poverty incidence of 33% of the

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population in 1997 and 43% in 2000, NCR had 6.5% in 1997 and 7.6% in 2000. The provinces

with the lowest poverty incidences for both years are Bulacan, Rizal, Bataan and Cavite while

those with the highest are Sulu, Masbate and Ifugao (Virola et al., 2004).

In the same study, Virola et al. have found that the distribution of income with respect to

the population is not so uneven. The Philippines annual per capita income grew from, Php

24,431 in 1997 to Php 28,808 in 2000 at an annual rate of 5.6%. In a survey conducted by the

SWS on September 2003, 79% of households in Mindanao considered themselves poor, 64% in

Visayas and 58% in Luzon (Mangahas, 2004).

The 2006 official poverty statistics as presented by the NSCB also indicated that there

was an increase in the number of Filipino individual and families that can be considered poor.

Based on the 2006 FIES, poverty incidence in the country increased from 30 percent in 2003 to

32.9 percent or almost 33 percent in 2006. They have also estimated that from around 4 million

poor families in the country in 2006 grew to about 4.7 million. There was also an increase in the

poverty threshold in the country. In 2003, the NSCB estimated that a typical Filipino family that

is composed of five or six members needs Php 5,129 per month in order to stay out of poverty,

but in 2006 it is already Php 6,274.

Based on the study of poverty trends by the Asian Development Bank (ADB) in 2005,

using comparable data, it was shown that while the income poverty incidence fell from 1985 to

2000, the number of poor people increased dramatically. The poverty incidence of families in the

Philippines declined from 44.2% in 1985 to 31.8% in 1997 and increased to 33.7% in 2000.

Similarly, the poverty incidence of individuals decreased from 49.2% in 1985 to 36.9% in 1997

before rising to 39.5% in 2000.

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The ADB also reported that there is an inconsistency in measuring poverty in the

Philippines. Despite a long tradition of poverty measurement in the Philippines, trends in income

poverty are not straightforward as one might hope. The most important issue is that there have

been two major methodology changes since the poverty incidence was first estimated for 1985.

The first major change occurred in 1992, and the second in 2003. In essence, there are three

different poverty series in existence for the Philippines.

Almost the same issue was raised by Albert and Collado in 2004. They said that official

poverty measurement actually started in 1985, but the measurement system has undergone a

number of changes since then. The latest changes in the official methodology involve the

generation of poverty lines for urban/rural areas of each province based on estimating per capita

minimal food and non-food requirements in order to meet data users’ demands for more

disaggregated statistics.

A specific model refinement can be in terms of assuming some “economies of scale” thus

clarifying the relationship between household size and membership to poverty status (Coudel,

Hentschel & Wodon, 2002 as cited by Reyes, 2004). Equivalised household income is the sum of

total household income adjusted with the use of an equivalence scale to take into account the

composition of a household. The income that a household needs depends or varies on its size.

For example, a larger household needs to have a higher level of income in order to attain the

same standard of living of a smaller household. For example, a one-adult household with a

median annual household income of $35,000 is likely to be able to access a higher standard of

living than a two-adult, two-child household with the same income (Jensen ,1988 as cited by

Statistics New Zealand, 2004).

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The equivalence scales used in research on income poverty in Britain have recently

changed. Originally, from the 1970s onwards, the McClements scale was used, named after the

government economist who derived it using econometric analysis of household expenditure data

(McClements, 1978 as cited by Bradshaw, 2008). According to the Department of Work and

Pensions (2004), the McClements scale points assignment for each household members are as

follows:

Head 0.61Spouse 0.39Other second adult 0.46Third adult 0.42Subsequent adults 0.36

and for each dependent aged:

0 to 1 0.092 to 4 0.185 to 7 0.218 to 10 0.2311 to 12 0.2513 to 15 0.2716 or over 0.36

Equivalised household income is the total household income divided by the total or the

sum of the equivalising scale, in equation form: where W is the equivalised

income, H is the total household income, Ni is the size of each type k of components of the

household (adult, adolescent, children), and αi is the relative weight given to them (Figini, 1998).

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The equivalence scale most commonly used outside the UK was the OECD (Organization

for Economic Co-operation and Development) scale, which was originally derived as a

consensus of scales used by national governments. It had no basis in science. As if to underline

this, when economists at the OECD working on the Luxembourg Income Study noticed that the

square root of the number of people in the household produced very similar results, they adopted

this as the preferred option (Bradshaw, 2008).

Equivalised household income is derived by calculating an equivalence factor according

to the 'modified OECD' equivalence scale, and then dividing income by the factor. The

equivalence factor is built up by allocating points to each person in a household (1 point to the

first adult, 0.5 point to each additional person who is 15 years and over, and 0.3 to each child

under the age of 15) and then summing the equivalence points of all household members (ABS,

2006).

The Australian Bureau of Statistics (2006) also defined equivalised household income as

a measure of the economic resources available to each member of the household. Mean

equivalised household income is, therefore, usually calculated by adding the equivalised

household income of all persons, and then dividing by the number of persons. This enables

people in large households to have the same contribution to the mean as people living alone.

Another scale was developed by Jensen (1988). This scale is made so that a two-adult

household has a rating of 1; households with fewer members score less than 1, those with more

members score more than 1. The scale also considered the fact that minor members of a

household are likely to require less income than adults to maintain a similar standard of living.

A study that was conducted by Norfolk County Council in 2006 showed that across the

United Kingdom, although the distribution of total and equivalised household income is only

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slightly different, there is a clear pattern to these differences. The effect of equivalising the data

is to increase the income measure of the highest income groups (£85,000 and over) and the

groups in the £5,000-£35,000 range (many of these will be young adults without dependants).

The most significant reduction is in the lowest income groups of all, those earning less than

£5,000, and in the middle income ranges (reflecting the fact that many households will comprise

couples with dependent children).

III. THEORETICAL FRAMEWORK

The National Statistical Coordination Board is currently using the household per capita

income in determining whether a household is poor or not. Household per capita income is

computed by

where: Yj – jth household per capita income,

Xij- income of the ith member in the jth household, and

nj- total number of members in the jth household.

This measure of household income is commonly used in economic analysis but it does

not take into account the fact that there are different economies of scale depending to the

household size. Many countries have already come up with different scales that can be used to

adjust for these economies of scale. In the Philippines, such scales are still under study.

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In this study, the four age classification, employment status and the combined age

classification and employment status were considered and the estimated population distributions

that were obtained were used as equivalising scales.

The four-age classification used was defined as:

These age classifications were based from the labor code of the Philippines. As stated in

Article 139.b, Chapter II, Title III, Book III “No child below fifteen (15) years of age shall be

employed, except when he works directly under the sole responsibility of his parents or guardian,

and his employment does not in any way interfere with his schooling” (Philippine Labor Code,

1974).

The age distribution was computed by:

where: wj – weight/distribution of the jth age classification,

nj- number of persons belonging in the jth age classification, and

N- total number of persons considered in the analysis.

Another procedure that was considered is the use of the distribution of the household

members by employment status. The employment status of the household members is defined as:

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This can be estimated by:

where: wj – weight/distribution of the jth employment status,

nj- number of persons belonging in the jth employment status, and

N- total number of persons considered in the analysis.

Lastly, the combined age class and employment status was obtained by cross-tabulating

the four age classifications and employment status of household members. The equivalising scale

can be also written as:

where the sum of the ei‘s is the denominator that will be used to compute for the equivalised

household income.

With an equivalising scale under the Philippine setting, the equivalised income is

computed by summing up the income of household and dividing it by the sum of the equivalising

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scale that was given to each member of the household. Mathematically, the equivalised

household income is then computed as

where: Ej – equivalised household income of the jth household,

Xij- income of the ith member in the jth household,

eij- equivalised scale/ weight that will be given to the ith member of the jth household, and

nj- total members of the jth household.

After deriving the equivalised household income, income classifications were constructed

and the number of families whose equivalised income and per capita income fell in a particular

income class was observed in order to model the Philippine income distribution. In the

Philippines, these income groups or also known as income classes are defined as follows:

Income Class Per Capita Income and or Equivalised Income

1st income class Under 40,000

2nd income class 40,000 – 59,999

3rd income class 60,000 – 99,999

4th income class 100,000 – 249,999

5th income class 250,000 and over

The income distributions obtained using different equivalising scales and per capita

income were compared using gini coefficient. The Gini coefficient is based on the Lorenz curve,

a cumulative frequency curve that compares the distribution of a specific variable with the

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uniform distribution that represents equality. This equality distribution is represented by a

diagonal line, and the greater the deviation of the Lorenz curve from this line, the greater the

inequality (PAHO, 2001). The coefficient ranges from zero to one. A coefficient of zero means

complete equality and one indicates complete inequality (one person has all the income or

consumption, all others have none).

Poverty incidence is the proportion of families with per capita incomes below the poverty

threshold or poverty line to the total number of families (NSCB, 2000). This can be estimated by:

where:

can be obtained by:

where: Yj- equal to 1 if the jth household is poor, and 0 otherwise;

- survey weight of the jth household; and

n- total number of households in the sample,

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IV. METHODOLOGY

A. Data Sets Used in the Study

The data that were used to model the Philippine income distribution were obtained from

the combined 2003 Family Income and Expenditure Survey (FIES) and Labor Force Survey

(LFS). The FIES is a household survey that is being conducted by the NSO every three years. It

gathers information about the income and expenditure from the sampled households. On the

other hand, the LFS gathers information about the individual members (gender, age, employment

status, etc.) of the sampled household.

There is a need to use the combined data set since there are certain variables needed in the

study that are not included on the FIES or LFS if each data set is treated separately. For example,

if only the FIES data will be used, variables such as household member’s age and gender are not

included and these variables are needed in the computation of the equivalised household income.

Conversely, if only LFS data will be used, the total income of a household is not included and

such information is also needed in this study.

B. Data Analysis

The equivalised income was constructed as discussed in the Theoretical Framework.

After the equivalised income was estimated, the per capita income of the Philippine households

was also measured.

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Descriptive statistics like the mean, median and measures of variability such as variance

and standard deviation were obtained and the characteristics of the income distribution obtained

using equivalised and per capita incomewere compared. Furthermore, gini coefficient was

estimated per type of income used in the construction of the income distribution.

Poverty incidence for each type of income constructed was also computed. The effects of

changing the measure of income on the estimation of poverty measure like poverty incidence and

gini coefficient were further studied.

V. RESULTS AND DISCUSSION

A. Characteristics of Filipinos in terms of their Age and Employment Status

Table 1 shows the age distribution of the Filipinos using the four age groupings and

employment status, respectively. It shows that the third and fourth age class has the highest and

lowest share of population, respectively. It can be seen that about 45 percent of the Filipinos

belong to the third age class or the working class while only 3.8 percent of the Filipinos belong

to the fourth age class. With majority of the Filipinos are in the working class, it can also be seen

in Table 1 that almost 92 out of every 100 Filipinos in the labor force are employed. Moreover,

59 out of every 100 are considered not in the labor force and only around 8 out of every 100

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Filipinos in the labor force are unemployed. These numbers indicate that majority of the

country’s population belongs to the dependent age.

Table 1. Estimated percentage distribution of Filipinos by age and employment status.

Age Classification Estimated Proportion14 years old and below 36.00

15 to 21 years old 14.3022 to 65 years old 45.90

66 years old and above 3.80

Employment Status Employment Rate 92.09

Unemployment Rate 7.91

Not in the Labor Force 58.91

Table 2 presents the estimated percentage distribution by age group and employment

status of Filipinos. The cross-tabulation is consistent with the distribution obtained in Table 1. It

can be observed that high percentages of distribution are found at the first and third age classes

and at those not in the labor force. Table 2 also shows that there are cases of child labor in the

country. There are households with members who are 14 years old and below and employed at

the same time. Looking at Table 2 it can be seen that 2.09% of Filipinos in the working age (22

to 65 years old) are in the labor force and at the same time unemployed. This shows that there is

still a scarcity in job opportunities in the country. It is also evident that almost 12 out of every

100 Filipinos who belong to the working age group are not in the labor force. These Filipinos

may either already gave up in searching for a job or they did not try to find a job at all thinking

that there is a lack of job opportunities in the country.

Table 2. Estimated percentage distribution by age classification and employment status.

Age Classification

Employment Status

TotalEmployed UnemployedNot in the

Labor Force

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14 years old and below 0.72 0.04 35.64 36.40

15 to 21 years old 4.49 1.09 8.90 14.48

22 to 65 years old 31.27 2.09 11.96 45.33

66 years old and above 1.35 0.02 2.41 3.79

Total 37.84 3.25 58.91 100.00

B. Equivalised Household Income

Table 3 shows the descriptive statistics for the different equivalised income and per

capita income computations. Results showed that the per capita income has the lowest mean at

Php35,677.27 while the equivalised income using the age classification by employment has the

highest mean at Php84,058.13. Considering OECD definition as the preferred option as quoted

from the study of Bradshow in 2008, the equivalised income computation from the employment

status has the nearest mean value to the computed mean using the OECD scale while the mean

per capita income is the farthest. Looking at the coefficient of variation (CV) of each estimate,

all of them range from one to two percent which shows that there is only a small variation of

income for every method that was used. It can also be seen that the per capita income has the

highest computed kurtosis while equivalised income computed by combined age class and

employment status has the lowest kurtosis. This implies that the variability of the computed

income using per capita income is due to a few extreme differences in the mean while the

variability of income using the combined age class and employment status is due to a frequent

modest differences from the mean.

Table 3. Descriptive Statistics of different income measures.

Descriptive Statistics

Equivalized Income Computed Using:

Per Capita IncomeOECD

DefinitionAge

ClassificationEmployment

Status

Age Classification

by Employment

StatusMean 64,193.29 70,174.91 59,286.57 84,058.13 35,677.27

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Median 35,827.96 39,276.46 31,973.39 47,254.29 19,723.67Min 757.67 834.32 534.84 897.74 1,257.00Max 8,488,434.00 11,915,792.00 7,841,769.00 14,343,671.00 8,064,012.00

Range 8,487,676.00 11,914,957.69 7,841,234.00 14,342,773.26 8,062,755.00CV 1.1654 1.1930 1.1966 1.2067 1.2580

Std. Error 748.14 837.20 709.43 1,014.30 448.84Kurtosis 1,646.69 3,126.25 1,506.62 3,372.61 5,548.55

Skewness 25.13 36.73 24.46 38.83 53.68

Figure 1 presents the income distribution using the different equivalised income and per

capita income computations. The results reveal that in all five (5) computations majority of the

respondents belong to the first income class (i.e. under 40,000). This means that it is probable to

have a high poverty incidence in the country based on per capita income since many if not

majority of the Filipinos belong to the first income class and the poverty line is also found in this

income class. Furthermore, Figure 1 also reveals that computing the Philippine income using the

combined age class and employment status as an equivalising scale resulted to the fewest number

of Filipinos that belong to the first income class. Overlapping the five income distributions, it

can be seen that there is a similar decreasing trend in the distribution as the income class

increases. Worth noting is that the distributions as one moves from income class 2 to income

class 4 in the equivalised income using age and employment status are almost equal. Although

all the five computations show income distributions which are skewed to the left, the per capita

income distribution which is most skewed to the left. Figure 1 also shows that per capita income

has the most peaked distribution while the computed equivalised income using combined age

class and employment status has the least peaked distribution.

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Figure 1. Income distribution using per capita income and different equivalising scales

Table 4 shows indeed that majority of the respondents belong to the first income class

except if their income will be computed using age class and employment status as an

equivalising scale. Using the per capita income resulted to the highest percentage in the first

income group which is equal to 77.13% of the respondents while equivalised income using age

class and employment status resulted to the lowest percentage in the first income class.

Table 4. Percentage of each estimated income class of different income computations

Income Class

Equivalized Income Computed Using:

Per Capita IncomeOECD

DefinitionAge

ClassificationEmployment

Status

Age Classification

by Employment

StatusUnder 40,000 54.96% 50.76% 59.08% 42.63% 77.13%

40,000 – 59,999 15.45% 16.39% 14.57% 17.42% 10.47%

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60,000 – 99,999 15.08% 16.23% 13.60% 18.27% 7.58%

100,000 – 249,999 12.07% 13.68% 10.56% 17.35% 4.22%

250,000 and over 2.44% 2.95% 2.20% 4.33% 0.60%

C. Measuring Poverty based on Different Measures of Annual Household Income

The poverty incidence was computed using the five different annual income measures

(i.e. using the OECD, age, employment status, combined age and employment status as a scale

and the household per capita income). The highest estimate of poverty incidence was estimated

using the per capita income which is 24 percent while the lowest estimate of poverty incidence

was obtained using the equivalising scale that considered the combined age class and

employment status of household members (see Table 5). Using this equivalising scale, it was

estimated that only 3 out of every 100 households are considered poor. It must be noted also that

among all the estimation procedures, this latter estimate resulted to the lowest standard error.

Results show that the current poverty estimation procedure (using per capita income) that

is being used might overestimate the incidence in the country. Moreover, all the computed

income using different equivalising scales resulted to a lower poverty incidence.

It can be seen that using the combined age class and employment status as an estimator

for household income has the lowest estimate of gini coefficient. This implies that the

equivalised income obtained using the combined age class and employment status as a scale is

more equally distributed compared to the other procedures of obtaining household income.

Furthermore, the per capita income resulted to the highest gini coefficient. It means that using

per capita as an estimator, the distribution of household income obtained was more unequal or

more dispersed compared to the other methods.

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Table 5. Poverty Incidence using the three equivalising scale and household per capita Income

 Poverty

IncidenceGini Coefficient

Per Capita Income0.24

(0.003287)0.54

Equivalised Income using OECD scale

0.08(0.001789)*

0.47

Equivalised Income using Age Class

0.06(0.001577)*

0.47

Equivalised Income using Employment Status

0.11(0.002143)*

0.47

Equivalised Income using combined Age Class and

Employment Status

0.03(0.001190)*

0.48

*Standard Error of the estimate

VI. SUMMARY and CONCLUSION

A “poor” household is defined as a household whose income is below the poverty

threshold. For the purpose of identifying households that classified as “poor” four different

methods of computing household income were compared (i.e. using the OECD, age, employment

status, combined age and employment status as a scale and the household per capita income).

The four different scales assign weights to the household members and the sum of these weights

for every household was used as a divisor for the household’s total income.

To obtain the equivalized household income, equivalising scales were first constructed.

Equivalising scales were computed by looking at the population distribution of the country.

Household members who belong to the third age group or the working class were given the

highest weights while household members who belong to the last age group were given the

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lowest weights using age class as the equivalising scale. It was found that 37.84 percent of

Filipinos are employed, so using the employment status as an equivalising scale, household

members who are employed were given the highest weights while unemployed household

members were given the lowest equivalising weight. Lastly, using the combined age class and

employment status, household members that belong to the third age class and considered

employed were given the highest equivalising weight.

Results showed that using the combined age class and employment status as an

equivalising scale resulted to the lowest estimate of poverty incidence in the country which is

equal to three percent. Thus, only 3 out of every 100 households in the country are considered

poor.

Moreover, using the OECD scale, age class and employment status of household

members as an equivalising scale resulted to a more equal income distribution of households

while using the per capita income resulted to a more unequal distribution among the other

estimation procedures.

The study shows that the estimation method using per capita income resulted to a higher

estimate of poverty incidence in the country. An alternative is the use of the equivalised income

using OECD scale that was developed by other countries and the three other equivalising scales

which give poverty measures that are more reliable since they considered the age and

employment status of each household member.

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VII. LITERATURE CITED

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AUSTRALIAN BUREAU OF STATISTICS. 2006 (Reissue). Census Dictionary.

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BRADSHAW, J. 2008. Minimum Income Standards Working Paper: A New Equivalence Scale. Social Policy Research Unit, University of York.

COUDEL, A., HENTSCHEL, J., & WODON, Q. 2002. Poverty Measurement and Analysis in

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JENSEN, J. 1988. Income equivalences and the estimation of family expenditure on children (Note). Wellington: Department of Social Welfare (unpublished).

MC CLEMENTS, L. 1978 The Economics of Social Security, London: Heinemann.

MANGAHAS M. 2004. The SWS Survey Time Series on Philippines Poverty and Hunger, 1983-Present. 9th National Convention on Statistics. 4-5 October 2004 EDSA Shangri-la Hotel, Mandaluyong City, Philippines

NATIONAL STATISTICS OFFICE. 2008. 2006 Family Income and Expenditure Survey. http://www.census.gov.ph/data/sectordata/2006/fies0605r.htm

NORFOLK COUNTY COUNCIL. Estimates of Equivalised Household Income. December 2006.http://www.norfolk.gov.uk/consumption/groups/public/documents/general_resources/ncc049022.pdf

PAN AMERICAN HEALTH ORGANIZATION. 2001. Measuring Health Inequalities: Gini Coefficient and Concentration Index.

REYES, M. 2004. Constuction of Unidimensional and Multidimensional Poverty Indices Using the FIES and APIS Data. 9th National Convention on Statistics. 4-5 October 2004 EDSA Shangri-la Hotel, Mandaluyong City, Philippines

SANTIANEZ, E.B. 2004. Income Distribution and Inequality of Three Key Cities in Mindanao. 9th National Convention on Statistics. 4-5 October 2004 EDSA Shangri-la Hotel, Mandaluyong City, Philippines

STATISTICS NEW ZEALAND. 2008. Technical Notes on Statistics on Housing Affordability. http://www.stats.govt.nz/analytical-reports/housing-affordability-report/technical-notes.htm#RJS

VIROLA, R., ENCARNACION J., BALAMBAN B., MILLENDEZ R.A., SANTOS F., MAGSOMBOL M.R., 2004. Concentration Indices of Production, Income and

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VIROLA, R.2005. Poverty Statistics 2000-2003: Has economic growth been pro-poor. February 14, 2005.

VIROLA R. 2005. Statistically Speaking: The FIES of the Rich: Truth or Consequence. December 12, 2005.

VIROLA, R. 2006. NSCB Grants Clearance to the 2006 Family Income and Expenditure Survey. http://www.nscb.gov.ph/pressreleases/2006/08Aug06_PR-200607-PP1-03_FIES.asp

VIROLA, R. 2006. 2006 Official Poverty Statistics. Press Conference on the 2006 Official Poverty Statistics March 5, 2008.

WORLD BANK. 2009. Measuring Inequality. http://go.worldbank.org/3SLYUTVY00.

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