1

MEASURING HOUSEHOLD VULNERABILITY TO POVERTY IN MINDANAO

By

Calixto C. Arbas III and Edgardo D. Cruz, PhD1

Abstract_____________________________________________________________________________

This paper studied the vulnerability to poverty in Mindanao using a cross-sectional dataand used consumption expenditure to estimate vulnerability. The Feasible Generalized LeastSquare (FGLS) was utilized to estimate the predicted log-consumption and variances, and theVulnerability as Expected Poverty (VEP) method to measure the vulnerability of the households.The result showed that 47% of the households in Mindanao are vulnerable to poverty, with 41%are relatively vulnerable and 6% highly vulnerable. Among the vulnerable households, 12.7% arerelatively vulnerable and 87.3% are highly vulnerable. Among these vulnerable households,nearly 61% of them are poor. Some 56% of the households in Mindanao are non-poor and amongthese non-poor, almost 33% of the households are vulnerable to poverty. In contrast, there is asignificant number of poor (29%) that are not vulnerable to remain poor. Of the vulnerablehouseholds, nearly 7% are vulnerable due to high volatility of consumption and 40% arevulnerable due to low mean consumption. The main source of vulnerability for the currently poorhousehold is the low mean consumption and the source of vulnerability for the non-poorhousehold is consumption volatility. The region with the highest vulnerability to poverty ratio wasRegion XI, and ARMM, the lowest. The sources of vulnerability and incidence of vulnerability, bydemographic characteristics were also explored. Most of the vulnerable households had higherdependency ratio, young headed households, low educational attainment of the heads, largenumber of family members, has household heads working in agriculture sector, no electricity, nopotable water connection and do not own house and lot

Keywords: vulnerability to poverty, vulnerability threshold, VEP, FGLS

___________________1BS Economics student and Faculty member, respectively, School of Applied Economics, University of

Southeastern Philippines, Obrero, Davao City.

_____________________________________________________________________________Chapter 1

INTRODUCTION

Background of the StudyPoverty is the lack of basic human needs, such as clean water, nutrition, health care,

education, clothing and shelter, because of the inability to afford them (wikipedia.com). Peopleare poor if their income and resources (material, cultural and social) are so inadequate as topreclude them from having a standard of living (socialinclusion.ie).

Poverty reduction has been one of the main programs of the Philippine Government forthe past two decades. A necessary prerequisite to reduce poverty is the economic growth of thecountry (Asian Development Bank, 2005). Philippines posted a real Gross Domestic Product(GDP) growth rate at a constant 1985 price of 5.3% in 2006 and 7.1% in 2007. In 2008, growthslowed down to 3.7% and in 2009, the real growth rate was 1.1%. For the first quarter of 2010,

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the economy grew rapidly by 7.3%. At the end of the second quarter of 2010, GDP growth posted7.9%. In 2006-2007, when the growth rate was increasing, Mindanao Regions posted the highestaverage Gross Regional Domestic Product (GRDP) growth rate. During that period, the averagegrowth rate of GRDP at a constant price (1985) of the Mindanao Regions is 7.11% while Luzonand Visayas Regions posted average growth rate of 7.01% and 6.53%, respectively (NSCB,2011).

While there has been an increasing economic growth in the Philippines, this has beenunable to lift many of the poor out of poverty who traditionally rely on the agriculture foremployment, particularly in Mindanao. They typically have less access to basic services, lowerlevels of education and larger families to support. Even during periods of stronger economicgrowth, such as 2004–2008, poverty continued to rise (ausaid.gov.au). Even though Mindanaoposted the highest average GRDP growth rate, the three Regions with the highest proportion ofpoor families in the Philippines are all located in Mindanao—Caraga has 47%, AutonomousRegion in Muslim Mindanao has 46% and Western Mindanao has 44% (AusAID, 2007).

In 2006, the poverty incidence in the Philippines was recorded at 26.9% of the totalfamilies and 32.9% of the population or an increase of 2.5% and 2.9% from 24.4% and 30% of2003, respectively (NSCB, 2008) and Mindanao has the highest average poverty incidence in2006 at 40.25% compared to the 25.2% and 34.03% in Luzon and Visayas, respectively (NSCB,2006).

According to the World Bank (2010), a person is considered poor if his or herconsumption or income level falls below some minimum level necessary to meet basic needs.This minimum level is usually called the "poverty line". The Philippine National StatisticsCoordination Board (NSCB) used methods to measure poverty in order to identify who are poorand who are not in the different regions the Philippines and assist policy makers how to reducepoverty. The usual poverty concepts and measures, however, do not “capture” the burden placedby the “insecurity”, in terms of consumption expenditure and meeting basic needs, on theshoulders of the poor. These concepts and measures typically focus on observed states ofdeprivation, making statements about singular or multiple dimensions of well-being. Thesemeasurements invoke an ex-post concept of poverty, devoid of the ex-ante uncertainty whichcompounds the distress of the poor. In a sense, the notion of vulnerability was conceptualized tomodify this omission (Calvo and Dercon, 2005).

Chaudhuri (2003) differentiated poverty from vulnerability. Poverty is an ex-post measureof a household’s well-being (or lack thereof) and it reflects a current state of deprivation, oflacking the resources or capabilities to satisfy current needs while, vulnerability may be broadlyconstrued as an ex-ante measure of well-being, reflecting not so much how well off a householdcurrently is, but what its future prospects are. What distinguishes the two is the presence of risk–the fact that the level of future well-being is uncertain. The uncertainty that households face aboutthe future stems from multiple sources of risk—harvests may fail, food prices may rise, the mainincome earner of the household may become ill, etc. If such risks are absent (and the future arecertain) there would be no distinction between ex-ante (vulnerability) and ex-post (poverty)measures of well-being.

Measuring the probability of falling to poverty in the future is called vulnerability (topoverty) analysis. Vulnerability is a forward-looking and stochastic poverty prediction, based onpast observations of income and shocks (Thomas, 2003). It is the probability or risk today ofbeing in the poverty or of falling into deeper poverty in the future (Coudouel, et al., 2002) or thepossibility of becoming or remaining poor in the future (Cafiero and Vakis). Pritchett, et al. (2000)define vulnerability to poverty as a probability, the risk that a household will experience at leastone episode of poverty in the near future. Calvo and Dercon (2005) viewed vulnerability as“magnitude of the threat of future poverty”.Rationale of the problem

According to Tandon and Hasan (2005) exposure to risk and vulnerability is not only aconstituent dimension of poverty but also an important cause of future poverty. Poverty is astochastic phenomenon. Today’s poor may or may not be tomorrow’s poor. Currently non-poorhouseholds, who face a high probability of a large adverse shock, may experience the shock andbecome poor tomorrow (Chaudhuri, 2003).

According to Asian Development Bank (2005), poverty in the Philippines is transient

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which means that people moved in and out of the poverty over a short period of time and therehas not been a great deal of empirical research in this phenomenon in the Philippines. The way toprotect against transient poverty is through the design of appropriate social safety nets, such asconditional cash transfer or labor-intensive public works programs (Asian Development Bank,2005). Chaudhuri (2003) listed why studying vulnerability to poverty should be conducted. Theseare:

a) First, a temporal or static approach to well-being, like poverty assessment, is of limiteduse in relation to policy interventions to improve well-being that can only occur in thefuture;

b) Second, vulnerability assessment highlights the distinction between ex ante povertyprevention interventions and ex post poverty alleviation interventions.

c) Thirdly, analyzing vulnerability helps to investigate sources and forms of riskshouseholds face. This helps to design appropriate safety net programs to reduce ormitigate risk, hence vulnerability.

d) Lastly, vulnerability is an intrinsic aspect of well-being when individuals are risk averse.

Many researchers used consumption as proxy in estimating vulnerability to poverty asrequired in the methodology of Chaudhuri (2003). The method, however, is not restricted in usingconsumption as proxy. Due to unavailability of data and arguments on his methods, many of thestudies of vulnerability to poverty used income as proxy [Zhang and Wan (2008), Witt and Waibel(2009) and Albert (2007)] since it affects the ability of household to smoothen consumption in thepresence of shocks. Coudouel, et al (2002) argued that actual consumption is more closelyrelated to a person’s well-being in the sense of having enough to meet current basic needs. Onthe other hand, income is only one of the elements that will allow consumption of goods; othersinclude questions of access and availability. According to Albert (2007), it is not clear enough touse current income poverty status as a proxy for vulnerability.

In the Philippines, there are few studies on vulnerability to poverty analysis. One of theseis the study of Albert et al (2007) which measured the vulnerability to poverty in the Philippinesusing income per capita. This study, unlike Albert, et al (2007), measured vulnerability to povertyusing consumption expenditure per capita. Chaudhuri (2000) measured the vulnerability topoverty in the Philippines; however, the data used were quite outdated. He used data of FamilyIncome and Expenditure Survey (FIES) and Annual Poverty Indicator Survey (APIS) 1997 and1998, respectively, while this study used the data of FIES 2006, there are no available data afterthis year. This study used data from Mindanao only and focused on measuring vulnerability ofhousehold in Mindanao since it currently has the highest average growth rate of GRDP comparedto Luzon and Visayas, but unfortunately has the highest poverty incidence in the Philippines.

Significance of the studyVulnerability to poverty analysis is an important anti-poverty study because this help

assists policy makers to protect the transitory poor household. The government should not onlyfocus on how to reduce poverty in a current situation, but also on how to prevent households fromfalling into poverty. Measuring the vulnerability of the household in Mindanao to fall into poverty inthe future will help assist policy maker in reducing poverty in the Philippines, in general, andMindanao, in particular.

The importance of this study, particularly in using single cross-sectional data, is to add tothe literatures on simple approaches of measuring vulnerability to poverty because in manydeveloping countries, rich and long panel data rarely exist.

Objective of the study:The main objective of the study is to measure the household vulnerability to poverty in

Mindanao, that is, the probability of each household being below a poverty threshold.To identify the possible causes of vulnerability, at the household level, the second

objective of the study is to measure the percentage of household vulnerable to poverty bydifferent household characteristics.

Lastly, vulnerability-to-poverty ratio is measured. The importance of vulnerability-to-poverty ratio is to identify whether the distribution of vulnerable household is dispersed or

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concentrated.Scope and limitation of the study

The study used the consumption expenditure per capita data as proxy variable tomeasure household vulnerability to poverty in Mindanao (total family expenditure over familymembers). Total family consumption expenditure refers to the expenses or disbursements madeby the family purely for personal consumption. Therefore, it excludes all expenses in relation tofarm or business operations, investments, ventures, purchase of real property and otherdisbursements that do not involve personal consumption. Gifts, support, assistance, or relief ingoods and services received by the family from friends, relatives etc. are considered as incomebut at the same time treated as expenditures of the family for purposes of balancing the familyaccount (FIES, 2006). The consumption expenditure data is composed of food and non-foodexpenses. It was assumed that the basic needs such as food, clothing, bills, rents and etc. areprioritized by each household. Only the household characteristics as independent variables forconsumption were used due to unavailability of data required for the method. According toChaudhuri (2003), ideally, some data that were supposed to be included are: observable locallycovariate shocks in a specific area e.g. weather, calamity shocks and agricultural pest shock;observable macro-economic shocks e.g. commodity price shocks, and inflation rate shocks;observable idiosyncratic shock experienced by household e.g. illness of the main income earner,time-period when a household head has no job; and other unobservable variables whether time-varying or not.

The household characteristics that are only included in the study are: gender of the head,age of the household head, marital status of the household head, educational attainment of thehead, occupation and source of income, family size, dependency ratio, ownership of house andlot, and household access to electricity and water. The household’s characteristics are assumedto be the same in every Region.

The measurement of vulnerability was applied in each region separately becauseaccording to Chaudhuri (2002) there are shocks that can only occur in a certain region; forexample, local price shock that can affect the consumption of the household in that certain area.If area dummies are included, these may possibly overestimate or underestimate the predictedlog-consumption of the household as a whole.

Two important thresholds were used in this study. First is the poverty threshold. The useof this threshold is to know at what point the household’s consumption will be considered poor sothat we can measure what fraction of population (households) are below this threshold (thus,considered poor) and we can also measure what is the probability of the households to fall belowthis threshold. The second one is the vulnerability threshold. The use of this threshold is toidentify at what point would the household’s probability to fall under poverty (vulnerability level) beconsidered vulnerable so that we can measure what fraction of population (households) arebelow this threshold, thus considered vulnerable household.

The choice of vulnerability threshold is quite arbitrary; and based on the literaturesreviewed, two important and commonly used vulnerability threshold stands out—the observedpoverty rate (mean vulnerability) and the 0.5 threshold. Therefore, this study used both observedpoverty and 0.5 as vulnerable threshold. It is assumed that the observed poverty rate to povertyestimates of each household in every region is approximately equal to the mean vulnerability.According to Chaudhuri (2002), observed poverty rate may represent one particular summarystatistic (namely, the mean) of the underlying distribution of poverty and this highlights the wealthof additional information that a vulnerability assessment can, in principle, provide in comparisonwith a poverty assessment, which, ultimately, focuses only on the mean. Since observed povertyrate represents vulnerability mean of each region, therefore any household with vulnerability levelhigher than the average vulnerability of each region is considered vulnerable.

The study measured the percentage of household vulnerable to poverty using a thresholdof 0.5, because this threshold allows 50/50 chance for the household to be considered as poor ornot. This threshold, however, in most study, is higher than the mean vulnerability. Therefore, inthis study, this threshold was considered as highly vulnerable household. Hence, if the probabilityof each household to fall below the poverty threshold exceeds 0.5, it is considered highlyvulnerable.

This study also estimated vulnerability-to-poverty by household characteristics. This

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study attempted to explain the possible causes of the vulnerability by using the given householdcharacteristics.

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Chapter 2REVIEW OF RELATED LITERATURE

The probability of falling into poverty tomorrow is impossible to measure, but one cananalyze income and consumption dynamics and variability as proxies for vulnerability (Coudouel,et al, 2002). Consequently, many studies on measuring vulnerability to poverty use income orconsumption as proxy.

The study of Chaudhuri, et al. (2002) used consumption as proxy for vulnerability tomeasure the vulnerability of the household in Indonesia. Ideally, to measure vulnerability topoverty, one must use a long and rich panel data, but such data rarely exist in a developingcountry. So they measured vulnerability using a single cross-sectional data from Mini-SUSENAS1998 and cross-validated their estimation to the 1999 data hoping that that despite the obviouslimitations of purely cross-sectional data, a detailed analysis of these data can potentially beinformative about the future. These data are detailed with household characteristics andconsumption expenditures. The results of their study indicated that the cross-sectionalvulnerability estimates did a reasonably good job of identifying, those among the non-poor whowere less vulnerable and were hence likely to remain non-poor, and those among the poor whowere more vulnerable and were hence likely to remain poor.

Jha, et al. (2009) measured the extent of vulnerability as expected poverty (VEP)—thenused consumption as proxy—and examined the importance of the determinants of VEP on thebasis of a household survey for Fiji. They found out that vulnerability (and poverty) is largely arural phenomenon. Moreover, the distribution of vulnerability across different segments of thepopulation can differ significantly from the distribution of poverty. In addition, 86.2% of thepopulation observed to be non-poor. Of the non-poor, 13.8% are estimated to be vulnerable topoverty.

Azam and Imai (2009) also used consumption to estimate the ex ante poverty andvulnerability of households in Bangladesh using Household Income and Expenditure Survey(HIES) data in 2005. Their results showed that poverty was not the same as vulnerability giventhat a sizeable portion of households that are now non-poor are certainly vulnerable to falling topoverty in the future—10.56% rural non-poor are vulnerable to poverty. The study found out thatthose without education or agricultural households are likely to be the most vulnerable.

Using income as proxy for vulnerability has also been widely used for measuringvulnerability to poverty. One of this studies conducted was by Zhang and Wan (2008) in ruralChina. The assessment was based on comparison between predicted vulnerability and actuallyobserved poverty. Results showed the precision on prediction of measuring vulnerability. First, itvaried depending on the vulnerability line; the results suggested setting the line at 50 per cent inorder to improve predictive power. Second, precision depended on how permanent income isestimated. Assuming log-normal distribution of income, it was preferable to use past weightedaverage income as an estimate of permanent income rather than using regressions to gaugepermanent income. And third, prediction precision depended on the chosen poverty line. Moreaccurate measurement of vulnerability to poverty was obtained with a higher poverty line of US$2instead of US$1.

Witt and Waibel (2009) measured vulnerability to poverty as a downside risk of householdincome in rural Cameroon. Results suggested that fishermen are less affected by adverse effectson income than other livelihood systems, while rice growers are the poorest and most vulnerable.Rice and millet growers are suffering from chronic poverty, while transient poverty is moreprevalent among the group of sorghum growers and fishermen.

Jha, et al (2008) examined the profile of poverty and vulnerability in Tajikistan. Resultsshowed that one half of the households observed to be non-poor are vulnerable to poverty. Usingthe expected utility approach, their analysis suggested that vulnerability associated with inequalityis very large, whereas that from idiosyncratic risk is moderate. Aggregate shocks have beenfavorable and reduced vulnerability.

In the Philippines, there are also studies conducted about vulnerability to poverty. Albert,et al (2007) estimated the household vulnerability to poverty using income data. Estimates ofhousehold vulnerability to income poverty are developed using a modified probit model thatconsiders volatilities in income as explained by some household characteristics. The

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measurement of vulnerability to poverty in the Philippines used income data from the 1997 FIESand the method of Chaudhuri (2000). About 54% of the household populations were found to bevulnerable (as compared to the official estimate of household poverty incidence at 28%). Theirresult showed that regions with highest poverty incidences appeared to be also those with highestestimates of vulnerability, but vulnerability rates are usually much higher than poverty rates.Moreover, while rural vulnerability is higher than urban poverty, the gap in estimates ofvulnerability is much lower than those pertaining to poverty. The result also showed thatvulnerable households have, on the average, much larger family sizes and vulnerability rates ofhouseholds with unemployed heads are much higher than their corresponding poverty rates.

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Chapter 3METHODOLOGY

Digression on VulnerabilityAccording to Dercon (2001), the household is the unit of analysis in vulnerability (to

poverty). They are composed of individuals, usually refers to family member and relatives, andthey belong in communities. Communities are usually referred to village, ethnic groups, extendedfamily and other social networks. Households and communities are entities of co-operation butalso of potential conflict, like vulnerability to poverty.

While there are new extensions and emerging of literatures on quantifying vulnerability,there are three approaches to measure vulnerability most commonly used in the differentliterature reviewed, these are: vulnerability as expected poverty (VEP), vulnerability as lowexpected utility (VEU) and vulnerability as uninsured exposure to risk (VER). VEP and VEUconstruct a measure of economic units and VER assesses the extent of deviation from theexpected welfare (Lim, 2010) therefore, it is an ex-post assessment of the extent to which anegative shock causes a household to deviate from expected welfare (Oni and Yusuf, 2008).

There is no clear definition of vulnerability to poverty and there are many arguments onmeasuring and defining it from quantitative to qualitative (see, Dercon, 2001, Coudouel, 2002 andChaudhuri, 2003). In this study, the vulnerability level V of a household h at time t is defined asthe probability that the household will find itself poor at time t+1:

Vh t = Pr(c h, t+1 < z)

where ch,t+1 is the household’s per capita consumption level at time t+1 and z is thecondition where the household will find itself poor or its consumption will be below the givenpoverty line. .According to Jha, et al (2008) the environment is assumed to be stationary so thatthe probability of future consumption ch,t+1 outcome remains the same across time.

Conceptual framework

It is known that income or consumption dynamics and variability is used as proxies forvulnerability to poverty. According Coudouel, et al (2002), to allow the researcher to identify whatcharacteristic of the household influenced ex ante distributions of the future consumption, oneshould analyze the determinants of poverty to see which factors influence the probability of lowincome in the future. This is because, the household determinants of poverty provide informationon the causes of poverty and can be analyzed by looking at households over time and analyzingtheir welfare changes in light of their characteristics (Coudouel, et al, 2002). Figure 1 shows thedeterminants of poverty that may contribute different shocks to the household that will affect theirfuture consumption.

According to Chaudhuri (2003) a large literature has developed which addressesprecisely these issues. This literature suggests that a household’s consumption in any period will,in general, depend on a number of factors. Among them its wealth, its current income, itsexpectations of future income (i.e., lifetime prospects), and the uncertainty it faces regarding itsfuture income and its ability to smooth consumption in the face of various income shocks. Each ofthese will, in turn, depend on a variety of household characteristics, those that are observableand possibly some that are not, as well as a number of features of the aggregate environment(macroeconomic and socio-political) in which the household finds itself.

The variables below that were used in the study were patterned from the study of Albert,et al. (2007) on the vulnerability to income poverty in the Philippines.

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Figure 1.The method of estimating vulnerability to poverty using the variables household

characteristics and consumption expenditure

HOUSEHOLD CHARACTERISTICSAge of household headGender of the household headEducational Attainment of the head Marital status of household head Family size

Dependency ratioOccupation and source of incomeOwn or owner-like possession of

house and lotHousehold access to water Household access to electricity

DEPENDENT VARIABLE Household

consumptionexpenditure

VULNERABILITYTO POVERTY

Vulnerability asExpected Poverty

(VEP)

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DEPENDENT VARIABLE

Household consumption expenditure per capita – vulnerability to poverty is assumed asmeasure of the ability of the household to smooth consumption in the presence of incomeshocks.

INDEPENDENT VARIABLE- HOUSEHOLD CHARACTERISTICS

Age of household head – age of the household head is an indicator of the householddecision making, assuming, in terms of consumption or income generation. The olderhousehold head, the higher and mature it’s the ability to cope up with different incomeshocks and etc. then the lower is the expected household’s vulnerability to poverty.

Educational attainment. The household head educational attainment is included in the studybecause this affects the chances of the household head to be employed in a higher-income joband etc. The higher the educational attainment of the household head, the expected vulnerabilityof the household will be lower. The dummy variables on educational attainment are:

Dummy on whether or not household head is illiterate1, if household head has illiterate0, otherwise

Dummy on whether or not household head has taken elementary education1, if household head has graduated and/or has taken elementary education0, otherwise

Dummy on whether or not household head has taken secondary education1, if household head has taken high school and/or high school graduate0, otherwise

Dummy on whether or not household head has taken tertiary education1, if household head has taken college and/or college graduate0, otherwise

Dummy on marital status of household head – can be an indicator of the householddecision making and planning. For instance, assuming they are married, household headthat is supported by another is better in decision making (to smooth consumption) than asingle household head, and etc.1, if the household head is married0, otherwise

Dummy on whether household head is female – this variable is included in order toidentify whether there are gender differences in terms of household ability to smoothconsumption or ability to generate income.1, if the household head is female0, female

Family size – larger number of family member in a household is expected to increase thevulnerability to poverty of the household because the consumption per capita of thehousehold will be smaller, ceteris paribus, and thus, its probability to be lower than thepoverty threshold increases.

Ratio of number of persons dependency ratio 0-14 years old to number of persons 15years old and over (in the household) or dependency ratio– the ratio can be an indicatorof the number of adults supporting his/her siblings/children for the household’s financialassistance (for consumption), assuming that adults are employed and/or belongs to laborforce. For example, 1 is to 2, one child is supported by two adults and etc.

Sources of income or occupation of the head. Sources of income are importantdeterminants/variables in vulnerability because these are indicators of the household capacityand capability to generate income for future expenditure (consumption). These dummy variablesare:

Dummy on whether or not household head works in Agriculture.1, if household head works in Agriculture0, otherwise

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Dummy on whether or not household head works in Industry1, if household head works in Industry0, otherwise

Dummy on whether or not household has income from Agriculture1, if household has income from Agriculture0, otherwise

Self-employed household head1, if household head is self employed0, otherwise

Dummy on whether or not household own or owner-like possession of house and lot –assets are important because this can be turn into income for future purposes1, if household own or owner-like possession of house and lot0, otherwise

The variables below are included because these are indicators of the householdcapability to access basic needs. It is expected that those without access to electricity affectbusinesses, comfort and etc. Thus, increases vulnerability of the household. Those that do nothave potable water may be prone to water-borne diseases and thus affect their vulnerabilitybecause the income intended for their daily consumption might be spend for the services ormedicine to cure the disease.

Household access to electricity1, if has access to electricity0, otherwise

Household access to potable water1, if has access to water0, otherwise

Economic modelThis study followed the generalized regression model used by Chaudhuri (2002). The

vulnerability to poverty estimation begins by assuming the stochastic process for generating theconsumption of a household h is given by:

(1)

Where,ln ch – per capita consumption expenditure of the householdXh – represents a bundle of observable household characteristics: gender of the head, ageof the household head, marital status of the household head, educational attainment of thehead, occupation and source of income of the household, family size, dependency ratio ofthe household, ownership of house and lot, and household access to electricity and water ofthe householdβ – vector of parameters to be estimated, andεh – is a mean-zero disturbance term that captures idiosyncratic factors that affectsobservationally equivalent to the consumption of household;

This model is modified by assuming consumption expenditures, ch to be log-normallydistributed and as such the disturbance term εh will be distributed normally (Azam and Imai,2009). According Chaudhuri (2003), this was assumed in order to capture the entire distribution ofconsumption by its mean and variances.

Generalized least square (GLS) is a technique for estimating the unknown parameters ina linear regression model. The GLS is applied when the variances of the observations areunequal (heteroscedasticity), or when there is a certain degree of correlation between theobservations. (wikipedia.org)

This method allowed heteroscedasticity when estimating vulnerability, therefore,

hhh ε+βX=lnc

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homoscedasticity is not satisfied or the variances of the observation are unequal. Variance isused as a measure of how far a set of numbers are spread out from each other. It is one ofseveral descriptors of a probability distribution, describing how far the numbers lie from the meanor average or the expected value (wikipedia.org). According to Gau (2002) when the variances ofthe observations are unequal (heteroscedasticity) or homoscedasticity is not satisfied, thefollowing model will be used:

hh εβXY

Assumptions:

0=X]|E[ε

Ωσ=X]|E[ 2εε'

where Ω is a known positive definite matrix. This model is called generalized regression model. Inreality, the Ω is not known directly, therefore, we need to choose an appropriate model that isclosely related to generalize regression model. The appropriate model to be used is the FeasibleGeneralized Least Square (FGLS) or also known as Estimated Generalized Least Square.Feasible generalized least square is similar to generalized least squares except that it uses anestimated variance-covariance matrix since the true matrix is not known directly (wikipedia.com).

To measure vulnerability to poverty, first, the variance of the residual εh in equation (1) isassumed to be dependent upon observable household characteristics X in some parametric way.Thus, the variance of εh can be modeled as follows:

σ2ε h = Xhθ (2)

According to Amemiya (1977, as cited by Albert et al, 2007), the parameters β and θ inequations (1) and (2) respectively, can be estimated using a three-step feasible generalized leastsquares (FGLS). The researcher used FGLS to estimate the parameters needed to measurevulnerability to poverty.

Data

The data used in this study is the household characteristic and consumption expenditureof Mindanao available in the Family Income and Expenditure Survey (FIES) 2006. These data areavailable in the Philippine’s National Statistics Office (NSO). The 2006 Family Income andExpenditure Survey (FIES 2006) is a nationwide survey of households undertaken by theNational Statistics Office. It is the main source of data on family income and expenditures in thePhilippines.

Estimation ProcedureHouseholds future consumption is further assumed to be dependent upon uncertainty

about some idiosyncratic and community characteristics. To have consistent estimates of theparameters, it is necessary to allow heteroscedasticity. Allowing heteroscedasticity is not a newprocess in estimating parameter in vulnerability to poverty measurement. Chaudhuri (2000), inmany of his research accommodated this method; Imai and Azam (2009), Jha, et al (2008),Zhang and Wan (2008), Pritchett, et al (2000) Ding et al (2007), Albert, et al (2007) also adaptedthe method of allowing heteroscedasticity in measuring vulnerability to poverty. In this study, theresearcher allowed heteroscedasticity and a three-step Feasible Generalized Least Squares(FGLS) procedure can to estimate the parameters.

The first step of FGLS is using OLS in the equation (1) to predict the residual:Xβlncε OLShOLSh,

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A model of squared residual:

hOLS,

2ε = Xhθ + ηh (3)

is formulated to allow having a measure of the idiosyncratic variance for each household; ηh is anunobserved time-invariant (constant) household effect this yield the initial estimate OLSθ

∧

of theparameter θ.

The second step of FGLS to use the predicted hOLSε ,

2to transform the following

equation:

hOLS,

^h

hOLS,

^

^

h

hOLS,

^hOLS,

2^

Xθ

η+

Xθ

θX=

Xθ

ε(4)

To estimate FGLS

^

, the equation (4).is estimated once again using OLS. According to

Chaudhuri (2003), FGLShθX^

is a consistent estimate of the variance of the idiosyncraticcomponent of the household consumption, σ2

εh and thus, estimates the following standard

deviation^

h :

FGLS

^

εh2 Xθ=σ

^

FGLS

^

εh Xθ=σ (5)

The third step is to use^

hεσ to transform equation (1) so that it yields the followingequation:

^

εh

h^

εh

h^

εh

h

σ

ε+β

σ

X=

σ

lnc(6)

By using OLS estimation of equation (6) yields an estimate of β denoted as, FGLSβ∧

. The

estimated FGLSθ^

and FGLSβ∧

can now be used to estimate the expected log consumption and

variance of log consumption of each household h as follows:

[ ]FGLS

^

hhh

^βX=X|lncE (7)

to estimate log-consumption and;

[ ] FGLShh

^θX=X|lncV∧

(8)

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to estimate variance of log-consumption.

To estimate vulnerability to poverty, define as vulnerability as expected poverty (VEP), thevulnerability to poverty of household h with characteristics Xh is measured by forming an estimateof probability and letting Φ as the cumulative log-normal distribution function:

)z<Pr(lnc=V h,h

∧

)X|lnz<Pr(lnc= hh

^

εh

FGLSh

σ

βXlnzΦ

-

hV

-- is the vulnerability to poverty of the household or the probability that the per capitaconsumption level c of the household h will be lower than the poverty threshold z conditional onhousehold characteristics Xh.; and if,

hV∧

> Mean vulnerability of the region, the household is considered vulnerable

a) 0.5> hV

> mean vulnerability of the region, the household is considered relatively

vulnerable

b) hV

> 0.5, the household is considered highly vulnerable

The poverty thresholds z used in the study were from each region in Mindanao:Php13,252.00 is used in region 9, Php14,184.00 in region 10, Php14,831.00 is used in region 11,Php13,982 in region 12, Php14,740.00 in region 13 and Php14,950.00 is used in ARMM. Thesource of these thresholds is from NSCB (2007).

15

Chapter 4

RESULTS AND DISCUSSION

This chapter discusses about the results and the interpretation of the results obtainedfrom the estimation. Attempts were also made to compare the results from other authors forconsistency and present some macro-economic factors and other poverty indicators such as,Human Development Index (HDI), and the Gini coefficient. This chapter also discusses about thepossible sources of vulnerability.

Table 1 shows the results of the estimation and other poverty indicators in Mindanao. Atthis point, it would be useful to revisit the general interpretation of the vulnerability indicators sothat a clearer understanding of the results may be gained. The estimation of vulnerability topoverty was clustered into observed poor and vulnerable household which is decomposed intorelatively vulnerable and highly vulnerable.

In this paper, the definition of vulnerability at the household level is the probability that ahousehold, regardless of whether it is poor today, will be consumption poor tomorrow. Thepercentage of vulnerable households (fraction of vulnerable) varies depending on the chosenvulnerability threshold. In Figure 2, for example, if the vulnerability threshold is placed at 0, thenall of the population will be considered vulnerable; and if 1, there will be no vulnerable household.Since the vulnerable household is whether consumption poor today or not, three different curvesare shown in figure 2, the observed poor, overall, and observed non-poor to identify what fractionof these population group (shown in the curves) are vulnerable. Hence, vulnerable household isdefined as percentage of household vulnerability level (probability) above vulnerability threshold(in this study, the mean vulnerability as threshold).

Figure 2. Poverty, observed poor and vulnerability in Indonesia, December 1998Source: Chaudhuri et.al (2002),

It is assumed that observed poverty rate is approximately equal to the mean vulnerabilityby region. For instance, suppose that 50% of a particular population has vulnerability level of 0.40

16

Table 1. Vulnerability estimates and other poverty assessment indicators, Mindanao, 2006.

Region9 Region10 Region11 Region12 Region13 ARMM Mindanao(in %)

Fraction of poor 46.04 39.73 34.05 38.58 45.86 62.88 43.95

Mean vulnerability 36.60 38.04 42.90 38.97 42.89 50.07 41.57

Vulnerable household 42.86 47.45 48.74 46.07 48.36 50.03 47.31

Highly vulnerable 36.35 33.12 43.12 40.67 44.46 50.33 41.36

Relatively vulnerable 6.51 14.33 5.62 5.4 3.9 0.0 5.95

Vulnerability-to-poverty ratio 0.93 1.19 1.43 1.19 1.05 0.80 1.02

Confirming Indicators

Albert, et al. (2007)Mean vulnerability (income) 42.0 33.0 34.0 53.0 49.0 54.0 44.2 (ave.)

Human Dev’t Index (HDI) 0.49 0.55 0.54 0.53 0.51 0.38 0.5 (ave.)

Income Inequality (Gini ratio) 0.51 0.48 0.42 0.40 0.45 0.31 0.43(ave.)

17

(e.g., they face a 40% probability of being poor) while the remaining 50% has a vulnerability levelof 0.10. The mean vulnerability level in this population is, therefore, 0.25. To identify the fractionof the poor that is considered poor in any period, say, year 1 or year 2, low vulnerability groupshould contribute 5% (10% of 50%) while the high vulnerability group should add 20% (40% of50%) for a total of 25% that should end up being poor. The idea is that because the observedpoverty rate represents the mean vulnerability level in the population (assuming that the sampleis large enough to represent the actual population poverty rate) anyone whose vulnerability levellies above this threshold faces a risk of poverty that is greater than the average risk in thepopulation and hence can be included among the vulnerable (Chaudhuri, 2002). Therefore, it isassumed that the observed poverty is expected to be the fraction of poor or the poverty rates inany given point in time (and also the vulnerability threshold of the household to be considered asvulnerable).

The vulnerability-to-poverty ratio, i.e. the ratio of the vulnerable fraction of poor is alsoestimated. The importance of the vulnerability-to-poverty ratio is to identify if the distribution ofvulnerable household is either concentrated within a few or dispersedly distributed. For any givenvulnerability threshold, a higher vulnerability-to-poverty ratio indicates that the distribution ofvulnerable household among the population is dispersed and a lower ratio suggests thatvulnerability is concentrated among a few. For example, two populations may have similarobserved poverty rates but very different incidences of vulnerability. Consider population X having20% of its household with a vulnerability level of 1 and the 80% has vulnerability level of 0. Theother one is population Y which has 100% of the households have vulnerability level of 0.20. Inboth populations, the observed poverty rate will be approximately 20%. But the fraction of thepopulation that is vulnerable (with a mean vulnerability to poverty threshold) is dramaticallydifferent. Only 20% of population X is vulnerable, whereas with the same threshold the entirepopulation of Y is vulnerable. This dramatic difference has important implications for policy. Theabove example illustrates the ratio of the fraction of the population that is vulnerable, of given athreshold, to the fraction that is observed poor. The vulnerability-to-poverty ratio can provide auseful measure of how dispersed vulnerability is in the population. For population X, wherevulnerability is limited to 20%, the vulnerability-to-poverty ratio is 1, while for population Y, wherethe entire population is vulnerable, the corresponding ratio is 5 which means that while 100% ofthe population is vulnerable, only 20% is poor at any point in time.

Vulnerability Estimation in Mindanao

Fraction of poor is defined as the percentage of household with consumption belowpoverty line/threshold. In Table 1, Region XI has the lowest fraction of poor while ARMM has thehighest at 62.88. This means that 63% of the populations in ARMM live below poverty line.

Mean vulnerability is defined as the average vulnerability level of the household within agroup. The region with the highest mean vulnerability is ARMM reaching up to 50% while Region9 has the lowest mean vulnerability of 36%.

Vulnerability-to-poverty ratio is the ratio of vulnerable households to the poor household.The importance of this measure is to determine how dispersed or concentrated vulnerablehouseholds within a group are. In Mindanao, the study found out that the region with the highestvulnerability-to-poverty ratio is Region 11, suggesting that vulnerable households in this area aredispersed. The region with the lowest vulnerability-to-poverty ratio, however, is ARMM, whichmeans vulnerable households in this area are concentrated

In terms of vulnerable households, it ranged between 42.9 in Region IX to 50.03 inARMM. The vulnerable household in Mindanao is 44.7. The ratio of vulnerable households isgreater than the fraction of poor because these include those poor and non-poor groups but arevulnerable to shocks in the consumption.

18

In order to identify whether the percentage of vulnerable households of each region inMindanao might be due to low or high mean vulnerability, the vulnerable households wereclustered into highly vulnerable and relatively vulnerable. Highly vulnerable household refers tothe percentage of households that have vulnerability levels exceeding 0.5 and relativelyvulnerable household are those above the mean vulnerability but are less than 0.5. This willensure whether the households in the particular region were greatly or slightly affected by therisks they face.

The results are little bit different from Albert’s (2007) study on vulnerability. Thedifferences are expected because Albert’s study used income while this study used consumption.According Coudouel, et al (2002) actual consumption is more closely related to a person’s well-being in the sense of having enough to meet current basic needs. On the other hand, income isonly one of the elements that will allow consumption of goods; others include questions of accessand availability. Using income-based numbers only overlooks the struggles of the people(households) (freakonomics.com). Therefore, it might over estimate the mean vulnerability of thehousehold in general.

Human Development Index (HDI) is one of the confirming indicators for the consistencyof the study. The HDI is a summary composite index that measures a country's averageachievements in three basic aspects of human development: longevity, knowledge, and a decentstandard of living. Longevity is measured by the life expectancy at birth; knowledge is measuredby a combination of the adult literacy rate and the combined primary, secondary, and tertiarygross enrollment ratio; and standard of living is measured by GDP per capita(glossary.econguru.com). In Mindanao, the region with the lowest HDI is ARMM while the highestis Region XI.

Income inequality is a measurement of the distribution of income that highlights the gapbetween individuals or households making most of the income in a given country and thosemaking very little (businessdictionary.com). Region with highest income inequality is Region IXand the lowest is ARMM.

The researcher found out that the region with the highest observed poverty rate (fractionof poor) is ARMM. ARMM is also the region with highest percentage of vulnerable household(fraction of vulnerable) and the region with the highest percentage of highly vulnerable household.It is observed that the highly vulnerable household in this region is higher compared to vulnerablehousehold. This is because the mean vulnerability level in this area is much higher that thethreshold the researcher considered as highly vulnerable, 0.5. Therefore, the vulnerablehousehold in this region are all considered as highly vulnerable.

This study found out that the region with highest mean vulnerability, vulnerablehousehold (fraction of vulnerable), highest poverty rate (fraction of poor), is also the region withthe least Human Development Index (which is ARMM) which means that the labor force in thisregion is less productive compared to other regions. This study also found out that ARMM is theregion with the most concentrated vulnerable households, or has the lowest vulnerability-to-poverty ratio which is also associated with low income inequality.

Aggregate poverty and vulnerability in Mindanao

Table 2 shows the aggregate poverty and vulnerability profile for Mindanao. Almost 44%of the household in Mindanao are poor and among the poor, 65.6% of them are vulnerable topoverty—60.1% are highly vulnerable while 5.51% of them are relatively vulnerable. Forty sevenpercent of the household are vulnerable to poverty in Mindanao—12.7% are relatively vulnerable

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Table 2. Aggregate poverty and vulnerability profiles for Mindanao (in %)

AmongMindanao

Among thenon-poor

Among thepoor

Among the non-vulnerable

Among thevulnerable

Amongthe

relativelyvulnerable

Amongthe highlyvulnerable

Mean vulnerability 41.57 30.87 55.22 15.01 71.15 44.60 74.97

Poor YES 43.95 0.00 100.00 28.70 60.93 40.73 63.79NO 56.05 100.00 0.00 71.30 39.07 59.27 36.21

VulnerableYES 47.31 32.97 65.59 0.00 100.00 100.00 100.00NO 52.69 67.03 34.41 100.00 0.00 0.00 0.00

Fraction relativelyvulnerable 5.95 6.25 5.53 0.00 12.66 100.00 0.00

Fraction highly vulnerable 41.36 26.72 60.06 0.00 87.34 0.00 100.00

20

and 87.3% are highly vulnerable. Among this vulnerable household, about 60.9% percent of themare poor. Fifty six percent of the household in Mindanao are non-poor and among these non-poora significant percentage of household are vulnerable to poverty, almost 33%. In contrast, there isa significant number of poor that are not vulnerable to remain poor, almost 29% of poor are notvulnerable to remain poor.

Sources of Vulnerability

To better understand the sources of vulnerability, an illustration might be useful. Considertwo households in Figure 3, Household A and Household B. These households have differentconsumption streams. On the average, Household A enjoys a much higher level of consumption,but its consumption is quite volatile. Household B, on the other hand, has a relatively stableconsumption profile, but with much lower levels of consumption. Despite the obvious differencesin their mean levels of consumption and in the volatility of their consumption streams, these twowere constructed so that their mean vulnerability levels are the same.

The figure illustrates that the two households with the same level of mean vulnerability,but looked very different, might be due to the different sources of vulnerability. The source ofvulnerability may stem primarily from low long-term consumption prospects and consumptionvolatility. In Household B, the vulnerability might be due low long-term consumption prospects.

Figure 3. Simulated consumption streams for two households for 50 time horizonSource: Chaudhuri et.al (2002)

For example, the household head is a farm laborer with incomes that cannot sustain the requiredper capita consumption. Then there is a huge possibility that the household will be vulnerablebecause their consumption is still low by the next time period, ceteris paribus (if there is no risk

21

given these households are considered poor). Household A consumption streams might be dueto consumption volatility, for example, a household with head working as a sales agent on a hugecompany, say, real estate, and has a commission-based income. If the head can sell a house andlot then, the household will enjoy higher consumption, assuming their income are spent only forconsumption and not on investment. But if the head cannot sell a house and lot because of therisk it faces, and then there is possibility that the household will be poor by next time period. Froma policy perspective, it will be important to distinguish between these two possibilities. Forinstance, vulnerability due to high volatility may call for ex-ante interventions that reduce the risksfaced by households or insure them against such risks. On the other hand, to addressvulnerability due to low endowments what might be needed are transfer programs. Clearly, adecomposition of the sources of vulnerability at the household level into the two componentsdescribed above can help inform that choice (Chaudhuri et al. 2002).

To decompose the households in Mindanao, the researcher clustered these householdsinto 3 categories—those that are not vulnerable, the low-mean consumption vulnerable, and highvolatility vulnerable. Those that are not vulnerable are the household that has predicted meanconsumption above poverty threshold and has vulnerability level below the vulnerable thresholdby region. The second is the low-mean consumption vulnerable. These households havepredicted consumption below the poverty threshold and have vulnerability level abovevulnerability threshold (even if these household are not affected by the risk, they would still bevulnerable). The third category is the high volatility vulnerable. These household havevulnerability level above the vulnerability threshold but has predicted consumption (consumptionaffected by risk) above the poverty threshold. These households are vulnerable primarily becauseof the risks they face and if the risks are taken, then these households are not considered poor orvulnerable.

Table 3 presents the sources of vulnerability for Mindanao. The results showed that47.31% of the household in Mindanao are vulnerable to poverty, composed of 6.87% ofvulnerable due to high volatility of consumption and 40.44% are vulnerable due to low meanconsumption. It is also observed that the percentage of non-poor among high volatility vulnerableis 59. 27% much higher compared among low mean consumption of 36.14%. The percentage ofpoor among the high volatility vulnerable is 40.72% and much lower than among low meanconsumption of 63.84%. Therefore, the main source of vulnerability for the currently poorhousehold is due to low mean consumption and the source of vulnerability for the non-poorhousehold is due to consumption volatility.

Vulnerability incidence by demographic characteristics

Household characteristics might have huge impact on household vulnerability to poverty.For example, a household is experiencing a temporary hunger because of its inability tosmoothen consumption in the presence of income shock. The primary reason, perhaps, is itsinability to generate income or the head of the household cannot be hired in a high-paying jobbecause of its low educational attainment, or the head is prone to illnesses because of waterborne diseases due to improper potable water connection. The researcher further estimated thevulnerability incidence in Mindanao by household characteristics to explore the possible patternssubject to household of vulnerability to poverty.

Table 4 shows the poverty and vulnerability within different segments of the population inMindanao. As observed in Table 4, there is no huge difference whether the household is headedby a female or a male, in terms of vulnerability. Although there is slight difference in the fraction ofvulnerable, but the share of population between male and female headed households are verydifferent and their mean vulnerability levels are close to each other. Single headed householdsare more vulnerable to poverty than married headed households. The fraction of poor among the

22

Table 3. Sources of vulnerability for Mindanao

Mindanao Among thenon-poor

Among thepoor

Among the non-vulnerable

Amongthe

vulnerable

Among high-volatility vulnerable

Among low-meanconsumption

Mean vulnerability 41.57 15.01 55.22 30.87 71.15 44.59 75.00

Fraction of Poor 43.97 0.00 100.00 28.70 60.93 40.72 63.84

Fraction of non-poor 56.05 100.00 0.00 71.30 39.05 59.27 36.14

Fraction of vulnerable 47.31 32.97 65.59 0.00 100.00 100.00 100.00

Fraction of high-volatility vulnerable 6.87 6.33 5.56 0.00 12.66 100.00 0.00

Fraction of low-meanconsumption 40.44 26.63 60.04 0.00 87.34 0.00 100.00

23

single headed households, however, is lower compared to the fraction of poor among the marriedcouples.

As the age of the household head increases, the vulnerability decreases. As thehousehold age increases, however, the vulnerable households are more concentrated. It wasalso observed that the higher the educational attainment of the household head, the lower is theirvulnerability to poverty. The vulnerable household in low educational attainment is much moreconcentrated. An opposite pattern was observed in the household size. As the household sizeincreases, the vulnerability of the households also increases. The vulnerable households areconcentrated in those households that have family size greater than 5.

In terms of dependency ratio, the higher ratio means the higher dependent familymembers, therefore, the higher dependency ratio means higher vulnerability, but the vulnerablehouseholds are concentrated within those whose dependency ratio was less than the average of0.72.

It was also observed that those who work in agriculture are more vulnerable to povertythan those who work in other industries and the vulnerable households were more concentratedin this sector. Those who have electricity were less vulnerable than those who do not have andthose who have access to potable water are less vulnerable than those who do not have. Thosehouseholds who do not own house and lot are more vulnerable than those households who ownhouse and lot.

24

Table 4. Poverty and vulnerability within different segments of the population, Mindanao

Populationshare

Shareof poor

Share ofvulnerable

Share of highlyvulnerable

Poor Meanvulnerability

Vulnerable Highlyvulnerable

Vulnerability topoverty ratio

Mindanao 100 100 100 100 43.95 41.57 47.31 41.36 1.07By Region (in %)

Region 9 14.89 15.59 13.48 13.08 46.04 36.60 42.86 36.35 0.93Region 10 16.36 14.79 16.41 13.10 39.73 38.04 47.48 33.12 1.20Region 11 19.26 14.92 19.84 20.08 34.05 42.90 48.74 43.12 1.43Region 12 18.25 16.03 17.78 17.94 38.58 38.97 46.07 40.67 1.19Region 13 15.59 16.26 15.93 16.75 45.86 42.89 48.36 44.46 1.05

ARMM 15.65 22.40 16.55 19.05 62.88 50.06 50.03 50.33 0.80By Gender (in %)

Femaleheaded

13.83 9.33 12.64 13.13 29.65 37.64 43.24 39.26 1.45

Male headed 86.17 90.67 87.36 86.87 46.24 42.20 47.96 41.71 1.03By Marital status (in %)

Single 16.80 11.27 17.94 81.32 29.49 43.32 50.51 45.99 1.71Married 83.20 88.73 82.06 18.68 46.87 41.22 46.67 40.43 1.00

By Age of head (in %)Age greater

than 4839.36 36.76 32.28 31.16 36.24 32.42 34.26 28.99 0.95

Age less than48

60.64 63.24 67.72 68.84 50.16 48.93 57.80 51.37 1.15

By Educational Attainment of head (in %)Illiterate 6.61 9.91 9.01 9.94 65.95 61.16 64.51 62.21 0.98

Elementary 45.89 60.48 50.56 51.95 57.92 44.55 52.13 46.83 0.90High school 29.28 25.10 28.97 27.30 37.67 40.71 46.81 38.57 1.24College level 9.83 3.73 6.68 6.29 16.69 30.47 32.14 26.45 1.92Above college 8.39 0.78 4.78 4.52 4.07 25.87 26.92 22.29 6.61

By Family size (in %)5 and above 54.63 73.24 69.76 72.51 58.92 51.82 60.41 54.91 1.03Less than 5 45.37 26.76 30.24 27.49 25.92 29.22 29.02 27.57 1.12

Table 4. Continued

25

Populationshare

Shareof poor

Share ofvulnerable

Share ofhighly

vulnerable

Fractionpoor

Meanvulnerability

Vulnerable Highlyvulnerable

Vulnerability topoverty ratio

Dependency Ratio (in %)Above 0.72 39.25 56.83 59.25 62.00 63.39 58.87 71.17 65.13 1.12

Less than 0.72 60.75 43.17 40.75 38.00 31.29 30.32 31.79 25.92 1.02Occupation of head (in %)

Work in Industry 39.23 68.70 32.60 30.98 28.12 35.81 39.32 32.66 1.40Work in agriculture 50.13 25.10 57.93 59.38 60.23 46.71 54.66 49.01 0.91

Self-employed 10.64 6.20 9.47 9.64 8.8 29.98 30.09 25.00 3.42Other source of Income (in %)

Has business YES 55.63 66.22 54.70 55.30 52.31 41.35 46.51 41.12 0.89NO 44.37 33.78 45.30 44.70 33.46 41.86 48.31 41.68 1.44

HasAgricultural

Income

YES 88.73 95.96 92.88 92.91 47.53 43.05 49.52 43.31 1.04NO 11.27 4.04 7.12 7.09 15.75 29.97 29.91 26.03 1.90

Access to potable water (in %)Has potable

waterconnection

YES 25.37 8.92 13.78 12.74 15.45 26.23 25.70 20.76 1.67NO 74.63 91.08 86.22 87.26 53.64 46.78 54.66 48.37 1.02

Access to electricity (in %)Has electricity YES 69.25 50.13 58.01 55.76 31.81 35.53 39.62 33.31 1.25

NO 30.75 49.87 41.99 44.24 71.29 55.17 64.62 59.52 0.91Ownership of House and Lot (in %)

Has Houseand Lot

YES 72.06 69.24 65.43 65.35 42.23 38.58 42.96 37.52 1.02NO 27.94 30.76 34.57 34.65 48.36 49.28 58.52 51.29 1.21

26

Chapter 5

SUMMARY, CONCLUSION AND RECOMMENDATION

Summary and Conclusion

Mindanao has the highest average GDP growth rate in 2006, however, has the highestaverage poverty incidence compared to Visayas and Luzon.

The study measured the vulnerability level (probability to be poor) of each samplehousehold and estimated the mean vulnerability within a group, measured the household fractionof vulnerable (if the vulnerability level is less than the vulnerability threshold or not) and measuredthe currently poor household. The study found out that vulnerable households are much higherthan the currently poor household because this includes the poor and non-poor who face the riskof remaining or becoming poor. This study found out that 47.31% of the household in Mindanaoare vulnerable to poverty, given the vulnerability threshold of each region, while the fraction ofpoor is 43.95%. Reducing poverty must not just consider who are currently poor but those whoare vulnerable to be poor. Hence, this study found out that among non-poor in Mindanao, 32.97%are vulnerable to poverty and among the poor, 34.41% are not vulnerable to remain poor.

The method used in the study is good enough that the results estimated are consistentwhen compared with other confirming indicators. The region with highest poverty and vulnerabilityincidence is also the region with lowest income inequality and human development index. Most ofthe vulnerable household has higher dependency ratio, young headed household, loweducational attainment of the head, large number of family members, has head working inagriculture sector, no electricity, no potable water connection and do not own house and lot.

Sources of vulnerability come primarily from consumption volatility and low meanconsumption. Consumption volatility and low mean vulnerability as source of vulnerability shouldbe considered in the study of vulnerability because this will give an idea on what preventivemeasure should the government implement. In Mindanao, 47.31% of the household arevulnerable to poverty—composed of 6.87% high consumption volatility and 40.44% low meanconsumption vulnerable household.

Recommendation

The conditions that vulnerable households face may permanently damage their long termwell being, or lead these vulnerable households to further risk-driven poverty traps which thenentrap them into a state of permanent poverty. Government will thus have to manage social risksby designing and implementing interventions that can strengthen informal, market based, orpublic arrangements that contribute to reducing the risk of households becoming consumptionpoor, or help to mitigate the impact of their becoming poor and assist them in coping withpoverty’s terrible consequences. For instance, in terms of location, the region with highestpercentage and concentrated vulnerable household should be prioritized in terms of cashtransfer, investment, peace and order, labor intensive income-generating programs, housingprograms, electrification, accessibility to water, and access to credit, such as loans for smallbusiness with low interest rate, because the percentage of vulnerable household in this region ishigh, concentrated and are all considered as highly vulnerable. Since, policy implementation islocalized the local government unit should manage and implement their policy efficiently, andcontinuously monitor whether the assistance was properly distributed to the target households fortransparency.

The government must conduct a seminar on how to cope up with the risk and seminarsand programs for long-term income-generation for the young headed vulnerable household—because this study found out that younger head are more prone to poverty than older headed

27

household—so that younger headed family can manage well the risk they faced. The governmentshould also prioritize those vulnerable households with low educational attainment head in termsof seminars. The seminar should focus on how to run a small time business for households withlow educational attainment, if the household heads cannot be hired on a more stable job, or teachthem to find a means of living so that the household head can, in turn, provide his children propereducation.

Government should control the increasing population growth. Policy must be conductedthat household size must be based on the capacity of the household head to provide the needs ofthe family or the implementation of family planning; because it is observed that households withlarger family size are more vulnerable to poverty.

Those who work in agricultural sector are more affected by risk (might be due macro-economic and localized shock, weather disturbances and pest, for instance) than any othersectors, which would make them more vulnerable to poverty. The government should offer someaccess to credit or loans with low interest rate, for the agricultural business owner or businessinsurance, and health insurance for the employers for future purposes.

The government should provide potable water connection and promote proper hygienefor the vulnerable household with no access to potable water because it is observed that thosewho do not have access to potable water are vulnerable to poverty. Electrification among thevulnerable households must also be implemented. Housing projects must also be provided by thegovernment. These housing projects may be rent to own house and lot to help those vulnerablehouseholds lower their cost of renting a house and lot by extending the amortization years,keeping in mind the capacity of the family to generate income.

The main source of vulnerability among the households that are currently poor householdis due to low mean consumption and the source of vulnerability for the non-poor household is dueto consumption volatility. The household vulnerability due consumption volatility might need agovernment programs that deal with the risks they face and a household with vulnerability leveldue to low mean consumption might need conditional cash transfer to increase its consumption.

Areas for further research

The data used in this study did not really meet the ideal vulnerability to poverty modelconceptualized by Chaudhuri (2000). If the data needed are available, include the data used forvulnerability as expected poverty method not just from micro-level shocks (i.e. householdscharacteristic, income shocks and etc.), but on a macro-level shocks (aggregate shock, such as,localized price shock, inflation rate, weather and etc) to get a rich and informative estimates ofvulnerability to poverty. The data should be large enough so that mean vulnerability wouldapproximately be equal to the current poverty rate. If the researcher is interested in studyingvulnerability it must be taken into account that the instead of regional estimation, it would bemuch better to conduct on a national estimation to obtain a better measurement for vulnerability(because the sample is much larger). The researcher can also compare different method inestimating vulnerability to poverty—the vulnerability as expected poverty (VEP), vulnerability asexpected utility (VEU) and vulnerability as uninsured exposure to risk (VER)—and differentiatethe uses of the three methods.

28

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