DP2015-16

Estim ation of Vulnerability to Poverty
Using a M ultile ve l Longitudinal
Model: Evide nce fromthe Philipp ine s*
Ch ristian D. MINA
Katsushi S. IMAI
April 7, 2015

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Estimation of Vulnerability to Poverty using a Multilevel Longitudinal
Model: Evidence from the Philippines

Christian D. Mina
Philippine Institute for Development Studies (PIDS), the Philippines&
Katsushi Imai
Economics, School of Social Sciences, University of Manchester, UK, & RIEB, Kobe
University, Japan

This Draft: 7th April 2015
Abstract

This study estimates household vulnerability in the Philippines using a three-level
and longitudinal linear random-coefficient model whereby vulnerability isdecomposed into idiosyncratic and covariate components. Our three-wave panel
data covering the period 2003-2009 allow us to analyse poverty situations in both
vulnerability and poverty persistence dimensions. A majority of the poor and athird of the non-poor are found to be vulnerable to unobservable shocks, whilemore susceptible to unobservable idiosyncratic shocks than to covariate shocks.
Adequate safety nets should be provided for vulnerable households with lesseducated and agriculturally-engaged or jobless heads, rural dwellers, or withmore members and/or dependents.
The JEL codes: C23, I32, O15
Key Words: Vulnerability, Poverty, Multilevel Model, Panel Data, the Philippines

Corresponding Author:
Katsushi Imai (Dr)Economics, School of Social Sciences,
University of Manchester,
Arthur Lewis Building, Oxford Road,
Manchester M13 9PL, UK;
Phone: +44-(0)161-275-4827 ; Fax: +44-(0)161-275-4928 ;E-mail: Katsushi.Imai@manchester.ac.uk

1

Estimation of Vulnerability to Poverty using a Multilevel Longitudinal
Model: Evidence from the Philippines
I. Introduction
During the past decade, the Philippines has faced a lot of challenges including a series ofextreme weather events, aftermath of the 2007/08 global financial crisis, and exorbitant andunpredictable rice and fuel prices, among others. The combined impacts of economic andnatural shocks could have largely contributed to the slow rate of progress in poverty
reduction in the country (NEDA and UNDP, 2010). Earlier studies on poverty argued that alarge component of the Philippine poverty is transient poverty, which is characterized byhigh vulnerability to shocks (Reyes, et al., 2011; 2013). Among the key reasons why many
Filipino households do not have the capacity to autonomously mitigate the adverse impactsof shocks include the lack of gainful employment, less access to credit and good-quality
health facilities, and lack of institutional support, among others (Reyes and Mina, 2013;
Reyes et al., 2009, 2013).
There has been a growing number of studies that emphasized the importance of looking
beyond the ex post measure of poverty. Assessing the ex ante measure of poverty, referred to
as vulnerability, and identifying determinants of vulnerability could provide essential inputs
to policymakers in addressing the issue of poverty (e.g. Chaudhuri et al., 2002; Pritchett etal., 2000; Zhang and Wan, 2006). In the Philippines, however, only a handful of studies haveestimated the vulnerability to poverty of households (Albert, Elloso and Ramos, 2008;
Albert, Ramos and del Prado, 2013). Thus far, no study yet has developed an econometricpanel data model in estimating Filipino households’ vulnerability to poverty.
This study primarily aims to estimate vulnerability to poverty using a three-level andlongitudinal linear random-coefficient model applied to a household-level panel data
covering recent three waves (2003, 2006 and 2009) in the Philippines. Specifically, it aims to2

address the following three research questions: (1) Who are vulnerable to poverty in thePhilippines?; (2) Which has a greater share in explaining the vulnerability to poverty,
idiosyncratic shocks and covariate shocks; and (3) What are the main characteristics ofvulnerable households. To our knowledge, this is the first study to estimate the vulnerability
to poverty using a multilevel longitudinal model. The advantage of our approach includes (i)decomposing the “ex-ante” vulnerability estimate1 into idiosyncratic and covariatecomponents, (ii) reducing the possible bias in vulnerability estimates by using a multilevel
model (e.g. controlling for possible downward bias of localized/aggregate shocks on theestimated mean of household’s welfare measure (Güntherand Harttgen, 2009)) and (iii)characterising household poverty situations in both vulnerability and poverty persistence
dimensions by utilising the panel data. Our study thus contributes to the growing but still
scarce literature on vulnerability estimation and assessment of impacts of shocks onhouseholds’ vulnerability. The findings from this study can also serve as critical inputs incrafting more specific policies and programs on poverty reduction.
The structure of the paper is as follows. Section II provides background of the study byreviewing macro economy, major shocks and poverty situation in the Philippines during theperiod 2003-2009. Section III then surveys the literature on conceptual frameworks ofvulnerability as well as on their applications. Data and variables are discussed in Section IV.
Section V describes the methodologies for multilevel analysis, estimation of vulnerability topoverty, and vulnerability assessment. Section VI provides estimation results andvulnerability profile of the panel households. Section VII concludes and provides somepolicy implications.

1

Ligon and Schechter (2003) decomposed the vulnerability into idiosyncratic and aggregate components
using the Bulgarian panel data, but this is essentially an ex-post measure based on the utility function
approach.

3

II. BackgroundThe 2010 Philippine Millennium Development Goals Progress Report noted that the
combined impacts of economic, natural and other shocks could have contributed in theslowdown of economic growth and the persistence of poverty in the country. During the past
decade, the Philippines have experienced a number of economic, natural and other shocks.
One of the most notable shocks is the global financial crisis, which originated in the UnitedStates in July 2007. The Philippines felt the impact of the crisis from the second half of 2008
until the end of 2009. Economic analysts argued that workers in the manufactured exports
sector, particularly those in electronics and garments sub-sectors, as well as the overseas
Filipino workers (OFWs) had borne the brunt of the crisis. During the same period, thecountry also faced significant rice and fuel price increases. Domestic rice prices had
dramatically increased up to 40 percent during the latter part of 2007 until the first half of2008 due to upsurge in global foodgrain prices.
Aside from economic shocks, the Philippines have also been frequently visited bytyphoons and other extreme weather events. Based on historical records of the National
Oceanic and Atmospheric Administration, four El Niño and three La Niña episodes had
occurred between 2003 and 2009. These had brought an incessant occurrence of destructive
typhoons, excessive flooding and even prolonged droughts to the country. Official statistics
show that these natural shocks have been getting more frequent and more intensified.
Despite these negative shocks, the Philippine economy had shown remarkable
performance during the period 2003-2007 in terms of gross domestic product (GDP) (4.86.7%), but the growth decelerated from 2008 (4.2%) to 2009 (1.1%). The main growth driverduring the period 2003-2006 had been the services sector, specifically the information andcommunications technology (ICT)-based tradable services. During the period 2006-2009,
however, agriculture and industry sectors had suffered negative growth. The damages caused4

by a number of devastating typhoons during the same period largely contributed tosignificant losses in agriculture. The global financial crisis, on the other hand, adversely
affected the industry sector, particularly manufacturing (Reyes et al., 2013).
From 2003 to 2009, the Philippine peso appreciated while the net factor income fromabroad almost doubled. In general, these trends could have been beneficial to Filipino
households. Although peso appreciation lowers the value of remittances received by familiesof overseas contract workers, it also lowers import prices. This results in cheaper domestic
rice, oil products and other basic commodities. Thus, although inflation was not maintained
below 3.0 percent after 2003, the growth in prices managed to decelerate from 8.3 percent in2008 to 4.2 percent in 2009. Apparently, effects of the global financial crisis did not persist.
Meanwhile, unemployment rate dropped to 7.5 percent in 2009, from 11.4 percent in 2003.
The poverty situation in the Philippines had not improved significantly during the period
2003-2009. The poverty incidence among households rose by 1.1 percent from 2003 to 2006
(20.0 to 21.1 percent), and then barely changed from 2006 to 2009 (20.9 percent). The
magnitude of poor households, on the other hand, grew from 3.3 million in 2003, 3.7 million
in 2006, to 3.9 million in 2009. Poverty has been a non-negligible issue that needs to beaddressed in the Philippines. However, no studies have characterised the country’s poverty
situations taking into account the effect of both macro and micro-level shocks in a dynamic
context.

III. Literature
Concepts of Vulnerability
Different definitions of vulnerability to poverty have been introduced in the literature. Somedefinitions highlighted the significant roles that risks and shocks play in explaining theconcept of vulnerability. Unlike the ex post poverty measure, vulnerability has been5

described as a forward-looking concept (e.g. Glewwe and Hall, 1998). Pritchett, Suryahadi
and Sumarto (2000, p. 2) among others defined vulnerability to poverty as the “risk ahousehold will fall into poverty in the future”, involving the comparison between predictedwelfare level and a particular poverty threshold.2 The 2000/01 World Development Reportpresents a slightly different definition, which is stated as: “the likelihood that a shock will
result in a decline in well-being” (World Bank, 2001).3 Vulnerability to poverty has also been
described as a “dynamic concept, involving a sequence of events after a [macroeconomic]
shock” (Glewwe and Hall, 1998, p. 182). Glewwe and Hall (1998) used rate of change inhousehold consumption” to measure vulnerability to poverty. Following this definition,
Corbacho, Garcia-Escribano and Inchauste (2007, p. 95) characterized vulnerable households
as “those that experience larger than average declines in socioeconomic status.” Meanwhile,
Gallardo (2013, p. 416) defined vulnerability to poverty as “the state of helplessness linked to
two types of situations, [namely:] expected poverty [and] downside risk of falling intopoverty.”4 Despite the many definitions or concepts of vulnerability to poverty being
presented in the literature, researchers have not yet reached a consensus on the most
preferred concept. Our definition thus follows the most widely used one, that is, theprobability that a household will fall into poverty in the future.

Empirical literature

2

This definition and its variants were adopted by many studies, such as, McCulloch and Calandrino(2003), Günther and Harttgen (2009), Chaudhuri, Jalan and Suryahadi (2002), Christiaensen and Subbarao(2005), and Échevin (2013b).
3
In line of this, some studies have highlighted the causal effect of a shock on a welfare measure in
defining vulnerability (e.g. Alem and Söderbom; 2012; Kühl, 2003; Zhang and Wan, 2006). For instance,
Kühl (2003) defined vulnerability as the propensity of a household to suffering a significant shock whichbrings its welfare below a socially accepted level.
4
Other concepts have been proposed, for instance, by Ligon and Schechter (2003) based on utility
function approach; Deng (2008) focusing on the exposure to risk and the capacity to resist downward
movements, and Calvo (2008) covering multidimensional aspects.

6

The literature on vulnerability to poverty has been growing since the early 2000s. Various
studies have adopted different measures of vulnerability and approaches on vulnerability
estimation. There are also studies that identified the determinants of vulnerability, assessed
the impact of different types of shocks on vulnerability, and decomposed poverty into
structural and risk-induced, among others. For instance, Pritchett et al. (2000) used thevulnerability as expected poverty (VEP) approach in measuring vulnerability to poverty ofIndonesian households. The study found that around 30-50 percent of Indonesian population
are vulnerable to poverty, given a 20-percent poverty rate. Chaudhuri et al. (2002) estimatedIndonesian households’ vulnerability through calculation of the expected value of poverty
based on a set of household characteristics. The study found that 45 percent of the Indonesian
population are considered as vulnerable, while 22 percent are classified as poor. 5 Calvo(2008) applied multidimensional approach to the 1998-2002 panel data from Peru and foundthat rural households are more vulnerable to consumption poverty, but less vulnerable toleisure-poverty.
In the context of the Philippines, Chaudhuri (2003) used the 1997/98 data from thePhilippines. The findings confirmed that vulnerable households outnumber those who are
currently poor. Applying Chaudhuri’s (2003) methodology, Albert et al. (2008) estimatedFilipino households’ vulnerability to poverty using the 1997 cross-sectional data. The
estimated proportion of vulnerable households is 54 percent, which is almost twice that ofhousehold poverty incidence (28%). Households in rural areas, with unemployed heads, andwith large size are those with higher vulnerability to poverty. Following the approach
adopted in this study, Albert et al. (2013) evaluated the vulnerability of households before
and amidst the global financial crisis using both cross-sectional and panel data. The study

5

Similar applications include McCulloch and Calandrino (2003) and Zhang and Wan (2006) for China,
and Imai, Gaiha and Kang (2011) for Vietnam.

7noted that vulnerable households are found to have high opportunity costs in sending
children to school and tend to be agricultural.6
The literature on vulnerability presents a wide range of methodologies; most common ofwhich are the fixed effects and generalized least squares (GLS) random effects regressions.
Only recently, Günther and Harttgen (2009) introduced multilevel modelling in vulnerability
estimation, which is later adopted by Échevin (2013a). These studies utilized cross-sectional
data and developed a two-level model. The first two studies estimated a random intercept
model while the latter estimated a random coefficient model. In addition, Échevin (2013a)
included shock variables in the set of observable covariates in the model while the first two
studies only included covariates. No study yet has estimated a three- or a higher-level linear
random coefficient model using panel data. The present study attempts to fill the gap.

IV. Data and Variables
The household-level panel data utilized in this study is the most recent three-wave panel data
generated from the 2003, 2006 and 2009 rounds of the Family Income and Expenditure
Survey (FIES) data. After excluding all other ineligible households and/or housing units andtaking into account non-responses, the final set of FIES panel households reached around
5,977. The data contain annual information on households’ socioeconomic characteristics
including income, expenditure, household head profile, and other household characteristics,
among others. The FIES data are supplemented with information on labour force,
employment and educational attainment of household members generated from the relevantrounds of the Labor Force Survey (LFS), namely: July 2003, January 2004, July 2006,

6

Other empirical studies on household vulnerability outside the Philippines include Échevin (2013b),
Gallardo (2013), Kochar (1995), Glewwe and Hall (1998), Dercon and Krishnan (2000), Christiaensen and
Subbarao (2005), Corbacho et al. (2007), Günther and Harttgen (2009), and Alem and Söderbom (2012).

8

January 2007, July 2009, and January 2010.7 Since the FIES dataset contains only householdlevel information, data on aggregate-level characteristics and shocks are sourced from theofficial statistics released by various government offices.
The official poverty statistics in the Philippines are generated regularly by the National
Statistical Coordination Board (NSCB) based on the results of the triennial FIES conductedby the NSO. A Filipino household is considered poor if its per capita income is below theofficial (provincial) poverty threshold.8 Since per capita income is the welfare measure usedin the generation of official poverty statistics in the country, (log of) per capita income wasused as the dependent variable in the empirical model.
The set of covariates considered in this study are selected based on the covariates used in
the previous poverty studies on the Philippines (Albert et al., 2008; Tabunda, 2001).
Appendix Table 1 reports the definition and summary statistics of the variables we haveused. They include household size, dependency ratio, and household head attributes (e.g. sex,age, education, and employment). We also use regional dummies as well as transportation
infrastructure index economic and social infrastructure index, and agriculture and utilities
index which have been generated using the Principal Component Analysis (PCA) because ofstrong correlations between some of the variables (see Appendix Table 1 for details). Except
for squares of household size and age of head, all main effect variables included in the model
are not strongly correlated.9

7

The set of information provided by the LFS July (January) round matches that of the first (second) roundof the FIES.
8
The official poverty thresholds, both at the regional and provincial level, are estimated by the NSCB
using the cost-of-basic needs approach. Per capita national poverty thresholds in 2003, 2006 and 2009 are
PhP10,976, PhP13,357 and PhP16,871, corresponding to US$1.543, US$1.682, and US$1.735 per capita
per day in 2005 PPP, which range between the two international poverty lines based on US$1.25 and
US$2.
9There is no pairwise correlation coefficient greater than or equal to 0.60.

9The idiosyncratic shocks considered in this study are labour market shocks generated
from the LFS10 (Table 1). It is hypothesized that changes in the labour force structure within
a household have a significant impact on household income. In particular, more joblessmembers could mean lower earning potentials of the household. Similarly, more vulnerable
workers11, more non-permanent wage/salary workers12 or fewer overseas contract workers(OCWs) could mean lower and/or reduced income. These labour market indicators aremutually exclusive13. Pairwise correlations also suggest that none among them are strongly
correlated with any covariates or aggregate-level shock variables.14 The covariate shocks, on
the other hand, that are considered in this study are rainfall and price (rice and fuel) shocks.
These covariate shocks can have both idiosyncratic and covariate components since theirimpacts vary across households. Rainfall shocks, specifically heavy rainfall and drought, canhave substantial impact on household welfare. This is particularly true in the Philippines
where a large proportion of households are engaged in agriculture - the sector that is quite
vulnerable to climate-related risks.
(Table 1 to be inserted)
The rice and fuel price shocks are hypothesized to have direct impact on householdincome and indirect impact on wage and employment. Consider an increase in both rice andfuel prices. Since the bulk of the rice being sold in the market is imported and most of thelocally produced rice is for subsistence, the rice price increase might not be felt by local rice
farmers. On the other hand, fuel price hike can substantially increase the cost of bringing
10

Only the primary occupation is considered“[Based on ILO’s (2009) definition,] vulnerable workers [are] those who are self-employed workers(without paid employees) and contributing family workers since they usually have relatively higher risk ofgetting zero or negative income in the face of economic, natural, and other types of shocks. These workers
are also said to have informal work arrangements and less likely to have access to employment benefits orsocial protection programs.” (Reyes and Mina, 2013, pp. 2-3)12Non-permanent wage/salary workers are considered to be informally employed since they have noformal contracts and/or not entitled to employment benefits (Cuevas et al., 2009).
13
Self-employed and unpaid family members are classified as non-wage/salary workers. This
classification of workers is only applied to domestic workers. Thus, OCWs cannot be part of either group.
14
There is no pairwise correlation coefficient greater than or equal to 0.50
1110

agricultural commodities to the market. It may be the case that the increase in transaction
costs due to the fuel price hike might exceed the benefit from increased demand for rice.
Another case is that a rice price hike might have an effect on income derived fromentrepreneurial activities that use rice as an intermediate input, e.g., rice cakes, rice flour,
among others. Decrease in income of households engaged in entrepreneurial activities might
have a negative impact on wages and employment of labourers.

V. Methodology
The methodology proposed in Günther and Harttgen (2009) is extended in this study byapplying it to short panel data with hierarchical structure and by taking into accountobservable shocks in income prediction. This section discusses this modified estimation
methodology and presents the vulnerability assessment approach that extends Suryahadi andSumarto (2003).

Multilevel analysis
Multilevel modelling is an appropriate approach if one wants to analyse “hierarchicallystructured data, with variables defined at all levels of the hierarchy” (Hox, 2000; p. 15).
When data contain variables measured at different levels, nesting of lower-level units within
higher-level ones produces additional sources of variation that violate the independence andhomoskedasticity assumptions. This is also true with panel data, where random fluctuations
can occur at repeated measurements leading to serially correlated errors (Gibbons, Hedeker
and DuToit, 2010). Traditional regression models are considered not robust against violationsof the aforementioned assumptions (Hox, 2000). One of the consequences of not taking into
account the hierarchical structure of the data is the misestimation of standard errors, resulting
in incorrect conclusions (Dupont and Martensen, 2007).

11

Unlike traditional regression models, “multilevel models are designed to analyse therelationship between variables that are measured at different hierarchical levels [with lowerlevels nested within higher-levels]” (Günther and Harttgen, 2009, p.1225). Multilevel
modelling is particularly appropriate if the study aims to assess the impact of idiosyncratic
and covariate shocks (Échevin, 2013a). Aside from the fact that a multilevel model cancontain explanatory variables defined at different hierarchical levels15 without violating theindependence assumption, it also gives correct standard errors and statistical results(Goldstein, 1999; Günther and Harttgen, 2009). A multilevel model also has the “ability tocontrol for possible downward bias of localized shocks” on the estimated mean ofhousehold’s welfare measure (ibid., 2009, p.1225). Another advantage is to decompose therelative impacts of household-specific and community-specific [or aggregate-specific] shocks
on households’ vulnerability (ibid., 2009). A multilevel model can also handle missing
observations and/or irregularly spaced measurements in panel data (Gibbons, Hedeker and
DuToit, 2010; Günther and Harttgen, 2009).

Three-level linear random coefficient mode
The empirical model estimated in this study is based on the formulation of a “three-level[multilevel] model for change” by Singer and Willett (2003). Let ln y tij be the log of percapita income of household i in province16 j at time t, where: level-1 units are themeasurement occasions17 indexed by t = 1, 2, 3; level-2 units are the households indexed by i
= 1,…, ni ; and, level-3 units are the provinces indexed by j = 1,…, n j . The three-level linear
random coefficient model for ln y tij can be written as follows:15

The model can also contain both time-varying and time-invariant variables.
Province is the largest unit in the political structure of the Philippines, consisting of municipalities and,in some cases, of component cities (NSCB, 2014).
17
Under fixed occasion design; wherein all households are measured at the same, regularly spaced time
points (Snijders and Bosker, 2012)1612

ln ytij  xT(1)tij β (1)  xT( 2)ij β ( 2)  xT(3) j β (3)  xT(1)tij β (1)β '( 2) xT( 2' )ij  xT(1)tij β (1)β '(3) xT(3') j xT(2)ij β (2)β '(3) xT(3') j  ZTtij v j  ZTtij u ij  etij .

(1)

The vector of all household-level and aggregate level (or province level) explanatory
variables,

xTtij  xT(1)tij , xT(2)ij , xT(3) j , includes the following: time-varying (level-1)
Tcovariates, x (1)tij , and their [same-level] interactions; time-invariant (level-2) covariates,

xT(2)ij , and their [same-level] interactions; aggregate-level (level-3) covariates, xT(3) j , andtheir [same-level] interactions; and, cross-level interaction terms - the fourth, fifth and sixthTterms in equation (1). The vector x (1)tij also contains a variable representing time (Frees,T2004). Associated with vector x tij is β  β(1) , β(2) , β(3) , which is a vector of fixed
T

T

T

T

regression coefficients. The first six terms in equation (1) comprise the fixed part of themodel. The last three terms in equation (1) comprise the random part. The seventh and eighthTterms involve the matrix Z tij and the associated vectors of random effects at the household1 z1 u 0ij 
and provincial levels, u ij and v j , respectively where Z  1 z 2  , u ij  , andu1ij 

1 z 3 Ttij

v 0 j 
v j    . The matrix Z Ttij contains the vectors of 1’s for the random intercept and the v1 j time variable z for the random coefficient. The random effect at the household level u ij
includes the random intercept u1ij and the random coefficient u 0ij , and is assumed as  u20  u 01 
follows: uij ~ N0, Σu  , where Σ u  
2  . The random effect at the provincial level u 01  u1 v j includes the random intercept v0 j and the random coefficient v1 j , and is assumed as13  v20  v 01 
follows: v j ~ N0, Σv  , where Σ v  
2  . The random intercept is interpreted as the v 01  v1 
initial status while the random coefficient for the time variable is interpreted as the rate of
growth. In this study, only the time variable was allowed to vary both at household andprovincial levels. Thus, the model in equation (1) only has the random coefficient for thetime variable. Meanwhile, the last term e tij is the level-1 residual and is assumed as follows:

etij ~ N(0, e2 ) . The composite residual is then defined as: rtij  Z Ttijv j  Z Ttiju ij  e tij . The
corresponding variance-covariance matrix have the following diagonal and off-diagonal
elements: diagonal = ZtijΣv Ztij  ZtijΣu Ztij   e ; off-diagonal = ZtijΣv Zt 'ij  ZtijΣu Zt 'ij
T

T

2

T

T

t  t ' .

The level-1 residual e tij represents the unexplained variance in households’ income andT

captures the impacts of idiosyncratic shocks. The level-2 residuals, Z tijuij , represent theunexplained variances across households and also capture the impacts of idiosyncraticTshocks. The level-3 residuals, Z tijv j , represent the unexplained variances across provinces
and capture the impacts of covariate shocks. It is assumed that “mainly economic variance[are captured by these residuals;] only to a lesser extent measurement error in [income]”(Günther and Harttgen, 2009, p. 1226).
For identification purposes, the covariates











xTtij

are assumed exogenous, with

E etij xTtij  0 , E uij xTtij  0 and E v j xTtij  0 . Moreover, the model in equation (1)allows for heteroskedasticity by introducing interactions between the time variable andhigher-level residuals. This particular feature of the model is suitable to vulnerability
analysis, where variances are usually assumed to be correlated with observable covariates.
The presence of time-invariant higher-level residuals in each of the composite residuals also
14

allows for autocorrelation (Graham, Singer and Willett, 2008), although independence of thelevel-1 residuals can be imposed on the covariance structure (StataCorp, 2011, p. 26).

Estimation method
The restricted (or residual) maximum likelihood (REML) is used in the estimation of themultilevel model in this study for the reasons that follow. First, “REML is preferable withrespect to the estimation of the variance parameters” (Snijders and Bosker, 2012, p. 60). Thisis important because one of the objectives of the study is to assess the impacts of shocks.
Second, Maximum Likelihood (ML) estimates fails to comply with consistency andasymptotic unbiasedness as the number of higher-level units becomes smaller (Raudenbush
and Bryk, 2002). Third, “REML estimates the variance components while taking into
account the loss of degrees of freedom resulting from the estimation of the regression
parameters, while ML does not” (Snijders and Bosker, 2012, p. 60). The number of
explanatory variables considered in this study, including the interaction terms, at different
levels is large enough that can eat up a lot of degrees of freedom. Fourth, REML estimates
are more reliable when dealing with unbalanced data, i.e., uneven distribution of lower-level
observations nested within higher-level units. The data used in this study is consideredunbalanced since the distribution of households nested within provinces is largely uneven.18
This study follows the approach outlined in Miller and Hollist (2007) in detecting andcorrecting attrition bias. Logistic regression is employed on ‘stayers’ using information fromthe first wave of panel data. The results of logistic regression indicate the presence of
attrition in panel data. Following the proposed two-step procedure by Heckman (1979), we
estimate the probit regression model of ‘stayers’ and compute the inverse Mills ratio (IMR).
18

We use the xtmixed program in Stata with the independence of level-1 residuals maintained and withheteroskedasticity with respect to the time variable. Because of the unavailability of weights in the panel
data used in this study and the fact that “[w]eighted estimation, whether frequency or sampling, is notsupported under [REML],” the models estimated in this study were unweighted (StataCorp, 2011, p. 305).

15

IMR is then included as an independent variable in the multilevel model to account for
attrition bias.

Estimation of vulnerability to poverty
The proposed methodology of estimating vulnerability to poverty is an extension of Günter
and Harttgen (2009) based on Chaudhuri’s (2003) method which involves estimation of
expected mean and variance in household’s welfare measure using cross-sectional data. In
our study, this is further extended by applying it to short panel data with hierarchicalstructure, and by taking into account observable shocks in the prediction of log of per capita
income (Échevin, 2013a).
Following Chaudhuri (2003), it is assumed that the variance of income both at household
and aggregate levels, or the impact of idiosyncratic and covariate shocks, depends on a set ofhousehold-level and aggregate-level characteristics. Thus, using the linear functional form inequation (1), the squared residuals at different levels are regressed on the aforementioned
covariates (excluding the shock variables)19, as in the following:
T
T
T
T
'
T'
e2tij  x(1)tijα(1)  x( 2 ) ijα ( 2 )  x( 3) jα ( 3)  x(1) tijα(1)α ( 2 ) x( 2 ) ij 

T
'
T'
T
'
T'
x(1)
tijα (1)α ( 3) x ( 3) j  x ( 2 ) ijα ( 2 ) α ( 3) x ( 3) j ;(2)

u 02ij  xT( 2)ijδ( 2)  xT(3) jδ(3)  xT( 2)ijδ( 2)δ'(3) xT(3') j ;(3)

v02 j  xT(3) j γ (3) ;

(4)

T
T
T
T
'
T'
s2tij  x(1)tijθ(1)  x( 2 ) ijθ( 2 )  x( 3) jθ( 3)  x(1) tijθ(1)θ( 2 ) x( 2 ) ij 

T
'
T'
T
'
T'
x(1)
tijθ(1)θ ( 3) x ( 3) j  x ( 2 ) ijθ ( 2 ) θ ( 3) x ( 3) j ;

(5)

19

Following Günter and Harttgen (2009) and Échevin (2013a), only random intercepts at levels 2 and 3
are used in equations (4) to (6). Also, similar to Échevin (2013a), only covariates are included; thus,
excluding observable shocks as well as the IMR since these are already captured by the estimatedresiduals.

16

where: stij  etij  u0ij  v0 j
Using the estimated coefficients from these regressions, the following expected variances
are estimated: unobservable idiosyncratic variances ˆ e tij and ˆ u 0 ij ; covariate variance22

̂ v2 ; and, total variance ˆ s2 . These variance estimates are then used to assess the impact
tij

0j

of idiosyncratic and covariate shocks on households’ vulnerability by applying the measureof vulnerability to poverty proposed by Chaudhuri (2003). The conditional probability of
being poor, or vulnerability to poverty, of household i in province j at time t is estimated asfollows:V̂tij  P̂ ln y tij  ln y x

T
tij



 ln y  ln ŷ 
tij 
 


ˆ tij2(6)where:  denotes the cumulative density of the standard normal distribution; ln y is thelog of poverty threshold; ln ŷ tij is the expected mean of log of per capita income ofhousehold estimated from equation (1); and,ˆ tij2 is the expected total variance ofunobservable shocks estimated from equation (5). Vulnerability estimation is also conducted
separately for different components of variance in income, namely: idiosyncratic variancesˆ e2 and ˆ u2 , and covariate variance ̂ v2 .
tij

0 ij

0j

Vulnerability assessment
Operational assessment of vulnerability depends on the choice of vulnerability threshold(“minimum level of vulnerability above which all households are classified as vulnerable”)
and the time horizon over which vulnerability is to be assessed. The following equation, as
presented in Günter and Harttgen (2009), is used for vulnerability assessment:
17





Vt* k ,ij  1  Pln ytij  ln y ,
k

(7)

*

where: Vt  k ,ij is the vulnerability threshold at time t to fall below the poverty threshold (atleast once) in the next k years; Pln ytij  ln y is the probability of having an income above
the poverty threshold in any given year. The vulnerability threshold of 0.5, which is the mostcommonly used threshold in the empirical literature (e.g., Échevin, 2013b; Zhang and Wan,
2006; Kühl, 2003; and, Pritchett, et al. 2000), is adopted in our study. On the other hand, atime horizon of 3 years is considered since the interval between the waves of the panel data
used in this study is 3 years. Thus, given equation (7), the estimated vulnerability threshold attime t to fall below the poverty threshold (at least once) in the next 3 years is 0.2063.
The categorization of poverty and vulnerability to poverty of households adopted in thisstudy slightly differs from that in Suryahadi and Sumarto (2003) as it takes into account thelongitudinal structure of the data. The poverty status is identified based on the observed
income of a household for the given period. A household is considered as poor (non-poor) if
its per capita income is below (above) the poverty threshold. As shown in Table 2, theoriginal eight categories are further grouped into four major categories, namely: chronicpoor, moderately poor, slightly poor, and never poor. The chronic poor are referred to as
households that are persistently poor from 2003 to 2009. The moderately poor are households
that became poor twice during the period 2003-2009. The slightly poor are households that
became poor only once during the period covered. The moderately poor and the slightly poor
are moving in and out of poverty and can then be collectively known as the ‘transient poor’.
Meanwhile, the never poor are referred to as households that are consistently non-poor
throughout the period.
(Table 2 to be inserted)

18

The vulnerability status, however, is identified based on the estimated vulnerability topoverty of households. A household is considered as vulnerable (not vulnerable) if its
estimated vulnerability to poverty is below (above) the vulnerability threshold. Similar topoverty groups, the major vulnerability groups of households (namely: highly vulnerable,
moderately vulnerable, less vulnerable, and not vulnerable) are defined based on the numberof times a household is classified as vulnerable. Moreover, the moderately vulnerable andless vulnerable households can be collectively known as ‘relatively vulnerable’.
Combining the poverty and vulnerability groups together, the following categories are usedin generating the vulnerability profile of panel households: (1) chronic poor, highly
vulnerable; (2) chronic poor, relatively vulnerable; (3) chronic poor, not vulnerable; (4)transient poor, highly vulnerable; (5) transient poor, relatively vulnerable; (6) transient poor,
not vulnerable; (7) never poor, highly vulnerable; (8) never poor, relatively vulnerable; and,(9) never poor, not vulnerable.

VI. Empirical results
This section presents the results of estimation of the three-level linear random coefficientmodel as well as the vulnerability profile of panel of Filipino households. The results of theMixed-effects REML regression are presented in Table 3. The estimated model (with random
effects) is preferred to an OLS regression model (without random effects) based on the resultof the likelihood ratio test. Likelihood ratio tests for additional random parameters also
supported the inclusion of random coefficients for the time variable both at the household
and provincial levels (Appendix Table 2). Moreover, given that random effects at all levels
are included, the full model (containing all the main effect and interaction variables)
presented in Table 3 is a big improvement from the intercept-only model in terms of
capturing dependencies (particularly at higher levels) in the hierarchical data used in this19

study (Appendix Table 3). Meanwhile, use of logarithmic form of per capita income resulted
in the satisfaction of the normality assumption of income and residuals at all levels(Appendix Figures 1-2). Scatter plots also indicate that outliers would not create a problem in
the analysis.
(Table 3 to be inserted)

Some variables are initially significant but became insignificant when they are allowed tointeract with other variables. This implies that the combined effects of these and othervariables matter more in explaining the variation in income. Thus, main effect variables withsignificant interaction effects are retained in the model. Inclusion of the inverse Mills ratio isalso justified by the significance of its coefficient.
Among the highly significant variables with large main effects are education andemployment of household head, dependency ratio, and some regional dummies. Householdswith more-educated heads tend to have higher per capita income than those with lesseducated heads. Less-educated people, in general, have lower employment opportunities andlower wage potential. In many Filipino families, the educational attainment of head(especially if either of the parents) gives an indication of the level of education of othermembers. Households with heads working in non-agriculture sector are also found to havehigher income than those with heads engaged in agriculture or not employed. Reyes andMina (2013) noted that the majority of the working poor are found in the agriculture sector.
Apparently, the working poor have lower education level and thus, lower chance of getting
higher-paying jobs. Dependency ratio is also considered as important predictor ofhousehold’s well-being. The presence of more children in a household means lower
proportion of labour force, which then limits the earning potentials of that household. Its
negative relationship with income confirmed the findings in many poverty studies. Many of20

the regional dummies are also found significant. Signs of the coefficients of the regionaldummies indicate that on average, incomes of households living in Caraga are among thelowest, if not the lowest. The majority of households living outside Caraga have relatively
higher per capita income, except for a few regions, namely: Central Visayas, Northern
Mindanao and Davao.
Other main effect variables that are found highly significant are as follows: time;
household size and its square; age of head and its square; sex of head; and, some shock
variables such as rainfall and fuel price shocks and decrease in the proportion of OCW
members. The time variable indicates that, in general, the per capita income of households(in real terms) had increased over time. Signs of squares of household size and age of head
and their squares confirm their quadratic relationships with income. Female-headedhouseholds are found to have relatively higher income than male-headed ones. Interestingly,
many female-headed households in the Philippines are heavily dependent on cash receipts or
support (either from abroad or domestic sources)20. Remittances (regardless of the source)
are usually higher in value because, apparently, Filipinos are willing to leave theirhouseholds only for better opportunities, e.g., higher-paying jobs.
Moreover, all highly significant shock variables have negative effects on income.
Households residing in provinces that experienced rainfall and fuel price shocks tend to haverelatively lower income. Because the majority of the working poor are engaged in agriculture(Reyes and Mina, 2013) and the agriculture sector is considered as highly vulnerable toclimate-related disasters, frequent occurrence of extreme weather events is expected toreduce income. Many households are also negatively affected by fuel price shocks through anumber of channels. For instance, large increases in fuel prices could lead to higher

20

Based on the FIES data, cash receipts both from abroad and domestic sources comprised around 25
percent of the total income of female-headed households during the period 2003-2009. In contrast, cash
receipts comprised only 3 to 5 percent of the total income of male-headed households.

21

transportation costs faced by entrepreneurs that regularly transport their produce to urban
centres, or higher variable costs faced by employers that could mean reduction in workers’
wages. Meanwhile, decrease in the proportion of OCW members could mean lower
contribution to household income.
A number of interaction variables have significant effects on income. The income
disparity between female- and male-headed households, in favour of the former, isparticularly true in certain regions like Ilocos and CALABARZON. This income disparity,
however, does not hold when the head is highly educated. This finding suggests that moreeducated heads have higher chance of getting high-paying occupations without the need toleave their households for better income opportunities.
The positive effect of education on income is more evident in Zamboanga Peninsula andNorthern Mindanao. Households with more-educated heads are also not significantly affectedby various employment shocks such as: increase in the proportion of working members who
are non-permanent wage workers, self-employed or unpaid family workers; or, decrease in
the proportion of members who are OCWs. More-educated individuals can easily find a
good-paying secondary job, as a way of augmenting their household income, once theirhouseholds face labour market shocks. In the Philippines, it is not unusual to find highly
educated workers having more than one job. The positive effect of education, however, is
weakened when interacted with household size. In fact, regardless of whether the head is
more-educated or is engaged in a higher-paying sector, an additional member in a household
reduces per capita income. The negative effects of household size and dependency ratio on
income are evident in Bicol and Zamboanga Peninsula—the regions with high poverty
incidence.
Households headed by workers in the non-agriculture sector in Davao region are
particularly better-off than those living in Caraga region and those headed by agricultural

22

workers or jobless individuals. Regardless of employment of head, however, households areadversely affected by rainfall shocks. Although an urban/rural dummy appears to haveinsignificant main effect, its interactions with a number of other variables are foundsignificant. Households located in urban areas are found to have higher income than thoseliving in rural areas. Infrastructure development, like that of irrigation, also strengthens the
positive effect of urbanity on household income. Meanwhile, living in urban areas or inregions where poverty incidence is relatively lower and nearer to the country’s capital (i.e.,
Central Luzon, CAR) somehow protects households from shocks like fuel price hike orreduction in the proportion of members who are OCWs.

Vulnerability profile
Estimates of vulnerability to poverty
Decomposition of poverty and vulnerability to poverty (by degree and by source), using thevulnerability estimates and the vulnerability threshold of 0.2063 (calculated using thevulnerability threshold of 0.5 and the time horizon of 3 years), is summarized in Table 4. It
should be noted, however, that the estimated vulnerability of a household in this our study isinterpreted as the household’s probability of falling into poverty at least once in the next 3
years.
(Table 4 to be inserted)
The results show that more than half (57.6%) of panel households are classified asvulnerable at least once in any of the periods covered, i.e., 2003, 2006 and 2009. Around 40
percent of panel households are classified as vulnerable to unobservable covariate shocks
while around 60 percent are vulnerable to unobservable idiosyncratic shocks. This finding
implies that households have higher probability of falling into poverty when faced with
idiosyncratic shocks than when faced with covariate shocks. That is, they are more23

vulnerable to idiosyncratic shocks probably because the impacts of these shocks are more
direct and more specific. The impacts of covariate shocks, on the other hand, are indirect andvary across households. This could point to the poor functioning of the insurance mechanism
within communities and the difficulty of anticipating idiosyncratic shocks.
Looking at the different poverty groups, it can be observed that a majority of poor
households in the panel are also vulnerable to unobservable shocks. In fact, almost all(98.6%) of the chronic poor and 85 percent of the transient poor are classified as vulnerable
in at least one of the periods covered. Only a small percentage of poor households (1.5% of
the chronic poor; 13.7% of the transient poor) are not vulnerable. Many of these households
have incomes that are not very far from the poverty threshold. On the other hand, a majorityof the never poor are not classified as vulnerable in any of the periods covered. Only 37
percent of the never poor are vulnerable, and almost all of them are classified as vulnerable
only once or twice.
Notably, more chronic and transient poor households are vulnerable to unobservable
idiosyncratic shocks than to unobservable covariate shocks. While this is true, the proportionof chronic poor households that are vulnerable to unobservable idiosyncratic shocks (97.4%)
does not largely differ from the proportion of those that are vulnerable to unobservablecovariate shocks (90.5%). On the other hand, the difference between the proportions of thetwo groups of transient poor households (23.1%) is considered substantial. Around 80
percent of transient poor households are vulnerable to unobservable idiosyncratic shocks
while only around 60 percent of the transient poor are vulnerable to unobservable covariate
shocks. Meanwhile, there are also more never poor households that are vulnerable tounobservable idiosyncratic shocks than to unobservable covariate shocks.

Characteristics of poor and vulnerable households

24In order to provide a better understanding of the vulnerability to poverty of Filipino
households, a snapshot of the key characteristics of different groups of panel households issummarised in Table 5. The relationships between the household’s degree of vulnerability topoverty and their key characteristics are consistently observed in all poverty groups, althoughnegative (positive) characteristics21 are more evident among the chronic (never) poor. A
selective summary is given below.
(Table 5 to be inserted)
One of the most interesting patterns that can be drawn from the table is that household
size and dependency ratio increase with the degree of vulnerability. As more members and/or
dependents join a household, the estimated vulnerability to poverty of that household tends toincrease. Age of head, however, appears to be negatively associated with the degree ofvulnerability. This can be explained by the fact that older heads tend to have more stablesource of income relative to younger ones. In terms of sex of head, it can be observed that the
proportion of female-headed (male-headed) households decreases (increases) as the degree ofvulnerability increases, and vice versa. As mentioned earlier, a large proportion of femaleheaded households are remittance-receiving.
Moreover, education of head is also observed to be negatively related to household’s
degree of vulnerability. As the degree of vulnerability increases, the proportion of householdswith less-educated heads increases while the proportion of those with more-educated heads
decreases. This particular finding supports Schultz’s (1975) hypothesis that more-educatedindividuals are assumed to be more adaptive to “new circumstances” and thus, have higher
coping capability (Glewwe and Hall, 1998; Christiaensen and Subbarao, 2005).

21

In this study, negative characteristics may include larger household size, higher dependency ratio, lower
level of education, more engaged in agriculture, lower access to basic amenities, rural dweller, among
others. The positive characteristics, on the other hand, are the opposites of the aforementioned
characteristics.

25

The proportion of households with heads who are working in the non-agriculture sector
decreases with the degree of vulnerability while the proportion of those with agriculturallyengaged or jobless heads increases with the degree of vulnerability. In the Philippines, a large
proportion of workers in the agriculture sector have been considered as vulnerable,
informally employed, or labourers/ unskilled - the “lowest paying occupational group”,(Reyes and Mina, 2013, p. 4). Apparently, many of the agricultural workers have lower levelof education, relative to those absorbed in the non-agriculture sector such as industry andservices.
In terms of access to basic amenities (i.e., electricity, safe water and sanitary toilet
facility) and land ownership, the patterns are clearly observed among chronic and transientpoor households. The proportion of households with access to basic amenities or that own aland decreases with the degree of vulnerability. Meanwhile, living in urban areas or inregions with lower poverty incidence is more associated with lower degree of vulnerability.
The proportion of urban (rural) households is higher in the ‘not vulnerable’ (‘highly
vulnerable’) group.
The results of the Multiple Correspondence Analysis (MCA)22 embolden the abovefindings and the findings from those in developing countries. As shown by Quadrant 4 inFigure 1, households with young, less-educated, agriculturally-engaged or jobless heads andthose that are living in rural areas are the most commonly associated characteristics of the
majority of the poor and highly vulnerable households. In particular, these households are
categorized under the following groups of households: chronic poor and vulnerable (either
highly or relatively vulnerable); transient poor and relatively vulnerable; and, never poor but
highly vulnerable. This finding confirms the agriculture-rural-poverty nexus in the context of
22

Multiple Correspondence Analysis (MCA) is a descriptive statistical technique used in handling highdimensional categorical data. It allows one to analyse the pattern of relationships of several nominal
variables with several levels or categories. One of its outputs is an n-dimensional map that displays themulti-way association among the levels of the variables, i.e., proximity among levels of different nominal
variables means that these levels tend to appear together in the observations (Greenacre and Blasius 2006).

26

the Philippines. Low level of education and lack of gainful employment, especially in rural
areas where opportunities are generally limited, are also among the key factors that increase
the household’s risk of falling into poverty. Meanwhile, workers aged below 20 are notexpected to have finished tertiary-level education. Those who are aged 21-24, although havecollege education, are just starting to build their career and might probably assume the lowerlevel positions in the organizations where they are working. Interestingly, these findings are
consistent with those that can be drawn from Quadrant 2. Urban households and householdswith heads who are more-educated and/or engaged in non-agriculture employment are mostprobably the not vulnerable households, regardless of the poverty status.
(Figure 1 to be inserted)
Other households that are poor and, at the same time, vulnerable - i.e., transient poor andhighly vulnerable - are commonly larger in size (more than 3 members), have higher
dependency ratio (more than half of the members are aged below 15), male-headed, orheaded by someone aged between 25 and 64. Quadrant 1 suggests that what makes atransient poor household highly vulnerable is that it might have higher number of members
and/or dependents. This group of households in the panel is largely composed of those withmale and/or prime-aged heads.
Meanwhile, Quadrant 3 suggests that the non-vulnerability of transient poor households,
which are mainly composed of those with female and/or older heads, is that they may beinsured by income from remittances and/or pension, respectively. This group of households
can also be protected from falling into poverty by maintaining a low number of members,
especially those below the working-age. The aforementioned characteristics, however, also
describe the group that are never poor but relatively vulnerable. Many factors can be
attributed to this finding. One possible explanation is that households with older heads are
not always protected against income shocks, particularly those who are no longer working

27but are not entitled to pension. Another one is that female-headed households may not always
be remittance-receiving households, or if remittance-receiving, the remittances are notreceived on a regular basis or are enough for the household.

VII. Concluding remarks
The vulnerability to poverty of Filipino households is estimated in this study using a threelevel longitudinal model and the most recent three-wave household-level panel data in thePhilippines. Chaudhuri’s (2003) method of estimating households’ vulnerability to poverty which has been widely adopted in a numerous empirical works on vulnerability based oncross-sectional data - has been further extended in our study by applying the multilevellongitudinal model to the panel data. This leads to our specific methodological contributions
to the empirical literature on vulnerability, such as, decomposing the “ex-ante” vulnerability
estimate into idiosyncratic and covariate components; reducing the possible bias invulnerability estimates by using a multilevel model; and characterising household poverty
situations in both vulnerability and poverty persistence dimensions by utilising the panel
data.23
Interestingly, the estimated multilevel model contains a set of significant and empiricallysound predictors of household income. Consistent with the findings from local poverty
studies, profile of heads (i.e., education, employment, sex, and age), composition (i.e.,household size and dependency ratio) and location (i.e., urban/rural and region) significantly
explain the variation in household income. Observable covariate (i.e., fuel price and rainfall)
and idiosyncratic (i.e., labour market) shocks also have significant (negative) impact onhousehold income.

23

Some of the limitations of the methodology, however, include non-testable assumptions in theestimation of the multilevel model; and unweighted analysis due to unavailability of sampling weights inthe panel data.

28

Further interesting findings can be drawn from the empirical results on our vulnerability
estimates. More than half of the panel households are classified as vulnerable at least once inany of the periods covered. Around 40 percent of the panel households are vulnerable tounobservable covariate shocks while around 60 percent are vulnerable to unobservable
idiosyncratic shocks. Decomposition of poverty and vulnerability to poverty revealed that thechronic and the transient poor, and even the never poor, are more vulnerable to unobservable
idiosyncratic shocks than to unobservable covariate shocks. Impacts of idiosyncratic shocks
might have been more direct and more specific compared to those of covariate shocks.
The results of the tabulation and multiple correspondence analysis suggest that highly
vulnerable households, regardless of the poverty status, are most commonly characterized by
the following: less-educated, agriculturally-engaged or jobless, and very young heads; and,rural dwellers. Other relatively vulnerable households have higher number of members andof dependents.
A number of policy implications can be drawn from the empirical results. Education is an
important determinant of both poverty and vulnerability. Highly educated individuals havehigher probability of gaining more stable and/or better-paying jobs. Education also serves as
an important investment and, at the same time, insurance tool against shocks. More-educatedindividuals are likely to be more adaptive to varying circumstances and have higher coping
capability (Glewwe and Hall, 1998; Christiaensen and Subbarao, 2005). In addition, moreeducated heads tend to keep their household size smaller because they better understand theimplications of having a larger household. Clearly, policies and programs aimed at humancapital investment are very important government interventions, especially in developing
countries like the Philippines. Thus, the current plan of expanding the Pantawid Pamilyang
Pilipino Program (4Ps), the Philippine version of the conditional cash transfer (CCT)
program, to ensure that the current children-beneficiaries finish not only elementary but also29high school is a good strategy. Extending the coverage to ensure college education andincreasing the number of beneficiaries could bring more promising outcomes.
Employment of head is also an important determinant of poverty and vulnerability, and it
is closely related to education. Since investment in human capital is more of a long-term
strategy, increasing labour demand for less-educated workers is a good short-term strategy.
One specific strategy is for the government to help improve the agriculture sector. Increasing
agricultural productivity can lead to improved competitiveness, and thus, increased labourdemand and/or higher wages. Another specific strategy that the government can adopt is tohelp increase productivity of non-agriculture industries (i.e., manufacturing industries thatparticipate in the regional production networks) in order to increase their labour absorption.
Remittances and pension can also be considered as risk-mitigating tools. In particular,
remittances are said to reflect some measure of diversification of earning sources and canthus insure households against shocks. In the same manner, pension is considered as a stablesource of income and could serve as insurance against labour market and other shocks.
Meanwhile, the government should provide adequate safety nets to poor and vulnerable
households in order to protect them against various economic, natural and other shocks.
These could include employment and skills training programs, which can be implemented ona regular basis and can be intensified in times of crisis.

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36

Figure 1. Multiple Correspondence Analysis two-dimensional map: poverty andvulnerability status of panel households

2

MCA coordinate plot
non-agriculture

urban

1

more dependents

never poor/not vulnerable
more-educatedchronic poor/not vulnerable

middle-age (25-64)
large (>3)
transient poor/highly vulnerable

0

male

chronic poor/relatively vulnerable
chronic poor/highly vulnerable

never poor/relatively vulnerable
few dependents

never poor/highly vulnerable
rural

-1

transient poor/not vulnerable
transient poor/relatively vulnerable
agriculture/no job

female-2small (3 or less)less-educatedyoung (<25)-3old (65 & over)

-2

-1

0

1
2
dimension 1 (52.3%)

3

4

coordinates in standard normalization

Source: Author's estimates using the 2003-2006-2009 FIES panel data.

TABLE 1
Definition of idiosyncratic and covariate shock variables
Shock variable

Definition

Idiosyncratic shocks
More jobless1 if the proportion of members who are either unemployed or not in the labor force
increased during the year; 0 otherwise

More vulnerable
workers

1 if the proportion of employed members who are either self-employed or unpaid family
workers increased during the year; 0 otherwise

More non-permanent
wage workers

1 if the proportion of employed members who are non-permanent wage/salary workers
increased during the year; 0 otherwise

Fewer OCWs

1 if the proportion of members who are overseas contract workers decreased during theyear; 0 otherwise

Covariate shocks
Rainfall shock

1 if the percentage deviation from normal rainfall went beyond the 80-120% range morethan 50 percent of the time during the year*; 0 otherwise

Rice price shock

1 if the annual average of rice inflation rate is 20% and above; 0 otherwise

Fuel price shock

1 if the annual average of fuel inflation rate is 20% and above; 0 otherwise* ‘Normal rainfall’ is the average amount of rainfall for the period 1971-2000. Annual figures onpercentage deviation could have been generated but it is not expected to capture the varying impactsof rainfall shocks throughout the year. An all-year round occurrence of slightly above-normal rainfall
usually has more adverse impact on a rural agricultural area and a low-lying urban area than a halfyear occurrence of highly above-normal rainfall.

37

TABLE 2
Categorization of poverty and vulnerability to poverty of households
Poverty status
Original category

Major Category

PPP- Poor in 2003, 2006 and 2009
PPN
- Poor in 2003 and 2006; Non-poor in 2009
PNP
- Poor in 2003 and 2009; Non-poor in 2006
NPP
- Non-poor in 2003; Poor in 2006 and 2009PNN
- Poor in 2003; Non-poor in 2006 and 2009
NPN
- Non-poor in 2003 and 2009; Poor in 2006
NNP
- Non-poor in 2003 and 2006; Poor in 2009NNN- Non-poor in 2003, 2006 and 2009
Vulnerability status
Original category
VVV- Vulnerable in 2003, 2006 and 2009
VVN
- Vulnerable in 2003 and 2006; Not vulnerable in 2009
VNV
- Vulnerable in 2003 and 2009; Not vulnerable in 2006
NVV
- Not vulnerable in 2003; Vulnerable in 2006 and 2009
VNN
- Vulnerable in 2003; Not vulnerable in 2006 and 2009
NVN
- Not vulnerable in 2003 and 2009; Vulnerable in 2006
NNV
- Not vulnerable in 2003 and 2006; Vulnerable in 2009NNN- Not vulnerable in 2003, 2006 and 200938

Chronic poor
Moderately poor
Transientpoor
Slightly poor
Never poor
Major Category
Highly vulnerable
Moderately vulnerable
Relatively
vulnerable
Less vulnerable
Not vulnerable

TABLE 3
Results of the Mixed-effects REML regression
Dependent variable: Log of per capita income
Variable
Fixed part
Time
Household composition
Household size
Square of household size
Dependency ratio
Household head profile
Sex
Age
Square of age
Educational attainment
At least elementary graduate
At least secondary graduate
At least college graduate
Employment
Location
Urban/rural
Regiona/
IlocosCagayanCentral Luzon
CALABARZONMIMAROPA
Bicol
Western Visayas
Central Visayas
Eastern Visayas
Zamboanga Peninsula
Northern Mindanao
Davao
SOCCSKSARGEN
Cordillera Administrative Region (CAR)
Autonomous Region in Muslim Mindanao (ARMM)
Other aggregate-level variables
Transportation infrastructure index
Economic and social infrastructure index
Irrigation development
Agriculture index
Utilities index
Idiosyncratic shocks

Parameter
0.0530 (0.0043)***-0.1651 (0.0081)***0.0066 (0.0006)***-0.4570 (0.0238)***-0.0587 (0.0157)***0.0141 (0.0023)***-0.0001 (0.0000)***0.1170 (0.0114)***0.2084 (0.0407)***0.9563 (0.0824)***0.1637 (0.0308)***0.0284 (0.0426)
0.2860 (0.1184)*
0.3605 (0.1030)***0.1476 (0.1010)
0.2989 (0.1108)**
0.0974 (0.0985)
0.1934 (0.1058)
0.0794 (0.0999)-0.1741 (0.1126)
0.2769 (0.0980)**0.0227 (0.1254)-0.0099 (0.1050)-0.018 (0.1043)
0.1009 (0.1024)
0.0489 (0.1452)
0.4305 (0.1406)**
0.0245 (0.0157)
0.0084 (0.0082)
0.0008 (0.0008)-0.0190 (0.0120)
0.0037 (0.0074)

More jobless0.0075 (0.0084)
More vulnerable workers-0.0031 (0.0076)
More non-permanent wage workers-0.0060 (0.0096)
Less OCWs-0.1583 (0.0234)***
Figures in parentheses are standard errors; *** p<0.001, ** p<0.01, * p<0.05.

39TABLE 3(continued)
Variable
Covariate shocks
Rainfall shock
Rice price shock
Fuel price shock
Interactions
Sex of head × Education of head (at least college graduate)
Sex of head × Ilocos
Sex of head × CALABARZON
Education of head (at least secondary graduate) × Morenon-permanent wage workers
Education of head (at least secondary graduate) × Less
OCWs
Education of head (at least college graduate) × Household
size
Education of head (at least college graduate) × Square ofhousehold size
Education of head (at least college graduate) × Zamboanga
Peninsula
Education of head (at least college graduate) × Northern
Mindanao
Education of head (at least college graduate) × Morevulnerable workers
Employment of head × Household size
Employment of head × Davao
Employment of head × Rainfall shock
Household size × Bicol
Dependency ratio × Zamboanga Peninsula
Urban/rural × CALABARZON
Urban/rural × Bicol
Urban/rural × Western Visayas
Urban/rural × Central Visayas
Urban/rural × Zamboanga Peninsula
Urban/rural × Davao
Urban/rural × Irrigation development
Urban/rural × Fuel price shock
Central Luzon × Fuel price shock
CAR × Less OCWs
Inverse Mills Ratio (IMR)
Intercept
Random part
Province-level
Variance (Random slope)
Variance (Random intercept)
Covariance (Random slope, Random intercept)
Household-level:
Variance (Random slope)
Variance (Random intercept)
Covariance (Random slope, Random intercept)
Occasion-level:
Time 0: Variance (Residual)
Time 3: Variance (Residual)
Time 6: Variance (Residual)

40

Parameter-0.0565 (0.0161)***-0.0300 (0.0653)-0.0761 (0.0114)***0.2025 (0.0412)***-0.1391 (0.0438)**-0.1326 (0.0414)**0.0581 (0.0187)**
0.1056 (0.0367)**-0.1283 (0.0278)***0.0099 (0.0024)***0.2722 (0.1057)*
0.2241 (0.0883)*
0.1151 (0.0303)***-0.0114 (0.0038)**
0.1120 (0.0422)**-0.0526 (0.0237)*-0.0277 (0.0081)**-0.2605 (0.0939)**0.1122 (0.0547)*
0.1513 (0.0582)**0.1507 (0.0515)**0.1041 (0.0530)*
0.3492 (0.0736)***
0.2344 (0.0713)**0.0033 (0.0007)***0.0288 (0.0123)*
0.0538 (0.0191)**0.2570 (0.1028)*-0.3023 (0.0502)***0.9174 (0.1187)

0.0003 (0.0001)
0.0211 (0.0057)-0.0012 (0.0006)
0.0024 (0.0003)
0.1989 (0.0074)-0.0061 (0.0012)
0.0960 (0.0064)
0.1166 (0.0035)
0.0872 (0.0063)

TABLE 4
Poverty and vulnerability status of panel households, by degree and by source
Vulnerability status
Chronic poor
Transient poor
Total vulnerability
Highly vulnerable
57.5
23.5
Relatively vulnerable
41.1
62.7
Not vulnerable
1.5
13.7
Covariate vulnerability
Highly vulnerable
25.6
5.9
Relatively vulnerable
64.9
53.2
Not vulnerable
9.5
40.9
Idiosyncratic vulnerability
Highly vulnerable
47.3
17.7
Relatively vulnerable
50.1
64.5
Not vulnerable
2.6
17.8
Source: Author's estimates using the 2003-2006-2009 FIES panel data.

41

Never poor

All

3.0
34.0
63.0

15.4
42.2
42.4

0.4
17.9
81.7

5.1
33.5
61.4

2.6
36.1
61.3

12.4
45.8
41.8

TABLE 5
Characteristics of different groups of panel households, 2009
Indicator
Mean household size
Mean dependency ratio
Mean age of head
Proportion of male-headed households
Proportion of households with heads who are at mostelementary undergraduate
Proportion of households with heads who are at least
elementary graduate
Proportion of households with heads who are at least secondary
graduate
Proportion of households with heads who are at least college
graduate
Proportion of households with heads who are engaged in nonagriculture
Proportion of households with access to electricity
Proportion of households with access to safe water
Proportion of households with access to sanitary toilet facility

Highlyvulnerable
7.5
0.5
44.8
96.3
58.6

Chronic poor
Relatively
Notvulnerable vulnerable
5.6
3.6
0.4
0.2
49.3
55.6
88.2
85.7
46.2
28.6

Highlyvulnerable
6.7
0.4
45.8
95.8
49.1

Transient poor
Relatively
Notvulnerable vulnerable
5.0
4.0
0.3
0.2
51.7
54.3
84.9
70.6
42.4
29.4

Highlyvulnerable
6.5
0.4
52.9
85.1
35.8

Never poor
Relatively
Notvulnerable vulnerable
4.7
3.8
0.2
0.2
53.7
54.7
78.7
71.8
27.4
12.0

All
households
4.8
0.3
52.4
79.7
28.7

34.8

32.3

28.6

44.0

36.1

37.3

49.3

35.6

24.0

31.8

6.6

21.5

42.9

6.9

21.4

30.2

14.9

33.4

39.3

29.1

0.0

0.0

0.0

0.0

0.2

3.2

0.0

3.6

24.7

10.4

16.5

28.2

57.1

25.5

29.9

35.7

34.3

45.0

55.5

41.9

46.2
62.1
71.4
70.4
77.4
78.6
91.0
94.0
95.7
60.8
69.2
71.4
59.7
74.1
83.3
94.0
85.6
91.9
56.0
70.8
85.7
67.6
75.0
80.2
91.0
91.3
95.4
60.4
67.2
100.0
63.9
75.2
83.3
79.1
81.1
85.4
Proportion of households that own a land
Proportion of urban households
5.5
20.5
57.1
6.9
17.9
49.2
7.5
25.7
59.1
Proportion of households living in Cagayan Valley
0.0
2.1
14.3
0.5
4.5
14.3
0.0
5.4
12.9
Proportion of households living in Central Luzon
0.7
5.1
0.0
0.0
7.8
18.3
3.0
13.2
16.5
Proportion of households living in CALABARZON
2.6
6.2
0.0
0.9
9.5
11.9
0.0
6.6
20.7
Proportion of households living in Bicol
13.9
9.2
14.3
11.6
7.6
7.9
6.0
5.8
5.8
Proportion of households living in Zamboanga Peninsula
16.8
5.6
0.0
12.0
3.5
2.4
11.9
2.9
1.6
Proportion of households living in Caraga
15.4
7.7
0.0
13.0
6.6
0.8
14.9
6.5
0.6
Note: Regions shown are those with the highest (CALABARZON, Central Luzon and Cagayan Valley) and lowest (Caraga, Bicol and Zamboanga Peninsula) poverty
incidences during the period 2003-2009.
Source: Author's estimates using the 2003-2006-2009 FIES
panel data.

84.6
82.0
84.7
78.6
35.0
7.5
11.4
11.9
7.3
4.4
5.3

42

Online Appendix
Appendix Table 1
Definition and summary statistics of variables
Variable
Log of per capita income
Time
Household composition
Household size
Square of household size
Dependency ratio
Household head profile
Sex
Age
Square of age
Educational attainment
At most elementary level
At least elementary graduate
At least secondary graduate
At least college graduate
Employment

2003

2006

2009

Log of per capita income (deflated by the 2003 provincial
poverty threshold)
Number of years from the baseline (2003)

0.54

Std
Dev
0.77

0

0

0

0

3

0

3

3

6

0

6

6

Average number of household members during the year
Square of household size
Proportion of household members aged below 15

5.08
30.37
0.33

2.14
25.32
0.24

1
1
0

15
225
1

5.02
29.91
0.30

2.18
25.96
0.24

1
1
0

15
225
1

4.85
28.26
0.27

2.17
24.96
0.23

1
1
0

17
272
1

Sex of household head: 1 if male; 0 if female
Age of household head, in years
Square of age of household head
Education dummies:
1 if either elementary undergraduate or have no grade
completed; 0 otherwise (base category)
1 if either elementary graduate or secondary undergraduate;
0 otherwise
1 if either secondary graduate or college undergraduate; 0otherwise
1 if either college graduate or postgraduate; 0 otherwise
1 if employed in non-agriculture sector; 0 if either employedin agriculture sector or not employed

0.85
47.51
2448.110.86
47.44
2440.67

0
14
1420

0
17
289

0.84
49.92
2673.480.37
13.45
1435.52

0
13
169

1
94
8836

0.81
52.10
2892.730.39
13.37
1474.97

0
11
121

1
98
9604

0.30

0.46

0

1

0.30

0.46

0

1

0.30

0.46

0

1

0.34

0.47

0

1

0.35

0.48

0

1

0.34

0.47

0

1

0.28

0.45

0

1

0.28

0.45

0

1

0.29

0.45

0

1

0.08
0.42

0.27
0.49

0
0

1
1

0.08
0.41

0.27
0.49

0
0

1
1

0.08
0.40

0.27
0.49

0
0

1
1

0.47

0

1

0.33

0.47

0

1

0.26
0.24
0.29
0.30
0.20
0.25
0.28
0.27

0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1

0.07
0.06
0.09
0.10
0.04
0.07
0.09
0.08

0.26
0.24
0.29
0.30
0.20
0.25
0.28
0.27

0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1

Definition

Mean

Min

Max

Mean

0.72

Std
Dev
0.77

-1.39

4.85

Min

Max

Mean

-1.69

4.73

Location
Urban/rural
Urban/rural indicator: 1 if urban; 0 if rural
0.33
0.47
0
1
0.33
Regiona/
Regional dummies:
Ilocos1 if a household resides in Ilocos; 0 otherwise
0.07
0.26
0
1
0.07
Cagayan
1 if a household resides in Cagayan Valley; 0 otherwise
0.06
0.24
0
1
0.06
Central Luzon
1 if a household resides in Central Luzon; 0 otherwise
0.09
0.29
0
1
0.09
CALABARZON
1 if a household resides in CALABARZON; 0 otherwise
0.10
0.30
0
1
0.10
MIMAROPA
1 if a household resides in MIMAROPA; 0 otherwise
0.04
0.20
0
1
0.04
Bicol
1 if a household resides in Bicol; 0 otherwise
0.07
0.25
0
1
0.07
Western Visayas
1 if a household resides in Western Visayas; 0 otherwise
0.09
0.28
0
1
0.09
Central Visayas
1 if a household resides in Central Visayas; 0 otherwise
0.08
0.27
0
1
0.08
a/ NCR was not included in the analysis because it is the only region that is not composed of provinces. It is composed of four districts, which are composed of cities.

43

Min

Max

0.97

Std
Dev
0.76

-1.51

5.19

Appendix Table 1 (Cont.)
Variable

Definition

2003

2006

2009

Eastern Visayas

1 if a household resides in Eastern Visayas; 0 otherwise

0.06

Std
Dev
0.23

0

1

0.06

Std
Dev
0.23

0

1

0.06

Std
Dev
0.23

Zamboanga Peninsula

1 if a household resides in Zamboanga Peninsula; 0 otherwise

0.05

0.22

0

1

0.05

0.22

0

1

0.05

0.22

Northern Mindanao

1 if a household resides in Northern Mindanao; 0 otherwise

0.05

0.22

0

1

0.05

0.22

0

1

0.05

Davao

1 if a household resides in Davao; 0 otherwise

0.06

0.23

0

1

0.06

0.23

0

1

SOCCSKSARGEN

1 if a household resides in SOCCSKSARGEN; 0 otherwise

0.06

0.23

0

1

0.06

0.23

0

1 if a household resides in CAR; 0 otherwise

0.04

0.20

0

1

0.04

0.20

0

1 if a household resides in ARMM; 0 otherwise

0.04

0.20

0

1

0.04

0.20

1 if a household resides in Caraga; 0 otherwise(base category)

0.04

0.21

0

1

0.04

Principal Component Analysis (PCA) index of road density, paved road ratio, andnumber of ports and airports (domestic and international)
Principal Component Analysis (PCA) index of the following: ratio of rural banks tototal barangays; ratio of elementary and secondary schools to total barangays;
ratio of barangay health stations to total barangays
Ratio of total service area to estimated total irrigable area

-0.53

1.18

-3.37

2.34

-0.03

1.35

-2.39

50.91

23.09

0.83
-0.14

Cordillera Administrative Region(CAR)
Autonomous Region in Muslim
Mindanao (ARMM)
Caraga

Mean

Min

Max

Mean

Min

Max

Mean

Min

Max

0

1

0

1

0.22

0

1

0.06

0.23

0

1

1

0.06

0.23

0

1

1

0.04

0.20

0

1

0

1

0.04

0.20

0

1

0.21

0

1

0.04

0.21

0

1

0.22

1.24

-3.11

4.03

0.34

1.20

-3.13

4.24

5.45

-0.03

1.28

-2.34

4.13

0.07

1.42

-2.26

5.73

6.46

155.98

52.72

23.86

6.56

160.52

55.64

23.57

7.50

161.80

1.11

-1.73

4.19

-0.38

0.78

-1.73

2.38

-0.43

0.79

-1.56

2.35

1.08

-3.21

1.97

0.03

0.91

-2.80

1.68

0.10

1.26

-5.57

3.83

Other aggregate-level variables
Transportation infrastructure index
Economic and social infrastructureindex
Irrigation development
Agriculture index
Utilities index

Principal Component Analysis (PCA) index of total area planted and average useof fertilizer
Principal Component Analysis (PCA) index of telephone density and percentage
of energization

44

Appendix Table 2
Results of Likelihood ratio tests for inclusion of random coefficients
Likelihood ratio test 1:
Model 1 (no random coefficient) vs. Model 2 (with random coefficient at level 2):
LR  22 = 78.00, Pr >  2 = 0.0000
Likelihood ratio test 2:
Model 2 (with random coefficient at level 2) vs. Model 3 (with random coefficients at levels
2 & 3):LR  42 = 112.50, Pr >  2 = 0.0000
Note: Models 1 to 3 have identical fixed-effects specifications.

Appendix Table 3
Comparison between Intercept-only and Full models
Random-effects Parameters
Province-level
Variance (Random slope)
Variance (Random intercept)
Covariance (Random slope, Random intercept)
Household-level:
Variance (Random slope)
Variance (Random intercept)
Covariance (Random slope, Random intercept)
Occasion-level:
Variance (Residual)
Time 0: Variance (Residual)
Time 3: Variance (Residual)
Time 6: Variance (Residual)Intercept-only

Full

0.00530.2316-0.02970.00030.0211-0.00120.00210.4237-0.0063

0.0024
0.1989-0.0061

0.1113

Note: Both models have random intercept and random slopes of time at all levels.
The Full model includes all the main effect and interaction variables.
Source: Author's estimates using the 2003-2006-2009 FIES panel data.

45

0.09600.11660.0872.1
0.05

Density.15.2

Appendix Figure 1a. Histogram (with normal-density plot) of per capita income

0

50

100

150

200

Per capita income

0.2

Density.4.6

Appendix Figure 1b. Histogram (with normal-density plot) of log of per capita income

-2

0

2

4

6

Log of per capita income

1500
1000

0

500

-1

0

-2

Residuals (level-1)

1

2000

2

Appendix Figure 2a. Scatter plot and histograms of the fitted values and level-1
residuals-202
Fitted values

4

-2

-1

0
Residuals

1

46

2

1500
1000

0

0

-2

500

-1

Residual (level-2)

1

2000

2

Appendix Figure 2b. Scatter plot and histograms of the household-level mean of fitted
values and level-2 residuals

-1

0

1
2
Mean of fitted values

3

4 -2

-1
0
1
Residuals, level(Household)

2

1000

1

500

0
-1

0

-2

Residual (level-3)

2

1500

3

2000

Appendix Figure 2c. Scatter plot and histograms of the provincial-level mean of fitted
values and level-3 residuals

0.5
1
Prov-level mean of fitted values

1.5 -2-1

0
1
2
Residuals, level(Province)

47

3