DP2016-16

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
Revised November 14, 2016
* 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, JapanFirst Draft: 7th April 2015
This Draft: 10th October 2016

Abstract

Using the panel data for the Philippines in 2003-2009, we estimate a three-level
random coefficient model to measure household vulnerability and to decompose
it into idiosyncratic and covariate components. We correct heterogeneity bias
using Bell and Jones’s (2015) ‘within-between’ formulation. A majority of thepoor and 18 percent of the non-poor are found to be vulnerable to unobservableshocks, while both groups of households are more susceptible to idiosyncratic
shocks than to covariate shocks. Adequate safety nets should be provided forvulnerable households that lack access to infrastructure, or are larger in size withmore dependents and less-educated heads.
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

Acknowledgements
The authors are grateful to valuable comments from two anonymous referees and a managing
editor. They also acknowledge valuable advice from Raghav Gaiha and participants in the
RIEB seminar at Kobe University in March 2016, the Manchester Workshop, ‘Pushing the
Frontiers of Poverty Research’ in May 2016, the research workshop at Bournemouth
University in October 2016, and the 13th National Convention on Statistics on the 3rd of
October. 2016. All the errors remain authors’.

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

1. Introduction
The Philippine economy showed remarkable performance during the period 2003-2006,
which our study focuses on, in terms of GDP. However, the growth decelerated from 2006
(5.2%) to 2009 (1.1%) (Table 1) and then recovered steadily in more recent years (3.7% in2010 to 6.1% in 2014). The main growth driver during the period 2003-2006 was the services
sector. Agriculture and industry sectors, on the other hand, suffered negative growth rates in2009. The gross national income consistently increased from 2003 to 2009. The current
account balance as a share of GDP went up significantly during this period, suggesting theimprovement in the country’s competitiveness. From 2003 to 2009, the Philippine peso
appreciated while the net factor income from abroad almost doubled. Philippines, however,was subject to a variety of macroeconomic and other shocks and households were likely to bevulnerable to both covariate and idiosyncratic shocks in this period.
(TABLE 1 to be inserted)
An important question in this context is how macroeconomic conditions influenced
household welfare. While peso appreciation lowered the value of international remittances, italso lowered import prices. This resulted in cheaper domestic rice, oil products and otherbasic commodities. Thus, although inflation was not maintained below 3.0 percent after
2003, the growth in prices decelerated from 8.3 percent in 2008 to 4.2 percent in 2009.
Apparently, effects of the global financial crisis did not persist. Meanwhile, theunemployment rate dropped from 11.4 percent in 2003 to 7.5 percent in 2009. However, thepoverty headcount ratio did not significantly decline during the period 2003-2009. It rosefrom 20 percent in 2003 to 21.1 percent in 2006, and then remained almost same (20.9%) in2

2009. The total number of poor households, on the other hand, grew from 3.3 million in 2003
to 3.9 million in 2009.1 Poverty in the Philippines is also characterised by spatial disparity(Figure 1). The provinces with the highest poverty incidence from 2003 to 2009 are locatedin the south region, the poorest region of the country. On the other hand, several provinces in
the central region had poverty rates higher than 30 percent while provinces in the northregion had relatively lower poverty rates. The Gini coefficient at the national level remained
high (48.7% in 2003 and 47.4% in 2009); higher than those in urban or rural areas (45.1% in2003 and 44.6% in 2009 in urban areas and 42.9% in 2003 and 42.8% in 2009 in rural areas).
The rural-urban disparity could have resulted in greater inequality at the national level than inurban or rural areas.
(FIGURE 1 to be inserted)
Earlier studies on poverty argued that a large component of the Philippine poverty istransient poverty, which is characterised by high vulnerability to shocks (Reyes et al., 2013).
Among the key reasons why many Filipino households do not have the capacity toautonomously mitigate the adverse impacts of shocks include the lack of gainful
employment, less access to credit and good-quality health facilities, and lack of institutional
support, among others (Reyes et al., 2009, 2013; Reyes and Mina, 2013). Thus, in analysing
further the underlying causes of persistence of poverty, it is necessary to take into account theeffect of macro and micro shocks on household welfare. The 2010 Philippine Millennium
Development Goals Progress Report noted that the combined impacts of economic, naturaland other shocks could have contributed to the persistence of poverty in the country. During
the past decade, the Philippines has faced many challenges including the aftermath of the
2007/08 global financial crisis, and exorbitant and unpredictable rice and fuel prices, and aseries of extreme weather events, among others. One of the most notable shocks is the global
financial crisis, which originated in the United States in July 2007. The Philippines felt the3

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 andgarments sub-sectors, as well as the overseas Filipino workers (OFWs) had borne the bruntof the crisis. During the same period, the country also faced significant rice and fuel priceincreases. Domestic rice prices had dramatically increased up to 40 percent during the latterpart of 2007 until the first half of 2008 due to upsurge in global foodgrain prices. Aside from
economic shocks, the Philippines have also been frequently visited by typhoons and otherextreme weather events. Based on historical records of the National Oceanic andAtmospheric Administration, four El Niño and three La Niña episodes occurred between
2003 and 2009. These brought an increased frequency of destructive typhoons, excessive
flooding and even prolonged droughts to the country. Official statistics show that thesenatural shocks have been getting more frequent and more intensified.
Bearing in mind these broad regional and economic contexts, this study aims to estimate
vulnerability to poverty using a three-level linear random coefficient (RC) model applied to a
Philippine household-level panel dataset covering three waves (2003, 2006 and 2009). We
draw upon the growing literature of quantitative studies of vulnerability as an ex ante
measure of poverty (Pritchett et al., 2000; Chaudhuri et al., 2002; Zhang and Wan, 2006).
Specifically, we will address the following three research questions: (1) Who are vulnerable
to poverty in the Philippines?; (2) Which has a greater share in explaining the vulnerability topoverty, idiosyncratic shocks and covariate shocks?; and, (3) What are the main
characteristics of vulnerable households?
To our knowledge, this is the first study to estimate the vulnerability to poverty using athree-level longitudinal model, or RC model to capture the effects of factors in different
levels (i.e., time, household, and province). Heterogeneity bias in the RC model is correctedby using Bell and Jones’s (2015) ‘within-between’ formulation to explicitly model both time-4

series (or ‘within’) variations in means of household-and province-level variables and crosssectional (or ‘between’) variations across different households and provinces. While Bell andJones argue that this method overcomes the limitation of the RC model and is preferable tothe fixed-effects (FE) model, both RC and FE models are estimated in this study. Morespecifically, we have applied the FE model to first differenced income to derive the predicted
income in 2012 to examine how a household is below or above the poverty threshold. In allcases, the attrition bias was corrected by the method of Fitzgerald et al. (1998).
The structure of the paper is as follows. Section 2 provides a brief summary of theempirical literature on vulnerability in developing countries. Data and variables are discussed
in Section 3. Section 4 describes the methodologies for multilevel analysis, estimation ofvulnerability to poverty, and vulnerability assessment. Section 5 provides estimation results
and vulnerability profile of the panel households. The final section concludes and provides
some policy implications.

2. Empirical Literature
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 the
expected poverty approach in measuring vulnerability to poverty of Indonesian households.
The study found that around 30-50 percent of Indonesian population are vulnerable topoverty, given a 20-percent poverty rate. Chaudhuri et al. (2002) estimated Indonesian
households’ vulnerability through calculation of the expected value of poverty based on a set

5

of household characteristics. The study found that 45 percent of the Indonesian population
are considered vulnerable while 22 percent are classified as poor.2
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 was later adopted by Échevin (2013). These studies utilized cross-sectional
data and developed a two-level model. Günther and Harttgen (2009) estimated a random
intercept model while Échevin (2013) estimated a RC model by including shock variables inthe set of explanatory variables. As an extension, this study proposes the use of a three-levellinear RC model as well as a FE model using panel data by introducing time as an additional
level in the multilevel model.
3. Data and Variables
We used the three-wave household-level panel data generated from the 2003, 2006 and 2009
rounds of the Family Income and Expenditure Survey (FIES) in the Philippines. The 2000
Census of Population and Housing served as the sampling frame for nationally representative
household-based surveys, including the FIES, Annual Poverty Indicators Survey (APIS) andLabour Force Survey (LFS). Households were tracked if at least one of the members
remained in all of the three rounds. The original cohort in 2003 was composed of 9,344
households. In 2006, the number of sample households that remained in the panel was 7,201;
that is, 2,143 households (22.9%) were dropped. In 2009, among those 7,201 sample
households, 1,215 (16.9%) were dropped. As a result, a total of 5,986 households comprised
the 2003-2006-2009 panel of Filipino households. According to the Philippine Statistics
Authority (PSA), the main reasons for attrition include the following: household units weredestroyed by natural calamities such as strong typhoon, landslide, earthquake, volcanic
eruption; residential area was converted to an industrial area; the entire household migrated

6

to other places because the head found a new job in another place; among others. Given thatvulnerable households tend to be dropped from the surveys, our vulnerability measures arelikely to be underestimated. While this is admittedly a major limitation of the study, our useof the panel data in deriving vulnerability estimates would offer rich policy implications as
the majority of the existing empirical works on vulnerability used cross-sectional data. We
used inverse probability weights as our approach for addressing attrition based on the methodof Fitzgerald et al. (1998) that used observable characteristics in correcting for attrition bias.
The data contain annual information on households’ socioeconomic characteristics,
including income, expenditure, household head profile, and other household characteristics.
The FIES data are supplemented by information on labour force, employment andeducational attainment of household members generated from the relevant rounds of the LFS,
namely: July 2003, January 2004, July 2006, January 2007, July 2009, and January 2010. 3
Since the FIES dataset contains only household-level information, data on aggregate-level
characteristics and shocks are sourced from the official statistics released by various
government offices.
The official poverty statistics in the Philippines are generated regularly by the PSA basedon the results of the triennial FIES. A Filipino household is considered poor if its per capita
income is below the official provincial poverty threshold.4 Since per capita income is thewelfare measure used in the generation of official poverty statistics in the country, (log of)
per capita income is used as a dependent variable in the empirical model.
The set of covariates used in this study are selected based on previous poverty studies on
the Philippines (Tabunda, 2001). Table 2 reports the definition and summary statistics ofthese variables. These variables include household size, dependency ratio, and household
head attributes (i.e., sex, age, education, and employment).We also included aggregate-levelvariables, namely the transportation infrastructure index, economic and social infrastructure7

index, irrigation development index, agriculture index, and utilities index. The indices weregenerated using the Principal Component Analysis (PCA) mainly because some of thecomponent variables of those indices are strongly correlated. Regional dummies were also
included to account for regional characteristics. Except for squares of household size and ageof head, all main effect variables included in the model are not strongly correlated. 5 While
the average real per capita household income increased over the years, most of the variables
on household characteristics were stable in 2003-2009. The quality of infrastructure (e.g.,paved roads; number of ports and airports; telephone density) improved while the total area
planted and/or average use of fertiliser declined.
(TABLE 2 to be inserted)
The rice and fuel price shocks are hypothesized to have a direct impact on householdincome. 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
agricultural commodities to the market.

4. Methodology
The methodology proposed in Günther and Harttgen (2009) is extended in this study byapplying it to panel data with hierarchical structure where time (or ‘occasion’) is included asanother level. Here we also take into account observable shocks in predicting income. We
propose to use the three-level model to decompose the ex ante vulnerability measure into
covariate (aggregate) and idiosyncratic components. We also employ fixed-effects (FE)model to derive the vulnerability estimate without decomposing it into covariate andidiosyncratic components, and to see how different methods yield different vulnerability

8estimates. Furthermore, the FE model is used for the first differenced income to predict
income in 2012 to define the vulnerability status.
We will use multilevel model or random-coefficient (RC) model to analyse hierarchicallystructured data with variables defined at all levels of the hierarchy (Hox, 2000; Gibbons etal., 2010; Dupont and Martensen, 2007; Singer and Willett, 2003). The multilevel model canbe used to decompose the relative impacts of household-specific and community-specific [oraggregate-specific] shocks on households’ vulnerability to poverty (Günther and Harttgen,2009).
Let ln y tij be the log of per capita income of household i in province j at time t, where:level-1 units are the measurement occasions indexed by t = 1, 2, 3; level-2 units are thehouseholds 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:

ln ytij  xT(1)tij β(1)  xT( 2)ij β( 2)  xT(3)tj β(3)  ZTtij v j  ZTtij uij  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 )tj , includes the following: time-varying (level-1) covariates,xT(1)tij ; time-invariant (level-2) covariates, xT(2)ij ; aggregate-level (level-3) covariates, x T( 3 )tj .

The vector x T(1)tij also contains a variable representing time (Frees, 2004). Associated with





T
T
T
T
T
vector xtij is β  β(1) , β(2) , β(3) , which is a vector of fixed regression coefficients. The

first three terms in equation (1) comprise the fixed part of the model. This is a baseline
specification and we have also tried the specification with interaction terms within/acrossdifferent levels. It is noted that all the explanatory variables, such as those representing theshocks, are included in “the fixed part”.

9The last three terms in equation (1) comprise the random part. ‘ ZTtij v j  ZTtij u ij ’ captures
the unobservable effects at province level j and household level i. v j , an unobservablerandom effect at the province level, captures, for instance, cultural or institutional factor at
the provincial level. It includes the random intercept v 0 j and the random coefficient v1 j , andis assumed as v j ~ N 0, Σv  . The random intercept is interpreted as the initial status of theunobservable random effect while the random coefficient for the time variable is interpreted
as the rate of growth of the random effect. On the other hand, u ij captures an unobservable
household random effect such as psychological factor or risk-aversion. The random effect at
the household level u ij also includes the random intercept u1ij and the random coefficient

u 0ij , and is assumed as follows: uij ~ N 0, Σu  . The matrix ZTtij contains the vectors of 1’s
for the random intercept and the time variable z for the random coefficient. In this study, only
the time variable was allowed to vary both at household and provincial levels. Thus, themodel in equation (1) only has the random coefficient for the time variable. Meanwhile, thelast term etij is the level-1 residual and is assumed as follows: etij ~ N (0, e ) . Residuals at2

different levels, etij , v j , and u ij may contain the impacts of aggregate or idiosyncratic shocks
as well as measurement errors and non-stochastic heterogeneity at different levels, which are
not captured by our model. We assume here that our flexible way of modelling the provincelevel effect by multilevel modelling has minimised the effect of measurement errors and nonT

stochastic heterogeneity. The level-2 residuals, Z tij u ij , represent the unexplained variancesacross households and also capture the impacts of idiosyncratic shocks. The level-3 residuals,
ZTtij v j , represent the unexplained variances across provinces and capture the impacts ofcovariate shocks.
10

T

For identification purposes, the covariates x tij are assumed to be exogenous with













E etij xTtij  0 , E uij xTtij  0 and E v j xTtij  0 , and residuals in levels 1, 2 and 3 areuncorrelated. Moreover, the model in equation (1) allows for heteroscedasticity byintroducing interactions between the time variable and higher-level residuals. This particularfeature of the model is suitable to vulnerability analysis, where variances are usually assumed
to be correlated with observable covariates. The presence of higher-level residuals in each of
the composite residuals also allows for autocorrelation (Graham et al., 2008), although
independence of the level-1 residuals can be imposed on the covariance structure.
To overcome the RC model’s limitation due to potential correlations between covariates
and an unobservable term at the household or province level, u ij or v j , or the heterogeneitybias, we adopted the ‘within-between’ formulation which Bell and Jones (2015) put forward
as an extension of Mundlak (1978). This formulation explicitly takes into account the ‘withinvariation’ by having a vector of demeaned terms of time-varying covariates in levels 1 and 3(time-varying covariates minus time-series mean of time-varying covariates: xT(1)tij  xT(1)ij andxT(3)tj  xT(3) j ) and the ‘between variation’ by having a vector of time-series means of timeTvarying covariates, xT(1)ij and x (3) j . This is a baseline specification and we have also tried
the specification with interaction terms within/across different levels.
ln ytij   xT(1)tij  xT(1)ij β (1)  xT(1)ij β (2)  xT( 2)ij β (3)   xT(3)tj  xT(3) j β ( 4)  xT(3) j β (5)  ZTtij v j  ZTtij u ij  etij





(1′)
Among various advantages, this formulation would enable us to capture the within- or
fixed-effect at household and province levels through β (1) and β ( 4) as well as the betweeneffect at household and province levels through β (2) and β (5) . This ‘within-between’

11formulation can overcome the main criticism of RE or RC that covariates and unobservable
terms are correlated and thus coefficient estimates of covariates are biased in the panel datasettings (Bell and Jones, 2015).
The restricted (or residual) maximum likelihood (REML) is used in the estimation of themultilevel model in this study for the following reasons. First, “REML is preferable withrespect to the estimation of the variance parameters” (Snijders and Bosker, 2012: 60). This isimportant because one of the objectives of the study is to assess the impacts of shocks.
Second, Maximum Likelihood (ML) estimates fail 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: 60).
We have also applied the FE model to the panel data in levels and in first differences byintroducing  ij , the unobservable fixed-effect at the household level, as in equations (3) and(3,).
ln ytij  xT(1)tij β (1)  xT(3)tj β (3)  ij  etij(3)

 ln ytij  x'T(1)tij β'(1) x'T(3)tj β'(3)   'ij e'tij(3’)

An advantage of the FE model is that we do not have to assume that  ij is correlated with aset of covariates. The disadvantages of the FE model, on the other hand, include thefollowing: (i) it ignores the effects of all time-invariant province- and household-levelvariables; (ii) it also ignores the hierarchical structure of the data and thus the coefficientestimates could be biased (Goldstein, 1999); and (iii) the relative impacts of householdspecific and community-specific factors cannot be identified.

12

Following Fitzgerald et al. (1998), this study tests for randomness of attrition, or whether
attrition has a significant effect on the model estimates by estimating Fitzgerald et al.’sunrestricted attrition probit model and performing the Becketti et al.’s (1988) pooling test for
attrition. The results of these models are reported in Online Appendix Tables 1 and 2. The
results suggest that the following variables can be considered as significant predictors of
attrition: household head profile (i.e., sex, age and its square, educational attainment
particularly elementary- and secondary-level education, employment), household size and its
square, urban/rural, and labour market shocks and attrition rate within the province 6. The
results of the post-estimation Wald test and F-test for attrition7, revealed that attrition in thehousehold-level panel data was non-random, suggesting that attrition bias needs to beaccounted for. Inverse probability weights were calculated as the ratio of predictedprobabilities from the unrestricted attrition probit to predicted probabilities from therestricted attrition probit (without the auxiliary variables). In all estimations in this paper,
these inverse probability (or attrition) weights are used to assign more weight to households
who remained in the panel.
Our methodology of estimating vulnerability to poverty is an extension of Günter andHarttgen (2009) based on Chaudhuri et al.’s (2002) method which involves estimation of
expected mean and variance in household’s welfare measure using cross-sectional data. In
our study, this is extended by applying it to the panel data with hierarchical structure and by
taking into account observable shocks in the prediction of log of per capita income (Échevin,2013). Following Chaudhuri et al., we assume 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 variance of residuals at each level is regressed on the aforementioned
covariates (excluding the shock variables)8, as in the following:13

 e2 tij  xT(1)tijα (1)  xT( 2)ij α ( 2)  xT(3)tj α (3)(4)

 u2 0ij  xT( 2)ij δ( 2)  xT(3)tj δ(3)(5)

 v2 0 j  xT(3)tj γ (3)(6)

 s2 tij  xT(1)tij θ(1)  xT( 2)ij θ( 2)  xT(3)tj θ(3)(7)

where: stij  etij  u0ij  v0 j .
Interactions within/across different levels are included in equations (4), (5) and (7) whileinteractions among level-3 covariates are included in equation (6) in cases where equation (1)or (1’) is estimated with interactions. Based on these equations, the expected variances are
estimated: unobservable idiosyncratic variances ˆ etij and ˆ u 0 ij ; covariate variance ˆ v0 j ;222

and, total variance ˆ stij . The conditional probability of being poor, or vulnerability to2poverty, of household i in province j at time tis estimated as follows:Vˆtij  Pˆ ln ytij  ln z x

T
tij



 ln z  ln yˆ 

tij 
 

2


ˆ tij




(8)

where:  denotes the cumulative density of the standard normal distribution; ln z is thelog of poverty threshold, z; ln ŷtij is log of per capita income of household predicted byequation (1); and, ˆ tij is the expected total variance of residuals in equation (5).
2

Vulnerability estimation is also conducted separately for different components of variance inincome, namely: idiosyncratic variances ˆ etij and ˆ u 0 ij , and covariate variance ˆ v0 j . In222

the case where the FE model is estimated, only the total vulnerability estimate is derived
because the variance cannot be decomposed into idiosyncratic and aggregate components.

14

It is noted here that the error terms at each level contain not only stochastic innovation(i.e., risk or shock) in the income-generating process, but also non-stochastic heterogeneity inthe income-generating process as well as measurement errors. If the error terms at each levelcontain non-stochastic heterogeneity and measurement errors, this will make the income
distribution in the equation (8) more widespread (as the denominator, ˆ tij2 , increases) andwill reduce the vulnerability measure ( Vˆtij ), rather than increase it. While estimating varianceterms by household and other characteristics in equations (4)-(7) will mitigate this problem,
our inability to distinguish between stochastic error terms and non-stochastic error term or
measurement error is admittedly a limitation of our study. However, the same limitation is
also applied to a number of studies on vulnerability drawing upon Chaudhuri et al.’s (2002)
method we have reviewed in Section 2. We will thus implement an alternative method basedon the FE model for the first differenced household income.
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:

Vt* k ,ij  1  P ln ytij  ln z k(9)*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 z  is the probability of having an income above
the poverty threshold in any given year. The vulnerability threshold of 0.5, the mostcommonly used threshold in the empirical literature (Pritchett et al., 2000; Kühl, 2003; Zhang
and Wan, 2006), is adopted in our study with a time horizon 3 years. Given equation (9), theestimated vulnerability threshold at time t to fall below the poverty threshold (at least once)in the next 3 years is 0.2063.
15

A household is considered as poor (non-poor) if its per capita income is below (above) thepoverty threshold. The chronic poor are referred to as households that are persistently poor
from 2003 to 2009. The transitory poor are households that became poor once or twice
during the period 2003-2009. This group is further disaggregated into two sub-groups: thehouseholds which were in poverty in 2003 but escaped from poverty in 2006 or later(‘moving up’) and thosenot in poverty in 2003 but slipped down into poverty in 2006 or later(‘slipping down’). The never poor are referred to as the households which were consistently
non-poor throughout the period. The vulnerability status is identified in a similar way. A
household is considered vulnerable (not vulnerable) if its estimated vulnerability to poverty is
below (above) the vulnerability threshold. The major vulnerability groups of households(namely: highly vulnerable, moderately vulnerable, less vulnerable, and not vulnerable) aredefined based on the number of times a household is classified as vulnerable. Moreover, themoderately vulnerable and the less vulnerable households can be collectively known as‘relatively vulnerable’.
As a robustness check, we have derived the predicted household income per capita in2012 as a sum of (i) the actual household income per capita in 2009 and (ii) the predicted
income change from 2009-2012 based on the prediction of the FE model for the firstdifferenced income for 2003-2006 and 2006-2009 (Equation (3’)). Then a household isdefined as vulnerable or not depending on whether its predicted income per capita in 2012 is
below or above the poverty line. This simple method has an advantage of modelling theunobservable household heterogeneity fixed over time. Hence, the prediction of incomechange reflects not only observable and time-variant household and community
characteristics but also unobservable household heterogeneity. However, this method
assumes that the income growth is deterministic in a sense that income growth derived as aprediction using the data in 2003-2009 will follow the same trend in 2009-2012.

16

Decomposition of the vulnerability into aggregate and idiosyncratic components is not
feasible with this method. Given these limitations, we will use two different methods tocharacterise household vulnerability.

5. Empirical results
The results of RC and FE models are presented in Table 3. A dependent variable is log
household income per capita (Models 1-4) or first difference of log household income percapita (Models 5-6). The first two columns of Table 3 show the results of RC model without
and with interaction terms, based on ‘within-between’ formulation (Bell and Jones, 2015).9
The next two columns provide those of Models 3 and 4, FE models without and withinteraction terms (e.g., household characteristics and time-varying province-level variables).
The last two columns show the result of Models 5 and 6, FE model applied to the firstdifferenced income. Attrition bias is corrected in all cases in Table 3 by using the method ofFitzgerald et al. (1998). The key results are discussed selectively below.
(TABLE3 to be inserted)
Highly significant variables include education of household head (positive for Models 1, 2
and 4; the interaction is positive for Model 3), household size (negative) and its square(positive), dependency ratio (negative for Models 1-3, 5 and 6; an interaction with household
size is significant and negative for Models 4), and regional dummies. Households with moreeducated heads tend to have higher per capita income than those with less-educated. A larger
household tends to have a lower per capita household income with some non-linear effect,
while dependency ratio is also considered as an important predictor of household’s wellbeing. The presence of more children in a household implies a lower share of adult membersin employment, which limits the earning potentials of that household.

17

On other results, female-headed households are found to have relatively higher income
than male-headed ones (Models 1 and 2). Interestingly, many female-headed households in
the Philippines are heavily dependent on cash receipts or support (either from abroad ordomestic sources, but chiefly remittances from abroad).10 Miralao (1992) compared maleand female-headed households and found that the latter, on average, have higher annual
income, are smaller in size, have older heads, and have higher share of property and rental
income than the former, while a male head is more likely to be in the labour market. Our datasuggest that remittances (regardless of the source) are usually higher in value because,
apparently, Filipinos are willing to leave their households only for better opportunities, e.g.,higher-paying jobs. However, as pointed by Miralao (1992), female-headed households arehighly heterogeneous and there exist very poor female-headed households that should be
supported by public policies. Our results cannot be generalised in a broader context ofdeveloping countries as female-headed households are generally poorer and more vulnerable
in other developing countries (e.g. Buvinić and Gupta, 1997, for Chile; Gangopadhyay and
Wadhwa, 2004, for India).
Households residing in provinces that experienced rainfall and fuel price shocks tend tohave relatively lower income (Models 1-4), while the rainfall shock tends to negatively
influence the income growth (Model 5). Because a majority of the working poor are engaged
in agriculture (Reyes and Mina, 2013) and the agriculture sector is considered highly
vulnerable to climate-related disasters, frequent occurrence of extreme weather events is
expected to reduce income. Many households are also negatively affected by fuel priceshocks through a number of channels. For instance, large increases in fuel prices could leadto higher transportation costs faced by entrepreneurs that regularly transport their produce tourban centres, or higher variable costs faced by employers that could mean reduction inworkers’ wages.

18

On the results of idiosyncratic shocks, having fewer overseas contact workers (OCW)
members in the household would lead to lower household income per capita for Models 1
and 2. This is understandable as the income from OCW would be a valuable source tosupplement household income or mitigate the household income shocks. But they are notsignificant in FE models (Models 3-6). Having more members with non-permanent orvulnerable job are associated with higher income for Models 3-4. This implies that theincrease in adult members in employment – even if they work as temporary workers invulnerable employment – will have a positive effect on the household income, while an
overall increase in household size or dependency ratio tends to reduce it.
A number of interaction variables have significant effects on income (Models 2 and 4).
The income disparity between female- and male-headed households, in favour of the former,
is observed in certain regions. This income disparity, however, does not hold when the headis highly educated (Model 2). Most of other interaction terms are statistically significant.11
Decomposition of poverty and vulnerability to poverty (by degree and by source), using
the vulnerability 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 study isinterpreted as the household’s probability of falling into poverty at least once in the next 3
years.
(TABLE4 to be inserted)
The results (displayed in Table 4, based on Model 2) show that 37.7 percent of panel
households are classified as vulnerable at least once in any of the periods covered, i.e., 2003,
2006 and 2009 (sum of ‘highly vulnerable’ and ‘relatively vulnerable’ households). Around15.9 percent of panel households are classified as vulnerable to unobservable covariate
shocks while around 34.5 percent are vulnerable to unobservable idiosyncratic shocks. This19

finding implies that households have a higher probability of falling into poverty when facedwith idiosyncratic shocks than when faced with covariate shocks. That is, they are more
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, 85.9 percent of
the chronic poor and 54.4 percent of the transitory poor are classified as vulnerable tounobservable idiosyncratic shocks in at least one of the periods covered. However, 62.3
percent of the chronic poor and 24.6 percent of the transitory poor are found to be vulnerable
to unobservable covariate shocks. Notably, more chronic and transitory poor households arevulnerable to unobservable idiosyncratic shocks than to unobservable covariate shocks. On
the other hand, a majority of the never poor are not classified as vulnerable in any of theperiods covered. Only 17.5 of the never poor are considered as vulnerable.
Our result that households are more vulnerable to idiosyncratic shocks rather thancovariate shocks - the pattern of which is broadly same at different income percentiles implies that there is imperfect risk-sharing because under perfect risk-sharing households
should be able to insure against idiosyncratic shocks, but not covariate shocks. Our result is
not specific to the Philippines and broadly consistent with the evidence from other parts ofthe developing world. For instance, it is consistent with Gaiha and Imai’s (2009) study onrural India which shows that the idiosyncratic component is much larger than the aggregate
component in household vulnerability, as well as with a number of empirical studies that
rejected the perfect risk-sharing hypotheses (e.g. Townsend, 1994; Ravallion and Chaudhuri,1997). Our result rather suggests that without policy interventions targeting households it is20difficult for a household to insure the idiosyncratic shocks. Possible policy options are
Conditional Cash Transfers (CCTs) (Skoufias, 2007) or microfinance (Feigenberg et al.
2013). Table 5 implies that the policies which develop infrastructure and irrigation facilities
at the village or province levels are likely to reduce household vulnerability, but these may be
more effective in reducing aggregate vulnerability, rather than idiosyncratic vulnerability.
If we use the FE model (Model 4, applied to log household income per capita in levels) inderiving vulnerable estimates, however, almost all the households (99.8%) are classified asvulnerable, while all the chronic and transitory poor households are classified as vulnerable.
It can then be inferred that the FE model cannot take account of time-invariant covariates(e.g. regional dummy variables) and unobservable heterogeneous effects (or randomcoefficients and intercepts) at the aggregate or province level. These factors are likely to becontained in the error terms and thus variances of the FE models are estimated to be higher
than those of the RC models.
Furthermore, we have used an alternative method to define vulnerability according towhether household’s per capita income is below the poverty line on the basis of the predicted
household income in 2012 as a sum of (i) the actual household income per capita in 2009 and(ii) the predicted household income per capita using the FE model for the first differenced
income. In this case 19.4% of the households are classified as vulnerable, while 80.6% wereas non-vulnerable in 2009 (Table 4). Most of the vulnerable households are either chronicallypoor or transitory poor, implying that the vulnerability status based on the alternative method
is positively correlated with the long-term poverty status.
In the last panel of Table 4, we have cross-tabulated vulnerability statuses based on
different measures to examine how they are correlated. 17.2% of the total households wereclassified as vulnerable in both measures and 60.1% of the households were non-vulnerable
in either of these two measures. On the other hand, 20.5% (= 3.3% + 17.2%) of the21households were either highly or relatively vulnerable based on the RC model but nonvulnerable based on the FE model for the first differenced income, while 8.9% (= 6.6% +2.3%) of the households are non-vulnerable based on the RC model but vulnerable based on
the FE model. We can conclude that these two measures are broadly correlated with eachother.
It is generally difficult to determine whether the RC (or RE) model or the FE model
should be selected, but a few recent studies have questioned the validity of the FE model
under certain circumstances. For instance, Gibbons et al. (2016) have replicated recent
influential papers published in American Economic Review and found that, in the presence ofheterogeneous treatment effects, the FE model tends to produce an inconsistent estimator of
the sample-weighted average treatment effect (SWE). The RC model offers a way toincorporate the heterogeneous group effect. Clarke et al. (2015) carefully compared the FEmodel and the RE model (two-level hierarchical linear regression model) and concluded that“when the available data on higher-level units are rich, RE models can be built that adjust forhigher-level selection” and “heterogeneous treatment effects are common and the SWE isoften statistically and economically different from the FE estimate” (p.275). They also
argued that “it is important to take a pragmatic view of what can reasonably be achieved byanalysing data from observational studies, whichever approach is used.” The choice between
FE and RE (or RC) models is essentially an empirical question (Rabe-Hesketh and Skrondal,
2012). Bell and Jones (2015) showed by Monte-Carlo simulations that “the RE approach is,
in fact, nearly always preferable” (p.149) if the ‘within-between’ formulation is used. They
argue that:
understanding the role of context (households, individuals, neighborhoods, countries,etc.) that defines the higher level, is usually of profound importance to a given researchquestion -one must model it explicitly - and requires the use of an RE model thatanalyzes and separates both the within and between components of an effect explicitly,
and assesses how those effects vary over time and space rather than assuming
heterogeneity away from FE (Bell and Jones, 2015, p.149).
22

In this regard, while we have shown the results of both FE and RC models, we will take the
RC model as our preferred model to derive the vulnerability estimates.
In order to characterise vulnerability in comparison with poverty, we have derived thepredicted value of vulnerability, Vˆt 3,ij in the equation (9), a probability of the household*

falling into poverty in the next three years for each household in 2009 (i.e., future
vulnerability) and estimated it by initial conditions, that is, covariates at household andprovince levels in 2003 to avoid the issue of endogeneity using Ordinary Least Squares(OLS) with heteroscedasticity-consistent standard errors. The result is shown in the firstcolumn of Table 5. To compare this with determinants of various categories of poverty, we
have also estimated a (robust) probit model for each of the following four categories:‘Chronic poverty’, ‘Moving up’ (from poverty in 2003 to non-poverty in 2006 and/or 2009),‘Slipping down’ (from non-poverty in 2003 to poverty in 2006 and/or 2009), and ‘Neverpoor’ (the second to the fifth column in Table 5) using the same set of covariates.
(TABLE 5 to be inserted)
We have highlighted the results selectively. First, the determinants of vulnerability andchronic poverty are broadly similar, reflecting the fact that the chronically poor in the past
are likely to be also vulnerable to poverty in the future. The factors which are correlated tohousehold vulnerability and chronic poverty include: (i) having a younger and less educatedhead; (ii) a higher dependency ratio; (iii) being located in rural areas; and (iv) lack of accessto irrigation. A larger household with more members is to less likely to be vulnerable (assuggested by a negative and significant coefficient estimate for vulnerability) and is more
likely to move up to “non-poverty” (a positive and significant estimate for “moving up”), butmore likely to be chronically poor (positive and significant for “chronic poverty”). This result
appears to be consistent with negative and significant estimates for (i) (having) ‘more23

members engaged in vulnerable employment’ in the first column to show the determinants of“vulnerability”, as well as for (ii) (having) “more members with non-permanent jobs” in thefirst column, whilst having more members in vulnerable employment prevented them from“moving up” from poverty to non-poverty. So these factors may serve as insurance for thehousehold coping with risks, but may not help them escape from the poverty situations. To
be able to escape from poverty, households may have to have more members with secure
employment.
Second, the factors which are only significantly associated with vulnerability, but not withchronic poverty, include lack of access to major transport infrastructure and lack of job
security. Third, lack of economic and social infrastructure is - contrary to our expectations associated with the higher probability of ‘moving up’. On the other hand, even if households
were not initially poor, they tended to slip down into poverty if they did not have access totransport infrastructure and/or irrigation facilities, or have more members in vulnerable
employment. Finally, consistent with our expectations, better education, a smaller household
size and a lower dependency ratio, living in urban areas, having access to better infrastructure
and/or better education are main determinants of being ‘never poor’.

6. Concluding remarks
The vulnerability to poverty of Filipino households is estimated in this study using a threelevel longitudinal model and three-wave household-level panel data in the Philippines.
Chaudhuri et al.’s (2002) method of estimating households’ vulnerability to poverty - which
has been widely adopted in numerous empirical works on vulnerability based on crosssectional data - has been further extended in our study by applying the multilevel longitudinalrandom coefficient (RC) model to the panel data. We have corrected heterogeneity bias using
Bell and Jones’s (2015) ‘within-between’ formulation. This leads to our specific24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 in vulnerability estimates by using a multilevel model; and,characterising household poverty situations in both vulnerability and poverty persistence
dimensions by utilising the panel data. As a robustness check we have applied the fixedeffects (FE) model for the level of household income per capita and its first-differences. In
case the FE model is applied for the first differenced income, the predicted income is derived
as a sum of the actual income in 2009 and the predicted growth in income. We define thevulnerability status according to whether the predicted income is below the poverty line or
not in 2012.
Interestingly, the estimated multilevel model contains a set of significant and empiricallysound predictors of household income. Consistent with the findings from local poverty
studies (e.g. Balisacan, 1997; Tabunda, 2000), profile of heads (education, sex, and age),composition (household size and dependency ratio) and location (urban/rural and region)
significantly explain the variation in household income. Observable covariate (fuel price andrainfall) and idiosyncratic (labour market) shocks also have significant (negative) impacts onhousehold income.
Further interesting findings can be drawn from the empirical results on our vulnerability
estimates based on the RC model. Around 37.7 percent of the panel households are classified
as vulnerable at least once in any of the periods covered. Only 15.9 percent of the panel
households are vulnerable to unobservable covariate shocks while 34.5 percent arevulnerable to unobservable idiosyncratic shocks. Decomposition of poverty and vulnerability
to poverty revealed that the chronic and the transitory poor, and even the never poor, aremore vulnerable to unobservable idiosyncratic shocks than to unobservable covariate shocks.
Impacts of idiosyncratic shocks might have been more direct and more specific compared to25those of covariate shocks. We have noted that the vulnerability statuses based on the FEmodel applied to the first differenced income are broadly consistent with those based on the
RC model.
Among a number of policy implications derived by our 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. More-educatedindividuals are likely to be more adaptive to varying circumstances and have higher coping
capability (Glewwe and Hall, 1998; Christiaensen and Subbarao, 2005). This is confirmed byour results comparing the determinants of vulnerability, chronic poverty, transitory poverty
and chronic non-poverty. Clearly, policies and programs aimed at human capital investment
are very important government interventions, especially in developing countries like thePhilippines. Meanwhile, the government should provide adequate safety nets to poor andvulnerable households in order to protect them against various economic, natural and othershocks. These policies could include conditional cash transfers or microfinance to help
communities or villages to strengthen risk-insurance mechanisms. Other policies are
employment and skills training programs, which can be implemented on a regular basis andcan be intensified in times of crisis. Furthermore, policies to improve transportation
infrastructure and/or irrigation facilities are also deemed important for reducing vulnerability.

26

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31

TABLE 1 Selected macroeconomic indicators, Philippines, 2003-2009
Indicator
2003
2004
2005
2006
Gross Domestic Product (GDP) growth rate, %
5.0
6.7
4.8
5.2
Agriculture
4.7
4.3
2.2
3.6
Industry
4.3
5.2
4.2
4.6
Services
5.5
8.3
5.8
6.0
Gross National Income (GNI), at constantprices
4,913
5,262
5,630
5,911
Current account balance, % of GDP
0.3
1.8
1.9
4.4
Net factor income from abroad, at constantprices
904
985
1,149
1,195
Exchange rate (PhP/US$), average of period
54.20
56.04
55.09
51.31
Inflation, %
2.3
4.8
6.5
5.5
Population growth rate, %
2.0
1.9
1.9
1.9
Source: Key Indicators for Asia and the Pacific 2013, Asian Development Bank.
PhP = Philippine peso; US$ = US dollar

32

2007
6.6
4.7
5.8
7.6

2008
4.2
3.2
4.8
4.0

2009
1.1
-0.7
-1.9
3.4

6,276
4.8

6,590
2.1

6,989
5.6

1,248
46.15
2.9
1.8

1,353
44.32
8.3
1.8

1,692
47.68
4.2
1.8

TABLE 2. 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
Location
Urban/rural
Other aggregate-level variables
Transportation infrastructure index
Economic and social infrastructureindex
Irrigation development
Agriculture index
Utilities index

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

Urban/rural indicator: 1 if urban; 0 if rural

0.33

0.47

0

1

0.33

0.47

0

1

0.33

0.47

0

1

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

0.22

1.24

-3.11

4.03

0.34

1.20

-3.13

4.24

-0.03

1.35

-2.39

5.45

-0.03

1.28

-2.34

4.13

0.07

1.42

-2.26

5.73

50.91

23.09

6.46

155.98

52.72

23.86

6.56

160.52

55.64

23.57

7.50

161.80

0.83

1.11

-1.73

4.19

-0.38

0.78

-1.73

2.38

-0.43

0.79

-1.56

2.35

-0.14

1.08

-3.21

1.97

0.03

0.91

-2.80

1.68

0.10

1.26

-5.57

3.83

Definition

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

Mean

Min

Max

Mean

0.72

Std
Dev
0.77

-1.39

4.85

Min

Max

Mean

-1.69

4.73

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.

33

Min

Max

0.97

Std
Dev
0.76

-1.51

5.19

TABLE3. Results of the Random Coefficient Model (RC) and Fixed-effects (FE) Model(with correction of attrition based on Fitzgerald et al. (1998))
RC Model:‘within-between’
formulation
Dependent Variable
Explanatory Variables

log household income pc

FE ModelLevel Data

FE ModelFirst Differenced Datalog household income pc

D.log household income pc

Model 1Model 2Model 3Model 4Model 5Model 6

0.0595(0.0032)***0.0605(0.0026)***

0.0652(0.0016)***

0.0672(0.0018)***-0.0277(0.0151)*-0.0376(0.0398)-0.1970(0.0093)***-0.0989(0.0151)***0.00887(0.00073)***0.00179(0.00073)-0.337(0.0276)***-0.850(0.0499)***-0.2646(0.0188)***--0.1998(0.0106)***--0.3161(0.0245)***--0.2433(0.0239)***--0.2427(0.0239)***-0.0266(0.0032)***-0.0091(0.0008)***-0.0322(0.0039)***-0.0103(0.0018)***-0.0092(0.0020)***-0.1007(0.0592)*--0.3355(0.0284)***-0.0016(0.0708)--0.2062(0.0720)***--0.1977(0.0719)***--0.0506(0.0140)***0.00822(0.0031)***0.0138(0.0039)***-0.00004(0.00003)-0.0001(0.00003)***-0.1700(0.0603)***- d)-0.0189(0.0189)0.0084(0.0032)***--0.0635(0.0709)0.0054(0.0036)--0.0201(0.0151)
0.0235(0.0001)**--0.0206(0.0398)
0.0232(0.0095)**-

0.00003(0.00003)-0.00009(0.00003)***-0.0001(<0.000)***--0.0001(<0.000)**--0.0002(0.0001)**--0.0002(0.0001)***-0.141(0.0119)***0.400(0.0146)***0.889(0.0226)***0.1820(0.0246)***
0.5130(0.0339)***0.7830(0.110)***0.0036(0.0151)
0.0158(0.0216)0.0003(0.0383)

0.0547(0.0256)**0.0804(0.0393)**
0.0664(0.1577)0.0408(0.0365)0.01823(0.051)-0.0815(0.0505)

0.0422(0.0365)0.0172(0.0514)0.1756(0.1533)

0.253(0.0159)***Yes

0.0623(0.0396)
Yes

-

-

-

-

No

No

No

No

-0.0259(0.0251)-0.00575(0.0219)-0.0133*(0.0080)*0.0492(0.0318)0.0013(0.0015)0.00167*(0.0009)-0.0135(0.0305)-0.00575(0.0219)0.0103(0.0114)0.0199(0.0169)- d)-0.0323(0.0155)**--0.0290(0.0167)*--0.4642(0.0472)--0.0508(0.0473)-0.0206(0.0091)**--0.0422(0.0252)*--0.0291(0.0113)***--0.0290(0.0113)***-0.0002(0.0010)--0.0001(0.0012)-0.0028(0.0023)-0.00160(0.00230--0.0223(0.0080)***--0.0043(0.0100)--0.0077(0.0252)-

0.0055(0.0254)-0.0120(0.0055)**-

0.0082(0.0059)---

Fixed part
Time
Household composition
Household size a)
Household size (between) b)
Square of household size a)
Square of household size (between)b)

Dependency ratio a)
Dependency ratio (between) b)
Household head profile
Sex
Age

a)

Age (between) b)
Square of age a)
Square of age (between)

b)

- d)

Educational attainment c)
At least elementary graduate
At least secondary graduate
At least college graduate

Location
Urban/rural
Regional Dummies
Aggregate-level variables
Transportation infrastructure index a)Transportation infrastructure (between) b)
Economic and social infrastructure indexa)Economic and social infrastructure(between) b)
Irrigation development index a)
Irrigation development index (between)
Agriculture index a)Agriculture index (between) b)
Utilities index a)
Utilities index (between) b)

b)

- d)
- d)
- d)
- d)
-

d)

- d)
-

d)

- d)
- d)

34

Idiosyncratic shocks
More jobless members
More members engaged in vulnerable
employment
More members with non-permanent jobs
Fewer overseas contract worker (OCW)
members
Covariate shocks
Rainfall shock
Rice price shock
Fuel price shock

0.00807(0.0086)0.00102(0.0086)-0.00029(0.00903)
0.109(0.0198)***0.01191(0.0087)
0.0168(0.0105)
0.0021(0.0091)
0.101(0.0197)***0.0057(0.0091)
0.0031(0.0085)0.0147(0.0089)*-0.0147(0.0221)0.0099(0.0092)
0.0187(0.0107)*0.0181(0.0089)**-0.0148(0.0219)-0.0072(0.0203)-0.0372(0.0195)*
0.0322(0.0215)0.0011(0.0512)0.1283(0.1119)-0.1083(0.0712)-0.0476(0.0504)-0.0656(0.0564)-0.0679(0.0132)***0.00762(0.0819)-0.0374(0.00661)***-0.0425(0.0169)**-0.0323(0.07469-0.0363(0.0075)***-0.0708(0.0119)***0.0007(0.0548)-0.0459(0.0063)***-0.054(0.0155)***0.0020(0.0536)-0.0404(0.0075)***-0.1197(0.0289)***0.0407(0.0289)-0.1271(0.0455)0.2419(0.2374)
0.0766(0.1003)-0.1244(0.1120)TABLE 3. (continued).
Variable

RC Model:‘within-between’
formulation

FE ModelFirst Differenced Data

FE ModelModel 1Model 2Model 3Model 4Model 5Model 6[Time × Region Dummies or Province
Variables][Household Characteristics –crossInteractions][Household Characteristics X Region
Dummies][Household Characteristics X Province
Variables][Region Dummies X Province Variables]

No

Yes

No

Yes

No

Yes

No

Yes

No

No

No

No

No

Yes

No

Yes

No

No

No

Yes

No

Yes

No

No

No

Yes

No

Yes

No

No

Sex X Age a)

-

-

-

-

0.0023(0.0012)*--

Sex X Age (between) b)--

Sex X Education, College

-

-

-

-

-

Education, College X Age (between) b)--0.0246(0.0608)0.0018(0.0028)--

Education, College X Age a)--

Education, Elementary X Rain

-

-

-

-

-

Education, Secondary X Household size
(between) b)
Education, College X Household size a)--0.0501(0.0226)**-0.0120(0.0060)**--

Education, Secondary X Household size

-

-

-

-0.0287(0.0109)***-0.0177(0.0272)-0.0177(0.0272)--

Education, College X Household size
(between) b)
Education, College X Transport
Infrastructure a)
Education, College X Transport
Infrastructure (between) b)
Household size a) X Dep. Ratio a)

-

-

-

-

-

-

-

-0.0684(0.0132)***--

Household size (between) b)X Dep. Ratio(between) b)
Irrigation a) X Utility Index a)--

Irrigation (between) X Utility Index(between) b)
Fuel price shock XMore members
engaged in vulnerable employment
Time X Education (college)-

0.0013(0.0005)***
-

-

b)

-

-

-

-0.0437(0.0170)**---0.0019(0.0017)0.0310(0.0010)***0.163(0.0417)***0.011(0.0025)***0.00418(0.00172)**-0.04839(0.0235)**-0.02229(0.0073)***-0.02416(0.00738)***-0.02839(0.01084)***-0.03337(0.01173)***-0.18925(0.05737)***-0.06311(0.01876)***-0.08814***(0.02992)-0.18906(0.02084)***-0.01126(0.00347)***0.00110(0.00045)***-0.04435(0.01684)***--

Time X Rainfall

-

-

-

-

-

Time X More jobless members

-

-

-

-

-

-0.0656(0.0160)**-0.0655(0.0368)*0.0147

Interactions

a)

-

-

-

-

35

-

(0.0145)
Time X More members engaged invulnerable employment
Time X More members with nonpermanent jobs
Fewer overseas contract worker (OCW)
members
Rainfall X Farm size

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

Agriculture index a)X More joblessmembers
Rainfall X More members engaged invulnerable employment
More members with non-permanent jobs
X Fewer overseas contract worker(OCW) members
Education (Secondary) X More members
engaged in vulnerable employment
Intercept

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

0.194
(0.135)0.5219(0.1068)***1.0066(0.1097)***1.864(0.2040)***1.0066(0.1097)***0.0249(0.0438)
0.5888(0.3649)0.0004(0.0001)***0.0265(0.0081)***-0.0025(0.0009)***0.0002(0.00007)***0.00716(0.0034)**-0.0008(0.0004)*

-

-

-

-

-

-

-

-

-

-

-

-

0.0027(0.0003)***0.2973(0.0155)***-0.0164(0.0020)***0.0027(0.0003)***0.2859(0.0152)***-0.0167(0.0019)***

-

-

-

-

-

-

-

-

-

-

-

-

0.0811(0.0057)***0.1152(0.0034)***0.0862(0.0056)***0.0805(0.0056)***
0.1130(0.0033)***0.0869(0.0055)***

-

-

-

-

-

-

-

-

-

-

-

-

0.0000(0.0000)
0.00003(0.00002)*

0.0012(0.0008)-0.0088(0.0179)
0.0860(0.0622)0.3396(0.1386)**

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)

36

TABLE 4.Poverty and vulnerability status of panel households, by degree and by source
Vulnerability status

Chronic poor

Transitory poor

Never poor

All

Highly vulnerable

56.3

19.9

2.4

13.9

Relatively vulnerable

32.1

38.9

15.1

23.8

Not vulnerable

11.6

41.2

82.5

62.4

Highly vulnerable

58.7

20.6

2.5

13.9

Relatively vulnerable

3.6

4.0

0.8

2.0

Not vulnerable

37.8

75.4

96.7

84.1

Highly vulnerable

56.3

19.9

2.4

13.9

Relatively vulnerable

29.6

34.5

12.4

20.6

Not vulnerable

14.1

45.6

85.2

65.6

Based on RandomCoefficient Model
Total vulnerability

Covariate vulnerability

Idiosyncratic vulnerability

Based on Fixed-Effects Model
Highly vulnerable

11.1

5.4

2.8

4.5

Relatively vulnerable

88.9

94.6

96.9

95.3

Not vulnerable

0.0

0.0

0.3

0.2

Based on Fixed-Effects Model for the first differenced log household income per capita (based on Model 6 of Table 3)
Vulnerable
Not vulnerable

Vulnerability status

9.9

8.7

0.8

2.4
19.3
58.9
Based on Fixed-Effects Model for the first differenced log
household income per capita (based on Model 6 of Table 3)

19.4
80.6

Vulnerable

Not vulnerable

All

Highly vulnerable

10.6

3.3

13.9

Relatively vulnerable

6.6

17.2

23.8

2.3

60.1

Based on Random Coefficient Model
Total vulnerability

62.4
Not vulnerable
Source: Authors’ estimates using the 2003-2006-2009 FIES panel data. Only sample households included in the estimation
sample were included (n = 5,199).

37

TABLE 5. Determinants of Vulnerability, Chronic Poverty, “Moving Up”, “Moving Down”
and “Never Poor” (based on covariates in 2003)
Dependent Variable:
Explanatory Variables
ModelHousehold head profile
Sex
Age
Square of age
At least elementary graduate

At least secondary graduate

At least college graduate
Household size
Square of household size
Dependency ratio
Urban/rural
Economic and social infrastructure index
Utilities index
Agriculture index
Transportation infrastructure index

Irrigation development index

More jobless members
More members engaged in vulnerable
employment

More members with non-permanent jobs
Fewer overseas contract worker (OCW)
members

OLS“Chronic
Poverty”
Probit“Moving
Up”
Probit“Slipping
Down”
Probit“NeverPoor”
Probit0.0161***(0.0056)-0.0146***(0.0011)0.00012***(0.000010)

0.0898(0.111)-0.0532***(0.0135)0.000432***(0.000132)

0.11(0.0799)
0.0157(0.0118)-0.000127(0.000113)

0.0905(0.0727)-0.0342***(0.01000)
0.000293***(0.0000933)-0.161**(0.0686)
0.0412***(0.00973)-0.000323***(0.0000920)-0.0461***-0.427***-0.174***-0.0691

0.458***(0.0060)(0.0634)(0.0555)(0.0536)(0.0517)-10.141***-0.948***-0.518***-0.361***1.131***(0.0062)-0.213***(0.00831)-0.0115***(0.00436)
0.00274***(0.000381)
0.294***(0.0123)-0.0828***(0.00447)-0.00973**(0.00415)-0.00336(0.00205)
0.00124(0.0798).
.
0.175***(0.0675)-0.00247(0.00479)
2.140***(0.182)-0.445***(0.0743)0.0421(0.0447)-0.123***(0.0344)0.0148(0.0658)-1.339***(0.180)0.291***(0.0518)-0.0173***(0.00412)
0.544***(0.138)-0.299***(0.0601)-0.115***(0.0390)-0.0646**(0.0269)
0.00968(0.0621)-1.533***(0.215)-0.0471(0.0431)0.00162(0.00345)
0.221*(0.132)-0.120**(0.0546)-0.0236(0.0383)0.00446(0.0248)0.0374(0.0588)
2.472***(0.181)-0.0758*(0.0436)-0.00347(0.00346)-1.563***(0.123)0.460***(0.0493)
0.0988***(0.0361)
0.0676***(0.0243)-0.0401(0.00310)(0.0422)(0.0365)(0.0347)(0.0313)-0.0216***

0.03680.0558-0.0652**-0.012(0.0426)(0.0396)(0.0357)(0.0329)(0.0307)-0.00659***-0.00335**

0.000288-0.00309**

0.00376***(0.00134)(0.00170)(0.00140)(0.00135)(0.00126)-0.00651-0.0981-0.07540.05980.0563(0.0052)(0.0791)(0.0630)(0.0570)(0.0548)-0.0163***0.0386-0.133**0.0941*-0.000652(0.0052)(0.0688)(0.0597)(0.0549)(0.0520)-0.0163***0.009210.01380.0667-0.0455(0.00544)(0.0711)(0.0598)(0.0569)(0.0530)-0.0269***

.

-0.25

-0.494***0.737***(0.0103).

(0.161)(0.173)(0.155)

Vulnerability

Regional Dummies

Yes
Yes
Yes
Yes
Yes
0.778
-0.399-2.3680.224-1.124(0.398)(0.396)(0.351)(0.299)(0.288)
5096
4655
5199
5199
5199
N
Figures in parentheses are standard errors; *** p<0.001, ** p<0.01, * p<0.05.Statistically significant coefficient estimates areshown in bold. All regressions are based on the Huber-White robust estimators. Source: Authors’ estimates using the 2003
FIES panel data.
_cons

38

2003

2006

2009

Source: GIS-based Socioeconomic Profile of the Philippines, Philippine Institute for Development Studies

FIGURE 1. Poverty incidence and magnitude of poverty among households by province in
the Phillipines39Online Appendices
Online Appendix Table 1. Fitzgerald et al.'s (1998) unrestricted attrition probit model
Dependent variable: Attrition dummy
Variable

Parameter
0.8218 (0.2319)***-0.0375 (0.0273)
0.0258 (0.0024)***

Intercept
Log of per capita income
Attrition dummy
Attrition rate within a province
Household composition
Household size
Square of household size
Dependency ratio
Household head profile
Sex
Age
Square of age
Educational attainmenta/
At least elementary graduate
At least secondary graduate
At least college graduate
Employment
Location
Urban/rural
Regional Dummies
Aggregate-level variables
Transportation infrastructure index
Economic and social infrastructure index
Irrigation development index
Agriculture index
Utilities index
Idiosyncratic shocks-0.2079 (0.0296)***0.0125 (0.0024)***0.0124 (0.0892)-0.1274 (0.0434)***-0.0367 (0.0065)***0.0003 (0.0001)***-0.1277 (0.0402)***-0.0859 (0.0460)*
0.1070 (0.0697)
0.0819 (0.0347)**0.1841 (0.0357)***
Yes-0.0143 (0.0243)-0.0192 (0.0164)-0.0009 (0.0010)
0.0031 (0.0254)
0.0110 (0.0281)

More jobless members
More members engaged in vulnerable employment
More members with non-permanent jobs
Fewer overseas contract worker (OCW) members
Covariate shocksc/
Rainfall shock
Fuel price shock
Figures in parentheses are robust standard errors; *** p<0.001, ** p<0.01, * p<0.05.
a/base category: At most elementary level-0.1111 (0.0405)***-0.5252 (0.0469)***-0.5475 (0.0497)***-0.4389 (0.1017)***-0.0604 (0.1051)
0.1345 (0.1834)

Online Appendix Table 2. Becketti, Gould, Lillard and Welch (BGLW) pooling test for attrition
Dependent variable: Log of per capita income
VariableIntercept
Log of per capita income
Attrition dummy
Attrition rate within a province
Household composition
Household size
Square of household size
Dependency ratio
Household head profile
Sex
Age
Square of age
Educational attainmenta/
At least elementary graduate
At least secondary graduate

Parameter
0.0249 (0.1258)-0.0249 (0.1258)
0.0034 (0.0013)***-0.1336 (0.0159)***0.0045 (0.0012)***-0.6821 (0.0451)***-0.0806 (0.0265)***0.0133 (0.0038)***-0.0001 (0.0000)*
0.1907 (0.0187)***0.5209 (0.0225)***40

At least college graduate
Employment
Location
Urban/rural
Region Dummies b/
Aggregate-level variables
Transportation infrastructure index
Economic and social infrastructure index
Irrigation development index
Agriculture index
Utilities index
Idiosyncratic shocks

1.1751 (0.0378)***0.2094 (0.0175)***0.1047 (0.0190)***Yes-0.0090 (0.0120)
0.0210 (0.0084)**0.0014 (0.0005)***-0.0398 (0.0131)***0.0264 (0.0135)*
0.0339 (0.0202)*
0.0298 (0.0199)

More jobless members
More members engaged in vulnerable
employment
More members with non-permanent jobs
Fewer overseas contract worker (OCW)
members
Covariate shocksc/
Rainfall shock
Fuel price shock-0.0241 (0.0188)
0.2976 (0.0480)***-0.1261 (0.0520)**-0.0356 (0.0929)

Online Appendix Table 2. (continued)
VariableInteractions with attrition dummy
Log of per capita income
Household head's sex
Household head's age
Household head's square of age
Household head's educational attainment: atleast elementary graduate
Household head's educational attainment: atleast secondary graduate
Household head's educational attainment: atleast college graduate
Household head's employment
Household size
Square of household size
Dependency ratio
Urban/rural
Regional Dummies
Transportation infrastructure index
Economic and social infrastructure index
Irrigation development index
Agriculture index
Utilities index
Fuel price shock
Rainfall shock

Parameter
1.0000 (0.0000)***0.0806 (0.0265)***-0.0133 (0.0038)***0.0001 (0.0000)*-0.1907 (0.0187)***-0.5209 (0.0225)***-1.1751 (0.0378)***-0.2094 (0.0175)***0.1336 (0.0159)***-0.0045 (0.0012)***0.6821 (0.0451)***-0.1047 (0.0190)***Yes
0.0090 (0.0120)-0.0210 (0.0084)**-0.0014 (0.0005)***0.0398 (0.0131)***-0.0264 (0.0135)*
0.0356 (0.0929)
0.1261 (0.0520)**-0.0339 (0.0202)*-0.0298 (0.0199)

More jobless members
More members engaged in vulnerable
employment
0.0241 (0.0188)
More members with non-permanent jobs
Fewer overseas contract worker (OCW)-0.2976 (0.0480)***members
Attrition rate within a province-0.0034 (0.0013)***Figures in parentheses are robust standard errors; *** p<0.001, **p<0.01, * p<0.05.
a/base category: At most elementary levelb/base category: Caraga; National Capital Region (NCR) was not included in the analysis because it is the only region thatis not composed of provinces. It is composed of four districts, which are composed of cities. The dummy for the
MIMAROPA region (Occidental and Oriental Mindoro, Marinduque, Romblon, Palawan) was dropped because
none of the sample households in that region were
included in the estimation sample.
c/
Rice price shock was dropped from the analysis.

41

Online Appendix Table 3. Results of Likelihood ratio tests for inclusion of randomcoefficients
Likelihood ratio test 1:
Model (without random coefficient) vs. Model (with random coefficient at level 2):

 22 = 89.37, Pr>  2 = 0.0000

LR

Likelihood ratio test 2:
Model (with random coefficient at level 2) vs. Model (with random coefficients at levels 2 & 3):

 42 = 40.54, Pr>  2 = 0.0000

LR

.1
0

.05

Density.15.2

Note:All models have identical fixed-effects specifications.

0

50

100

150

200

Per capita income

0.2

Density.4.6

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

-2

0

2

4

6

Log of per capita income

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

42

2500
2000

2

1500

1

1000

0

0

-2

500

-1

Residuals (level-1)

-2

0

2
Fitted values

4

-2

-1

0
Residuals

1

2

1500
1000

0

500

-1

0

-2

Residual (level-2)

1

2000

2

2500

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

-1

0

1
2
Mean of fitted values

3

4 -2

-1

0
1
Residuals, level(HH_ID)

2

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

43

2000
1500

3
2

0

-2

-1

500

0

1000

1

Residual (level-3)

0.51Prov-level mean of fitted values

1.5 -2-1

0
1
2
Residuals, level(prov)

3

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

Endnotes1

Based on Family Income and Expenditure Surveys, Philippine Statistics Authority.

2

Similar applications include McCulloch and Calandrino (2003) and Zhang and Wan (2006)

for China, and Imai, Gaiha and Kang (2011) for Vietnam.
3

The set of information provided by the LFS July (January) round matches that of the first(second) round of the FIES.
4

The official poverty thresholds, both at the regional and provincial level, are estimated by

the PSA 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,

44

US$1.682, and US$1.735 per capita per day in 2005 PPP, which range between the twointernational poverty lines based on US$1.25 and US$2.
5

There is no pairwise correlation coefficient greater than or equal to 0.60.

6

Attrition rate within the province is also included because it is related to attrition albeit notdirectly related to household income (Baulch and Quisumbing, 2011).
7

Wald test’s Chi2(14) = 754.55 and Prob> chi2 = 0.0000; F-test’s F(33, 7968) = 180.07 andProb> F = 0.0000.
8

Following Günter and Harttgen (2009) and Échevin (2013), only random intercepts at levels

2 and 3 are used in equations (4) to (6). Also, similar to Échevin (2013), only covariates areincluded; thus, excluding observable shocks since these are already captured by the estimatedresiduals.
9

For RC models (Models 1-2 in Table 3) the estimated model with random effects ispreferred to OLS without random effects based on the result of the likelihood ratio test.
Likelihood ratio tests for additional random parameters also supported the inclusion of
random coefficients for the time variable both at household and provincial levels (Online
Appendix Table 3). Meanwhile, the normality assumption of income and residuals are
satisfied at all levels (Online Appendix Figures 1-2). Scatter plots also indicate that outliers
would not create a problem in the analysis.
10

Based on the FIES data, cash receipts both from abroad and domestic sources comprisedaround 25 percent of the total income of female-headed households during the period 20032009. In contrast, cash receipts comprised only 3 to 5 percent of the total income of maleheaded households.
11Interaction terms are reported selectively in Table 3. A full set of the results will be

provided on request.

45