00074918.2013.772939
Bulletin of Indonesian Economic Studies
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
The determinants of poverty dynamics in
Indonesia: evidence from panel data
Teguh Dartanto & Nurkholis
To cite this article: Teguh Dartanto & Nurkholis (2013) The determinants of poverty dynamics
in Indonesia: evidence from panel data, Bulletin of Indonesian Economic Studies, 49:1, 61-84,
DOI: 10.1080/00074918.2013.772939
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Date: 17 January 2016, At: 23:40
Bulletin of Indonesian Economic Studies, Vol. 49, No. 1, 2013: 61–84
THE DETERMINANTS OF POVERTY
DYNAMICS IN INDONESIA:
EVIDENCE FROM PANEL DATA
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Teguh Dartanto*
Nurkholis*
University of Indonesia, Jakarta
We use the ‘spell’ approach to identifying poverty and apply an ordered logit
model to examine the determinants of poverty dynamics in Indonesia, categorising
households as poor, transient poor (–), transient poor (+) or non-poor. Observing
the National Socio-Economic Survey (Susenas) balanced-panel data sets of 2005 and
2007, we found that 28% of poor households are classiied as chronically poor (that
is, remaining poor in two periods) while 7% of non-poor households are vulnerable
to being transient poor (–). Our estimations conirmed that the determinants of poverty dynamics in Indonesia are educational attainment, the number of household
members, physical assets, employment status, health shocks, the microcredit program, access to electricity, and changes in employment sector, employment status
and the number of household members. We also found that households in Java–Bali
are more vulnerable to negative shocks than those outside Java–Bali.
Keywords: poverty dynamics, transient poverty, vulnerability, shocks, government
assistance
BACKGROUND
Poverty in Indonesia has been much researched, but most of this research – for
example, Bidani and Ravallion (1993) – focuses on static poverty and analyses the
proportion of the population falling below a given income threshold at a given
time. Poverty is not, however, a purely static phenomenon (Muller 2002); households currently not poor may later fall below the poverty line, owing to shocks
* We would like to thank the University of Indonesia and the Directorate General of Higher Education, Ministry of National Education, the Republic of Indonesia, for inancing this
research through the National Research Strategic Fund 2010 (DRPM/Hibah Strategis Nasional/2010/I/4024). We would also like to thank Ms Lily Yunita and Mr Usman from the
Institute for Economic and Social Research, University of Indonesia, for their assistance.
We would like to thank Professor Mohamad Ikhsan (University of Indonesia) for providing
Susenas panel data sets. Professor Shigeru Otsubo (Nagoya University) and his seminar
participants provided valuable comments, as did Professor Hal Hill, and other participants of the 2011 Singapore Economic Review Conference, and Assistant Professor Mark
Rebuck. We would also like to thank the three anonymous referees for their constructive
and valuable comments and suggestions, which helped improve the quality of this paper.
Any remaining errors are our responsibility.
ISSN 0007-4918 print/ISSN 1472-7234 online/13/010061-24
http://dx.doi.org/10.1080/00074918.2013.772939
© 2013 Indonesia Project ANU
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62
Teguh Dartanto and Nurkholis
such as crop loss, job loss or death.1 Conversely, poor households may escape
from poverty by gaining employment or a better job (Fields et al. 2003), increasing
their level of education (Herrera 1999) or having access to improved infrastructure (Sawada et al. 2008).
Under President Susilo Bambang Yudhoyono, the Indonesian government
has changed its poverty alleviation policies from a macro, top-down approach
to a community or household participatory approach. It has developed and
implemented several policies to alleviate chronic poverty, including educational
subsidies (Bantuan Operasional Sekolah), scholarships, conditional cash transfers, community empowerment programs (Program Nasional Pemberdayaan
Masyarakat), credits for small and medium enterprises (SMEs) (microinance)
and infrastructure development projects (Program Pengembangan Kecamatan).
The government has also provided social safety nets, including subsidised rice
(Raskin), cash transfers (Bantuan Langsung Tunai) and health insurance targeted
to the poor (Askeskin).2 These policies are intended to address transient poverty
and protect the poor from external shocks. However, the effectiveness of government policies in alleviating poverty is questionable. It can be dificult to evaluate
the impact of poverty alleviation policies in the short term, because many policies
experience a delay between implementation and results. For instance, the impact
of microcredit on SMEs often becomes apparent only after two or more years,
requiring a longer period of observation. Further, it is generally acknowledged
that the impact of investment in human capital (such as education and health) on
household welfare cannot be investigated immediately.
As the poverty incidence can change over time, it is important to conduct a
dynamic analysis to distinguish between chronic poor, transient poor and never
poor; to discover which determinants differentiate among groups; and to evaluate
the effectiveness of government policies in changing poverty status in Indonesia.
The distinction between chronic and transient poverty is important not only for
an accurate measurement of poverty but also for policy implication purposes;
chronic and transient poverty call for different alleviation strategies. In a country
or region where the poverty problem is characterised by the chronically poor,
the appropriate strategy would be to redistribute assets, providing basic physical
and human-capital infrastructure. If the predominant poverty problems relate to
transient poverty, the strategy should be geared towards providing safety nets
and coping mechanisms to reduce the vulnerability of the poor and to help them
return to a non-poor situation (Hulme and Shepherd 2003; McCulloch and Calandrino 2003).
This paper has three objectives. First, it aims to contribute to the literature of
poverty studies. To date, there has been very little analysis of poverty dynamics
in Indonesia – for example, studies that investigate the welfare movements of a
1 For example, Contreras et al. (2004) found that health problems were correlated with
households falling into poverty in Chile. Dercon and Krishnan (2000) showed that risk
contributes to poverty luctuations in Ethiopia.
2 Sparrow, Suryahadi and Widyanti (2012), using the National Socio-Economic Survey
(Susenas) panels of 2005 and 2006, showed that Askeskin increases the use of outpatient
health care among the poor. This policy may therefore protect households from falling into
transient poverty because of health shocks.
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The determinants of poverty dynamics in Indonesia: evidence from panel data
63
set of households over time. Second, it provides information to deepen understanding of poverty in present-day Indonesia, particularly the factors that determine households’ movements into and out of poverty and why some households
remain poor. Third, as a pioneer paper, it deals with how economic shocks and
risks, government assistance, and changes in socio-economic variables can change
poverty status in Indonesia.3
This paper explains the concepts of poverty dynamics, and then describes the
changes in household poverty status in Indonesia during 2005-07. It reviews the
research methods of the ordered logit model and analyses the estimation results
of determinants of poverty dynamics. It ends with some important indings and
policy suggestions.
THEORETICAL FRAMEWORK
Concepts and measures of chronic and transient poverty, based on panel data
Researchers commonly identify and measure chronic and transient poverty
(income- and consumption-based poverty) on the basis of panel data, using the
‘spell’ and ‘components’ approaches (Yaqub 2000). The spell approach focuses on
the number or length of spells of poverty experienced by households, because the
deining feature of both chronic and transient poverty is their extended duration
(Hulme and Shepherd 2003). The components approach distinguishes the permanent component of a household’s income or consumption from its transitory
variations, classifying the chronically poor as those whose permanent component
– commonly the intertemporal average of household income or consumption – is
below the poverty line (McKay and Lawson 2003).
Under the spell approach, the term ‘chronic poor’ indicates that consumption
expenditure or household income remained below the poverty line in all observation periods. ‘Transient poor’ indicates that consumption expenditure or household income was not always below the poverty line and was sometimes above it.
‘Non-poor’ (never poor) indicates that consumption expenditure or household
income remained above the poverty line in all observation periods (Hulme, Moore
and Shepherd 2001). Figure 1 shows a simple illustration of the spell approach.
Chronic poverty, then, can be described as the household condition of being
poor over an extended period, whereas transient poverty (– or +) refers to a state
of occasionally being poor or non-poor. This difference is typically based on
longitudinal (or panel) data or a life-history survey, both of which observe the
living conditions of the same individual or households at several points in time
(McKay and Lawson 2002). The former provides information about individuals
or households during one period or consecutive periods, while the latter captures
the dynamic aspect of living conditions from a list of retrospective questions. A
life history – for instance, the weight-for-height anthropometric measure – can
luctuate signiicantly in a short time. These luctuations may relect, for example,
3 Dercon and Shapiro (2007) contend that the impact of shocks and risks on poverty mobility has received relatively limited attention in the literature of poverty dynamics. Analysing poverty dynamics can provide insights into the effects of socio-economic and antipoverty policies on household poverty status. It can also help policy makers to identify
effective ways to help households escape poverty.
64
Teguh Dartanto and Nurkholis
FIGURE 1 The Distinction between Chronic Poor, Transient Poor (–),
Transient Poor (+) and Never Poor a
Y2
Transient poor (+)
Never poor
Chronic poor
Transient poor (–)
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Z2
0
Z1
Y1
a Y and Y represent individual or household income or consumption in period 1 and period 2
1
2
respectively. Variables Z1 and Z2 represent the poverty lines in the same periods.
Source: Adapted from Grab and Grimm (2007).
the period of the agricultural season or the effects of chronic disease. Over an
extended observation period, an individual with a weight-for-height measurement below the standard could therefore be categorised as chronically poor,
whereas an individual with a weight-for-height measurement occasionally equal
to or below the standard could be categorised as transiently poor.
Previous research on poverty dynamics
Many studies have found that human capital, demographics, geographical location, physical assets and occupational status help determine poverty status. An
increase in human capital, for example, indicated by educational attainment (years
of schooling), decreases the probability of being chronically poor and improves
a household’s ability to respond to transitory shocks (Adam and Jane 1995; Alisjahbana and Yusuf 2003). Jalan and Ravallion (1998) contend that changes in
demographics, such as increased household size, are related to chronic poverty.
McCulloch and Calandrino (2003), in Sichuan, China, conirmed that chronic poverty is commonly found in rural (especially remote) areas, and Fields et al. (2003)
found that households in urban areas have a higher probability of escaping from
poverty. A lack of physical assets is also often associated with chronic poverty
(Adam and Jane 1995), and occupational status can help determine household
poverty status. Okidi and Kempaka (2002) found that self-employed farming
households in Uganda are more likely to be chronically poor, and Kedir and
McKay (2005) found that households in Ethiopia with a head working as a waged
employee can escape poverty.
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The determinants of poverty dynamics in Indonesia: evidence from panel data
65
Grab and Grimm (2007), using the Indonesia Family Life Survey (IFLS) data
set to measure poverty dynamics, compared chronic and transient poverty in two
timespans. They found that absolute comparisons show a signiicant decline in
chronic poverty from 1993–97 to 1997–2000. The decline in both chronic and transient poverty was largely due to a substantial decline in poverty in rural Indonesia. Fields et al. (2003), using the 1993 and 1997 IFLS panel data sets, found that
the determinants of household income dynamics at those times were household
location, age of the household head, employment status of the head, change in the
gender of the head, change in employment status of the head, and change in the
number of children. Alisjahbana and Yusuf (2003), using the IFLS data sets from
1993 and 1997, observed that of the 84.8 percentage points of non-poor in 1993,
11.6 percentage points had fallen into poverty by 1997. Of the 15.2 percentage
points of poor in 1993, 7.8 percentage points had remained poor whereas the other
7.4 percentage points had escaped poverty.
OVERVIEW OF POVERTY DYNAMICS IN INDONESIA DURING 2005–07
Susenas and the economic condition
We use data from the 2005 and 2007 National Socio-Economic Survey (Susenas), conducted by Indonesia’s Badan Pusat Statistik (BPS), the national statistics agency, to measure poverty dynamics in Indonesia.4 Susenas consists of two
main data sets: core and module. The 2005 Susenas core data set recorded detailed
characteristics of 278,352 households, from an estimated 59 million households
nationally and covering various geographic regions of Indonesia. The 2005 Susenas module data set collected additional information on a subset (or 68,288 households) of core households. It recorded detailed information on food and non-food
consumption, as well as on household shocks and coping strategies.
BPS revisited around 10,600 households from the 2005 Susenas module sample
in 2007. Merging the 2005 and 2007 Susenas panels and dropping observations
that contained incomplete household information or that were outliers yielded a
total of 8,726 households (balanced-panel data). The 2007 Susenas did not revisit
those households that migrated, so the 8,726 revisited households are those that
remained in one location during 2005–07.5
4 We intended to use a longer sequence of Susenas data sets (for instance, 2002–07), to capture greater changes in poverty status. The 2002 and 2007 databases do not match, however,
because Susenas modules collect information from different categories every three years. We
also found many inconsistencies in the 2006 data, so we could not include them. Analysing
poverty dynamics using panel data covering a short period (three years) may not reveal all of
the long-run changes in poverty. Given the limitations of available data, however, analysing
a short period of poverty dynamics using a Susenas data set that provides rich information
about household socio-economic conditions and covers all provinces will nevertheless contribute to a deeper understanding of the recent situation of poverty. It will also provide useful
insights into why some households remain poor and why others escape poverty.
5 In merging the 2005 and 2007 sample identiiers of Susenas core and module data sets,
we found 9,491 balanced-panel samples. Around 1,120 samples were lost during the merger, which might be due to a split of provinces. We not only merged the sample identiiers
but also included household information such as educational attainment, physical assets,
shocks and the poverty line.
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66
Teguh Dartanto and Nurkholis
In order to provide basic information about household welfare status, our
analysis of poverty dynamics starts with a discussion of household expenditure,
the poverty line and poverty incidence during 2005–07. In this period, household
expenditure in Indonesia increased by an average of 30.4% (table 1). Households
outside Java–Bali experienced an increase in expenditure of 39.4%, while households in Java–Bali experienced an increase of only 23.6%. This large increase in
household expenditure outside Java–Bali was not followed by a corresponding
reduction in poverty in those areas, because the poverty line in those areas rose
by 31.9% (largely owing to the rise of rice prices in 2005–06 (Lindblad and Thee
2007)). The national poverty incidence remained unchanged during 2005–07, but
the poverty incidence outside Java–Bali decreased by around 0.5 of a percentage point. Surprisingly, urban poverty also decreased by around 0.5 of a percentage point, but rural poverty increased by almost 1.0 percentage point. Although
both rural and urban households experienced a similar proportion of increase in
expenditure, the rural poverty line increased by more than 25% while the urban
poverty line increased by only 14%.
Poverty dynamics in Indonesia during 2005–07
This paper uses the spell approach (as illustrated in igure 1), the poverty lines of
2005 and 2007, and the poverty measures of the Foster–Greer–Thorbecke (FGT)
formula (Foster, Greer and Thorbecke 1984) to identify and measure poverty status in Indonesia.6 It analyses only the P0 (head-count index) of the FGT poverty
measurement. As this paper uses a short period of panel data, it may be inappropriate to refer to the chronic poor and never poor; both categories require at least
ive years of longitudinal data to provide a clear deinition and analysis. Using
expenditure-based poverty measures, we have categorised households into just
four groups: poor, transient poor (–), transient poor (+) and non-poor. This adjustment does not reduce the signiicance of this analysis of poverty dynamics in
Indonesia. The paper also applies three different poverty lines: the oficial poverty
line, published by BPS; the lower poverty line (75% of the oficial poverty line);
and the upper poverty line (125% of the oficial poverty line). These three different poverty lines help examine the sensitivity of poverty incidence to changes in
the poverty line. Figure 2 shows Indonesian poverty dynamics during 2005–07 at
a national level, using the oficial poverty line.
Table 2 shows that poverty in Indonesia is predominantly a rural phenomenon and very sensitive to changes in the poverty line: an increase of 25% in the
poverty line causes an increase of more than 100% in the poverty rate. At the
disaggregated level, 95% of urban poor households in 2005 were able to escape
poverty during 2005–07, while 64% of rural poor households were able to do the
same. Around 11% of rural non-poor households in 2005 subsequently fell into
6 The FGT class of poverty measures follows:
1 q ⎛ z − yi ⎞
πα = ∑ ⎜
n i=1⎝ z ⎟⎠
α
where π is the poverty index, n is the total population size, z is the poverty line, yi is the
income of the ith individual (or household), q represents the number of individuals just
below or on the poverty line, and α is a parameter for the FGT class.
The determinants of poverty dynamics in Indonesia: evidence from panel data
67
TABLE 1 Summary of Household Expenditure, the Poverty Line
and Poverty Incidence (2005–07)
Household Expenditure Calculated Based on Balanced Panels of 2005 and 2007
(Rp/month/capita)
2005
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Region
2007
Change
(%)
Mean
SDa
Mean
SD
National
Urban
288,579
401,305
260,391
348,171
376,175
521,161
330,679
409,812
30.4
29.9
Rural
208,434
119,911
273,093
205,269
31.0
Java–Bali
Outside Java–Bali
312,278
261,840
301,724
200,639
386,130
364,944
337,318
322,697
23.6
39.4
Oficial Poverty Line
(Rp/month/capita)
2005
2007
Change
(%)
141,465
165,565
167,390
187,942
18.3
13.5
Rural
117,365
146,837
25.1
Java–Bali
145,569
169,031
16.1
Urban
170,153
192,974
13.4
Rural
120,985
145,088
19.9
Outside Java–Bali
135,768
179,015
31.9
Urban
Rural
156,456
115,080
197,909
160,121
26.5
39.1
Region
National
Urban
Poverty Incidence, Calculated Based on the Total Sample of Susenas 2005 and 2007
(%)
Region
2005
2007
Change
National
Urban
16.6
13.0
16.6
12.5
0.0
–0.5
Rural
19.4
20.4
1.0
Java–Bali
Outside Java–Bali
15.8
18.0
16.0
17.5
0.2
–0.5
a SD = standard deviation.
Source: Authors’ calculations and several BPS publications.
68
Teguh Dartanto and Nurkholis
FIGURE 2 National Poverty Dynamics during 2005–07 a
No. of HH
8,726
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P-05
1,061 (12.2%)
P-07
Poor
292
(3.4%)
NP-07
Transient
poor (+)
769 (8.8%)
NP-05
7,665 (87.8%)
P-07
Transient
poor (–)
509 (5.8%)
NP-07
Non-poor
7,156
(82.0%)
a HH= households; P = poor; NP = non-poor. Figures in parentheses are the percentage of the total
sample.
Source: Authors’ calculations.
poverty, compared with only 1% of urban non-poor households. Urban households contributed more transient poor (+) and non-poor while rural households
contributed more transient poor (–) and poor. Rural households rely mostly on
agricultural activities for income, which are relatively unstable compared with
industrial or service sectors in the urban area. Negative shocks such as crop loss,
falling agricultural prices, or death and illness can therefore easily send rural
households into poverty.
Table 2 also shows poverty dynamics at the disaggregated, regional level of
Java–Bali and outside Java–Bali.7 In Indonesia, it is generally observed that there
are two types of regional segregation: Java–Bali versus outside Java–Bali, and
Western Indonesia versus Eastern Indonesia. Western Indonesia comprises Sumatra, Java, Bali and Kalimantan, while Eastern Indonesia consists of Sulawesi, Nusa
Tenggara, Maluku and Papua. Java and Bali have signiicantly larger populations
and more developed economic activities and infrastructure than the other islands.
Manufacturing activities and service sectors dominate the economy of Java–Bali;
agricultural and mining activities dominate outside Java–Bali. Suryadarma et al.
(2006), using the 2003 Village Potential Survey (Podes) and the 2002 and 2004
Susenas panel data sets, showed that households in Java–Bali had better access to
basic services, such as education and health, than households outside Java–Bali.
7 According to BPS, the 2005 and 2007 Susenas panel data sets should be presented at
the national, rural and urban levels but not at the provincial level. However, there is still
the possibility and validity of analysing at the regional level both Java–Bali and outside
Java–Bali, because the 2005 and 2007 Susenas balanced panel had been distributed proportionally between Java–Bali (4,626 households) and outside Java–Bali (4,100 households).
A regional analysis would follow Suryadarma et al. (2006), who used the 2002 and 2004
Susenas panel data sets to analyse the level of access to basic services at regional level.
The determinants of poverty dynamics in Indonesia: evidence from panel data
69
TABLE 2 Overview of Poverty Status in 2005 and 2007 a
(number of households)
Condition in 2007
Lower Poverty Line
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Total
Condition in 2005
Urban
74
Poor
3,552
Non-poor
Rural
209
Poor
4,891
Non-poor
Poor
Oficial Poverty Line
Nonpoor
Total
Poor
Nonpoor
Upper Poverty Line
Total
Poor
Nonpoor
2
2
72
3,550
281
3,345
13
32
268
3,313
690
2,936
171
220
519
2,716
35
153
174
4,738
780
4,320
279
477
501
3,843
1,627
3,473
832
783
795
2,690
Java–Bali
108
Poor
4,518
Non-poor
Outside Java–Bali
175
Poor
3,925
Non-poor
16
16
92
4,502
475
4,151
143
243
332
3,908
1,088
3,538
472
513
616
3,025
21
139
154
3,786
586
3,514
149
266
437
3,248
1,229
2,871
531
490
698
2,381
National
Poor
Non-poor
283
8,443
37
155
246
8,288
1,061
7,665
292
509
769
7,156
2,317
6,409
1,003
1,003
1,314
5,406
Total
8,726
192
8,534
8,726
801
7,925
8,726
2,006
6,720
a The oficial poverty line is that published by BPS. The lower poverty line is 75% of the BPS line, and
the upper poverty line is 125% of the BPS line.
Source: Authors’ calculations based on Susenas data.
Almost 20% of villages outside Java–Bali had no primary school, compared with
less than 1% of villages in Java–Bali.
The regional segregation between Java–Bali and outside Java–Bali might inluence household poverty characteristics, owing to differences in economic structure and infrastructure availability. At the disaggregated, regional level, we found
that 70% of poor households in Java–Bali in 2005 were able to escape poverty
during 2005–07 and that 75% of poor households outside Java–Bali were able to
do the same (table 2). Around 6% of non-poor households in Java–Bali in 2005
fell into poverty, compared with 8% of those outside Java–Bali. Further, around
30% of poor households in Java–Bali and around 25% of poor households outside
Java–Bali remained poor in two periods of observation.
Non-poor households outside Java–Bali seemed more vulnerable to falling
into poverty than those in Java–Bali, while poor households outside Java–Bali
tended to move more easily out of poverty than those in Java–Bali. The economic
dependence of areas outside Java–Bali on agricultural and mining commodities
may explain this vulnerability: luctuations in the prices of these commodities
often lead to luctuations in household income and expenditure.
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70
Teguh Dartanto and Nurkholis
RESEARCH METHODOLOGY
Model speciication
As discussed above, the spell approach categorises households in Indonesia as
poor, transient poor (–), transient poor (+) or non-poor. We contend that household poverty status has an order in which one status might be more favourable
than others. Non-poor is the most preferred condition; poor the least preferred.
The order of transient poor (–) and transient poor (+) is in between poor and nonpoor. This paper assumes that the improvement condition of transient poor (+) is
more favourable than the degradation condition of transient poor (–).
We used an ordered logit model to examine the determinants that can change
household poverty status and enable the poor to escape from poverty. Such a
model is useful for understanding the relative effect of different household characteristics on poverty status, but it is less useful for distinguishing between poverty categories. Independent variables (predictors) in this model are essentially
divided into two groups: the 2005 initial variables and the 2005–07 change variables.
The initial variables represent household conditions and positions that may
change household poverty status in the future. For instance, poor agricultural
households with a small area of land in the initial year might become continuously poor later: the land might not produce above a subsistence level, and the
household might not have enough resources to invest in modern agricultural
technology or to buy good seed for the next production. Uninsured households
that experience health shocks in the initial year might become poor in the future –
their members might be unable to work, or they might have to allocate all of their
resources to medical treatments. Households forced to sell land for medical treatments might later become impoverished; and non-poor households in the initial
year might become poor households in the next, because of changes in variables
such as marital or job status.
Independent variables included in the model consider the data availability in
the 2005 and 2007 Susenas, as well as variables used in research by Jalan and
Ravallion (1998), Herrera (1999), Okidi and Kempaka (2002), Alisjahbana and
Yusuf (2003), Bigsten et al. (2003), Fields et al. (2003), Haddad and Ahmed (2003),
McCulloch and Calandrino (2003), McKay and Lawson (2003), Contreras et al.
(2004), Kedir and McKay (2005), Woolard and Klasen (2005), and Widyanti et al.
(2009). The ordered logit model (equation (1)) is as follows:
yi =
HHCi0β + S COi0 x + ShockGovi0ϕ + ΔVARi05−07φ + ei
(1)
where
• yi = a household’s poverty status: 0 = poor, 1 = transient poor (–), 2 = transient
poor (+), 3 = non-poor;
• i0 = a vector of family characteristics in 2005, including marital status, age,
educational attainment, number of household members, dummy variables for
location and island;
• SEOi0 = a vector of socio-economic characteristics in 2005, including dummy
variables for employment sector and employment status, land ownership (in
hectares), size of house (in square metres), access to electricity for lighting, and
The determinants of poverty dynamics in Indonesia: evidence from panel data
•
•
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•
•
71
a dummy variable for households with a family member working as a migrant
overseas;
Shockovi0 = a vector of shocks, risks and policy variables received by a
household in 2005;8
ΔVRi05−07 = a vector of changes in variables during 2005–07, including
change in marital status, number of household members, employment sector,
employment status, access to electricity for lighting, and microcredit;
e is an error term; and
i is the household identiier (i = 1,…, 8,726).
Appendix table 1 describes the variables and their expected signs.
Ordered response model
Equation (1) is an ordered response model (probit or logit) with four outcomes,
y = 0,1,2,3 . An ordered logit model for y (conditional on explanatory variables
x) can be derived from a latent variable model. Assume that a latent variable, y∗,
is determined by:
{
}
y∗ = xβ + e, e x Noma 0,1
( )
(2)
where β is a Kx1 coeficient vector and where vector x does not contain a constant
(for a detailed explanation of the ordered response model, see Wooldridge 2010).
The parameters of the model can be estimated by using maximum likelihood
estimation. The signs of the estimated coeficients from the ordered probit (logit)
models have the exact meaning with the result of ordinary least square (OLS)
estimations. A negative sign determines whether the choice probabilities shift
to lower categories when the independent variable increases. The partial effect
of estimated coeficients, however, cannot be interpreted directly as the result of
OLS estimation. In most cases, we are interested in the response probabilities or
partial effects, P y = j x , of the ordered probit model (see Wooldridge 2010).
The ordered logit model (equation (1)) uses three sample groups: Java–Bali,
outside Java–Bali and national (entire sample). Although our analysis of poverty
dynamics focuses on the national group, dividing the sample helps show the consistency and robustness of estimation results. It also checks whether there are signiicant differences in poverty characteristics between Java–Bali and the rest of
the country.
(
)
DESCRIPTIVE DATA ANALYSIS
Households in Indonesia can be divided into four groups, based on their poverty experience in 2005–07 (table 3): poor (292 households), transient poor (–) (509
households), transient poor (+) (769 households) and non-poor (7,156 households).
8 Negative shocks and risks include economic risks and health shocks. Positive shocks include improvements to public facilities surrounding living areas, more new jobs, and microcredit. Economic risks include crop loss, job loss, falling crop prices and increased production
costs. This vector also includes interaction variables between savings and socio-economic
shocks, and the policy variables of Raskin and Askeskin. These variables are intended to examine the effectiveness of saving and government policies in coping with negative shocks.
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:40 17 January 2016
72
Teguh Dartanto and Nurkholis
We observed that members of the poor group are largely uneducated or have a
low level of educational attainment; live in a rural area, rely on income from agricultural production and the informal sector; and either own a small area of land
or are landless. Unlike the other groups, the poor group is excluded from accessing modern utilities (around 39% of households in the poor group do not have
electricity) and inancial services (none of the households in the poor group have
received microcredit from either the government or other sources).
The demographic characteristics and socio-economic variables of the transient poor (–) group were slightly better than those of the poor group – including
higher educational attainment, better access to electricity, and ownership of larger
land areas – and fewer transient poor (–) households experienced economic risks
and health shocks. We found that the major variable change faced by the transient
poor (–) group during 2005–07 was an increase of one household member moving
from formal to informal employment (14%).
Compared with the transient poor (–) group, the transient poor (+) group has
a higher level of educational attainment; lives in an urban area; has better access
to electricity in 2007 than it did in 2005; has a low percentage of members working in the agricultural sector; and has a low percentage of households experiencing economic and health shocks, as well as suficient savings to cope with such
shocks. The greatest differences in the change variables of the transient poor (+)
group and the poor and transient poor (–) groups is a decrease in household size
by almost one member, a higher proportion of households receiving microcredit,
a higher proportion of households gaining access to electricity, and a lower proportion of households moving from formal to informal employment.
The non-poor group is generally more educated than other groups; has fewer
household members; lives in urban areas; has a larger proportion of households
connected to electricity; has experienced fewer economic and health shocks; and
has suficient savings to cope with such shocks. The daily activities of non-poor
households are disrupted by health shocks only 3.7 days per month, around half
the time experienced by poor households. Furthermore, members of households
in the non-poor group tend to work in the formal and non-agricultural sectors, so
their income is less volatile and less likely to depend on government assistance.
Table 4 shows that signiicant differences exist between households in Java–
Bali and those outside Java–Bali. The latter, for example, have more family members, live mostly in rural areas and have larger areas of agricultural land. Fewer
households outside Java–Bali have electricity for lighting. Furthermore, households outside Java–Bali experienced more economic risks and health shocks than
households in Java–Bali. The daily activities of households outside Java–Bali are
disturbed by health shocks half a day more than those of households in Java–Bali.
Households outside Java–Bali are more vulnerable to being transient poor, both
(–) and (+), than households in Java–Bali.
THE DETERMINANTS OF POVERTY DYNAMICS IN INDONESIA
This paper uses three models – Java–Bali, outside Java–Bali and national – based
on household location, which were estimated using maximum likelihood estimation, with robust standard errors. Tables 5 and 6 show the estimation results of
the ordered logit model. The signs of coeficients in the three models are almost
The determinants of poverty dynamics in Indonesia: evidence from panel data
73
TABLE 3 Descriptive Data on Poverty Status a
Poor
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Mean
Demographic variables in 2005
Marital status of HH head (1 = married; 0 = other)
Age of HH head (years)
Educational attainment of HH head (years)
Number of HH members
Island dummy (1 = Java–Bali; 0 = other)
Location dummy (1 = urban; 0 = rural)
Socio-economic variables in 2005
Employment sector of HH head (1 = agriculture;
0 = other)
Employment status of HH head (1 = formal;
0 = other)
Land ownership (ha)
House size (m2)
HH with a migrant-worker (TKI) member
(1 = has TKI member; 0 = other)
Access to electricity for lighting
(1= no; 0 = yes)
Shocks/risks and policy variables in 2005
ECSHRS (1= experience; 0 = no experience)
Used Raskin for ECSHRS (1 = yes; 0 = no)
Daily activities disrupted by health problems
for all family members (days/month)
Health insurance (1 = Askeskin; 0 = other)
Saving as a coping strategy for ECSHRS
(1 = having savings; 0 = no savings)
Microcredit (1 = yes; 0 = no)
Source of microcredit (1 = government;
0 = other)
Family member gaining employment
(1 = yes; 0 = other)
Improved public facilities nearby
(1 = yes; 0 = no)
Change variables during 2005–07
Change in number of HH members
Change in marital status of HH head
(1 = divorced; 0 = other)
Change in employment sector of HH head
(1 = agricultural to non-agricultural; 0 = other)
Change in employment status of HH head
(1 = formal to informal; 0 = other)
Change in access to electricity for lighting
(1 = access in 2007 but not in 2005; 0 = other)
Change in access to credit (1 = credit in 2007
but not in 2005; 0 = other)
Number of observations
Transient
Poor (–)
SD Mean
Transient
Poor (+)
SD Mean
Non-poor
SD Mean
SD
0.88 0.33 0.85 0.35 0.87 0.34 0.85 0.36
47.43 14.28 46.17 14.90 47.43 14.23 45.53 13.71
4.74 3.15 5.10 3.37 5.65 3.19 6.91 4.38
4.72 1.79 4.06 1.74 4.88 1.77 3.85 1.60
0.49 0.50 0.48 0.50 0.43 0.50 0.55 0.50
0.04 0.21 0.06 0.24 0.35 0.48 0.46 0.50
0.80
0.40
0.72
0.45
0.64
0.48
0.45
0.50
0.16
0.36
0.18
0.38
0.17
0.38
0.30
0.46
0.64 0.79 0.86 1.19 0.74 1.26 0.52 1.59
59.77 50.19 58.17 27.92 56.67 55.95 70.32 65.37
0.04 0.19 0.04 0.20 0.04 0.19 0.04 0.21
0.49
0.27
0.44
0.27
0.44
0.10
0.30
0.28 0.45
0.02 0.14
6.36 11.20
0.26
0.02
4.45
0.44
0.12
8.61
0.23
0.03
4.85
0.42
0.16
8.70
0.16
0.01
3.73
0.37
0.08
7.80
0.04
0.01
0.19
0.08
0.03
0.01
0.16
0.08
0.02
0.02
0.15
0.14
0.01
0.03
0.10
0.16
0.00
0.00
0.00
0.00
0.03
0.01
0.16
0.09
0.02
0.01
0.12
0.07
0.03
0.01
0.18
0.10
0.06
0.24
0.05
0.21
0.10
0.30
0.08
0.27
0.13
0.34
0.09
0.29
0.08
0.27
0.10
0.29
–0.07
0.05
1.27
0.23
0.64
0.05
1.50 –0.59
0.21 0.06
1.67
0.24
0.07
0.06
1.53
0.23
0.11
0.32
0.11
0.31
0.13
0.34
0.14
0.35
0.11
0.32
0.14
0.34
0.08
0.27
0.12
0.32
0.11
0.31
0.08
0.27
0.13
0.34
0.04
0.21
0.03
0.16
0.04
0.19
0.05
0.22
0.07
0.26
0.39
292
509
769
7,156
SD = standard deviation; HH = household; TKI = Tenaga Kerja Indonesia (Indonesian migrant worker);
ECSHRS = economic shocks and risks; Raskin = rice for the poor; Askeskin = health insurance for the poor.
a
Source: Authors’ calculations based on Susenas data.
74
Teguh Dartanto and Nurkholis
TABLE 4 Descriptive Data Used in the Ordered Logit Model a
Java–Bali
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Mean
Demographic variables in 2005
Marital status of HH head (1 = married; 0 = other)
Age of HH head (years)
Educational attainment of HH head (years)
Number of HH members
Island dummy (1 = Java–Bali; 0 = other)
Location dummy (1 = urban; 0 = rural)
Socio-economic variables in 2005
Employment sector of HH head (1 = agriculture; 0 = other)
Employment status of HH head (1 = formal; 0 = other)
Land ownership (ha)
House size (m2)
HH with a migrant-worker (TKI) member
(1 = has TKI member; 0 = other)
Access to electricity for lighting (1= no; 0 = yes)
Shocks/risks and policy variables in 2005
ECSHRS (1= experience; 0 = no experience)
Used Raskin for ECSHRS (1 = yes; 0 = no)
Daily activities disrupted by health problems
for all family members (days/month)
Health insurance (1 = Askeskin; 0 = other)
Saving as a coping strategy for ECSHRS
(1 = having savings; 0 = no savings)
Microcredit (1 = yes; 0 = no)
Source of microcredit (1 = government; 0 = other)
Family member gaining employment (1 = yes; 0 = other)
Improved public facilities nearby (1 = yes; 0 = no)
Change variables during 2005–07
Change in number of HH members
Change in marital status of HH head (1 = divorced; 0 = other)
Change in employment sector of HH head
(1 = agricultural to non-agricultural; 0 = other)
Change in employment status of HH head
(1 = formal to informal; 0 = other)
Change in access to electricity for lighting
(1 = access in 2007 but not in 2005; 0 = other)
Change in access to credit (1 = credit in 2007 but not in 2005; 0
= other)
Outside
Java–Bali
SD Mean
National
SD Mean
SD
0.85 0.36 0.85 0.35 0.85 0.36
46.73 14.03 44.75 13.59 45.80 13.86
6.51 4.27 6.74 4.22 6.62 4.24
3.79 1.54 4.21 1.76 3.98 1.66
0.53 0.50
0.51 0.50 0.31 0.46 0.42 0.49
0.41 0.49 0.58 0.49 0.49 0.50
0.30 0.46 0.26 0.44 0.28 0.45
0.23 1.09 0.94 1.83 0.56 1.53
73.38 62.55 62.04 62.37 68.05 62.72
0.04 0.20 0.05 0.21 0.04 0.20
0.03
0.16
0.26
0.44
0.13
0.34
0.16
0.01
3.74
0.37
0.08
7.67
0.19
0.01
4.21
0.39
0.12
8.53
0.17
0.01
3.96
0.38
0.10
8.09
0.01
0.03
0.10
0.16
0.02
0.02
0.12
0.14
0.01
0.02
0.11
0.15
0.05
0.02
0.08
0.10
0.21
0.13
0.27
0.30
0.01
0.00
0.08
0.09
0.10
0.04
0.26
0.28
0.03
0.01
0.08
0.10
0.17
0.10
0.27
0.29
0.07
0.05
0.14
1.42
0.22
0.34
0.01
0.06
0.14
1.69
0.24
0.34
0.04
0.06
0.14
1.55
0.23
0.34
0.12
0.32
0.12
0.32
0.12
0.32
0.02
0.13
0.10
0.30
0.06
0.23
0.08
0.27
0.05
0.22
0.07
0.25
Poverty status
Poor
Transient poor (–)
Transient poor (+)
Non-poor
143
243
332
3,908
149
266
437
3,248
292
509
769
7,156
Number of observations
4,626
4,100
8,726
SD = standard deviation; HH = household; TKI = Tenaga Kerja Indonesia (Indonesian migrant worker);
ECSHRS = economic shocks and risks; Raskin = rice for the poor; Askeskin = health insurance for the poor.
a
Source: Authors’ calculations based on Susenas data.
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The determinants of poverty dynamics in Indonesia: evidence from panel data
75
the same, except in the following variables: age of household head (outside Java–
Bali), economic shocks and risks (outside Java–Bali), source of microcredit (outside Java–Bali) and change in marital status (Java–Bali). All three models show
that the Wald chi-square statistics of the ordered logit model are statistically signiicant, indicating that at least one of the covariates or independent variables
affects household poverty status. Generally, the ordered logit models of poverty
dynamics are consistent and robust.
Table 6 shows the partial effects (dy/dx) of changes in the probability of households categorised as poor, transient poor (–), transient poor (+) and non-poor,
responding to change in independent variables (predictors). The partial effects
(the predicted probability of household poverty status) were evaluated at means
of independent variables y = x .
(
)
Demographic variables
All three models in Table 5 conirm that educational attainment, location and
the number of household members are the most important demographic determinants of household poverty status. The variables of marital status and age of
the household head are both statistically signiicant to poverty status in model 3
(national level); but marital status is not signiicant in model 1 (Java–Bali), nor is
age in model 2 (outside Java–Bali). Married households outside Java–Bali have a
higher probability of being non-poor; most of the households outside Java–Bali
are working in the labour-intensive agricultural sectors, so a married household
has more workers than a single household and therefore has the potential to produce greater output and generate greater income.
Table 6 shows that an increase in the number of household members decreases
the probability of being non-poor, while increasing the probability of being poor,
transient poor (–) and transient poor (+). This inding is similar to those of Herrera (1999), Haddad and Ahmed (2003), and Woolard and Klasen (2005). Given a
ixed income, an increase in the number of members forces households to reduce
per-person consumption to support the additional members. A better education
increases the probability of being non-poor, because a higher level of education
provides greater opportunities for a better job and, subsequently, a higher income.
These indings conirmed the conclusions of other studies, such as Bigsten et al.
(2003), and Widyanti et al. (2009).
The location dummy variable reveals that those living in urban areas have a
higher probability of being non-poor. This inding conirms indings in studies
of other countries, such those of Fields et al. (2003), and Kedir and McKay (2005).
Urban areas, where most industries and economic activities are located, provide
more job opportunities in the formal and the informal sectors.
Socio-economic variables
As many studies have found (Dercon and Krishnan 2000; Okidi and Kempaka
2002), households with a head working in the agricultural sector have a high
probability of being poor, owing to low productivity and wage rates. This probability increases by 1.3% in Java–Bali, 1.1% outside Java–Bali and 1.4% nationally,
as table 6 shows. Furthermore, households with a head working in the formal sector – that is, working for an agency, ofice or company for a ixed salary, either in
cash or in goods – have a higher probability of being non-poor. Those working in
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76
Teguh Dartanto and Nurkholis
formal sectors increase their probability of being non-poor by 4.6% in Java–Bali,
6.8% outside Java–Bali and 5.8% nationally. The formal sector in Indonesia guarantees a stable income and pays higher wage rates than the informal sector, which
complements the inding of Kedir and McKay (2005) that waged employees in
rural Ethiopia have a higher probability of escaping from poverty.
Owing to the lack of job opportunities in Indonesia, individuals who cannot
ind jobs in the formal sector or start a business (as an entrepreneur) are forced to
work either in the domestic, informal sector, for a low wage, or outside Indonesia,
as migrant workers overseas. Most migrant workers also work in the informal
sector, as domestic helpers, but they are paid a higher wage. This paper conirms that households with a family member working outside Indonesia tend to
be non-poor. Their remittances may take the form of family transfers to support
basic needs, or entrepreneur capital transfers to support their families starting a
business. Hall (2007) showed that remittances play an important role in poverty
dynamics in Latin America. Notably, the coeficient for this variable is insigniicant for the outside Java–Bali sample.
Land ownership as an indicator of physical assets signiicantly affects household poverty status. Table 6 shows that a one-hectare increase in land size would
increase the probability of being non-poor by 1.7% in Java–Bali, 1.3% outside
Java–Bali and 1.7% nationally. Landless and small-landholder households tend
to be chronically poor, because their productive assets are inadequate for increasing their income. Land reforms aimed at increasing access to land as a productive
asset by poor households could alleviate chronic poverty. This inding is similar
to those of Haddad and Ahmed (2003), and Woolard and Klasen (2005). House
size as an indicator of physical assets can also determine a household’s poverty
status: a larger house is associated with a lower probability of being non-poor.
Both indings may possibly also imply that the certiication of agricultural land
and house ownership may help alleviate poverty: certiication would legalise
land and house ownership, which could then be used as collateral for gaining
productive credit from formal institutions.
Other socio-economic variables, such as access to modern electricity utilities,
signiicantly increase the probability of escaping poverty. The unit cost of lighting with electricity is cheaper per kilowatt-hour than lighting with candles or an
oil lamp (Foster and Tre 2003). Households could possibly reduce their energy
expenditure and potentially re-allocate savings to income-generating activities or,
if children are part of the household, to education. This could ultimately help lift
households out of poverty. Table 4 shows that households in Java–Bali have better
access to electricity than households outside Java–Bali, owing more to the availability of the electricity gri
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
The determinants of poverty dynamics in
Indonesia: evidence from panel data
Teguh Dartanto & Nurkholis
To cite this article: Teguh Dartanto & Nurkholis (2013) The determinants of poverty dynamics
in Indonesia: evidence from panel data, Bulletin of Indonesian Economic Studies, 49:1, 61-84,
DOI: 10.1080/00074918.2013.772939
To link to this article: http://dx.doi.org/10.1080/00074918.2013.772939
Published online: 21 Mar 2013.
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Date: 17 January 2016, At: 23:40
Bulletin of Indonesian Economic Studies, Vol. 49, No. 1, 2013: 61–84
THE DETERMINANTS OF POVERTY
DYNAMICS IN INDONESIA:
EVIDENCE FROM PANEL DATA
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:40 17 January 2016
Teguh Dartanto*
Nurkholis*
University of Indonesia, Jakarta
We use the ‘spell’ approach to identifying poverty and apply an ordered logit
model to examine the determinants of poverty dynamics in Indonesia, categorising
households as poor, transient poor (–), transient poor (+) or non-poor. Observing
the National Socio-Economic Survey (Susenas) balanced-panel data sets of 2005 and
2007, we found that 28% of poor households are classiied as chronically poor (that
is, remaining poor in two periods) while 7% of non-poor households are vulnerable
to being transient poor (–). Our estimations conirmed that the determinants of poverty dynamics in Indonesia are educational attainment, the number of household
members, physical assets, employment status, health shocks, the microcredit program, access to electricity, and changes in employment sector, employment status
and the number of household members. We also found that households in Java–Bali
are more vulnerable to negative shocks than those outside Java–Bali.
Keywords: poverty dynamics, transient poverty, vulnerability, shocks, government
assistance
BACKGROUND
Poverty in Indonesia has been much researched, but most of this research – for
example, Bidani and Ravallion (1993) – focuses on static poverty and analyses the
proportion of the population falling below a given income threshold at a given
time. Poverty is not, however, a purely static phenomenon (Muller 2002); households currently not poor may later fall below the poverty line, owing to shocks
* We would like to thank the University of Indonesia and the Directorate General of Higher Education, Ministry of National Education, the Republic of Indonesia, for inancing this
research through the National Research Strategic Fund 2010 (DRPM/Hibah Strategis Nasional/2010/I/4024). We would also like to thank Ms Lily Yunita and Mr Usman from the
Institute for Economic and Social Research, University of Indonesia, for their assistance.
We would like to thank Professor Mohamad Ikhsan (University of Indonesia) for providing
Susenas panel data sets. Professor Shigeru Otsubo (Nagoya University) and his seminar
participants provided valuable comments, as did Professor Hal Hill, and other participants of the 2011 Singapore Economic Review Conference, and Assistant Professor Mark
Rebuck. We would also like to thank the three anonymous referees for their constructive
and valuable comments and suggestions, which helped improve the quality of this paper.
Any remaining errors are our responsibility.
ISSN 0007-4918 print/ISSN 1472-7234 online/13/010061-24
http://dx.doi.org/10.1080/00074918.2013.772939
© 2013 Indonesia Project ANU
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:40 17 January 2016
62
Teguh Dartanto and Nurkholis
such as crop loss, job loss or death.1 Conversely, poor households may escape
from poverty by gaining employment or a better job (Fields et al. 2003), increasing
their level of education (Herrera 1999) or having access to improved infrastructure (Sawada et al. 2008).
Under President Susilo Bambang Yudhoyono, the Indonesian government
has changed its poverty alleviation policies from a macro, top-down approach
to a community or household participatory approach. It has developed and
implemented several policies to alleviate chronic poverty, including educational
subsidies (Bantuan Operasional Sekolah), scholarships, conditional cash transfers, community empowerment programs (Program Nasional Pemberdayaan
Masyarakat), credits for small and medium enterprises (SMEs) (microinance)
and infrastructure development projects (Program Pengembangan Kecamatan).
The government has also provided social safety nets, including subsidised rice
(Raskin), cash transfers (Bantuan Langsung Tunai) and health insurance targeted
to the poor (Askeskin).2 These policies are intended to address transient poverty
and protect the poor from external shocks. However, the effectiveness of government policies in alleviating poverty is questionable. It can be dificult to evaluate
the impact of poverty alleviation policies in the short term, because many policies
experience a delay between implementation and results. For instance, the impact
of microcredit on SMEs often becomes apparent only after two or more years,
requiring a longer period of observation. Further, it is generally acknowledged
that the impact of investment in human capital (such as education and health) on
household welfare cannot be investigated immediately.
As the poverty incidence can change over time, it is important to conduct a
dynamic analysis to distinguish between chronic poor, transient poor and never
poor; to discover which determinants differentiate among groups; and to evaluate
the effectiveness of government policies in changing poverty status in Indonesia.
The distinction between chronic and transient poverty is important not only for
an accurate measurement of poverty but also for policy implication purposes;
chronic and transient poverty call for different alleviation strategies. In a country
or region where the poverty problem is characterised by the chronically poor,
the appropriate strategy would be to redistribute assets, providing basic physical
and human-capital infrastructure. If the predominant poverty problems relate to
transient poverty, the strategy should be geared towards providing safety nets
and coping mechanisms to reduce the vulnerability of the poor and to help them
return to a non-poor situation (Hulme and Shepherd 2003; McCulloch and Calandrino 2003).
This paper has three objectives. First, it aims to contribute to the literature of
poverty studies. To date, there has been very little analysis of poverty dynamics
in Indonesia – for example, studies that investigate the welfare movements of a
1 For example, Contreras et al. (2004) found that health problems were correlated with
households falling into poverty in Chile. Dercon and Krishnan (2000) showed that risk
contributes to poverty luctuations in Ethiopia.
2 Sparrow, Suryahadi and Widyanti (2012), using the National Socio-Economic Survey
(Susenas) panels of 2005 and 2006, showed that Askeskin increases the use of outpatient
health care among the poor. This policy may therefore protect households from falling into
transient poverty because of health shocks.
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The determinants of poverty dynamics in Indonesia: evidence from panel data
63
set of households over time. Second, it provides information to deepen understanding of poverty in present-day Indonesia, particularly the factors that determine households’ movements into and out of poverty and why some households
remain poor. Third, as a pioneer paper, it deals with how economic shocks and
risks, government assistance, and changes in socio-economic variables can change
poverty status in Indonesia.3
This paper explains the concepts of poverty dynamics, and then describes the
changes in household poverty status in Indonesia during 2005-07. It reviews the
research methods of the ordered logit model and analyses the estimation results
of determinants of poverty dynamics. It ends with some important indings and
policy suggestions.
THEORETICAL FRAMEWORK
Concepts and measures of chronic and transient poverty, based on panel data
Researchers commonly identify and measure chronic and transient poverty
(income- and consumption-based poverty) on the basis of panel data, using the
‘spell’ and ‘components’ approaches (Yaqub 2000). The spell approach focuses on
the number or length of spells of poverty experienced by households, because the
deining feature of both chronic and transient poverty is their extended duration
(Hulme and Shepherd 2003). The components approach distinguishes the permanent component of a household’s income or consumption from its transitory
variations, classifying the chronically poor as those whose permanent component
– commonly the intertemporal average of household income or consumption – is
below the poverty line (McKay and Lawson 2003).
Under the spell approach, the term ‘chronic poor’ indicates that consumption
expenditure or household income remained below the poverty line in all observation periods. ‘Transient poor’ indicates that consumption expenditure or household income was not always below the poverty line and was sometimes above it.
‘Non-poor’ (never poor) indicates that consumption expenditure or household
income remained above the poverty line in all observation periods (Hulme, Moore
and Shepherd 2001). Figure 1 shows a simple illustration of the spell approach.
Chronic poverty, then, can be described as the household condition of being
poor over an extended period, whereas transient poverty (– or +) refers to a state
of occasionally being poor or non-poor. This difference is typically based on
longitudinal (or panel) data or a life-history survey, both of which observe the
living conditions of the same individual or households at several points in time
(McKay and Lawson 2002). The former provides information about individuals
or households during one period or consecutive periods, while the latter captures
the dynamic aspect of living conditions from a list of retrospective questions. A
life history – for instance, the weight-for-height anthropometric measure – can
luctuate signiicantly in a short time. These luctuations may relect, for example,
3 Dercon and Shapiro (2007) contend that the impact of shocks and risks on poverty mobility has received relatively limited attention in the literature of poverty dynamics. Analysing poverty dynamics can provide insights into the effects of socio-economic and antipoverty policies on household poverty status. It can also help policy makers to identify
effective ways to help households escape poverty.
64
Teguh Dartanto and Nurkholis
FIGURE 1 The Distinction between Chronic Poor, Transient Poor (–),
Transient Poor (+) and Never Poor a
Y2
Transient poor (+)
Never poor
Chronic poor
Transient poor (–)
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Z2
0
Z1
Y1
a Y and Y represent individual or household income or consumption in period 1 and period 2
1
2
respectively. Variables Z1 and Z2 represent the poverty lines in the same periods.
Source: Adapted from Grab and Grimm (2007).
the period of the agricultural season or the effects of chronic disease. Over an
extended observation period, an individual with a weight-for-height measurement below the standard could therefore be categorised as chronically poor,
whereas an individual with a weight-for-height measurement occasionally equal
to or below the standard could be categorised as transiently poor.
Previous research on poverty dynamics
Many studies have found that human capital, demographics, geographical location, physical assets and occupational status help determine poverty status. An
increase in human capital, for example, indicated by educational attainment (years
of schooling), decreases the probability of being chronically poor and improves
a household’s ability to respond to transitory shocks (Adam and Jane 1995; Alisjahbana and Yusuf 2003). Jalan and Ravallion (1998) contend that changes in
demographics, such as increased household size, are related to chronic poverty.
McCulloch and Calandrino (2003), in Sichuan, China, conirmed that chronic poverty is commonly found in rural (especially remote) areas, and Fields et al. (2003)
found that households in urban areas have a higher probability of escaping from
poverty. A lack of physical assets is also often associated with chronic poverty
(Adam and Jane 1995), and occupational status can help determine household
poverty status. Okidi and Kempaka (2002) found that self-employed farming
households in Uganda are more likely to be chronically poor, and Kedir and
McKay (2005) found that households in Ethiopia with a head working as a waged
employee can escape poverty.
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The determinants of poverty dynamics in Indonesia: evidence from panel data
65
Grab and Grimm (2007), using the Indonesia Family Life Survey (IFLS) data
set to measure poverty dynamics, compared chronic and transient poverty in two
timespans. They found that absolute comparisons show a signiicant decline in
chronic poverty from 1993–97 to 1997–2000. The decline in both chronic and transient poverty was largely due to a substantial decline in poverty in rural Indonesia. Fields et al. (2003), using the 1993 and 1997 IFLS panel data sets, found that
the determinants of household income dynamics at those times were household
location, age of the household head, employment status of the head, change in the
gender of the head, change in employment status of the head, and change in the
number of children. Alisjahbana and Yusuf (2003), using the IFLS data sets from
1993 and 1997, observed that of the 84.8 percentage points of non-poor in 1993,
11.6 percentage points had fallen into poverty by 1997. Of the 15.2 percentage
points of poor in 1993, 7.8 percentage points had remained poor whereas the other
7.4 percentage points had escaped poverty.
OVERVIEW OF POVERTY DYNAMICS IN INDONESIA DURING 2005–07
Susenas and the economic condition
We use data from the 2005 and 2007 National Socio-Economic Survey (Susenas), conducted by Indonesia’s Badan Pusat Statistik (BPS), the national statistics agency, to measure poverty dynamics in Indonesia.4 Susenas consists of two
main data sets: core and module. The 2005 Susenas core data set recorded detailed
characteristics of 278,352 households, from an estimated 59 million households
nationally and covering various geographic regions of Indonesia. The 2005 Susenas module data set collected additional information on a subset (or 68,288 households) of core households. It recorded detailed information on food and non-food
consumption, as well as on household shocks and coping strategies.
BPS revisited around 10,600 households from the 2005 Susenas module sample
in 2007. Merging the 2005 and 2007 Susenas panels and dropping observations
that contained incomplete household information or that were outliers yielded a
total of 8,726 households (balanced-panel data). The 2007 Susenas did not revisit
those households that migrated, so the 8,726 revisited households are those that
remained in one location during 2005–07.5
4 We intended to use a longer sequence of Susenas data sets (for instance, 2002–07), to capture greater changes in poverty status. The 2002 and 2007 databases do not match, however,
because Susenas modules collect information from different categories every three years. We
also found many inconsistencies in the 2006 data, so we could not include them. Analysing
poverty dynamics using panel data covering a short period (three years) may not reveal all of
the long-run changes in poverty. Given the limitations of available data, however, analysing
a short period of poverty dynamics using a Susenas data set that provides rich information
about household socio-economic conditions and covers all provinces will nevertheless contribute to a deeper understanding of the recent situation of poverty. It will also provide useful
insights into why some households remain poor and why others escape poverty.
5 In merging the 2005 and 2007 sample identiiers of Susenas core and module data sets,
we found 9,491 balanced-panel samples. Around 1,120 samples were lost during the merger, which might be due to a split of provinces. We not only merged the sample identiiers
but also included household information such as educational attainment, physical assets,
shocks and the poverty line.
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66
Teguh Dartanto and Nurkholis
In order to provide basic information about household welfare status, our
analysis of poverty dynamics starts with a discussion of household expenditure,
the poverty line and poverty incidence during 2005–07. In this period, household
expenditure in Indonesia increased by an average of 30.4% (table 1). Households
outside Java–Bali experienced an increase in expenditure of 39.4%, while households in Java–Bali experienced an increase of only 23.6%. This large increase in
household expenditure outside Java–Bali was not followed by a corresponding
reduction in poverty in those areas, because the poverty line in those areas rose
by 31.9% (largely owing to the rise of rice prices in 2005–06 (Lindblad and Thee
2007)). The national poverty incidence remained unchanged during 2005–07, but
the poverty incidence outside Java–Bali decreased by around 0.5 of a percentage point. Surprisingly, urban poverty also decreased by around 0.5 of a percentage point, but rural poverty increased by almost 1.0 percentage point. Although
both rural and urban households experienced a similar proportion of increase in
expenditure, the rural poverty line increased by more than 25% while the urban
poverty line increased by only 14%.
Poverty dynamics in Indonesia during 2005–07
This paper uses the spell approach (as illustrated in igure 1), the poverty lines of
2005 and 2007, and the poverty measures of the Foster–Greer–Thorbecke (FGT)
formula (Foster, Greer and Thorbecke 1984) to identify and measure poverty status in Indonesia.6 It analyses only the P0 (head-count index) of the FGT poverty
measurement. As this paper uses a short period of panel data, it may be inappropriate to refer to the chronic poor and never poor; both categories require at least
ive years of longitudinal data to provide a clear deinition and analysis. Using
expenditure-based poverty measures, we have categorised households into just
four groups: poor, transient poor (–), transient poor (+) and non-poor. This adjustment does not reduce the signiicance of this analysis of poverty dynamics in
Indonesia. The paper also applies three different poverty lines: the oficial poverty
line, published by BPS; the lower poverty line (75% of the oficial poverty line);
and the upper poverty line (125% of the oficial poverty line). These three different poverty lines help examine the sensitivity of poverty incidence to changes in
the poverty line. Figure 2 shows Indonesian poverty dynamics during 2005–07 at
a national level, using the oficial poverty line.
Table 2 shows that poverty in Indonesia is predominantly a rural phenomenon and very sensitive to changes in the poverty line: an increase of 25% in the
poverty line causes an increase of more than 100% in the poverty rate. At the
disaggregated level, 95% of urban poor households in 2005 were able to escape
poverty during 2005–07, while 64% of rural poor households were able to do the
same. Around 11% of rural non-poor households in 2005 subsequently fell into
6 The FGT class of poverty measures follows:
1 q ⎛ z − yi ⎞
πα = ∑ ⎜
n i=1⎝ z ⎟⎠
α
where π is the poverty index, n is the total population size, z is the poverty line, yi is the
income of the ith individual (or household), q represents the number of individuals just
below or on the poverty line, and α is a parameter for the FGT class.
The determinants of poverty dynamics in Indonesia: evidence from panel data
67
TABLE 1 Summary of Household Expenditure, the Poverty Line
and Poverty Incidence (2005–07)
Household Expenditure Calculated Based on Balanced Panels of 2005 and 2007
(Rp/month/capita)
2005
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Region
2007
Change
(%)
Mean
SDa
Mean
SD
National
Urban
288,579
401,305
260,391
348,171
376,175
521,161
330,679
409,812
30.4
29.9
Rural
208,434
119,911
273,093
205,269
31.0
Java–Bali
Outside Java–Bali
312,278
261,840
301,724
200,639
386,130
364,944
337,318
322,697
23.6
39.4
Oficial Poverty Line
(Rp/month/capita)
2005
2007
Change
(%)
141,465
165,565
167,390
187,942
18.3
13.5
Rural
117,365
146,837
25.1
Java–Bali
145,569
169,031
16.1
Urban
170,153
192,974
13.4
Rural
120,985
145,088
19.9
Outside Java–Bali
135,768
179,015
31.9
Urban
Rural
156,456
115,080
197,909
160,121
26.5
39.1
Region
National
Urban
Poverty Incidence, Calculated Based on the Total Sample of Susenas 2005 and 2007
(%)
Region
2005
2007
Change
National
Urban
16.6
13.0
16.6
12.5
0.0
–0.5
Rural
19.4
20.4
1.0
Java–Bali
Outside Java–Bali
15.8
18.0
16.0
17.5
0.2
–0.5
a SD = standard deviation.
Source: Authors’ calculations and several BPS publications.
68
Teguh Dartanto and Nurkholis
FIGURE 2 National Poverty Dynamics during 2005–07 a
No. of HH
8,726
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P-05
1,061 (12.2%)
P-07
Poor
292
(3.4%)
NP-07
Transient
poor (+)
769 (8.8%)
NP-05
7,665 (87.8%)
P-07
Transient
poor (–)
509 (5.8%)
NP-07
Non-poor
7,156
(82.0%)
a HH= households; P = poor; NP = non-poor. Figures in parentheses are the percentage of the total
sample.
Source: Authors’ calculations.
poverty, compared with only 1% of urban non-poor households. Urban households contributed more transient poor (+) and non-poor while rural households
contributed more transient poor (–) and poor. Rural households rely mostly on
agricultural activities for income, which are relatively unstable compared with
industrial or service sectors in the urban area. Negative shocks such as crop loss,
falling agricultural prices, or death and illness can therefore easily send rural
households into poverty.
Table 2 also shows poverty dynamics at the disaggregated, regional level of
Java–Bali and outside Java–Bali.7 In Indonesia, it is generally observed that there
are two types of regional segregation: Java–Bali versus outside Java–Bali, and
Western Indonesia versus Eastern Indonesia. Western Indonesia comprises Sumatra, Java, Bali and Kalimantan, while Eastern Indonesia consists of Sulawesi, Nusa
Tenggara, Maluku and Papua. Java and Bali have signiicantly larger populations
and more developed economic activities and infrastructure than the other islands.
Manufacturing activities and service sectors dominate the economy of Java–Bali;
agricultural and mining activities dominate outside Java–Bali. Suryadarma et al.
(2006), using the 2003 Village Potential Survey (Podes) and the 2002 and 2004
Susenas panel data sets, showed that households in Java–Bali had better access to
basic services, such as education and health, than households outside Java–Bali.
7 According to BPS, the 2005 and 2007 Susenas panel data sets should be presented at
the national, rural and urban levels but not at the provincial level. However, there is still
the possibility and validity of analysing at the regional level both Java–Bali and outside
Java–Bali, because the 2005 and 2007 Susenas balanced panel had been distributed proportionally between Java–Bali (4,626 households) and outside Java–Bali (4,100 households).
A regional analysis would follow Suryadarma et al. (2006), who used the 2002 and 2004
Susenas panel data sets to analyse the level of access to basic services at regional level.
The determinants of poverty dynamics in Indonesia: evidence from panel data
69
TABLE 2 Overview of Poverty Status in 2005 and 2007 a
(number of households)
Condition in 2007
Lower Poverty Line
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Total
Condition in 2005
Urban
74
Poor
3,552
Non-poor
Rural
209
Poor
4,891
Non-poor
Poor
Oficial Poverty Line
Nonpoor
Total
Poor
Nonpoor
Upper Poverty Line
Total
Poor
Nonpoor
2
2
72
3,550
281
3,345
13
32
268
3,313
690
2,936
171
220
519
2,716
35
153
174
4,738
780
4,320
279
477
501
3,843
1,627
3,473
832
783
795
2,690
Java–Bali
108
Poor
4,518
Non-poor
Outside Java–Bali
175
Poor
3,925
Non-poor
16
16
92
4,502
475
4,151
143
243
332
3,908
1,088
3,538
472
513
616
3,025
21
139
154
3,786
586
3,514
149
266
437
3,248
1,229
2,871
531
490
698
2,381
National
Poor
Non-poor
283
8,443
37
155
246
8,288
1,061
7,665
292
509
769
7,156
2,317
6,409
1,003
1,003
1,314
5,406
Total
8,726
192
8,534
8,726
801
7,925
8,726
2,006
6,720
a The oficial poverty line is that published by BPS. The lower poverty line is 75% of the BPS line, and
the upper poverty line is 125% of the BPS line.
Source: Authors’ calculations based on Susenas data.
Almost 20% of villages outside Java–Bali had no primary school, compared with
less than 1% of villages in Java–Bali.
The regional segregation between Java–Bali and outside Java–Bali might inluence household poverty characteristics, owing to differences in economic structure and infrastructure availability. At the disaggregated, regional level, we found
that 70% of poor households in Java–Bali in 2005 were able to escape poverty
during 2005–07 and that 75% of poor households outside Java–Bali were able to
do the same (table 2). Around 6% of non-poor households in Java–Bali in 2005
fell into poverty, compared with 8% of those outside Java–Bali. Further, around
30% of poor households in Java–Bali and around 25% of poor households outside
Java–Bali remained poor in two periods of observation.
Non-poor households outside Java–Bali seemed more vulnerable to falling
into poverty than those in Java–Bali, while poor households outside Java–Bali
tended to move more easily out of poverty than those in Java–Bali. The economic
dependence of areas outside Java–Bali on agricultural and mining commodities
may explain this vulnerability: luctuations in the prices of these commodities
often lead to luctuations in household income and expenditure.
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70
Teguh Dartanto and Nurkholis
RESEARCH METHODOLOGY
Model speciication
As discussed above, the spell approach categorises households in Indonesia as
poor, transient poor (–), transient poor (+) or non-poor. We contend that household poverty status has an order in which one status might be more favourable
than others. Non-poor is the most preferred condition; poor the least preferred.
The order of transient poor (–) and transient poor (+) is in between poor and nonpoor. This paper assumes that the improvement condition of transient poor (+) is
more favourable than the degradation condition of transient poor (–).
We used an ordered logit model to examine the determinants that can change
household poverty status and enable the poor to escape from poverty. Such a
model is useful for understanding the relative effect of different household characteristics on poverty status, but it is less useful for distinguishing between poverty categories. Independent variables (predictors) in this model are essentially
divided into two groups: the 2005 initial variables and the 2005–07 change variables.
The initial variables represent household conditions and positions that may
change household poverty status in the future. For instance, poor agricultural
households with a small area of land in the initial year might become continuously poor later: the land might not produce above a subsistence level, and the
household might not have enough resources to invest in modern agricultural
technology or to buy good seed for the next production. Uninsured households
that experience health shocks in the initial year might become poor in the future –
their members might be unable to work, or they might have to allocate all of their
resources to medical treatments. Households forced to sell land for medical treatments might later become impoverished; and non-poor households in the initial
year might become poor households in the next, because of changes in variables
such as marital or job status.
Independent variables included in the model consider the data availability in
the 2005 and 2007 Susenas, as well as variables used in research by Jalan and
Ravallion (1998), Herrera (1999), Okidi and Kempaka (2002), Alisjahbana and
Yusuf (2003), Bigsten et al. (2003), Fields et al. (2003), Haddad and Ahmed (2003),
McCulloch and Calandrino (2003), McKay and Lawson (2003), Contreras et al.
(2004), Kedir and McKay (2005), Woolard and Klasen (2005), and Widyanti et al.
(2009). The ordered logit model (equation (1)) is as follows:
yi =
HHCi0β + S COi0 x + ShockGovi0ϕ + ΔVARi05−07φ + ei
(1)
where
• yi = a household’s poverty status: 0 = poor, 1 = transient poor (–), 2 = transient
poor (+), 3 = non-poor;
• i0 = a vector of family characteristics in 2005, including marital status, age,
educational attainment, number of household members, dummy variables for
location and island;
• SEOi0 = a vector of socio-economic characteristics in 2005, including dummy
variables for employment sector and employment status, land ownership (in
hectares), size of house (in square metres), access to electricity for lighting, and
The determinants of poverty dynamics in Indonesia: evidence from panel data
•
•
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•
•
71
a dummy variable for households with a family member working as a migrant
overseas;
Shockovi0 = a vector of shocks, risks and policy variables received by a
household in 2005;8
ΔVRi05−07 = a vector of changes in variables during 2005–07, including
change in marital status, number of household members, employment sector,
employment status, access to electricity for lighting, and microcredit;
e is an error term; and
i is the household identiier (i = 1,…, 8,726).
Appendix table 1 describes the variables and their expected signs.
Ordered response model
Equation (1) is an ordered response model (probit or logit) with four outcomes,
y = 0,1,2,3 . An ordered logit model for y (conditional on explanatory variables
x) can be derived from a latent variable model. Assume that a latent variable, y∗,
is determined by:
{
}
y∗ = xβ + e, e x Noma 0,1
( )
(2)
where β is a Kx1 coeficient vector and where vector x does not contain a constant
(for a detailed explanation of the ordered response model, see Wooldridge 2010).
The parameters of the model can be estimated by using maximum likelihood
estimation. The signs of the estimated coeficients from the ordered probit (logit)
models have the exact meaning with the result of ordinary least square (OLS)
estimations. A negative sign determines whether the choice probabilities shift
to lower categories when the independent variable increases. The partial effect
of estimated coeficients, however, cannot be interpreted directly as the result of
OLS estimation. In most cases, we are interested in the response probabilities or
partial effects, P y = j x , of the ordered probit model (see Wooldridge 2010).
The ordered logit model (equation (1)) uses three sample groups: Java–Bali,
outside Java–Bali and national (entire sample). Although our analysis of poverty
dynamics focuses on the national group, dividing the sample helps show the consistency and robustness of estimation results. It also checks whether there are signiicant differences in poverty characteristics between Java–Bali and the rest of
the country.
(
)
DESCRIPTIVE DATA ANALYSIS
Households in Indonesia can be divided into four groups, based on their poverty experience in 2005–07 (table 3): poor (292 households), transient poor (–) (509
households), transient poor (+) (769 households) and non-poor (7,156 households).
8 Negative shocks and risks include economic risks and health shocks. Positive shocks include improvements to public facilities surrounding living areas, more new jobs, and microcredit. Economic risks include crop loss, job loss, falling crop prices and increased production
costs. This vector also includes interaction variables between savings and socio-economic
shocks, and the policy variables of Raskin and Askeskin. These variables are intended to examine the effectiveness of saving and government policies in coping with negative shocks.
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:40 17 January 2016
72
Teguh Dartanto and Nurkholis
We observed that members of the poor group are largely uneducated or have a
low level of educational attainment; live in a rural area, rely on income from agricultural production and the informal sector; and either own a small area of land
or are landless. Unlike the other groups, the poor group is excluded from accessing modern utilities (around 39% of households in the poor group do not have
electricity) and inancial services (none of the households in the poor group have
received microcredit from either the government or other sources).
The demographic characteristics and socio-economic variables of the transient poor (–) group were slightly better than those of the poor group – including
higher educational attainment, better access to electricity, and ownership of larger
land areas – and fewer transient poor (–) households experienced economic risks
and health shocks. We found that the major variable change faced by the transient
poor (–) group during 2005–07 was an increase of one household member moving
from formal to informal employment (14%).
Compared with the transient poor (–) group, the transient poor (+) group has
a higher level of educational attainment; lives in an urban area; has better access
to electricity in 2007 than it did in 2005; has a low percentage of members working in the agricultural sector; and has a low percentage of households experiencing economic and health shocks, as well as suficient savings to cope with such
shocks. The greatest differences in the change variables of the transient poor (+)
group and the poor and transient poor (–) groups is a decrease in household size
by almost one member, a higher proportion of households receiving microcredit,
a higher proportion of households gaining access to electricity, and a lower proportion of households moving from formal to informal employment.
The non-poor group is generally more educated than other groups; has fewer
household members; lives in urban areas; has a larger proportion of households
connected to electricity; has experienced fewer economic and health shocks; and
has suficient savings to cope with such shocks. The daily activities of non-poor
households are disrupted by health shocks only 3.7 days per month, around half
the time experienced by poor households. Furthermore, members of households
in the non-poor group tend to work in the formal and non-agricultural sectors, so
their income is less volatile and less likely to depend on government assistance.
Table 4 shows that signiicant differences exist between households in Java–
Bali and those outside Java–Bali. The latter, for example, have more family members, live mostly in rural areas and have larger areas of agricultural land. Fewer
households outside Java–Bali have electricity for lighting. Furthermore, households outside Java–Bali experienced more economic risks and health shocks than
households in Java–Bali. The daily activities of households outside Java–Bali are
disturbed by health shocks half a day more than those of households in Java–Bali.
Households outside Java–Bali are more vulnerable to being transient poor, both
(–) and (+), than households in Java–Bali.
THE DETERMINANTS OF POVERTY DYNAMICS IN INDONESIA
This paper uses three models – Java–Bali, outside Java–Bali and national – based
on household location, which were estimated using maximum likelihood estimation, with robust standard errors. Tables 5 and 6 show the estimation results of
the ordered logit model. The signs of coeficients in the three models are almost
The determinants of poverty dynamics in Indonesia: evidence from panel data
73
TABLE 3 Descriptive Data on Poverty Status a
Poor
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Mean
Demographic variables in 2005
Marital status of HH head (1 = married; 0 = other)
Age of HH head (years)
Educational attainment of HH head (years)
Number of HH members
Island dummy (1 = Java–Bali; 0 = other)
Location dummy (1 = urban; 0 = rural)
Socio-economic variables in 2005
Employment sector of HH head (1 = agriculture;
0 = other)
Employment status of HH head (1 = formal;
0 = other)
Land ownership (ha)
House size (m2)
HH with a migrant-worker (TKI) member
(1 = has TKI member; 0 = other)
Access to electricity for lighting
(1= no; 0 = yes)
Shocks/risks and policy variables in 2005
ECSHRS (1= experience; 0 = no experience)
Used Raskin for ECSHRS (1 = yes; 0 = no)
Daily activities disrupted by health problems
for all family members (days/month)
Health insurance (1 = Askeskin; 0 = other)
Saving as a coping strategy for ECSHRS
(1 = having savings; 0 = no savings)
Microcredit (1 = yes; 0 = no)
Source of microcredit (1 = government;
0 = other)
Family member gaining employment
(1 = yes; 0 = other)
Improved public facilities nearby
(1 = yes; 0 = no)
Change variables during 2005–07
Change in number of HH members
Change in marital status of HH head
(1 = divorced; 0 = other)
Change in employment sector of HH head
(1 = agricultural to non-agricultural; 0 = other)
Change in employment status of HH head
(1 = formal to informal; 0 = other)
Change in access to electricity for lighting
(1 = access in 2007 but not in 2005; 0 = other)
Change in access to credit (1 = credit in 2007
but not in 2005; 0 = other)
Number of observations
Transient
Poor (–)
SD Mean
Transient
Poor (+)
SD Mean
Non-poor
SD Mean
SD
0.88 0.33 0.85 0.35 0.87 0.34 0.85 0.36
47.43 14.28 46.17 14.90 47.43 14.23 45.53 13.71
4.74 3.15 5.10 3.37 5.65 3.19 6.91 4.38
4.72 1.79 4.06 1.74 4.88 1.77 3.85 1.60
0.49 0.50 0.48 0.50 0.43 0.50 0.55 0.50
0.04 0.21 0.06 0.24 0.35 0.48 0.46 0.50
0.80
0.40
0.72
0.45
0.64
0.48
0.45
0.50
0.16
0.36
0.18
0.38
0.17
0.38
0.30
0.46
0.64 0.79 0.86 1.19 0.74 1.26 0.52 1.59
59.77 50.19 58.17 27.92 56.67 55.95 70.32 65.37
0.04 0.19 0.04 0.20 0.04 0.19 0.04 0.21
0.49
0.27
0.44
0.27
0.44
0.10
0.30
0.28 0.45
0.02 0.14
6.36 11.20
0.26
0.02
4.45
0.44
0.12
8.61
0.23
0.03
4.85
0.42
0.16
8.70
0.16
0.01
3.73
0.37
0.08
7.80
0.04
0.01
0.19
0.08
0.03
0.01
0.16
0.08
0.02
0.02
0.15
0.14
0.01
0.03
0.10
0.16
0.00
0.00
0.00
0.00
0.03
0.01
0.16
0.09
0.02
0.01
0.12
0.07
0.03
0.01
0.18
0.10
0.06
0.24
0.05
0.21
0.10
0.30
0.08
0.27
0.13
0.34
0.09
0.29
0.08
0.27
0.10
0.29
–0.07
0.05
1.27
0.23
0.64
0.05
1.50 –0.59
0.21 0.06
1.67
0.24
0.07
0.06
1.53
0.23
0.11
0.32
0.11
0.31
0.13
0.34
0.14
0.35
0.11
0.32
0.14
0.34
0.08
0.27
0.12
0.32
0.11
0.31
0.08
0.27
0.13
0.34
0.04
0.21
0.03
0.16
0.04
0.19
0.05
0.22
0.07
0.26
0.39
292
509
769
7,156
SD = standard deviation; HH = household; TKI = Tenaga Kerja Indonesia (Indonesian migrant worker);
ECSHRS = economic shocks and risks; Raskin = rice for the poor; Askeskin = health insurance for the poor.
a
Source: Authors’ calculations based on Susenas data.
74
Teguh Dartanto and Nurkholis
TABLE 4 Descriptive Data Used in the Ordered Logit Model a
Java–Bali
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Mean
Demographic variables in 2005
Marital status of HH head (1 = married; 0 = other)
Age of HH head (years)
Educational attainment of HH head (years)
Number of HH members
Island dummy (1 = Java–Bali; 0 = other)
Location dummy (1 = urban; 0 = rural)
Socio-economic variables in 2005
Employment sector of HH head (1 = agriculture; 0 = other)
Employment status of HH head (1 = formal; 0 = other)
Land ownership (ha)
House size (m2)
HH with a migrant-worker (TKI) member
(1 = has TKI member; 0 = other)
Access to electricity for lighting (1= no; 0 = yes)
Shocks/risks and policy variables in 2005
ECSHRS (1= experience; 0 = no experience)
Used Raskin for ECSHRS (1 = yes; 0 = no)
Daily activities disrupted by health problems
for all family members (days/month)
Health insurance (1 = Askeskin; 0 = other)
Saving as a coping strategy for ECSHRS
(1 = having savings; 0 = no savings)
Microcredit (1 = yes; 0 = no)
Source of microcredit (1 = government; 0 = other)
Family member gaining employment (1 = yes; 0 = other)
Improved public facilities nearby (1 = yes; 0 = no)
Change variables during 2005–07
Change in number of HH members
Change in marital status of HH head (1 = divorced; 0 = other)
Change in employment sector of HH head
(1 = agricultural to non-agricultural; 0 = other)
Change in employment status of HH head
(1 = formal to informal; 0 = other)
Change in access to electricity for lighting
(1 = access in 2007 but not in 2005; 0 = other)
Change in access to credit (1 = credit in 2007 but not in 2005; 0
= other)
Outside
Java–Bali
SD Mean
National
SD Mean
SD
0.85 0.36 0.85 0.35 0.85 0.36
46.73 14.03 44.75 13.59 45.80 13.86
6.51 4.27 6.74 4.22 6.62 4.24
3.79 1.54 4.21 1.76 3.98 1.66
0.53 0.50
0.51 0.50 0.31 0.46 0.42 0.49
0.41 0.49 0.58 0.49 0.49 0.50
0.30 0.46 0.26 0.44 0.28 0.45
0.23 1.09 0.94 1.83 0.56 1.53
73.38 62.55 62.04 62.37 68.05 62.72
0.04 0.20 0.05 0.21 0.04 0.20
0.03
0.16
0.26
0.44
0.13
0.34
0.16
0.01
3.74
0.37
0.08
7.67
0.19
0.01
4.21
0.39
0.12
8.53
0.17
0.01
3.96
0.38
0.10
8.09
0.01
0.03
0.10
0.16
0.02
0.02
0.12
0.14
0.01
0.02
0.11
0.15
0.05
0.02
0.08
0.10
0.21
0.13
0.27
0.30
0.01
0.00
0.08
0.09
0.10
0.04
0.26
0.28
0.03
0.01
0.08
0.10
0.17
0.10
0.27
0.29
0.07
0.05
0.14
1.42
0.22
0.34
0.01
0.06
0.14
1.69
0.24
0.34
0.04
0.06
0.14
1.55
0.23
0.34
0.12
0.32
0.12
0.32
0.12
0.32
0.02
0.13
0.10
0.30
0.06
0.23
0.08
0.27
0.05
0.22
0.07
0.25
Poverty status
Poor
Transient poor (–)
Transient poor (+)
Non-poor
143
243
332
3,908
149
266
437
3,248
292
509
769
7,156
Number of observations
4,626
4,100
8,726
SD = standard deviation; HH = household; TKI = Tenaga Kerja Indonesia (Indonesian migrant worker);
ECSHRS = economic shocks and risks; Raskin = rice for the poor; Askeskin = health insurance for the poor.
a
Source: Authors’ calculations based on Susenas data.
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The determinants of poverty dynamics in Indonesia: evidence from panel data
75
the same, except in the following variables: age of household head (outside Java–
Bali), economic shocks and risks (outside Java–Bali), source of microcredit (outside Java–Bali) and change in marital status (Java–Bali). All three models show
that the Wald chi-square statistics of the ordered logit model are statistically signiicant, indicating that at least one of the covariates or independent variables
affects household poverty status. Generally, the ordered logit models of poverty
dynamics are consistent and robust.
Table 6 shows the partial effects (dy/dx) of changes in the probability of households categorised as poor, transient poor (–), transient poor (+) and non-poor,
responding to change in independent variables (predictors). The partial effects
(the predicted probability of household poverty status) were evaluated at means
of independent variables y = x .
(
)
Demographic variables
All three models in Table 5 conirm that educational attainment, location and
the number of household members are the most important demographic determinants of household poverty status. The variables of marital status and age of
the household head are both statistically signiicant to poverty status in model 3
(national level); but marital status is not signiicant in model 1 (Java–Bali), nor is
age in model 2 (outside Java–Bali). Married households outside Java–Bali have a
higher probability of being non-poor; most of the households outside Java–Bali
are working in the labour-intensive agricultural sectors, so a married household
has more workers than a single household and therefore has the potential to produce greater output and generate greater income.
Table 6 shows that an increase in the number of household members decreases
the probability of being non-poor, while increasing the probability of being poor,
transient poor (–) and transient poor (+). This inding is similar to those of Herrera (1999), Haddad and Ahmed (2003), and Woolard and Klasen (2005). Given a
ixed income, an increase in the number of members forces households to reduce
per-person consumption to support the additional members. A better education
increases the probability of being non-poor, because a higher level of education
provides greater opportunities for a better job and, subsequently, a higher income.
These indings conirmed the conclusions of other studies, such as Bigsten et al.
(2003), and Widyanti et al. (2009).
The location dummy variable reveals that those living in urban areas have a
higher probability of being non-poor. This inding conirms indings in studies
of other countries, such those of Fields et al. (2003), and Kedir and McKay (2005).
Urban areas, where most industries and economic activities are located, provide
more job opportunities in the formal and the informal sectors.
Socio-economic variables
As many studies have found (Dercon and Krishnan 2000; Okidi and Kempaka
2002), households with a head working in the agricultural sector have a high
probability of being poor, owing to low productivity and wage rates. This probability increases by 1.3% in Java–Bali, 1.1% outside Java–Bali and 1.4% nationally,
as table 6 shows. Furthermore, households with a head working in the formal sector – that is, working for an agency, ofice or company for a ixed salary, either in
cash or in goods – have a higher probability of being non-poor. Those working in
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76
Teguh Dartanto and Nurkholis
formal sectors increase their probability of being non-poor by 4.6% in Java–Bali,
6.8% outside Java–Bali and 5.8% nationally. The formal sector in Indonesia guarantees a stable income and pays higher wage rates than the informal sector, which
complements the inding of Kedir and McKay (2005) that waged employees in
rural Ethiopia have a higher probability of escaping from poverty.
Owing to the lack of job opportunities in Indonesia, individuals who cannot
ind jobs in the formal sector or start a business (as an entrepreneur) are forced to
work either in the domestic, informal sector, for a low wage, or outside Indonesia,
as migrant workers overseas. Most migrant workers also work in the informal
sector, as domestic helpers, but they are paid a higher wage. This paper conirms that households with a family member working outside Indonesia tend to
be non-poor. Their remittances may take the form of family transfers to support
basic needs, or entrepreneur capital transfers to support their families starting a
business. Hall (2007) showed that remittances play an important role in poverty
dynamics in Latin America. Notably, the coeficient for this variable is insigniicant for the outside Java–Bali sample.
Land ownership as an indicator of physical assets signiicantly affects household poverty status. Table 6 shows that a one-hectare increase in land size would
increase the probability of being non-poor by 1.7% in Java–Bali, 1.3% outside
Java–Bali and 1.7% nationally. Landless and small-landholder households tend
to be chronically poor, because their productive assets are inadequate for increasing their income. Land reforms aimed at increasing access to land as a productive
asset by poor households could alleviate chronic poverty. This inding is similar
to those of Haddad and Ahmed (2003), and Woolard and Klasen (2005). House
size as an indicator of physical assets can also determine a household’s poverty
status: a larger house is associated with a lower probability of being non-poor.
Both indings may possibly also imply that the certiication of agricultural land
and house ownership may help alleviate poverty: certiication would legalise
land and house ownership, which could then be used as collateral for gaining
productive credit from formal institutions.
Other socio-economic variables, such as access to modern electricity utilities,
signiicantly increase the probability of escaping poverty. The unit cost of lighting with electricity is cheaper per kilowatt-hour than lighting with candles or an
oil lamp (Foster and Tre 2003). Households could possibly reduce their energy
expenditure and potentially re-allocate savings to income-generating activities or,
if children are part of the household, to education. This could ultimately help lift
households out of poverty. Table 4 shows that households in Java–Bali have better
access to electricity than households outside Java–Bali, owing more to the availability of the electricity gri