Household Energy Consumption and Its
Determinants in Timor-Leste
∗
DIL BAHADUR RAHUT, KHONDOKER ABDUL MOTTALEB, AND AKHTER ALI

Using data from the 2007 Timor-Leste Living Standards Survey, this paper
examines the determinants of household energy choices in Timor-Leste. El
majority of households are dependent on dirty fuels such as fuelwood and
kerosene for energy. Only a small fraction of households use clean energy
such as electricity. Econometric results show that wealthy households, urban
households, and those headed by individuals with higher levels of education are
less likely to use and depend on kerosene and more likely to use and depend
on electricity. While female-headed households are generally more likely to use
and depend on fuelwood, richer female-headed households are more likely to
use and depend on electricity. Our findings highlight the importance of ensuring
an adequate supply of clean energy for all at affordable prices and of investing
in education to raise awareness about the adverse impacts of using dirty fuels.

Palabras clave: education, energía, fuelwood, household, income, Timor-Leste
JEL codes: D12, I25, I31, Q42

I. Introducción

Más que 1.4 billion people worldwide lack access to clean energy such
as electricity, mientras 2.7 billion people rely on dirty energy such as biomass and
fuelwood for cooking (Kaygusuz 2012).1 Enhancing access to clean energy is a
prerequisite for sustainable economic development (Spalding-Fecher 2005, Abebaw
2007). Alarmingly, a lack of access to clean energy is found to be associated
with ill health and the prevalence of poverty (Ekholm et al. 2010). Desafortunadamente,
the majority of households, particularly in rural areas in developing economies,
lack access to clean energy sources such as electricity even though demand for
clean energy consistently increases in line with rising household incomes in these
economías.

∗Dil Bahadur Rahut (Autor correspondiente): Program Manager, Socioeconomics Program, International Maize and
Wheat Improvement Center (CIMMYT) (Texcoco, México). Correo electrónico: d.rahut@cgiar.org; Khondoker Abdul Mottaleb:
Agricultural Economist, Socioeconomics Program, CIMMYT (Texcoco, México). Correo electrónico: k.mottaleb@cgiar.org;
Akhter Ali: Agricultural Economist, Socioeconomics Program, CIMMYT (Islamabad, Pakistán). Correo electrónico:
akhter.ali@cgiar.org. The authors would like to thank the managing editor and anonymous referees for helpful
comments. The usual disclaimer applies.

1As electricity and gas pollute the atmosphere less than coal, kerosene, and fuelwood, the former are referred

to as “clean energy,” while the later are referred to as “dirty energy.”

Asian Development Review, volumen. 34, No. 1, páginas. 167–197

C(cid:3) 2017 Asian Development Bank
and Asian Development Bank Institute

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168 ASIAN DEVELOPMENT REVIEW

Inadequate supply, the consequent high costs, and a lack of purchasing power
are the major barriers to a household’s conversion to clean energy sources in
developing economies (Arntzen and Kgathi 1984; Heltberg, Arndt, and Sekhar
2000). The price of energy increases with improvements in energy quality and its
ease of use (Behera et al. 2015, Rahut et al. 2014).2 Por ejemplo, fuel costs increase
as a household shifts from solid fuels such as biomass to other fuels such as gas and
electricidad. The energy ladder hypothesis postulates that with increases in income and
awareness, households gradually shift from solid fuels to more modern and efficient
energy sources such as liquid petroleum gas, natural gas, and electricity (Leach 1975,
1992). Several studies have documented that the energy sources used by households
change as income levels increase (Rao and Reddy 2007; Khandker, Barnes, y
Samad 2012; Rahut, Behera, and Ali 2016), with a shift from traditional to modern
fuels (Daioglou, Van Ruijven, and Van Vuuren 2012), particularly electricity (Hills
1994). A few studies, sin embargo, have found that increased incomes do not always lead
to households switching to cleaner fuels (Masera, Saatkamp, and Kammen 2000;
Nansaior et al. 2011; Huang 2015). De este modo, the direction of the relationship between
income and the demand for clean energy remains uncertain and thus requires further
investigation using large samples across economies (Khandker, Barnes, and Samad
2012).

Using data from the 2007 Timor-Leste Survey of Living Standards (TLSLS),
this paper analyzes the influences of income and human capital on household energy
choices in developing economies. Understanding patterns of household energy
consumption and the determinants of energy choices is important. Timor-Leste,
a newly independent small country in Southeast Asia with an area of 15,410 square
kilometers and a population of 1.2 millón, is one of the poorest economies in the
world with a poverty rate of 27% (Datt et al. 2008). It was a Portuguese colony
para 450 years and later governed by Indonesia from 1976 a 2002. On 20 Puede
2002, Timor-Leste became a sovereign state, joining the United Nations and the
Community of Portuguese Language Countries.

Since independence, Timor-Leste has aspired to boost

the provision
of electricity through a grid extension program based on the national rural
electrification master plan (Government of Timor-Leste 2012). En 2002, solo 36%
of Timor-Leste’s 0.825 million people had access to electricity, most of whom were
concentrated in the capital of Dili (International Monetary Fund 2004). In its most
recent survey, the World Bank found that access to electricity was limited to 6%–10%
of rural households (Banco mundial 2005). The nearly two-thirds of all households in
Timor-Leste that lack access to electricity mainly depend on kerosene and candles
to meet their lighting needs. Fuelwood is the cheapest form of fuel available and

2en este documento, the quality of an energy source is defined in terms of the nature of its pollution. Sources of
energy that emit smoke and pollute the environment like fuelwood, dung cake, coal, and kerosene are regarded as low
quality sources of energy. Sources like liquid petroleum gas and electricity are regarded as high quality.

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HOUSEHOLD ENERGY CONSUMPTION AND ITS DETERMINANTS IN TIMOR-LESTE 169

is used by 95% of households in Timor-Leste for cooking (Banco mundial 2005).
This heavy reliance on fuelwood is the main cause of rapid deforestation in Timor-
Leste. Además, the indoor air pollution generated using fuelwood is a major
concern for human health. En 2003, total health expenditure from indoor air pollution
was estimated at $12.4 millón, o 1.4% of gross national income (Arcenas et al. 2010). Households in Timor-Leste spend an average of $14.3 on energy per month,
which is the equivalent of 20% of a typical rural household’s monthly income and
on average, members of a household spend 3.5 hours per day for cooking and
allocate 6 hours per week for collecting fuelwood (Mercy Corps 2011). An average
household uses 9.3 kilograms of fuelwood daily and 3 tons annually (Mercy Corps
2011). In addition to being the primary source of deforestation, this massive use of
fuelwood negatively affects the agricultural systems of Timor-Leste (Banco mundial
2010).

Timor-Leste has vast reserves of natural gas in the Timor Sea and thus
has great potential for generating electricity cheaply (Strategic Development Plan
2011). Against this backdrop, an analysis of household energy choices in a newly
independent and poverty-stricken developing economy can provide guidance to
policy makers and international donors on what types of energy should be promoted
for facilitating rapid economic development and reducing widespread poverty.

This paper makes four distinct contributions to the existing literature. To the
best of our knowledge, no such energy study has been carried out in Timor-Leste
using large, nationally representative household data sets. De este modo, this study can
provide insight to policy makers and donor agencies on domestic energy policy
in Timor-Leste. Segundo, the study confirms the existing energy ladder hypothesis,
which suggests there is (i) an inverse relationship between household wealth and
education levels and the use of traditional energy such as biomass, y (ii) a
positive relationship between household wealth and education levels and the use
of clean energy such as electricity. Tercero, this paper is unique in using econometric
modelos, including a multivariate probit model to analyze the factors influencing
household energy choices and a Tobit model to examine the intensity of energy
consumption based on the share of household expenditure allocated for different
energy sources. Finalmente, we reestimate our econometric models by splitting and
employing the sampled observations into 75%, 50%, y 25% segments to examine
the robustness and sensitivity of the findings.

The paper is organized as follows. Section II includes a brief literature
review and two testable hypotheses. Section III outlines the data sources and
data collection process, as well as the specification of econometric models. Nosotros
subsequently present descriptive analyses, empirical results, and discussions of
the determinants of household energy choices in section IV. Section V presents
consumption intensity. Section VI presents major empirical findings. Section VII
concludes with a discussion of the policy implications.

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170 ASIAN DEVELOPMENT REVIEW

II. Literature Review and Testable Hypotheses

The energy ladder hypothesis postulates that as incomes rise households
gradually shift from solid fuels to more modern and efficient energy sources such
as kerosene, liquid petroleum gas, natural gas, and electricity (Leach 1975, 1992).
De este modo, the transition from solid fuels to more efficient and modern energy sources is
greatly influenced by household income (Hills 1994; Rao and Reddy 2007; Daioglou,
Van Ruijven, and Van Vuuren 2012; Khandker, Barnes, and Samad 2012). With an
increase in income, the opportunity cost of collecting fuelwood increases. In many
casos, it might be more efficient for high-income households to switch to natural
gas, kerosene, or electricity as a source of fuel rather than collecting fuelwood given
the rising opportunity cost involved. A few studies, sin embargo, failed to establish any
correlation between rising incomes and households switching to efficient energy
(Masera, Saatkamp, and Kammen 2000; Nansaior et al. 2011). To understand the
direction of the relationship between income and energy choices as incomes rise,
we postulate the following hypothesis:

Hipótesis (1): It is highly likely that households with relatively higher incomes are
less likely to depend on kerosene and fuelwood and more likely to choose electricity
and other efficient fuels. De este modo, they will spend relatively more income on clean
energy such as electricity.

Household demographics such as the sex of a household head can have a
significant influence on energy choices as female members have a strong preference
for using cleaner and more convenient energy sources. In developing economies,
female household members are generally responsible for collecting fuelwood and
cooking (Farhar 1998). Por ejemplo, en la India, females are more involved in
collecting fuelwood from forests than their male counterparts (Heltberg, Arndt,
and Sekhar 2000). De este modo, female household members play an active role in energy
use from collecting fuel to making decisions on fuel sources (Reddy and Srinivas
2009). Use of clean energy has a positive impact on the health and well-being
of households, particularly children and female members. Por eso, when a female
member is the principal decision-making agent (household head), higher priority
will be given to the use of clean energy (Parikh 1995; Rahut, Behera, and Ali
2016), which is why empirical evidence strongly suggests that per capita fuelwood
consumption in female-headed households is less than in male-headed households
(Israel 2002). The age of the household head and family size can also play important
roles in energy choices. While households with more family members need more
energía, such households are also able to supply more labor for fuelwood collection
and other activities in rural areas (Dewees 1989; Heltberg, Arndt, and Sekhar
2000; Nepal, Nepal, and Grimsrud 2011). Empirical evidence indicates an inverse
relationship between family size and the use of clean fuel (Pandey and Chaubal
2011).

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HOUSEHOLD ENERGY CONSUMPTION AND ITS DETERMINANTS IN TIMOR-LESTE 171

In addition to income and household demographics, the level of education
of the household head, which can serve as a proxy for the level of human capital
at the household level, can also affect household energy choices through enhanced
nonfarm income and thus the affordability of more efficient energy sources, el
increased opportunity cost of the time required for fuelwood collection, and raised
awareness of the harmful effects of dirty fuel on the environment and health (Leach
1975, 1992). It is well documented that the use of solid fuels is detrimental to the
environment and health (bruce, Perez-Padilla, and Albalak 2000; Holdren et al.
2000; Rehfuess, Mehta, and Pr¨uss- ¨Ust¨un 2006). Empirical evidence confirms that
education is a strong determinant of switching from traditional solid fuels to more
efficient modern fuels (Heltberg 2005, Pachauri and Jiang 2008). To examine the
relationship between choice of energy sources and household demographics and
capital humano, the following hypothesis is formulated:

Hipótesis (2): While households with more family members are more likely to
depend on fuelwood and electricity for energy and therefore spend a relatively
larger share of total energy expenditure on these sources, relatively more educated
household heads are less likely to choose kerosene and therefore spend relatively
less on it and more likely to choose clean energy such as electricity and therefore
spend relatively more on it.

Generally, the focus of energy policy is to create incentives and enable
households in developing economies to switch from traditional fuels such as
biomass and fuelwood to clean energy such as electricity. By examining our testable
hypotheses, this paper investigates household patterns of energy consumption
and analyzes the factors that influence household energy choices in developing
economies by using data collected under the TLSLS 2007 from more than 4,000
rural and urban households in Timor-Leste.

III. Data and Methodology

A.

Data and Sampling

This paper uses data from the TLSLS 2007 to analyze household-level
energy consumption and its determinants. The TLSLS is a government-administered
activity with financial, intellectual, and technical support from the multidonor
Planning and Financial Management Capacity Building Program managed by the
World Bank.3 The TLSLS is a comprehensive multimodule survey encompassing
broad topics. Samples were selected in two stages. In the first stage, 300 census

3Meta data and detailed documentation can be found at http://econ.worldbank.org/WBSITE/EXTERNAL
/EXTDEC/EXTRESEARCH/EXTLSMS/0,,contentMDK:22764522∼pagePK:64168445∼piPK:64168309∼theSite
PK:3358997,00.html

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172 ASIAN DEVELOPMENT REVIEW

Mesa 1. TLSLS Distribution of Enumeration Areas and Full Sample by
Region and Household Rural–Urban Status

Número de

Enumeration Areas Sampled Households

Regions

Rural Urban Total Rural Urban Total

1 (Baucau, Lautem, and Viqueque)
2 (Ainaro, Manufahi, and Manatuto)
3 (Aileu, Dili, and Ermera)
4 (Bobonaro, Cova Lima, and Liquica)
5 (Oecussi)

35
35
35
35
28

25
25
37
25
20

60
60
72
60
48

524
517
522
520
419

899
375
374
891
552 1,074
895
375
648
229

Total
168
TLSLS = Timor-Leste Survey of Living Standards.
Fuente: Government of Timor-Leste, Ministry of Finance. “Timor-Leste Survey of Living
Standards 2007.” http://www.statistics.gov.tl/wp-content/uploads/2013/12/Timor-Leste-Survey-of
-Living-Standards-2007.pdf

1,905 4,407

2,502

132

300

Enumeration Areas were selected as the primary sampling units; in the second
stage, 15 households were selected from each Enumeration Area. The first sampling
stage used the list of 1,163 Enumeration Areas generated by the 2004 census as a
sampling frame. Within each stratum, the allocated number of Enumeration Areas
was selected with probability proportional to size, using the number of households
reported by the census as a measure of size. The second sampling stage used
an exhaustive household listing operation in all selected Enumeration Areas as
its sampling frame. Sampled households in each Enumeration Area were selected
from the list by systematic equal probability sampling. Mesa 1 shows the TLSLS
distribution of the Enumeration Areas and full sample by region and by household
rural–urban status.

B. Metodología

Generally, households depend on energy from multiple sources. Por lo tanto,
the choices to use a variety of individual energy sources are correlated with each
otro. To capture the mutually inclusive behavior of household energy choices, a
multivariate probit model was employed to analyze the determinants of a household’s
energy choices. To test hypothesis 1 and hypothesis 2, we randomly split the total
sample into four equal groups. While we first ran the multivariate probit model
using the total sample, we subsequently ran the same model using 75%, 50%,
y 25% segments of the total sample. We then compared the coefficients of
different household income levels and different levels of education of the household
head against energy use choices and the expenditure shares on different energy
sources. In the multivariate probit model, sources of energy such as fuelwood,
kerosene, electricidad, and others are considered dependent variables. The independent

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HOUSEHOLD ENERGY CONSUMPTION AND ITS DETERMINANTS IN TIMOR-LESTE 173

variables include household demographic characteristics, labor supply, human and
physical capital, and location dummies. One advantage of the multivariate probit
model is that, unlike single-equation probit and logit models, the multivariate
probit model simultaneously analyzes the choice of energy by the source of
energía.

We follow Lin, Jensen, and Yen (2005) in formulating the multivariate model,

which has four dependent variables, y1 . . . y4:

yi = 1 if βi X (cid:5) + εi > 0

y

yi = 0 if βi X (cid:5) + εi ≤ 0, i = 1, 2, . . . , 5

(1)

(2)

where x is a vector of the explanatory variables; β1, β2, β3, β4, and β5 are
conformable parameter vectors; and ε1, ε2, ε3, ε4, and ε5 are random errors
distributed as a multivariate normal distribution with zero mean, unitary variance,
and an n X n.

As information on household expenditure on fuel by source is available,
we generated a variable by dividing the fuel expenditure for each source by total
energy expenditure per household.4 The proportion of expenditure on each energy
source reveals the dependency on different sources of energy at the household
nivel. Since the dependent variable is a fraction ranging from 0 a 1, we employed
a Tobit model (censored at 0) to analyze the determinants of household energy
dependency.

To examine hypotheses 1 y 2 with respect to the influence of a household’s
income and the level of education of the household head on expenditure on different
energy sources, we ran a Tobit model first using the entire sample and then using
segments equal to 75%, 50%, y 25% of the total observations. Due to a previous
lack of information on expenditure on energy sources, most past studies have focused
simply on choices (Rahut et al. 2014), which is an approach that fails to capture
the level of dependency on energy sources as measured by expenditure size. Nuestro
study fills in this research gap by using data on expenditure to determine household
dependency on particular fuel sources.

The intensity of consumption of different sources of energy is estimated
using a censored Tobit model. The ratio of a household’s expenditure on different
sources of energy to total expenditure on energy is used to measure the intensity of
consumption.

4Por ejemplo, household expenditure on kerosene is divided by total household expenditure on fuel.

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174 ASIAN DEVELOPMENT REVIEW

The intensity of fuel consumption is censored from the lower tail by specifying
the level of intensity below which a household is not regarded as having consumed a
particular source of energy. De este modo, the Tobit model assumes a latent variable x ∗
i that
is generated by the following function:

x ∗
i

= β (cid:5)

x zi + εxi

(3)

where x ∗
i is the latent variable that truncates the consumption of particular sources
of energy, zi is a vector of household and location characteristics, βxi is a vector
of coefficients to be estimated, and εxi is a scalar of error terms assumed to be
independently and normally distributed with mean 0 and constant variance σ 2.
Given this function, the specification of household intensity of consumption of a
particular source of energy is expressed as

xi = x ∗

i

if x ∗
i

≥ d

y

xi = 0 if x ∗

i

< d (4) (5) Where d is an established threshold that distinguishes households that use a particular source of energy from those that do not. The probability function for nonusers is p(x ∗ i < d) = (cid:5) (cid:3) (cid:2) β (cid:5) x zi σ and the density for households that use a particular source of energy is f (xi |x ∗ i ≥ d) = f (xi ) p(x ∗ i ≥ d) = (cid:3) zi ∗ i (cid:3) φ 1 σ (cid:2) x ∗ i −β(cid:5) x (cid:2) σ zi β(cid:5) ∗ x i σ (cid:5) (6) (7) where (cid:5)(.) and φ(.) are the standard normal cumulative and probability density functions, respectively. The density function represents the truncated regression model for those households whose observed consumption of a particular source of energy is greater than the threshold. The log-likelihood function for the Tobit model is given as a summation of the probability functions for both users and nonusers of a particular source of l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 1 1 6 7 1 6 4 2 9 3 0 a d e v _ a _ 0 0 0 8 5 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 HOUSEHOLD ENERGY CONSUMPTION AND ITS DETERMINANTS IN TIMOR-LESTE 175 Table 2. Household Energy Sources and Expenditure as a Share of the Total Household Energy Sources Kerosene Fuelwood Electricity Other fuels Expenditure per Energy Source Kerosene Fuelwood Electricity Other fuels Frequency of Use (%) 74.9 85.3 23.2 5.1 Share of Total (%) 31.8 56.8 9.9 1.5 Note: Energy choices are not mutually exclusive; that is, households can simultaneously use a mix of energy sources. Source: Authors’ calculations based on Government of Timor-Leste, Ministry of Finance. “Timor-Leste Survey of Living Standards 2007.” http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC /EXTRESEARCH/EXTLSMS/0,,contentMDK:22764522∼pagePK :64168445∼piPK:64168309∼theSitePK:3358997,00.html energy: ln L = (cid:4) x ∗ i chi2 = 0.0000.
Fuente: Authors’ calculations.

between clean and dirty sources of energy. A positive and significant correlation is
observed between the use of kerosene and fuelwood, both of which are considered
dirty sources of energy. A positive correlation is noted between kerosene and other
fuels. Curiosamente, Mesa 3 shows negative and significant correlations between
kerosene and electricity, and fuelwood and electricity, indicating that a household
which depends on electricity as a source of energy also tends to use fuels other
than kerosene or fuelwood. This is likely because of the relatively high purchasing
power of households that use electricity. Mesa 3 generally confirms that households
usually depend on more than a single source of energy. Por ejemplo, a household
may depend on electricity for lighting and fuelwood for cooking. De este modo, energía
sources are not mutually exclusive within a single household, which allows us to
employ a multivariate probit model in estimating household choices of different
energy sources.

Mesa 4 presents the estimated functions of household energy sources in
relation to household characteristics. Results from the multivariate probit on
energy choices show that with an increase in the age of the household head, el
likelihood of using electricity increases up until 54 years of age. The coefficient
of the female-headed household variable (yes = 1) is negative and significant for
kerosene and other fuels, and is positive and highly significant for fuelwood (PAG < 0.00). This finding confirms that in developing economies, female members are more involved in collecting fuelwood from forests than their male counterparts (Heltberg, Arndt, and Sekhar 2000). Consequently, a female-headed household is more likely to choose fuelwood as a source of energy (Reddy and Srinivas 2009). The multiplicative dummies in Table 4, which are generated by multiplying the female-headed household dummy with consumption quintiles, show that relatively rich female-headed households are less likely to use fuelwood as a source of energy since there is a higher opportunity cost of collecting fuelwood for these households. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 1 1 6 7 1 6 4 2 9 3 0 a d e v _ a _ 0 0 0 8 5 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 HOUSEHOLD ENERGY CONSUMPTION AND ITS DETERMINANTS IN TIMOR-LESTE 181 Table 4. Functions Estimated Using a Multivariate Probit Model to Explain Household Energy Choices Estimation Method Multivariate Probit Dependent variables: Energy source Kerosene Fuelwood Electricity Other Fuels Demographics Age, household head Age squared, household head Female-headed householda,b Household size (no. of family members) Human capital Primary completeda,c Presecondary completeda,c Secondary completeda,c University completeda,c Consumption quintile Consumption quintile 2a,d Consumption quintile 3a,d Consumption quintile 4a,d Consumption quintile 5a,d Location Rural householde 0.001 (0.01) −0.00003 (0.00) 0.01 (0.23) −0.05∗∗∗ (0.01) −0.15∗ (0.08) −0.07 (0.11) −0.17∗ (0.10) −0.47∗∗ (0.18) 0.03 (0.11) −0.17 (0.11) −0.40∗∗∗ (0.11) −0.38∗∗∗ (0.12) 0.043∗∗∗ (0.01) −0.018 (0.01) 0.0001 −0.0004∗∗∗ (0.00) (0.00) 0.71∗∗∗ −0.02 (0.26) (0.22) 0.08∗∗∗ 0.11∗∗∗ (0.01) (0.02) −0.17∗∗ (0.08) −0.0033 (0.12) −0.34∗∗∗ (0.10) −0.63∗∗∗ (0.23) 0.18∗ (0.10) 0.59∗∗∗ (0.11) 0.71∗∗∗ (0.11) 0.79∗∗∗ (0.14) 0.43∗∗∗ (0.08) 0.61∗∗∗ (0.11) 0.58∗∗∗ (0.10) 0.50∗∗∗ (0.18) 0.30∗∗∗ (0.11) 0.38∗∗∗ (0.11) 0.57∗∗∗ (0.12) 0.84∗∗∗ (0.13) 0.86∗∗∗ (0.06) −0.33∗∗∗ −0.55∗∗∗ (0.07) (0.07) Gender and consumption quintile Female-headed household × consumption quintile 2 Female-headed household × consumption quintile 3 Female-headed household × consumption quintile 4 Female-headed household × consumption quintile 5 Regions Region 2 (Manatuto, Manufahi, Ainaro)d,f Region 3 (Dili, Aileu, Ermera)d,f Region 4 (Bobonaro, Cova Lima, Liquic¸ ´a)d,f Region 5 (Oecusse)d,f −0.13 (0.30) −0.38 (0.29) −0.06 (0.28) −0.18 (0.28) 1.23∗∗∗ (0.10) 0.77∗∗∗ (0.08) 1.11∗∗∗ (0.09) 1.62∗∗∗ (0.15) −0.55∗ (0.29) −0.82∗∗∗ (0.30) −0.61∗∗ (0.30) −0.54∗ (0.29) 0.14 (0.32) 0.07 (0.32) −0.07 (0.32) −0.14 (0.30) −0.64∗∗∗ −0.40∗∗∗ (0.09) −0.18∗∗ (0.09) 0.21∗∗ (0.09) 1.00∗∗∗ −0.91∗∗∗ (0.17) (0.08) −1.02∗∗∗ (0.09) −0.70∗∗∗ (0.09) (0.08) 0.048∗ (0.03) −0.001∗∗ (0.00) 0.51 (0.43) 0.08∗∗∗ (0.02) −0.050 (0.13) 0.16 (0.17) −0.12 (0.15) 0.57∗∗ (0.27) 0.53∗∗∗ (0.18) 0.70∗∗∗ (0.18) 1.02∗∗∗ (0.18) 1.13∗∗∗ (0.19) 0.07 (0.10) −1.04∗ (0.54) −0.58 (0.60) −0.76 (0.54) −0.86 (0.56) 0.005 (0.20) −0.44∗∗∗ (0.16) 0.20 (0.16) 1.39∗∗∗ (0.16) Continued. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 1 1 6 7 1 6 4 2 9 3 0 a d e v _ a _ 0 0 0 8 5 p d . / f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 182 ASIAN DEVELOPMENT REVIEW Estimation Method Multivariate Probit Table 4. Continued. Dependent variables: Energy source Constant No. of observations Wald Chi2 (84) Prob. > chi2
Log pseudolikelihood

Kerosene
−0,08
(0.35)

Fuelwood
1.10∗∗∗
(0.37)

Electricity
−2.25∗∗∗
(0.38)

Other Fuels
−4.11∗∗∗
(0.66)

4,357
1,586.27
0.000
−233,621.94

aDummy variables
bExcluded category: male-headed households
cExcluded category: household head with no education
dExcluded category: consumption quintile 1
eExcluded category: urban households
f Excluded region: Region I: (Baucau, Laut´em, Viqueque)
Notas: Standard errors in parentheses. ∗ = 10% level of significance, ∗∗ = 5% level of significance, ∗∗∗ = 1% nivel
of significance.
Fuente: Authors’ calculations.

The findings confirm that, while in general households headed by a female are
more likely to use fuelwood as their primary source of energy, relatively wealthy
female-headed households are less likely to use fuelwood as their primary source of
energía.

The coefficient of household size is positive and significant with respect to
the use of fuelwood, electricidad, and other fuels, while it is negative and significant
for kerosene. The findings in Table 4 strongly support the first part of hypothesis
(2), which is that household size positively and significantly influences the choice
of and expenditure on fuelwood, electricidad, and energy sources other than kerosene.
The positive relationship between household size and fuelwood can be explained by
the increased availability of family labor to collect fuelwood and the greater demand
for energy in larger households. This finding supports results from past studies on
household energy use in developing economies that illustrate the positive correlation
between fuelwood and household size (Heltberg 2004).

In order to examine the influence of education on energy choices, cual es
covered in the second part of hypothesis (1), we included four dummies for the
level of education of the household head: primary completed (1), presecondary
completed (2), secondary completed (3), and university completed (4). De este modo, el
excluded category is no education (0). The results in Table 4 show that compared with
households headed by individuals with no education, the probability of choosing
kerosene and wood as sources of fuel decreases as the level of education rises. Para
kerosene, the coefficients of the variables are as follows: primary completed (−0.15
[PAG < 0.10]), secondary completed (−0.17 [P < 0.10]), and university completed (−0.47 [P < 0.05%]). For fuelwood, the coefficients of the variables are as follows: (−0.17 [P < 0.05]), secondary completed (−0.34 [P < 0.10]), and university l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 1 1 6 7 1 6 4 2 9 3 0 a d e v _ a _ 0 0 0 8 5 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 HOUSEHOLD ENERGY CONSUMPTION AND ITS DETERMINANTS IN TIMOR-LESTE 183 completed (−0.63 [P < 0.05]). The coefficients of the dummies for presecondary completed for kerosene and fuelwood are both negative but insignificant. Table 4 clearly shows that the probability of the choice of electricity for domestic energy use increases with an increase in the level of education of the household head. In the energy choice model, the coefficient of the primary completed variable for the household head is 0.43, for presecondary completed it is 0.61, for secondary completed it is 0.58, and for a university degree it is 0.5. All of these coefficients are significant at the 1% level. To examine hypothesis (1), which covers the effects of income on the choice of domestic energy use, we used the consumption quintiles as independent variables in the estimated functions shown in Table 4. The results indicate that the likelihood of the choice of kerosene decreases, while the choice of fuelwood, electricity, and other fuels increases progressively in relation to consumption quintiles. For example, the coefficients for the choice of kerosene are −0.40 (P < 0.00) for consumption quintile 4 and −0.38 (P < 0.00) for consumption quintile 5. (Consumption quintile 1 is the base in this case.) The coefficients for the choice of fuelwood are 0.18 (significant at the 10% level) for consumption quintile 2, 0.59 (significant at the 1% level) for consumption quintile 3, 0.71 (significant at the 1% level) for consumption quintile 4, and 0.79 (significant at the 1% level) for consumption quintile 5. The coefficients for the choice of electricity are 0.3 for consumption quintile 2, 0.38 for consumption quintile 3, 0.57 for consumption quintile 4, and 0.84 for consumption quintile 5. All are significant at the 1% level. Coefficients for the choice of other energy sources are 0.53 for consumption quintile 2, 0.7 for consumption quintile 3, 1.02 for consumption quintile 4, and 1.13 for consumption quintile 4. All are significant at the 1% level. The findings indicate that relatively affluent households are more likely to choose fuelwood as well as clean energy such as electricity as the main sources of energy for their homes. The coefficients of the rural household dummy (yes = 1) are 0.86 (significant at the 1% level) for the choice of kerosene, −0.33 (significant at the 1% level) for fuelwood, and −0.55 (significant at the 1% level) for electricity, indicating that, when compared with urban households, rural households are more likely to choose kerosene and less likely to choose fuelwood and electricity. To capture the effects of regional heterogeneity in fuel choices among sampled households, four regional dummies for five regions were included in estimating the functions in Table 4. The base region is Region 1, comprising Baucau, Lautem, and Viqueque districts. The regional dummies in Table 4 show that compared with households located in Region 1, households in all other regions are more likely to use kerosene and less likely to use electricity as a source of fuel. The households in Region 4, comprising Bobonaro, Coval Mia, and Liquica districts, and Region 5, comprising Oecusse district, are more likely to choose fuelwood than households located in the base region. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 1 1 6 7 1 6 4 2 9 3 0 a d e v _ a _ 0 0 0 8 5 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 184 ASIAN DEVELOPMENT REVIEW 2. Intensity of Consumption of Energy by Sources—Results and Discussion from the Tobit Model The multivariate probit model in Table 4 only assesses the choice of a particular energy source at the household level. It does not tell the extent to which households are dependent on different sources of energy. In order to assess a household’s dependency on a particular source of energy, we employed a tobit model in which the dependent variable is expenditure on a particular source of energy divided by the total energy expenditure of a household (Table 5). Estimated functions in Table 5 present the intensity of a particular energy source used by households. Similar to the energy choice model (Table 4), the results show that with an increase in the age of the household head the consumption of both electricity and other fuels increases in relation to total energy consumption. However, dependency on electricity and other fuels, in terms of the share of household expenditure, declines with the age of the household head. Female-headed households are less likely to depend on kerosene and more likely to depend on fuelwood than their male-headed counterparts. However, there is no statistically significant relationship between wealthy female-headed households and dependency on a particular fuel. This means that the share of expenditure on all fuels almost remains the same among female-headed households irrespective of income. With an increase in family size, households are more likely to be dependent on fuelwood, electricity, and other fuels, while dependence on kerosene decreases as households expand in size. Importantly, there is no significant relationship between the level of education of the household head and dependency on kerosene. This means that the use of kerosene remains nearly the same among all households irrespective of the level of education of the household head. The degree of dependency on fuelwood decreases with an increase in the level of education of the household head. In contrast, the degree of dependency on electricity increases with an increase in the level of education of the household head. The function explaining expenditure share on fuelwood shows that the coefficient of the dummy for a household head who has completed a primary education is −0.05 (significant at the 1% level), a presecondary education is 0.06 (significant at the 1% level), a secondary education is −0.10 (significant at the 1% level), and a university education is −0.15 (significant at the 5% level). In contrast, the coefficient of the dummy variable for a household head with a primary education is 0.29, a presecondary education is 0.37, a secondary education is 0.42, and a university education is 0.37. All of these coefficients are significant at the 1% level. In the case of other fuels, the dummy variable for a household head with a university degree is positive and significant at the 5% level. Table 5 shows that with an increase in wealth, dependency on kerosene decreases and dependency on fuelwood, electricity, and other fuels increases. The coefficient of the rural dummy is 0.24 (significant at the 1% level) for the share of l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 1 1 6 7 1 6 4 2 9 3 0 a d e v _ a _ 0 0 0 8 5 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 HOUSEHOLD ENERGY CONSUMPTION AND ITS DETERMINANTS IN TIMOR-LESTE 185 Table 5. Functions Estimated Using a Two-Limit Tobit Model to Explain Household Expenditure on Different Energy Sources Dependent variables Demographics Age, household head Age squared, household head Female-headed householda,b Household size (no. of family members) Human capital Primary completeda,c Presecondary completeda,c Secondary completeda,c University completeda,c Income Consumption quintile 2a,d Consumption quintile 3a,d Consumption quintile 4a,d Consumption quintile 5a,d Location Rural householda,e Gender and wealth Female-headed household × consumption quintile 2 Female-headed household × consumption quintile 3 Female-headed household × consumption quintile 4 Female-headed household × consumption quintile 5 Regions Region 2 (Manatuto, Manufahi, Ainaro)a,f Region 3 (Dili, Aileu, Ermera)a,f Region 4 (Bobonaro, Cova Lima, Liquic¸ ´a)a,f Region 5 (Oecusse)a,f Share of expenditure on kerosene Share of expenditure on fuelwood Share of expenditure on electricity Share of expenditure on other fuels 0.0017 (0.00) −0.0000096 (0.00) −0.12∗∗∗ (0.04) −0.032∗∗∗ (0.00) −0.0019 (0.02) 0.0061 (0.03) 0.0078 (0.02) −0.038 (0.07) −0.043∗ (0.02) −0.14∗∗∗ (0.02) −0.19∗∗∗ (0.02) −0.20∗∗∗ (0.03) 0.24∗∗∗ (0.02) 0.088 (0.06) 0.057 (0.06) 0.075 (0.06) 0.074 (0.06) 0.38∗∗∗ (0.02) 0.23∗∗∗ (0.02) 0.27∗∗∗ (0.02) 0.10∗∗∗ (0.02) −0.0061∗ (0.00) 0.000044 (0.00) 0.10∗∗∗ (0.04) 0.020∗∗∗ (0.00) −0.055∗∗∗ (0.02) −0.066∗∗∗ (0.02) −0.10∗∗∗ (0.02) −0.15∗∗ (0.06) 0.022 (0.02) 0.12∗∗∗ (0.02) 0.13∗∗∗ (0.02) 0.10∗∗∗ (0.03) −0.15∗∗∗ (0.01) −0.071 (0.06) −0.086 (0.06) −0.077 (0.05) −0.046 (0.05) −0.34∗∗∗ (0.02) −0.11∗∗∗ (0.02) −0.15∗∗∗ (0.02) 0.064∗∗∗ (0.02) 0.026∗∗ (0.01) −0.00021∗ (0.00) 0.0090 (0.19) 0.047∗∗∗ (0.01) 0.040∗ (0.02) −0.00052∗∗ (0.00) 0.41 (0.34) 0.068∗∗∗ (0.02) 0.29∗∗∗ (0.06) 0.37∗∗∗ (0.08) 0.42∗∗∗ (0.07) 0.37∗∗∗ (0.13) 0.19∗∗ (0.09) 0.16∗∗ (0.08) 0.32∗∗∗ (0.09) 0.48∗∗∗ (0.09) −0.27∗∗∗ (0.04) −0.047 (0.23) 0.0094 (0.24) −0.018 (0.24) −0.11 (0.22) −0.12∗∗ (0.06) −0.57∗∗∗ (0.06) −0.35∗∗∗ (0.06) −0.68∗∗∗ (0.05) −0.072 (0.10) 0.14 (0.15) −0.062 (0.12) 0.50∗∗ (0.21) 0.37∗∗ (0.15) 0.51∗∗∗ (0.15) 0.75∗∗∗ (0.14) 0.88∗∗∗ (0.15) 0.022 (0.08) −0.83∗∗ (0.42) −0.51 (0.46) −0.56 (0.43) −0.69 (0.44) −0.065 (0.16) −0.38∗∗∗ (0.14) 0.11 (0.13) 0.92∗∗∗ (0.12) Continued. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / 3 4 1 1 6 7 1 6 4 2 9 3 0 a d e v _ a _ 0 0 0 8 5 p d / . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 186 ASIAN DEVELOPMENT REVIEW Dependent variables Constant Sigma No. of observations Left-censored observations at tker_exp <= 0 Uncensored observations Right-censored observations Pseudo R2 F Prob. > F
Log pseudolikelihood

Mesa 5. Continuado.

Share of
expenditure
on kerosene

0.12
(0.08)
0.35∗∗∗
(0.01)

4,357
1,093

3,264
0
0.21
44.04
0.00
−89,439.94

Share of
expenditure
on fuelwood
0.81∗∗∗
(0.08)
0.34∗∗∗
(0.01)

4,357
639

3,718
0
0.16
30.90
0.00
−83,897.11

Share of
expenditure
on electricity
−1.64∗∗∗
(0.28)
0.77∗∗∗
(0.03)

4,357
3,345

1,012
0
0.10
25.80
0.00
−78,432.60

Share of
expenditure on
other fuels
−3.18∗∗∗
(0.58)
0.80∗∗∗
(0.06)

4,357
4,135

222
0
0.19
10.93
0.00
−20,503.39

aDummy variables
bExcluded category: male-headed households
cExcluded category: household head with no education
dExcluded category: consumption quintile 1
eExcluded category: urban households
f Excluded region: Region I: (Baucau, Laut´em, Viqueque)
Notas: Robust standard errors in parentheses. ∗∗∗ = 1% level of significance, ∗∗ = 5% level of significance, ∗ = 10%
level of significance.
Fuente: Authors’ calculations.

expenditure on kerosene, indicating that rural households are more dependent on
kerosene than urban households. The coefficients of the rural dummy, sin embargo, son
−0.15 and −0.27, respectivamente, for fuelwood and electricity (both are significant at
el 1% nivel), indicating that fuelwood and electricity are less important as sources
of energy to rural households than urban households.

The regional dummies included in Table 5 show that compared with Region
1, households in all other regions are more likely to depend on kerosene and less
likely to depend on wood and electricity.

3.

Sensitivity Analysis

In Tables 6 y 7, we apply the same estimation methods (multivariate probit
for estimating the energy choice function and Tobit for estimating the expenditure
share function) to reestimate the functions by using different combinations of the
muestras. Mesa 6 presents estimated functions applying a multivariate probit model
explaining household choices of different energy sources. In the first segment of
Mesa 6, we include 75% of total sampled households (3,267 out of 4,357). En el
second segment, we include 50% (2,178) of total sampled households. In the third
segmento, we include 25% (1,089) of total sampled households.

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HOUSEHOLD ENERGY CONSUMPTION AND ITS DETERMINANTS IN TIMOR-LESTE 187

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6
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