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Bulletin of Indonesian Economic Studies
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Labour market dimensions of poverty in Indonesia
Armida S. Alisjahbana & Chris Manning
To cite this article: Armida S. Alisjahbana & Chris Manning (2006) Labour market dimensions
of poverty in Indonesia , Bulletin of Indonesian Economic Studies, 42:2, 235-261, DOI:
10.1080/00074910600873674
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Date: 18 January 2016, At: 21:42
Bulletin of Indonesian Economic Studies, Vol. 42, No. 2, 2006: 235–61
LABOUR MARKET DIMENSIONS
OF POVERTY IN INDONESIA
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Armida S. Alisjahbana*
Padjadjaran University, Bandung
Chris Manning*
Australian National University
This paper focuses on labour market issues relevant to poverty alleviation. Patterns of participation, unemployment and employment are examined among the
poor compared with the non-poor in general, among urban and rural households,
and among various socio-demographic groups. Using data from the 2002 National
Socio-Economic Survey, the paper finds that low participation in the workforce
and high unemployment, while important, are less closely related to poverty status than expected, especially among spouses of household heads. However, sector of employment and underemployment are closely associated with poverty,
especially for those in informal jobs in urban areas; in rural areas, the poor are
heavily concentrated in agriculture. Among the poor, young people and females
are more likely to be underemployed and to work in agriculture than primeage workers. The data suggest that labour market policies that tend to protect
those in formal sector employment are unlikely to reduce poverty much, if at all.
INTRODUCTION
Study of the labour market dimensions of poverty is a surprisingly neglected subject in the now voluminous academic literature on the poor in Indonesia, and in
related policy documents.1 In one sense this is not surprising. Labour market participation can be conceived of as an intermediate variable for poverty alleviation
on both the supply and the demand side: on the supply side, it intervenes between
poverty status and policies that seek to address the welfare of households (such as
social expenditures on education and health); on the demand side, it intervenes
between poverty status and changes in demand that affect output. Policy makers have typically focused on supply and demand determinants, assuming that
labour markets will act as a conduit through which growth and economic and
social policies have an impact on the welfare of the poor.
However, labour market imbalance, reflected in unemployment, underemployment or low earnings (relative to the earnings of people with similar
human capital in similar jobs), can contribute independently to poverty. The poor
may be more affected by low levels of mobility, and have less capacity to under-
* This paper had its origins in a note prepared for the World Bank office in Jakarta on the
labour dimensions of poverty in Indonesia. The authors would like to thank an anonymous referee for insightful comments on the paper.
1 Important exceptions are Huppi and Ravallion (1991) and Smith et al. (2002).
ISSN 0007-4918 print/ISSN 1472-7234 online/06/020235-27
DOI: 10.1080/00074910600873674
© 2006 Indonesia Project ANU
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236
Armida S. Alisjahbana and Chris Manning
take job searches or bargain with employers, than the non-poor. At the same time,
it is useful for policy makers not only to understand the dimensions of labour
market problems experienced by the poor, but also to ask which socio-demographic groups among the poor feel labour market difficulties most acutely.
Specifically, we are interested in the extent to which the poor suffer, relative to
the non-poor, as a result of low participation in the workforce (including through
unemployment or underemployment), rather than as a result of the kinds of
jobs they hold. We also focus on the extent to which labour market participation
among the poor, compared with the non-poor, might differ between urban and
rural areas, and by gender and age. It might be expected, for example, that the
differences will be greater in urban areas, where there is a wider range of jobs and
skills, than in rural areas; it is also reasonable to expect the differences between
the poor and non-poor in labour market participation to be greater among groups
who are likely to be more vulnerable in the labour markets, especially females,
young people and less educated people.2
Policy approaches to poverty alleviation should be based in part on an understanding of some of these relationships. For example, if the issue is mainly low
participation and high unemployment rather than the kinds of jobs held by the
poor, then greater emphasis might be given to making it easier for individuals to
enter the labour market (through improved information, greater mobility and so
on), rather than adopting strategies that seek to raise living standards among those
already in work. Or, if the poor in certain demographic groups, such as females
or youth, are over-represented among the unemployed and low wage earners,
then anti-poverty strategies might concentrate on these demographic groups. The
focus would be on examining which factors—such as low mobility, inadequate
human capital or ‘discrimination’ (statistical or otherwise)—might contribute to
high levels of poverty among females or young people in particular.
This paper addresses several of these issues. It focuses on data from the National
Socio-Economic Survey (Susenas) for 2002, but also makes some comparisons
with data from the Susenas undertaken in 1996, just before the economic crisis
of 1997–98. These data allow us to examine the labour force characteristics of the
poor, the near-poor and the non-poor, distinguished according to levels of consumption in relation to the official poverty line set by the central statistics agency
(BPS). Other labour force data are taken from the National Labour Force Surveys
(Sakernas) for various years. Poor people are those falling below the BPS poverty
lines for urban and rural areas by province based on the 2002 Susenas (18% of all
individuals in 2002).3 The near-poor are defined as all persons above the poverty
line in the first two quintiles of consumption per capita (that is, the remaining 2%
of the first quintile and all of the second quintile).4
2 Some of these issues are addressed in the international literature, although even here
there is a remarkable paucity of studies dealing with the labour market dimensions of
poverty. Lipton (1983) is probably the most comprehensive study. See also Quibria (1993),
Mazumdar (1994) and the section on labour in Winters, McCulloch and McKay (2004).
3 In 2002, the poverty lines were Rp 130,000 (approximately $14) and Rp 97,000 (approximately $11) per capita per month, in urban and rural areas respectively.
4 In some of the later empirical analysis we combined the poor and near-poor into one
group, since we found very little difference between these two groups in terms of labour
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Labour market dimensions of poverty in Indonesia
237
In the second section, we discuss some general dimensions of poverty in Indonesia and other developing countries in relation to the labour market. This is
followed by an overview of labour market conditions in Indonesia in 2002. The
findings of a logit analysis focusing on the labour market characteristics of poor
households are summarised in the fourth section. The fifth section looks at participation rates, unemployment and underemployment among the poor, and other
labour market dimensions of poverty: the types of jobs held by the poor and earnings per hour. In the discussion we pay special attention to the labour market
characteristics of females and young people aged 15–24, in contrast to prime-age
male workers. The former two groups typically demonstrate substantial variation
in labour market participation and outcomes across regions and countries, and
over time. We then examine regional dimensions of poverty status in relation to
labour market characteristics, and some changing aspects of this relationship over
time. Some policy implications are given in the concluding section.
LABOUR MARKETS AND POVERTY: SOME KEY RELATIONSHIPS
One of the most valuable entitlements of the poor is access to a stable job with a
sufficient income stream. In Indonesia, as in other developing countries, lack of
access to such jobs distinguishes the poor from more fortunate members of society. Typically, inadequate access may take two forms: less than full involvement in
work by household members, and engagement in activities where the returns to
labour are low and/or uncertain. The first is typically reflected in low labour force
participation or activity rates, unemployment or underemployment—what we
might refer to as the ‘participation problem’. The second concerns the nature of
the jobs undertaken, or the ‘earnings-cum-productivity problem’, as manifested
in the kinds of jobs people hold by occupation, industry and work status—wage
or non-wage, formal or informal (Lipton 1983).
From the standpoint of economic analysis and policy, and for determining
priorities in anti-poverty programs, one challenge is to identify which problem
dominates—the participation problem or the earnings-cum-productivity problem—and to find correlates of each. In a perfectly operating labour market, the
first problem would not exist, since people would have access to jobs at a level of
remuneration where the labour market clears. Nor would earnings differ between
similar jobs requiring comparable skills, in the same regional or national labour
market. In practice, a host of frictions in the labour market means that workers
are unable to move seamlessly from one job to another (Bhaskar, Manning and To
2002; Ehrenberg and Smith 2006: ch. 5). In addition to pecuniary costs related to
distance, other factors such as imperfect information and poor social networks,
the high psychological costs of moving from one job to another and discrimination may contribute to both low participation and segmented labour markets.5
force patterns. Both differed quite significantly from the non-poor, however, in many labour market dimensions.
5 Differences in wages for similar jobs, which might be considered evidence of labour market segmentation, are inherent in decisions made by profit-maximising employers: wages
vary partly because firms incur different quasi-fixed labour costs (for example, hiring and
firing costs, contributions to pension funds and firm-specific investment in training).
238
Armida S. Alisjahbana and Chris Manning
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It is arguable that labour market frictions resulting from the cost of moving
from one job to another, and which are reflected in low participation or remuneration among working-age persons, are likely to be greater for the poor. Where
people are living close to the subsistence level, the risks and psychological costs of
movement are high relative to the potential incomes of such people.
Contrasts between urban and rural labour markets and by sex
In developing countries like Indonesia, wage employment is only part—sometimes a small part—of the labour market; self-employed and family workers,
commonly considered part of the informal sector, account for a significant proportion of the workforce. Urban labour markets tend to display a wider range
of earnings between enterprises and within the informal sector than rural labour
markets, even after differences in individual and job characteristics are taken into
account (Mazumdar 1994). The greater availability of formal work in towns and
cities is reflected in significant urban–rural differences in the key participation
variables: labour force participation rates tend to be lower and unemployment
rates higher in urban areas – especially among women, many of whom are ‘secondary’ workers.
What then are the key relationships between the labour market and poverty in
urban, mainly non-agricultural sectors likely to be, compared with rural, mainly
agricultural activities? In an earlier study, Rodgers (1989) summarised the findings of several studies on poverty and urban labour markets in Latin America and
Asia, including Indonesia, conducted in the 1980s. Three main findings emerged
from this research.
• Labour force participation rates tended to be high among the poor, although
poor health and related factors had a negative influence on participation by
some households (Rodgers 1989: 13).
• The poor tended to experience higher rates of unemployment than the nonpoor. In general, none of the studies cited by Rodgers confirmed the so-called
‘luxury’ unemployment hypothesis, which contends that better-off individuals
experience higher rates of unemployment than the poor (who cannot afford to
be without a job). However, even if rates were not necessarily higher among
the non-poor, relatively well-educated youth were an important segment of
the unemployed, especially in several Asian countries (Rodgers 1989: 17).
• The poor tended to be concentrated in low-income occupations with uncertain
work, especially in ‘unprotected’ wage jobs, self-employment, and family
work in small-scale production and low-income activities such as hawking,
shoe-shining and the like (Rodgers 1989: 21).
In these urban contexts, higher levels of education (in part acting as a signalling
or screening device for employers), more contacts and more extensive social networks, access to credit, location (close to a major industrial or service centre) and
work experience in the formal sector all tended to be negatively related to poverty
(Rodgers 1989: 13).
In rural areas, poverty was much more likely to be associated with lack of
access to land among individuals who were dependent on the agricultural sector
for a high proportion of their household income (Quibria 1993). The poor were
typically over-represented among farm labourers, and under-represented among
Labour market dimensions of poverty in Indonesia
239
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individuals who had found work in small-scale business operations or who had
migrated on a temporary basis (involving commuting, or circular or seasonal
migration) to find jobs in towns. As research in Indonesia has also found (Hart
1986; White and Wiradi 1989), there was a close correlation between access to land
and earnings outside agriculture: individuals in larger land-owning households
tended to earn a high proportion of household income from non-farm activities.
We examine whether some of these propositions apply to Indonesia in the subsequent sections of the paper.
THE INDONESIAN LABOUR MARKET IN 2002
In discussing the relationship between participation in the labour market and
poverty, it is important to bear in mind the structure of the labour market and
the economic context prevailing in 2002, the year of the Susenas survey on which
our analysis is based. In Indonesia, the informal sector—defined as including
all self-employed and family workers as well as casual wage employees—made
up 92% of agricultural employment and 52% of non-agricultural employment in
2002 (table 1).6 Just over 70% of the total workforce was employed in this sector.
Rates of poverty can be expected to be higher than in the formal sector, given that
many job seekers resort to self-employed or casual work, or work in small family
enterprises, owing to the relative lack of regular wage employment opportunities
(Mazumdar 1994).7
The structure of employment differed significantly between urban and rural
areas and by sex: urban informal employment accounted for just over half of all
jobs compared with close to 75% in rural areas, and females were more heavily
concentrated in informal jobs. Not surprisingly, these contrasts partly reflect the
dominance of agriculture in rural areas, where self-employment and family work
on small farms predominate, and in which females play a major role.8 However,
even in the non-agricultural sector, informal employment is considerably higher
in rural than in urban areas in the Indonesian case (table 1).
By 2002 the economy had largely recovered from the crisis, although per capita income levels were still below those prevailing immediately before the crisis.
6 This definition of the informal sector, which lumps less skilled workers together with
highly skilled professionals, is only a rough approximation. However, given that only
around 10% of all self-employed workers, casual wage employees and family workers had
a post-secondary education in 2002 (according to the Sakernas), it should give a reasonable
estimate of the size of the informal sector.
7 It should be noted nevertheless that entry into some informal sector activities may be
difficult because they tend to be dominated by specific ethnic groups or people from a particular region. This can partly be explained by activity-specific knowledge and imperfect
information flows beyond the insider group, as well as possible discrimination in favour of
insiders in the price and quality of inputs and in other aspects of business operation. It is
also the case that earnings are not always higher in the formal sector than in the informal
sector, especially where activity in the latter involves higher skill levels and significant
capital investments.
8 See, for example, Booth (2003) on the distribution of employment according to farm size
based on the agricultural census conducted in 1993.
240
Armida S. Alisjahbana and Chris Manning
TABLE 1 Labour Market Structure by Urban/Rural Area and by Sex, 2002
(%)
Urban
Rural
Males
Females
Total
62.6
55.1
7.5
72.0
67.0
5.0
85.6
79.2
6.4
50.1
44.2
5.9
67.8
61.6
6.1
Share of employment
Agriculture
Non-agriculture
Total
13.0
87.0
100.0
65.6
34.4
100.0
43.7
56.3
100.0
45.4
54.6
100.0
44.3
55.7
100.0
Agriculture
Informal
Self-employed
Family worker
Casual wage
Formal
Regular wage
Employer
Total
84.9
45.2
19.9
19.7
15.1
10.9
4.2
100.0
92.9
52.3
30.6
9.9
7.1
5.0
2.1
100.0
90.0
66.7
12.3
11.0
10.0
6.8
3.2
100.0
95.2
25.5
58.4
11.3
4.8
3.9
0.9
100.0
91.9
51.5
29.3
11.1
8.1
5.7
2.3
100.0
Non-agriculture
Informal
Self-employed
Family worker
Casual wage
Formal
Regular wage
Employer
Total
43.7
32.2
6.5
5.0
56.3
52.5
3.8
100.0
65.9
44.4
11.1
10.4
34.1
30.9
3.2
100.0
48.2
36.2
2.9
9.1
51.8
47.0
4.9
100.0
58.6
37.7
17.8
3.1
41.4
40.1
1.2
100.0
51.9
36.7
8.2
7.0
48.1
44.5
3.6
100.0
67.3
81.4
74.0
74.7
148.7
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Labour force participation rate
Employment rate
Unemployment ratea
Memo item: working-age
(15+) population (million)
a Based on the pre-2001 definition of unemployment, which excludes discouraged workers, those
establishing a new business and those not yet started in a new job.
Source: Sakernas, 2002.
Macroeconomic conditions were far from stable in this first year of the Megawati government, and Indonesia was still experiencing a significant net outflow
of capital.9 Poverty incidence, however, had almost recovered to pre-crisis levels:
according to official (revised) data, the proportion of the population below the
poverty line had fallen back to 18% from a high of 23% in 1999, compared with
15% in 1996, just before the crisis.
9 See, for example, Waslin (2003) and MacIntyre and Resosudarmo (2003).
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Labour market dimensions of poverty in Indonesia
241
In line with the slow recovery in investment, most labour indicators suggest
Indonesia was experiencing a major problem in formal sector job creation. While
the overall unemployment rate in 2002 (6.1% according to the pre-2001 definition)
was 20% above rates in the pre-crisis period (although still quite low by developing country standards), most of the new jobs after the crisis were created in
agriculture and the informal sector. Agricultural employment increased by over
1% p.a. between 1996/97 and 2002/03—faster than the growth in non-agricultural jobs—after declining in the decade before the crisis.10 The share of non-wage
employees in non-agricultural employment rose from 50% to 56%, while the share
of wage employees fell by a similar percentage over the same period. In absolute
terms the latter sector actually lost over 1 million jobs during this period. This
marked a significant reversal of pre-crisis trends, when the formal sector was the
major source of employment growth, expanding by over 5% p.a. over the decade
from 1986/87 to 1996/97.
Finally, the Indonesian government embarked on an aggressive minimum wage
policy to restore and increase real wages after a sharp decline during the crisis, in
addition to radically rewriting labour protection laws to introduce a number of
new minimum standards for wage workers. By 2002, real minimum wages were
already 30–50% higher in real terms in major urban centres (Jakarta, Bandung and
Surabaya) than they had been immediately before the crisis (Manning 2003).
OVERALL RELATIONSHIPS:
A STATISTICAL ANALYSIS OF LABOUR AND POVERTY
A logit regression model was employed, using the 2002 Susenas data, to identify
the variables associated with household poverty status, taking poor and nearpoor households as the reference category.11 The model seeks to incorporate the
key labour market relationships that could be expected to be associated with poverty, most importantly the activities of the household head and participation in
work by other household members.12 A set of other control variables was also
included in the equation, such as the age, gender and education of the household
head, the household dependency ratio and locational characteristics.
Table 2 provides parameter estimates of the logit estimation for all households,
single-headed households and households with married couples. The key variable was the labour market status of the household head: whether he or she was
in the labour market and, if in the labour market, in what sector and whether fully
employed or underemployed. Households in which the head was fully employed
10 These trends are all based on data from the annual Sakernas; see Alisjahbana and Manning (2005) for a fuller exposition.
11 Casual observation suggests that near-poor households have much in common with
poor households. We tested the model for all households with married heads, taking poor
households, rather than the poor and near-poor, as the reference group. The signs were the
same for all coefficients, and the parameter values very similar in most cases; statistical
significance was also similar for all variables in both cases. However, the parameter values
were smaller for the sex (male) variable, and larger (negative) for urban areas.
12 We do not address the issue of causality in this model. That is, the logit model does not
distinguish whether poverty is a cause or a consequence of various labour market characteristics.
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Variable
Single-headed Households
Married Couple Households
m.e.b Coef.
Std
Err.
***
***
–0.02 –0.09
0.00 0.00
0.01 –10.60
0.00
11.76
***
***
0.02 –16.02
***
–0.07
0.39
0.04
9.50
***
0.06
0.15
0.24
0.61
–0.01
0.54
0.96
0.01
0.02
0.02
–0.76
31.72
53.61
***
***
0.00
0.11
0.18
0.04
0.44
0.19
0.05
0.08
0.04
0.76
5.87
4.59
***
***
0.01
0.07
0.03
0.17
0.66
0.84
0.02
0.02
0.02
10.12
31.86
45.98
***
***
***
0.04
0.15
0.19
–0.34
–0.42
1.09
–0.83
–0.67
–0.56
0.02
0.02
0.03
0.05
0.02
0.09
–19.08
–18.21
32.63
–16.05
–39.14
–6.06
***
***
***
***
***
***
–0.08
–0.10
0.19
–0.20
–0.15
–0.13
–0.39
–0.91
0.84
–1.09
–0.91
–0.90
0.05 –7.40
0.07 –13.17
0.14
5.84
0.16 –6.95
0.06 –15.22
0.27 –3.32
***
***
***
***
***
***
–0.07
–0.18
0.11
–0.23
–0.18
–0.19
–0.45
–0.44
0.75
–0.86
–0.60
–0.58
0.02
0.03
0.04
0.06
0.02
0.11
–21.68
–16.44
20.73
–14.23
–29.81
–5.47
***
***
***
***
***
***
–0.11
–0.11
0.16
–0.21
–0.15
–0.14
–0.51
0.08
–6.77
***
–0.12 –0.79
0.25
–3.20
***
–0.16 –0.58
0.09
–6.75
***
–0.14
0.26
0.02
14.35
***
0.06 –0.08
0.07
–1.17
–0.01
0.02
6.30
***
0.03
Coef.
Std
Err.
Z
–0.10
0.00
0.00 –34.79
0.00 37.07
–0.32
Z
m.e.b Coef.
Std
Err.
–0.02 –0.09
0.00 0.00
0.00 –26.42
0.00 26.98
0.13
m.e.b
Z
***
***
–0.02
0.00
0.04
Armida S. Alisjahbana and Chris Manning
Household head characteristics
Age
Age squaredc
Sex
Male
Schooling (reference variable:
primary or less)
Primary or less
Junior secondary
Senior secondary & above
Labour force status (reference
variable: in non-agriculture,
IFS & fully employed)
Outside workforce
Unemployed
Professional, managerial & clerical
In agriculture & underemployed
In agriculture & fully employed
In non–agriculture, FS
& underemployed
In non–agriculture, IFS
& underemployed
In non–agriculture, FS
& fully employed
All Households
242
TABLE 2 The Probablility of Being Non-Poor, 2002a
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Household characteristics
Dependency ratiod
Employment ratioe
Location characteristics
Urban–rural (reference variable: rural)
Urban
Region (reference variable:
Eastern Indonesia)
Sumatra
Java–Bali
Kalimantan
Sulawesi
Constant
No. of observations
Likelihood ratio statistic
–2.25
0.62
0.06 –34.86
0.07 30.23
***
***
0.03 10.23
0.04 14.79
0.07 23.24
0.02 –25.91
0.03
3.95
***
***
***
***
***
0.07
0.13
0.28
–0.15
0.03
–0.36 –2.40
0.37 1.21
0.03 –79.46
0.04 28.93
***
***
–0.58
0.29
0.01 –0.10
0.02
–6.37
***
–0.02
0.02 –92.68
0.02 28.38
***
***
0.04
0.01
2.70
***
0.01
0.04
0.04
0.86
0.56
0.32
1.00
0.46
0.02
0.02
0.03
0.03
24.50
14.59
37.84
18.64
***
***
***
***
0.12
0.07
0.18
0.09
0.93
0.42
0.71
0.37
0.07
0.06
0.08
0.07
13.51
6.50
8.56
4.95
***
***
***
***
0.14
0.07
0.10
0.06
0.50
0.12
1.05
0.40
0.03
0.03
0.03
0.03
19.04
4.66
34.94
13.90
***
***
***
***
0.12
0.03
0.22
0.09
2.84
0.08
36.34
187,783
26,317
***
0.00
1.43
0.25
5.75
23,078
3,177
***
0.00
1.88
0.25
7.43
138,742
23,091
***
0.00
***
–0.49 –2.12
0.14 2.19
0.27
0.58
1.51
–0.61
0.12
***
Labour market dimensions of poverty in Indonesia
Spouse characteristics
Labour force status (reference
variable: in non–agriculture, IFS)
Outside workforce
Unemployed
Professional, managerial & clerical
In agriculture
In non-agriculture, FS
***
243
Coef. = coefficient; std err. = standard error; Z = Z statistic; m.e. = marginal effect; IFS = in the informal sector; FS = in the formal sector.
* = significant at 10%; ** = significant at 5%; *** = significant at 1%.
a Bivariate division of the dependent variable into two groups: poor and near-poor (combined), and non-poor. The poor and near-poor is the omitted category.
b Marginal effect is the rate of change in the probability of a household being non-poor due to a unit change in the regressor. Marginal effects are evaluated at the
regressor means.
c The estimated coefficients on this variable were all 0.001.
d Dependency ratio = ratio of children aged less than 15 to number of working-age persons in each household.
e Employment ratio = ratio of employed persons to number of working-age persons in each household.
Source: Susenas, 2002.
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Armida S. Alisjahbana and Chris Manning
in the informal sector, outside agriculture, were chosen as the reference category.
Most of the implied relationships were as expected between the main ‘explanatory’ variables on the one hand (the labour market characteristics of the household head and the spouse of the head, as well as the participation of household
members in the labour market) and poverty status on the other. Fully employed
formal sector workers in the non-agricultural sector, and professional, managerial
and clerical employees, were more likely to be among the non-poor, while those
in all other labour force categories were less likely to live in non-poor households
than the reference group. These latter categories comprised household heads who
were outside the workforce, unemployed, employed as agricultural workers (both
fully employed and underemployed) and underemployed in the non-agricultural
formal sector. All coefficients were significant at the 1% level. Underemployed
family heads working in agriculture were especially likely to be in households
below or close to the poverty line.
Three results stand out with regard to the labour market and employment status of the household head. First, household heads outside the workforce, and
those who were unemployed, were less likely to be non-poor (a negative coefficient in table 2); that is, they were more likely to be members of a poor or nearpoor household. Second, people employed in agriculture—regardless of whether
they were fully employed or underemployed—were similarly more likely to be
poor or near-poor. And third, the underemployed were also more likely to be poor
or near-poor, irrespective of whether they worked in the formal or informal sector.
All these results are relative to the reference group of households whose heads
were fully employed in the informal, non-agricultural sector.
The first finding is not controversial. The second reminds us of the extent to
which attachment to the agricultural sector in many parts of Indonesia (especially
Java–Bali and Eastern Indonesia) is associated with limited access to fertile land,
and with uncertain and low-paid employment.13 The third challenges the notion
that formal sector work per se is likely to lift individuals out of poverty; the utilisation of labour in terms of hours worked, in addition to productivity and wages,
also matters considerably.
Other labour market characteristics tested in the regression included the
number of employed persons in the household (employment ratio in table 2)
and the labour force status of the spouse of the household head (in households
in which the head was married; see last five columns in table 2). As might be
expected, the number of working household members was positively related to
the probability of being non-poor; a higher employment ratio would mean that
households could take advantage of additional sources of income from working
household members.
However, the variable measuring the spouse’s labour force status produced
some unexpected results.14 In contrast to household heads, spouses outside the
labour market and unemployed spouses were more likely to be among the nonpoor (a positive coefficient in table 2) than spouses in the reference group (those
employed in informal work outside agriculture). These findings provide some
13 See especially World Bank (1983), Hart (1986) and Breman and Wiradi (2001).
14 As might be expected, a high proportion of spouses were female.
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Labour market dimensions of poverty in Indonesia
245
support for the ‘luxury unemployment’ hypothesis—that spouses of the household head are more likely to be outside the workforce, or looking for work, precisely because they are less likely to work out of economic necessity. However,
like household heads, spouses employed in the formal, non-agricultural sector,
or working as professionals, managers and clerks, were more likely to belong to
non-poor households.
Among other characteristics of the household head, higher levels of education
were strongly associated with the probability of a household escaping poverty,
but the age and sex variables gave mixed results. The probability of being nonpoor was higher if the household head had at least a junior secondary education,
relative to those who had a primary education or less. The coefficients for junior
secondary, and for senior secondary or higher education, were both positive and
significant, with that for the latter being larger than that for the former, as one
would expect. Surprisingly, however, the probability of households being nonpoor was negatively related to the age of household head up to age 45.5 years,
and positively related beyond this (bearing in mind the positive coefficient on the
age-squared variable). The Z statistics show that both the age and age-squared
coefficients are significant at the 1% level; on the other hand, the marginal effects
are very small, suggesting that the overall relationship between age of household
head and household poverty status is of trivial importance once all the other factors are accounted for.15 Male household heads were more likely to be among
the non-poor (a positive coefficient) in the equation for single-headed households
and households with married heads but, surprisingly, not for the sample containing all households.
Turning to household characteristics, table 2 shows that poor and near-poor
households were more likely to have a higher dependency ratio; that is, households were more likely to be poor if they had more children relative to the number
of household members of working age (i.e. all those aged 15 years and above). A
set of locational variables indicates the expected positive and significant effect of
urban location on a household’s probability of being non-poor, although the coefficient is small and the sign is reversed for urban households that include married couples. Similarly, the probability of being in a poor household was lower
for households located in Java–Bali, Sumatra, Kalimantan and Sulawesi than
for those located in the poorer regions of Eastern Indonesia. Among the island
groups, the marginal effect for all households was much larger for Kalimantan
and Sumatra than for Java–Bali.
To summarise, the labour force and work characteristics of the household head
and spouse are significantly correlated with poverty status (i.e. being poor or
near-poor), even after we control for a range of other personal and household
characteristics. Whether household heads work, and the sector in which they
work, are important, and participation in the non-agricultural sector in particular
(whether in the formal or informal sector) is associated with a higher probability
of being non-poor. Participation by spouses per se tends to matter much less than
15 The unexpected negative sign for age may be the result of a positive association between age and attachment to the informal sector. Proportionately fewer young people were
employed in the informal sector than in the formal sector in 2002; the opposite was the case
for older people.
246
Armida S. Alisjahbana and Chris Manning
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the participation of household heads. For spouses, the nature of work is much
more closely correlated with poverty status. We examine some of these relationships in more detail in the following section.
PATTERNS OF LABOUR FORCE PARTICIPATION,
EMPLOYMENT AND EARNINGS
One might expect the poor in Indonesia to share many of the labour market characteristics of the poor in other developing countries discussed earlier, given the
patterns of participation, unemployment and employment shown in table 1: moderate unemployment rates but much higher underemployment, and a substantial
share of the workforce employed in agriculture and in the informal sector. However, there were some specific characteristics of the labour market that might be
expected to contribute to rather different patterns: slowing labour force growth
rates from the 1990s; relatively low unemployment rates among prime-age workers; quite high female participation rates; and a sustained period of rapid economic growth, brought to an end by the crisis in 1997–98 (Manning 2003).
In the logit analysis we have seen that most of the labour market variables were
significant and had the expected signs for household heads, although there were
some unexpected results for the spouse of the household head. Here we examine some of these relationships in greater detail, focusing on three sub-groups in
the population: females; youth (aged 15–24); and prime-age males (aged 25–59).
Both of the first two groups typically contain a significant proportion of secondary workers, that is, workers other than the main breadwinner in the household.
In addition, we examine participation and employment among the poor in urban
and rural locations in greater detail, before turning briefly to a consideration of
some regional dimensions and trends over time.
Participation rates, unemployment and underemployment
Indonesia is not unlike many other developing countries in that the main labour
market correlates of poverty appear to be sector of employment and work status rather than the extent of participation in the workforce. Participation and
unemployment rates were relatively uniform across expenditure classes in 2002
(table 3).16 The major exception was participation by young people: individuals
aged 15–24 in poor and near-poor households displayed significantly higher participation rates than youth in non-poor households.
In contrast to participation and unemployment rates, underemployment was
significantly higher among the poor and near-poor than among the non-poor.17
16 Following BPS, we include in the unemployed category those not working but looking
for work, and also people neither working nor looking for work but available for work
(‘discouraged’ workers). There are some conceptual problems in including discouraged
workers as unemployed, especially without reference to reservation wages (Suryadarma,
Suryahadi and Sumarto 2005). Discouraged workers are more likely to include older, rural and female people, who are also likely to be more heavily represented in poor households.
17 The underemployed are defined here as those working more than one but less than 35
hours per week, and looking for more work.
Labour market dimensions of poverty in Indonesia
247
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TABLE 3 Participation, Unemployment and Underemployment
Rates by Poverty Status, 2002a
(%)
Poor
Near-poor
Non-poor
Total
Participation rate
Prime-age malesb
Females
Youthc
98.1
51.8
61.0
98.3
51.1
57.7
97.8
47.8
52.1
98.0
49.1
54.8
Total
69.0
68.9
67.1
67.8
Unemployment rate
Prime-age malesb
Females
Youthc
3.1
15.7
29.6
3.1
14.6
28.0
3.1
15.3
28.8
3.1
15.2
28.8
Total
11.2
10.2
9.8
10.1
Underemployment rate
Prime-age malesb
Females
Youthc
15.9
18.0
26.1
13.8
15.3
24.8
10.0
8.3
13.3
11.7
11.4
18.3
Total
17.3
15.0
10.1
12.3
a
Participation rate = labour force/working-age population. Unemployment rate = unemployed/
labour force (using the 2002 revised definition, which includes discouraged workers). Underemployment rate = population working less than 35 hours per week and prepared to take on more work/total
employed. All three are expressed as percentages.
b
Prime-age males are workers aged 25–59.
c
Youth are workers aged 15–24.
Source: Authors’ calculations, based on the 2002 Susenas.
Figure 1 shows that underemployment was especially high among females and
young people in poorer households compared with the same groups in non-poor
households.
Thus, in terms of participation in work, the main differences between poor and
non-poor households were experienced most sharply among youth and, to a lesser
extent, females. Younger people from poor and near-poor families who were just
out of school, or in families unable to afford schooling, were much more likely to
be in the workforce and, if they did work, to be underemployed, than their better-off contemporaries.18 Higher participation among poor youth is presumably
18 Note that full-time students are classified as being outside the labour force (along with
housewives and the infirm), provided they are not looking for work. Anyone who works
a minimum of one hour a week is considered to be employed, even though the principal
activity may be school, university or household work.
248
Armida S. Alisjahbana and Chris Manning
FIGURE 1 Rate of Underemployment among Prime-age Males,
Females and Youth, 2002a
(%)
30
Poor
Near-poor
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20
Non-poor
10
0
Prime-age males
a
Females
Youth
Prime-age males are workers aged 25–59; youth are workers aged 15–24.
Source: Authors’ calculations, based on the 2002 Susenas.
partly a reflection of above-average school dropout rates among the poor, and
higher rates of school participation among the non-poor.
Consistent with the findings of the logit model above, the ‘luxury’ unemployment hypothesis does not appear to be confirmed as a dominant feature of
employment in Indonesia, even if a relatively high proportion of young and bettereducated people dominates among the unemployed.19 Labour markets do not
clear for many younger, disadvantaged people as well, suggesting that policies
that support job creation for these groups could contribute to poverty reduction.
It is worth mentioning that even though underemployment was a greater problem in rural areas, and unemployment in urban areas (among all income classes),20
younger and female workers in poor households were especially disadvantaged
by shorter hours of work relative to their better-off counterparts in towns and
cities. Thus 15–20% of younger and female workers in poor urban households
worked less than 35 hours per week and were searching for more work. In contrast, only around 5% of such workers in non-poor urban households were underemployed and looking for more work.
19 See, for example, Manning and Junankar (1998).
20 For example, according to the 2002 Susenas, 19% of females were unemployed and 8%
were underemployed in urban areas, compared with 12% and 14% respectively in rural
areas.
Labour market dimensions of poverty in Indonesia
249
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TABLE 4 Distribution of Workers by Industry, Work Status
and Household Poverty Status, 2002
(%)
Employment by industrya
Activities in which the poor/near-poor
are over-representedb
Agriculture
Construction
Activities in which the poor/near-poor
are under-representedc
Professional, managerial
& clerical (all sectors)
Manufacturing
Trade
Transport
Services
Other
Total
Employment by working status
Formal employment
Employees
Employers
Informal employment
Self-employed
Family workers
Casual
Total
Memo items:
Total number of employed (million)
Total number of households (million)
a
Poor
Nearpoor
Nonpoor
Total
61.3
4.7
56.9
4.6
36.2
3.8
44.6
4.2
1.2
2.0
8.5
5.9
10.2
12.2
4.0
5.6
0.8
100.0
11.6
14.0
4.0
6.0
0.8
100.0
12.3
21.3
4.9
12.0
1.0
100.0
11.8
18.3
4.6
9.7
0.9
100.0
24.8
23.0
1.8
26.4
24.4
2.0
42.1
38.3
3.7
35.9
32.9
3.0
75.2
41.4
22.2
11.6
100.0
73.6
43.0
21.2
9.4
100.0
57.9
38.9
13.8
5.3
100.0
64.1
40.2
16.7
7.2
100.0
13.7
7.2
18.7
10.5
53.9
35.4
86.3
53.1
The distribution of workers by industry excludes professional, managerial and clerical workers.
b
Over-represented = the proportion of poor/near-poor employed is greater than the proportion of
the total employed in this activity.
c
Under-represented = the proportion of poor/near-poor employed is less than the proportion of the
total employed in this activity.
Source: Authors’ calculations, based on the 2002 Susenas.
Patterns of employment and earnings
In relation to employment, the differences between household expenditure
groups were significant for households with members working in agriculture
and in the informal sector, as earlier studies of poverty in Indonesia have suggested (Huppi and Ravallion 1991; Smith et al. 2002). These patterns are evident
in the proportion of workers from poor households, compared with those from
250
Armida S. Alisjahbana and Chris Manning
FIGURE 2 Distribution of Manufacturing Workers between Formal and
Informal Sectors by Poverty Status, 2002
(%)
120
Poor
Near-poor
Non-poor
80
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55.9
76.6
40
25.8
15.3
0
8.2
Formal sector
18.3
Informal sector
Source: Authors’ calculations, based on the 2002 Susenas.
non-poor households, employed in agriculture and the informal sector—the latter especially among casual employees and, to a lesser extent, family workers
(table 4). The distribution of the near-poor also differed from that of the non-poor
in this respect.
Outside agriculture, however, the poor were more heavily concentrated than
the non-poor only in construction. In contrast, they were under-represented
among white-collar workers and in trade and services (although less so in manufacturing), in both urban and rural areas. As suggested in some of the studies of
poverty discussed above, both self-employed and family workers were less likely
to be members of poor households if they worked outside agriculture than if they
worked within it.21
The findings for manufacturing are surprising, given that movements out of
agriculture into industry are commonly believed to be associated with improved
wages and living standards. These results are partly explained by differences in
the poverty status of formal and informal sector workers within manufacturing.22
Thus, as shown in figure 2, there was a higher probability of manufacturing workers who were self-employed, family workers or casual employees being attached
to poor or near-poor households. Nevertheless, at the same time, a significant
number of formal sector workers in manufacturing (close to 25%) were members
21 Half of all informal sector workers in agriculture are attached to poor households; this
proportion falls to one-third for informal workers in non-agricultural jobs.
22 Bear in mind that many manufacturing plants are cottage industries employing less
than five workers (often working on an irregular basis and consisting largely of family
members). Approximately 40% of all employees in manufacturing were working in cottage
enterprises in 2002.
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Labour market dimensions of poverty in Indonesia
251
of poor or near-poor households. The large majority were regular wage employees rather than casual workers. It seems likely that many of them worked in small
and cottage industries in rural areas, where reservation wages were more likely to
be set by standards in agriculture than in modern industry.
Within the informal sector, casual wage workers were more likely than either
family workers or the self-employed to be members of poor households. Thus it is
important to underline the fact, emphasised in several studies of India in particular, that the contrast in poverty among various work status groups is not merely a
question of whether people are engaged in wage or non-wage employment.23 The
kind of wage employment also matters.
The data also reveal some important differences in the participation of individuals in poor/near-poor and non-poor households in urban compared with rural
areas. Workers in poor and near-poor urban households were heavily concentrated in several non-agricultural sectors, especially construction and transport,
in which short-term contracts and low-income urban jobs such as becak (trishaw)
driving are common. This is shown by the ratio of the share of the poor/near-poor
to that of the non-poor in particular sectors (table 5). Thus, for example, almost
twice (1.83 times) as many poor as non-poor urban workers were employed in
construction. On the other hand, workers from poor urban households were relatively under-represented in trade (including restaurants and hotels) and services.
However, among poor and non-poor workers, the pattern of participation in
jobs outside agriculture was different for rural areas. In general, the data provide
some indication that most non-farm sectors, even construction and transport, offer
more opportunities for households to move out of poverty in rural areas, compared to the greater bunching of poor workers in some non-agricultural sectors
in cities and towns. Trade in particular is a good example of a sector in which the
proportion of poor individuals employed in rural areas was considerably smaller
than that of the non-poor (nearly half); in urban areas the situation was different,
with a similar share of poor/near-poor and non-poor being employed in trade.
Many of the former presumably earned an income from hawking or other itinerant activities in middle-class neighbourhoods.
To what extent do employed persons in poor/near-poor households differ
from the non-poor in terms of work patterns by age and sex? Agriculture was
over-represented among the poor, especially among prime-age males, but also
among youth and females (data not shown in table 5). It is instructive nevertheless that a smaller proportion of prime-age m
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
Labour market dimensions of poverty in Indonesia
Armida S. Alisjahbana & Chris Manning
To cite this article: Armida S. Alisjahbana & Chris Manning (2006) Labour market dimensions
of poverty in Indonesia , Bulletin of Indonesian Economic Studies, 42:2, 235-261, DOI:
10.1080/00074910600873674
To link to this article: http://dx.doi.org/10.1080/00074910600873674
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Date: 18 January 2016, At: 21:42
Bulletin of Indonesian Economic Studies, Vol. 42, No. 2, 2006: 235–61
LABOUR MARKET DIMENSIONS
OF POVERTY IN INDONESIA
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Armida S. Alisjahbana*
Padjadjaran University, Bandung
Chris Manning*
Australian National University
This paper focuses on labour market issues relevant to poverty alleviation. Patterns of participation, unemployment and employment are examined among the
poor compared with the non-poor in general, among urban and rural households,
and among various socio-demographic groups. Using data from the 2002 National
Socio-Economic Survey, the paper finds that low participation in the workforce
and high unemployment, while important, are less closely related to poverty status than expected, especially among spouses of household heads. However, sector of employment and underemployment are closely associated with poverty,
especially for those in informal jobs in urban areas; in rural areas, the poor are
heavily concentrated in agriculture. Among the poor, young people and females
are more likely to be underemployed and to work in agriculture than primeage workers. The data suggest that labour market policies that tend to protect
those in formal sector employment are unlikely to reduce poverty much, if at all.
INTRODUCTION
Study of the labour market dimensions of poverty is a surprisingly neglected subject in the now voluminous academic literature on the poor in Indonesia, and in
related policy documents.1 In one sense this is not surprising. Labour market participation can be conceived of as an intermediate variable for poverty alleviation
on both the supply and the demand side: on the supply side, it intervenes between
poverty status and policies that seek to address the welfare of households (such as
social expenditures on education and health); on the demand side, it intervenes
between poverty status and changes in demand that affect output. Policy makers have typically focused on supply and demand determinants, assuming that
labour markets will act as a conduit through which growth and economic and
social policies have an impact on the welfare of the poor.
However, labour market imbalance, reflected in unemployment, underemployment or low earnings (relative to the earnings of people with similar
human capital in similar jobs), can contribute independently to poverty. The poor
may be more affected by low levels of mobility, and have less capacity to under-
* This paper had its origins in a note prepared for the World Bank office in Jakarta on the
labour dimensions of poverty in Indonesia. The authors would like to thank an anonymous referee for insightful comments on the paper.
1 Important exceptions are Huppi and Ravallion (1991) and Smith et al. (2002).
ISSN 0007-4918 print/ISSN 1472-7234 online/06/020235-27
DOI: 10.1080/00074910600873674
© 2006 Indonesia Project ANU
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Armida S. Alisjahbana and Chris Manning
take job searches or bargain with employers, than the non-poor. At the same time,
it is useful for policy makers not only to understand the dimensions of labour
market problems experienced by the poor, but also to ask which socio-demographic groups among the poor feel labour market difficulties most acutely.
Specifically, we are interested in the extent to which the poor suffer, relative to
the non-poor, as a result of low participation in the workforce (including through
unemployment or underemployment), rather than as a result of the kinds of
jobs they hold. We also focus on the extent to which labour market participation
among the poor, compared with the non-poor, might differ between urban and
rural areas, and by gender and age. It might be expected, for example, that the
differences will be greater in urban areas, where there is a wider range of jobs and
skills, than in rural areas; it is also reasonable to expect the differences between
the poor and non-poor in labour market participation to be greater among groups
who are likely to be more vulnerable in the labour markets, especially females,
young people and less educated people.2
Policy approaches to poverty alleviation should be based in part on an understanding of some of these relationships. For example, if the issue is mainly low
participation and high unemployment rather than the kinds of jobs held by the
poor, then greater emphasis might be given to making it easier for individuals to
enter the labour market (through improved information, greater mobility and so
on), rather than adopting strategies that seek to raise living standards among those
already in work. Or, if the poor in certain demographic groups, such as females
or youth, are over-represented among the unemployed and low wage earners,
then anti-poverty strategies might concentrate on these demographic groups. The
focus would be on examining which factors—such as low mobility, inadequate
human capital or ‘discrimination’ (statistical or otherwise)—might contribute to
high levels of poverty among females or young people in particular.
This paper addresses several of these issues. It focuses on data from the National
Socio-Economic Survey (Susenas) for 2002, but also makes some comparisons
with data from the Susenas undertaken in 1996, just before the economic crisis
of 1997–98. These data allow us to examine the labour force characteristics of the
poor, the near-poor and the non-poor, distinguished according to levels of consumption in relation to the official poverty line set by the central statistics agency
(BPS). Other labour force data are taken from the National Labour Force Surveys
(Sakernas) for various years. Poor people are those falling below the BPS poverty
lines for urban and rural areas by province based on the 2002 Susenas (18% of all
individuals in 2002).3 The near-poor are defined as all persons above the poverty
line in the first two quintiles of consumption per capita (that is, the remaining 2%
of the first quintile and all of the second quintile).4
2 Some of these issues are addressed in the international literature, although even here
there is a remarkable paucity of studies dealing with the labour market dimensions of
poverty. Lipton (1983) is probably the most comprehensive study. See also Quibria (1993),
Mazumdar (1994) and the section on labour in Winters, McCulloch and McKay (2004).
3 In 2002, the poverty lines were Rp 130,000 (approximately $14) and Rp 97,000 (approximately $11) per capita per month, in urban and rural areas respectively.
4 In some of the later empirical analysis we combined the poor and near-poor into one
group, since we found very little difference between these two groups in terms of labour
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Labour market dimensions of poverty in Indonesia
237
In the second section, we discuss some general dimensions of poverty in Indonesia and other developing countries in relation to the labour market. This is
followed by an overview of labour market conditions in Indonesia in 2002. The
findings of a logit analysis focusing on the labour market characteristics of poor
households are summarised in the fourth section. The fifth section looks at participation rates, unemployment and underemployment among the poor, and other
labour market dimensions of poverty: the types of jobs held by the poor and earnings per hour. In the discussion we pay special attention to the labour market
characteristics of females and young people aged 15–24, in contrast to prime-age
male workers. The former two groups typically demonstrate substantial variation
in labour market participation and outcomes across regions and countries, and
over time. We then examine regional dimensions of poverty status in relation to
labour market characteristics, and some changing aspects of this relationship over
time. Some policy implications are given in the concluding section.
LABOUR MARKETS AND POVERTY: SOME KEY RELATIONSHIPS
One of the most valuable entitlements of the poor is access to a stable job with a
sufficient income stream. In Indonesia, as in other developing countries, lack of
access to such jobs distinguishes the poor from more fortunate members of society. Typically, inadequate access may take two forms: less than full involvement in
work by household members, and engagement in activities where the returns to
labour are low and/or uncertain. The first is typically reflected in low labour force
participation or activity rates, unemployment or underemployment—what we
might refer to as the ‘participation problem’. The second concerns the nature of
the jobs undertaken, or the ‘earnings-cum-productivity problem’, as manifested
in the kinds of jobs people hold by occupation, industry and work status—wage
or non-wage, formal or informal (Lipton 1983).
From the standpoint of economic analysis and policy, and for determining
priorities in anti-poverty programs, one challenge is to identify which problem
dominates—the participation problem or the earnings-cum-productivity problem—and to find correlates of each. In a perfectly operating labour market, the
first problem would not exist, since people would have access to jobs at a level of
remuneration where the labour market clears. Nor would earnings differ between
similar jobs requiring comparable skills, in the same regional or national labour
market. In practice, a host of frictions in the labour market means that workers
are unable to move seamlessly from one job to another (Bhaskar, Manning and To
2002; Ehrenberg and Smith 2006: ch. 5). In addition to pecuniary costs related to
distance, other factors such as imperfect information and poor social networks,
the high psychological costs of moving from one job to another and discrimination may contribute to both low participation and segmented labour markets.5
force patterns. Both differed quite significantly from the non-poor, however, in many labour market dimensions.
5 Differences in wages for similar jobs, which might be considered evidence of labour market segmentation, are inherent in decisions made by profit-maximising employers: wages
vary partly because firms incur different quasi-fixed labour costs (for example, hiring and
firing costs, contributions to pension funds and firm-specific investment in training).
238
Armida S. Alisjahbana and Chris Manning
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It is arguable that labour market frictions resulting from the cost of moving
from one job to another, and which are reflected in low participation or remuneration among working-age persons, are likely to be greater for the poor. Where
people are living close to the subsistence level, the risks and psychological costs of
movement are high relative to the potential incomes of such people.
Contrasts between urban and rural labour markets and by sex
In developing countries like Indonesia, wage employment is only part—sometimes a small part—of the labour market; self-employed and family workers,
commonly considered part of the informal sector, account for a significant proportion of the workforce. Urban labour markets tend to display a wider range
of earnings between enterprises and within the informal sector than rural labour
markets, even after differences in individual and job characteristics are taken into
account (Mazumdar 1994). The greater availability of formal work in towns and
cities is reflected in significant urban–rural differences in the key participation
variables: labour force participation rates tend to be lower and unemployment
rates higher in urban areas – especially among women, many of whom are ‘secondary’ workers.
What then are the key relationships between the labour market and poverty in
urban, mainly non-agricultural sectors likely to be, compared with rural, mainly
agricultural activities? In an earlier study, Rodgers (1989) summarised the findings of several studies on poverty and urban labour markets in Latin America and
Asia, including Indonesia, conducted in the 1980s. Three main findings emerged
from this research.
• Labour force participation rates tended to be high among the poor, although
poor health and related factors had a negative influence on participation by
some households (Rodgers 1989: 13).
• The poor tended to experience higher rates of unemployment than the nonpoor. In general, none of the studies cited by Rodgers confirmed the so-called
‘luxury’ unemployment hypothesis, which contends that better-off individuals
experience higher rates of unemployment than the poor (who cannot afford to
be without a job). However, even if rates were not necessarily higher among
the non-poor, relatively well-educated youth were an important segment of
the unemployed, especially in several Asian countries (Rodgers 1989: 17).
• The poor tended to be concentrated in low-income occupations with uncertain
work, especially in ‘unprotected’ wage jobs, self-employment, and family
work in small-scale production and low-income activities such as hawking,
shoe-shining and the like (Rodgers 1989: 21).
In these urban contexts, higher levels of education (in part acting as a signalling
or screening device for employers), more contacts and more extensive social networks, access to credit, location (close to a major industrial or service centre) and
work experience in the formal sector all tended to be negatively related to poverty
(Rodgers 1989: 13).
In rural areas, poverty was much more likely to be associated with lack of
access to land among individuals who were dependent on the agricultural sector
for a high proportion of their household income (Quibria 1993). The poor were
typically over-represented among farm labourers, and under-represented among
Labour market dimensions of poverty in Indonesia
239
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individuals who had found work in small-scale business operations or who had
migrated on a temporary basis (involving commuting, or circular or seasonal
migration) to find jobs in towns. As research in Indonesia has also found (Hart
1986; White and Wiradi 1989), there was a close correlation between access to land
and earnings outside agriculture: individuals in larger land-owning households
tended to earn a high proportion of household income from non-farm activities.
We examine whether some of these propositions apply to Indonesia in the subsequent sections of the paper.
THE INDONESIAN LABOUR MARKET IN 2002
In discussing the relationship between participation in the labour market and
poverty, it is important to bear in mind the structure of the labour market and
the economic context prevailing in 2002, the year of the Susenas survey on which
our analysis is based. In Indonesia, the informal sector—defined as including
all self-employed and family workers as well as casual wage employees—made
up 92% of agricultural employment and 52% of non-agricultural employment in
2002 (table 1).6 Just over 70% of the total workforce was employed in this sector.
Rates of poverty can be expected to be higher than in the formal sector, given that
many job seekers resort to self-employed or casual work, or work in small family
enterprises, owing to the relative lack of regular wage employment opportunities
(Mazumdar 1994).7
The structure of employment differed significantly between urban and rural
areas and by sex: urban informal employment accounted for just over half of all
jobs compared with close to 75% in rural areas, and females were more heavily
concentrated in informal jobs. Not surprisingly, these contrasts partly reflect the
dominance of agriculture in rural areas, where self-employment and family work
on small farms predominate, and in which females play a major role.8 However,
even in the non-agricultural sector, informal employment is considerably higher
in rural than in urban areas in the Indonesian case (table 1).
By 2002 the economy had largely recovered from the crisis, although per capita income levels were still below those prevailing immediately before the crisis.
6 This definition of the informal sector, which lumps less skilled workers together with
highly skilled professionals, is only a rough approximation. However, given that only
around 10% of all self-employed workers, casual wage employees and family workers had
a post-secondary education in 2002 (according to the Sakernas), it should give a reasonable
estimate of the size of the informal sector.
7 It should be noted nevertheless that entry into some informal sector activities may be
difficult because they tend to be dominated by specific ethnic groups or people from a particular region. This can partly be explained by activity-specific knowledge and imperfect
information flows beyond the insider group, as well as possible discrimination in favour of
insiders in the price and quality of inputs and in other aspects of business operation. It is
also the case that earnings are not always higher in the formal sector than in the informal
sector, especially where activity in the latter involves higher skill levels and significant
capital investments.
8 See, for example, Booth (2003) on the distribution of employment according to farm size
based on the agricultural census conducted in 1993.
240
Armida S. Alisjahbana and Chris Manning
TABLE 1 Labour Market Structure by Urban/Rural Area and by Sex, 2002
(%)
Urban
Rural
Males
Females
Total
62.6
55.1
7.5
72.0
67.0
5.0
85.6
79.2
6.4
50.1
44.2
5.9
67.8
61.6
6.1
Share of employment
Agriculture
Non-agriculture
Total
13.0
87.0
100.0
65.6
34.4
100.0
43.7
56.3
100.0
45.4
54.6
100.0
44.3
55.7
100.0
Agriculture
Informal
Self-employed
Family worker
Casual wage
Formal
Regular wage
Employer
Total
84.9
45.2
19.9
19.7
15.1
10.9
4.2
100.0
92.9
52.3
30.6
9.9
7.1
5.0
2.1
100.0
90.0
66.7
12.3
11.0
10.0
6.8
3.2
100.0
95.2
25.5
58.4
11.3
4.8
3.9
0.9
100.0
91.9
51.5
29.3
11.1
8.1
5.7
2.3
100.0
Non-agriculture
Informal
Self-employed
Family worker
Casual wage
Formal
Regular wage
Employer
Total
43.7
32.2
6.5
5.0
56.3
52.5
3.8
100.0
65.9
44.4
11.1
10.4
34.1
30.9
3.2
100.0
48.2
36.2
2.9
9.1
51.8
47.0
4.9
100.0
58.6
37.7
17.8
3.1
41.4
40.1
1.2
100.0
51.9
36.7
8.2
7.0
48.1
44.5
3.6
100.0
67.3
81.4
74.0
74.7
148.7
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Labour force participation rate
Employment rate
Unemployment ratea
Memo item: working-age
(15+) population (million)
a Based on the pre-2001 definition of unemployment, which excludes discouraged workers, those
establishing a new business and those not yet started in a new job.
Source: Sakernas, 2002.
Macroeconomic conditions were far from stable in this first year of the Megawati government, and Indonesia was still experiencing a significant net outflow
of capital.9 Poverty incidence, however, had almost recovered to pre-crisis levels:
according to official (revised) data, the proportion of the population below the
poverty line had fallen back to 18% from a high of 23% in 1999, compared with
15% in 1996, just before the crisis.
9 See, for example, Waslin (2003) and MacIntyre and Resosudarmo (2003).
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Labour market dimensions of poverty in Indonesia
241
In line with the slow recovery in investment, most labour indicators suggest
Indonesia was experiencing a major problem in formal sector job creation. While
the overall unemployment rate in 2002 (6.1% according to the pre-2001 definition)
was 20% above rates in the pre-crisis period (although still quite low by developing country standards), most of the new jobs after the crisis were created in
agriculture and the informal sector. Agricultural employment increased by over
1% p.a. between 1996/97 and 2002/03—faster than the growth in non-agricultural jobs—after declining in the decade before the crisis.10 The share of non-wage
employees in non-agricultural employment rose from 50% to 56%, while the share
of wage employees fell by a similar percentage over the same period. In absolute
terms the latter sector actually lost over 1 million jobs during this period. This
marked a significant reversal of pre-crisis trends, when the formal sector was the
major source of employment growth, expanding by over 5% p.a. over the decade
from 1986/87 to 1996/97.
Finally, the Indonesian government embarked on an aggressive minimum wage
policy to restore and increase real wages after a sharp decline during the crisis, in
addition to radically rewriting labour protection laws to introduce a number of
new minimum standards for wage workers. By 2002, real minimum wages were
already 30–50% higher in real terms in major urban centres (Jakarta, Bandung and
Surabaya) than they had been immediately before the crisis (Manning 2003).
OVERALL RELATIONSHIPS:
A STATISTICAL ANALYSIS OF LABOUR AND POVERTY
A logit regression model was employed, using the 2002 Susenas data, to identify
the variables associated with household poverty status, taking poor and nearpoor households as the reference category.11 The model seeks to incorporate the
key labour market relationships that could be expected to be associated with poverty, most importantly the activities of the household head and participation in
work by other household members.12 A set of other control variables was also
included in the equation, such as the age, gender and education of the household
head, the household dependency ratio and locational characteristics.
Table 2 provides parameter estimates of the logit estimation for all households,
single-headed households and households with married couples. The key variable was the labour market status of the household head: whether he or she was
in the labour market and, if in the labour market, in what sector and whether fully
employed or underemployed. Households in which the head was fully employed
10 These trends are all based on data from the annual Sakernas; see Alisjahbana and Manning (2005) for a fuller exposition.
11 Casual observation suggests that near-poor households have much in common with
poor households. We tested the model for all households with married heads, taking poor
households, rather than the poor and near-poor, as the reference group. The signs were the
same for all coefficients, and the parameter values very similar in most cases; statistical
significance was also similar for all variables in both cases. However, the parameter values
were smaller for the sex (male) variable, and larger (negative) for urban areas.
12 We do not address the issue of causality in this model. That is, the logit model does not
distinguish whether poverty is a cause or a consequence of various labour market characteristics.
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Variable
Single-headed Households
Married Couple Households
m.e.b Coef.
Std
Err.
***
***
–0.02 –0.09
0.00 0.00
0.01 –10.60
0.00
11.76
***
***
0.02 –16.02
***
–0.07
0.39
0.04
9.50
***
0.06
0.15
0.24
0.61
–0.01
0.54
0.96
0.01
0.02
0.02
–0.76
31.72
53.61
***
***
0.00
0.11
0.18
0.04
0.44
0.19
0.05
0.08
0.04
0.76
5.87
4.59
***
***
0.01
0.07
0.03
0.17
0.66
0.84
0.02
0.02
0.02
10.12
31.86
45.98
***
***
***
0.04
0.15
0.19
–0.34
–0.42
1.09
–0.83
–0.67
–0.56
0.02
0.02
0.03
0.05
0.02
0.09
–19.08
–18.21
32.63
–16.05
–39.14
–6.06
***
***
***
***
***
***
–0.08
–0.10
0.19
–0.20
–0.15
–0.13
–0.39
–0.91
0.84
–1.09
–0.91
–0.90
0.05 –7.40
0.07 –13.17
0.14
5.84
0.16 –6.95
0.06 –15.22
0.27 –3.32
***
***
***
***
***
***
–0.07
–0.18
0.11
–0.23
–0.18
–0.19
–0.45
–0.44
0.75
–0.86
–0.60
–0.58
0.02
0.03
0.04
0.06
0.02
0.11
–21.68
–16.44
20.73
–14.23
–29.81
–5.47
***
***
***
***
***
***
–0.11
–0.11
0.16
–0.21
–0.15
–0.14
–0.51
0.08
–6.77
***
–0.12 –0.79
0.25
–3.20
***
–0.16 –0.58
0.09
–6.75
***
–0.14
0.26
0.02
14.35
***
0.06 –0.08
0.07
–1.17
–0.01
0.02
6.30
***
0.03
Coef.
Std
Err.
Z
–0.10
0.00
0.00 –34.79
0.00 37.07
–0.32
Z
m.e.b Coef.
Std
Err.
–0.02 –0.09
0.00 0.00
0.00 –26.42
0.00 26.98
0.13
m.e.b
Z
***
***
–0.02
0.00
0.04
Armida S. Alisjahbana and Chris Manning
Household head characteristics
Age
Age squaredc
Sex
Male
Schooling (reference variable:
primary or less)
Primary or less
Junior secondary
Senior secondary & above
Labour force status (reference
variable: in non-agriculture,
IFS & fully employed)
Outside workforce
Unemployed
Professional, managerial & clerical
In agriculture & underemployed
In agriculture & fully employed
In non–agriculture, FS
& underemployed
In non–agriculture, IFS
& underemployed
In non–agriculture, FS
& fully employed
All Households
242
TABLE 2 The Probablility of Being Non-Poor, 2002a
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Household characteristics
Dependency ratiod
Employment ratioe
Location characteristics
Urban–rural (reference variable: rural)
Urban
Region (reference variable:
Eastern Indonesia)
Sumatra
Java–Bali
Kalimantan
Sulawesi
Constant
No. of observations
Likelihood ratio statistic
–2.25
0.62
0.06 –34.86
0.07 30.23
***
***
0.03 10.23
0.04 14.79
0.07 23.24
0.02 –25.91
0.03
3.95
***
***
***
***
***
0.07
0.13
0.28
–0.15
0.03
–0.36 –2.40
0.37 1.21
0.03 –79.46
0.04 28.93
***
***
–0.58
0.29
0.01 –0.10
0.02
–6.37
***
–0.02
0.02 –92.68
0.02 28.38
***
***
0.04
0.01
2.70
***
0.01
0.04
0.04
0.86
0.56
0.32
1.00
0.46
0.02
0.02
0.03
0.03
24.50
14.59
37.84
18.64
***
***
***
***
0.12
0.07
0.18
0.09
0.93
0.42
0.71
0.37
0.07
0.06
0.08
0.07
13.51
6.50
8.56
4.95
***
***
***
***
0.14
0.07
0.10
0.06
0.50
0.12
1.05
0.40
0.03
0.03
0.03
0.03
19.04
4.66
34.94
13.90
***
***
***
***
0.12
0.03
0.22
0.09
2.84
0.08
36.34
187,783
26,317
***
0.00
1.43
0.25
5.75
23,078
3,177
***
0.00
1.88
0.25
7.43
138,742
23,091
***
0.00
***
–0.49 –2.12
0.14 2.19
0.27
0.58
1.51
–0.61
0.12
***
Labour market dimensions of poverty in Indonesia
Spouse characteristics
Labour force status (reference
variable: in non–agriculture, IFS)
Outside workforce
Unemployed
Professional, managerial & clerical
In agriculture
In non-agriculture, FS
***
243
Coef. = coefficient; std err. = standard error; Z = Z statistic; m.e. = marginal effect; IFS = in the informal sector; FS = in the formal sector.
* = significant at 10%; ** = significant at 5%; *** = significant at 1%.
a Bivariate division of the dependent variable into two groups: poor and near-poor (combined), and non-poor. The poor and near-poor is the omitted category.
b Marginal effect is the rate of change in the probability of a household being non-poor due to a unit change in the regressor. Marginal effects are evaluated at the
regressor means.
c The estimated coefficients on this variable were all 0.001.
d Dependency ratio = ratio of children aged less than 15 to number of working-age persons in each household.
e Employment ratio = ratio of employed persons to number of working-age persons in each household.
Source: Susenas, 2002.
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244
Armida S. Alisjahbana and Chris Manning
in the informal sector, outside agriculture, were chosen as the reference category.
Most of the implied relationships were as expected between the main ‘explanatory’ variables on the one hand (the labour market characteristics of the household head and the spouse of the head, as well as the participation of household
members in the labour market) and poverty status on the other. Fully employed
formal sector workers in the non-agricultural sector, and professional, managerial
and clerical employees, were more likely to be among the non-poor, while those
in all other labour force categories were less likely to live in non-poor households
than the reference group. These latter categories comprised household heads who
were outside the workforce, unemployed, employed as agricultural workers (both
fully employed and underemployed) and underemployed in the non-agricultural
formal sector. All coefficients were significant at the 1% level. Underemployed
family heads working in agriculture were especially likely to be in households
below or close to the poverty line.
Three results stand out with regard to the labour market and employment status of the household head. First, household heads outside the workforce, and
those who were unemployed, were less likely to be non-poor (a negative coefficient in table 2); that is, they were more likely to be members of a poor or nearpoor household. Second, people employed in agriculture—regardless of whether
they were fully employed or underemployed—were similarly more likely to be
poor or near-poor. And third, the underemployed were also more likely to be poor
or near-poor, irrespective of whether they worked in the formal or informal sector.
All these results are relative to the reference group of households whose heads
were fully employed in the informal, non-agricultural sector.
The first finding is not controversial. The second reminds us of the extent to
which attachment to the agricultural sector in many parts of Indonesia (especially
Java–Bali and Eastern Indonesia) is associated with limited access to fertile land,
and with uncertain and low-paid employment.13 The third challenges the notion
that formal sector work per se is likely to lift individuals out of poverty; the utilisation of labour in terms of hours worked, in addition to productivity and wages,
also matters considerably.
Other labour market characteristics tested in the regression included the
number of employed persons in the household (employment ratio in table 2)
and the labour force status of the spouse of the household head (in households
in which the head was married; see last five columns in table 2). As might be
expected, the number of working household members was positively related to
the probability of being non-poor; a higher employment ratio would mean that
households could take advantage of additional sources of income from working
household members.
However, the variable measuring the spouse’s labour force status produced
some unexpected results.14 In contrast to household heads, spouses outside the
labour market and unemployed spouses were more likely to be among the nonpoor (a positive coefficient in table 2) than spouses in the reference group (those
employed in informal work outside agriculture). These findings provide some
13 See especially World Bank (1983), Hart (1986) and Breman and Wiradi (2001).
14 As might be expected, a high proportion of spouses were female.
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Labour market dimensions of poverty in Indonesia
245
support for the ‘luxury unemployment’ hypothesis—that spouses of the household head are more likely to be outside the workforce, or looking for work, precisely because they are less likely to work out of economic necessity. However,
like household heads, spouses employed in the formal, non-agricultural sector,
or working as professionals, managers and clerks, were more likely to belong to
non-poor households.
Among other characteristics of the household head, higher levels of education
were strongly associated with the probability of a household escaping poverty,
but the age and sex variables gave mixed results. The probability of being nonpoor was higher if the household head had at least a junior secondary education,
relative to those who had a primary education or less. The coefficients for junior
secondary, and for senior secondary or higher education, were both positive and
significant, with that for the latter being larger than that for the former, as one
would expect. Surprisingly, however, the probability of households being nonpoor was negatively related to the age of household head up to age 45.5 years,
and positively related beyond this (bearing in mind the positive coefficient on the
age-squared variable). The Z statistics show that both the age and age-squared
coefficients are significant at the 1% level; on the other hand, the marginal effects
are very small, suggesting that the overall relationship between age of household
head and household poverty status is of trivial importance once all the other factors are accounted for.15 Male household heads were more likely to be among
the non-poor (a positive coefficient) in the equation for single-headed households
and households with married heads but, surprisingly, not for the sample containing all households.
Turning to household characteristics, table 2 shows that poor and near-poor
households were more likely to have a higher dependency ratio; that is, households were more likely to be poor if they had more children relative to the number
of household members of working age (i.e. all those aged 15 years and above). A
set of locational variables indicates the expected positive and significant effect of
urban location on a household’s probability of being non-poor, although the coefficient is small and the sign is reversed for urban households that include married couples. Similarly, the probability of being in a poor household was lower
for households located in Java–Bali, Sumatra, Kalimantan and Sulawesi than
for those located in the poorer regions of Eastern Indonesia. Among the island
groups, the marginal effect for all households was much larger for Kalimantan
and Sumatra than for Java–Bali.
To summarise, the labour force and work characteristics of the household head
and spouse are significantly correlated with poverty status (i.e. being poor or
near-poor), even after we control for a range of other personal and household
characteristics. Whether household heads work, and the sector in which they
work, are important, and participation in the non-agricultural sector in particular
(whether in the formal or informal sector) is associated with a higher probability
of being non-poor. Participation by spouses per se tends to matter much less than
15 The unexpected negative sign for age may be the result of a positive association between age and attachment to the informal sector. Proportionately fewer young people were
employed in the informal sector than in the formal sector in 2002; the opposite was the case
for older people.
246
Armida S. Alisjahbana and Chris Manning
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the participation of household heads. For spouses, the nature of work is much
more closely correlated with poverty status. We examine some of these relationships in more detail in the following section.
PATTERNS OF LABOUR FORCE PARTICIPATION,
EMPLOYMENT AND EARNINGS
One might expect the poor in Indonesia to share many of the labour market characteristics of the poor in other developing countries discussed earlier, given the
patterns of participation, unemployment and employment shown in table 1: moderate unemployment rates but much higher underemployment, and a substantial
share of the workforce employed in agriculture and in the informal sector. However, there were some specific characteristics of the labour market that might be
expected to contribute to rather different patterns: slowing labour force growth
rates from the 1990s; relatively low unemployment rates among prime-age workers; quite high female participation rates; and a sustained period of rapid economic growth, brought to an end by the crisis in 1997–98 (Manning 2003).
In the logit analysis we have seen that most of the labour market variables were
significant and had the expected signs for household heads, although there were
some unexpected results for the spouse of the household head. Here we examine some of these relationships in greater detail, focusing on three sub-groups in
the population: females; youth (aged 15–24); and prime-age males (aged 25–59).
Both of the first two groups typically contain a significant proportion of secondary workers, that is, workers other than the main breadwinner in the household.
In addition, we examine participation and employment among the poor in urban
and rural locations in greater detail, before turning briefly to a consideration of
some regional dimensions and trends over time.
Participation rates, unemployment and underemployment
Indonesia is not unlike many other developing countries in that the main labour
market correlates of poverty appear to be sector of employment and work status rather than the extent of participation in the workforce. Participation and
unemployment rates were relatively uniform across expenditure classes in 2002
(table 3).16 The major exception was participation by young people: individuals
aged 15–24 in poor and near-poor households displayed significantly higher participation rates than youth in non-poor households.
In contrast to participation and unemployment rates, underemployment was
significantly higher among the poor and near-poor than among the non-poor.17
16 Following BPS, we include in the unemployed category those not working but looking
for work, and also people neither working nor looking for work but available for work
(‘discouraged’ workers). There are some conceptual problems in including discouraged
workers as unemployed, especially without reference to reservation wages (Suryadarma,
Suryahadi and Sumarto 2005). Discouraged workers are more likely to include older, rural and female people, who are also likely to be more heavily represented in poor households.
17 The underemployed are defined here as those working more than one but less than 35
hours per week, and looking for more work.
Labour market dimensions of poverty in Indonesia
247
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TABLE 3 Participation, Unemployment and Underemployment
Rates by Poverty Status, 2002a
(%)
Poor
Near-poor
Non-poor
Total
Participation rate
Prime-age malesb
Females
Youthc
98.1
51.8
61.0
98.3
51.1
57.7
97.8
47.8
52.1
98.0
49.1
54.8
Total
69.0
68.9
67.1
67.8
Unemployment rate
Prime-age malesb
Females
Youthc
3.1
15.7
29.6
3.1
14.6
28.0
3.1
15.3
28.8
3.1
15.2
28.8
Total
11.2
10.2
9.8
10.1
Underemployment rate
Prime-age malesb
Females
Youthc
15.9
18.0
26.1
13.8
15.3
24.8
10.0
8.3
13.3
11.7
11.4
18.3
Total
17.3
15.0
10.1
12.3
a
Participation rate = labour force/working-age population. Unemployment rate = unemployed/
labour force (using the 2002 revised definition, which includes discouraged workers). Underemployment rate = population working less than 35 hours per week and prepared to take on more work/total
employed. All three are expressed as percentages.
b
Prime-age males are workers aged 25–59.
c
Youth are workers aged 15–24.
Source: Authors’ calculations, based on the 2002 Susenas.
Figure 1 shows that underemployment was especially high among females and
young people in poorer households compared with the same groups in non-poor
households.
Thus, in terms of participation in work, the main differences between poor and
non-poor households were experienced most sharply among youth and, to a lesser
extent, females. Younger people from poor and near-poor families who were just
out of school, or in families unable to afford schooling, were much more likely to
be in the workforce and, if they did work, to be underemployed, than their better-off contemporaries.18 Higher participation among poor youth is presumably
18 Note that full-time students are classified as being outside the labour force (along with
housewives and the infirm), provided they are not looking for work. Anyone who works
a minimum of one hour a week is considered to be employed, even though the principal
activity may be school, university or household work.
248
Armida S. Alisjahbana and Chris Manning
FIGURE 1 Rate of Underemployment among Prime-age Males,
Females and Youth, 2002a
(%)
30
Poor
Near-poor
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20
Non-poor
10
0
Prime-age males
a
Females
Youth
Prime-age males are workers aged 25–59; youth are workers aged 15–24.
Source: Authors’ calculations, based on the 2002 Susenas.
partly a reflection of above-average school dropout rates among the poor, and
higher rates of school participation among the non-poor.
Consistent with the findings of the logit model above, the ‘luxury’ unemployment hypothesis does not appear to be confirmed as a dominant feature of
employment in Indonesia, even if a relatively high proportion of young and bettereducated people dominates among the unemployed.19 Labour markets do not
clear for many younger, disadvantaged people as well, suggesting that policies
that support job creation for these groups could contribute to poverty reduction.
It is worth mentioning that even though underemployment was a greater problem in rural areas, and unemployment in urban areas (among all income classes),20
younger and female workers in poor households were especially disadvantaged
by shorter hours of work relative to their better-off counterparts in towns and
cities. Thus 15–20% of younger and female workers in poor urban households
worked less than 35 hours per week and were searching for more work. In contrast, only around 5% of such workers in non-poor urban households were underemployed and looking for more work.
19 See, for example, Manning and Junankar (1998).
20 For example, according to the 2002 Susenas, 19% of females were unemployed and 8%
were underemployed in urban areas, compared with 12% and 14% respectively in rural
areas.
Labour market dimensions of poverty in Indonesia
249
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TABLE 4 Distribution of Workers by Industry, Work Status
and Household Poverty Status, 2002
(%)
Employment by industrya
Activities in which the poor/near-poor
are over-representedb
Agriculture
Construction
Activities in which the poor/near-poor
are under-representedc
Professional, managerial
& clerical (all sectors)
Manufacturing
Trade
Transport
Services
Other
Total
Employment by working status
Formal employment
Employees
Employers
Informal employment
Self-employed
Family workers
Casual
Total
Memo items:
Total number of employed (million)
Total number of households (million)
a
Poor
Nearpoor
Nonpoor
Total
61.3
4.7
56.9
4.6
36.2
3.8
44.6
4.2
1.2
2.0
8.5
5.9
10.2
12.2
4.0
5.6
0.8
100.0
11.6
14.0
4.0
6.0
0.8
100.0
12.3
21.3
4.9
12.0
1.0
100.0
11.8
18.3
4.6
9.7
0.9
100.0
24.8
23.0
1.8
26.4
24.4
2.0
42.1
38.3
3.7
35.9
32.9
3.0
75.2
41.4
22.2
11.6
100.0
73.6
43.0
21.2
9.4
100.0
57.9
38.9
13.8
5.3
100.0
64.1
40.2
16.7
7.2
100.0
13.7
7.2
18.7
10.5
53.9
35.4
86.3
53.1
The distribution of workers by industry excludes professional, managerial and clerical workers.
b
Over-represented = the proportion of poor/near-poor employed is greater than the proportion of
the total employed in this activity.
c
Under-represented = the proportion of poor/near-poor employed is less than the proportion of the
total employed in this activity.
Source: Authors’ calculations, based on the 2002 Susenas.
Patterns of employment and earnings
In relation to employment, the differences between household expenditure
groups were significant for households with members working in agriculture
and in the informal sector, as earlier studies of poverty in Indonesia have suggested (Huppi and Ravallion 1991; Smith et al. 2002). These patterns are evident
in the proportion of workers from poor households, compared with those from
250
Armida S. Alisjahbana and Chris Manning
FIGURE 2 Distribution of Manufacturing Workers between Formal and
Informal Sectors by Poverty Status, 2002
(%)
120
Poor
Near-poor
Non-poor
80
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55.9
76.6
40
25.8
15.3
0
8.2
Formal sector
18.3
Informal sector
Source: Authors’ calculations, based on the 2002 Susenas.
non-poor households, employed in agriculture and the informal sector—the latter especially among casual employees and, to a lesser extent, family workers
(table 4). The distribution of the near-poor also differed from that of the non-poor
in this respect.
Outside agriculture, however, the poor were more heavily concentrated than
the non-poor only in construction. In contrast, they were under-represented
among white-collar workers and in trade and services (although less so in manufacturing), in both urban and rural areas. As suggested in some of the studies of
poverty discussed above, both self-employed and family workers were less likely
to be members of poor households if they worked outside agriculture than if they
worked within it.21
The findings for manufacturing are surprising, given that movements out of
agriculture into industry are commonly believed to be associated with improved
wages and living standards. These results are partly explained by differences in
the poverty status of formal and informal sector workers within manufacturing.22
Thus, as shown in figure 2, there was a higher probability of manufacturing workers who were self-employed, family workers or casual employees being attached
to poor or near-poor households. Nevertheless, at the same time, a significant
number of formal sector workers in manufacturing (close to 25%) were members
21 Half of all informal sector workers in agriculture are attached to poor households; this
proportion falls to one-third for informal workers in non-agricultural jobs.
22 Bear in mind that many manufacturing plants are cottage industries employing less
than five workers (often working on an irregular basis and consisting largely of family
members). Approximately 40% of all employees in manufacturing were working in cottage
enterprises in 2002.
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Labour market dimensions of poverty in Indonesia
251
of poor or near-poor households. The large majority were regular wage employees rather than casual workers. It seems likely that many of them worked in small
and cottage industries in rural areas, where reservation wages were more likely to
be set by standards in agriculture than in modern industry.
Within the informal sector, casual wage workers were more likely than either
family workers or the self-employed to be members of poor households. Thus it is
important to underline the fact, emphasised in several studies of India in particular, that the contrast in poverty among various work status groups is not merely a
question of whether people are engaged in wage or non-wage employment.23 The
kind of wage employment also matters.
The data also reveal some important differences in the participation of individuals in poor/near-poor and non-poor households in urban compared with rural
areas. Workers in poor and near-poor urban households were heavily concentrated in several non-agricultural sectors, especially construction and transport,
in which short-term contracts and low-income urban jobs such as becak (trishaw)
driving are common. This is shown by the ratio of the share of the poor/near-poor
to that of the non-poor in particular sectors (table 5). Thus, for example, almost
twice (1.83 times) as many poor as non-poor urban workers were employed in
construction. On the other hand, workers from poor urban households were relatively under-represented in trade (including restaurants and hotels) and services.
However, among poor and non-poor workers, the pattern of participation in
jobs outside agriculture was different for rural areas. In general, the data provide
some indication that most non-farm sectors, even construction and transport, offer
more opportunities for households to move out of poverty in rural areas, compared to the greater bunching of poor workers in some non-agricultural sectors
in cities and towns. Trade in particular is a good example of a sector in which the
proportion of poor individuals employed in rural areas was considerably smaller
than that of the non-poor (nearly half); in urban areas the situation was different,
with a similar share of poor/near-poor and non-poor being employed in trade.
Many of the former presumably earned an income from hawking or other itinerant activities in middle-class neighbourhoods.
To what extent do employed persons in poor/near-poor households differ
from the non-poor in terms of work patterns by age and sex? Agriculture was
over-represented among the poor, especially among prime-age males, but also
among youth and females (data not shown in table 5). It is instructive nevertheless that a smaller proportion of prime-age m