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Bulletin of Indonesian Economic Studies
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
Poverty in Indonesia 1984–2002: the impact of
growth and changes in inequality
Riyana Miranti
To cite this article: Riyana Miranti (2010) Poverty in Indonesia 1984–2002: the impact of
growth and changes in inequality, Bulletin of Indonesian Economic Studies, 46:1, 79-97, DOI:
10.1080/00074911003642252
To link to this article: http://dx.doi.org/10.1080/00074911003642252
Published online: 17 Mar 2010.
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Date: 18 January 2016, At: 19:25
Bulletin of Indonesian Economic Studies, Vol. 46, No. 1, 2010: 79–97
POVERTY IN INDONESIA 1984–2002:
THE IMPACT OF GROWTH AND CHANGES IN INEQUALITY
Riyana Miranti*
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University of Canberra
This paper examines the growth elasticity of poverty across three development episodes in Indonesia between 1984 and 2002, after controlling for inequality. It relies
on estimation of panel data from the National Socio-Economic Survey conducted
by the central statistics agency. Contrary to expectations, the growth elasticity of
poverty was virtually indistinguishable across the three development episodes – a
period of far-reaching policy liberalisation (1984–90); a second period of slower
liberalisation (1990–96); and the period of recovery from the Asian financial crisis
(1999–2002). Growth was pro-poor in all three periods, while the impact of growth
on poverty was either augmented or offset by changes in inequality, depending on
the period. Only during the first liberalisation period did a reduction in inequality
serve to augment the impact of growth on poverty.
INTRODUCTION
The incidence of poverty in Indonesia fell from 29.5% in 1984 to 18.2% in 2002,
although the decline was interrupted for a few years by the financial crisis of the
late 1990s. The first objective of this paper is to examine the impact of economic
growth on poverty, with a special focus on differences during periods of policy
liberalisation and crisis recovery. It does not attempt, however, to reach definitive
conclusions about why the impact differed across time.
Most previous studies measuring the impact of economic growth on poverty,
including those on Indonesia, have used a short time-frame and have not examined differences in impact across different time periods (for example, Bidani
and Ravallion 1993; Balisacan, Pernia and Asra 2003; Miranti and Resosudarmo
2005). This may be due to data limitations, as a longer time series is necessary for
an extended analysis. Friedman (2005) did extend his focus to a 15-year period
(1984–99), but did not decompose this longer time-frame into shorter development episodes. The analysis in this paper is undertaken for the years 1984–2002,
and the period is divided into three development episodes, 1984–90, 1990–96 and
1999–2002. The purpose of considering these three sub-periods separately is to
shed some empirical light on the view that differing government policies across
these sub-periods had different impacts on poverty.
*
riyana.miranti@natsem.canberra.edu.au. I am grateful to Chris Manning, Hal Hill, Budy
Resosudarmo, Ross McLeod and two anonymous referees for their advice on previous
drafts. Those who gave advice bear no responsibility for any errors or deficiencies in the
final version.
ISSN 0007-4918 print/ISSN 1472-7234 online/10/010079-19
DOI: 10.1080/00074911003642252
© 2010 Indonesia Project ANU
80
Riyana Miranti
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If the distribution of income is constant, economic growth must reduce poverty. If income distribution becomes more equal, this tends to reduce poverty, and
conversely. The second objective of this paper is therefore to determine the extent
to which poverty reduction was simply the consequence of growth, and to what
extent this effect was augmented or offset by changes in inequality.
DEFINITIONS AND DATA
Poverty
To calculate the growth elasticity of poverty we focus on the head-count ratio or
incidence of poverty – that is, the proportion of poor people in the total population. People are categorised as poor when their consumption is below a certain threshold, referred to as the poverty line. Henceforth, ‘poverty’ as used in
this paper refers to poverty as measured by the poverty head-count ratio. This
paper applies the poverty line methodology currently used by the central statistics agency (BPS), the official institution publishing poverty data for Indonesia
(BPS 2003). BPS adopts the ‘basic needs’ approach, defining the poverty line as
the cost of consuming 2,100 calories per person per day, plus a pro-rata allowance
for non-food requirements. This approach differs from the analysis of Friedman
(2005), who applied the methodology of Bidani and Ravallion (1993) and Ravallion (1994) in constructing his poverty lines, and calculated the cost of non-food
needs in terms of the cost of foregone food items.
BPS claims that its current methodology is more comparable across regions
and consistent across time than previous methods. It has used this method
since it revised the 1996 official poverty figure. Three significant methodological
improvements have been introduced (BPS 2003). First, the measure includes more
commodities as basic needs. Second, consistency across time has been improved
by applying the 1999 shares of total consumption (proxied by expenditure) for
each of 52 products. The expenditure share of each product in the bundle differs
across provinces but is similar over time. Third, comparability across provinces
has been improved by indexing the price level in each province to the price level in
Jakarta, such that the cost of the ‘basic needs’ basket of goods is comparable for the
reference populations in all provinces. Thus, regional disparities in consumption
arise only because of differences in consumption patterns and prices, and not
because of differences in income levels.1 The methodology is considered dynamic
in the sense that different consumption tastes and patterns among provinces are
taken into account.
As a preliminary step, this paper uses the improved BPS methodology to recalculate poverty incidence between 1984 and 1996, the year the official poverty
figure was revised.2 Applying the new methodology results in poverty levels
for the period 1984–93 that are different from those officially published by BPS.
Table 1 shows the official poverty series and the re-calculated poverty levels used
in this paper.
1 However, the BPS poverty line has some shortcomings, especially in relation to regional
price differences, which are calculated and discussed in Nashihin (2007).
2 For a full description of the process, see Miranti (2007).
Poverty in Indonesia 1984–2002: the impact of growth and changes in inequality
81
TABLE 1 Poverty, 1984–2002
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(% of population)
1984
1987
1990
1993
1996
1996 revised (see text)
1999
2002
Official BPS Poverty Series
New Poverty Seriesa
21.7
17.4
15.1
13.7
11.3
17.6
23.4
18.2
29.5
25.7
23.4
20.6
17.6
23.4
18.2
a Recalculated by the author for 1984, 1987, 1990 and 1993, using the 2003 poverty line methodology
of the central statistics agency (BPS).
Source: BPS, National Socio-Economic Survey (Susenas), various years.
The poverty level is calculated using a panel of seven consecutive crosssectional National Socio-Economic Surveys (Survei Sosial Ekonomi Nasional, or
Susenas) undertaken by BPS from 1984 to 2002, covering 26 provinces.3 While
the core Susenas survey is conducted annually, a Susenas module covering consumption is published every three years. Since 1981, the consumption module
sample has covered approximately 65,000 households. Quantity and value data
are collected on more than 300 items for representative households in each province. The present study covers the period 1984–2002, using the 1984, 1987, 1990,
1993, 1996, 1999 and 2002 consumption modules (that is, seven waves). Use of
provincial-level data provides far more observations of growth and inequality
than would relying on national data, and hence results in more reliable econometric estimates.
Growth
The term ‘economic growth’ is most commonly understood to refer to a change
in the level of gross domestic product (GDP), a measure reported in the national
accounts, and its regional counterpart, gross regional domestic product (GRDP).
However, changes in the welfare of a country’s people may also be represented
by other measures. Aggregate household income, estimated from household surveys, is one of these.
Deaton (2001: 127) has criticised attempts to use growth data from the national
accounts to study the relationship between growth and poverty. He argues that
this growth measure ‘has at best a weak relationship with poverty’, since the
national accounts data and the household surveys used for calculating poverty
3 For consistency, four new provinces – Bangka Belitung, Banten, Gorontalo and North
Maluku – which first appeared in the 2002 Susenas as a result of province fragmentation
– have been re-combined with the provinces they were separated from, in order to match
the former provinces of South Sumatra, West Java, North Sulawesi and Maluku.
82
Riyana Miranti
TABLE 2 Growth Rates by Growth Measure and Development Episode, 1984–2002
(% p.a)
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First
Second
Liberalisation Liberalisation
Period
Period
National accounts
GDP
GDP per capita
Manufacturing GDP
Household surveys
Mean consumption expenditure
per capita
Crisis
Period
Recovery
Period
6.3
4.1
10.0
7.2
5.3
9.9
–13.1
–14.4
–11.4
4.0
2.7
4.2
1.1
1.3
–17.0
3.3
Source: BPS, Susenas and national accounts, various years; CEIC Asia Database, various years. Data
on mean consumption expenditure per capita for the crisis period are from Suryahadi, Sumarto and
Pritchett (2003).
levels measure different things. There are marked discrepancies in both direction
and rate of change between the two types of growth data (table 2). It can therefore
be argued that using an income proxy from the national accounts in conjunction
with household survey poverty data introduces new errors, in addition to those
found in the national accounts (Ravallion and Chen 1997: 363). The two types of
errors do not cancel each other out (Ravallion 2003). We would expect the same
problem to occur if an income proxy from the regional accounts were used.
Adams (2004) found that the growth elasticity of poverty is sensitive to the measure of income used. Most traditional empirical estimations of the growth elasticity
of poverty use mean consumption expenditure from household surveys as a proxy
for income (Adams 2004). It has been argued that this proxy is a better reflection of
welfare than is income calculated from the same source (Ravallion 1995), because
it gives a more accurate indication of ‘life-cycle’ or permanent (that is, long-term)
income (Balisacan, Pernia and Asra 2003: 332). Moreover, the data collected on consumption are more accurate than the income data, given that people may have reasons to hide some of their income (Ravallion 2001) and that measured consumption
patterns are less variable than measured income patterns (Deaton 1997).
This study uses mean consumption from the household surveys as a proxy for
income, and uses change in this variable as a proxy for economic growth. It uses
the province as the spatial unit for calculating income and changes in income, and
calculates monthly mean household consumption expenditure per capita for each
province in each Susenas consumption module period. Thus, ‘growth’ is defined
in this study as the percentage change in provincial monthly mean household
consumption expenditure per capita (for simplicity, we refer to this below as ‘consumption expenditure per capita’). The term ‘growth elasticity of poverty’ refers
to the percentage reduction in poverty given a 1% increase in mean consumption
expenditure per capita (rather than given a 1% change in GDP or GRDP).
In this paper, mean consumption expenditure per capita is calculated in real
terms, to allow comparison across time. Real mean consumption expenditure per
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Poverty in Indonesia 1984–2002: the impact of growth and changes in inequality
83
capita is calculated by deflating mean consumption to 1984 prices. The deflator
used is the implicit deflator of the weighted average of the urban and rural poverty lines, following Friedman (2001), Tarp et al. (2002) and Grimm and Gunther
(2006). The poverty line has been chosen as the deflator in preference to the consumer price index (CPI), because it gives a better representation of the spending
patterns of the poor (Adams 2004). Food items comprise only 40% of the CPI, but
they represent more than 70% of the items used to calculate the poverty line (BPS
2003). A further consideration is that the CPI is calculated only for 44 urban areas
in Indonesia, and does not cover rural areas, even though price movements in
urban and rural areas may differ. Use of the CPI as the deflator could therefore
result in biased estimates of real consumption.
DEVELOPMENT EPISODES
Between the 1970s and 2002, Indonesia experienced several distinct episodes of
development, including the oil boom from 1972 to around 1981; the rice boom
of 1978–83; a period of wide-ranging liberalisation throughout the mid- to late
1980s (Woo, Glassburner and Nasution 1994; Hill 2000); a second period of more
cautious liberalisation (Hill 1997) – and some back-sliding on reform – during the
first half of the 1990s; the financial and economic crisis of 1997–98; and, finally,
recovery after the crisis, beginning in 1999. There is no clear consensus on when
either of the two liberalisation periods started. One strand of the literature refers
mainly to microeconomic reform – especially trade liberalisation – commencing
in 1986 (Woo, Glassburner and Nasution 1994: 115) or 1987 (Hill 2000: 17), following a sharp decline in world oil prices in 1986. Several liberalisation packages
were introduced in 1986 to ease import and export procedures. They included
a duty exemption and drawback scheme that allowed export-oriented firms to
purchase imported inputs at international prices – the initial step towards an
export-promoting path of industrialisation. But in addition to trade liberalisation there had been some earlier fiscal reforms (such as tax reforms in 1983 and
1985) and exchange rate reforms (a devaluation in 1983). Soesastro (2006) discusses these, and identifies liberalisation as commencing in 1982. Aswicahyono
and Feridhanusetyawan (2004: 13) too have argued that microeconomic reform
began in the period 1982–85, although they note that this reform was slower and
less effective than the liberalisation of the mid-1980s.4
The delineation of periods for analysis in this paper is therefore to some extent
arbitrary, and has been driven partly by the availability of data at three-year intervals from the Susenas consumption module. The timing of the latter made 1984 a
convenient starting point for the analysis, and 1984–90 and 1990–96 appropriate
time-spans for the first and second liberalisation periods, while 1999–2002 realistically reflects Indonesia’s crisis recovery period. Data for 1990, the transitional
year linking the first and the second liberalisation periods, is included in both.
The following section discusses each of the development episodes. In explaining economic growth during each of the development episodes, two sources of
4 While trade liberalisation would have had a predictable impact on poverty, the impact of
other kinds of reform on poverty is less clear.
84
Riyana Miranti
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growth data are discussed: the national accounts and the household surveys.
Growth data from the latter source are then used in the estimation below.
The first liberalisation period (1984–90)
During the first liberalisation period, GDP grew by 6.3% annually. Manufacturing
GDP expanded by 10% p.a. (table 2) – almost twice the rate of GDP in agriculture (5.1%; figure not shown). Per capita GDP growth was 4.1%, while per capita
expenditure consumption based on household survey data grew more slowly, at
1.1%. The difference in the rate of change between the national accounts (GDP)
data and the household survey data is the result of differences in the definition
and coverage of GDP and household consumption per capita.
This first liberalisation period can be called one of transformation, in which
Indonesia experienced rapid industrialisation. There was initially a movement
away from heavy reliance on oil and gas exports and towards a more diversified
economy based on a strongly export-oriented manufacturing sector. This was followed by a transformation of the structure of employment, away from the primary sector and towards manufacturing and services.
Reform in the banking sector began in 1983, when the government removed
ceilings on time deposit rates at state banks and lending controls at all banks. This
was followed by a further banking deregulation package in 1988, which removed
restrictions on the expansion of bank branch networks and the establishment
of new banks, except in the case of purely foreign-owned banks (McLeod 1993;
Aswicahyono and Feridhanusetyawan 2004). From March 1985 through May
1990, a number of other deregulation packages were launched to liberalise the
economy, and especially to promote non-oil exports. The new policies included
reform of export incentives and administrative improvements to simplify procedures by reducing the number of licences required to export goods.5 As a result of
the trade reform and deregulation, tariffs and non-tariff barriers (NTBs) declined
significantly (Fane 1996; Hill 2000; Aswicahyono and Feridhanusetyawan 2004).
Significant growth in exports, structural transformation and poverty reduction
took place during this first period (Hill 2000).
The sectoral transformation away from agriculture and towards manufacturing
and services was mirrored in employment during the mid-1980s, with an expansion of jobs in footloose labour-intensive manufacturing sectors such as clothing, woven fabrics, footwear, electronics, furniture, yarn, toys and sporting goods
and glass and glassware (Hill 2000). Previous researchers (Hill 2000; Temple 2003)
have argued that Indonesia’s growth during the 1980s was not only rapid but
particularly favourable to the poor, because trade liberalisation policy meant that
Indonesia could make better use of one of its abundant resources – low-skilled
labour. This was readily available in large quantities in agriculture, and could be
drawn out of that sector into more modern activities in manufacturing, construction and other services, where its productivity would be higher and hence it could
earn higher incomes. Thus policies at the time were consistent with the reality that
the most straightforward way to reduce poverty is to increase the demand for
5 Hill (2000) points out that the strongest trade reform took place in the 1986–89 period,
when protection was reduced progressively.
Poverty in Indonesia 1984–2002: the impact of growth and changes in inequality
85
TABLE 3 Structural Transformation of Employment
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Growth episodes
First liberalisation
period a
Second liberalisation
period
Recovery period
Agriculture, Forestry
& Fisheries
Manufacturing
Services
Employment
Growth
(% p.a.)
Average
Share
of Total
Employment
(%)
Employment
Growth
(% p.a.)
Average
Share
of Total
Employment
(%)
Employment
Growth
(% p.a.)
Average
Share
of Total
Employment
(%)
3.1
55.8
9.8
9.2
2.9
30.5
–1.9
49.6
5.8
11.6
5.9
34.1
1.9
44.2
1.7
13.1
0.8
38.1
a Owing to data limitations, the first liberalisation period starts at 1987 for agriculture, forestry and
fisheries and for manufacturing, and at 1989 for services, instead of at 1984. Data on employment in
other sectors (mining and quarrying; electricity, gas and water; and construction) are not shown in
this table.
Source: CEIC Asia Database.
labour supplied by the poor. Restructuring the economy in line with comparative
advantage through trade liberalisation was an obvious way to do this.
It has been argued that growth of the labour-intensive manufacturing sector,
and of the economy as a whole, reduces poverty. For example, Manning (1998:
ch. 5) and Osmani (2004) have argued that declining poverty during the first liberalisation period resulted mainly from the employment expansion created by
economic growth, facilitated by slow growth in real wages. Table 3 illustrates this
structural transformation of employment. The proportion of jobs in the agriculture, forestry and fisheries sector declined, while the share in manufacturing and
the services sector increased.
Average annual growth of employment in agriculture, forestry and fisheries during the first liberalisation period was 3.1%, whereas in the manufacturing sector it was more than three times as rapid, at 9.8%. Large quantities of
labour were indeed drawn out of agriculture into more modern activities in
manufacturing and services. During this period, the proportion of employed
persons working in agriculture, forestry and fisheries was around 55.8% on
average, while 9.2% of employment was in the manufacturing sector and 30.5%
in services.
The second liberalisation period (1990–96)
During the period 1990–96, GDP and GDP per capita grew even more rapidly, by
7.2% and 5.3% annually, while manufacturing output continued to grow at 9.9%
(table 2). Mean consumption expenditure per capita grew slightly more rapidly
than in the first liberalisation period, by 1.3% annually. Although the magnitude
of the two per capita growth rates differs, both measures increased during the
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86
Riyana Miranti
second liberalisation period. Liberalisation through deregulation policies weakened, however (Hill 1997). Aswicahyono and Feridhanusetyawan (2004) have
labelled the (slightly different) period 1992–97 as one of ‘deregulation fatigue’.
Reforms were slower and less comprehensive, and there was some return to more
interventionist policy (Fane 1996). For example, a new monopoly on clove trading
was introduced in 1991, as were new restrictions on inter-island trade in oranges
from West Kalimantan in 1991; the tariff surcharge on imports of propylene and
ethylene was increased in 1993. The government also established a ‘national’ car
project involving Kia Motors of South Korea and the Indonesian car maker Timor
Putra Nasional, under which Timor was exempted from paying luxury taxes
(Fane 1996; Aswicahyono and Feridhanusetyawan 2004).
During the second liberalisation period, and especially between 1993 and 1996,
there was a decline in the rate of job creation, particularly in manufacturing and
agriculture, probably indicating that the previous labour surplus had begun to
be exhausted. Employment growth in the manufacturing sector was slower during the second liberalisation period, at 5.8%, than it had been in the first, while
employment in the agricultural sector contracted by 1.9% per year (table 3).
Do such data imply that growth was less pro-poor during this second liberalisation period? In fact, poverty fell by nearly six percentage points in these years,
from 23.4% in 1990 to 17.6% in 1996 – a slightly larger relative decline than in
the first period (table 1). The faster rate of poverty decline was probably due to
increases in real wages in all sectors, including construction, textiles and government administration (Manning 1998: ch. 5; Feridhanusetyawan 2002), as the rapid
growth of manufactured exports finally began to have an impact on the general
price of unskilled labour. Real wages in the manufacturing sector grew by 33%
between 1990 and 1996 – an annual growth rate of around 5%. On the other hand,
as we will see below, inequality worsened during this period, with the Gini coefficient increasing noticeably, from 0.321 in 1990 to 0.355 in 1996. This seems to
indicate that as trade liberalisation progressed, its impact was less heavily concentrated on the poor.
The crisis period (1997–98)
The Asian financial crisis hit Indonesia in 1997. A political crisis followed that
culminated in Soeharto’s resignation from the presidency in 1998. The economy
fell into turmoil, with GDP contracting by more than 13% (and GDP per capita by
more than 14%) in 1998 (table 2). Mean consumption expenditure per capita also
fell, by 17% (Suryahadi, Sumarto and Pritchett 2003).
It was decided not to include the period 1997–98 in the present analysis, because
the growth figure for the period as a whole masks enormous underlying volatility: the GDP growth rate fell from 4.7% in 1997 to –13.1% in 1998, before bouncing
back to 0.8% in 1999. Under such circumstances, and recalling that the Susenas
consumption module data are produced only every third year, it would be very
difficult to draw any meaningful interpretation of the overall change in the poverty level during this period of turmoil.
The recovery period (1999–2002)
Following the crisis there was little by way of the kinds of policy reform that had
been seen during the 1980s and early 1990s. Indeed, policy in the labour market
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Poverty in Indonesia 1984–2002: the impact of growth and changes in inequality
87
area – which is of crucial importance to poverty – began to become much more
interventionist. The economy rebounded, with GDP growing at an average rate
of 4% per annum, and per capita GDP at 2.7% per annum (table 2), though this
growth was much slower than that in the two periods of liberalisation. However, mean per capita consumption expenditure grew markedly in the recovery
period, at 3.3% p.a. This growth was more rapid than that in the first and second
liberalisation periods, and suggests that, coming from a very low base during
the crisis, consumption was quick to recover relative to other sectors. Miranti
(2007: 77–8) shows that this recovery of mean consumption expenditure was
robust to the choice of deflator.
The manufacturing sector recovered slowly to grow by only 4.2% per year
during this period (table 2), less than half the rate during the two liberalisation
phases. Despite the severe economic contraction during the crisis, unemployment
had remained lower than expected (Feridhanusetyawan 2002). Instead, there was
a shift of main occupation, with displaced workers moving from shrinking sectors to other sectors – especially agriculture and the informal sector. In contrast
with the two liberalisation periods, employment growth of 1.9% in agriculture,
forestry and fisheries during the recovery phase, while slow, was slightly more
rapid than growth of employment in manufacturing, where jobs expanded at
just 1.7% annually; services employment grew by only 0.8% during this period
(table 3). This adjustment reflected the flexibility of the labour market (Manning
2000; Feridhanusetyawan 2002).
Nevertheless, poverty fell by 5.2 percentage points in this recovery period,
from 23.4% in 1999 to 18.2% in 2002, a level only slightly higher than that in 1996
(17.6%) (table 1). Suryahadi and Sumarto (2001) categorised people with per
capita consumption below the poverty line immediately after the crisis as either
chronic or transient poor. The first category refers to those likely to remain poor
in the future, and the second to those likely to increase their consumption sufficiently to elevate themselves above the poverty line in the future. Suryahadi and
Sumarto found that the head-count ratio of transient poor increased from 12.4%
of the population in 1996 to 17.9% in 1999, whereas the head-count ratio of the
chronic poor increased by almost a factor of three, from 3.2% to 9.5%, during the
same period. This shows that rising numbers of chronic poor contributed most
to the increase in poverty, such that their share of the total rose from 20% to
35%. The fact that poverty declined during the recovery period may reflect the
impact of renewed GDP growth in raising the consumption of those in transient
poverty. Thus it is not surprising that inequality worsened during this period,
because the recovery may have had less impact on the chronic poor than on
other groups.
Changes in poverty and inequality
Table 4 shows the trends in poverty and in inequality (as indicated by the Gini
coefficient) during the three periods under consideration. During the first
liberalisation period, poverty fell by about six percentage points (from 29.5% in
1984 to 23.4% in 1990), while inequality declined very slightly from 0.330 to 0.321.
Poverty again fell by almost six percentage points – a larger relative decline – during the second liberalisation period, even though inequality increased noticeably,
to 0.355 in 1996.
88
Riyana Miranti
TABLE 4 Poverty and Inequality 1984–2002
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First liberalisation period
Second liberalisation period
Year
Head-count
Poverty
(%)
Gini
Coefficient
1984
29.5
0.330
1987
25.7
0.322
1990
23.4
0.321
1993
20.6
0.335
1996
17.6
0.355
1999
23.4
0.308
2002
18.2
0.329
Recovery period
Source: BPS, Susenas, various years.
These changes were reversed during the crisis, but the recovery period saw
poverty fall back to 18.2%, only a little higher than its level in 1996, while inequality returned to around its 1984 level (table 4).
We turn now to estimate the extent to which changes in poverty can be attributed to economic growth, on the one hand, and changing inequality on the other.
The following section provides the empirical methodology for this estimation.
EMPIRICAL METHODOLOGY AND APPROACH
An absolute poverty measure will be a strictly decreasing function of an increase
of mean consumption (growth), given a fixed poverty line and fixed income distribution (or given that the consumption of each individual changes in the same
proportion) (Ravallion 1995). This means that when income distribution is held
constant, a positive change in mean consumption expenditure per capita (growth)
will reduce poverty. This assumption of unchanging income distribution has been
made in various studies that measure the impact of economic growth on poverty,
including Datt and Ravallion (1992); Kakwani (1993); and Bourguignon (2003).
Thus the growth elasticity of poverty (GEP) is defined as the percentage reduction
in poverty given a 1% increase in consumption expenditure per capita, with other
factors held constant.
In reality, however, income distribution, or the level of inequality, is also
likely to vary over time. In this paper, the degree of inequality of the income
distribution is measured by the Gini coefficient, which has a value between
zero and one. A value of zero means perfect equality, such that everyone in the
Poverty in Indonesia 1984–2002: the impact of growth and changes in inequality
89
population has the same level of income. A value of one indicates perfect inequality, where one person accounts for all income. More generally, the smaller
the Gini coefficient, the more equal the distribution of income. An increase in
the Gini coefficient is likely to contribute positively to absolute poverty, other
things being equal. The inequality elasticity of poverty (IEP) is defined as the
percentage change in poverty given a 1% increase in the Gini coefficient, with
other factors held constant.
To estimate the growth and inequality elasticities of poverty, I follow a basic
model suggested by Ravallion and Chen (1997):
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e=6
ln Pi,t = γ0 + γ1 ln MEAN i,t + γ2 ln GINI i,t + ∑ βe dt + δ + εi,t
(1)
e =1
where
Pi,t is poverty in province i at time t (%);
MEAN i,t is mean consumption expenditure per capita (Rp/month, 1984 prices)
for province i at time t, as a proxy for income;
GINI i,t is the Gini coefficient for province i at time t, as a proxy for inequality.
t is the year index (t = 1984, 1987, 1990, 1993, 1996, 1999, 2002);
dt are the year dummies for the years when the Susenas was conducted:
d87 = 1 if t = 1987, and 0 otherwise;
d90 = 1 if t = 1990, and 0 otherwise;
d93 = 1 if t = 1993, and 0 otherwise;
d96 = 1 if t = 1996, and 0 otherwise;
d99 = 1 if t = 1999, and 0 otherwise;
d02 = 1 if t = 2002, and 0 otherwise.
The base period is 1984.
δi is the province fixed effect (unobserved heterogeneity).
εi,t is a white-noise error term that includes errors in the poverty measure.
As equation (1) is in logarithmic format for both dependent and independent
variables, the coefficient of ln MEAN refers to a 1% change in monthly mean consumption per capita (as a proxy for income) and the coefficient of ln GINI refers to
a 1% change in inequality.
While Ravallion and Chen (1997) and Adams (2004) use first differences estimation, this paper uses fixed effects, for the following reasons. First, fixed-effects
estimation allows the use of all the information available, even though these are
unbalanced panel data. They are unbalanced because there are several missing
observations for poverty in 1990 in nine provinces (BPS did not publish the data
for those provinces in that year), and because of the absence of consumption
data for Aceh, Maluku and Papua in 2002 (when a Susenas module could not be
undertaken in these provinces because of significant social and political unrest).
Second, the fixed-effects approach is more efficient than the first-differences
approach, because in any province random errors are usually assumed to be
serially independent – that is, not serially correlated with each other across time
periods (Wooldridge 2003).
Equation 1 assumes that the impact of growth on poverty is similar across
periods, but responds to the possibility of omitted variable bias by incorporating province and year dummies. The latter also capture, in general, the impact of
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90
Riyana Miranti
macroeconomic conditions in each Susenas consumption module period. Comparison of the coefficients of the year dummies with that for the base period of 1984
will indicate whether poverty was higher on average (if there is a significant positive sign) or lower (if there is a significant negative sign) than for the base period,
after the analysis has controlled for changes in consumption and in inequality.
However, we also aim to discover whether there are differences in the growth
and inequality elasticities of poverty across development episodes. Equation 2
therefore incorporates a set of interaction terms intended to determine whether the
impact of growth and inequality on poverty differs across development episodes.
The six year dummies are still included to avoid omitted variable bias and to
capture, in general, the impact of macroeconomic conditions in each Susenas consumption module period. In addition, there are interactions between the second
liberalisation period (EPISODE2) and the recovery period (EPISODE3) with the
growth variable (ln MEAN). Similarly, in calculating the IEP across development
episodes, there are interactions between the Gini coefficient variable (ln GINI) and
both the second liberalisation period (EPISODE2) and the recovery period (EPISODE3). The base period is the first liberalisation period, 1984–90.
Thus we now have:
ln Pi,t = ρ0 + ρ1 ln MEAN i,t + ρ2 ln GINI i,t + ρ3 EPISODE2,t * ln MEAN i,t +
ρ4 EPISODE3,t * ln MEAN i,t + ρ5 EPISODE2,t * ln GINI i,t +
e=6
ρ6 EPISODE3,t * ln GINI i,t + ∑ βe dt + δi + εi,t
(2)
e =1
where:
EPISODE j ,t , j = 2, 3, are the dummies for development episodes;6
EPISODE2,t = 1 if t = 1990, 1993 or 1996, and 0 otherwise (the second liberalisation period); and
EPISODE3,t = 1 if t = 1999 or 2002, and 0 otherwise (the recovery period).
The reference development episode is 1984–90 (the first liberalisation period).
To test the structural stability of the regression model (that is, to check whether
there is a structural change in the overall relationship between growth and poverty and between inequality and poverty), I performed an F-test of joint significance of the interaction terms.
EMPIRICAL RESULTS
Full period
We begin by assuming that the relationships between growth and poverty and
inequality and poverty remained unchanged throughout the whole period.
The middle column of table 5 shows that, after we had controlled for inequality, the GEP (ln MEAN) was –2.43 during the period 1984–2002. This means that
a 10% increase in consumption per capita reduced poverty proportionately by
24.3%. (By way of illustration, suppose that a particular province had a real mean
6 The dummies for the development episodes are used to capture the macroeconomic conditions during the specific development episodes.
Poverty in Indonesia 1984–2002: the impact of growth and changes in inequality
91
TABLE 5 Regression Resultsa
Explanatory Variable
ln MEAN
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ln GINI
For Full Period
(Equation 1)
For Individual
Development Episodes
(Equation 2) b
–2.43***
(–21.68)
0.92***
(5.85)
–2.37 ***
(–21.62)
0.78 ***
(5.11)
–0.09 **
(–2.01)
–0.12 *
(–1.86)
0.32 **
(2.01)
0.52 ***
(2.56)
–0.02
0.01
–0.07
–0.36 ***
–0.43
–0.53
30.62 ***
0.90
EPISODE2*ln MEAN
EPISODE3*ln MEAN
EPISODE2*ln GINI
EPISODE3*ln GINI
d87
d90
d93
d96
d99
d02
Constant
R-sq within
–0.02
0.02
–0.06*
–0.34***
–0.23**
–0.31***
29.94***
0.89
a ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively. The figures in parentheses
are t ratios. The number of observations is 189.
b For equation (2), the first liberalisation period, 1984–90, is the reference period. Therefore the
coefficients of ln MEAN and ln GINI in the last column refer to the first period of liberalisation.
consumption level of Rp 100,000 per month and a head-count poverty ratio of
20%. At a later time, when consumption had increased by 10% to Rp 110,000, poverty would have decreased by 24.3% of 20%, to 15.1%, other things being equal.)
The IEP (ln GINI) was 0.92, meaning that a 10% increase in inequality (as measured by the Gini coefficient) increased poverty proportionately by 9.2%.
The GEP in table 5 is similar to that found by Friedman (2005) for Indonesia
over the period 1984–99. It is also comparable with that found in studies of other
countries that also used the head-count poverty ratio measure as the dependent
variable. For Bangladesh, Wodon (2002) found growth elasticities for 1983–86 in
the range –1.6 to –2.6, depending on which of various poverty lines was used; this
was slightly higher than the GEP for Thailand of –2.2 in 1992–99 (Deolalikar 2002).
At 0.92, the IEP in table 5 is lower than those estimated by Friedman (2005), which
ranged from 1.3 to 1.9. It is also much smaller than those found in other countries.
For example, Thailand (Deolalikar 2002) recorded an IEP of 3.0, which is around
three times higher.
92
Riyana Miranti
TABLE 6 Summary of Growth and Gini Elasticities of Poverty, 1984–2002
Development Episode
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First liberalisation period
Second liberalisation period
Recovery period
Entire period
Growth
Elasticity of Povertya
–2.37
–2.46
–2.49
–2.43
Inequality (Gini)
Elasticity of Poverty
0.78
1.10
1.30
0.92
a See text for an explanation of ‘growth’ as used in this paper.
Source: Author’s calculations.
The estimations were carried out by applying the fixed-effects approach to control for provincial heterogeneity, an approach similar to that of applying ordinary
least squares (OLS) estimation, with 25 provincial dummies as explanatory variables. The results show that, with the province of Papua as the base category, all
province dummies except that for East Kalimantan are significant at the 1% level.
If other factors are held constant, including inequality, the other 24 provinces on
average had head-count poverty ratios that fell short of that estimated for Papua,
by percentages ranging from 26% (Riau and the Jakarta Special Region) to 82%
(South Sulawesi).
Poverty across development episodes
What happened to the GEP across periods? The last column of table 5 shows
that both elasticity measures differed across the three development episodes.
Table 6 provides a summary (in which the elasticities for each period are obtained
by adding the coefficients on the interaction terms to those for the first liberalisation period – that is, the reference development episode). The GEP was –2.37
during the first liberalisation period. The coefficients of the interaction terms
EPISODE2*ln MEAN and EPISODE3*ln MEAN are –0.09 and –0.12, respectively
(table 5), both being significantly different from zero at the 1% level. This means
the GEP for the second liberalisation period was –2.46, increasing slightly in magnitude to –2.49 in the recovery period. This is not unexpected. While poverty was
lower during the second and third periods than during the first, simple arithmetic
indicates that the GEP will tend to increase as poverty declines. From a different
perspective, with the manufacturing sector now quite large, a given amount of
growth in manufacturing would create far more job opportunities for people who
would otherwise still be in agriculture than it would have done when manufacturing was relatively small.
Table 6 also shows the IEP across the three periods. The elasticity was 0.78
during the first liberalisation period, meaning that a 10% increase in the Gini coefficient would increase poverty by 7.8%, other things being equal. The impact of
inequality on poverty was higher during the second liberalisation period and the
recovery period, however, when the IEPs were 1.10 and 1.30, respectively.
These results lend little support to the hypothesis that the GEP differed significantly across different development episodes. It turns out that growth was
pro-poor regardless of the development episode. However, the IEP during the
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Poverty in Indonesia 1984–2002: the impact of growth and changes in inequality
93
first liberalisation period was significantly different from that during the second
liberalisation and recovery periods.
When the different development episodes are taken into account, it is interesting to find that the only year dummy coefficient that was significant at the
1% level was that for 1996. This is in contrast to the full period estimation, in
which the 2002 dummy coefficient was also significant at 1%. This indicates that
growth of mean consumption expenditure per capita and changes in inequality
have explained almost all the change in poverty, and that macroeconomic conditions played a role only in 1996. (However, all the coefficients on the province
dummies, except that for East Kalimantan – not shown here – are still significant
at the 1% level.) As with the estimates for the full period, so in the individual
development episodes, other things being equal, the other 24 provinces on average had estimated head-count poverty ratios lower than that in Papua, in this case
by percentages ranging from 24% (Riau) to 81% (South Sulawesi).
Quantifying the growth and inequality effects on poverty
Having measured the magnitude of the growth and inequality elasticities of poverty across different development episodes, and knowing the amount of growth
and degree of change in the Gini coefficients that actually occurred, it is possible to
calculate the average implied change in poverty resulting from growth (changes
in consumption expenditure) and changes in inequality. For example, the coefficient γ1 in the main poverty equation (equation 1) represents the impact of growth
on poverty when other variables are held constant. The magnitude of this partial
impact of growth on poverty can be estimated as follows:
P
t
Pt
ln Pt
( * MEANt * Pt )
= 1 Pt = 1
= 1
ln MEAN t
MEAN t
MEAN
t
MEAN t
(3)
In the same way, the partial impact of a change in the Gini coefficient on poverty can also be estimated:
P
t
Pt
ln Pt
( * GINI t * Pt )
= 2 Pt = 2
= 2
ln GINI t
GINI t
GINI
t
GINI t
(4)
Table 7 shows the respective contributions of growth (as defined here) and
change in inequality to the change in national poverty. When we apply the
estimated parameters from the regressions above (using the growth and inequality
elasticities of poverty shown in table 6), the contribution of growth to the decline
in poverty can be seen to be largest during the recovery period, while its lowest
contribution was during the second liberalisation period. By contrast, changes
in inequality resulted in an offsetting impact on poverty, particularly during the
second liberalisation and recovery periods.
94
Riyana Miranti
TABLE 7 Contribution of Growth and Inequality to Change in Poverty
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Development Growtha Inequality Consumption Inequality Contribution to
Total
Episode
Change
at
at
Poverty Change Change
(∆MEAN) (∆GINI)
Beginning Beginning
(%)
a
of Period
of Period Growth Inequality
Change
(Rp/month, (Gini coef(%)
(%)
1984 prices)
ficient)
First
14,376
liberalisation
period
–0.01
211,374
0.33
–4.75
–0.61
–5.36
Second
18,144
liberalisation
period
0.03
225,750
0.32
–4.62
2.71
–1.92
Recovery
period
22,554
0.02
217,550
0.31
–6.05
2.12
–3.93
Entire
period
28,730
0.00
211,374
0.33
–9.74
–0.08
–9.83
a See text for an explanation of ‘growth’ as used in this paper.
Source: Author’s calculations.
The negative impact of change in inequality on poverty during the first liberalisation period seems credible, given that inequality declined slightly during this
period (table 4). The structural transformation away from agriculture and towards
labour-intensive manufacturing and services created many new job opportunities, and this impact was probably more significant for unskilled labour – which is
found near the bottom of the income distribution – thus reducing inequality.
The offsetting impact on poverty of changes in inequality during the second
liberalisation period may reflect the fact that liberalisation had begun to benefit
the population more widely, rather than mainly just the poor. The Gini coefficient
increased rather noticeably during this period (table 4). Further, as a study by
Akita and Alisjahbana (2002) reveals, the rapid GDP growth just before the 1997
economic crisis was characterised by increasing within-province inequality, particularly in Riau, and in Jakarta and West and East Java, where the manufacturing sector was mainly concentrated. The offsetting impact of increased inequality
on poverty during the recovery period suggests that the recovery might have
affected those in transient poverty to a greater extent than those in chronic poverty. A thorough examination of the factors that might explain the different signs
of the contribution of changes in inequality to poverty changes requires further
research, however.
CONCLUSIONS
This paper has used seven waves of the Susenas consumption module to track
the dynamics of poverty change across three development episodes. It began with
the presumption that different policies followed during each of these episodes
could be expected to have differential impacts on poverty during the respective
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Poverty in Indonesia 1984–2002: the impact of growth and changes in inequality
95
periods, especially through the employment effect of labour-intensive growth.
Various analysts have argued that, viewed from the demand side, the growth of
export-oriented, labour-intensive manufacturing can be seen as one factor among
several that help to explain Indonesia’s good record of poverty alleviation since
the early 1980s. (At the same time, on the supply side, improvements in education
and health standards have also benefited the poor.)
However, our results show that the GEP was remarkably stable across the
three development episodes, moving within the narrow range –2.37 to –2.49. The
presumption of substantial differences in the GEP between the sub-periods is
therefore not supported by the data. By contrast, the inequality elasticity of poverty ranged much more widely – between 0.78 and 1.30 – across the development
episodes, with the change between the first and second periods of liberalisation
especially noticeable.
The results also show that change in inequality re-inforced the impact of
growth on poverty only during the first liberalisation pe
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
Poverty in Indonesia 1984–2002: the impact of
growth and changes in inequality
Riyana Miranti
To cite this article: Riyana Miranti (2010) Poverty in Indonesia 1984–2002: the impact of
growth and changes in inequality, Bulletin of Indonesian Economic Studies, 46:1, 79-97, DOI:
10.1080/00074911003642252
To link to this article: http://dx.doi.org/10.1080/00074911003642252
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Date: 18 January 2016, At: 19:25
Bulletin of Indonesian Economic Studies, Vol. 46, No. 1, 2010: 79–97
POVERTY IN INDONESIA 1984–2002:
THE IMPACT OF GROWTH AND CHANGES IN INEQUALITY
Riyana Miranti*
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University of Canberra
This paper examines the growth elasticity of poverty across three development episodes in Indonesia between 1984 and 2002, after controlling for inequality. It relies
on estimation of panel data from the National Socio-Economic Survey conducted
by the central statistics agency. Contrary to expectations, the growth elasticity of
poverty was virtually indistinguishable across the three development episodes – a
period of far-reaching policy liberalisation (1984–90); a second period of slower
liberalisation (1990–96); and the period of recovery from the Asian financial crisis
(1999–2002). Growth was pro-poor in all three periods, while the impact of growth
on poverty was either augmented or offset by changes in inequality, depending on
the period. Only during the first liberalisation period did a reduction in inequality
serve to augment the impact of growth on poverty.
INTRODUCTION
The incidence of poverty in Indonesia fell from 29.5% in 1984 to 18.2% in 2002,
although the decline was interrupted for a few years by the financial crisis of the
late 1990s. The first objective of this paper is to examine the impact of economic
growth on poverty, with a special focus on differences during periods of policy
liberalisation and crisis recovery. It does not attempt, however, to reach definitive
conclusions about why the impact differed across time.
Most previous studies measuring the impact of economic growth on poverty,
including those on Indonesia, have used a short time-frame and have not examined differences in impact across different time periods (for example, Bidani
and Ravallion 1993; Balisacan, Pernia and Asra 2003; Miranti and Resosudarmo
2005). This may be due to data limitations, as a longer time series is necessary for
an extended analysis. Friedman (2005) did extend his focus to a 15-year period
(1984–99), but did not decompose this longer time-frame into shorter development episodes. The analysis in this paper is undertaken for the years 1984–2002,
and the period is divided into three development episodes, 1984–90, 1990–96 and
1999–2002. The purpose of considering these three sub-periods separately is to
shed some empirical light on the view that differing government policies across
these sub-periods had different impacts on poverty.
*
riyana.miranti@natsem.canberra.edu.au. I am grateful to Chris Manning, Hal Hill, Budy
Resosudarmo, Ross McLeod and two anonymous referees for their advice on previous
drafts. Those who gave advice bear no responsibility for any errors or deficiencies in the
final version.
ISSN 0007-4918 print/ISSN 1472-7234 online/10/010079-19
DOI: 10.1080/00074911003642252
© 2010 Indonesia Project ANU
80
Riyana Miranti
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If the distribution of income is constant, economic growth must reduce poverty. If income distribution becomes more equal, this tends to reduce poverty, and
conversely. The second objective of this paper is therefore to determine the extent
to which poverty reduction was simply the consequence of growth, and to what
extent this effect was augmented or offset by changes in inequality.
DEFINITIONS AND DATA
Poverty
To calculate the growth elasticity of poverty we focus on the head-count ratio or
incidence of poverty – that is, the proportion of poor people in the total population. People are categorised as poor when their consumption is below a certain threshold, referred to as the poverty line. Henceforth, ‘poverty’ as used in
this paper refers to poverty as measured by the poverty head-count ratio. This
paper applies the poverty line methodology currently used by the central statistics agency (BPS), the official institution publishing poverty data for Indonesia
(BPS 2003). BPS adopts the ‘basic needs’ approach, defining the poverty line as
the cost of consuming 2,100 calories per person per day, plus a pro-rata allowance
for non-food requirements. This approach differs from the analysis of Friedman
(2005), who applied the methodology of Bidani and Ravallion (1993) and Ravallion (1994) in constructing his poverty lines, and calculated the cost of non-food
needs in terms of the cost of foregone food items.
BPS claims that its current methodology is more comparable across regions
and consistent across time than previous methods. It has used this method
since it revised the 1996 official poverty figure. Three significant methodological
improvements have been introduced (BPS 2003). First, the measure includes more
commodities as basic needs. Second, consistency across time has been improved
by applying the 1999 shares of total consumption (proxied by expenditure) for
each of 52 products. The expenditure share of each product in the bundle differs
across provinces but is similar over time. Third, comparability across provinces
has been improved by indexing the price level in each province to the price level in
Jakarta, such that the cost of the ‘basic needs’ basket of goods is comparable for the
reference populations in all provinces. Thus, regional disparities in consumption
arise only because of differences in consumption patterns and prices, and not
because of differences in income levels.1 The methodology is considered dynamic
in the sense that different consumption tastes and patterns among provinces are
taken into account.
As a preliminary step, this paper uses the improved BPS methodology to recalculate poverty incidence between 1984 and 1996, the year the official poverty
figure was revised.2 Applying the new methodology results in poverty levels
for the period 1984–93 that are different from those officially published by BPS.
Table 1 shows the official poverty series and the re-calculated poverty levels used
in this paper.
1 However, the BPS poverty line has some shortcomings, especially in relation to regional
price differences, which are calculated and discussed in Nashihin (2007).
2 For a full description of the process, see Miranti (2007).
Poverty in Indonesia 1984–2002: the impact of growth and changes in inequality
81
TABLE 1 Poverty, 1984–2002
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(% of population)
1984
1987
1990
1993
1996
1996 revised (see text)
1999
2002
Official BPS Poverty Series
New Poverty Seriesa
21.7
17.4
15.1
13.7
11.3
17.6
23.4
18.2
29.5
25.7
23.4
20.6
17.6
23.4
18.2
a Recalculated by the author for 1984, 1987, 1990 and 1993, using the 2003 poverty line methodology
of the central statistics agency (BPS).
Source: BPS, National Socio-Economic Survey (Susenas), various years.
The poverty level is calculated using a panel of seven consecutive crosssectional National Socio-Economic Surveys (Survei Sosial Ekonomi Nasional, or
Susenas) undertaken by BPS from 1984 to 2002, covering 26 provinces.3 While
the core Susenas survey is conducted annually, a Susenas module covering consumption is published every three years. Since 1981, the consumption module
sample has covered approximately 65,000 households. Quantity and value data
are collected on more than 300 items for representative households in each province. The present study covers the period 1984–2002, using the 1984, 1987, 1990,
1993, 1996, 1999 and 2002 consumption modules (that is, seven waves). Use of
provincial-level data provides far more observations of growth and inequality
than would relying on national data, and hence results in more reliable econometric estimates.
Growth
The term ‘economic growth’ is most commonly understood to refer to a change
in the level of gross domestic product (GDP), a measure reported in the national
accounts, and its regional counterpart, gross regional domestic product (GRDP).
However, changes in the welfare of a country’s people may also be represented
by other measures. Aggregate household income, estimated from household surveys, is one of these.
Deaton (2001: 127) has criticised attempts to use growth data from the national
accounts to study the relationship between growth and poverty. He argues that
this growth measure ‘has at best a weak relationship with poverty’, since the
national accounts data and the household surveys used for calculating poverty
3 For consistency, four new provinces – Bangka Belitung, Banten, Gorontalo and North
Maluku – which first appeared in the 2002 Susenas as a result of province fragmentation
– have been re-combined with the provinces they were separated from, in order to match
the former provinces of South Sumatra, West Java, North Sulawesi and Maluku.
82
Riyana Miranti
TABLE 2 Growth Rates by Growth Measure and Development Episode, 1984–2002
(% p.a)
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First
Second
Liberalisation Liberalisation
Period
Period
National accounts
GDP
GDP per capita
Manufacturing GDP
Household surveys
Mean consumption expenditure
per capita
Crisis
Period
Recovery
Period
6.3
4.1
10.0
7.2
5.3
9.9
–13.1
–14.4
–11.4
4.0
2.7
4.2
1.1
1.3
–17.0
3.3
Source: BPS, Susenas and national accounts, various years; CEIC Asia Database, various years. Data
on mean consumption expenditure per capita for the crisis period are from Suryahadi, Sumarto and
Pritchett (2003).
levels measure different things. There are marked discrepancies in both direction
and rate of change between the two types of growth data (table 2). It can therefore
be argued that using an income proxy from the national accounts in conjunction
with household survey poverty data introduces new errors, in addition to those
found in the national accounts (Ravallion and Chen 1997: 363). The two types of
errors do not cancel each other out (Ravallion 2003). We would expect the same
problem to occur if an income proxy from the regional accounts were used.
Adams (2004) found that the growth elasticity of poverty is sensitive to the measure of income used. Most traditional empirical estimations of the growth elasticity
of poverty use mean consumption expenditure from household surveys as a proxy
for income (Adams 2004). It has been argued that this proxy is a better reflection of
welfare than is income calculated from the same source (Ravallion 1995), because
it gives a more accurate indication of ‘life-cycle’ or permanent (that is, long-term)
income (Balisacan, Pernia and Asra 2003: 332). Moreover, the data collected on consumption are more accurate than the income data, given that people may have reasons to hide some of their income (Ravallion 2001) and that measured consumption
patterns are less variable than measured income patterns (Deaton 1997).
This study uses mean consumption from the household surveys as a proxy for
income, and uses change in this variable as a proxy for economic growth. It uses
the province as the spatial unit for calculating income and changes in income, and
calculates monthly mean household consumption expenditure per capita for each
province in each Susenas consumption module period. Thus, ‘growth’ is defined
in this study as the percentage change in provincial monthly mean household
consumption expenditure per capita (for simplicity, we refer to this below as ‘consumption expenditure per capita’). The term ‘growth elasticity of poverty’ refers
to the percentage reduction in poverty given a 1% increase in mean consumption
expenditure per capita (rather than given a 1% change in GDP or GRDP).
In this paper, mean consumption expenditure per capita is calculated in real
terms, to allow comparison across time. Real mean consumption expenditure per
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Poverty in Indonesia 1984–2002: the impact of growth and changes in inequality
83
capita is calculated by deflating mean consumption to 1984 prices. The deflator
used is the implicit deflator of the weighted average of the urban and rural poverty lines, following Friedman (2001), Tarp et al. (2002) and Grimm and Gunther
(2006). The poverty line has been chosen as the deflator in preference to the consumer price index (CPI), because it gives a better representation of the spending
patterns of the poor (Adams 2004). Food items comprise only 40% of the CPI, but
they represent more than 70% of the items used to calculate the poverty line (BPS
2003). A further consideration is that the CPI is calculated only for 44 urban areas
in Indonesia, and does not cover rural areas, even though price movements in
urban and rural areas may differ. Use of the CPI as the deflator could therefore
result in biased estimates of real consumption.
DEVELOPMENT EPISODES
Between the 1970s and 2002, Indonesia experienced several distinct episodes of
development, including the oil boom from 1972 to around 1981; the rice boom
of 1978–83; a period of wide-ranging liberalisation throughout the mid- to late
1980s (Woo, Glassburner and Nasution 1994; Hill 2000); a second period of more
cautious liberalisation (Hill 1997) – and some back-sliding on reform – during the
first half of the 1990s; the financial and economic crisis of 1997–98; and, finally,
recovery after the crisis, beginning in 1999. There is no clear consensus on when
either of the two liberalisation periods started. One strand of the literature refers
mainly to microeconomic reform – especially trade liberalisation – commencing
in 1986 (Woo, Glassburner and Nasution 1994: 115) or 1987 (Hill 2000: 17), following a sharp decline in world oil prices in 1986. Several liberalisation packages
were introduced in 1986 to ease import and export procedures. They included
a duty exemption and drawback scheme that allowed export-oriented firms to
purchase imported inputs at international prices – the initial step towards an
export-promoting path of industrialisation. But in addition to trade liberalisation there had been some earlier fiscal reforms (such as tax reforms in 1983 and
1985) and exchange rate reforms (a devaluation in 1983). Soesastro (2006) discusses these, and identifies liberalisation as commencing in 1982. Aswicahyono
and Feridhanusetyawan (2004: 13) too have argued that microeconomic reform
began in the period 1982–85, although they note that this reform was slower and
less effective than the liberalisation of the mid-1980s.4
The delineation of periods for analysis in this paper is therefore to some extent
arbitrary, and has been driven partly by the availability of data at three-year intervals from the Susenas consumption module. The timing of the latter made 1984 a
convenient starting point for the analysis, and 1984–90 and 1990–96 appropriate
time-spans for the first and second liberalisation periods, while 1999–2002 realistically reflects Indonesia’s crisis recovery period. Data for 1990, the transitional
year linking the first and the second liberalisation periods, is included in both.
The following section discusses each of the development episodes. In explaining economic growth during each of the development episodes, two sources of
4 While trade liberalisation would have had a predictable impact on poverty, the impact of
other kinds of reform on poverty is less clear.
84
Riyana Miranti
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growth data are discussed: the national accounts and the household surveys.
Growth data from the latter source are then used in the estimation below.
The first liberalisation period (1984–90)
During the first liberalisation period, GDP grew by 6.3% annually. Manufacturing
GDP expanded by 10% p.a. (table 2) – almost twice the rate of GDP in agriculture (5.1%; figure not shown). Per capita GDP growth was 4.1%, while per capita
expenditure consumption based on household survey data grew more slowly, at
1.1%. The difference in the rate of change between the national accounts (GDP)
data and the household survey data is the result of differences in the definition
and coverage of GDP and household consumption per capita.
This first liberalisation period can be called one of transformation, in which
Indonesia experienced rapid industrialisation. There was initially a movement
away from heavy reliance on oil and gas exports and towards a more diversified
economy based on a strongly export-oriented manufacturing sector. This was followed by a transformation of the structure of employment, away from the primary sector and towards manufacturing and services.
Reform in the banking sector began in 1983, when the government removed
ceilings on time deposit rates at state banks and lending controls at all banks. This
was followed by a further banking deregulation package in 1988, which removed
restrictions on the expansion of bank branch networks and the establishment
of new banks, except in the case of purely foreign-owned banks (McLeod 1993;
Aswicahyono and Feridhanusetyawan 2004). From March 1985 through May
1990, a number of other deregulation packages were launched to liberalise the
economy, and especially to promote non-oil exports. The new policies included
reform of export incentives and administrative improvements to simplify procedures by reducing the number of licences required to export goods.5 As a result of
the trade reform and deregulation, tariffs and non-tariff barriers (NTBs) declined
significantly (Fane 1996; Hill 2000; Aswicahyono and Feridhanusetyawan 2004).
Significant growth in exports, structural transformation and poverty reduction
took place during this first period (Hill 2000).
The sectoral transformation away from agriculture and towards manufacturing
and services was mirrored in employment during the mid-1980s, with an expansion of jobs in footloose labour-intensive manufacturing sectors such as clothing, woven fabrics, footwear, electronics, furniture, yarn, toys and sporting goods
and glass and glassware (Hill 2000). Previous researchers (Hill 2000; Temple 2003)
have argued that Indonesia’s growth during the 1980s was not only rapid but
particularly favourable to the poor, because trade liberalisation policy meant that
Indonesia could make better use of one of its abundant resources – low-skilled
labour. This was readily available in large quantities in agriculture, and could be
drawn out of that sector into more modern activities in manufacturing, construction and other services, where its productivity would be higher and hence it could
earn higher incomes. Thus policies at the time were consistent with the reality that
the most straightforward way to reduce poverty is to increase the demand for
5 Hill (2000) points out that the strongest trade reform took place in the 1986–89 period,
when protection was reduced progressively.
Poverty in Indonesia 1984–2002: the impact of growth and changes in inequality
85
TABLE 3 Structural Transformation of Employment
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Growth episodes
First liberalisation
period a
Second liberalisation
period
Recovery period
Agriculture, Forestry
& Fisheries
Manufacturing
Services
Employment
Growth
(% p.a.)
Average
Share
of Total
Employment
(%)
Employment
Growth
(% p.a.)
Average
Share
of Total
Employment
(%)
Employment
Growth
(% p.a.)
Average
Share
of Total
Employment
(%)
3.1
55.8
9.8
9.2
2.9
30.5
–1.9
49.6
5.8
11.6
5.9
34.1
1.9
44.2
1.7
13.1
0.8
38.1
a Owing to data limitations, the first liberalisation period starts at 1987 for agriculture, forestry and
fisheries and for manufacturing, and at 1989 for services, instead of at 1984. Data on employment in
other sectors (mining and quarrying; electricity, gas and water; and construction) are not shown in
this table.
Source: CEIC Asia Database.
labour supplied by the poor. Restructuring the economy in line with comparative
advantage through trade liberalisation was an obvious way to do this.
It has been argued that growth of the labour-intensive manufacturing sector,
and of the economy as a whole, reduces poverty. For example, Manning (1998:
ch. 5) and Osmani (2004) have argued that declining poverty during the first liberalisation period resulted mainly from the employment expansion created by
economic growth, facilitated by slow growth in real wages. Table 3 illustrates this
structural transformation of employment. The proportion of jobs in the agriculture, forestry and fisheries sector declined, while the share in manufacturing and
the services sector increased.
Average annual growth of employment in agriculture, forestry and fisheries during the first liberalisation period was 3.1%, whereas in the manufacturing sector it was more than three times as rapid, at 9.8%. Large quantities of
labour were indeed drawn out of agriculture into more modern activities in
manufacturing and services. During this period, the proportion of employed
persons working in agriculture, forestry and fisheries was around 55.8% on
average, while 9.2% of employment was in the manufacturing sector and 30.5%
in services.
The second liberalisation period (1990–96)
During the period 1990–96, GDP and GDP per capita grew even more rapidly, by
7.2% and 5.3% annually, while manufacturing output continued to grow at 9.9%
(table 2). Mean consumption expenditure per capita grew slightly more rapidly
than in the first liberalisation period, by 1.3% annually. Although the magnitude
of the two per capita growth rates differs, both measures increased during the
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Riyana Miranti
second liberalisation period. Liberalisation through deregulation policies weakened, however (Hill 1997). Aswicahyono and Feridhanusetyawan (2004) have
labelled the (slightly different) period 1992–97 as one of ‘deregulation fatigue’.
Reforms were slower and less comprehensive, and there was some return to more
interventionist policy (Fane 1996). For example, a new monopoly on clove trading
was introduced in 1991, as were new restrictions on inter-island trade in oranges
from West Kalimantan in 1991; the tariff surcharge on imports of propylene and
ethylene was increased in 1993. The government also established a ‘national’ car
project involving Kia Motors of South Korea and the Indonesian car maker Timor
Putra Nasional, under which Timor was exempted from paying luxury taxes
(Fane 1996; Aswicahyono and Feridhanusetyawan 2004).
During the second liberalisation period, and especially between 1993 and 1996,
there was a decline in the rate of job creation, particularly in manufacturing and
agriculture, probably indicating that the previous labour surplus had begun to
be exhausted. Employment growth in the manufacturing sector was slower during the second liberalisation period, at 5.8%, than it had been in the first, while
employment in the agricultural sector contracted by 1.9% per year (table 3).
Do such data imply that growth was less pro-poor during this second liberalisation period? In fact, poverty fell by nearly six percentage points in these years,
from 23.4% in 1990 to 17.6% in 1996 – a slightly larger relative decline than in
the first period (table 1). The faster rate of poverty decline was probably due to
increases in real wages in all sectors, including construction, textiles and government administration (Manning 1998: ch. 5; Feridhanusetyawan 2002), as the rapid
growth of manufactured exports finally began to have an impact on the general
price of unskilled labour. Real wages in the manufacturing sector grew by 33%
between 1990 and 1996 – an annual growth rate of around 5%. On the other hand,
as we will see below, inequality worsened during this period, with the Gini coefficient increasing noticeably, from 0.321 in 1990 to 0.355 in 1996. This seems to
indicate that as trade liberalisation progressed, its impact was less heavily concentrated on the poor.
The crisis period (1997–98)
The Asian financial crisis hit Indonesia in 1997. A political crisis followed that
culminated in Soeharto’s resignation from the presidency in 1998. The economy
fell into turmoil, with GDP contracting by more than 13% (and GDP per capita by
more than 14%) in 1998 (table 2). Mean consumption expenditure per capita also
fell, by 17% (Suryahadi, Sumarto and Pritchett 2003).
It was decided not to include the period 1997–98 in the present analysis, because
the growth figure for the period as a whole masks enormous underlying volatility: the GDP growth rate fell from 4.7% in 1997 to –13.1% in 1998, before bouncing
back to 0.8% in 1999. Under such circumstances, and recalling that the Susenas
consumption module data are produced only every third year, it would be very
difficult to draw any meaningful interpretation of the overall change in the poverty level during this period of turmoil.
The recovery period (1999–2002)
Following the crisis there was little by way of the kinds of policy reform that had
been seen during the 1980s and early 1990s. Indeed, policy in the labour market
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Poverty in Indonesia 1984–2002: the impact of growth and changes in inequality
87
area – which is of crucial importance to poverty – began to become much more
interventionist. The economy rebounded, with GDP growing at an average rate
of 4% per annum, and per capita GDP at 2.7% per annum (table 2), though this
growth was much slower than that in the two periods of liberalisation. However, mean per capita consumption expenditure grew markedly in the recovery
period, at 3.3% p.a. This growth was more rapid than that in the first and second
liberalisation periods, and suggests that, coming from a very low base during
the crisis, consumption was quick to recover relative to other sectors. Miranti
(2007: 77–8) shows that this recovery of mean consumption expenditure was
robust to the choice of deflator.
The manufacturing sector recovered slowly to grow by only 4.2% per year
during this period (table 2), less than half the rate during the two liberalisation
phases. Despite the severe economic contraction during the crisis, unemployment
had remained lower than expected (Feridhanusetyawan 2002). Instead, there was
a shift of main occupation, with displaced workers moving from shrinking sectors to other sectors – especially agriculture and the informal sector. In contrast
with the two liberalisation periods, employment growth of 1.9% in agriculture,
forestry and fisheries during the recovery phase, while slow, was slightly more
rapid than growth of employment in manufacturing, where jobs expanded at
just 1.7% annually; services employment grew by only 0.8% during this period
(table 3). This adjustment reflected the flexibility of the labour market (Manning
2000; Feridhanusetyawan 2002).
Nevertheless, poverty fell by 5.2 percentage points in this recovery period,
from 23.4% in 1999 to 18.2% in 2002, a level only slightly higher than that in 1996
(17.6%) (table 1). Suryahadi and Sumarto (2001) categorised people with per
capita consumption below the poverty line immediately after the crisis as either
chronic or transient poor. The first category refers to those likely to remain poor
in the future, and the second to those likely to increase their consumption sufficiently to elevate themselves above the poverty line in the future. Suryahadi and
Sumarto found that the head-count ratio of transient poor increased from 12.4%
of the population in 1996 to 17.9% in 1999, whereas the head-count ratio of the
chronic poor increased by almost a factor of three, from 3.2% to 9.5%, during the
same period. This shows that rising numbers of chronic poor contributed most
to the increase in poverty, such that their share of the total rose from 20% to
35%. The fact that poverty declined during the recovery period may reflect the
impact of renewed GDP growth in raising the consumption of those in transient
poverty. Thus it is not surprising that inequality worsened during this period,
because the recovery may have had less impact on the chronic poor than on
other groups.
Changes in poverty and inequality
Table 4 shows the trends in poverty and in inequality (as indicated by the Gini
coefficient) during the three periods under consideration. During the first
liberalisation period, poverty fell by about six percentage points (from 29.5% in
1984 to 23.4% in 1990), while inequality declined very slightly from 0.330 to 0.321.
Poverty again fell by almost six percentage points – a larger relative decline – during the second liberalisation period, even though inequality increased noticeably,
to 0.355 in 1996.
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TABLE 4 Poverty and Inequality 1984–2002
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First liberalisation period
Second liberalisation period
Year
Head-count
Poverty
(%)
Gini
Coefficient
1984
29.5
0.330
1987
25.7
0.322
1990
23.4
0.321
1993
20.6
0.335
1996
17.6
0.355
1999
23.4
0.308
2002
18.2
0.329
Recovery period
Source: BPS, Susenas, various years.
These changes were reversed during the crisis, but the recovery period saw
poverty fall back to 18.2%, only a little higher than its level in 1996, while inequality returned to around its 1984 level (table 4).
We turn now to estimate the extent to which changes in poverty can be attributed to economic growth, on the one hand, and changing inequality on the other.
The following section provides the empirical methodology for this estimation.
EMPIRICAL METHODOLOGY AND APPROACH
An absolute poverty measure will be a strictly decreasing function of an increase
of mean consumption (growth), given a fixed poverty line and fixed income distribution (or given that the consumption of each individual changes in the same
proportion) (Ravallion 1995). This means that when income distribution is held
constant, a positive change in mean consumption expenditure per capita (growth)
will reduce poverty. This assumption of unchanging income distribution has been
made in various studies that measure the impact of economic growth on poverty,
including Datt and Ravallion (1992); Kakwani (1993); and Bourguignon (2003).
Thus the growth elasticity of poverty (GEP) is defined as the percentage reduction
in poverty given a 1% increase in consumption expenditure per capita, with other
factors held constant.
In reality, however, income distribution, or the level of inequality, is also
likely to vary over time. In this paper, the degree of inequality of the income
distribution is measured by the Gini coefficient, which has a value between
zero and one. A value of zero means perfect equality, such that everyone in the
Poverty in Indonesia 1984–2002: the impact of growth and changes in inequality
89
population has the same level of income. A value of one indicates perfect inequality, where one person accounts for all income. More generally, the smaller
the Gini coefficient, the more equal the distribution of income. An increase in
the Gini coefficient is likely to contribute positively to absolute poverty, other
things being equal. The inequality elasticity of poverty (IEP) is defined as the
percentage change in poverty given a 1% increase in the Gini coefficient, with
other factors held constant.
To estimate the growth and inequality elasticities of poverty, I follow a basic
model suggested by Ravallion and Chen (1997):
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e=6
ln Pi,t = γ0 + γ1 ln MEAN i,t + γ2 ln GINI i,t + ∑ βe dt + δ + εi,t
(1)
e =1
where
Pi,t is poverty in province i at time t (%);
MEAN i,t is mean consumption expenditure per capita (Rp/month, 1984 prices)
for province i at time t, as a proxy for income;
GINI i,t is the Gini coefficient for province i at time t, as a proxy for inequality.
t is the year index (t = 1984, 1987, 1990, 1993, 1996, 1999, 2002);
dt are the year dummies for the years when the Susenas was conducted:
d87 = 1 if t = 1987, and 0 otherwise;
d90 = 1 if t = 1990, and 0 otherwise;
d93 = 1 if t = 1993, and 0 otherwise;
d96 = 1 if t = 1996, and 0 otherwise;
d99 = 1 if t = 1999, and 0 otherwise;
d02 = 1 if t = 2002, and 0 otherwise.
The base period is 1984.
δi is the province fixed effect (unobserved heterogeneity).
εi,t is a white-noise error term that includes errors in the poverty measure.
As equation (1) is in logarithmic format for both dependent and independent
variables, the coefficient of ln MEAN refers to a 1% change in monthly mean consumption per capita (as a proxy for income) and the coefficient of ln GINI refers to
a 1% change in inequality.
While Ravallion and Chen (1997) and Adams (2004) use first differences estimation, this paper uses fixed effects, for the following reasons. First, fixed-effects
estimation allows the use of all the information available, even though these are
unbalanced panel data. They are unbalanced because there are several missing
observations for poverty in 1990 in nine provinces (BPS did not publish the data
for those provinces in that year), and because of the absence of consumption
data for Aceh, Maluku and Papua in 2002 (when a Susenas module could not be
undertaken in these provinces because of significant social and political unrest).
Second, the fixed-effects approach is more efficient than the first-differences
approach, because in any province random errors are usually assumed to be
serially independent – that is, not serially correlated with each other across time
periods (Wooldridge 2003).
Equation 1 assumes that the impact of growth on poverty is similar across
periods, but responds to the possibility of omitted variable bias by incorporating province and year dummies. The latter also capture, in general, the impact of
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Riyana Miranti
macroeconomic conditions in each Susenas consumption module period. Comparison of the coefficients of the year dummies with that for the base period of 1984
will indicate whether poverty was higher on average (if there is a significant positive sign) or lower (if there is a significant negative sign) than for the base period,
after the analysis has controlled for changes in consumption and in inequality.
However, we also aim to discover whether there are differences in the growth
and inequality elasticities of poverty across development episodes. Equation 2
therefore incorporates a set of interaction terms intended to determine whether the
impact of growth and inequality on poverty differs across development episodes.
The six year dummies are still included to avoid omitted variable bias and to
capture, in general, the impact of macroeconomic conditions in each Susenas consumption module period. In addition, there are interactions between the second
liberalisation period (EPISODE2) and the recovery period (EPISODE3) with the
growth variable (ln MEAN). Similarly, in calculating the IEP across development
episodes, there are interactions between the Gini coefficient variable (ln GINI) and
both the second liberalisation period (EPISODE2) and the recovery period (EPISODE3). The base period is the first liberalisation period, 1984–90.
Thus we now have:
ln Pi,t = ρ0 + ρ1 ln MEAN i,t + ρ2 ln GINI i,t + ρ3 EPISODE2,t * ln MEAN i,t +
ρ4 EPISODE3,t * ln MEAN i,t + ρ5 EPISODE2,t * ln GINI i,t +
e=6
ρ6 EPISODE3,t * ln GINI i,t + ∑ βe dt + δi + εi,t
(2)
e =1
where:
EPISODE j ,t , j = 2, 3, are the dummies for development episodes;6
EPISODE2,t = 1 if t = 1990, 1993 or 1996, and 0 otherwise (the second liberalisation period); and
EPISODE3,t = 1 if t = 1999 or 2002, and 0 otherwise (the recovery period).
The reference development episode is 1984–90 (the first liberalisation period).
To test the structural stability of the regression model (that is, to check whether
there is a structural change in the overall relationship between growth and poverty and between inequality and poverty), I performed an F-test of joint significance of the interaction terms.
EMPIRICAL RESULTS
Full period
We begin by assuming that the relationships between growth and poverty and
inequality and poverty remained unchanged throughout the whole period.
The middle column of table 5 shows that, after we had controlled for inequality, the GEP (ln MEAN) was –2.43 during the period 1984–2002. This means that
a 10% increase in consumption per capita reduced poverty proportionately by
24.3%. (By way of illustration, suppose that a particular province had a real mean
6 The dummies for the development episodes are used to capture the macroeconomic conditions during the specific development episodes.
Poverty in Indonesia 1984–2002: the impact of growth and changes in inequality
91
TABLE 5 Regression Resultsa
Explanatory Variable
ln MEAN
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ln GINI
For Full Period
(Equation 1)
For Individual
Development Episodes
(Equation 2) b
–2.43***
(–21.68)
0.92***
(5.85)
–2.37 ***
(–21.62)
0.78 ***
(5.11)
–0.09 **
(–2.01)
–0.12 *
(–1.86)
0.32 **
(2.01)
0.52 ***
(2.56)
–0.02
0.01
–0.07
–0.36 ***
–0.43
–0.53
30.62 ***
0.90
EPISODE2*ln MEAN
EPISODE3*ln MEAN
EPISODE2*ln GINI
EPISODE3*ln GINI
d87
d90
d93
d96
d99
d02
Constant
R-sq within
–0.02
0.02
–0.06*
–0.34***
–0.23**
–0.31***
29.94***
0.89
a ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively. The figures in parentheses
are t ratios. The number of observations is 189.
b For equation (2), the first liberalisation period, 1984–90, is the reference period. Therefore the
coefficients of ln MEAN and ln GINI in the last column refer to the first period of liberalisation.
consumption level of Rp 100,000 per month and a head-count poverty ratio of
20%. At a later time, when consumption had increased by 10% to Rp 110,000, poverty would have decreased by 24.3% of 20%, to 15.1%, other things being equal.)
The IEP (ln GINI) was 0.92, meaning that a 10% increase in inequality (as measured by the Gini coefficient) increased poverty proportionately by 9.2%.
The GEP in table 5 is similar to that found by Friedman (2005) for Indonesia
over the period 1984–99. It is also comparable with that found in studies of other
countries that also used the head-count poverty ratio measure as the dependent
variable. For Bangladesh, Wodon (2002) found growth elasticities for 1983–86 in
the range –1.6 to –2.6, depending on which of various poverty lines was used; this
was slightly higher than the GEP for Thailand of –2.2 in 1992–99 (Deolalikar 2002).
At 0.92, the IEP in table 5 is lower than those estimated by Friedman (2005), which
ranged from 1.3 to 1.9. It is also much smaller than those found in other countries.
For example, Thailand (Deolalikar 2002) recorded an IEP of 3.0, which is around
three times higher.
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TABLE 6 Summary of Growth and Gini Elasticities of Poverty, 1984–2002
Development Episode
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First liberalisation period
Second liberalisation period
Recovery period
Entire period
Growth
Elasticity of Povertya
–2.37
–2.46
–2.49
–2.43
Inequality (Gini)
Elasticity of Poverty
0.78
1.10
1.30
0.92
a See text for an explanation of ‘growth’ as used in this paper.
Source: Author’s calculations.
The estimations were carried out by applying the fixed-effects approach to control for provincial heterogeneity, an approach similar to that of applying ordinary
least squares (OLS) estimation, with 25 provincial dummies as explanatory variables. The results show that, with the province of Papua as the base category, all
province dummies except that for East Kalimantan are significant at the 1% level.
If other factors are held constant, including inequality, the other 24 provinces on
average had head-count poverty ratios that fell short of that estimated for Papua,
by percentages ranging from 26% (Riau and the Jakarta Special Region) to 82%
(South Sulawesi).
Poverty across development episodes
What happened to the GEP across periods? The last column of table 5 shows
that both elasticity measures differed across the three development episodes.
Table 6 provides a summary (in which the elasticities for each period are obtained
by adding the coefficients on the interaction terms to those for the first liberalisation period – that is, the reference development episode). The GEP was –2.37
during the first liberalisation period. The coefficients of the interaction terms
EPISODE2*ln MEAN and EPISODE3*ln MEAN are –0.09 and –0.12, respectively
(table 5), both being significantly different from zero at the 1% level. This means
the GEP for the second liberalisation period was –2.46, increasing slightly in magnitude to –2.49 in the recovery period. This is not unexpected. While poverty was
lower during the second and third periods than during the first, simple arithmetic
indicates that the GEP will tend to increase as poverty declines. From a different
perspective, with the manufacturing sector now quite large, a given amount of
growth in manufacturing would create far more job opportunities for people who
would otherwise still be in agriculture than it would have done when manufacturing was relatively small.
Table 6 also shows the IEP across the three periods. The elasticity was 0.78
during the first liberalisation period, meaning that a 10% increase in the Gini coefficient would increase poverty by 7.8%, other things being equal. The impact of
inequality on poverty was higher during the second liberalisation period and the
recovery period, however, when the IEPs were 1.10 and 1.30, respectively.
These results lend little support to the hypothesis that the GEP differed significantly across different development episodes. It turns out that growth was
pro-poor regardless of the development episode. However, the IEP during the
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Poverty in Indonesia 1984–2002: the impact of growth and changes in inequality
93
first liberalisation period was significantly different from that during the second
liberalisation and recovery periods.
When the different development episodes are taken into account, it is interesting to find that the only year dummy coefficient that was significant at the
1% level was that for 1996. This is in contrast to the full period estimation, in
which the 2002 dummy coefficient was also significant at 1%. This indicates that
growth of mean consumption expenditure per capita and changes in inequality
have explained almost all the change in poverty, and that macroeconomic conditions played a role only in 1996. (However, all the coefficients on the province
dummies, except that for East Kalimantan – not shown here – are still significant
at the 1% level.) As with the estimates for the full period, so in the individual
development episodes, other things being equal, the other 24 provinces on average had estimated head-count poverty ratios lower than that in Papua, in this case
by percentages ranging from 24% (Riau) to 81% (South Sulawesi).
Quantifying the growth and inequality effects on poverty
Having measured the magnitude of the growth and inequality elasticities of poverty across different development episodes, and knowing the amount of growth
and degree of change in the Gini coefficients that actually occurred, it is possible to
calculate the average implied change in poverty resulting from growth (changes
in consumption expenditure) and changes in inequality. For example, the coefficient γ1 in the main poverty equation (equation 1) represents the impact of growth
on poverty when other variables are held constant. The magnitude of this partial
impact of growth on poverty can be estimated as follows:
P
t
Pt
ln Pt
( * MEANt * Pt )
= 1 Pt = 1
= 1
ln MEAN t
MEAN t
MEAN
t
MEAN t
(3)
In the same way, the partial impact of a change in the Gini coefficient on poverty can also be estimated:
P
t
Pt
ln Pt
( * GINI t * Pt )
= 2 Pt = 2
= 2
ln GINI t
GINI t
GINI
t
GINI t
(4)
Table 7 shows the respective contributions of growth (as defined here) and
change in inequality to the change in national poverty. When we apply the
estimated parameters from the regressions above (using the growth and inequality
elasticities of poverty shown in table 6), the contribution of growth to the decline
in poverty can be seen to be largest during the recovery period, while its lowest
contribution was during the second liberalisation period. By contrast, changes
in inequality resulted in an offsetting impact on poverty, particularly during the
second liberalisation and recovery periods.
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TABLE 7 Contribution of Growth and Inequality to Change in Poverty
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Development Growtha Inequality Consumption Inequality Contribution to
Total
Episode
Change
at
at
Poverty Change Change
(∆MEAN) (∆GINI)
Beginning Beginning
(%)
a
of Period
of Period Growth Inequality
Change
(Rp/month, (Gini coef(%)
(%)
1984 prices)
ficient)
First
14,376
liberalisation
period
–0.01
211,374
0.33
–4.75
–0.61
–5.36
Second
18,144
liberalisation
period
0.03
225,750
0.32
–4.62
2.71
–1.92
Recovery
period
22,554
0.02
217,550
0.31
–6.05
2.12
–3.93
Entire
period
28,730
0.00
211,374
0.33
–9.74
–0.08
–9.83
a See text for an explanation of ‘growth’ as used in this paper.
Source: Author’s calculations.
The negative impact of change in inequality on poverty during the first liberalisation period seems credible, given that inequality declined slightly during this
period (table 4). The structural transformation away from agriculture and towards
labour-intensive manufacturing and services created many new job opportunities, and this impact was probably more significant for unskilled labour – which is
found near the bottom of the income distribution – thus reducing inequality.
The offsetting impact on poverty of changes in inequality during the second
liberalisation period may reflect the fact that liberalisation had begun to benefit
the population more widely, rather than mainly just the poor. The Gini coefficient
increased rather noticeably during this period (table 4). Further, as a study by
Akita and Alisjahbana (2002) reveals, the rapid GDP growth just before the 1997
economic crisis was characterised by increasing within-province inequality, particularly in Riau, and in Jakarta and West and East Java, where the manufacturing sector was mainly concentrated. The offsetting impact of increased inequality
on poverty during the recovery period suggests that the recovery might have
affected those in transient poverty to a greater extent than those in chronic poverty. A thorough examination of the factors that might explain the different signs
of the contribution of changes in inequality to poverty changes requires further
research, however.
CONCLUSIONS
This paper has used seven waves of the Susenas consumption module to track
the dynamics of poverty change across three development episodes. It began with
the presumption that different policies followed during each of these episodes
could be expected to have differential impacts on poverty during the respective
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Poverty in Indonesia 1984–2002: the impact of growth and changes in inequality
95
periods, especially through the employment effect of labour-intensive growth.
Various analysts have argued that, viewed from the demand side, the growth of
export-oriented, labour-intensive manufacturing can be seen as one factor among
several that help to explain Indonesia’s good record of poverty alleviation since
the early 1980s. (At the same time, on the supply side, improvements in education
and health standards have also benefited the poor.)
However, our results show that the GEP was remarkably stable across the
three development episodes, moving within the narrow range –2.37 to –2.49. The
presumption of substantial differences in the GEP between the sub-periods is
therefore not supported by the data. By contrast, the inequality elasticity of poverty ranged much more widely – between 0.78 and 1.30 – across the development
episodes, with the change between the first and second periods of liberalisation
especially noticeable.
The results also show that change in inequality re-inforced the impact of
growth on poverty only during the first liberalisation pe