00074918.2013.809841
Bulletin of Indonesian Economic Studies
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
Regional convergence and the role of the
neighbourhood effect in decentralised Indonesia
Yogi Vidyattama
To cite this article: Yogi Vidyattama (2013) Regional convergence and the role of the
neighbourhood effect in decentralised Indonesia, Bulletin of Indonesian Economic Studies,
49:2, 193-211, DOI: 10.1080/00074918.2013.809841
To link to this article: http://dx.doi.org/10.1080/00074918.2013.809841
Published online: 26 Jul 2013.
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Date: 17 January 2016, At: 23:44
Bulletin of Indonesian Economic Studies, Vol. 49, No. 2, 2013: 193–211
REGIONAL CONVERGENCE AND THE ROLE OF
THE NEIGHBOURHOOD EFFECT IN
DECENTRALISED INDONESIA
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Yogi Vidyattama*
University of Canberra
More than a decade since Indonesia’s radical decentralisation process commenced,
this article examines whether the economic performance of neighbouring regions
– the neighbourhood effect – can determine the speed of regional convergence. The
results suggest that the inequality of gross regional domestic product per capita, as
indicated by the Williamson index of regional inequality, may increase slightly in
times of insigniicant estimated speeds of convergence – especially because of the
growth of Jakarta. In contrast, changes in the Human Development Index numbers
for Indonesia indicate that regional convergence is taking place, although its speed
is decreasing. The neighbourhood effect could be signiicant in both cases, but it has
had little effect on the speed of convergence.
Keywords: regional convergence, regional inequality, Indonesia after decentralisation
INTRODUCTION
Regional inequality is a major issue in Indonesia, from the different levels of development and resource endowments among its regions to its population’s distribution and ethnicity (Tadjoeddin, Suharyo and Mishra 2001; Aspinall and Berger
2001). In the past decade, regional convergence, or the decline in dispersion of a
development indicator such as per-capita income across different regions, and its
effect in reducing inequality, has been the subject of many studies in Indonesia.
These studies have applied various techniques, such as statistical disparity measures (see, for example, Akita and Lukman 1995; Tadjoeddin, Suharyo and Mishra
2001; Akita and Alisjahbana 2002; and Milanovic 2005) and regional growth convergence frameworks (see, for example, Garcia-Garcia and Soelistianingsih 1998;
Resosudarmo and Vidyattama 2006; and Hill, Resosudarmo and Vidyattama
2008). Yet few have focused on disparity at the district level, and fewer again have
looked at the process since 1999, when the Indonesian government announced its
decentralisation reforms.
*
The author would like to thank the University of Canberra, for funding this study
through its Vice-Chancellor’s Awards for Early Career Researchers, and NATSEM, for use
of its staff development fund. The author would also like to thank Bana Bodri and Agusman Simbolon, from the Badan Pusat Statistik (BPS), Indonesia’s Central Statistics Agency,
for providing the data used in this study, as well as Riyana Miranti, Rebecca Cassells, Hal
Hill and the two anonymous referees, for their valuable comments.
ISSN 0007-4918 print/ISSN 1472-7234 online/13/020193-19
http://dx.doi.org/10.1080/00074918.2013.809841
© 2013 Indonesia Project ANU
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Yogi Vidyattama
This article contributes to the existing body of knowledge of regional income
inequality in Indonesia, by looking at the pattern of regional convergence at the
provincial and district levels after decentralisation was formalised, by Indonesian
Law 22/1999 and Law 25/1999, and then implemented, in 2001. Since decentralisation commenced, the central government has delegated much greater responsibility for education, agriculture, industry, trade, investment and infrastructure
to district authorities, with the aim of delivering better public services – particularly in less developed regions (Alm, Aten and Bahl 2001; World Bank 2003). The
examination of regional convergence has since become increasingly important,
especially at the district level. This article, however, does not speciically assess
the impact of decentralisation itself on convergence, despite the growing literature on how decentralisation may increase or decrease the speed of convergence
(Rodriguez-Pose and Ezcurra 2010). Instead, this article examines the impact of
the neighbourhood effect at the district level on regional convergence, to ascertain whether the neighbourhood effect shapes Indonesia’s regional growth.
As acknowledged by McCulloch and Sjahrir (2008) and Akita, Kurniawan and
Miyata (2011), one of the main challenges of conducting an analysis at a subnational level, especially in a lower level such as the district, is how to account
for the possible spatial, or neighbourhood, effect: the economic performance of
neighbouring regions often has a signiicant impact (Anselin 1988; LeSage 1999;
Rey 2001), affecting convergence (Egger and Pfaffermayr 2006) by reducing inequality in only certain locations. This is particularly relevant in Indonesia, given
the allegation that most of its development has occurred in only a few provinces
(Suryadarma et al. 2006; Hill, Resosudarmo and Vidyattama 2008).
This article also uses patterns of change in the Human Development Index
(HDI) as an alternative indicator of regional development to gross regional
domestic product (GRDP) per capita. There are advantages and disadvantages
in using GRDP per capita as a measure of economic performance. One disadvantage is the inclusion of the mining sector, which, in Indonesia, operates in several
areas (that is, districts or provinces) with limited backward and forward linkages,
which implies that GRDP does not necessarily reveal a shared level of economic
development throughout a region (Tadjoeddin, Suharyo and Mishra 2001; Brodjonegoro and Martinez-Vazquez 2002; Milanovic 2005; Hill, Resosudarmo and Vidyattama 2008; Akita and Lukman 1995). Another problem is that GRDP data do
not take account of regional cross-border transactions. For the purpose of approximating the regional standard of living, this article therefore uses the HDI, which
is widely regarded as a comprehensive index and combines measures of health,
education, and income or expenditure. HDI has been widely used, especially in
studies of developing nations (Anand and Sen 2000). Other common measures of
regional inequality may include the distribution of disadvantage, such as poverty,
unemployment or homelessness, but these are not the focus of this article.
CONVERGENCE STUDIES IN INDONESIA
As there is a large body of literature on regional inequality and convergence which
follows the seminal work of Williamson (1965), Barro and Sala-I-Martin (1991), and
Sala-I-Martin (1996), this article refers only to those studies that look speciically
at Indonesia or examine the neighbourhood effect’s impact on convergence. These
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Regional convergence and the role of the neighbourhood effect
195
studies form a comparative base for examining convergence in Indonesia after
decentralisation; comparisons between Indonesia and other countries have been
discussed in Shankar and Shah (2003), Milanovic (2005), and Hill, Resosudarmo
and Vidyattama (2008).
In Akita and Lukman’s (1995) study of regional convergence in Indonesia, they
use the Williamson index, a coeficient of variation measure, to look at changes
in regional inequality at the provincial level during 1975–92, which shows a large
decrease in inequality of GRDP per capita among regions. Their decomposition of
the inequality index shows the increasing importance of the manufacturing and
construction sectors in examining inequality in Indonesia. Despite this, inequality of non-mining GRDP per capita remained relatively stagnant during 1975–92,
except for a discontinued trend of increased inequality during the export-oriented
reforms of the mid-1980s.
Garcia-Garcia and Soelistianingsih (1998) produced the irst estimate of the
speed of convergence (β-convergence) of Indonesian provincial incomes of 1975–
83, which conirmed statistically signiicant convergence of GRDP per capita and
estimated its speed to be around 2%. This speed implied that the convergence
process would be at its halfway point after 35 years, or, in other words, that the
observed regional inequality (that is, the average deviation from the mean) would
roughly be halved in that period. This was similar to the results from several
OECD countries (Sala-I-Martin 1996). Hill, Resosudarmo and Vidyattama (2008)
show that the indings of Garcia-Garcia and Soelistianingsih (1998) are sensitive to
the choice of time period analysed and are also heavily inluenced by the decline
in the contribution since 1975 of Indonesia’s resource-rich provinces to Indonesia’s GDP, as the oil and gas sector has become less important. This later study
also shows that the speed of β-convergence varies signiicantly across Indonesia’s
development periods. It was quite rapid (2%) during the oil boom of 1975–81; and
it accelerated after oil prices stabilised during 1981–86, with the speed of convergence estimated to be 2.8%. The speed of β-convergence then collapsed, however,
from 1.7%, in 1986–92, to just 1%, in the 1990s, as the government’s export-oriented reforms took hold. During the inancial crisis and its aftermath, from 1997
to 2002, there was no signiicant convergence. Hill, Resosudarmo and Vidyattama
(2008) also conirm Akita and Lukman’s inding that convergence is fairly stable
after the output of the mining sector has been removed from GRDP per capita.
Tadjoeddin, Suharyo and Mishra (2001) conirm that regional inequality is stable at the district level, having examined the Williamson index and the Theil and
Gini coeficients of GRDP per capita from 1993, when the data was irst released,
to 1998. Although their estimations, similarly to those of Hill, Resosudarmo and
Vidyattama (2008), show that regional inequality remained relatively unchanged
during this period, they ind that the inequality of GRDP per capita, without oil
and gas, increased slightly at the district level until 1998. Akita and Alisjahbana
(2002) draw similar conclusions at the district level, inding that regional income
inequality increased during 1993–97. This does not contradict the relatively stable
levels of regional inequality estimated by other studies at the provincial level;
rather, it shows that inequality increased among certain districts within some
provinces. Akita, Kurniawan and Miyata (2011) show that the Asian inancial crisis reduced inequality in Indonesia, especially as the crisis hit Jakarta harder than
it did less developed areas. This trend did not last – regional inequality increased
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196
Yogi Vidyattama
again until 2004, in the wake of Jakarta’s recovery – but it remains uncertain what
the trend has been since decentralisation intensiied. Moreover, Tadjoeddin (2013)
argues the inequality among districts could follow a Kuznets curve, where inequality increases when high levels of growth bring an economy to a certain level
of income and then decreases with further increases in income. However, he notes
that this relationship is observed only when cities are integrated within their surrounding regencies.
Studies using a regional growth framework at the district level – such as those
noted above – have prompted examinations of whether including spatial factors
would change the result. This is related to the argument of Sala-I-Martin (1996)
that convergence is more achievable in a sub-national setting, owing to greater
interaction among smaller regions as economic entities. It also means that the
neighbourhood effect is more likely to exist among smaller regions. Another common reason that emerges in regional studies for the existence of the neighbourhood effect is that the administrative boundaries used to identify regions do not
necessarily relect the boundaries of economic activities (LeSage 1999; Rey 2001).
As a result, some economic activities within borders or across borders, such as
trade and commuting, relate the economic performances of the regions involved,
so that a change of conditions in one region’s economy could well affect that of
another.
Egger and Pfaffermayr (2006) point out that the neighbourhood effect can also
produce biased analyses of convergence. They argue that the speed of convergence can vary across regions, and that convergence in major growth centres can
prevent remote regions from catching up. Indonesia’s remote east, for example,
which comprises most of the country’s least developed districts, could be left
behind as inequality levels in the rest of the country converge. Akita, Kurniawan
and Miyata (2011), however, show that the differences in inequality among Indonesia’s largest regions (Java–Bali, Sumatra–Kalimantan–Papua, and other regions
in the country’s east) are small compared with the levels of inequality within those
regions, and that the levels of cross-regional inequality have been relatively constant throughout the years. Instead, Akita, Kurniawan and Miyata (2011) detect
increasing levels of inequality not only within regions but also among districts
within provinces in those regions.
Applying the neighbourhood effect to convergence has brought mixed results
in other countries’ regional growth analyses. Rey and Montouri (1999), pioneers
in this ield, ind that the neighbourhood effect among US states is not only statistically signiicant; it also signiicantly alters convergence. Niebuhr (2001) inds
that although the neighbourhood effect in West Germany affected growth signiicantly, it slowed convergence only slightly. His indings differ from those of
Kosfeld, Eckey and Dreger (2002), who, concentrating on a uniied Germany, note
that the inclusion of the neighbourhood effect slowed convergence signiicantly.
Magalhães, Hewings and Azzoni (2005), in an example of the neighbourhood
effect’s impact in a developing country, ind that it did little to alter the speed of
regional convergence in Brazil.
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Regional convergence and the role of the neighbourhood effect
197
METHODOLOGY AND DATA
Empirical method
The concept of convergence refers to the decline in dispersion of a development
indicator, such as per-capita income distribution, across regions as economic entities. In growth analyses, convergence has a slightly different meaning: it refers
to relatively lower growth of an economy, with relatively higher income. Sala-IMartin (1996) argues that the concept of convergence in growth analyses is a necessary but not suficient condition in reducing income inequality, or the inequality
of any other development indicator.
A measure of regional inequality popularised by Williamson (1965) – the Williamson index – is often used to examine regional inequality at one point in time
and, hence, to measure changes in inequality over time. This article uses the
population-weighted version of the index:
n
∑ (Yi − Y )2
CVw =
i=1
Pi
P
Y
(1)
where:
•
•
•
•
•
•
CVw = population-weighted Williamson index;
n = number of regions;
Yi = income in region i;
Y = average income;
Pi = population in region i; and
P = total population.
Although the Williamson index has been used widely to analyse the changes in
regional inequality and, hence, regional convergence, it cannot indicate the signiicance of convergence itself.
The speed of convergence is introduced in growth analyses. Known as as
β-convergence, it can be used to examine whether the economies of relatively
poorer regions grow signiicantly faster than richer regions, as an indication of
decreasing levels of regional inequality. Barro and Sala-I-Martin (1991) made the
concept famous, using the following regression assessment:
( ) u
g y = α + e− β − 1 ln y0 +
(
)
(2)
where:
• gy = the growth rate of per-capita output;
• y0 = the initial economic output value; and
• u = the error term of this estimation.
Empirically, convergence occurs when the coeficient of (e-β –1) shows a negative correlation between growth and the initial condition of the economy. Consequently, β should be positive in times of regional convergence. The absolute value
of β represents the speed of the catch-up process, or the speed of convergence.
198
Yogi Vidyattama
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This article considers two well-known spatial models, as popularised by Anselin
(1988) – the spatial autoregressive lag model (SAR) and the spatial autoregressive
error model (SEM) – to see whether the neighbourhood effect is signiicant and/
or if it signiicantly alters the speed of convergence.
The SAR assesses the direct connection between the development of one region
and that of its neighbours. If the growth of one region can directly affect that of its
neighbours, then neighbouring regions are more likely to grow at similar rates. As
Fingleton and Lopez-Bazo (2006) demonstrated, the spatial autoregressive lag can
be introduced to the growth regression in equation (2) as:
( ) W
g y = α + e− β − 1 ln y0 + ρ g y + u
(
)
(3)
where ρWgy is the spatial lag of the dependent variable and W is the spatial weight
matrix.
The SEM examines the indirect existence of the neighbourhood effect. The
development of one region may not be affected directly by that of its neighbours,
but it can still be affected by the undisclosed determinants in the equation. For
example, it is possible that although the growth of one region may have an insigniicant effect on that of its neighbours, the increasing human capital in that region
would affect its neighbours signiicantly. Anselin (1988) formalises the structure
of the error term affected by the neighbourhood effect:
u = λ u+ε
(4)
or, considering the spatial multiplier effect and combined with equation (2), the
SEM can be written as:
( ) ( I − λu )−1 ε
g y = α + e− β − 1 ln y0 +
(
)
(5)
where u is the error term in the panel estimation and ε is the real random factor.
Growth and development indicators
Most growth analyses focus on GDP or GRDP per capita as a proxy for per-capita
income and as an indicator of development. The data for provincial-level GRDP
are available from the regional accounts of BPS from 1975 onwards, whereas the
district-level data are available only from 1993 onwards.
Various proxy measures for different industrial sectors are used to calculate
GRDP in Indonesia. BPS coordinated and conducted GRDP calculations since
1975, and formalised a process that had previously relied on universities producing GRDP data for their area (Arndt 1973). Output statistics reported by various
regional BPS ofices are used as proxy measures for the output of the agricultural sector, and the output of the industrial sector has been calculated based on
data from industrial censuses and sample surveys. While a proxy measure for
the construction sector can be based on the consumption of building material
and surveys of local contractors, measuring the output of the services sector has
been more challenging: market surpluses, public-sector wages and sales statistics all need to be estimated. In 1983, 1993 and 2000, BPS revised the baskets of
goods and services used as proxies for the estimation of regional GDP, to relect
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Regional convergence and the role of the neighbourhood effect
199
the changes in the compositions of output and consumption in different sectors.
It also changed the base year for the estimation of constant price series. Besides
releasing a new series of GRDP data based on the new proxies, BPS also released
the data based on the previous procedure for particular years, as a concordance
tool. The GRDP used in this article is based on the 1993 constant price series and
all other data are converted to that time series, using the concordance tool.
Calculating GRDP using proxy measures of output often fails to show the real
level of development, especially when the wealth created from the output of a
region is not necessarily distributed within the same region. In Indonesia, GRDP
may overstate true wealth, because many regions have high levels of GRDP per
capita but relatively low levels of individual income (Tadjoeddin, Suharyo and
Mishra 2001; Brodjonegoro and Martinez-Vazquez 2002). In Papua’s Mimika
regency, for example, which, in 1999, was part of the Fakfak district, the high output from PT Freeport Indonesia’s Grasberg mine has not translated to the people
of the region, where the poverty rate exceeds 40%. Lhokseumawe, in North Aceh,
the location of the Arun liqueied-natural-gas plant, and Sumbawa, the location
of Newmont’s Batu Hijau mine, are other examples of regions with high levels of
GRDP and relatively high poverty rates. This issue centres largely on the output
of the mining industry, which, while allocated to the incomes of central government and mining companies, is accrued in regional GDP statistics (Akita and Lukman 1995; Milanovic 2005; Hill, Resosudarmo and Vidyattama 2008).
The HDI, another indicator of development, has been used since 1990 to compare worldwide development levels. Based on three equally weighted dimensions – life expectancy; education or literacy; and standard of living, or per-capita
income – it is still the most widely used index of development (Anand and Sen
2000). There are advantages and disadvantages in using a composite index such
as the HDI in convergence analyses: it has the advantage of representing the total
package of living standards in the economy, but it can also hide the importance
of certain variables or be less effective if all variables have a very similar regional
distribution (McGillivray 1991).
There are also advantages and disadvantages in interpreting convergence values based on the HDI. The advantage comes from the assumption, or the hope,
that the index will move, or converge, to the expected (maximum) value, but this
means that the traditional thinking behind the catch-up process is not really relevant. There is a maximum value of convergence that the most developed region
will be able to achieve, so the speed of convergence would be expected to be
higher in HDI than in GRDP per capita. This article therefore looks at HDI convergence simply from the point of whether, on average, regions with low levels
of HDI have a higher mobility towards the maximum compared with those with
high levels of HDI, as well as the signiicant level of this mobility and its changes
over time.
The data for HDI are available for Indonesia at both a provincial and a district level.1 The calculation of HDI data at the district level relies on Indonesia’s
National Socio-Economic Survey (Susenas). The income proxy in Indonesia’s HDI
1 BPS has published HDI data regularly since 2002, with the index appearing irst in the
2001 Indonesia Human Development Report, by the United Nations Development Project’s
UNSFIR (the United Nations Support Facility for Indonesian Recovery).
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200
Yogi Vidyattama
calculation is based on the average expenditure of the households in Susenas.
While education variables such as the literacy rate and the mean number of years
of schooling can also be estimated directly from Susenas, calculating life expectancy involves using data from the 2000 Population Census to continue the series
from previous censuses, and the infant mortality variable from both the Population Census and Susenas. Given the high number of Susenas observations – at
around 157,000 households in 1999 and 278,000 in 2005 – the district-level data
can be considered reliable. However, Susenas enumerators often cannot capture
data in very remote areas, which detracts from this reliability. In the 1999 and 2002
surveys, for example, BPS acknowledged the dificulty of reaching conlict areas
in Aceh, Maluku and Papua. The dificulty of interviewing those in the highest
and lowest income brackets is another challenge recognised by the survey (Leigh
and Van der Eng 2009).
The regions in an Indonesian context
Administrative divisions are the most common economic entities in a country’s
regional economy. In Indonesia, the irst, or highest, administrative division is the
province, followed by the district. The latter consists of regencies and cities, or
kabupaten and kota. The third administrative division comprises sub-districts, or
kecamatan, while the fourth, or lowest, division comprises urban and rural villages,
or kelurahan and desa. According to the Indonesian Department of Internal Affairs,
in 2005 Indonesia had 33 provinces, 349 regencies, 91 cities, 5,263 sub-districts,
7,123 urban villages and 62,806 rural villages.
As Indonesia has embraced decentralisation, the boundaries of its provinces
and districts have changed rapidly. While the number of provinces increased
from 26 (excluding East Timor) to 33 during 1999–2008, the number of districts
increased from around 350 to more than 450. To obtain a consistent database, and
to ensure that every region is represented throughout the period of decentralisation, BPS has amalgamated the provinces into the 26 that existed prior to 1999 and
the districts into the 294 that existed in the 1996 BPS database.
An essential component of including spatial autocorrelation in acknowledging the neighbourhood effect in growth analyses is the speciication of neighbour
as represented in a spatial weighting matrix. This article uses a distance-decay
parameter, to recognise that the farther apart the regions the lower their levels of
autocorrelation (Cliff and Ord 1973). The distance is measured based on the geographical distance between the centroid of two regions.
RESULTS AND DISCUSSION
Convergence process after decentralisation
Figure 1 presents the Williamson index of GRDP per capita during and after
decentralisation at both the provincial and district levels. It shows that inequality
at the district level is considerably higher than at the provincial level, conirming
the conclusion of Akita and Alisjahbana (2002) that the use of the provincial unit
tends to underestimate regional inequality at the district level. The Williamson
index numbers for both levels are estimated to have increased gradually during
1999–2005 and then decreased slightly during 2005–08, but regional inequality is
still estimated to be have been higher in 2008 than in 2002.
Regional convergence and the role of the neighbourhood effect
201
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FIGURE 1 Williamson Index of GRDP per Capita, 1999–2008
Source: Author’s calculations based on provincial and district BPS data from various years, in 1993
constant prices.
Note: GRDP = gross regional domestic product.
Figure 2 shows the Williamson index with HDI as an indicator. As in the case
with GRDP per capita, inequality at the district level is considerably higher than
it is at the provincial level. However, the regional inequalities of HDI declined
at both levels – district and provincial – during 1999–2008. This not only shows
that regional convergence may still have occurred in a decentralised Indonesia
but also conirms the absence of signiicant correlation between regional output
and the development index in Indonesia. Therefore, the increasing inequality of
regional output does not necessarily translate to increasing inequality of other
development indicators.
Table 1 presents the estimate of β-convergence, which reveals that there was no
regional convergence of GRDP per capita during or after the implementation of
decentralisation, especially in 1999–2002 and 2002–05. With the estimated speed
of convergence insigniicant at around 0.4%, the period of 1999–2008 had the lowest speed of convergence since observations began, in 1975. Yet this result still
contradicts the observation from the Williamson index, which indicates a diverging instead of a converging pattern of GRDP per capita.
Both the β-convergence estimates and the Williamson index indicate that convergence occurred again in Indonesia in 2005–08. Akita, Kurniawan and Miyata
(2011) argue that the increase in inequality after the Asian inancial crisis hit
Indonesia, in 1997–98, is mainly due to the recovery of major cities, especially
Jakarta. Therefore, it is possible that convergence reoccurred after 2005 because
the major cities had by then recovered from the crisis, and their growth rates had
fallen below those of districts with relatively lower levels of GRDP per capita.
Nevertheless, this is certainly not the case for Jakarta, which continues to have
relatively high (albeit more modest) growth. This will be discussed further below,
in a comparison between convergence levels in Java, Sumatra and Indonesia as a
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Yogi Vidyattama
FIGURE 2 Williamson Index of HDI, 1999–2008
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Source: Author’s calculations based on provincial and district BPS data from various years, in 1993
constant prices.
Note: HDI = Human Development Index.
whole. The other main driver of this convergence is the recovery in the GRDP of
some districts in Aceh after the 2004 tsunami and the fall in the GRDP of Mimika.
The Williamson index and the estimate of β-convergence also indicate that HDI
convergence is taking place in Indonesia. The β-convergence, however, shows the
speed of convergence declining from 7.4% in 1999–2002 to 3.0% in 2005–08 at the
district level, and from 5.3% to 2.4% in the same periods at the provincial level.
This slowing in regional HDI convergence could be alarming, since HDI relates
mostly to public services that have been delegated to the district level. Brodjonegoro (2009) notes that although most districts are starting to cope to the decentralised system, local governments still tend to focus on their budgets rather than
on the delivery of public services in their new authorities.
The impact of the neighbourhood effect
Table 1 also presents diagnostic statistics for the neighbourhood effect, using the
inverted distance matrix in the β-convergence estimation. It shows that the neighbourhood
effect is statistically signiicant in estimations at the district level but
insigniicant when based on distance at the provincial level. The neighbourhood
effect is often detected as signiicant in conjunction with a signiicant level of convergence. This raises the questions of whether convergence occurs in only certain
areas, and, as posed by Egger and Pfaffermayr (2006), whether districts farther
from those areas are left behind in the process.
This article uses the spatial lag and spatial error models, as described in equations (3) and (5), to analyse the impact of the distance-based neighbourhood
effect on convergence. The results show insigniicant changes in the speed of
convergence, even when the spatial lag or the introduced errors are signiicant
Regional convergence and the role of the neighbourhood effect
203
TABLE 1 β-convergence Estimates of GRDP per Capita and HDI, and
the Statistical Diagnostic of the Neighbourhood Effect, 1999–2008
GRDP per Capita
Province
District
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1999–2008 1999–2002 2002–2005 2005–2008 1999–2008 1999–2002 2002–2005 2005–2008
(e–β –1)
Std errora
β
–0.004
0.009
0.004
–0.012
0.031
0.013
0.006
0.012
–0.006
–0.015*
0.008
0.015
–0.007
0.007
0.007
–0.012
0.019
0.012
–0.002
0.005
0.002
–0.011***
0.003
0.011
0.299
1.599
0.150
1.663
0.038
0.542
0.106
0.895
0.177
0.565
0.003
0.068
0.086
0.395
4.277**
5.362**
Spatial error
Moran’s I
–0.039
LM
0.368
Robust LM
1.668
0.236
0.192
1.706
1.302
0.053
0.557
0.639
0.029
0.818
–0.010
0.137
0.526
0.359
0.002
0.067
0.095
0.077
0.386
3.563***
7.623***
8.708***
Spatial lag
LMb
Robust LM
HDI
Province
District
1999–2008 1999–2002 2002–2005 2005–2008 1999–2008 1999–2002 2002–2005 2005–2008
(e–β –1)
Std error
β
–0.034*** –0.051*** –0.026*
0.006
0.013
0.013
0.034
0.053
0.027
–0.024*** –0.043*** –0.071*** –0.046*** –0.030***
0.004
0.002
0.008
0.005
0.003
0.024
0.044
0.074
0.047
0.030
Spatial lag
LM
Robust LM
0.017
0.902
0.479
1.084
0.000
0.857
0.119
0.229
Spatial error
Moran’s I
LM
Robust LM
2.177**
0.525
1.409
0.467
0.095
0.699
1.374
0.046
0.903
–0.346
0.659
0.770
0.433
3.479*
0.337
11.214***
4.212*** 2.022**
11.037*** 1.977
14.082*** 12.855***
0.714
0.185
2.785*
0.716
1.893*
1.655
1.126
2.079**
2.104
0.035
Source: Author’s calculations based on provincial and district BPS data from various years, in 1993
constant prices.
Note: GRDP = gross regional domestic product. HDI = Human Development Index. N = 294.
a
Standard error.
b
LM = Lagrange multiplier.
*
p < 0.1; ** p < 0.05; *** p < 0.01
204
Yogi Vidyattama
TABLE 2 Impact of Spatial Lag and Error Inclusion on
β-convergence Estimates of GRDP per Capita and HDI, 1999–2008
(district level)
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GRDP per Capita
β without spatial model
β with spatial lag
ρ
D(β)
β with spatial error
λ
D(β)
1999–2008
1999–2002
2002–2005
0.007
0.007
–0.216
0.000
0.007
–0.189
0.000
0.012
0.012
–0.029
0.000
0.012
–0.024
0.000
0.002
0.002
–0.112
0.000
0.002
–0.107
0.000
2005–2008
0.011
0.011
0.628**
0.000
0.013
0.723***
0.002
HDI
β without spatial model
β with spatial lag
ρ
D(β)
β with spatial error
λ
D(β)
1999–2008
1999–2002
2002–2005
2005–2008
0.044
0.043
0.190
–0.001
0.043
0.782***
–0.001
0.074
0.075
–0.270
0.001
0.075
0.488*
0.001
0.047
0.045
0.289
–0.002
0.045
0.412
–0.002
0.030
0.029
0.348
–0.002
0.029
0.437
–0.002
Source: Author’s calculations based on provincial and district BPS data from various years, in 1993
constant prices.
Note: GRDP = gross regional domestic product. HDI = Human Development Index. N = 294. ρ and λ
are spatial lag and error based on equations (3) and (5), respectively. D(β) is the difference in the speed
of convergence due to the introduction of the spatial model.
*
p < 0.1; ** p < 0.05; *** p < 0.01
(table 2). The signiicance of the changes in the speed of convergence is determined by conducting the following t-test:
atial =
(e− β − 1) non-sp
t
(e− β − 1)sp
(6)
In estimations based on GDP per capita, a positively signiicant spatial error
and lag occurred in only the most recent period (2005–08). Introducing spatial
error increases the speed of convergence insigniicantly, by only 0.2 percentage
points, whereas introducing spatial lag does not change the speed. Any increase
would mean that the positive neighbourhood effect had hindered convergence,
but the effect is insigniicant in this estimation. The only prominent example of
how the neighbourhood effect reduced the speed of convergence is the growth of
the Jakarta district and the areas surrounding it, such as Tanggerang and Bekasi.
Regional convergence and the role of the neighbourhood effect
205
FIGURE 3 Williamson Index of GRDP per Capita in Java and Sumatra, 1999–2008
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Source: Author’s calculations based on provincial and district BPS data from various years, in 1993
constant prices.
Note: GRDP = gross regional domestic product.
In
estimations based on HDI, the spatial lag is not signiicant in any period,
whereas spatial error is positively signiicant in the whole period, 1999–2008, and
the speciic period 1999–2002. As in estimations based on GDP per capita, however, there is no signiicant change (only that of 0.1 percentage point) in the speed
of convergence. Although the distance-based neighbourhood effect is signiicant
on regional development, especially through the undisclosed variable (that is, the
error term), its impact on district-level convergence is insigniicant.
To further examine the neighbourhood effect’s impact on convergence, this
article assesses the speeds of convergence in Indonesia’s two most populated
islands – Java and Sumatra. Table 3 shows large differences in their speeds of
convergence, and, although it is not signiicant, there is an indication that GRDP
per capita diverges among districts in Java. Conversely, GRDP per capita converges signiicantly among Sumatra’s districts, with a speed of around 3.6% during 1999–2008.
HDI, as an alternative indicator, shows signiicant convergence in both islands.
The speed of this convergence, after decentralisation, is estimated to be increasing
in Java but decreasing in Sumatra. The increasing speed of convergence in Java is
dominated not only by faster development in districts in East Java with low HDI
numbers (such as Sampang, Bondowoso, Sumenep and Situbondo) but also by
slower development in several parts of Jakarta and in some provincial big cities,
such as Bandung, Yogyakarta, Surakarta and Semarang. The catch-up process of
districts with the lowest HDI numbers in Sumatra, such as Nias and Musi Banyuasin, has also been slowing down.
Figure 3 shows the Williamson indexes for the GRDP per capita of districts
in Java and Sumatra. In 1999, the level of inequality in Sumatra was just below
the national level, while the level in Java was just above the national level. The
206
Yogi Vidyattama
TABLE 3 β-convergence Estimates of GRDP per Capita and HDI in
Java and Sumatra, 1999–2008
GRDP per Capita
Java
Sumatra
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1999–2008 1999–2002 2002–2005 2005–2008 1999–2008 1999–2002 2002–2005 2005–2008
(e–β –1) island 0.002
β island
–0.002
β Indonesia
0.007
D(β)
–0.010
0.012
–0.012
0.012
–0.024
–0.006
0.006
0.002
0.004
0.001
–0.001
0.011
–0.013
–0.036*** –0.053*** –0.033*** –0.038***
0.036
0.055
0.033
0.039
0.007
0.012
0.002
0.011
0.029
0.042
0.031
0.028
HDI
Java
Sumatra
1999–2008 1999–2002 2002–2005 2005–2008 1999–2008 1999–2002 2002–2005 2005–2008
(e–β –1) island –0.032*** –0.013
β island
0.033
0.013
β Indonesia
0.044
0.074
D(β)
–0.011
–0.061
–0.057*** –0.034*** –0.060*** –0.126*** –0.044*** –0.037***
0.059
0.035
0.062
0.134
0.045
0.037
0.047
0.030
0.044
0.074
0.047
0.030
0.012
0.005
0.018
0.060
–0.002
0.007
Source: Author’s calculations based on provincial and district BPS data from various years, in 1993
constant prices.
Note: GRDP = gross regional domestic product. HDI = Human Development Index. N (Java) = 108, N
(Sumatra) = 73. D(β) is the difference between the speed of convergence in Java or Sumatra and the
speed of convergence in Indonesia.
*
p < 0.1; ** p < 0.05; *** p < 0.01
increasing inequality of GRDP per capita among districts in Java at the same time
as a decreasing trend in Sumatra has seen inequality increase and decrease in Java
and Sumatra, respectively, compared with the national average. Akita, Kurniawan
and Miyata (2011) have offered an explanation for this trend: the decreasing levels
of
inequality in Sumatra are likely to be inluenced by the continuing decline of
its mining sector and the increasing spread of its manufacturing industry. This
can be illustrated by the relatively lower levels of growth in Dumai, an oil-mining
district that was part of Bengkalis in 1999, and Batam, where Sumatra’s manufacturing industry had previously been concentrated. The growth of Aceh has
also clearly contributed to convergence in Sumatra. At the same time, Java is facing increasing inequality of GRDP per capita, which is likely to be dominated by
Jakarta’s continuous economic growth and leave less developed regions behind.
Given Jakarta’s potentially important role in inluencing inequality in Java, igure 3 includes an inequality index for Java without Jakarta. Inequality in Java is
much lower once Jakarta has been removed. Inequality in Java decreased during
the 1998 inancial crisis, owing to the fall in the output of the region’s inancial,
construction and manufacturing sectors, which are located mainly in its relatively
Regional convergence and the role of the neighbourhood effect
207
FIGURE 4 Williamson Index of HDI in Java and Sumatra, 1999–2008
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Source: Author’s calculations based on provincial and district BPS data from various years, in 1993
constant prices.
Note: HDI = Human Development Index.
richer districts, especially in Jakarta (Hill, Resosudarmo and Vidyattama 2008;
Akita, Kurniawan and Miyata 2011). By 2001–02, however, these sectors had
started to return to their previous levels of growth (Hill 2011) – as had inequal.ity
The relative stability of Java’s inequality levels without Jakarta conirms the
observation of Akita, Kurniawan and Miyata (2011) that Jakarta is the main source
of Java’s increasing inequality.
Jakarta’s inluence may also affect inequality in Indonesia as a whole. In fact,
it could partially explain why the Williamson index for Indonesia’s GRDP per
capita has continued to increase while the β-convergence estimate has indicated
insigniicant speeds of convergence. The Williamson index used in this article
applies a population-weighted coeficient of variation, so, given that Java’s population accounts for 60% of the total Indonesian population, the increasing levels
of inequality in Java are dominating the index because of the statistical weight of
Jakarta. This also means that inequality may not be increasing at all, if it is examined with a measure less inclined to be affected by an extreme value – such as the
Theil inequality index, which uses a logarithmic function for such a purpose.
Figure 4 shows that the inequality of regional development, as measured by
HDI at the district level, decreased in both Sumatra and Java. Although the Williamson index indicates that these levels of inequality are higher when Jakarta is
included, the inclusion does not reverse the decreasing trend. Figure 4 also shows
that the HDI inequality among districts in Sumatra decreased more considerably
than for those in Java, especially in 1999–2002. In this period, Sumatra has a much
faster speed of convergence than Java, as conirmed by the estimated speed of
convergence in table 3: Java at 3.3%, and Sumatra at 13.4%. Figure 4 indicates that
inequality in both Java and Sumatra is still lower than inequality in Indonesia
as a whole, which suggests higher levels of inequality of regional development
208
Yogi Vidyattama
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among districts outside the two islands (or considerable differences in the HDI
number between districts in Java and Sumatra and those in other islands). More
work needs to be done to look at the inequality among districts outside Java and
Sumatra and those in Java and Sumatra.
CONCLUSION
More than a decade has passed since the Indonesian government implemented
its decentralisation policy. This article has attempted to examine regional convergence following the central delegation of most authorities in the areas of education, agriculture, industry, trade, investment and infrastructure to an increasing
number of local governments at a district level. It has also examined whether the
neighbourhood effect played a role in the process, by introducing a spatial model
into β-convergence estimates and by looking at whether there is a difference in
regional convergence in the two most populous islands in Indonesia – Java and
Sumatra.
The initial result of this assessment is rather inconclusive. The Williamson
indexes for GRDP per capita, for example, show slight increases in regional inequality
at both the district and the provincial levels, and, although insigniicant,
the β-convergence estimates suggest that convergence occurred at both administrative levels during 1999–2008. The speciic period 2005–08 saw signiicant
convergence occur at the district level, owing partly to the impact of the neighbourhood effect. Although this may seem to promise lower levels of inequality,
the rise of Aceh after the conlict at the end of the 1990s and the early 2000s, in
addition to the fall in the number of mining areas in Papua, has also contributed
considerably to convergence. Moreover, the overall trend of convergence is still
very weak, calling for a longer period of observation.
The increasing and decreasing trends of economic growth in Aceh and Papua,
respectively, has increased the impact of the neighbourhood effect on GRDP convergence. This is an early indication that these districts’ economies are connecting
to those of their neighbours – once an unlikely scenario, given the prevalence of
mining in these areas. The neighbourhood effect could be crucial in indicating the
socio-economic connectivity and the relationship between Aceh and other provinces in Indonesia – especially North Sumatra – as parts of the economy interact with each other, but it could also be a sign that socio-economic events such
as conlict and economic downturns could spread more easily. Furthermore, the
question of whether GRDP per capita could really represent the condition of the
people has yet to be answered. Aceh, for example, may have risen in terms of
GRDP per capita because of the low of aid and aid workers after the 2004 tsunami, rather than because of the refunctioning of its economy.
Given the complexities of using GRDP per capita in growth analyses of Indonesia, this article has used HDI as a comparative indicator of development. The
result shows signiicant regional convergence in HDI numbers during 1999–2008,
despite the absence of signiicant convergence in regional GRDP per capita. The
speed of HDI convergence seems to have slowed, however, and the impact of
the neighbourhood effect lessened. This could be alarming in the future, because
HDI indicators are closely related to the authorities delegated during decentralisation – especially education and health. The ability to lift the HDI numbers of
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Regional convergence and the role of the neighbourhood effect
209
Indonesia’s less developed regions to the level of its more developed regions will
therefore measure the success of this delegation process. Furthermore, the slowing
of regional convergence has been followed by fewer instances of the neighbourhood effect, which, in turn, could indicate fewer spillovers from one area to its
neighbours. The data are not clear on whether this is the case, but there are many
reasons for spillovers to reduce in number. For example, a local authority’s limited budget could force it to provide some services only to certain residents, based
on their identity card (kartu tanda penduduk, KTP). More work needs to be done in
this area, as well as on the use of HDI numbers to examine regional convergence
and its implications – especially since HDI is a composite index. Furthermore, the
results of distance-based spatial models show that although the neighbourhood
effect could have an impact on convergence in certain periods, it does not alter the
speed
of convergence signiicantly.
A more interesting inding comes from this article’s analysis of Java and Sumatra. The convergence of GRDP per capita is estimated to be signiicant in Sumatra,
while Java’s GRDP per capita is estimated to diverge. In contrast, the regional
convergence in HDI numbers is strong in both islands, although the speed of convergence is increasing in Java and decreasing in Sumatra. The pattern of inequality of GRDP per capita in Java shows that despite the recovery of Java’s big cities
after the Asian inancial crisis of 1997–98 contributing to increased inequality on
the island, Jakarta was the main source of this increase, especially during 2002–08.
Jakarta’s hampering of convergence in GRDP per capita during decentralisation
does not reduce the importance of regional inequality. In contrast, it could complicate the issue. Jakarta is often seen as representing central government, and
Jakarta’s absorbing the inancial resources of other regions was one of the main
arguments for decentralisation.
The pattern of HDI convergence is less dominated by the development of
Jakarta’s districts. Although the HDI numbers for Jakarta’s districts increased
more modestly than they did for other districts, the rise of the less developed districts in Java and Sumatra have played a greater role in converg
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
Regional convergence and the role of the
neighbourhood effect in decentralised Indonesia
Yogi Vidyattama
To cite this article: Yogi Vidyattama (2013) Regional convergence and the role of the
neighbourhood effect in decentralised Indonesia, Bulletin of Indonesian Economic Studies,
49:2, 193-211, DOI: 10.1080/00074918.2013.809841
To link to this article: http://dx.doi.org/10.1080/00074918.2013.809841
Published online: 26 Jul 2013.
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Date: 17 January 2016, At: 23:44
Bulletin of Indonesian Economic Studies, Vol. 49, No. 2, 2013: 193–211
REGIONAL CONVERGENCE AND THE ROLE OF
THE NEIGHBOURHOOD EFFECT IN
DECENTRALISED INDONESIA
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Yogi Vidyattama*
University of Canberra
More than a decade since Indonesia’s radical decentralisation process commenced,
this article examines whether the economic performance of neighbouring regions
– the neighbourhood effect – can determine the speed of regional convergence. The
results suggest that the inequality of gross regional domestic product per capita, as
indicated by the Williamson index of regional inequality, may increase slightly in
times of insigniicant estimated speeds of convergence – especially because of the
growth of Jakarta. In contrast, changes in the Human Development Index numbers
for Indonesia indicate that regional convergence is taking place, although its speed
is decreasing. The neighbourhood effect could be signiicant in both cases, but it has
had little effect on the speed of convergence.
Keywords: regional convergence, regional inequality, Indonesia after decentralisation
INTRODUCTION
Regional inequality is a major issue in Indonesia, from the different levels of development and resource endowments among its regions to its population’s distribution and ethnicity (Tadjoeddin, Suharyo and Mishra 2001; Aspinall and Berger
2001). In the past decade, regional convergence, or the decline in dispersion of a
development indicator such as per-capita income across different regions, and its
effect in reducing inequality, has been the subject of many studies in Indonesia.
These studies have applied various techniques, such as statistical disparity measures (see, for example, Akita and Lukman 1995; Tadjoeddin, Suharyo and Mishra
2001; Akita and Alisjahbana 2002; and Milanovic 2005) and regional growth convergence frameworks (see, for example, Garcia-Garcia and Soelistianingsih 1998;
Resosudarmo and Vidyattama 2006; and Hill, Resosudarmo and Vidyattama
2008). Yet few have focused on disparity at the district level, and fewer again have
looked at the process since 1999, when the Indonesian government announced its
decentralisation reforms.
*
The author would like to thank the University of Canberra, for funding this study
through its Vice-Chancellor’s Awards for Early Career Researchers, and NATSEM, for use
of its staff development fund. The author would also like to thank Bana Bodri and Agusman Simbolon, from the Badan Pusat Statistik (BPS), Indonesia’s Central Statistics Agency,
for providing the data used in this study, as well as Riyana Miranti, Rebecca Cassells, Hal
Hill and the two anonymous referees, for their valuable comments.
ISSN 0007-4918 print/ISSN 1472-7234 online/13/020193-19
http://dx.doi.org/10.1080/00074918.2013.809841
© 2013 Indonesia Project ANU
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194
Yogi Vidyattama
This article contributes to the existing body of knowledge of regional income
inequality in Indonesia, by looking at the pattern of regional convergence at the
provincial and district levels after decentralisation was formalised, by Indonesian
Law 22/1999 and Law 25/1999, and then implemented, in 2001. Since decentralisation commenced, the central government has delegated much greater responsibility for education, agriculture, industry, trade, investment and infrastructure
to district authorities, with the aim of delivering better public services – particularly in less developed regions (Alm, Aten and Bahl 2001; World Bank 2003). The
examination of regional convergence has since become increasingly important,
especially at the district level. This article, however, does not speciically assess
the impact of decentralisation itself on convergence, despite the growing literature on how decentralisation may increase or decrease the speed of convergence
(Rodriguez-Pose and Ezcurra 2010). Instead, this article examines the impact of
the neighbourhood effect at the district level on regional convergence, to ascertain whether the neighbourhood effect shapes Indonesia’s regional growth.
As acknowledged by McCulloch and Sjahrir (2008) and Akita, Kurniawan and
Miyata (2011), one of the main challenges of conducting an analysis at a subnational level, especially in a lower level such as the district, is how to account
for the possible spatial, or neighbourhood, effect: the economic performance of
neighbouring regions often has a signiicant impact (Anselin 1988; LeSage 1999;
Rey 2001), affecting convergence (Egger and Pfaffermayr 2006) by reducing inequality in only certain locations. This is particularly relevant in Indonesia, given
the allegation that most of its development has occurred in only a few provinces
(Suryadarma et al. 2006; Hill, Resosudarmo and Vidyattama 2008).
This article also uses patterns of change in the Human Development Index
(HDI) as an alternative indicator of regional development to gross regional
domestic product (GRDP) per capita. There are advantages and disadvantages
in using GRDP per capita as a measure of economic performance. One disadvantage is the inclusion of the mining sector, which, in Indonesia, operates in several
areas (that is, districts or provinces) with limited backward and forward linkages,
which implies that GRDP does not necessarily reveal a shared level of economic
development throughout a region (Tadjoeddin, Suharyo and Mishra 2001; Brodjonegoro and Martinez-Vazquez 2002; Milanovic 2005; Hill, Resosudarmo and Vidyattama 2008; Akita and Lukman 1995). Another problem is that GRDP data do
not take account of regional cross-border transactions. For the purpose of approximating the regional standard of living, this article therefore uses the HDI, which
is widely regarded as a comprehensive index and combines measures of health,
education, and income or expenditure. HDI has been widely used, especially in
studies of developing nations (Anand and Sen 2000). Other common measures of
regional inequality may include the distribution of disadvantage, such as poverty,
unemployment or homelessness, but these are not the focus of this article.
CONVERGENCE STUDIES IN INDONESIA
As there is a large body of literature on regional inequality and convergence which
follows the seminal work of Williamson (1965), Barro and Sala-I-Martin (1991), and
Sala-I-Martin (1996), this article refers only to those studies that look speciically
at Indonesia or examine the neighbourhood effect’s impact on convergence. These
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Regional convergence and the role of the neighbourhood effect
195
studies form a comparative base for examining convergence in Indonesia after
decentralisation; comparisons between Indonesia and other countries have been
discussed in Shankar and Shah (2003), Milanovic (2005), and Hill, Resosudarmo
and Vidyattama (2008).
In Akita and Lukman’s (1995) study of regional convergence in Indonesia, they
use the Williamson index, a coeficient of variation measure, to look at changes
in regional inequality at the provincial level during 1975–92, which shows a large
decrease in inequality of GRDP per capita among regions. Their decomposition of
the inequality index shows the increasing importance of the manufacturing and
construction sectors in examining inequality in Indonesia. Despite this, inequality of non-mining GRDP per capita remained relatively stagnant during 1975–92,
except for a discontinued trend of increased inequality during the export-oriented
reforms of the mid-1980s.
Garcia-Garcia and Soelistianingsih (1998) produced the irst estimate of the
speed of convergence (β-convergence) of Indonesian provincial incomes of 1975–
83, which conirmed statistically signiicant convergence of GRDP per capita and
estimated its speed to be around 2%. This speed implied that the convergence
process would be at its halfway point after 35 years, or, in other words, that the
observed regional inequality (that is, the average deviation from the mean) would
roughly be halved in that period. This was similar to the results from several
OECD countries (Sala-I-Martin 1996). Hill, Resosudarmo and Vidyattama (2008)
show that the indings of Garcia-Garcia and Soelistianingsih (1998) are sensitive to
the choice of time period analysed and are also heavily inluenced by the decline
in the contribution since 1975 of Indonesia’s resource-rich provinces to Indonesia’s GDP, as the oil and gas sector has become less important. This later study
also shows that the speed of β-convergence varies signiicantly across Indonesia’s
development periods. It was quite rapid (2%) during the oil boom of 1975–81; and
it accelerated after oil prices stabilised during 1981–86, with the speed of convergence estimated to be 2.8%. The speed of β-convergence then collapsed, however,
from 1.7%, in 1986–92, to just 1%, in the 1990s, as the government’s export-oriented reforms took hold. During the inancial crisis and its aftermath, from 1997
to 2002, there was no signiicant convergence. Hill, Resosudarmo and Vidyattama
(2008) also conirm Akita and Lukman’s inding that convergence is fairly stable
after the output of the mining sector has been removed from GRDP per capita.
Tadjoeddin, Suharyo and Mishra (2001) conirm that regional inequality is stable at the district level, having examined the Williamson index and the Theil and
Gini coeficients of GRDP per capita from 1993, when the data was irst released,
to 1998. Although their estimations, similarly to those of Hill, Resosudarmo and
Vidyattama (2008), show that regional inequality remained relatively unchanged
during this period, they ind that the inequality of GRDP per capita, without oil
and gas, increased slightly at the district level until 1998. Akita and Alisjahbana
(2002) draw similar conclusions at the district level, inding that regional income
inequality increased during 1993–97. This does not contradict the relatively stable
levels of regional inequality estimated by other studies at the provincial level;
rather, it shows that inequality increased among certain districts within some
provinces. Akita, Kurniawan and Miyata (2011) show that the Asian inancial crisis reduced inequality in Indonesia, especially as the crisis hit Jakarta harder than
it did less developed areas. This trend did not last – regional inequality increased
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196
Yogi Vidyattama
again until 2004, in the wake of Jakarta’s recovery – but it remains uncertain what
the trend has been since decentralisation intensiied. Moreover, Tadjoeddin (2013)
argues the inequality among districts could follow a Kuznets curve, where inequality increases when high levels of growth bring an economy to a certain level
of income and then decreases with further increases in income. However, he notes
that this relationship is observed only when cities are integrated within their surrounding regencies.
Studies using a regional growth framework at the district level – such as those
noted above – have prompted examinations of whether including spatial factors
would change the result. This is related to the argument of Sala-I-Martin (1996)
that convergence is more achievable in a sub-national setting, owing to greater
interaction among smaller regions as economic entities. It also means that the
neighbourhood effect is more likely to exist among smaller regions. Another common reason that emerges in regional studies for the existence of the neighbourhood effect is that the administrative boundaries used to identify regions do not
necessarily relect the boundaries of economic activities (LeSage 1999; Rey 2001).
As a result, some economic activities within borders or across borders, such as
trade and commuting, relate the economic performances of the regions involved,
so that a change of conditions in one region’s economy could well affect that of
another.
Egger and Pfaffermayr (2006) point out that the neighbourhood effect can also
produce biased analyses of convergence. They argue that the speed of convergence can vary across regions, and that convergence in major growth centres can
prevent remote regions from catching up. Indonesia’s remote east, for example,
which comprises most of the country’s least developed districts, could be left
behind as inequality levels in the rest of the country converge. Akita, Kurniawan
and Miyata (2011), however, show that the differences in inequality among Indonesia’s largest regions (Java–Bali, Sumatra–Kalimantan–Papua, and other regions
in the country’s east) are small compared with the levels of inequality within those
regions, and that the levels of cross-regional inequality have been relatively constant throughout the years. Instead, Akita, Kurniawan and Miyata (2011) detect
increasing levels of inequality not only within regions but also among districts
within provinces in those regions.
Applying the neighbourhood effect to convergence has brought mixed results
in other countries’ regional growth analyses. Rey and Montouri (1999), pioneers
in this ield, ind that the neighbourhood effect among US states is not only statistically signiicant; it also signiicantly alters convergence. Niebuhr (2001) inds
that although the neighbourhood effect in West Germany affected growth signiicantly, it slowed convergence only slightly. His indings differ from those of
Kosfeld, Eckey and Dreger (2002), who, concentrating on a uniied Germany, note
that the inclusion of the neighbourhood effect slowed convergence signiicantly.
Magalhães, Hewings and Azzoni (2005), in an example of the neighbourhood
effect’s impact in a developing country, ind that it did little to alter the speed of
regional convergence in Brazil.
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Regional convergence and the role of the neighbourhood effect
197
METHODOLOGY AND DATA
Empirical method
The concept of convergence refers to the decline in dispersion of a development
indicator, such as per-capita income distribution, across regions as economic entities. In growth analyses, convergence has a slightly different meaning: it refers
to relatively lower growth of an economy, with relatively higher income. Sala-IMartin (1996) argues that the concept of convergence in growth analyses is a necessary but not suficient condition in reducing income inequality, or the inequality
of any other development indicator.
A measure of regional inequality popularised by Williamson (1965) – the Williamson index – is often used to examine regional inequality at one point in time
and, hence, to measure changes in inequality over time. This article uses the
population-weighted version of the index:
n
∑ (Yi − Y )2
CVw =
i=1
Pi
P
Y
(1)
where:
•
•
•
•
•
•
CVw = population-weighted Williamson index;
n = number of regions;
Yi = income in region i;
Y = average income;
Pi = population in region i; and
P = total population.
Although the Williamson index has been used widely to analyse the changes in
regional inequality and, hence, regional convergence, it cannot indicate the signiicance of convergence itself.
The speed of convergence is introduced in growth analyses. Known as as
β-convergence, it can be used to examine whether the economies of relatively
poorer regions grow signiicantly faster than richer regions, as an indication of
decreasing levels of regional inequality. Barro and Sala-I-Martin (1991) made the
concept famous, using the following regression assessment:
( ) u
g y = α + e− β − 1 ln y0 +
(
)
(2)
where:
• gy = the growth rate of per-capita output;
• y0 = the initial economic output value; and
• u = the error term of this estimation.
Empirically, convergence occurs when the coeficient of (e-β –1) shows a negative correlation between growth and the initial condition of the economy. Consequently, β should be positive in times of regional convergence. The absolute value
of β represents the speed of the catch-up process, or the speed of convergence.
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Yogi Vidyattama
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This article considers two well-known spatial models, as popularised by Anselin
(1988) – the spatial autoregressive lag model (SAR) and the spatial autoregressive
error model (SEM) – to see whether the neighbourhood effect is signiicant and/
or if it signiicantly alters the speed of convergence.
The SAR assesses the direct connection between the development of one region
and that of its neighbours. If the growth of one region can directly affect that of its
neighbours, then neighbouring regions are more likely to grow at similar rates. As
Fingleton and Lopez-Bazo (2006) demonstrated, the spatial autoregressive lag can
be introduced to the growth regression in equation (2) as:
( ) W
g y = α + e− β − 1 ln y0 + ρ g y + u
(
)
(3)
where ρWgy is the spatial lag of the dependent variable and W is the spatial weight
matrix.
The SEM examines the indirect existence of the neighbourhood effect. The
development of one region may not be affected directly by that of its neighbours,
but it can still be affected by the undisclosed determinants in the equation. For
example, it is possible that although the growth of one region may have an insigniicant effect on that of its neighbours, the increasing human capital in that region
would affect its neighbours signiicantly. Anselin (1988) formalises the structure
of the error term affected by the neighbourhood effect:
u = λ u+ε
(4)
or, considering the spatial multiplier effect and combined with equation (2), the
SEM can be written as:
( ) ( I − λu )−1 ε
g y = α + e− β − 1 ln y0 +
(
)
(5)
where u is the error term in the panel estimation and ε is the real random factor.
Growth and development indicators
Most growth analyses focus on GDP or GRDP per capita as a proxy for per-capita
income and as an indicator of development. The data for provincial-level GRDP
are available from the regional accounts of BPS from 1975 onwards, whereas the
district-level data are available only from 1993 onwards.
Various proxy measures for different industrial sectors are used to calculate
GRDP in Indonesia. BPS coordinated and conducted GRDP calculations since
1975, and formalised a process that had previously relied on universities producing GRDP data for their area (Arndt 1973). Output statistics reported by various
regional BPS ofices are used as proxy measures for the output of the agricultural sector, and the output of the industrial sector has been calculated based on
data from industrial censuses and sample surveys. While a proxy measure for
the construction sector can be based on the consumption of building material
and surveys of local contractors, measuring the output of the services sector has
been more challenging: market surpluses, public-sector wages and sales statistics all need to be estimated. In 1983, 1993 and 2000, BPS revised the baskets of
goods and services used as proxies for the estimation of regional GDP, to relect
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Regional convergence and the role of the neighbourhood effect
199
the changes in the compositions of output and consumption in different sectors.
It also changed the base year for the estimation of constant price series. Besides
releasing a new series of GRDP data based on the new proxies, BPS also released
the data based on the previous procedure for particular years, as a concordance
tool. The GRDP used in this article is based on the 1993 constant price series and
all other data are converted to that time series, using the concordance tool.
Calculating GRDP using proxy measures of output often fails to show the real
level of development, especially when the wealth created from the output of a
region is not necessarily distributed within the same region. In Indonesia, GRDP
may overstate true wealth, because many regions have high levels of GRDP per
capita but relatively low levels of individual income (Tadjoeddin, Suharyo and
Mishra 2001; Brodjonegoro and Martinez-Vazquez 2002). In Papua’s Mimika
regency, for example, which, in 1999, was part of the Fakfak district, the high output from PT Freeport Indonesia’s Grasberg mine has not translated to the people
of the region, where the poverty rate exceeds 40%. Lhokseumawe, in North Aceh,
the location of the Arun liqueied-natural-gas plant, and Sumbawa, the location
of Newmont’s Batu Hijau mine, are other examples of regions with high levels of
GRDP and relatively high poverty rates. This issue centres largely on the output
of the mining industry, which, while allocated to the incomes of central government and mining companies, is accrued in regional GDP statistics (Akita and Lukman 1995; Milanovic 2005; Hill, Resosudarmo and Vidyattama 2008).
The HDI, another indicator of development, has been used since 1990 to compare worldwide development levels. Based on three equally weighted dimensions – life expectancy; education or literacy; and standard of living, or per-capita
income – it is still the most widely used index of development (Anand and Sen
2000). There are advantages and disadvantages in using a composite index such
as the HDI in convergence analyses: it has the advantage of representing the total
package of living standards in the economy, but it can also hide the importance
of certain variables or be less effective if all variables have a very similar regional
distribution (McGillivray 1991).
There are also advantages and disadvantages in interpreting convergence values based on the HDI. The advantage comes from the assumption, or the hope,
that the index will move, or converge, to the expected (maximum) value, but this
means that the traditional thinking behind the catch-up process is not really relevant. There is a maximum value of convergence that the most developed region
will be able to achieve, so the speed of convergence would be expected to be
higher in HDI than in GRDP per capita. This article therefore looks at HDI convergence simply from the point of whether, on average, regions with low levels
of HDI have a higher mobility towards the maximum compared with those with
high levels of HDI, as well as the signiicant level of this mobility and its changes
over time.
The data for HDI are available for Indonesia at both a provincial and a district level.1 The calculation of HDI data at the district level relies on Indonesia’s
National Socio-Economic Survey (Susenas). The income proxy in Indonesia’s HDI
1 BPS has published HDI data regularly since 2002, with the index appearing irst in the
2001 Indonesia Human Development Report, by the United Nations Development Project’s
UNSFIR (the United Nations Support Facility for Indonesian Recovery).
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200
Yogi Vidyattama
calculation is based on the average expenditure of the households in Susenas.
While education variables such as the literacy rate and the mean number of years
of schooling can also be estimated directly from Susenas, calculating life expectancy involves using data from the 2000 Population Census to continue the series
from previous censuses, and the infant mortality variable from both the Population Census and Susenas. Given the high number of Susenas observations – at
around 157,000 households in 1999 and 278,000 in 2005 – the district-level data
can be considered reliable. However, Susenas enumerators often cannot capture
data in very remote areas, which detracts from this reliability. In the 1999 and 2002
surveys, for example, BPS acknowledged the dificulty of reaching conlict areas
in Aceh, Maluku and Papua. The dificulty of interviewing those in the highest
and lowest income brackets is another challenge recognised by the survey (Leigh
and Van der Eng 2009).
The regions in an Indonesian context
Administrative divisions are the most common economic entities in a country’s
regional economy. In Indonesia, the irst, or highest, administrative division is the
province, followed by the district. The latter consists of regencies and cities, or
kabupaten and kota. The third administrative division comprises sub-districts, or
kecamatan, while the fourth, or lowest, division comprises urban and rural villages,
or kelurahan and desa. According to the Indonesian Department of Internal Affairs,
in 2005 Indonesia had 33 provinces, 349 regencies, 91 cities, 5,263 sub-districts,
7,123 urban villages and 62,806 rural villages.
As Indonesia has embraced decentralisation, the boundaries of its provinces
and districts have changed rapidly. While the number of provinces increased
from 26 (excluding East Timor) to 33 during 1999–2008, the number of districts
increased from around 350 to more than 450. To obtain a consistent database, and
to ensure that every region is represented throughout the period of decentralisation, BPS has amalgamated the provinces into the 26 that existed prior to 1999 and
the districts into the 294 that existed in the 1996 BPS database.
An essential component of including spatial autocorrelation in acknowledging the neighbourhood effect in growth analyses is the speciication of neighbour
as represented in a spatial weighting matrix. This article uses a distance-decay
parameter, to recognise that the farther apart the regions the lower their levels of
autocorrelation (Cliff and Ord 1973). The distance is measured based on the geographical distance between the centroid of two regions.
RESULTS AND DISCUSSION
Convergence process after decentralisation
Figure 1 presents the Williamson index of GRDP per capita during and after
decentralisation at both the provincial and district levels. It shows that inequality
at the district level is considerably higher than at the provincial level, conirming
the conclusion of Akita and Alisjahbana (2002) that the use of the provincial unit
tends to underestimate regional inequality at the district level. The Williamson
index numbers for both levels are estimated to have increased gradually during
1999–2005 and then decreased slightly during 2005–08, but regional inequality is
still estimated to be have been higher in 2008 than in 2002.
Regional convergence and the role of the neighbourhood effect
201
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FIGURE 1 Williamson Index of GRDP per Capita, 1999–2008
Source: Author’s calculations based on provincial and district BPS data from various years, in 1993
constant prices.
Note: GRDP = gross regional domestic product.
Figure 2 shows the Williamson index with HDI as an indicator. As in the case
with GRDP per capita, inequality at the district level is considerably higher than
it is at the provincial level. However, the regional inequalities of HDI declined
at both levels – district and provincial – during 1999–2008. This not only shows
that regional convergence may still have occurred in a decentralised Indonesia
but also conirms the absence of signiicant correlation between regional output
and the development index in Indonesia. Therefore, the increasing inequality of
regional output does not necessarily translate to increasing inequality of other
development indicators.
Table 1 presents the estimate of β-convergence, which reveals that there was no
regional convergence of GRDP per capita during or after the implementation of
decentralisation, especially in 1999–2002 and 2002–05. With the estimated speed
of convergence insigniicant at around 0.4%, the period of 1999–2008 had the lowest speed of convergence since observations began, in 1975. Yet this result still
contradicts the observation from the Williamson index, which indicates a diverging instead of a converging pattern of GRDP per capita.
Both the β-convergence estimates and the Williamson index indicate that convergence occurred again in Indonesia in 2005–08. Akita, Kurniawan and Miyata
(2011) argue that the increase in inequality after the Asian inancial crisis hit
Indonesia, in 1997–98, is mainly due to the recovery of major cities, especially
Jakarta. Therefore, it is possible that convergence reoccurred after 2005 because
the major cities had by then recovered from the crisis, and their growth rates had
fallen below those of districts with relatively lower levels of GRDP per capita.
Nevertheless, this is certainly not the case for Jakarta, which continues to have
relatively high (albeit more modest) growth. This will be discussed further below,
in a comparison between convergence levels in Java, Sumatra and Indonesia as a
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Yogi Vidyattama
FIGURE 2 Williamson Index of HDI, 1999–2008
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Source: Author’s calculations based on provincial and district BPS data from various years, in 1993
constant prices.
Note: HDI = Human Development Index.
whole. The other main driver of this convergence is the recovery in the GRDP of
some districts in Aceh after the 2004 tsunami and the fall in the GRDP of Mimika.
The Williamson index and the estimate of β-convergence also indicate that HDI
convergence is taking place in Indonesia. The β-convergence, however, shows the
speed of convergence declining from 7.4% in 1999–2002 to 3.0% in 2005–08 at the
district level, and from 5.3% to 2.4% in the same periods at the provincial level.
This slowing in regional HDI convergence could be alarming, since HDI relates
mostly to public services that have been delegated to the district level. Brodjonegoro (2009) notes that although most districts are starting to cope to the decentralised system, local governments still tend to focus on their budgets rather than
on the delivery of public services in their new authorities.
The impact of the neighbourhood effect
Table 1 also presents diagnostic statistics for the neighbourhood effect, using the
inverted distance matrix in the β-convergence estimation. It shows that the neighbourhood
effect is statistically signiicant in estimations at the district level but
insigniicant when based on distance at the provincial level. The neighbourhood
effect is often detected as signiicant in conjunction with a signiicant level of convergence. This raises the questions of whether convergence occurs in only certain
areas, and, as posed by Egger and Pfaffermayr (2006), whether districts farther
from those areas are left behind in the process.
This article uses the spatial lag and spatial error models, as described in equations (3) and (5), to analyse the impact of the distance-based neighbourhood
effect on convergence. The results show insigniicant changes in the speed of
convergence, even when the spatial lag or the introduced errors are signiicant
Regional convergence and the role of the neighbourhood effect
203
TABLE 1 β-convergence Estimates of GRDP per Capita and HDI, and
the Statistical Diagnostic of the Neighbourhood Effect, 1999–2008
GRDP per Capita
Province
District
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1999–2008 1999–2002 2002–2005 2005–2008 1999–2008 1999–2002 2002–2005 2005–2008
(e–β –1)
Std errora
β
–0.004
0.009
0.004
–0.012
0.031
0.013
0.006
0.012
–0.006
–0.015*
0.008
0.015
–0.007
0.007
0.007
–0.012
0.019
0.012
–0.002
0.005
0.002
–0.011***
0.003
0.011
0.299
1.599
0.150
1.663
0.038
0.542
0.106
0.895
0.177
0.565
0.003
0.068
0.086
0.395
4.277**
5.362**
Spatial error
Moran’s I
–0.039
LM
0.368
Robust LM
1.668
0.236
0.192
1.706
1.302
0.053
0.557
0.639
0.029
0.818
–0.010
0.137
0.526
0.359
0.002
0.067
0.095
0.077
0.386
3.563***
7.623***
8.708***
Spatial lag
LMb
Robust LM
HDI
Province
District
1999–2008 1999–2002 2002–2005 2005–2008 1999–2008 1999–2002 2002–2005 2005–2008
(e–β –1)
Std error
β
–0.034*** –0.051*** –0.026*
0.006
0.013
0.013
0.034
0.053
0.027
–0.024*** –0.043*** –0.071*** –0.046*** –0.030***
0.004
0.002
0.008
0.005
0.003
0.024
0.044
0.074
0.047
0.030
Spatial lag
LM
Robust LM
0.017
0.902
0.479
1.084
0.000
0.857
0.119
0.229
Spatial error
Moran’s I
LM
Robust LM
2.177**
0.525
1.409
0.467
0.095
0.699
1.374
0.046
0.903
–0.346
0.659
0.770
0.433
3.479*
0.337
11.214***
4.212*** 2.022**
11.037*** 1.977
14.082*** 12.855***
0.714
0.185
2.785*
0.716
1.893*
1.655
1.126
2.079**
2.104
0.035
Source: Author’s calculations based on provincial and district BPS data from various years, in 1993
constant prices.
Note: GRDP = gross regional domestic product. HDI = Human Development Index. N = 294.
a
Standard error.
b
LM = Lagrange multiplier.
*
p < 0.1; ** p < 0.05; *** p < 0.01
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Yogi Vidyattama
TABLE 2 Impact of Spatial Lag and Error Inclusion on
β-convergence Estimates of GRDP per Capita and HDI, 1999–2008
(district level)
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GRDP per Capita
β without spatial model
β with spatial lag
ρ
D(β)
β with spatial error
λ
D(β)
1999–2008
1999–2002
2002–2005
0.007
0.007
–0.216
0.000
0.007
–0.189
0.000
0.012
0.012
–0.029
0.000
0.012
–0.024
0.000
0.002
0.002
–0.112
0.000
0.002
–0.107
0.000
2005–2008
0.011
0.011
0.628**
0.000
0.013
0.723***
0.002
HDI
β without spatial model
β with spatial lag
ρ
D(β)
β with spatial error
λ
D(β)
1999–2008
1999–2002
2002–2005
2005–2008
0.044
0.043
0.190
–0.001
0.043
0.782***
–0.001
0.074
0.075
–0.270
0.001
0.075
0.488*
0.001
0.047
0.045
0.289
–0.002
0.045
0.412
–0.002
0.030
0.029
0.348
–0.002
0.029
0.437
–0.002
Source: Author’s calculations based on provincial and district BPS data from various years, in 1993
constant prices.
Note: GRDP = gross regional domestic product. HDI = Human Development Index. N = 294. ρ and λ
are spatial lag and error based on equations (3) and (5), respectively. D(β) is the difference in the speed
of convergence due to the introduction of the spatial model.
*
p < 0.1; ** p < 0.05; *** p < 0.01
(table 2). The signiicance of the changes in the speed of convergence is determined by conducting the following t-test:
atial =
(e− β − 1) non-sp
t
(e− β − 1)sp
(6)
In estimations based on GDP per capita, a positively signiicant spatial error
and lag occurred in only the most recent period (2005–08). Introducing spatial
error increases the speed of convergence insigniicantly, by only 0.2 percentage
points, whereas introducing spatial lag does not change the speed. Any increase
would mean that the positive neighbourhood effect had hindered convergence,
but the effect is insigniicant in this estimation. The only prominent example of
how the neighbourhood effect reduced the speed of convergence is the growth of
the Jakarta district and the areas surrounding it, such as Tanggerang and Bekasi.
Regional convergence and the role of the neighbourhood effect
205
FIGURE 3 Williamson Index of GRDP per Capita in Java and Sumatra, 1999–2008
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Source: Author’s calculations based on provincial and district BPS data from various years, in 1993
constant prices.
Note: GRDP = gross regional domestic product.
In
estimations based on HDI, the spatial lag is not signiicant in any period,
whereas spatial error is positively signiicant in the whole period, 1999–2008, and
the speciic period 1999–2002. As in estimations based on GDP per capita, however, there is no signiicant change (only that of 0.1 percentage point) in the speed
of convergence. Although the distance-based neighbourhood effect is signiicant
on regional development, especially through the undisclosed variable (that is, the
error term), its impact on district-level convergence is insigniicant.
To further examine the neighbourhood effect’s impact on convergence, this
article assesses the speeds of convergence in Indonesia’s two most populated
islands – Java and Sumatra. Table 3 shows large differences in their speeds of
convergence, and, although it is not signiicant, there is an indication that GRDP
per capita diverges among districts in Java. Conversely, GRDP per capita converges signiicantly among Sumatra’s districts, with a speed of around 3.6% during 1999–2008.
HDI, as an alternative indicator, shows signiicant convergence in both islands.
The speed of this convergence, after decentralisation, is estimated to be increasing
in Java but decreasing in Sumatra. The increasing speed of convergence in Java is
dominated not only by faster development in districts in East Java with low HDI
numbers (such as Sampang, Bondowoso, Sumenep and Situbondo) but also by
slower development in several parts of Jakarta and in some provincial big cities,
such as Bandung, Yogyakarta, Surakarta and Semarang. The catch-up process of
districts with the lowest HDI numbers in Sumatra, such as Nias and Musi Banyuasin, has also been slowing down.
Figure 3 shows the Williamson indexes for the GRDP per capita of districts
in Java and Sumatra. In 1999, the level of inequality in Sumatra was just below
the national level, while the level in Java was just above the national level. The
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Yogi Vidyattama
TABLE 3 β-convergence Estimates of GRDP per Capita and HDI in
Java and Sumatra, 1999–2008
GRDP per Capita
Java
Sumatra
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1999–2008 1999–2002 2002–2005 2005–2008 1999–2008 1999–2002 2002–2005 2005–2008
(e–β –1) island 0.002
β island
–0.002
β Indonesia
0.007
D(β)
–0.010
0.012
–0.012
0.012
–0.024
–0.006
0.006
0.002
0.004
0.001
–0.001
0.011
–0.013
–0.036*** –0.053*** –0.033*** –0.038***
0.036
0.055
0.033
0.039
0.007
0.012
0.002
0.011
0.029
0.042
0.031
0.028
HDI
Java
Sumatra
1999–2008 1999–2002 2002–2005 2005–2008 1999–2008 1999–2002 2002–2005 2005–2008
(e–β –1) island –0.032*** –0.013
β island
0.033
0.013
β Indonesia
0.044
0.074
D(β)
–0.011
–0.061
–0.057*** –0.034*** –0.060*** –0.126*** –0.044*** –0.037***
0.059
0.035
0.062
0.134
0.045
0.037
0.047
0.030
0.044
0.074
0.047
0.030
0.012
0.005
0.018
0.060
–0.002
0.007
Source: Author’s calculations based on provincial and district BPS data from various years, in 1993
constant prices.
Note: GRDP = gross regional domestic product. HDI = Human Development Index. N (Java) = 108, N
(Sumatra) = 73. D(β) is the difference between the speed of convergence in Java or Sumatra and the
speed of convergence in Indonesia.
*
p < 0.1; ** p < 0.05; *** p < 0.01
increasing inequality of GRDP per capita among districts in Java at the same time
as a decreasing trend in Sumatra has seen inequality increase and decrease in Java
and Sumatra, respectively, compared with the national average. Akita, Kurniawan
and Miyata (2011) have offered an explanation for this trend: the decreasing levels
of
inequality in Sumatra are likely to be inluenced by the continuing decline of
its mining sector and the increasing spread of its manufacturing industry. This
can be illustrated by the relatively lower levels of growth in Dumai, an oil-mining
district that was part of Bengkalis in 1999, and Batam, where Sumatra’s manufacturing industry had previously been concentrated. The growth of Aceh has
also clearly contributed to convergence in Sumatra. At the same time, Java is facing increasing inequality of GRDP per capita, which is likely to be dominated by
Jakarta’s continuous economic growth and leave less developed regions behind.
Given Jakarta’s potentially important role in inluencing inequality in Java, igure 3 includes an inequality index for Java without Jakarta. Inequality in Java is
much lower once Jakarta has been removed. Inequality in Java decreased during
the 1998 inancial crisis, owing to the fall in the output of the region’s inancial,
construction and manufacturing sectors, which are located mainly in its relatively
Regional convergence and the role of the neighbourhood effect
207
FIGURE 4 Williamson Index of HDI in Java and Sumatra, 1999–2008
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Source: Author’s calculations based on provincial and district BPS data from various years, in 1993
constant prices.
Note: HDI = Human Development Index.
richer districts, especially in Jakarta (Hill, Resosudarmo and Vidyattama 2008;
Akita, Kurniawan and Miyata 2011). By 2001–02, however, these sectors had
started to return to their previous levels of growth (Hill 2011) – as had inequal.ity
The relative stability of Java’s inequality levels without Jakarta conirms the
observation of Akita, Kurniawan and Miyata (2011) that Jakarta is the main source
of Java’s increasing inequality.
Jakarta’s inluence may also affect inequality in Indonesia as a whole. In fact,
it could partially explain why the Williamson index for Indonesia’s GRDP per
capita has continued to increase while the β-convergence estimate has indicated
insigniicant speeds of convergence. The Williamson index used in this article
applies a population-weighted coeficient of variation, so, given that Java’s population accounts for 60% of the total Indonesian population, the increasing levels
of inequality in Java are dominating the index because of the statistical weight of
Jakarta. This also means that inequality may not be increasing at all, if it is examined with a measure less inclined to be affected by an extreme value – such as the
Theil inequality index, which uses a logarithmic function for such a purpose.
Figure 4 shows that the inequality of regional development, as measured by
HDI at the district level, decreased in both Sumatra and Java. Although the Williamson index indicates that these levels of inequality are higher when Jakarta is
included, the inclusion does not reverse the decreasing trend. Figure 4 also shows
that the HDI inequality among districts in Sumatra decreased more considerably
than for those in Java, especially in 1999–2002. In this period, Sumatra has a much
faster speed of convergence than Java, as conirmed by the estimated speed of
convergence in table 3: Java at 3.3%, and Sumatra at 13.4%. Figure 4 indicates that
inequality in both Java and Sumatra is still lower than inequality in Indonesia
as a whole, which suggests higher levels of inequality of regional development
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Yogi Vidyattama
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among districts outside the two islands (or considerable differences in the HDI
number between districts in Java and Sumatra and those in other islands). More
work needs to be done to look at the inequality among districts outside Java and
Sumatra and those in Java and Sumatra.
CONCLUSION
More than a decade has passed since the Indonesian government implemented
its decentralisation policy. This article has attempted to examine regional convergence following the central delegation of most authorities in the areas of education, agriculture, industry, trade, investment and infrastructure to an increasing
number of local governments at a district level. It has also examined whether the
neighbourhood effect played a role in the process, by introducing a spatial model
into β-convergence estimates and by looking at whether there is a difference in
regional convergence in the two most populous islands in Indonesia – Java and
Sumatra.
The initial result of this assessment is rather inconclusive. The Williamson
indexes for GRDP per capita, for example, show slight increases in regional inequality
at both the district and the provincial levels, and, although insigniicant,
the β-convergence estimates suggest that convergence occurred at both administrative levels during 1999–2008. The speciic period 2005–08 saw signiicant
convergence occur at the district level, owing partly to the impact of the neighbourhood effect. Although this may seem to promise lower levels of inequality,
the rise of Aceh after the conlict at the end of the 1990s and the early 2000s, in
addition to the fall in the number of mining areas in Papua, has also contributed
considerably to convergence. Moreover, the overall trend of convergence is still
very weak, calling for a longer period of observation.
The increasing and decreasing trends of economic growth in Aceh and Papua,
respectively, has increased the impact of the neighbourhood effect on GRDP convergence. This is an early indication that these districts’ economies are connecting
to those of their neighbours – once an unlikely scenario, given the prevalence of
mining in these areas. The neighbourhood effect could be crucial in indicating the
socio-economic connectivity and the relationship between Aceh and other provinces in Indonesia – especially North Sumatra – as parts of the economy interact with each other, but it could also be a sign that socio-economic events such
as conlict and economic downturns could spread more easily. Furthermore, the
question of whether GRDP per capita could really represent the condition of the
people has yet to be answered. Aceh, for example, may have risen in terms of
GRDP per capita because of the low of aid and aid workers after the 2004 tsunami, rather than because of the refunctioning of its economy.
Given the complexities of using GRDP per capita in growth analyses of Indonesia, this article has used HDI as a comparative indicator of development. The
result shows signiicant regional convergence in HDI numbers during 1999–2008,
despite the absence of signiicant convergence in regional GRDP per capita. The
speed of HDI convergence seems to have slowed, however, and the impact of
the neighbourhood effect lessened. This could be alarming in the future, because
HDI indicators are closely related to the authorities delegated during decentralisation – especially education and health. The ability to lift the HDI numbers of
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Regional convergence and the role of the neighbourhood effect
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Indonesia’s less developed regions to the level of its more developed regions will
therefore measure the success of this delegation process. Furthermore, the slowing
of regional convergence has been followed by fewer instances of the neighbourhood effect, which, in turn, could indicate fewer spillovers from one area to its
neighbours. The data are not clear on whether this is the case, but there are many
reasons for spillovers to reduce in number. For example, a local authority’s limited budget could force it to provide some services only to certain residents, based
on their identity card (kartu tanda penduduk, KTP). More work needs to be done in
this area, as well as on the use of HDI numbers to examine regional convergence
and its implications – especially since HDI is a composite index. Furthermore, the
results of distance-based spatial models show that although the neighbourhood
effect could have an impact on convergence in certain periods, it does not alter the
speed
of convergence signiicantly.
A more interesting inding comes from this article’s analysis of Java and Sumatra. The convergence of GRDP per capita is estimated to be signiicant in Sumatra,
while Java’s GRDP per capita is estimated to diverge. In contrast, the regional
convergence in HDI numbers is strong in both islands, although the speed of convergence is increasing in Java and decreasing in Sumatra. The pattern of inequality of GRDP per capita in Java shows that despite the recovery of Java’s big cities
after the Asian inancial crisis of 1997–98 contributing to increased inequality on
the island, Jakarta was the main source of this increase, especially during 2002–08.
Jakarta’s hampering of convergence in GRDP per capita during decentralisation
does not reduce the importance of regional inequality. In contrast, it could complicate the issue. Jakarta is often seen as representing central government, and
Jakarta’s absorbing the inancial resources of other regions was one of the main
arguments for decentralisation.
The pattern of HDI convergence is less dominated by the development of
Jakarta’s districts. Although the HDI numbers for Jakarta’s districts increased
more modestly than they did for other districts, the rise of the less developed districts in Java and Sumatra have played a greater role in converg