Published online: 26 Jul 2013.

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1 This article was downloaded by: [Macquarie University] On: 29 July 2013, At: 08:28 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: Registered office: Mortimer House, Mortimer Street, London W1T 3JH, UK Bulletin of Indonesian Economic Studies Publication details, including instructions for authors and subscription information: Regional convergence and the role of the neighbourhood effect in decentralised Indonesia Yogi Vidyattama a a University of Canberra Published online: 26 Jul 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, , DOI: / To link to this article: PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the Content ) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at

2 Bulletin of Indonesian Economic Studies, Vol. 49, No. 2, 2013: REGIONAL CONVERGENCE AND THE ROLE OF THE NEIGHBOURHOOD EFFECT IN DECENTRALISED INDONESIA 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 insignificant 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 significant 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 print/issn online/13/ Indonesia Project ANU

3 194 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 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 specifically 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 significant 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 specifically at Indonesia or examine the neighbourhood effect s impact on convergence. These

4 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 coefficient of variation measure, to look at changes in regional inequality at the provincial level during , 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 , except for a discontinued trend of increased inequality during the export-oriented reforms of the mid-1980s. Garcia-Garcia and Soelistianingsih (1998) produced the first estimate of the speed of convergence (β-convergence) of Indonesian provincial incomes of , which confirmed statistically significant 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 findings of Garcia-Garcia and Soelistianingsih (1998) are sensitive to the choice of time period analysed and are also heavily influenced 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 significantly across Indonesia s development periods. It was quite rapid (2%) during the oil boom of ; and it accelerated after oil prices stabilised during , with the speed of convergence estimated to be 2.8%. The speed of β-convergence then collapsed, however, from 1.7%, in , to just 1%, in the 1990s, as the government s export-oriented reforms took hold. During the financial crisis and its aftermath, from 1997 to 2002, there was no significant convergence. Hill, Resosudarmo and Vidyattama (2008) also confirm Akita and Lukman s finding that convergence is fairly stable after the output of the mining sector has been removed from GRDP per capita. Tadjoeddin, Suharyo and Mishra (2001) confirm that regional inequality is stable at the district level, having examined the Williamson index and the Theil and Gini coefficients of GRDP per capita from 1993, when the data was first released, to Although their estimations, similarly to those of Hill, Resosudarmo and Vidyattama (2008), show that regional inequality remained relatively unchanged during this period, they find that the inequality of GRDP per capita, without oil and gas, increased slightly at the district level until Akita and Alisjahbana (2002) draw similar conclusions at the district level, finding that regional income inequality increased during 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 financial 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

5 196 Yogi Vidyattama again until 2004, in the wake of Jakarta s recovery but it remains uncertain what the trend has been since decentralisation intensified. 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 reflect 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 field, find that the neighbourhood effect among US states is not only statistically significant; it also significantly alters convergence. Niebuhr (2001) finds that although the neighbourhood effect in West Germany affected growth significantly, it slowed convergence only slightly. His findings differ from those of Kosfeld, Eckey and Dreger (2002), who, concentrating on a unified Germany, note that the inclusion of the neighbourhood effect slowed convergence significantly. Magalhães, Hewings and Azzoni (2005), in an example of the neighbourhood effect s impact in a developing country, find that it did little to alter the speed of regional convergence in Brazil.

6 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-I- Martin (1996) argues that the concept of convergence in growth analyses is a necessary but not sufficient 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: CVw = where: n (Y i Y ) 2 P i i=1 P Y CVw = population-weighted Williamson index; n = number of regions; Y i = income in region i; Y = average income; P i = 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 significance 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 significantly 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: (1) ( ) ln y 0 g y = α + e β 1 where: ( ) + u (2) g y = the growth rate of per-capita output; y 0 = the initial economic output value; and u = the error term of this estimation. Empirically, convergence occurs when the coefficient 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.

7 198 Yogi Vidyattama 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 significant and/ or if it significantly 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: ( ) ln y 0 g y = α + e β 1 ( ) + ρwg 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 insignificant effect on that of its neighbours, the increasing human capital in that region would affect its neighbours significantly. Anselin (1988) formalises the structure of the error term affected by the neighbourhood effect: u = λwu + ε (4) or, considering the spatial multiplier effect and combined with equation (2), the SEM can be written as: ( ) ln y 0 g y = α + e β 1 ( ) + ( I λwu) 1 ε (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 offices 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 reflect

8 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 liquefied-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 significant 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 first in the 2001 Indonesia Human Development Report, by the United Nations Development Project s UNSFIR (the United Nations Support Facility for Indonesian Recovery).

9 200 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 difficulty of reaching conflict areas in Aceh, Maluku and Papua. The difficulty 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 first, 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 , 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 specification 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, confirming 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 and then decreased slightly during , but regional inequality is still estimated to be have been higher in 2008 than in 2002.

10 Regional convergence and the role of the neighbourhood effect 201 FIGURE 1 Williamson Index of GRDP per Capita, 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 This not only shows that regional convergence may still have occurred in a decentralised Indonesia but also confirms the absence of significant 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 and With the estimated speed of convergence insignificant at around 0.4%, the period of had the lowest speed of convergence since observations began, in 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 Akita, Kurniawan and Miyata (2011) argue that the increase in inequality after the Asian financial crisis hit Indonesia, in , 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

11 202 Yogi Vidyattama FIGURE 2 Williamson Index of HDI, 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 to 3.0% in 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 significant in estimations at the district level but insignificant when based on distance at the provincial level. The neighbourhood effect is often detected as significant in conjunction with a significant 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 insignificant changes in the speed of convergence, even when the spatial lag or the introduced errors are significant

12 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, GRDP per Capita Province District (e β 1) * *** Std error a β Spatial lag LM b ** Robust LM ** Spatial error Moran s I *** LM *** Robust LM *** Province HDI District (e β 1) 0.034*** 0.051*** 0.026* 0.024*** 0.043*** 0.071*** 0.046*** 0.030*** Std error β Spatial lag LM * Robust LM * *** Spatial error Moran s I 2.177** *** 2.022** 1.893* 2.079** LM *** Robust LM *** *** 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

13 204 Yogi Vidyattama TABLE 2 Impact of Spatial Lag and Error Inclusion on β-convergence Estimates of GRDP per Capita and HDI, (district level) GRDP per Capita β without spatial model β with spatial lag ρ ** D(β) β with spatial error λ *** D(β) HDI β without spatial model β with spatial lag ρ D(β) β with spatial error λ *** * D(β) 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 significance of the changes in the speed of convergence is determined by conducting the following t-test: (e β 1) non-spatial = (e β 1) spatial (6) In estimations based on GDP per capita, a positively significant spatial error and lag occurred in only the most recent period ( ). Introducing spatial error increases the speed of convergence insignificantly, 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 insignificant 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.

14 Regional convergence and the role of the neighbourhood effect 205 FIGURE 3 Williamson Index of GRDP per Capita in Java and Sumatra, 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 significant in any period, whereas spatial error is positively significant in the whole period, , and the specific period As in estimations based on GDP per capita, however, there is no significant change (only that of 0.1 percentage point) in the speed of convergence. Although the distance-based neighbourhood effect is significant on regional development, especially through the undisclosed variable (that is, the error term), its impact on district-level convergence is insignificant. 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 significant, there is an indication that GRDP per capita diverges among districts in Java. Conversely, GRDP per capita converges significantly among Sumatra s districts, with a speed of around 3.6% during HDI, as an alternative indicator, shows significant 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

15 206 Yogi Vidyattama TABLE 3 β-convergence Estimates of GRDP per Capita and HDI in Java and Sumatra, GRDP per Capita Java Sumatra (e β 1) island *** 0.053*** 0.033*** 0.038*** β island β Indonesia D(β) Java 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 influenced 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 influencing inequality in Java, figure 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 financial crisis, owing to the fall in the output of the region s financial, construction and manufacturing sectors, which are located mainly in its relatively HDI Sumatra (e β 1) island 0.032*** *** 0.034*** 0.060*** 0.126*** 0.044*** 0.037*** β island β Indonesia D(β) 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

16 Regional convergence and the role of the neighbourhood effect 207 FIGURE 4 Williamson Index of HDI in Java and Sumatra, 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 , however, these sectors had started to return to their previous levels of growth (Hill 2011) as had inequality. The relative stability of Java s inequality levels without Jakarta confirms the observation of Akita, Kurniawan and Miyata (2011) that Jakarta is the main source of Java s increasing inequality. Jakarta s influence 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 insignificant speeds of convergence. The Williamson index used in this article applies a population-weighted coefficient 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 In this period, Sumatra has a much faster speed of convergence than Java, as confirmed 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

17 208 Yogi Vidyattama 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 insignificant, the β-convergence estimates suggest that convergence occurred at both administrative levels during The specific period saw significant 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 conflict 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 conflict 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 flow 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 significant regional convergence in HDI numbers during , despite the absence of significant 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

18 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 significantly. A more interesting finding comes from this article s analysis of Java and Sumatra. The convergence of GRDP per capita is estimated to be significant 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 financial crisis of contributing to increased inequality on the island, Jakarta was the main source of this increase, especially during 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 financial 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 convergence. The catch-up process seems to have slowed during , however, and the pattern suggests that more attention needs to be paid to inequality and convergence among those districts outside Java and Sumatra, because it is less evident that they are catching up. While there are some districts in Nusa Tenggara, in the east of Java, showing some evidence of doing so, some districts in Papua, such as Jayawijaya and Merauke, saw their already relatively low HDI numbers improve the least during This assessment of regional convergence during and after the start of Indonesia s decentralisation process shows some promising trends in GRDP per capita but challenging conditions for HDI convergence. It has not, however, examined what effect, if any, decentralisation itself has had on the speed of convergence. There were some indications that the new districts formed during decentralisation initially had high levels of growth, owing to new investment in building new governments, but this trend seems to have faded away fairly quickly.

19 210 Yogi Vidyattama REFERENCES Akita, T. and Alisjahbana, A.S. (2002) Regional income inequality in Indonesia and the initial impact of the economic crisis, Bulletin of Indonesian Economic Studies 38 (2): Akita, T., Kurniawan, P.A. and Miyata, S. (2011) Structural changes and regional income inequality in Indonesia: a bidimensional decomposition analysis, Asian Economic Journal 25 (1): Akita, T. and Lukman, R.A. (1995) Interregional inequalities in Indonesia: a sectoral decomposition analysis for , Bulletin of Indonesian Economic Studies 31(2): Alm, J., Aten, R.H. and Bahl, R. (2001) Can Indonesia decentralise successfully? Plans, problems and prospects, Bulletin of Indonesian Economic Studies 37 (1): Anand, S. and Sen, A. (2000) The income component of the Human Development Index, Journal of Human Development 1 (1): Anselin, L. (1988) Spatial Econometrics: Methods and Models, Kluwer Academic Publishers, Dordrecht, Netherlands. Arndt, H.W. (1973) Regional income estimates, Bulletin of Indonesian Economic Studies 9 (3): Aspinall, E. and Berger, M.T. (2001) The break-up of Indonesia? Nationalisms after decolonisation and the limits of the nation-state in post-cold war Southeast Asia, Third World Quarterly 22 (6): Barro, R.J. and Sala-I-Martin, X. (1991) Convergence across states and regions, Brookings Papers on Economic Activity 1: Brodjonegoro, B. (2009) Fiscal decentralization and its impact on regional economic development and fiscal sustainability, in Decentralization and regional autonomy in Indonesia: Implementation and challenges, eds C.J.G. Holtzappel and M. Ramstedt, Institute of Southeast Asian Studies, Singapore: Brodjonegoro, B. and Martinez-Vazquez, J. (2002) An analysis of Indonesia s transfer system: recent performance and future prospects, Paper presented at Can decentralization help rebuild Indonesia?, Georgia State University, Atlanta GA, 1 3 May. Cliff, A.D. and Ord, J.K. (1973) Spatial Autocorrelation, Pion Ltd., London. Egger, P. and Pfaffermayr, M. (2006) Spatial convergence, Papers in Regional Science 85 (2): Fingleton, B. and Lopez-Bazo, E. (2006) Empirical growth models with spatial effects, Papers in Regional Science 85 (2): Garcia-Garcia, J. and Soelistianingsih, L. (1998) Why do differences in provincial incomes persist in Indonesia?, Bulletin of Indonesia Economic Studies 34 (1): Hill, H., Resosudarmo, B. and Vidyattama, Y. (2008) Indonesia s changing economic geography, Bulletin of Indonesian Economic Studies 44 (3): Kosfeld, R., Eckey, H.F. and Dreger, C. (2002) Regional convergence in unified Germany: a spatial econometric perspective, Economic Discussion Paper No. 39/02, University of Kassel, Germany. Leigh, A. and Van der Eng, P. (2009) Inequality in Indonesia: what can we learn from top incomes?, Journal of Public Economics 9 3(1 2): LeSage, J.P. (1999) Spatial Econometrics, The Web Book of Regional Science, Regional Research Institute, West Virginia University, Morgantown WV. Magalhães, A., Hewings, G.J.D. and Azzoni, C.R. (2005) Spatial dependence and regional convergence in Brazil, Investigaciones Regionales 6: McCulloch, N. and Sjahrir, B.S. (2008) Endowments, location or luck? Evaluating the determinants of sub-national growth in decentralized Indonesia, Policy Research Working Paper No. 4769, World Bank, Washington DC. McGillivray, M. (1991) The Human Development Index: yet another redundant composite development indicator?, World Development 19 (10):