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Structural Change and Regional Economic Growth in #

Andriansyah Andriansyaha,*, Asep Nurwandaa and Bakhtiar Rifaia

aCentre for Macroeconomic Policy Fiscal Policy Agency, Ministry of Finance of the Republic of Indonesia

Abstract

This paper investigates the relationship between structural change and regional economic growth in Indonesia. We utilize several measures of structural change, i.e. structural change index, norm absolute value index, shift-share method, and effective structural change index, for 30 provinces over the period 2005-2018. We show that the structural change has occurred across provinces, even though it is slowing, towards an agricultural-services transition. By employing dynamic panel data models, it has shown that structural change is a significant determinant to growth. However, structural change matters for growth only if there is an increase in productivity, not only the movement of labor across sectors. Growth can be happened if there is an improvement in productivity within sectors as well as a movement of labors to other sectors with better productivity.

Subject Keywords: Structural Change, Regional Growth, Indonesia, Productivity

JEL Codes: L16, O40, R11

*Corresponding author. Postal Address: Centre for Macroeconomic Policy, Fiscal Policy Agency, Ministry of Finance of the Republic Indonesia. RM Notohamiprodjo Building 8th Floor. Jl. Dr. Wahidin Raya No. 1 10710. E-mail: [email protected]

# We would like to thank to the participants of the 15th Indonesian Regional Science Association (IRSA) International Conference held in Banda , Indonesia, on 22-23 July 2019 for their valuable comments.

Structural Change and Regional Economic Growth in Indonesia

1. Introduction

Structural change is an important determinant of economic growth. Kuznets (1973) claims that the high rate of structural change is one of six characteristics of the modern economic.

Theorically, the transmission channel linking structural change to economic growth is through productivity, where there is a cross-sectoral reallocation from low productivity economic sectors to higher productivity sectors. Traditionally, the reallocation occurs from agriculture to manufature (Chenery, Robinson, Syrquin, & Feder, 1986). Increasing role of the manufacturing sector, well-known as industrialization, as argued by Rodrik (2013) and later supported by Felipe,

Kumar, and Abdon (2014), is claimed as an engine of growth and empirically creates unconditional convergence of increased labor productivity with its capacity to absorb capital and technology.1 Lately, however, the reallocation can also occurs from agriculture to services, as took place in many other developing countries (Timmer, de Vries, & de Vries, 2015), particularly in

Asia (Asian Development Bank, 2013).

Hausmann and Klinger (2006), in general, define structural change as a development process from a poor economy with simple products into a rich economy with more complex products. Alternatively, Laitner (2000) shows that structural change leads to a higher income through an increase in a national savings rate. Moreover, Herrendorf, Rogerson, and Valentinyi

(2014) develop a multi-sector extension of the one-sector growth model accounting for many

1 O’Rourke and Williamson (2017) provide a comprehensive review of industrialization; while Assunção, Burity, and Medeiros (2015) show that the convergence depends on country-specific characteristics, such as political institutions, trade openness and education. aspects of structural change such as regional income, aggregate productivity, working hours, wages, and business cycles. On the other hand, the cost of transition, consisting of structural unemployment and social costs, may hamper the benefit of structural change on economic growth (GHK, 2011).

Empirically, the relationship between structural change and economic growth either in a regional or a national level is rather inconclusive. Regionally, an evidence of a positive relationship has been supported, among others, for, OECD countries (Dietrich, 2011) and Asian economies (Vu, 2017). Szirmai (2012), however, argues that manufacturing sector is an important growth determinant for developing economies in Asia and Latin America, while service sector is more important for developed economies. On the other hand, a negative evidence is also found for Africa and Latin America (McMillan, Rodrik, & Verduzco-Gallo, 2014). At an individual economy level, Padilla-Perez and Villarreal (2017), for instance, show that Mexico has experienced no positive impact of structural change due to the wrong direction of reallocation which is from high labor productivity sectors to lower or declining productivity growth ones.

The recent overview of the relationship between structural change and growth is given by

McMillan, Rodrik, and Sepúlveda (2017) which also discuss for the case of India, Viet Nam,

Botswana, Ghana, Nigeria, Zambia, and Brazil.

Meanwhile, research at subnational level have also been conducted even though mainly on

China and India. In China, for example, Jiang (2011) argues that regional growth is depended on structural change for labor productivity growth as the economy evolves. In the case of India, Babu and Raj (2011) and Thind and Singh (2018) show that structural changes have contributed positively to regional economic growth. The latter, however, argues that contribution of productivity growth within individual sectors is found to be more important than structural the productivity effect of labor reallocations across different sectors. Indonesia has similar characteristics with China and India particularly in term of high population and regional diversity. However, there has been no study on the impact of structural change on regional growth in Indonesia. The studies on general regional growth determinants have been conducted by Hill, Resosudarmo, and Vidyattama (2008), McCulloch and Sjahrir

(2008), and Vidyattama (2010). All studies, however, do not examine the structural change impact. The latter uses GDP shares of manufacture, agriculture, and services as determinants of regional growth, but these metrics are not a direct mesurement of structural change. Similar to the GDP share approcah, the Structrural Change (SC) index is used by Hill et al. (2008), explaining the sector share change between the agriculture, industry, and services. By using a simple scatter plot, they show that there is a weak correlation between structural change and regional growth.

This index, howewer, according to Vu (2017), is less meaningful because there is no indication if the structural change is productivity-enhancing or decreasing. To fill in this gap, this study is aimed at measuring structural change dynamics and examining their impact to regional growth in Indonesia. This is our main contribution to the structural change and regional growth nexus.

The structure of this paper is as follows. Section 2 explains the structural change in

Indonesia, including the structural change measures used. Next, Section 3 describes the data and methodology employed to estimate the impact of structural change on regional economic growth.

Section 4 presents the empirical results and Section 5 concludes the paper.

2. Structural Change in Indonesia

Economic transformation managed to quickly propel Indonesia’s income per capita and helped raise the nation’s status from underdeveloped to developing country by the late 1980s.

Indonesia’s income per capita (in current US$) rose from only $53.5 in 1967 to $3,893.6 in 2018.

However, the episode of exponential growth was ended abruptly by the 1997-1998 Asian financial crisis (leading to twin crises in Indonesia – the banking and currency ones). Up to this point,

Indonesia has not fully recovered from this crisis (Basri, Rahardja, & Fitrania, 2016). Pre-crisis from 1961 to 1997, GDP growth was 5.9% on average. Meanwhile, post-crisis (from 2000 to 2018), it was only 5.3%. In parallel with the national economic performance, manufacturing grew by

9.3% prior to the crisis, proving the sector to be the engine of growth at the time. Yet, following the crisis, the sector never quite recovered to its pre-crisis rate and grew below the national average, 4.7% in 2000-2018.

INSERT FIGURE 1 ABOUT HERE

A stellar growth of the manufacturing sector before the crisis has guided the Indonesian economy through a structural transformation. Figure 1 shows Indonesia’s gradual structural transformation from a traditional agricultural-driven economy in the early 1960s into a manufacturing and services-driven economy within a few decades. The agricultural sector’s contribution to GDP declined from around 50% in the 1960s to only around 13% in 2018, while manufacturing occupied a larger share from below 10% to around 20% in the same period. On the other hand, the services sector was on a consistent climb throughout the period from around

30% to 43%. Indonesia also underwent a structural transformation from a regional perspective but showed disproportionate outcomes between different regions. Judging from the pace of economic transformation and industrialization, there is a glaring disproportion between and

Non-Java region. A number of factors are contributing the widening gap, one of which is a poor national logistics system that supposedly integrates the market and the domestic production chain (Axelsson & Palacio, 2017). In terms the Gross Domestic Regional Product’s (GDRP) contribution to the total GDP, Java accounted for 57.9% of the total GDP, followed by Sumatera

(22.5%). Meanwhile, , , & Nusa Tenggara, and Maluku- together only made up about 20% of the total GDP, despite representing more than two-thirds of the

Indonesian territory. In terms of manufacturing, Java accounted for 71.1% of total national manufacturing amplify the argument of highly concentrated economic activity in this small island and denote the industrialization process did not occur on other islands.

So far, the structural change measurements in national and regional level have only been based on changes in sectoral share. We need more formal measurements of structural change. In addition to SC index, following Vu (2017) this study utilizes three other structural change measurements, which are norm absolute value index, shift-share method, and effective structural change index with brief explanation as follows: a. Norm Absolute Value (NAV) index

The NAV index provides a simple calculation to measure the overall magnitude of structural change by seeing the change of employment share of sector. The index is calculated for period [0, T], as follows:

1 푁퐴푉 = ∑푛 |푆 − 푆 | (1) 2 푖=1 푖푇 푖0

2 where 푛 denotes the number of sectors, i.e. 9 sectors, 푆푖푇 and 푆푖0 denotes the employment share of sector 𝑖 at time T and 0, respectively.3 This method provides quick structural change measure from the total reallocation of employment, but has limitation to explain whether the reallocation occurred to a better sector or worse, hence this index failed to explain the effect of structural change to economic growth.

2 In 2010, the component of GDP has changed into 17 sectors so we reclassify the data from 2010-2018 into 9 sectors. See Appendix I for the detail conversion method. 3 SC index is calculated with a similar formula for NAV by replacing the employment share with value- added share. b. Shift-Share (SS) method

The SS method is a more common measurement of structural change. The method decomposes the sectoral contribution to overall labour productivity growth into three terms, as follows:

∆푃 푛 푆푖0∆푃푖 푛 푃푖0∆푆푖 푛 ∆푆푖∆푃푖 = ∑푖=1 + ∑푖=1 + ∑푖=1 (2) 푃0 푃0 푃0 푃0

where 푛, 푆푖푇 and 푆푖0 are same variable from the NAV Index, ∆푆푖 is the change of employment share of sector 𝑖 over the time 0 and T, 푃0 is the level of labour productivity at time 0 (i.e. the value-added measured at constant price divided by the number of workers), ∆푃푖 is the change of level of labor productivity of sector 𝑖 over the time 0 and T.

푛 푆푖0∆푃푖 The first term (∑푖=1 ), commonly known as “within-effect” (Within), captures the 푃0 improvement productivity sourced from the same sector. The number is usually positive since the labour productivity tends to increase overtime. It may happen even when a sector has maintained the same amount of shares in total employment as during the base year. The second

푛 푃푖0∆푆푖 (∑푖=1 ) is commonly known as “static structural effect” (Static). It represents the 푃0 contribution of reallocation of employment among sectors, given the initial level of productivity.

The number is positive (negative) if the employees reallocate to above (below) average productivity. In other words, sectors with higher productivity attract more (less) labor resources.

푛 ∆푆푖∆푃푖 The third term (∑푖=1 ) can be called “dynamic structural effect” (Dynamic), capturing the 푃0 effect of both employment reallocation and productivity growth. Dynamic is positive if and only if there are both an increase in employment share and in productivity (∆푆 > 0 and ∆푃 > 0), or there are both a decrease in employment share and in productivity (∆푆 < 0 and ∆푃 < 0). The interaction term becomes larger (smaller) when sectors with fast productivity growth are faced with more (less) labour resources. In other words, the number is positive (negative) if workers tend to shift from productivity declining (improving) sector to productivity improving

(declining) sectors. Vries, Timmer, and Vries (2013) argue it is important to distinguish between static and dynamic reallocation effects in order to understand differences in the role of structural change. c. Effective Structural Change (ESC) index

This index is proposed by Vu (2017) to combine the strength of NAV index and SS method.

The index is basically similar with NAV but only consider the sectors that positively contribute to labour productivity. Hence, Equation (1) is modified is as follows:

1 퐸푆퐶 = ∑ |푆 − 푆 | 푤𝑖푡ℎ 푌 = {𝑖} such that 퐶 > 0 (3) 2 푖∈푋 푖푇 푖0 푖

where 푛, 푆푖푇 and 푆푖0 are same variable from the NAV Index, and 푌 is the set of sectors that have positive contribution to labor productivity growth and 퐶푖 is the total contribution of sector i to the total overall economy’s productivity growth. This index is believed to be better in measuring structural change since it eliminates the declining sector in which the reallocation (changes) not contributing to the overall productivity as well as to growth.

In addition to the national level, this study also focuses on structural change in the 30 sub- nationals (provinces) in Indonesia, in which some of them have been experienced a fragmentation

(Pemekaran in Indonesian), such as (some areas become ),

(), () and Papua ()4. The data used is

4 See Appendix II for details. collected from CEIC which is mainly derived from Badan Pusat Statistik (Indonesia Statistics) covering the 2005-2018 period (Panel A Table 1)5.

INSERT TABLE 1 ABOUT HERE

INSERT TABLE 2 ABOUT HERE

The four measurements of structural change (SC, NAV, ESC, and SS) are presented in Table

2. Over the period 2005-2018, in general the progress of structural change in Indonesia has been slowing, towards a new economic structure. This can be shown by Indonesia’s SC index for the period 2005-2018 is 0.095, but the last 5-year period (2015-2018) has index of 0.017 which is much lower than the first 5-year period (2005-2009) index of 0.035. On the other hand, the employment reallocation has been increasing, shown by NAV index. Two sectors have experiencing the largest increase in value added (employment) shares are Transportation and Trade (Trade and

Government). On the other hand, those have experiencing the largest decrease in value added

(employment) shares are Mining and Agriculture (Agriculture and Transportation).

These changes are followed by an increase in productivity as presented by the positive total value of SS. This increasing productivity however is slowing, shown by the value of the 2015-

2018 period that is lower than that of the 2005-2014 period. Two sectors have experiencing the largest increase in productivity are Trade and Manufacturing. It is interesting, however, to notice that the highest productivity occurred in the period of 2010-2014 due to the period of commodity boom benefiting Indonesia as a large producer of some commodity-based products i.e. crude palm oil, coal, and mineral. The increase in productivity at the national level mostly happens at the same sectors and due to the reallocation of employment among sectors as shown by positive

5 The period of 2005-2018 is selected to avoid the outlier data in the crises period as well as to consider the availibility of provincial data in CEIC values of Within and Static. Meanwhile, the slowing productivity is caused by the negative dynamic structural effect. This indicates that workers tend to shift from productivity improving sector to productivity declining sectors6.

The slowing productivity is confirmed by decreasing ESC index, showing that reallocating of employment occurs more to the sectors that have not increased in productivity. Over the period

2005-2009, there were only six sector having a positive value of C, i.e. Agriculture, Manufacture,

Utilities, Construction, Trade, and Transport, while in the period 2015-2018 the number of sectors has been reduced to five, i.e. Agriculture, Construction, Transport, Finance, and Government.

Regionally, it is clear that the provinces in Sulawesi relatively have higher indexes than those in Java, in particular in terms of NAV and ESC. This means that reallocation of employment to more productive sectors are more happening in Sulawesi than in Java. In terms of SC index, the largest structural change occurred in Papua and over the period 2005-2018.

While in the period 2005-2009, Aceh experienced the fastest change before it was replaced by

West Nusa Tenggara in the next two 5-year periods. In Java, the slowing down trend has also occurred in the period 2005-2014, in particular in and . Meanwhile in terms of NAV index, the structural change has been caused by the change in labor structure. Over the period of study, the largest NAV index belonged to while the lowest one belonged to DKI Jakarta. It seems the labor structure in the capital city has been stable and concentrated in sectors Trade and Government. Meanwhile in the period 2010-2018, the largest change has occurred in Sulawesi Island, in particular in central and south east parts.

In terms of SS, the higher productivity in Indonesia may be caused by the higher productivity in the same sector (Within) and the higher share of labour in the same sector (Static),

6 See Appendix III for details. not caused by the movement of labour to more productive sectors. The one and only province that experienced the movement of labour to more productive sectors was , shown by a positive dynamic structural effect. In terms of the within effect, the largest improvement in productivity was experienced by Riau in the period 2005-2009, while Aceh and

Bali and East Kalimantan experienced the decrease in productivity. In general, and Bali experienced the consistent decline in the same sector productivity. In terms of static structural change, the decreasing trend was occurred in Riau and . In general, at the national level, the productivity has been increased with the best period has been shown in the period 2010-2014 where all province has a positive value of labour productivity. However, three provinces experienced decreasing productivity, that is Riau, Papua and Aceh. The decrease in

Aceh happened in the period 2005-2008 might be due to Tsunami. While in the period 2015-2018, the decrease has been occurred in West Nusa Tenggara.

We can also see even though the variation of within effect relative small for Indonesia as at the national level, the variation across provinces is relative high in particular for Papua. In terms of static structural effect, most provinces have a positive value, showing its labor share is expanding. South East Sulawesi, , and exhibit their labor share expand much faster than other provinces. Interestingly, the contribution of dynamic structural effect have been negative for all provinces, meaning that workers have shifted from productivity growing sectors to productivity declining sectors, or at least to a sector with no improvement in productivity growth.7

Interestingly, the pattern in NAV index is different to that in ESC index because in general the number of productive sectors is unchanged (6 sectors) even though the cluster of sectors is

7 In 2005-2018 some sectors i.e. Mining, Utilities, and Finance, are negative in term of change of labor productivity. Meanwhile, Agriculture and Transport are negative in term of change of labor share. different from Mining, Utilities, Construction, Trade, Finance, and Government in 2005-2009 into

Mining, Manufacture, Utilities, Trade, Transport, and Finance in 2015-2018. Structural change occurring in Maluku is more effective than other provinces shown by the higher ESC values and the increase in the number of sectors experiencing higher productivity. The provinces experiencing slower effectivity of the structural change are Aceh, Riau, and Jambi.

INSERT TABLE 3 ABOUT HERE

Judging from the shift in labor (Table 3), the share of agricultural labor in Indonesia has decreased 15.2% over the period 2005-2018, or roughly 1% decrease every year. At the same time, the employment in services sector improved 10,7%, which mainly distributed to trade (5.7%) and government sectors (4.0%). The pattern of double digit declining in agricultural labor share occured in all provinces, except DKI Jakarta and Bangka Belitung. As expected he share of agricultural labor has been very low in DKI Jakarta, while the share of agricultural has increased

2.8% in Bangka Belitung probably due to a significant decrease of employment in mining sector.

Overall, there has been a shift of employment from agricultural sector to services, especially in trade and government sector. On the other hand, sectors with high productivity, such as manufacturing, construction, and financial services on average only shown a slight increase.

Share of manufacturing employment has shown only a slight improvement of 2.0%, where only two provinces (Maluku and North Sulawesi) who rose more than 5%. The share of manufacturing sector in Riau, DKI Jakarta, Bali, West Nusa Tenggara, and East Kalimantan has been decreased.

The slight increase of manufacturing amidts a significant decrease of agricultural sector indicates a less manufacturing jobs created relative to services jobs during the period.

Above descriptions of regional structural change are illustrated by Figure 2. Panel A and

Panel B map the effective structural change index and shift share method or real labor productivity across provinces in Indonesia over the period 2005-5018. It can be also seen that almost all major provinces in Java only grew in the range of the national average. While the provinces with the fastest growth and high structural change index are mostly small provinces such as North Maluku, Maluku, and Central Kalimantan. These figures demand explanation if the structural change matters for regional economic growth. Figure 3 is set as an early indication the relationship between GDP per capita growth, NAV index, and ESC index.

Panel A shows that there may be a positive relationship between regional growth and NAV index, while Panel B shows an inverse relationship between regional growth and ESC index. These indications are similar with Hill et al. (2008) that also show a weak relationship between structural change and regional growth. The formal examination about the relationships therefore is needed as explained below.

INSERT FIGURE 2 ABOUT HERE

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3. Data and Methodology

Regional growth models have been employed by many studies such as Chen and Wu (2005) for China and Vidyarthi (2017) for India. In order to assess the relationship between the structural change and growth, we are interested in a stable dynamic model, provided |훼| < 1, in the following model specification set laid out by Barro and Sala-i-Martin (2004) for a basis of empirical analysis:

′ 푃푅퐺퐷푃푖,푡 − 푃푅퐺퐷푃푖,푡−푗 = 훼푃푅퐺퐷푃푖,푡−푗 + 훾푆푐ℎ표표푙𝑖푛𝑔푖,푡−푗 + 훽 푋푖,푡 + 휂푖 + 휀푖,푡 (4)

where 푃푅퐺퐷푃푖,푡 − 푃푅퐺퐷푃푖,푡−푗 denotes province i per capita regional GDP growth rate in j-year period from year t-j to year j; 푃푅퐺퐷푃푖,푡−푗 is initial the logarithm of real per capita regional GDP, representing a stock of physical capital; 푆푐ℎ표표푙𝑖푛𝑔푖,푡−푗 is the number of average years spent by the residents of a province in formal education, representing a stock of human capital; X is a vector of our variable of interests (i.e. SC, NAV, ESC, Within, Static, or Dynamic);  is an unobservable province effect; and  is the error term that is assumed to be homoscedastic and mutually uncorrelated over time, and the subscript i and t denote province and time period, respectively.

To accommodate business cycles and identify long-term relationship between the variables of interest (Harris & Tzavalis, 1999), we firstly examine 5-year average growth regressions as utilized by Vidyattama (2010). Three five-year average periods are used here: 2005–2009, 2010–

2014 and 2015–2018.8 The first term in Equation 4 is therefore the value of PRGDP in year 2005,

2010 and 2015. Equation 4 is estimated by employing the two-steps system GMM developed by

Arellano and Bond (1991), Arellano and Bover (1995), and Blundell and Bond (1998), as also used in Vidyattama (2010).

We also use a set of control variables used for excluded instruments, that is the length of roads (Roads), the importance of mining (Mining), government spending for capital expenses

(Government), commercial banking loans (Credit) and foreign direct investments (FDI). These variables are mainly found to be statistically significant to growth in existing studies such as in

Hill et al. (2008), McCulloch and Sjahrir (2008), and Vidyattama (2010). The constant term is excluded because we include all year dummies as extra exogenous instruments as suggested by

Han and Kim (2014). We also follow Roodman (2009) to employ the collapsing technique and only use one lag to reduce the number of instruments used. We transform the data by taking the

8 The last period only consists of four years due to data availability. natural logarithm of all variable except for the structural change variables. For 5-year data, we use the standard practice in the empirical studies by averaging the data.

To attend that imposing an average effect limitation makes it impossible to capture each country’s individual idiosyncrasy (Aretis & Demetriades, 1997), we adapt Andriansyah and

Messinis (2014)’s approach by also using annual time series data and employing a first-order autoregressive distributed lag (ARDL) version of Equation 4. GMM estimators are also used for annual data; however, we utilize the level GMM estimators instead of the two-steps system GMM system. This choice is based on the argument of Jenkinson and Ljungqvist (2001) and Mitze (2012) that the level GMM estimators are relatively better than the system GMM estimators for a small sample (the number of cross sections is about 25, 35, or 50 and the number of years is about 12 or

15).

4. Results

4.1. Descriptive statistics

Table 4 and Table 5 present descriptive statistics for the variables of interest. Table 4 summaries the main economic data of Indonesia which divided into 30 provinces. Various indicators indicate that the characteristics of economic data are vary across regions in Indonesia.

GDP per capita from the poorest regions is IDR 18.4 million per capita, lagged very far behind the richest province with IDR 248 million per capita.

In terms of growth, the range of average growth also varies from 0.3% to 8.8%. Based on the growth trend, most provinces experienced an in line trend with the national average, which increased in the 2010-2014 period but declined in the 2015-2018 period. This is mainly influenced by the condition of the commodity boom in the era of 2010-2014 which was not only felt by natural resources-rich provinces in, but also by other regions that did not rely on natural resources. INSERT TABLE 4 ABOUT HERE

INSERT TABLE 5 ABOUT HERE

Table 5 shows the strength of relationship among variables. Mostly there is a weak negative correlation between structural change measures and PRGD, except for Static. In terms of cross- section dependence in macro panel data, Pesaran CD cross-sectional independent tests show that there is a correlation among provinces that may arise from globally common shocks with heterogeneous or as the result of local spillover effects between provinces.

4.2. The 5-Year Average Data

The estimates of the traditional growth model using five-year average data are presented in Table 6. Model 1 to Model 4 presents the estimates for SC, NAV, ESC, and SS, respectively. We find that the coefficient estimate of SC is statistically insignificant, while other structural change measures are statistically significant. SC is not significantly determine GDRP per capita growth, possibly due to the index only measures the change of value added share. Interestingly, the impacts of NAV and ESC on growth are negative, while the impacts of Within, Static, and Dynamic are positive. This means that the movement of labor across sectors may hamper economic growth if the movement does lead to a higher productivity. This is confirmed by the three measures of shift-share method that are statistically important to determine growth because since they are link to productivity growth.

Model 4 also shows that within sector productivity improvement is more important than static and dynamic structural effects for the case of Indonesia as in India (Thind & Singh, 2018).

The coefficient of Static is also higher than that of Dynamic, meaning that the impact of reallocating employees to above average productivity sectors must be get more concern rather than the movement of employees across sectors. 4.3. The Annual Data

In estimating the dynamic model using annual data, we firstly employ two panel unit root tests: the Im-Pesaran-Shin (IPS W-t-bar) test (Im, Pesaran, & Shin, 2003) and the Pesaran's simple panel unit root test in presence of cross section dependence (CADF Z-t-bar) test (Pesaran, 2007).

The tests’ results presented in Table 7 generally show that all variables contain a unit root except for structural change and government expenditure variables. The results of the panel unit root tests in first difference confirm that the series are non-stationary. To be consistent with interpretation of change, we therefore estimate GMM by using first differences of all examined variables. Next, we examine the possibility of a long- relationship and employ the Westerlund error-correction-based panel cointegration tests (Westerlund, 2007). The results appeared in Table

8 fail to reject the null hypothesis of no cointegration between regional economic growth and structural change measures.

The estimates of the dynamic growth model using annual data are presented in Table 9.

Two specification tests, i.e. the AR(2) test in difference and the Hansen (1982) test concerning the joint validity of the instruments, suggest that our models are acceptable. The results of annual data are similar to those of 5-year average data, even make it stronger to conclude that SC, NAV, and ESC have a negative relationship with growth where has a positive relationship with growth.

Model 4 in Table 9 display a contrasting result with Model 4 in Table 8. For annual data, it seems that dynamic structural effects have more impact to growth than static structural effect and within sector productivity improvement. This may due the movement of labor occurs gradually.

Again, the results in general show that structural change matters for growth only if there is an increase in productivity, not only the movement of labor across sectors.

4.3. Discussions

The results have confirmed that there is a positive relationship between structural change and regional economic growth, in particular the shift-share method indicators. This is in line with the theory that there is link between economic growth and structural change through productivity. Empirically this finding is also similar with India (Babu & Raj, 2011; Thind & Singh,

2018) and China (Jiang, 2011). On the other hand, although dynamic structural effect has a positive impact on growth, this indicator for Indonesia were always in the negative zone during

2005-2018 period. This indicates that the reallocation of labor that occurred on average does not shift to growth improving sectors. This is confirmed by the fact that the largest reallocation occurred from the agriculture sector to the services sector, particularly trade and government

(which includes social and community services). Those two destination sectors have lower productivity level compare to manufacturing and construction sector. The phenomena of agriculture-services transition is commonly happen in developing countries as discussed by

Chenery et al. (1986). The lack of labor education and skill are believed to be the main reason behind which translate into low productivity.

From a subnational perspective, the progress of structural change and improvement productivity in 30 provinces were varied greatly across time and across regions. Judging from the productivity growth, the provinces are divided into three major groups (Figure 2 Panel B).

The first group is The outperformer regions such as provinces in Sulawesi (except Gorontalo),

Central Java and East Java. Productivity in the outperformer group grew far above the national average. The improvement is mainly driven by the growth of new industries. For example, in

Sulawesi there are new industrial estates developed in recent years for agro and metal processing industries. Central Java and East Java are also benefited from new investment such as the garment and electronics industries. The second group is the underperformer regions, covering the province of Aceh, Riau, Bangka Belitung, East Kalimantan, Maluku, and Papua. Productivity in the underperformer group grew far below national average. Most of the underperforming regions are commodity-driven regions which experiencing a downward trend of production in the last 14 years, such as Riau and East Kalimantan. The last group is the median regions, including provinces in Sumatera (other than Aceh, Riau, and Bangka Belitung), provinces in Java

(other than Central Java and East Java), provinces in Kalimantan (except East Kalimantan),

Gorontalo, North Maluku, Bali, and Nusa Tenggara regions. Productivity in the median group grew at the range of the national average. Even though they are in the same range, the pattern of structural change in these regions varies greatly. Some regions have relatively mature industries, such as , but others have not yet undergone the process of industrialization, such as

East Nusa Tenggara and North Maluku.

With this varied productivity performance, policies to support the structural change among regions need to be customized based on the character of each region. We have to learn for the case of Mexico where the wrong direction of reallocation make the structural change has no impact to growth (Padilla-Perez & Villarreal, 2017). Asian Development Bank and Bappenas

(2019) show that primary gross enrolment ratio, foreign direct investment, manufacturing exports, economic complexity index, high technology export and current account openness, are, among other, the important factors determining productivity growth. There is also a need to further examine the factors that determine productivity for each region.

5. Conclusion

This paper has examined the dynamics of structural change and investigated the relationship between structural change and regional economic growth in Indonesia. By calculating four measures of structural change, namely structural change index, norm absolute value index, shift-share method, and effective structural change index, this study finds that structural change has occurred across 30 provinces over the period 2005-2018 toward an agricultural-services transition. The progress of structural change is relatively slowing, towards a new economic structure. The provinces in Sulawesi outperformed other regions.

By utilizing 5-year average data and annual data as and employing dynamic panel data models with GMM estimators, this study empirically confirms that productivity is an important factor that link structural change to economic growth. In particular, shift-share method shows that for annual data dynamic structural effects have more impact to growth than static structural effect and within sector productivity improvement. Even though the role of manufacturing is need to be improved as the engine of growth, the industrialization policies must be based on characteristics of each provinces. Structural change matters for growth only if there is an increase in productivity, not only the movement of labor across sectors. Growth can be happened if there improvement in productivity within sectors as well as by shifting to other sector with better productivity. Policies to support the structural change among provinces however need to be customized based on the characteristics of each province. There is a need to further examine the factors that determine productivity for each province.

References

Andriansyah, A., & Messinis, G. (2014). Equity Markets and Economic Development: Does the Primary Market Matter? Economic Record, 90(Special Issue June), 127-141. Arellano, M., & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application toEmployment Equations. The Review of Economic Studies, 58(2), 277-297. Arellano, M., & Bover, O. (1995). Another Look at the Instrumental Variables Estimation of Error-components Models. Journal of Econometrics, 68, 29-51. Aretis, P., & Demetriades, P. (1997). Financial Development and Economic Growth: Assessing the Evidence. The Economic Journal, 107(442), 783-799. Asian Development Bank. (2013). Asia’s Economic Transformation: Where to, How, and How Fast? - Key Indicators for Asia and the Pacific 2013 Special Chapter. Retrieved from Mandaluyong City, Philippines: Asian Development Bank, & Bappenas. (2019). Plocies to Support the Development of Indonesia's Manufacturing Sector During 2020-2040 Retrieved from Jakarta: Assunção, J., Burity, P., & Medeiros, M. C. (2015). Is the convergence of the manufacturing sector unconditional? EconomiA, 16(3), 273-294. doi:10.1016/j.econ.2015.09.005 Axelsson, T., & Palacio, A. (2017). Transforming Indonesia: Structural change in a regional perspective 1968-2010. Lund Papers in Economic History, 164(Lund University, Department of Economic History). Babu, M. S., & Raj, S. N. R. (2011). Trends in regional industrial growth in India. Journal of Indian Business Research, 3(2), 132-143. doi:10.1108/17554191111132242 Barro, R. J., & Sala-i-Martin, X. (2004). Economic Growth (Second edition ed.). Cambridge: The MIT Press. Basri, M. C., Rahardja, S., & Fitrania, S. N. (2016). Not a Trap, But Slow Transition? Indonesia's Pursuit to High Income Status. Asian Economic Papers, 15(2), 1-22. doi:10.1162/ASEP_a_00422 Blundell, R., & Bond, S. (1998). Initial Conditions and Moment Restrictions in Dynamic Panel Data Models. Journal of Econometrics, 87, 115-143. Chen, C. H., & Wu, H. L. (2005). Determinants of regional growth disparity in China's transitional economy. Journal of Economic Studies, 32(5), 406-419. doi:10.1108/01443580510622397 Chenery, H. B., Robinson, S., Syrquin, M., & Feder, S. (1986). Industrialization and growth: Oxford University Press New York. Dietrich, A. (2011). Does growth cause structural change, or is it the other way around? A dynamic panel data analysis for seven OECD countries. Empirical Economics, 43(3), 915-944. doi:10.1007/s00181-011-0510-z Felipe, J., Kumar, U., & Abdon, A. (2014). How rich countries became rich and why poor countries remain poor: It's the economic structure…duh! Japan and the World Economy, 29, 46- 58. doi:10.1016/j.japwor.2013.11.004 GHK. (2011). Impacts of Structural Change: Implications for policies supporting transition to a Green Economy: Final Report. Retrieved from http://ec.europa.eu/environment/enveco/resource_efficiency/pdf/transition_costs.pdf Han, C., & Kim, H. (2014). The role of constant instruments in dynamic panel estimation. Economics Letters, 124(3), 500-503. doi:10.1016/j.econlet.2014.07.021 Hansen, L. P. (1982). Large Sample Properties of Generalized Method of Moments Estimators. Econometrica, 50, 1029-1054. Harris, R. D. F., & Tzavalis, E. (1999). Inference for unit roots in dynamic panels where the time dimension is fixed. Journal of Econometrics, 91(2), 201-226. Hausmann, R., & Klinger, B. (2006). Structural Transformation and Patterns of Comparative Advantage in the Product Space. Retrieved from https://EconPapers.repec.org/RePEc:cid:wpfacu:128 Herrendorf, B., Rogerson, R., & Valentinyi, Á. (2014). Growth and Structural Transformation. 2, 855-941. doi:10.1016/b978-0-444-53540-5.00006-9 Hill, H., Resosudarmo, B. P., & Vidyattama, Y. (2008). Indonesia's Changing Economic Geography. Bulletin of Indonesian Economic Studies, 44(3), 407-435. doi:10.1080/00074910802395344 Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testingfor unit roots in heterogeneous panels. Journal of Econometrics, 115, 53-74. Jenkinson, T., & Ljungqvist, A. (2001). Going Public: The Theory and Evidence on How Companies Raise Equity Finance (Second ed.). New York: Oxford University Press. Jiang, Y. (2011). Structural Change and Growth in China under Economic Reforms: Patterns, Causes and Implications. Review of Urban & Regional Development Studies, 23(1), 48-65. doi:10.1111/j.1467-940X.2011.00177.x Kuznets, S. (1973). Modern Economic Growth: Findings and Reflections. American Economic Review, 63(3), 247-258. Laitner, J. (2000). Structural Change and Economic Growth. The Review of Economic Studies, 67(3), 545-561. McCulloch, N., & Sjahrir, B. S. (2008). Endowments, Location or Luck? Evaluating the Determinants of Sub-National Growth in Decentralized Indonesia. Policy Research Working Paper. Pacific Poverty Reduction and Economic Management Unit. The World Bank. McMillan, M., Rodrik, D., & Sepúlveda, C. (2017). Structural, Fundamentals, and Growth: A Framework and Case Studies. Washington, DC: International Food Policy Research Institute (IFPRI). McMillan, M., Rodrik, D., & Verduzco-Gallo, Í. (2014). Globalization, Structural Change, and Productivity Growth, with an Update on Africa. World Development, 63, 11-32. doi:10.1016/j.worlddev.2013.10.012 Mitze, T. (2012). Dynamic Simultanoeus Equations with Panel Data: Small Sample Properties and Application to Regional Economeric Modelling Empirical Modelling in Regional Science. Lecture Notes in Economics and Mathematical Systems 657 (pp. 219-271). Berlin Hidenberg: Springer-Verlag. O’Rourke, K. H., & Williamson, J. G. (2017). The spread of modern industry to the periphery since 1871 (K. H. O’Rourke & J. G. Williamson Eds.): Oxford University Press. Padilla-Perez, R., & Villarreal, F. G. (2017). Title: Structural change and productivity growth in Mexico, 1990-2014. Structural Change and Economic Dynamics, 41(17), 53-63. doi:10.1016/j.strueco.2017.02.002 Pesaran, M. H. (2007). A Simple Panel Unit Root Test in the Presence of Cross-sectional Dependence. Journal of Applied Econometrics, 22, 265-312. Rodrik, D. (2013). Unconditional Convergence in Manufacturing. The Quarterly Journal of Economics, 128(1), 165-204. doi:10.1093/qje/qjs047 Roodman, D. (2009). Practitioner's Corner: A Note on the Theme of Too Many Instruments. Oxford Bulletin of Economics and Statistics, 71(1), 135-158. Szirmai, A. (2012). Industrialisation as an engine of growth in developing countries, 1950–2005. Structural Change and Economic Dynamics, 23(4), 406-420. doi:10.1016/j.strueco.2011.01.005 Thind, M., & Singh, L. (2018). Structural Change and Economic Growth across Major States of India. Millennial Asia, 9(2), 162-182. doi:10.1177/0976399618786329 Timmer, M., de Vries, G., & de Vries, K. (2015). Patterns of structural Change in Developing Countries. In J. W. M. Tribe (Ed.), Routledge Handbook of Industry and Development (pp. 65-83 ): Routledge. Vidyarthi, H. (2017). Banking outreach, infrastructure development and regional growth. World Journal of Entrepreneurship, Management and Sustainable Development, 13(2), 114-127. doi:10.1108/wjemsd-11-2016-0054 Vidyattama, Y. (2010). A Search for Indonesia’s Regional Growth Determinants. ASEAN Economic Bulletin, 27(3), 281. doi:10.1355/ae27-3c Vries, G. J. d., Timmer, M. P., & Vries, K. d. (2013). Structural transformation in Africa: Static gains, dynamic losses. GGDC Working Papers. GGDC. Groningen. Vu, K. M. (2017). Structural change and economic growth: Empirical evidence and policy insights from Asian economies. Structural Change and Economic Dynamics, 41, 64-77. doi:10.1016/j.strueco.2017.04.002 Westerlund, J. (2007). Testing for Error Correction in Panel Data*. Oxford Bulletin of Economics and Statistics, 69(6), 709-748. doi:10.1111/j.1468-0084.2007.00477.x

60%

50%

40%

30%

20%

10%

0%

1962 1964 1960 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 Agriculture Manufacture Services

Figure 1. Economic Transformation, in terms of output share, over the Period 1961-2018 Source: , GGDC (own calculation)

NAD 0.1396 MALUT 0.1686 SUMUT SULUT 0.1367 0.2037

RIAU KALTIM KALBAR 0.1245 0.1333 GORO 0.0831 SULTENG SUMBAR BABEL KALTENG 0.1502 JAMBI 0.1850 0.1276 0.1302 0.1876 0.1200 SULTRA PAPUA SUMSEL KALSEL 0.2268 0.1315 0.1368 DKI MALUKU 0.1698 0.1363 0.0707 JATENG 0.1285 Legend: JATIM LAMPUNG 0.1614 SULSEL JABAR 0.1462 >0.218 0.1639 0.1581 0.1531 0.168 to 0.218 0.148 to 0.168 0.1129 DIY

0.128 to 0.148 0.1100 NTB NTT BALI 0.1535 0.1241 0.098 to 0.128 0.1577 National ESC 2005-2018: 0.1484

Panel A: Effective Structural Change Index 2005-2018 The index based on employment share change for the sectors that recorded positive productivity growth in 2005-2018

NAD -0.4217 MALUT 0.5519 SUMUT SULUT 0.5075 0.7818

RIAU KALTIM KALBAR -0.0102 0.0792 GORO 0.5602 SULTENG SUMBAR BABEL KALTENG 0.4620 JAMBI 1.1472 0.4841 0.1405 0.5819 0.4067 SULTRA PAPUA SUMSEL BENGKULU KALSEL 0.8652 -0.1691 0.4494 DKI MALUKU 0.5800 0.4378 0.5979 JATENG 0.2270 Legend: JATIM LAMPUNG 0.7891 SULSEL Growth >1.03 0.8162 0.7959 0.4951 JABAR Growth 0.78 to 1.03 BANTEN 0.4661 Growth 0.53 to 0.78 0.5051 DIY

Growth 0.28 to 0.53 0.5291 NTB NTT BALI 0.4096 0.5604 Growth 0.03 to 0.28 0.7152 Growth <0.03 National LP Growth 2005-2018: 0.5312

Panel B: Shift-Share Method (Real Labor Productivity 2005-2018) The index basically similar with the overall labor productivity growth during the period of 2005-2018

Figure 2. Map of Effective Structural Change and Shift Share Method, 2005-2018

Panel A: GDP Per Capita Growth and NAV Index

Panel B: GDP Per Capita Growth and ESC Index Figure 3. Scatter Plot Structural Change and Economic Development

Table 1 List of variables and their definitions Variable Definition Unit Source A. Structural Change Variables GDRP Regional GDP per capita (at constant price) IDR million BPS - National labour force S Employment share by sector in the economy survey (SAKERNAS) Labour productivity (regional GDP by sector per IDR million P Own Calculation employment) NAV See Equation (1) - Own Calculation Within See Equation (2) - Own Calculation Static See Equation (2) - Own Calculation Dynamic See Equation (2) - Own Calculation ESC See Equation (3) - Own Calculation

B. Control Variables Schooling The number of average years spent by the Year BPS residents (aged 25 years and over) in formal education. BPS used a new methodology in calculating the variable after 2010. Hence, we do backcast for the data before 2010 using splicing method by applying the growth rate of data before 2010 (the previous method) to the 2010 data. Roads Length of road by province and level of Km BPS (collected from government responsibility CEIC) Mining Share of mining sector relative to total GDRP (in Percent BPS (collected from nominal price) CEIC) Government Realization of local government budget IDR billion BPS (collected from expenditure for capital expenses CEIC) Credit Outstanding of loans in rupiah and foreign IDR billion BI (collected from BPS) currency of commercial and rural banks by project location of provinces FDI Investment activity to conduct business in the USD Indonesia Investment territory of Indonesia by foreign investor, Million Coordinating Board whether fully funded by foreign capital or in (collected from CEIC) partnership with domestic investor by provinces

Table 2 Structural Change Measurements, 2005-2018 SC NAV ESC Provinces 2005- 2005- 2010- 2015- 2005- 2005- 2010- 2015- 2005- 2005- 2010- 2015- 2018 2009 2014 2018 2018 2009 2014 2018 2018 2009 2014 2018 Aceh 0.310 0.188 0.062 0.034 0.174 0.079 0.023 0.067 0.140 0.042 0.018 0.033 North 0.131 0.034 0.039 0.014 0.211 0.101 0.046 0.067 0.137 0.052 0.040 0.034 0.097 0.026 0.028 0.033 0.140 0.048 0.071 0.066 0.128 0.026 0.062 0.033 Riau 0.178 0.054 0.058 0.042 0.185 0.105 0.052 0.044 0.124 0.077 0.026 0.022 Jambi 0.065 0.025 0.031 0.022 0.161 0.079 0.062 0.061 0.120 0.045 0.038 0.030 0.142 0.078 0.039 0.052 0.192 0.071 0.051 0.084 0.137 0.049 0.046 0.059 Bengkulu 0.148 0.027 0.023 0.031 0.218 0.096 0.074 0.080 0.170 0.056 0.052 0.044 Lampung 0.141 0.062 0.029 0.024 0.266 0.154 0.082 0.064 0.164 0.078 0.061 0.032 Bangka Belitung 0.150 0.062 0.065 0.044 0.183 0.109 0.048 0.064 0.130 0.054 0.035 0.032 DKI Jakarta 0.099 0.030 0.019 0.019 0.095 0.043 0.043 0.045 0.071 0.025 0.026 0.023 West Java 0.109 0.052 0.030 0.015 0.171 0.038 0.055 0.049 0.153 0.025 0.040 0.024 Central Java 0.095 0.014 0.025 0.021 0.172 0.050 0.051 0.056 0.161 0.038 0.042 0.043 East Java 0.114 0.031 0.013 0.020 0.149 0.041 0.053 0.061 0.146 0.039 0.040 0.047 DI 0.089 0.025 0.026 0.013 0.141 0.051 0.054 0.071 0.110 0.023 0.039 0.036 Banten 0.263 0.025 0.058 0.031 0.153 0.078 0.070 0.035 0.113 0.073 0.058 0.031 Bali 0.126 0.032 0.043 0.021 0.174 0.035 0.089 0.047 0.158 0.033 0.071 0.030 West Nusa Tenggara 0.224 0.030 0.179 0.086 0.158 0.051 0.071 0.077 0.153 0.039 0.062 0.056 0.175 0.026 0.024 0.020 0.232 0.108 0.051 0.073 0.124 0.054 0.047 0.041 0.165 0.023 0.045 0.018 0.166 0.059 0.042 0.070 0.083 0.029 0.032 0.042 Central Kalimantan 0.205 0.075 0.028 0.036 0.283 0.092 0.051 0.080 0.188 0.046 0.048 0.046 0.117 0.036 0.030 0.028 0.147 0.082 0.038 0.051 0.136 0.042 0.029 0.032 East Kalimantan 0.142 0.059 0.050 0.035 0.180 0.055 0.036 0.045 0.133 0.031 0.021 0.020 North Sulawesi 0.055 0.040 0.037 0.014 0.249 0.113 0.069 0.073 0.204 0.075 0.043 0.042 Central Sulawesi 0.235 0.051 0.056 0.061 0.211 0.059 0.106 0.062 0.185 0.032 0.071 0.040 South Sulawesi 0.103 0.050 0.028 0.025 0.168 0.051 0.071 0.066 0.158 0.030 0.045 0.053 South East Sulawesi 0.233 0.077 0.043 0.010 0.270 0.100 0.075 0.107 0.227 0.074 0.060 0.054 Gorontalo 0.184 0.044 0.016 0.036 0.229 0.108 0.043 0.052 0.150 0.054 0.034 0.029 Maluku 0.240 0.035 0.036 0.011 0.242 0.059 0.062 0.100 0.129 0.005 0.053 0.063 North Maluku 0.270 0.025 0.052 0.044 0.262 0.132 0.058 0.047 0.169 0.074 0.046 0.044 Papua 0.353 0.160 0.113 0.017 0.160 0.074 0.079 0.069 0.132 0.048 0.051 0.040 Indonesia 0.095 0.035 0.028 0.017 0.160 0.049 0.051 0.052 0.148 0.036 0.045 0.030

Table 2 Structural Change Measurements, 2005-2018 (Continued) SS Within Static Dynamic Provinces 2005- 2005- 2010- 2015- 2005- 2005- 2010- 2015- 2005- 2005- 2010- 2015- 2018 2009 2014 2018 2018 2009 2014 2018 2018 2009 2014 2018 Aceh -0.480 -0.533 -0.013 -0.024 1.798 1.098 0.078 0.064 -1.740 -0.987 -0.038 -0.036 0.382 -0.017 0.236 0.014 0.629 0.338 0.113 0.054 -0.503 -0.254 -0.029 -0.038 West Sumatra 0.469 0.058 0.169 0.044 0.156 0.058 0.062 0.032 -0.141 -0.036 -0.042 -0.020 Riau 1.047 1.002 0.052 0.000 -0.309 -0.302 0.037 -0.028 -0.748 -0.684 -0.042 -0.008 Jambi 0.321 0.211 0.191 -0.080 0.186 -0.028 0.137 0.171 -0.100 -0.059 -0.029 -0.061 South Sumatra 0.019 -0.042 0.055 0.022 0.957 0.290 0.128 0.125 -0.527 -0.144 -0.021 -0.051 Bengkulu 0.370 0.029 0.109 0.074 0.558 0.217 0.116 0.053 -0.349 -0.081 -0.026 -0.037 Lampung 0.302 -0.011 0.254 0.039 2.450 2.355 0.095 0.019 -2.257 -2.246 -0.067 -0.016 Bangka Belitung 0.141 0.041 0.115 -0.027 0.148 0.017 0.130 0.063 -0.148 -0.041 -0.042 -0.025 DKI Jakarta 0.493 0.040 0.199 0.235 0.133 0.043 0.115 -0.006 -0.028 -0.016 -0.022 -0.036 West Java 0.400 0.073 0.106 0.063 0.175 0.015 0.031 0.014 -0.109 -0.006 -0.020 -0.013 Central Java 0.629 0.202 0.135 0.082 0.219 0.026 0.056 0.039 -0.059 -0.008 -0.019 -0.009 East Java 0.635 0.149 0.165 0.042 0.195 0.000 0.084 0.086 -0.014 -0.006 -0.015 -0.017 DI Yogyakarta 0.322 -0.010 0.052 0.005 0.245 0.111 0.095 0.085 -0.038 -0.028 -0.031 -0.042 Banten 0.337 0.219 0.155 0.051 0.281 0.060 0.092 0.033 -0.113 -0.002 -0.032 -0.018 Bali 0.661 0.158 0.245 0.131 0.224 0.047 0.046 0.007 -0.170 -0.014 -0.046 -0.024 West Nusa Tenggara 0.671 0.538 0.120 -0.004 -0.051 -0.127 -0.038 0.028 -0.210 -0.286 -0.016 -0.026 East Nusa Tenggara 0.057 -0.177 0.094 0.039 1.240 0.576 0.124 0.050 -0.737 -0.298 -0.048 -0.020 West Kalimantan 0.361 0.104 0.173 -0.011 0.820 0.156 0.047 0.180 -0.621 -0.158 -0.048 -0.062 Central Kalimantan 0.277 0.084 0.210 0.037 0.680 0.275 0.000 0.117 -0.375 -0.199 -0.055 -0.035 South Kalimantan 0.263 0.028 0.174 0.044 0.267 0.080 0.015 0.058 -0.092 -0.036 -0.021 -0.022 East Kalimantan -0.071 -0.067 -0.094 -0.047 0.463 0.074 0.175 0.006 -0.312 -0.074 -0.038 -0.020 North Sulawesi 0.680 0.091 0.153 0.067 0.369 0.167 0.108 0.053 -0.267 -0.073 -0.036 -0.029 Central Sulawesi 0.820 0.189 0.277 0.111 0.321 0.092 0.072 0.072 0.006 -0.035 -0.102 -0.039 South Sulawesi 0.612 0.038 0.208 0.118 0.331 0.122 0.087 0.043 -0.147 -0.066 -0.040 -0.020 South East Sulawesi 0.395 -0.059 0.249 0.050 2.427 2.359 0.136 0.080 -1.956 -2.117 -0.028 -0.052 Gorontalo 0.566 0.008 0.199 0.107 0.189 0.116 0.057 -0.009 -0.293 -0.080 -0.042 -0.021 Maluku 0.045 -0.118 0.186 0.070 0.651 0.178 0.111 0.131 -0.470 -0.125 -0.051 -0.092 North Maluku 0.114 -0.121 0.105 0.102 2.254 1.294 0.060 0.051 -1.816 -1.048 -0.009 -0.002 Papua -0.483 -0.497 -0.124 0.035 0.705 1.319 0.175 0.162 -0.391 -0.928 -0.049 -0.069 Indonesia 0.367 0.081 0.121 0.042 0.225 0.027 0.081 0.046 -0.064 -0.004 -0.019 -0.008

Table 3 Change in Employment Share by Sector (in % points), 2005-2018 Province Total AGRI MINE MANU UTIL CONS TRAD TRAN FINA GOVE SERV

1 Aceh 17.4 -15.8 0.6 4.7 0.3 2.0 3.0 -1.6 1.3 5.6 8.2 2 North Sumatra 21.1 -20.2 0.6 3.3 0.3 0.3 9.3 -0.9 1.4 5.9 15.6 3 West Sumatra 14.0 -12.7 1.0 0.5 0.4 2.3 8.7 -1.2 1.1 -0.1 8.5 4 Riau 18.5 -9.9 -6.8 -1.6 0.5 -1.5 10.1 -1.5 1.4 3.6 13.6 5 Jambi 16.1 -14.3 0.3 1.2 0.2 2.2 5.5 -1.8 1.2 5.6 10.5 6 South Sumatra 19.2 -19.0 1.0 3.0 -0.2 0.6 6.5 1.0 1.2 5.9 14.7 7 Bengkulu 21.8 -20.9 1.0 4.1 0.8 2.4 7.4 -0.9 1.3 4.7 12.7 8 Lampung 26.6 -26.6 0.6 2.4 0.2 1.8 13.2 0.9 0.9 6.6 21.6 9 Bangka Belitung 18.3 2.8 -15.8 2.1 0.1 -1.1 7.3 -1.4 1.2 4.9 11.9 10 DKI Jakarta 9.5 0.1 0.4 -5.6 0.6 0.0 -3.9 4.0 3.8 0.6 4.5 11 West Java 17.1 -13.4 -0.1 3.1 0.7 1.8 5.4 -3.6 2.8 3.4 8.0 12 Central Java 17.2 -16.5 0.0 3.9 0.4 3.2 5.2 -0.7 1.7 2.8 9.1 13 East Java 14.9 -13.5 -0.1 2.7 0.5 2.4 5.3 -1.3 1.2 2.7 7.9 14 DI Yogyakarta 14.1 -14.0 0.4 3.3 0.3 -0.1 4.2 1.0 2.3 2.7 10.2 15 Banten 15.3 -13.4 0.0 2.7 0.7 3.0 2.5 -1.9 4.3 2.1 6.9 16 Bali 17.4 -13.4 -0.5 -1.9 0.4 -1.1 11.0 -0.5 3.0 3.1 16.6 West Nusa 17 Tenggara 15.8 -10.3 -2.4 -1.0 1.0 2.3 7.5 -2.1 0.8 4.3 10.5 East Nusa 18 Tenggara 23.2 -23.1 -0.1 1.6 0.4 3.1 6.6 2.1 1.2 8.2 18.1 19 West Kalimantan 16.6 -14.6 -0.4 1.3 0.3 3.7 4.6 -1.7 1.8 4.9 9.6 Central 20 Kalimantan 28.3 -27.6 4.9 1.1 0.4 3.2 8.1 -0.8 1.2 9.4 18.0 21 South Kalimantan 14.7 -14.6 1.0 0.0 0.5 -0.1 5.8 0.1 1.7 5.7 13.2 22 East Kalimantan 18.0 -12.8 4.6 -1.5 0.2 -1.8 5.9 -1.9 3.2 4.0 11.2 23 North Sulawesi 24.9 -23.2 1.8 5.0 0.4 2.1 7.9 -1.7 1.8 5.9 13.9 24 Central Sulawesi 21.1 -20.2 0.8 3.6 0.6 3.6 3.1 -0.9 1.0 8.4 11.7 25 South Sulawesi 16.8 -14.9 0.4 2.4 0.3 2.2 6.8 -2.0 1.3 3.5 9.6 South East 26 Sulawesi 27.0 -26.7 2.4 2.9 0.5 4.5 8.9 -0.3 1.2 6.6 16.4 27 Gorontalo 22.9 -21.0 1.3 3.6 0.2 2.8 7.1 -1.9 1.2 6.8 13.2 28 Maluku 24.2 -24.2 1.3 5.3 0.5 3.1 4.7 1.5 1.1 6.8 14.1 29 North Maluku 26.2 -26.2 2.4 3.4 0.4 1.5 4.3 1.8 1.4 11.1 18.6 30 Papua 16.0 -16.0 0.4 1.1 0.2 1.7 5.1 0.4 0.3 6.8 12.6 Indonesia 16.0 -15.2 0.1 2.0 0.4 1.8 5.7 -0.9 1.9 4.0 10.7 Note: Total Change in employment refer to total magnitude change in employment share by sector correcting for double counting (known as Norm of Absolute Values). AGRI = Agriculture, MINE = Mining and & Quarrying, MANU = Manufacturing, UTIL = Utilities which includes Electricty, Gas and Water, CONS = Construction, TRAD = Trade, Hotel and Restaurant, TRAN = Transportation and Communication, FINA = Finance and Business Services, GOVE = Government, Social and Other Services, SERV = Combination of TRAD, TRAN, FINA, and GOVE (detail of sector definition in Appendix II).

Table 4 Selected Statistics of 30 Regional and Indonesia Average GDRP Growth Average GDRP/Capita Growth Level in 2018 (in Million) GDP Per Labor Province 2005 2005 2010 2015 2005 2005 2010 2015 Popu- Capita Prod -18 -09 -14 -18 -18 -09 -14 -18 lation (IDR) (IDR) 0.3 -4.3 2.8 2.8 -1.4 -5.0 0.4 1.0 24.01 57.55 5,281.3 1 Aceh 5.8 6.0 6.2 5.1 4.8 4.8 5.4 3.9 35.57 76.21 14,415.4 2 North Sumatra 5.8 5.9 6.1 5.3 4.4 4.3 4.8 4.1 30.47 68.04 5,382.1 3 West Sumatra 4.1 5.0 4.6 2.5 0.3 0.0 1.0 0.0 73.26 171.82 8,951.4 4 Riau 6.2 6.4 7.3 4.5 3.7 4.3 3.8 2.8 40.05 83.07 3,570.3 5 Jambi 5.4 5.0 5.8 5.3 4.4 5.5 3.8 3.9 35.67 75.32 8,370.3 6 South Sumatra 5.8 5.9 6.3 5.1 4.6 6.0 4.1 3.5 22.50 45.85 1,963.3 7 Bengkulu 5.4 5.1 5.9 5.2 4.2 4.1 4.5 4.1 27.74 57.19 8,370.5 8 Lampung 4.7 4.1 5.6 4.3 1.6 0.4 2.2 2.1 35.76 74.44 1,459.9 9 Bangka Belitung 6.1 5.9 6.3 6.0 4.8 4.9 4.5 5.0 165.86 367.31 10,467.6 10 DKI Jakarta 5.8 5.7 6.1 5.4 3.8 3.6 4.0 4.0 29.16 68.32 48,683.7 11 West Java 5.4 5.4 5.4 5.3 5.0 5.3 5.0 4.6 27.29 54.58 34,490.8 12 Central Java 5.9 5.8 6.3 5.5 5.1 4.8 5.6 4.9 39.59 76.47 39,500.9 13 East Java 5.0 4.4 5.2 5.4 3.6 2.4 4.4 4.2 25.78 46.27 3,802.9 14 DI Yogyakarta 7.1 9.0 6.4 5.6 4.2 6.2 2.7 3.5 34.19 81.37 12,689.7 15 Banten 6.4 6.5 6.6 6.1 4.4 4.8 3.6 4.9 35.91 61.89 4,292.2 16 Bali 4.2 4.9 2.2 5.8 3.0 4.0 0.8 4.5 18.02 41.93 5,013.7 17 West NusaTenggara 5.0 4.6 5.4 5.1 2.9 1.9 3.6 3.4 12.28 27.34 5,371.5 18 East Nusa Tenggara 5.3 5.0 5.6 5.1 4.1 4.9 3.8 3.5 26.11 55.64 5,001.7 19 West Kalimantan 6.4 5.9 6.8 6.4 4.2 5.0 3.5 4.2 35.56 72.71 2,660.2 20 Central Kalimantan 5.4 5.6 5.7 4.7 3.2 3.3 3.3 3.0 30.63 63.37 4,182.7 21 South Kalimantan 3.1 3.0 4.4 1.5 0.0 -0.8 1.5 -0.9 119.73 161.48 4,365.2 22 East Kalimantan 6.7 7.2 6.6 6.2 5.4 5.8 5.1 5.1 33.92 76.94 2,484.4 23 North Sulawesi 8.8 8.2 8.5 9.7 6.7 6.6 5.7 8.0 34.42 71.39 3,010.4 24 Central Sulawesi 7.4 6.7 8.2 7.2 6.1 5.8 6.5 6.0 33.61 77.47 10,127.6 25 South Sulawesi 7.8 7.6 8.9 6.6 5.5 5.9 5.8 4.5 33.29 73.15 2,653.7 26 South East Sulawesi 7.2 7.5 7.6 6.5 5.0 5.1 5.0 4.9 22.54 48.10 1,185.5 27 Gorontalo 5.8 5.2 6.4 5.7 3.1 3.6 2.0 4.0 16.61 42.09 1,773.8 28 Maluku 6.8 6.8 6.7 6.9 3.5 2.4 3.5 4.8 20.32 48.58 1,232.6 29 North Maluku 6.1 8.5 3.4 6.4 1.7 4.2 -3.0 4.4 51.69 100.32 4,260.0 30 Papua Indonesia 5.5 5.6 5.8 5.0 4.0 4.2 4.0 3.8 39.47 84.89 264,161.6 Min 0.3 -4.3 2.2 1.5 -1.4 -5.0 -3.0 -0.9 12.3 27.3 1,185.5 Max 8.8 9.0 8.9 9.7 6.7 6.6 6.5 8.0 165.9 367.3 48,683.7 Mean 5.7 5.6 6.0 5.4 3.7 3.8 3.6 3.9 39.1 80.9 8,833.8 STDEV 1.5 2.3 1.5 1.5 1.8 2.5 2.0 1.7 30.9 62.0 11,533.7

Table 5 Descriptive statistics Statistic PRGDP SC NAV ESC Within Static Dynamic Correlation (No. observations = 420) PRGDP 1.000 SC -0.080 1.000 NAV -0.031 -0.019 1.000 ESC -0.040 -0.025 0.929 1.000 Within -0.036 -0.010 0.082 0.199 1.000 Static 0.009 0.045 0.239 0.185 -0.444 1.000 Dynamic -0.021 -0.073 -0.338 0.296 -0.056 -0.842 1.000

Pesaran CD Cross-sectional Independence test Average 0.780 0.692 0.343 0.282 0.184 0.122 0.498 coefficient CD-statistic 60.84 54.01 26.74 21.99 14.32 9.52 38.87 Note: The null hypothesis is there is cross-section independence. All CD-statistic is significant at 1 per cent level

Table 6 Two-step system GMM estimates regressions of the relationship between structural change and regional growth, 5-year average data Variables Model 1 Model 2 Model 3 Model 4

PRGDPi,0 -0.168*** -0.156** -0.163*** -0.065** (0.043) (0.064) (0.059) (0.030) SCi,t 0.504 (0.858)

NAVi,t -2.428* (1.268) ESCi,t -4.835* (2.402) Withini,t 3.442*** (0.532) Statici,t 3.400** (1.400) Dynamici,t 3.151*** (0.974)

Schoolingi,o 0.332*** 0.364*** 0.379*** 0.107* (0.074) (0.100) (0.100) (0.054)

Observations 90 90 90 90 Number of instruments 13 13 13 17 AR(1) 0.063 0.051 0.046 0.208 Hansen test 0.113 0.102 0.127 0.234 Notes: ***, **, and * indicate significance at 1, 5 and 10 per cent levels, respectively. Standard errors are in parentheses. The standard errors are robust to heteroscedasticity and autocorrelation with Windmeijer correction. Covariance matrix estimate is based on small sample correction. The number of instruments used is reduced both by using only one lag (i.e. lag 1) and collapsing the instrument matrix. The instrument variable set is one-period lag exogenous variables.

Table 7 Panel unit root tests Variable IPS Test [W-t-bar] CADF Test [Z-t-bar] Lag(1) Lag(2) Lag(1) Lag(2) In Level PRGDPi,t 0.331 -5.751*** -5.911*** 1.313 SCi,t -3.930*** -2.034** -3.068*** 0.647 NAVi,t -5.353*** -3.190*** -1.306* 0.732 ESCi,t -4.978*** -1.969** -2.557*** 1.541 Withini,t -9.965*** -5.869*** -5.615*** -0.651 Statici,t -15.617*** -6.947*** -6.053*** 0.494 Dynamici,t -39.482*** -18.597*** -5.783*** -1.716** Schooling 0.3162 -0.1256 0.998 4.035 Road -0.551 -1.627* 1.440 5.788 Mining 1.135 0.094 0.362 1.231 Government -2.503*** -1.507* -1.990** 2.859 Credit -0.560 0.154 3.944 1.000 FDI -2.255** -0.413 -3.782*** 5.551

In First Difference ΔPRGDPi,t -3.939*** -7.507*** -6.388*** -0.604 ΔSCi,t -7.088*** -3.072*** -3.146*** -1.302* ΔNAVi,t 8.496*** -5.142*** -3.333*** -0.261 ΔESCi,t -8.463*** -2.686*** -5.773*** 2.554 ΔWithini,t -12.015*** -5.208*** -5.702*** 2.353*** ΔStatici,t -18.565*** -6.382*** -9.765*** 1.160 ΔDynamici,t -20.757*** -10.489*** -6.055*** -2.591*** ΔSchoolingi,t -2.423*** -1.0176 0.511 3.142 ΔRoadi,t -11.171*** -3.941*** -3.938*** 19.366 ΔMiningi,t -1.515* -1.545* 1.689 0.510 ΔGovernmenti,t -5.514*** -2.444*** -4.333*** 6.132 ΔCrediti,t -3.5748*** 1.121 3.479 5.543 ΔFDIi,t -7.536*** -2.533*** 3.456 Notes: ***, ** and * indicate significance at 1%, 5% and 10% level, respectively. The unit root tests remove cross-sectional effects and include a time trend. The null hypothesis is panels contain unit roots. We set the number of lag for ADF regression in the tests up to two due to our limited sample size.

Table 8 Westerlund ECM panel cointegration tests Statistics Variable SC NAV ESC Within Static Dynamic Gt -1.961 -1.281 -1.122 -2.020 -2.047 -1.621 Ga -3.681 -2.023 -2.288 -2.670 -2.744 -1.597 Pt -4.033 -6.688 6.442 -7.010 -5.470 -7.831 Pa -1.590 -2.139 -2.170 -2.585 -1.279 -1.698 Notes: No statistic is significant. The null hypothesis is panels contain co integration. The tests use a constant, a deterministic trend, one lag and lead length.

Table 9 One-step level GMM estimates regressions of the inter-relationship between the structural change and PRGDP growth, selective sample, annual data Variables Model Model 1 Model 2 Model 3 Model 4

ΔPRGDPi,t-1 0.828*** 0.602*** 0.587*** 0.785*** (0.118) (0.141) (0.170) (0.247) ΔSCi,t-1 -0.242** (0.100)

ΔNAVi,t-1 -0.311*** (0.099) ΔESCi,t-1 -0.380 (0.300) ΔWithini,t-1 0.209** (0.087)

ΔStatic i,t-1 0.326*** (0.115)

ΔDynamic i,t-1 0.331*** (0.118)

ΔSchooli,t-1 -0.166 -0.476 -0.449 -0.986 (0.416) (0.652) (0.747) (0.865)

ΔPRGDPi,t -0.043 (0.120) ΔSCi,t -0.392** (0.165)

ΔNAVi,t -0.965*** (0.336) ΔESCi,t -0.052 (0.112) ΔWithini, -0.114 (0.100)

ΔStatici,t -0.022 (0.123)

ΔDynamici,t 0.584 1.600** 1.612*** 1.507 (0.513) (0.596) (0.560) (0.906)

ΔSchooli,t -0.043 (0.120)

Observations 309 309 309 309 Number of instruments 19 19 19 21 AR(2) 0.0941 0.327 0.491 0.464 Hansen test 0.0373 0.227 0.189 0.205 Notes: ***, **, and * indicate p<0.01, p<0.05, and p<0.1 respectively. Robust standard errors with Windmeijer correction are in parentheses. Covariance estimates are also based on a small sample correction. The number of instruments used is reduced by using only one lag (i.e. lag 1) and collapsing the instrument matrix to one-period lag of each exogenous variable.

Appendix I Conversion 17 Sectors into 9 Sectors, Based on System of National Account 2008

17 SECTORS 9 SECTORS Code

Agriculture, Forestry and Fisheries Agriculture AGRI Mining and Quarrying Mining and quarrying MINE Manufacturing Industry Manufacturing MANU Electricity and Gas Supply Electricty, gas and water UTIL Water Supply, Sewerage, Waste and Recycling Mgt Electricty, gas and water UTIL Construction Construction CONS Wholesale and Retail Trade Trade, hotel and restaurant TRAD Transportation and Storage Transportation and communication TRAN Accommodation and Food Beverages Activity Trade, hotel and restaurant TRAD Information and Communication Transportation and communication TRAN Financial and Insurance Activity Financial services FINA Real Estate Financial services FINA Business Services Financial services FINA Public Admin, Defense and Social Security Services GOVE Education Services Services GOVE Other Services Services GOVE Human Health and Social Work Activity Services GOVE

Appendix II Conversion 34 Provinces to 30 Provinces for Data Analysis List of Provinces No Notes Combined Provinces Current for Data Analysis 1 Aceh Aceh 2 North Sumatra North Sumatra 3 West Sumatra West Sumatra Riau Riau Island established on 25 Oktober 2002, but 4 Riau Riau Island initial economic data start available in 2006. 5 Jambi Jambi 6 South Sumatra South Sumatra 7 Bengkulu Bengkulu 8 Lampung Lampung 9 Bangka Belitung Bangka Belitung 10 DKI Jakarta DKI Jakarta 11 West Java West Java 12 Central Java Central Java 13 East Java East Java 14 DI Yogyakarta DI Yogyakarta 15 Banten Banten 16 Bali Bali 17 West NusaTenggara West NusaTenggara 18 East Nusa Tenggara East Nusa Tenggara 19 West Kalimantan West Kalimantan 20 Central Kalimantan Central Kalimantan 21 South Kalimantan South Kalimantan East Kalimantan North Kalimantan established in 25 October 22 East Kalimantan North Kalimantan 2012. 23 North Sulawesi North Sulawesi 24 Central Sulawesi Central Sulawesi South Sulawesi West Sulawesi established on 25 October 2004, 25 South Sulawesi West Sulawesi but initial economic data start available in 2006 26 South East Sulawesi South East Sulawesi 27 Gorontalo Gorontalo 28 Maluku Maluku 29 North Maluku North Maluku Papua West Papua established on 21 November 2001, 30 Papua West Papua but initial economic data start available in 2006

Appendix III Change of Labor Productivity and Change of Employment Share Matrix (2005-2018) ∆푺 Positive Negative ∆푷

Manufacture, Construction, Positive Agriculture, Transport Trade, Government

Negative Mining, Utilities, Finance -