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Journal Article•DOI•

Structural Change and Regional Economic Growth in Indonesia

Andriansyah Andriansyah, Asep Nurwanda, Bakhtiar Rifai
08 Apr 2021-Bulletin of Indonesian Economic Studies (Informa UK Limited)-pp 1-34
TL;DR: In this paper, the authors investigated the relationship between structural change and regional economic growth in Indonesia and showed that structural change is a significant determinant of economic growth, but only if there is an increase in productivity, not only a movement of labor across sectors.
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, this study shows that structural change is a significant determinant of growth. However, structural change matters for growth only if there is an increase in productivity, not only a movement of labor across sectors. An improvement in productivity within sectors and a movement of labors to other sectors with better productivity lead to a better economic development.

Summary (4 min read)

Jump to: [1. Introduction] – [2.1. Measurement of Structural Change] – [2.2. Regional Growth Model] – [2.3. Data] – [3.1. Patterns] – [National Patterns] – [Regional Patterns] – [INSERT FIGURE 1 ABOUT HERE] – [3.2. Descriptive Statistics] – [INSERT TABLE 5 ABOUT HERE] – [3.3. The Five-Year Average Data] – [3.4. The Annual Data] – [4. Discussions] and [5. Conclusions]

1. Introduction

  • Structural change is an important determinant of economic growth.
  • Empirically, the relationship between structural change and economic growth either in a regional or at a national level is rather inconclusive.
  • Studies at the subnational level have also been conducted although mainly on China and India.

2.1. Measurement of Structural Change

  • The measurements of structural change are usually calculated in terms of employment shares or value added shares.
  • B. Norm Absolute Value (NAV) index NAV index is similar to SC index, but uses employment share rather than value added share, and is calculated as follows: 𝑁𝐴𝑉 = 12 ∑ |𝑆𝑖𝑇 − 𝑆𝑖0|𝑛𝑖=1 (2) where 𝑆𝑖𝑇 and 𝑆𝑖0 denote the employment share of sector 𝑖 at time T and 0, respectively.
  • Again, this index only measures the shifting of employment, with no direct link between employment shifting and productivity, and does not make a distinction as to whether structural change experienced by a sector is productivity-enhancing or decreasing.
  • The method decomposes the sectoral contribution to overall labor productivity growth into three terms as follows: ∆𝑃𝑃0 = ∑ 𝑆𝑖0∆𝑃𝑖𝑃0𝑛𝑖=1 + ∑ 𝑃𝑖0∆𝑆𝑖𝑃0𝑛𝑖=1 + ∑ ∆𝑆𝑖∆𝑃𝑖𝑃0𝑛𝑖=1 (3) where 𝑛, 𝑆𝑖𝑇 and 𝑆𝑖0 are the same variables as in the NAV index.

2.2. Regional Growth Model

  • Silva and Teixeira (2008) classify the studies on structural change into 11 main topics, where this study focuses on the topics of convergence and growth as well as regional and urban economics.
  • This study focuses on the direction from structural change to economic growth because it is interested in regional growth determinants.
  • The authors also use a set of other control variables for excluded instruments (Panel B Table 1), which are the length of roads , the importance of mining , government spending for capital expenses , commercial banking loans and foreign direct investments (FDI).
  • Collapsing technique and only use one lag to reduce the number of instruments used.
  • GMM estimators are also used for annual data; however, the authors utilize the level GMM estimators instead of the two-steps system GMM system.

2.3. Data

  • In addition to the national level, this study calculated the four structural change measurements above for the 30 sub-nationals in Indonesia.
  • It is worth noting that some of the provinces have experienced a fragmentation, such as Riau (some areas became Riau Islands), East Kalimantan (North Kalimantan), South Sulawesi (West Sulawesi) and Papua (West Papua).
  • The data used for the calculation is 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.

3.1. Patterns

  • Indonesia’s gradual structural transformation from a traditional agricultural-driven economy into a manufacturing and services-driven economy helped to boost Indonesia’s income per capita and to raise the nation’s status from an underdeveloped to a developing country by the late 1980s.
  • The 1997–98 Asian financial crisis ended the episode of exponential growth abruptly, and Indonesia has not fully recovered from this crisis (Basri et al. 2016).
  • Judging from the pace of economic transformation and industrialization, there is a clear disproportion among regions due to a poor national logistics system (Axelsson & Palacio 2018).
  • In terms of the regional gross domestic product (RGDP)’s contribution to the total GDP.
  • Griffith (1983) reports that the decision to define the boundary affects spatial distribution identification and statistical parameter estimation.

National Patterns

  • Over the period 2005-2018, the progress of Indonesia’s structural transformation into a services-driven one has been slowing.
  • The fragmentation of administrative entities at the sub-national level has been mirrored by a boom in the number of public-service jobs, at around 17.5 public servants per 1000 population (Vujanovic 2017).
  • This indicates that workers tend to shift from productivityimproving sectors to productivity-declining sectors.
  • The sectors experiencing both an increase in labor share and an increase in productivity are Manufacture, Construction, Trade, and Government.
  • Agriculture, Manufacture, Utilities, Construction, Trade, and Transport, while in the period 2015–2018 the number of sectors has been reduced to five, i.e.

Regional Patterns

  • Figure 2 illustrates the distribution of regional structural change in Indonesia.
  • Panel A and Panel B map the effective structural change index and shift-share method (real labor productivity) 7 across provinces over the period 2005–5018, respectively.
  • Almost all major provinces in Java Islands only grew in the range of the national average.
  • While the provinces with the fastest growth and highest structural change index are mostly small provinces such as North Maluku, Maluku, Gorontalo, and Central Kalimantan.

INSERT FIGURE 1 ABOUT HERE

  • Regionally, it is clear that the provinces in Sulawesi Island relatively have higher structural change measurements than those in Java Island,8 in particular in terms of NAV and ESC.
  • This means that reallocation of employment to more productive sectors is happening more in Sulawesi Island than in Java Island.
  • The share of the manufacturing sector in Riau, DKI Jakarta, Bali, West Nusa Tenggara, and East Kalimantan has been decreased.
  • 8 Sulawesi Island, also known as Celebes, consists of five provinces: North Sulawesi, Central Sulawesi, South Sulawesi, South East Sulawesi, and Gorontalo.
  • In terms of the within effect, the largest improvement in productivity was experienced by Riau in the period 2005–2018, while Aceh and East Kalimantan experienced a consistent decline in the same sector productivity in the four different periods of study.

3.2. Descriptive Statistics

  • Table 3 and Table 4 present descriptive statistics for the variables of interest.
  • Table 3 summaries the main economic data of Indonesia divided into 30 provinces.
  • GDP from the poorest province is Rp 18 million per capita, lagging very far behind the richest province with Rp 248 million per capita.
  • Most provinces experienced an inline trend with the national average, which increased in the 2010– 2014 period but declined in the 2015–2018 period.
  • The commodity boom in the era of 2010–2014 plays an important factor here not only for natural resources-rich provinces but also by other regions that do not rely on natural resources.

INSERT TABLE 5 ABOUT HERE

  • Table 5 shows the strength of the relationship among variables.
  • Mostly there is a weak negative correlation between structural change measures and PRGDP, except for Static.
  • In terms of cross-section dependence in macro panel data, Pesaran CD cross-sectional independent tests show that there is an interlinkage among provinces that may arise from globally common shocks as the result of local spillover effects between provinces.
  • There may be a positive relationship between regional growth and the NAV index, which is similar to Hill et al. (2008) that also shows a weak relationship between structural change and regional growth.
  • The next sub-section explains the formal examination of the relationships.

3.3. The Five-Year Average Data

  • Table 5 presents the estimates of the traditional growth model using five-year average data.
  • Model 1 to Model 4 presents the estimates for SC, NAV, ESC, and SS, respectively.
  • The authors find that the coefficient estimate of SC is statistically insignificant, while other structural change measures are statistically significant.
  • This means that the movement of labor across sectors may hamper economic growth if the movement does lead to higher productivity.
  • The coefficient of Static is insignificant, even though positive.

3.4. The Annual Data

  • In estimating the dynamic model using annual data, the authors firstly employ two panel unit root tests: the Im-Pesaran-Shin (IPS W-t-bar) test (Im et al. 2003) and the Pesaran's simple panel unit root test in the presence of the cross-section dependence (CADF Z-t-bar) test (Pesaran 2007).
  • The test results generally show that (1) all variables contain a unit root except for structural change and government expenditure variables, and (2) there is no cointegration between regional economic growth and structural change measures.
  • To be consistent with the interpretation of change, the authors estimate GMM by using first differences of all examined variables.
  • For annual data, it seems that dynamic structural effects have more impact on growth than static structural effects and within-sector productivity improvement.
  • This may due because the movement of labor occurs gradually.

4. Discussions

  • Recent studies from Hill et al. (2008) and Axelsson and Palacio (2018) have examined the pattern of structural change from Indonesia’s subnational perspective.
  • The outperformer regions such as provinces in Sulawesi (except Gorontalo), Central Java, and East Java are characterized by their productivity that grows far above the national average, mainly driven by the growth of new industries.
  • Meanwhile, the large gaps of share employment and value added in agriculture lead to a surplus of labor that has been unable to be absorbed by other more productive sectors (Axelsson & Palacio 2018).
  • The speed of structural change in this region needs to be interpreted with great caution, given the low level of productivity and small economic share of the provinces to the national economy.
  • In terms of the relationship between structural change and economic growth, this study has confirmed that there is a positive relationship between structural change and regional economic growth, in particular the shift-share method indicators.

5. Conclusions

  • 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 provinces in Sulawesi outperformed other regions.
  • Even though the role of manufacturing needs to be improved as the engine of growth, industrialization policies must be based on the characteristics of each province.
  • Structural change matters for growth only if there is an increase in productivity, not just the movement of labor across sectors.

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Munich Personal RePEc Archive
Structural Change and Regional
Economic Growth in Indonesia
Andriansyah, Andriansyah and Nurwanda, Asep and Rifai,
Bakhtiar
aCentre for Macroeconomic Policy Fiscal Policy Agency, Ministry of
Finance of the Republic of Indonesia
7 August 2020
Online at https://mpra.ub.uni-muenchen.de/105177/
MPRA Paper No. 105177, posted 07 Jan 2021 10:45 UTC
1
Structural Change and Regional Economic Growth in Indonesia
#
Andriansyah Andriansyah
a,*
, Asep Nurwanda
a
, and Bakhtiar Rifai
a
a
Centre for Macroeconomic Policy
Fiscal Policy Agency, Ministry of Finance of the Republic of Indonesia
This Version: 7 August 2020
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, this study shows that structural change is a significant determinant of growth.
However, structural change matters for growth only if there is an increase in productivity, not
only a movement of labor across sectors. An improvement in productivity within sectors and a
movement of labors to other sectors with better productivity lead to a better economic development.
Subject Keywords: Structural Change, Regional Growth, Indonesia, Productivity
JEL Codes: L16, O40, R11
#
We would like to thank to the participants of the 15
th
Indonesian Regional Science Association
International Conference held in Banda Aceh, Indonesia, on 22-23 July 2019 for their valuable
comments.
*Corresponding author. Postal Address: Centre for Macroeconomic Policy, Fiscal Policy Agency,
Ministry of Finance of the Republic Indonesia. RM Notohamiprodjo Building 8
th
Floor. Jl. Dr.
Wahidin Raya No. 1 Jakarta 10710. E-mail:
andriansyah@kemenkeu.go.id
2
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 economy.
Theoretically, 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 industry
(Chenery et al. 1986). The increasing role of the manufacturing sector, well known as
industrialization, as argued by Rodrik (2013) and later supported by Felipe et al. (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 occur from agriculture to services, as took place in many other developing countries
(Timmer et al. 2015), particularly in Asia (Asian Development Bank 2013).
The definition for the term of structural change varies. Silva and Teixeira (2008) identify
that there are at least nine meanings of structural change. In their literature survey, the term
structure refers to the division of the economic system into a limited number of subsystems and
the term structural change then refers to a change from one classification scheme to another.
Krüger (2008), for instance, borrows the definition used by Erich Streissler, i.e. “long-term changes
in the composition of economic aggregates,” while GHK (2011) defines structural change as “a dynamic
and turbulent process associated with very substantial changes of growth and contraction at the sectoral
1
O’Rourke and Williamson (2017) provide a comprehensive review of industrialization; while Assunção
et al. (2015) show that the convergence depends on country-specific characteristics, such as political
institutions, trade openness, and education.
3
and business levels which yield small, but persistent, net economic benefits over the long-term.” Mostly,
the concepts of structural change are related to the results of the structural change process itself.
As an example, Hausmann and Klinger (2006) 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.
GHK (2011) documents that structural change is mainly caused by technological, societal,
political, financial, and ecological transformations. Theoretically, a structural change occurs due
to preference changes in demand and sector-specific productivity (Dietrich 2011). Moreover,
Herrendorf et al. (2014) develop a multi-sector extension of the one-sector growth model
accounting for many aspects of a structural change such as regional income, aggregate
productivity, working hours, wages, and business cycles. In terms of policy and institutional
settings, Dabla-Norris et al. (2013) show that structural reform policies such as the openness to
trade, and human and physical capital as well as finance, can be the factors that can improve the
growth-enhancing structural change.
The cost of transition, consisting of structural unemployment and social costs, on the other
hand, may hamper the benefit of structural change on economic growth (GHK 2011). This may
cause a country to experience a wrong-direction structural change from more productive to less
productive activities such as in Africa (McMillan et al. 2014). Some factors can contribute to this
negative flow such as the endowment of natural resources and globalization as well as policy and
institutional settings. The high endowment of natural resources, in general, has a growth-
reducing effect of structural change because even though the extractive sectors typically operate
at very high productivity, they do not generate much employment that absorbs the surplus labor
from agriculture (McMillan et al. 2014). The other important factors that may disrupt the
4
structural change contribution to economic growth are the non-economic factors such as social
conflict and natural disaster (Rao & Vidyattama 2017; Heger & Neumayer 2019).
Empirically, the relationship between structural change and economic growth either in a
regional or at a national level is rather inconclusive. Regionally, evidence of a positive
relationship has been supported, among others, for OECD countries (Dietrich 2011) and Asian
economies (Vu 2017). Szirmai (2012) argues that the manufacturing sector is an important growth
determinant for developing economies in Asia and Latin America, while the service sector is more
important for developed economies. On the other hand, negative evidence is found for Africa and
Latin America (McMillan et al. 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 et al. (2017) who also discuss cases in India, Viet Nam,
Botswana, Ghana, Nigeria, Zambia, and Brazil.
Studies at the subnational level have also been conducted although mainly on China and
India. In China, for example, Jiang (2011) argues that regional growth depends 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 change has positively contributed to
regional economic growth. The latter, however, argues that the contribution of productivity
growth within individual sectors is found to be more important than the structural productivity
effect of labor reallocations across different sectors.
Indonesia has similar characteristics to China and India, particularly in terms of high
population and regional diversity. However, there has been no study on the impact of structural
change on regional growth in Indonesia so far. The studies on general regional growth
Citations
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Kevin H. O'Rourke, Jeffrey G. Williamson
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TL;DR: The authors provide a systematic, comparative, historical account of the spread of modern manufacturing beyond its traditional heartland, to Southern and Eastern Europe, the Middle East, Asia, Africa, and Latin America, or what we call the poor periphery.
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01 May 1992-Quarterly Journal of Economics
TL;DR: The authors examined whether the Solow growth model is consistent with the international variation in the standard of living, and they showed that an augmented Solow model that includes accumulation of human as well as physical capital provides an excellent description of the cross-country data.
Abstract: This paper examines whether the Solow growth model is consistent with the international variation in the standard of living. It shows that an augmented Solow model that includes accumulation of human as well as physical capital provides an excellent description of the cross-country data. The paper also examines the implications of the Solow model for convergence in standards of living, that is, for whether poor countries tend to grow faster than rich countries. The evidence indicates that, holding population growth and capital accumulation constant, countries converge at about the rate the augmented Solow model predicts. This paper takes Robert Solow seriously. In his classic 1956 article Solow proposed that we begin the study of economic growth by assuming a standard neoclassical production function with decreasing returns to capital. Taking the rates of saving and population growth as exogenous, he showed that these two vari- ables determine the steady-state level of income per capita. Be- cause saving and population growth rates vary across countries, different countries reach different steady states. Solow's model gives simple testable predictions about how these variables influ- ence the steady-state level of income. The higher the rate of saving, the richer the country. The higher the rate of population growth, the poorer the country. This paper argues that the predictions of the Solow model are, to a first approximation, consistent with the evidence. Examining recently available data for a large set of countries, we find that saving and population growth affect income in the directions that Solow predicted. Moreover, more than half of the cross-country variation in income per capita can be explained by these two variables alone. Yet all is not right for the Solow model. Although the model correctly predicts the directions of the effects of saving and

14,402 citations

Journal Article•DOI•
Large sample properties of generalized method of moments estimators

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Lars Peter Hansen
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Frequently Asked Questions (15)
Q1. What have the authors contributed in "Structural change and regional economic growth in indonesia" ?

This paper investigates the relationship between structural change and regional economic growth in Indonesia. The authors 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, this study shows that structural change is a significant determinant of growth. 

Thedecentralization and proliferation of a district may be the main reason behind the failure ofreallocation of labor to more productive sectors due to diminishing marginal return in thegovernment sector. 

The other important factors that may disrupt thestructural change contribution to economic growth are the non-economic factors such as socialconflict and natural disaster (Rao & Vidyattama 2017; Heger & Neumayer 2019). 

Twospecification tests, i.e. the AR(2) test in first differences and the Hansen (1982) test concerning thejoint validity of the instruments, suggest that their models are acceptable. 

Mahi (2016) finds that over 2 million public servants or almost two-thirds of the centralgovernment workforce were transferred to the regions. 

Manufacture, Utilities, Construction, Trade, and Transport, while in the period 2015–2018 the number of sectors has been reduced to five, i.e. 

Growth can happen if there is an improvement in productivity within sectors as well as by shifting to other sectorswith better productivity. 

The cost of transition, consisting of structural unemployment and social costs, on the otherhand, may hamper the benefit of structural change on economic growth (GHK 2011). 

The only province that experienced the movementof labor to more productive sectors is Central Sulawesi, shown by a positive dynamic structural effect. 

The phenomena of agriculture-services transition are commonly happening in developing countries as discussed by Chenery et al. (1986). 

Structural changeoccurring in Maluku is more effective than other provinces shown by the higher ESC values andan increase in the number of sectors experiencing higher productivity. 

the large gaps of share employment and value added in agriculture lead to a surplusof labor that has been unable to be absorbed by other more productive sectors (Axelsson &Palacio 2018). 

Krüger (2008), for instance, borrows the definition used by Erich Streissler, i.e. “long-term changes in the composition of economic aggregates,” while GHK (2011) defines structural change as “a dynamic and turbulent process associated with very substantial changes of growth and contraction at the sectoral1 O’Rourke and Williamson (2017) provide a comprehensive review of industrialization; while Assunção et al. (2015) show that the convergence depends on country-specific characteristics, such as political institutions, trade openness, and education. 

In particular, the shift-share methodshows that for annual data dynamic structural effects have more impact on growth than staticstructural effects and within-sector productivity improvement. 

The sectors experiencing both an increase inlabor share and an increase in productivity are Manufacture, Construction, Trade, andGovernment.