labor movement from low to high productivity sector: i...

iLabor Movement from Low to High Productivity Sector: Evidence from Indonesia’s Provincial Data i

APINDO Policy Seriesii

APINDO-EU ACTIVE Project Team Members:

Maya Safira (Project Manager)Riandy Laksono (Lead Economist)

Muhammad Rizqy Anandhika (Economist)Sehat Dinati Simamora (Junior Economist)

Nuning Rahayu (Project Assistant)

The content of APINDO-EU ACTIVE working papers is the sole responsibility of the author(s) and can in no way be taken to reflect the views of Indonesia Employers Association (APINDO) or its partner instututions. APINDO-EU ACTIVE working papers are preliminary documents posted on the APUNDO website (www. apindo.or.id) and widely circulated to stimulate discussion and critical comment.

Disclaimer

APINDO–EU ACTIVE working papers are issued in joint cooperation between Indonesia Employer Association (APINDO) and Advancing Indonesia’s Civil Society in Trade and Investment (ACTIVE), a project co-funded by the European Union. ACTIVE project aims to strengthen APINDO’s policy making advocacy capabilities in preparing the business environment and to empower national competitiveness in facing global integration.

For more information, please contact ACTIVE Team at [email protected] or visit www.apindo.or.id

Dewan Redaksi

Pelindung : Sofjan Wanandi

Pembina : Chris Kanter Suryadi Sasmita

Shinta Widjaja Kamdani Anthony Hilman

Pemimpin Redaksi : P. Agung Pambudhi

Tim Penyusun : Diana M. Savitri Riandy Laksono M. Rizqy Anandhika Sehat Dinati Simamora I.B.P. Angga Antagia Jefri Butarbutar Adrinaldi

Wahyu Handoko

Penyunting : Septiyan Listiya Eka R.

Copyright©APINDO-EU ACTIVELabor Movement from Low To High Productivity Sectors: Evidence from Indonesian Provincial Data

Published in July 2014

Acknowledgement

iiiLabor Movement from Low to High Productivity Sector: Evidence from Indonesia’s Provincial Data iii

Productivity issues become crucial in every business and economic progress in developing countries. Indonesia, one of the next big-player in world economy, cannot ignore the importance to improve the productivities, including it labor productivities. In the condition of lagging productivities

among ASEAN Countries, addressing labor productivities issue is urgent in order evaluate and improve our industries’ capability to compete in ASEAN Economic Community starting in 2015 and benefit our decades of demographic dividend.

This second edition of APINDO Policy Series brings the productivity issue, particularly structural change as a channel to gain productivity growth. The research about the changes of productivity across Indonesian provinces becomes a significant input for industrial strategies, especially in this decentralized governance era. It maps which provinces gain and loss the productivity, as well as which sector allocates more or less labor, as a measure of productivity. It also tends to explain some determinant that related with structural change.

As the employer organization concerning the employer interests, this paper should offer a significant contributions of APINDO to its stakeholder, by showing its consistency to encourage research-based advocacy to tackle strategic issues, such as minimum wage determination. Supporting by APINDO-EU ACTIVE Project, APINDO Policy Series hopefully can bring more industry, trade, and investment issues into the research-based analysis to recommend suitable policies.

Finally, we appreciate APINDO-EU ACTIVE Team which deliver this policy paper and we would like to thank Muhammad Rizqy Anandhika and Riandy Laksono for studying this issue. We hope this policy paper could benefit Indonesian businesses in the future.

Sofjan Wanandi Chris KanterGeneral Chairman Vice ChairmanIndonesian Employers Association(APINDO) Indonesian Employers Association(APINDO)

Foreword

APINDO Policy Seriesiv

AEC ASEAN Economic CommunityASEAN Association of Southeast Asian NationsBPS Badan Pusat Statistik (Indonesian Statistic Agency)FTA Free Trade AgreementGCI Global Competitiveness IndexGDP Gross Domestic ProductGRP Gross Regional ProductINDO-DAPOER Indonesia Database for Policy and Economic ResearchISIC International Standard Industrial ClassificationKHL Kebutuhan Hidup Layak (Decent Life Component)SAKERNAS Survei Angkatan Kerja Nasional (National Survey of Labor Force)

List of Abbreviations

vLabor Movement from Low to High Productivity Sector: Evidence from Indonesia’s Provincial Data v

Acknowledgement ..................................................................................................................................................................................... ii

Forewords ......................................................................................................................................................................................................... iii

List of Abbreviation ................................................................................................................................................................................. iv

Content ................................................................................................................................................................................................................ v

List of Figures ............................................................................................................................................................................................... vi

List of Tables .................................................................................................................................................................................................. vi

Abstract ............................................................................................................................................................................................................ 07

1 INTRODUCTION ........................................................................................................................................................................... 07

2 LITERATURE REVIEWS ............................................................................................................................................................ 09

3 METHODOLOgy AND DATA ............................................................................................................................................. 11

3.1 Methodology.............................................................................................................................................................................. 11

3.1.1 Structural Changes Decomposition ............................................................................................................... 11

3.1.2 Determinant of Structural Change in Indonesia, period 2001-2011 ...................................... 11

3.2 Data ................................................................................................................................................................................................ 12

4 THE RESULTS ................................................................................................................................................................................... 12

4.1 The Pattern of Productivity Growth and Structural Change in Indonesia ...................................... 12

4.2 Determinant of Structural Changes ........................................................................................................................... 15

5 Conclusion and Policy Recommendations ............................................................................................................... 19

5.1 Conclusion ................................................................................................................................................................................... 19

5.2 Policy Implications ................................................................................................................................................................ 20

5.3 Recommendation for Further Research .................................................................................................................. 21

References ....................................................................................................................................................................................................... 22

Appendices ................................................................................................................................................................................................... 23

Appendix A: 9 sectors - ISIC rev. 2 .................................................................................................................................... 23

Appendix B: Variable definitions, sources, descriptive statistics ......................................................................24

Contents

APINDO Policy Seriesvi

Figure 1 Labor Productivity of Indonesia and other ASEAN 5

countries (excluding Singapore) ..................................................................................................................... 08

Figure 2 Decomposition of Labor Productivity Growth in Indonesia 1971-2011 ............................. 12

Figure 3 Decomposition of Labor Productivity Growth

in Indonesia 1971-2011: Sectoral Figures ................................................................................................. 14

Figure 4 ‘Within’ and ‘Structural Change’ Productivity Growth, 2001-2011 .............................................. 16

List of Figure

List of Table

Table 1 ASEAN-5’s Competitiveness world ranks in Flexibility .................................................................. 09

Table 2 Summary Statistics on Sectoral Labor Productivity ......................................................................... 16

Table 3 Summary Statistics ................................................................................................................................................. 17

Table 4 Regression results .................................................................................................................................................... 18

7Labor Movement from Low to High Productivity Sector: Evidence from Indonesia’s Provincial Data 7

L abor productivity has become a pressing development agenda for Indonesia, at least, for two reasons. The first is because Indonesian productivity is lagging

behind its neighbors. Between ASEAN countries, Indonesia’s productivity level has not shown any significant changes over time, compared to its counterparts. Amongst countries in the Figure 1, Indonesia’s progress in productivity growth

Abstract

Indonesian labor productivity faces a serious challenge ahead: its lag with the ASEAN neighbors and ever-increasing minimum wage. To map the productivity problems, productivity growth can be decomposed into two: (i) ‘within’ component and (ii) structural change component of productivity growth. This paper aims to document the progress of structural change among Indonesia’s provinces, and identifies the relevant factors behind it.

This study demonstrates that the recent structural transformation in Indonesia has not only been slower, but also tends to left manufacturing sector behind. The finding also shows that agriculture employment share, institution, and education are positively related to structural change, whilst primary sector share and minimum wage growth are negatively related. In order to boost growth-enhancing structural change, several policies are recommended: (1) supporting manufacturing sector for pro-employment growth, (2) reevaluating minimum wage and other barriers of labor flexibility, (3) promoting better access to education, (4) Diversifying economies in primary-sector dependent provinces.

Keywords: Structural change, Indonesia, labor productivity, province

Labor Movement from Low to High Productivity Sectors: Evidence from Indonesia’s Provincial Data*

Muhammad Rizqy AnandhikaRiandy Laksono

* We want to thank to Dr. Arianto Patunru for detailed comments. Comments from seminar participants at Indonesian Development Research Workshop 2014 held by ANU Indonesia Project and SMERU Research Institute are greatly appreciated.

1

is placed in second lowest, only better than Philippines. In 2012, Indonesia marks 1.3 times of its productivity compared to its 1980’s productivity, lower than Malaysia (1.45), Singapore (1.49), Thailand (1.91), even with ASEAN latecomers such as Cambodia (1.6) and Vietnam (2.19). As the implementation of ASEAN Economic Community (AEC) is near approaching, productivity issue becomes

INTrODuCTION

APINDO Policy Series8

more substantial, especially when Indonesia seeks to be a competitive and attractive investment destination in the pursuit of single production base of ASEAN. The figure on productivity implies that Indonesia’s firms will face even more difficult competition with other developing ASEAN countries, especially in winning the ASEAN market and attracting foreign investor.

The other important reason why Indonesia’s policy makers should concentrate more on enhancing its productivity is because labor productivity improvement is urgently needed to offset the distortive effect arising from ever-increasing minimum wage in Indonesia. Having hit by the repression of labor rights in pre-reformasi era, Indonesian labor unions since the enactment of Manpower Protection Law of 2003 has gained more powerful position to press and lobby the politicians (including the government), especially regarding labor welfare and minimum wage increase (Chowdury et al. 2009). In line with that, the 2013-2014 global competitiveness index data shows that Indonesia is among the most underdeveloped countries in term of its labor market efficiency (overall rank 103rd out of 148 economies), with extremely inflexible regime on wage determination and very high redundancy cost (See Table 1). Improvement on productivity could therefore compensate the high cost incurred to employers which

is stimulated by the current ever-increasing minimum wage regime.

The increasingly high labor cost in Indonesia will generate a substantial high-cost business environment to the private sectors, and is suspected as the main barrier of massive and good employment creation. In the case of expansion, the expensive labor cost understandably might encourage private sectors to commit more on technological and capital deepening, rather than hiring more new workers (McMillan & Rodrik 2011). The data from Badan Pusat Statistik (BPS) supports this early indication. In 2007, it is observed that 1% economic growth could contribute to about 700,000 new employment creation, while in 2012, 1% of economic growth can only absorb less than 200,000 additional workers. Furthermore, the more disaggregated data tells that between the periods, the major contributor of employment creation is the less productive, non-tradable services sectors, namely wholesale, trade, restaurant, and accommodation sector. Not only does the Indonesia’s economic growth become increasingly jobless, but also less productive. Regarding Indonesia’s demographic dividend within decades ahead, the additional pool of labor in the coming future will tend to unoptimalized if Indonesia’s economy is under-capacity in providing it with highly productive jobs.

FIgURE 1 Productivity changes between ASEAN countries as compared with US’s productivity, 1980=1

Source: The Conference Board, accessed 2014

Indonesia

Thailand

Malaysia

Vietnam

Cambodia

Philippines

Singapore

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2.3

2.1

1.9

1.7

1.5

1.3

1.1

0.9

0.7

0.5

Prod

uctiv

ity c

hang

es a

s co

mpa

red

with

19

80=1


Referring McMillan and Rodrik (2011), there are essentially two sources of productivity growth, namely within and structural change productivity. Within productivity growth demonstrates the productivity enhancement within the sectors; while structural change growth denotes labor movement from less to more productive activity. This paper put emphasis on labor flows from low to higher productivity jobs, as it is a key driver of development. Documenting the evolution and the progress of structural change in Indonesia is undeniably a very important task to do, as it needs to provide its people with more and better jobs.

2

7th pillar: Labor market efficiency

Sub Pillar: Flexibility (7A)

Cooperation in labor-employer

relation (701)

Flexibility of wage

determination (702)

Hiring and firing practices (703)

Redundancy costs (704)

Effects of taxation on incentive to work (705)

Cont. Rank Cont. Rank Cont. Rank Cont. Rank Cont. Rank Cont. Rank Cont. Rank

SIN 1 SIN 1 SIN 2 SIN 5 SIN 3 SIN 6 SIN 4

MAL 25 MAL 29 MAL 19 MAL 33 MAL 26 MAL 110 MAL 10

THA 62 PHI 108 PHI 34 IND 106 THA 31 PHI 124 IND 27

PHI 100 THA 120 THA 37 PHI 109 IND 39 THA 135 PHI 40

IND 103 IND 133 IND 49 THA 111 PHI 117 IND 141 THA 44

Source: Global Competitiveness Index data platform, WEF, accessed in 2014.

TABLE 1 ASEAN-5’s Competitiveness world ranks in Flexibility

The main objectives of this study are to map the structural change in Indonesia’s provinces, and identify the drivers that distinguish the successful provinces from the unsuccessful ones in term of structural change growth, meaning labor movement from low to higher productivity jobs. Chapter I presents about background and motivation of the study, while Chapter II is the section of literature review. Chapter III describes research methodology and data. Chapter IV is the elaboration of structural change mapping and regression result. Finally, Chapter V summarizes the finding and derives the policy implications.

D eveloping economies are characterized by the experiences of structural change, demonstrated by the significant change of productivity within

and across sectors. Recalling Lewis (1954) dual economy models, the income differences between subsistence and modern sectors will increase the employment of modern sector. Before the competitive subsistence sector’s wage is establisehed, labors from subsistence sector are attracted to work in modern sector because of higher wage, that lower the employment share in subsistence, low-productivity agriculture sector. This movement will increase the modern sector’s output, until the surplus of labor from subsistence sector is depleted. Thus, the more movement of labor into modern sectors will generate higher productivity output, and usually happens simultaneously with the increase in agriculture productivity.

Harris and Todaro (1957) explains that the migration from rural to urban area, as well as structural change from agriculture to modern sectors, when the politically determined minimum urban wage is imposed, in the higher level than agricultural earnings. The structural change and migration happen as a response of urban-rural differences in expected wages, with urban employment rate as equilibrating property on the migration, which will increase the informality.

Structural change is one of the most important parts of the development process in most developing countries. The movement of labor from low-productivity sectors (usually agriculture) to higher productivity sectors contributes to overall increase in productivity.

Furthermore, the structural change is driven by two forces (Maddison 1987). First, the elasticity of demand for

LITErATurE rEvIEwS


certain product that become more similar at given level of income, thus reduce the demand in agriculture goods and increase the demand for products of services and industry. Second, the different of speed of technological advance across sectors, i.e. productivity growth is slower in service than commodity production.

Alvarez-Cuadrado and Poschke (2009) research the structural change out of agriculture by employing ‘labor push’ and ’labor pull’ channels. The ‘labor push’ hypothesis represents the improvements in agricultural technology combined with Engel’s law of demand, which push resources away from agricultural sector.1 Therefore, the firms in non-agricultural sector will increase the employment. The ‘labor pull’ hypothesis explains the improvements in industrial technology attracts worker into this higher-productivity sector.

In general, Maddison (1987) and Alvarez-Cuadrado and Poschke (2009) share similar arguments: both of their first argument is similar (‘labor push’ hypothesis), although the Alvarez-Cuadrado’s (2009) second argument, ‘labor pull’ hypothesis, could be seen as an implication of Maddison’s (1987) speed of technological advance argument.

In the result of structural change, McMillan and Rodrik (2011) investigates, whilst the movement to higher productivity occurs in East Asian countries, some cases show the opposite movement could happens, such as in Latin American and Sub-Sahara African countries. They examines 38 countries within 1990-2005 using decomposition of productivity into within- and structural change-productivity growth, conclude three factors that explain whether the structural change is in the expected direction: (1) Countries with initial comparative advantage in primary products are disadvantaged; (2) Countries which keep competitive currencies encounter positive structural change; (3) Flexible labor market system could advantage countries to earn growth-enhancing structural change.

Pieper (2000) examines 30 developing countries within 1975 to 1984 and 1985 and 1993, observes that Asian countries has increased their industry’s contributions (positive structural change), whereas the opposite happens in many countries in Latin America and Sub-Saharan Africa.

1 Engel’s law states that as income rises, the proportion of expenditure on foods are decreasing, even the actual expenditure on food rises.

One of the important findings is the evidence of Asian countries that able to increase both labor productivity and employment in industry and a whole economy, indicating there is no trade-off between them.

Other decomposition is presented in Ocampo et al. (2009) which involving 57 countries within 1990-2004. It founds that industry sector is the most gaining in productivity in Asian Tigers (Malaysia, Singapore, South Korea, and Taiwan), China, generated by within-productivity, and Southeast Asia, driven by structural change-productivity. Services become dominant contributor of South Asia, and driven more by within-productivity. A different picture is shown in Sub-Saharan Africa which demonstrates stagnant productivity growth with low positive within-productivity growth and negative structural change- produtivity growth. Latin American countries show similar trend with Asia, but experience lower within-productivty growth.

A different approach, by employing Total Factor Productivity is investigated by Ngai and Pissarides (2007). They show various TFP growth across sectors predict employment changes in sectors that consistent with low substitutability between final good produced by each sector. In balance aggregate growth, there is a shifting of employment from sector with high technological progress into lower growth sector, while in the limit, all employment converges into two: sector producing capital goods and sector with lowest rate of productivity growth. Their findings also show the decrease of agriculture’s employment share, the increase and decline of manufacturing share, and the rise of service share.

The impact of technological changes is founded by Fagerberg (2000). He investigates to see structural change in technology side. He studies the productivity of 39 countries between 1973 and 1990, found that structural changes are more likely to be influenced by technological changes than the period before, suggesting the inclusion of technological progress could advantage the growth.

The specific structural change investigation in Indonesia is lacking. Hill et al. (2008) briefly shows some progresses in Indonesian structural change between 1975 and 2004. They found that the provinces with agriculture


dependency (more than one-third of GRP) shrunk from 21 provinces in 1975 to only eight provinces in 2004. Further, in manufacturing sectors, the provinces progressed to produce more manufacturing output at least 20% of GRP from zero province in 1975 to seven provinces in 2004. Lastly, the services sectors also shows progressive

3

growth. Started by only two provinces with one-half of GRP from services sectors in 1975, five provinces now in this group, whilst several comes up approaching. They also found weak correlation between non-mining growth and structural change in Indonesia, but becomes stronger when mining sector is included.

3.1 Methodology3.1.1 Structural Changes Decomposition

T his research borrows McMillan and Rodrik (2011) methodology that simply decompose productivity growth into two: (1) productivity growth ‘within’

sector, and (2) structural change productivity growth. The decomposition is written as:

(1)

Where and are economy-wide and sectoral labor productivity level, respectively. represents the employment share of sector i in time t. ∆ denotes the change of both productivity and employment share between time t-k and t. The first term is productivity “within” term whilst the second term denotes “structural change” term.

The compartmentalization of those two components is very useful in tracking the source of productivity growth, whether it is from productivity enhancement within the industry or from the labor re-allocation effect across different economic sectors. The positive sign of within productivity growth demonstrate the productivity enhancement within the sectors, i.e. the sector earns more output by increasing efficiency, mechanization, and improved know-how; while positive structural change growth denotes labor movement from less to more productive activity. Positive structural change growth means that the country/region is on the right track of development process and able to diversify away from agriculture and other traditional activities with low productivity, towards modern economic activities with higher productivity (e.g. manufacturing, services, etc.). In this study, the speed of the structural change differentiates successful provinces from unsuccessful ones.

3.1.2 Determinant of Structural Change in Indonesia, period 2001-2011

Following McMillan and Rodrik (2011), this research employs one determinant in that relevant for this provincial study in Indonesia: agriculture share in employment. This research uses primary sector share in GDP (i.e. agriculture and mining sector) as a modification of their raw material share in export, due to domestic economic context on the research. the using of share to GDP to figure the dependency into certain sectors similar with approach by Hill et al (2009).

This paper captures the role of tradable industries by adding provincial trade openness. High trade openness could positively or negatively related with structural changes. Positive correlation happens if the export dominates more the domestic business and employs labor from lower productivity’s sectors. In contrast, negative correlation happens when domestic import-competing business will lose its competitiveness, thus discouraging the growth-enhancing structural change.

This paper also tests the variable that mentioned in McMillan and Rodrik (2011) but insignificant: institutional quality. In this study, institutional quality is represented by share of public, law, and order function expenditure to total government expenditure in each province. Higher public, law, and order expenditure expectantly represents higher attention of institutional reform by government, which will encourage structural change by fairer assistance on negotiation of industrial relation issues, as well as effectiveness in delivering infrastructure and education development in provincial level.

METHODOLOgy AND DATA


The employment rigidity variable is captured by the variation of minimum wage as a barrier for firm to recruit new employee. The sharp increases of minimum wage prevent job creation and retention, and reduction in formal employment (rising agriculture sector share), especially if the economy is dominated by small firms (Del Carpio et al. 2012, Mason & Baptist 1996). These will negatively affects structural change. Other variable that could represent rigidity is severance pay, but since its rate is determined nationally, it is impossible to capture the variation.

Finally, this research adds infrastructure and education factors as determinant of structural change. Intuitively, better public infrastructure will give a better access for employee to move into higher productivity sector and firm to recruit more workers from subsistence sectors, whilst the higher logistic cost will discourage firms to expand their employment. The evidence in China shows that structural change is positively correlated with physical infrastructure, besides human capital and capital stock (Biggeri 2010).

Education is employed as determinant because of its role to upgrading technical absorption that facilitate labor to be qualified in higher productivity jobs. It is strengthened by Artuç et al. (2013), proving that labor mobility cost—which becomes barrier of structural changes—is negatively correlated with education. A clearer evidence comes from Lee and Malin (2013) showing that 11% of aggregate growth of productivity in China comes from education, consisting 9% from labor reallocation and 2% of increase of within-sector human capital. This paper

4

2 New provinces are created after 2001: West Papua (2003) from Papua, Riau Islands (2004) from Riau, and West Sulawesi (2004) from South Sulawesi. Thus, we define the provinces in 2011: Papua (West Papua and Papua), Riau (Riau and Riau Island), and South Sulawesi (South Sulawesi and West Sulawesi).

3 See the Appendix B for complete description of ISIC rev. 2

uses Barro and Lee (1993) calculation method of mean years of schooling.

3.2 Data

This paper uses Indonesian provincial data from 2001-2011 from World Bank’s Indonesia Database for Policy and Economic Research (INDO-DAPOER), National Survey of Labor Force (SAKERNAS), and Statistic of Indonesia from Central Statistic Agency of Indonesia (BPS). It accounts 30 provinces used in 2001 and 2011, adopting the 33 provinces at the latter years into 30 provinces to create a balanced panel data2. This research uses period between 2001 and 2011 to see the recent trend of Indonesian productivity growth during the economics emergence after Asian Financial Crisis that followed by the fall of authoritarian regime. The period is also interesting, especially for provincial study, because Decentralization Act is legislated in 1999, thus increases local governments’ (including province’s) discretion unlike years before. In 2003, The Manpower Act is enacted, starting a period of more stringent labor protection that increases employment rigidity in higher level, e.g. by the ever-increasing minimum wage among provinces.

This research classifies nine sectors with International Standard Industrial Classification (ISIC) revision 2.3 For Indonesian historical comparison, this research using database from 10-Sector Productivity Database, by Timmer and de Vries (2009). For regression, this research shows the complete description of the dependent and independent variables that can be seen in Appendix A.

T his section will be divided in two main parts, the first part is the mapping on structural change and productivity growth in Indonesia (national

and provincial level) from 2001-2011, while the second part is devoted to analyze the determinants of structural change, or in other words, the labor movement from low to high productivity sectors.

4.1. The Pattern of Productivity Growth and Structural Change in Indonesia

National Level

Indonesia experiences positive and increasing productivity growth from period to period. In 2001-2011, Indonesia

THE rESuLT


experienced notable productivity growth, that is, 3.33% per annum, which mostly comes from within component (2.37% per annum) of productivity growth, rather than the structural changes (0.96% per annum). The composition of productivity growth is quite reversed, if it is compared with the productivity growth in 1971 to 2000. As depicted in Figure 2, from the period of 1971-1985 to 1986-2000, the structural change component is always higher than the within component. The positive sign of structural change component in 2001-2011 means that Indonesia, in general, is still on the “right track” of the development process, as it succeeds on moving its employment away from low productivity jobs (e.g. agriculture) towards higher productivity jobs (e.g. services). However, the decreasing trend of structural change component indicates that the pace of the economy to move its labor away from low to higher productivity job becomes slower time by time.

The economic sectors disaggregation of labor productivity growth decomposition can be classifi ed into three groups. The fi rst group is the sectors that have both positive sign on within and structural change component, while the second groups is the sectors which experienced growth enhancing structural change (positive structural change)

yet having negative within productivity component. The third (last) group comprises of the economic sectors which have positive within component, yet experiencing growth-reducing structural change (negative structural change). This study fi nds no sectors having both negative within and structural change component.

There are 4 sectors which have both positive sign on within and structural change component, namely public utilities; construction; trade, restaurant and accommodation; as well as government (social) sectors. The government, construction and public utilities sectors are related to each other. The positive growth of within and structural change indicates the active expansion of government/public works, especially in the area of basic infrastructure/delivery, such as road construction, electricity, and water supply. Such expansion contributes positively to productivity growth and attracts more employment. Trade, restaurant, and accommodation sector experiences highest productivity growth; its within productivity growth is the highest among all, while the structural change growth is the second highest, after financial, insurance, and real estate sector. The strong productivity growth of the trade, restaurant, and accommodation sector as well as its ability to absorb more

Note: Data from 1971 to 2000 is from Groningen Growth and Development Centre 10-sector database, June 2007, http://www.ggdc.net/, de Vries and Timmer (2007) using ISIC rev.3 classifi cation; while the 2001 to 2011 data is aggregated from provincial data as provided by The World Bank, INDO-DAPOER, using ISIC rev. 2. The diff erence between ISIC rev. 2 and rev. 3 mostly on the detail, not on the aggregate classifi cation, thus making them somewhat comparable, especially in aggregate level.

Source: authors’ calculation based on Timmer and de Vries (2007); The World Bank, INDO-DAPOER (accessed in 2014).

FIgURE 2 Decomposition of Labor Productivity Growth in Indonesia 1971-2011

within

structural

0.000% 0.500% 1.000% 1.500% 2.000% 2.500% 3.000% 3.500%

2001-2011

1986-2001

1971-1986

0.961%2.370%

1.469%1.114%

0.995% 1.300%


employment than the others are the logical consequences of Indonesia’s increasing volume of trade, induced by many FTAs signed in recent years, as well as its increasingly competitive and attractive tourism-travel destination in the world. Robust trade sector productivity is also linear with the fact that most of Indonesia’s capital cities has rapidly transformed themselves into more competitive services-trade city (e.g. Jakarta, Surabaya, Medan and Makassar)

The economic sectors which have positive sign on structural change component, but experiencing negative within-productivity growth are mining-quarrying, and fi nancial, insurance, and real estate sectors. Both sectors are among the well-paid, high productivity, and most attractive employment destination for the workers. In fact, in 2001-2011 period, fi nancial, real estate and insurance sectors experience the highest growth on the structural change, where its employment share in 2011 is almost doubled than that of 2001. Yet, they now experience diminishing rate of return on their productivity, meaning that the increase of output is much lower than the additional level of input (labor) coming to that sectors. In other words, these sectors are ‘overcrowded’ by surge of labor coming from lower productivity sector

so that its effi ciency depleted. It is even worse for mining and quarrying sector, that its negative within productivity growth surpasses its structural change growth. It means, from 2001 to 2011, the net outcome of absorbing more labor to mining and quarrying sector tends to create ineffi ciency and reduce productivity. It is fair to say that the sector is in the middle of their saturation point.

The last group of sector is the sectors that have positive sign on their within productivity growth, but experiencing negative structural change growth (growth-reducing structural change), comprising of agriculture, transportation and manufacturing sectors. Agriculture sector expectedly experiences negative structural change, as it is the main source of workers for other sectors. It is not the case of negative structural change growth in transportation and manufacturing sector, as they are not a natural source of workers for other sectors. Growth reducing structural change, yet signifi cant positive within productivity growth in transportation and telecommunication sector suggests that there are now more effi cient operator in transportation and telecommunication services available domestically. It is quite logical to see that effi ciency sometimes requires labor

FIgURE 3 Decomposition of Labor Productivity Growth in Indonesia 1971-2011: Sectoral Figures

Note: see the notes in Figure 2

Source: authors’ calculation based on de Vries and Timmer (2007); The World Bank, INDO-DAPOER (accessed in 2014).


restructuring, and at the same time, greater utilization of high technological and capital content.

The negative structural change growth (growth-reducing structural change) in manufacturing sector from 2001 to 2011 is the most striking finding in this study. Since 1971, at least until the beginning of 2000’s, manufacturing sector has always recorded positive significant growth both in within and structural change component. In 1971-1986, manufacturing productivity grew at a considerable level, that is, 0.73% per annum. While in 1986 to 2001, manufacturing grew even higher at 1.23% per annum and is among the major absorber of surplus of workers around that time. In 2001-2011, the manufacturing productivity growth has reduced notably to only around 0.58% per annum, and at the same time, the portion of labor working in the manufacturing sector has been reduced (see Figure 3). From the earlier finding, it is implied that the structural change has begun to slower from time to time. The negative structural change growth in manufacturing sector provides additional insight that the recent structural transformation in Indonesia has not only been slower, but also tends to left manufacturing sector behind.

This finding is a bad sign for Indonesia, as manufacturing sector is the only possible, yet productive sector which can absorb abundant additional pool of labor in the years

ahead. If Indonesia seeks to transform its economy into a higher path of productivity without losing the capacity to absorb massive new employment, it has to strengthen manufacturing base in the country. The progress of services sectors is inevitable; but losing manufacturing base while there will be one-time “demographic dividend” doesn’t seem quite strategic.

Provincial level

The general labor productivity figure in Indonesia in 2011 showed that 1 unit of labor in Indonesia, averagely, can produce around Rp 21.53 million per year. The highest and lowest average productivity belong to DKI Jakarta and Nusa Tenggara Timur (NTT) by 92 and 6.3 million Rupiah, respectively. This reflects an extreme disparity of productivity between provinces. Manufacturing, finance, mining, and public utilities sectors are among the highest productivity job in most of Indonesia’s provinces, while agriculture sector is expectedly the least productive activity.

The most productive province in doing the primary (resource-based) economic activity mostly located in Sumatera island, namely Bangka-Belitung Islands for agriculture, as well as Riau (and Riau Islands) for mining and quarrying activities. Public utilities sector is the

Sectoraverage sectoral labor

productivity (Million IDR)

Maximum Sectoral Labor Productivity (Million IDR)

Minimum Sectoral Labor Productivity (Million IDR)

ProvinceLabor

ProductivityProvince

Labor Productivity

Agriculture, Hunting, Forestry, and Fishing

Agr 8.498 Bangka Belitung Islands

17.067 NTT 3.500

Mining and Quarrying Min 118.764 kepri (riau) 950.269 Banten 1.613

Manufacturing Man 38.943 kaltim 342.387 NTT 1.516

Utilities (Electricity, Gas, and Water)

Uti 107.307 West Java 211.704 Maluku 8.970

Construction Con 22.486 DKI Jakarta 270.524 North Maluku 3.314

Wholesale and Retail Trade, Hotels, and Restaurants

Trd 21.280 DKI Jakarta 56.235 Gorontalo 7.016

Transport, Storage, and Communications

Tra 37.827 DKI Jakarta 135.356 North Maluku 9.407

Finance, Insurance, Real Estate, and Business Service

Fin 79.608 DKI Jakarta 265.843 Banten 17.310

Community, Social, Personal and Government

Soc 13.291 DKI Jakarta 41.185 North Maluku 3.538

Economy-Wide Sum 21.553 DKI Jakarta 92.022 NTT 6.322


TABLE 2 Summary Statistics on Sectoral Labor Productivity


most productive in West Java, while East Kalimantan is recorded as the most productive region for conducting manufacturing activities. Jakarta, as a services capital of Indonesia, expectedly showed the highest labor productivity score for the entire services activity in Indonesia (see Table 2).

There are generally two types of region in Indonesia, the one is the region which is successful in moving its labor away from low to higher productivity jobs, and the other one is the region that fails to do so. Almost the entire province in Indonesia is considerably successful in moving its labor away from low to high productivity sectors, or in other words, experiencing positive structural change growth, except for Banten, Jambi, West Kalimantan, Central Kalimantan, Bangka Belitung Islands, North Maluku, and NTB—that are experiencing negative structural change growth in the period of 2001-2011 (see Table 3). Among the successful region, there are provinces which records positive within-productivity growth, and there are regions showing the opposite sign (negative within-productivity growth). Aceh, Riau (and Riau Islands), East Kalimantan, and Papua (and West Papua) are among the provinces having

FIgURE 4 ‘Within’ and ‘Structural Change’ Productivity Growth, 2001-2011

Notes: See table 4 for the provinces’ code used in graph


4 Breusch-Pagan/Cook-Weisberg test for heteroscedasticity rejects alternative hypothesis (H1), meaning that the model free from heteroscedasticity problem. VIF test shows a number that is not between the range to be judged as having multicollinearity problem, i.e. 1.51 (mean VIF). The individuals VIF value are also not in the multicollinearity’s range. The model are free from omitted variable problem, which is indicated by Ramsey RESET test accepting null hypothesis (model has no omitted variables).

negative within productivity growth. This study fi nds no single province experiencing both negative within and structural change growth (see Figure 4).

This study identifies such a significant gap on the performance of labor productivity and structural change/transformation growth between the provinces. The next section will discuss deeper on the driver/enabling factors that might explain why a region are doing quite well, while the other is not, in term of structural transformation/change, that is to moving its labor away from low to higher productivity jobs. From the regression result, this paper derives policy implication needed to promote provincial structural change/transformation back on the right track.

4.2 Determinant of Structural Changes

Before formal regression equation is run, we conduct several test on the variable and model specifi cation. The result suggests that the model is free from the classical problems such as multicollinearity, heteroscedasticity, as well as omitted variable.4


No

Prov

ince

Code

Econ

omy-

wid

e La

bor

Prod

uctiv

ity

Sect

or w

ith H

ighe

st L

abor

Pr

oduc

tivit

ySe

ctor

with

Low

est

Labo

r Pr

oduc

tivit

yCo

mpo

und

Ann

ual g

row

th R

ate

of E

cono

mic

-w

ide

Prod

uctiv

ity

Sect

orLa

bor

Prod

uctiv

itySe

ctor

Labo

r Pr

oduc

tivity

annu

al g

row

th

rate

of ‘

with

in’

prod

uctiv

ity

annu

al g

row

th

rate

of ‘

stru

ctur

al

chan

ge’

prod

uctiv

ity

annu

al g

row

th

rate

of t

otal

pr

oduc

tivity

1N

angg

roe

Aceh

D

arus

sala

mN

AD18

.775

Min

222.

583

Agr

10.4

08-1

.68%

0.47

%-1

.21%

2N

orth

Sum

ater

aN

SM21

.412

Fin

84.5

03Ag

r11

.325

2.42

%1.

20%

3.61

%

3W

est

Sum

ater

aW

SM19

.941

Tra

58.6

87Ag

r11

.649

2.58

%1.

09%

3.67

%

4Ri

au +

Ri

au Is

land

sRI

A45

.671

Min

950.

269

Soc

12.7

92-2

.15%

1.52

%-0

.63%

5Ja

mbi

JAM

13.2

15M

in12

2.88

8So

c7.

171

2.80

%-0

.01%

2.79

%

6So

uth

Sum

ater

aSS

M19

.140

Min

345.

587

Agr

6.47

51.

76%

1.61

%3.

37%

7Ba

ngka

Bel

itung

Isla

nds

BBE

19.6

52M

an75

.596

Soc

9.70

52.

42%

-1.0

1%1.

41%

8Be

ngku

luBE

N10

.161

Min

33.4

85Co

n6.

331

2.21

%1.

00%

3.21

%

9La

mpu

ngLA

M11

.733

Fin

102.

478

Soc

7.14

82.

77%

1.13

%3.

90%

10Ba

nten

BAN

20.7

98U

ti19

0.70

2M

in1.

613

3.39

%-0

.34%

3.05

%

11D

KI J

akar

taD

KI92

.022

Con

270.

524

Agr

10.0

862.

21%

0.58

%2.

79%

12W

est

Java

WJA

19.6

57U

ti21

1.70

4So

c8.

746

2.83

%0.

66%

3.50

%

13Ce

ntra

l Jav

aCJ

A12

.457

Uti

58.6

99Ag

r6.

584

4.36

%0.

32%

4.68

%

14D

I Yo

gyak

arta

DIY

12.3

04U

ti47

.385

Agr

8.24

92.

77%

0.85

%3.

62%

15Ea

st J

ava

EJA

19.3

76U

ti20

2.14

3Ag

r6.

998

3.94

%0.

57%

4.51

%

16Ba

liBA

L13

.950

Uti

68.6

44Co

n6.

658

2.93

%0.

51%

3.44

%

17N

usa

Teng

gara

Bar

atN

TB9.

903

Min

81.3

12Ag

r5.

420

2.93

%-0

.10%

2.83

%

18N

usa

Teng

gara

Tim

urN

TT6.

322

Fin

24.7

65M

an1.

516

2.28

%1.

35%

3.63

%

19W

est

Kalim

anta

nW

KA14

.972

Fin

86.0

82Ag

r6.

119

2.57

%-0

.28%

2.29

%

20So

uth

Kalim

anta

nSK

A17

.838

Min

97.6

92Ag

r9.

961

2.50

%0.

06%

2.56

%

21Ce

ntra

l Kal

iman

tan

CKA

18.1

59Fi

n89

.253

Agr

9.91

22.

68%

-0.4

7%2.

21%

22Ea

st K

alim

anta

nEK

A72

.435

Man

342.

387

Soc

8.29

4-1

.59%

0.25

%-1

.34%

23G

oron

talo

GO

R7.

056

Uti

102.

932

Min

2.35

63.

06%

0.99

%4.

04%

24N

orth

Sul

awes

iN

SU19

.920

Fin

58.1

27Ag

r11

.083

3.20

%0.

95%

4.15

%

25Ce

ntra

l Sul

awes

iCS

U15

.255

Uti

74.4

95Ag

r11

.499

3.55

%1.

15%

4.70

%

26So

uth

Sula

wes

i + W

est

Sula

wes

iW

SU15

.424

Min

121.

204

Agr

9.61

22.

07%

1.59

%3.

66%

27So

uthe

ast

Sula

wes

iSS

U12

.334

Fin

71.2

51Ag

r7.

851

2.75

%2.

17%

4.93

%

28N

orth

Mal

uku

NM

A7.

339

Fin

40.5

02Co

n3.

314

3.09

%-1

.16%

1.92

%

29M

aluk

uM

AL6.

933

Fin

29.2

87Co

n3.

735

0.14

%1.

16%

1.30

%

30Pa

pua

+ W

est

Papu

a PA

P18

.261

Min

195.

823

Agr

4.91

4-3

.67%

0.80

%-2

.87%

Indo

nesi

aID

N21

.553

Min

118.

764

Agr

8.49

82.

37%

0.96

%3.

33%

Not

e: A

ll nu

mbe

rs a

re fo

r 201

1.Cu

rrenc

y is

in c

onst

ant 2

000

IDR.

Gro

wth

s are

in a

nnua

l rat

e, be

twee

n 20

01 a

nd 2

011.

Abb

revi

atio

ns a

re fo

llow

s: (A

gr) A

gric

ultu

re; (

min

) Min

ing,

(Man

) Man

ufac

turin

g; (U

ti) P

ublic

U

tiliti

es; (

Con)

Con

stru

ctio

n; (

Tra)

Who

lesa

le a

nd T

rade

; (Tr

a) T

rans

port

and

Com

mun

icat

ion;

(Fin

) Fin

ance

and

Bus

ines

s Se

rvic

e; (S

oc) C

omm

unity

, So

cial

, and

Gov

ernm

ent S

ervi

ces

Sour

ce: A

utho

rs’ c

alcu

latio

n ba

sed

on T

he W

orld

Ban

k, IN

DO

-DAP

OER

(acc

esse

d 20

14)

TABL

E 3

Sum

mar

y St

atist

ics


TABLE 4 Regression results

Dep. var: annual structural-change growth variables

agricultural share in employment 0.033 ***

(0.095)

annual growth of primary sectors share in GDP-0.125 *

(0.072)

annual growth of institution spending share0.020 ***

(0.005)

annual growth of infrastructure spending -0.006

(0.005)

primary education 0.010 **

(0.004)

openness to trade-0.006

(0.004)

minimum wage growth-0.201 ***

(0.068)

East-Indonesia dummy0.004

(0.002)

constant-0.049 *

(0.025)

observations 30

R-squared 0.613

Robust t-statistics in parentheses

* denotes significant at 10% level, ** denotes significant at 5% level, *** denotes significant at 1% level

Source: authors’ calculation

In general, agriculture employment, primary sector, institution, primary education, and minimum wage shows significant result in the regression. Only openness and East-Indonesia dummy that show insignificancy. The good R-squared value (0.613) shows good representation of variables in the model

The agriculture share in employment, at the beginning of period (2001), is a proxy to document the role of initial development gaps on structural change growth. Theoretically (based on standard dual economy model), the wider the development gaps, the larger the room for growth enhancing structural change. Provinces that have a significant share of agriculture labor force may have greater potential for positive structural change growth. Positive and significant sign in the regression result, then, implies that the structural change growth between more advanced and less-developed provinces in Indonesia are converged.

Primary sector, as an indicator of comparative advantage, also play a significant role in structural change speed. The estimation suggests that there is a quite strong and negative association between province’s reliance on primary economic activities and the rate of structural change growth. Provinces with heavy reliance on primary economic activities are disadvantaged.

The variable of institutional quality shows positive and very significant correlation with structural change growth. This research use public, law, and order function of expenditure as the proxy of institutional quality, and prove that the increasing of such expenditure will encourage structural change. The logical argument arising from the regression result is that higher spending on public, law, and order will create stronger law enforcement and advancing fairer regulation that supporting the employment and firms’ expansion. Thus, the more budget allocated to institutional enhancement, i.e. public, law, and order spending, will increase the speed of structural change.

Two variables proposed in this research, namely education and infrastructure, have different results. Education shows positive and significant correlation with structural change, whilst Infrastructure shows negative and insignificant correlation. The significance gained by primary-education’s mean year of schooling shows that Indonesia’s increase in labor education attainment in recent decade is supportive to structural change. This research tested that the primary-education’s mean years of schooling performs better significance-level when it is compared to total-education’s mean years of schooling. This indicates that the primary education is crucial in current Indonesian labor market movement, and thereby it is urgent for the government to improve basic education as a foundation to higher education. It also indicates that current industries’ landscape are favoring less-skilled worker, since Indonesia is still advantageous in labor-intensive industries with the cheap labor wages. Nevertheless, education is fundamental to prepare the labor force with the skills that needed by the industry in the future. The increasing of GDP per capita will increase the wage and will leave Indonesian labor-intensive industries (e.g, textiles) behind. When labors want to move into higher productivity, they should have a qualification that required by the industries. It is strictly sure that such upgrade is empowered by the education.


The infrastructure’s support supposes to increase the structural change. Not only because a good infrastructure has a direct influence in technological progress of the industry, it also boosts the production by lowering logistic cost. However, the insignificant variable does not means that infrastructure is not significant to structural change. Instead, it possibly indicates that infrastructure is not affecting structural change via its spending. The using of physical measures such as road length and port capacity could be more reliable for this study. Therefore, the utilization of actual physical infrastructure condition in provincial level for future studies is highly urged.

Although openness to trade shows insignificancy, the sign of the estimated variable reflects the concerns raised by McMillan and Rodrik (2011). They argue that openness to trade can be a factor that will discourage structural change. In the case of intense liberalization, the uncompetitive, import-competing businesses will suffer during the liberalization. In critical condition, it could push the employers to shut down those higher productivity industries (although not the most efficient one), thus will force the workforce to go to the less-productive sectors, e.g. agriculture or even worse the informal sector.

The coefficient of growth of minimum wage shows negative and highly significant. The estimation result shows that every increasing of minimum wage by 1% will

discourage structural change by 0.2%. This is consistent with the findings about the influence of ease of entry and exit into industry and flexibility of labor markets. Recalling the GCI result for 2010-2011, Indonesian labor flexibility is ranked 98th, consisting of low flexibility of wage determination (rank 114th). The ever-increasing minimum wage will prevent employer from hiring more new workers in the case of expansion. Instead, under the condition of expensive labor cost, employer will upgrade plant and equipment (capital deepening) to improve their productivity, thus reduces the opportunity of labor moving into higher productivity and formal jobs. Based on this finding, this paper argues that negative structural change in several Indonesia’s provinces could happen because pushing the minimum wage up every year could decrease the financial ability of firms to pay the wage bill, therefore the additional pool of labor will be more likely to end up in a low-paid agriculture sector, informal and other less productive activity. The other implication could be related with firms’ reallocations. When the regional minimum wage significantly increase, the firms will alter their production bases to the province with lower minimum wage. Under this argument, it is possible to see that the negative structural change in one province could increase the structural change in neighboring provinces.

5.1 Conclusion

The movement of labor from low to higher productivity sectors (structural change) is evident, though its proportion on the overall Indonesia’s labor productivity growth has decreased over time. In the period of 2001-2011, structural change growth accounted for 0.96% out of 3.33% compound annual productivity growth. Although the positive sign on Indonesia’s recent structural change means that Indonesia is on the right track of development process, there are two trends worth to be taken seriously as policy issues. Firstly, the decreasing trend of structural

change component indicates that the pace of the economy to move its labor away from low to higher productivity job becomes slower time by time. Secondly, in the last decade, structural change has tended to left manufacturing sector behind, and bias towards services sectors instead.

The estimation result of this study suggests that: (i) education and government institution matter for successful transformation into higher productivity sectors; (ii) the rates of structural change growth among Indonesia’s provinces are converged; (iii) rigid labor market as approached by

5 CONCLuSION AND POLICy rECOMMENDATION


increase on minimum wage is a barrier to labor movement into formal and higher productivity sectors; and (iv) provinces with high dependency on primary economic activities are disadvantages from structural change.

5. 2 Policy implications

The st ruc tura l change analys i s resu l ts some recommendation that could be relevant with the findings.

a) Supporting manufacturing sector for pro-employment growth

Manufacturing sector shows negative, small structural changes in recent decade, implying that Indonesia’s structural change is driven mostly by service sectors (financial, wholesale and trade, social and government, and construction services). However, the most elastic employment-enhancing growth is manufacturing sector, due to its capacity to create high employment. Future policies should support employment in manufacturing, especially in labor-intensive sectors that should accommodate high population growth within decades ahead.

The fact that China’s labor-intensive industries become less competitive should be seen as an opportunity to take 10% of its manufacture market by 2019, accounting for 19% growth of Indonesia’s manufacturing export (Papanek et al. 2014). This is clearly a window of opportunity as Indonesia needs to employ its worker massively to feed its 3 million population addition each year, i.e. high growth-enhancing structural changes. The failure of this employment creation could miss Indonesian productive-age’s boom from demographic dividend into massive unemployment or informality.

b) Reevaluating minimum wage

Government should play an active role to promote ‘pro-employment’ growth, by re-evaluating minimum wage determination into fairer calculation, of what this study proposes as technocratic approach. In doing so, a policy reform is needed to put the minimum wage as a safety net, instead of negotiation wage. By improving the calculation method and objectivity of ‘decent life component’ (Kebutuhan Hidup Layak/

KHL), the minimum wage should be set at the lowest minimum that can still provide a decent life for workers, but is fixed/not negotiable in a certain period. The negotiation process is best done in a plant level which reduces the political attempt to make it rather as a popular policy to gain vote from labor class. This study proposes more stability and technocratic component in wage determination, so that it can be more relaxing for private sectors, and at the same time makes it possible for the additional pool of labor force to get job in formal and productive sectors.

c) Promoting better access to education

The results indicate that higher primary education attainment in labor force increases structural changes. However, Indonesian primary education’s mean years of schooling within labors shows low figure (average 6.1 out of 9), with the most of labor force is elementary school graduate or lower (48%). Based on observation, the primary education shows significant result in determining structural change. This could happen because the demand of labor within industries is still dominated by labor-intensive, low education industries, as most of developing countries advantage by their low-wage industries. It doesn’t mean that the higher education should be discouraged, because in line with the increasing of GDP per capita, Indonesian wages will increase, and higher-skilled employee will be in higher demand when Indonesia lost it competitiveness in labor-intensive industries. Better education qualification helps labor to graduate into higher productivity sector that needs higher cognitive and analytical skills, that supporting the technology application in firms.

d) Diversifying economies in primary-sector dependent provinces

The provinces with high dependency in raw materials should diversify its economy into other high-value added sectors. It is evidenced that raw materials dependency will discourage structural changes. In doing so, the local governments should prepare some policy packages to attract high productivity industry and services to come and present in their region, such as tax incentives, subsidy, R&D and training program.


5.3 Recommendation for Further Research

Nevertheless, this research contains some caveats regarding the methods and data used. First, it should be realized that productivity in sector is an overall productivity, which cannot represents any certain profession in such sector. Thus, a movement of labor from lower productivity sector, for example when an unskilled construction worker moves into higher productivity sector, such as mining and quarrying sector, cannot be generalized that his productivity will increase in new sector if he still works as unskilled worker. Hence, in some extent, the position with higher wage matters, but in most case of movement from agriculture, it is sure that any moving away from agriculture will increase the aggregate productivity.

Second, regarding the significant finding of minimum wage correlation with structural change, it is should be remembered that minimum wage is used as a proxy of rigidity of labor. World Economy Forum lists there are other three aspects related with labor rigidity: cooperation in labor-employer relations, hiring and firing practices, and redundancy costs. This research employs minimum wage growth because its variation is captured in provincial level.

Finally, future research is expected to analyze deeper in interesting fact about the driver of manufacturing sector’s shrinking structural growth and other determinants relating labor rigidity.


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Appendices

Appendix A: 9 sectors - iSiC rev. 2

Classification Abbreviation Contents

1-Agriculture, Hunting, Forestry and Fishing Agr 11 - Agriculture and Hunting

12 - Forestry and logging

13 - Fishing

2 - Mining and Quarrying Min 21 - Coal Mining

22 - Crude Petroleum and Natural Gas Production

23 - Metal Ore Mining

29 - Other Mining

3 - Manufacturing Man 31 - Manufacture of Food, Beverages and Tobacco

32 - Textile, Wearing Apparel and Leather Industries

33 - Manufacture of Wood and Wood Products, Including Furniture

34 - Manufacture of Paper and Paper Products, Printing and Publishing

35 - Manufacture of Chemicals and Chemical, Petroleum, Coal, Rubber and Plastic Products

36 - Manufacture of Non-Metallic Mineral Products, except Products of Petroleum and Coal

37 - Basic Metal Industries

38 - Manufacture of Fabricated Metal Products, Machinery and Equipment

39 - Other Manufacturing Industries

4 - Public Utilities (Electricity, Gas and Water) Uti 41 - Electricity, Gas and Steam

42 - Water Works and Supply

5 - Construction 50 - Construction

6 - Wholesale and Retail Trade and Restaurants and Hotels

Trd 61 - Wholesale Trade

62 - Retail Trade

63 - Restaurants and Hotels

7 - Transport, Storage and Communication Tra 71 - Transport and Storage

72 - Communication

8 - Financing, Insurance, Real Estate and Business Services

Fin 81 - Financial Institutions

82 - Insurance

83 - Real estate and Business Services

9 - Community, Social and Personal Services Soc 91 - Public Administration and Defence

92 - Sanitary and Similar Services

93 - Social and Related Community Services

94 - Recreational and Cultural Services

95 - Personal and Household Services

96 - International and Other Extra-Territorial Bodies

Source: https://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=8&Lg=1

Appendix A: 9 sectors - iSiC rev. 2


Variable Source Obs Mean Std. Dev. Min Max Description

annual growth of structural change INDO-DAPOER, Timmer and de Vries (2009)

30 0.006 0.008 -0.012 0.022 growth is in level

agriculture employment share INDO-DAPOER 30 0.505 0.153 0.008 0.506 share is in level

annual growth of primary sectors share to GDP

INDO-DAPOER 30 -0.018 0.017 -0.078 0.016 growth is in level

annual growth of institutional (public, law, order) spending share with expenditure

INDO-DAPOER 30 0.089 0.23 -0.357 0.513 share is in level

annual growth of infrastructure spending share with expenditure

INDO-DAPOER 30 0.049 0.228 -0.982 0.461 share is in level

primary education’s mean years of schooling

SAKERNAS 30 6.13 0.37 5.202 7.18 the calculation is based on Barro & Lee (1993), not schooling=0, not completing elementary school=3, completeing elementary school=6, completing junior high school=9.

openness to trade INDO-DAPOER 30 0.745 0.325 0.086 1.53 in level

annual growth of minimum wage Statistics of Indonesia 30 0.131 0.162 0.074 0.162 growth is in level

Note: INDO-DAPOER: Indonesia Database for Policy and Economic Research, SAKERNAS: Survey Angkatan Kerja Nasional (National Labor Forces Survey), data for institution and infrastructure uses 2001-2008 due to limitation.

Appendix B: variable definitions, sources, descriptive statistics


APiNDO-EU ACTivE Project

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labor movement from low to high productivity sector: i...

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