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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 relect 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.

D i s c l a i m e r

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 active@apindo.or.id 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 ACTIVE

Labor Movement from Low To High Productivity Sectors: Evidence from Indonesian Provincial Data

Published in July 2014

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P

roductivity 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 beneit 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 signiicant 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 ofer a signiicant 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 beneit Indonesian businesses in the future.

Sojan Wanandi Chris Kanter

General Chairman Vice Chairman

Indonesian Employers Association(APINDO) Indonesian Employers Association(APINDO)

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AEC ASEAN Economic Community

ASEAN Association of Southeast Asian Nations

BPS Badan Pusat Statistik (Indonesian Statistic Agency)

FTA Free Trade Agreement

GCI Global Competitiveness Index

GDP Gross Domestic Product

GRP Gross Regional Product

INDO-DAPOER Indonesia Database for Policy and Economic Research

ISIC International Standard Industrial Classiication

KHL Kebutuhan Hidup Layak (Decent Life Component)

SAKERNAS Survei Angkatan Kerja Nasional (National Survey of Labor Force)

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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 deinitions, sources, descriptive statistics ...24

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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

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L

abor productivity has become a pressing development agenda for Indonesia, at least, for two reasons. The irst is because Indonesian productivity is lagging behind its neighbors. Between ASEAN countries, Indonesia’s productivity level has not shown any signiicant 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 identiies 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 inding 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 lexibility, (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

*

M uhammad Rizqy Anandhika

Riandy 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.

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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

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more substantial, especially when Indonesia seeks to be a competitive and attractive investment destination in the pursuit of single production base of ASEAN. The igure on productivity implies that Indonesia’s irms will face even more diicult 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 ofset the distortive efect 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 eiciency (overall rank 103rd out of 148 economies), with extremely inlexible 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

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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 lows 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

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 inding and derives the policy implications.

D

eveloping economies are characterized by the experiences of structural change, demonstrated by the signiicant change of productivity within and across sectors. Recalling Lewis (1954) dual economy models, the income diferences 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 diferences 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

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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 diferent 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 irst 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 indings 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-of 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 diferent 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 diferent 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 inal 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 indings 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 inluenced by technological changes than the period before, suggesting the inclusion of technological progress could advantage the growth.

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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

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growth. Started by only two provinces with one-half of GRP from services sectors in 1975, ive 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. sector, and (2) structural change productivity growth. The decomposition is written as:

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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 irst 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 efect across diferent 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 eiciency, 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 diferentiates 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 modiication of their raw material share in export, due to domestic economic context on the research. the using of share to GDP to igure 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 insigniicant: 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 efectiveness in delivering infrastructure and education development in provincial level.

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The employment rigidity variable is captured by the variation of minimum wage as a barrier for irm 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 irms (Del Carpio et al. 2012, Mason & Baptist 1996). These will negatively afects 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 irm to recruit more workers from subsistence sectors, whilst the higher logistic cost will discourage irms 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 qualiied 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

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 classiies nine sectors with International Standard Industrial Classiication (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 irst 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

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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 classii 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 i 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%

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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 i 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, i 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 ei 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 inei 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 signii cant positive within productivity growth in transportation and telecommunication sector suggests that there are now more ei cient operator in transportation and telecommunication services available domestically. It is quite logical to see that ei ciency sometimes requires labor FIgURE 3 Decomposition of Labor Productivity Growth in Indonesia 1971-2011: Sectoral Figures

Note: see the notes in Figure 2

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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 inding in this study. Since 1971, at least until the beginning of 2000’s, manufacturing sector has always recorded positive signiicant 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 inding, 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 inding 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 igure 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 relects an extreme disparity of productivity between provinces. Manufacturing, inance, 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

Sector average sectoral labor productivity (Million IDR)

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

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

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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

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

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 i 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

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1

No Province Code wide Labor

Productivity Sector Labor

NAD 18.775 Min 222.583 Agr 10.408 -1.68% 0.47% -1.21%

2 North Sumatera NSM 21.412 Fin 84.503 Agr 11.325 2.42% 1.20% 3.61%

3 West Sumatera WSM 19.941 Tra 58.687 Agr 11.649 2.58% 1.09% 3.67%

4 Riau + Riau Islands RIA 45.671 Min 950.269 Soc 12.792 -2.15% 1.52% -0.63%

5 Jambi JAM 13.215 Min 122.888 Soc 7.171 2.80% -0.01% 2.79%

6 South Sumatera SSM 19.140 Min 345.587 Agr 6.475 1.76% 1.61% 3.37%

7 Bangka Belitung Islands BBE 19.652 Man 75.596 Soc 9.705 2.42% -1.01% 1.41%

8 Bengkulu BEN 10.161 Min 33.485 Con 6.331 2.21% 1.00% 3.21%

9 Lampung LAM 11.733 Fin 102.478 Soc 7.148 2.77% 1.13% 3.90%

10 Banten BAN 20.798 Uti 190.702 Min 1.613 3.39% -0.34% 3.05%

11 DKI Jakarta DKI 92.022 Con 270.524 Agr 10.086 2.21% 0.58% 2.79%

12 West Java WJA 19.657 Uti 211.704 Soc 8.746 2.83% 0.66% 3.50%

13 Central Java CJA 12.457 Uti 58.699 Agr 6.584 4.36% 0.32% 4.68%

14 D I Yogyakarta DIY 12.304 Uti 47.385 Agr 8.249 2.77% 0.85% 3.62%

15 East Java EJA 19.376 Uti 202.143 Agr 6.998 3.94% 0.57% 4.51%

16 Bali BAL 13.950 Uti 68.644 Con 6.658 2.93% 0.51% 3.44%

17 Nusa Tenggara Barat NTB 9.903 Min 81.312 Agr 5.420 2.93% -0.10% 2.83%

18 Nusa Tenggara Timur NTT 6.322 Fin 24.765 Man 1.516 2.28% 1.35% 3.63%

19 West Kalimantan WKA 14.972 Fin 86.082 Agr 6.119 2.57% -0.28% 2.29%

20 South Kalimantan SKA 17.838 Min 97.692 Agr 9.961 2.50% 0.06% 2.56%

21 Central Kalimantan CKA 18.159 Fin 89.253 Agr 9.912 2.68% -0.47% 2.21%

22 East Kalimantan EKA 72.435 Man 342.387 Soc 8.294 -1.59% 0.25% -1.34%

23 Gorontalo GOR 7.056 Uti 102.932 Min 2.356 3.06% 0.99% 4.04%

24 North Sulawesi NSU 19.920 Fin 58.127 Agr 11.083 3.20% 0.95% 4.15%

25 Central Sulawesi CSU 15.255 Uti 74.495 Agr 11.499 3.55% 1.15% 4.70%

26 South Sulawesi + West

Sulawesi

WSU 15.424 Min 121.204 Agr 9.612 2.07% 1.59% 3.66%

27 Southeast Sulawesi SSU 12.334 Fin 71.251 Agr 7.851 2.75% 2.17% 4.93%

28 North Maluku NMA 7.339 Fin 40.502 Con 3.314 3.09% -1.16% 1.92%

29 Maluku MAL 6.933 Fin 29.287 Con 3.735 0.14% 1.16% 1.30%

30 Papua + West Papua PAP 18.261 Min 195.823 Agr 4.914 -3.67% 0.80% -2.87%

Indonesia IDN 21.553 Min 118.764 Agr 8.498 2.37% 0.96% 3.33%

Note: All numbers are for 2011.Currency is in constant 2000 IDR. Growths are in annual rate, between 2001 and 2011. Abbreviations are follows: (Agr) Agriculture; (min) Mining, (Man) Manufacturing; (Uti) Public Utilities; (Con) Construction; (Tra) Wholesale and Trade; (Tra) Transport and Communication; (Fin) Finance and Business Service; (Soc) Community , Social, and Government Services

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TABLE 4 Regression results

Dep. var: annual structural-change growth variables

agricultural share in employment 0.033 ***

annual growth of infrastructure spending -0.006 (0.005)

Robust t-statistics in parentheses

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

Source: authors’ calculation

In general, agriculture employment, primary sector, institution, primary education, and minimum wage shows signiicant result in the regression. Only openness and East-Indonesia dummy that show insigniicancy. 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 signiicant share of agriculture labor force may have greater potential for positive structural change growth. Positive and signiicant 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 signiicant 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 signiicant 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 irms’ expansion. Thus, the more budget allocated to institutional enhancement, i.e. public, law, and order spending, will increase the speed of structural change.

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The infrastructure’s support supposes to increase the structural change. Not only because a good infrastructure has a direct inluence in technological progress of the industry, it also boosts the production by lowering logistic cost. However, the insigniicant variable does not means that infrastructure is not signiicant to structural change. Instead, it possibly indicates that infrastructure is not afecting 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 insigniicancy, 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 sufer during the liberalization. In critical condition, it could push the employers to shut down those higher productivity industries (although not the most eicient 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 signiicant. The estimation result shows that every increasing of minimum wage by 1% will

discourage structural change by 0.2%. This is consistent with the indings about the inluence of ease of entry and exit into industry and lexibility of labor markets. Recalling the GCI result for 2010-2011, Indonesian labor lexibility is ranked 98th, consisting of low lexibility 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 inding, 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 inancial ability of irms 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 irms’ reallocations. When the regional minimum wage signiicantly increase, the irms will alter their production bases to the province with lower minimum wage. Under this argument, it is 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

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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

T h e s t r u c t u r a l c h a n g e a n a l y s i s re s u l t s s o m e recommendation that could be relevant with the indings.

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 (inancial, 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 ixed/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 igure (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 qualiication helps labor to graduate into higher productivity sector that needs higher cognitive and analytical skills, that supporting the technology application in irms.

d) Diversifying economies in primary-sector dependent provinces

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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 signiicant inding 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 iring practices, and redundancy costs. This research employs minimum wage growth because its variation is captured in provincial level.

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Alvarez-Cuadrado, F & Poschke, M 2009, ‘Structural change out of agriculture: labor push versus labor pull’, IZA Discussion Paper, no. 4247, The Institute for the Study of Labor (IZA), Germany.

Artuç, E, Lederman, D, & Porto, G 2013, ‘A mapping of labor mobility costs in developing countries’, Policy Research Working Paper, no. 6556, Development Research Group, Poverty Reduction and Economic Management Network, The World Bank.

Barro, RJ & Lee, J 1993, ‘International comparisons of educational attainment’, NBER Working Paper Series, no. 4349, National Bureau of Economic Research, MA.

Biggeri, M 2003,’Key factors of recent Chinese provincial economic growth’, Journal of Chinese Economic and Business Studies, vol. 1, no. 2, pp. 159-183.

Chowdhury, A, Islam, I, & Tadjoeddin, MZ 2009.’Indonesia’s employment challenges: growth, structural change and labour market rigidity’, European Journal of East Asian Studies, vol. 8, no. 1, pp. 31-59.

Del Carpio, X, Nguyen, & H, Wang, LC 2012,’Does the minimum wage affect employment? evidence from the manufacturing sector in Indonesia’, Policy Research Working paper, no. 6147, The World Bank.

Fagerberg, J 2000,’Technological progress, structural change and productivity growth: a comparative study’, Structural Change and Economic Dynamics, vol. 11, pp. 393-411.

Harris, JR & Todaro, MP 1970, ‘Migration, unemployment and development: a two-sector analysis’, The American Economic Review, vol. 60, no. 1, pp. 126-142.

Hill, H, Resosudarmo, BP, & Vidyattama, Y 2008, ‘Indonesia’s changing economic geography’, Bulletin of Indonesian Economic Studies, vol. 44, no. 3, pp. 407-435.

Lee, S & Malin, BA 2013,’Education’s role in China’s structural transformation’, Journal of Development Economics, no. 101, pp. 148-166.

references

Lewis, AW 1954,’Economic development with unlimited supplies of labour’, Manchester School, vol. 22, no. 2, pp. 139-191.

Maddison, A 1987,’Growth and slowdown in advanced capitalist economies: techniques of quantitative asessment’, Journal of Economic Literature, vol. 25, no. 2, pp. 649-698.

Mason, AD & Baptist, J 1996, ‘How important are labor markets to the welfare of Indonesia’s poor?’, Policy Research Working Paper, no. 1665, The World Bank.

McMillan, MS & Rodrik, D 2011, ‘Globalization, structural change and productivity growth’, NBER Working Paper Series, No. 17143, National Bureau of Economic Research, MA.

Ngai, LR & Pissarides, CA 2007,’Structural change in multisector model of growth’, The American Economic Review, vol. 97, no. 1, pp. 429-443.

Ocampo, JA, Rada, C, & Taylor, L 2009, Growth and Policy in Developing Countries: A Structuralist Approach, Columbia University Press, New York.

Papanek, G, Pardede, R, & Nazarra, S 2014, ‘The choice for the next 5 years: 5% growth and 0.8 million jobs a year or 10% growth and 3 million good jobs’, Apindo Indonesian Economics Road Map, Slides Presentation,11 March 2014.

Pieper, U 2000, ‘Deindustrialization and the social and economic sustainability nexus in developing countries: cross-country evidence on productivity and employment’, Journal of Development Studies, vol. 36, no. 4, pp. 66-99.

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Appendices

Appendix A: 9 sectors - ISIC rev. 2

Classiication 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

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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.

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APINDO-EU ACTIVE Project Gedung Permata Kuningan Lantai 10 Jl. Kuningan Mulia Kav. 9C, Guntur – Setiabudi, Jakarta 12980 – Indonesia

Telp. +62-21 8378 0824 Fax. +62-21 8378 0823, 8378 0746 Email: active@apindo.or.id

timkajian@apindo.or.id

Gambar

FIgURE 1     Productivity changes between ASEAN countries as compared with US’s productivity, 1980=1
FIgURE 2     Decomposition of Labor Productivity Growth in Indonesia 1971-2011
FIgURE 3     Decomposition of Labor Productivity Growth in Indonesia 1971-2011: Sectoral Figures
TABLE 2      Summary Statistics on Sectoral Labor Productivity
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