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Employment Dimension Of Trade Liberalization With China: Analysis Of The Case Of Indonesia With Dynamic Social Accounting Matrix

Case study by Ernst, Christoph, Peters, Ralf, 2011

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The ASEAN – China FTA raises concerns regarding its employment impact in Indonesia. The loss of millions of jobs has been predicted as a consequence of the final liberalization round, though few studies on ACFTA consider employment explicitly. This paper has two objectives. First, the employment effects of ACFTA in Indonesia on different groups of the labour market such as rural and youth employment are assessed. Second, a relatively simple methodology is developed that can be used by government officials, employers, trade unions and civil society organizations to assess and quantify the impact of trade policy changes on employment and to deepen their understanding of the complex relationship between trade and employment. The methodology combines two analytical models. Trade shocks are assessed using SMART that calculates import changes resulting from tariff reductions. The resulting effects on employment are evaluated using a multiplier analysis based on the 2008 Social Accounting Matrix component of a Dynamic Social Accounting Matrix. The impact of the final step of ACFTA is likely to be limited for Indonesia in terms of employment. Our analysis shows a small net loss of employment in Indonesia in the short run with some losses for certain groups, including female and young workers, and gains for other groups, for example agriculture employment.

UNCTAD series on assuring development gains from the international trading system and trade negotiations


U N I T E D N AT I O N S C O N F E R E N C E O N T R A D E A N D D E V E L O P M E N T


EMPLOYMENT DIMENSION OF TRADE
LIBERALIZATION WITH CHINA:


ANALYSIS OF THE CASE OF INDONESIA WITH
DYNAMIC SOCIAL ACCOUNTING MATRIX




   


UNITED NATIONS CONFERENCE ON TRADE AND DEVELOPMENT










EMPLOYMENT DIMENSION OF TRADE
LIBERALIZATION WITH CHINA:


ANALYSIS OF THE CASE OF INDONESIA WITH
DYNAMIC SOCIAL ACCOUNTING MATRIX












Christoph Ernst and Ralf Peters











New York and
Geneva, 2011


 




ii  EMPLOYMENT DIMENSION OF TRADE LIBERALIZATION WITH CHINA :
ANALYSIS OF THE CASE OF INDONESIA WITH DYNAMIC SOCIAL ACCOUNTING MATRIX


 


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Series Editor:


Ms. Mina Mashayekhi
Head, Trade Negotiations and Commercial Diplomacy Branch


Division on International Trade in Goods, and Services, and Commodities
United Nations Conference on Trade and Development


Palais des Nations
CH-1211 Geneva 10





UNCTAD/DITC/TNCD/2011/4










UNITED NATIONS PUBLICATION


ISSN 1816-2878







Copyright © 2011 United Nations and International Labour Organization
All rights reserved worldwide


 




PREFACE iii


 


Preface

As the focal point of the United Nations for the integrated treatment of trade and development and
interrelated issues, and in accordance with the São Paulo Consensus adopted at the eleventh session of
UNCTAD, the UNCTAD secretariat supports member States in assuring development gains from
international trade, the trading system and trade negotiations, with a view to their beneficial and fuller
integration into the world economy, and to the achievement of the United Nations Millennium
Development Goals. Through intergovernmental deliberations and consensus-building, policy research
negotiations and commercial diplomacy aims at enhancing the human, institutional and regulatory
capacities of developing countries to analyse, formulate and implement appropriate trade policies and
strategies in multilateral, interregional and regional trade negotiations.

This paper is part of a series entitled Assuring Development Gains from the International Trading System
and Trade Negotiations. It builds on the previous series entitled Selected Issues in International Trade
Negotiations. Experts are invited to express their own views, which do not necessarily reflect those of the
UNCTAD secretariat. The targeted readership is government officials involved in trade negotiations, trade
and trade-related policymakers, and other stakeholders involved in trade negotiations and policymaking,
including non-governmental organizations, private sector representatives and the research community.

The objective of the series is to improve understanding and appreciation of key and emerging trade policy
and negotiating issues facing developing countries in international trade, the trading system and trade
negotiations. The series seeks to do so by providing a balanced, objective and sound analysis of the
technical issues involved, drawing implications for development and poverty reduction objectives, and
exploring and assessing policy options and approaches to international trade negotiations in goods,
services and trade-related issues. It seeks to contribute to the international policy debate on innovative
ideas to realize a development dimension for the international trading system, with a view to achievement
of the Millennium Development Goals.

The series is produced by a team led by Mina Mashayekhi, Head, Trade Negotiations and Commercial
Diplomacy Branch, Division on International Trade in Goods and Services, and Commodities.



 




ABSTRACT AND ACKNOWLEDGEMENTS v


 


 


Abstract
 
The ASEAN – China FTA raises concerns regarding its employment impact in Indonesia. The loss of
millions of jobs has been predicted as a consequence of the final liberalization round, though few studies
on ACFTA consider employment explicitly. This paper has two objectives. First, the employment effects of
ACFTA in Indonesia on different groups of the labour market such as rural and youth employment are
assessed. Second, a relatively simple methodology is developed that can be used by government
officials, employers, trade unions and civil society organizations to assess and quantify the impact of trade
policy changes on employment and to deepen their understanding of the complex relationship between
trade and employment. The methodology combines two analytical models. Trade shocks are assessed
using SMART that calculates import changes resulting from tariff reductions. The resulting effects on
employment are evaluated using a multiplier analysis based on the 2008 Social Accounting Matrix
component of a Dynamic Social Accounting Matrix. The impact of the final step of ACFTA is likely to be
limited for Indonesia in terms of employment. Our analysis shows a small net loss of employment in
Indonesia in the short run with some losses for certain groups, including female and young workers, and
gains for other groups, for example agriculture employment.








Acknowledgements
 
This paper has been developed as a contribution to the International Collaborative Initiative on Trade and
Employment. The views expressed are those of the authors and do not necessarily reflect those of the
participating organizations.


The paper was prepared by Christoph Ernst, Employment Sector, International Labour Organization (ILO)
and Ralf Peters, Trade Negotiations and Commercial Diplomacy Branch, Division on International Goods
and Services, and Commodities, UNCTAD. Support from the EU financed ILO project on “Assessing and
Addressing the Effects of Trade on Employment” is gratefully acknowledged. The authors would like to
thank Ana Iturriza, Marion Jansen, Shigehisa Kasahara, David Vanzetti and participants of the International
Collaborative Initiative on Trade and Employment (ICITE) conference at the Asian Development Bank in
Manila in 2011 for helpful comments on an earlier version. Sophie Munda was responsible for the
formatting of this publication. The views expressed are those of the authors and do not necessarily reflect
the views of the United Nations or of the ILO.









CONTENTS vii


 


 


Contents


Preface .................................................................................................................................. iii


Abstract .................................................................................................................................. v


Acknowledgements.............................................................................................................. v


1. Introduction ......................................................................................................................1


2. Trade between China and Indonesia, trade policy and employment .............3


Employment in Indonesia ............................................................................................................3


Trade between Indonesia and China .......................................................................................4


Indonesia’s tariff structure ........................................................................................................7


3. Social Accounting Matrix to measure the effects of trade on
employment..........................................................................................................................11


Social Accounting Matrix..........................................................................................................11


Static SAM and trade................................................................................................................12


Dynamic SAM ...............................................................................................................................13


Trade policy analysis in dySAM ..............................................................................................14


Backward and forward linkages in SAM .............................................................................15


Labour satellite ............................................................................................................................18


Employment effects of trade...................................................................................................20


4. Results and employment effects from the scenario ........................................27


Calculation of trade shocks using SMART .........................................................................27


Employment effects from ACFTA...........................................................................................30


5. Conclusion .......................................................................................................................35


References ...........................................................................................................................37


Annex......................................................................................................................................39




1. INTRODUCTION 1


1. Introduction

The Association of Southeast Asian Nations (ASEAN)–China Free Trade Area (ACFTA) is a free trade area
among the 10 member States of ASEAN and China. The initial framework agreement was signed in 2002
with the intent of establishing a free trade area among the 11 nations by 2010. Liberalization started in
2005 and the free trade area came into effect on 1 January 2010.

The ACFTA is seen as an opportunity as well as a challenge in Indonesia. On the one hand, concerns exist
that import competition resulting from the final step of liberalization may destroy industrial jobs. The
director of the Indonesian Institute for Development of Economic and Finance, for example, said that
ACFTA would increase unemployment by 1 million people because many labour-intensive industries
would be adversely affected (Jakarta Post, 28 January 2010). Others predict losses of 2.5 million workers
in labour intensive industries such as leather processing, clothing production, textiles and steel (Jakarta
Globe, 11 January 2010). On the other hand, new export opportunities are seen as well. Agricultural
exporters hope, for example, that exports will be boosted. Due to its high unemployment and
underemployment rate as well as its relatively high growth of the labour force, the impact on employment
is a major issue in the discussion.

An Asian Development Bank study (Park et al., 2008) based on a sectoral general equilibrium model
concluded that the ACFTA will lead to moderate welfare gains for Indonesia, by increasing its exports to
China and higher imports that increase productivity and efficiency in Indonesia. Total output in Indonesia,
however, is predicted to fall slightly by 0.17 per cent, mainly due to decreasing output and exports of
heavy manufacturing while output and exports in food products increase significantly. Another study by
Kiyota et al. (2008) using a general equilibrium model confirms the positive long-term impact on welfare
(1.2 per cent of GDP) of such a free trade agreement for Indonesia through resource allocation towards
sectors with comparative advantages (see also OECD, 2010). Tsigas and Wang (2010) apply a general
equilibrium model with explicit modeling of export processing zones in China and predict an increase of
Indonesian imports by 4.32 per cent and an increase of exports by 3.31 per cent. Welfare rises by $397
million in Indonesia. Vanzetti and Oktaviani (2011) combine a global and a country specific CGE model
and show that in agriculture the employment effects of the ACFTA are small.

Most of the quantitative analysis of the economic impact of free trade agreements (FTAs) does not
explicitly analyse the impact on labour markets. The focus is usually on welfare, sectoral output and trade.
Furthermore, CGE models are often based on relatively aggregated databases. The GTAP database, for
example, distinguishes only between skilled and unskilled labour which is often not enough to assess the
impact on, say, rural or urban poverty.

This paper attempts to determine aspects of the socio-economic impact of an FTA and tries to assess
specifically the employment effects of the final step of the ACFTA agreement in the entire economy. It
combines two techniques to analyze details of the employment implications in Indonesia. The potential
impact of the elimination of Indonesia’s tariffs on imports from China and the impact of the elimination of
tariffs on Indonesia’s exports to China are assessed using the SMART model which is accessible through
the World Integrated Trade Solution (WITS) developed by UNCTAD and World Bank.1 The impact of these
estimated changes of imports and exports on the labour market in Indonesia are assessed using a social
accounting matrix (SAM) in a Leontief multiplier model for Indonesia that has been updated on the basis of
a dynamic social accounting matrix (dySAM). The dySAM has been developed by the Government of
Indonesia with technical support by the ILO (see Alarcón and Ernst, 2011).

The objective of the paper is twofold. First, the results provide information about the impact of the ACFTA
on the Indonesian labour market. An advantage of the approach that is chosen is that relatively detailed


                                                            
1 For detailed information about SMART see wits.worldbank.org.


 




2 EMPLOYMENT DIMENSION OF TRADE LIBERALIZATION WITH CHINA :
ANALYSIS OF THE CASE OF INDONESIA WITH DYNAMIC SOCIAL ACCOUNTING MATRIX




 


information about the impact on various groups within the labour force becomes available. For example,
the impact on rural and urban male and rural and urban female workers can be differentiated as well as
the impact on youth employment.

Another objective of the paper is to develop a relatively simple quantitative methodology to assess the
impact of regional trade agreements and other trade policy changes on employment. This is important,
since more sophisticated models such as general equilibrium models are often considered a black box,
even by many economists, and are based on a large number of behavioural equations and assumptions.
Policymakers should be guided by sound analysis and the acceptance of quantitative models is most
likely higher when the technique is fairly simple and more related to real data. Specialists in quantitative
analysis in Ministries or in research units in trade unions or employers’ organizations are rare. Policy
advisors have made the experience that complicated models may not be used after their development if
they are not well understood by the targeted users. Thus, methodologies that can rather easily be used in
Ministries can support the acceptance of quantitative analysis. Both the SMART and the multiplier model
have limitations. For example, they do not take potential dynamic effects into account and the multiplier
analysis is based on a linear approach. These shortcomings are discussed in the last section. Thus, there
is a trade-off between more sophisticated but also much more complex models such as dynamic
computable general equilibrium models (CGE) that may capture some of those shortcomings and simpler
models that may have a higher acceptance among policy makers.

The paper is organized as follows. Section 2 discusses the trade pattern between Indonesia and China,
the employment structure in Indonesia and both countries’ trade policy. In section 3, the SAM and the
employment satellite are discussed, both theoretically and the specific Indonesian SAM. Based on a
hypothetical change of exports or imports of 1 million rupiah, employment impacts are discussed. In
section 4, the change of exports and imports triggered by the ACFTA using SMART is calculated. Results
are discussed in section 4 and section 5 concludes.




2. TRADE BETWEEN CHINA AND INDONESIA, TRADE POLICY AND EMPLOYMENT 3


2. Trade between China and Indonesia, trade
policy and employment




Employment in Indonesia



South-East Asia’s largest economy Indonesia has shown a healthy improvement since 2000 with
remarkable growth rates of 6.3 per cent in 2007 and 6.1 per cent in 2008. This robust economic growth,
however, has not necessarily translated into better labour market outcomes. The unemployment rate in
2007, for example, was higher than in 2002. Since November 2005, the unemployment rate has been
falling and stood at 7.3 per cent in 2010. Vulnerable employment2, which provides an indication of job
quality and the extent of informalization of the labour market, remains high, above 60 per cent. The
vulnerable remain mostly in agriculture although the urban informal economy is expanding rapidly. Due to
the recent economic crisis growth has slowed in 2009, though Indonesia’s economy has continued to
grow and growth increased again to about 6 per cent in 2010. However, this subdued expansion may not
be sufficient to absorb all the new entrants to the labour force. The crisis has impacted Indonesia most
severely through falling exports, with oil and gas exports declining by 55.4 per cent and manufactured
goods by 26.9 per cent. Slowing economic growth has prompted a steep fall in the growth of wage
employment, which grew just 1.4 per cent between February 2008 and February 2009, compared with 6.1
per cent in the previous period. The global crisis has also resulted in widespread job losses, with
subcontracted, casual and temporary workers in export-orientated industries being hardest hit. However,
the downward-trending unemployment rate suggests that many of the displaced workers have been
absorbed into other jobs, including in the informal economy, which has seen a marked expansion, in
contrast to previous years.3

Since unemployment and underemployment of the rapidly growing workforce in Indonesia is high,
employment creation is a priority area. From 2005 to 2009 there were some significant shifts in
employment in the different sectors: in agriculture employment decreased while it increased in
manufacturing and services. Differences between sectors within the three broad sectoral categories are
also high (table 2.1). Employment in the services sector restaurant, for example, has increased by almost
300 per cent but employment in trade services has remained almost unchanged. In agriculture there was a
shift in employment from other agriculture such as vegetable products, oils and fats towards livestock.


                                                            
2 Vulnerable employment is defined as the sum of own-account workers and contributing family workers.


3 This paragraph is mainly based on (ILO, 2008 and 2009).


 




4 EMPLOYMENT DIMENSION OF TRADE LIBERALIZATION WITH CHINA :
ANALYSIS OF THE CASE OF INDONESIA WITH DYNAMIC SOCIAL ACCOUNTING MATRIX




Table 2.1. Estimates of employment in Indonesia


Sectors 2'005 2'006 2007 2008 2009
Crops 31'814'800 30'676'471 30'638'909 30'527'137 30'444'676
Other Agriculture 5'615'930 4'510'376 3'536'325 3'452'175 2'822'795
Livestock 2'447'670 2'532'195 3'303'705 3'407'475 3'613'888
Forestry 504'050 608'293 585'130 635'565 623'032
Fishery 1'625'280 1'577'043 1'835'992 1'775'711 1'800'069
Coal, Metal, Petroleum Mining 314'940 347'491 359'276 394'512 455'904
Mining and Quarry 550'730 517'510 607'137 611'464 593'073
Food, Beverages and Tobacco 2'433'250 2'665'828 2'651'551 2'673'431 2'719'591
Textile, Wearing Apparel, Garment, Leather 2'806'210 2'713'336 2'591'724 2'636'927 2'764'606
Wood 2'388'440 2'140'502 2'117'101 1'987'930 1'887'400
Paper, Print, Transp, Metal Prod, other Ind. 2'511'380 2'314'682 2'668'504 2'687'494 2'640'180
Chemical, Fertilizer, Clay and Cement 1'732'490 1'769'089 1'857'509 1'986'709 1'995'125
Electricity, Gas and Water 191'190 219'507 163'706 187'709 204'480
Construction 4'497'560 4'529'653 4'927'595 5'087'646 5'039'220
Trade Services 16'226'480 16'528'646 15'323'415 15'869'948 16'124'131
Restaurant 2'076'660 3'482'202 8'127'491 8'159'323 8'292'462
Hotel Affairs 190'510 211'818 222'450 237'433 242'998
Land Transportation Services 3'365'410 3'189'497 3'011'323 3'046'302 2'698'742
Air, Water Transportation and Communic. 1'705'540 1'816'219 2'140'932 2'672'169 5'196'328
Storage, Other Transportation Service 540'110 664'352 971'010 978'121 629'977
Bank, Insurance, and Services 541'460 647'236 686'320 639'501 675'984
Real Estate and Business Services 904'060 1'010'097 972'040 1'128'659 1'072'642
Government, Defense, Education, SocSer 6'502'620 7'043'591 7'090'888 6'866'101 7'816'930
Other Individual and Household Services 3'977'090 4'258'318 4'552'102 5'801'244 5'455'543


Source: Authors’ calculation based on DySAM. For a list with full names of sectors see the Annex.




Trade between Indonesia and China

Trade between Indonesia and China has been very dynamic since 2000. Imports from China in 2010 were
10 times as high as they were in 2000, and exports to China were more than 6 times as high (figure 2.1).
Although Indonesia’s trade with other trading partners also grew significantly its trade with China was
much more dynamic (figure 2.1 and table 2.2). Trade with China as a share of Indonesia’s total trade has
increased: from 6 to 15 per cent for imports and from 5 to 10 per cent for exports. China is now one of
Indonesia’s major trading partners. Indonesia has a trade surplus with the world but a trade deficit with
China. Other major trading partners of Indonesia are ASEAN countries accounting for about 20 per cent of
Indonesia’s exports. Due to a lot of trade in intermediate goods, intra-ASEAN trade is significantly higher
than other south-south trade agreements, for example, intra-MERCUSOR or intra-SADC trade. However,
Indonesia’s ASEAN-trade share is slightly below the ASEAN average which is above one quarter. The
bilateral trade structures between ASEAN members and China suggest that there is a greater danger for
Indonesia of a trade deficit following the ACFTA compared with other ASEAN members (Vanzetti and
Oktaviani, 2011).



 




2. TRADE BETWEEN CHINA AND INDONESIA, TRADE POLICY AND EMPLOYMENT 5


Figure 2.1. Indonesia’s trade with China from 1996 to 2010 in $ billions


0


5


10


15


20


25


19
96


19
97


19
98


19
99


20
00


20
01


20
02


20
03


20
04


20
05


20
06


20
07


20
08


20
09


20
10


Imports


Exports


 
Source: United Nations Comtrade.


 
 


Table 2.2. Indonesia’s share of trade with China to
total trade in per cent


Year Share of imports Share of exports
2000 6.0 4.5
2001 6.0 3.9
2002 7.8 5.1
2003 9.1 6.2
2004 8.8 6.4
2005 10.1 7.8
2006 10.9 8.3
2007 11.5 8.5
2008 11.8 8.5
2009 14.5 9.9
2010 15.1 9.9


Source: United Nations Comtrade.



Indonesia has successfully diversified its exports. In 1980, about 70 per cent of its exports were in mineral
fuels and lubricants but in 2009 these products accounted for less than 30 per cent of the total
merchandise exports (figure 2.2). However, Indonesia’s exports to China are less diversified than its total
exports. Major exports to China are mineral products followed by vegetable products (table 2.3). Mineral
products are usually not very labour-intensive so that employment creation of these exports is limited.
However, agriculture is labour-intensive and here; exports are much larger than the imports from China.
Imports from China are high in machinery and electrical products, metals, chemicals and textiles, some of
which are also relatively labour-intensive.


 




6 EMPLOYMENT DIMENSION OF TRADE LIBERALIZATION WITH CHINA :
ANALYSIS OF THE CASE OF INDONESIA WITH DYNAMIC SOCIAL ACCOUNTING MATRIX




Figure 2.2. Indonesia’s total merchandise exports by sectors


0


10


20


30


40


50


60


70


80


Fo
od


an
d l


ive
a


nim
als


Be
ve


ra
ge


s a
nd


to
ba


cc
o


Cr
ud


e m
at


er
ial


s,
ine


di
bl


e,
ex


ce
pt


fu
els


M
ine


ra
l f


ue
ls,


lu
br


ica
nt


s,
re


lat
ed


m
at


er
ial


s


An
im


al
an


d
ve


ge
ta


bl
e o


ils
a


nd
fa


ts


Ch
em


ica
ls


M
an


uf
ac


t g
oo


ds
cl


as
sif


ie
d b


y m
at


er
ial


M
ac


hin
er


y a
nd


tr
an


sp
or


t e
qu


ipm
en


t


M
isc


ell
an


eo
us


m
an


uf
ac


tu
re


d
ar


tic
les


Pe
r c


en
t


1980
2010



Source: United Nations Comtrade





Table 2.3. Indonesia’s trade with China by sector
in 2008 in $ millions


Sector
HS


chapter
Imports Exports


Animal & Animal


Products 01-05 38 60


Vegetable Products 06-15 527 2,354


Foodstuffs 16-24 258 63


Mineral Products 25-27 774 5,252


Chemicals & Allied


Industries 28-38 1,724 670


Plastics/Rubbers 39-40 426 1,135


Raw Hides, Skins,


Leather & Furs 41-43 54 62


Wood & Wood


Products 44-49 166 1,181


Textiles 50-63 1,435 204


Footwear/Headgear 64-67 141 75


Stone/Glass 68-71 259 21


Metals 72-83 2,425 445


Machinery/Electrical 84-85 6,674 1,356


Transportation 86-89 675 34


Miscellaneous 90-97 646 69


Total All 16,221 12,980


Source: United Nations Comtrade.


 




2. TRADE BETWEEN CHINA AND INDONESIA, TRADE POLICY AND EMPLOYMENT 7


Indonesia’s tariff structure

The ratio of Indonesia’s merchandise trade to its GDP (exports plus imports divided by GDP) is increasing
and is currently about 50 per cent. Indonesia significantly reduced its applied tariffs between 1989 and
2001 (figure 2.3). In 2009 the simple average most favoured nation (MFN) tariff was 7.56 per cent (tariff line
level, table 2.5). Tariffs in agriculture are slightly higher than in industrial goods. Most tariffs, more than
three quarters, are between 0 and 10 per cent and about one quarter is duty-free (table 2.4). Peak tariffs
apply to motor vehicles, chemicals, fabricated metal products, bicycles and alcohol products. As a World
Trade Organization (WTO) member, Indonesia has committed itself to maximum legally binding tariffs
(bound rates). These are considerably higher than Indonesia’s applied rates (bound rates are 37 per cent
on average). As in most countries, tariffs tend to be higher on processed products, i.e. there is some
degree of tariff escalation, for example on semi-processed food.



Figure 2.3. Tariff reduction in Indonesia


0


5


10


15


20


25


19
89


19
90


19
93


19
95


19
96


19
99


20
00


20
01


20
02


20
03


20
04


20
05


20
06


20
07


20
09


Applied Total


Agriculture


Industrial


 
Source: UNCTAD TRAINS.



In addition to these relatively low MFN tariffs4, Indonesia provides – and benefits from – preferential
access within regional trade agreements such as ASEAN. As a member of ASEAN Indonesia is part of the
free trade agreements with Australia, India and the Republic of Korea, and it is a member of the Global
System of Trade Preferences among Developing Countries (GSTP) trade agreement where many major
developing countries provide each other preferential benefits.



                                                            
4 The average applied industrial tariff in other developing countries is 13 per cent and in agriculture 20 per cent.


 




8 EMPLOYMENT DIMENSION OF TRADE LIBERALIZATION WITH CHINA :
ANALYSIS OF THE CASE OF INDONESIA WITH DYNAMIC SOCIAL ACCOUNTING MATRIX




Table 2.4. Tariff distribution of Indonesia in percentage
Ad valorem


applied tariff
MFN 2009 China 2009


range in % distribution in % distribution in %


0 24.0 65.5
5 41.4 25.6


5<x<50 33.8 8.1
50≤x 0.8 0.8


Source: UNCTAD TRAINS.





Table 2.5. Average tariffs of Indonesia in per cent


MFN
2009


China
2009


All tariff lines 7.57 3.65
Dutiable lines only (i.e. > 0) 9.96 10.6
Lines where China has non-
zero preference 12.28 5.2
Source: UNCTAD TRAINS.



The ACFTA was signed in November 2002. China and ASEAN began lowering barriers to trade in 2005. In
2009 tariffs on imports from China were already low on average. More than 65 per cent of imports were
duty-free and the average tariff was 3.65 per cent (tables 2.4 and 2.5). A few sensitive products were
excluded, and these were protected with relatively high tariffs that were equal to the MFN tariffs. Under
the ACFTA, China, Brunei Darussalam, Indonesia, Malaysia, the Philippines, Singapore and Thailand
removed almost all tariffs as of 1 January 2010. A list of Highly Sensitive Track codes has been specified
and exempt from tariff elimination. Indonesia’s list of sensitive products comprises 76 products at the HS
6-digit level and China’s list has 86 products. Some agricultural products, especially rice and sugar, and
parts for motor vehicles and heavy machinery still face tariffs in 2010 and thereafter in Indonesia. China
also excluded some agricultural products, including tobacco, some wood products and some vehicles.
Apart from these exceptions, almost all goods have been set to be tariff-free between China and
Indonesia, the Philippines, Thailand, Singapore, Malaysia and Brunei Darussalam. The other four ASEAN
members – Cambodia, the Lao People’s Democratic Republic, Myanmar and Viet Nam – are expected to
realize zero tariffs on a majority of Chinese commodities by 2015.

China’s average tariff on ASEAN commodities was reduced in 2010 as well. The simple average MFN tariff
in China is 9.5 per cent (table 2.7). Less than 10 per cent of China’s tariffs are MFN duty free (table 2.6). In
2009, nearly 60 per cent of tariff lines in China were duty-free for Indonesian products and the average
tariff was already relatively low at 2.6 per cent. Thus, Indonesia and other ASEAN countries had already
considerable preferential access in 2009 which was further improved in 2010.



 




2. TRADE BETWEEN CHINA AND INDONESIA, TRADE POLICY AND EMPLOYMENT 9


 


Table 2.6. Tariff distribution of China
in percentage


Ad valorem
applied tariff


MFN 2009
Indonesia


2009


range in %
distribution


in %
distribution in


%
0 9.4


0<x<50 89.6 39.6
50≤x 1.0 0.9


59.5



Source: UNCTAD TRAINS.





Table 2.7. Average tariffs of China in percentage


MFN 2009
Indonesia


2009
All tariff lines 9.5 2.6
Dutiable lines (i.e. > 0) 10.5 5.0
Lines where China has non-
zero preference 14.4 6.4
Source: UNCTAD TRAINS.



The fact that low tariffs between China and Indonesia before ACFTA came fully into force in 2010 is one
obvious reason for predicting small changes of trade as a result of the full implementation of the ACFTA in
2010 (see section 4).





3. SOCIAL ACCOUNTING MATRIX TO MEASURE THE EFFECTS OF TRADE ON EMPLOYMENT 11


3. Social Accounting Matrix to measure the
effects of trade on employment




Social Accounting Matrix

A SAM is a representation of all economic transactions that take place within an economy. Trade is a
component of the national aggregate demand and can therefore through this enter a Leontief multiplier
model. Thus, a SAM used in a Leontief multiplier model can be an instrument to analyse the direct,
indirect and induced effects of trade changes on employment. Direct effects stem from increased demand
from higher exports (or lower demand due to higher imports) and indirect effects from second round
effects due to changed incomes and consumption of workers that work in export sectors. A SAM can be
considered to be an extension of the input-output tables, which have been extensively used by the ILO
and others in recent decades to measure the direct and indirect employment effects of public investment
through a multiplier analysis. The major drawback of input-output tables is that they do not include any
detailed data on the distributional side of economic processes. That is, they do not contain data on socio-
economic transfers of and between the economic actors (governments, enterprises, and households) and
only describe a truncated socio-economic circle of a given country.


Figure 3.1. Input-output table and SAM




Institutions


SAM


Households
GovernmentEnterprises




I
N
P
U
T


OUTPUT


Social Transfers
Transfer


Wages


Taxes


Social Transfers


Subsidies/credits


Consumption


Production


 




12 EMPLOYMENT DIMENSION OF TRADE LIBERALIZATION WITH CHINA :
ANALYSIS OF THE CASE OF INDONESIA WITH DYNAMIC SOCIAL ACCOUNTING MATRIX


 
A SAM coherently brings together data on income creation and production such as national accounts and
input-output tables, and also includes information on incomes received by different institutions and
related spending (see figure 3.2).5 A SAM is an accounting framework that takes trade with the rest of the
world into account. Exports enter as an output of domestic production directed towards the rest of the
world (line-wise). Imports contribute to the production as an import or are part of national consumption.
Imports are considered leakages to the domestic economy. Traditionally, exports and imports are
considered exogenous factors.


Figure 3.2. Principal circular “closed” economic flow6




Final Demand
Households (FDH)


FD Other Institutions
(FDOI)


Factors and
Factorial


Income Distribution
(FP)


PRODUCTION:


COMMODITY (CM)


ACTIVITIES (PA)




Static SAM and trade



The ILO has used static SAMs to analyse the impact of FDI and trade on employment, for example in the
case of Costa Rica (Ernst and Sanchez-Ancochea, 2008). A SAM was used to measure the local linkages
between the Export Processing Zones (EPZs) – which are dominated by offshoring activities – and other
sectors of the economy. The research reveals the low linkages of EPZs when compared with other
sectors. As an extension to the SAM, a small satellite employment account was created. The calculation
of employment multipliers shows that the lack of linkages leads to limited employment effects. The weak
backward linkages are partly the result of the insufficient capabilities of domestic firms. Traditionally,
Costa Rica has not been a producer of manufacturing inputs and so domestic firms tend to be small and
experience difficulties in meeting high quality standards and production levels (Gamboa et al., 2006).

Another example for measuring the employment impact of trade comes from Kucera et al. (2010) who
measured employment changes in India and South Africa as a result of trade contraction with the


                                                            
5 A SAM, therefore, displays the following elements: 1. Inputs; 2. Outputs; 3. Factor incomes created in domestic production;
4. Distribution of these factor incomes; 5. Redistribution of these factor incomes over these institutions, 6. Expenditure of the
institutions on consumption, investment, 7. Savings made by them. For more information, see van Heemst (1991) and
Breisinger et al. (2010).


6 See Pyatt and Thorbecke (1976).


 




3. SOCIAL ACCOUNTING MATRIX TO MEASURE THE EFFECTS OF TRADE ON EMPLOYMENT 13


European Union and United States during the 2008–09 global crises. They used static SAMs in a Leontief
multiplier model with a more comprehensive employment satellite account. The trade shocks used are
observed declines in trade during the “Great Trade Collapse”. One outcome was that India and South
Africa suffered from significant income and employment reduction as a consequence of the contraction of
trade with the United States and Europe. Most of the decline was in the non-tradable sector as a result of
the induced effect via income and consumption, which shows that the tradable and non-tradable sectors
are linked and interdependent. The study discusses the effects on specific labour market groups such as
female workers and income inequality. In India, there is no gender bias of the employment contraction in
the total figures, although a slightly higher share (than men) of less educated women has lost jobs. In
South Africa, there is a general gender bias in favour of women since sectors dominated by women were
less affected by the contraction. Regarding income inequality, trade decline had a lesser impact on lower
income quintiles, thus contributing to lower income inequality in South Africa. In India, trade contraction
did not have a significant impact on income distribution. Similar approaches assessing the impact of trade
with a SAM have been used in other studies. Sachs and Shatz, (1994) and Wood (1994) are examples of
earlier studies.

Another strand of the literature use SAMs as the underlying equilibrium data framework for CGE models.
De Melo and Tarr (1992) use a SAM based CGE for the United States to analyse its foreign trade policy.
Reinhart and Roland-Holst (1997) discuss the SAM based CGE approach and provide several examples,
including where such an approach has been used to assess the GATT Uruguay Round agreement. A more
recent example of this approach is Polaski et al. (2009). The model is a social accounting matrix–based
CGE model, the Static Applied General Equilibrium-Labor (STAGE_LAB) model developed by McDonald
and Thierfelder. The single country model for Brazil contains information about 11 regions and provides
significant details on labour markets. A result is that any of the trade agreements explored will have
positive, but very modest, effects on the Brazilian economy.


Dynamic SAM

Standard trade simulations use one period SAMs and the base year is often several years back due to
infrequent updating of SAMs. In this paper the last available year, 2008, of a dynamic SAM (DySAM) has
been used to bring the base year for the analysis closer to the current edge. The DySAM is based on the
Indonesian 2005 SAM.

A DySAM is a time series of data (see figure 3.3) and addresses four main problems of a static SAM that
often add limitations to the use of SAMs in various research areas: (a) a SAM model is static with fixed
coefficients; (b) data in a SAM refer to one single period (normally one year); (c) the year of a SAM is
normally not current; and (d) a SAM lacks behavioural responses, e.g. to changes of relative prices.



Figure 3.3. Comparison static SAM and DySAM


SAM




2010


2009


2008


DySAM


 




14 EMPLOYMENT DIMENSION OF TRADE LIBERALIZATION WITH CHINA :
ANALYSIS OF THE CASE OF INDONESIA WITH DYNAMIC SOCIAL ACCOUNTING MATRIX


 
A DySAM is based on an existing “static” SAM, but while the static SAM gives a snapshot of the
economy, a DySAM shows the evolution of the economic structure over time, for periods covering the
years before and after the static SAM. It displays several sequential SAMs over time, thus implying
dynamics. This time dimension helps to relax some strong assumptions of a static SAM framework:


 Over time, changes of the SAM reflect technology and price changes.
 A DySAM lessens the need to calculate expenditure income elasticities in order to introduce


behaviour.
 There will always be one DySAM period that matches surveys (e.g. labour, household


expenditure, population), which eliminates the need to introduce time-bound assumptions.

Moreover, an employment satellite account for one or several years with disaggregated labour market
data was added and coupled with the DySAM, and matched with exact years of the particular survey. A
DySAM multiplier analysis can build a greater understanding of the dynamic-interdependent linkages
(forward, backward) between the different sectors and the institutional agents at work within the economy.


A SAM based model or “multiplier framework” requires the adoptions of a number of assumptions, among
others, the specification of the SAM accounts as exogenous and endogenous. Endogenous accounts are,
in general, commodities, activities, factors of production, enterprises and households. Exogenous are in
general the government, capital accounts and the rest of the world (trade). Accounts intended to be used
as policy instruments are made exogenous. Thus,


Y=Ma * x,


where Y are endogenous accounts, Ma is the multiplier matrix and x are exogenous accounts.




Trade policy analysis in dySAM

Though tariffs and taxes are government variables that could be exogenous, an analysis of a regional
trade agreement where tariffs are only reduced vis-à-vis imports from certain trade partners can in general
not be conducted with a SAM where the “rest of the world” is not disaggregated. Furthermore, even if
trade was disaggregated between China and the other rest of the world, trade diversion effects, i.e. lower
imports from third countries due to a change in relative import prices as a result of the ACFTA would not
be taken into account. Therefore, our analysis of the impact of a trade policy change like joining or
extending an RTA on households and employment is split into two parts. First, the impact of the trade
policy change on exports and imports is calculated outside of the SAM using the SMART trade simulation
model. Second, the changes in imports and exports are used as exogenous shocks in the DySAM model.
Exports are exogenous in the SAM. Imports are leakages and as a component of aggregate demand they
will indirectly be affected by changes in demand from the rest of the world. Aggregate demand is:


Y = C + I + G + X – M,


where C is private consumption, I investments, G government demand, X exports and M imports. The
approach used in this paper is roughly equivalent to analysis using a SAM based country CGE model with
exogenous international trade and with specific functional forms such as Leontief production functions.
Gibson (2011) discusses methodological approaches to assess employment effects of trade and provides
technical details on input-output and general equilibrium models.

Our specific focus is on bilateral trade relations between two countries. The change in imports and
exports is modeled as a shock to demand for commodities from the rest of the world. In the case of rising
exports, the rest of the world increases its demand for the corresponding commodities. In the case of
higher imports it is assumed that domestic production is replaced by imports and thus demand for
domestically produced commodities falls by the same amount as imports increase. In a CGE model this


 




3. SOCIAL ACCOUNTING MATRIX TO MEASURE THE EFFECTS OF TRADE ON EMPLOYMENT 15


corresponds to infinite Armington elasticities, i.e. foreign and domestically produced goods are perfect
substitutes. Thus,


ΔY=Ma * Δx,


where ΔY is the change in total domestic production, Ma is the multiplier matrix and Δx is the change in
exports or imports. The change in total domestic production does reflect the multiplier effect determined
by the direct and indirect effects which are discussed below.



Backward and forward linkages in SAM

SAM based approaches use a simple and straightforward multiplier methodology for short- to medium-
term policymaking. Multipliers can be decomposed into intra-account and induced effects. The intra-
account effect shows how much an activity is integrated with the rest of the economy. It shows not only
the direct effect of, for example, the specific activity within the activity account (e.g. car producer), but
also the indirect impact on other activities within the activity account, e.g. suppliers to the automobile
industry and the feedback loops between the different activities. The induced effect is via the “workings of
the economy,” or via the cascading effect throughout the economy (through other accounts, such as
factor income and institutions). For example, the injection could occur in a specific activity, which leads to
higher wage of their workers active in this sector, who consume more boosting the production of goods
(thus enterprises) they purchase, they pay more taxes (the enterprises as well), leading to increased tax
collection, etc.


Moreover, each of the columns of the matrix of accounting multipliers shows the effects which each
corresponding exogenous injection on the incomes of endogenous accounts will have. Equally to the Input-
Output model, the sum total for a column or a row is equivalent to the backward and forward (income or
expenditure) linkages. Moreover, in a SAM framework it is possible to calculate within account sums by
column or row (so-called “total backward or total forward linkages”), module sums (so called “partial
backward or forward linkages”) and within (the same) account backward or forward linkages.


The interpretation of partial backward and forward linkages in a SAM based model is close to that of Input-
Output models. Backward linkages occur where inputs are used in the supply chain. Greater agricultural
exports and production, for example, increase the demand for fertilizers. Forward linkages involve processing
and other downstream activities. Even though the sum of all the elements can be read as the backward
(forward) linkages of the trade-induced multipliers, the interpretation is not so straightforward in a SAM
framework, as the linkages are composites of the effects of several types of accounts effects. For example, an
exogenous increase of exports will have as the first effect the increase of the income of the corresponding
account (partial linkage). This income rise will then generate income effects on all other endogenous accounts;
the sum of all these effects represents its total backward or forward linkage. This indicator, even though a
crude one, can be helpful in the evaluation of the total expected impact at the macro national level and thus it
can give a clearer effect of the impact that trade changes can have on the economy (Alarcón, 2006).7
Figure 3.4 shows the partial backward linkages of activities, i.e. domestic production, for the year 2008.
This is the latest available year for which the DySAM is available based on reliable macroeconomic data.


The service Restaurant has the highest partial backward linkage (rank 1, unweighted). Crops as an
Activity, for example, has high partial backward linkages within the Activity account, in popular terms, with
other economic sectors, number four ranked, but weighted by the total output of all activities, much less
so, ranked number 8.


                                                            
7 In other words, by comparing the sums of the different column totals in the Ma matrix, one can explain when an increased
demand of, for example, one million monetary units, will create the highest total income for an endogenous account. From a
policy perspective, however, it might still be interesting to use within account linkages, for example to discriminate and
provide a better understanding into the degree of effects per account (See Alarcón, 2006). 


 




16 EMPLOYMENT DIMENSION OF TRADE LIBERALIZATION WITH CHINA :
ANALYSIS OF THE CASE OF INDONESIA WITH DYNAMIC SOCIAL ACCOUNTING MATRIX


 
Figure 3.4. Rank of partial backward linkages of Activities (A A), Ma, weighted and unweighted,


DySAM, 2008


0 5 10 15 20 25 30


CoalMetalPetrol
ChemFertClayCement


RealEstate BusinessServ
AirWaterTranspCommun


PaperPrintTranspMetal
ForestryHunt


BankInsuranceServ
Fishery


OthIndivHHServ
RoadCapitInt


ElectrGasWater
ConstructionRest


Storage OthTranspServ
HotelAffairs


WeaveTextileGarmentLeather
Irrigation


Wood
MiningQuarry


TradeServ


GovDefEduHlthFilm
OthSocServ


OthAgricult
LandTranspServ


Livestock
Crops


RoadLabInt
FoodDrinkTobacco


Restaurant


weigthed


unweighted



Source: Authors’ calculations based on Indonesia DySAM.


Note: The values for partial weighted backward linkages (Ma) vary between 0.1


and 0.8, for unweighted Ma between 2.0 and 3.2. The graph shows the


ranking for the 27 sectors to allow comparisons. Smaller bars imply higher


partial backward linkages, i.e. a higher rank.

The analysis of the unweighted partial8 backward linkages by Activities within the Activity account shows
the high level of linkages for primary products, such as agriculture and agro-business and mining as well
as for basic services such as restaurants. On the other end of the spectrum are manufacturing sectors
such as chemicals, fertilizers and cements or paper, transport, metal, the petroleum industry, and some
more sophisticated services such as communication or real estate/business services. Their backward
integration with the rest of the economy in Indonesia is relatively low compared with other countries in
South-East Asia. An interesting observation is the higher backward linkage level for agricultural products
when weighted, as well as for land and transport services. Agriculture and related activities in Indonesia
are important economic sectors and employment providers often dominated by subsistence farming and
low productivity activities. Labour productivity is also low for basic services as well. Manufacturing,
however, including petroleum extraction, a rather sophisticated extracting activities, is more capital-
intensive and more productive, but their socio-economic dimension is limited thus contribute relatively
less to the total economy having smaller weighted values.




                                                            
8 Because of the issue of double counting, we limit our analysis to partial backward linkages of selected accounts, namely
Activities and Commodities.


 




3. SOCIAL ACCOUNTING MATRIX TO MEASURE THE EFFECTS OF TRADE ON EMPLOYMENT 17


Figure 3.5. Rank of partial backward linkages of Commodities (Co Co), Ma, weighted and
unweighted (ranked), DySAM, 2009


0 5 10 15 20 25 30


CoalMetalPetrol
ChemFertClayCement


RealEstate BusinessServ
ForestHunt


AirWaterTransp Commun.
PaperPrintTranspMetal


BankInsuranceServ
Fishery


OthIndivHHServ
ElectGasWater


HotelAffairs
Storage OthTranspServ


Construction
WeaveTextGarmentLeather


MiningQuarry
TradeServ


Wood
FoodDrinkTobacco


OthAgric


GovDefEduHlthFilm
OthSocServ


LandTranspServ
Livestock


Crops
Restaurant


weighted


unweighted



Source: Authors’ calculations based on Indonesia DySAM.


Note: The values for weighted partial backward linkages (Ma) vary between 0.08


and 0.26, for unweighted Ma between 2.0 and 3.3. The graph shows the ranking


for comparison reasons. Restaurant, for example, has high partial backward


linkages within the Commodities account, number 1 ranked, but weighted by the


total output of all commodities, less so, ranked number 4.



An analysis of the partial backward linkages of Commodities within the Commodities account including
imports from abroad, confirms more or less the results found in Activities, with low values for primary
products, with the exception of petroleum, and much higher values when weighted, and high values for
petroleum, manufacturing and sophisticated services. The fact that the results are similar to the ones
related to the Activities account shows indirectly and to a certain extent that Indonesia is still a relatively
closed economy and depend less on imports or international trade in general than other countries in the
region.

Nevertheless, it is also helpful to analyse the total forward linkages representing the amounts of
expenditures per account which are made “available” for the expansion in other accounts (Alarcón, 2006). It
shows the level of potential demand and helps to identify potential bottlenecks.



 




18 EMPLOYMENT DIMENSION OF TRADE LIBERALIZATION WITH CHINA :
ANALYSIS OF THE CASE OF INDONESIA WITH DYNAMIC SOCIAL ACCOUNTING MATRIX


 
Figure 3.6. Rank of total forward linkages, Ma, Activities, unweighted and ranked,


DySAM, 2009


0 5 10 15 20 25 30


RoadLabInt
ConstructRest


RoadCapInt
Irrigation


HotelAffairs
MiningQuarry
ForestryHunt


Storage OthTranspServ
Wood


Fishery
Livestock


ElectrGasWater
OthAgric


CoalMetalPetrol
WeaveTextGarmentLeather


LandTranspServ
OthIndivHHServ


RealEstate BusinessServ
AirWaterTransp Communic.


GovDefEduHlthFilmOthSocSrv
BankInsuranceServ


Restaurant
Crops


ChemFertClayCement
PaperPrintTranspMetal


TradeServ
FoodDrinkTobacco



Source: Authors’ calculations based on Indonesia DySAM and Employment


Satellite.


Note: The graph shows the ranking. Food, drinks and tobacco as an


Activity, for example, has the highest total forward linkages, while road


construction has the lowest.



As expected, all construction activities, with labour-intensive road building as the number one ranked,
have the highest forward linkages, as the outcome of construction is highly demanded by other activities,
it has a potentially high market in the Indonesian economy. The same holds for mining and quarry, an
important input for other sectors. On the other end of the spectrum is food, drink, tobacco, which is at the
end of the production chain. Similarly, final services such as trade services, which depend on demand
related to other activities, have also low forward linkages. An analysis by Commodities shows similar
results.


Labour satellite


The employment effect is determined in a satellite to the SAM that contains detailed information about the
labour market in real (number of workers) and not in monetary terms. Some products, for example, are
highly capital-intensive, others rather labour-intensive. The employment satellite contains disaggregated
labour market information, such as on employment by gender, age groups or geographic location of
workers, whereas the SAM only contains information about the wage bill, meaning a lump sum of
wages/income times workers/employment. But combining the SAM with the employment satellite allows
the calculation of the labour intensity of the sectors and calculation of employment effects, or, the inverse,
their labour productivity.

The matrix λ contains the information how much labour is used per output, thus information on the labour
intensity of sectors. Thus, if λ is multiplied by the change in total domestic production ΔY (=Ma * Δx) the
number of created or lost jobs triggered by a change in demand Δx is received,



 




3. SOCIAL ACCOUNTING MATRIX TO MEASURE THE EFFECTS OF TRADE ON EMPLOYMENT 19


i.e.


ΔLabour = λ * Ma * Δx,


where ΔLabour is the change in employment and λ is the labour output.



Figure 3.7. Output per labour in billion rupiah, ranked by Activities, 2009


0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6


Crops
Livestock


Restaurant
OthAg


OthIndivHHSrv
RoadLI


TradeSrv
Storage OthTrpSrv


Fishery
ForestHunt


Wood
GovDefEduHlthFilm


LandTrpSrv
MiningQuarry


AirWaterTrp Communicatn
ConstRest


WeaveTextileGarmentLeather
HotelAffairs


RealEstate BusinessSrv
FoodDrinkTobacco


RoadKI
PaperPrintTranspMetal


BankInsuranceSrv
ChemFertClayCement


Irrig
ElecGasWater


CoalMetalPetrol




Source: Authors’ calculations based on Indonesia DySAM.


Note: The output per labour or labour productivity is calculated by dividing gross output by employment.9 The values vary


between 1,307,500 for coal, metal, petroleum, a high-capital intensive sector and 12,855 for other crops, with a lot of


subsistence farming.


The productivity figure above shows low levels for agricultural activities, which can be explained by the
high level of subsistence farming in Indonesia and its low capital-intensity. Other activities with low
productivity and high employment intensities are labour-intensive road construction as well as some basic
services such as restaurant or other individual and household services. At the opposite end, is the
petroleum industry, utilities and the majority of the manufacturing services (e.g. chemical industry) as well
as some sophisticated services such as banking and insurances, all sectors with relatively high
productivity and capital intensity, but less important as employment providers.10


                                                            
9 It is the opposite of λ, which is employment divided by gross output and which describes the labour-intensity of each
activity. In Indonesia, agriculture crops production is the most labour-intensive activity: to produce an output of 1 billion
rupiah, 85 workers are needed. Other activities in agriculture such as other agriculture and livestock are also labour intensive.
Restaurant services are also labour intensive and requires 27 for each billion rupiah output. Production of coal, metal and
petroleum is the other extreme: only 1 worker per 1 bill. Rupiah output is employed (Source: DySAM Indonesia and
employment satellite).


10 2009 data confirms these results as data are very similar. But as 2008 are more reliable, we stick to 2008 data for our
analysis.


 




20 EMPLOYMENT DIMENSION OF TRADE LIBERALIZATION WITH CHINA :
ANALYSIS OF THE CASE OF INDONESIA WITH DYNAMIC SOCIAL ACCOUNTING MATRIX


 
More detailed information about specific groups of workers such as female and youth employment and
the urban-rural distribution or employment is contained in the employment satellite. With this information,
the effects of trade on various employment groups can be assessed. The additional information on
gender, youth and regional distribution for each sector is contained in the matrix μ. Thus,


ΔEmploymentDistribution = μ * ΔLabour .

Finally, since not all workers are working full time a full-time equivalent can be calculated using the
information about average working hours in each sector in Indonesia that is available in matrix ν. Average
working time is defined as 40 hours by week. Agriculture, for example suffers from under-employment
with an average of 32 hours (subsistence farming), while in construction, over-employment is very
common (over 46 hours). Thus,


ΔEmploymentFullTimeEquivalent = ν * ΔLabour .


Employment effects of trade

Box 1 summarizes the approach chosen in this paper to estimate the employment effects of the ACFTA
on employment in Indonesia. In step 1 the change in total domestic production from a hypothetical
change of exports by 1 billion rupiah is calculated and used for step 2 to assess the implications for
employment using the labour satellite. In step 3 the above discussed effects on various labour market
groups is estimated. The effect of a change by 1 billion rupiah, the unit definition within the DySAM and
SAMs more generally, on employment is calculated in order to separate the impact of the backward and
forward linkages and the labour intensity from the effect of the magnitude and the composition of the
trade changes. This analysis provides interesting general results for policymakers regarding the
implications for the labour market of industrial policies such as export promotion.

The specific effect of the Asian – China FTA on trade is calculated in step 4 and these trade changes are
used in steps 5 and 6 to assess the employment effects of the ACFTA.



Box 1. Summary of methodology

Step 1: ΔY=Ma * Δx, where ΔY is the change in total domestic production, Ma is the multiplier matrix and


Δx is the change in exports or imports of 1 billion rupiah in each sector
Step 2: ΔLabour = λ * Ma * Δx, where ΔLabour is the change in employment and λ is the employment


satellite
Step 3: ΔEmploymentDistribution = μ * ΔLabour , where μ is the share of employment in rural and urban


areas, male and female employment and youth employment share
Step 4: Calculate change of exports and imports, ΔxACFTA, using SMART
Step 5: ΔLabourACFTA = λ * Ma * ΔxACFTA, where ΔLabourACFTA is the change in employment as a result of


changed exports and imports induced by the ACFTA
Step 6: ΔEmploymentDistribution = μ * ΔLabourACFTA and ΔEmploymentFullTimeEquivalentACFTA = ν *


ΔLabourACFTA , where ν is the full-time equivalent matrix

The employment effects of a change of exports by 1 billion rupiah on employment depends on the
backward and forward linkages, which are determined by Ma and the labour intensity, determined by λ
(steps 1 and 2).

The employment effects from an increase of exports or imports vary greatly among sectors.


 




3. SOCIAL ACCOUNTING MATRIX TO MEASURE THE EFFECTS OF TRADE ON EMPLOYMENT 21


 


While an increase of exports of coal, metal or petroleum by 1 billion rupiah creates 14.1 more jobs in
Indonesia an increase of exports of other agriculture products raise the number of jobs by 119.9 (see
figure 3.8).





Figure 3.8. Employment Effects of export change of 1 billion rupiah


0 20 40 60 80 100 120 140


CoalMetalPetroleum


Electricity, Gas & Water


ChemicalFertilClayCement


PaperPrintTranspMetal


BankInsuranceServices


RealEstate BusinessServices


AirWaterTrp Communication


Construction


WeaveTextileGarmentLeather


HotelAffairs


Forestry &Hunting


Mining & Quarry


Fishery


LandTransportServices


Storage OthTransport
Services


Personal Services


Wood


Trade Services


GovDefEduHealthFilm
OthSocSrv


FoodDrinkTobacco


Other Agriculture


Livestock


Restaurant


Crops



Source: Authors’ calculation using DySAM.


Note: Technically, the employment effect of an export rise of coal, metal or


petroleum is the result of ΔLabour = Δ * Ma * Δx (see above), where Δx=


(Δxcrops, ΔxOthAg, …, ΔxOthIndivHHSrv)=(0, …, 0, ΔxCoalMetalPetrol, 0, …, 0),


with ΔxCoalMetalPetrol=1 and the effect regarding other agriculture where


ΔxOthAg=1 and all other Δxi=0.



The jobs that would be created would be located in different geographical areas depending on the sector
and would vary with respect to their impact on female and youth employment (step 3). Naturally, in
agriculture most jobs would be created in rural areas while banking and insurance services would create
relatively more jobs in urban areas. However, due to the multiplier effect higher exports of all products
would lead to higher employment in all areas. A particularly positive impact on urban female employment
would be experienced through an increase of exports in the sectors restaurant, transport services and
textile and leather. In rural areas agriculture has the most positive impact including creating most jobs for
young workers, though this effect is not dominated by a high share of young workers in these sectors but
the high overall effect on employment due to the high labour intensity. In terms of intensity of young
workers the sector mining and quarry has the highest share with about one third of all workers being
younger than 30 years. In agriculture it is about one quarter. In absolute terms and apart from agriculture,
exports in restaurant services create many jobs for young people (17.3 per 1 billion rupiah of exports,
Table 3.1).




22 EMPLOYMENT DIMENSION OF TRADE LIBERALIZATION WITH CHINA :
ANALYSIS OF THE CASE OF INDONESIA WITH DYNAMIC SOCIAL ACCOUNTING MATRIX


 
Table 3.1. Total employment effects from an increase in exports by 1 billion rupiah per commodity


Total
Urban


Male


Urban


Female


Rural


Male


Rural


Female


Share of 15-29 in


all age cohorts


Crops 119.9 11.8 8.1 58.8 41.3 28.4


Other Agriculture 52.2 7.2 5.3 24.5 15.1 13.1


Livestock 63.7 8.6 6.2 28.3 20.6 17.3


Forestry &Hunting 34.8 5.5 3.8 17.5 8.0 9.5


Fishery 38.6 8.7 3.9 18.7 7.3 11.4


CoalMetalPetroleum 14.1 3.2 2.4 5.1 3.4 3.9


Mining & Quarry 37.3 8.1 5.9 14.8 8.6 11.3


FoodDrinkTobacco 45.2 6.8 5.3 19.4 13.7 11.6


WeaveTextileGarmentLeather 29.1 7.3 7.3 7.7 6.9 10.0


Wood 41.4 9.2 6.2 15.3 10.7 12.2


PaperPrintTranspMetal 19.2 5.4 3.6 6.0 4.1 5.7


ChemicalFertilClayCement 17.9 4.2 3.1 6.4 4.3 5.2


Electricity, Gas & Water 17.5 4.6 3.0 6.0 3.9 4.9


Construction 27.7 7.8 4.1 10.4 5.3 7.9


Trade Services 44.5 11.9 10.2 12.3 10.1 12.8


Restaurant 64.8 14.2 15.4 19.1 16.0 17.3


HotelAffairs 32.9 8.0 5.2 11.6 8.1 9.6


LandTransportServices 38.7 12.5 6.7 13.2 6.3 11.1


AirWaterTrp Communication 26.9 8.9 5.3 7.6 5.1 8.5


Storage OthTransport Services 39.5 14.1 8.1 10.0 7.2 11.6


BankInsuranceServices 21.0 5.5 4.1 6.7 4.6 6.2


RealEstate BusinessServices 21.6 6.4 4.4 6.5 4.3 6.1


GovDefEduHealthFilm OthSocSrv 44.6 12.1 8.7 14.3 9.6 11.7


Personal Services 40.4 8.3 16.1 8.2 7.8 12.6


Source: Authors calculations based on Indonesia SAM and Employment Satellite.



If exports of crops increase by 1 billion rupiah, a total of 119.9 additional jobs would be generated across
all sectors, i.e. in crops and through income effects also in other agriculture, livestock, etc (Table 3.1). Of
these 119.9 jobs, 11.8 would be male in urban areas, 8.1 women in urban areas, 58.8 male in rural areas
and 41.3 women in rural areas. Of the 119.9 additional jobs, 28.4 would be young employees.

Conversely, the results show that additional imports of agricultural goods would have the highest
employment destruction effect. An increase of imports of other agriculture by 1 bill. rupiah would reduce
employment by about 52 workers. An increase in imports of crops or livestock by the same amount would
eliminate 120 or 64 jobs, respectively. This compares with low employment effects in the banking and
insurance sector where 21 jobs would be lost if exports would increase by 1 bill. rupiah in this sector.



Due to the income and multiplier effect, jobs would not only be positively or negatively affected in the
sector where trade changes, but also in all other sectors. The extent to which jobs are affected in the
sector with the trade changes itself or other sectors vary considerably. Table 3.2 shows that the direct
employment effect in the sector where trade changes is often dominating. The column in the table gives
the sector where exports increase and the row in which sector the employment gains occur. If exports of
crops increase by 1 billion rupiah, 101.29 additional jobs will be created in the crops sector itself. Through
income and multiplier effects 1.8 additional jobs would be created in the sector “other agriculture” and so
on. An increase in the exports of livestock increases employment in that sector by 32 persons but would
also have relatively high positive effects on employment in the crops sector with 16 additional jobs.
Interestingly, an increase in the food, beverage and tobacco sector would lead to a higher job creation in
the crops sector than in the food, beverage and tobacco sector itself. This demonstrates the strong link
between these two sectors and the high labour intensity in the crops sector. Additional exports of 1 billion
rupiah of crops lead to high employment effects that are concentrated in the crops sector itself (84 per
cent of additional jobs in the same sector) while in Chemicals and Fertilizers and Paper Industry only 13
per cent and 9 per cent, respectively, are in the same sector.


 




 


Table 3.2. Distribution of employment effects by
sectors


Crops OthAg
Livestoc


k
ForestH


unt
Fishery


CoalM
etalPetr


ol


MiningQ
uarry


FoodDri
nkToba


cco


WeaveTe
xtileGarm


entLeath
er


Wood
PaperPri


ntTransp
Metal


ChemFe
rtClayCe


ment


ElecGas
Water


Constru
ction


TradeS
rv


Restaura
nt


HotelAf
fairs


LandTrp
Srv


AirWater
Trp


Commu
nicatn


Storage
OthTrpS


rv


BankIns
uranceSr


v


RealEsta
te


Busines
sSrv


GovDefE
duHlthFil


m
OthSoc


Srv


OthIndi
vHHSrv


Crops 101.29 11.75 15.58 8.07 9.82 5.11 10.21 24.81 6.60 7.61 5.34 5.54 5.50 6.76 9.10 18.31 12.47 8.01 6.29 7.88 6.74 6.09 12.96 6.62
OthAg 1.80 24.84 1.40 1.11 0.83 0.41 0.84 2.66 0.88 0.73 0.48 1.25 0.61 0.66 0.76 1.44 0.96 0.81 0.57 0.65 0.54 0.50 0.94 0.67
Livestoc 1.89 1.43 32.27 0.92 1.07 0.62 1.23 2.24 0.79 0.91 0.64 0.67 0.66 0.82 1.12 3.53 2.36 0.97 0.77 0.95 0.82 0.74 1.38 0.81
ForestH 0.06 0.06 0.05 14.21 0.05 0.03 0.08 0.05 0.04 2.10 0.06 0.03 0.03 0.35 0.05 0.05 0.04 0.04 0.03 0.06 0.03 0.04 0.05 0.05
Fishery 0.76 0.66 0.73 0.47 16.13 0.31 0.62 0.97 0.38 0.46 0.32 0.33 0.33 0.41 0.56 1.05 0.69 0.48 0.37 0.47 0.41 0.37 0.67 0.40
CoalMet 0.03 0.03 0.02 0.02 0.02 0.74 0.03 0.02 0.03 0.03 0.06 0.12 0.15 0.04 0.03 0.02 0.02 0.04 0.02 0.03 0.02 0.02 0.03 0.03
MiningQ 0.02 0.03 0.02 0.02 0.01 0.01 10.23 0.02 0.02 0.02 0.02 0.06 0.02 0.58 0.02 0.02 0.01 0.03 0.02 0.05 0.01 0.03 0.05 0.02
FoodDri 1.05 0.92 1.65 0.64 0.87 0.42 0.83 3.96 0.58 0.63 0.44 0.46 0.45 0.55 0.76 1.64 1.18 0.66 0.54 0.65 0.56 0.51 0.97 0.55
WeaveT
extileGar 0.46 0.41 0.37 0.30 0.31 0.20 0.39 0.29 10.04 0.33 0.22 0.22 0.21 0.26 0.39 0.44 0.31 0.33 0.24 0.32 0.25 0.25 0.40 0.32
Wood 0.14 0.14 0.12 0.10 0.11 0.06 0.15 0.10 0.09 17.00 0.13 0.07 0.07 0.68 0.15 0.11 0.09 0.10 0.08 0.14 0.08 0.09 0.13 0.10
PaperPri 0.37 0.36 0.31 0.34 0.28 0.22 0.38 0.27 0.28 0.34 3.01 0.21 0.25 0.58 0.39 0.31 0.26 0.39 0.29 0.35 0.30 0.29 0.49 0.56


ChemFe 0.36 0.44 0.25 0.20 0.24 0.14 0.37 0.25 0.34 0.32 0.28 2.25 0.60 0.48 0.32 0.26 0.20 0.65 0.33 0.27 0.19 0.19 0.33 0.38
ElecGas 0.05 0.04 0.04 0.03 0.03 0.02 0.04 0.04 0.08 0.05 0.05 0.03 1.39 0.04 0.07 0.05 0.04 0.05 0.04 0.09 0.04 0.04 0.05 0.05
Constru 0.06 0.13 0.04 0.08 0.04 0.04 0.19 0.05 0.05 0.05 0.04 0.03 0.06 5.46 0.11 0.05 0.04 0.06 0.07 0.35 0.06 0.20 0.08 0.04
TradeSr 3.46 3.35 3.96 2.54 3.09 1.83 3.66 3.62 3.44 4.07 3.17 2.29 2.68 4.03 22.14 4.87 3.56 3.71 2.70 3.15 2.39 2.37 4.02 3.26
Restaura 2.77 2.43 2.27 1.82 1.96 1.34 2.76 1.88 1.77 2.06 1.49 1.46 1.46 1.93 2.64 27.96 2.07 2.17 1.87 2.10 1.85 1.78 2.66 1.87
HotelAff 0.03 0.03 0.03 0.02 0.02 0.02 0.03 0.02 0.03 0.02 0.02 0.02 0.02 0.03 0.04 0.03 4.83 0.03 0.03 0.03 0.03 0.03 0.03 0.03
LandTrp 0.91 0.85 0.82 0.63 0.64 0.41 0.87 0.72 0.72 0.95 0.65 0.48 0.51 0.73 1.03 0.85 0.65 12.62 0.52 0.68 0.57 0.54 0.82 0.62
AirWater 0.66 0.60 0.59 0.52 0.49 0.35 0.63 0.51 0.51 0.61 0.45 0.39 0.40 0.55 0.81 0.63 0.55 0.71 9.02 1.10 0.59 0.60 0.69 0.51
Storage 0.20 0.19 0.20 0.16 0.16 0.10 0.20 0.18 0.19 0.26 0.17 0.13 0.13 0.19 0.21 0.22 0.18 0.33 0.74 16.68 0.15 0.15 0.21 0.16
BankIns 0.20 0.27 0.17 0.15 0.14 0.10 0.18 0.17 0.18 0.20 0.14 0.12 0.13 0.16 0.28 0.19 0.15 0.21 0.18 0.16 2.94 0.22 0.20 0.14
RealEsta 0.29 0.27 0.26 0.23 0.21 0.15 0.33 0.23 0.24 0.28 0.24 0.17 0.23 0.33 0.54 0.29 0.25 0.32 0.25 0.44 0.31 4.11 0.35 0.33
GovDefE
duHlthFil 1.42 1.25 1.17 0.90 0.98 0.60 1.19 0.93 0.77 0.93 0.67 0.67 0.66 0.83 1.10 1.05 0.90 0.97 0.80 1.05 0.93 0.96 15.45 0.81
OthIndiv 1.63 1.69 1.39 1.31 1.13 0.89 1.91 1.22 1.11 1.48 1.05 0.90 0.91 1.20 1.88 1.41 1.11 4.99 1.11 1.85 1.21 1.52 1.65 22.03
Total 119.91 52.16 63.71 34.77 38.63 14.11 37.33 45.20 29.15 41.43 19.17 17.89 17.48 27.66 44.48 64.76 32.91 38.70 26.89 39.50 21.01 21.62 44.61 40.36




3.S
O


C
IA


L A
C


C
O


U
N


TIN
G


M
A


TR
IX


TO
M


E
A


S
U


R
E


TH
E


E
FFE


C
TS


O
F TR


A
D


E
O


N
E


M
P


LO
Y


M
E


N
T






23


Source: Authors calculations.


 




24  EMPLOYMENT DIMENSION OF TRADE LIBERALIZATION WITH CHINA :
ANALYSIS OF THE CASE OF INDONESIA WITH DYNAMIC SOCIAL ACCOUNTING MATRIX


 


A disaggregation into intra-account and induced effects helps to better understand the nature of the
multiplier effect, which has interesting policy implications. Sectors with high intra-account effect show a
high integration with the rest of the economy, while sectors where the induced effect dominates have a
stronger cascading effect on other accounts, mostly through higher income and related consumption. Of
the 120 new jobs created by higher exports of crop products, 90 would be created through intra account
effects and 30 through induced effects as shown in Figure 3.9. Coal, Metal and Petroleum, for example,
have a very low intra-account effect, which shows their lack of integration with the rest of the economy.
Most of the total effect Ma can be explained by the induced effect, which mostly results from higher
wages in this sector and cascading effects. The situation is similar for high productive services such as
banks and insurances, real estate and business services or utilities. Agricultural and food products,
however, have a higher intra-account effect than an induced effect, as they are well integrated into the
national economy and most farming is characterized by low productivity and income. The cascading
effect to other accounts via income increase is therefore rather limited. The situation is similar in the case
of restaurants, which mainly depend on national inputs, e.g. food, personal services and which is in
general a low wage/income sector.



Figure 3.9. Total (Ma), intra-account (M1) and induced (O+C) employment effect


0 20 40 60 80 100 120 140


Crops
OthAg


Livestock
ForestHunt


Fishery
CoalMetalPetrol


MiningQuarry
FoodDrinkTobacco


WeaveTextileGarmentLeather
Wood


PaperPrintTranspMetal
ChemFertClayCement


ElecGasWater
Construction


TradeSrv
Restaurant
HotelAffairs
LandTrpSrv


AirWaterTrp Communicatn
Storage OthTrpSrv
BankInsuranceSrv


RealEstate BusinessSrv
GovDefEduHlthFilmOthSocSrv


OthIndivHHSrv


Ma


M1


OC


 
Note: The total employment multiplier effect in crops, based on the multiplier Ma, was 119.9, which can be disaggregated


into the intra-account effect (M1) of 90.1 and the induced effect (OC) of 29.8.




Industrial policy or export promotion would have the highest job creating effect in agricultural sectors as
well as in basic services. Countries that aim to create many jobs in the short term because of high
unemployment or high growth of their labour force may focus on those sectors. The longer term growth
potential of certain sectors may add another criterion that is not addressed here. Other countries such as
Malaysia that rather face labour shortages may focus on higher quality jobs in manufacturing or business
services sectors.

The trade policy may be adapted accordingly. Employment could be an important criterion to select
sensitive products that are often excluded in multilateral and regional trade agreements. An example is


   




3. SOCIAL ACCOUNTING MATRIX TO MEASURE THE EFFECTS OF TRADE ON EMPLOYMENT 25


 


 


Special Products in the Doha round that are discussed not to receive the full formula cuts discussed for
other products. The labour intensity alone may be a relatively poor indicator as it does not take into
account backward and forward linkages and second round effects as shown in tables 3.1 and 3.2.

A SAM analysis with a hypothetical change of trade of one unit could enable policymakers to better target
labour, trade and industrial policies.




4. RESULTS AND EMPLOYMENT EFFECTS FROM THE SCENARIO 27


 


4. Results and employment effects from the
scenario



The analysis above has shown employment effects as a result of trade changes of 1 billion rupiah in all
sectors, respectively. However, due to different absolute cuts of applied tariffs and different import
demand elasticities for each product the impact of the free trade agreement on exports and imports is not
equal across sectors.


Calculation of trade shocks using SMART

The analysis above uses only the DySAM based framework. It provides useful information on the
employment effects if exports or imports change as a result of a trade policy change. Since “the rest of
the world” is not disaggregated into trading partners and trade diversion effects cannot be modeled in a
SAM either changing tariffs vis-à-vis China only within the SAM model is not possible. Therefore a model
that is specifically designed for the analysis of trade policy changes is used. SMART quantifies the change
in imports if import tariffs for one or more trading partners are changed. SMART is used to identify and
quantify changes of imports in China and Indonesia at the HS 6-digit when all tariffs for Indonesia and
China that are not on the list of highly sensitive products are eliminated and tariffs for the other trading
partners remain unchanged. The initial applied tariffs are the 2009 tariffs in UNCTAD’s TRAINS database.
The change in China’s imports from Indonesia is taken as the change in Indonesia’s exports to China.

Trade creation and diversion effects are taken into account in the SMART model. It is based on product
specific import demand elasticities, supply elasticities and substitution elasticities that determine the
substitution between different sources of supply, i.e. products from different countries, if relative prices
change.11 For example, if tariffs on imports from China are reduced in Indonesia, it can be expected that
imports from China increase and that imports of the same product from other countries decrease because
they become relatively more expensive (trade diversion). The reduced imports from third countries are
taken into account in the calculation of employment effects. Thus, the effective increase of Indonesia’s
imports is the change of imports from China reduced by the lower imports from third countries.

The results from SMART, i.e. the expected gross changes in imports, Δximports, and exports, Δxexports, are
then separately combined with the SAM model to calculate the employment effects. Tables 4.1, 4.2, 4.3
and 4.4 show the change of Indonesia’s imports and its exports to China (step 4).





Table 4.1. Change of Indonesia’s imports at the HS level in $ millions


Product HS chapter Additional imports
Animal & Animal Products 01-05 0.0


Vegetable Products 06-15 0.2


Foodstuffs 16-24 22.7


Mineral Products 25-27 0.7


Chemicals & Allied Industries 28-38 20.5


Plastics/Rubbers 39-40 48.5


Raw Hides, Skins, Leather, & Furs 41-43 4.3


Wood & Wood Products 44-49 5.0


Textiles 50-63 393.7


                                                            
11 For a description of SMART see wits.worldbank.org.


 




28  EMPLOYMENT DIMENSION OF TRADE LIBERALIZATION WITH CHINA :
ANALYSIS OF THE CASE OF INDONESIA WITH DYNAMIC SOCIAL ACCOUNTING MATRIX


 
Footwear/Headgear 64-67 30.8


Stone/Glass 68-71 83.6


Metals 72-83 171.9


Machinery/Electrical 84-85 144.2


Transportation 86-89 341.2


Miscellaneous 90-97 25.3


Service 98-99 0.0


Total 1’292.5


Source: Author’s calculation using SMART.





Table 4.2. Change of Indonesia’s imports in $millions


Product group
Additional


imports
Total 1,292.5


PaperPrintTransportMetal 682.6


WeaveTextileGarmentLeather 428.8


Mining & Quarry 83.6


ChemicalFertilizerClayCement 69.0


FoodDrinkTobacco 26.7


Wood 5.0


CoalMetalPetroleum 0.7


Other Agriculture 0.2



Additional imports from China are estimated to be concentrated in a few sectors (table 4.1). Due to the
aggregation in the DySAM, two sectors, Paper Print Transport Metal and Weave Textile Garment Leather,
would account roughly for half of the additional imports. Additional exports are predicted to be more
diversified (table 4.3).



Table 4.3. Change of Indonesia’s exports to China at the HS level in $ millions


Product HS chapter Additional exports
Animal & Animal Products 01-05 0.0


Vegetable Products 06-15 172.2


Foodstuffs 16-24 5.8


Mineral Products 25-27 254.4


Chemicals & Allied Industries 28-38 86.0


Plastics/Rubbers 39-40 39.3


Raw Hides, Skins, Leather, & Furs 41-43 89.4


Wood & Wood Products 44-49 39.9


Textiles 50-63 38.6


Footwear/Headgear 64-67 14.4


Stone/Glass 68-71 6.4


Metals 72-83 37.9


Machinery/Electrical 84-85 99.4


Transportation 86-89 18.0


Miscellaneous 90-97 14.9


Service 98-99 0.0


Total 916.5
Source: Author’s calculation using SMART.


   




4. RESULTS AND EMPLOYMENT EFFECTS FROM THE SCENARIO 29


 
Table 4.4. Change of Indonesia’s exports to China in $millions


Product group
Additional


exports
Total 916.5
Coal, Metal & Petroleum 253.1
PaperPrintTransportMetal 170.3
Other Agriculture 169.8
WeaveTextileGarmentLeather 142.3
ChemicFertilizerClayCement 125.3
Wood 39.9
Mining & Quarry 7.6
Food, Drink & Tobacco 5.8
Crops 2.5
Livestock 0.0
Forestry & Hunting 0.0
Fishery 0.0



It is estimated that exports from Indonesia to China would increase by about $916 million. Imports by
Indonesia are expected to increase by about $1.29 billion. The latter is composed of increased imports
from China minus lower imports of replaced imports (trade diversion).

A reason for the relatively small estimated change of trade between China and India is that the trade in
2009 between China and Indonesia was in sectors that had already relatively little protection (Figure 4.1)
and that highly protected sectors such as beverages and tobacco were partly declared as sensitive
products and exempt from tariff elimination. Furthermore, the estimated changes are in addition to the
ongoing trend of increasing trade between China and Indonesia.



Figure 4.1. Bilateral trade in $ millions and tariffs in per cent by sector
















0


1000


2000


3000


4000


5000


6000


7000


8000


Fo
od


a
nd


li
ve


an
im


al
s


B
ev


er
ag


es
an


d
to


b
ac


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at


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in
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ry
an


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tr


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en
t


M
is


ce
lla


ne
ou


s
m


an
uf


ac
tu


re
d


ar
tic


le
s


Imports
Exports


 




30  EMPLOYMENT DIMENSION OF TRADE LIBERALIZATION WITH CHINA :
ANALYSIS OF THE CASE OF INDONESIA WITH DYNAMIC SOCIAL ACCOUNTING MATRIX


 








0


5


10


15


20


25


30


35


40


45


Fo
od


a
nd


liv
e


an
im


al
s


Be
ve


ra
ge


s
an


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to


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cc


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ru


de
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at
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ia
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ed
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,


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in


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br
ic


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d


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od
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an


uf
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re


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go


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as


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fie


d
M


ac
hi


ne
ry


an
d


tra
ns


po
rt


eq
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en


t
M


is
ce


lla
ne


ou
s


m
an


uf
ac


tu
re


d
ar


tic
le


s


Indonesia
China





















Source: United Nations Comtrade and UNCTAD TRAINS.




Employment effects from ACFTA

The estimated changes in trade between Indonesia and China are used to calculate the employment
effects from the ACFTA (step 5). Thus,


ΔLabourACFTA = λ * Ma * ΔxACFTA,


where ΔxACFTA is the estimated change in import from table 4.2 for the calculation of losses in employment
due to increasing imports or ΔxACFTA is the estimated change in export from table 4.4 for the calculation of
employment generation due to increased exports.

Table 4.2 shows that the highest increase in imports is in the sector PaperPrintTransportMetal followed by
WeaveTextileGarmentLeather, two sectors with employment effects of 19.2 and 29.1 (table 3.1),
respectively, in the lower and middle range. Thus, the negative impact on employment in Indonesia from
higher imports from China is smaller than if imports in sectors with higher employment effects would
increase. However, the employment sensitivity in the middle range of 29 of textile coupled with the
relatively high increase of imports in this sector could result in a significant employment reduction in the
textile sector. The total estimated loss of employment in Indonesia is 284’397 jobs (table 4.5). Most of the
jobs would be lost in the crops, textile and trade services sector.



Table 4.5. Total job losses through import competition from China


Results for Word Total
Urban
Male


Urban
Female


Rural
Male


Rural
Female


Crops 75,853 4,810 2,690 40,136 28,217
Other Agriculture 8,490 508 235 4,994 2,753
Livestock 8,966 747 390 4,488 3,341
Forestry & Hunting 736 66 15 516 139
Fishery 4,425 1,171 55 2,858 342
Coal, Metal & Petroleum 578 239 17 303 19
Mining & Quarry 8,004 1,768 757 4,086 1,393
Food, Drink & Tobacco 6,776 1,456 1,678 1,710 1,933


   




4. RESULTS AND EMPLOYMENT EFFECTS FROM THE SCENARIO 31


 
WeaveTextileGarmentLeather 41,176 12,096 15,790 4,250 9,039
Wood 2,095 476 241 777 600
PaperPrintTransportMetal 20,353 11,304 3,877 3,971 1,200
ChemFertClayCement 4,847 1,922 885 1,459 582
Electricity, Gas & Water 677 435 46 185 12
Construction 599 280 10 305 5
TradeServices 38,363 12,294 11,066 7,170 7,832
Restaurant 19,686 5,703 7,764 2,492 3,727
HotelAffairs 258 154 57 33 14
LandTransport Services 8,038 4,367 197 3,365 108
AirWaterTrp Communication 5,674 3,032 1,229 954 458
Storage Other TransportServ 2,111 1,116 484 282 228
BankInsuranceServices 1,831 957 544 246 84
RealEstate Business Services 2,826 1,736 663 377 50
GovDefenseEducatHealthFilm
OthSocSrv 8,791 3,673 2,260 1,894 964
Other Personal Services 13,245 2,413 7,903 994 1,936
Total 284,397 72,722 58,852 87,845 64,978


Source: Authors calculations.



As shown in table 4.4 the exports increase mainly in the sector CoalMetalPetroleum. Since this sector
generates the least number of jobs per billion rupiah the employment generation of the ACFTA agreement
may be limited. On the other hand, exports of other agriculture and textile and leather products, where
employment effects are high, are also to be expected to increase by more than hundred million each. The
total amount of created jobs is estimated to be 224,092 of which almost one half is created in crops and
other agriculture sector (table 4.6). Job creation effects are also high in trade services. This is not a direct
effect since no changes in services trade policy is modeled here but indirect effects through backward
and forward linkages as well as income effects.





Table 4.6. Total job gains from increasing exports to China


Results for Word Total
Urban
Male


Urban
Female


Rural
Male


Rural
Female


Crops 60,257 3,821 2,137 31,884 22,415
Other Agriculture 43,204 2,587 1,193 25,415 14,009
Livestock 6,997 583 304 3,503 2,607
Forestry & Hunting 1,123 101 23 787 212
Fishery 3,391 897 42 2,190 262
CoalMetalPetrol 2,031 840 61 1,065 65
Mining & Quarry 896 198 85 457 156
Food, Drink & Tobacco 4,866 1,045 1,205 1,228 1,388
WeaveTextileGarmentLeather 14,892 4,375 5,711 1,537 3,269
Wood 6,964 1,583 803 2,583 1,995
PaperPrintTransportMetal 6,520 3,621 1,242 1,272 385
ChemicalFertilizerClayCement 4,619 1,831 843 1,390 555
Electricity, Gas & Water 363 233 25 99 6
Road Labour Intensive 259 84 2 172 1
Road Capital Intensive 81 59 1 20 1
Irrigation 47 25 1 20 1
Construction Rest 108 64 3 40 1
Trade Services 23,419 7,505 6,756 4,377 4,781
Restaurant 14,230 4,122 5,612 1,802 2,694


 




32  EMPLOYMENT DIMENSION OF TRADE LIBERALIZATION WITH CHINA :
ANALYSIS OF THE CASE OF INDONESIA WITH DYNAMIC SOCIAL ACCOUNTING MATRIX


 
HotelAffairs 172 102 38 22 9
LandTransport Services 5,230 2,842 128 2,190 70
AirWaterTransp Communicatn 3,850 2,058 834 648 311
Storage Other Transport Serv 1,315 695 301 176 142
BankInsuranceSrv 1,319 689 392 177 61
RealEstate BusinessSrv 1,778 1,092 417 237 32
GovDefenseEducatHealthFilm
OthSocSrv 6,623 2,767 1,703 1,427 726
Other Personal Services 9,537 1,737 5,690 716 1,394
Total 224,092 45,557 35,552 85,433 57,550
Source: Authors calculations




More jobs would be created in agriculture than lost while in some other sectors the reverse is expected to
happen. Thus, there will be adjustment processes, even within the “winner”, the agricultural sector.
However, with a total work force of 103 million in the sectors covered in the analysis the effects from the
free trade agreement are relatively low. Only about 0.2 per cent of jobs would be affected. This is mainly
due to two factors:

First, tariffs in 2009 between China and Indonesia were already fairly low. The average tariff was 3.65 per
cent and 65 per cent of the tariff lines were duty free in Indonesia. Thus, the additional reduction of
preferential tariff rates was not very high and since Indonesia also has fairly low MFN rates, also the
preference margin is not very high. Second, only merchandise trade is considered regarding the trade
liberalization but the impact on the total employment in Indonesia, i.e. including services sector, is
assessed. The impact in specific sectors can therefore be more significant. In the textile sector, for
example, about 2 per cent of jobs could be at risk due to higher imports.

Another factor is, of course, the static nature of the approach. Dynamic effects such as higher exports and
employment in some sectors that benefit from cheaper imports of inputs are in this approach not
accounted for, neither inflows of FDI or higher domestic investment as a result of the FTA. Some of these
shortcomings of the present approach will be discussed in the conclusion.

However, the strength of the approach is to analyse in a relatively easy way the potential impact on a
number of employment groups. Important groups are often rural, young and female workers, for example,
due to higher unemployment rates or rural-urban migration that imposes difficulties for cities. The job
balance for rural workers is close to be balanced, i.e. the number of destroyed and created jobs is close to
be equal. In Indonesia relatively more jobs in rural areas are created than in urban areas compared to the
potential job losses. This is the case since those sectors where more jobs are created than potentially lost,
i.e. Other Agriculture, Forestry & Hunting, CoalMetalPetroleum, Wood, ChemicalFertilizerClayCement, are
relatively more intensive in rural areas. Regarding female workers, about 71 per cent of potentially created
jobs are for female workers while 77 per cent of destroyed jobs are female jobs. Thus, female employment
is relatively more adversely affected than male jobs. This is largely driven by the increase in imports of
textile products which is among the most women intensive sectors in Indonesia with a share of 60 per
cent of women. This sector also has a strong impact on the effect of youth employment. The free trade
agreement with China does not appear to be very beneficial for youth employment since in many sectors
with a high share of young workers more jobs may be destroyed than created. The textile sector,
PaperPrintTransportMetal, HotelAffairs and BankInsuranceServices all have a high share of young workers
and lose more jobs than are created.


Another advantage of the approach used is that additional labour market details may be assessed if the
corresponding information is available. Here, a full-time equivalent of lost and created jobs can be
calculated, for example (step 6). The estimated employment changes in Indonesia of 284,397 lost and
224,092 created jobs are not fully comparable since in some sectors the average working hours are higher
than in other sectors. The average working time ranges from 31.8 to 51.3 hours per week. Adjusting the
created and lost jobs in each sector to calculate a full time equivalent (40 hours per week) shows that the


   




4. RESULTS AND EMPLOYMENT EFFECTS FROM THE SCENARIO 33


 


 


total number of full-time equivalent jobs that could be destroyed is 297’142 and the total number of
created jobs is 225’018 (table 4.7) thus slightly increasing the number of net job losses.





Table 4.7. Full time equivalent employment, created and lost




Average
working
hours


Export
gains


FTE
exp.


Gains


Imports
losses


FTE
imp.


Losses
Crops 31.8 60,257 47,950 75,853 60,360
Other Agriculture 35.8 43,204 38,614 8,490 7,588
Livestock 39.2 6,997 6,850 8,966 8,778
Forestry 40.5 1,123 1,136 736 745
Fishery 43.1 3,391 3,650 4,425 4,763
Coal, Metal, Petroleum Mining 44.6 2,031 2,265 578 644
Mining and Quarry 44.6 896 999 8,004 8,928
Food, Beverages and Tobacco 47.4 4,866 5,767 6,776 8,031
Textile, Wearing apparel, Garment and
Leather 44.1 14,892 16,415 41,176 45,386
Wood 44.9 6,964 7,815 2,095 2,351
Paper, Print, Transport, Metal Product, other
industry 45.1 6,520 7,350 20,353 22,943
Chemical, Fertilizer, Clay and Cement 44.5 4,619 5,137 4,847 5,390
Electricity, Gas and Water 45.4 363 412 677 769
Road Labour Intensive 46.4 259 300 313 363
Road Capital Intensive 46.4 81 94 99 114
Irrigation 46.4 47 55 57 66
Construction 46.4 108 125 130 151
Trade Services 47.3 23,419 27,716 38,363 45,402
Restaurant 49.4 14,230 17,563 19,686 24,297
Hotel Affairs 48.8 172 210 258 315
Land Transportation Services 51.3 5,230 6,708 8,038 10,309
Air, Water Transportation and
Communication 47.3 3,850 4,552 5,674 6,708
Storage, Other Transportation Service 49.2 1,315 1,619 2,111 2,598
Bank, Insurance, and Services 45.4 1,319 1,497 1,831 2,079
Real Estate and Business Services 46.8 1,778 2,079 2,826 3,304
Government, Defensive, Education, and 42.7 6,623 7,069 8,791 9,382
Other Personal Services 46.4 9,537 11,072 13,245 15,377
Total 40.7 224,092 225,018 284,397 297,142


Source: Authors calculations.




5. CONCLUSION 35


 


5. Conclusion


Creation of productive jobs is a key policy issue for policymakers. In the evaluation of trade agreements,
the focus is often on exports and imports, on net trade effects and sometimes on a general notion of net
welfare effects. However, these may be weak indicators of the socio-economic implications of trade
policies and related trade shifts. Employment is a key socio-economic element, which is mostly neglected
in such an analysis. Employment has a social dimension through social cohesion, but also an economic
dimension, as it contributes to strengthen the internal demand and to a sustainable growth path. Analysis
of employment effects should not stop short at the level of overall net employment gains or losses. It is
important to understand where the adjustment occurs in the case of trade changes, as the transition
between winning and losing sectors is not automatic, even less from an employment perspective. A
female urban worker losing a job in the textile sector may not become a palm oil farmer in the rural area
within a couple of months. Labour mobility depends, among other things, on geographic and skills
mobility. A better understanding of the winning and losing workers will help identify appropriate labour
market policies to mitigate social costs and to maximize potential benefits. It also supports determining
trade policies that minimize adjustment costs, for example, or selecting sensitive products that may be
exempt from liberalization in regional or multilateral trade agreements.

Moreover, apparent welfare gains in a neo-classical sense could still lead to net employment losses for
the following reasons: First, sectors vary considerably in terms of the labour intensity. Second, backward
linkages and income effects also vary across sectors. Thus, even if imports increase more than exports
this may not mean a net job loss. Furthermore, policymakers often target specific groups such as low
skilled, rural, female or young workers if specific needs have been identified. An analysis disaggregating
these groups may enable policymakers to better target labour, social, industry and trade policy.

In this paper, a methodology to assess the impact of trade on employment has been developed by
combining two well known approaches. The SMART model that quantifies at a very disaggregated level,
e.g. HS 6-digit level, the potential impact of trade policy changes on imports has been combined with a
SAM based approach that allows a relatively disaggregated assessment of the impact on various
employment groups. Furthermore, the SAM has been derived from a DySAM that allows using a more
recent year as the base for the SAM data. Similar approaches have been used by using the results from
global CGE models on export and import changes with national household panel data or national CGE
models (Hertel and Winters, 2006). Though these approaches may better capture certain complexities of
markets than the approach used here, they are fairly complex and difficult to be used by policymakers and
their direct support staff. Quantitative economists in ministries and employers’ or workers’ associations
able to use CGE models are rare. The approach developed here is rather easy and could thus contribute
to the understanding and use of quantitative trade and labour market policy analysis of a broader public.
Once the multiplier matrix Ma is calculated, all remaining calculations can easily be done in one single
Excel worksheet. SMART is easily accessible through a graphical user interface in the internet. This
approach does, however, not preclude additional analysis using other approaches to guide policy makers.

The SAM based analysis is similar to a CGE analysis with specific functional forms, e.g. Leontief
production functions. This functional form is restrictive as it does not support behavioral changes of
economic agents if relative prices change. There is a trade off between more complex approaches, that
may capture better certain complexities of markets, if the parameters and functional forms are correct, but
may be considered as black boxes and may be less accepted by policy makers, and more simplistic
approaches.

The assessed total impact on employment is relatively small and slightly negative for Indonesia. Our
results thus confirm results from previous studies. What may matter even more for policymakers is not the
total impact, but to identify the potential winners and losers of the FTA and take respective actions. An
FTA creates new opportunities, but the potential positive impact on some sectors has to be realized.
Supportive industrial policies (e.g. FDI policies, innovation, R&D, education) and related labour market


 




36  EMPLOYMENT DIMENSION OF TRADE LIBERALIZATION WITH CHINA :
ANALYSIS OF THE CASE OF INDONESIA WITH DYNAMIC SOCIAL ACCOUNTING MATRIX


 


   


policies (e.g. skills development, promotion of geographic mobility) as well as trade policies may be
considered and put in place in a targeted way. The same holds for sectors with net losses (e.g. activation
policies or social policies such as temporary income support through cash transfer programmes).
Furthermore, the transition of workers from a losing to a winning sector is far from obvious from an
economic and an employment point of view as discussed before. We observed that female and young
workers could be affected by the final step of the FTA with China, special considerations are therefore
needed for these target groups. Employment in the agricultural sector, however, is expected to increase in
Indonesia.

The sectors vary considerably in their employment sensitivity to trade changes. It has been shown that
this is not only a result of labour intensity but also due to differing backward and forward linkages as well
as income effects. Interesting is the much higher intra-account effect (employment effects from backward
and forward linkages, i.e. the degree of integration of a sector in the economy) than induced effect
(income effects) in Activities and Commodities. The FTA will therefore mostly stimulate inter-related
productive activities and will lead less so to cascading effect of higher income in specific high productive
sectors. In this paper, only the last step of trade liberalization, i.e. the final reduction of tariffs in 2010, has
been analyzed since this was the concern of interest groups. The effect of the full ACFTA where
liberalization started in 2005 has not been considered here.

Further limitations to the analysis should be kept in mind. Our analysis does not capture dynamic long-
term effects which could result from the ACFTA. More trade could lead to higher (foreign direct)
investments causing more formal employment or cheaper and higher quality intermediate imports could
increase the competitiveness of labour intensive sectors. These effects are underestimated in our paper
and could easily outweigh the small negative employment impact calculated so far. Furthermore, the
quality of employment may change. Agriculture as one of the winning sectors is currently dominated by
subsistence farming and is rather labour-intensive, even though in some activities (e.g. palm oil),
agriculture is highly productive and orientated towards exports. It is possible that the increasing export
potential to China may not lead to many new jobs, but to different types of jobs, possibly better jobs, in
the agricultural sector due to changing ways of agricultural production leading to higher productivity and
eventually higher income. Potential changes of the quality of employment and movements between formal
and informal sectors have not been analyzed explicitly. Another shortcoming of the paper is the high level
of aggregation of the economic sectors; a common problem of economic models in general.

In spite of these limitations, the methodology can be a useful approach for policymakers and their support
staff, trade unions, employers’ representatives and other civil society organizations to assess the impact
of trade policy changes on employment. The results provide a useful indication of the possible impact of
the ACFTA on Indonesia. The impact of the last step of the implementation of the FTA with China is likely
to be limited for Indonesia in terms of employment. Our analysis shows a small net loss of employment in
Indonesia in the short run, but long run effects could be different. Furthermore, the analysis allows a
relatively detailed understanding of effects on specific groups such as rural, urban, female, male and
young workers in order to support targeted trade, sectoral, labour market and social policies.




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ANNEX 39


 


Annex


Full name Abbreviation
Crops Crops
Other Agriculture OthAg
Livestock Livestock
Forestry ForestHunt
Fishery Fishery
Coal, Metal, Petroleum Mining CoalMetalPetrol
Mining and Quarry MiningQuarry
Food, Beverages and Tobacco FoodDrinkTobacco
Textile, Wearing apparel, Garment and Leather WeaveTextileGarmentLeather
Wood Wood
Paper, Print, Transp, Metal Product, other industry PaperPrintTranspMetal
Chemical, Fertilizer, Clay and Cement ChemFertClayCement
Electricity, Gas and Water ElecGasWater
Construction Construction
Trade Services TradeSrv
Restaurant Restaurant
Hotel Affairs HotelAffairs
Land Transportation Services LandTrpSrv
Air, Water Transportation and Communication AirWaterTrp Communicatn
Storage, Other Transportation Service Storage OthTrpSrv
Bank, Insurance, and Services BankInsuranceSrv
Real Estate and Business Services RealEstate BusinessSrv
Government, Defense, Education, & other social
services GovDefEduHlthFilm OthSocSrv
Other Individual and Household Services OthIndivHHSrv


 




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