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Impacts of Trade Liberalization on Households’ Welfare

and Labor Market: A CGE Analysis for Cambodia

r

Final Report

Heng Dyna

Senh Senghor

Ngim Sokrachany

Ear Sothy

Em Kagna

Chan Theary

March 2014

Month Year (of publication)

1

Impacts of Trade Liberalization on Households’ Welfare and Labor

Market: A CGE Analysis for Cambodia

Abstract

This study analyzes households’ welfare and labor market impacts from trade liberalization

measures using Computable General Equilibrium (CGE) calibrated to the Cambodia’s Social

Accounting Matrix (SAM) built for year 2011. Cambodia’s textile industry remains buoyant

with its domestic output increases in every scenario. This industry will continue to be a

backbone for the growth and job employment for a short and medium term. Manufacturing

industry is relatively more beneficial from trade liberalization compared to agriculture and

service sectors. In terms of effect on labor market, it is obvious that wage increase is relatively

higher for higher skilled workers than low skilled workers. Among different household groups,

Rattanakiri household benefits less while Phnom Penh household gains the most from the rise

of their nominal incomes. With the decline of consumer prices, it is Phnom Penh household

again who benefit the most due to their greater consumptions of manufacturing goods. Welfare

gains go to households living in Phnom Penh and its surrounding provinces as well as other big

provinces with active economic activities. Trade liberalization seems to bring the widening gap

between urban households and rural households. Complementary policies in terms of

infrastructure investment, government transfer, and agriculture supports should be given a

priority to target those living in rural and remote areas.

JEL: C63, C67, C68.

Keywords: trade liberalization, welfare, CGE, labor market.

Authors

Heng Dyna:

President, Cambodian Economic Association

Phnom Penh, Cambodia

[email protected]

Senh Senghor:

Coordinator, Cambodian Economic association


[email protected]

Ngim Sokrachany:

Lecturer, Royal University of Phnom Penh


[email protected]

2

Ear Sothy:

Official, Ministry of Industry


[email protected]

Kagna Em

Research Associate, Center for Policy Studies


[email protected]

Theary Chan Analyst, Ministry of Economy and Finance


[email protected]

Acknowledgements

This research work was carried out with financial and scientific support from the Partnership for

Economic Policy (PEP) (www.pep-net.org) with funding from the Department for International

Development (DFID) of the United Kingdom (or UK Aid), and the Government of Canada through the

International Development Research Center (IDRC). The authors are also grateful to Bernard Decaluwe,

Hélène Maisonnave, and Erwin Corong for technical support and guidance, as well as to participants at

the Cambodian Economic Association’s CGE Seminar for valuable comments and suggestions.

http://portal.pep-net.org/users/view/id/9756

3

Contents

Executive summary ...................................................................................................................... 6

1 Introduction ......................................................................................................................... 7

1.1 Context of the Study ......................................................................................................... 7

1.2 Research Questions and Objectives .................................................................................. 8

2 Cambodia's Economy and Measures affecting Trade .......................................................... 8

3 Literature review ............................................................................................................... 12

4 Methodology ..................................................................................................................... 13

5 Data ................................................................................................................................... 14

6 Constructing Cambodia’s Social Accounting Matrix ........................................................ 14

6.1 Construction of MacroSAM ........................................................................................... 14

6.2 Construction of MicroSAM ............................................................................................ 17

6.2.1 Factor Payment Estimation......................................................................................... 18

6.2.2 Household Income Estimation ................................................................................... 20

6.2.3 Household Expenditure Estimation ............................................................................ 21

6.2.4 Reconciliation and Balancing ..................................................................................... 24

7 Initial Structure of the Economy in Base Scenario ............................................................ 25

8 Simulation Designs ............................................................................................................ 29

9 Simulation Results ............................................................................................................. 29

9.1 Resource Allocation ........................................................................................................ 29

9.2 Factor Market .................................................................................................................. 35

9.3 Household Income ........................................................................................................... 36

9.4 Household Expenditure ................................................................................................... 37

9.5 Household Welfare .......................................................................................................... 38

10 Conclustion and Policy Recommendations ....................................................................... 40

References ................................................................................................................................. 41

Annex ........................................................................................................................................ 42

4

List of Tables

Table 1: Share of international trade taxes in total tax revenue, 2004-2010 ............................. 10

Table 2: Tariff commitments under FTAs between ASEAN and its dialogue partners ............ 11 Table 3: Cambodia’s MacroSAM ............................................................................................. 16

Table 4: Share of Employed Persons by Employment Status ................................................... 17

Table 5: Sectoral Factor Payments of Cambodia ...................................................................... 19

Table 6 Sources of Household Income ..................................................................................... 20

Table 7: Household Income by Household Groups................................................................... 21

Table 8: Household Consumption Comparison: Pre and Post Disaggregation ......................... 22

Table 9: Consumption by Household Groups (Millions US$) .................................................. 23

Table 10: Import/Export and Tariff by Goods (%).................................................................... 25

Table 11: Value-Added and Labor by Sector (%) ..................................................................... 26

Table 12: Sources of Household Income (%) ............................................................................ 27

Table 13: Household Consumption Pattern (%) ........................................................................ 28

Table 14: Effect on Prices and Volumes (Sim 1) ...................................................................... 31




Table 18: Effect on Production Factors by Sector ..................................................................... 35

Table 19: Effect on Wage Rate ................................................................................................. 36

Table 20: Effect on Household Income ..................................................................................... 37

Table 21: Effect on Household Expenditure ............................................................................. 38

Table 22: Effect on Household Welfare .................................................................................... 39

List of Figures

Figure 1: Cambodia’s GDP per capita and GDP Growth 1994-2011 ....................................... 09

Figure 2: Government’s Revenue 2004-2011 (% of GDP) ....................................................... 11

List of Abbreviations

ADB Asian Development Bank

AEC ASEAN Economic Community

AFTA ASEAN Free Trade Agreement

ASEAN Association of South East Asian Nations

CGE Computable General Equilibrium

CMDG Cambodian Millennium Development Goal

CSES Cambodian Socio-Economic Survey

IMF International Monetary Fund

MEF Ministry of Economy and Finance

IO Table Input-Output Table

NBC National Bank of Cambodia

5

NGO Non-Governmental Organization

NIS National Institute of Statistics

NSDP National Strategic Development Plan

SAM Social Accounting Matrix

SUT Supply-Use Table

VAT Value-Added Tax

WTO World Trade Organization

6

Executive summary

Since switching to the market economy in 1993, Cambodia has embraced a series of trade

liberalization measures to fulfill its commitment for freer trade openness in accordance with

the AFTA and WTO agreements and other regional trade negotiations. Amid strong economic

growth resulting from trade liberalization measures averaging 7% between 1994 and 2011,

there is an allegedly growing gap between poor households and rich households in terms of

income distribution.

This raises a number of crucial issues pertaining to social goals such as: How are different

household group especially the poor impacted by significant trade liberalization measures? Are

countervailing policies needed to promote trade liberalization in a more equitable manner? The

study, thus, analyzes households’ welfare and labor market impacts from trade liberalization

measures using Computable General Equilibrium (CGE) calibrated to the Cambodia’s Social

Accounting Matrix (SAM) built for year 2011.

The study is divided into two stages. First, the Social Accounting Matrix (SAM) of Cambodia

has been constructing using various data from National Account and Government Budget

(2011), Balance of Payment (2011) from the National Bank of Cambodia, the Asian

Development Bank’s Supply-Use Table (2011), and Cambodia Socio-Economic Survey (CSES

2009), and Cambodia’s Custom System (2011). The final balanced MicroSAM includes 23

activities, 28 commodities, 3 types of labors and a capital, 24 types of households (each

province), and 5 types of taxes. It is essential to have a multi-household MicroSAM when the

objectives of the study are to find out welfare impacts on different household groups.

Second, four simulation scenarios have been conducted to quantify the impacts of trade

liberalization on households’ welfare and labor market. Following trade liberalization,

Cambodia’s textile industry remains buoyant with its domestic output increases in every

scenario. This industry will continue to be a backbone for the growth and job employment for a

short and medium term. Manufacturing industry is relatively more beneficial from trade

liberalization compared to agriculture and service sectors. In terms of effect on labor market, it

is obvious that wage increase is relatively higher for higher skilled workers than low skilled

workers.

Among different household groups, Rattanakiri household benefits less while Phnom Penh

household gains the most from the rise of their nominal incomes. With the decline of consumer

prices, it is Phnom Penh household again who benefit the most due to their greater

consumptions of manufacturing goods. Welfare gains go to households living in Phnom Penh

and its surrounding provinces as well as other big provinces with active economic activities.

Trade liberalization seems to bring the widening gap between urban households and rural

households.

Complementary policies in terms of infrastructure investment, government transfer, and

agriculture supports should be given a priority to target those living in rural and remote areas.

Agriculture need to be protected for the time-being with more incentives from government to

attract investment in this sector. Domestic tax codes need to be restructured and simplified in

order to increase or at least maintain government revenues stemming from the revenue loss of

trade-related taxes so that government can have ability to sustain its complementary and

compensatory policies against poverty and vulnerability.

7

1 Introduction

1.1 Context of the Study

‘We have nothing to fear from openness and competitions as we strengthen and diversity our

economy and build our nation’

The Minister of Commerce of Cambodia

(The Southeast Asia Weekly, June 17th, 2012).

‘Trade liberalization and integration in to the region and the world is one of the top policy

priorities of the Royal Government of Cambodia...Since trade is a major source for economic

growth and poverty reduction, the Royal Government will make further strides on the path of

trade liberalization aimed at free movement of goods and services between Cambodia and her

trade partners...This will create opportunity to avail economies of scales and bring other

benefits to Cambodian economy in terms of investment, jobs, income, and economic growth’

(National Strategic Development Plan 2009-13, p.113).

‘… Royal Government of Cambodia-not to mention Cambodian public- still seems to know

very little about the impacts of trade liberalization on the poor and vulnerable. There is a

tendency to accept it too easily as a requirement for Cambodia’s sustainable development’

(NGO Forum, Trade Issues, p.1).

Cambodia started its trade reforms toward a market-oriented economy in the late 1980s by

abolishing state monopoly of foreign trade. After the first UN-sponsored election in 1993, a

number of reform programs have been continuously undertaken including the sequential

reduction of tariff and non-tariff barriers, dismantling of quantitative restrictions on imports,

and deregulation of export and import procedures. Those reforms are clearly stated in

Cambodia MDGs, Cambodia National Poverty Reduction Strategy 2008-2013, and

Rectangular Strategy 2009-2013. Trade Policy Reviews Report of the Royal Government of

Cambodia to WTO also mentions that ‘Trade, employment and poverty reduction are tightly

linked in Cambodia, and trade policy is an integral and central element in the Royal

Government's efforts to promote development and improve living standards’ (WTO 2011, p.5).

Cambodia is currently a member of ASEAN and WTO, and is actively engaging itself into

various bilateral and multilateral trade agreements. Cambodia believes that by making itself

into the integrated regional and international economic system would bring about technological

transfer, knowledge spillover effects, investments, job creation, and ultimately poverty

reduction. Since the reform, Cambodia’s economy achieved rapid growths in an average of 7%

and GDP per capital has tripled from just $250 in 1994 to almost $900 in 2011 (WDI 2012).

Poverty headcount ratio dropped from 39% in 1994 to 30.14% in 2007 (WorldBank 2009).The

latest rate available from government shows that poverty headcount ratio continued dropping

to 22.89%.

However, amid strong economic growth resulting from trade liberalization measures, there is

an allegedly growing gap between poor households and rich households in terms of income

distribution. This raises a number of crucial issues pertaining to social goals such as: How are

different household group especially the poor impacted by significant trade liberalization

measures? Are countervailing policies needed to promote trade liberalization in a more

equitable manner?

http://www.opendevelopmentcambodia.net/category/news-source/southeast-asia-weekly/

8

Less is understood about the potential costs and benefits of such trade liberalization, especially

in terms of growth and employment, and their magnitudes. In addition, there has been criticism

that Cambodia is not ready for ASEAN Economic Community (AEC) 2015 given its low-skills

labour force and limited infrastructures. The concern is that trade liberalization could have

negative impacts on employment and could lead to revenue loss due to reduction of tariffs. In

that regards, some argues that Cambodia should strengthen its governance and infrastructures

as well as its labour market first before full trade liberalization.

Despite these debates, however, there has been no serious attempt to quantify the impacts of

trade liberalization on households’ welfare and labor market in Cambodia. The scientific,

evidence-based policy research is needed to contribute to the debates among policy makers

and to the formulation of policy options in the face of further trade liberalization measures.

Such analysis on the impacts of trade openness and fiscal policy is even more important for

Cambodia where poverty remains high, especially in rural areas.

1.2 Research Questions and Objectives

This study seeks to address the following questions:

1. What would be impacts of trade liberalization on outputs and production factors?

2. What would be the changes in welfare arising from the trade openness and the change

in fiscal policies?

These are important questions whose answers can shed light on the whether the on-going trade

liberalization can actually benefits Cambodia as believed, and what can be done to maximize

the benefits and to minimize the costs. The finding of the study can also have some policy

implications on what Cambodia should do to prepare itself for the full trade liberalization.

2 Cambodia’s Economy and Measures affecting Trade Prior to the global economic crisis Cambodia has been a star growth performer in the East

Asian region. The economic performance has been remarkably impressive between 1998 and

2007. Its annual economic growth stood at a high record of 9.4% on average. If calculated

from 1994 to 2011, an average growth is around 7% and GDP per capita has tripled. The

Cambodia’s growth and export remain narrowly based, giving limited benefits to the vast

needs of majority of people to move out of poverty. The concentration of growth and export

are namely in the sectors of garment and tourism. During the crisis, it could prove that the

competitiveness of these two sectors remain extensively low and fragile.

9

Figure 1: Cambodia’s GDP per capita and GDP Growth 1994-2011

Source: WDI (2012)

Cambodia’s trading regimes are affected from its membership in ASEAN and WTO and its

unilateral trade liberalization. It joined ASEAN in 1999 and committed to implementing the

so-called ASEAN Common External Preferential Tariff (CEPT) in 2000. In 2004, it was

acceded to WTO and also committed to a number of reforms on its institutional and trading

system in compliance with WTO regulations. The Customs Law was amended in 2007 to pay

the way for fulfilling Cambodia’s commitments to the ASEAN’s CEPT, the 1999 revised

Kyoto Convention, and the WTO’s Customs Valuation Agreement. At the end of 2010, all

imports to Cambodia are in line with the WTO valuation method. Cambodia also does not

apply PSI-related laws from 2010.

As the result of Cambodia’s import and export procedure streamlining, numbers of days

needed for necessary administrative documents for imports and exports as well as export costs

per container decreased. With the introduction of ASYCUDA World System, to clear a

shipment, it now takes an average 24 hours for both imports and exports. Within these 24

hours, almost 90% of import declarations are cleared, from a filing of goods declaration to a

release of goods. A number of steps required for obtaining a certificate of origin and an export

license at the Ministry of Commerce also reduced to 8 steps from 11 steps since 2004 through

the new application of a single administrative document (SAD).

In regard to reforming tariff structure, before joining WTO tariff bands were reduced to 4 from

12. There were no highest tariff rates of 40%, 50%, 90%, and 120% anymore. Instead, the

highest tariff rates have been reduced to 0%, 7%, 15%, and 35%. As of 2011, among all tariff

lines, 13.7% are bound for duty free while 39.7%, 36.7%, and 9.9% are bound for highest rates

of 7%, 15%, and 35% respectively. In addition, from the time of its accession to WTO, the

numbers of tariff lines were reduced from 10,700 to 8,300 in 2011, based on HS 2007

nomenclature.

Cambodia has three types of duties for imported goods, i.e. customs duties (tariff), VAT, and

Excise taxes. Additional taxes are to apply on gasoline and diesel oil, with a tax of US$0.02

per litre for gasoline and US$0.04 per litre for diesel oil. All tariffs are MFN bound and

9,10

6,44 5,41 5,62

5,01

11,91

8,77 8,04

6,69

8,51

10,34

13,25

10,77 10,21

6,69

0,09

5,96

7,07

0%

2%

4%

6%

8%

10%

12%

14%

$0

$100

$200

$300

$400

$500

$600

$700

$800

$900

$1 0001

994

19

95

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

20

11

GDP per capita (US$) GDP Growth (%)

10

uniform for all countries except ASEAN Dialogue Partners under CEPT with incentives of

lower tariff rates. There are also tariff exemptions for the imports of production materials for

qualified investments approved by the Council for the Development of Cambodia (CDC),

imports of some agricultural inputs and machinery, and the imports of aid-providing

international organizations, embassies, and certain development projects. Cambodia applies the

10% VAT uniform tax covering goods and services through all stages of importation,

production, and distribution. As for excise tax, it is levied on selected products whether they

are locally produced or imported. Since the customs duties were in decline, excise tax has been

increased to ensure revenue neutral for the country. In 2010, share of customs duties, VAT on

imports, and excise tax to the total tax revenues accounted for 16.9%, 19.8%, and 14.6%

respectively.

Table 1: Share of international trade taxes in total tax revenue, 2004-2010

2004 2005 2006 2007 2008 2009 2010

Total tax revenue (billion riels) 1656.2 1989.8 2391.6 3584.7 4688.7 4332.2 5070

of which (%)

Customs duties (after exemption) 21.9 23.1 22.7 19.5 19.2 17.3 16.9

Excise duties on imports 15 16.3 14.9 14.7 16.5 13.7 14.6

Gasoline/diesel taxes 5.3 4 4.3 3.3 2.5 3.7 3.7

VAT on imports 24.4 24.3 24 20 20.3 21.4 19.8

Export taxes 1.2 0.9 1 0.6 0.5 0.3 0.4

Others (fees & penalties) 1.8 2 2.2 2 1.8 1.1 0.8

Total international trade taxes 69.5 70.6 69 60.1 60.7 57.5 56.3

Source: WTO (2011), p.34

The challenge faced by Cambodia now is to reduce its reliance on trade-related revenues by

simplifying and broadening domestic taxes. The introduction of tax on property (initially in the

capital Phnom Penh) and the increase of road tax are the two tax policies currently

implemented by the government. Even with the government’s efforts to reduce revenue

contribution from trade-related taxes, they still comprise 56.3% of total revenues in 2010,

down from 69.5% in 2004. The total revenue to GDP still stagnated at around 10-11%, the

level considered as one of the lowest in the world although Cambodia has enjoyed high growth

rates.

This shows that revenue generation is irresponsive to the real economic activities which have

very much improved in the last period, indicating that the country is significantly

underperforming its revenue administration potential. To finance its fiscal deficit, Cambodia

still needs to depend on foreign loans and grants to meet its huge demands for the country

development investments. Foreign financing accounted for 5.3% of GDP in 2010. Given the

currently growing globalization period, custom tax (trade tax) will eventually become less

contributing to the country’s tax revenues in the future. With 56.3% of the Cambodia’s tax

revenues coming from international trade, the country continues its long tradition of relying on

more distortive import-based taxes. Thus, in the future prospect, it is believed that personal

income tax, corporate income tax, and VAT would be a cornerstone for the Cambodia’s tax

revenue.

11

Figure 2: Government’s Revenue 2004-2011 (% of GDP)

Source: IMF (2012), p.07

Cambodia also has to follow the established FTA agreement between ASEAN and its dialogue

trading partners. Those partners include Japan, China, India, Australia/New Zealand, and

South Korea. Cambodia has obligation to set its tariff at zero with China by 2015, South Korea

and India by 2018, New Zealand/Australia by 2024, and Japan by 2026.

Table 2: Tariff commitments under FTAs between ASEAN and its dialogue partners

ASEAN

AFTAs with

Schedule of Zero Tariff Rates

2010 2011 2015 2018 2024 2026

China ASEAN 6 CLMV

India

ASEAN 5

(except the

Philippines)

Cambodia

South Korea ASEAN 6 Cambodia

Japan ASEAN 6 CLM

Australia ASEAN 6 Cambodia

New Zealand Myanmar

Source: WTO (2011), p.24

12

3 Literature review

Although the impacts of trade liberalization are subject to debates and need to be understood

more through ex-ante analysis, there have been very few studies related to the impacts of trade

liberalization in Cambodia. For instance, Naron (2003) provides a description of Cambodia’s

economy and trade structure (Cambodia’s main exports and imports), and the potentials of

trade liberalization. The paper, however, does not provide any analyses of the impact of trade

liberalization on households’ welfare and labor market. Similarly, Neak (2006) tries to assess

the impact of garment industry on poverty reduction using descriptive data and interviews with

policy makers. On the other hand, Chan and Oum (2011) assess the impact of the US tariff

exemption on garment and textile imports on the Cambodian economy and livelihoods, using

Input-Output table for 2008. They found that the reduction in poverty rate is approximately

1.5% in urban areas as compared to 1.1% in rural areas.

There are also few studies that use CGE model to investigate the impacts of trade

liberalization. The first study carried out by Fukase & Martin (2001) to examine economic and

fiscal implications of Cambodia’s accession to the ASEAN Free Trade Area (AFTA). They

reveal small gains from AFTA liberalization package, saying that trade diversion offsets the

benefits of trade creation. Terms of trade with partner markets that are expected to improve are

limited. Although broader liberalization would yield significantly larger welfare gains, this

requires more attention to the development of alternative revenue sources. Another study was

done by Strutt et al., (2010) to explore the poverty impacts of ASEAN Trade Liberalization for

Cambodia, Lao PDR, Thailand, and Vietnam. Using a global trade model, Strutt et al., (2010)

find that ASEAN liberalization is likely to bring significant gains to the regions and lead to

substantial poverty reduction. For Cambodia, poverty reduction of agricultural and rural

diversified households is particularly beneficial from this ASEAN liberalization. Likewise,

under broader ASEAN+3 and ASEAN+6 liberalization, they show a much higher poverty

reduction.

Besides the two studies above, other CGE-used studies were done by Bargawi (2005) and Sak

& Ryuata (2009). These studies focused on garment industry, the main exporting sector of

Cambodia. Bargawi (2005) shows that Cambodia’s garment industry is likely to be able to

withstand the effect of falling export prices after the expiry of the Multi-Fiber Arrangement

(MFA). Sak & Ryuata (2009) investigate Cambodian economic impacts of the Vietnam

membership to WTO and the abolition of restrictions on Chinese exports of textiles and

clothing to the US and EU. Their results show that the welfare loss would be about 905 million

US dollars with the decrease of both export and import of apparel products. However, these

two studies have some loopholes to be questioned. A study by Bargawi (2005) did not provide

in detail data sources and model they used for conducting simulation. On the other hand, SAM

used by Sak & Ryuata (2009) could not well replicate the real structure of the Cambodia’s

economy as there are many missing data and unrealistic data in that SAM, and their simulation

scenarios have been formed without strong hypothesis.

It is noted that all studies mentioned above focus more on the export side, i.e. Cambodia’s

export of garment, and largely ignore Cambodia’s import and its impact. In addition, none of

these studies provide a clear analysis of potential costs and benefits from trade liberalization in

terms of welfare gain/loss and its implication on labor market, which is important for informed

decision making to policy makers.

13

In short, there has been no evidence-based in-depth analysis to support the policies and to

measure the potential costs and benefits of such trade liberalization. This is the important gap

that this study intends to fill.

Among the trade liberalization studies of other countries, the synthesis work by Cockburn,

Decaluwé, & Robichaud (2008), in collaboration with various researchers in Asia and Africa,

have been a useful and guiding study for the Cambodia’s trade liberalization case in this

research. There are seven Asian and African countries- Bangladesh, Benin, India, Nepal,

Pakistan, the Philippines and Senegal, which have been used to examine the poverty and

welfare impact from trade liberalization. Each country’s household has been disaggregated

according to the country’s specific socio-economic characteristics. It compares and contrasts

welfare, poverty, and inequality impact results from trade liberalization in those countries,

explaining where there are similarities and why there are differences.

What is an important point noteworthy for this study is that particular attention is paid to

indentifying how the specific characteristics of each country such as initial tariff structure,

trade patterns, relative factor endowments, production patterns, income sources and

consumption patterns of the poor, and so on can modify the poverty and inequality results,

rather avoiding from differences in methodological approaches used. Findings suggest that

trade liberalization has small, but positive impacts on welfare and poverty. Overall, industrial

sectors benefit compared to agriculture from trade liberalization, as do urban households

compared to their rural counterparts. There are seven lessons learned to be drawn from this

study about the impacts of trade liberalization on welfare, poverty, and inequality:

1. Trade liberalization increases welfare and reduces poverty marginally

2. Trade liberalization is pro-urban and may increase rural poverty

3. Industrial output increases relative to agriculture as a result of a stronger

export response and greater input cost savings

4. Relative wages increase, returns to capital fall

5. Nominal income tends to fall most in rural areas

6. Nominal consumer prices fall more in industry than agriculture or services

7. Cost of living effects vary.

4 The methodology

This study employs the computable general equilibrium (CGE) model, as it is widely used for

economic-wide impact studies and recognized as powerful tool in welfare and poverty

analysis. Using this framework analysis, consequences of several measures on allocation of

resources, distribution of income, distribution of consumption, and welfare situation of

different household groups will be examined.

The study uses the CGE model of PEP-1-1 (version 2.1), developed by Bernard Decaluwe,

Andre Lemelin, Veronique Robichaud, and Helene Maisonnave. Using the PEP-1-1 is capable

of taking into account a broader set of tax instruments, particularly as Cambodia has five main

tax instruments reported in the government budget, namely direct income tax, VAT tax,

custom tax, excise tax, and export tax.

For the purpose of policy analysis, simulation exercises will be conducted using the multi-

sectoral, multi-factor, and multi-households computable general equilibrium (CGE) model

14

calibrated to the social accounting matrix (SAM) of the Cambodia’s economy. Given the lack

of official Accounting Matrix (SAM) of Cambodia, the study first constructs both the

MacroSam and MicroSam for Cambodia.

5 Data

In order to build the Macro-SAM and Micro-SAM for our analysis, the study uses the data

from National Account and Government Budget (2011), Balance of Payment (2011) from the

National Bank of Cambodia, the Asian Development Bank’s Supply-Use Table (2011), and

Cambodia Socio-Economic Survey (CSES 2009), and Cambodia’s Custom System (2011).

6 Constructing Cambodia’s Social Accounting Matrix

6.1 Construction of MacroSAM

Cambodia’s MacroSAM 2011 was built with data from various sources including National

Account, Government Budget, Customs Tariff of Cambodia, ADB’s Supply-Use Table, and

Balance of Payment. GDP calculated from this MacroSAM is approximately 13 billions $US

for fiscal year 2011. The main components of the MacroSAM are explained as below.

i. (factors,act)…11,960

It is the value of GDP at factor cost, generated by labor and capital. This value is derived from

ADB’s Supply-Use Table (SUT). In MicroSAM, labor and capital will be disaggregated across

different activities, and labor income will be also split into three categories of low-skilled,

semi-skilled, and high-skilled.

ii. (com,act)…11,551

It is the value of intermediate consumption used in production process. This value is also

derived from the ADB’s SUT. Technical coefficient from the SUT for disaggregation between

commodities and activities will be used in MicroSAM.

iii. (act,com)…23,511

It is the value of output at market price. This value is equal to gross output which consists of

GDP at factor cost and intermediate consumption (23,511=11,551+11,960).

iv. (com,hhd), (com,gov), (com,sav-inv), and (com,dstk)

(com,hhd)…9,767 is the value of households consumption denoted by C. (com,gov)…1,479 is

the value of government recurrent expenditure denoted by G. (com,sav-inv)…2,191 is the

value of gross fixed capital formation while (com,dstk)…181 is the value of changes in

inventories or stock. These values are taken from government’s National Account and

Government Budget. Noted that (com,sav-inv)+(com,dstk) = Total Investment denoted by I.

15

v. (row,…) and (…,row)

Values in these cells are all derived from Balance of Payment’s data, including exports/imports

of goods and services i.e. (com,row)…7,432, (row,com)…8,033 and other transfers into/out

the country.

vi. (tax-…,com) and (tax-dir,hhd)

Values in these cells are taken from Government Budget. Tax rates from the Customs Tariff of

Cambodia are used to disaggregate taxes across each commodity when constructing

MicroSAM.

vii. (hhd,f-lab)…4,424 and (hhd,f-cap)…6,813

These are the household incomes from the factors of production i.e. labor and capital. The

values of these two cells are derived from subtraction of (f-lab,act) and (f-cap,act) with that of

(row,f-lab) and (row,f-cap).

viii. (sav-inv,gov)…239, (sav-inv,row)…712, and (sav-inv,hhd)…1,422

Value of government saving (sav-inv,gov) is taken from National Account of the government

while value of foreign saving (sav-inv,row) is the sum of trade balance, net factor income, and

net cash transfers. This cell is also called current account balance of a country. With value of

712, it can be said that Cambodia has a negative current account balance. Household saving

(sav-inv,hhd) is what remains from the subtraction of household incomes with household

expenditures.

It is noted that we used import/export data from NBC as data there are more reliable than other

sources. We adjusted values of household consumption, saving-investment, and change of

stock to make total sum of rows and columns equal. However, data changes are not that big.

Only Total row sum and Total column sum for Commodities are not equal, providing us a

reason to adjust only household consumption, saving-investment, and change of stock. After

this small adjustment, we have completed the Cambodia’s 2011 MacroSAM as shown below.

16

Table 3: Cambodia’s MacroSAM

Source: Authors’

Cambodia's Macro SAM, fiscal year 2011 (millions $US)

act com f-lab f-cap hhd gov row tax-vat tax-excise tax-imp tax-exp tax-dir sav-inv dstk total

Activity act 23,511 23,511

Commodity com 11,551 9,767 1,479 7,432 2,191 181 32,602

Labor f-lab 4,548 5 4,553

Capital f-cap 7,411 7 7,418

Household hhd 4,424 6,813 132 215 11,583

Government gov 19 440 494 291 255 18 365 1,882

Rest of

the world row 8,033 130 586 30 32 8,811

VAT tax tax-vat 494 494

Excise tax tax-excise 291 291

Tariff tax tax-imp 255 255

Export tax tax-exp 18 18

Direct tax tax-dir 365 365

Saving/

investment sav-inv 1,422 239 712 2,372

Change

of stock dstk 181 181

Total total 23,511 32,602 4,553 7,418 11,583 1,882 8,811 494 291 255 18 365 2,372 181

17

6.2 Construction of MicroSAM

This section is to illustrate the process on how to construct a MicroSAM with different types of

households. There are obviously two reasons to do so. First of all, to filter out the magnitude of trade

liberalization impacts on different household groups, it is required to disaggregate households and their

labors into different categories. Therefore, microdata on household’s income/expenditure and labor from

household survey are to be used for constructing a MicroSAM. Second, total labor compensation in

Cambodia’s MacroSAM is smaller than total capital, meaning that the economy appear to be highly

endowed with capital, which is unlikely for the case of developing country like Cambodia. For instance,

compensation of employees in Cambodia only accounts for around 38% of value-added. As parameters

used in CGE model are functions of factor shares, the results of CGE models that use data from an

uncorrected MacroSAM are put in doubt.

Labor compensation in SUT does not take into account the so-called self-employment records, leading us

to believe that labor compensation is perhaps underestimated. Based on 2009 CSES, self-employment

workers include own account worker, unpaid family worker, and others. They represent about 77% of the

total labor share in the economy. This suggests that a high proportion of those self-employment workers

should not be overlooked when carrying out the welfare and poverty analysis. The underestimation of

labor compensation in SUT is common for developing countries where a majority of labor force is in the

self-employment sector. Thus, we need to do wage imputation for those whose statuses are self-

employment.

Table 4: Share of Employed Persons

Employment Status Number Percent

Employee 1,700,755 22.78%

Employer 22,398 0.30%

Own account worker 3,908,451 52.35%

Unpaid family worker 1,827,677 24.48%

Other 7,466 0.10%

Total 7,466,000 100%

Source: Author’s calculation from CSES 2009

This method of wage imputation can be found in the literature. From the approaches applied by Fofana &

Cockburn (2003) and Fontana & Wobst (2001) in constructing a MicroSAM for Nepal and Bangladesh,

they use socio-economic data to reconcile with Macro SAM. Fofana & Cockburn (2003) use the 1995/96

Nepal Living Standard Survey (NLSS) to reconcile with the 1996/97 Nepal Social Accounting Matrix.

Fontana & Wobst (2001) also apply this approach to reconcile the 1995/96 Labor Force Survey (LFS) to

make a data reconciliation with the 1993/94 SAM of Bangladesh. As for the Cambodia’s case, we follow

this procedure to reconcile data of the 2009 Cambodia Socio-Economic Survey (CSES 2009) with that of

data in the 2011 MacroSAM constructed in above. Normally, there are four steps in the data reconciliation

process.

18

Before we proceed with the four steps in reconciling data in SAM with household survey, we first need to

have structures of production factors, intermediate consumption, export/import, household consumption,

etc. by utilizing shares from Supply-Use Table with MacroSAM serving as control totals. The resulting

SAM is called a preliminary MicroSAM. Then the reconciliation process involves only the estimation of

household groups’ income/consumptions and their labor payments in each activity from household survey

to disaggregate households and to correct structure of labor/capital in the preliminary MicroSAM.

6.2.1 Factor Payment Estimation

In Cambodia’s household survey i.e. CSES 2009, formal salary can only be known for those whose

employment occupation are employee. To estimate labor compensations for own account worker, unpaid

family worker, and others, there is a need to impute those labor compensations by multiplying the

numbers of working hours with the average wage in the formal sector (employee) for the same type of

labor. Data in CSES 2009 record the numbers of working hours for self-employment labor, but without

recording the amount of wage payments. Labor type is classified into three kinds: Low-Skilled Labor,

Medium-Skilled Labor, and High-Skilled Labor. The criteria to separate different skills type for each

individual are based on their highest education attainment, i.e. low-skilled: grade 0-5, medium-skilled:

grade 6-10, and high-skilled: grade 11 and above.

It is noteworthy that in CSES 2009, there are two records of a person’s employment occupation. A person

could have either one main employment occupation or second employment occupation, or they might have

both main and second employment occupation as sources of their labor incomes. It is a common thing in

developing country like Cambodia. For example, it often happens that a person whose main occupation is

government employee would rather find other secondary jobs to support their daily expenditures due to

the insufficient salary from government. They can be an employee in a private company, a part-time

teacher, motor taxi driver, or street vender. Sometimes, earnings from those secondary jobs far outweigh

that from government’s salary. Therefore, it is very crucial not to exclude those workers who have both

the main employment occupation and second employment occupation when estimating factor payments in

a MicroSAM.

After summing up the factor payments of formal and self-employment labor forces in both main and

second employment occupation, total factor payments for each activity are finally made by an addition of

factor payments in main employment occupation with that of second employment occupation. Table 5

below shows factor payments in each activity after the values from CSES2009 are scaled up to match with

control totals in preliminary MicroSAM.

It is noted that return to capital of each sector is calculated from what remains after subtracting labor

payments from fixed value-added in the preliminary MicroSAM. Fixing total value-added will constrain

the total GDP at factor costs to its original value as set in the MacroSAM.

19

Table 5: Sectoral Factor Payments of Cambodia (Millions US$)

ID Activities Low-

skilled

Medium-

skilled High-skilled

1 Agriculture, Hunting, Forestry, and Related Service Activities

1,654.09

766.85

146.76

2 Fishing, Aquaculture, and Service Activities Incidental to Fishing

309.60

102.12

8.71

3 Mining and Quarrying

9.48

4.50

0.19

4 Manufacture of Food Products, Beverages, and Tobacco

68.87

43.56

6.37

5 Manufacture of Textiles, Wearing Apparel, and Footwear

138.31

207.66

42.28

6 Manufacturing of Wood, Wood Products, Paper, and Paper

Products

23.87

9.35

1.78

7 Manufacture of Rubber and Plastic Products

22.26

14.37

8.80

8 Manufacture of Basic Metals

0.79

2.91

21.79

9 Manufacture of Fabricated Metal Products; and Office and

Computing Machinery

14.49

13.54

4.96

10 Manufacture of Motor Vehicles and Other Transport Equipment

2.28

3.06

0.32

11 Other Manufacturing

19.45

25.81

9.44

12 Electricity, Gas, and Water Supply

7.82

7.60

3.29

13 Construction

127.63

123.27

20.26

14 Wholesale and Retail Trade; and Repair of Motor Vehicles

377.12

296.69

154.42

15 Hotels and Restaurants

67.08

72.84

35.42

16 Transport Services and Storage

99.31

148.16

49.99

17 Post and Telecommunications

2.30

2.80

21.30

18 Financial Intermediation and Insurance

4.61

12.51

38.12

19 Real Estate, Renting, and Business Services

37.56

72.23

150.71

20 Public Administration and Defense

5.03

25.46

65.05

21 Education

4.86

26.37

111.65

22 Health and Social Work

5.73

17.98

53.67

23 Other Community Service Activities

40.39

113.62

132.11

Source: Author’s calculation from CSES 2009

20

6.2.2 Household Income Estimation

24 different household groups are categorized according to their living locations in each province. To get

household incomes for incorporating into a MicroSAM, it is better to look first at the sources of household

incomes. In the Table 6 below, again it shows the high percentage (63.41%) of household’s incomes from

self-employment sector. If all incomes from self-employment activities are considered to be return to

capital, it will underestimate labor endowment in the economy.

Table 6: Sources of Household Income

Sources of Income Amount (US dollar) Percent

Salary 2,070,000,000 31.86%

Self-Employment (Agr & Non-Agr) 4,120,000,000 63.41%

Capital 34,100,000 0.52%

Transfer 273,372,898 4.21%

Total 6,497,472,898 100%

Source: Authors’ calculation from CSES 2009

The tasks now are to:

1. Disaggregate formal salary into returns to three classifications of labor, i.e. Low-skilled, Medium-

skilled, and High-skilled, corresponding to the 24 different household groups in each province. This

could be done since the data also record living locations and education attainment of every working

family member.

2. Separate wage and return to capital from what is recorded as self-employment revenues in the Table

above. The method is similar to step 1 in estimating factor payments, i.e. wage imputation. For

those whose statuses are self-employed also have a record of their working time, thus the imputed

wage is then calculated as their working hours multiplied by the average formal wage for

households’ specific skill types and their specific sectors of employment. The return to capital is

calculated as a residual after subtraction imputed wage from households’ self-employment incomes.

3. The imputed labor can also be categorized by three different skill types. The result is then the

estimation of income sources for each household, separating 24 classifications of household groups,

three classifications of labor, capital, government transfer, and foreign transfer. Table 6 presents the

sources of incomes after the imputation of wage from self-employment.

21

Table 7: Household Income by Household Groups (Millions US$)

Households Low-skilled Medium-skilled High-skilled Capital Govt RoW

Banteay 171.53 108.92 51.10 139.73 5.67 8.61

Battambang 167.63 143.91 56.23 286.75 7.76 13.44

K.Cham 487.28 234.73 46.39 348.93 13.20 14.37

K.Chhnang 107.63 65.47 15.26 181.30 4.32 7.37

K.Speu 159.53 117.56 20.57 272.30 6.49 3.41

K.Thom 140.08 71.42 23.37 253.65 5.64 8.31

Kampot 114.31 91.09 30.04 128.06 4.23 1.92

Kandal 254.85 227.88 102.29 564.79 14.22 2.02

Kep 3.08 2.19 0.27 6.59 0.14 0.09

Koh Kong 23.51 10.49 5.29 55.93 1.08 1.30

Kratie 96.97 39.03 6.57 74.64 2.51 1.24

Mondul Kiri 28.07 1.40 1.40 22.21 0.60 0.45

Oddor 27.74 17.50 0.92 24.11 0.82 0.42

Pailin 7.75 9.40 0.67 70.17 0.96 1.23

Phnom Penh 165.26 331.64 511.87 1,429.03 28.09 42.88

Preah Vihear 49.39 6.35 1.62 29.67 0.98 0.08

Prey Veng 223.45 127.01 23.50 171.30 6.72 17.49

Pursat 134.85 73.39 19.70 109.60 4.06 7.19

Rattanak Kiri 46.37 3.44 2.07 32.78 1.04 3.58

Shv 80.13 51.21 33.62 78.15 2.89 3.47

Siem Reap 233.55 112.61 47.84 417.66 9.35 13.31

Stung Treng 30.26 17.64 2.75 33.29 0.96 0.27

Svay Rieng 72.18 53.58 11.26 92.65 2.68 2.58

Takeo 159.09 151.02 50.91 294.52 7.67 9.73

Source: Authors’ calculation from CSES 2009

6.2.3 Household Expenditure Estimation

The third step is to estimate household expenditure from the household survey data. 24 types of households

are classified and distributed their expenditures to each commodity classification. Commodity classification

is based on the Central Product Classification (CPC)1 for the types of household expenditures from each

commodity. In mapping SUT commodity and CSES commodities, it is based on codes from NIS (National

Institute of Statistics) about items households spend on. Since there is no available official mapping list

between SUT commodity and CSES commodities, mapping was done through discussion among team

members and with NIS staff, though this would not give a good mapping.

Regarding the disaggregation of household consumption, structure of household consumption from 2009

CSES has been preserved. How could that structure be preserved? There is a link of labor/capital account,

household income account, and household consumption account in SAM. Labor and capital are sources of

household income. Since labor payment has already been estimated in Section 6.2.1, we have Total amount

of each type of labor. Capital is a residual of value added minus labor payment. We then used Total Labor

(each labor)/Capital amount as a control Total to disaggregate household income by using income shares

1 To be consistent with product classification in ADB’s Supply-Use Table

22

from 2009 CSES. After that we got the total income of each 24 household (also include Row transfer and

Govt transfer to HH).Those total incomes are used as control Total for disaggregating HH consumption on

each commodity, using shares from 2009 CSES (because in SAM Total row sum must be equal to Total

column sum). It is also noted that HH saving is what remains from HH income minus HH expenditure in

2009 CSES. Other household’s expenditure is the government tax. From data in CSES 2009, households

also have a record of paying tax to the government. Table 8 shows HH consumption structure before and

after disaggregation. It is just a small difference.

Table 8: Household Consumption Comparison: Pre and Post Disaggregation (Millions $US)

HH consumption comparison: Before and After disaggregation

(Millions $US)

HH consumption

(Before

disaggregation:

from SUT)

HH consumption

(After

disaggregation: from

2009 CSES)

Agriculture, Forestry, and Logging Products 2,208.46 2,586.83

Fish and Other Fishing Products 864.39 1,012.48

Coal and Lignite; Peat, Crude Petroleum, and Natural Gas 22.75 21.79

Other Minerals, n.e.c. 32.14 30.78

Electricity, Gas, and Water 158.60 151.90

Food, Beverages, and Tobacco 1,679.19 1,608.25

Clothing and Wearing Apparel; and Leather and Leather Products 372.93 357.18

Products of Wood, Paper, and Paper Products 79.26 75.92

Basic Chemicals and Other Chemicals 305.89 292.96

Rubber and Plastics Products 88.63 84.88

Furniture and Other Transportable Goods, n.e.c. 126.43 121.09

Basic Metals 40.97 39.24

Fabricated Metal Products, Except Machinery and Equipment 109.06 104.45

General and Special Purpose Machinery 106.67 102.16

Office, Accounting, and Computing Machinery 310.45 297.34

Transport Equipment 303.80 290.97

Other Manufacturing 371.62 355.92

Construction Services 88.38 76.32

Wholesale and Retail Trade Services 0 0

Lodging, Food, and Beverage Serving Services 283.60 244.89

Transport Services, and Supporting and Auxiliary Transport Services 255.40 220.54

Postal, and Courier and Telecommunications Services 86.56 74.74

Financial Intermediation, Insurance, and Auxiliary Services 64.85 56.00

Real Estate, Leasing Services, and Other Business Services 945.10 816.10

Public Administration and Compulsory Social Security Services 5.51 4.76

Education Services 232.33 200.62

Health and Social Services 145.73 125.84

Other Services, n.e.c. 478.04 412.79

Total 9,766.74 9,766.74

Source: Authors’ calculation

23

Table 9: Consumption by Household Groups (Millions US$)

Commodities Bant Battam K.Cham K.Chhnang K.Speu K.Thom Kampot Kandal Kep K.Kong Kratie M.Kiri Oddor Pailin PP P.Vihear P.Veng Pursat RK Shv SR ST S.Rieng Takeo

Agriculture, Forestry, and Logging Products 111.57 154.41 266.15 90.48 138.01 119.28 100.57 261.77 3.67 22.04 58.74 14.64 17.82 19.86 471.15 23.61 126.60 88.91 21.40 57.13 183.27 23.04 52.92 159.82

Fish and Other Fishing Products 43.67 60.44 104.17 35.41 54.02 46.69 39.36 102.46 1.43 8.63 22.99 5.73 6.97 7.77 184.41 9.24 49.55 34.80 8.37 22.36 71.73 9.02 20.71 62.56

Coal and Lignite; Peat, Crude Petroleum, and Natural Gas 0.91 1.26 2.37 0.74 1.14 1.02 0.56 2.44 0.02 0.16 0.32 0.10 0.13 0.17 4.47 0.14 1.19 0.63 0.16 0.46 1.42 0.14 0.50 1.33

Other Minerals, n.e.c. 1.28 1.79 3.35 1.04 1.61 1.44 0.80 3.44 0.03 0.23 0.45 0.15 0.19 0.24 6.31 0.20 1.69 0.89 0.23 0.64 2.00 0.19 0.70 1.88

Electricity, Gas, and Water 6.33 8.81 16.53 5.14 7.95 7.11 3.93 16.98 0.13 1.14 2.24 0.72 0.93 1.18 31.16 0.98 8.32 4.41 1.14 3.18 9.89 0.96 3.46 9.30

Food, Beverages, and Tobacco 66.99 93.30 174.98 54.43 84.15 75.31 41.58 179.81 1.35 12.07 23.70 7.61 9.83 12.48 329.94 10.33 88.10 46.71 12.07 33.62 104.69 10.13 36.58 98.49

Clothing and Wearing Apparel; and Leather and Leather Products 14.88 20.72 38.86 12.09 18.69 16.73 9.23 39.93 0.30 2.68 5.26 1.69 2.18 2.77 73.28 2.29 19.57 10.37 2.68 7.47 23.25 2.25 8.12 21.87

Products of Wood, Paper, and Paper Products 3.16 4.40 8.26 2.57 3.97 3.55 1.96 8.49 0.06 0.57 1.12 0.36 0.46 0.59 15.57 0.49 4.16 2.21 0.57 1.59 4.94 0.48 1.73 4.65

Basic Chemicals and Other Chemicals 12.20 17.00 31.87 9.92 15.33 13.72 7.57 32.75 0.25 2.20 4.32 1.39 1.79 2.27 60.10 1.88 16.05 8.51 2.20 6.12 19.07 1.85 6.66 17.94

Rubber and Plastics Products 3.54 4.92 9.24 2.87 4.44 3.97 2.19 9.49 0.07 0.64 1.25 0.40 0.52 0.66 17.41 0.55 4.65 2.47 0.64 1.77 5.53 0.53 1.93 5.20

Furniture and Other Transportable Goods, n.e.c. 5.04 7.02 13.17 4.10 6.34 5.67 3.13 13.54 0.10 0.91 1.78 0.57 0.74 0.94 24.84 0.78 6.63 3.52 0.91 2.53 7.88 0.76 2.75 7.42

Basic Metals 1.63 2.28 4.27 1.33 2.05 1.84 1.01 4.39 0.03 0.29 0.58 0.19 0.24 0.30 8.05 0.25 2.15 1.14 0.29 0.82 2.55 0.25 0.89 2.40

Fabricated Metal Products, Except Machinery and Equipment 4.35 6.06 11.36 3.54 5.47 4.89 2.70 11.68 0.09 0.78 1.54 0.49 0.64 0.81 21.43 0.67 5.72 3.03 0.78 2.18 6.80 0.66 2.38 6.40

General and Special Purpose Machinery 4.26 5.93 11.12 3.46 5.35 4.78 2.64 11.42 0.09 0.77 1.51 0.48 0.62 0.79 20.96 0.66 5.60 2.97 0.77 2.14 6.65 0.64 2.32 6.26

Office, Accounting, and Computing Machinery 12.39 17.25 32.35 10.06 15.56 13.92 7.69 33.24 0.25 2.23 4.38 1.41 1.82 2.31 61.00 1.91 16.29 8.64 2.23 6.22 19.35 1.87 6.76 18.21

Transport Equipment 12.12 16.88 31.66 9.85 15.22 13.62 7.52 32.53 0.24 2.18 4.29 1.38 1.78 2.26 59.69 1.87 15.94 8.45 2.18 6.08 18.94 1.83 6.62 17.82

Other Manufacturing 14.83 20.65 38.72 12.05 18.62 16.67 9.20 39.79 0.30 2.67 5.25 1.68 2.17 2.76 73.02 2.29 19.50 10.34 2.67 7.44 23.17 2.24 8.10 21.80

Construction Services 3.06 4.26 6.02 2.21 3.40 2.64 2.67 7.09 0.06 0.77 1.73 0.25 0.41 0.62 20.85 0.67 3.30 2.19 0.56 1.74 6.31 0.58 1.07 3.84

Lodging, Food, and Beverage Serving Services 9.82 13.68 19.32 7.09 10.90 8.48 8.57 22.75 0.21 2.48 5.56 0.82 1.33 1.98 66.90 2.16 10.60 7.02 1.78 5.58 20.26 1.86 3.44 12.31

Transport Services, and Supporting and Auxiliary Transport Services 8.85 12.32 17.40 6.39 9.81 7.63 7.71 20.49 0.18 2.24 5.01 0.74 1.19 1.78 60.25 1.95 9.54 6.32 1.61 5.03 18.24 1.67 3.10 11.09

Postal, and Courier and Telecommunications Services 3.00 4.17 5.90 2.16 3.33 2.59 2.61 6.94 0.06 0.76 1.70 0.25 0.40 0.60 20.42 0.66 3.23 2.14 0.54 1.70 6.18 0.57 1.05 3.76

Financial Intermediation, Insurance, and Auxiliary Services 2.25 3.13 4.42 1.62 2.49 1.94 1.96 5.20 0.05 0.57 1.27 0.19 0.30 0.45 15.30 0.49 2.42 1.60 0.41 1.28 4.63 0.42 0.79 2.82

Real Estate, Leasing Services, and Other Business Services 32.73 45.58 64.40 23.63 36.31 28.25 28.55 75.83 0.68 8.27 18.52 2.72 4.42 6.60 222.96 7.20 35.32 23.38 5.95 18.60 67.51 6.19 11.48 41.03

Public Administration and Compulsory Social Security Services 0.19 0.27 0.38 0.14 0.21 0.16 0.17 0.44 0.00 0.05 0.11 0.02 0.03 0.04 1.30 0.04 0.21 0.14 0.03 0.11 0.39 0.04 0.07 0.24

Education Services 8.05 11.20 15.83 5.81 8.93 6.94 7.02 18.64 0.17 2.03 4.55 0.67 1.09 1.62 54.81 1.77 8.68 5.75 1.46 4.57 16.59 1.52 2.82 10.09

Health and Social Services 5.05 7.03 9.93 3.64 5.60 4.36 4.40 11.69 0.11 1.28 2.86 0.42 0.68 1.02 34.38 1.11 5.45 3.61 0.92 2.87 10.41 0.95 1.77 6.33

Other Services, n.e.c. 16.56 23.05 32.57 11.95 18.37 14.29 14.44 38.35 0.35 4.18 9.37 1.38 2.23 3.34 112.77 3.64 17.87 11.83 3.01 9.41 34.14 3.13 5.81 20.75

Direct tax 15.43 21.62 34.09 11.55 16.59 15.07 9.98 41.08 0.41 2.96 6.11 1.54 2.10 2.82 87.87 2.05 16.21 9.41 2.74 7.38 27.88 2.28 8.03 19.48

Savings 60.12 84.25 132.88 45.02 64.66 58.73 38.97 160.14 1.59 11.54 23.89 6.02 8.17 10.98 342.50 7.99 63.18 36.66 10.70 28.82 108.64 8.90 31.31 75.94

Source: Authors’ calculation from 2009 CSES

24

6.2.4 Reconciliation and Balancing

The final step is to put factor payments, household incomes, household expenditures estimated above into

the preliminary MacroSAM. After incorporating those data, there are some small differences in SAM’s

total column and total row of commodities. This is a common problem in the reconciliation process, as it

can be explained through the following reasons: the Supply-Use Table and CSES 2009 are recorded in

different periods, under-reporting in either the Supply-Use Table or CSES 2009 or both, sample bias in

CSES 2009, mapping error namely between household survey commodity classification and the Supply-

Use commodity classification. This could also reflect through a small difference of HH consumption

before-disaggregation (from SUT) and after-disaggregation, as provided in Table 8.

It is noted that household incomes and household consumptions are balanced because of the adjustment of

household saving. As mentioned above, household saving had been derived from household income minus

household consumption in 2009 CSES before disaggregation was done. The remaining unequal numbers

happened only on Total row sum and Total column sum of each commodity. To make MicroSAM

balanced, adjustment has been done on the value of the change of stock (dstk). Since Total column sum of

each commodity was not affected by disaggregation process, value of dstk is what remains between Total

column sum of each commodity and other accounts in the row of each commodity (intermediate

consumption, HH consumption, Govt expenditure…).

This final balanced MicroSAM includes 23 activities, 28 commodities, 3 types of labors and a capital, 24

types of households (each province), and 5 types of taxes. This SAM had been converted to the format of

PEP’s SAM sample for the use with GAMS code of PEP 1-1 Model.

25

7 Initial Structure of the Economy in Base Scenario Understanding the magnitude of trade liberalization results requires an examination of initial situation,

particularly initial tariff level, value-added share, sectoral exports/imports, sectoral labor remuneration

share, and so forth. Tables below feature the basic information of the Cambodia’s economic structure in

the base case. Table 10: Import/Export and Tariff by Goods (%)

GOODS ttim Mi/M EXi/EX Mi/Qi EXi/XSi

AGR 4.30 0.65 1.15 1.35 2.06

FISH 5.46 0.01 0.04 0.03 0.23

CPETRO 5.01 0.27 0.00 100.00 0.00

OTHMIN 5.02 0.35 0.00 27.64 0.17

EGW 1.76 0.22 0.00 6.87 0.00

FBT 5.74 6.10 0.42 28.75 2.38

TEXTILE 2.89 31.34 69.71 98.08 99.00

WP 5.31 1.90 0.92 58.35 37.02

CHEM 4.21 7.12 0.06 93.78 10.14

RP 6.02 1.65 0.09 34.84 2.43

FURN 4.74 2.19 0.93 79.12 58.24

METAL 2.87 1.45 0.36 63.95 27.94

FMETAL 5.13 1.78 0.05 33.38 1.27

MACHINE 4.19 4.47 0.09 99.35 73.70

OFFICE 4.46 6.06 0.41 99.98 99.58

TRANS 5.64 3.76 0.16 90.90 27.04

OTHMNU 1.58 23.96 0.09 93.06 4.06

CON 0.95 0.93 0.05 4.32 0.22

WTT 0.92 3.27 8.69 7.90 16.56

HR 2.26 0.03 14.30 0.65 71.27

POST 1.72 0.25 1.13 9.61 29.74

FINANCE 1.04 0.36 0.30 11.53 8.73

REAL 0.97 1.07 1.04 6.78 5.79

ADM 0.98 0.16 0.00 2.43 0.00

EDU 0.00 0.19 0.00 3.87 0.00

HEALTH 0.00 0.12 0.00 3.46 0.00

OTHSER 0.96 0.33 3.98 3.91 29.97


Tariff rates are high in rubber and plastics products, food beverages tobacco, and transport equipments

with rates of 6.02%, 5.74%, and 5.64% respectively, followed by 4.30% in agriculture and 5.46% in fish

and other fishing products. Service industry’s tariff rate is almost nil except in the sectors of hotel,

finance, post and telecommunication, where the estimated rates are 2.26%, 1.04%, and 1.72%. These

initial tariff rates suggest that industries with the higher rates will see the increase of imported goods,

resulting from the declining domestic price of imported goods relative to the price of domestically

produced goods. It is expected that industries of rubber and plastics products, food beverages tobacco, and

transport equipments will be the most affected by tariff elimination measures in trade liberalization.

26

The structure of production shows that agriculture and fish, trade and transport service, and textile

industries share the most contribution, combing for almost 62% in the total value-added. This proves that agriculture is still an important and largest sector for Cambodia’s job employment. As for sectoral

import/export shares (Mi/M and EXi/EX), textile industry accounts for 31% in the total import and 70% in

total export. Overall, the total share of textile import in local consumption (Mi/Qi) is approximately 98%

and the total share of its export in local production (EXi/XS) is almost 99%. It is not surprising with these

figures as the textiles produced in Cambodia are mostly for exporting markets, particularly for EU and US

markets. Only around 1% is for local consumption. Agriculture, on the other hand, has smaller

percentages for both import and export shares. Interestingly, agricultural export is only 1.15% of the total

export and around 2% of total local production.

In regard to sectoral labor forces, it is common in Cambodia’s context that the majority shares of labor

forces are low skilled labors in the agricultural and industrial sectors. High skilled labors are observed

bigger in service sector except in hotel and trade and transport services. The three service sectors with

majority shares of high skilled labors are post and telecommunication, education, and health with the rates

of 81%, 78%, and 69% accordingly.

Table 11: Value-Added and Labor by Sector (%)

SECTOR VAi/VA LSK MSK HSK

AGR 24.88 64.42 29.87 5.72

FISH 7.82 73.64 24.29 2.07

MINQ 0.40 66.90 31.78 1.32

FBT 2.49 57.97 36.67 5.36

TEXTILE 12.96 35.62 53.49 10.89

WP 0.61 68.21 26.71 5.08

RP 0.51 49.01 31.63 19.36

METAL 0.26 3.09 11.42 85.49

FMETAL 0.92 43.92 41.05 15.04

MOTORT 0.11 40.20 54.10 5.71

OTHMNU 0.92 35.55 47.19 17.26

EGW 0.51 41.77 40.63 17.60

CON 6.68 47.07 45.46 7.47

WTT 16.13 42.32 39.52 18.16

HR 4.45 38.26 41.54 20.20

POST 1.25 8.72 10.62 80.66

FINANCE 1.20 8.35 22.65 69.00

REAL 7.00 14.42 27.73 57.85

ADM 1.86 5.27 26.65 68.09

EDU 2.25 3.40 18.45 78.15

HEALTH 1.35 7.41 23.23 69.36

OTHSER 5.42 14.12 39.71 46.17


27

Looking at sources of household income, the majority of households get income come from their labor

remuneration in a bigger share. The exception is only Phnom Penh, Kep, Koh Kong, and Pailin, where

income from capital is bigger. Other sources of income including government transfer and rest of world

transfer account for approximately 0.10%-3% in a range. The same for household expenditure pattern, spending on agricultural and fish products, food beverages tobacco, and textile represents over 50% of

total spending. Households living in far remote provinces such as Mondul Kiri, Rattanakiri, Kep, for

example, have higher spending share on those commodities at even around 60%-63%. Phnom Penh,

households living in the Capital, has smaller share of just 51%. Spending on health, education,

telecommunication, and so forth of service sector is observed high for household of Phnom Penh and other

households with high incomes.

Table 12: Sources of Household Income (%)

HOUSEHOLD LSK MSK HSK CAP GVT ROW

BANTEAY 35.33 22.43 10.52 28.78 1.17 1.77

BATT 24.81 21.30 8.32 42.44 1.15 1.99

CHAM 42.56 20.50 4.05 30.48 1.15 1.26

CHHNANG 28.22 17.17 4.00 47.54 1.13 1.93

SPEU 27.51 20.27 3.55 46.96 1.12 0.59

KTHOM 27.88 14.21 4.65 50.48 1.12 1.65

KAMPOT 30.93 24.64 8.13 34.64 1.14 0.52

KANDAL 20.96 18.74 8.41 46.45 1.17 4.28

KEP 24.89 17.75 2.18 53.37 1.11 0.70

KK 24.09 10.75 5.42 57.31 1.10 1.33

KRATIE 43.89 17.66 2.97 33.78 1.13 0.56

MONDUL 51.85 2.59 2.59 41.02 1.11 0.84

ODDOR 38.79 24.47 1.28 33.72 1.14 0.59

PAILIN 8.59 10.42 0.75 77.81 1.07 1.36

PP 6.49 13.15 20.37 56.68 1.12 2.19

VIHEAR 56.06 7.21 1.84 33.68 1.12 0.10

PREY 39.24 22.30 4.13 30.08 1.18 3.07

PURSAT 38.66 21.04 5.65 31.42 1.16 2.06

RATTANAK 51.94 3.85 2.32 36.72 1.16 4.01

SHV 32.12 20.53 13.48 31.32 1.16 1.39

SIEM 27.99 13.50 5.73 50.06 1.12 1.60

STUNG 35.53 20.72 3.22 39.09 1.13 0.32

SVAY 30.72 22.81 4.79 39.44 1.14 1.10

TAKEO 23.64 22.44 7.57 43.77 1.14 1.45


28

Table 13: Household Consumption Pattern (%)

BANTEAY BATT CHAM CHHNANG SPEU KTHOM KAMPOT KANDAL KEP KK KRATIE MONDUL ODDOR PAILIN PP VIHEAR PREY PURSAT RATTANAK SHV SIEM STUNG SVAY TAKEO

AGR 27.30 27.19 27.31 27.95 27.75 27.90 31.45 25.88 35.65 26.61 30.85 31.51 29.24 26.05 22.73 30.34 25.93 29.44 28.31 26.87 26.34 31.22 27.20 27.77

FISH 10.69 10.64 10.69 10.94 10.86 10.92 12.31 10.13 13.95 10.41 12.08 12.33 11.45 10.19 8.90 11.88 10.15 11.52 11.08 10.52 10.31 12.22 10.65 10.87

CPETRO 0.22 0.22 0.24 0.23 0.23 0.24 0.18 0.24 0.18 0.20 0.17 0.22 0.22 0.22 0.22 0.18 0.24 0.21 0.22 0.21 0.20 0.19 0.25 0.23

OTHMIN 0.31 0.31 0.34 0.32 0.32 0.34 0.25 0.34 0.25 0.28 0.24 0.31 0.31 0.31 0.30 0.25 0.35 0.30 0.31 0.30 0.29 0.26 0.36 0.33

EGW 1.55 1.55 1.70 1.59 1.60 1.66 1.23 1.68 1.24 1.38 1.18 1.55 1.52 1.55 1.50 1.25 1.70 1.46 1.51 1.49 1.42 1.30 1.78 1.62

FBT 16.39 16.43 17.95 16.81 16.92 17.62 13.00 17.77 13.15 14.58 12.45 16.39 16.13 16.37 15.92 13.27 18.04 15.47 15.97 15.81 15.05 13.73 18.80 17.11

TEXTILE 3.64 3.65 3.99 3.73 3.76 3.91 2.89 3.95 2.92 3.24 2.76 3.64 3.58 3.64 3.54 2.95 4.01 3.44 3.55 3.51 3.34 3.05 4.18 3.80

WP 0.77 0.78 0.85 0.79 0.80 0.83 0.61 0.84 0.62 0.69 0.59 0.77 0.76 0.77 0.75 0.63 0.85 0.73 0.75 0.75 0.71 0.65 0.89 0.81

CHEM 2.99 2.99 3.27 3.06 3.08 3.21 2.37 3.24 2.40 2.66 2.27 2.99 2.94 2.98 2.90 2.42 3.29 2.82 2.91 2.88 2.74 2.50 3.43 3.12

RP 0.87 0.87 0.95 0.89 0.89 0.93 0.69 0.94 0.69 0.77 0.66 0.87 0.85 0.86 0.84 0.70 0.95 0.82 0.84 0.83 0.79 0.72 0.99 0.90

FURN 1.23 1.24 1.35 1.27 1.27 1.33 0.98 1.34 0.99 1.10 0.94 1.23 1.21 1.23 1.20 1.00 1.36 1.16 1.20 1.19 1.13 1.03 1.42 1.29

METAL 0.40 0.40 0.44 0.41 0.41 0.43 0.32 0.43 0.32 0.36 0.30 0.40 0.39 0.40 0.39 0.32 0.44 0.38 0.39 0.39 0.37 0.33 0.46 0.42

FMETAL 1.06 1.07 1.17 1.09 1.10 1.14 0.84 1.15 0.85 0.95 0.81 1.06 1.05 1.06 1.03 0.86 1.17 1.00 1.04 1.03 0.98 0.89 1.22 1.11

MACHINE 1.04 1.04 1.14 1.07 1.08 1.12 0.83 1.13 0.84 0.93 0.79 1.04 1.02 1.04 1.01 0.84 1.15 0.98 1.01 1.00 0.96 0.87 1.19 1.09

OFFICE 3.03 3.04 3.32 3.11 3.13 3.26 2.40 3.29 2.43 2.69 2.30 3.03 2.98 3.03 2.94 2.45 3.34 2.86 2.95 2.92 2.78 2.54 3.48 3.16

TRANS 2.97 2.97 3.25 3.04 3.06 3.19 2.35 3.22 2.38 2.64 2.25 2.97 2.92 2.96 2.88 2.40 3.26 2.80 2.89 2.86 2.72 2.48 3.40 3.10

OTHMNU 3.63 3.64 3.97 3.72 3.75 3.90 2.88 3.93 2.91 3.23 2.76 3.63 3.57 3.62 3.52 2.94 3.99 3.42 3.54 3.50 3.33 3.04 4.16 3.79

CON 0.75 0.75 0.62 0.68 0.68 0.62 0.83 0.70 0.62 0.93 0.91 0.55 0.68 0.81 1.01 0.87 0.68 0.72 0.74 0.82 0.91 0.78 0.55 0.67

WTT 2.16 2.17 1.79 1.97 1.97 1.79 2.41 2.03 1.80 2.70 2.63 1.58 1.96 2.34 2.91 2.50 1.95 2.09 2.13 2.36 2.62 2.27 1.59 1.93

HR 2.40 2.41 1.98 2.19 2.19 1.98 2.68 2.25 2.00 3.00 2.92 1.76 2.18 2.60 3.23 2.78 2.17 2.32 2.36 2.62 2.91 2.52 1.77 2.14

POST 0.73 0.74 0.61 0.67 0.67 0.61 0.82 0.69 0.61 0.91 0.89 0.54 0.66 0.79 0.99 0.85 0.66 0.71 0.72 0.80 0.89 0.77 0.54 0.65

FINANCE 0.55 0.55 0.45 0.50 0.50 0.45 0.61 0.51 0.46 0.69 0.67 0.40 0.50 0.59 0.74 0.63 0.50 0.53 0.54 0.60 0.67 0.58 0.40 0.49

REAL 8.01 8.03 6.61 7.30 7.30 6.61 8.93 7.50 6.66 9.99 9.73 5.86 7.25 8.66 10.76 9.25 7.23 7.74 7.87 8.75 9.70 8.39 5.90 7.13

ADM 0.05 0.05 0.04 0.04 0.04 0.04 0.05 0.04 0.04 0.06 0.06 0.03 0.04 0.05 0.06 0.05 0.04 0.05 0.05 0.05 0.06 0.05 0.03 0.04

EDU 1.97 1.97 1.62 1.79 1.80 1.62 2.19 1.84 1.64 2.45 2.39 1.44 1.78 2.13 2.64 2.27 1.78 1.90 1.93 2.15 2.38 2.06 1.45 1.75

HEALTH 1.23 1.24 1.02 1.13 1.13 1.02 1.38 1.16 1.03 1.54 1.50 0.90 1.12 1.34 1.66 1.43 1.12 1.19 1.21 1.35 1.50 1.29 0.91 1.10

OTHSER 4.05 4.06 3.34 3.69 3.69 3.34 4.52 3.79 3.37 5.05 4.92 2.97 3.67 4.38 5.44 4.68 3.66 3.92 3.98 4.42 4.91 4.25 2.98 3.61


29

The above examination of the structure of economy can principally suggest that impacts of tariff

elimination might be likely channeled to all sectors through the variation in import prices/volumes, sectoral

output, labor and capital demand, and so forth.

8 Simulation Designs In order to assess the impacts of trade liberalization on households’ welfare and labor market in Cambodia’s

context, four main simulation scenarios have been carried out in the model:

Simulation 1: Complete tariff removal with exogenous CAB and fixed capital

Simulation 2: Complete tariff removal with endogenous CAB and fixed capital

Simulation 3: Complete tariff removal with exogenously 10%2 CAB increase and fixed capital

Simulation 4: Complete tariff removal with exogenously 10% CAB increase and fixed capital. Government

revenue is compensated by the increase of indirect tax.

The first two simulations are the extreme scenarios while simulation 3 and simulation 4 reflect the reality of

the Cambodia’s economy and the government policy of increasing indirect taxes to compensate for the loss

of trade-related taxes’ revenue in the future fiscal direction. Fixed capital in the model suggests that there is

a rigid movement of capital across production sectors in the Cambodia’s context.

9 Simulation Results To better understand and track out the channel through which tariff elimination has impacts on households’

welfare and labor market, five sections have been analyzed in the following manner, starting from the

influence of tariff elimination on resource allocation, factor market, household income, household

consumption, and finally households’ welfare.

9.1 Resource Allocation The initial effect of tariff reduction will immediately translate into the decline of import prices in each

sector. Considering baseline structure of tariff rates, it is the sectors of rubber and plastics products, food

beverages tobacco, and transport equipments which see the highest reduction of import prices in every

simulation, with the estimated decrease of -5.7%, -5.4%, and -5.3% respectively in simulation 1, simulation

2, and simulation 3. In the same simulations, other sectors expecting to have an approximately 5% decrease

of import prices are fish products, wood and paper products, petroleum natural gas and other minerals, and

fabricated metal products. It is noted that import prices decrease of service sector is very small due to its

lower initial tariff rates compared to other sectors.

In simulation 4, the drop in import prices is smaller than that in other simulations. This is due to effect of

the government increase in indirect tax to compensate for the loss of government revenue stemming from

tariff cut. However, the pattern of import price drop in each sector is the same as in simulation 1, simulation

2, and simulation 3. In response to these declines in import prices, the total demand for imports increase.

The response magnitude depends on both import prices decrease and import penetration in each sector.

2 This percentage increase is viewed and analyzed as affordable for Cambodia to borrow money from abroad

30

In simulation 1, import volumes surge in most sectors except sectors such as minerals, basic metal,

machinery, and especially construction. This can be explained by the effect of consumers switching from

import to cheaper local product given the greater fall in domestic prices relative to the reduction in imported

prices. It is also partly due to the investment effect on the demand for imported goods as current account

balance is fixed in the model, limiting the country’s ability to get fund for investment projects. The increase

in the import demand is only financed by the increase in export. The exports can be easily sold on the

foreign market due to the assumption that elasticity of export demand is infinite. Since international prices

are fixed, the fall in domestic price index suggests that the real exchange rate depreciates. With the effect of

this depreciation, it is enough to allow exports to achieve the required level. In short, local producers

reallocate parts of their production to foreign market in response to the falling domestic prices. Only three

sectors of basic metal, fabricated metal, and construction see the drop of export volumes. This drop comes

with no surprise as it is the result of investment effect of fixed current account balance, where higher

investment shares are observed in these three sectors compared to the others.

For domestic output in simulation 1, it reveals that output increase happens on agriculture and service and

some industries such as textile, food beverages and tobacco, electricity gas and water, machinery, and so on.

Factors determining the increase/decrease of domestic outputs rely on the response of export and domestic

consumption volumes as well as their shares in the domestic output.

In simulation 2, where the current account balance is endogenous, it is expected that the volume variation of

import, domestic consumption, and domestic output will increase. However, volume change of export in

some sectors will decrease due to the fact that real exchange rate will be more appreciated as the current

account balance is not fixed anymore in the model. Simulation 2 is significantly different from simulation 1

for a reason that county can afford to get more fund from outside to finance its investment projects. Due to

this positive investment effect, it gives a good boost for domestic output in simulation 2 compared to

simulation 1.

A realistic reflection of the Cambodia’s economy is in simulation 3, where it is assumed that current

account balance is exogenous with its increase of 10%. Simulation result in this scenario is somehow in

between the results in simulation 1 and simulation 2. In another word, it is an average figure of numbers in

the previous two scenarios. It is noteworthy that domestic output in simulation 3 is a slight increase of

domestic output in simulation 1, reflecting through the 10% increase of current account balance for the

country’s capacity to borrow a certain level of fund.

The last simulation is the situation when the country increases indirect tax as an alternative source of

revenue to maintain government fiscal balance. The expected negative effect of increasing indirect tax will

cut down the growth of domestic output and eventually put some sectors in negative growth. The magnitude

of indirect tax effect on domestic output will be attributed to the initial structure of indirect tax placing on

each sector, as it is assumed a uniform increase of indirect tax for revenue compensation. Interestingly,

textile industry is still buoyant in each scenario, as reflected through its growth in both export and domestic

output. This suggests that trade liberation will not be negatively affected textile, which is a backbone

industry for the Cambodia’s economic growth and job creation.

As such, in terms of resource allocation resulted from trade liberalization, it can be concluded that it is

manufacturing sector which is relatively beneficial the most compared to agriculture and service sectors.

Those manufacturing sectors include textile, basic metal, fabricated metal, machinery, office and computing

machinery with the output growth rates of 1.21%, 0.54%, 0.15%, 1.08%, and 1.49% respectively. In service

sector, there exist a very small output increase in sectors such as construction, trade and transport service,

hotel and restaurant, post and telecommunication. In short, trade liberalization in Cambodia is relatively

pro-manufacturing industry.

31

Table 14: Effect on Prices and Volumes (Sim 1)

Price Change Volume Change

GOODS PM PD PE_FOB P IM DD Q EX XS

AGR -4.12 -0.61 -0.25 -0.60 4.64 0.22 0.28 0.50 0.22

FISH -5.18 -0.52 -0.32 -0.52 6.43 0.48 0.48 0.64 0.48

CPETRO -4.77

2.68

2.68

OTHMIN -4.78 -4.11 -0.17 -4.10 -2.03 -2.85 -2.61 0.33 -2.84

EGW -1.73 -1.37

-1.37 1.46 1.01 1.04

1.01

FBT -5.43 -1.38 -0.58 -1.36 5.71 0.52 2.05 1.17 0.54

TEXTILE -2.81 -1.88 -0.89 -0.90 2.14 0.98 2.12 1.80 1.79

WP -5.05 -2.96 -0.59 -2.08 1.89 -0.74 0.82 1.20 -0.01

CHEM -4.04 -2.35 -0.15 -2.13 0.60 -1.49 0.47 0.29 -1.30

RP -5.68 -2.89 -0.65 -2.86 3.01 -0.53 0.74 1.31 -0.50

FURN -4.53 -2.38 -0.51 -1.29 2.20 -0.49 1.66 1.03 0.40

METAL -2.79 -3.44 0.53 -2.31 -4.96 -4.19 -4.69 -1.05 -3.30

FMETAL -4.88 -5.14 0.08 -5.07 -4.66 -4.35 -4.46 -0.16 -4.30

MACHINE -4.02 -6.51 -1.07 -2.47 -5.38 -2.35 -5.36 2.17 1.01

OFFICE -4.27 -6.20 -1.49 -1.51 -3.31 -0.92 -3.31 3.04 3.02

TRANS -5.34 -3.38 -0.81 -2.68 2.01 -0.47 1.79 1.64 0.11

OTHMNU -1.56 -1.57 -0.41 -1.52 -0.12 -0.11 -0.12 0.83 -0.07

CON -0.94 -6.33 0.79 -6.32 -13.18 -7.15 -7.42 -1.55 -7.14

WTT -0.91 -1.36 -0.43 -1.21 -0.43 0.12 0.07 0.87 0.24

HR -2.21 -1.07 -0.53 -0.68 2.03 0.63 0.63 1.06 0.94

POST -1.69 -0.32 -0.16 -0.27 1.87 0.18 0.35 0.31 0.22

FINANCE -1.03 -0.37 -0.14 -0.35 0.89 0.09 0.19 0.28 0.11

REAL -0.96 -0.66 -0.22 -0.63 0.46 0.09 0.11 0.44 0.11

ADM -0.97 -0.76

-0.76 1.78 1.53 1.54

1.53

EDU -0.25

-0.25 0.07 0.37 0.36

0.37

HEALTH -0.55

-0.55 -0.02 0.65 0.63

0.65

OTHSER -0.95 -0.40 -0.25 -0.35 1.05 0.38 0.41 0.50 0.42


32




AGR -4.12 2.02 0.47 1.99 8.04 0.28 0.39 -0.94 0.26

FISH -5.18 1.90 0.35 1.89 9.61 0.54 0.54 -0.69 0.53

CPETRO -4.77

4.50

4.50

OTHMIN -4.78 -1.08 0.37 -1.08 2.70 -1.89 -0.59 -0.74 -1.89

EGW -1.73 -0.04

-0.04 3.58 1.48 1.63

1.48

FBT -5.43 0.55 -0.11 0.54 8.44 0.75 3.00 0.22 0.74

TEXTILE -2.81 -1.59 -0.48 -0.49 1.58 0.07 1.56 0.97 0.96

WP -5.05 -1.31 0.13 -0.78 3.26 -1.41 1.35 -0.26 -0.98

CHEM -4.04 -0.98 0.29 -0.85 2.19 -1.59 1.96 -0.58 -1.49

RP -5.68 -1.81 -0.25 -1.79 4.13 -0.77 0.98 0.49 -0.75

FURN -4.53 -0.88 0.07 -0.33 3.66 -0.89 2.73 -0.13 -0.45

METAL -2.79 -2.05 0.19 -1.42 -1.29 -2.18 -1.61 -0.39 -1.67

FMETAL -4.88 -2.35 0.03 -2.32 1.17 -1.97 -0.90 -0.06 -1.95

MACHINE -4.02 -3.13 -0.47 -1.17 -0.12 -1.21 -0.13 0.95 0.39

OFFICE -4.27 -2.56 -0.68 -0.69 1.98 -0.16 1.98 1.37 1.37

TRANS -5.34 -1.70 -0.34 -1.33 4.20 -0.41 3.79 0.69 -0.11

OTHMNU -1.56 -0.52 0.14 -0.49 0.46 -0.80 0.38 -0.27 -0.78

CON -0.94 -2.27 0.61 -2.26 -5.03 -3.48 -3.55 -1.21 -3.47

WTT -0.91 0.52 0.16 0.46 1.71 -0.02 0.12 -0.31 -0.07

HR -2.21 1.67 0.25 0.66 5.43 0.63 0.66 -0.50 -0.17

POST -1.69 1.22 0.28 0.94 3.77 0.20 0.54 -0.55 -0.03

FINANCE -1.03 1.33 0.41 1.25 2.76 -0.10 0.23 -0.82 -0.16

REAL -0.96 1.53 0.28 1.45 3.46 0.43 0.63 -0.56 0.37

ADM -0.97 0.04

0.04 1.18 -0.04 -0.01

-0.04

EDU 1.29

1.29 1.38 -0.17 -0.11

-0.17

HEALTH 0.84

0.84 1.31 0.30 0.34

0.30

OTHSER -0.95 1.77 0.34 1.34 3.78 0.47 0.60 -0.67 0.13


33




AGR -4.12 0.87 0.16 0.86 6.55 0.26 0.34 -0.31 0.25

FISH -5.18 0.84 0.06 0.84 8.22 0.51 0.51 -0.12 0.51

CPETRO -4.77

3.71

3.71

OTHMIN -4.78 -2.39 0.14 -2.39 0.66 -2.29 -1.45 -0.27 -2.29

EGW -1.73 -0.62

-0.62 2.65 1.28 1.37

1.28

FBT -5.43 -0.29 -0.32 -0.29 7.25 0.65 2.59 0.63 0.65

TEXTILE -2.81 -1.71 -0.66 -0.67 1.82 0.46 1.80 1.33 1.32

WP -5.05 -2.03 -0.18 -1.34 2.67 -1.12 1.12 0.37 -0.56

CHEM -4.04 -1.58 0.10 -1.40 1.51 -1.54 1.32 -0.20 -1.40

RP -5.68 -2.28 -0.42 -2.26 3.64 -0.66 0.88 0.85 -0.64

FURN -4.53 -1.54 -0.18 -0.75 3.03 -0.72 2.27 0.37 -0.08

METAL -2.79 -2.65 0.33 -1.81 -2.86 -3.03 -2.92 -0.66 -2.36

FMETAL -4.88 -3.56 0.05 -3.51 -1.36 -2.98 -2.43 -0.09 -2.95

MACHINE -4.02 -4.59 -0.73 -1.74 -2.40 -1.69 -2.39 1.48 0.66

OFFICE -4.27 -4.14 -1.03 -1.04 -0.31 -0.48 -0.31 2.09 2.08

TRANS -5.34 -2.43 -0.55 -1.92 3.25 -0.43 2.92 1.10 -0.01

OTHMNU -1.56 -0.97 -0.10 -0.94 0.21 -0.50 0.16 0.20 -0.47

CON -0.94 -4.02 0.68 -4.01 -8.57 -5.04 -5.20 -1.34 -5.03

WTT -0.91 -0.30 -0.10 -0.27 0.78 0.04 0.10 0.20 0.06

HR -2.21 0.47 -0.09 0.07 3.95 0.63 0.65 0.18 0.30

POST -1.69 0.55 0.09 0.41 2.94 0.19 0.46 -0.18 0.08

FINANCE -1.03 0.59 0.17 0.55 1.95 -0.01 0.21 -0.34 -0.04

REAL -0.96 0.58 0.06 0.55 2.15 0.28 0.41 -0.12 0.26

ADM -0.97 -0.30

-0.30 1.44 0.63 0.65

0.63

EDU 0.62

0.62 0.81 0.06 0.09

0.06

HEALTH 0.23

0.23 0.73 0.45 0.46

0.45

OTHSER -0.95 0.83 0.08 0.60 2.59 0.43 0.51 -0.16 0.25


34




AGR -3.04 0.47 0.00 -0.64 3.81 -0.52 -0.46 0.00 -0.51

FISH -4.11 0.22 -0.12 -0.90 5.04 -0.39 -0.38 0.24 -0.38

CPETRO -3.96

1.94

1.94

OTHMIN -3.73 -0.44 -0.20 -1.53 3.42 -0.67 0.49 0.40 -0.67

EGW -0.63 0.14

-0.98 0.60 -0.32 -0.25

-0.32

FBT -4.46 0.03 0.15 -0.96 4.38 -1.22 0.43 -0.31 -1.20

TEXTILE -1.77 -0.82 -0.60 -0.62 1.35 0.18 1.33 1.22 1.21

WP -4.01 -1.46 -0.26 -1.68 1.84 -1.31 0.56 0.52 -0.63

CHEM -3.02 -0.63 0.17 -1.49 1.10 -1.80 0.93 -0.33 -1.65

RP -4.64 -0.89 0.07 -1.94 2.87 -1.77 -0.11 -0.14 -1.75

FURN -3.50 -1.08 -0.08 -0.93 1.47 -1.50 0.87 0.16 -0.53

METAL -1.73 -1.07 -0.69 -1.72 1.02 0.20 0.73 1.39 0.54

FMETAL -3.83 -0.32 -0.46 -1.40 4.55 0.15 1.65 0.92 0.15

MACHINE -3.00 -0.74 -0.65 -0.95 3.21 0.39 3.19 1.32 1.08

OFFICE -3.25 -0.84 -0.74 -0.74 3.57 0.56 3.57 1.50 1.49

TRANS -4.54 -1.59 -0.37 -1.86 2.77 -0.91 2.45 0.74 -0.46

OTHMNU -0.51 0.06 -0.06 -0.96 0.06 -0.62 0.01 0.13 -0.59

CON 0.17 0.58 -0.43 -0.54 1.28 0.79 0.81 0.87 0.79

WTT 0.19 0.07 -0.29 -0.91 -0.16 -0.01 -0.03 0.58 0.08

HR -1.10 0.02 -0.23 -0.48 1.12 -0.24 -0.23 0.46 0.26

POST -0.59 0.04 -0.24 -0.82 0.57 -0.19 -0.12 0.48 0.01

FINANCE 0.08 0.40 -0.17 -0.66 0.28 -0.10 -0.06 0.33 -0.07

REAL 0.15 0.08 -0.21 -0.98 -0.31 -0.23 -0.23 0.43 -0.19

ADM 0.15 0.04

-1.07 -0.22 -0.09 -0.10

-0.09

EDU 1.13 0.18

-0.93 -1.48 -0.37 -0.42

-0.37

HEALTH 1.13 -0.05

-1.16 -1.52 -0.13 -0.18

-0.13

OTHSER 0.17 0.05 -0.21 -0.81 -0.41 -0.26 -0.27 0.43 -0.06


35

9.2 Factor Market Following trade liberalization, the variation of domestic output in each simulation will result in the same

variation of value added used in each sector production, which subsequently determines the value added

prices. Usually, it is expected that production factors used intensively in sectors with declining value-added

prices will experience a fall in factor prices. It is observable that a greater fall in construction output price

will translate into greater decline in construction value-added price relative to other sectors. In the contrary

manner, for every simulation, value added of textile relatively sees the highest increase among other

sectors. Table 18: Effect on Production Factors by Sector

Simulation 1 Simulation 2 Simulation 3 Simulation 4

SECTOR PVA VA WC RC PVA VA WC RC PVA VA WC RC PVA VA WC RC

AGR -0.44 0.22 -0.46 -0.29 2.36 0.26 2.33 2.54 1.14 0.25 1.11 1.30 -0.83 -0.51 -0.78 -1.17

FISH -0.13 0.48 -0.52 0.18 2.77 0.53 2.33 3.14 1.51 0.51 1.09 1.85 -1.13 -0.38 -0.82 -1.38

MINQ -5.03 -2.84 -0.51 -6.84 -0.76 -1.89 2.32 -2.01 -2.60 -2.29 1.09 -4.10 -1.82 -0.67 -0.77 -2.26

FBT 0.09 0.54 -0.45 0.45 3.08 0.74 2.32 3.59 1.78 0.65 1.11 2.22 -1.94 -1.20 -0.74 -2.72

TEXTILE 3.20 1.79 -0.34 4.43 4.27 0.96 2.32 4.94 3.81 1.32 1.16 4.72 1.76 1.21 -0.63 2.58

WP -0.49 -0.01 -0.48 -0.50 1.60 -0.98 2.33 0.93 0.69 -0.56 1.11 0.31 -1.25 -0.63 -0.80 -1.66

RP -0.40 -0.50 -0.28 -0.73 2.16 -0.75 2.35 1.65 1.05 -0.64 1.20 0.61 -1.15 -1.75 -0.74 -2.31

METAL 0.06 -3.30 0.52 -2.15 2.23 -1.67 2.46 1.08 1.29 -2.36 1.62 -0.31 -0.63 0.54 -0.70 -0.28

FMETAL -5.81 -3.50 -0.32 -8.02 -0.21 -1.59 2.33 -1.27 -2.63 -2.40 1.18 -4.20 -0.23 0.30 -0.69 -0.03

MOTORT -0.31 0.11 -0.41 -0.24 2.21 -0.11 2.31 2.14 1.11 -0.01 1.13 1.10 -1.06 -0.46 -0.64 -1.37

OTHMNU -0.32 -0.07 -0.28 -0.37 1.81 -0.76 2.33 1.30 0.88 -0.46 1.20 0.58 -1.13 -0.72 -0.65 -1.60

EGW 1.21 1.01 -0.29 1.89 4.59 1.48 2.34 5.62 3.12 1.28 1.19 3.99 -1.16 -0.32 -0.69 -1.37

CON -9.87 -7.14 -0.41 -14.21 -2.35 -3.47 2.32 -4.62 -5.59 -5.03 1.13 -8.78 0.33 0.79 -0.68 0.86

WTT -0.17 0.24 -0.28 -0.01 2.31 -0.07 2.34 2.26 1.23 0.06 1.20 1.27 -0.65 0.08 -0.69 -0.60

HR 1.02 0.94 -0.25 1.65 2.10 -0.17 2.34 1.99 1.63 0.30 1.21 1.84 -0.33 0.26 -0.68 -0.16

POST 1.16 0.22 0.46 1.31 2.37 -0.03 2.45 2.35 1.84 0.08 1.59 1.90 -0.70 0.01 -0.72 -0.69

FINANCE 0.45 0.11 0.34 0.53 2.25 -0.16 2.43 2.15 1.47 -0.04 1.52 1.44 -0.75 -0.07 -0.68 -0.79

REAL 0.37 0.11 0.20 0.44 2.97 0.37 2.41 3.23 1.84 0.26 1.45 2.01 -0.95 -0.19 -0.67 -1.07

ADM 1.68 1.53 0.33 2.71 2.39 -0.04 2.42 2.36 2.08 0.63 1.51 2.51 -0.74 -0.09 -0.66 -0.80

EDU 0.67 0.37 0.44 0.92 2.34 -0.17 2.45 2.22 1.61 0.06 1.57 1.65 -0.90 -0.37 -0.68 -1.15

HEALTH 0.81 0.65 0.34 1.25 2.65 0.30 2.43 2.86 1.85 0.45 1.52 2.16 -0.77 -0.13 -0.67 -0.85

OTHSER 0.43 0.42 0.08 0.71 2.49 0.13 2.38 2.58 1.59 0.25 1.38 1.76 -0.68 -0.06 -0.63 -0.71


36

In simulation 1, for instance, decline of value added price in agriculture is -0.44%. Since labor is used

greater than capital in this sector, the decline of wage rate is -0.46% compared to -0.29% of return to capital

rate. Construction, on the other hand, where capital is used intensively, the drop of wage rate is just -0.41%

against -14.21% for return to capital rate. The analysis for factor price variation in other sectors is applied in

the same manner, considering the labor and capital shares in the production sectors.

Distinguishing the analysis when the current account balance is exogenous or endogenous can provide

insightful conclusion that wage rate and return to capital rate are better off in case of endogenous current

account balance due to the above explanation of positive investment effect on domestic output. In

simulation 3, the factor price variation is between the results in simulation 1 and simulation 2. The

introduction of indirect tax increase in simulation 4 poses a negative impact on factor prices. Nevertheless,

factor price in textile industry relatively experiences a surge even with the existence of indirect tax increase.

As for labor market, it can be said that those working in manufacturing and service sectors benefit much

compared to those in agriculture. In all scenarios, wage rate increase of high skilled workers is greater than

those of low skilled workers in a relative term. In simulation 1, for instance, wage rate increase is 0.69% for

high skilled workers compared to -0.60% and -0.38% of low skilled and medium skilled workers. In a

realistic scenario of simulation 3, for high skilled workers, the rate is 1.71% against 1.06% and 1.11% of

low skilled and medium ones. Thus, Cambodia’s trade liberalization is relatively pro-high skilled workers.

Table 19: Effect on Wage Rate

Sim 1 Sim 2 Sim 3 Sim 4

Labor Wage Rate

LSK -0.60 2.34 1.06 -0.96

MSK -0.38 2.27 1.11 -0.38

HSK 0.69 2.49 1.71 -0.74


9.3 Household Income The manner in which the factor prices change and sources of income that household own will consequently

result in household income variation. In all simulations, income variation is not much different among 24

households. The income change is positive in simulation 2 and simulation 3. The increase of indirect tax

will turn income change to be negative, but at a minimal number. By observing all results in the four

scenarios, it is clear to say Phnom Penh household most benefits from income gain relative to other

household groups. Rattanakiri household is viewed as a loser in terms of income change from trade

liberalization. As such, effect of trade liberalization measures on household income is relatively pro-Phnom

Penh household.

37

Table 20: Effect on Household Income


HOUSEHOLDS YHL YHK YHTR YH YHL YHK YH YHL YHK YHTR YH YHL YHK YHTR YH

BANTEAY -0.33 -0.22 -1.69 -0.34 2.34 2.35 2.28 1.18 1.23 -0.74 1.14 -0.74 -0.08 -0.61 -0.54

BATT -0.32 -0.22 -1.69 -0.32 2.34 2.35 2.27 1.18 1.23 -0.74 1.14 -0.70 -0.08 -0.61 -0.43

CHAM -0.45 -0.22 -1.69 -0.41 2.33 2.35 2.28 1.11 1.23 -0.74 1.11 -0.77 -0.08 -0.61 -0.56

CHHNANG -0.42 -0.22 -1.69 -0.36 2.33 2.35 2.27 1.13 1.23 -0.74 1.12 -0.74 -0.08 -0.61 -0.42

SPEU -0.42 -0.22 -1.69 -0.35 2.32 2.35 2.30 1.13 1.23 -0.74 1.14 -0.72 -0.08 -0.61 -0.42

KTHOM -0.40 -0.22 -1.69 -0.35 2.33 2.35 2.28 1.14 1.23 -0.74 1.13 -0.76 -0.08 -0.61 -0.41

KAMPOT -0.35 -0.22 -1.69 -0.33 2.33 2.35 2.30 1.16 1.23 -0.74 1.16 -0.71 -0.08 -0.61 -0.49

KANDAL -0.29 -0.22 -1.69 -0.33 2.34 2.35 2.22 1.19 1.23 -0.74 1.11 -0.70 -0.08 -0.61 -0.41

KEP -0.45 -0.22 -1.69 -0.35 2.32 2.35 2.29 1.11 1.23 -0.74 1.14 -0.72 -0.08 -0.61 -0.38

KK -0.37 -0.22 -1.69 -0.32 2.34 2.35 2.29 1.16 1.23 -0.74 1.16 -0.78 -0.08 -0.61 -0.37

KRATIE -0.48 -0.22 -1.69 -0.41 2.33 2.35 2.30 1.10 1.23 -0.74 1.12 -0.79 -0.08 -0.61 -0.55

MONDUL -0.53 -0.22 -1.69 -0.43 2.34 2.35 2.30 1.09 1.23 -0.74 1.11 -0.93 -0.08 -0.61 -0.57

ODDOR -0.49 -0.22 -1.69 -0.42 2.32 2.35 2.29 1.09 1.23 -0.74 1.11 -0.74 -0.08 -0.61 -0.51

PAILIN -0.44 -0.22 -1.69 -0.30 2.31 2.35 2.29 1.11 1.23 -0.74 1.16 -0.65 -0.08 -0.61 -0.20

PP 0.13 -0.22 -1.69 -0.13 2.39 2.35 2.29 1.41 1.23 -0.74 1.24 -0.66 -0.08 -0.61 -0.33

VIHEAR -0.54 -0.22 -1.69 -0.45 2.34 2.35 2.31 1.08 1.23 -0.74 1.11 -0.89 -0.08 -0.61 -0.61

PREY -0.44 -0.22 -1.69 -0.43 2.33 2.35 2.24 1.12 1.23 -0.74 1.07 -0.75 -0.08 -0.61 -0.54

PURSAT -0.42 -0.22 -1.69 -0.40 2.33 2.35 2.26 1.13 1.23 -0.74 1.10 -0.76 -0.08 -0.61 -0.54

RATTANAK -0.53 -0.22 -1.69 -0.48 2.34 2.35 2.22 1.09 1.23 -0.74 1.05 -0.91 -0.08 -0.61 -0.59

SHV -0.27 -0.22 -1.69 -0.29 2.35 2.35 2.29 1.21 1.23 -0.74 1.17 -0.74 -0.08 -0.61 -0.53

SIEM -0.38 -0.22 -1.69 -0.34 2.34 2.35 2.28 1.15 1.23 -0.74 1.14 -0.77 -0.08 -0.61 -0.42

STUNG -0.45 -0.22 -1.69 -0.38 2.32 2.35 2.30 1.11 1.23 -0.74 1.13 -0.75 -0.08 -0.61 -0.48

SVAY -0.41 -0.22 -1.69 -0.36 2.33 2.35 2.28 1.13 1.23 -0.74 1.13 -0.72 -0.08 -0.61 -0.46

TAKEO -0.33 -0.22 -1.69 -0.32 2.33 2.35 2.28 1.17 1.23 -0.74 1.15 -0.69 -0.08 -0.61 -0.42


9.4 Household Expenditure The preceding analysis shows that trade liberalization is pro-Phnom Penh in terms of its impact on nominal

income in every scenario. Following trade liberalization, however, the decline in import and domestic prices

will also lead to the fall in consumer prices. The analysis of households’ welfare ultimately needs to take

into account both the effects of nominal income and consumer prices changes. The consumer price index

see the decline of -1.69%, -0.74%, and -0.61% in simulation 1, simulation 3, and simulation 4 accordingly.

At the commodity level, all consumer prices are in decline, suggesting that the real household consumption

is improved following trade liberalization measures.

This can be proved in Table below showing that real consumptions for all household groups increase in all

simulations. Phnom Penh household can enjoy the relative decline in the consumer price of manufacturing

38

goods due to their greater consumption shares of these goods. Therefore, in terms of trade liberalization

effects on consumer prices, it can be seen that Phnom Penh household benefit from the general falling

consumer prices. Real consumption increases for Phnom Penh household are 1.58%, 2.29%, 1.99%, and

0.28% in the respective simulation scenarios. The household consumption impact of trade liberalization is,

thus, again Pro-Phnom Penh.

Table 21: Effect on Household Expenditure


HOUSEHOLDS CTH CTH_Real CTH CTH_Real CTH CTH_Real CTH CTH_Real

BANTEAY -0.34 1.37 2.28 2.28 1.14 1.89 -0.54 0.06

BATT -0.32 1.39 2.27 2.27 1.14 1.89 -0.43 0.17

CHAM -0.41 1.30 2.28 2.28 1.11 1.85 -0.56 0.05

CHHNANG -0.36 1.35 2.27 2.27 1.12 1.87 -0.42 0.18

SPEU -0.35 1.36 2.30 2.30 1.14 1.89 -0.42 0.19

KTHOM -0.35 1.36 2.28 2.28 1.13 1.88 -0.41 0.19

KAMPOT -0.33 1.38 2.30 2.30 1.16 1.90 -0.49 0.12

KANDAL -0.33 1.38 2.22 2.22 1.11 1.86 -0.41 0.20

KEP -0.35 1.36 2.29 2.29 1.14 1.89 -0.38 0.23

KK -0.32 1.40 2.29 2.29 1.16 1.90 -0.37 0.23

KRATIE -0.41 1.30 2.30 2.30 1.12 1.87 -0.55 0.06

MONDUL -0.43 1.28 2.30 2.30 1.11 1.86 -0.57 0.03

ODDOR -0.42 1.29 2.29 2.29 1.11 1.86 -0.51 0.09

PAILIN -0.30 1.41 2.29 2.29 1.16 1.91 -0.20 0.40

PP -0.13 1.58 2.29 2.29 1.24 1.99 -0.33 0.28

VIHEAR -0.45 1.27 2.31 2.31 1.11 1.86 -0.61 -0.01

PREY -0.43 1.28 2.24 2.24 1.07 1.82 -0.54 0.06

PURSAT -0.40 1.31 2.26 2.26 1.10 1.85 -0.54 0.07

RATTANAK -0.48 1.23 2.22 2.22 1.05 1.80 -0.59 0.01

SHV -0.29 1.42 2.29 2.29 1.17 1.92 -0.53 0.08

SIEM -0.34 1.38 2.28 2.28 1.14 1.89 -0.42 0.19

STUNG -0.38 1.33 2.30 2.30 1.13 1.88 -0.48 0.12

SVAY -0.36 1.35 2.28 2.28 1.13 1.88 -0.46 0.14

TAKEO -0.32 1.40 2.28 2.28 1.15 1.90 -0.42 0.19


9.5 Household Welfare Putting together the variation in households’ nominal incomes and consumer prices, a welfare effect, as

measured by equivalent variation (EV) as a percentage of initial incomes is calculated in the provided table.

Generally, it can be said that welfare impacts from trade liberalization is positive, as it is a consequence of a

reduction in consumer prices to boost/offset the increase/decrease in households’ nominal incomes in

different scenarios. In realistic simulation 3, both income and real consumption see a growth, while in

simulation 4 the effect of indirect tax increase makes income shrink but is offset by the consumer price

reduction. As a result in simulation 4, households’ welfare as a whole is still positive, but marginally small.

39

Table 22: Effect on Household Welfare

Sim 1 Sim 2 Sim 3 Sim 4

HOUSEHOLD Equivalent Variation (EV)

BANTEAY 1.16 1.92 1.59 0.05

BATT 1.18 1.92 1.60 0.15

CHAM 1.18 2.07 1.69 0.11

CHHNANG 1.17 1.96 1.62 0.17

SPEU 1.20 2.02 1.66 0.18

KTHOM 1.22 2.04 1.68 0.21

KAMPOT 1.02 1.70 1.40 -0.06

KANDAL 1.22 1.97 1.64 0.23

KEP 0.96 1.60 1.32 0.03

KK 1.10 1.82 1.51 0.12

KRATIE 0.92 1.65 1.33 -0.13

MONDUL 1.09 1.94 1.58 0.01

ODDOR 1.08 1.92 1.56 0.06

PAILIN 1.20 1.95 1.63 0.34

PP 1.30 1.90 1.64 0.22

VIHEAR 0.95 1.78 1.42 -0.15

PREY 1.19 2.06 1.68 0.13

PURSAT 1.09 1.88 1.53 0.01

RATTANAK 1.02 1.85 1.49 -0.01

SHV 1.19 1.92 1.60 0.04

SIEM 1.09 1.82 1.50 0.10

STUNG 1.01 1.76 1.43 -0.02

SVAY 1.23 2.08 1.71 0.22

TAKEO 1.23 2.01 1.67 0.19

Source: Authors’ calculation At each household group, choosing results of EV in simulation 3 to show the effect on households’ welfare

as this simulation is more realistic to reflect the situation of the Cambodia’s economy. It is noted that

welfare are relatively gained most to households living in Phnom Penh and its surrounding provinces as

well as other big provinces with active economic activities. Those households include Phnom Penh,

Banteay Meanchey, Battambang, Kompong Cham, Kompong Chhnang, Kompong Speu, Kompong Thom,

Kandal, Prey Veng, Svay Rieng, Takeo, Sihanouk Ville, and Pailin. Other households also receive the

welfare gains from trade liberalization measures but with smaller level compared to households mentioned

above. In simulation 4 with the effect of indirect tax, households who experience the negative welfare

impacts are Stung Treng, Rattanakiri, Preah Vihear, Kratie, and Kompot.

Therefore, trade liberalization will lead to be pro-urban households. Households who are considered as

losers in a relative term compared to other household group are people living far from the Capital with

small economy base such as Stung Treng, Rattanakiri, Preah Vihear, Kratie, Mondol Kiri, Kep, and Pursat.

This leaves the conclusion that Cambodia’s trade liberalization is widening the inequality gap between

urban households and rural households. Overall, trade liberalization brings welfare gains to the country as a

whole but small and also to each household group.

40

10 Conclusion and Policy Recommendations Having now followed the channels and consequences of trade liberalization impacts on households’ welfare

and labor market, it can be concluded that a relation between trade reform measures, households’ welfare,

and labor market is complex. Factors which should be focused on prior to the analysis of its impact and

relation are the review of initial structure of the country’s economy, labor and capital shares in the

production, trade pattern, sources of income for different household groups, household groups’ expenditure

pattern, and so forth. Examining those factors would shed a light on the channels through which the impact

starts from and its end on households’ welfare.

Following trade liberalization, Cambodia’s textile industry remains buoyant with its domestic output

increases in every scenario. This industry will continue to be a backbone for the growth and job

employment for a short and medium term. Manufacturing industry is relatively more beneficial from trade

liberalization compared to agriculture and service sectors. In terms of effect on labor market, it is obvious

that wage increase is relatively higher for higher skilled workers than low skilled workers. Among different

household groups, Rattanakiri household benefits less while Phnom Penh household gains the most from

the rise of their nominal incomes. With the decline of consumer prices, it is Phnom Penh household again

who benefit the most from this general price fall due to their greater consumptions of manufacturing goods.

Finally, welfare gains go to households living in Phnom Penh and its surrounding provinces as well as other

big provinces with active economic activities. Trade liberalization seems to bring the widening gap between

urban households and rural households.

As stated in the Cambodia’s poverty reduction strategy paper (PRSP), priority of the paper is aimed at

alleviating the poverty for those living in rural area. Since following trade liberalization, rural households

are relative loser in terms of welfare; policy implications to be drawn from this experiment should aim to

develop better strategies in a fight against poverty, or at least aim not to worsen the poverty situation of

rural households. Therefore, the following policies are suggested to address this issue:

1. Agriculture should be protected for the time-being with more incentives from government to attract

investment in this sector. In case of any complete trade liberalization, special treatment and some

exemptions in term of tariff reduction and export tax should be a focus point for government to

discuss with its trade partners. Government can base on the ground that as it is still a least

developed country with the majority of the poor working in the sector, subsidies and export tax to

agriculture, for instance, is justified for the time-being with the set of committed timeframe it

promises to gradually drop these special treatment.

2. Government’s complementary policies including building physical infrastructures of road, bridge,

telecommunication, providing water and electricity access, etc. in rural area should be continuously

implemented so that rural households can be connected to the international trade.

3. Domestic tax codes need to be restructured and simplified in order to increase or at least maintain

government revenues stemming from the revenue loss of trade-related taxes so that government can

have ability to sustain its complementary and compensatory policies against poverty and

vulnerability.

4. Redistribution policy in terms of government transfer and income tax exemption should be given a

priority to target those living in rural and remote areas.

41

References

Asian Development Bank (2012): “Supply and Use Tables for selected Economies in Asia and the Pacific”. Manila: A Research

Study.

Bargawi (2005): “Cambodia’s Garment Industry-Origins and Future Prospects,” ESAU Working Paper 13, Overseas

Development Institute, London.

Chan, S., & Oum, S. (2011): “Impact of garment and textile trade preferences on livelihoods in Cambodia,” Research

Background Paper, Oxfam America, Cambodia.

Cockburn, J. (2005): “Procedures for conducting poverty/distribution analysis of CGE simulation results with DAD,” Centre

Interuniversitaire sur le Risque, les Politiques Économiques et l’Emploi, Université Laval, Québec. Retrieved from

http://www.pep-net.org/fileadmin/medias/pdf/microsim.pdf.

Cockburn, J., Decaluwé, B., & Robichaud, V. (2008): “Trade Liberalization and Poverty: A CGE Analysis of the 1990s

Experience in Africa and Asia,” Poverty and Economic Policy (PEP) Research Network. Retrieved from http://www.pep-

net.org/fileadmin/medias/pdf/promotionnal_material/TradeLiberalization_Poverty-final.pdf

Fofana, I., & Cockburn, J. (2003): “Microsimulations in Computable General Equilibrium: Procedures for analyzing and

reconciling data,” Poverty and Economic Policy Network and Centre Interuniversitaire sur le Risque, les Politique

Economiques et l’Emploi, Université de Laval. Retrieved from http://www.pep-

net.org/fileadmin/medias/pdf/Reconciliation.pdf

Fontana, M., & Wobst, P. (2001): “A gendered 1993-94 social accounting matrix for Bangladesh,” TMD Discussion papers.

Retrieved from http://ideas.repec.org/p/fpr/tmddps/74.html Fukase, E., & Martin, W. (2001): “Economic and Fiscal Implications of Cambodia’s Accession to the ASEAN Free Trade Area,”

Asian Economic Journal, 15(2), 139–172.

GDCE (2011): “Cambodia’s Custom System,” Royal Government of Cambodia.

IFPRI. (2010): “Social Accounting Matrices and Multiplier Analysis. Washington: Food Security in Practice”.

IMF (2012): “Article IV: Country Consultation Report,” Phnom Penh, Cambodia.

NSDP (2009-2013): “National Strategic Development Plan,” Royal Government of Cambodia.

Naron, Hang Chuon (2003): “Trade Liberalization: a Cambodia Perspective,” Working Papers 03-8, Asia Pacific School of

Economics and Government.

Neak, Samsen (2006): “Trade and Povety Link: The Case of the Cambodian Garment,” EIC report, Cambodia.

Sak, S., & Ryuta, K. 2009: "Future Prospects of the Garment Industry of Cambodia," Working Papers EMS_2009_12, Research

Institute, International University of Japan.

Strutt, Anna, Thomas W. Hertel and Susan Stone (2010): “Exploring Poverty Impacts of ASEAN Trade Liberalization for

Cambodia, Lao PDR, Thailand and Vietnam,” In John Gilbert (ed.). New Developments in Computable General

Equilibrium Analysis for Trade Policy. Bingley, U.K.:Emerald.

WDI (2012): “WordBank’s WDI 2012-Cambodia Data,” Retrieved April 26, 2013, from

http://data.worldbank.org/country/cambodia

WorldBank (2009): “Poverty Profile and Trend in Cambodia : Findings from the 2007 Cambodia Socio-economic Survey

(CSES),” (No. 48618-KH). Poverty Reduction and Economic Management Sector Unit, East Asia and Pacific Region,

World Bank.

WTO (2011): “Trade Policy Review: Cambodia,” (No. WT/TPR/S/253). World Trade Organization.

http://www.pep-net.org/fileadmin/medias/pdf/microsim.pdf

http://www.pep-net.org/fileadmin/medias/pdf/Reconciliation.pdf

http://www.pep-net.org/fileadmin/medias/pdf/Reconciliation.pdf

http://ideas.repec.org/p/fpr/tmddps/74.html

http://ideas.repec.org/p/iuj/wpaper/ems_2009_12.html

http://ideas.repec.org/s/iuj/wpaper.html

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Annex

Abbreviation Description

Abbreviation Description

LSK Low-skilled

AGR Agriculture, Forestry, and Logging Products

MSK Medium-skilled

FISH Fish and Other Fishing Products

HSK High-skilled

CPETRO Coal and Lignite; Peat, Crude Petroleum, and Natural Gas

BANTEAY Banteay Meanchey

OTHMIN Other Minerals, n.e.c.

BATT Battambang

EGW Electricity, Gas, and Water

CHAM Kompong Cham

FBT Food, Beverages, and Tobacco

CHHNANG Kompong Chhnang

TEXTILE Clothing and Wearing Apparel; and Leather and Leather Products

SPEU Kompong Speu

WP Products of Wood, Paper, and Paper Products

KTHOM Kompong Thom

CHEM Basic Chemicals and Other Chemicals

KAMPOT Kampot

RP Rubber and Plastics Products

KANDAL Kandal

FURN Furniture and Other Transportable Goods, n.e.c.

KEP Kep

METAL Basic Metals

KK Koh Kong

FMETAL Fabricated Metal Products, Except Machinery and Equipment

KRATIE Kratie

MACHINE General and Special Purpose Machinery

MONDUL Mondul Kiri

OFFICE Office, Accounting, and Computing Machinery

ODDOR Oddor Meanchey

TRANS Transport Equipment

PAILIN Pailin

OTHMNU Other Manufacturing

PP Phnom Penh

CON Construction Services

VIHEAR Preah Vihear

WTT Wholesale, Retail Trade, and Transport Service

PREY Prey Veng

HR Lodging, Food, and Beverage Serving Services

PURSAT Pursat

POST Postal, and Courier and Telecommunications Services

RATTANAK Rattanak Kiri

FINANCE Financial Intermediation, Insurance, and Auxiliary Services

SHV Sihanouk Ville

REAL Real Estate, Leasing Services, and Other Business Services

SIEM Siem Reap

ADM Public Administration and Compulsory Social Security Services

STUNG Stung Treng

EDU Education Services

SVAY Svay Rieng

HEALTH Health and Social Services

TAKEO Takeo

OTHSER Other Services, n.e.c.

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