Pr-MPIA-0470 Working Paper Version

Poverty Impacts of Preferential and Multilateral Trade Liberalization

on the Philippines: A Computable General Equilibrium Analysis♦

Erwin L. Coronga

Rachel C. Reyes b

Angelo B. Taningcoc

November 2008

aLaval University, Quebec, Canada (erwincorong@yahoo.com) bEconomics Department, De La Salle University, Manila, Philippines (reyesrc@dlsu.edu.ph) cEconomics Department, De La Salle University, Manila, Philippines (taningcoa@dlsu.edu.ph) ♦ This paper was carried out with funding and technical assistance from the Poverty and Economic Policy (PEP) Research Network, which is financed by the Australian Agency for International Development (AusAID) and the Government of Canada through the International Development Research Centre (IDRC) and the Canadian International Development Agency (CIDA). The authors are grateful to Ramon Clarete, John Cockburn, Caesar Cororaton, Bernard Decaluwe, and Veronique Robichaud for their valuable comments.

Abstract

The Philippines has been participating in preferential and multilateral trade liberalization

since the 1990s. However, the poverty effects of these trading arrangements are not yet

fully known. This paper, using a computable general equilibrium (CGE) model, finds

that reducing both Most-Favored-Nation (MFN) tariff rate and ASEAN Free Trade

Area’s Common Effective Preferential Tariff (CEPT) rate, combined with enhancing

direct income taxes to offset the loss in tariff revenue, are instrumental in reducing

poverty in the country. It also shows that the relatively poor and less-skilled household

groups, like agricultural workers and industrial workers, as well as the poorest of the

poor, gain from these trading arrangements because of its ability to substantially lower

consumer prices. In this regard, this paper proposes that the Philippine government be

more active and further promote preferential and multilateral trade liberalization in order

to help eradicate poverty.

Keywords: Computable general equilibrium, international trade, social

accounting matrix, Philippines, poverty

JEL Classification: D33, D58, F13, F14, I32

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1. Introduction

The participation of the Philippines in preferential trading arrangements (PTAs) as

well as in the multilateral trading system presents an interesting question as to whether

and to what extent these trade liberalization measures affect the level of poverty in this

Southeast Asian developing economy. Being one of the original member countries of the

World Trade Organization (WTO), the Philippines is very much committed to

multilateralism as it provides for Most-Favored-Nation (MFN) treatment to all of its

trading partners. It is also involved in a number of bilateral and regional PTAs such as

the ASEAN Free Trade Area (AFTA), the ASEAN-China Free Trade Area (ACFTA),

and the Japan-Philippines Economic Partnership Agreement (JPEPA), among others. At

present, the Philippines is engaged in talks with some countries with the intention of

forging new PTAs1. It is important to note also that the country is geared towards

eradicating poverty as reflected in its commitment to the Millenium Development Goals.

Amidst all of these developments, there is scant evidence to show that preferential trade

liberalization and multilateralism are both poverty-reducing in the Philippines.

The theoretical arguments for and against PTAs are widely documented. It is noted

that one of its potential benefits is trade creation accorded to its member countries. It has

been pointed out that the economic benefits from PTAs are relatively greater when there

is wider difference in the comparative advantage among member countries (Clarete,

Edmonds, and Wallack, 2002). More recent literature suggests that under certain

conditions, there can also be an increase in the volume of trade between PTA members

and the rest of the world (Ornelas, 2005). It has likewise been argued that forming PTAs

is easier and faster than following the route of multilateralism, which is perceived to be

slow and not fully effective. Also, such trading arrangements are likely to deepen

economic integration by strengthening economic cooperation and the harmonization of

economic policies among its member countries (Lawrence, 1996). Others believe that

PTAs complement the nature of the multilateral trading system (Baldwin, 2006), and this

appears to be the case in Asia (Clarete, 2004; Low, 2004).

1 For an updated list of ongoing and proposed PTAs involving the Philippines, see Menon (2006).

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Conversely, skeptics argue that there are economic costs associated with PTAs.

Generally, the main criticism hurled against such arrangements is that these serve as

“stumbling blocks” to multilaterism. For one, PTAs may be more trade-diverting than

trade-creating. Furthermore, it may have redistribution effects on income that could

worsen the overall welfare of member countries. Another possible negative outcome is

that PTAs may undermine the multilateral trading system as countries may lose interest

in promoting its initiatives, especially that of the WTO (Panagariya, 1999; Bhagwati and

Panagariya, 1996). Also, regionalism may erode the principle of non-discrimination and

MFN treatment and that it tends to present a “spaghetti-bowl” problem, which essentially

could make more difficult the identification of product origins and may thereby result in

an inefficient allocation of resources (Bhagwati, 2005).

Finally, there are a few studies that offer a more neutral view towards PTAs

suggesting that these arrangements are “not necessarily either beneficial or harmful, nor

are they unavoidable stumbling blocks” towards a freer global trade (ADB, 2002;

Edmonds and Verbiest, 2002).

Overall, the theoretical arguments on PTAs are mixed and ambiguous. But one thing

is certain, and that is PTAs are here to stay (Baldwin, 2006). Also, multilateral efforts are

expected to be continued by WTO member countries, including the Philippines. But so

far, little has been known about the poverty impacts of these trading arrangements on

developing economies. In this regard, this paper intends to address this research gap by

using a computable general equilibrium (CGE) analysis to investigate the potential

effects of preferential liberalization and multilateralism on poverty in the Philippines.

The results of this study will be useful for Philippine policymakers in terms of having a

better understanding of the economic and poverty effects of the country’s existing trading

arrangements.

The next section discusses existing and related literature. In section 3, the recent

economic and trade performances of the Philippines are presented. The country’s

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unilateral trade reforms and its commitments to a number of PTAs and the multilateral

trading system are discussed in section 4. Section 5 briefly describes the country’s

poverty profile. The pertinent characteristics of the CGE model are captured in section 6.

Section 7 highlights the policy simulations and results. Lastly, section 8 presents the

conclusion and policy recommendations.

2. Literature Review

The evidence regarding the economic effects of PTAs and multilateralism around the

world is mixed.2 On one hand, there are empirical studies that empirically show that

PTAs are trade-augmenting and welfare-enhancing. For instance, Burfisher, Robinson,

and Thierfelder (2000), using a 26-sector multicountry CGE model, find that member

countries of the North American Free Trade Area—United States (US), Canada, and

Mexico, have bigger trade creation compared to trade diversion and all experience

welfare gains as long as there are appropriate domestic reforms in place. Hertel et al.

(2004) assess the economic impacts of the Free Trade Agreement of the Americas

(FTAA) using the Global Trade Analysis Project (GTAP) model with econometric

estimates of trade elasticities; they show that the formation of this regional trading

arrangement led to an increase in imports for all and welfare gains to most of its member

countries. Baier and Bergstrand (2007) employ a gravity model that corrects for

endogeneity bias by using a panel data approach with 96 countries and covering the

period 1960-2000; their results confirm that free trade areas (FTAs) approximately leads

to a doubling of bilateral trade between members after 10 years. Medvedev (2006) finds

that PTAs leads to approximately a 114% increase in intra-bloc trade.

On the other hand, some studies point out that multilateral trading arrangements offer

larger benefits to the world economy than having a network of PTAs (see Harrison,

Rutherford, and Tarr, 2003; Pomfret, 2006 ). Others argue that multilateralism is a better

trade policy than a PTA. For example, using a CGE model with oligopolistic behavior

2 In PTA-related studies, the most widely used methodologies are ex-post analyses, which use gravity models, and ex-ante analyses, which use CGE models.

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and scale economies, Aziz and Hertel (2004) find that Morocco will benefit more from

multilateral trade liberalization than having a Morocco-European Union (EU) FTA

because of the latter’s adverse effects like deterioration in its terms-of-trade, trade

diversion, lower firm output in industries with scale economies, and lower labor demand.

Soloaga and Winters (2001) find no significant intra-bloc trade among nine PTAs and

they even show evidence of trade diversion in the EU and the European Free Trade

Association. In some studies, the positive impacts for both preferential and multilateral

trade liberalization are emphasized. For instance, Harrison et al. (2003) use a CGE model

to show that the FTAA, the EU-Mercosur agreement, and the multilateral trade

liberalization are all beneficial to Brazil, especially to its poor.

Empirical studies that focused on Asian PTAs seem to suggest that Asian countries

could derive certain economic benefits from preferential trade liberalization. For

example, Clarete, Edmonds, and Wallack (2002) find that Asian PTAs have increased its

intra-bloc trade while Frankel (1997) shows that APEC enhances trade among its member

countries as well as with the rest of the world. Furthermore, Edmonds and Verbiest

(2002) conjecture that these preferential arrangements in Asia were able to foster regional

cooperation. Another, Baharumshah et al (2007) proves that the ASEAN trading bloc is

moving towards multilateralism.

It may be worthwhile to note that some of the papers that lend support to PTAs in

Asia use CGE models. For example, Kawai and Wignaraja (2007) conducts different

scenarios of possible East Asian FTAs using a CGE model based on a modified version

of the GTAP model, and they find that a more expanded regional FTA like an ASEAN+6

FTA, comprising of all 10 ASEAN member countries plus Australia, India, Japan, New

Zealand, People’s Republic of China (PRC), and Republic of Korea, yield the biggest

gain in terms of higher national income for all of its member countries and the least cost

for its non-member countries. On the other hand, were not that optimistic about the

positive role of Asian PTAs. For example, Elliot and Ikemoto (2004), using a gravity

model, argue that ASEAN intra-regional trade was not strongly influenced by the

creation of AFTA.

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In the Philippines, most studies that use CGE models focus on the country’s unilateral

trade reforms and its poverty effects. According to Clarete (2005), these studies present

ex-ante findings of net positive gains for the country as a result of these unilateral

reforms. For example, Cororaton and Cockburn (2007), using a CGE microsimulation

model calibrated to the 1994 Social Accounting Matrix (SAM), find that unilateral tariff

cuts between 1994 and 2000 tend to lower consumer prices and thereby result in poverty

reduction.3

Other Philippine studies also adapt CGE modeling to investigate the economywide

and poverty impacts of multilateralism and bilateral PTAs on this country. For instance,

Cororaton, Cockburn, and Corong (2005) utilize a 35-sector CGE model in examining the

possible outcomes of the WTO-led Doha Development Agenda, and their results show a

mild increase in national poverty with rural households suffering the most. Focusing on a

planned bilateral PTA between the Philippines and Japan, Yasutake (2004) employs a 12-

sector CGE model and finds that this trade arrangement would lead to a small increase in

the Philippines’ consumer welfare and volume of trade as a result of its tariff cuts on

imports coming from Japan. Also, Cororaton (2004) utilizes an integrated CGE

microsimulation analysis and reveals that lowering tariffs on Philippine imports on

Japanese non-agricultural goods benefits the country’s industrial sector, expecially the

non-food manufacturing sector; that household income will rise, thereby leading to a

decline in poverty; and that the agricultural sector will be adversely affected, with a

worsening of its income inequality. Rodriguez and Cabanilla (2006) analyze the

potential economic impacts of a Philippine-US FTA on Philippine agricultural sector;

their results depict that the removal of tariffs on Philippine agricultural imports from the

US enhances domestic output and employment in most of the agricultural sub-sectors.

3 These findings are also similar to the other studies that use CGE models focusing on other developing countries in Asia and Africa. For a survey of these studies, see Cockburn, Decaluwe, and Robichaud (2006).

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3. Philippine growth and trade performances

3.1 Growth performance

The Philippine economic growth has undergone a boom-bust cycle over the past years

(Table 1). Coinciding with the near end of the Aquino administration when coup d’état

attempts occurred, together with natural disasters and the worst energy crisis in the

country in modern history, was the economic contraction of 0.6% in 1991. Recovery

succeeded during the Ramos administration from a low gross domestic product (GDP)

growth of 0.3% in 1992 to a peak of 5.8% in 1996. This boom period was tempered by

the 1997 Asian financial crisis and the El Niño phenomenon that had its full impact

evidenced by another 0.6% contraction in 1998. While the Philippine economy was still

picking itself up having brought its growth to 4.4% in 2000, another political related

event, i.e., Estrada’s political scandal that led to his ouster, and the El Niño accounted for

the minimal growth of 1.8% in 2001. The following years under Arroyo’s administration

mark another expansionary mode surpassing the good performance in the 1990s.

Over the years, the country’s output has become less and less agricultural, averaging

only 1/5 of the gross value added share, although the agricultural sector accounted for the

highest employment until the mid-1990s. This low agricultural productivity may explain

the shift in employment to the service sector, from 40.1% in 1990 to 50.5% in 2006.

Likewise, service’s share in gross value added has consistently increased from 42.2% to

48.7% over the same period. The industry’s 1/3 share in the output has been relatively

stable with only a marginal decrease from 35.5% in 1990 to 32.5% in 2006. The

industrial sector’s employment was the smallest among the sectors comprising of only

around 1/6 of total employment.

3.2 Trade performance

In the last 15 years or so, the Philippines experienced rapid expansion in its volume of

trade with the rest of the world. Exports and imports as ratios to GDP both increased

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dramatically from 30.2% to 49.1% from 1990 to 2006 for exports and from 37.3% to

51.7% during the same period for imports (Table 1). It is noteworthy to point out though

that the interim foreign trade performance was even more striking until the 1997 Asian

financial crisis when the respective export and import intensity ratios peaked at 52.1%

and 63.6%, respectively. Table 2 shows that much of the exports over the years comprise

primarily of manufactures where electrical equipment including semi-conductors belong.

Manufactures share in the exports was 69.7% in 1990, grew to 79.5% and 90.2% in 1995

and 2000, respectively, and stabilized to 89.6% in 2005. On the other hand, the export

shares of agricultural and mineral products have both consistently declined. Also, the

largest magnitude of imports was of raw materials and intermediate goods that include

semi-processed raw materials. Raw materials’ share in the imports was 47.6% in 1990

and settled to 59% in 2005. Finally, it appears that importation of capital goods was

relatively higher in the 1990’s than they have been in the early 2000s, while import

shares of mineral fuels and lubricants, and consumer goods did not vary too much

averaging at 12% and 8%, respectively, for the periods presented.

Intra-ASEAN exports increased from US$795.3 million which is 1.8% of total intra-

ASEAN exports in 1993 to US$6.8 billion or 5.8% of total in 2004 (Table 3). The same

direction was seen in intra-ASEAN imports during the same period. It is important to

note that because of the high level of intra-regional trade in ASEAN over the years, the

region became the top destination and source for its exports and imports respectively

(Table 4). The impressive inter and intra-regional trade performance by the Philippines

may be attributed to its trade and investment liberalization policies and structural

reforms. For instance, the country conducted unilateral trade reforms since the 1980s,

specifically reforming its tariff structure with the aim of improving the competitiveness

of its local industries. In the 1990s, it participated in PTAs and multilateralism by joining

AFTA in 1992 and the WTO in 1994. To further boost exports, the country tried to attract

foreign direct investments through (i) investment promotion schemes, (ii) export

processing zones, (iii) investment priorities plans, and (iv) fiscal incentives such as

custom duty exemptions and tax holidays. It may be worthwhile to point out, however,

that amidst these policy measures, the country’s industrial sector is still small and has a

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narrow base, and its manufacturing export growth in recent years has been slower

compared to other Southeast Asian countries like Indonesia, Malaysia, and Thailand

(ADB 2007).

4. Trade policy

4.1 Unilateral trade reforms

Since the 1980s, the Philippines entered four different phases of tariff reform

programs (TRPs) with the aim of improving the operating efficiency and competitiveness

of its domestic industries. The first phase of the TRP was introduced in the early 1980s.

This program was complemented by an import liberalization program and indirect tax

realignment. Altogether, it led to a reduction in average nominal tariff from 42% to 28%

and the narrowing of the tariff structure from 0-100% to 10-50%.

The second phase started in 1991. The rationale for this phase was to continue the

reforms implemented in the previous phase and to lower tariff dispersion by setting tariffs

within the 10-30% range by 1995. From 28% in 1991, the average nominal tariff was

reduced to 20% in 1995. In 1992, quantitative restrictions (QRs) on certain commodities

were converted into their tariff equivalents. However, certain QRs on agricultural

commodities were brought back a year later due to strong clamor for protection from the

agricultural sector.

The third phase was initiated in 1994 calling for tariff cuts on industrial and “non-

sensitive” agricultural commodities and tariff protection on “sensitive” agricultural

products. A four-tier tariff structure was adopted with 3% for raw materials and capital

equipment not available locally; 10% for raw materials and capital equipment available

domestically; 20% for intermediate goods; and 30% for final goods. Overall, the average

nominal tariff further decreased from 19.7% in 1994 to 13.4% in 1997.

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The most recent phase of unilateral trade reforms took place starting in the late 1990s

up to the present. It followed an eight-tier tariff structure and continued its pursuit of

achieving a uniform tariff of 5% by 2004. Along the way, policy reversals were initiated

and these include a postponement of tariff reduction and a tariff hike on “sensitive”

agricultural items.

4.2 Commitments to multilateral trading system

In 1994, the Philippines participated in the multilateral trading system by joining the

WTO. Being a WTO member country required committing to MFN principles and

national treatment, the former calling for non-discrimination between trading partners

and the latter promoting equal treatment between domestic and foreign products and

workers. Such adherence, it is argued, will enable the country to reap benefits other than

market access like “fair competition, transparency, stability, and predictability” (Austria,

2002). The WTO (1999) reports that during the 1990s, the Philippines complied with all

of its multilateral commitments within the agreed timeframes. Among its commitments

were the binding of tariff rates at a maximum of 10 percentage points over the 1995

applied rate on around 64.8% of all tariff lines; the binding of tariff rates on selected

information technology products; and the binding of market access in selected service

sectors (Austria, 2001). As it turned out, the bound rates exceeded the applied rates. The

Philippines also enacted laws that were consistent with multilateralism. Among these

were Republic Act (RA) No. 8178 in 1996, calling for the “tariffication” of QRs on

agricultural imports; RA No. 8293 passed in 1998, allowing for the compliance to the

WTO’s Trade-Related Aspects of Intellectual Property Rights agreement; and RA No.

8752, which is also known as the “Anti-Dumping Act of 1999”. Given these multilateral

commitments, the country was able to lower its simple average applied MFN rate from

26% in 1992 to 9.7% in 1999 (WTO, 1999).

Since 1999, the Philippines achieved “little progress”, however, in further promoting

trade liberalization reforms (WTO, 2005). Although the average level of its applied

MFN rate again declined from 9.7% in 1999 to 5.8% in 2003, it however went up to 7.4%

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in 2004. This reversal in the MFN rate was due to tariff hikes accorded by the

government to problematic domestic industries. In spite of these “policy reversals”, new

laws consistent with WTO commitments were enacted. In 2000, RA No. 8181 and RA

No. 8800 were passed, the former legalized the adoption of transactions value as the new

method of customs valuation, while the latter was also known as the “Safeguard

Measures Act”. In 2003, RA No. 9184 was enacted into law and this allowed for the

regulation of government procurement.

4.3 Commitments to PTAs

4.3.1 AFTA

The Philippines is currently involved in a number of existing bilateral and regional

PTAs. Being one of the original member countries of ASEAN, established in 1967, it

joined AFTA in 1992. The main objective of AFTA is to improve the competitiveness of

industries in the ASEAN region, leading to the formulation of the CEPT scheme. The

latest version of the CEPT called for the reduction of intra-regional tariffs to 0%-5%

range by 2002 for the original six member countries of ASEAN, a.k.a., ASEAN-6—

Brunei Darussalam, Indonesia, Malaysia, Philippines, Singapore, and Thailand, and by

2008 for ASEAN’s new members—Cambodia, Laos, Myanmar, and Vietnam.

At present, tariff rates of 99% of all products included in the CEPT Inclusion List of

ASEAN-6 are within the 0%-5% range. For the whole ASEAN, around 90% of total

tariff lines in the CEPT Inclusion List have tariffs below 5%. ASEAN member countries

have the option, however, to exclude certain products from the CEPT Inclusion List.

These excluded products are normally identified in the Temporary Exclusions List,

Sensitive List, and General Exceptions List.4

4 The Temporary Exclusions List normally includes products that have tariffs within the 0%-5% range but are currently being protected due to delays in tariff reduction. Those products in the Sensitive List are primarily agricultural goods while those in the General Exceptions List are goods that are important to the country in order to protect national security, public morals, human, animal, or plant life, or documents of artistic, archaelogical, or historical importance.

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It is reported that trade liberalization in the Philippines appears to perform better

under AFTA compared to the multilateral trading system route (WTO, 2005). For

instance, as of 2004, around 99% of tariff lines under the CEPT scheme are within 0%-

5% while only half of tariff lines under MFN are within the same range. Also, in the

same year, the country’s average preferential rate to products coming from five of its

CEPT partners was far lower than its MFN rate.

4.3.2 Other PTAs

One of the early PTAs participated in by the Philippines is APEC, formed in 1989,

and consisting of twenty-one member economies.5 Its main goal is to promote freer trade

and investments in the Asia-Pacific region, specifically for its industrializing member

economies by 2010 and for its developing member economies by 2020.6 Its three main

areas are: trade and investment liberalization, economic and technical cooperation, and

business facilitation. According to Intal and Austria (2000), this regional group made

significant progress in terms of achieving its objectives within these areas as well as in

institutional development. For instance, average tariffs of member economies were

substantially reduced from 16.6% in 1988 to 6.4% in 2004 (APEC, 2006). In improving

business facilitation, the group helped in reducing transactions costs by 5% from 2001

and 2006, and aims to lower these costs by another 5% by 2010. APEC continues to

support the multilateral trading system and the “promotion of key regional trade

arrangements and free trade agreements”, two key areas that are contained in the Hanoi

Action Plan crafted in 2006 (APEC, 2007).

In 2002, ASEAN leaders signed a bilateral agreement with PRC that led to the

formation of ACFTA. It envisioned the six major ASEAN member countries – Brunei

Darussalam, Indonesia, Malaysia, Philippines, Singapore, and Thailand – to have an FTA

with PRC by 2010 and the rest of ASEAN – Cambodia, Laos, Myanmar, and Vietnam –

5 APEC member economies are Australia, Brunei Darussalam, Canada, Chile, China, Hong Kong (China), Indonesia, Japan, South Korea, Malaysia, Mexico, New Zealand, Papua New Guinea, Peru, Philippines, Russia, Singapore, Chinese Taipei, Thailand, United States, and Vietnam.

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to partake in this bilateral FTA by 2015. An important component of ACFTA agreement

is the Early Harvest Programme (EHP), which provides for tariff reduction and

elimination on a list of selected products. The EHP for ASEAN (except the Philippines)

and PRC started in January 2004. In April 2005, the Philippines and PRC signed a

Memorandum of Understanding on the EHP pushing for an elimination of tariffs on

certain products by January 2006. Last December 2005, the Philippines formalized its

entry into the EHP through the issuance of EO 485, which called for the adoption of

ACFTA rates on articles and specific products that are granted concessions under the

EHP. It is also noted that products listed in the ACFTA ageement’s Normal Track must

face tariff reduction or elimination within the period 2005-2010 while those products

listed in the program’s Sensitive Track must reduce or phase out its tariffs within the

2012-2018 period (BITR, 2006).

In September 2006, the Philippines and Japan entered into a bilateral trading

arrangement called JPEPA. This agreement centered on three pillars, namely,

cooperation, facilitation, and liberalization (Yap, Medalla, and Aldaba, 2006). Among

the liberalization provisions of JPEPA include the elimination of tariffs on selected

agricultural and manufactured products in both countries, and promoting a freer

movement of natural persons under certain conditions. Improvements in business

facilitation entails the use of information and communications technology in business

transactions; adoption of more efficient customs procedures; and formulation of

regulatory and policy frameworks that are conducive for business and competition,

among others. Cooperation between the two countries is centered on key areas such as

energy, environment, human resource development, financial services, science and

technology, transportation, and tourism.

4.4 Trends in CEPT and MFN rates

Table 5 presents CEPT and MFN tariff rates by input-output (I-O) sector in the

Philippines for the years 2000, 2003, and 2006. In 2000, the average CEPT and MFN

tariff rates across all 168 sectors were 10.8% and 6.4%, respectively. In 2006, both were

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8.8% and 3.1%, respectively. Over 2000-2006 period, there has been a decline, on the

average, in both CEPT and MFN tariff rates with the former having a bigger fall than the

latter. In the same period, 15 sectors experienced full elimination of CEPT rates, while

the sector “embroidery establishments” had the biggest decline in MFN rate. Conversely,

between 2000 and 2006, 7 sectors had an increase in their CEPT rates while 22 sectors

were accorded higher MFN rates. As of 2006, “rice and corn milling” sector is the most

protected sector vis-à-vis non-ASEAN member countries as its MFN rate is a high

44.5%. Similarly, the “hog” sector is the most protected sector vis-à-vis ASEAN member

countries given its 2006 CEPT rate of 26.2%.

5. Poverty and income distribution

Philippine poverty indices dramatically went down from 1991 to 1997 but slightly

rose in 2000 due to the adverse economic effects brought by the El Nino phenomenon

and the East Asian financial crisis (Table 6). The same trend can be seen among rural

and urban areas, including the country’s main urban area, the National Capital Region

(NCR). It is apparent though that the NCR, having the lowest poverty incidence, posted

the biggest poverty reduction while the rural areas, predominantly occupied by poor

households, experienced the smallest decline in poverty. Another disturbing finding is

the widening income inequality over the years as shown by the rising gini coefficient.

During the 1990s, workers in the agricultural, fishery, and forestry sector were the

poorest while “professional, technical, and related workers” had the lowest poverty

incidence by the end of the decade (Table 7). Unsurprisingly, workers who have no

education were the poorest while workers with college degrees were the least poor (Table

8). Poverty incidence for most of these workers were going down during the early to mid

1990s until the negative economic shocks in the late 1990s reversed this trend.

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6. The model

Basic structure

The model is calibrated to the 2000 Philippine SAM. It comprises of 35 production

sectors, 10 of which are in the agricultural sector, 19 in the industry, and 6 in the services

sector. The agricultural sector is composed of the chicken, coconuts, fishing, forestry,

fruits, hogs, other crops, rice, sugar, and vegetables sub-sectors. The industrial sector

consists of the following: alcohol, canning, chemicals, coal, construction, electricity, food

processing, hydro source, mining, meat, milk, milling, natural gas, oil, other

manufacturing, steam, textile, water, and wood. And the services sector includes

banking, transport, professional, public, wholesale, and other services. Except for public

services sector, the rest produce tradeables.

Figure 1 illustrates the model’s production structure, which assumes perfect

competition and constant returns to scale technology. Gross output is produced using

intermediate inputs and value added with the combination of the two being specified by a

Leontief production function. Both labor and capital are utilized to generate the value

added using a Cobb-Douglas7 production function. It is noted that capital is sector-

specific while labor is assumed to be a mobile factor.8

7 The use of Cobb-Douglas production function is to ensure consistency with related Philippine studies that used CGE modeling in trade and poverty analyses. Furthermore, the lack of econometric estimates constrained us to use this type of production function. Alternative production functions may be pursued in future research. 8 Land is not included as a factor of production because it is not disaggregated owing to lack of information.

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Figure 1: Production Structure

Intermediate Inputs

Value-Added

Cobb-Douglas

Labor Capital

Cobb-Douglas

Capital-Labor

Output

Leontief

In our model, there are 6 household groups: agricultural workers, clerk and sales

workers, industrial workers, government workers, professional workers, and households

that are not classified elsewhere. The classification of households is done via extracting

these 6 representative household groups (RHGs) from the 2000 SAM and map the

relevant characteristics of these RHGs in the 2000 Philippine Family Income and

Expenditure Survey (FIES), which has around 39,041 households.

Figure 2 depicts the model’s price relationships. Output price affects both export and

local prices. The domestic price is determined by adding an indirect tax to the local

price. The import price is in local currency and is influenced by the exchange rate, tariff

rate, indirect tax rate, and the world price of imports. Combining both domestic and

import prices yields the composite price. All prices automatically adjust to clear both

factor and product markets.

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Figure 2: Price Relationship in Model

The basic structure of the model’s output allocation, which describes its quantity or

volume relationships, is shown in Figure 3. Output is determined as a Constant Elasticity

of Transformation between domestic sales and exports. Both export and local prices

together with the elasticity of substitution would have an influence over the allocation

between exports and domestic sales. Domestic demand is defined by Cobb-Douglas

utility functions and it follows the Armington assumption wherein domestic and foreign

goods (imports) are considered to be substitutes. The consumption allocation between

these goods would depend on domestic and import prices and in the elasticity of

substitution. Also, both the indirect tax and the tariff rate can affect the domestic price

and the import price, respectively.

Figure 3: Structure of Output Allocation in Model

Output

Export

Domestic Sales

Import

Composite Good

CET

Output Price

Export Price

Local Price

Indirect Tax Domestic Price

Import Price

Composite Price +

Import Tariff

+

CET = Constant Elasticity of Transformation

The model has 4 closures. First is the fixed current account balance which also

implies constant foreign savings. Its nominal exchange rate is assumed to be fixed as

18

well and serves as the numeraire. Second is having a constant investment-saving

balance. Third is a clearing labor market where labor demand equals labor supply.

Fourth is maintaining government account balance with government spending and

revenue both held fixed. It is important to note that any reduction in government revenue

coming from tariff cuts is equally compensated by a corresponding increase in direct

income taxes, which is neutral in terms of distributional impact. It is noted that the

application of income tax is the same across households at the marginal level, but is

progressive in nominal terms, i.e., households who pay high taxes would have higher

income tax increase.

The CGE model is linked to the FIES via a top-down approach, generating

macroeconomic, sectoral, price, and volume effects based on a policy simulation. These

are then transmitted to household income and the cost of consumer basket, both will have

its respective changes, which would then be applied to all households in FIES having the

same characteristics as in the 6 RHGs. Hence, the link between CGE and FIES allows the

model to determine the effects of a policy simulation on the poverty profile of individual

households.

6.2 Poverty measure

The Foster-Greer-Thorbecke indices are used as measures of poverty. This is based

on the formula:

1

1 qi

i

z yP

n z

α

α=

−⎛ ⎞= ⎜ ⎟⎝ ⎠

∑

where α is the poverty aversion parameter; Pα is the poverty index at a given level of α; n

refers to the size of the population; z is the poverty line; q is the number of households

below z; and yi is household income. When α = 0, Pα becomes the poverty headcount

index and this takes into account the proportion of the population that falls below z. If α

= 1, then Pα represents the poverty gap index, which measures how far the poor are under

19

z. Lastly, Pα denotes the poverty severity index, i.e., a measure that puts more weight

on the poorest of the poor, whenever α = 2.

6.3 Production and trade structures at the base

The structure of the Philippine economy based on the 2000 SAM reveals the

dominance of certain sectors with respect to foreign trade and production (Table 9).

Specifically, the industrial sector has the biggest share in foreign trade as its export and

import shares are 92.5% and 87.1%, respectively. This sector also posts the highest

overall export and import intensity ratios of 42.7% and 41.0%, respectively9. The largest

industrial contributor to foreign trade is the “other manufacturing” sub-sector, which

encompasses electronics, semiconductors, appliances, and machinery; it has the highest

in terms of export share (39.4%), import share (29.4%), export intensity (90.5%), and

import intensity (87.7%). Textile and canning has the second and third-highest export-

intensity ratios with 41.7% and 27.3%, respectively, while high import-intensity ratios

were registered in coal (83.4%), oil (50.1%), chemicals (40.6%), and mining (39.5%).

The agricultural sector has the smallest export and import shares with 1.7% and 2.0%,

respectively and it also has the lowest overall import-intensity ratio (5.1%). Within

agriculture, its fruits, forestry, and fishing sub-sector has above-average export-intensity

ratios with 21.6%, 10.3%, and 7.9%, respectively, while those with high import-intensity

ratios were other crops (28.6%), fruits (10.9%), and vegetables (8.8%).

In production, the highest overall ratios of capital-labor-energy-value-added to output

and capital-labor-value-added to output belong to the agricultural sector with 76.5% and

74.5%, respectively. The lowest goes to the industrial sector (48.9% and 40.3%,

respectively). Across all sectors, it can be gleaned that the highest capital-labor-energy-

value-added-output ratio is in electricity with 95.0% while the lowest belongs to meat

with 21.7%. The highest and lowest capital-labor-value-added-output ratios are seen in

forestry (89.5%) and oil (14.3%) respectively. On the other hand, the industrial sector is

9 Export intensity refers to the sector’s exports as a percentage of domestic output while import intensity is the ratio of sector’s imports to domestic consumption.

20

the most energy-intensive sector as it has the highest overall energy-value-added-output

ratio with 8.6%. Agriculture has the lowest energy-value-added-output ratio with 2.0%.

Unsurprisingly, oil is the most energy-intensive across all sectors with a ratio of energy-

energy-value-added to output of 75.3%.

The services sector has the highest overall value added share with 48.6% followed by

industry with 38.5%. “Other manufacturing” is the biggest contributor to national value

added (16.1%). It can also be observed that agriculture is labor-intensive as it has the

highest overall labor-capital ratio with 100.8% while industry is capital-intensive due to

having the lowest overall labor-capital ratio of 64.5%.

6.4 Household income sources and poverty profile at the base

Table 10 depicts the sources of household income and poverty profile based on the

2000 SAM. It illustrates that wages is the major source of income for government

workers, professionals, clerks and sales workers, industrial workers, and other households

whose occupation is not elsewhere classified. On the other hand, agricultural workers get

most of their income from capital, primarily land and other assets, while wages is their

secondary income source. This surprising phenomenon could be explained by the greater

share of capital income generated by land-rich agricultural workers relative to labor

income derived by poor agricultural workers. Interestingly, transfers is the secondary

income source for other households and also that one-fifth of this group’s income is in

the form of remittances.

The overall picture of poverty depicts that 33.9% of the country’s population were

below the poverty threshold. As expected, majority of the poor (and the poorest)

households are agricultural workers for they have the highest poverty headcount, poverty

gap, and poverty severity indices of 57.8%, 20.0%, and 9.0% respectively. Conversely,

professional workers have the lowest poverty indices which means that they are the least-

poor.

21

7 Policy simulations and results

Two policy simulations are conducted in this study. The first simulates actual

reductions in CEPT and MFN tariff rates between 2000 and 2006 (a.k.a., SIM 1). This

was made in order to examine the relative magnitudes of the economic, sectoral, price,

volume, and poverty effects of actual CEPT and MFN tariff rate cuts during this period.

The other simulation requires eliminating both CEPT and MFN rates in order to

investigate the potential effects of a full liberalization scenario (a.k.a., SIM 2). For each

of these simulations, a corresponding increase in direct income taxes is made in order to

maintain government revenue neutrality, which is one of the model’s closures. It is also

worthwhile to note that in these simulations, the CEPT rate refers to the tariff rate on

Philippine imports sourced from the original ASEAN member countries, whereas MFN

rate is the tariff rate on Philippine imports that come from non-ASEAN trading partners.

In this context, the “rest of the world” has been disaggregated into two regions—CEPT

region, consisting of ASEAN member countries, and MFN region, which encompasses

all non-ASEAN member countries.

Table 11 shows the macro welfare effects of the two simulations. Based on the

results of SIM 1, the actual MFN tariff and CEPT rate reductions between 2000 and 2006

lead to a less than 1% fall in import and consumer prices and production costs. This

translates to a real peso depreciation by 0.35%, thereby enhancing import and export

volumes by less than 1%. Notably, the increase in the volume of exports and imports is

slightly higher under CEPT than MFN. However, domestic production for local sales

and consumption of composite goods fall by 0.14% and 0.02% respectively and this is

due to the more intense import competition arising from a more liberalized environment.

Overall, there is minimal negative change in national output (-0.01%). This is also true

for SIM 2, except that the changes are larger and that national output marginally rises by

0.08%, suggesting that the 1.02% reduction in domestic production for local sales is

offset by the economic gains coming from higher trade volumes and domestic

consumption.

22

The price and volume effects across sectors are presented in tables 12a and 12b. Total

elimination of CEPT and MFN tariff rates benefits the industrial sector only, raising its

output by 0.61%, while agricultural and services sectors’ outputs contract by 1.70% and

0.34%, respectively (Table 12b). The output expansion of the import-intensive industrial

sector is due to its relatively large price reductions, which in turn enhances its trade

volumes and composite commodity production, and thereby offsetting the decline in its

domestic sales. The biggest gainer in the industrial sector is “other manufacturing” with

2.46% output expansion. In agriculture, the biggest loser is the palay sector with a 3%

decline in output. On the other hand, the actual MFN and CEPT rate reductions between

2000 and 2006 would lead to a marginal agricultural output expansion of 0.05% with no

change in industrial output (Table 12a). Noticed that import price of agricultural

products rose by 3.55%, leading to a decline in import volume by 4.3%. This is because

the agricultural sector was accorded an increase in protection from foreign competition in

recent years. Overall, it can be inferred that trade liberalization lowers prices with the

largest magnitude in the price of imports. Across sectors, the decline in import prices is

larger in agriculture than in industry due to the relatively high level of protection in the

former. Greater access to imports leads to slightly lower domestic sales for majority of

the sectors. The improvement in the competitiveness position of these industries enable

exports to grow, albeit marginally. However, with the exception of other manufacturing,

the export growth is not able to fully reverse the contraction in domestic sales and

composite commodity output, allowing for minimal changes in sectoral output.

Table 13 shows the simulation results on factor markets, pinpointing the increase in

industry’s value added and value added prices for the two simulations. This is largely due

to the “other manufacturing” sector. Conversely, the agricultural sector posts negative

changes in its value added and value added prices given full liberalization and uniform

reductions in both MFN and CEPT rates. The average wage marginally rises with large

tariff cuts on both CEPT and MFN (while small tariff cuts yield small negative change in

the price of labor). Under a full liberalization scenario, labor demand by the “other

manufacturing” sector grows by 7.04% but this is at the expense of most of the other

sectors which suffers from labor demand reductions. The return to capital rises also for

23

the “other manufacturing” sector by 7.1% with tariff elimination but the actual

experience of MFN and CEPT rate reductions as well as a uniform rate reduction for both

generates less than 1% increases for this sector. In general, reductions in MFN tariff and

CEPT rates result in a gain for industrial capitalists while its counterparts in agriculture

and services suffer.

The preferential and multilateral liberalization effects on gross household income,

disposable income, and consumer prices are found in Table 14. First, the actual MFN

and CEPT rate cuts between 2000 and 2006 raise gross household income for all

household groups by less than 1%. Also, their disposable incomes and consumer prices

lower by less than 1% as well, with the decline in the former explained by the imposition

of higher direct income taxes to compensate for the loss in tariff revenue and maintain

government revenue neutrality (Figure 4a). Both government workers and professionals

experienced the biggest negative change in disposable income. Also, all household

groups had almost the same magnitudes in terms of the negative change in consumer

prices, hovering at around -0.3%.

Figure 4a: SIM 1 Effects on Disposable Income and Consumer Prices, (% change)

-0.80

-0.70

-0.60

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

Disposable income -0.11 -0.29 -0.69 -0.19 -0.32 -0.68

Consumer prices -0.27 -0.29 -0.29 -0.28 -0.28 -0.28

Agricultural workers

Clerks and sales workers

Government workers

Industrial workers

Other households

(nec)Professionals

nec = not elsewhere classified, SIM 1 = Simulation 1

24

Eliminating both CEPT and MFN rates, as in SIM 2, yields huge declines in both

disposable income and consumer prices for all households (see Figure 4b). Government

workers and professionals are again the top two household groups with the largest drop in

disposable income, while agricultural workers had the smallest decline. The relatively

large reduction in the disposable income levels of government workers and professionals

is expected because these two household groups are huge income tax payers. On the

other hand, the relatively poor household groups, namely, agricultural and industrial

workers, have the least negative change in their respective disposable incomes primarily

because they are not heavy income tax payers. Also, this full liberalization scenario

results in a fall in consumer prices for all household groups, ranging from -2.53% for

professionals to -2.82% for agricultural workers.

Figure 4b: SIM 2 Effects on Disposable Income and Consumer Prices, (% change)

-7.00

-6.00

-5.00

-4.00

-3.00

-2.00

-1.00

0.00

Disposable income -1.12 -2.71 -5.98 -1.94 -2.86 -5.69

Consumer prices -2.82 -2.67 -2.64 -2.73 -2.57 -2.53

Agricultural workers

Clerks and sales workers

Government workers

Industrial workers

Other households

(nec)Professionals

nec = not elsewhere classified, SIM 2 = Simulation 2.

Table 15 illustrates the poverty impacts of regional and multilateral liberalization on

our 6 RHGs and also on the whole Philippine population. Our simulations prove that

both MFN and CEPT reductions are poverty-reducing at the national level. For instance,

these actual tariff cuts between 2000 and 2006 lower the national headcount, gap, and

severity indices by 0.09%, 0.21%, and 0.28%,respectively (see Figure 5a). Further

promoting regional and multilateral liberalization via reducing both MFN and CEPT rates

25

to 0% yields larger negative changes on these national poverty indices (see Figure 5b).

Although such poverty reduction is not consistent across household groups. Specifically,

a relatively high degree of MFN and CEPT rates reductions tend to raise the poverty

situation of relatively skilled and less-poor workers – professionals, government workers,

clerks and sales workers, and other households – that are not elsewhere classified. This is

because these workers pay more direct taxes, thereby leading to a decrease in their

disposable income which more than offsets the reduction in the cost of their consumption

basket.

On the other hand, the relatively poor and unskilled workers, specifically the

agricultural and blue-collar industrial workers, benefit from preferential and multilateral

trading arrangements and the imposition of direct income taxes. This is mainly because

the decrease in consumer prices for these two RHGs are larger than the reduction in their

disposable incomes since these groups do not fully feel the burden of paying direct

income taxes. For example, the actual tariff cuts under MFN and CEPT schemes

between 2000 and 2006 translates to a less than 1% decrease in all poverty indices for

both agricultural and industrial workers while a fully liberalized environment lowers the

headcount, gap, and severity indices of agricultural workers by 1.79%, 3.27%, and

4.22%, respectively, and of industrial workers by 1.49%, 2.22%, and 2.61%,

respectively.

26

Figure 5a: Poverty Effects of SIM 1, by Household Group (% change)

-0.50

0.00

0.50

1.00

1.50

2.00

Headcount -0.09 -0.11 0.00 0.78 -0.20 0.00 0.42

Gap -0.21 -0.30 0.02 1.03 -0.26 0.12 1.13

Severity -0.28 -0.39 0.00 1.06 -0.30 0.15 1.53

PhilippinesAgricultural

workersClerks and

sales workersGovernment

workersIndustrial workers

Other households

(nec)Professionals

nec = not elsewhere classified, SIM 1 = Simulation 1

Figure 5b: Poverty Effects of SIM 2, by Household Group (% change)

-6.00

-4.00

-2.00

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

Headcount -1.21 -1.79 0.09 4.45 -1.49 0.87 5.03

Gap -2.35 -3.27 0.14 8.93 -2.22 0.78 9.56

Severity -3.14 -4.22 0.13 9.74 -2.61 0.87 12.62

PhilippinesAgricultural

workersClerks and

sales workersGovernment

workersIndustrial workers

Other households

(nec)Professionals

nec = not elsewhere classified, SIM 3 = Simulation 3

In Tables 16a and 16b, we compare the poverty effects of CEPT and MFN rate

reductions between two compensatory tax measures—direct income tax and indirect tax

on domestic use, that offset the loss in government tariff revenues. The choice of indirect

27

tax as an alternative compensatory tax is made in order to capture the tight fiscal situation

in the Philippines as it’s collection of direct income taxes is found to be a challenging

endeavor (ADB 2007). Our results yield marked contrasts between these compensatory

tax mechanisms. It may be recalled that for all simulations with compensatory direct tax,

poverty measures all went down with agricultural and industrial workers benefiting the

most. Conversely, as shown in most of our simulations, an indirect tax as a compensatory

tax mechanism alongside reductions in CEPT and MFN rates increase poverty indices.

For example, with an compensatory indirect tax and actual reductions in CEPT and MFN

rates between 2000 and 2006, the headcount, gap, and severity ratios for the whole

Philippines rose by 0.13%, 0.15%, and 0.17% respectively (Table 16a). In a full

liberalization scenario, all poverty indices go up and with bigger magnitudes compared to

the first simulation (Table 16b). In addition, looking at the full liberalization scenario and

with compensatory indirect tax, those RHGs that are worse off in terms of having higher

poverty levels are agricultural workers, clerks and sales workers, government workers,

industrial workers, and professionals, whereas other households not elsewhere classified

are slightly better off. The decline in the poverty levels for most of the RHGs is evident

in the larger decrease in their disposable incomes compared to the decrease in consumer

prices.

8 Conclusion

The Philippines has embarked on a series of preferential and multilateral trading

arrangements during the 1990s and early 2000s. It is widely conjectured that such trend

will be the future of the country’s trade policy. Amidst its popularity, its potential

economywide and poverty impacts are still ambiguous. Hence, this paper employs a top-

down representative household CGE model calibrated to Philippine data to investigate

the possible effects of MFN treatment and CEPT scheme on poverty in this small

developing economy in Southeast Asia.

Our simulation findings generally show that both the PTA, specifically the CEPT

scheme, and multilateral trade liberalization help promote a better macroeconomic

28

environment as well as reduce poverty. Specifically, both trade liberalization measures

have the potential to lower import and domestic prices; raise domestic output, volume of

trade, and local consumption; and improve national output. Furthermore, it is found that

preferential and multilateral trade liberalization tends to increase the national wage rate.

But it is seen also that the industrial sector gains more than agriculture and services in

terms of lower prices, higher output, greater labor demand, and higher returns to capital.

More importantly, it is found that reductions in both MFN and CEPT rates together

with the imposition of an appropriate compensatory fiscal measure, i.e., direct income

tax, lower national poverty. All household groups experience a fall in their respective

consumption baskets’ costs and also in their disposable incomes. But for the relatively

poor and unskilled, namely, the agricultural and blue-collar industrial workers,

preferential and multilateral liberalization benefits them more as their poverty indices go

down. This is because for these household groups, the decline in consumer prices is

bigger than the decrease in their disposable income.

Given our results, it is proposed that the Philippines further promote preferential and

multilateral trading commitments and adopt a compensatory direct income tax measure in

order to reap the economic rewards brought by trade liberalization and help eradicate

poverty.

29

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33

Table 1 Selected Philippine Economic Indicators, 1990-2006

GDP Exports/ Imports/Year Growth GDP GDP Agriculture Industry Service Agriculture Industry Service1990 3.0 30.2 37.3 22.3 35.5 42.2 44.4 15.5 40.11991 -0.6 32.3 37.1 22 .7 34.7 42.5 44.7 16.4 38.91992 0.3 33.6 40.2 22.8 34.4 42.8 45.5 15.8 38.71993 2.1 34.9 43.9 22.8 34.3 43.0 45.2 15.7 39.11994 4.4 40.1 48.2 22.4 34.7 42.9 43.1 16.3 40.61995 4.7 42.9 53.4 21.5 35.4 43.1 43.3 16.0 40.71996 5.8 46.8 58.9 21.1 35.6 43.3 40.4 17.0 42.61997 5.2 52.1 63.6 20.7 35.9 43.4 38.5 17.1 44.41998 -0.6 41.4 54.5 19.5 35.4 45.1 47.3 20.0 32.71999 3.3 41.5 51.3 20.0 34.5 45.5 38.3 15.9 45.82000 4.4 45.8 50.4 19.8 35.5 44.8 36.5 16.7 46.82001 1.8 43.5 51.3 20.2 34.0 45.9 37.1 15.5 47.52002 4.4 43.3 51.9 20.1 33.8 46.1 37.0 15.2 47.82003 4.9 43.3 54.8 19.8 33.5 46.7 35.4 16.0 48.62004 6.2 46.5 54.5 19.6 33.1 47.3 35.9 15.7 48.42005 5.0 46.2 53.2 19.1 32.8 48.1 36.5 15.1 48.42006 5.4 49.1 51.7 18.8 32.5 48.7 34.7 14.8 50.5

National Statistical Coordination Board (NSCB) National Income Accounts, Bangko Sentral ng Pilipinas (BSP) Selected

Philippine Economic Indicators (various issues).

Gross value added share (%) Employment structure (%)

Sources. Asian Development Bank (ADB) Key Economic Indicators, Philippine National Statistics Office (NSO) Labor Force Statistics,

34

Table 2: Philippine Foreign Trade, by Sector, 1990, 1995, 2000, and 2005

1990 1995 2000 2005Export shares (%) Agricultural products 18.1 12.2 4.5 4.9 Mineral products 8.8 5.1 1.7 2 Petroleum products 1.9 1 1.1 1.4 Manufactures 69.7 79.5 90.2 89.6 Other exports 1.4 2.2 2.5 2.1

Total 100 100 100 100Value (million US dollars) 8,186 17,447 38,078 41,255

Import shares (%) Capital goods 25.6 30.4 17.8 18.6 Raw materials and intermediate goods 47.6 46.1 65.7 59 Unprocessed raw materials 7.1 5.9 3.1 3.1 Semi-processed raw materials 40.5 40.2 62.6 56.9 Mineral fuels and lubricants 15.1 9.3 9 13.1 Consumer goods 8.7 10.5 5.8 7.1 Others 3.1 3.6 1.7 2.2

Total 100 100 100 100Value (million US dollars) 12,206 26,391 43,318 48,036Source. Bangko Sentral ng Pilipinas (BSP) Selected Philippine Economic Indicators (various issues).

35

Table 3: Philippine Intra-ASEAN Exports and Imports, 1993-2004

(in US$ million) (% of ASEAN-6a) (in US$ million) (% of ASEAN-6a)1993 795.3 1.8 1883.0 4.91994 1425.5 2.4 2463.8 5.31995 2357.5 3.4 2489.1 4.61996 2970.3 3.7 4011.8 6.21997 3436.2 4.0 4872.8 7.51998 3821.0 5.5 4428.9 8.61999 4989.1 6.7 4461.0 7.92000 5982.6 6.4 4955.4 6.92001 4986.0 6.1 4664.8 7.22002 5529.7 6.5 5542.0 7.82003 6581.7 6.8 6398.1 8.72004 6837.9 5.8 8335.9 8.3

Source: ASEAN Statistical Yearbook 2005a ASEAN-6 refers to Brunei Darussalam, Indonesia, Malaysia, Philippines, Singapore, and Thailand.

Intra-ASEAN Exports Intra-ASEAN Imports

ASEAN = Association of Southeast Asian Nations Table 4: Major Markets of ASEAN Exports and Imports, 2004

Exports ImportsMarket/Supplier (%) (%)

ASEAN 23.0 22.0Australia 3.0 2.0

China 7.0 10.0European Union 11.0 9.0

Hong Kong 5.0 -Japan 12.0 15.0

Saudi Arabia - 2.0South Korea 4.0 4.0

Taiwan 3.0 4.0Other European Countries 3.0 2.0

Others 20.0 22.0United States of America 9.0 8.0

Source: ASEAN Statistical Pocketbook, 2006 ASEAN = Association of Southeast Asian Nations

36

Table 5: CEPT and MFN Tariff Rates by Sector, 2000, 2003, and 2006

2000 2003 2006 2000 2003 2006Palay 50.0 25.0 25.0 Corn 27.5 23.3 23.3 6.5 23.3 20.0 Vegetables 15.4 9.8 14.9 6.4 4.2 2.8 Roots and Tubers 17.2 13.7 16.0 5.3 13.7 10.7 Banana 15.0 10.0 15.0 15.0 5.0 5.0 Pineapple 15.0 10.0 10.0 15.0 5.0 5.0 Mango 15.0 10.0 15.0 15.0 5.0 5.0 Citrus Fruits 11.7 7.0 9.4 11.7 5.0 5.0 Fruits and Nuts Excluding Coconuts 8.8 6.5 7.6 7.4 4.3 1.7 Coconut 15.0 10.0 15.0 15.0 5.0 5.0 Sugarcane 10.0 7.0 10.0 3.0 3.0 - Tobacco 7.9 5.5 5.5 7.9 3.5 2.5 Abaca 3.0 3.0 3.0 3.0 3.0 - Other fiber crops 3.0 3.0 5.0 3.0 3.0 3.0 Coffee 43.8 36.3 37.5 17.5 5.0 5.0 Cacao 3.0 3.0 3.0 3.0 3.0 3.0 Rubber 2.8 2.7 2.8 2.9 2.8 1.2 Other agricultural production, n.e.c. 4.0 3.5 4.2 3.7 3.1 0.5 Hog 28.0 25.2 26.2 3.0 25.6 24.0 Cattle and Other Livestock 7.9 7.5 7.9 5.3 3.9 1.8 Chicken 25.8 19.7 21.5 3.0 19.5 16.6 Hen's Egg 0.9 5.8 5.8 11.3 4.4 3.2 Other poultry and poultry products 44.6 35.5 36.3 16.5 16.6 14.8 Ocean, coastal, and inland fishing 5.7 5.8 6.0 5.3 3.9 1.9 Aquaculture and other fishery activities, n.e.c. 11.3 7.8 8.9 7.6 4.7 2.8 Forestry 3.2 3.1 3.4 3.9 3.0 0.4 Gold and Silver Mining 18.6 14.0 14.0 6.4 4.8 4.6 Copper Mining 3.0 2.2 2.2 3.0 2.2 0.2 Nickel mining 3.0 2.1 2.1 3.0 2.1 - Chromite Mining 3.0 3.0 3.0 3.0 3.0 - Other Metal Mining 3.0 2.3 2.1 3.0 2.3 0.0 Coal Mining 5.4 5.0 5.0 5.4 2.6 2.6 Crude Petroleum and Natural Gas 5.0 4.1 3.3 3.0 3.0 2.1 Stone quarrying, clay and sand pits 3.3 3.3 3.1 3.0 3.0 1.2 Salt Mining 4.0 1.0 1.0 3.0 1.0 1.0 Other non-metallic mining and quarrying 3.4 2.7 2.4 3.0 2.5 0.6 Slaughtering and Meat Packing 22.5 18.3 18.3 7.1 15.8 12.4 Meat and Meat Processing 33.5 27.2 27.7 13.5 4.2 3.5 Milk Processing 4.5 3.6 2.4 4.1 3.2 0.5 Butter and Cheese Manufacturing 5.2 4.6 4.6 5.2 3.8 2.0 Ice Cream, Sherbets and other flavored ices 10.0 7.0 10.0 10.0 5.0 5.0 Other Dairy Products 4.9 3.9 3.5 4.0 3.2 1.8 Canning and Preserving of Fruits and Vegetables 9.3 7.2 9.6 7.8 4.5 2.9 Fish Canning 7.3 6.7 7.9 7.2 3.7 3.0 Fish drying, smoking and manufacturing of other seafood products 13.8 9.2 11.2 8.9 4.9 4.6 Production of crude coconut oil, copra cake and meal 15.0 10.0 10.0 15.0 3.0 3.7

IO Sector MFN CEPT

37

Table 5: CEPT and MFN Tariff Rates by Sector, 2000, 2003, and 2006 (cont.)

2000 2003 2006 2000 2003 2006Other Crude Vegetable Oil excluding coconut oil, fish and other marine products 5.8 4.4 4.9 5.2 3.2

0.8 Manufacture of Refined Coconut and Vegetable Oil 7.5 5.2 5.2 5.7 3.4 1.1 Rice and Corn Milling 44.2 42.3 44.5 11.5 4.0 3.0 Flour, Cassava and Other Grains Milling 8.4 6.3 6.6 5.9 3.3 2.6 Manufacture of Bakery Products excluding Noodles 13.4 9.2 13.4 10.6 4.1 4.5 Noodles Manufacturing 12.0 9.0 11.9 9.4 4.1 4.3 Sugar Milling and Refining 47.4 47.2 31.6 9.0 4.0 18.2 Manufacture of Cocoa, Chocolate and Sugar Confectionery 7.8 6.4 7.1 7.4 4.2 3.7 Manufacture of Desiccated Coconut 15.0 10.0 15.0 15.0 5.0 5.0 Manufacture of Ice Excluding Dry Ice 10.0 7.0 10.0 5.0 5.0 - Coffee Roasting and Processing 50.6 37.5 39.1 20.0 5.0 5.0 Manufacture of Animal Feeds 10.1 8.3 8.4 5.8 5.4 3.4 Manufacture of Starch and Starch Products 18.3 14.6 17.7 6.3 13.4 11.2 Manufacture of Flavouring Extracts, Mayonnaise and food coloring products 3.9 3.8 4.4 3.3 3.1

0.5 Miscellaneous food products 11.0 8.1 8.5 5.2 3.6 2.1 Alcoholic Liquors and Wine 10.6 7.9 9.6 8.7 5.0 2.9 Malt and Malt Liquors 7.4 4.9 6.3 7.4 2.7 2.1 Softdrinks and Carbonated Water 10.0 8.5 9.1 5.6 5.0 2.3 Cigarette Manufacturing 8.4 5.9 7.1 7.9 4.1 2.9 Cigar, Chewing and Smoking Tobacco 9.0 6.3 7.3 8.3 4.4 3.3 Tobacco Leaf Flue-Curing and Redrying 10.0 7.0 7.0 10.0 4.0 4.0 Textile spinning, weaving, texturizing and finishing 9.4 6.7 8.2 4.7 4.2 3.2 Fabric knitting mills 9.7 6.5 8.2 5.1 4.9 4.5 Hosiery, underwear and outerwear knitting 20.0 15.0 15.0 5.0 5.0 5.0 Manufacture of made up textile goods, except wearing apparel 19.8 17.4 17.4 8.6 5.0 4.9 Manufacture of carpets and rugs 20.0 15.0 15.0 6.9 5.0 5.0 Cordage, rope, twine and net manufacturing 12.8 11.0 12.9 5.6 4.2 4.7 Manufacture of articles made of native materials 10.4 9.1 9.1 4.5 4.4 2.6 Manufacture of artificial leather and impregnated and coated fabrics 10.0 7.0 13.6 8.6 5.0 5.0 Manufacture of fibre batting, padding and upholstery fillings including coir 20.0 15.0 15.0 5.0 5.0

5.0 Custom tailoring and dressmaking shops 8.9 3.7 3.7 5.0 3.7 - Manufacture of ready-made clothing 18.6 13.9 13.9 8.8 4.9 4.6 Embroidery establishments 13.3 2.3 2.3 5.0 2.3 - Manufacture of other wearing apparel except footwear 19.4 14.0 14.0 6.5 4.9 4.6 Tanneries and leather finishing 3.4 3.4 2.5 3.0 3.0 0.6 Manufacture of products of leather and leather substitutes, except footwear and wearing apparel

13.4 12.9 12.9 12.6 4.9 4.1

Manufacture of leather footwear and footwear parts 12.0 10.8 10.6 10.5 4.6 3.6 Sawmills and planing mills 4.3 3.0 2.8 1.5 1.4 1.2 Manufacture of veneer and plywood 15.5 12.4 10.7 13.0 4.2 3.8 Manufacture of hardboard and particle board 20.0 15.0 15.0 10.5 5.0 5.0 Wood drying and preserving plants 3.0 1.0 1.0 3.0 1.0 - Millwork plants 2.7 1.5 1.5 2.7 1.5 - Manufacture of wooden and cane containers and small cane wares 7.5 5.5 5.5 4.5 4.5 2.5 Manufacture of wood carvings 15.4 11.4 11.4 4.6 4.4 3.8 Manufacture of miscellaneous wood, cork and cane products, n.e.c. 7.7 5.5 5.5 5.5 3.7 1.7 Manufacture and repair of wooden furniture including upholstery 14.3 10.0 10.1 4.4 4.1 3.1

IO Sector MFN CEPT

38

Table 5: CEPT and MFN Tariff Rates by Sector, 2000, 2003, and 2006 (cont.)

2000 2003 2006 2000 2003 2006Manufacture and repair of rattan furniture including upholstery 8.4 5.0 5.0 3.8 3.2 1.3 Manufacture and repair of other furnitures and fixtures, n.e.c. 13.9 10.0 10.1 4.9 3.8 3.2 Pulp, paper and paperboard 9.1 6.7 5.1 7.1 3.0 2.3 Paper and paperboard containers 10.0 7.0 7.0 5.0 5.0 5.0 Manufacture of articles of paper and paperboard 7.7 13.6 8.4 5.3 2.7 2.9 Newspapers and periodicals 7.7 5.4 5.4 6.1 4.6 0.7 Printing and publishing of books and pamphlets 8.4 5.8 6.1 5.4 4.6 1.7 Commercial and job printing and other allied industries 8.6 7.2 6.4 7.1 3.4 2.8 Mfr. of Basic Industrial Chemicals 3.2 2.4 2.3 3.0 2.3 0.2 Manufacture of Fertilizers 1.0 2.6 2.2 3.0 2.9 0.7 Manufacture of synthetic resins, plastics materials and other man-made products 6.7 8.4 8.6 5.0 3.6

3.1 Mfr. of Pesticides, Insecticides, etc. 4.3 5.9 5.7 5.4 3.8 2.2 Mfr. of paints, varnish and lacquers 9.4 7.1 7.0 5.7 4.8 4.5 Mfr. of Drugs and Medicines 4.1 4.2 3.8 4.0 4.0 2.8 Manufacture of soap and detergents 4.0 3.9 5.7 3.8 3.0 1.5 Mfr. of Perfumes, cosmetics and other toilet preparations 7.8 6.6 6.6 6.3 4.2 4.1 Manufacture of chemical products 4.0 3.5 3.7 3.6 2.8 1.3 Petroleum Refineries 3.0 2.9 2.8 3.0 2.9 2.6 Manufacture of asphalt, lubricants and miscellaneous products of petroleum and coal 3.0 2.8 2.4 3.0 2.8

0.2 Rubber tire and tube manufacturing 3.8 5.2 4.7 5.2 3.5 2.3 Manufacture of rubber footwear 14.5 13.2 13.2 13.0 4.8 4.7 Mfr. of other rubber products n.e.c. 9.1 6.9 7.0 6.4 4.7 4.2 Manufacture of plastic furniture , plastic footwear, plastic industrial supplies and other fabricated plastic products

11.1 12.1 12.7 8.3 4.6 4.3

Manufacture of pottery, china and earthernware 9.7 9.5 9.5 5.5 4.5 3.9 Manufacture of flat glass 8.6 7.3 7.6 7.0 3.9 2.5 Manufacture of glass containers 8.6 7.3 7.6 7.0 3.9 2.5 Manufacture of other glass and glass products 8.5 7.3 7.5 6.9 3.9 2.4 Cement Manufacture 4.1 4.1 4.1 3.0 3.0 1.7 Manufacture of structural clay products 12.2 8.0 8.0 9.3 4.4 3.8 Manufacture of structural concrete products 9.3 9.4 9.4 6.2 4.9 4.7 Manufacture of other non-metallic mineral products 5.0 4.1 4.2 3.9 3.1 1.4 Blast furnace and steel making furnace, steel works and roll 6.9 4.5 4.9 4.8 3.0 1.6 Iron and steel foundries 3.0 1.0 1.0 3.0 1.0 - Non-ferrous smelting and refining plants, rolling, drawing and extrusion mills 4.2 3.3 3.0 3.2 2.4

0.5 Non-ferrous foundries 3.0 1.0 1.0 3.0 1.0 - Cutlery, handtools, general hardware 9.8 5.9 6.0 4.4 3.1 1.9 Structural metal products 11.0 7.3 6.8 7.7 4.3 2.8 Manufacture of metal containers 7.9 9.0 8.4 7.6 4.6 3.6 Metal stamping, coating, engraving mills 8.9 5.0 6.6 5.1 2.6 1.8 Manufacture of wire nails 15.0 10.0 10.0 5.0 5.0 5.0 Manufacture of other fabricated wire and cable products except insulated wire and cable

12.7 10.2 10.2 6.2 4.7 4.5

Manufacture of non-electric lighting and heating fixtures 13.3 8.2 8.2 5.8 4.0 2.5 Manufacture of fabricated metal products except machinery and equipment 10.4 7.8 7.6 7.1 4.2

3.0 Manufacture of agricultural machinery and equipment 4.0 2.5 2.5 3.5 1.9 0.8 Manufacture of metal and wood working machinery 3.0 1.0 1.0 3.0 1.0 -

IO Sector MFN CEPT

39

Table 5: CEPT and MFN Tariff Rates by Sector, 2000, 2003, and 2006 (cont.)

2000 2003 2006 2000 2003 2006Manufacture of engines and turbines except for transport equipment and manufacture of special industry and equipment

3.0 2.3 1.0 3.0 2.3 -

Manufacture assembly and repair of office, computing and accounting machines - - - - - -

Manufacture of pumps, compressors, blowers, airconditioners 3.0 1.0 1.0 3.0 1.0 - Machine shops and of non-electrical machinery and equipment, n.e.c. 4.8 3.3 3.1 3.7 2.2

1.1 Manufacture of electrical industrial machinery and apparatus 4.8 3.3 3.1 3.7 2.2 1.1 Manufacture of radio and TV receiving sets, sound recording and reproducing equipment including records and tapes

19.1 12.5 12.7 8.0 4.8 4.4

Manufacture of communication and detection equipment 6.4 4.6 4.1 3.9 2.8 1.5 Manufacture of parts and supplies for radio, television and communication (semi-conductors)

6.4 4.6 4.1 3.9 2.8 1.5

Manufacture of appliances and housewares, n.e.c. 5.1 3.5 3.3 3.7 2.3 1.1 Manufacture of primary cells and batteries and electric accumulators 15.0 15.0 15.0 11.0 5.0 5.0 Insulated wires and cables 15.8 9.0 4.2 10.8 5.0 2.5 Manufacture of current-carrying wiring devices, conduits and fittings 14.7 10.3 8.3 6.0 4.7 3.4 Manufacture of electrical lamps, flourescent tubes and other electrical apparatus and supplies, n.e.c.

6.1 5.3 5.4 4.8 3.6 3.0

Shipyards and boatyards 4.2 3.4 2.9 3.7 3.6 0.6 Manufacture and assembly of motor vehicles 23.1 23.4 22.5 17.6 4.7 4.4 Rebuilding and major alteration of motor vehicles 18.4 18.7 19.1 13.7 4.5 4.1 Manufacture of motor vehicles parts and accessories 21.2 21.0 17.4 15.0 4.3 3.5 Manufacture, assembly of motorcycles and bicycles 23.7 24.3 22.8 16.6 4.6 4.1 Manufacture, assembly, rebuilding and major alteration of aircraft and animal or hand-drawn vehicles

5.1 4.2 4.2 4.3 3.3 0.9

Manufacture of professional, scientific measuring and controlling equipment 3.1 2.2 1.9 2.8 2.2 0.2

Manufacture of photographic and optical instrument 3.1 2.2 1.9 2.8 2.2 0.2 Manufacture of watches and clocks 5.3 4.2 4.1 3.9 3.8 0.1 Manufacture and repair of furniture and fixtures, made primarily of metal 13.6 9.4 9.4 7.3 4.8

4.0 Manufacture of jewelry and related articles 5.7 5.2 5.2 5.2 3.5 1.3 Manufacture of musical instruments 4.5 4.5 4.6 3.6 3.6 0.8 Manufacture of sporting and athletic goods 7.4 6.5 6.5 4.8 4.1 1.6 Manufacture of surgical, dental, medical and orthopedic supplies 3.1 2.2 1.9 2.8 2.2 0.2 Manufacture of opthalmic goods 10.0 5.0 5.0 5.0 5.0 5.0 Manufacture of toys and dolls excluding rubber and plastic toys 2.6 0.9 0.9 2.6 0.9 - Manufacture of stationers', artists, and office supplies 7.5 5.0 5.0 4.0 3.8 2.1 Miscellaneous manufacturing 7.8 6.9 6.9 4.4 3.9 2.2

Average 45.7 43.5 43.8 41.6 39.6 38.5 Source: Tariff Commission, Republic of the Phiippines.

IO Sector MFN CEPT

40

Table 6: Poverty and Income Distribution, 1991, 1994, 1997, and 2000

1991 1994 1997/a/ 2000/a/Philippines Headcount 45.2 40.6 33.0 34.0 Gap 15.4 13.5 10.3 10.6 Severity 7.0 6.1 4.4 4.5

National Capital Region (NCR) Headcount 16.6 10.4 6.5 7.6 Gap 3.8 2 1.2 1.6 Severity 1.3 0.6 0.4 0.5

Urban Headcount 42.7 34.7 18.4 18.6 Gap 14.9 11.4 5.1 5.0 Severity 6.9 5.2 2.0 2.0

Rural Headcount 55.0 53.1 46.3 48.8 Gap 19.0 18.2 15.1 15.9 Severity 8.7 8.3 6.6 6.9

Poverty Distribution NCR 5.1 3.5 2.7 3.1 Urban 34.1 30.7 25.7 26 Rural 60.7 65.7 71.6 70.9

Income Distribution Gini Coefficient 0.48 0.464 0.507 0.505Source: 1991, 1994, 1997, and 2000 Family Income and Expenditure Surveys as reported in Cororaton and Cockburn (2007) and Cororaton (2006)

41

Table 7: Poverty Incidence of Families, by Occupation of the Household Head, 1991, 1994, 1997, and 2000 Major Occupation Group 1991 1994 1997 2000

Not specified 46.7 48.6 - -Professional, Technical and RelatedWorkers 11.8 10.6 6.7 5.9Administrative, Executive and ManagerialWorkers 6.4 7.4 5.3 10.8Clerical & Related Workers 12.0 8.2 8.0 9.4Sales Workers 24.0 18.1 15.5 17.0Service Workers 32.8 19.2 18.7 18.2Agricultural, Animal Husbandry andForestry Workers, Fishermen and Hunters 55.8 55.9 50.0 55.5Production & Related Workers, Transportand Equipment Operators 32.9 26.8 23.9 33.8Other Occupations not Classifiable 14.5 8.2 9.4 26.5Armed Forces 7.4 17.2 3.8 10.7Non-gainful Occupation - 41.8 44.3 29.2Unemployed 25.0 20.6 17.6 19.4Source: 1991, 1994, 1997, and 2000 Family Income and Expenditure Surveys as reported in Reyes (2002)

Table 8: Poverty Incidence, by Educational Attainment of Household Head, 1991, 1994, 1997, and 2000 Highest Educational Attainment 1991 1994 1997 2000

No Grade 55.8 55.2 52.5 60.5Elementary Undergraduate 53.2 50.7 48.6 45.2Elementary Graduate 48.7 43.6 39.8 26.01st-3rd Year High School 43.1 35.3 33.2 11.9High School Graduate 29.6 23.6 21.0 18.2College Undergraduate 16.2 11.7 10.9 10.3At least College Graduate 4.0 4.0 2.4 2.5Source: 1991, 1994, 1997, and 2000 Family Income and Expenditure Surveys as reported in Reyes (2002)

42

Table 9: Production and trade structures based on 2000 SAM (%)

LaborExport as a Import as a Value to percentage percentage Added Capitalof sectoral of composite Share

output commodityPalay 0.001 0.01 0.1 2 79.5 77.7 1.8 2.6 244.3Coconut 0.002 0.2 - - 89.3 88.9 0.4 0.6 146.1Fruits 0.5 21.6 0.2 10.9 79.3 78.7 0.6 1.5 76.4Sugar - - - - 74.3 69.7 4.6 0.3 95.4Vegetables 0.03 2.9 0.1 8.8 81.8 81.2 0.6 0.8 149.1Other crops 0.01 0.4 0.5 28.6 81.8 80.6 1.1 0.9 95.1Hogs 0.002 0.1 0.1 2.2 66 65.5 0.5 1.8 98Chicken 0.001 0 0.01 0.4 63.9 60.7 3.2 1.3 90.6Fishing 0.3 7.9 0.01 0.3 81.3 77.4 3.8 2.8 52.3Forestry 0.03 10.3 0.001 0.6 93.2 89.5 3.8 0.2 26.8

Agriculture 1.7 4.4 2 5.1 76.5 74.5 2 12.9 100.8

Mining 0.2 16.4 0.6 39.5 75.8 63.2 12.7 0.6 43.1Coal - - 0.2 83.4 64.9 58.1 6.8 0 57Oil 0.7 11.8 5 50.1 89.5 14.3 75.3 0.7 100Natural gas - - - - 42 27.6 14.4 0 84.6Meat 0.002 0.04 0.2 3.4 21.7 20.5 1.1 1.1 52.7Milk 0.02 1.7 0.5 33.6 36.1 31.1 4.9 0.3 51.2Canning 1.2 27.3 0.5 14.1 34.5 30.7 3.9 1.2 34.6Milling 0.1 1.1 0.2 3.7 30.2 28.7 1.5 1.6 86.3Food 0.2 5.3 0.5 10.1 34.8 31.6 3.2 1.2 67.4Alcohol 0.04 1.4 0.2 5.7 45.5 40.4 5.1 1 72.2Textile 4.1 41.7 1.9 24.9 47.5 42.9 4.5 3.6 75.8Wood 1 19.7 1 19.3 43.9 39.3 4.6 1.7 68.4Chemicals 0.6 9.2 3.9 40.6 49.3 41.1 8.2 2.2 56.7Other manufacturing 39.4 90.5 29.4 87.7 47 43 3.9 16.1 55.5Construction 0.1 1.5 0.2 1.9 54.8 53 1.8 3.9 179.1Electricity - - - - 95 67.7 27.3 2.8 31.3Steam - - - - 79.8 67.5 12.3 0.1 25.2Hydro Source - - - - 79.8 67.6 12.3 0.1 25.3Water - - - - 81.4 75.3 6.1 0.3 73.4

Industry 92.5 42.7 87.1 41 48.9 40.3 8.6 38.5 64.5

Transport 1.6 10.2 4.4 24.2 66 53.6 12.4 7 52.6Wholesale 1.2 5.1 0.3 1.5 68.7 65.8 2.9 13.5 59.3Banking 0.2 1.1 0.8 4.7 78.4 76.5 1.8 11.3 18.4Professional - - - - 52.4 49.8 2.6 4.8 274Other services - - - - 61.7 55.5 6.2 3.9 191Public services - - - - 74.1 72.2 1.8 8.2 -

Services 5.8 3.3 10.9 6.1 68.2 63.8 4.4 48.6 93.1KLEVA, capital-labor-energy value added; KLVA, capital-labor value added; X, output.

Sectors

Exports,% Imports,%

(KLEVA/X)Share ShareRatio

Trade Production

(KLVA/X) (EVA/X)

SAM = Social Accounting Matrix

43

Table 10: Sources of Household Income and Poverty Profile Based on 2000 SAM (%)

Labor income

Capital income Transfers Remittances Headcount Gap Severity

Philippines 33.9 10.7 4.6

Government workers 74.1 14.8 5.6 5.5 14.2 4 1.7Professionals 51.2 35.2 4.9 8.7 7.8 2 0.7Clerks and sales workers 56.9 31 4.9 7.2 17.3 4.3 1.6Agricultural workers 28.4 61.1 5.4 5.1 57.8 20 9Blue-collar industrial workers 61.8 28.4 3.1 6.6 26.4 7 2.6Other households (NEC) 46.4 16.7 27.7 9.2 18.4 5 2NEC, not elsewhere classified

Sources of household income Poverty index

SAM = Social Accounting Matrix Table 11: Simulation Results on Macro Effects (% change) SIM 1 SIM 2Overall nominal tariff rate

Tariff Rate - MFN -9.70 -100.00Tariff Rate - CEPT -18.52 -100.00

PricesImport prices in local currency - Total -0.65 -5.21Import prices in local currency - MFN -0.47 -4.91Import prices in local currency - CEPT -0.90 -5.67Consumer (Composite prices) -0.34 -2.94Local cost of production -0.23 -2.11

Real exchange rate 0.35 2.98Import volume - Total 0.38 3.29 Import volume - MFN 0.10 3.22 Import volume - CEPT 0.82 3.40Export volume - Total 0.38 3.24 Export volume - MFN 0.37 3.24 Export volume - CEPT 0.41 3.30Domestic production for local sales -0.14 -1.02Consumption (composite) goods -0.02 0.09Price of Household Consumer Basket -0.28 -2.65Overall output, % -0.01 0.08SIM 1, Simulation 1; SIM 2, Simulation 2CEPT, Common Effective Preferential Tariff; MFN, Most-Favored-Nation

44

Table 12a: Simulation 1 Results on Prices and Volume, by Sector (% change)

pmi pdi pqi pxi pli mi ei di qi xiPalay 36.12 0.29 0.95 0.29 0.29 -45.31 0.15 0.74 -0.57 0.74Coconut - -0.05 -0.05 -0.05 -0.05 - -0.07 -0.16 -0.16 -0.16Fruits -5.57 -0.59 -1.21 -0.46 -0.59 10.07 0.51 -0.68 0.56 -0.43Sugar - -0.47 -0.47 -0.47 -0.47 - - -0.60 -0.60 -0.60Vegetables 19.50 0.93 2.70 0.90 0.93 -27.69 -0.50 1.36 -2.10 1.31Other Crops 0.04 -0.06 -0.03 -0.06 -0.06 -0.31 0.02 -0.11 -0.17 -0.11Hogs -0.20 -0.18 -0.18 -0.18 -0.18 -0.20 0.13 -0.23 -0.23 -0.23Chicken -1.63 -0.21 -0.22 -0.21 -0.21 2.67 0.20 -0.23 -0.22 -0.23Fishing -2.28 -0.16 -0.17 -0.15 -0.16 4.26 0.21 -0.11 -0.10 -0.09Forestry 0.01 -0.74 -0.74 -0.66 -0.74 -1.85 1.13 -0.36 -0.37 -0.21

Agriculture 3.55 -0.06 0.11 -0.06 -0.06 -4.29 0.38 0.04 -0.31 0.05

Mining -1.44 -0.61 -0.95 -0.51 -0.61 1.22 0.74 -0.48 0.21 -0.28Coal -2.09 -1.41 -1.99 -1.41 -1.41 0.11 - -1.28 -0.12 -1.28Oil -0.71 -0.57 -0.64 -0.51 -0.57 0.19 1.08 -0.08 0.06 0.06Natural Gas - -0.14 -0.14 -0.14 -0.14 - - 0.32 0.32 0.32Meat -4.35 -0.30 -0.48 -0.30 -0.30 8.32 0.30 -0.30 0.07 -0.30Milk -2.31 -0.85 -1.37 -0.84 -0.85 2.46 1.18 -0.54 0.50 -0.51Canning -2.05 -0.46 -0.70 -0.33 -0.46 2.94 0.58 -0.33 0.16 -0.08Milling -3.11 0.25 0.08 0.24 0.25 6.37 -1.11 -0.63 -0.30 -0.63Food -1.97 -0.29 -0.48 -0.27 -0.29 3.07 0.21 -0.37 0.01 -0.34Alcohol -1.83 -0.31 -0.41 -0.31 -0.31 2.98 0.49 -0.13 0.07 -0.12Textile -1.48 -0.47 -0.75 -0.28 -0.47 1.65 0.56 -0.39 0.16 0.01Wood -3.33 -1.05 -1.53 -0.84 -1.05 3.82 1.23 -0.90 0.07 -0.48Chemicals -0.43 -0.30 -0.35 -0.27 -0.30 0.17 0.50 -0.10 0.01 -0.05Other Manufacturing -0.64 -0.32 -0.60 -0.03 -0.32 0.31 0.32 -0.33 0.23 0.26Construction 0.00 -0.21 -0.20 -0.20 -0.21 -0.40 0.43 0.01 0.01 0.02Electricity - -0.21 -0.21 -0.21 -0.21 - - -0.04 -0.04 -0.04Steam - -0.17 -0.17 -0.17 -0.17 - - -0.04 -0.04 -0.04Hydro - -0.17 -0.17 -0.17 -0.17 - - -0.04 -0.04 -0.04Water - -0.15 -0.15 -0.15 -0.15 - - -0.16 -0.16 -0.16

Industry 0.59 -0.33 -0.55 -0.19 -0.33 -0.82 0.38 -0.29 0.09 0.00

Transport - -0.22 -0.17 -0.15 -0.22 - 0.41 -0.16 -0.15 0.00Wholesale - -0.15 -0.14 -0.20 -0.15 - 0.21 -0.04 -0.09 -0.07Banking - -0.30 -0.28 -0.14 -0.30 - 0.52 -0.08 -0.11 -0.07Professional - -0.12 -0.12 -0.29 -0.12 - - -0.08 -0.06 -0.06Other Services - -0.14 -0.14 -0.12 -0.14 - - -0.06 -0.05 -0.05Public Services - 0.00 0.00 -0.14 0.00 - - -0.05 0.00 0.04

Services 0.00 -0.19 -0.18 -0.18 -0.19 0.00 0.33 -0.07 -0.05 -0.05pmi, import prices; pdi, domestic prices, pqi, composite commodity prices; pxi, output prices; pli, local pricesmi, imports; ei, exports; di, domestic sales; qi, composite commodity; xi, total output

Price Changes (%) Volume Changes (%)

45

Table 12b: Simulation 2 Results on Prices and Volume, by Sector (% change)

pmi pdi pqi pxi pli mi ei di qi xiPalay -20.18 -1.61 -2.17 -1.61 -1.61 47.40 0.21 -2.98 -1.86 -2.98Coconut - -0.76 -0.76 -0.76 -0.76 - 0.79 -0.74 -0.74 -0.73Fruits -8.42 -1.91 -2.73 -1.49 -1.91 12.79 2.19 -1.68 -0.02 -0.84Sugar - -2.35 -2.35 -2.35 -2.35 - - -1.94 -1.94 -1.94Vegetables -22.82 -2.73 -5.46 -2.64 -2.73 52.81 1.68 -3.79 1.85 -3.63Other Crops -4.57 -2.39 -3.05 -2.38 -2.39 2.32 2.65 -2.20 -0.87 -2.18Hogs -11.09 -2.19 -2.43 -2.18 -2.19 18.78 2.58 -1.85 -1.36 -1.85Chicken -15.62 -2.22 -2.29 -2.22 -2.22 32.27 3.05 -1.48 -1.34 -1.48Fishing -9.75 -1.60 -1.62 -1.47 -1.60 17.90 2.42 -0.82 -0.77 -0.56Forestry -3.92 -1.68 -1.69 -1.50 -1.68 3.95 2.69 -0.73 -0.71 -0.38

Agriculture -9.01 -1.91 -2.39 -1.83 -1.91 14.62 3.27 -1.89 -0.97 -1.70

Mining -3.40 -1.27 -2.14 -1.06 -1.27 3.92 2.06 -0.51 1.26 -0.09Coal -5.29 -3.80 -5.06 -3.80 -3.80 0.40 - -2.69 -0.09 -2.69Oil -2.10 -2.09 -2.09 -1.84 -2.09 0.15 4.45 0.13 0.14 0.65Natural Gas - -1.50 -1.50 -1.50 -1.50 - - 1.12 1.12 1.12Meat -24.24 -2.82 -4.01 -2.82 -2.82 61.46 3.90 -1.88 0.58 -1.87Milk -6.00 -3.36 -4.30 -3.30 -3.36 4.16 5.51 -1.45 0.50 -1.33Canning -8.26 -2.65 -3.54 -1.91 -2.65 10.74 3.75 -1.66 0.17 -0.17Milling -23.89 -2.33 -3.62 -2.30 -2.33 61.26 2.63 -2.09 0.56 -2.03Food -9.76 -2.89 -3.70 -2.73 -2.89 13.94 4.34 -1.61 0.06 -1.29Alcohol -9.50 -2.38 -2.85 -2.34 -2.38 15.30 3.96 -0.92 0.05 -0.85Textile -9.80 -3.50 -5.28 -2.01 -3.50 11.65 4.75 -2.45 1.25 0.59Wood -8.22 -3.33 -4.38 -2.66 -3.33 8.65 4.81 -2.05 0.11 -0.68Chemicals -5.12 -3.03 -3.92 -2.74 -3.03 2.67 4.51 -1.72 0.11 -1.14Other Manufacturing -5.80 -2.96 -5.48 -0.27 -2.96 2.97 3.02 -2.98 2.26 2.46Construction 0.00 -1.75 -1.72 -1.72 -1.75 -3.49 3.57 -0.02 -0.09 0.03Electricity - -1.32 -1.32 -1.32 -1.32 - - -0.30 -0.30 -0.30Steam - -1.48 -1.48 -1.48 -1.48 - - -0.30 -0.30 -0.30Hydro - -1.48 -1.48 -1.48 -1.48 - - -0.30 -0.30 -0.30Water - -1.54 -1.54 -1.54 -1.54 - - -0.98 -0.98 -0.98

Industry -5.77 -2.52 -3.96 -1.44 -2.52 3.98 3.27 -1.38 0.90 0.61

Transport - -1.85 -1.41 -1.54 -1.85 -4.13 3.32 -0.98 -1.36 -0.08Wholesale - -1.41 -1.39 -1.66 -1.41 -3.33 2.33 -0.47 -0.58 -0.40Banking - -2.69 -2.57 -1.34 -2.69 -5.88 4.98 -0.54 -0.85 -0.53Professional - -1.49 -1.49 -2.66 -1.49 - - -0.60 -0.27 -0.27Other Services - -1.42 -1.42 -1.49 -1.42 - - -0.27 -0.36 -0.36Public Services - 0.00 0.00 -1.42 0.00 - - -0.36 0.00 0.77

Services 0.00 -1.81 -1.68 -1.73 -1.81 -4.34 3.02 -0.48 -0.33 -0.34pmi, import prices; pdi, domestic prices, pqi, composite commodity prices; pxi, output prices; pli, local pricesmi, imports; ei, exports; di, domestic sales; qi, composite commodity; xi, total output

Price Changes (%) Volume Changes (%)

46

Table 13: Simulation Results on Factor Markets, by Sector (% change)

SIM 1 SIM 2 SIM 1 SIM 2 SIM 1 SIM 2 SIM 1 SIM 2Palay 0.74 -2.98 0.39 -1.18 1.13 -4.13 1.04 -4.18Coconut -0.16 -0.73 -0.02 -0.45 -0.18 -1.19 -0.27 -1.23Fruits -0.43 -0.84 -0.47 -1.05 -0.89 -1.88 -0.98 -1.93Sugar -0.60 -1.94 -0.54 -1.98 -1.14 -3.88 -1.23 -3.93Vegetables 1.31 -3.63 0.97 -2.40 2.29 -5.94 2.20 -5.98Other Crops -0.11 -2.18 -0.02 -2.25 -0.13 -4.38 -0.22 -4.43Hogs -0.23 -1.85 -0.15 -1.84 -0.38 -3.66 -0.47 -3.70Chicken -0.23 -1.48 -0.16 -1.58 -0.40 -3.04 -0.49 -3.09Fishing -0.09 -0.56 -0.08 -1.03 -0.16 -1.58 -0.25 -1.63Forestry -0.21 -0.38 -0.69 -1.35 -0.89 -1.72 -0.98 -1.77

Agriculture 0.07 -1.70 0.00 -1.40 -0.09 -2.84

Mining -0.28 -0.09 -0.56 -0.16 -0.84 -0.25 -0.93 -0.30Coal -1.28 -2.69 -2.15 -4.62 -3.40 -7.18 -3.49 -7.23Oil 0.06 0.65 0.15 0.70 0.22 1.34 0.12 1.29Natural Gas 0.32 1.12 0.47 1.38 0.79 2.52 0.70 2.47Meat -0.30 -1.87 -0.48 -3.48 -0.77 -5.28 -0.86 -5.33Milk -0.51 -1.33 -0.91 -2.54 -1.42 -3.84 -1.51 -3.89Canning -0.08 -0.17 -0.15 -0.44 -0.23 -0.61 -0.32 -0.66Milling -0.63 -2.03 -0.64 -2.30 -1.27 -4.29 -1.36 -4.34Food -0.34 -1.29 -0.41 -1.85 -0.75 -3.12 -0.84 -3.16Alcohol -0.12 -0.85 -0.07 -1.13 -0.19 -1.98 -0.28 -2.02Textile 0.01 0.59 0.10 0.82 0.11 1.41 0.02 1.36Wood -0.48 -0.68 -0.61 -0.94 -1.08 -1.61 -1.17 -1.66Chemicals -0.05 -1.14 0.01 -1.96 -0.04 -3.08 -0.13 -3.12Other Manuf. 0.26 2.46 0.56 4.53 0.82 7.10 0.73 7.04Construction 0.02 0.03 0.10 0.07 0.12 0.10 0.03 0.05Electricity -0.04 -0.30 -0.05 -0.91 -0.09 -1.21 -0.18 -1.26Steam -0.04 -0.30 -0.08 -1.14 -0.13 -1.44 -0.22 -1.49Hydro -0.04 -0.30 -0.08 -1.14 -0.12 -1.44 -0.22 -1.49Water -0.16 -0.98 -0.12 -1.28 -0.28 -2.25 -0.37 -2.30

Industry 0.02 0.75 0.15 1.43 0.19 2.36

Transport 0.00 -0.08 0.10 -0.11 0.10 -0.19 0.01 -0.24Wholesale -0.07 -0.40 -0.02 -0.62 -0.09 -1.01 -0.18 -1.06Banking -0.07 -0.53 -0.29 -2.81 -0.36 -3.33 -0.45 -3.37Professional -0.06 -0.27 0.07 -0.05 0.02 -0.32 -0.08 -0.37Other Services -0.05 -0.36 0.06 -0.14 0.01 -0.50 -0.08 -0.55Public Services 0.04 0.77 0.09 0.05 0.00 0.00 0.04 0.77

Services -0.05 -0.36 -0.06 -1.03 -0.15 -1.67

Wage rate 0.091 0.049SIM 1, Simulation 1; SIM 2, Simulation 2

Value added Value added priceRate of return to

capital Labor demand

47

Table 14: Simulation Results on Household Income and Consumer Prices, by Household Group (% change)

IncomeDisposable

incomeConsumer

prices IncomeDisposable

incomeConsumer

pricesGovernment workers 0.07 -0.69 -0.29 0.02 -5.98 -2.64Professionals 0.05 -0.68 -0.28 -0.01 -5.69 -2.53Clerks and sales workers 0.05 -0.29 -0.29 0.00 -2.71 -2.67Agricultural workers 0.03 -0.11 -0.27 -0.04 -1.12 -2.82Industrial workers 0.06 -0.19 -0.28 0.00 -1.94 -2.73Other households (nec) 0.04 -0.32 -0.28 0.01 -2.86 -2.57SIM 1, Simulation 1; SIM 2, Simulation 2

SIM 1 SIM 2

Table 15: Poverty Impacts, by Simulation and Household Group (% change)

Headcount Gap Severity Headcount Gap SeverityAll Philippines -0.09 -0.21 -0.28 -1.21 -2.35 -3.14

Government workers 0.78 1.03 1.06 4.45 8.93 9.74Professionals 0.42 1.13 1.53 5.03 9.56 12.62Clerks and sales workers 0.00 0.02 0.00 0.09 0.14 0.13Agricultural workers -0.11 -0.30 -0.39 -1.79 -3.27 -4.22Industrial workers -0.20 -0.26 -0.30 -1.49 -2.22 -2.61Other households (nec) 0.00 0.12 0.15 0.87 0.78 0.87SIM 1, Simulation 1; SIM 2, Simulation 2

nec, not elsewhere classified.

SIM 1 SIM 2

48

Table 16a: Poverty Impacts of SIM 1, by Household Type and Compensatory Tax (% change)

Direct tax Indirect tax Direct tax Indirect tax Direct tax Indirect taxAll Philippines -0.09 0.13 -0.21 0.15 -0.28 0.17Government workers 0.78 0.00 1.03 0.08 1.06 0.06Professionals 0.42 0.00 1.13 0.10 1.53 0.14Clerks and sales workers 0.00 0.09 0.02 0.12 0.00 0.13Agricultural workers -0.11 0.14 -0.30 0.17 -0.39 0.21Industrial workers -0.20 0.17 -0.26 0.13 -0.30 0.15Other households (nec) 0.00 0.00 0.12 0.00 0.15 0.00SIM 1, Simulation 1nec, not elsewhere classified

Headcount Gap Severity

Table 16b: Poverty Impacts of SIM 2, by Household Type and Compensatory Tax (% change)

Direct tax Indirect tax Direct tax Indirect tax Direct tax Indirect taxAll Philippines -1.21 0.36 -2.35 0.57 -3.14 0.70Government workers 4.45 0.78 8.93 1.00 9.74 1.06Professionals 5.03 0.42 9.56 1.08 12.62 1.39Clerks and sales workers 0.09 1.12 0.14 0.97 0.13 1.08Agricultural workers -1.79 0.31 -3.27 0.50 -4.22 0.64Industrial workers -1.49 0.50 -2.22 0.99 -2.61 1.17Other households (nec) 0.87 -0.28 0.78 -0.16 0.87 -0.20SIM 2, Simulation 2nec, not elsewhere classified

Headcount Gap Severity

49