Trade Liberalization and Wage Inequality in the Philippines

7/30/2019 Trade Liberalization and Wage Inequality in the Philippines

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Asian Development Bank

6 ADB Avenue, Mandaluyong City

1550 Metro Manila, Philippines

www.adb.org/economics

2010 by Asian Development BankMarch 2010

ISSN 1655-5252

Publication Stock No. WPS09_______

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are those of the author(s) and do not

necessarily reect the views or policies

of the Asian Development Bank.

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I. Introduction

An important insight from trade theory is that reductions in trade protection have

distributional implications. Moreover, based largely on the logic of the workhorse

Heckscher-Ohlin (HO) model of trade, conventional wisdom has held that trade

liberalization leads to declines in income inequality in developing countriesi.e.,

countries abundant in unskilled/less skilled workers.1 Recent empirical work has not

been supportive of the conventional wisdom, however. As Goldberg and Pavcnik (2007)

note in their survey of the literature, carefully conducted studies for Argentina; Brazil;

Chile; Colombia; Hong Kong, China; India; and Mexico tend to show trade liberalization

in these economies to be closely associated with increases in various measures of

inequality.2 Various factors have been put forward to explain the apparent deviations

from the predictions of standard trade theory, including the possibility of skill-biased

technological change induced by trade, barriers to within-country factor mobility, and

trade in intermediate products. It has also been noted that patterns of protection prior to

liberalization, and differential degrees of liberalization across sectors, could be driving

some of the results one sees.3

As may be noted from Goldberg and Pavcniks survey, much of the rigorous empirical

work on the effects of trade on wage inequality has focused on the experience of various

Latin American countries, with a few contributions considering experiences from Asia. In

particular, there is a dearth of evidence from Southeast Asian countries, especially the

Philippinesan economy where merchandise trade as a share of gross domestic product

(GDP) has grown rapidly: from less than 50% in 1990 to a little over 100% by 2000.

Exceptions include the work of Lanzona (2000) and Hasan and Chen (2004).4 While the

rst uses a factor returns approach and uses data from 1989 to 1995 to understand how

changes in export prices have affected wages of different types of workers and industries,

the second examines the relationship between trade and industry wage premia (i.e., the

1 Because developing countries are typically presumed to be abundant in unskilled rather than skilled labor, trade

liberalization in such countries may be expected to raise the relative actor price o unskilled labor.2 Note, however, the recent work o Ferreira, Leite, and Wai-Poi (2007) who nd that trade liberalization in Brazil has

helped reduce wage inequality there.3 For example, it is typically assumed that developing countries are more likely to protect skill- or capital-intensive

sectors. In reality, in a number o countries, trade protection is highest among labor-intensive sectors. As we will

see below, this is also the case in the Philippines.4 A study by Orbeta (2002) uses two data sets or the manuacturing sectorone at the three-digit level and

covering the years 1993-1997 and another at the two-digit level covering the 1980-1995to examine the impact

o changes in export and import volumes on employment across manuacturing subsectors. The study nds some

support or a positive relationship between export volumes and employment levels.


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portion of wages that are purged of workers observable characteristics and accrue to

their industry of employment alone) in the manufacturing sector from 1988 to 1997.

In this paper we analyze the relationship between trade liberalization and wage inequality

in the Philippines in much greater detail than the Hasan and Chen study mentionedabove. In particular, we use a comprehensive approach to capture trade liberalization

wage inequality linkagesdeveloped recently by Ferreira, Leite, and Wai-Poi (2007) and

henceforth referred to as FLW. While details are provided later, some salient features of

FLWs approach can be noted here. First, the approach enables us to work with wage

inequality as it pertains to all workers and not just those in tradable sectors. Second, it

enables us to quantify the extent to which trade liberalization has contributed to changes

in overall wage inequality. Third, the approach not only allows trade liberalization to

affect wage inequality through its inuence on industry wage premia and industry skill

premia (i.e., wages accruing to industry of employment for high skilled workersproxied

here by a college degree), but also through employment reallocation effects that then

affect the wage distribution. Finally, FLWs approach allows us to consider the effects ofeconomywide (as opposed to industry-specic) returns to education on wage inequality.

While no attempt is made to establish how much of the changes in economywide returns

to education are driven by trade per se, FLWs approach does give us some sense of

upper and lower bounds on the effects of trade on inequality under varying assumptions

about the relationship between economywide returns to education and trade.

Another way we in which we build over the existing (but limited) work on trade and

wage inequality in the Philippines is by extending its analysis to more recent years. It

is important to point out, however, that while our data allow us to examine the trade

wage inequality relationship all the way up to 2006 (something that we do), we focus

most of our attention on the 19942000 period during which trade policy was liberalizeddramatically. Examining these years in detail as opposed to the longer 19882006 period

has several advantages.

First, trade liberalization, as opposed to large expansions in foreign direct investment

(FDI) and/or outsourcing of services to the Philippines, represented the main channel

through which the country experienced globalization during 19942000. As Figure 1

shows quite clearly, tariff rates declined considerably over these years, and trade volumes

seem to have responded in the expected manner, while FDI inows as a proportion of

GDP remained relatively unchanged. Indeed, the share of merchandise trade in GDP

increased from 56% in 1994 to 101% in 2000the highest share recorded even as

of 2008. Second, data from labor force surveys reveal that wage inequality increasedconsiderably between 1994 and 2000for example, the Gini coefcient over hourly

wages increased from 36% to 41%. If trade liberalization is responsible for increasing

wage inequality, as found in other countries, we would be well placed to nd evidence for

it by focusing on 19942000. Finally, and most importantly, as we shall describe below,

the wage data for 2006 raises some serious concerns about its comparability with earlier

2 | ADB Economics Working Paper Series No. 195


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27.93 in 1994 and 2000, respectively, while the Ginis over wages were 36% and 41%,

respectively. The corresponding numbers for wages and inequality in 1997 are roughly

in between and certainly in no way out of line with those for 1994 and 2000: P26.1 for

wages and 38% for the Gini. In summary, it appears unlikely that the nancial crisis

had signicant and lasting effects that would seriously contaminate the analysis of tradeliberalization and wage inequality carried out in this paper.

With that as a caveat, our main ndings are that trade-induced effects on industry

wage premia and industry-specic skill premia account for an economically insignicant

increase in wage inequality. A more substantial role for trade liberalization comes through

trade-induced employment reallocation effects whereby reductions in protection appear

to have led to a shift of employment to more protected sectors, especially services where

wage inequality tended to be high to begin with. Nevertheless, changes in economywide

returns to education and changes in industry membership over and above those

accounted for by our estimates of trade-induced employment reallocation effects are

much more important drivers of wage inequality. In order for trade liberalization to accountfor a relatively large portion of the increases in wage inequality, it would have to be a

major driver of changes in economywide returns to education.

The remainder of this paper is organized as follows. Section II discusses data and

measurement issues pertaining to trade and wages. In addition to commenting briey on

the patterns of protection in the Philippines and describing the construction of industry

specic tariff rates and other trade-related variables, the section discusses available labor

force survey data and how these are used to construct measures of wage inequality.

Section III provides details on the methodology of FLW used here to understand the

relationship between trade liberalization and wage inequality. Section IV describes the

results of our empirical analysis while Section V concludes.

II. Data and Measurement

Our analysis of trade, wage inequality, and employment linkages makes use of two

sources of data: trade-related data, which allows us to quantify the patterns of protection

and trade ows across industries; and the Philippines Labor Force Survey (LFS) data,

which provides information on workers.

A. Trade Protection and Trade Flows

Like many other developing countries, the Philippines pursued protectionist policies

from the 1950s to the 1970s. Although there were some attempts at liberalizing trade in

the 1960s and 1970s, it was only in the early 1980s that serious efforts at liberalization



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Table 2 presents summary statistics of the sample of these wage and salary workers.

A quick examination of wages across columns 14 reveals a decline in average wages

across all major production sectors between 2000 and 2006 though employment shares

remain stable (columns 58). The data also indicate a sharp decline in inequality between

2000 and 2006 (column 11 versus 12 for the Gini coefcient and column 15 versus 16for the 90th and 10th percentile wage differential). A closer examination of the data reveal

that the decrease in inequality from 2000 to 2006 is due to a dramatic reduction in wages

in the top three deciles (ranging from 10% for 70 th percentile wages to 20% for 90th

percentile wages). Whether this reects reality or is on account of survey and nonsurvey

errors is something that is beyond the scope of this paper to determine. However, a

large discrepancy between top wages reported in the LFS for 2006 and those described

in published compilations of average salaries in the corporate sector (ADB 2007), along

with the fact that the Philippines economy performed reasonably between 2000 and

2006 (GDP per capita grew at an average annual growth rate of 2.66 over 20002006)

suggests that the 2006 wage data may not be comparable with previous years.

Focusing attention on the 19942000 period, we nd that real average wages grew by

close to 4% annually, driven partly by wage growth in the services sector (column 2

versus column 3) and partly by the increases in employment in the better paying (on

average) services sector (column 6 and 7).12 As for wage inequality, examination of

the 90th10th percentile ratio and the Gini coefcients reveals that wages in services

tend to be more dispersed. While the P90P10 differentials registered a slight decrease

in inequality for both agriculture and industry from 1994 to 2000, the Gini coefcient

nevertheless increased. What drives this seemingly paradoxical result is that the

wages of the highest earners in these sectors (i.e., those above the 90 th percentile)

increased rapidly. These statistics reveal a pattern of wage adjustments over a period

of liberalization that are similar with those typically found for previous studies from LatinAmerican countries. For example, Feliciano (2001) reports increasing inequality in the

tradables sector in Mexico driven by rapid growth of the highest wage earners and

declines in wage growth of the lowest wage earners.

Next, we turn to examining the sample worker characteristics across tradable industries

(i.e., agriculture and manufacturing) by matching the industry-level trade data with

workers industry of employment. Table 3 presents various summary statistics by level

of protection in 1994. Industries with lower tariff rates (below the median in the tariff

distribution) on average paid the highest wages, had the highest share of educated

workers, but accounted for the lowest share of employment. In contrast, industries

with tariff rates in the upper part of the distribution on average paid the lowest wages,employed the largest share of females, and had the lowest share of workers with more

than a high school education. Thus, protection as captured by average tariff rates tended

to be lower for relatively skill-intensive industries.

12 The comparative real average wage growth gures or 19881994 and 20002006 are 1.6% and 2.8%,

respectively.

Trade Liberalization and Wage Inequality in the Philippines | 9


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of interactions between industry indicators and a dummy indicator for college-educated

workers in order to capture industry-specic skill premia:13

lnw X I wp I S spij ij ij j ij i j j ij = + + ( ) +

We estimate this wage equation in order to derive the industry wage premia (wpj) and

industry-specic skill premia (spj). We estimate the wage equation for the years 1988,

1994, 2000, and 2006 and pool the resulting industry wage and industry-specic skill

premia to be used in Step 3 later. Although our decompositions of wage inequality are

mainly restricted to analyzing changes over 1994 and 2000, we also analyze changes

over 1988 and 1994, and 2000 and 2006.

Step 2: Estimation o Model o Employment/Occupation Status

The second step is accomplished by estimating a multinomial logit model employment/

occupation status.14,15 This involves regressing an individuals employment/occupationstatus on a set Zij( ) of personal and household characteristics:

Pr ,j s P Ze

e e

s

i

Z

Z Z

j s

i s

i s i j

={ } = ( ) =+

The above equation includes 10 possible employment/occupation categories

corresponding to combinations of industry afliation, tradable/nontradable status,

and employment type. The categories are: (1) inactive (not in the labor force or

unemployed); (2) self-employed in manufacturing sectors; (3) self-employed in

nonmanufacturing tradable sectors; (4) self-employed in the nontradable sectors;

(5) permanently-employed in manufacturing sectors; (6) permanently-employed

in nonmanufacturing tradable sectors; (7) permanently-employed in nontradablesectors; (8) casually-employed in manufacturing sectors; (9) casually-employed

in nonmanufacturing tradable sectors; and (10) casually-employed in nontradable

sectors.16

13 Agricultural crops is the omitted industry in the wage equations.14 As in Step 1, we estimate this equation or the years 1988, 1994, 2000, and 2006.15 The spirit behind this model o occupational choice closely resembles McFadden (1974). Although the McFadden

occupational choice model gives a description o preerence by an individual, it may not be ully justied since

the individuals choice may in reality be held in check by the demand side o the labor market (Bourguignon

and Ferreira 2005). A complete model must thereore include a mixture o both preferences and rationing. The

interpretation o this model must be taken with a grain o salt.16 Although we restrict our analysis to wage workers, our multinomial logit model allows or the possibility o

individuals being predicted to be sel-employed. Ater obtaining the counteractual occupations, those who were

predicted to be sel-employed were excluded in constructing the counteractual wages in Step 4, while those who

were predicted to be wage workers were included and their counteractual wages were computed.



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Step 3: Estimating the Impact o Trade on Industry Wage/Skill Premia

and Employment/Occupation Status

This step requires collecting the three sets of estimated coefcients from the previous two

stepsi.e., the industry wage premia (wpjt ) and the industry-specic skill premia(spjt ) from the rst step and the occupational constant terms in the multinomial logit

model (jt) from the second stepand regressing these on industry-specic and time-

varying measures of trade protection and other trade-related variables in addition to

various controls. The trade-related variables Tij( ) include industry-specic tariff rates,import-weighted exchange rates, and import penetration rates and exports as a proportion

of the value of domestic production:17

v T v wp spjt jt v ij jt jt jt jt= + = { } , ; ; 0

Step 4: Decomposing and Attributing Changes in Wage Inequality

The last step involves decomposing changes in the wage distribution over any two

years and determining the quantitative importance of the various trade-induced effects

in accountingfor the observed changes in wage inequality between them.18 The

decompositions used by FLW draw on the approach of Juhn, Murphy, and Pierce

(1993) whereby the difference between the wage distributions of any two years can

be decomposed into three components: (i) those due to changes in observed worker

characteristics X( ) ; (ii) those due to changes in the return to these characteristics (theregression coefcients ( ) ); and (iii) those due to changes in the distribution of theresiduals ( ) .

In particular, FLW construct six counterfactual wage distributions that are used to isolate

the effects of the different channels by which reductions in trade protection affect wage

inequality (either by inuencing some component of theXs ors).19 Consider 1994 and

2000 as the two years over which we would like to decompose and attribute changes in

inequality.

The rst counterfactual wage distribution (C1) is then estimated as:

17 Tari rates or nontradables, such as services, are set at zero. This is not problematic since, as will be made clear

later, what matters or the inequality decompositions that are carried out in this paper are changes in protection.

For the other trade-related variables such as import penetration and export shares, we likewise set their value to

zero or nontradables. This makes it unnecessary to deal with the issue o what an exchange rate or nontradables

means or would look like given that our specications introduce exchange rates only as in interaction with import

penetration rates and export shares.18 Is crucial to note that the decompositions do not inorm us about the causal relationships involved. The exercise

carried out here is an accounting decomposition.19 It may be noted that the results o the Juhn, Murphy, Pierce (1993) decompositions are sensitive to the precise

order in which the various counteractuals are carried out. There is no reason, however, to suspect that the results

would be qualitatively very dierent i a dierent ordering had been utilized.



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into the multinomial logit model in Step 2 so as to predict the counterfactual distribution

of occupations.20 This simulation captures the effect of trade-induced employment

reallocation on wages.

Another important channel through which wage dispersion may change is throughchanges in the economywide skill premium (as opposed to just trade induced industry-

specic skill premiums). These effects can be captured by a fourth counterfactual (C4):

( ) ( )4 94 00 94 00 194 94ln s s sij ij ij j ij ij j iw X I wp I S sp F = + + + (C4)

where sed ed

= { }00 94; ~ . In this simulation, the coefcients on all education dummies andindustry wage premiums and the industry skill premiums are replaced with their 2000

estimates. Doing this extends the price effect of trade liberalization to include changes

in the returns to education and to industry membership beyond those induced by changes

in trade variables as reected in Step 3. As FLW argue, this stimulation corresponds

to a more generous estimate of the price effects of trade liberalization, in which thefull changes in returns to education and industry membershiprather than only those

mandated by the second stageare included (Ferreira, Leite, and Wai-Poi 2007, 20).

The other two remaining counterfactual distributions account for changes that may have

been driven by other channels apart from trade reforms. The rst of these two, C(5),

represents changes in the structure of returns to observed characteristics other than that

of education and industry membership (for instance, sex, age, and region of employment,

etc.):

lnw X I wp I S sp F ij ij ij s

j ij

s

ij j i

5 94 00 00 94 00

94

1

94= + + ( ) + ( ) (C5)

The nal simulation, C(6), introduces the 2000 residuals consistent with a rank-preserving

transformation:21

lnw X I wp I S sp F ij ij ij s

j ij

s

ij j i

6 94 00 00 94 00

00

1

94= + + ( ) + ( )

(C6)

The difference between C(6) and the estimated equation for 2000 is:

lnw X I wp I S sp F ij ij ij j ij ij j i 00 00 00 00 00 00 00 00

00

1

00= + + ( ) + ( ))

20Workers whose predicted occupations are dierent rom their original 1994 occupations are allocated to specicindustries by random draws with probabilities derived rom the 2000 employment distribution.

21 A rank-preserving transormation is carried out by replacing the residual in the nth percentile (o residuals) at

time t by the residual in the nth percentile at time t. In our case our rank-preserving transormation involves an

approximate solution that assumes that both distribution o residual terms are the same up to a proportional

transormation (e.g., when residuals are normally distributed with mean zero). Thus, it is equivalent to multiplying

the residual observed at time t by the ratio o standard deviations at time t and t. Thus, the residuals are estimated

as F Fi i001

94

00

94 94

1

94

( ) =

( )

. See Bourguignon and Ferreira (2005).



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Turning to the results of the multinomial logit model estimated in Step 2, these show

some familiar (if not unsurprising) results (Tables 6a and 6b). College-educated (or

skilled) workers tend to be employed in permanent jobs in manufacturing (in 2000) and

nontradables (both 1994 and 2000). Furthermore, more males seem to be entering into

the labor force as reected by the increasing coefcients of the male dummy on all thebroad industrial categories. Finally, those with longer work experience do not tend to be

employed as casual workers, suggesting that rms test workers who are early in their

career by offering them shorter contracts.

As explained in the previous section, in Step 3 we regress separately the pooled

industry wage premiums, industry skill premiums and the multinomial logit constants

on a vector of trade variables. Results are provided in Tables 7, 8, and 9, respectively.

While specications 16 in Tables 7, 8, and 9 do not control for the effects of time,

specications 712 do so by including year xed effects while those in 1318 include a

time trend instead of year xed effects. All specications in the industry wage premium

and industry skill premium regressions include industry xed effects to control for timeinvariant industry-specic characteristics.

An examination of the regression results for the industry wage premiums (Table 7)

shows that the specications without year xed effects yield a positive and statistically

signicant relationship between tariff movements and the movement in industry wage

premiums. In other words, declines in tariff reductions are associated with declines in

industry wage premium in these specications. For instance, a 10 percentage point

decline in average tariffs will translate into at most a little over a 5 point decline in

average industry wage premiums (i.e., from an industry wage premium of, say, 0.40 to

0.35). Interestingly, an increase in export shares is associated with a decline in industry

wage premiums. Finally, a currency appreciationas measured by the increase inimport-weighted industry specic exchange rates (interacted with either lagged import

penetration or export shares)decreases the industry wage premium, although this effect

is not statistically signicant in all specications. This nding is consistent with a scenario

whereby an industry-specic appreciation of the peso and/or larger import penetration

leads to a decline in the wage premium of the affected industry due to a decline in the

competitiveness of the sector. For the industry skill premium regressions, we nd that

tariff declines are associated with increases in the industry skill premium, especially in

industries with lowerimport penetration (Table 8). The latter can be inferred from the

positive and statistically signicant interaction term involving tariffs and lagged import

penetration. As in the case of the industry wage premiums, the effects of tariffs become

statistically insignicant once year xed effects are introduced.



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Table8:IndustrySkillPremiumTradeExposureRegression

DependentVariable:

Industry-SkillPremium

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Tari

-0.0

0011

-0.0

091**

-0.0

0856**

-0.0

0896**

-0.0

0792**

-0.0

0917**

-0.0

0538

-0.0

0956

-0.0

098

-0.0

0897

Tari*LaggedImportP

enetration

0.0

0104***

0.0

0109***

LaggedImportPenetration

0.0

2267**

0.0

2646***

LaggedExports/ValueoDomestic

Production

-0.0

9041

0.0

0009

LaggedImportPenetration*Import

WeightedRER

0.0

0126


Production*ImportWe

ightedRER

-0.0

3658

Constant

0.1

2529**

0.2

037***

0.1

8872***0.2

4279***0.1

8934***0.24312***

0.3

8416**

0.1

205

0.10

581

0.1

2025

YearDummy

No

No

No

No

No

No

Yes

Yes

Yes

Yes

TimeTrend

No

No

No

No

No

No

No

No

No

No

IndustryFixedEects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

104

78

78

78

78

78

104

78

78

78

R-squared

0

0.1

7

0.1

6

0.1

5

0.1

4

0.1

6

0.2

5

0.3

3

0.3

3

0.2

9

(11)

(12)

(13)

(14)

(15)

(16)

(17)

(18)

Tari

-0.0

1042

-0.0

0904

-0.0

1067**

-0.0

1668***

-0.0

1679***

-0.0

1582***

-0.0

1715***

-0.0

1566***

Tari*LaggedImportP

enetration

0.0

0098***


0.0

2528***


Production

-0.0

4332


WeightedRER

0.0

0263

0.0

0296


Production*ImportWe

ightedRER

-0.0111

-0.0

1949

Constant

0.1

0251

0.13547

0.5

0989***

0.5

2567***

0.5

3162***

0.5

3***

0.5

7086***

0.5

1895***

YearDummy

Yes

Yes

No

No

No

No

No

No

TimeTrend

No

No

Yes

Yes

Yes

Yes

Yes

Yes

IndustryFixedEects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

78

78

104

78

78

78

78

78

R-squared

0.3

1

0.2

9

0.1

1

0.2

5

0.2

5

0.2

2

0.24

0.2

2

*signicantat10%;**sig

nicantat5%;***signicantat1%.

Robusttstatisticsinbrackets.



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Table9:IndustryParticipation(MultinomialLogitConstant)andTradeExposureRegression

DependentVariable:

Multinomial

LogitConstants

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Tari

0.2

7191***0.5

8629***

0.4

4596***0.4

0982***0.3

6274***0.41656***

0.0

1144

0.0

2044

0.03

633

0.0

3967

Tari*LaggedImportP

enetration

-0.0

3544***

0.0

0248


-

0.3

5607***

0.0

033


Production

0.7

5209

0.0

6125


WeightedRER

-0.0

2306***


Production*ImportWe

ightedRER

0.1

9027*

Constant

-12.3

276***-13.9

4479***-1

3.1

1778***-13.7

0958***-12.7

1233***-13.78823***-12.4

9636***-12.5

6085***-12.5

9946***-12.6

3643***

YearDummy

No

No

No

No

No

No

Yes

Yes

Yes

Yes

TimeTrend

No

No

No

No

No

No

No

No

No

No

BroadIndustryFixedEects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

36

27

27

27

27

27

36

27

27

27

R-squared

0.5

6

0.6

7

0.6

6

0.6

5

0.6

8

0.6

5

0.9

8

0.9

8

0.9

8

0.9

8

(11)

(12)

(13)

(14)

(15)

(16)

(17

)

(18)

Tari

0.0

3632

0.03949

0.0

4877

0.0

3578

0.0

812

0.1

902***

0.1

4505**

0.1

9842***

Tari*LaggedImportP

enetration

0.0

1618


0.2

3503


Production

1.7

5093***


WeightedRER

0.0

0103

0.0

0354


Production*ImportWe

ightedRER

0.01125

0.3

8429***

Constant

-12.6

2683***

-12.6

308

9***

-7.1

5226***

-6.8

2355***

-7.1

009***

-7.9

7409***

-7.5

2597***

-8.0

8525***

YearDummy

Yes

Yes

No

No

No

No

No

No

TimeTrend

No

No

Yes

Yes

Yes

Yes

Yes

Yes

BroadIndustryFixedEects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

27

27

36

27

27

27

27

27

R-squared

0.9

8

0.9

8

0.8

5

0.8

6

0.8

7

0.8

9

0.86

0.8

9

Robusttstatisticsinbrackets

*signicantat10%;**

signicantat5%;***signicantat1%

Robusttstatisticsinbrackets

*signicantat10%;**sig

nicantat5%;***signicantat1%



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The regressions involving the multinomial logit constants (i.e., those capturing

employment/occupation status) yield some interesting results (Table 9). First, the positive

and signicant relationship between the constants and tariffs suggests that industries

that experienced larger declines in protection experienced reductions in employment

(though this effect is moderated by larger levels of import penetration in one outof the three specications that introduces an interaction between tariffs and import

penetrationi.e., in specication 2). In other words, workers in the hardest hit industries

(presumably the unskilled, labor-intensive ones) seem to have been reallocated toward

other (more protected) industries such as services. This explanation is quite plausible,

since by looking back at Table 2 we can see that the sector with the largest increases in

employment are in the services sectori.e., industries that are nontraded. Also equally

interesting is the relationship between conditional employment and exports: the positive

sign on this relationship suggests that those industries that exported more hired more

workers.

In summary, we see from the three trade exposure regressions that the fall in tariffrates has tended to exert downward pressure on industry wage premiums and induce

employment to reallocate away from the industries that experienced a heavier tariff

decline and/or from those industries that did not export more. Moreover, greater

competition from imports and an appreciating currency has also put downward pressure

on industry wage premiums. In contrast, the fall in tariff rates has shown some tendency

to raise industry skill premiums, especially in industries with low levels of import

penetration. The combined quantitative importance of these effects on wage inequality is

unclear, however, without further analysis. To get a sense of this, we turn to the analysis

of wage decompositions.

B. Wage Decompositions (Step 4)

In Step 4 we use the results of the trade exposure regressions to construct counterfactual

wage distributions to determine the effects of trade-induced changes on wage inequality.

In constructing these decompositions, we use the estimated coefcients on tariffs from the

specications that include time-trends and yield the largest (and statistically signicant)

impact of tariffs.23 In other words, we are allowing trade to have its largest possible

impact on wage inequality (within the context of the approach we are using).

Table 10 reports four inequality measures for both 1994 and 2000 (actual wage

distributions) and also for the six counterfactual wage distributions (simulated). In

addition, we also show different wage growth incidence curves corresponding to 1994

2000 and the various counterfactual wage distributions. (The growth incidence curves

show the growth in wages at different statistical percentiles of the wage distribution for

any two wage distributions.)

23 The results hardly change when we use coecients rom the alternative specications.



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Table 10: Wage Inequality Indicators, Actual (1994 and 2000) and Counteractuals

P90/P10 GE(0) GE(1) Gini

1994 6.315 0.230 0.207 0.355

C(1) 6.569 0.233 0.209 0.357

C(2) 6.616 0.236 0.213 0.360C(3) 6.552 0.243 0.224 0.366

C(4) 7.313 0.270 0.250 0.387

C(5) 7.351 0.271 0.250 0.387

C(6) 7.796 0.287 0.264 0.396

2000 7.616 0.294 0.295 0.408

To determine the effect of trade liberalization on changes in the wage distribution

through the industry wage premium channel, we compare the actual change in the wage

distribution between 1994 and 2000as depicted by the growth incidence curve G(94-

00) in Figure 5with the change in the wage distribution between 1994 and the rst

counterfactual wage distributionas depicted by the growth incidence curve G(94-C1)in Figure 5. Recall from the previous section that that the rst counterfactual wage

distribution C(1) allows us to capture the change to the 1994 wage distribution resulting

from trade-induced changes in industry wage premiums.

Figure 5: Wage Growth Incidence Curves I: Actual and Counteractual, 1994 and 2000

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

G(94-00) G(94-C1) G(C1-C2)

90

70

60

50

40

30

20

10

0

-10

Wage Percentile

PercentGrowth

As can be seen quite clearly from Figure 5 and the rst two rows of Table 10, the industry

wage premium channel exerts a negligible effect on the actual changes in the wage

distribution registered between 1994 and 2000. Most inequality measures hardly move

across the rst two rows of Table 10 and the growth incidence curve G(94-C1) lies very

close to the horizontal axis, depicting an insignicant change in wages from their 1994

values. Thus, the industry wage premium channel is economically insignicant in terms



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of contributing to changes in inequality despite the positive and statistically signicant

relationship between trade protection and industry wage premiums seen in Table 7.

The situation is similar for the industry skill premium channel. This can be seen by

comparing the inequality measures across rows 2 and 3 in Table 10 and the growthincidence curve G(C1-C2). As with G(94-C1), this lies close to the x-axis.

The decomposition results so far suggest that declines in tariff rates did not affect the

wage distribution through the industry wage or industry-specic premium channels.

However, trade liberalization may have affected the wage distribution through other

channels. The counterfactual wage distribution C(3) incorporates the inuence of trade

liberalization induced employment reallocation effects on the wage distribution (in

addition to the trade liberalization induced effects on industry wage and skill premiums).

Figure 6 shows the wage growth incidence curve that results from a comparison of the

counterfactual distributions C(2) and C(3) (i.e., G(C2-C3)). For purposes of comparison,

the gure also shows the growth incidence curve for the actual 1994 and 2000distributions (i.e., G(94-00)). The G(C2-C3) is closer to the actual 19942000 growth

incidence curve, save for those workers with wages above the 80 th percentile level. There

is also a more noticeable change in the four inequality measures across rows 3 and 4 in

Table 10.

Figure 6: Wage Growth Incidence Curves II: Actual and Counteractual, 1994 and 2000

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

G(94-00) G(C2-C3) G(C3-C4)

80

70

60

50

40

30

20

10

0

-10

-20

Wage Percentile

PercentGrow

th

Taken at face value, the results suggest that trade liberalization has had a largerimpact on the wage distribution through employment reallocation effects than either

the industry wage and industry skill premium effects combined. Thus one way in which

trade liberalization may have increased wage inequality is by precipitating the movement

of workers from agriculture and manufacturing (i.e., tradables) toward services (i.e.,

nontradables). As we have seen from Table 2 earlier, the services sector is characterized

by greater inequality in wages than either agriculture or manufacturing.



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While the employment reallocation effects are certainly not large enough to approximate

the actual increases in wage inequality between 1994 and 2000 (compare, for example,

the Gini coefcients for the 1994, C(3), and 2000 wage distribution in Table 10) they

are not trivial either. Nevertheless, the biggest, most conspicuous jump in the inequality

measures among all counterfactuals considered so far happens with C(4)thecounterfactual wage distribution that takes into account changes in the economywide

returns to education and industry membership. Inequality is clearly higher for this

counterfactual distribution compared to C(3). Compare, for example, the Gini coefcients

reported and Table 10 for these two distributions: 36% versus 39%. This may also be

seen by examining the upward-sloping growth incidence curve G(C3-C4) in Figure 6. This

result may seem puzzling since the wage equations in Table 4 show that the returns to

tertiary education fell between 1994 and 2000 thereby suggesting slow growth in wages

in the upper part of the distribution. However, this counterfactual also incorporates the

effects of changes in industry membership including changes in industry-specic skill

premiums not captured by the reduction in tariff rates as calculated in Step 3. It also

incorporates the effects of rising returns to primary and secondary education between1994 and 2000 relative to uneducated workerstypically the ones with the lowest wages.

Thus while we can expect some erosion of wage growth of skilled workers because

of the drop in the economywide returns to tertiary education, this seems to have been

offset by nontrade-related changes in the industry skill premium so that these relatively

higher-earning workers beneted from increasing industry-specic returns to education.24

Indeed, the sharp increase in the skill premium for nontradables (Table 5) certainly

points to this.25 Overall, the results show us that changes in the economywide returns

to education, combined with (possibly) nontrade-induced changes in industry-specic

returns, have been inequality-increasing.

The remaining results from the last two counterfactuals take into account changes inthe structure of returns to observed characteristics other than education and industry

membership, C(5), and the 2000 residuals, C(6). The corresponding growth incidence

curves and inequality estimates are described in Figure 7 and Figure 8 and rows 6 and

7 of Table 10, respectively. An examination of the various inequality measures indicates

that the move from C(4) to C(5) leaves inequality essentially unchanged. However,

incorporating the 2000 residuals is clearly inequality-increasing, leading to an increase

in the Gini coefcient by around 1 point. Increases in inequality of a similar magnitude

take place (at least in terms of the Gini coefcient) in moving from C(6) to the actual 2000

distribution.26

24 Recall rom columns 1 and 2 o Table 4 that i we run the Mincerian regressions without controlling or industry-

skill eects, the returns to tertiary education actually increased.25 The increase in the skill premium may be due partly to a very rapid pace o labor productivity growth during the

period 1994 to 2000 (Felipe and Sipin 2004). This growth was particularly infuenced by quality upgrading among

Philippine industries brought about by both trade (e.g., lower capital importation costs) and nontrade actors (e.g.,

fexible compensation schemes or managers).26 This involves introducing the 2000 characteristics or all observables (other than the employment/occupational

changes induced by trade and already incorporated) and accounting or the changes between observables and

the 2000 residuals.



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The results from executing Step 4 for analyzing changes in wage inequality over 2000

and 2006 are similar in that reductions in tariffs have a mild inuence on wage inequality.

But beyond this there are some differences in results. First, in contrast to the ndings

above for 19942000 and 19881994, employment reallocation effects work to reduce

wage inequality (row 4 versus either row 3 or row 1 in the second panel of Table 11).Second, there are two nontrade-related counterfactuals that generate large changes in

inequality. The rst arises from changes in the economywide skill premium and industry

membership beyond trade and works to reduce inequality (row 5 versus row 4). The

second arises from changes in observable worker characteristics, i.e., moving from 2000

values of the Xs to the 2006 values, and work to increase inequality (row 8 versus row 7).

It is difcult to be sure about what is driving these changes. As noted earlier, wages for

2006 seem to be unreasonably low compared to those for 2000.

Table 11: Wage Inequality Indicators, Actual and Counteractuals

19881994


1988 6.983 0.251 0.224 0.371

C(1) 7.354 0.257 0.228 0.373

C(2) 7.406 0.259 0.229 0.375

C(3) 7.242 0.262 0.236 0.378

C(4) 6.951 0.250 0.224 0.368

C(5) 6.530 0.239 0.216 0.362

C(6) 6.497 0.238 0.215 0.361

1994 6.315 0.230 0.207 0.355

20002006


2000 7.616 0.294 0.295 0.408

g1 8.036 0.300 0.297 0.411g2 8.037 0.300 0.297 0.412

g3 6.459 0.268 0.266 0.395

g4 5.464 0.223 0.222 0.360

g5 5.656 0.226 0.226 0.364

g6 4.463 0.166 0.166 0.316

2006 6.158 0.222 0.211 0.356

V. Conclusion

This paper analyzed the role of trade liberalization in inuencing changes in wageinequality in the Philippines between 1994 and 2000. Tariff rates declined considerably

between these 2 years while both exports and imports rose sharply. Unlike the post-2000

period, FDI and/or outsourcing of services to the Philippines did not expand in a big way.

Thus, trade liberalization represented the main channel through which the Philippines

experienced globalization. In the meantime, data from labor force surveys reveal that



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wage inequality increased considerably. In particular, the Gini coefcient over hourly

wages increased from 35.5% to 40.8% between 1994 and 2000.

While these two sets of factsi.e., increasing openness to trade and increasing

inequalityare consistent with a growing body of literature that has found tradeliberalization to lead to increases in inequality, the analysis of this paper nds little

evidence to suggest that trade liberalization had an important role to play in increasing

inequality in the Philippines. Using the approach of Ferreira, Leite, and Wai-Poi (2007),

this paper nds trade-induced effects on industry wage premia, industry-specic skill

premia, and employment reallocation to account for slightly less than 17% the total

increase in the Gini coefcient between 1994 and 2000. Interestingly, the effects of

trade on industry wage premia and industry-specic skill premia are found to account

for very little of the increases in wage inequality. The bulk of trade-induced increases in

inequalityalmost three fourths in the case of the Gini coefcientare captured by the

employment reallocation effects of trade. In particular, reductions in protection appear to

have led to a shift of employment to more protected sectors, especially services wherewage inequality tends to be high to begin with and increased still further.

A much more important driver of wage inequality appears to be changes in economywide

returns to education and changes in industry membership over and above those

accounted for by our estimates of trade-induced employment reallocation effects. Of

course, we cannot discount the possibility that both factors are somehow linked to trade

liberalization.

These ndings suggest several areas for future work. First, a deeper understanding

of how trade liberalization, or for that matter any major change in economic policy,

inuences employment opportunities across sectors is required. Second, understandingthe drivers of inequality in the services sector requires some attention. Finally,

understanding the connections between economywide changes in the returns to

education and trade liberalization is needed.



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