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Global Social Sciences Review (GSSR)
Vol. IV, No. I (Winter 2019) | Page: 128 – 141
Impact of Trade Liberalization on the Industry Wages in
Pakistan (1995 – 2015)
Umer Khalid PhD Scholar, School of Economics, Quaid-i-Azam University,
Islamabad, Pakistan. Email: umerkhalid@hotmail.com
The paper estimates the effects of trade reforms on workers’ earnings
in Pakistan’s manufacturing sector during 1995-2015, employing data
from 14 rounds of the Pakistan Labour Force Survey. OLS technique has been used for
estimation and separate analysis for workers engaged in informal manufacturing
activities is also undertaken. The results indicate that a tariff fall on intermediate products
is associated with a rise in real earnings of workers employed in the manufacturing sector
during this period, while a corresponding decline in tariffs on final goods has no effect
on worker’s wages. The results show that real wages of
workers employed in the mainly export oriented
industries of food, beverages & tobacco, textiles, apparel
& leather and non-metallic mineral industries have
declined over the twenty years period of trade reforms
implemented in Pakistan. On the other hand, real wages
are observed to have increased in the chemical &
petroleum and basic metals industries.
. .
Introduction
Trade liberalization has been shown to have increased growth, productivity and
efficiency across the developing economies (Busse & Koniger, 2012). Subsequent
research has explored the issue of trade reforms on labour markets in developing
economies. The Stolper-Samuelson Theorem (1941) stipulates that developed
countries produce skill intensive products, whereas developing countries produce
labour intensive goods, offers clear theoretical predictions about the influence of
trade reforms on worker’s earnings around developing countries. The linkages
between trade liberalization and wages have been examined by numerous studies
encompassing both the developed and developing countries. This strand of
literature has mainly made use of the industry wage premium methodology
introduced by Krueger and Summers (1988) and mostly covers Latin American
countries, which pursued trade liberalization policies relatively earlier in the
1980s.
The findings of a large section of this body of empirical evidence contradict
the a priori expectations of the Stolper-Samuelson theorem, as they show that
trade liberalization has widened wage-gap among unskilled and skilled workers
[Feliciano (2001), Galiani and Sanguinetti (2003), Pavcnik et. al (2004), Pavcnik
and Goldberg (2005), Harrison and Hanson (1999), Revenga (1997), and
Robertson (2005)]. However, some studies [Kumar and Mishra (2007), Galiani
and Porto (2010) and Amiti and Cameron (2012)] find that trade reforms resulted
in a reduction in the skilled-unskilled wage-gap within the manufacturing
Abstract
Key Words
Trade Liberalization,
Wages, Input/ Output
Tariffs
p
-ISSN 2520-0348
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e-ISSN 2616-793X
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L-ISSN 2616-793X
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DOI:
10.31703/gssr.2019(IV
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I).
12
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URL:
http://dx.doi.org/10.31703/gssr.2019(IV
-
I).
12
Impact of Trade Liberalization on the Industry Wages in Pakistan (1995 – 2015)
Vol. IV, No. I (Winter 2019) 129
industries in India, Argentina and Indonesia, respectively. In case of Thailand,
Jayanthakumaran et al. (2013) observe increase in wage premiums due to a fall in tariffs
on final goods, while a decline in tariffs of intermediate goods exerts a stronger positive
effect on wage premiums.
The present study offers newer perspectives on distributional consequences of trade
liberalization on wages, in respect of a developing country – Pakistan, which has
implemented wide ranging trade liberalization reforms since 1990s. These trade reforms
encompassed not only reduction in tariffs but also focused on lowering non-tariff barriers,
like import quotas and import surcharges (Liaqat 2013). Pakistan significantly liberalized
its tariffs, resulting in the peak tariff rate falling from 225 percent in 1987 to 65 percent by
1996, which was subsequently brought down to 25 percent in 2002. The unweighted
average tariff rate went down from 61.1 percent in 1992 to 42 percent in 1996, which slid
down to 17.3 percent by 2002 (Pursell, Khan & Gulzar, 2011).
Pakistan initiated trade reforms under the framework of an IMF sponsored structural
adjustment program that the country entered into due to severe balance of payments crisis.
Since this program of trade reforms was exogenous, it can be used as a natural experiment
to investigate the influence of trade opening on the labour market. The present study
extends the existing literature in a number of ways. It focuses on both the formal and
informal segments of Pakistan’s manufacturing sector, as previous research has mainly
examined the impact of trade liberalization on formal segments. Moreover, it also examines
the effect of trade liberalization separately for workers engaged in informal sector
employment. In addition, the study explores the effect of fall in both tariffs on final goods
and those on intermediate products to examine overall effect on wage earnings. The study
employs a large sample of pooled worker level data from 14 rounds of the Labour Force
Survey over a twenty-year period to determine trade liberalization’s impact on real
earnings of workers in the country’s manufacturing sector.
The paper is comprised of six sections. Section 2 presents empirical methodology,
while the data used, and construction of variables is discussed in Section 3. The findings
of the empirical analysis are presented in Section 4. Concluding remarks are given in
Section 5, while the last section provides policy implications.
Empirical Specification
The analysis of industry-level trade liberalization on real wages is started off by estimating
a basic model, based on the human capital literature (Mincer, 1958, & Becker, 1962). This
model which includes human capital characteristics, such as education and experience
(proxied by age), worker characteristics, such as marital status and gender and educational
attainment and indicators of broad manufacturing industrial sector, is given as:
where real wagei, the logarithm of yearly real wages is the dependent variable. Agei and
agei2 are proxies for experience and experience square, respectively ( As age and
experience are highly correlated, the former can be used as a suitable proxy for the latter).
Dummy variables are included for marital status, gender and various levels of educational
attainment as well as for technical training received. A dummy variable for informal sector
employment accounts for the impact of wages if a worker is employed in informal sector.
Umer Khalid
130 Global Social Sciences Review (GSSR)
S represents dummies for the level of education while the manufacturing industry dummies
are represented by I. Manufacturing dummies are added to capture the industry wise
variations within the manufacturing sector.
Using Ordinary Least Squares (OLS) method, the basic equation for pooled sample
comprising of data from 14 rounds of the LFS is estimated to highlight the important
determinants of wages in the manufacturing sector during selected time period. This serves
as a baseline for the subsequent estimation of the impact of trade liberalization on real
wages.
Following this, the basic model (eq. 1) is extended for modeling influence of trade
liberalization on wages through controlling for year fixed effects and including interaction
term of a manufacturing industry with its respective tariff rates. The industry-wise tariff
rates (output and input tariffs) indicate industry-specific trade liberalization, while the year
fixed effects capture the economy-wide effect of a specific time period on wages. This
extended model is represented as:
Where real wageit shows the wage for the ith cross-sectional unit at time t and
is the interaction term of a particular manufacturing industry and tariff rate. Yt represents
the time dummies for the 14 years (year 1994-95 is taken as the base category). All other
variables are the same as used in model 1.
Data and Variables
The study utilizes micro data from 14 rounds of the Pakistan Labour Force Survey (LFS),
conducted over the period 1994-95 to 2014-15, encompassing the period of trade
liberalization reforms undertaken in Pakistan. The use of this long-term series of
employment data over a period of 20 years helps in carrying out a robust analysis of the
trade reforms effect on wages in the manufacturing sector of Pakistan. The LFS captures
employment at two-digit Pakistan Standard Industrial Classification (PSIC), although more
recent rounds of LFS have employment information available at the four-digit PSIC level.
To make the definition of industry employment consistent over this 20 year period, the
study uses employment at the two-digit PSIC level. In each of the 14 rounds of the LFS,
only the sample of workers engaged in different forms of paid employment in the 9 two-
digit industries of the manufacturing sector have been used, as LFS only reports wages for
paid employees. The sample of workers has been restricted to the age group of 15-65 years
as per the international definition of working age population.
In line with the existing literature, the outcomes of trade liberalization on wages in the
manufacturing sector are analyzed using two measures of tariffs – output tariff and input
tariffs. The output tariff represents the tariffs on final goods, as shown in the country’s
tariff schedule; while input tariffs show the tariffs applicable on intermediate goods/ raw
materials. Both these tariff measures have been defined at two-digit PSIC industry level,
for which tariff data during this period classified under the Harmonized System has been
converted into the corresponding 9 two-digit industries using the concordance developed
by Sarwar (2016).
Impact of Trade Liberalization on the Industry Wages in Pakistan (1995 – 2015)
Vol. IV, No. I (Winter 2019) 131
The output tariff measure, representing tariff on final produced goods, is the trade
weighted average of the two-digit HS tariff lines falling under each of the nine
manufacturing industries has been obtained from the United Nations Conference on Trade
and Development’s (UNCTAD) Trade Analysis Information System (TRAINS) database.
The input tariffs represent a weighted mean of output tariffs, with the import shares of raw
materials by each two-digit PSIC industry taken as weights. This import share is obtained
from the Census of Manufacturing Industries (CMI) data. (The import shares of raw
material at firm level, across the two-digit PSIC industries in the manufacturing sector,
have been obtained from the 2000-01 round of the CMI. As other rounds of CMI do not
give this information, it is assumed that the import shares stay constant over the period of
our analysis, i.e., 1994-2015.)
Table 1 presents the variables used in our analysis. As the wage data obtained from
different rounds of the LFS is time series data, it has been adjusted for inflation. The
nominal wage data from different rounds of LFS has been adjusted for inflation using GDP
deflator, with 2014-15 used as base year to deflate the wage variable.
The summary statistics of the pooled dataset, comprising of 14 rounds of LFS,
employed in the regression analysis is shown in table 2.Table 2 also gives the summary
statistics of the sample of the workers involved in informal sector employment across the
different industries of Pakistan’s manufacturing sector during the period of analysis. A
comparison of the full sample with the sample of informal sector workers shows a slightly
lower mean age of workers in informal employment. A higher proportion of informal sector
workers had no formal education/ less than primary level of education compared to the full
sample (61 percent vs. 58 percent), while surprisingly a higher share of informal sector
workers had degree and above educational attainment (10 percent vs. 7 percent) and had
obtained technical training (30 percent vs. 23 percent).
The trends in output and input tariffs over the period under review are presented in
table 3. The analysis indicates that towards start of the trade reforms (1995), most of the
manufacturing industries were operating behind high levels of tariff protection, with tariffs
being highest for the non-metallic mineral products; food, beverages & tobacco and wood,
wood products & furniture industries. As a result of the subsequent trade liberalization
reforms, both output and input tariff rates have declined considerably over time across the
nine two-digit PSIC industries. The decline in tariff rates has been greater during the 1995-
2005 period, while tariffs in some industries have gone up slightly during 2010-15.
Table 1. Variables used for Examining Impact of Trade Liberalization on Wages
Variable
Description
Dependent variables
Log real wages (two-digitPSIC level)
Log of annual wages (in Rupees) divided by
the GDP deflator
Independent/ explanatory variables
Worker characteristics
Age
Age of worker (proxy for experience)
Age Squared
Square of age
Gender
=1 if male, 0 otherwise
Umer Khalid
132 Global Social Sciences Review (GSSR)
Never married
=1 if never married, 0 otherwise
Currently Married
=1 if currently married, 0 otherwise
Widow/ divorced
=1 if widowed/ divorced, 0 otherwise
Education
No formal education/ below primary
=1 if no formal education/ education below
primary level, 0 otherwise
Middle
=1 if primary to middle level education, 0
otherwise
Secondary
=1 if above middle and upto intermediate, 0
otherwise
Degree and above
=1 if education of bachelor’s degree and
above, 0 otherwise
Technical training
=1 if worker has acquired technical training,
0 otherwise
Informal employment
=1 if working in informal sector enterprise, 0
otherwise
Industrial dummies
Industry 1
=1 if employed in Food, Beverages &
Tobacco, 0 otherwise
Industry 2
=1if employed in Textile, Wearing Apparel
and Leather, 0 otherwise
Industry 3
=1 if employed wood and wood products, 0
otherwise
Industry 4
=1 if employed in paper and paper products,
printing and publishing, 0 otherwise
Industry 5
=1 if employed in chemicals, petroleum,
coal, rubber & plastic, 0 otherwise
Industry 6
=1 if employed in non-metallic mineral
products, 0 otherwise
Industry 7
=1 if employed in basic metal industries, 0
otherwise
Industry 8
=1 if employed in fabricated metal products,
machinery & equipment, 0 otherwise
Industry 9
=1 if employed in other manufacturing
industries and handicrafts (reference
category), 0 otherwise
Tariffs
Output tariff rate
Weighted applied tariff rates for industries at
two-digitPSIC level
Input tariff rate
Output tariff rates weighted by share of
imported inputs for industries at two digit
PSIC level
Sana Tariq and Bahramand Shah
133 Global Social Sciences Review (GSSR)
Table 2. Summary Statistics of Dataset used for Examining Impact of Trade
Liberalization on Wages
Variables
Full Sample
Informal
Workers
Sample
Dependent variables
Log of annual real wages (two-digit PSIC level)
11.670
11.407
(0.766)
(0.780)
Independent variables
Age
30.595
29.237
(11.629)
(11.462)
Age squared
1071.304
986.210
(828.564)
(801.604)
Gender
0.888
0.800
(0.315)
(0.399)
Marital status
Unmarried
0.421
0.458
(0.494)
(0.498)
Married
0.561
0.518
(0.496)
(0.499)
Widow/Divorced
0.018
0.022
(0.133)
(0.149)
Educational Status
No formal education
0.575
0.697
(0.494)
(0.459)
Middle
0.152
0.151
(0.359)
(0.358)
Secondary
0.204
0.140
(0.103)
(0.347)
Degree & above
0.069
0.010
(0.254)
(0.102)
Technical Training
0.230
0.298
(0.421)
(0.457)
Informal Employment
0.446
-
(0.497)
Industrial dummies
Industry 1
0.116
0.103
(0.321)
(0.305)
Industry 2
0.476
0.497
(0.500)
(0.500)
Industry 3
0.040
0.074
(0.196)
(0.261)
Industry 4
0.027
0.026
(0.164)
(0.161)
Industry 5
0.058
0.022
Umer Khalid
134 Global Social Sciences Review (GSSR)
Variables
Full Sample
Informal
Workers
Sample
(0.234)
(0.147)
Industry 6
0.104
0.072
(0.305)
(0.259)
Industry 7
0.021
0.009
(0.142)
(0.096)
Industry 8
0.090
0.097
(0.286)
(0.296)
Industry 9
0.068
0.096
(0.252)
(0.295)
Tariff
Output tariff
18.961
17.853
(9.770)
(7.715)
Input tariff
2.382
2.084
(2.345)
(1.932)
Number of observation
58,003
25,896
Mean in top row
Standard deviation in parenthesis
Table 3. Trends in Industrial Tariff Rates (%)
Tariff Rates
1995
2000
2005
2010
2015
Industry 1
Output
62.49
28.32
22.22
26.71
20.32
Input
6.73
3.05
2.39
2.88
2.19
Industry 2
Output
49.36
25.32
14.90
13.91
14.31
Input
3.68
1.89
1.11
1.04
1.07
Industry 3
Output
57.19
26.15
15.02
13.65
12.72
Input
1.27
0.58
0.33
0.30
0.28
Industry 4
Output
46.61
17.16
12.63
11.25
11.68
Input
11.40
4.20
3.09
2.75
2.86
Industry 5
Output
45.37
19.87
11.80
10.42
10.51
Input
19.65
8.60
5.11
4.51
4.55
Industry 6
Output
67.52
32.99
21.39
24.42
21.55
Input
3.79
1.85
1.20
1.37
1.21
Industry 7
Output
38.53
17.45
9.25
7.56
8.07
Input
11.77
5.33
2.83
2.31
2.46
Industry 8
Output
45.82
28.53
13.90
14.22
13.56
Input
17.80
11.09
5.40
5.52
5.27
Industry 9
Output
54.62
27.77
15.55
15.73
15.13
Input
7.56
3.85
2.15
2.18
2.10
Impact of Trade Liberalization on the Industry Wages in Pakistan (1995 – 2015)
Vol. IV, No. I (Winter 2019) 135
Results
The table 4 reported the OLS estimates of the basic model, eq. (1). According to the
estimation results age has a positive relationship with real wages during the period under
review, with wages of workers increasing with age at a decreasing rate. Male workers earn
more than their female counterparts. The analysis by marital status shows that in
comparison to unmarried workers, their married counterparts have higher earnings, while
widowed/ divorced workers, on average, have lower wages.
The findings with respect to educational attainment show that in comparison to
workers with no formal education/ education below primary level, the wages of workers
increase monotonically across the subsequent three education levels – middle, secondary
and degree and above, with the returns to education being highest for workers with
educational level of degree and above. Workers with technical training are observed to
have significantly higher earnings, while workers engaged in informal sector employment
earn lower than their counterparts employed in formal sector.
There is considerable variation observed in wages across the nine manufacturing
industries, over the sample period, in comparison to wage levels in ‘other manufacturing
industries’ which is the reference category. Real wages of workers in four industries – food
& beverages, textile & apparel, paper & publishing and non-metallic mineral products are
observed to have declined over the 20-year period in comparison to the base category; with
this finding being statistically significant. On the other hand, real wages in the remaining
four industries increased during the period under review, although the finding for fabricated
metals and equipment is not statistically significant.
Table 4. Regression Results of Basic Model for Pooled Sample, 1994-95 to 2014-15
Variables
Coefficient
Age
0.045***
(0.002)
Age Squared
-0.0005***
(0.000)
Gender
0.757***
(0.015)
Marital status
Married
0.018**
(0.009)
Widow/Divorced
-0.060***
(0.023)
Educational Status
Middle
0.102***
(0.007)
Secondary
0.210***
(0.007)
Degree & above
0.855***
(0.018)
Technical Training
0.095***
(0.009)
Umer Khalid
136 Global Social Sciences Review (GSSR)
Informal Employment
-0.191***
(0.007)
Industrial dummies
Industry_1
-0.093***
(0.015)
Industry _2
-0.071***
(0.013)
Industry _3
0.007
(0.021)
Industry _4
-0.051***
(0.020)
Industry _5
0.038**
(0.017)
Industry _6
-0.047***
(0.016)
Industry _7
0.127***
(0.028)
Industry _8
0.002
(0.016)
Constant
10.384***
(0.079)
R-squared
0.3762
Province x time dummies
Yes
Number of observations
58,003
Robust standard errors in parenthesis.
***, **, * show significance at 1 %, 5 % and 10 % respectively.
The results of eq. (2) showing the effects of trade reforms on real earnings, estimated using
OLS are presented in table 5. Since micro level data from LFS is obtained from cluster
sampling, the standard errors are corrected for clustering at the primary sampling unit
(PSU) level in the two models shown in table 5.
The results indicate that a reduction in output tariffs leads to a fall in real wages, while
a decline in input tariffs results in a rise in the real wages; with only the results with respect
to input tariffs being statistically significant. The effect on real wages is observed to vary
across the different industries, with workers in the food & beverages, textile & apparel,
wood & wood products, paper & publishing and non-metallic mineral products industries
experiencing a fall in real wages over this period, while real wages of workers in the
remaining three industries witnessed an increase.
The results of the model run on the pooled sample of informal sector workers over the
14 rounds of the LFS are given in column 2 of table 5. Goldberg and Pavcnik (2003)
postulate that firms respond to increased competition from cheaper imports in the wake of
lower tariffs brought about by trade reforms by reducing formal employment and
substituting it with cheaper informal employment. The results point towards a positive
association between both final goods tariffs (output tariffs) and intermediate goods tariffs
Impact of Trade Liberalization on the Industry Wages in Pakistan (1995 – 2015)
Vol. IV, No. I (Winter 2019) 137
(input tariffs) and real wages. However, both the results are not statistically significant,
thus we can infer that there are no systematic linkages between trade liberalization and real
wages in the informal segment of Pakistan’s manufacturing sector.
Table 5. OLS Regression Results for Models Examining Impact of Trade Liberalization
on Wages
Model 1
Model 2
Informal Workers
Sample
Full Sample
Coefficient
Coefficient
Age
0.045***
0.059***
(0.002)
(0.003)
Age Squared
-0.0005***
-0.0006***
(0.00002)
(0.00003)
Male
0.757***
0.883***
(0.015)
(0.017)
Married
0.017***
-0.017
(0.009)
(0.013)
Widow/ Divorced
-0.060***
-0.067**
(0.023)
(0.032)
Middle
0.102***
0.111***
(0.007)
(0.011)
Secondary
0.210***
0.159***
(0.007)
(0.012)
Degree & above
0.855***
0.437***
(0.018)
(0.053)
Technical training
0.096***
0.098***
(0.009)
(0.013)
Informal employment
-0.190***
-
(0.007)
Output tariff
0.002
-0.0003
(0.002)
(0.003)
Input tariff
-0.005**
0.004**
(0.003)
(0.006)
Industry 1
-0.111***
-0.097***
(0.021)
(0.035)
Industry 2
-0.074***
-0.101***
Umer Khalid
138 Global Social Sciences Review (GSSR)
Model 1
Model 2
Informal Workers
Sample
Full Sample
Coefficient
Coefficient
(0.014)
(0.020)
Industry 3
-0.003
-0.036
(0.022)
(0.027)
Industry 4
-0.035
-0.093***
(0.022)
(0.035)
Industry 5
0.067***
-0.041
(0.021)
(0.039)
Industry 6
-0.068***
-0.066**
(0.020)
(0.032)
Industry 7
0.153***
0.009
(0.033)
(0.052)
Industry 8
0.025
-0.102***
0.019
0.030
Province x time dummies
Yes
Yes
Constant
10.327***
9.803***
(0.101)
(0.133)
R-Squared
0.3763
0.3196
Number of observations
58,003
25,896
***, **, * significant at 1 %, 5 % and 10 % respectively.
Overall, our results only provide confirmation of inverse relationship between trade
liberalization and real wages through the impact of tariffs on intermediate goods. This
implies that access of firms to cheaper and better-quality imported inputs helps in
increasing productivity, which leads to increase in real wages. However, in case of workers
employed in informal segments of the different two-digit industries, no increase in real
wages is observed. This may be attributable to the fact that firms working in informal
activities do not make use of higher quality imported inputs and raw materials and prefer
to rely on cheaper locally available inputs.
Conclusion
The present study analyzed the impact of trade liberation reforms carried out in Pakistan
over the period 1994-2015 on wages in the country’s manufacturing sector. The study
employed micro level data on employment at the two-digit PSIC industry level from 14
rounds of the Pakistan Labour Force Survey combined with macro level data on two types
of tariffs, including tariff on final products and tariff on intermediate goods/ raw materials.
For the empirical analysis the study used OLS technique.
Impact of Trade Liberalization on the Industry Wages in Pakistan (1995 – 2015)
Vol. IV, No. I (Winter 2019) 139
Firstly, a simple linear regression is used to find the important determinants of real
wages of workers in Pakistan’s manufacturing sector during the period under review. In
the second stage, tariffs on final goods and those on intermediate goods are included in the
regression framework, to ascertain the effect of trade reforms on wages of workers in the
manufacturing sector. The results show that a fall in input tariffs positively impacts wages
of manufacturing workers.
The results further indicate that real wages of workers employed in food, beverages&
tobacco, textiles, apparel& leather and non-metallic mineral industries have declined over
the twenty years period of trade reforms implemented in Pakistan. On the other hand, real
wages are observed to have increased in the chemical & petroleum and basic metals
industries. The four industries that experienced fall in real wages, on average, accounted
for slightly under three-fourths of total employment in the country’s manufacturing sector
over the twenty-year period reviewed.
Contrary to a priroi expectations, the analysis was unable to uncover a systematic
relationship between trade liberalization and real wages of workers employed in Pakistan’s
large and growing informal sector. The real earnings of informal workers in all the two-
digit PSIC industries are observed to have declined during this period of trade reforms,
except for workers employed in the basic metal industry. The lack of any well-defined
relationship output and input tariffs on real wages of informal sector workers can be
attributable to the fact that goods manufactured by firms in the informal sector are not close
substitutes of imported goods and that the production of these goods does not involve use
of imported inputs and raw materials.
Policy Implications
This study’s main finding indicates that productivity improvements in the country’s
manufacturing sector have been driven by use of higher quality imported inputs and raw
materials due to a fall in tariffs on intermediate inputs. In view of this finding, the
Government should revise the country’s tariff structure to make it more cascading, i.e.,
there should be lower tariffs on raw materials and other low value-added imports and
proportionately higher tariffs with each stage of value addition. This will ensure that the
manufacturing industries continue to have access to cheaper imported inputs, while direct
competition from imported finished goods is kept at a reasonable level to promote the
sector’s future growth and development prospects.
Umer Khalid
140 Global Social Sciences Review (GSSR)
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