NUST JOURNAL OF SOCIAL SCIENCES AND HUMANITIES
Vol.3 No.2 (July-December 2017) pp. 132-151
Valuing the Recreational Uses of Pakistan’s Wetlands:
An Application of the Travel Cost Method
Iftikhar Hussain Adil* and Ali Dehlavi†
Abstract:
The Keenjhar Lake, Pakistan’s largest freshwater lake and a Ramsar site, is
located in the Lower Indus Basin of the Indus Ecoregion. Global 200, which
scientifically ranks outstanding terrestrial and aquatic ecosystems in 238 ecoregions
worldwide, the Indus Ecoregion is one of the 40 priority Ecoregions. This study uses a
single-site truncated count data travel cost method to estimate the access values of
visitors to Keenjhar Lake. Policy makers may use these estimates on the recreational
value of the lake to assess the returns on conservation investments. A basic version of
the model applied to a subset of visitors using charter transportation allows analysis
of impacts on welfare measurement from altering assumptions about embarkation
points. This study finds the assumption that this category of visitor does not incur travel
and time costs before boarding charter transport to be both unrealistic and simplifying,
leading in turn to an underestimate of consumer surplus values. The strongest argument
in favour of revising data collection and processing strategies in this regard is perhaps
the finding that shared and rented transportation is common in developing countries,
while cost coefficients tend to figure prominently in welfare measurement irrespective
of the functional form.
Keywords: Travel Cost Method, Truncated Count Data Model, Ecotourism, Keenjhar
Lake
1. INTRODUCTION
Keenjhar is Pakistan’s largest freshwater lake (14,000 ha) and is
situated approximately 120km north of Karachi. A wildlife sanctuary
and a Ramsar site, it is set in a stony desert composed of alternating
layers of sandstone and limestone. Approximately 50,000 people, from
12 large and 20 small surrounding villages, are dependent on the lake.
Another predominant use of the lake, which might be labeled indirect
* Iftikhar Hussain Adil <[email protected]> is Professor of Economics at
School of Social Sciences and Humanities (S3H), National University of Sciences and
Technology, Islamabad, Pakistan. † Ali Dehlavi <[email protected]> is Advance Data Analytic Lead, Data Strategy
& Analytic Unit HBL, I.I. Chudrigar Road, Karachi, Pakistan.
133 Adil and Dehalvi
because consumption occurs off-site, is the supply of water for
residential and commercial use in Karachi. The major, direct
consumptive use of the lake among the local population takes the form
of fishing. However, tourists, mainly from Karachi, also enjoy swim-
ming, boating, and other entertainment activities offered by the Sindh
Tourism Development Corporation (STDC) at a resort on the lake’s
western banks.
This study estimates the access value of Keenjhar using a travel
cost method (TCM). It is expected that this would replace existing
decision-making with regard to pricing which does not rely on
quantitative tools but on intuition and experience. Adding the use value
of recreation to the already measured use value of fisheries and other
indirect use values such as the water supply by determining the need to
preserve Keenjhar would provide the planners with more accurate
estimates of its value. After reviewing the existing literature, we have
confined the modeling approach to a count data model for a single site.
The study also addresses the issues associated with multiple purpose
trips and the impacts of labour decisions on time valuation, in addition
to truncation and endogenous stratification via data analysis.
The total economic value of Keenjhar Lake, based on a recent
estimate of the direct consumptive use value (i.e., the producer surplus
from commercial fisheries), the indirect use value (i.e., the residential
water supply to 1 million of the 15 million population of Karachi), and
the non-use value (based on an application of the “choice experiment”
technique administered in Karachi to examine the willingness to pay for
species protection) is in the order of PKR 9 billion annually or USD
145million [Dehlavi, et al. (2008)].
At present, STDC does not employ valuation or similar advanced
quantitative techniques in their planning or pricing of accommodation
and recreational activities. This is unfortunate as models of recreational
demand can be put to a number of uses, including addressing economic
(for e.g., measuring the welfare derived from the reserve) as well as
financial (for e.g., responsiveness to cost components with bearing on
overall revenue or revenue per unit of on-site paying activities)
questions. This paper addresses the economic question of whether
investments in recreational sites provide a return on equity by estimating
Valuing the Recreational Uses of Pakistan’s Wetlands 134
the monetary access value figure associated with the recreational uses of
the Lake.
This study addresses labour market constraints while estimating
time costs by distinguishing between recreationists who are committed
to a fixed work week and fixed vacation allotments and those who are
not constrained in this fashion. The approach followed in this study is
developed by Bockstael, Strand and Hanemann (1987) which argued that
discontinuous labour market constraints lead to corner or interior solu-
tions.
In the case of Keenjhar, it is necessary to take into account the
concerns of the public regarding polluted water which is not only to its
recreational use but also to domestic and commercial uses of the Lake
by Karachi and a local population, mainly inhabitants of the surrounding
twelve large and twenty small villages [WWF-Pakistan (2006)].
Amongst others, pollution of the lake is caused by upstream tanneries,
sewerage, and grease from vehicle-washing and motorized fishing boats.
In a noteworthy economic and epidemiological contingent valuation
survey undertaken at two beaches, Georgiou, et al. (1996) established
that the British public was prepared to pay an amount in excess of the
total clean-up cost that would be incurred to bring British beaches up to
the standard required by the European Community (which in 1995 was
approximately GBP 9 billion).
TCM is used to a subset of visitors using charter transportation.
This was possible owing to a design feature in the questionnaire that
permitted us to analyze the impacts on welfare measurement of visitors
with different embarkation points for their trip to the Lake. The
unrealistic and simplifying assumption that this category of visitor does
not incur travel and time costs before boarding charter transport results
in an underestimate of consumer surplus values. We understand that our
analysis makes the case for revising data collection and processing
strategies since shared and rented transportation is common in
developing countries while cost coefficients tend to figure prominently
in welfare measurement irrespective of the functional form. This study
is useful in Pakistan to estimate non-market values for public policy
purposes. An earlier study by Khan (2004) adopted the TCM with an
objective to shape national policy on the regulation of a national park in
135 Adil and Dehalvi
Islamabad while this study is an attempt to shape national policy for
wetlands.
2. THE STUDY SITE AND SAMPLING
Choice of the study site in part was motivated by the STDC’s
own interest in providing economic values for the recreational services
it provides. However, in addition to aiding management decisions, we
were also interested in complementing Keenjhar’s total economic value
estimates USD 145 m [Dehlavi, et al. (2008)].This includes a direct use
component for commercial fisheries, which would be complemented by
recreational use, another direct use value. Even if an augmented value
were not to be applied in a benefit cost analysis in the context of Keenjhar
since it already benefits from protected status as a Ramsar site, the
exercise would be useful for its replicability within the Indus Ecoregion
and elsewhere in Pakistan. Furthermore, the valuable dataset accomp-
anying our analysis allows for further analysis, which would help with
the allocation of scarce resources, allowing planners in turn to assess
whether and by how much to raise fees depending on the magnitude of
the consumer surplus.
Seven-day reconnaissance survey (in February and March of
2009) was held for the purpose of designing a reliable survey instrument.
We conducted a count at the two entrance gates of the site which showed
that 5,892 individuals had visited it during this period. Based on the
findings from the reconnaissance survey, an innovative survey
instrument design element was generated that was incorporated into the
final questionnaire: that is, questions which partitioned travel costs for
those using chartered transport into within-city travel to a common point
of departure and travel onwards to Keenjhar. The form of chartered
transport mentioned here refers to the renting of a bus/van typically by a
single but large family. As there was no reason to assume that all
members of an extended family were picked up from their front door, we
asked respondents using chartered transport if they incurred time and
petrol costs to reach a “common point of departure”. During the main
survey (undertaken between 12-18 August, 2009), the chartered mode
remained the most popular (59 percent), with only a fraction not picked
Valuing the Recreational Uses of Pakistan’s Wetlands 136
up from home and thus incurring travel costs before boarding the
chartered transport. Privately owned cars (35 percent) and motorcycles
(6 percent) came second and third among preferred modes of travel.
Our Sampling plan for 1,000 observations which was designed
based on weights of the total observed participation in: (a) activities on
zone basis; (b) activities by each day of a 7-day week; (c) activities by
time periods within a single day and (d) activities by category. This
formulation yielded a convenient way to determine the specific number
of questionnaires to be filled within a given zone, day, time, and activity
category.
The main survey was conducted from 12th to 18thAugust, 2009,
and coincided with a national holiday, the Pakistan Independence Day,
which fell on a Friday in 2009. The survey yielded a sample of 741
visitors.
3. METHODOLOGY
The paper estimates access value to Keenjhar using the TCM.
After describing the theoretical construction of TCM, welfare
measurement using Poisson and negative binomial regression models is
described. This section described the analytical techniques used to
address the separate issues of multiple purpose trips, the impact of labour
decisions on time valuation, truncation and endogenous stratification.
3.1. The Model
The basic recreational demand model used in this paper may be
written as follows:
Pr(𝑥𝑗 = 𝑛) = 𝑓 (𝑛, 𝑧𝑗 , 𝛽), 𝑛 = 0, 1, 2, . . . , 𝑘 … (1)
where, the demand variable x can take an integer value from 0 to k; 𝑧𝒊 is
the row vector of M demand arguments (including the vector of prices
and qualities for recreational sites and the amount of income that could
be earned if the person worked all of the available time); and, β is an M
1 column vector of parameters to be estimated.
137 Adil and Dehalvi
TCMs are based on an idea first introduced by Hotelling (1949).
Researchers can derive resource values through the use of TCM by
estimating a demand curve for complementary market goods (for
example a day visitor’s costs of travelling to Keenjhar) and calculating
the welfare value for the household by integrating between the present
price faced by the household for the complementary good and the choke
price, i.e., the price at which the quantity demanded goes down to zero.
3.2. Welfare Measurement in the Poisson Regression Model
Welfare measurement or the value of access to Keenjhar in its
general form is calculated and approximated as the willingness to pay for
use of the site. The computation then is that of the area under the utility-
constant demand curve for the site, or the income-constant demand
curve, given expected low income effects and budget shares of recrea-
tional demand models [Haab, and McConnell (2002)]
𝑊𝑇𝑃(𝑎𝑐𝑐𝑒𝑠𝑠) = ∫ 𝑓(𝑠, 𝐶2𝑖 + 𝑤𝑖𝑡2𝑖𝑝∗
𝑝𝑖0 , 𝑦𝑖
𝑓)𝑑𝑠 … (2)
where, iiii twcP 11
0 (P, the price of a trip to the primary site) and P*is
the relevant choke price; ( iii twc 22 denotes travel cost to the substitute
site, c denotes the round-trip travel cost, w is the after-tax wage rate, and
t is a unit of time for the trip, while 𝑦𝑖𝑓 is a measure of full income, i.e.,
the amount that would be earned if all available time were used up for
work, and s is the dummy variable of integration). Each household is de-
noted by i, while subscripts 1 and 2 index the primary and substitute sites.
As this is an on-site sample with the number of visits expressed as counts,
Poisson regression model is suitable whose probability density function
is given by Haab and McConnell (2002) as follows
Pr(𝑥𝑖 = 𝑛) = 𝑒−𝜆𝑖𝜆𝑖
𝑛
𝑛! 𝑤ℎ𝑒𝑟𝑒, 𝑛 = 0, 1, 2, … … (3)
where, λi > 0 is mean of number of visits and according to characteristics
of poisson distribution equal to the variance.
Valuing the Recreational Uses of Pakistan’s Wetlands 138
𝜆𝑖 = exp(𝑧𝑖, 𝛽) … (4)
Willingness to pay for access using the Poisson regression model
is calculated and assuming an exponential function, the choke price is
infinite. Defining P0 as the current travel cost, consumer surplus for
access is given by Haab and McConnell (2002):
𝑊𝑇𝑃(𝑎𝑐𝑐𝑒𝑠𝑠) = ∫ 𝑒𝛽0+𝛽1𝑠𝑑𝑠∞
𝑃𝑖0 = [
𝑒𝛽0+𝛽1𝑠
𝛽1]
𝑃𝑖0
𝑃⟶∞
= −𝑥
𝛽1 … (5)
where, β1 < 0
The Poisson regression model is commonly used in recreational
demand models. Since the Poisson regression model is subject to
misspecification owing to its implicit restriction on the number of
counts: 𝐸 (𝑥𝑖|𝑧𝑖, 𝛽) = 𝑉 (𝑥𝑖|𝑧𝑖 , 𝛽) = 𝜆𝑖 (the conditional mean and
variance are equal). One consequence of variance exceeding the mean
(over dispersion), as is characteristic in recreational data, is that the
Poisson regression model’s standard errors are underestimated, leading
often to the rejection of the null hypothesis of no association. The
Negative Binomial is used to test for over dispersion, a common version
of which is a Poisson regression model with a gamma distributed error
term [Green (2005)]. In such a case, the Negative Binomial’s probability
function can be written as Haab and McConnell (2002):
Pr(xi) = Γ (xi+
1
α)
Γ (xi+ 1) Γ (1
α)
(1
α1
α+ 𝜆𝑖
)
1
α
(𝜆𝑖
1
α+ 𝜆𝑖
)
xi
… (6)
where, 𝜆𝑖 = exp(𝑧𝑖, 𝛽)
The mean and variance of the negative binomial distribution are
𝐸 (𝑥𝑖) = 𝜆𝑖 = exp(𝑧𝑖, 𝛽) and 𝑉 (𝑥𝑖) = 𝜆𝑖 (1 + 𝛼𝜆𝑖), respectively.
The parameter is the over dispersion parameter. If α > 0, over dispersion
is said to exist. If α = 0, no over dispersion or under dispersion exists and
the Negative Binomial collapses to the Poisson distribution in the limit.
If, on the other hand, α < 0, the data are under dispersed so that the
Poisson regression model should be rejected in favour of the Negative
139 Adil and Dehalvi
Binomial model, revealing that the test is also one of the Negative
Binomial models against the null hypothesis of a Poisson.
3.3. Endogenous Stratification and Truncation
Count models with truncated samples, i.e., models where only
those visiting the site are sampled, should make use of the appropriate
functional form and also be observant of the effects of functional form
choice and truncation on consumer surplus estimates [Ozuna, et al.
(1993)].
It is expected the sample average number of trips to be higher
than the population mean (endogenous stratification) since on-site
interviewing process is inherently likely to have intercepted avid visitors
to Keenjhar. To obtain the correct likelihood function, it is needed to
account for this oversampling of visitors who have a high use level.
Endogenously stratified and truncated Poisson Regression Model may
be estimated by running a standard Poisson regression of 𝑥𝑖 − 1 on the
independent variables and can be written as its probability [Haab and
McConnell (2002)]:
ℎ (𝑥𝑖 𝑎𝑛𝑑 𝑖𝑛𝑡𝑒𝑟𝑣𝑖𝑒𝑤|𝑥𝑖 > 0) = 𝑒𝜆𝑖𝜆
𝑖
𝑤𝑖
𝑤𝑖! … (7)
where, 𝑤𝑖 = 𝑥𝑖 − 1 and right hand term is the probability function for a
Poisson distribution for the random variable wi. To address over
dispersion relative to the Poisson, truncation at zero, and endogenous
stratification due to oversampling of frequent visitors at Keenjhar,
Negative Binomial regression model should be estimated [Haab and
McConnell (2002)].
𝒉(𝒙𝒊𝒂𝒏𝒅 𝒊𝒏𝒕𝒆𝒓𝒗𝒊𝒆𝒘|𝒙𝒊 > 𝟎) = 𝐱𝐢𝚪 (𝐱𝐢+
𝟏
𝛂)
𝚪 (𝐱𝐢+ 𝟏) 𝚪 (𝟏
𝛂)
(𝟏
𝛂𝟏
𝛂+ 𝝀𝒊
)
𝟏
𝛂
(𝝀𝒊
𝟏
𝛂+ 𝝀𝒊
) 𝛌𝐢𝐱𝐢−𝟏 … (8)
Valuing the Recreational Uses of Pakistan’s Wetlands 140
3.4. Multiple Purpose Visits
The model, as is standard, distinguishes single purpose visits
from multiple purpose visits, that is, visits made to destinations on the
way to Keenjhar or on the way back home. This turned out to be very
important since as much as 42 percent of our sample undertook
incidental visits. Using a somewhat recent approach Parsons and Wilson
(1997), one can interact a dummy variable with price to capture both the
shift and rotation of the demand function due to the existence of
complementary sites, thereby adjusting the reported total trip cost of
multiple purpose visitors in our sample.
3.5. Implications of Labour Decisions on Time Valuation
Model in the study also attempted to reflect the implications of
labour decisions on time valuation (opportunity cost of time) and
allowed these decisions to vary over individuals in our sample. In
particular, adopting an approach based on Bockstael (1987), this study
distinguished visitors to Keenjhar who give up on the opportunity to earn
income for a day trip to the Lake from those who do not face any such
trade off. The former “unconstrained” category is different from the
“constrained” one in that it describes individuals whose labour/leisure
choice is at an “interior” and whose opportunity cost of time is reflected
in the wage rate. The modeling structure adopted here is:
𝑥𝑖 = ℎ𝐼(𝑃𝑖 + 𝑤𝐷𝑡𝑖, 𝑃𝑜 + 𝑤𝐷𝑡𝑜 , �̅� + 𝑤𝐷�̅�)
𝑥𝑖 = ℎ𝐶(𝑃𝑖, 𝑡𝑖, 𝑃𝑜 , 𝑡𝑜 , �̅�, �̅�)
𝑙𝑛𝑥𝑚1 = 𝑔 (𝑃𝑚 + 𝑊 𝑡𝑚, 𝐼 + 𝑊 𝑇) 𝑓𝑜𝑟 𝑚 = 𝑗, 𝑘, 𝑙 … (9)
𝑙𝑛𝑥𝑚2 = 𝑔 (𝑝𝑚, 𝑡𝑚, 𝐼 , 𝑇) 𝑓𝑜𝑟 𝑚 = 𝑗, 𝑘, 𝑙 … (10)
where, Equation [9] describes the natural log of the quantity of trips
demanded in the mth mode by unconstrained individuals and Equation 10
describes this with reference to constrained individuals; pm is the travel
cost and tm is the travel time, both associated with the mth mode; w is the
wage rate, I is household income, and T is discretionary time. Time and
budget constraints are collapsed into a single constraint in the case of
141 Adil and Dehalvi
Equation (9), which describes “unconstrained” individuals, while time
and budget constraints are separately binding in the case of Equation (10)
“constrained” individuals.
4. RESULTS AND DISCUSSION
The study began by selecting the best estimator for our TCM
through estimating two simple versions of our model. One version uses
travel cost and income variables while the other adds travel time as a
separate variable for so-called “constrained” individuals. Table 1 present
the descriptive statistics of the explanatory variables and theoretically
expected signs. While in Table 2, the two estimators tested were the
Poisson and Negative Binomial, both corrected for zero-truncation and
endogenous stratification. As over dispersion is characteristic in
recreational data, the Negative Binomial tested for over dispersion but
found Poisson more suitable as compare to Negative Binomial.
Table 1. Explanatory Variables and Associated Hypotheses
Variable +/- Mean Std. Dev. Min Max
Travel Cost (TC) (PKR) - 1,283 1,162.30 0 16,078
Travel Time (Ti) (minutes) - 174.05 90.83 3 900
Household Income (mon_income
PKR) + 43,000 74,343.36 2,000 1,000,000
Education (Years of schooling) + 11.85 3.58 0 21
Age (Years) - 31.66 9.97 12 73
Distance (KM) - 49.57 79.84 28 487
Interacted_TC ? 568.29 969.97 0 8212
Interacted_Travel_Time
Gender (male=1, Female=0)
Unmarried (yes=1, No=0)
Region(Urban=0, Rural=1)
?
+
+/-
-
1.65
0.99
0.43
0.85
2.95
0.11
0.50
0.36
0
0
0
0
30
1
1
1
Source: Survey (12-18 August 2009); the sample size is 741.
Having determined an appropriate model, the endogenously
stratified and truncated Poisson alone to estimate demand models were
selected, the first time using seven variables and the next time eleven. In
Valuing the Recreational Uses of Pakistan’s Wetlands 142
each of the seven- and eleven-variable versions of the TCM, we carried
out one regression in which incidental consumption effects are ignored
(Model 1); one in which an indicator variable for multiple destination
visits is included (Model 2); and a third one, a fully interacted model, in
which the indicator variable is interacted with the price variable and
travel time (Model 3).
4.1. Estimator Selection for the TCM
The study began by noting some interesting results from the
estimator selection process before proceeding to discuss the travel cost
model. Firstly, in the case of the endogenously stratified and truncated
Poisson, the signs on our coefficients were as expected (see, Table 1).
Secondly, for the purpose of computing the marginal change with
variables held at their means, it is possible to interpret the coefficient of
the total trip cost as semi-elasticity showing the percentage by which
visits would drop for a single unit (i.e., PKR 1) increase in TC. The
coefficients show an extremely low degree of elasticity in both models.
As discussed in Section 3 above, a Keenjhar trip is a unique and high
quality recreational experience and, based on the empirical evidence
reviewed in Woodall, et al. (2002), unique and high quality recreational
experiences have been shown to have low price elasticities. In an
analysis of day trips to Canyon County for wine tourism, the same
authors include a “stay home” dummy variable which confirms the site
in question to be unique, with few substitutes.
As it is an on-site sample with self-selection on the part of those
who go to Keenjhar, the data are truncated because only those visitors
are considered who have visited Keenjhar Lake at least once. Hence,
zero-truncated Poisson regression model is taken. Again, owing to the
fact that ours is an on-site sample, it is inherently likely to have
intercepted avid visitors to Keenjhar. The endogenous stratified
truncated Poisson addresses this problem because it is expected that the
sample average number of trips to be higher than the population mean.
143 Adil and Dehalvi
4.2. Estimation of the TCM Model
The results for the selected estimator; the zero-truncated and
endogenously stratified Poisson regression model are presented in
Tables 3 and 4. Seven-variable model is discussed first. Its overall
significance, as measured by χ2, was 0.0000 in all three models showing
that the sign of the coefficients across all variables in Table 3 is as
expected. In general, when displaying marginal effects for travel cost
models we are presented with the predicted number of events in the
dependent variable against each of the independent variables.
Table 2. Estimator Selection for the Travel Cost Model
Endogenous Stratified and
Truncated Poisson
Endogenous Stratified
Negative Binomial
1 2 3 4
Variable Estimate
(S.E.)
Estimate
(S.E.)
Estimate
(S.E.)
Estimate
(S.E.)
Constant
-0.195***
(0.053)
0.373***
(0.065)
-14.347
(196.956)
-12.988
(93.716)
Travel Cost -0.00005*
(0.00003)
-0.00006**
(0.00004)
-0.00005
(0.00005)
-0.00005
(0.00005)
Monthly
Income
1.09 e-08
(5.13 e-07)
7.58 e-08
(5.09 e-07)
-3.62 e-08
(7.45 e-07)
-8.29 e-09
(7.46 e-07)
Travel Time -0.048***
(0.0106)
-0.051***
(0.016)
LR / Wald χ2 2.41 24.12 1.16 11.84
Level of sig. 0.2998 0.0000 0.5601 0.0080
Pseudo R2 0.0010 0.0096
Note: ***, ** and * indicate significance at the 1 percent, 5 percent and 10 percent levels, respectively.
Results are for a sample size of 741.
Based on a high Pseudo R2 value as compared to Models 1 and
2, the marginal effects in Model 3 are examined in Table 3. It can be
observed that trips are predicted by the Model to increase by 0.03 percent
for a PKR. 1000 increase in monthly income. By looking at elasticities
after Poisson, a 10 percent increase in travel costs would result in a 1.3
percent decrease in the frequency of trips. It is noteworthy that travel
cost variable is significant at the 5 percent level for all three models.
Valuing the Recreational Uses of Pakistan’s Wetlands 144
The coefficient of monthly income is insignificant in all the
models, considering that nearly half the sample has undertaken an
approximately 3-hour long journey. Supporting hypotheses that males,
more importantly single males, face nominal constraints when it comes
to traveling unaccompanied and exercising travel decision prerogatives,
gender and married coefficients show significance at the 5 percent and
10 percent levels, respectively.
Table 3. Endogenous Stratified and Truncated Poisson
Regression- Basic Models
1 2 3
Variable Estimate
(S.E.)
Estimate
(S.E.)
Estimate
(S.E.)
Constant
-1.192
(0.728)
-1.288*
(0.729)
-1.218*
(0.729)
Travel Cost -0.00006**
(0.00003)
-0.00007**
(0.00004)
-0.0001**
(0.00004)
Travel Time -0.052***
(0.011)
-0.053***
(0.011)
-0.080***
(0.015)
Monthly Income 2.17 e-07
(5.06 e-07)
2.91 e-07
(5.11 e-07)
3.19 e-07
(5.13 e-07)
Gender 1.696**
(0.709)
1.719**
(0.709)
1.783**
(0.710)
Married 0.231***
(0.072)
-0.216***
(0.072)
-0.218***
(0.072)
Urban 0.299***
(0.088)
-0.301***
(0.088)
-0.304***
(0.088)
Waterac_pref 0.255*
(0.139)
0.234*
(0.139)
0.220
(0.139)
D_mp 0.225***
(0.070)
-0.048
(0.132)
Interacted_TC 0.00007
(0.00007)
Interacted_T_Time 0.058***
(0.022)
LR (χ2) 58.19 68.5 76.43
Level of sig 0.0000 0.0000 0.0000
Pseudo R2 0.0233 0.0274 0.0306
Note: Note: ***, ** and * indicate significance at the 1 percent, 5 percent and 10 percent levels, respectively.
The correlation between the decision to travel to Keenjhar and
the preference for water-based activities appears to be supported by the
145 Adil and Dehalvi
significance of the coefficient of our “water based activities” variable at
the 10 percent level except in Model 3. Moreover, the coefficient of the
dummy variable for multiple purpose visits is highly significant in Model
2 but not so when interacted with travel time in Model 3.
Table 4. Endogenous Stratified and Truncated Poisson
Regression - Extended Models
Variable
Model 1 Model 2 Model 3
Estimate
(S.E.)
Estimate
(S.E.)
Estimate
(S.E.)
Constant -1.299*
(0.751)
-1.424*
(0.752)
-1.352*
(0.753)
Travel Cost -0.00007**
(0.00003)
-0.00007**
(0.00004)
-0.0001***
(0.00005)
Travel Time -0.051***
(0.011)
-0.052***
(0.011)
-0.079***
(0.016)
Monthly Income 1.72 e-07
(5.22e-07)
2.31 e-07
(5.29 e-07)
2.65e-07
(5.32 e-07)
Gender 1.699**
(0.710)
1.725**
(0.711)
1.777**
(0.711)
Married -0.254***
(0.085)
-0.241***
(0.084)
-0.239***
(0.085)
Urban -0.296***
(0.089)
-0.310***
(0.089)
-0.304***
(0.089)
Waterac_pref 0.254*
(0.139)
0.234*
(0.139)
0.221
(0.139)
Education 0.004
(0.010)
0.006
(0.010)
0.007
(0.010)
Unemp_09 0.061
(0.089)
0.073
(0.089)
0.050
(0.090)
Wtp_50 0.080
(0.080)
0.083
(0.079)
0.094
(0.080)
Age -0.0005
(0.004)
-0.0004
(0.004)
-0.0005
(0.004)
D_mp 0.231***
(0.071)
-0.047
(0.133)
Interacted_TC 0.00007
(0.00007)
Interacted_Travel_Time 0.058***
(0.022)
LR χ2 60.10 71.00 78.95
Level of Sig. 0.0000 0.0000 0.0000
Pseudo R2 0.0240 0.0284 0.0316
Note: ***, ** and * indicate significance at the 1 percent, 5 percent and 10 percent levels, respectively.
The mean consumer surplus result relates to Model 1 and needs
to be interpreted in light of Models 2 and 3 (Table 3) which exhibit
Valuing the Recreational Uses of Pakistan’s Wetlands 146
incidental visits to complementary sites such as Badshahi Masjid,
Bhamboor, Makli and Chowkandi. In Model 2, following the introduct-
ion of an indicator variable, the statistical significance of the differential
intercept implies that the intercept for the multi-purpose trips (the 58
percent of our sample undertaking single purpose visits and the 42
percent undertaking multiple purpose visits) is different. As the coeffi-
cient is positive, the incidental visits may be interpreted to serve as
complements [Loomis, et al. (2000)].
All the models estimated are presented in Tables 3 and 4 while
segregating visitors to Keenjhar who forego the opportunity to earn
income for the purpose of enjoying a day trip to the Lake from those who
do not. The former “constrained” category describes individuals whose
labour/leisure choice is at an “interior” and whose opportunity cost of
time is reflected in the wage rate. In demand function, the opportunity
cost of time of the latter “unconstrained” category is reflected in the
absolute value of the coefficient of the travel time variable in Tables 3
and 4.
Constrained individuals’ opportunity cost of time was based on
empirical evidence presented by Cesario (1976) where the most common
assumption is that the price of time spent travelling can be valued at
between a ¼ and ½ of the wage rate. The unconstrained individuals’
time was set at zero. When this structure of modeling is not followed,
and all individuals are treated equally for time valuation, the estimated
mean consumer surplus per visit is PKR27,322 (or USD329). As shown
in Table 4, for Model 3, the estimated mean consumer surplus per visit
in the endogenous stratified truncated Poisson is PKR9,024 (or
USD109). This estimate is only marginally smaller (a 5 percent diff-
erence) when compared to the result obtained from our 7-variables
endogenous stratified truncated Poisson regression.
4.3. Impact of Outset Origins on Welfare Measurement
A design feature in our questionnaire permitted the analysis of
impacts on welfare measurement arising from differences among visitors
in terms of their point of departure. In particular, respondents travelling
as a large group in rented buses/vans (437 visitors or 59 percent of our
147 Adil and Dehalvi
sample of 741) were asked if they incurred petrol and time costs before
boarding their charter transport. In other words, we did not assume that
all members of an extended family or all members of a group of friends
were picked up from their front door. As referred in Table 5 to a
“common point of departure” (CPD) to describe the point of boarding
chartered transport for those who were not picked up from their home (6
percent of sample). The term “home” indicates that welfare was cal-
culated on the assumption that all chartered transport visitors were
picked up from their doorstep. Common point of departure “CPD” and
“home” is the embarkation points for each of the two sub-samples that
are used to estimate consumer surplus in Table 5. The impact on
consumer surplus, an increase of 41 percent, is pronounced when the
sub-sample is restricted to only those who reported costs before boarding
their chartered transport.
However, the analysis does show that the unrealistic and
simplifying assumption that this category of visitor does not incur travel
and time costs before boarding charter transport results in a significant
underestimate of consumer surplus values. The design feature is cumber-
some and cause respondent fatigue. Moreover, in a review of revealed
preference valuation techniques, Bockstael (2007) found that cost
coefficients tend to figure prominently in welfare measurement
irrespective of the functional form. With regard to the zonal travel cost
method, Bateman, et al. (1997) undertook research on embarkation
points for the trip emphasizing the accuracy of road distances and routing
to recreational sites and the impact that this has on welfare measurement.
This study was based on a sample of 351 visitors to a woodland
recreation site. The authors use actual road network distance in order to
compare the consumer surplus estimates derived using it with those
obtained by assuming straight line travel, where they found the latter to
underestimate welfare values up to 20 percent. The revision of data
collection and processing strategies in our case is all the more important
given the fact that chartered transport of the kind used by Karachiites is
common in the urban centers of developing countries. For example, in
an application of the travel cost method in Bangladesh, Shammin (1999)
found as many as 58 percent of visitors to the Dhaka zoo to use a bus as
compared with 20 percent in the tempo/scooter category. The easy avail-
Table 5. Results of Access Values for Different Specification of Time Cost and Out of
Pocket Expenses in the Travel Cost Variable
Sample Used
Outset
Origin
TC
Coefficient
Standard
Error
t - Value Log
Likelihood
Prob > chi2 Consumer
Surplus
(mean per
visit, USD)
47 charter transport users
who were not picked up
from home (6 percent of
the sample)
CPD -0.000189 0.000374 -0.51 -47.23 0.12 64
Home -0.000266 0.000373 -0.71 -47.10 0.11 45
Entire sub-sample of 437
charter transport visitors
(59 percent of the sample)
CPD -0.000281 0.0001 -2.81 -699.2 0.02 43
Home -0.000296 0.000010 -2.97 -698.7 0.02 41
Full sample (741
visitors)*
Home -0.000111 0.000051 -2.19 -1211 0.00 109
Full sample (741
visitors)**
Home -0.000105 0.000050 -2.11 -1213 0.00 115
Note: The term “home” indicates that welfare was calculated assuming that all chartered transport visitors were picked up from their doorstep; conversely, the
welfare measurement incorporating time and out-of-pocket expenses incurred before boarding chartered transport is denoted by “common point of departure”
(CPD).
14
8 V
alu
ing
the R
ecreatio
na
l Uses o
f Pa
kistan
’s Wetla
nd
s
149 Adil and Dehalvi
ability of buses, microbuses and other shared/chartered transport for low
income groups visiting popular public attractions is also underlined in
Mahat and Koirala (2006), which applies the travel cost method to study
visitors to the Jawalakhel Central Zoo of Nepal where this category
represented 80 percent of all transport modes.
5. CONCLUSION AND POLICY IMPLICATIONS
This study used a single-site truncated count data travel cost
method to estimate the access values of visitors to Keenjhar Lake. A
basic version of the model applied to a subset of visitors using charter
transportation allowed to analyze the impacts on welfare measurement
from altering assumptions about embarkation points. Policy makers may
use these estimates on the recreational value of the lake to assess the
returns on conservation investments.
Using the TCM, in this study we estimate the recreational value
of Keenjhar Lake. The estimated value is PKR3.46 billion (or USD42.2
million) calculated on the basis of an annualized PKR9,500 (or USD116)
illustrating consumer surplus per visit value, that is, assuming average
daily visits at 1,000.
From the above results it can be inferred easily that amenity value
of the site can be improved by providing facilities to the visitors. It is
well known fact that amenity sites are scare in Pakistan. People chose to
go for recreational trips if facilities are available at the site. Facilities
may be provided to the visitors when the funds are available. Funds can
be generated through increase in entry ticket, introducing parking fee,
taxing vendors and minor percentage raise in the STDC huts charges.
Sindh Tourism and Development Corporation and WWF Pakistan may
collaborate so that to generate revenue and save the environment at the
same time.
Valuing the Recreational Uses of Pakistan’s Wetlands 150
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University, U.K.
Bockstael, N.E. and K.E. McConnell (2007) Environmental and
Resource Valuation with Revealed Preferences – A Theoretical
Guide to Empirical Models. Bateman (ed.) Economics of Non-
Market Goods and Resources, Vol. 7, Dordrecht, Netherlands.
Bockstael, N.E., I.E. Strand and W.M. Hanemann (1987) Time and the
Recreational Demand Model. American Journal of Agriculture
Economics, 69:2, 293–302.
Cesario, F.J. (1976) Value of Time in Recreational Benefit Studies. Land
Economics 52:32, 41.
Dehlavi, A., B. Groom, B.N. Khan and A. Shahab (2008) Total
Economic Value of Wetland Sites on the Indus River. Report by
the World Wide Fund for Nature – Pakistan, Indus for All
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151 Adil and Dehalvi
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Study of Environmental Valuation of Dhaka Zoological Garden’.
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Karachi.
NUST JOURNAL OF SOCIAL SCIENCES AND HUMANITIES
Vol.3 No.2 (July-December 2017) pp. 152-177
Decomposition Analysis of Energy Consumption in
Pakistan for the Period 1990-2013
Faisal Jamil* and Arbab Shahzad†
Abstract:
The final energy consumption in Pakistan has doubled during the last two
decades. Investigating the factors responsible for changes in energy use is important
for future projections. Decomposition techniques enable us to quantify the contributing
factors in aggregate energy change. This study attempts to investigate the factors
behind the aggregate change in energy consumption over the period 1990-2013 using
Logarithmic Mean Divisia Index (LMDI) decomposition technique. LMDI decomposes
the overall change in energy use into three effects namely, activity, structural, and
intensity effects. Results of the study suggest that observed increase in Pakistan’s
energy consumption is primarily due to the activity and structural effects. The energy
intensity of overall economy has decreased showing an increase in energy efficiency,
though at a decreasing rate over time. The quantification of energy imports based on
projections shows that Pakistan may face serious fiscal challenge by 2025 due to
extremely large energy import bill and possible energy price shocks. There is a need to
put efforts towards reducing the gap between energy supply and demand, diversifying
domestic energy production including increased reliance on renewables, efforts
towards energy and environment conservation, and efficient use of available resources.
Keywords: Energy, Decomposition Analysis, LMDI, Intensity Effect
1. INTRODUCTION
Energy is an essential input affecting the output and the overall
economic welfare. Various past studies examine the relationship
between economic growth and energy consumption and find different
results for different economies [Masih and Masih (1996); Soytas and
Sari (2003); Lee (2007); Apergis and Payne (2010); Jamil and Ahmad
(2010); Belke, et al. (2011); and, Shahbaz, et al. (2012)]. The literature
* Faisal Jamil <[email protected]> is Assistant Professor at School of
Social Sciences and Humanities (S3H), National University of Sciences & Technology
(NUST), Islamabad, Pakistan. † Arbab Shshzad <[email protected]> School of Social Sciences and
Humanities (S3H), National University of Sciences & Technology (NUST), Islamabad,
Pakistan.
153 Jamil and Shahzad
finds that growth in energy consumption is inevitable in the economic
growth although there is lack of consensus on the direction of causality.
The final energy consumption in Pakistan increased from 21.58
million tons of oil equivalents (MTOE) in 1990 to 40.18 MTOE in 2013
[Energy Yearbook (2014)]. The role of economic growth is considerable
in raising energy consumption. The energy mix and consumer mix for
different energy sources are changing over time making the job of policy
makers and energy planners challenging. The investigation of changing
energy consumption patterns and the factors responsible for this change
is an important research topic. Therefore, this study seeks to quantify the
impact of structural shift of economic activity on total energy demand in
Pakistan so as to have better understanding of the energy use
mechanisms.
Energy decomposition is a widely used method that tracks the
relative contributions of various factors to changes in energy consump-
tion, energy intensity and the environment [Ang and Zhang (2000);
Mairet and Decellas (2009)]. Different decomposition techni-ques have
been used in various past studies such as, Arithmetic Mean Divisia Index
method-1 (AMDI-1), Modified Fisher Ideal Index method, Conventional
Fisher Ideal Index method, Shapley/Sun method and Marshall-
Edgeworth method, Laspeyres, Paasche, Sato-Vartia and Tornqvist [Ang
and Zhang (2000); Liu and Ang (2003); Ang (2004)]. Some studies
provide a comparison of various decomposition methods and show that
log mean divisia index (LMDI) method is robust due to being consistent
in aggregation, residual free, and easy interpretation. These advantages
lead to an extensive use of LMDI in different past studies [Ang and Choi
(1997); Ang and Liu (2001); Ang (2004); Ang (2005); Ediger and Huvaz
(2006); Ma and Stern (2008); Hatzigeorgiou, et al. (2008); Mairet and
Decellas (2009); Zhang, et al. (2011); Balezentis and Strienikiene
(2011); Nasab, et al. (2012); Hasanbeigi and Sathaye (2012); Zhao, et al.
(2012); and Lotz and Pouris (2012)].
LMDI enables to investigate the responsible factors of aggregate
energy consumption change over time by decomposing the overall
change into certain effects including activity effect, structural effect and
intensity effect. The activity effect shows the impact of a change in
energy consumption associated with level of economic activity;
Decomposition Analysis of Energy Consumption 154
structural effect summarizes the impact of composition of energy
consuming activities; while, intensity effect highlights the impact
associated with sectoral use of energy per unit of output. In a nutshell,
literature has established the usefulness of decomposition in energy
related forecasting as it helps to track down factors that are responsible
for change in aggregate energy use. Most of the past studies decompose
either total energy consumption or carry out the analysis for a particular
sector especially the manufacturing sector. Moreover, a range of
decomposition methods are found in the literature, but a consensus about
the best decomposition method is still lacking.
This study applies period-wise decomposition analyses for the
total period as well as decade-wise analysis separately for 1990-2000 and
2000-2013 and also quantifies the cost to the economy of the projected
energy imports by 2025. The economy is broadly classified into
agriculture, industrial and services sectors and energy consumption rises
in all the sectors that contributed to the energy imports of Pakistan. The
results suggest that observed increase in the energy consumption is
primarily attributed to the activity and structural effects. The energy
intensity of overall economy shows a decreasing trend signifying
improvements in energy efficiency over the period. However, the
compensating share of intensity effect was higher during the period
1990-2000 as compared to 2000-2013. The sector-wise analysis shows
that agriculture is responsible for a decline in total energy consumption,
while industrial and services sectors have contributed in energy con-
sumption growth. The quantification of energy imports based on official
projections indicates that the country may face serious balance of
payment challenges by 2025 due to heavy reliance on imported energy.
It suggests diversifying energy mix towards more renewable and
sustainable energy sources.
The rest of the paper is as follows. Section 2 provides an over-
view of energy sector in Pakistan. Section 3 describes the methodology
and the data while Section 4 discusses the results. Section 5 shows the
impact of future energy imports on Pakistan’s external account. Finally,
Section 6 concludes the study.
155 Jamil and Shahzad
2. AN OVERVIEW OF PAKISTAN ENERGY SECTOR AND
THE ECONOMY
Total final energy consumption is increasing in Pakistan and is
projected to reach 142 MTOE by 2025 [Pakistan Integrated Energy Plan
(2013)]. The final energy mix is mainly comprised of four sources
including natural gas, oil, hydroelectricity and coal. The share of natural
gas and oil is about 74% in the total mix. Petroleum imports constitute
about 30% of total energy use in Pakistan in 2014 and are increasing as
indigenous natural gas resources are depleting. Energy efficiency is the
major future energy source in the world. It is valid in the case of Pakistan
as the energy intensity calculated by energy use per unit of output has
declined over time, from 9.42 TOE in 1990 to 7.09 TOE and 6.64 TOE
per unit of output respectively in 2000 and 2014. Figure 1 shows the
period-wise changing patterns of Pakistan’s fuel mix [Energy Yearbook
(2000, 2014)].
Total energy consumption in different sectors shows changing
pattern as the share of agriculture is declining whereas, shares of industry
and service sectors have slightly increased (see, Figure 2). A similar
pattern is evident in the sector-wise economic activity shares during
1990-2013 as shown in Figure 3. The economy has experienced struc-
tural shifts especially during the last decade wherein the share of
agriculture in the economy falls and the shares of industry and services
sectors increase. The composition of intra-sector economic activity is
given in the subsequent figures. Figure 4 depicts the changing pattern of
sub-sectoral shares in the agriculture sector. The share of crops in agri-
cultural GDP has fallen from 65 to 42% while, livestock subsector
witnessed a phenomenal rise in its share from 30 to 55% over the period.
This structural pattern clearly indicates that agricultural sector has been
rapidly moving from relatively energy intensive crop sub-sector towards
less energy intensive livestock sub-sector. Figure 5 shows the shares of
sub-sectors in the industrial sector. Manufacturing sector’s share in the
total sectoral output has increased from 68% to 73% over the same time
period. The other two sub-sectors, i.e., construction and utilities’ supply
have witnessed decreasing share in total industrial output.
Decomposition Analysis of Energy Consumption 156
Figure 1. Share of Fuels in total energy Mix in the period 1990-2014
Source: Hydrocarbon Development Institute of Pakistan (2004 and 2014).
Figure 2. Sectoral share in total energy consumption in Pakistan
1990-2013
Source: Hydrocarbon Development Institute of Pakistan (2004 and 2014).
23.83
32
45
38
48.32
2931.72
14.68 166.43 4.98
10
0
10
20
30
40
50
60
1990 2000 2014
Fuel
Mix
% i
n t
ota
l E
ner
gy
Years
Natural Gas Petroleum Electricity Coal
6.92 2.66 1.64
33.9134.62 35.48
59.15 63.06 62.88
0
20
40
60
80
100
120
1990 2000 2013
Per
cen
tage
Shar
e
Years
Agriculture Industry Services
157 Jamil and Shahzad
Figure 3. Sectoral share of Economic Activity 1990-2013
Source: Pakistan Economic Survey (Various Issues)
Figure 4. Sub-sector share in Agriculture Output
Source: Pakistan Economic Survey (Various Issues)
25.59 25.92 21.05
23.07 23.3225.25
51.33 50.74 53.7
0
20
40
60
80
100
120
1990 2000 2013
Agriculture Industry Services
6550.7
41.66
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1990 2000 2013
% S
hare
in
To
tal
Ag
ricu
ltu
re
Crops livestock Fisheries Forestry
Decomposition Analysis of Energy Consumption 158
Figure 5. Sub-Sector Share in Industrial Output in Pakistan (%)
Source: Pakistan Economic Survey (Various Issues)
Figure 6. Sub-Sector Share in Services Output (Percentage)
Source: Pakistan Economic Survey (Various Issues)
2.179.77 9.44
68.4562.92
73.42
16 10.51
8.513.38 16.8
8.64
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1990 2000 2013
% S
hare
in
To
tal
Ind
ust
ry
Min & Quar Manuf Construction Elect, Gas, Water Supply
19.76 22.14 18.02
34 34.431.98
4.587.3
8.9411.27 6.2
5.08
14.25 12.1812.37
16.14 17.78 23.61
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1990 2000 2013
% S
hare
in
To
tal
Serv
ices
Tra & Com WS & RT Fin & Ins OD P Adm & Def SCS
159 Jamil and Shahzad
Figure 6 shows sub-sectoral shares in the services. Finance and
insurance sub-sectors have doubled over the period 1990-2013 whereas,
social and other community services rose from 16% to 24%. The analysis
shows that energy use, composition of energy sources, and sectoral share
in energy consumption is changing. It signifies the importance of investi-
gating energy consumption trends across different sectors to formulate
an optimal energy supply system in the country. This analysis will help
in understanding the energy consumption behaviour vis-à-vis the re-
quirements for infrastructure and future planning for making the desired
energy sources available in sufficient amounts. The analysis will also
guide the policy makers to plan for sustainable energy future in the
country.
3. METHOD OF DECOMPOSITION AND DATA
During the last two decades, several studies decompose the
changes in energy consumption into specific predetermined effects to
capture the factors that may explain the change over a period of time
[Choi, et al. (1995); Ang and Liu (1995); Ang and Lee (1996); Hoekstra
and Bergh (2003); Liu and Ang (2003); Ang, et al. (2004); and Reddy
and Ray (2010); Ullah, et al. (2014)]. Ang and Zhang (2000) and Ang
(2004) provide a survey of literature on several decomposition methods
including LMDI, AMDI-1, Modified Fisher Ideal Index method,
Conventional Fisher Ideal Index method, Shapley/Sun method and
Marshall-Edgeworth method. Past studies distinguish between two
alternative techniques of decomposition, i.e., structural decomposition
analysis (SDA) and index decomposition analysis (IDA). SDA method
is based on input-output coefficients, while IDA is based on aggregate
input-output data. Ang and Lee (1996) extend the energy consumption
approach by replacing it with energy coefficient approach where the
impact of structural and sectoral energy efficiency changes are measured
in terms of coefficients and elasticities.
IDA is useful to decompose the change in aggregate variables in
two or more different components with their shares in the aggregate
change. LMDI is one of the index based techniques of IDA [Ang and
Zhang (2000)]. An LMDI with both multiplicative and additive
Decomposition Analysis of Energy Consumption 160
techniques is proposed by Ang and Choi (1997). LMDI has been
extensively used for decomposition of energy consumption in different
countries and identified the impact of predefined factors in energy
consumption change Ediger and Huvaz (2006); Ma and Stern (2008);
Hatzigeorgiou, et al. (2008); Mairet and Decellas (2009); Zhang, et al.
(2011); and Zhao, et al. (2012)]. In some of the studies, sectoral effects
representing a transition from agriculture to industrialization explains
increase in energy demand [Lotz and Pouris (2012); Zhao, et al. (2012)],
while in many other studies, activity effect is the main driver of change
in aggregate energy consumption [Balezentis, et al. (2011); Nasab, et al.
(2012)]. Hasabeigi, et al. (2012) incorporate additive LMDI approach
for decomposing energy intensity of Californian industries for the period
1997-2008 and find that structural effect that cause a shift from high
energy intensive industries to low energy intensive industries play an
important role in reduction of energy intensity.
Among different IDA techniques, LMDI is found to be a superior
decomposition method in various past studies Ang and Liu (2001); Ang
(2004); and Ang (2005); Xu and Ang (2014). Ang, et al. (2004)] compare
LMDI results with many other methods including (AMDI)-1,
Conventional and Modified Fisher Ideal Index method, Shapley/Sun
method and Marshall-Edgeworth method, Simple Average Parametric
Divisia Index, Laspeyres Index, and Divisia Index Arithmetic Mean
Function and find that LMDI is preferable on the basis of properties like
theoretical foundation, ease of use, result interpretation and adaptability.
Ang and Liu (2001, 2007) reiterate that LMDI a better decomposition
method with no residual term (an overview of some of the selected
studies on the energy decomposition is given in Annexure). Other
identified features of LMDI include comprehensive formula, error free
decomposition, perfect aggregation and capability of handling zero
values in the data set. Due to these merits, both multiplicative and
additive LMDI are considered superior to other methods. Therefore, we
employed LMDI for decomposing total energy consumption in Pakistan.
Decomposition is a top down approach that has been greatly used
for apportioning the aggregates into different components. This study
aims to investigate the factors that are accountable for change in
aggregate energy consumption in Pakistan for the period 1990-2013
161 Jamil and Shahzad
method. The total energy consumption is decomposed using LMDI for
three periods of different lengths, (i) the analysis for overall period that
is, 1990 to 2013; (ii) 1990-2000; and (iii) 2000 to 2013. The first set
analyzes the aggregate changes for the whole time period to get historical
trends with the year 1990 as a base period and 2013 as a current period.
The second and third sets are for the decade-wise analysis, where base
years are 1990 and 2000 respectively. Change in aggregate energy use is
a long term phenomena hence, we conduct a period-wise decomposition
rather than time series decomposition. The analysis classifies the overall
economy into three major sectors including agriculture, industry and
services. Main variables in the data set are the final energy consumption
in Tonnes of Oil Equivalent (TOE) and output level (GDP in million
rupees) for all the three sectors. Sector-wise energy consumption data is
obtained from Hydrocarbon Development Institute of Pakistan
(Unpublished) and collected from Pakistan Energy Yearbook
(Published). The economic activity data are collected from various issues
of Pakistan Economic Survey.
The study employs IDA based method of LMDI proposed by
Ang and Choi (1997) and Ang (2005). LMDI is based on an identity,
where the left-hand-side must equal the right-hand-side making a
decomposition model with no residual. There are two main approaches
in LMDI: (a) ratio decomposition also known as multiplicative
decomposition and (b) difference decomposition also known as additive
decomposition. In this study, the latter is adopted, as the former gives
results in ratio form that are difficult to interpret. The method examines
the impact of pre-defined effects explaining the change in total energy
consumption in absolute form by adding all the factors on right-hand side
of the identity. The overall change is divided into the above mentioned
three effects as defined below:
1. Activity effect refers to the change in aggregate energy
consumption due to change in overall activity level.
2. Structural effect refers to the change due to a change in activity
mix by a sub-sector.
3. Intensity effect refers to the use of energy per million rupees of
output.
Decomposition Analysis of Energy Consumption 162
Let the total energy change be represented by G, assuming that there
are a number of factors represented by n associated with change in the
aggregate over the period of time with quantitative variables, i.e., x1, x2,
x3 , ..., xn. The subscript i represents sub-sector. The general IDA identity
is given as follows:
∑ 𝐺𝑖 𝑛𝑖=1 = ∑ 𝑛
𝑖=1 𝑥1𝑖, 𝑥2𝑖, … , 𝑥𝑛𝑖 … (1)
The aggregate change from G0 to GT takes the form:
𝐺0 = ∑ 𝑥1𝑖𝑜 , 𝑥2𝑖
𝑜 , … , 𝑥𝑛𝑖𝑜𝑛
𝑖=1 and 𝐺𝑇 = ∑ 𝑥1𝑖𝑇 , 𝑥2𝑖
𝑇 , … , 𝑥𝑛𝑖𝑇𝑛
𝑖=1
where, 0 is the base period and T the current period. Decomposition
through additive technique takes the following form.
∆DTot = GT- G0 = ∆Gx1+ ∆Gx2 + … + ∆Gxn. … (2)
Additive decomposition gives results in absolute form by adding all the
factors on the right-hand-side making them exactly equal to the left-
hand-side of the identity. The general LMDI formula for the kth factor
in Equation (2) is as follows:
∆Gxk= ∑ 𝐿(𝐺𝑖𝑇 𝐺𝑖
0)𝑙𝑛 (𝑥𝑘𝑖𝑇/𝑥𝑘𝑖
0𝑛𝑖=1 ) …(3)
The IDA for the three factors case is given below,
E= ∑ 𝑛𝑖=1 Ei = ∑ 𝑄𝑛
𝑖=1 𝑄𝑖
𝑄
𝐸𝑖
𝑄𝑖= ∑i QSiIi … (4)
where,
E = The total energy consumption of all the sectors in the economy
Q = ∑i Qi is the total economic activity/output level of all sectors in the
economy
Qi is the output level of the ith sector
Si = Qi/Q is the activity share of the ith sector
Ei is the energy consumption of the ith sector
163 Jamil and Shahzad
Ii = Ei /Qi is the energy intensity of the ith sector
For additive decomposition, we decompose the difference such as,
∆ETOT= ET-E0= ∆EAct+ ∆EStr+∆EInt … (5)
where,
∆ETOT denotes aggregate change in energy consumption,
ET denotes energy consumption in current year,
E0 denotes energy consumption in base year,
∆EAct denotes change in energy consumption due to activity effect,
∆EStr denotes change in energy consumption due to structural effect,
∆EInt denotes change in energy consumption due to intensity effect.
Three effects on right-hand-side of the Equation (5) are estimated by
employing the following Equations:
∆EAct = ∑i [𝑊𝑖 ∗ 𝑙𝑛 (𝑄𝑇
𝑄0)] … (5a)
∆EStr = ∑i [𝑊𝑖 ∗ 𝑙𝑛 (𝑆𝑖𝑇
𝑆𝑖0)] … (5b)
∆EInt = ∑i [𝑊𝑖 ∗ 𝑙𝑛 (𝐼𝑖𝑇
𝐼𝑖0)] … (5c)
where,
𝑊𝑖 = 𝐸𝑇−𝐸0
𝑙𝑛 𝐸𝑖𝑇− 𝑙𝑛 𝐸𝑖0
To calculate activity effect (∆EAct) for the three sectors, we divide total
current period output by total base period output for all three sectors
(QT/Q0) and take their natural log. The results are multiplied to aggregate
Wi of all the three sectors that gives us final value for activity effect. The
structural effect (∆EStr) is calculated for the three sectors by dividing the
current period output share of the ith sector in total output by base period
output share of the same sector in total output, i.e., (SiT/Si
0). Similarly,
intensity effect (∆Eint) is calculated for the three sectors by dividing the
current period energy intensity of the ith sector by base period energy
intensity of the same sector, i.e., (IiT/Ii
0). The sum value of three
Equations (5a), (5b) and (5c) must be equal to the term ∆E= ET - E0 in
Equation (5).
Decomposition Analysis of Energy Consumption 164
4. RESULTS AND DISCUSSIONS
The study is carried out for three time periods using methodo-
logy described in Section 4. First analysis gives us overall energy
consumption decomposition for the whole period of 1990-2013. Table 1
shows the decomposition results for all three periods. In each period, the
first row show decomposition results in absolute form with unit of energy
consumption change, while second row shows the relative contribution
of the three effects in total change in percentage form. The total change
in energy consumption is 18.603 MTOE by taking 1990 as base year and
2013 as current year. The dominant contribution of 159.97% comes from
activity effect with 29.780 MTOE.
Intensity effect is the second dominant contributor having a share
of -12.722 MTOE, implying 68% reduction in overall change in energy
consumption, thus compensating the inflated demand of energy use
driven by activity and structural effects. It means that energy intensity
has decreased leading to a contraction of 12.722 MTOE in overall energy
change. Since energy intensity is inversely related to energy efficiency,
a decrease in energy intensity implies a rise in energy efficiency. Several
factors that contribute directly and indirectly towards energy efficiency
are innovative energy saving policies such as promotion of energy
efficient appliances, growth of natural gas consumption and LPG in all
the three sectors of economy during the period.
The third contribution of 8.41% with 1.565 MTOE in total
change comes from structural effect, showing that structure of the
economy experienced slight transition from agrarian to industrial and
services dominated economy. The sectoral composition of the economy
changed especially in the 2000s. The share of agriculture in economy
falls by 17% between 1990 and 2013. The shares of industry and services
sector increase by 10% and 5% respectively during the period. Statistics
show structural shift from low energy intensive agriculture sector to high
energy intensive industrial and service sector thus contributing in rise of
energy consumption.
The overall increase of energy consumption is dominantly
contributed by services and industrial sector. The cumulative effects of
service sector indicate that this sector is responsible for 12.501 MTOE
165 Jamil and Shahzad
in total change. Similarly, industrial sector is responsible for an increase
of 6.937 MTOE in total energy consumption over the period 1990-2013.
Agriculture is responsible for a slight decline of 0.835 MTOE in the
overall change. The sector wise decomposition analysis shows a decline
of -0.835 MTOE in agriculture sector. Activity effect is responsible for
an increase of 1.022 MTOE due to growth in agriculture sector, while
structural and intensity effects dominantly decrease the total change.
Structural pattern also indicates within sector transition from energy
intensive crop sector towards less energy intensive livestock sector.
A rise of 6.937 MTOE in industry is mainly due to activity effect
followed by structural effect having share of 150% and 14% respec-
tively. Due to growth in industrial sector, activity effect is the main
source of growth in energy consumption. Structural effect shows that
industrial sector has been changing its sub-sector composition with rising
share of mining, quarrying and manufacturing. The compensating share
of 63% is reflected through intensity effect with decline of 3.689 MTOE
in total industrial energy consumption change. It means industrial sector
has witnessed energy efficiency due to modern techniques of production,
change in sub-sectoral composition with slight shift from more energy
intensive sub-sectors to less energy intensive sub-sectors as mentioned
in Figure 5.
An increase of 12.501 MTOE in services sector is mainly
attributed to activity and structural effects with share of 147% and 7%
respectively. Due to growth in services sector, activity effect is
responsible for 18.325 MTOE rise in total sectoral energy consumption
change. The structural changes in sub-sector composition of services
sector as shown in Figure 6, is captured by a rising share of sub-sectors
like finance and insurance and social and community services in total
service industry over the period 1990-2013. Intensity effect has
compensating share of 53% causing total energy consumption change
decrease by 6.56 MTOE. Energy efficiency in services sector is mainly
attributed to shift from high energy intensive subsectors to less energy
intensive subsectors. The dominant share of service sector in total energy
use implies that energy is used as a final good to maximize well-being
although it may not directly generate economic activity.
Decomposition Analysis of Energy Consumption 166
Table 1 shows the results of total change in energy consumption
which stands at 3.70 MTOE over the period 1990-2000. The dominant
factor behind this change is activity effect followed by intensity effect.
Activity effect (∆EAct) is responsible for 278% increase in total change
equivalent to 10.28 MTOE. The second dominant contributor is intensity
effect (∆EInt) with -6.51 MTOE holding a share of -175.85% in overall
change. The contribution of structural effect (∆EStr) is negligible with
0.066 MTOE (1.77%) decline in overall energy consumption change.
Figure 3 shows that sectoral composition of Pakistan economy in the
1990s was traditional with little structural shifts towards industriali-
zation. The negative contribution of structural effect in overall energy
consumption change is due to slight decrease in the service sector share.
To summarize, the total change of 3.703 MTOE is dominantly contri-
buted by services and industry. The cumulative effects of services sector
is 3.179 MTOE followed by industrial with 1.344 MTOE in total
increase. Agriculture sector is responsible for a decrease of 0.820 MTOE
in overall change in this period.
Table 1. LMDI Decomposition Results of Pakistan Economy:
1990-2013
Category (Unit) ∆EAct ∆EStr ∆EInt ∆ETOT
1990-2013
Absolute change
(MTOE) 29.78 1.565 -12.722 18.603
Share in change (%) 159.97 8.41 -68.38 100
1990-2000
Absolute change
(MTOE) 10.282 -0.066 -6.513 3.703
Share in change (%) 277.62 -1.77 -175.85 100
2001-2013
Absolute change
(MTOE) 17.98 1.900 -4.986 14.90
Share in change (%) 120.71 12.75 -33.46 100
167 Jamil and Shahzad
The structural and intensity effect results for the Period 2000-
2013 are different as compared to preceding period. Structural effect has
a higher share of 12.75 % in the energy consumption change as compared
to -1.77 % in 1990s which clearly shows that economy had experienced
structural changes. The share of intensity effect falls from 176% in the
1990s to just 34% in the 2000s, which shows that energy efficiency still
provide relief to inflating energy consumption but its share is declining.
During 2000-2013, total energy consumption increases by an
amount of 14.90 MTOE. Due to fast pace economic growth in the mid-
2000s, the activity effect is the major contributor having share of 17.98
MTOE (121%). The share of activity effect in overall energy
consumption falls from 278% to 114% during 2000-2013 as compared
to 1990-2000 implying that rapid growth was absorbed and spread by
structural and intensity effects. The share of structural effect in total
change has increased from 1.77% in 1990s to 13% in this period.
Intensity effect is the most considerable factor in this period, showing
low trends of energy efficiency. In this period, intensity effect is
responsible for a decline in overall energy consumption change
equivalent to 4.98 MTOE (33%).
Table 2 shows sector-wise decomposition results. The agriculture
sector is responsible for 0.820 MTOE decline in total energy
consumption change mainly due to structural and intensity effects.
Activity effect explains the change in energy consumption by 0.45
MTOE (55%) mainly due to growth in agriculture sector. Intensity effect
has compensating share of 1.28 MTOE (157%) using decrease in total
agricultural energy consumption. Energy efficiency is mainly due to shift
from high energy intensive crop sector to low energy intensive livestock
sector along with the modernized farming techniques and energy
efficient farm machinery. The table shows a rise of 1.344 MTOE in
industrial sector’s energy consumption, due to activity and structural
effects and compensating intensity effect. The activity effect is
responsible for 262% increase in energy consumption accruing from
growth in the industrial sector. Structural effect accounts for 6% share in
total industrial energy consumption change. The industrial sector has
witnessed energy efficiency as depicted by a compensating share of
168% in total change. Modern production techniques, energy saving
Decomposition Analysis of Energy Consumption 168
machineries, high value added output and changing sub-sector com-
position of industrial sector are the main reasons of energy efficiency in
the industrial sector.
An increase of 3.179 MTOE in services sector is mainly due to
activity effect with a share of 198.43%. Structural and intensity effect
has compensating share of 5% and 93% respectively due to changing
sub-sectoral shares. Last panel of Table 2 shows an increase of 14.90
MTOE in 2000-2013, which is dominantly contributed by services and
industrial sector. The combined effect of services sector is 9.32 MTOE
followed by industrial sector with 5.59 MTOE over this period. The
effect of agriculture sector is negative with a decrease of 0.015 MTOE
in overall energy consumption change.
Table 2. Sector wise LMDI Decomposition Results of Pakistan
Economy 1990-2013
Sector ∆EAct
(MTOE)
∆EStr
(MTOE)
∆EInt
(MTOE)
∆ETOT
(MTOE)
∆EAct
%
∆EStr
%
∆EInt
%
Total
%
1990-2013
Agriculture 1.02 -0.209 -1.59 -0.84 -122.35 23.93 198.41 100
Industry 10.41 0.938 -3.68 6.94 150.09 13.52 -63.62 100
Services 18.32 0.827 -6.56 12.50 146.58 6.61 -53.20 100
Total (MTOE) 29.76 1.565 -11.84 18.60 _ _ _ _
1990-2000
Agriculture 0.45 0.013 -1.28 -0.82 -55.47 -1.65 157.12 100
Industry 3.51 0.081 -2.25 1.34 261.63 6.35 -167.99 100
Services 6.30 -0.162 -2.96 3.18 198.43 -5.17 -93.25 100
Total (MTOE) 10.28 -0.064 -6.51 3.70 _ _ _ _
2001-2013
Agriculture 0.37 -0.139 -0.25 -0.015 -2483 927.29 1655.75 100
Industry 6.28 0.891 -1.58 5.59 112.33 15.94 -28.28 100
Services 11.33 1.148 -3.16 9.32 121.53 12.31 -33.85 100
Total (MTOE) 17.98 1.900 -4.98 14.90 _ _ _ _
169 Jamil and Shahzad
Sector-wise decomposition results show that compensating 0.015
MTOE change in the agriculture sector is attributed to structural effect
(927%) and intensity effect (1656%). Activity effect is responsible for
an increase of 0.37 MTOE in total change in agricultural sector energy
consumption due to the sectoral growth. Structural shifts are responsible
for decrease of 0.14 MTOE. Intensity effects also have compensating
share of 0.24 MTOE in the total change arising mainly from change in
sub-sector composition. The activity and structural effects with share of
112% and 16% respectively account for an increase of 5.59 MTOE in
the industrial sector. Activity effect is responsible for an increase of 6.28
MTOE in total industrial energy consumption change due to growth in
that sector. Intensity effect has compensating share of 3.16 MTOE,
indicating energy efficiency achievements in industrial sector. A rise of
9.32 MTOE in services sector is contributed by activity and structural
effects with share of 122% and 12%, respectively. Activity effect brings
about 11.33 MTOE increase in total service sector energy consumption
change due to massive growth particularly in the mid-2000s. Change in
sub-sector composition is responsible for an increase of 1.148 MTOE
through structural effect. Energy efficiency brings a compensating share
of 3.16 MTOE reduced through the intensity effect.
5. IMPACT OF ENERGY IMPORTS ON PAKISTAN
EXTERNAL ACCOUNT
Pakistan is a net energy importer and relies both on domestic and
imported resources to meet its requirements. Natural gas, oil and
hydroelectricity account for about 90% of energy supply in the country.
The government regulates the distribution of almost all major energy
sources. Natural gas supply is made out of domestic resource and often
its supply falls short of demand especially during the peak demand
season. In that case, oil or LNG imports satisfy excess demand and bring
the market into equilibrium. However, heavy share of imports in total
energy consumption puts pressure on the balance-of-payments account.
Some earlier studies highlight the impact of energy imports on the
external account of Pakistan [Malik, 2007; Jamil (2012)]. The depend-
ence on imported energy will further increases due to depleting domestic
Decomposition Analysis of Energy Consumption 170
resources in future that will create negative impact on country’s external
account. Jamil (2012) argues that internal energy policy and regulations
are causing a shortage of indigenous energy resources that resulted in
increased imports earlier than it is binding. Hussain (2010) also argues
that planning of energy supply composition should integrate the external
cost of energy use.
Sustainable and affordable energy supply drives to economic
growth and well-being. The rising demand for fossil fuels and its price
volatility put extra burden on energy importing economies and make
energy sector management and planning quite challenging. Energy
security especially in uncertain international oil market has been an
important political and economic concern. Energy dependence to fulfill
the total energy requirement can be measured as net energy import as
percent of total energy consumption. High energy dependence leads a
country to vulnerability with negative impacts on foreign exchange
reserves and expose the economy to international economic shocks.
The world is facing serious challenges to sustainably meet its
growing energy requirements and Pakistan, being an energy deficient
country, finds it hard to ensure reliable energy supplies. The rising
energy demand coupled with limited and depleting domestic energy
resources has unfolded since the mid-2000s. According to SBP Annual
Report 2012-13, Pakistan spent approximately USD 14.9 billion (33%
of total imports) on petroleum imports with a quantity of 17.9 MTOE.
According to Pakistan Integrated Energy Plan 2012-2025, if GDP grows
at the rate 2-4%, then the final energy consumption of Pakistan will reach
142 MTOE by 2025 with cumulative growth rate of almost 250%
(equivalent to 40 MTOE).
For many years, the country meets only 20% of its total oil
requirements through domestic production and the rest is imported.
Keeping the same ratio of 80:20 till 2025 implies that the country will
require to import 32 MTOE of oil which is equivalent to 220.8 million
barrels per annum. The forecasted world oil price for 2025 is $100/barrel,
taken from the World Bank commodity price index. At price $100/barrel,
Pakistan will have to spend $22.85 billion on oil imports by 2025. It is
pertinent to note that any increase in price of crude oil may further
increase the import bill.
171 Jamil and Shahzad
According to Pakistan Integrated Energy Plan 2012-2025, by the
year 2025, Pakistan will also require 10.12 billion cubic feet per day
(BCFD) of natural gas with expected domestic production of 2.17
BCFD. The imports of 7.95 BCFD will be equivalent to 2992 million
British thermal Unit (MBTU) per annum. If the international price of gas
forecasted by the World Bank Commodities Price Forecast remains
$10/BTU, Pakistan will have to spend $ 29.92 billion by 2025. Table 3
shows the impact of projected imports on external account in 2025.
Projected estimates show that the country will have to spend $52.76
billion on imports of oil and gas by 2025. The total value of oil and gas
imports in 2013 were $14.9 billion that will witness a growth of 250%
by 2025.
Table 3. Import Bill based on Projections
Domestic Imports Total World Price Value
($ billion)
Oil 2025
(MTOE)
8
32 40
$ 100/barrel 22.85
Gas 2025
(BCFD)
2.17
7.95 10.12 $
10/(MBTU)
29.91
Oil 2013
(MTOE)
11.3 19 30.3 $ 107/barrel 14.9
Sources: Pakistan Integrated Energy Plan, 2012-2025, Pakistan Energy Year Book, HDIP, 2013, WB Commodity
Price Forecast, 2015.
High dependence on imported energy will further expose the
economy to vulnerability with interrupted energy supplies and price
shocks especially in international oil market. The negative impacts of
$52.76 billion worth imports will severely reflect through both internal
account (budget) and external account (balance of payment). The ever
rising-energy imports have serious implications for an economy with
trade deficits. In consideration of limited foreign exchange reserves,
there is a need to reduce our dependence on energy imports by domestic
resource mobilization, through inflow of foreign direct investment,
efficient use of available energy resources, and balanced energy mix.
Energy conservation is the best method to lower the energy supply and
demand deficit. Currently, Pakistan has the potential to save 25 % of
energy on average worth $5billion annually [ENERCON (2015)].
Decomposition Analysis of Energy Consumption 172
6. CONCLUSION
This study provides a comprehensive analysis of the factors
responsible for growing energy consumption in Pakistan over the period
1990-2013 with segmented time period analysis using LMDI
decomposition technique. Finding the determinants of change in overall
energy consumption is important for appropriate energy policy design
and its implementation. In Pakistan energy mix is mainly comprised of
four fuel types; natural gas, oil, hydroelectricity and coal. LMDI techni-
que tracks down the pre-defined determinants (activity effect, structural
effect and intensity effect) responsible for increased energy consumption
in three major sectors.
Total energy consumption in Pakistan increased by 86.2%
(equivalent to 18.60 MTOE) over the period 1990-2013. Even so, there
is prevalence of enormous unmet energy demand due to natural gas and
electricity shortage especially since the mid-2000s. The energy shortage
is a result of the domestic energy market control by the government, who
are responsible for overall energy planning and influence the prices and
quantity supplied to different sectors. This increase is mainly due to the
activity effect followed by an intensity effect. By 2025, the final energy
consumption is projected to reach 142 MTOE level. In analysis of the
period 1990-2013, activity effect is found to be the main contributor in
overall energy consumption growth, which reiterates the positive
relationship between economic growth and energy consumption.
Intensity effect is the second major compensating contributor in the
overall energy consumption change showing strong indications of
energy efficiencies in overall economy as well as in sector wise analysis.
The share of structural effects are low in volume but indicates that the
economy experiences a slow pace structural shift from traditional
economy to industrialized and service-led economy.
Analysis of the period 1990-2000 shows the highest share of
energy efficiency through intensity effect in the overall change. Activity
effect remains the most dominant contributor in the overall energy
consumption change with services and industrial sectors holding the
dominant share in overall activity effect. The share of structural effects
is very little due to stagnant sectoral composition of economy over this
173 Jamil and Shahzad
period. The compensating share of intensity effect in total energy
consumption is comparatively less than the preceding period showing an
overall slowdown energy efficiency.
The quantification of energy imports based on projections shows
that Pakistan faces serious challenge of rising import bill due to
dwindling domestic energy reserves and energy price volatility. The
findings of our study are consistent with various past studies conducted
for different countries mentioned in literature review [Ediger and Huvaz
(2006); Mairet and Decellas (2009); Sahu and Narayanan (2010); Zhang,
et al. (2011); and Nasab, et al. (2012)]. The decomposition findings of
Pakistan economy is consistent with other developing countries in which
activity and intensity effect contribute dominantly with structural effects
up to some extent. The findings of this study provide basic foundations
for energy related policies both in their formulation as well as
implementation. For policy makers, the result of energy intensity effect
is startling as it shows an increase in energy intensity during recent
decade, which is a sign of concern. In order to reduce gap between
energy demand and supply, energy conservation is the best and cheapest
method as Pakistan has potential to save sufficient energy. Furthermore,
the petroleum exploration policies should be designed judiciously to
attract investment in order to reduce import dependence.
Annexure
List of Selected Studies on Energy Decomposition
Study Country Method Sector Effects/Factors
Choi, et al. (1995) Korea Divisia Index Manufacturing Aggregate
intensity
Ang and Lee (1996) Singapore
Taiwan
Energy Coefficient
Approach Electricity
Sectoral
intensity
Ediger and Huvaz (2006) Turkey LMDI Whole
Economy Sectoral
Ma and Stern (2008) China LMDI Whole
Economy Technological
Mairet and Decellas
(2009) France LMDI Services Activity
Sahu, and Narayanan
(2010) India
General Parametric
Divisia Index and
Laspeyers
Decomposition Index
Manufacturing
Structural
Intensity
Activity
Zhang, et al. (2011) China LMDI Transport Activity
Intensity
Decomposition Analysis of Energy Consumption 174
Balezentis and
Strienikiene (2011) Lithuania LMDI
Whole
Economy Intensity
Nasab, et al. (2012) Iran LMDI Industrial
Activity
Structural
Intensity
Hasabeigi and Sathaye
(2012) California LMDI Industrial Structural
Zhao, et al. (2012) China LMDI Residential
Income
Structural
Intensity
Activity
Population
Lotz and Pouris (2012) South
Africa LMDI
Whole
Economy Structural
Ullah, et al. (2014) Pakistan Fischer Ideal Index
(IDA)
Whole
Economy Intensity
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NUST JOURNAL OF SOCIAL SCIENCES AND HUMANITIES
Vol.3 No.2 (July-December 2017) pp. 178-192
The Productivity Paradox: Does Happiness Matter?
Farooq Rasheed* and Eatzaz Ahmad†
Abstract:
Productivity paradox refers to a situation when investment in information
technology is inversely related with economic growth. We test the hypothesis whether
economic happiness plays a moderating role between information related
capitalization and economic growth and thus may or may not invalidate the
productivity paradox. Using 20 years annual panel data of OECD and APT economic
blocs, happiness moderates the relationship between inputs labour and capital
productivities with output growth in both the economic blocs. Economic Happiness is
thus recommended to be boosted in getting effective labour and capital productivities.
Keywords: Productivity Paradox, Happiness, Information Technology
1. INTRODUCTION
a. Productivity Paradox
Economic inefficiencies are often seen in various regions of the
world. It is observed that economic growth often lags the amazing
technological progress and South Asian region is no exception. Islam,
Salim and Bloch (2016) examine the impact of intra-regional initiatives
on various aspects of efficiency along with growth of productivity in
South Asia. They observed that the South Asian region has suffered from
a total factor productivity shrinkage and economic hammering of some
degree through technological inefficiencies and are slow in adopting
such technological innovations.
Amjad and Awais (2016) review Pakistan’s productivity
performance over the period of 1980 to 2015. Authors examined the
contribution of physical capital, human capital and TFP to labour
* Mr Farooq Rasheed <[email protected]> is Assistant Professor at Air
University, Islamabad, Pakistan. † Eatzaz Ahmad <[email protected]> is Chair Professor of the State Bank of
Pakistan at University of Peshawar, Peshawar, Pakistan.
179 Rasheed and Ahmad
productivity and observed that the contributions of physical capital and
education remained modest and there has been a declining trend in TFP
growth. They identified the declining trend in labour productivity and
total factor productivity (TFP) for which the lack of sustained growth
and declining levels of technological investment were found to be the
key causes. They concluded that Pakistan’s economy has not taken full
advantage of the favourable technological developments. This is
somewhat similar to the predictions of Moore’s Law, which has held for
more than four decades. It was noted that companies bought computers
on the guarantee that the “computer age” would revolutionize business.
In the 1970s, information technology (IT) related technical
equipment accounted for about 25% of all information technology
related business investments. However, a number of researches in the
1980s and 1990s failed to get any evidence for improvement in such a
technological productivity contribution [Berndt and Morrison (1995)].
In the 1980s and the early 1990s, the “productivity paradox” was widely
debated. The productivity paradox is the unusual observation made that
as more investment is made in IT, workers’ productivity instead of
growing actually declines. Despite striking advances in computer field
and increasing capacity of the IT sector, growth rate of productivity
declined in the US economy. Labour productivity growth rate in the
1960s was around 3% and fell to approximately 1% in the beginning of
the new millennium. These paradoxical productivity patterns are also
referred to or termed as “Solow Computer Paradox” due to Solow’s
(1987) statement “You can see the computer age everywhere but in the
productivity statistics”. Researches point out three possibilities for such
a paradox:
i. Data and analytical problems hide productivity revenues, i.e., the
ratios for input and output are not easy to measure. As a culture
moves progressively from a qualitative one to a quantitative one, its
effect on productivity increases even more, thus further hiding the
gains which could be ascribed to technology.
ii. Revenues gained by a company through productivity may not always
be easy to account for, because these could be offset by losses in
other divisions or departments. But as the overall productivity is
The Productivity Paradox: Does Happiness Matter? 180
considered, these could be buried in the details. Once again, profits
accrued just by investments in productivity become hard to measure.
iii. Complex designing, administering and maintaining of IT systems.
These costs are due to rapid obsolescence of equipment and software,
incompatible software and network platforms and issues with
security such as data theft and viruses. This drives a continuous cycle
of technology replacement.
Wetherbe, et al. (2007) emphasize that in order to interpret the
paradox, the concept of productivity has to be well understood. The
existence of the paradox may not be the same among different firms or
economies. This could be due to the differences in their efforts of
adopting technological development and possibly the differences in
standards of information technology.
To answer how information technology affects productivity, it
seems important to understand the functional role of information
technology towards production. Economists consider its role as a factor
of production and thus provide vital statistics for growth accounting and
help in defining meaningful linkage between inputs like labour, capital
and IT capitalization with growth rate of the output of an economy. Table
1.1 showing the trends and reoccurrence of such a paradox is visible
beyond year 2001.
Most of the studies have also found that in comparison to other
capital investment, IT investments were associated with higher marginal
productivity. Some studies translate these returns into “excess returns”,
by stressing a perspective that investments should pay the same risk
adjusted returns thus ending up with lower net returns. In contrast,
Brynjolfsson and Hitt (1996) observed that the net returns from IT
investments were nonetheless more than the returns in non-IT
investments; partly due to the complementary nature of such investments
that the firms establish for raising assets.
Acemoglu, et al. (2014) have revisited the productivity growth
and IT related issue and found no significant evidence of increasing
productivity growth in labour working in IT exhaustive industries-
bearing high cost of technological investments. Furthermore, authors are
of the view that such IT related investments are one of the causes of
181 Rasheed and Ahmad
workplace distractions. Arguably this could have negative effects at
workplace productivity till such noise pollution factors are controlled.
Table 1.1. Reoccurrence of Productivity Paradox
In their study on information technology business value,
Melville, et al. (2004) split performance into two categories: business
process performance and organizational productivity and define that
business process related performance is actually operational efficiency
measured by factors like customer service, flexibility and culture of
information sharing, while the factors like market value, profitability,
competitive advantage account for firm’s productivity. Sunny, et al.
(2005) noted significant association between IT investments and
performance efficiencies in the hotel industry through factors like
enhanced annual sales, greater level of repeat business, increased
occupancy rate, enhanced positive word of mouth and reduced operating
costs. Gartner (2012) states “However, despite unclear causality, on an
industry level there appear to be interesting relationships between the
level of investment and the operating profits of organizations… many
organizations with high operating margins also have high IT spending as
a percentage of revenue. This view should not imply that, by investing
more in IT, an organization should expect to get better profitability,
The Productivity Paradox: Does Happiness Matter? 182
rather, it should simply outline how different industries behave under
varied economic conditions.” This suggests that financial return cannot
be considered as the sole indicator of performance, rather other factors
are required to be explored.
b. Economic Happiness
Kazi (2002) using Figures 1 and 2 demonstrates how the
technological reliance and economic dependence are complementary in
nature, but often forms a vicious circle of technological reliance. This
vicious circle encircles increased dependence on foreign inputs that lead
to weak indigenous capacity and thus an ineffective economic growth
and development. It may result in possibilities like retreating economic
growth, enhancing poverty index, reduction in wellbeing index for the
society, etc. The idea that wellbeing or happiness is becoming important.
Magnus (2001) using Swedish micro data reported that the economics of
happiness is quantitative and theoretical in nature that studies well-being,
life satisfaction, quality of life, psychological and health aspects.
Though the wealth accumulation is considered as the key
objective in life, but should not be at the cost of happiness. Mostly the
measures of economic improvements do not reflect the happiness
aspects. Can money buy happiness? The debate on this query is now
taking place frequently. Anielski (2009) classifies assets as natural,
financial, human & social types and stresses to improve them to build
not just a wealth but a genuine wealth.
This helps recent literature also emphasize on happiness and
labor productivity linkage. The dimension of happiness has been taken
into account to test such linkage to support the happy society view of
philosophers like Aristotle, Confucius and Plato.
Does a rise in happiness affect productivity? Through conducting
three different styles of experiments, Oswald, Proto & Sgroi (2015)
found that happiness makes labors more efficient and productive. In the
experiments, the selected persons were made happier and found that the
treated people were 12% more productive and observed that lower
happiness is associated with low productivity.
183 Rasheed and Ahmad
Figure 1. The Vicious Circle of Technological Reliance
L o w D e m an d f o r I n d i g e n o u s S & T Se r v i c e s
W e ak I n d i g e n o u sC ap ac i t y
S & T
R e l i an c e o nF o r e i g n I n p u t s
M o r e F o r e i g nI n p u t s
I n e f f e c t i v e d e v e l o p m e n t o f I n d i g e n o u s S & T
F u r t h e r m ar g i n a lI n d i g e n o u s S & T
i sat i o no f
I n c r e ase d d e p e n d e n c eo n f o r e i g n I n p u t s
Source: Adopted from Kazi (2002)
To see the impact on economic growth, two variables - life
expectancy and investment ratio representing happiness were
considered to test the significance in the relationship between them, Li
& Lu (2010) found a robust positive correlation.
Guriev and Zhuravskaya (2009) in contrast found a positive and
significant effect on life satisfaction with Gini coefficient. Rasheed,
Ahmad and Rauf (2011) using pooled data of industrial / developed
economies, observed a significant impact of economic happiness on
GDP growth.
Veenhoven (2000) established counter logical connection
between economic happiness and income equality and found that the
presumed link fails to exist. Average economic happiness was high in
countries where income related equality was poor.
The Productivity Paradox: Does Happiness Matter? 184
Figure 2. The Vicious Circle of Economic Dependence Superimposed
on the Circle of Technological Development
R e t ar t e d D e v e l o p m e n to f L o c a l I n d u st r y
H i g h E c o n o m i cD e p e n d e n c e
H i g h D e m an d f o rF o r e i g n L o an s
W e ak I n d i g e n o u s C ap ac i t y
S & T
M o r e I m p o r t s
L e ss R e so u r c e s f o rI n d i g e n o u s S & T
D e v e l o p m e n t
I n e f f e c t i v eD e v e l o p m e n t o f
I n d i g e n o u s S & TD e v e l o p m e n t
I n c r e ase d D r ag o nF o r e i g n E x c h an g e R e se r v e s
Source: Adopted from Kazi (2002)
Since a happy state of labour is more productive therefore a
happiness factor can influence the existing relationship between
capitalization in information technology (IT) and economic growth
through effective labour productivity and thus invalidates productivity
paradox. Happiness aspect as a policy objective in achieving higher
productivities is generally overlooked. This study thus attempts to test
the hypothesis whether economic happiness plays moderating role
between information related capitalization and economic growth.
2. DATA AND METHODOLOGY
In order to test the argument we raised, the annual time series
data of total employment to represent participatory labour force (N)1,
1 Expectedly, labour force in general is more the more IT literate over the time.
185 Rasheed and Ahmad
Investment in Information and Communication Technologies (ICT) to
represent capital and GDP(Y) at factor cost for the all OECD and
ASEAN plus three (APT2) countries are taken from The World Bank’s
data bank for a period of 20 years from 1996 to 2015. The happiness (H)
data are taken from world data base of happiness and New Economic
Foundation, UK. Generally in OECD countries the growth in investing
in information technology is higher and modern than in Asian
economies, therefore we have considered OECD and APT blocs to be
compared for the hypotheses we have developed.
The data are transformed into natural log form to get
responsiveness of each input, i.e., labour and capital towards output
productivity using panel least square regression estimates of the
following equations with happiness as a moderator in which Equations
2.1 and 2.2 will test the moderating role3 of happiness factor. Since
pooled ordinary least square (PLS) mitigates the existence of non-
independent observations, thus catering to the issue of serial correlation;
thus we preferred PLS over other regression models such as fixed or
random effect models.
Ln Yt = β0 + β1 Ln Kt + β2 Ln Nt+ εt …(2.1)
TFPt = λ0 + λ1Ln Ht +εt …(2.2)
Ln Yt = b0 + b1 Ln Kt + b2 Ln Ht + b3 Ln KtHt + εt …(2.3)
Ln Yt = d0 + d1 Ln Nt + d2 Ln Ht + d3 Ln NtHt+ εt …(2.4)
where,
Ln Yt = Natural Log of GDP at factor cost,
Ln Kt = Natural Log of Investment in Information and Communication
Technologies,
Ln Nt = Natural Log of Labour,
Ln Ht = Happiness index,
Ln HtKt and Ln NtKt are the cross product terms to test moderation,
2Consists of countries from ASEAN bloc plus China, Japan, and South Korea. 3Testing Moderation: Moderator effects are indicated by the interaction of an independent
variable and the moderating variable in explaining dependent variable. Following equation
demonstrates estimation of moderated regression Y = Z + aX + bM+ cXM + E. The interaction
of X and M measures the moderation effect via slope ‘c’. Slope ‘a’ measures the main effect of
X. The effect of X on Y is a + cM, thus, the impact of X on Y depends on M is also justified
[Baron & Kenny (1986)].
The Productivity Paradox: Does Happiness Matter? 186
TFPt = Total factor productivity represented by the residual series ‘ε’ is
the error tem, Subscript‘t’ is the time period.
3. RESULTS
The panel regression estimates of the testing hypotheses are
provided in Appendix I. Since each country specific cross section
selected in the pooled data has different types of stochastic components
which means some countries may have a unit root and some may not. In
this situation we have applied common unit root test [Levin, Lin and Chu
(2002)] to test the stationarity of all the selected series (see Table 3a in
the Appendix I). The results show that non-stationarity in the series
found at levels and stationarity at the first difference of the selected
series, suggesting a possibility of long run policy implications.
Table 3.1. Results for Economic Growth
(OECD Bloc)
Variable Coefficient Prob. R-squared Durbin-Watson
C 11.15 0.00 0.96 1.12
Ln(K) 0.31 0.00
Ln(N) 0.43 0.00
(APT Bloc)
C 23.14 0.00 0.99 0.81
Ln(K) 0.53 0.00
Ln(N) 0.27 0.04
Note: Estimates are based on Equation 2.1.
For both OECD and APT countries, with strong degree of
coefficient of determination and no issue of autocorrelation, the response
of investment in information communication technology (ICT) on GDP
is found significant at 5% level of significance and the response of labour
on GDP is also found significant at 5% level of significance (see, Table
3.1).
TFP incorporates technological change, i.e., adopting new
technologies and happiness through sociopolitical factors [Bosworth and
Collins (2008)]. To test economic happiness factor as a determinant of
total factor productivity (TFP), we estimated Equation 2.2 using the
187 Rasheed and Ahmad
residuals from Equation 2.1 and found economic happiness as
statistically significant in both OECD and APT economic blocs at 10%
and thus gives a reason to test economic happiness factor as a moderating
variable (see, Table 3.2).
Table 3.2. Results for Total Factor Productivity
(OECD Bloc)
Variable Coefficient Prob. R-squared Durbin-Watson
C 19.30 0.34 0.99 2.88
Ln(H) 1.56 0.09
(APT Bloc)
C 1.25 0.12 0.98 2.18
Ln(H) 0.85 0.06
Note: Estimates are based on Equation 2.2.
The possibility of moderation of economic happiness on
investment in ICT and GDP relationship is also proved significant at 5%
level of significance for OECD bloc and significant for APT bloc at 10%
level of significance (see, Table 3.3). Finally in the case of labour and
GDP association, the positive moderating role of happiness is again
statistically established for both OECD and APT blocs at 1% level of
significance (see, Table 3.4).
Table 3.3. Results for Moderator on IT
(OECD Bloc)
Variable Coefficient Prob.
C 5.08 0.00
Ln(K) 0.10 0.09
Ln(H) 0.29 0.00
Ln(KH) 0.16 0.03
(APT Bloc)
Variable Coefficient Prob.
C 0.85 0.00
Ln(K) 0.02 0.00
Ln(H) 0.19 0.00
Ln(KH) 0.08 0.00
Note: Estimates are based on Equation 2.3.
The Productivity Paradox: Does Happiness Matter? 188
Table 3.4. Results for Moderator on Labour
(OECD Bloc)
Variable Coefficient Prob.
C 3.31 0.00
Ln(N) 0.80 0.00
Ln(H) 1.12 0.00
Ln(NH) 0.19 0.00
(APT Bloc)
Variable Coefficient Prob.
C 1.48 0.00
Ln(N) 1.21 0.00
Ln(H) 1.19 0.00
Ln(NH) 0.54 0.00
Note: Results are based on Equation 2.4.
4. CONCLUSION
Acemoglu, et al. (2014) observing growth patterns among IT
based manufacturing industries in US economy from 1980-2009, found
some evidence of productivity growth in IT based industries but not
beyond the 1990s; accompanied by observation of a steep rise in
unemployment. However, authors are of the view that rejection of Solow
Paradox by that proponents of the technological discontinuity view may
have been premature. We believe there can be some other factors that
can reverse the situation. We, by introducing economic happiness as an
intangible capital, claim that economic happiness can be augmented with
labour force to make them as effective labour force that can help in
raising production of efficiencies IT based. Our results approve that
economic happiness is pivotal in enhancing the impact of labour and
capital productivities on output growth.
The policy makers for an economy must aim at enhancing this
intangible capital which in the end heaves economic values. The vast
literature suggests quality of life, life satisfaction, social security,
religious independence, access to justice, equality, health, education are
vital determinants of economic happiness and among others thus may be
aimed at developing economic policies.
189 Rasheed and Ahmad
APPENDIX I
Table 3a Common Unit Root Process for all series
Method Statistic Prob.**
Unit root test at level
Levin, Lin & Chu t* 14.4001 1.0000
Unit root test at first difference
Levin, Lin & Chu t* -4.11307 0.0000
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NUST JOURNAL OF SOCIAL SCIENCES AND HUMANITIES
Vol.3 No.2 (July-December 2017) pp. 194-231
Rehabilitation of 2010 Flood Affectees in Pakistan:
The Role of Development Partners
Sheeba Farooq*
Abstract:
Pakistan was hit by catastrophic floods in 2010, which inundated
infrastructure spread over 100,000 sq kms and affected over 20 million people with
large scale displacement. The study explored the impact of floods on the lives of people;
especially their livelihoods and gauged the extent to which the donors contributed
towards restoration of livelihoods of the flood affected population. This was
substantiated through a case study undertaken in Nowshera District, Pakistan, which
was amongst the worst hit areas during 2010 floods. The study used qualitative
research tools to make an in-depth analysis by gathering facts from the affectees and
validating it through the published reports. Semi-structured interviews were held with
the government functionaries, affected population and donor officials. The findings
indicate that almost seventy percent of the population received the Watan Card, which
helped them in resettling and rehabilitating their livelihoods but the rest thirty percent
were unhappy as they thought that the amount was not sufficient to re-activate their
livelihoods.
Keywords: Livelihoods, Rehabilitation, Watan Card
1. INTRODUCTION
1.1. Background
Located in South Asia, Pakistan is the sixth most populated
country in the world, housing a population of 184.35 million [GOP
(2013)] and stretching over an area of 700,000 sq km. Nature has blessed
the country with all endowments in abundance. Its vast expanse includes
long coastal areas, mineral rich deserts, fertile farmlands and lofty
mountains. Five of the fourteen tallest peaks including the second tallest
K-2 are located in Pakistan [Khan (2013)]. However, ever since its birth
* Sheeba Farooq <[email protected]> is Lecturer at School of Social
Sciences and Humanities (S3H), National University of Sciences and Technology
(NUST), Islamabad, Pakistan.
195 Sheeba Farooq
in 1947 the country has remained embroiled in territorial conflict with
neighboring India. In addition, Pakistan has also been facing continuous
ethnic and racial issues as well as political upheavals internally.
Resultantly, except for a few brief periods, the country has been ruled by
the military dictators. The Soviet occupation of Afghanistan, their
retreat, subsequent Talibanization and the post 9/11 war on terror has
taken a major toll upon the already meager economy of Pakistan and has
wrecked its socio-political fabric.
Pakistan has been enduring troubles throughout which has not
allowed its decision-makers to focus upon the issues of socio-economic
development. The lofty mountains, whose melting glaciers could be
utilized for hydel power generation and preservation of water resources
with some planning, now instead bring destruction to infrastructure and
losses to human lives by causing floods with increasing frequency.
1.2. Magnitude of Floods in 2010
Pakistan since its inception has experienced twenty floods1;
however the 2010 floods were the worst in its history. The United
Nations Secretary-General Ban Ki-moon highlighted the devastation
caused by 2010 flood by stating that, “in the past I have witnessed many
natural disasters around the world, but nothing like this” [Brooker
(2011:5)]. This flood according to him was a ‘slow-motion tsunami’. The
studies indicate that “heavy rainfall, flash floods and riverine floods
combined to create a moving body of water equal in dimension to the
land mass of the United Kingdom” [UNOCHA, (2010:10); World Bank,
(2011:7)].
The brutal impact of floods destroyed the lives of millions of
people living in 78 out of 141 districts in Pakistan [Brooker (2011)].
Reports produced by House (2012) and FAO (2012) illustrated that 20
million people and 1.74 million houses were affected by the floods. Not
only that it also brought large scale destruction to infrastructure, water
1As reported by the Ministry of Water & Power (2015), the floods from 1950 till
2015 have affected total area of 616,598 sq km, snatched 12,177 human lives and
caused direct losses worth US$38,165 million.
Rehabilitation of 2010 Flood Affectees 196
channels, sanitation, healthcare, housing and educational institutions
resulting in total disruption of life [UNOCHA (2010)].
The floods jolted the whole economy [Tahir, et al. (2011)].
People became homeless, lost their properties and instruments of income
generation and poverty level rose significantly. For instance, in Punjab
it reached 32.7% after 2010 floods as against 19.7% before floods and in
Khyber Pakhtunkhuwa it rose to 33.2% as against 19.4% pre-floods
[World Bank (2011)].
1.3. Destruction Caused by 2010 Floods in Pakistan
Government of Pakistan requested ADB and WB to carry out the
Damage and Needs Assessment (DNA) in the aftermath of the floods.
The findings of the report were that “the overall recovery and
reconstruction cost associated with the floods is estimated at approxi-
mately US$ 8.74 billion to 10.85 billion, which includes estimated costs
for relief, early recovery and medium to long-term reconstruction”
[World Bank (2010:24)].
The floods caused total or partial damage to almost 1.6 million
housing units and a total of 10,407 educational institutions in the country
were destroyed. Physical infrastructure was also marred by the floods as
estimates have indicated 10% of the road network and 16% railways
were damaged. The agriculture, livestock and fisheries sectors suffered
the highest damages estimated at US$5 billion [World Bank (2010)].
Hence, the floods caused destruction in all the sectors on a wide scale.
The study further highlights that the World Bank in the wake of
the floods initially proposed three options to reconstruct the damages
incurred by the floods as they were not in favour of giving cash assist-
ance. The first option was the Base case with a cost estimated at US$6.8
billion, second option was Building Back Smarter that would ensure
cost-optimization in reconstruction with a cost of US$7.4 billion and the
third was Building Back Better which comprised of larger infrastructure
improvements and was the recommended option by the World Bank with
a cost calculated at US$8.9 billion [World Bank (2010)]. But for one
reason or another and perhaps because of some political pressure the
government opted for cash assistance from donors rather than any of the
options mentioned above.
197 Sheeba Farooq
Government of Pakistan took immediate steps to provide
emergency relief which entailed distribution of essential items. But with
its meager resources and weak economy Pakistan was hardly prepared to
cope with the crises. It entailed rescue of the disaster-effected
population, provision of food and shelter, rehabilitating the people back
to pre-flood level and re-building the inundated infrastructure. Thus, the
country needed support in cash and kind from international donors which
did come.
How adequate and effective was the assistance extended by the
international community, is what this study intends to assess and
ascertain. The study estimates the wider debate of aid effectiveness in
the context of rehabilitation of disaster-effected population with
particular reference to the role of donors and their contribution during
2010 floods in Pakistan. The study shall also ascertain whether
international assistance has helped the Government of Pakistan in
preparing a disaster risk reduction strategy for future. Since the intensity
of floods in Pakistan seems to have rather increased, perhaps due to the
effects of climatic changes, it needs to adopt a pro-active approach
ensuring that the impact of natural disasters like floods is minimized and
that people whose socio-economic activities are disrupted are supported
to rehabilitate their livelihoods as fast and as effectively as possible.
Moreover, the issue of elite capture and its prevalence during the 2010
floods in various areas of Pakistan will also be highlighted in the study.
2. REVIEW OF LITERATURE
The territory of Pakistan comprises valleys and delta wherefrom
the mighty Indus River and all its tributaries flow. Floods in these rivers
are caused by heavy rainfall in upper catchments and Himalayan
foothills resulting from monsoon currents originating from Bay of
Bengal” [Tahir, et al. (2011:4)]. Sixty percent of Pakistan’s population
is still living below poverty line estimated according to the international
poverty line of two dollar a day [WDR (2013)]. The country is facing
multiple socio-economic problems and since 2008-09, the economy on
average grew at 2.94%. During FY12 and FY13 the power shortage
became so severe that it wiped out 2% of GDP [GOP (2013)]. Though
Rehabilitation of 2010 Flood Affectees 198
economy has the growth trajectory of more than 6%, but the worst energy
crisis, bleeding public sector enterprises, economic mismanagement and
menace of informal economy has hemorrhaged the system.
Some governments in the developing countries, with the help of
western donors and development partners are trying to build better
infrastructure to minimize the impact of such disasters but more often
than not the approach is that of a reactive nature rather than proactive
preparation. It is when the disasters have already played havoc with
people and infrastructures the governments wake up to the situation and
look for remedies with the meager resources available to the developing
world. It has been frequently observed that most of the developing
countries tend to make investments on recovery from a disaster rather
than creating adaptive capacity [Mirza (2003)].
The 2010 floods in Pakistan were according to all assessments,
of much greater magnitude than the 1998 floods in Bangladesh. As
earlier stated in World Bank (2011:7). The Government of Pakistan, civil
society and development partners such as the multilateral and bilateral
aid donors in the country, came forward with the best of their capacity
and efforts to save lives, provide immediate relief and support
rehabilitation of the population hit and displaced by the floods.
Since the major focus of this paper is to ascertain the role of
donors towards the rehabilitation of the flood affectees, we will start our
Literature Review by first discussing the wider debates such as Aid
Effectiveness and Disaster Risk Reduction and Livelihood Restoration
measures adopted in the developing countries, by applying a sustainable
livelihood framework to increase the resilience of the poor people. The
review will then ascertain the role of donors towards the rehabilitation
of the flood affectees. The review will study in depth the Citizen’s
Damage Compensation Program (Watan Card), the cash assistance
scheme launched by the Government of Pakistan with the support of the
development partners, and will assess its effectiveness towards the
rehabilitation of the flood affectees and restoration of their livelihoods.
During this discussion we will also throw light upon such themes as Elite
Capture, which prevails in most of the cash assistance scenarios.
A lot of aid has flowed to underdeveloped world after the World
War II and it perhaps has led to considerable socio-economic progress.
199 Sheeba Farooq
There is a continuing debate however, that has this aid been as effective
as it should have been in terms of value for each dollar. The governments
and the development agencies in the developed world are suffering from
aid fatigue and their tax payers are increasingly getting critical of their
tax money being thrown into ‘bottomless pits’ [OECD (2014)]. Have the
billions of aid dollars spent in Africa, South America and Asia been
effective to combat disease, illiteracy and poverty is the question being
asked at a number of foras with increasing frequency.
Masud and Yontcheva (2005) state that aid flows are predomi-
nantly meant to fill the gap between domestic savings and investment
needs. But drawing on reviews of the wider literature it has been argued
that aid has no significant impact on growth, savings or investment.
According to Mosley et al. (1992) aid indicated an increase in
unproductive public consumption and failed to promote investment but
then the proponents of aid have argued that developing countries would
have been poorer in the absence of aid. Gilbert and Vines (2006)
highlight the wider debates around aid which are conditionality and
dependency; the former has been criticized as the donor agencies; desire
to bring in policy reforms in poor environments and the latter is believed
to undermine the national capacities. Burnside and Dollar (1997) go on
to argue that these two are the main reasons due to which aid has no
significant effect on growth.
One of the major issues with aid is that donors impose
conditionalites. While they are not very well versed with local conditions
and requirements they ask the recipient governments to undertake
projects which are neither very feasible neither in economic terms nor
are responsive to the local context and development need. This is
particularly true in the context of disaster risk reduction, where
readymade formulas, architectured in the developed world are affected
and little effort is made to strengthen the capacity of the local population
to cope with their problems including the natural calamities which can
best be done by building upon their existing and traditional
arrangements. One of the factors which militate against the effectiveness
of disaster risk reduction strategies is the pressure on the development
agencies to disburse funds without knowing the capacities of the
implementing bodies [Seck (2007)]. For instance, during the 2000 floods
Rehabilitation of 2010 Flood Affectees 200
in Mozambique it was noted that some British NGOs were under
pressure from the Disaster Emergency Committee to spend money for
DRR activities on immediate basis [Hanlon (2004)].
It has been noted that the poor people in disaster-prone
developing countries are the ones who bear maximum economic losses,
and their livelihoods get affected due to their higher vulnerability to
disasters [Oxley (2013)]. The risks from the disasters can be mitigated
by adopting disaster risk reduction strategies which strengthen the
resilience of the vulnerable population. The Global Network of Civil
Society Organizations for Disaster Reduction (GNDR) (2013) supports
the Hyogo Framework for Action (HFA) which entails increased
awareness and understanding of disaster risk reduction at national and
international levels. GNDR has given recommendations for effective
DRR framework that encompasses prioritizing the poorest and
marginalized people, tackling the causes of people’s vulnerability and
improving public access to information regarding DRR.
Pakistan is a disaster prone country and is exposed to high risks
of floods, earthquakes, cyclones and landslides. Keeping this in view the
government formulated a strategy for disaster risk management known
as National Disaster Risk Management Framework (NDRMF) in 2007
[Ahmad (2013)]. The critics, however, believe that it has failed to reduce
human suffering due to bad governance, corruption, lack of political will
and overlapping responsibilities. “The government after every disaster,
perhaps in an attempt to mitigate its responsibility, cites the ‘will of God’
and punishment for wrongful deeds of the people” [Fisher (2010:552)].
On the other hand, even a still poorer country Mozambique, has
prioritized disaster risk reduction strategy for which they have a
dedicated department named as National Institute for Disaster
Management. This institute efficiently handles local operations as it has
government’s support which reflects that they value their people [UNDP
(2010)].
According to Dasgupta and Beard (2007), in order to promote
pro-poor growth, World Bank uses the term community-driven projects
that empower poor people as they have control over the development
process. Wong (2010) in his study states that “community-based
development has been criticized for its inadequate understanding of
201 Sheeba Farooq
power relationships at the local level, which thus leaves room for elite
capture”. The author further argues that these development projects tend
to fail because of weak institutional control mechanisms that create
opportunities for the local elites to gain benefit out of the opportunities.
Platteau (2004) believes that the aid given by donor agencies can only
reach the intended beneficiaries if the issue of elite capture is solved.
Wong (2010) also supports this view and suggests that a ‘co-opt-elite’
approach should be adopted which solicits cooperation of the local elites,
as they can play a vital role in the community development.
The governments of developing countries are unable to give
social protection to the poor people on long-term basis due to which the
elite get an opportunity to gain maximum shares out of the development
projects, meant for the poorest of the poor.
Natural calamities, particularly floods, disrupt the socio-
economic fabric of the society, destroy infrastructure and badly affect
the livelihoods. Populations are displaced, their regular socio-cultural
network break-down resulting into loss of ages-old social safety nets,
and their income generating activities discontinued through loss of crops,
industrial tools, cattle/poultry, shops, etc. Besides, floods make
rehabilitation of life and livelihoods even more difficult, by rendering
the farmlands uncultivable and destroying homes and businesses [Tahir,
et al. (2011)]. Mwape (2009) in his study states that floods have become
an annual event in few parts of Zambia, the floods of 2007-2008 were
the worst in terms of the amount of rainfall and the level of impact
especially on the socio-economic livelihoods of Sikaunzwe community
in Zambia. The main source of livelihood of that area was crop
production followed by trading which engaged a small proportion of the
population. The crop fields were damaged due to floods which resulted
in reduced staple crop production. This reflects their overdependence on
crop production which ultimately increased their vulnerability. Another
example is highlighted by Arnall, et al. (2013) stating that Mozambique
is a flood-prone country where more than eighty percent population
depends on agriculture which takes place in the country’s low-lying
floodplain. The worst flooding occurred in 2000 which impacted the
livelihoods of the people badly in the country. The author further
mentions about a locality Chicomo in Manhica city, where majority of
Rehabilitation of 2010 Flood Affectees 202
the people were involved in low-area agriculture before floods but the
percentage drastically fell from ninety nine percent to sixty five percent
after the floods due to lack of seeds, equipment and shortage of cattle for
ploughing. The severe impact of floods on the livelihoods was also
evident in the 2010 floods in Pakistan where agriculture, livestock and
fisheries suffered the highest damages, estimated at US$5.0 Billion
[World Bank (2010)]. The report produced by state that more than 80%
of the households relied on agriculture for their livelihoods. With the loss
of harvest, many poor people were unable to pay back their debts which
they had taken to buy agricultural inputs. In fact many people were
forced to do casual labour and relied on humanitarian assistance pro-
vided by the international community or richer community.
This depicts that natural disasters can lead to the long-term
impoverishment and add to the misery of the affected people who end up
losing not only their instruments of income generation, property, life-
time savings, but also get homeless and lose their social networks. This
makes them vulnerable to hazards so the need is to build their resilience
to shocks and diversify their livelihoods to make them sustainable.
Disasters not only rob people of their property, social and human
capital but also destroy their livelihoods. Effective interventions to
reduce disaster risk can lower the vulnerability of the people against
shocks and hazards [AusAid (2009)]. For instance, IFAD ensures
sustainable livelihoods by conducting vulnerability context in the flood
hit areas by incorporating DRR activities and accordingly formulate
interventions [IFAD (2013)].
The sustainable livelihood idea was first introduced by the
Brundtland Commission on Environment and Development which was
further expanded by the UN in a conference in 1992. Chambers and
Conway proposed definition of sustainable livelihood stating that “a live-
lihood comprises the capabilities, assets (including both material and
social resources) and activities required for a means of living. A lively-
hood is sustainable when it can cope with and recover from stresses and
shocks, maintain or enhance its capabilities and assets, while not under-
mining the natural resource base” [Chambers and Conway (1992: 3)].
Sustainable livelihood framework has three components;
livelihood resources, institutional processes, organizational structures
203 Sheeba Farooq
and livelihood strategies [Krantz (2001)] as shown in Figure 1.
According to this framework is used to analyze different vulnerabilities
faced by the poor people for whom they do not have the capacity to cope
with, like shocks which are at times unpredictable and traumatic in the
form of floods and epidemics. Carney (1999) has argued that livelihood
can be sustainable only if poor people have the ability to recover from
these shocks and stresses. Krantz (2001) states that people use tangible
and intangible assets to construct their livelihoods which are termed as
‘capital’ which have five types namely human capital (skills and ability
to labour), physical capital (infrastructure, shelter), natural capital
(natural resources), social capital (social relations) and financial capital
(savings and incomes) upon which people draw when pursuing different
livelihood strategies. It is argued by Scoones (1998) that institutional
processes also play a vital role as they often mediate access to livelihood
resources which make understanding of institutions important in
designing interventions which improve livelihood outcomes.
Regnier (2008) has given an example of Tamil Nadu, India,
where NGOs had restored the livelihoods of people affected by tsunami,
by adopting a sustainable development approach. The community’s main
source of livelihood was fishing, so the NGOs with the help of donors
supplied boats, motors and nets to the communities to resume their
fishing activities. The quality and diversity of the inputs have made them
capable of generating substantial revenues. In addition, they were also
given access to microcredit which made their livelihoods sustainable. A
report produced by Oxfam (2013) points out that they conduct DRR
analysis before an intervention in disaster hit area which enables them to
ensure protection of financial asset of the vulnerable people which
strengthens the performance of their livelihood programs. For instance,
heavy flooding in Beni District, Bolivia, in 2007 resulted in loss of
traditional crops and disrupted livelihood activities in twelve commu-
nities. Oxfam GB in order to make the livelihoods sustainable re-
introduced a technique that was being practiced three thousand years ago
in that particular district that comprised of modification of the landscape
such as planting of elevated seedbeds to cope with such challenges.
Rehabilitation of 2010 Flood Affectees 204
Figure 1. Sustainable Livelihoods Framework
Source: Krantz (2001).
Likewise DFID (1999) has also adopted SLA framework to
eliminate poverty by firstly understanding how many assets poor people
possess and then capitalizing on those assets as they are interconnected.
There are different factors that either hinder or enhance the livelihood
opportunities of poor people such as access to different assets, culture,
norms and institutional processes which should ultimately be responsive
to the needs of the poor. For instance, provision of microcredit to the
poor people or government providing social safety nets can increase their
sources of income. The more sources of income people have the more
strength they have to cope with and recover from shocks and stresses
which make their livelihood sustainable [Chambers and Conway
(1992)].
This study will be adopting this holistic framework and examine
as to how were the people affected by 2010 floods in Pakistan and what
did the donors do in order to restore their social, physical, human, natural
and financial assets which are the capital endowments required to create
livelihoods, but the main focus will be on financial capital.
The Citizen Damage Compensation Program (CDCP) emerged
to be the single major initiative launched by the Government of Pakistan
to support the rehabilitation of the 2010 flood affectees. The programme
was undertaken in two phases. During Phase I the Federal Government
VULNERABILITY
CONTEXT
• SHOCKS
• TRENDS
• SEASONALITY
STRUCTURES • Levels of government • Private Sector • laws
• Policies
• Culture
• Institutions
PROCESSES
TRANSFORMING
STRUCTURES &
PROCESSES
LIVELIHOOD STRATEGIES
LIVELIHOOD
OUTCOMES
• More income • Increased Wellbeing • Reduced vulnerability • More sustainable use of NR base
i n o r d e r t o a c h i e v e
S
H
N
P F
LIVELIHOOD ASSETS
Influence & access
Key H = Human Capital S= Social Capital N = Natural Capital P = Physical Capital F = Financial Capital
205 Sheeba Farooq
in collaboration with the Provincial Governments embarked upon the
ambitious programme of unconditional cash transfers to flood affectees
to help them restore their livelihoods. A total fund of US$400 million
with the contribution of US$200 million by Federal Government and
US$200 million by Provincial Governments was arranged. It was agreed
to provide cash assistance of US$230 approximately to each affected
family. The Provincial Disaster Management Authorities (PDMAs) were
required to identify flood affected areas in each province; National
Database and Registration Authority (NADRA) an arm of Ministry of
Interior, GOP, was to verify the lists of family heads within the affected
areas and commercial banks were to make the payments to the
beneficiaries through a debit card termed as the “Watan Card”.
According to a study by Emergency Relief Cell (2012) of the Cabinet
Division, GOP, within three months of the programs initiation, 1.4
million families were registered and paid US$230 each.
Some studies for instance that of Hunt, et al. (2011), however,
are skeptical about the effectiveness and performance of this program
and observes that the pre-condition to become a beneficiary of CDCP
was to have a valid CNIC (Computerized National Identity Card), which
most of the poor and vulnerable did not have. Moreover, people who did
not update their details at NADRA database and who were living in small
communities, far away from the main villages and NADRA offices were
filtered out. The study also highlighted the factors such as lack of
communication between policy makers and implementers, lengthy
procedures and capacity issues at the administration level, due to which
the eligible beneficiaries were unable to get access to the CDCP cash
transfer. The authors also point out that out of 100 eligible family heads
only 43 received Watan Cards on average in Sindh and Balochistan
provinces which shows that many eligible people were left out and the
grievance mechanism did not function properly to give justice to the
aggrieved.
On the contrary, the National Database and Registration
Authority (NADRA) claims that total appeals filed with grievance
redressal cell were 385,010 out of which 39,105 had been resolved and
345,905 were under scrutiny as of May, 2012 (NADRA, 2012). It has
also been claimed that “Ninety percent genuine flood affectees had
Rehabilitation of 2010 Flood Affectees 206
received Watan Cards (Government functionary (personal comment) 26
June 2013)”. The Grievance Redressal Cell was set up right after the
implementation of Phase I so that the beneficiaries who had been left out
could be facilitated. Its purpose was to scrutinize beneficiaries’ requests
and complaints vis-à-vis the payments and provide a mechanism for
social accountability of the program.
It was because of the overall satisfactory results of CDCP Phase
I which encouraged the development partners to collaborate with the
GOP for CDCP Phase II. Accordingly, World Bank pledged to
contribute US$125 million, DFID US$100 million, USAID US$190
million and Government of Italian Republic US$65 million. This total of
US$480 million with a contribution of US$100 million by GOP was
agreed to be disbursed to 1.21 million households in two equal tranches
to help them rebuild their lives [World Bank (2011); GOP (2012)]. In
order to make the implementation of Phase II effective the World Bank
had undertaken rapid evaluation of Phase I in January 2011 which ascer-
tained and highlight the weak areas of the first phase. For instance, some
of the flood affected families’ names were missing in the beneficiaries
list. Moreover, events of malpractices were noticed such as unsystematic
handling of complaints and demands of bribes by the local officials who
were conducting the survey to expedite the complaints process. But
overall the evaluation mission considered the progress of program to be
satisfactory as 900,000 beneficiary households had been given Watan
Card [World Bank (2011); GOP (2012); World Bank (2012)].
However, to further improve the programme and to maximize its
effectiveness at the suggestion of the World Bank, a refined targeting
mechanism was adopted whereby better-off families were filtered out
and legitimate vulnerable beneficiaries such as female and disabled
headed households were included in Phase II [ERC (2012)]. In addition,
it was decided that the selection criteria used as a proxy for eligibility,
should be based on the beneficiary’s damaged house. Each province
conducted a house damage assessment survey which identified the Heads
of the Household of the damaged houses and PDMA shared the data with
NADRA for verification which was further sent to Third Party
Verification Firm for validation to ensure maximum accuracy and
transparency [ERC (2011)]. A similar study carried out by the World
207 Sheeba Farooq
Bank (2011) further emphasized that the payment to beneficiaries under
CDCP be made conditional on having a valid CNIC. The payment cycle
was of three months between each installment. The beneficiaries were
expected to prudently use these funds to recapitalize their assets, restore
their livelihoods and repair their houses.
Accordingly the CDCP, particularly the Phase II, which was
undertaken primarily with the support of the development partners, did
contribute towards restoration of livelihoods, as people in most cases
utilized the cash assistance provided to them for buying cattle, poultry,
basic tools for cottage industry and fertilizer for reactivating their
agricultural activities. It also helped them to repair their dwellings and to
render them livable. Most of the people moved back to their places of
residence and restarted their normal socio-economic activities in their
familiar surroundings with the help of Watan Card assistance.
In summation, international aid and donor’s assistance has
actually led to improvement in the well-being of the poor in the
developing world and has saved lives at the times of natural calamities
and disasters. However, it has not led to a process of sustainable growth
and countries around the developing world continue to depend upon
financial and technical support from multilateral and bilateral donors.
Whether these are purely economic crisis such as widening budget
deficits, depleting foreign exchange reserves or the nature inflicted
catastrophes like tsunami, earthquake and floods, the governments keep
looking towards industrial west for support.
In spite of being repeatedly exposed to natural calamities and
having seen thousands of lives lost and their infrastructures destroyed
the developing countries still continue to remain reactive in their
approach rather than pro-active to prepare for the natural disasters. Some
countries have established organizations such as National Disaster
Management Authority (NDMA) and Provincial Disaster Management
Authority (PDMA) in Pakistan, but they are rather at an embryonic stage,
without adequate technical expertise and know how. They badly lack
necessary equipments, technical facilities and necessary training.
During 2010 floods Pakistan was in dire need of help from the
international community to cope with the catastrophe. The international
community came forward with committing an amount of US$1.371
Rehabilitation of 2010 Flood Affectees 208
billion against some one hundred and forty four projects as already stated
above, covering a wide spectrum from provision of medical health to
food items and shelter. But it should be noted that the main instrument
for the restoration of livelihoods was by Watan Card under CDCP. The
Watan Card Scheme was divided in two phases, the purpose of the first
phase was to give immediate relief to flood affectees to fulfill their
pressing needs and the aim of cash assistance given in Phase II was to
restore the livelihoods of the flood affected population. There were some
issues and snags faced by the program, as has been mentioned above,
and there have been criticism of the same, but if we look at the bigger
picture Watan Card has greatly contributed in the restoration of poor
people’s livelihoods, as most of the people utilized the cash for
purchasing agricultural inputs, livestock, hand looms and restoring their
agricultural lands, and have started to generate their income which is
being further utilized in sending their children back to schools and
ultimately spending on health and nutrition.
In view of the above analysis, this study adopts the following
research objectives in order to ascertain the level and effectiveness of the
support extended by the donors for the rehabilitation of 2010 flood
affected population.
3. METHODOLOGY
3.1. Research Approach
This study is based on a case-study of a village named Misri
Banda in Nowshera district in Khyber Pakhtunkhwa (KP) Pakistan, to
examine the impact of 2010 floods and the role of donors in bringing the
people back to pre-flood conditions, with a focus on restoration of their
livelihoods. The method of case study was used because in this approach
different methods are combined with the purpose of illuminating a case
from different angles [Johansson (2003)]. It is a tool to find out the true
picture which is mostly not depicted in the reports and articles.
In order to carry out the case study a qualitative research
approach was used shown in Table 1, as it allows the evaluator to study
the issues in depth and data collection is not restricted by predetermined
categories [Patton (2002)]. The qualitative instruments administered
209 Sheeba Farooq
included focus group discussions (FGDs) with bene-ficiaries, in-depth
interviews with non-beneficiaries, semi-structured interviews with the
beneficiaries, officials of donor agencies and the community leaders to
validate the data collected through questionnaires. Focus group
discussions were carried out to make in-depth analysis of the experience
of beneficiaries. The groups were identified through community support
[Chaudhry (2013)]. The sample included normal beneficiaries, female
headed-household and vulnerable people ensuring equal inclusion of
males and females.
Table 1. Research Methods Used in Study
Method Number of Exercises Exercises Conducted With
1.Semi-
structured
interviews
-Seventy interviews were conducted
with the flood affected people.
-Ten interviews were conducted
with the government officials.
-Ten interviews were conducted
with the development functionaries.
-Meetings with Government of
Pakistan functionaries dealing with
development assistance.
-Meetings with relevant functionaries
of donor organizations operating in
Pakistan.
-Assessing affected population’s
perception in this regard.
2.Focus
Group
Discussion
-Ten focus group discussions were
carried out to find out the views of
people on cash assistance.
-The discussions were conducted with
f female-headed households, males and
vvulnerable people.
The sampling technique used was Simple Random Sampling,
because each element has an equal probability of being chosen as the
subject [Sekaran and Bougie (2010)]. The first step was to determine an
appropriate sample of beneficiaries which could represent the target
population. Based on the sampling technique the total sample size
selected was 80 beneficiaries, out of which seventy were interviewed,
comprising of 35 men and 35 women, from different households and 10
focus group discussions were held with equal representation from the
males and females of that community. Four specific situations were
analyzed in-depth to document respondent’s experience regarding the
effectiveness of the donor’s assistance.
Rehabilitation of 2010 Flood Affectees 210
3.2. Data Collection
The data were collected over a period of three to six months in
2013. The identification of village and data collection facilitation was
provided by the Deputy Commissioner, Nowshera. The researcher
undertook the interviews along with the support from local admin-
istration. The research used both primary and secondary sources of data.
Primary data, refer to information obtained first-hand on the variables of
interest for the specific purpose of the study [Sekaran and Bougie
(2010)]. Examples of sources of primary data are interviews, admin-
istered questionnaires, focus groups in which people from different age
groups are invited to give their opinion on specific issues, video
conferencing and the internet if the questionnaire is administered over it.
Accordingly, focus group discussions and semi-structured interviews
were held in 2013 with the respondents identified by the focal person to
get villagers’ opinion on the specific issues to determine the level of their
satisfaction and to explore the issues pertaining to that context. The other
source of data used is secondary data, which refers to information
gathered from company archives or records, websites, periodicals,
census data, statistical abstracts and the media [Sekaran and Bougie
(2010)]. But in this research the data were collected by the existing
literature which includes government and donor publications, reports
published by the NGOs, newspapers and relevant articles.
Ethical clearance was obtained prior to the commencement of
research and permission of interviewees was also sought before inter-
views were held. All information obtained from the interviews was
treated as confidential. The interviews generated a wide variety of
opinions on how impact of floods could have been mitigated and as to
how successful the rehabilitation process has been.
The findings provide substantial representation of affectees’
experiences which reflect the voice of the majority of people living in
that particular community.
4. RESULTS AND ANALYSIS
This section addresses the three sub-objectives of the study,
which seek to explore as to how the people were impacted by 2010
211 Sheeba Farooq
floods, with particular reference to Nowshera district in Khyber
Pakhtunkuwa province in Pakistan, and to find out the value and extent
of donor’s contribution towards restoration of the livelihoods of the flood
affectees, particularly for building their financial capital. The results and
analysis of the interviews and focus group discussions held with the
flood affectees, functionaries of multilateral as well as bilateral donor
organization based in Pakistan, and government officials are presented
throughout this section.
While the development partners were initially reluctant to
participate in CDCP, they joined it at a later stage for Phase II with a
combined contribution of US$480 million. Out of the total US$480
million, USAID contributed US$190 million, World Bank US$125
million, DFID US$100 million and Government of Italy US$65 million.
The GOP also made a contribution equal to US$100 million [World
Bank (2011)].
In the meanwhile, the bilateral/multilateral donors and aid
agencies continued to provide support and assistance to flood affectees
through provision of food items, blankets and health services which
greatly helped the early recovery efforts. The main window for
restoration of livelihoods of flood affectees, however, was the CDCP
which provided a sum of US$200 and $400 to each flood affectee during
Phases I and II, respectively.
Our literature review as well as field study amply manifests that
the donors made substantial contribution towards the rehabilitation of
flood affectees, particularly for CDCP Phase II, which was the main
instrument to restore the livelihoods of the flood affectees. While the
cash assistance provided in Phase I was predominantly used by people
to meet their immediate needs, it was the assistance received during
Phase II which was utilized for purchase and restoration of basic
instruments of income generation. People purchased livestock and
fertilizers with the money, restored their agricultural lands, started small
businesses, and repaired their houses to render them livable. This finding
is fully substantiated by our field study of Nowshera District.
Rehabilitation of 2010 Flood Affectees 212
4.1. Case Study of Misri Banda Village in Nowshera District
Nowshera District is situated at south east of provincial capital Peshawar
in Khyber Pakhtunkawa (KP) province, where Kabul River joins River
Indus. The total area of the district is 1748 sq km with a population of
1.1 million [NADRA (2012)]. The main source of income of the people
is agriculture. Nowshera was one of the worst affected districts in KP,
the floods wrecked the infrastructure, sweeping away houses, roads,
bridges, electricity network and agricultural land [UNDP (2013)]. Out of
the total population, almost ninety percent was displaced from its
original place of residence, losing their livestock, cattle, small businesses
and cottage industry [PDMA (2010)]. The destruction caused by the
floods can be well assessed from Table 2.
The devastation was caused in all sections of life, livelihoods
both agriculture and non-agriculture were directly or indirectly affected
as depicted in Table 2 [UNDP (2013)].
Table 2. Summary of Losses/Damages
District Total
Population Dead Injured
Total
Population
Displaced
H.H
Affected
Educational
Facilities
Health
Facilities
Nowshera 1.2 Million 167 10 350,336 71,403 134 11
Source: The data have been taken by the PDMA report on Summary of Losses/ Damages in 2010.
4.1.1. Misri Banda
Misri Banda is a small village in Nowshera District with a
population of 12,303 and literacy rate of about 31% [Pakistan Bureau of
Statistics (2013)]. It has a high poverty rate with people meeting two
ends with difficulties and residing in mud houses. Most of the people are
engaged in agricultural activities though some earn their livelihood
through livestock, cottage industry, small enterprise, and masonry work.
Being one of the poorest communities worst hit by the 2010 floods with
a major part of the population displaced and severely affected, Misri
Banda was considered to be the most appropriate place for a case study
for this research and thus selected.
213 Sheeba Farooq
4.2. Impact of Floods on Misri Banda
Interviews conducted at Misri Banda indicate that 94% of the
population was affected by 2010 floods and as a result, a number of
families lost one or two members, 35% had their properties inundated
and 94% lost their instruments of income generating activities. The
economy of Misri Banda is agriculture based with 58% engaged in
agriculture as their main source of livelihood while 80% depend on
livestock and poultry, 13% run small enterprises and 21% works as daily
wagers/labourers [NADRA (2013)]. Eighteen percent2 of the people
owned 1-2 hectares of agricultural land before floods from which they
earned £100 to £120 per month, while farm-tenants and other daily
workers earned approximately £2-3 a day. The average monthly income
of the people engaged in small enterprises and cottage industry stood at
around £100, a rather meager amount to fulfill family needs. The
residents of Misri Banda were thus surviving at a subsistence level with
minimum possible facilities of life even before the floods, which too,
were taken away from them by this unexpected catastrophe.
One of the residents of Misri Banda interviewed regarding the
impact of floods stated that “the flood was a dreadful incident, it struck
the village at night when the electricity was out, people were screaming
for help and it seemed that nobody will be able to survive. People were
running for their lives leaving behind their entire assets made out of life
time earnings and important documents such as identity cards. There was
chaos everywhere and with a blink of an eye the flood washed away
houses, bridges, infrastructure and people were helpless”.
4.3. Restoration of Livelihoods of the Flood Affected People:
Role of Donors
As in other flood-affected areas the government undertook relief
and recovery efforts in Misri Banda as well. People were rescued out of
heavy flowing water with the help of helicopters and boats and over 70%
of the population was moved to IDP camps where they were provided
with food items, tents, medicines, etc.
2 Tabulated information is available with the author.
Rehabilitation of 2010 Flood Affectees 214
After the floods, to rehabilitate the livelihoods of the people the
government initiated the cash transfer program under CDCP Phase II. As
has been stated above, CDCP Phase I was for early recovery while CDCP
Phase II (Watan Card Scheme) was for rehabilitation of the flood
affectees, where the development partners joined hands with a
contribution of US$ 480 million.
During the field visit a large number of people were encountered
who had benefitted from the Watan Card Scheme but on the other hand,
there were people who were not contented with this compensation. When
asked from the respondents about the utilization of cash; 30% reported
that the cash received from Watan Card was utilized in purchasing
livestock, 18% restored their agricultural land, 12% repaired houses,
10% started small business, 12% met their daily expenses, 8% used it for
educational purpose and 4% used it on health care. It should be noted
that people had spent part of the money in repairing their houses, part of
the money in purchasing instruments of production and part of the money
on meeting daily expenses. Critics argue that majority of the bene-
ficiaries of Watan Card had spent the amount on their immediate
consumption needs, particularly food, clothing and healthcare rather than
on restoring their livelihoods [Hunt, et al. (2011)]. Even if this was true
in some cases, most of the families did utilize the amount for re-
activation of their income generating activities. Besides, the amount
spent for immediate consumption too led towards the restoration of their
lives and was unavoidable.
Most interviews indicate that the cash assistance was enough to
restore small enterprises such as a shop or cottage industry or to purchase
poultry and basic gadgets such as a hand loom as is evident from Figure
2. For instance, 82% respondents agreed that they were able to restore
their cottage industry but 18% were unable to do so as they had spent the
money elsewhere. On the other hand, 74% respondents said that the
amount was inadequate to purchase livestock; reason being that a buffalo
costs about £300-350 and the amount that was given was £258
approximately in Phase II which too was given in two tranches. But the
twenty six percent who were satisfied were those who bought poultry or
a small goat. When asked the respondents about the difficulties they
faced in getting the compensation; ten percent replied that the authorities
215 Sheeba Farooq
were not accessible, 24% replied that they were not helpful, they were
rude, and indifferent, ten percent reported that they needed contacts, as
the field functionaries tried to fleece people who had no references, and
nine percent reported that officials wanted bribes. On the other hand,
36% respondents stated that they were not aware of any cash assistance
or compensation available but 64% respondents reported that the local
mobilizers, national television broadcast and radio disseminated the
information among flood-affectees. Findings suggest that the people who
could not receive compensation were those who had either lost necessary
documents like national identity card to establish their eligibility or did
not have the transportation to reach the facilitation centre.
In addition to the regular multilateral and bilateral donors such as
the World Bank, USAID, DFID and Italian Republic which made
substantive contribution towards cash assistance (Watan Card Phase II),
a number of other countries and international humanitarian organizations
also came forward with assistance in cash and kind to assist the flood
affec-tees. For instance, Solidar Switzerland supported the displaced
people by building their capacities and restoring livelihoods in Misri
Banda village. Two hundred and five women received training in
embroidery and dress designing and in addition a number of villagers
were given tool kits and their links were developed with the market so
that they can sell their products and earn their livelihood [Solidar
(2013)].
Likewise, IOM’s initiation of Cash for Work Scheme in Misri
Banda, in which the flood affectees were given training in house building
and construction so that they can become productive and earn cash and
may also construct their own houses, was a win-win situation for them.
Another International NGO Action against Hunger, developed
new water supply schemes that provided safe drinking water to the Misri
Banda community [ACF International, (2012)]. Hence, number of
international organizations played a vital role in the rehabilitation of the
flood-victims.
Results show that 86% people received basic necessities from the
donors in relief phase which comprised of bedding, cooking utensils,
medicines, food items and tents. The other 14% respondents informed
that they did not receive immediate assistance from the donors or
Rehabilitation of 2010 Flood Affectees 216
government agencies as they lived in far flung areas that were not
accessible.
Figure 2. Utilization of Cash for Restoration of Livelihood
Source: Based on the Author’s calculation.
When specifically enquired about the support provided by the
international community/donors while some respondents expressed their
dissatisfaction about 69% acknowledged the presence of international
community and appreciated their contribution towards socio-economic
rehabilitation of the flood-affectees.
4.4. Discussion of Results
The field study of Misri Banda, and the focus group discussions
as well as one-to-one interviews, however, bring us to a safe conclusion
that almost 70% of the population received Watan Card which helped
them in resettling and restoring their financial capital. Out of total
respondents nine percent claimed that they did not receive the Watan
Card due to the non-availability or loss of documents like national
identity cards during floods, which was necessary for proving eligibility
for Watan Card. They were unable to get the documents made due to
30%
18%12%
10%
12%
8%4%
30% purchasedlivestock
18% restoredagricultural land
12% repaired houses
10% started smallbusiness
12% met daily expenses
8% educational purpose
4% health care
217 Sheeba Farooq
inaccessibility to the offices responsible for issuing documents as they
were living in far flung areas. Out of the total respondents ten percent of
the population tried to become illegal beneficiaries of the cash assistance.
It was reported about a boy, Raja, who had left his job in the city and
came back to his village hoping that he would receive Watan Card,
although his house had not been damaged. This incident made the
researcher curious to probe further and a few young boys in their twenties
were interviewed. It was a shocking revelation that hundreds of boys left
their jobs in cities hoping that they will become claimants of Watan Card
back in their villages. These jobs were their only source of income and
they left them even though there was no surety whether they will get the
Watan Card amount or not. This in fact is a negative aspect of cash
assistance which creates dependency syndrome among the people.
Semple (2011) also refers to this in his study, stating that aid had a
negative impact on majority of the people during 2010 floods in
Pakistan, as they became dependent on aid and did not want to become
self-sufficient.
During the interview with an INGO officer it was found out that
“46000 complaints had been registered by February, 2013 in Nowshera
for not receiving Watan Card and almost ninety percent of them had been
resolved by now” (INGO officer3 (personal comment) 5 June, 2013).
The findings also reveal that in a very few cases the whole
amount received through Watan Card was spent on one particular
activity. Almost everywhere it was utilized for number of needs though
major amounts were spent to repair houses or purchase cattle, hand
looms, goods for sale at a shop or reactivation of a commercial activity
disrupted by the floods. It may however be noted that before the cash
assistance, flood affectees were borrowing money to fulfill their
immediate needs either from friends, relatives or shopkeepers [IOM
(2012)]. That was the reason the government came up with the idea of
giving unconditional cash grant to the flood-affected population. A
report produced by International Office of Migration stated that
“international evidence suggests that cash grants allow the recipients the
3 Interview transcript is available with the author.
Rehabilitation of 2010 Flood Affectees 218
flexibility of choosing where to put their resources based on their specific
conditions and priorities” [IOM (2012: 24)].
The Watan Card helped flood affectees to restore their liveli-
hoods and made them capable to revive their income generating
activities, and to rebuild their lost assets. According to some respondents,
the amount of Watan Card was not sufficient but as a matter of fact, the
assistance did help them to revive their incomes to pre-flood level as
shown in Figure 3. As has been stated earlier most of the people living
in Misri Banda were earning £95-100 per month before floods, the
amount of Watan Card which was almost £258 was thus a very
substantive support.
Figure 3. Respondent’s Views on Cash Assistance
Source: Based on the Author’s calculation.
Empirical evidence also shows instances of Elite Capture during
the 2010 floods in Pakistan whereby the local politicians and feudal lords
managed diversion of funding to their constituencies to strengthen and
develop their vote banks. It was informed by a senior officer of Ministry
of Finance and Economic Affairs during an interview that “a famous lady
politician, the then Minister of State for Economic Affair Division, had
called a meeting of Pakistan Poverty alleviation Fund (PPAF) whose
mandate is to reduce poverty around the country. PPAF were advised to
discontinue other projects and rush teams to her constituency to establish
camp offices and provide relief to the flood affectees to strengthen her
70%11%
9%
10%
70% satisfied withWatan Card
11% not satisfied withWatan Card
9% non-availability ofdocuments
10% illegal beneficiaries
219 Sheeba Farooq
vote bank [Anon. (personal comment) (28 June, 2013)]”. This incident
was also confirmed by the senior management of PPAF. It was also
endorsed by an NGO functionary4 who did extensive research in the
context of rehabilitation of flood affectees and studied the phenomenon
of elite capture in depth. According to him “a good amount of cash
assistance was diverted to the lady politician’s constituency / home
district while the needy did not get cash in some parts of Muzaffargarh,
South Punjab. She had asked most of the ambassadors to give everything
to her constituency [NGO functionary (personal comment) (7 June,
2013)]”. Another powerful feudal family in Shikarpur district of Sindh,
the Bajranis managed to get hold of most of the assistance meant for
rehabilitation of flood affectees of that area, on the pretext that they will
directly be distributing it to the deserving people.
An interesting incident was quoted in a study by Semple (2011)
which is another example of elite control. He states that 100%
destruction was reported in Sabzujat Union Council in the district
Muzaffargarh, South Punjab, and the whole council was submerged in
water but as per his research ‘hardly anybody there could qualify for
Watan Card as they had voted against the Minister for Economic Affairs,
during the last national elections [Semple (2011: 77)]. It is the same lady
Minister who has been referred above. Likewise there were also reports
confirmed by a number of media and civil society observers that some
powerful feudal lords and politicians such as Jamalis, a very big clan in
the border areas of Boluchistan and Sindh, had the rivers and canal
embankments fractured at specific points to reduce the flow of water
before reaching their lands [Ahmed (2013)]. This did save the crops and
lands of a few wealthy and mighty but aggravated human miseries, as
the specific points where the water was allowed to outflow were densely
populated areas with small villages, which as a result were exposed to
unexpected levels of floods.
In spite of the misuses of the distribution of Watan cards, the
process in general has been quite fair as claimed by the World Bank and
NADRA and confirmed through third party validation in the field. One
million beneficiaries received assistance through Watan Card and the
4 Interview transcript is available with the author.
Rehabilitation of 2010 Flood Affectees 220
World Bank declared it as the flagship programme of GOP and a symbol
of one of the largest humanitarian cash transfer programs. Moreover, a
survey conducted by FAO has emphasized that “Watan card had the
broadest coverage of any kind of intervention in the early recovery
period and it has been recognized as a model that other disaster stricken-
countries might consider emulating” [FAO, (2012: 7)].
To the extent of immediate rehabilitation of livelihoods, the
foreign aid received for the 2010 flood affectees has thus been fairly
effective. However, if assessed from the perspective of Disaster Risk
Reduction, it does not seem to have made much difference. The people
as well as their socio-economic infrastructure remain as vulnerable to
natural calamities as before. Their dwellings and commercial set-ups
have not improved to better resist future floods or similar disasters nor
have any measures been taken to reduce the destructive impact of floods,
by strengthening river banks or managing the flow of torrential waters.
For this, as has been discussed above perhaps the political expediencies
and the element of Elite Capture is more to blame, whereby the proposed
plan of development partners to ‘Build Back Better’ was rejected and
instead the cash assistance was opted for.
5. CONCLUSION
Pakistan was hit by a catastrophic flood in 2010, which affected
over 20 million people in terms of loss of lives, shelter and livelihoods
and inundated an area stretching over 100,000 sq km destroying all kind
of infrastructure and facilities. The country was hardly prepared to meet
the disaster and its after effects. However, rescue and relief efforts were
undertaken with the active support of the international community. Yet
the more daunting task was to restore the damaged infrastructure and to
rehabilitate the displaced population. For this the Government of
Pakistan in collaboration with the development partners undertook
CDCP Phase II as has been discussed in detail in this paper.
The GOP and development partners claim that the CDCP Phase
II has been a great success and is one of the best cash transfer
programmes ever under taken in the world for disaster effected
population. The programme has come to finality and most of the flood
221 Sheeba Farooq
affectees have resumed their regular socio-economic activities, at their
places of pre-floods residence.
However, there have been instances of mismanagement and
misuse of the funds during the process for instance; some politicians
manipulated the situation to divert some funds to their constituencies to
strengthen their vote banks, even though those areas were not hit by the
floods. Similarly, some field functionaries of the government managed
to exploit the illiterate population by demanding share in the cash
assistance to which they were entitled. There were also instances of field
functionaries refusing to accept even genuine documents such as identity
cards and authenticated payment of assistance only at the receipt of
bribes. In some cases the badly damaged infrastructure and inadequate
data also militated against the funds reaching to the deserving people.
Some cases of the affected population trying to get more than their
entitlement by offering bribes and presenting fake documents were also
noted. Some people who had not been affected by the floods also tried to
make hay while the sun shines. In spite of all the above issues CDCP has
attained its objective to rehabilitate the flood affected population to a
good extent as per the claim of the GOP, the World Bank and other
development partners. The interviews conducted for this research and the
field study undertaken in Misri Banda, Nowshera District, also support
this claim.
The ground reality that majority of people have moved back to
their native towns and original places of residence, have resumed
professional/income generating activities, their children have started
going to schools/madrasas and their social lives have more or less been
brought back to pre-flood mode; bears ample witness to the fact that the
efforts by the GOP and the donors for the rehabilitation of the 2010 flood
affectees have been fairly successful.
For future the GOP needs to put in place necessary mechanism to
meet such disasters. Pakistan is a flood-prone country and the monsoon
rains in 2013 have again been very heavy, resulting into loss of life and
destruction of infrastructure. The Daily News in its editorial on August
6th, 2013 reads that “100 people have died due to the latest monsoon
which has become an annual lament but unfortunately the country is
never prepared to deal with the destruction caused by floods”. The report
Rehabilitation of 2010 Flood Affectees 222
further highlights that ‘the National Disaster Management Authority
(NDMA) has, for some reason, only been allocated Rs.180 million (£1.1
million) for the coming fiscal year, even though it ended up needing Rs.5
billion (£33 million approximately) last year and that the NDMA needs
to be proactive and formulate a strategy rather than wait for the disaster
to hit the country (The News, (2013: 7)].
Thus, the GOP must formulate long-term and effective strategy
and allocate adequate funds for Disaster Risk Reduction. NDMA and
DMAs need to be fully equipped with modern machinery/ technology
and its functionaries must be provided training perhaps with the help of
development partners to meet future eventualities. The government must
develop and put in place a comprehensive policy framework and
required wherewithal in which the development partners can support it
through advice and assistance.
On their part, the development partners and international
community, to ensure effective and best utilization of their aid, should
insist that the governments in the disaster prone countries improve
capacities and infrastructure to minimize the destruction and damages by
the natural calamities. They should also support such initiatives which
enhance self-sufficiency of the populations to meet their economic needs
and to cope with their natural disasters rather than be dependent on cash
assistance and government support.
The scope of the study was towards a specific area i.e. impact of
floods on livelihoods but future research can be conducted to find out
how to bring in behaviour change in the people who reside in flood-prone
areas to build their resilience and ultimately transform the society. This
assessment will give an explicit direction to the government to come up
with more effective programs in the future. The study’s limitation were
the non-availability of extensive data, comparative analysis of various
districts. Also, very few organizations had studied the impact assessment
of Watan Cards.
6. POLICY IMPLICATIONS
Based on the research, several policy implications are formulated are
stated below:
223 Sheeba Farooq
1. The cash assistance policy was good on short-term basis as it
facilitated rehabilitation of the flood affectees. However, it did not
contribute enough on long-term basis such as to prepare the
population for similar disasters in the future. The best way could
have been to provide support for immediate rehabilitation but
enjoining it with long term planning and undertaking, what the World
Bank called ‘Build Back Better’. The reconstruction, particularly of
infrastructure essential for maintaining the income generating
activities of the flood-prone areas should have been done at a level
that it could resist damage and destruction by future floods.
2. While the 2010 flood affectees have more or less been rehabilitated
and have resumed their normal lives, the broader issue of reducing
their vulnerability to floods and other natural calamities remains
unaddressed. People have moved back to their original places of
residence but these exactly are the areas which have maximum
exposure to floods and similar disasters. The monsoon rains in 2013
had again been unexpectedly very heavy and triggered heavy floods
resulting into loses of lives, properties and infrastructure. As per the
report of The News dated 4th August, 2013 “in one day the flash
floods have cost over sixty lives, disrupted communication network
in most of KP, Azad Jammu Kashmir and Baluchistan and caused
heavy damage to infrastructure”. In yet another report the newspaper
quoted KP Minister for Information and PDMA Chief saying that
“efforts are on to minimize flood damage”. Now this is what has been
referred as a reactive approach in this study. The Federal and
Provincial governments in Pakistan need to come out of this mode
by adopting a pro-active approach which will be a two-pronged
strategy. For instance, risk-assessment should be conducted prior to
emergency planning to ensure that organizations possess sufficient
supplies to respond to emergencies. Dedicated structures should be
in place for imparting technical training to staff for crises
preparedness [OECD (2013)].
3. Instead of making efforts with a reactive approach, to minimize the
damages after the calamity has occurred, there must be standing
operating procedures or SOPs available to relevant agencies such as
NDMA, PDMAs and district governments, who should remain
Rehabilitation of 2010 Flood Affectees 224
prepared to meet an exigency. The Government should formulate a
National Flood Management Policy as in Indonesia whereby their
government has organized disaster task force to coordinate the
emergency management. A national body provides central
coordination, with support from technical ministries (Studari, 2004).
Secondly, the government should develop relocation plans for flood-
prone areas, build rescue stations and invest in human capital to
overcome emergencies.
4. According to another report in The News, 8th August, 2013 World
Bank has issued a policy note titled “Managing Natural Disaster” to
Pakistan to improve disaster management by creating city emergency
centers in major urban centers. The policy note further recommends
“establishing linkages between city emergency centers and current
disaster management structures at federal, provincial and district
levels to ensure timely response in case of any natural disasters such
as floods, earthquakes, excess rainfalls, cyclones, tsunamis etc.” The
policy note also states that government should ensure systematic
methods for information gathering regarding disaster risk. The paper
further highlights that ‘the capacity of municipal governments is
quite limited due to lack of communication systems, equipment’s and
technical capacity’. It thus advises the government to ensure physical
resilience by making new infrastructure flood-prone and abiding by
the building codes through improved supervision of new
construction taking place [Haider (2013: 15)]. The government
policy, therefore, must ensure that the issues highlighted by the
World Bank, which have been time and again flashed by other
development partners, civil society and the media, are addressed on
war footing. The GOP with the help of development partners also
need to institute Rapid Response Force well-equipped with technical
skills and necessary tools. The government should conduct damage
assessment in collaboration with donors for targeting flood affectees
effectively. Secondly, the government can establish an emergency
contingency fund in collaboration with donors which will help in
restoring their livelihoods and reconstructing homes [World Bank
(2010)].
225 Sheeba Farooq
5. The GOP and development partners need to make comprehensive
impact assessment of the cash assistance programme, which is
reportedly under process to identify the gaps and weaknesses, if any
so that they are properly addressed in such future situations.
6. While some cash assistance to meet immediate needs may be
necessary, donors should as a matter of policy prevail upon
governments in the developing countries to institute projects with
donor funding, which productively employ disaster hit populations
leading towards their income generation on the one hand and skill
development for maintaining livelihoods on self-sufficient basis on
the other hand.
The government should be focusing on developing proper
relocation plans for flood-prone areas, build rescue stations, install
earning warning system and invest in reconstruction of the infrastructure.
Every constituency should be allocated funds for rehabilitation during
emergencies to curb elite capture. The government should conduct risk
assessment and ensure that relevant organizations have sufficient
mechanism in place to deal with disasters. The government should be
working in collaboration with donors to devise disaster risk reduction
strategies which will help in minimizing the damages caused by natural
disasters.
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NUST JOURNAL OF SOCIAL SCIENCES AND HUMANITIES
Vol.3 No.2 (July-December 2017) pp. 232-264
Trade Costs of Pakistan with its Major Trading Partners:
Measurement and its Determinants
Saba Altaf*, Zafar Mahmood† and Shabana Noureen§
Abstract:
Trade costs are cited as an important determinant of volume of trade. Higher
trade cost is an obstacle to trade as it impedes the realization of gains from trade
liberalisation. Determinants of trade costs of Pakistan for the period 2003-2012 with
their major trading partners across Asia, European Union and North America are
investigated. Several gravity type variables have been used as trade cost determinants.
Trade costs for agricultural and non-agricultural sector are also calculated using a
micro-founded trade costs measure. Estimates of trade costs equivalents show a
declining trend of trade costs estimates over the period of study. Fully Modified
Ordinary Least Square estimation of the model shows that tariff rates and distances
between the trading partners increase the bilateral trade costs and thus adversely affect
trade. Results show that improvements in port infrastructure and membership of free
trade agreement significantly reduce the trade costs. Z-test shows that the effect of
determinants of trade costs for agricultural and non-agricultural sectors is invariant.
This paper recommends that the agreement on trade facilitation be implemented and
reduce the red tape at border crossings to cut down the trade costs.
Keywords: Trade Costs, Gravity Model, Liner Shipping Connectivity Index
1. INTRODUCTION
International trade is significantly affected by the trade costs
incurred locally and across the borders. Trade costs form a potentially
important barrier to trade. Higher trade costs are an obstacle to trade and
impede the realization of gains from trade liberalization,1 therefore
* Saba Altaf <[email protected]> is a graduate of Economics at School of Social
Sciences and Humanities (S3H), National University of Sciences and Technology,
Islamabad, Pakistan. † Zafar Mahmood <[email protected]> is Professor of Economics at School of
Social Sciences and Humanities (S3H), National University of Sciences and
Technology, Islamabad, Pakistan. §
Shabana Noureen <[email protected]> is a PhD Scholar at
Economics Department, School of Social Sciences and Humanities (S3H),
National University of Sciences and Technology, Islamabad, Pakistan.
233 Altaf, Mahmood and Noreen
special attention is given to trade costs. Owing to the importance of trade
costs in explaining the volume and direction of trade, international trade
economists are increasingly focusing upon trade costs and this has
become an area of key interest within the modern stream of international
trade research. The gradual decreeing of trade cost has been resulting
major risen in international trade thus this tremendous change has
brought improvement in every country for international trading over a
past years.
The pertinent question is what exactly are the trade costs? They
include all the costs incurred in getting a good to the final user, excluding
the marginal cost of producing the good itself. Hence, trade costs include
transportation costs (both freight costs and time costs), policy barriers
(tariffs and non-tariff barriers), information costs, contract enforcement
costs, costs associated with the use of different currencies, local
distribution costs (wholesale and retail) and legal and regulatory costs
[Singh, et al. (2014)].
Sources of trade costs are mainly divided into two main
categories. First category totally consists of bilateral factors to put
segregation between imported and exported and such factors are wide-
ly depending upon exogenous factors such as geographical distance,
common border or sharing a common language than particular policy
choices. The second category is composed of endogenous trade costs,
which are international connectivity such as air or maritime transport
services, tariffs and non-tariff measures, and other factors that facilitate
trade.
Evidence shows that with growing regionalism in the world,
countries have considerably reduced the tariff rates, i.e., 5% in
developed countries and 10 to 20% in underdeveloped countries
[Anderson and Van Wincoop (2004)]. With a drastic fall in tariffs on the
one hand, there are, on the other hand, some other barriers to trade that
are hampering the trade performance. Most important among those are
barriers relating to infrastructure quality besides the tariff and non-tariff
barriers, collectively these are referred to as policy barriers. Poor
1A growing literature has documented the impact of trade costs on the volume of trade
(see, for example, Anderson and Van Wincoop, 2004).
Trade Costs of Pakistan 234
institutions and poor infrastructure distort strategic trade policy focus,
not only in terms of the traditional mechanisms of tariffs and quotas but
also of infrastructure and logistics,2 the so-called ‘behind the border
issues’. Thus, besides the differences in economic size and endowments,
the differences in trade costs, which act as a friction to trade, is important
reason as to why some countries trade more than others.
In an increasingly globalized and networked world, trade costs
are of great importance from a policy perspective. This is because they
act as a determinant of the pattern of bilateral trade and investment as
well as of the geographical distribution of production. International trade
costs are large and vary widely across countries and sectors. These costs
are likely to be higher in developing countries as compared to the
developed countries due to the existence of substantial tariffs and non-
tariff measures accompanied by poor infrastructure, dysfunctional
transport and logistics.
Pakistan is a country heavily enriched with natural resources.
Pakistan’s major trade partners are Asian, European Union and North
American countries. These include China, USA, UK, India, Bangladesh,
Saudi Arabia, Malaysia, Japan, Germany and UAE. EU has now
emerged as Pakistan’s largest trading partner.3 Total trade between the
two amounts to about $10 billion with Pakistan’s share in EU market of
about 0.09% and the share of EU in Pakistani market is 11.39%. Pakistan
also has very strong trade ties with Asian economies like China, UAE,
Saudi Arabia, and Malaysia. The main reason behind massive trade of
Pakistan with Asian countries is low transportation costs, similarities of
consumer tastes and trading priorities. USA is also one of the strongest
trade partners of Pakistan.
The size of Pakistan’s current trade doesn’t truly reflect its trade
potential. This is mainly because the direction of Pakistan’s foreign
trade, which is trade cost dependent, has not changed virtually since its
independence. Keeping in view the trade potential of Pakistan and to
reap full benefits from international trade, it is thus imperative to have a
2See, for example, Khan and Weiss (2006), who explain how and why infrastructure
can assist the regional cooperation process. 3EUROSTAT (2013).
235 Altaf, Mahmood and Noreen
detailed insight into the determinants of trade costs. Pakistan needs to
pay serious attention to the trade costs because only then it will be in a
position to improve its ability to position better in global networks of
trade and production. A detailed study on the determinants and
calculation of trade costs will help identify the areas which need to be
given special attention to identify policies and measures that have a
significant effect on trade costs, and to prioritize them thus affecting the
overall trade flows and composition of trade consequently.
The research problem which is to be addressed and assessed in
this paper is “What are the factors that affect trade costs incurred by
Pakistan with its major trading partners”? The study uses a set of selected
trading partners of Pakistan due to the paucity of available data. The
main objective of the study is to measure the trade costs incurred by
Pakistan in agricultural and non-agricultural sector with its major trading
partners in three different regions of the world, i.e., Asia, Europe and
North America including USA, Germany, UK, Japan, China, UAE,
Saudi Arabia, Bangladesh, India and Malaysia and empirically
investigate the determinants of trade costs.
This area is virtually untapped in case of Pakistan. Therefore,
there is a need to have a research study that can show Pakistan’s position
in terms of trade costs and identify its determinants. Such a study can
provide insights that if properly targeted, trade costs can not only be
reduced but also proper policies can be formulated to help boost the
overall trade as well improving Pakistan’s position in global trade
network. This study would add to the literature by disaggregating trade
into two macro-sectors; agricultural and non-agricultural. Harmonized
System (HS)4 based on two digit level with its major trading partners in
three different regions of the world, i.e., European Union, Asia and
North America. The countries include USA, Germany, UK, Japan,
China, UAE, Saudi Arabia, Bangladesh, India and Malaysia.
4It is a coding system known for coding Harmonized Commodity Description of tariff
nomenclature. It is a system of International standard of names and codes in order to
classify traded products maintained by the World Customs Organization (WCO).
Trade Costs of Pakistan 236
2. LITERATURE REVIEW
Trade costs have become a key area of interest for researchers.
In this regard, it is important to understand which factors trigger the trade
costs of a particular economy. Existing literature draws attention to some
of important determinants of trade costs. This section sheds light on the
existing literature in this area.
Limao and Venables (1999) identified the determinants of
transport costs and showed how they depend on geography and
infrastructure. Tobit model was estimated for the year 1990 taking 93
countries. Distance, contiguity and landlocked-ness were taken as
geographical determinants and quality of transport and communication
infrastructure were studied as infrastructural determinants. They
discussed that land distance is much more costly than sea distance.
Landlocked countries have high transport costs which can be reduced by
better infrastructure facilities. They further argued that trade volume can
be increased by a factor of five if transport costs are halved. The study
highlighted the cost of being landlocked as far as bilateral trade flows
are concerned.
Arvis, et al. (2007) estimated the cost attached to landlocked-
ness with regards to the international trade. Based on empirical analysis,
the study found out that large proportion of least developed countries are
landlocked and their market access depends upon the availability of
trade corridor or a transit system. High degree of unpredictability
associated with transportation time increases the trade costs of
landlocked economies along with high freight charges. The study
highlighted the need for reliable logistic services which are hampered by
flaws in implementation of transit system. They pointed that the business
community should design and implement comprehensive trade facili-
tation strategies. In addition to the physical constraints, least developed
countries are also faced with a problem of widespread rent seeking
activities. Thus, they showed that high trade costs of LDCs are mainly
due to high transportation costs which explain major proportion of high
logistic costs and vulnerability of supply chains and these areas need to
be targeted specifically.
Novy (2007) analysed the patterns of trade costs of UK and USA
with 31 trading partners from a period of 1960-2002. His study found
237 Altaf, Mahmood and Noreen
out that tariff equivalents of trade costs for USA have declined over the
period of study with US showing lowest trade costs with Canada and
Mexico while UK exhibited a remarkable increase in its bilateral trade
costs over time. Novy used micro founded trade cost measure for the
calculation of trade costs. The main determinants of trade costs were
classified into geographical, historical and institutional factors. Distan-
ce, landlocked-ness and exchange rate volatility ad tariffs showed a
positive relationship with trade costs while common border,
membership of free trade agreement negatively affected the trade costs.
Olper and Valentina (2007) examined the patterns of inter-
national trade costs in processed foods industry for a large cross section
of developed and developing countries over the period of 1976-2000.
Panel data estimation technique with country and time fixed effects was
used in this study. Tariff equivalents of trade costs were taken as the
dependent variable and the independent variables were divided into four
categories as geographical factors, historical and cultural linkage,
institutional factors and infrastructure development. Their study found
out that geographical and historical factors dominate the infrastructural
and institutional determinants of trade costs. Empirical results showed
that tariff equivalents of trade costs for the Emerging countries declined
by 13% over the period of study. However, developing countries showed
a low reduction pattern thus highlighting a need for government to focus
on the issue in order to achieve the goal of economic growth. They also
highlighted the need for freer trade environment keeping in view the
influential role played by trade policy in reduction of trade costs.
Duval and Utoktham (2011) examined the trade costs of Indian
Mekong sub-region and also evaluated the policy related and other
factors in order to facilitate trade and reduce trade costs. Novy’s trade
cost measure has been employed for calculating the trade costs. Various
trade related factors which possibly effected the trade costs of the Indian
Mekong sub-region were found to be bilateral distance between the
trading partners, cultural distance, tariffs between the trading countries,
liner shipping connectivity index, internet users per hundred people, ease
of doing business and monetary costs of moving a container from factory
to port and port to warehouse. Cross sectional data set of 64 countries
Trade Costs of Pakistan 238
has been estimated for the year 2006 using the Ordinary Least Square
estimation.
The results show that trade costs between India and Mekong
countries are high. However, China, India, Thailand and most of other
Mekong countries are making progress in reducing trade costs among
themselves as compared to other countries like Japan and USA which is
mainly due to the enhanced regional connectivity. The study also
investigated the contribution of explanatory variables. Results revealed
that the natural barriers contribute about 22 percent to the total variations
in trade costs followed by the differences in maritime logistics and then
the trade related but non-trade specific measures such as credit
information, extent of information disclosure accounting for about 16
percent and 7 percent variations respectively in trade costs. The study
highlighted the importance of logistics and information technology
services regulation as important issues to reduce the trade costs.
Several researches have been conducted in different countries of
the world as far as measurement and determinants of trade costs are
concerned, but there is hardly any research on measurement of trade
costs of Pakistan and investigation of determinants of trade costs with
its major trading partners. Thus, the study at hand becomes all more
important to fill this research gap.
3. OVERVIEW OF THE ECONOMY IN THE CONTEXT
OF TRADE COSTS
Economic journey of Pakistan has faced serious global and
internal challenges since independence. Despite the critical circum-
stances, the country, however, managed to gain a momentum. In this
regard, the period of the 1960s was marked as the golden economic era
of Pakistan. Trade policies in that era focused on industrial development
and import substitution. Various incentives like tax rebates and exempt-
ions as well as export bonuses were offered on exports, which resulted
in a remarkable increase in export volume, with exports showing a
growth rate of 16.19%. In the late 1980s, due to increased economic
pressures and globalization forces, Pakistan initiated the process of trade
reforms and its intensity increased in the first half of the 1990s. Wide
239 Altaf, Mahmood and Noreen
ranging thorough liberalization programmes started in 1996-97 in the
agricultural sector. Government reduced average tariffs to a level of 15
percent compared to a high tariff rate of 51 percent in 1994/95 [WTO
(2007)]. Trade volumes of Pakistan increased sharply in the 2000s. Total
trade volume increased from $23,380 million in 2003 to $69,410 million
in 2013 [GOP (2014)].
Analysis of the trade costs of Pakistan for agricultural and non-
agricultural sector with its trading partners shows that on average
Pakistan is facing high levels of trade costs despite substantial fall in
tariffs worldwide. Quality of institutions and infrastructure differs across
countries thus causing a difference in their levels of trade and trade costs.
Therefore, today’s trade strategy goes beyond the traditional mechan-
isms of tariffs and quotas and includes “behind-the-border” issues, such
as the role of infrastructure and governance in supporting a well-
functioning trading economy. For instance, many studies show that
liberalisation of international transport services foster international trade
similar to tariff liberalization [Baier and Bergstrand (2001)].
Estimates of trade costs equivalents show that trade costs have
declined over the period of study thus showing an increase in
international trade volumes of the country (Table 1). It may be noted that
the agricultural sectors trade costs are comparatively higher than the non-
agricultural sector due to the existence of policy barriers including high
tariffs and non-tariff barriers. In addition, arguably the processing and
storage costs of agricultural commodities are higher than such costs on
industrial consumer goods.
Trade costs (TC) of Pakistan in agricultural and non-agricultural
sectors on average show a declining trend for the period 2003-2012
(Table 1). The reduction in trade costs (TC) is consistent with the
lowering of tariff rates. Tariffs not only make imports costly but also
discourage exports by raising the cost of imported inputs and act as an
implicit tax on exports. Thus, a fall in simple average tariff from 16.8%
in 2003 to 13.9% in 2012 has resulted into a rise in exports and imports,
also consistent with trade costs (TC) reduction.
Trade Costs of Pakistan 240
Table 1. Trend in Trade Costs of Pakistan for Agricultural &
Non-Agricultural Sectors
Year TC Agr TC NAgr LSCI Pak Tariff ΔER
(Dep/App)
TV
(US$ million)
2003 204.08 159.93 19.29 16.81 0.008759 23380
2004 202.70 156.51 20.18 16.17 0.021759 27905
2005 197.26 154.50 21.49 14.61 0.012992 34989
2006 196.02 150.13 21.82 14.79 0.00785 45032
2007 198.41 150.55 24.77 14.9 0.160433 47516
2008 192.14 150.01 24.61 14.08 0.186123 59018
2009 193.70 151.84 26.58 14.78 0.049439 52510
2010 189.23 148.15 29.48 14.51 0.014068 54000
2011 190.39 147.83 30.54 14.25 0.082774 65224
2012 187.70 144.94 31.97 13.99 0.09536 68540
Source: Authors’ estimations, except for LSCI, average tariff based on World Bank (2013) and trade volume
based on GOP (2013).
Note: Positive change in exchange rate represents depreciation and negative change in exchange rate represents
appreciation. LSCI stands for liner shipping connectivity index represents Port infrastructure, TV represents the
trade volume.
An analysis of changes in the nominal exchange rate (ER) shows
depreciation of nominal exchange rate (ER) over the period of study.
Depreciation of exchange rate (ER) has increased the bilateral trade
flows relative to domestic trade, thus, causing a reduction in overall trade
costs (TC). Hence, depreciation of nominal exchange rate (ER) is seen
as a factor helping in trade costs reduction.
Reduction in trade costs can also be attributed to improvement in
port infrastructure and shipment. Table 1 shows a significant improve-
ment in liner shipping connectivity index (LSCI) from 19% in 2003 to
32 % in 2012. More than 95% of total freight trade of Pakistan is sea
borne; an improved and efficient port infrastructure facilitates trade and
reduces trade costs. Keeping this in view, Ministry of Ports and Shipping
of Pakistan is focused to achieve the objective of modernization and
corporatization of ports introducing modern technology and data base in
line with the present day trends, reviving ship-owning in the private
sector by removing the impediments, and enhancing tonnage and
profitability of Pakistan National Shipping Corporation. Fulfilment of
these objectives will further enhance port efficiency, reduce the costs for
241 Altaf, Mahmood and Noreen
port users and enhance port management accountability; consequently
reducing trade costs.
3.1. Sectoral Trade Costs
In trade costs equivalent terms, Pakistan and UAE on average
have the lowest levels of trade costs in their bilateral trade, i.e., 146.5%
for agricultural sector and 104% for non-agricultural sector. Table 2 and
3 provide trade costs of agricultural and non-agricultural sectors. There
are many factors behind these lower trade costs between two partners;
these include geographical proximity, cultural linkage, no currency
restrictions from UAE, abundant energy supplies, and no corporate
taxation [Hamid and Hayat (2012)]. Trade costs between two countries
are expected to decline further with the decrease in oil prices, which will
reduce transportation costs.
Another interesting finding of trade costs analysis is that despite
being neighbouring countries, tariff equivalents of trade costs between
Pakistan and India are quite high, i.e., 218% for agricultural and 176%
for non-agricultural sector (Tables 2 and 3). Trade costs are not low
between these two countries owing to the economic, political and
military tensions. There is discriminatory stringent application of non-
tariff barriers by India, i.e., regulatory and safety requirements that
dampens Pakistani exports to India. Political uncertainty, strict pro-
cedures for licensing permits and visa hassles also act as barriers to trade,
thus increasing trade costs. India follows a restrictive trade regime
especially in case of agricultural goods which is depicted by the high
trade costs of agricultural sector. Similarly, for textile exports, India
observes a large number of non-tariff barriers including para-tariffs,
sanitary and photo sanitary (SPS) measures and pre-shipment inspection.
Some goods can only be imported through specified ports and road
routes between the two countries are only open for exports of limited
number of commodities. These bottlenecks on road and rail route and
weak and inadequate transportation links between the two countries
further increases the trade costs. Also, Pakistan maintained a “Positive
list” for the Indian imports until 2011, which only allowed the imports
of these 1,946 items from India. Later on the approach of “Negative list”
Trade Costs of Pakistan 242
was adopted by Pakistan which prohibited the import of 1209 items to
be imported from India. Positive list had also hindered the free flow of
goods between the two partners thus aggravating the overall trade costs
[Saleem, et al. (2014)]. With the adoption of a negative list, almost 85%
of goods can be imported from India compared to level of 25%
previously.
Pakistan and China are leading trading partners and neighbouring
countries, sharing a common border. However, bilateral trade costs
between two countries remain high. The government of China promotes
domestic consumption through structural tax reduction policies and there
is a strong domestic demand in China. Although, bilateral trade flows
between two countries are very large, yet China’s customs procedures
still require harmonization. Besides, its tariff regimes have not changed
substantially, which is a possible reason behind high trade costs.
In addition, China maintains restrictions, licensing and prohibit-
ions on grounds of state security and morality, all these factors add to the
levels of trade costs. Bilateral costs of trade between two countries can
be reduced by upgrading the Karakoram Highway which is the shortest
overland route between the two countries. Also, the construction of an
economic corridor is foreseen as a great opportunity to reduce the
staggering amount of time and distance consequently reducing the trade
costs. Long shipping routes between the two countries add to the costs
of trade which can be lessened by the construction of a direct corridor
from Kashgar to Gwadar, which is estimated to cut down the existing
costs associated to long distance by one-third of the current levels
[Kayani, et al. (2013)].
USA is also among the top ten major trading partners of Pakistan.
Trade costs between the two countries are high owing to the long
distance as well as many other contributing factors. USA’s domestic
trade relative to international trade with Pakistan is very high as
compared to Pakistan. The reason behind high values of domestic trade
is that there is an excellent working relationship between US
manufacturers and other distributors that provides wholesale customers
with access to barge product wherever and whenever they need it. Also,
there is an ease of transport (ground versus air/sea) which makes
domestic trade more feasible. Trade costs between two countries are high
243 Altaf, Mahmood and Noreen
because of large distance, stressed relationship between the government,
licensing and quality control requirements from USA government.
Pakistan is a country that is included in the list of Restricted Entities by
USA, imposition of non-tariff barriers makes textile and clothing
products of Pakistan suffer the most.
Table 2. Estimates of Trade Costs Equivalents for Agricultural Sector US Dollar (USD)
Year IND UAE CHN SA UK USA MYS JPN GMY BD
2003 241.26 148.28 222.76 217.26 190.21 205.22 200.31 239.38 224.92 151.18
2004 239.87 148.19 219.47 215.38 194.16 186.24 201.76 242.01 224.27 156.65
2005 217.54 142.31 207.87 210.65 191.83 201.09 199.33 240.87 220.86 150.09
2006 196.67 144.37 217.21 208.85 188.42 192.01 203.25 244.09 216.73 158.92
2007 222.88 148.86 196.07 208.87 190.65 193.84 199.07 233.54 218.59 151.07
2008 218.38 147.27 207.21 197.99 185.16 181.39 192.41 233.81 214.41 151.24
2009 221.74 146.92 206.35 199.52 184.25 190.03 180.85 229.68 212.57 150.12
2010 219.91 142.73 203.41 196.37 182.96 188.95 185.80 226.03 208.57 149.04
2011 213.50 149.18 199.91 206.34 181.23 192.93 179.77 219.32 209.42 149.82
2012 212.88 147.20 191.78 202.98 180.01 187.63 179.84 214.91 208.68 149.01
Avg. 218.66 146.53 206.62 204.42 192.68 191.93 192.24 232.57 209.76 151.71
Source: Authors’ calculations.
Note: IND stands for India, CHN stands for China, BD stands for Bangladesh, SA stands for Saudi Arabia, MYS
stands for Malaysia, JPN stands for Japan, GMY stands for Germany.
Pakistan and EU enjoy very strong and rapidly growing trade ties.
We have chosen two countries from EU, United Kingdom and Germany
for the purpose of trade costs analysis. Estimated trade costs show that
despite the fact there is no cultural or geographical proximity between
Pakistan and selected EU member states, trade costs on average are not
very large. A further decline in trade costs is expected to occur by the
GSP plus status granted by EU to Pakistan in 2014. Before that, Pakistan
was given a general GSP status, and Pakistani exports faced some sort
of non-tariff barriers like standard intellectual property rights, rules of
origin and competition policy.
Pakistan and Bangladesh have not been able to bring about a
significant reduction in their bilateral trade costs. Though trade between
two countries is growing progressively and has crossed $1 billion mark
but there is a need to develop trade facilitation strategies that can further
Trade Costs of Pakistan 244
reduce trade costs. At present there is no direct air link between two
countries, especially between Lahore and Dhaka. Infrequent shipping
arrangements between the two countries hamper flow of goods between
Pakistan and Bangladesh.
Japan is an important trading partner of Pakistan. There is a huge
potential for further increase in trade volume between the two countries.
Trade costs estimates, however, do not present a very encouraging
picture. Trade costs equivalents are very high. The import regulations,
applicable standards and quarantine requirements make it all the more
difficult to export Pakistani products specially food items. Pakistani
exports also have the disadvantage of being more distant from the market
than its competitors, such as China, Russia, Thailand, South Korea, etc.
This not only increases transportation costs but also delays the delivery
of goods, whereas Japanese importers prefer small size lots with short
delivery schedules. Both the countries need to overcome these
impediments to bilateral trade.
In the modern time, importance of trade costs as a determinant of
national trade performance and competitiveness has been seriously
Table 3. Estimates of Trade Costs Equivalents for
Non-Agricultural Sector US Dollar (USD)
Year IND UAE CHN SA UK USA MYS JPN GMY BD
2003 203.47 107.78 162.67 150.15 157.63 171.20 164.49 161.65 180.25 139.10
2004 185.64 109.32 160.49 147.19 160.33 165.90 160.30 160.76 176.01 139.18
2005 182.60 106.39 153.83 148.71 158.85 164.03 160.52 156.64 176.86 136.60
2006 162.07 105.77 148.30 144.82 152.83 165.49 161.97 151.43 175.02 133.68
2007 167.94 104.07 146.72 138.61 160.79 166.59 155.62 157.19 174.32 133.98
2008 164.32 103.23 153.62 137.68 157.70 165.90 151.17 156.63 175.14 134.78
2009 174.46 103.62 158.80 135.74 157.75 163.74 149.79 164.01 172.78 137.91
2010 171.49 103.06 152.93 130.70 147.88 163.32 145.96 159.42 171.89 133.82
2011 169.34 101.52 154.58 131.80 148.71 163.63 147.88 158.34 171.37 131.54
2012 172.11 100.48. 154.64 127.84 139.88 158.63 142.73 160.10 162.04 130.99
Avg. 176.12 103.72 154.66 139.33 154.20 164.01 154.04 158.61 173.57 134.24
Source: Authors’ calculations.
Note: IND stands for India, CHN stands for China, BD stands for Bangladesh, SA stands for Saudi Arabia, MYS
stands for Malaysia, JPN stands for Japan, GMY stands for Germany.
245 Altaf, Mahmood and Noreen
recognized by the developed countries. Their governments have been
critically analysing and performing research for making effective
policies for reduction of trade costs. On the other hand, developing
countries have been rather ignorant and little efforts have been made so
far at policy level to address this issue. Pakistan is not different from
other developing nations. By looking at trade costs estimates, we find
that the country still faces high bilateral trade costs viz a viz its major
trading partners. This shows government’s lack of policy attention
towards trade facilitation. Pakistan still exports large volume of agri-
cultural products, while trade costs for agricultural sector are sub-
stantially higher than that of non-agricultural sector, which speaks of
sectoral inefficiency and bias in policies. Thus, the key need is to identify
the primary sources of trade costs and formulate what government
should do to address them so that trade can be used to sustain high rate
of economic growth over a longer period of time.
4. THE TRADE COSTS MODEL
4.1. Theoretical Framework
Trade costs are cited as important determinant of international
trade. Given the nature and pattern of trade costs, the Gravity model of
international trade is most suitable to determine factors that affect trade
costs. This is because the model provides main link between trade flows
and trade barriers. The Gravity model has become a major pillar in
applied international economics [Evenett and Hutchinson (2002)]. It is
basically motivated by the Newton’s gravitational law in which the
gravitational force utilized among two bodies is determined by their
distance and mass. This model became popular in international
economics with the pioneering work of Tinbergen (1962). It relates
bilateral trade flows to the GDP, distance, and other factors including
trade barriers. Anderson (1979), Deardoff (1998), Hummels (1999),
Baier and Bergstrand (2001), Limao and Venables (2001) have applied
it in a wider sense to infer trade flow effects of institutions such as
customs unions, exchange rate mechanisms, ethnic ties, linguistic
identity and international borders.
Trade Costs of Pakistan 246
This paper makes use of Novy’s (2008) trade costs measure. This
is a micro founded measure of trade cost that has been derived from
Anderson and Van Wincoop (2003) model based on the Gravity
equation. The Gravity equation has been most widely used instrument
for modelling the bilateral trade flows. As a workhorse of international
trade, it relates countries bilateral trade with their economic sizes and
trade costs. This measure analytically solves the theoretical gravity
equation for the trade cost parameters that capture the barriers to
international trade.
Novy (2007) explained multilateral trade hindrance factors in
detail and solved that trade function too. These new strategies are
applicable to both international and domestic trade resistance. Basically,
when the cost of a particular product reduces then such items are shipped
out of countries and this implies that such hindrance have huge impact
on domestic trade too. Previous theories don’t justify this boarder
hindrance and also, they don't take domestic trade in any account. A
slight change in trade barriers can bring noticeable change in resources
and can shift recourses into tradeable and non-tradable sectors and this
will result in changes in trade flows (either bilaterally or multilaterally).
Hence, multilateral resistance of the trading countries explains domestic
trade very well so it is important to include domestic factor into the
equation also to address the home biased.
The motivation behind Novy’s approach was to overcome the
drawbacks that were associated with the theory-based gravity framework
by Anderson and Van Wincoop (2003), which imposed certain arbitrary
trade cost functions. The theory-based gravity formulation was a
refinement of the traditional gravity equation to include multilateral trade
resistance variables.
Anderson and Van Wincoop [AvW (2003)] derived a micro
founded trade cost measure based on a multi-country general equilibrium
model expressed as:
... (1)
where, 𝓧𝒊𝒋 is the bilateral trade from i to j, 𝓨𝓲 & 𝓨𝓳 are nominal income
of country i and j, 𝓨𝔀 is the world income, 𝚷𝓲 is the outward multilateral
resistance of country i, 𝓟𝓳 is the outward multilateral resistance of
247 Altaf, Mahmood and Noreen
country j, and 𝓽𝓲𝓳 is the bilateral trade cost measure, σ is the elasticity
of substitution between goods. The main innovation in AvW's (2003)
model is to incorporate exporter and importer price indices (Π and P )
such that trade not only depends on bilateral trade costs between the two
countries but also on the trade “resistance” they face with all of their
trading partners in the rest of the world. That is, country i is more likely
to trade with country j if πi is higher, meaning the multilateral resistance
of country i to all other partners is higher.
Using Equ. (1), consider the intra-national trade of country i as:
… (2)
and rewrite it as:
… (3)
which solves for country i's multilateral resistance. Multiplying Equ. (1)
with 𝓧𝓳𝓲, we obtain:
… (4)
substitute Equ. (3) for country i and j into (2), we can derive the bilateral
trade costs relative to domestic trade costs expressed as tariff equivalent
by subtracting 1:
… (5)
where,
τij = tariff equivalent trade cost (i.e., measures domestic trade relative to
bilateral trade).
Trade Costs of Pakistan 248
tij = international trade costs from country i to country j.
tji = international trade costs from country j to country i.
tii= intra-national trade costs of country i tjj denotes intra-national trade
costs of country j.
xij =international trade flows from country i to country j.
xji =international trade flows from country j to country i.
xii =intra-national trade of country i.
xjj =intra-national trade of country j.
σ denotes elasticity of substitution between all goods.5
τij is defined as a ratio of trade cost across national border relative to
trade cost within national border weighted by the elasticity of
substitution. It must be noted that τij is not directional, i.e., τij measures
the barrier between country i and j on average, so that it is a two-way
trade cost measure. Intuitively, it measures the bilateral trade cost for
both importing and exporting countries. Trade costs τij, thus represent the
geometric average of international trade costs between countries i and j
relative to domestic trade costs within each country. Intuitively, trade
costs are higher when countries tend to trade more with themselves than
they do with each other, i.e., as 𝑋𝑖𝑖𝑋𝑗𝑗/𝑋𝑖𝑗𝑋𝑗𝑖 increases. As the ratio falls
and countries trade more internationally than domestically, international
trade costs must be falling relative to domestic trade costs.
An additional advantage of Novy’s trade cost measure is that it
allows time-varying measurement of bilateral trade barriers. With
readily available trade and production data in tradable goods categories,
we are able to measure and explain the determinants of bilateral border
effects.
The gravity equation represents one of the greater successes in
empirical economics, as it describes the value of bilateral trade, which is
function of the market size of the importer as well as exporter, and
5See, Anderson and Van Wincoop (2003) for detailed discussion on elasticity of
substitution between goods. This thesis follows AVW’s and Novy (2008) σ=8, which
is the middle point of available estimates. Smaller value of σ results in higher trade
costs showing that consumers are irresponsive to prices and trade costs and consume
larger amounts of foreign goods.
249 Altaf, Mahmood and Noreen
distance among them [Lili (2011)]. Market sizes embody push and pull
factors that affect value of trade flows, and are usually characterized by
the GDP. Distance is generally measured by geographic distance among
two regions (absolute distance). It is anticipated that large distance
between trading partners leads to a decrease in trade, as trade will
become complicated and bring transaction costs. The basic Gravity
model is as the following:
𝑇𝑖𝑗 = 𝐺 (𝑌𝑖𝑌𝑗
𝐷𝑖𝑗) … (6)
where, Tij is bilateral trade volume, for sum of exports and imports; Yi
is country i's GDP; Yj is country j's GDP, Dij is the distance among
country i and country j; and G is a constant; and is independent of any
subscript as it links to a standard Gravity equation in the following form.
The multiplicative nature of Equ. (6) Suggests that by taking logs it can
be made linear in parameters:
lnTij = lnG + a1lnGDPi + a2lnGDPj - a3lnDij + εij … (7)
Objectives of this paper are to test the following two hypotheses:
H1: Connectivity constraints are more important trade deterrents than
tariff barriers.
H2: Determinants of trade costs have similar effect on agricultural and
non-agricultural sectors.
The relationship between trade costs and its determinants is
difficult to capture given the paucity of data on all the factors involved.
However, in order to explore the determinants of trade costs, our
empirical analysis has used several gravity-type variables including
distance, infrastructure development, exchange rate, tariff, area and two
dummy variables for contiguity and free trade agreement between the
trading partners.
Trade Costs of Pakistan 250
4.2. Empirical Model
Following Novy (2007), joint observation of non-bilateral
variables for country i and j are constructed by multiplying the single
country variables to lead to symmetric and constant interaction effects.
All variables are taken in the log natural form.
τij = ƒ (DIST, TARIFF, EXCH, LSCI, AREA, CONT, FTA) … (8)
where, τij is the dependent variable representing tariff equivalent of
trade costs, DIST is the distance among Pakistan and partner country,
TARIFF is the product of tariffs imposed by Pakistan and other trading
partner, EXCH is the official exchange rate with respect to Pakistan
(taken in current US dollars), LSCI is the linear shipping connectivity
index of Pakistan and partner country, AREA represents product of land
area of two trading partners, CONT and FTA are dummies for
contiguity and free trade area, which take the value one if two partner
countries are contiguous and members of FTA and zero otherwise.
Distance appears in the Gravity model as proxy of remoteness or
transportation costs implying that coefficient of distance is expected to
have a positive impact on trade costs. This paper uses liner shipping
connectivity index (LSCI) as a measure of infrastructure development of
the trading countries. Our model includes a dummy variable to show
common border with the trading partner. Those countries that share a
common border are reflected by a unitary value of dummy variable,
known as contiguity. Common border again is a proxy for transportation
and information costs, which tend to be lower for contagious trading
partners as they are well aware of consumer’s choices and trading
prospects, thus making mutual trade less costly. Coefficient of conti-
guity is expected to be negative.
Ample land is an indicator of big economy and bigger population
with high domestic demands. In order to fulfil that high demand foreign
goods are also accepted and larger countries have cultural diversity,
residents have greater acceptability for a variety of cultures, which calls
for greater imports [Saleem and Mahmood (2014)]. Thus, trade
increases and overall trade costs decrease. Coefficient of area of trading
251 Altaf, Mahmood and Noreen
partners is expected to have a negative sign. Another dummy has also
been included to evaluate the effect of Free Trade Area (FTAs) on trade
costs. Dummy for FTA is expected to have a negative impact on trade
costs.
Tariffs and exchange rate are two policy related or institutional
determinants of trade costs. Tariffs imposed by partner countries are
used as a measure of restrictiveness to trade flows. Aggravation of tariffs
imposed by the trading partners is expected to increase the bilateral trade
costs, not only it affects imports but the level of exports also declines if
tariffs are imposed on raw materials. Issues of duty draw back further
add to the level of trade costs. Thus, overall international trade declines
and intra national trade increases consequently increasing trade costs.
Exchange rate is used as a measure of competiveness in
international trade flows. The study uses official exchange rate as a
determinant of trade costs. Increase in nominal exchange rate leads to an
increase in overall volume of trade is a well-established fact. An increase
in trade flows with nominal depreciation therefore leads to decline in
trade costs as trade flows and trade costs are inversely related. Keeping
this in view, coefficient of exchange rate is expected to have a negative
sign.
4.3. Empirical Specification
The general empirical model reported in Equ. (8) is transformed
as the following econometric equation, which links tariff equivalents of
trade costs with its determinants and is given as:
τij = β0 + B1EXCHijt + B2TRit*TRjt + B3DISTij + B4LSCIit*LSCIjt +
B5CONTij + B6AREAi*AREAj + B7FTAijt + εijt … (9)
In our opinion, model in Equ. (9) will help us determine the
impact of these variables on trade costs of Pakistan. The findings from
this model will have important implications for the policy, as it will help
the policy makers to figure out those areas that can bring about
significant reductions in trade costs and prioritize policies accordingly.
Trade Costs of Pakistan 252
Table 4. Definition and Sources of Data Variables
Variable Definition Proxy of Data Source
Export/
Import
Bilateral trade flows between
country i and j
Direct Variable UN Comtrade
GDP Output of agricultural and non-
agricultural sectors of country i and
j in current US Dollars
Direct Variable WDI, World Bank
TARIFF Product of simple average tariffs
imposed by Pakistan and partner
country.
Measure of
restrictiveness
WDI, World Bank
EXCH Average official exchange rate of
Pakistan (US Dollar)
Competitiveness Pakistan Economic
Survey, GOP
DIST Distance between Pakistan and
partner countries capital cities.
Transportation
costs
CEPII
AREA Product of country i and j land area. Size of
economy
CEPII
FTA Dummy equal to unity if two
countries are a member of free trade
area.
Market access WTO website
CONT Dummy equal to unity if two
countries share a common border.
Information
costs
CEPII
LSCI Product of country i and j scores on
liner shipping connectivity index.
Trade
infrastructure
WDI, World Bank
5. RESULTS AND DISCUSSION
5.1. Summary Statistics
Summary statistics is a quantitative description of the main
features of the data used in the study. Mean and median are used as
measures of central tendency while standard deviation, maximum and
the minimum values represent measures of variability. Table 5 provides
summary statistics of Pakistan’s trade costs with reference to the
variables included in the study. A fleeting look at the summary statistics
shows that highest mean value of total trade costs for Pakistan is
138.50% with maximum value of 191.8% and minimum value of 98.4%.
To identify whether a long run relationship between trade cost
and explanatory variables exists or not, the prerequisite is to analyse the
time series properties of all the variables first. As the co-integration tests
253 Altaf, Mahmood and Noreen
Table 5. Summary Statistics
Variable Mean Median Std. Dev Minimum Maximum Observations
TC 138.50 135.74 22.702 98.4 191.8 140
TARIFF 0.924 0.633 0.737 0.205 4.59 140
DIST 4299.13 3916.826 2472.13 683.369 11091 140
AREA 1042615 625300.8 873038.8 464239 2907092 140
LSCI 36.064 37.697 13.466 9.504 68.146 140
Dummy Variables
CONT 0.214 0 0.410 0 1 140
FTA 0.285 0 0.451 0 1 140
can only be performed when the panels are non-stationary. For the
purpose of checking the stationarity of the series, panel unit root test
[Levin, Lin and Chu (2002)] is run on the basis of the following
hypothesis:
H0: Variables exhibit a unit root.
H1: All the variables are stationary.
5.2. Empirical Results
5.2.1 Empirical Results of Pooled Unit Root Test
In order to check the presence of unit root in selected countries,
pooled unit root test is conducted using the Eviews-8 Software. Table 6
reports the results of Levin, Lin and Chu (2002) stationarity test showing
that the variables TC, TARIFF, EXCH and LSCI are stationary at the first
difference, i.e., I(1). Distance between countries and country area fail to
show any result because they are independent of time. Remaining two
variables included in the model are dummy variables.
5.2.2. Empirical Results of Kao (1999) Co-integration Test
To determine whether variables with first difference orders of
integration, i.e., I(1) yield spurious regression or a long run relationship
does exist, Kao (1999) panel co-integration test is run based on the null
hypothesis of no co-integration. Table 7 shows that the null hypothesis
Trade Costs of Pakistan 254
of no co-integration is rejected thus confirming that a long run relation-
ship does exist. In other words, the possibility of spurious results has
been ruled out.
Table 6. Levin, Lin & Chu Test for Stationarity
Variable
Level First Difference
Order of Integration
Stat. Prob. Stat. Prob.
TC -1.315 0.0946 -8.717 0.0000 I(1)
TARIFF 0.151 0.5603 -4.736 0.0000 I(1)
EXCH -28.001 0.0000 I(1)
LSCI 0.370 0.6444 -5.273 0.0000 I(1)
Table 7. Kao (1999) Residual based Co-integration Test Estimation
Kao Co-integration Test Dependant variable: D (RESID)
Included Observation: 139 after
adjustment
Variable Coefficient t-statistic P-value
RESID(-1) -0.233 -3.749 0.0003
Null Hypothesis: No co-integration
The results of Kao (1999) co-integration tests confirm the
existence of a long run relationship between the dependent and
explanatory variables. Therefore, the application of OLS technique will
yield biased and inconsistent estimators. Fixed effects model cannot be
applied to models involving time invariant variables such as distance as
it leads to problem of endogeneity. We thus need to adopt an alternative
method to estimate the co-integrated panel. In this regard, Panel Fully
Modified Ordinary Least Square (FMOLS) method was developed by
Pedroni (1996), which uses a correction approach to deal with the
nuisance parameters and thus gives long run coefficients for the
estimated model correcting for endogeneity and serial correlation.
FMOLS has an advantage over other techniques as it allows for
estimation of common co-integration vectors while allowing for
heterogeneity both across time and cross sections [Pedroni (2004)].
255 Altaf, Mahmood and Noreen
Thus, the resultant estimates are more consistent, free of serial
correlation and endogeneity.
5.2.3. Empirical Results of FMOLS: Total Merchandise Trade
Results of fully modified ordinary least square model show that
trade costs equivalents for selected trading partners of Pakistan are
significantly dependent on the explanatory variables included in the
model.
Table 8 shows the estimated results of determinants of trade costs
for overall merchandise trade with Pakistan’s major trading partners.
Dependent variable is the log of trade costs equivalents for total
merchandise trade.
The results depict that nominal exchange rate (EXCH) is
statistically significant at 1% level and has a negative sign. There is an
inverse relationship between depreciation of nominal exchange rate and
trade costs. In other words, with depreciation of the exchange rate, total
volume of trade rises. As trade goes up, intra-national trade goes down
resulting into a decline in trade costs. The coefficient for exchange rate
suggests that 1% depreciation of exchange rate reduces trade costs by
0.03% (Table 8). This result is consistent with the findings of Singh, et
al. (2012). Thus, an increase in international trade greater than the
increase in intra-national trade as a result of currency depreciation
implies that it has become easier for countries to have more trade
internationally rather than trading internally, which is tantamount to a
decline in trade costs. It is pertinent to note that with depreciation of
nominal exchange rate it is realized that the growth in total bilateral trade
with selected countries over the period of 2003-2012 is 50.4%, which is
larger than the growth of intra-national trade which increased by 37.2%.
Tariffs always act as an obstacle to international trade, thus,
increasing the trade costs. Imposition of tariffs not only decreases the
level of imports as well as exports, because tariffs imposed on imported
raw materials and inputs used in production of export tables, causing a
switch towards intra-national trade leading to increase in trade costs.
Thus, increase in tariffs adversely affects overall trade flow. Here,
product of tariffs imposed by Pakistan and its trading partner is used,
Trade Costs of Pakistan 256
reflecting degree of market access in two countries, which leads to
increase in trade costs. Estimated coefficient sign for this variable is
positive but is statistically insignificant. Results show that 1 % increase
in tariffs will increase the trade costs by 0.02% (Table 8). These results
are in line with the findings of Novy (2013) and Wincoop, et al. (2004).
* Significant at 1%, ** significant at 5%.
Distance, area and common border are geographic determinants
of trade costs. Distance between the trading partners affects the physical
transport cost. Geographic distance between the trading countries is a
hindrance to bilateral trade flows. Estimated results show that
geographic distance between Pakistan and its trading partners is
positively related to the trade costs (Table 8). It indicates that 1%
increase in distance increases the trade costs by 0.28%. Our result is
consistent with the study of Duan and Jason (2012).
The regression coefficient of the variable land area of Pakistan
and the trading partner is negative and statistically significant at 1%
Table 8. Empirical Results of FMOLS: Total Merchandise Trade
Variable Coefficient Std. Error t-Statistics p-values
TARIFF( TRi*TRj) 0.019663 0.051249 0.383675 0.7020
EXCH -0.028292 0.009666 -2.927088 0.0042*
LSCI( LSCIi*LSCIj) -0.179337 0.045527 -3.939140 0.0002 *
DIST 0.278061 0.071994 3.862296 0.0002 *
AREA(Areai*Areaj) -0.047967 0.015814 -3.159048 0.0031 *
CONT -0.131884 0.099674 -1.323153 0.1888
FTA -0.166789 0.071971 -2.31745 0.0224**
C 2.644451 0.553275 4.779636 0.0000*
R-squared 0.678589 Mean dependent var 4.916574
Adjusted R-squared 0.656313 S.D. dependent var 0.181657
S.E. of regression 0.106496 Sum squared resid 1.145483
Durbin-Watson stat 0.592009 Long-run variance 0.028958
257 Altaf, Mahmood and Noreen
confidence level. It implies that when there is 1% increase in land area,
trade costs decrease by 0.04% (Table 8). Intuition behind this result is
that generally countries with large land areas have large economies and
populations, and thus have high domestic demand. To fulfil domestic
demand, foreign goods are also accepted by local population, which
results into trade. Moreover, in large size countries, cultural diversity is
a hallmark and the residents have greater acceptability for a variety of
culture including foreign cultural goods, which also causes greater
import of cultural goods [Salim and Mahmood (2014)]. Thus, inter-
national trade flow increases and trade costs decrease. The present
study’s empirical result is same as that of Lili (2011).
Liner shipping connectivity index (LSCI) is used as a proxy for
infrastructure development. Estimates of regression show that LSCI has
a negative and statistically significant impact on trade costs. Better
maritime connectivity and port efficiency reduce the level of delays in
shipment of goods and thus lower trade costs. Results show that 1%
increase in LSCI decreases the trade costs by 0.17% (Table 8). These
results corroborate with the findings of Duval and Chorthip (2010),
Singh, et al. (2012) and Olper and Valentina (2007).
Dummy variable for free trade agreement (FTA) exhibits a
negative and significant relationship with trade costs. According to the
regression results, Pakistan’s membership in a free trade area reduces
trade costs by 0.16% (Table 8). Free trade area reduces barriers to
exchange and increases international trade through specialization, divis-
ion of labour and comparative advantage. Thus, an increase in
international trade in the aftermath of free trade agreement reduces trade
costs. Our results are in line with the findings of Novy (2007).
R-square is used to measure the regression’s success in
determining the values of dependent variables. Overall, our model per-
forms reasonably well and about more than half of the variation in
dependent variable is being explained by independent variables.
Adjusted R-square is 0.66, which shows that the above determinants are
explaining 67% of variation in trade costs. Standard deviation of
dependent variable is less than which indicates greater reliability of the
results. Model is also adjusted for serial correlation and possible
endogeneity problem because of FMOLS.
Trade Costs of Pakistan 258
Estimation results for trade costs equivalents for agricultural and
non-agricultural sectors along with the z-test results of cross model
coefficients comparison are given in Appendix, the included explanatory
variables yield same statistical relationship with the dependent variable
as in the case of total merchandise trade.
6. CONCLUSION AND POLICY IMPLICATIONS
This study analysed the estimates of trade costs for overall trade,
agricultural trade and non-agricultural trade of Pakistan with its major
trading partners across Asia, Europe and North America over the period
2003-2012. Moreover, it examined the relationship between trade costs
and its major determinants using the panel data estimation techniques.
The study adds to the literature by disaggregating trade into two macro-
sectors, agriculture and non-agriculture. Existing studies only used total
trade, without attempting on sectoral trade details.
Despite the fact that international economy has considerably
integrated, our analysis of tariff equivalents of trade costs emphasises
that large unexploited gains can be reaped by further reducing the wedge
between the cost of producing a good and price paid by ultimate
consumer, i.e., by cutting down the trade costs.
Our estimates of trade costs reveal that Pakistan’s trade costs are
following a disproportionate pattern with its trading partners. Although,
the estimates show a considerable reduction in trade costs, yet they
indicate that substantial room remains for lowering them further. High
bilateral trade costs with some of its very largest trading partners in
particular calls for policies that can effectively reduce trade costs
between the trading partners. Policy makers need to address the
dynamics of higher trade costs in order to improve country’s absolute
and relative position in the global trade.
At the sectoral level, costs of trade for agricultural sector tend to
bypass the costs of trade for non-agricultural sector. The fact that
agricultural trade costs in many developing countries are relatively
larger than that of the non-agricultural sector suggests that focusing on
trade facilitation efforts for agricultural sector would be particularly
259 Altaf, Mahmood and Noreen
productive for Pakistan as WTO’s agreement on trade facilitation also
emphasis on the release of perishable goods at the earliest possible.
In addition to mapping out the level of trade costs of Pakistan in
the recent decade, we used econometric method to investigate various
determinants of trade costs. For this purpose, we decompose the trade
cost components into various policy and non-policy features. A key
finding is that distance, maritime transport and trade facilitation matter
for trade costs. Two areas which are highly amenable to policy
intervention for reduction of trade costs are the trade infrastructure and
free trade areas with the trading partners. UNCTAD’S liner shipping
connectivity index is a more important source of trade costs than tariffs.
This is because better shipment connectivity with the trading
partners efficiently improves transportation routes thus reducing time
and other costs. Similarly, we find that free trade agreements also play a
significant role in reducing the costs of trade; this implies that the FTAs
of modern era including a fall of non-tariff and behind the border
regulatory measures will be helpful to achieve the target of trade costs
reduction. Empirical analysis allowed to identify those trade facilitation
measures and policies which are most effective determinants of trade
costs. It suggests that an increase in geographical distance between
trading partners, and tariffs are positively linked with the trade costs.
However, land area and common border between trading partners,
nominal depreciation of exchange rate, liner shipping connectivity index
and membership of a free trade area all because a decline in trade costs.
The benefits of trade as an engine of economic growth and
sustainable development as well as means of poverty reduction can only
be achieved if these high trade costs are controlled. Higher trade costs
lower the competitiveness, thus limiting the potential benefits of trade.
Pakistan is a developing country and trade can turn out to be a helpful
instrument to achieve sustainability and economic welfare provided
these large trade costs are taken care of.
The study evidently shows that there is ample room for reduction
in trade costs if proper policy actions are taken. Findings of the study
have the following implications for policy making:
Trade Costs of Pakistan 260
Pakistan should actively participate in WTO’s agreement on
trade facilitation and reduce the red tape at border crossings to
cut down the trade costs.
Shipment of perishable agricultural goods must be expedited and
releasing these goods at the earliest could help reduce trade costs.
Improve port connectivity, cargo handling and means of
transportation, i.e., roads, railways and air links.
In addition to tariff reduction, NTB’s must be streamlined and
harmonized to reduce trade costs.
Effect of longer distance can be limited by the development of
both hard and soft infrastructures by applying modern
technological methods: internet, publicity campaigns and
electronic media.
Initiation of mega projects like CPEC can bring about the much
needed trade costs reduction.
261 Altaf, Mahmood and Noreen
APPENDIX
A. Empirical Results of FMOLS: Agricultural Sector Trade
Variable Coefficient Std. Error t-Statistics p-values
TARIFF( TRi*TRj) 0.072242 0.052622 1.372851 0.1732
EXCH -0.052983 0.011580 -4.575206 0.0000*
LSCI( LSCIi*LSCIj) -0.171619 0.054041 -3.175734 0.0020 *
DIST 0.149250 0.071237 2.095166 0.0389 **
AREA(Areai*Areaj) -0.059225 0.012518 -4.731075 0.0000 *
CONT -0.040529 0.030851 -1.313729 0.1922
FTA -0.144177 0.067401 -2.139099 0.0351**
C 4.578977 0.260851 17.55403 0.0000*
R-squared 0.586571 Mean dependent var 5.257956
Adjusted R-squared 0.554769 S.D. dependent var 0.146936
S.E. of regression 0.098044 Sum squared resid 0.874743
Durbin-Watson stat 0.728349 Long-run variance 0.021633
*significant at 1%, ** significant at 5%.
B. Empirical Results of FMOLS: Non-Agricultural Sector Trade
Variable Coefficient Std. Error t-Statistics p-values
TARIFF( TRi*TRj) 0.004906 0.037630 0.130386 0.8965
EXCH -0.027262 0.013489 -2.021037 0.0462**
LSCI( LSCIi*LSCIj) -0.186992 0.070409 -2.653159 0.0094 *
DIST 0.282673 0.069443 4.070551 0.0001*
AREA(Areai*Areaj) -0.049181 0.012387 -3.970405 0.0001*
CONT -0.009774 0.079129 -0.123524 0.9020
FTA -0.292959 0.077992 -3.756287 0.0003*
C 6.164287 1.114648 5.530256 0.0000*
R-squared 0.524590 Mean dependent var 5.660031
Adjusted R-squared 0.488020 S.D. dependent var 0.149255
S.E. of regression 0.106796 Sum squared resid 1.037896
Durbin-Watson stat 0.520878 Long-run variance 0.029268
*significant at 1% ,** significant at 5% .
Trade Costs of Pakistan 262
C. Z-scores for Cross Model Coefficients
Variable Calculated Z- score
Tariff 1.041
EXCH -1.44
LSCI 0.173
DIST 4.33
AREA 0.570
CONT 0.36
FTA 0.828
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BOOK REVIEW
Mundial, B. Poverty and Shared Prosperity 2016: Taking on
Inequality. World Bank Group, Washington DC: Banco Mundial DOI,
10, 978-1. 2016. 193 pages. (Paperback): $39.95.
Poverty and shared prosperity 2016 – Taking on Inequality, is
the first version of a series of annual reports, which is designed and
generated by the World Bank (WB) group. In the book form, the report
is written by several experts from the field of rural development, coming
together as an expert team, thus publishing this comprehensive
manuscript under the name banner of WB as their first edition. It
highlights four key areas; status of poverty through citing actual
statistical figures, shared prosperity and inequality, pressures on the
significance of reduction in inequality and promotion of shared
prosperity throughout the globe. Similarly, utmost importance of
equality along with shared prosperity within a country. The report also
sheds light on the failings and limitations in the capacities of research,
data collection, availability and adequacy of evidence, which hinder the
prosperity and develop causes of poverty and inequality. This report
provides an analysis of comparison among and within the countries, also
evaluates their past state to present state in terms of poverty and
inequality (Hardoon, 2017). Furthermore, how these countries have
developed over a course of time to overcome poverty and promote shared
prosperity is also discussed. The series of reports aims to deliver a set of
rules and examples of policy components and interventions. It helps the
countries to overcome poverty and develop into more equal and
prosperous states.
Chapter 1 discusses the two objectives of WB group, along with
the way to achieve them, i.e., end extreme poverty and promote shared
prosperity. These objectives are very ambitious and are expected to be
met by 2030. The chapter further discusses each objective as a separate
component. The first half discusses poverty; how it is measured by
comparing cost of living and purchasing power parities across countries,
and how it can be monitored against the international poverty line,
BOOK REVIEW 267
standards of basic needs and a proper aggregation of the poverty
calculation. The second half discusses shared prosperity; how it can be
measured by the consumption dispersal and development experienced by
the poorest segment of the country, besides how it can be monitored by
the per capita gross domestic product (GDP). This measure of shared
prosperity helps countries to identify the least wealthy people and also
evaluate the levels of inequality in the income distribution. The chapter
further discourses the range of WB for these goals and how other
organizations and agencies have also adopted similar or somewhat alike
goals to reduce poverty and improve equality, where exists.
Chapter 2 features global poverty. It gives not only precise
figures, but also country-wise comparison of poverty, estimation of the
international, and state personal poverty line and standards against which
different countries evaluate their lowest income consumers. The chapter
explains the multiple characteristics of poverty, conditions of the people
living in poverty and the common lifestyle of the global poor across;
unemployment, over populate households, illiteracy, bad health, im-
proper shelter, WASH, and lack of a stable livelihood, etc. It further
voices how gender affects the poverty graph and what difference women
can make to the household’s income increase. Further, the chapter points
out that the countries with the highest number of poor people and
countries with the highest graph of poverty are often not the same, e.g.,
countries of Middle East, Asia and Africa.
A promotion in the shared prosperity worldwide is reported in
Chapter 3. A total of 83 countries were monitored in 2008-13, out of
which 60 countries (89% world population) have experienced positive
income growth, in spite of the global financial crises of 2008-09 (mb-
KP, 2016). However, the analysis presented in the chapter shows that 23
countries indicated negative income growth. Further, two different
assessments have been made to measure supplementary advancement
and the shared prosperity premium; which compares the relative growth
in income or consumption [Cobham (2016)]. The chapter accounts the
findings that 49 out of these 83 countries showed greater income growth
than the mean (positive shared prosperity premium), while the remaining
34 showed less thriving results (negative shared prosperity premium).
This shows that according to the trend of the Gini coefficient by region,
268 BOOK REVIEW
the highest levels of inequality are observed in Latin America and the
Caribbean. From all the data received and studied, the chapter concludes
that positively shared prosperity premium can lead to the achievement of
the World Bank’s goal of ending extreme poverty by 2030.
Chapter 4 reveals why poverty is reliant on the inequality of
earnings and consumption outgoings. As inequality is the prime focus of
this report, this chapter is also divided in two sections. The first half
stresses upon why and how inequality matters i.e. how it affects poverty
and country’s economic health. The second half discusses global trends
regarding inequality. Different countries define and counter inequality
and equity differently. The chapter provides a cross-country analysis,
which examines income poverty and inequality in terms of sex, health-
care, education, ethnicity, individual or groups. It is to end poverty in a
country through advanced development, reduction in inequality or a
blend of them. The chapter shows that according to the trend of the Gini
coefficient by region, the highest levels of inequality are observed in
Latin America and the Caribbean [PAHO (2017)].
Chapter 5 educates the drivers behind the significant progress in
a shared prosperity, narrowing inequality and reducing poverty achieved
by five countries of the world, i.e., Brazil, Cambodia, Mali, Peru, and
Tanzania. The chapter describes how low to middle income countries
have succeeded in cutting down poverty and inequality – setting an ex-
ample for the world to follow. Each country’s outstanding programmes
are analyzed in detail to measure the ‘whys and wherefores’ that made
these programs successful in the longer run. Concluding this chapter, the
authors come up with a set of options and opportunities that work in the
real world. Also, the drivers for poverty reduction and dismantling
inequality at the country level have been highlighted.
Last two chapters are devoted to policy interventions, which are
based on the understandings and dependable success models from all
around the world. It is to help developing countries to reduce poverty,
enhance shared prosperity and educate developed countries to cut down
inequalities. The chapters advocate the establishment of basic human
rights in the form of healthcare, quality education, food and nutrition,
livelihood opportunities in the form of cash transfers and microcredit
programmes, communication and transport infrastructure development
BOOK REVIEW 269
and advancement of the rural sector as it homes the general poor
community all around the world. The chapters provide rapid strategies
and also long term policy interventions that can contribute in attaining
the universal goal of ending extreme poverty, inequality and sharing
prosperity.
Poverty and Shared Prosperity 2016-Taking on Inequality, is a
successful milestone in the journey to eradicate world poverty. While
reporting the major cause of poverty i.e. inequality, the book opens the
pathways to report other associated key issues of the underdeveloped
societies. It is to discuss in detail that how other issues, e.g., policy
interventions, play an important role in poverty reduction. The report
series will serve as a landmark in development and assist governments,
state collaborations, international and national organizations, private
local bodies and individuals as well. The first report stands as an
impressive work that sheds light over multiple aspects of poverty, around
the world situation, statistics and success models. The report then
delivers need based policy narratives that are definite to be effective in
combating inequality and eradicating world poverty by 2030.
Mahrukh H. Durrani
Post-Graduate Student,
Department of Development Studies,
School of Social Sciences and Humanities (S3H),
National University of Sciences and Technology (NUST),
Islamabad.
Umer Khayyam
Assistant Professor,
Department of Development Studies,
School of Social Sciences and Humanities (S3H),
National University of Sciences and Technology (NUST),
Islamabad.
270 BOOK REVIEW
REFERENCES
Cobham, A., & Sumner, A. (2016). Is It All About the Tails? The Palma
Measure of Income Inequality. Center for Global Development
Working Paper, 343.
Hardoon, D. (2017). Oxfam Report Of 2017 "An Economy for 99%".
Oxfam GB for Oxfam International.
The Dawn. (2016). Key points of the Land Law in Ecuador. (Retrieved
from http://www.thedawn-news.org/2016/01/12/key-points-of-
land-law-in-ecuador/, dated: Oct. 07, 2017))
PAHO. (2017). Values And Principles Of Universal Health. Pan
American Health Organization (Retrieved from paho.org-salud
en las Americas: http://www.paho.org/salud-en-las-americas-
2017/?lang=es&post_t_es=valores-y-principios-de-la-salud-
universal, dated: Oct. 28, 2017)
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the other three sides. The files of tables and charts should be submitted in MS Excel and
stata-generated figures should be saved and sent in Encapsulated PostScript (eps) format.
Each table should a separate set of footnotes.
5. Spell out all abbreviations and acronyms when they are first mentioned in the text. This rule
does not apply for the commonly known and exceptionally long abbreviation. Do not use
abbreviations and acronyms in titles and headings. Abbreviations in tables and figures are
allowed provided these are spelled out in a footnote.
6. Place appendixes at the end of the paper and number them as Appendix 1, Appendix 2, and
so on. Appendixes should carry complete titles.
7. Refer to all graphs, diagrams, and charts as “figures” and number them consecutively in the
text with Arabic numerals. Place all figures on the page where they are first cited.
8. Authors should be careful to ensure accuracy and consistency in the use of mathematical
material. Discussions in the text must be consistent with figures given in tables and
appendixes. In cases where the derivation of formulas has been abbreviated, present the full
derivation on a separate page or as an appendix. Encode formulas using MS Word Equation
Editor.
9. Present all notes as footnotes. Keep footnotes to a minimum, ensuring that they carry
substantive related material. Do not place reference details in the footnotes; rather present
all bibliographic details in a Reference List. Number footnotes consecutively throughout the
text with Arabic numerals. Use a size 9 point for footnotes.
10. Reference lists are strictly required of all submissions. A one-to-one correspondence
between text citations and the bibliography must be observed. The reference list appears at
the end of the main text (after Appendixes). References should carry complete information.
11. Table values should be rounded to one decimal place. Number tables consecutively in the
text using Arabic numerals. Present tables on the page where they are first cited. All tables
should carry the table number and title. Use a size 9 to 11 point within tables, and size 7.5
point for table footnotes.
12. Manuscripts (hard copy) of articles, comments, rejoinders, notes and book reviews in
English only should be sent in triplicate together with a soft copy on CD to the Managing
Editor, NUST Journal of Social Sciences and Humanities, School of Social Sciences and
Humanities, National University of Sciences and Technology (NUST), Sector H-12,
Islamabad, Pakistan. Submissions via e-mail (as attachment in MS Word only) are
acceptable for consideration. Submissions and queries regarding submission may be sent to
13. For further detail please, do visit our website: http://www.nust.edu.pk/INSTITUTIONS/School/S3H/NJSSH/Pages/Default.aspx