The role of power generation and industrial consumption uncertainty in De-industrialising Pakistan.
Yasmin, Bushra ; Qamar, Wajeeha
The declining time path of the industrial share in GDP and
employment has long been viewed as a natural outcome of the matured
stages of development analogous to the radical decline in agricultural
sector and a persistent move towards services sector. But the situation
in country like Pakistan is not due to such structural transformation.
Rather, the energy crises is expected to play a detrimental role in the
growth of industrial sector. The volatility in power consumption by
industrial sector and stagnant power generation not only hurts the
industrial sector but also has devastating effects on the other
interlinked sectors of the economy. This study endeavours to identify
the role of power sector's crises behind the industrial downfall in
Pakistan and attempts to work out the extent to which this phenomenon
may prevail in future. The attempt is made for Pakistan over a time span
of 1970-2010. The Johansen Cointegration, Error Correction Model,
Impulse Response Functions and Variance Decomposition techniques are
applied to explore the short and long-run relationships among selected
variables from the power sector. The uncertainty in industrial
consumption of electricity and power generation are identified as major
factors in undermining industrial growth whereas domestic consumption
did not appear significantly volatile. For the future, power generation
will remain the major contributing factor in shaping the time path of
industrialisation in Pakistan.
JEL Classifications: 013, 014, D81
Keywords: Industrialisation, Energy, Uncertainty
1. INTRODUCTION
The term deindustrialisation refers to the process of
socio-economic changes taking place due to reduction in the industrial
capacity and/or the loss of industrial potential of an economy. This
also connotes the secular decline in the share of industrial sector
employment as observed in developed countries since 1970s. The secular
shift from manufacturing to services sector reflects the impact of
discrepancy in productivity growth between the said sectors. A faster
rise in productivity in manufacturing sector than in services switches
the employment from manufacturing to the services sector, as suggested
by Rowthom and Ramaswamy (1997).
Generally, deindustrialisation is considered as the natural
outcorrte of economic development because it involves the transformation
from primitive agriculture-based economy to the modern industrial-based.
After the establishment of manufacturing sector, the long-run economic
growth stimulates an innovation-based economy implying the services
sector's growth [Galor (2005)]. However, the process requires a
gradual shift accompanied by allied institutional and infrastructural
reforms and the process of deindustrialisation occurs at the later stage
of development.
The economic history of today's developed world discloses that
the process of deindustrialisation started in these economies in late
1970s and the share of industrial output and employment tended to fall
since then. Comparatively, in the developing world several attempts have
been made for industrialisation, for sustainable economic growth. But,
most of these were unable to develop their industrial sector and hence
they are lagging behind the others in the pursuit of economic
development. This inability can be attributed to the policy bottlenecks
and the challenges faced by the industrial sector that lead toward the
path of deindustrialisation, in contrast to the developed world. Such
economic scenario is termed as 'premature deindustrialisation'
in the literature.1 Such deindustrialisation can have negative
implications for the economy because the labour shed from industries may
not be absorbed into the services sector and hence leads to
rising unemployment in the economy. Moreover, the vulnerable growth
of industrial sector may negatively affect the growth of other sectors
due to its forward and backward linkages to the other sectors in
economy.
The economy of Pakistan has been facing inconsistent industrial
policies, liberalisation reforms and the macro-economic challenges in
the form of energy crisis and political instability that ultimately
reduced the potential of industrial sector. The country has been facing
deindustrialisation since 1990s and the efforts to put the sector back
on its trail are all in vain. The acute energy shortage, continuous
power breaks down and government issues with Independent Power Producers
(IPPs) on payment have badly affected the sector's capacity in
power generation and distribution. In this regard, deindustrialisation
in Pakistan with sup-optimal industrial growth can be attributed to the
energy crises that prevented the industries to operate at their capacity
level and hereby lowered the output growth. A consistent attrition in
the economy's growing capability and the domestic energy shortages
and excessive rise of electricity prices can be considered as factors
thwarting the sector's competitiveness, as well.
In view of the role of energy crises in hampering industrial sector
growth, an attempt is made to empirically investigate the extent to
which the most significant component of energy i.e., electricity crises
has played its role in deindustrialising Pakistan. The power generation
and volatile power consumption by industrial sector along with domestic
consumption, inflation and energy imports are selected as the major
factors determining the time path of industrial sector's share in
GDP. The Johansen Cointegration and the Error Correction Model are
applied for this purpose. The Impulse Response Function (IRF) and
Variance Decomposition are obtained to observe the effect of shocks to
selective variables on the industrial share in GDP and to forecast the
future role of the factors in determining industrial variation,
respectively. The data from World Development Indicators and Economic
Survey is used over the time period of 1970-2010.
The rest of the paper is organised as follows. Second section deals
with the literature review. Third part discusses the trends of
industrial sector growth and evolution of power sector in Pakistan.
Fourth section discusses the methodology and the fifth section reports
and interprets the empirical findings. Final section concludes the paper
with some policy suggestions.
II. LITERATURE REVIEW
The understanding of possible impact of power sector crisis on the
process of deindustrialisation is important as it provides the
theoretical and empirical support to the analyses undertaken. The
literature provides empirical evidence for the determinants of
deindustrialisation in developed and developing economies, generally and
is discussed below.
1. Evidence from the Developed World
The developed world has been on the path of services sector growth
since 1970s and the economists have considered it as a process of
"restructuring" or "creative destruction". This
transition has been attributed to the higher productivity growth of
industrial sector, North-South trade and outsourcing of manufacturing
activities to the labour abundant developing countries [Alderson (1999);
Lee and Wolpin (2006)]. Alderson (1999) analysed the impact of
globalisation on the process of deindustrialisation in the selected OECD
countries. By using the panel data fixed effect regression technique, he
concluded that the fall in manufacturing employment in the developed
world is the result of outflow of direct investment and North-South
trade. Additionally, the inverted U hypothesis has also been proven
indicating the fact that the economic development in these countries has
reached at a point after which there is a decline in manufacturing
employment. However, Rowthorn and Ramaswamy (1999) established that
deindustrialisation in the advanced economies is the result of the
economic development and higher productivity of manufacturing sector as
compared to other sectors. The role of North-South trade and problems
faced by manufacturing sector in these economies has little contribution
towards the process of deindustrialisation.
Nickell, et al. (2008) explained that across the OECD countries,
difference in the pace of deindustrialisation can be attributed mainly
to the differences in the productivity across manufacturing,
agricultural and services sector. Apart from that, differences in the
relative prices, technology and factor endowment also play vital role in
determining the pace of deindustrialisation.
2. Evidence from the Developing World
There has been some pessimist view regarding the phenomenon of
deindustrialisation in the developing part of the world. It is
considered that deindustrialisation is a process of betrayal to the
industrialist workers and the propaganda to deprive the developing world
from its industrial power [Cowie and Heathcott (2003)].
Noorbakhsh and Paloni (1999) considered the Structural Adjustment
Programme (SAP) of IMF and World Bank as responsible for the low per
capita growth of SubSaharan Africa claiming that SAP has resulted in the
declining performance of industrial sector as compared to the period
before the adoption of SAP. And SAP could not lead towards a rise in the
export competitiveness of industrial sector with presumably attached
technology transfer.
According to Palma (2005 and 2008) the developing world has been
facing the declining share of industrial sector in GDP/employment
because of the policy shifts faced by most of the economies. Trade
liberalisation along with the financial liberalisation has resulted in
inverse relationship between the manufacturing employment and the income
per capita. Dasgupta and Singh (2005) have provided the evidence of
deindustrialisation at the low level of income, jobless growth and the
development of informal sector. They used the concept of "premature
deindustrialisation" because of its negative implications for
growth as it lowers the capacity and hence growth of industrial sector.
For the Latin American countries, Brady, et al. (2008) suggested
that deindustrialising took place in these countries despite the sheer
need of strong industrial base because of the MERCOSUR trade agreement,
dependency on the United States, inward FD) inflows, military spending
and institutional problems.
This completes the review of literature. Next section presents an
overview of Pakistan's industrial sector growth performance and
energy crises.
III. DEINDUSTRIALISATION AND POWER CRISIS IN PAKISTAN: AN OVERVIEW
The industrialisation has been considered as engine of growth that
has held true for almost 200 years, since the start of Industrial
Revolution [Chenery (1960); Kaldor (1966)]. It is well established that
industrial sector development is fundamental for overall economic
development. The historical evidence portrays that currently developed
countries have developed with the help of sound industrialisation
strategies. The industrial sector of Pakistan is the second largest
sector of the economy comprising of small, medium and large scale
industries. Currently, industrial sector contributes 20.9 percent to GDP
having sub-sectors: manufacturing, construction, mining and quarrying
and electricity and gas distribution. According to Economic Survey
(2012-13), the growth of manufacturing sector is estimated at 3.5
percent compared to the growth of 2.1 percent last year. The employment
share by manufacturing sector has increased from 13.2 percent in 2009-10
to 13.7 percent in 2010-11.
However, the fact remains that the performance of industrial sector
has remained below potential and is impediment in the way of sustainable
economic growth and development. There are various reasons for the poor
performance of industrial sector but the concern of the current paper is
to examine the role of acute power crisis in the industrial downfall in
Pakistan. A detailed analysis of deindustrialisation and power crisis
trend is made in this section.
1. An Overview of 1970s
The industrial performance of Pakistan was meander in the first two
decades, in view of the negligible industrial base. The establishment of
Pakistan Industrial Development Corporation (PIDC) in 1952 helped the
economy to create an industrial base for self-sustained growth. In
1970s, Pakistan adopted the Indian development strategy of state-led and
heavy industry based industrialisation. However, separation of East
Pakistan, war with India, oil price shocks and the public deficits
reduced the manufacturing growth in 1960s from 7.8 percent to 2.8
percent in 1970s [Federal Bureau of Statistics (2011)]. The dismal
performance of industrial sector in 1970s cannot be attributed soley to
the power shortage as the electricity situation was quite good that
time. The cost of production and demand of electricity were quite low as
the total consumption of electricity in 1970s was 7739 GWH against the
generation of 11373 GWH on average [Pakistan (2010)].
2. Moving towards Denationalisation and Industrial Sector: 1980s
With the change in government, decade of 1980s witnessed the
reversal in policies which moved towards the denationalisation with the
mixed economy and import substitution.
The denationalisation took place in few industries but the public
sector continued to invest in the heavy industries. The expansion in
domestic demand led to the industrial growth in that period almost equal
to that of 1960s. With the outbreak of Afghan war, the country had
inflow of foreign capital in form of assistance from USA and other
financial institutions. However, the industrial sector growth was
unbalanced and most of the investment was concentrated in the textile
and sugar industries. The value addition of industrial sector in GDP was
23.2 percent with a nominal rise from previous year's figure i.e.,
22.7 percent [WorldDevelopment Indicators (2010)].
On energy front, the need for additional power generation capacity
was realised in the power sector in mid 1980s. The concept of Integrated
Energy Planning and Policy Formulation (IEP) and the institutional
structure was introduced in early 1980s but gradually lost its favour
with international institutions on the presumption of market forces
leading towards right policy choice. And the task was given to the
private sector in the form of Independent Power plants (IPPs) instead of
adding the additional capacity in public sector, the first step towards
the power crisis emerged in the following years.
3. Declining Industrial Growth and Rising Energy Shortage: 1990s
The industrial sector performance was disappointing in the 1990s as
the growth of large scale manufacturing sector that was 8.2 percent in
1989 reduced to 4.7 percent in the first half and 2.5 percent in the
second half of the decade [Federal Bureau of Statistics (2011)]. The
implementation of reforms suggested by "Washington Consensus"
and Structural Adjustment Programme by International Monetary Fund (IMF)
led to the deregulation which created an anti-industrial bias in the
country and economy observed a sharp move towards services sector growth
thereafter. The value addition of industrial sector in GDP was 24.3
percent for the decade against the 49.4 percent by the services sector.
Following the reforms, the new power policy was announced in 1994. The
policy was based on the cost-plus-return with 15-18 percent internal
rate of return along with the repayment of fixed as well as variable
cost of production in terms of US dollars irrespective of the efficiency
by the Pakistan Electric Power Company (PEPCO)AVAPDA and Karachi
Electric Supply Company (KESC) [Munir and Khalid (2012)]. The policy
clearly marked the accumulation of the acute circular debt with the
devaluation of the Rupee in the 2000s.
4. Sufferings of Industrial Sector and Energy Crises: 2000 Onwards
The industrial performance of Pakistan from 2000 till 2010 was
highly volatile as the growth rate of industrial sector was as high as
12.1 percent in 2005 while it drastically declined to -3.6 percent in
2009. Similarly, the large scale manufacturing growth declined to -7.7
percent from 19.9 percent in the same time period. On the contrary, the
growth rate of services sector was satisfactory at 3.6 percent in 2009
although lower than 2005s figure i.e., 8.5 percent [Pakistan (2010)].
The first half of the decade was accompanied by sound macroeconomic
policies, strengthening domestic demand, suitable financial conditions
and stable exchange rate that encouraged the industrial sector growth.
However, in the later half, severe energy shortages, global recession of
2008, oil price hike and sharp depreciation in the local currency led to
the decline of industrial sector growth [Jaleel (2012)].
The decade of 2000s can be considered as the decade of power crisis
as the economy faced such electricity problems which have never been
experienced before. On the one hand, the demand of electricity is rising
enormously and the number of electricity consumers increased from 7.9
million in 1992 to 19.9 million in 2008 while on the other hand, the
shortfall was recorded to be 37 percent as demand for electricity was
11,509 MW against the supply of 7237 MW [Khan (2012)]. These issues are
the direct outcome of poor power policy adopted in 1994 and 2002 power
policy. Despite knowing the fact
that Pakistan has advantage in hydropower, the fuel mix between
hydro and thermal was modified from 60:40 in 1980 to 30:70 in 2000 which
raised the cost of generating electricity from Rs 1.03 per kwh by WAPDA
to Rs 9.58 per kwh by IPPs [Munir and Khalid (2012)]. Additionally, the
fiscal crunch faced by the government has led to the inability to pay
the debt to IPPs and further aggravated the situation as electricity
generation is not meeting the demand and the industries are forced to be
shut down or to move the entire set up elsewhere.
In short, industrial sector growth has gradually declined with the
power shortage as represented by figures and facts discussed above. The
comparison of sectoral share in GDP and growth trends in industrial and
domestic consumption of electricity (gice, gdce) and power generation
(geg) are displayed in Graph 1 and Graph 2, respectively. Graph 1
provides the sectoral share of industrial (ind), agricultural (agr) and
services (serv) sector as percentage of GDP.
[GRAPHIC 1 OMITTED]
[GRAPHIC 2 OMITTED]
This portrays the emerging significance of services sector, which
was taken up by traditional agricultural sector in Pakistan where
agricultural sector's share declined over time while services
sector's share increased on a sharp pace over the same time span.
While the industrial sector has remained stagnant throughout the time
period, maintaining a GDP share around 25 percent with nominal
fluctuations. Such trends in sectoral shares in GDP indicate the
industrial sector's status, which is functioning sub-optimally, on
the one hand the services sector is replacing the other sectors of the
economy.
According to Graph 2, the power generation appears highly volatile
and has remained lower than domestic use of electricity throughout the
time. For industrial use a wide gap is observed between demand and
supply underpinning the rising power crises over time. Besides, the
growth touched negative digits in last years and so is the case for
electricity consumption. This completes the overview of Pakistan's
economy for power crises and industrialisation trends. Now we turn to
the methodology.
IV. METHODOLOGY AND DATA DESCRIPTION
1. Model Specification and Data Description
In order to achieve the objectives of the research, the variables
related to the power sector including power generation, domestic
consumption and industrial consumption volatility, industrial imports
and inflation are included in the deindustrialisation equation.
Following is the equation for estimation.
[IGDP.sub.t] = [[alpha].sub.0] + [[beta].sub.1] GEG, +
[[beta].sub.2] [GDCE.sub.t], + [[beta.sub.]4] [INF.sub.t], +
[[beta].sub.5], + [GIIMP.sub.t] + [mu] ... (1)
Where,
[IGDP.sub.t] = Industrial share in GDP (%)
[GEG.sub.t] = Growth rate of electricity generation (Gwh)
[GDCE.sub.t] = Growth rate of domestic consumption of electricity
(Gwh)
[VGICE.sub.t] = Volatility in Industrial consumption of electricity
(Gwh)
[INF.sub.t] = Inflation (annual CPI growth)
[GIIMP.sub.t] = Growth rate of industrial imports (2)
The share of industrial sector as % of GDP, dependent variable, is
used to measure the deindustrialisation time path for Pakistan over the
period of 1970-2010. The electricity generation and consumption are
measured in Gwh and its growth rate is expected to have a positive
relationship with industrial share in GDP. However, the disaggregated
industrial and domestic need for electricity may yield variant effects
as the electricity shortage makes the domestic and industrial sectors
compete for energy. The industrial use may be significant in promoting
industrial sector but domestic use may or may not be significant. The
power generation is expected to affect industrial sector positively. The
industrial consumption volatility is expected to affect industrial
sector significantly.
The industrial imports are measured in million rupees and the
variable is expected to promote the industrial sector due to heavy
reliance on imports, import intensity of industrial production and a
meagre and less competitive export base. The data on all variables is
collected from Handbook of Statistics (SBP) and Economic Survey (various
issues).
In order to measure the uncertainty in power generation and
consumption, volatility of the series was derived using Generalised
Autoregressive Conditional Heteroscedasticity (GARCH) technique.
Following Aizenman and Marion (1993), the forecasting equation is
specified as below to determine the unexpected part as measure of
uncertainty for industrial consumption. (3)
[P.sub.t] = [alpha].sub.1] + [alpha].sub.2] T + [alpha].sub.3]
[P.sub.t-1] + [alpha].sub.4],[P.sub.t-2] + [[epsilon].sub.t] ... ... ...
... (i)
where [P.sub.t] is the variable under consideration, T is time
trend; [alpha].sub.1] is an intercept, [alpha].sub.3] and [alpha].sub.4]
are the autoregressive parameters and [[epsilon].sub.t], is the error
term. After estimating Equation (i), the Garch term ([[sigma].sup.2])
will be regressed on one year lag of the error term square and its own
lag. Following is the equation for that purpose:
[[sigma].sup.2] t = [gamma].sub.0] + [gamma].sub.t]
[[epsilon].sub.2.sub.t-1] +[[delta].sub.1] [[sigma].sup.2.sub.t-1] ...
... ... ... ... ... (ii)
2. Estimation Technique
The short and long-run effect of volatile industrial energy
consumption and power generation on emerging phenomena of
deindustrialisation is assessed through Johansen (1998) and Johansen and
Juselius (1990) cointegration technique. The series is checked for
stationarity purpose by Augmented Dickey Fuller (1979) that serves to
identify the order of integration of all variables in the model. ADF
test includes the estimation of following regression equation.
[DELTA][X.sub.t] = [alpha] + [[beta].sub.t] + [[gamma].sub.i ]+
[n.summation over (i=1) [[phi].sub.i] [DELTA][X.sub.t- 1] +
[epsilon].sub.t] ... ... ... (iii)
Where [X.sub.t] is the variable under consideration, [DELTA] is the
first difference operator, t captures the time trend, [[member
of].sub.t], is the random error term and n is the maximum lag length.
The optimal lag length is determined to ensure that the error term is
white noise, while [alpha], [beta], [gamma] and [phi] are the parameters
to be estimated. The non rejection of the null hypothesis depicts the
presence of unit root. Hereafter, the selection of an optimal lag length
is essential at the onset of cointegration analysis because multivariate
cointegration analysis is very sensitive to the lag length selection.
This would be done with the help of two available criterions namely
Akaike Information Criterion (AIC) and Schwarz Information Criterion
(SC).
2.1. Johansen Cointegration Test
Next step in the estimation procedure is the application of
Johansen Cointegration test. This proposes two tests namely; trace test
([[lambda].sub.trace]) and maximum eigen test ([[lambda].sub.max]) in
order to determine the existence and number of cointegrating vectors in
the model. The null hypothesis under the trace test is that the number
of cointegrating vectors is less than or equal to r where r = 0,1, 2,
3..., etc. While in the null hypothesis for Eigen test, the existence of
r cointegrating vectors is tested against the alternative of r + 1
co-integrating vectors.4 The multivariate co-integration test can be
expressed as:
[Z.sub.t] = [K.sub.I] [Z.sub.t-1] + [K.sub.2] [Z.sub.t-2] + ... +
[K.sub.k-l], [Z.sub.t-k] + [mu] + [v.sub.t] ... ... ... ... (iv)
Where [Z.sub.t] ([GEG.sub.t], [GDCE.sub.t], [VGICE.sub.t],
[INF.sub.t], [GUMP.sub.t]) i.e., a 6 x 1 vector of variables of I (1)
where I(1) refers to the integration of order 1, [mu] is a vector of
constant and [v.sub.t] vector of normally and independently distributed
error term.
2.2. Vector Error Correction Model
The next step is the application of the vector Error Correction
Model (VECUM). The model yields the effects which are considered as the
limit to which the behaviour of dependent variable will tend, ceteris
paribus. The regulator of the behaviour of the variable in the short run
is taken into account, up to a certain point, as shown by Engle and
Granger (1987). Equation (ii) can be reformulated in a Vector Error
Correction Model (VECM) as follows:
[DELTA]Zt = [GAMMA]1[DELTA]Zt - 1 + [GAMMA]2[DELTA]Zt-2 + ... +
[GAMMA]k -1[DELTA]Zt - k - 1 + [PI]Zt +1 + [mu] + vt ... ... (v)
Where, [GAMMA]i = (I - [A.sub.1] - [A.sub.2] ... [A.sub.1]), I =
1,2,3 ... k-1 and n = - (I - [A.sub.1]- [A.sub.2] - [A.sub.3] ...
[A.sub.k]). The coefficient matrix [PI] provides information about the
long-run relationships among the variables in the data, n can be
factored into [alpha][beta]' where a will include the speed of
adjustment to the equilibrium coefficients while the [beta]' will
be the long-run matrix of coefficients. The presence of r cointegrating
vectors between the elements of Z implies that n is of the rank r, (0
< r < 5). (5)
V. RESULTS AND INTERPRETATION
This section deals with the empirical findings and interpretation.
1. Test for Order of Integration
The stationary properties of the individual series are examined
before proceeding to establish the long-run relationship. The results of
ADF reported in Table 1 yields the existence of unit roots at level but
stationary at its first order. (6) Hence, all variables in the model are
integrated of order one i.e., 1(1) and allow to proceed with the
cointegration process.
As mentioned in methodology, Johansen's maximum likelihood
approach is used for the cointegration test. The optimal lag length is
one according to the both and is reported in Table A2.
2. Johansen Cointegration Test
Table 2 reports the findings for co-integration based on
Johansen-Juselius cointegration test. The maximal eigenvalue
([[lambda].sub.max]) traces two cointegrating vector, suggesting a
stable long-run relationship among selected variables. This implies the
existence of significant co-movement of selected variables in the long
run. It is pertinent to mention that the results for error correction
model are reported with 1 cointegrating vector keeping in view that;
first, the 1 st cointegrating vector has the highest eigenvalue and is
therefore the "most associated with the stationary part of the
model". (7) Second, the results yielded by the first cointegration
vector are consistent with expectations and theory, as well. Hence, the
first vector is normalised by the deindustrialisation variable and the
results are reported in Table 2.
The short-run dynamics of the industrial share in GDP was estimated
following general-to-specific modelling approach. The results for the
Error Correction Model for deindustrialisation are reported in Table 3.
The results reported in Table 3 postulate a long-run relationship
among the variables. A number of diagnostic tests are applied to the
Error Correction Model. [R.sup.2] implies that model is a good fit. The
serial correlation-Lagrange Multiplier test indicates no signs of
autocorrelation of the residuals. Normality test, based on [chi square]
statistic, does not reject the null hypothesis of residuals multivariate
normality. The growth rate of electricity generation appeares as
significantly positive, as expected and the coefficient is highest (0.46
percent) among all other parameters. It is obvious from the findings
that the power shortage is partly responsible for de-industrialsing
Pakistan's economy in the long run as perceived in Section III.
Pakistan has long been relying on imported coal and furnace oil for
thermal power generation that kept on adding the energy bill.
According to 'US Department of Energy Estimates 2012',
published in Energy Outlook, the price of electricity has gone up
approximately 530 percent for the average consumer since 1990 due to the
switch in the energy mix from cheaper hydropower to the thermal power in
Pakistan.
In the 1980s, the country's electricity generation was based
on a fuel mix of approximately 60:40 percent in favour of hydropower
versus thermal. A dramatic change was observed in 90s in fuel mix and
was switched to a fuel mix of 30:70 percent for hydropower versus
thermal by the end of 2010. According to a recent World Bank Report, oil
accounts for nearly 40 percent of electricity generation with gas and
hydropower at 29 percent each. (8)
Munir and Khalid (2012) provided,
"the dramatic shift in generation source occurred because the
1994 power policy (and later the 2002 power policy) did not discriminate
on the fuel source being employed and made the country hostage to
fluctuations in international oil prices".
The incentives were given to Independent Power Producers in energy
policy 1994 for thermal power units but the economy faced a sharp rise
in the price of electricity afterwards in 90s. The gap between growth
rate of supply and consumption of electricity has widened afterwards
till today. In this regards Asian Development Bank's Energy Outlook
(2013) expressed, "despite economic rebound, the energy shortages
have been constraining economic growth. Pakistan is faced with domestic
energy supply shortages of coal, oil and natural gas, as well as a
shortage of hydro generation capacities. These fuel constraints have
severely affected the power sector, resulting in a significant decline
in the power production". The lack of concern for the proper source
of fuel for electricity generation has added to the existing shortage.
To this end, it has raised the overall cost of electricity generation
and created acute power shortage.
The power generation shortfall makes the industrial power
consumption uncertain. According to our findings, volatile industrial
consumption has declined industrial share by 0.05 percent in total GDP
over 1970-2010. (9) The high energy prices, power breakdowns and
relentless load shedding made industrial consumption highly uncertain
and have long been upsetting the industrial production. The gap in
growth of power demand and supply is expanding due to rising population
pressure and hinders the steady power flow to the most critical sector
of the economy i.e., industrial sector. While the supply of power is
required to be continuous and price competitive for industrial sector
growth. If not done so, it can hard hit the overall economy. The figure
says that 44 percent of thermal fuel resources make electricity
expensive and 25-28 percent loss occurs due to mismanagement in power
transmission, theft and poor infrastructure. (10) Regarding the
emergence of services sector in Pakistan, the historic and momentous
role of industrial sector in economic development can't be
abandoned.
According to the findings, the industrial imports have positive
impact on the industrial share in GDP. The result shows a nominal but
significant role of industrial imports in industrial growth. The
industrial sector needs imported material and advanced technology due to
the import-intensive nature of domestic production and consumption with
a narrow export-base. Although, the industrial imports appeared as
positively significant in affecting industrial sector's share in
GDP but they are generally considered as an impediment to the economic
growth by deteriorating its external balance. With the every rise in the
import bill, the economy can face imbalance in trade. However, the
positive impact is quite negligible and dependence on imports can be
overcome by additional and dedicated efforts to expand export base. (11)
This is worth mentioning that purpose is not only to re-industrialise
the economy; it is also to enhance the capacity and growth of industrial
sector to promote employment generation.
The relationship between industrial share and inflation appeared as
positive. The findings are consistent with the theory of inflation
indicating a link between rising cost of production and rising inflation
of consumer goods. The rising prices of consumer goods can serve as an
incentive to the producers to enhance industrial production and its
share in GDP, consequently. Such behaviour can also be explained by the
'misperception theory' on the part of producers and also by
'Tobin effect' that explains a positive link between inflation
and higher output.
From the experience of countries, the literature on inflation
presents a positive impact of inflation on economic growth at low or
moderate level of inflation whereas negative at higher level of
inflation. Similarly, literature suggests positive impact for
single-digit inflation while negative for double-digit inflation
[Phillips (1958); Nell (2000); Chowdhury and Mallik (2001)]. Such
evidences suggest that whenever the economy enters into double-digit
inflation it will hit the industrial sector hard.
The Error Correction Term (ECT) represents the percentage of
correction to the deviation in the long-run equilibrium in
deindustrialisation and also represents how fast the deviations from the
long-run equilibrium will be adjusted. According to the result reported
in Table 3, the error correction term, measuring the speed of
adjustment, appears to be negatively significant i.e., -0.318,
reflecting the model stability. The value of ECT implies a marginal rate
of convergence to equilibrium over a period of 10 years and implies that
in any disturbance in the industrial share in GDP in the long run, 0.318
percent correction to disequilibrium will take place each year.
3. Impulse Response Function (IRF) and Variance Decomposition
The responses of deindustrialisation to one standard deviation
shock to the selective variables are presented in Graph 3. The first
graph shows model's stability and displays that one time shock to
industrial share will eventually converge to its equilibrium in next 10
years. The response of inflation, volatility of industrial consumption
of electricity and domestic consumption growth have appeared as
insignificant whereas the response of industrial share to one s-d shock
to power generation and industrial imports is significant.
Notwithstanding, the electricity generation shows a rising trend in
industrial share in GDP till 3rd year and than declines, touching
negative zone, but does not show tendency to converge till the end of
10th year. This implies that electricity generations shocks have
long-run impact on the industrial sector. The one time shock is
persistent and sequel for deindustrialisation. According to IRF, the
response of industrial share in GDP to one time shock to inflation,
domestic consumption of electricity and industrial consumption
volatility is likely to be converged towards the equilibrium after 9
years of shock.
[GRAPHIC 3 OMITTED]
Similar are the findings from variance decomposition reported in
Table 4. Thais identifies electricity generation growth (geg) as the
major contributor to industrial sector's share in the economy. It
is worth mentioning that its contribution in forecasted error increases
gradually over the time. The electricity generation and industrial
consumption volatility contributes to the industrial share's
standard error negligibly but in the long-term horizon it explains
around 50 percent of the forecasted error variance of industrial share
in GDP. The industrial share is contributing 73 percent in 1st year but
then declines to 40 percent. Industrial consumption volatility is
contributing around 4 percent of variations while the rest of the
variations in the forecasted error of deindustrialisation are due to
other selective variables.
VI. CONCLUSIONS AND POLICY SUGGESTIONS
The paper endeavoured to assess the role of electricity demand,
supply and industrial consumption volatility on the industrial share in
GDP. The declining share of industrial sector has raised questions about
the reasons of such trends. Some regarded it as pathological problem,
where it stops the economy from being able to achieve its full potential
of growth, employment and resource utilisation while some other
considered it as premature de-industrialisation. Kaldor (1966, 1967) in
his seminal contribution, emphasised on the spillover effects of
industrial development due to its dynamic economies of scale. (12) The
industrial sector has long been considered as an engine of growth, in
that regards. Kaldor (1966) materialised,
"on the supply side, industrial sector has greater potential
for productivity growth and hence, for employment generation as compared
to services sector. While on the demand side the income elasticity of
demand for manufacturing products was greater than that for
agriculture".
This perspective classifies industrial sector as a critical sector
of the economy. The industrial exports are a major source of foreign
exchange earnings in Pakistan. The share of industrial sector in GDP and
in employment is not only declining in Pakistan but also Shafedin (2005)
suggested that, "a premature decline in industry value added as
percentage of GDP without recovering is due to re-orientation of the
production structure of the economy from import substitution strategies
towards production on the basis of static comparative advantage due to
trade liberalisation". The findings by Dasgupta (2006) suggested
that manufacturing sector continues to be a critical sector in economic
development, but services sector also made a positive contribution in a
number of developing countries like India. Conclusively, the services
sector can be considered as an additional engine of growth provided that
a well-developed and diversified industrial base has already been
developed in the economy.
The findings of this study connote the role of electricity
generation and industrial consumption volatility to the
industrialisation in Pakistan. The power generation and volatile
industrial consumption have significant impact on the industrial share
in GDP. The electricity generation will have the highest contribution to
the forecasted variations in industrial sector's share in GDP in
next 10 years according to variance decomposition and will have a
persistent and long lasting effect of its own shock. In view of the
gravity of power crises and intensity of the issue that made industrial
sector vulnerable to internal and external shocks, an adequate and
pertinent power policy is still awaited to be implemented in Pakistan.
The policy target should be focused on finding cheaper and sustainable
energy alternate to electricity like small hydropower projects, lower
reliance on imported oil and better provision of gas and coal to
efficient power firms and extraction of new coal sources to end the
power shortage. Consequently, it will make industrial consumption of
electricity more certain and industrial output can come out of energy
crises trap rendering a U turn in industrial sector performance.
[FIGURE 1 OMITTED]
Comments
The paper titled "The Role of Power Generation and Industrial
Consumption Uncertainty in De-industrialising Pakistan" is an
interesting paper in the where the authors explores the reasons for
reduction of manufacturing industry share in the GDP and having a lesser
employment share in Pakistan.
Following are some of the observations which if incorporated may
improve the quality of paper and in terms of contribution to the
academic knowledge on the subject.
(i) Using terminologies like "de-industrialisation" needs
a clear explanation at the very outset to make the reader more aware of
what is to follow. Especially if the paper is going to extend the
existing knowledge on that subject. In terms of how it should be
accounted for. May be some cross country and Pakistan data tables could
help more in terms of taking into account what is proposed.
(ii) When we talk specifically about the "premature
de-industrialisation" then what exactly it means in terms of the
variable we are referring to. e.g. if it is the industrial share in the
GDP, then does that mean that some other sector is improving and why is
it bad?
(iii) There are specific studies on the losses of employment and
economic loss due to the load shedding. For example see our study titled
" The Cost of Unserved Energy: Evidence from Selected Industrial
Cities of Pakistan" published in PDR.
(iv) Using qualifiers such as ... as a rule of thumb, industrial
sector has to face 33 percent ... needs citation. Referencing is in
general weak.
(v) Data for 2014 publication needs an update especially if used
from Economic Survey.
(vi) The Literature review is devoid of any study which studies the
"premature de-industrialisation" the present study is
discussing. I doubt it, it may be with some other name, such as the cost
of unserved energy etc.
(vii) The variables in graph needs to be explained in terms of what
they are referring to.
(viii) Some theoretical model has to be referred to.
(ix) The selection of variables seems arbitrary and without
explanation. E.g. VGICE: volatility in industrial consumption of
electricity, is not an exogenous variable or a variable of choice for
the firms to take, its an out come variable, which may be due to one of
the explanatory variables such as the growth of electricity generation
and domestic use etc.
(x) Some other variables are missing in the specification for
control such as the openness and law and order situation.
(xi) Uncertainty may not be the case here, its simply and excess
demand situation with prices capped. Supply increases so will the
utilisation increase.
(xii) The results for unit root test are not provided for inclusion
of intercept and trend or there is no plot of the data. Further for
robust results especially for data sets with structural breaks often PP
test is also applied but not in this case.
(xiii) Results need a proper validation through cross referencing.
(xiv) Results such as cost push inflation ... consumer prices
increase ... incentives to producers ... is a bit A-theoretical. Like
stagflation, but micro is more of a settled thing I guess.
(xv) Take the later half of first para and 2, 3rd paragraph in the
situation analysis.
(xvi) Random thoughts should not be placed in the conclusion.
References to be placed in the conclusion also needs a careful revisit.
Conclusions such as employment share (it could be the absolute value)
declining needs some evidence and not hard to get. Further basing policy
recommendations which are not arrived at from the authors estimation
should not be put forth.
(xvii) Editing is required.
Mahmood Khalid
Pakistan Institute of Development Economics, Islamabad.
APPENDIX
Table A1
Volatility of Industrial Consumption of Electricity
GARCH = C(1) + C(2)*RESID(-1)^2 + C(3)*GARCH(-1)
Variable Coefficient Std. Error Prob.
C 7.137974 6.913685 0.3019
RESID(-1)^2 -0.225884 0.154326 0.1433
GARCH(-1) 0.850106 0.323772 0.0086
Table A2
VAR Lae Order Selection
Lag SC
0 36.126
1 35.667 *
2 51.97
3 52.38
* Indicates lag order selected by criterion.
REFERENCES
Aftab, M. (2011) Analysis: Energy Crises and Industry. Daily Times,
January 11.
Aizenman, J. and N. Marion (1993) Policy Uncertainty, Persistence,
and Growth. Review of International Economics 1:2, 145-163.
Alderson, A. S. (1999) Explaining Deindustrialisation:
Globalisation, Failure or Success? American Sociological Review 64:5,
701-721.
Brady, D. Y. Kaya and G. Gereffi (2008) Why is Latin America
Deindustrialising? Paper presented at the annual meeting of the SASE
Annual Conference, Sciences Po, Paris, France. July 16.
Chowdhury, A. and G. Malik (2001) Inflation and Economic Growth:
Evidence from South Asian Countries. Asia-Pacific Development Journal
8:1.
Dasgupta, S. and A. Singh (2006) Manufacturing, Services and
Premature Deindustrialisation in Developing Countries. World Institute
for Development Economics Research. Finland. (Discussion Paper No. 49).
Dutta, S. and A. Ahmed (1997) An Aggregate Import Demand Function
for Bangladesh: A Cointegration Approach. Applied Economics 31, 465-472.
Galor, O. (2005) From Stagnation to Growth: Unified Growth Theory.
Handbook of Economic Growth, 171-293.
Jaleel, S. (2012). Pakistan Industrial Growth. Rawalpindi:
Rawalpindi Chamber of Commerce.
Johansen, S. and K. Juselius (1990) Maximum Likelihood Estimation
and Inference on Cointegration--With Application to the Demand for
Money. Oxford Bulletin of Economics and Statistics 52, 169-210.
Khan, A. N. (2012) Energy Crisis in Pakistan: Causes and
Consequence. Abasyn Journal of Social Science 4:2, 341-363.
Lee, D. and K. Woplin (2006) Inter-sectoral Labour Mobility and the
Growth of the Service Sector. Econometrica 74:1, 1-46.
Munir, K. A. and S. Khalid (2012) Pakistan's Power Crisis: How
Did We Get Here. Lahore Journal of Economics 17:3, 73-82.
Nell, K. (2000) Is Low Inflation a Precondition for Faster Growth?
The Case of South Africa. University of Kent. Department of Economics.
(Discussion Paper 00/11).
Nickell, S., S. Redding, and J. Swaffield (2008) The Uneven Pace of
Deindustrialisation in the OECD. The World Economy 31:9, 1154-1184.
Noorbakhsh, F. and A. Paloni (1999) Structural Adjustment
Programmes and Industry in Sub-Saharan Africa: Restructuring or
De-Industrialisation? The Journal of Developing Areas 33:4, 549-570.
Pakistan, Government of (Various Issues) Economic Survey. Ministry
of Finance.
Palma, G. (2005) Four Sources of 'De-Industrialisation'
and a New Concept of the 'Dutch Disease'. Beyond Reforms:
Structural Dynamics and Macroeconomic Vulnerability. New York: Stanford
University Press, World Bank.
Palma, G. (2008) Deindustrialisation, Premature
Deindustrialisation, and the Dutch Disease. The New Palgrave: A
Dictionary of Economics (2nd edition). Basingstoke: Palgrave Macmillan.
Philips, A. W. (1958) The Relationship between Unemployment and the
Rate of Change of Money in the UK 1861-1957. Economica 25, 283-99.
Rowthom, R. and R. Ramaswamy (1999) Growth, Trade and
Deindustrialisation. International Monetary Fund. (Working Paper No.
60).
Shafaeddin, S. M. (2005) Trade Liberalisation and Economic Reform
in Developing Countries: Structural Change for De-Industrialisation?
UNCTAD (Discussion Paper 179).
Trimble, C., N. Yoshida, and M. Sakib (2011) Rethinking Electricity
Tariffs and Subsidies in Pakistan. Washington, DC: World Bank. (Report
No. 62971-PK).
World Bank (2012) World Development Indicators 2012. Washington,
DC.
(1) See Dasgupta and Singh (2006), p. 2 for reference.
(2) Trade liberalisation and industrial policy dummies were used in
the model as exogenous variables. For trade liberalisation and
industrial policy, a value 1 is assigned to post-trade liberalisation
period i.e., 1988 onward and to the successful 5 year industrial plans
compared with base category i.e., assigned value 0, pretrade
liberalisation period and unsuccessful industrial policies,
respectively. The variable on electricity loss was also added but
dropped in final model for being insignificant.
(3) The volatility appeared to be statistically significant only
for industrial consumption of electricity following Equation (2). The
significance and graph of volatility series is given in Appendix Table
A1 and Figure A 1.
(4) In case of divergence among the results of two tests, the \max
test is recommended because it is more reliable especially in small
samples [see Dutta and Ahmed (1997) for reference].
(5) It is important to point out that the long-run effects should
be considered with some caution in that they are not the real measures,
rather they can inform of what impact would be if economy had reached
its equilibrium behaviour.
(6) It is done with the intercept and trend option.
(7) See, Johansen and Juselius (1995) for fuller discussion on this
issue.
(8) C.f., Trimble, Yoshida, and Sakib (2011).
(9) The Appendix Table A1 and Figure A1 depict the significant
volatility measure from GARCH in industrial energy consumption. Besides,
the domestic power consumption appeared insignificant in results.
(10) In this study, the electricity loss in distribution appeared
as insignificant to industrial share in GDP, hence dropped from the
model.
(11)Hypothetically, industrial imports may have bidirectional
relationship with industrialisation but the empirical findings from the
Granger Causality between IIMP and IGDP suggested only one-way pass
through to industrial share of GDP from industrial imports.
(12) Faster the growth of manufacturing output, faster will be the
growth of manufacturing productivity.
Bushra Yasmin <bushrayasmin@yahoo.com> is Associate
Professor/Chairperson Economics Department, Fatima Jinnah Women
University, Rawalpindi. Wajeeha Qamar is MPhil Student at the Department
of Economics, Fatima Jinnah Women University, Rawalpindi.
Table 1
Unit Root Test
First Order of
Variables Level Difference Integration
IGDP -2.895 -6.442 I(1)
GEG -0.457 -5.88 I(1)
GDCE -3.475 -8.080 I(1)
VG1CE -3.527 -7.229 I(1)
INF -3.086 -6.100 I(1)
GUMP -0.217 -4.331 I(1)
1 % Critical Value -4.219 -4.219
Table 2
Johansen's Cointegration Test Results
Trace Test Maximal
95%
Alternative Critical
Null Hypothesis Hypothesis Statistics Value
r = 0 r = 1 153.02 * 95.75
r [less than or equal to] 1 r = 2 93.54 * 69.81
r [less than or equal to] 2 r = 3 47.12 47.86
Eigenvalue Test
95%
Critical
Null Hypothesis Statistics Value
r = 0 59.47 * 40.07
r [less than or equal to] 1 46.42 * 33.87
r [less than or equal to] 2 22.81 27.58
Note: * Implies that null hypothesis is rejected at 5 percent
confidence level.
Table 3
Error Correction Results for Deindustrialisation
ECM based on Johansen Technique
Variables (se in parentheses)
Constant -18.13
GEG 0.46 *
(0.08)
GDCE 0.075
(0.07)
VGICE -0.05 **
(0.02)
INF 0.25 *
(0.03)
GUMP 1.26E-05 *
(1.4E-06)
ECT -0.3185
(0.098)
Diagnostic Tests
[R.sup.2] 0.46
F statistic 3.00
Normality Test (Cholesky) [chi square](6) = 1.858 (0.932)
Serial Correlation (LM stat) 30.44 (0.729)
Note: (1.) **, * indicates statistical significance at 5 percent and
1 percent, respectively.
(2.) p-values in parentheses of diagnostic tests.
Table 4
Forecast Error Variance Decomposition (%)
Forecasted Industrial Electricity
Standard Share Generation
Period Error in GDP Growth
1 0.752941 100.0000 0.000000
2 0.961956 73.55454 20.81417
3 1.143897 52.47500 39.20005
4 1.275434 42.72156 47.96662
5 1.346050 39.84180 50.52904
6 1.373335 39.72425 50.50973
7 1.381248 40.07852 50.02002
8 1.386109 40.02647 49.88767
9 1.393718 39.59599 50.09299
10 1.403368 39.10992 50.34025
Industrial
Consumption
Period Volatility Inflation
1 0.000000 0.000000
2 1.615167 0.264892
3 3.590812 0.712668
4 4.405961 1.157688
5 4.615723 1.532751
6 4.621744 1.779320
7 4.588492 1.884827
8 4.558937 1.894833
9 4.529756 1.874369
10 4.492912 1.863517
