The impact and cost of power load shedding to domestic consumers.
Pasha, Hafiz A. ; Saleem, Wasim
This paper analyses the impact and cost of the high level of power
load shedding to domestic consumers in 2012 by a survey based approach.
The paper develops a methodology for quantification of the cost of
outages by deriving the utility loss, cost of self-generation and other
costs incurred. Overall, the total outage cost to residential consumers
in the urban areas of Pakistan is estimated at close to Rs 200 billion.
The willingness to pay more for uninterrupted electric supply is also
determined. Policy recommendations are made to mitigate the impact of
load shedding on domestic consumers.
1. INTRODUCTION
The widespread and growing phenomenon of power load shedding has
emerged as one of the principal supply-side constraints to growth of the
economy of Pakistan. Not only has this led to significant losses of
output, employment and exports but also during periods of high outages
there have been large-scale protests, particularly in Punjab and KPK.
Households have faced severe disruptions due to the high and
growing incidence of load shedding. These have led to mass protests on
streets resulting in disruption of other economic activities. As such,
the economic return of reducing outages and of facilitating the process
of adjustment to these outages is likely to be high.
This paper provides an approach and methodology for quantifying
cost of load shedding to households in Pakistan. It is organised as
follows: Section 2 highlights some key trends in the power sector of
Pakistan. Section 3 will present a detailed literature review on the
methodology used for quantification of costs due to outages. Section 4
describes the methodology used for qualification of costs due to outages
and for estimation of willingness to pay. Section 5 presents estimates
of the cost of load shedding in the domestic sector of Pakistan.
Finally, Section 6 highlights the major policy implications emerging
from the research.
2. THE POWER SECTOR
The growth in installed capacity and generation of electricity in
Pakistan is presented in Table 1 since 1970-71. The growth in installed
capacity has been more than doubling every decade up to 2000-01, with
annual growth rate of over 7 percent. It is only during the last decade
that the rate of expansion in capacity has substantially slowed down to
less than 3 percent per annum. In the initial years of the decade there
was significant excess capacity, due to the hump in investment by the
IPPs in the mid-to late- 90s. But adequate provisions were not made to
cater for the future growth in demand.
The growth in electricity generation was rapid in the 70s and 80s.
In particular, the commissioning of the Tarbela Dam in the early 80s
enabled a quantum jump in supplies at low cost. During the 90s as the
growth rate of the economy slowed down, demand for electricity was not
so buoyant and the rate of increase annually in power generation
declined to 5 percent. During the last decade, this has fallen further
to only 3 percent.
An index of capacity utilisation1 is constructed in Table 1. The
rate of capacity utilisation exceeded 100 percent by 1990-91 and the
load shedding which occurred in a significant way in the mid-to-late-80s
can be attributed to a shortage of capacity. It was during this period
that the first study in Pakistan on costs of load shedding was
undertaken by Pasha, Ghaus and Malik (1989). As opposed to this, the
upsurge in load shedding once again since 2007-08 can be attributed
primarily to a lack of full capacity utilisation arising from lack of
adequate maintenance of older plants and liquidity problems due to the
ballooning of circular debt and the slow expansion in capacity.
The growth in electricity consumption by type of consumer during
the last decade is presented in Table 2. The analysis is broken up into
two sub-periods, the years prior to commencement of significant load
shedding in 2007-08 and the years thereafter. In the latter period, the
overall level of power consumption has declined with marginal growth
only in the case of industrial consumers.
The surplus/deficit between demand and supply during system peak
hours for National Transmission and Despatch Company (NTDC) and Karachi
Electric Supply Corporation (KESC) combined is given in Table 3. The
supply gap was 1912 MW in 2007 which has risen to 6518 MW, equivalent to
29 percent of demand. It is important to note that in 2011-12 National
Electric Power Regulatory Authority (NEPRA) reports the generation
capability as less than 70 percent of the installed capacity.
According to NEPRA, the highest incidence of outages regionally is
in the area served by Multan Electric Power Company, Peshawar Electric
Supply Company and Lahore Electric Supply Company. The least outages are
in areas served by Islamabad Electric Supply Company. Most areas of
Punjab and Khyber-Pakhtunkhwa are more vulnerable to load shedding.
Incidence of Load Shedding
The costs of load shedding, to a large extent, depend on the
frequency and duration of outages. The incidence of load shedding is
given in Table 4.
Overall, on an average outages occurred 5 times a day in Pakistan
in 2012, highest being in Punjab, 6 time's. Households, on an
average did not have electricity supply from power distribution
companies for 1453 hours in 2012. The highest load shedding has occurred
in Punjab at 1683, followed by KPK, 1216. Clearly, the average incidence
is lower in Sindh and Balochistan.
3. LITERATURE REVIEW
Various approaches have been developed in the literature for
quantification of the cost incurred by different types of consumers as a
result of power outages. These approaches vary greatly in terms of data
requirements and level of complexity. This section starts with the
simple value added approach and ends with the full-blown survey based
and contingent valuation approaches.
The Simple Value Added Approach
A relatively high estimate of the cost of load shedding is as
follows:
[V.sub.i] = Value added by sector i in absence of load shedding
[E.sub.i] = Electricity consumption in the absence of load shedding
Then the cost [C.sub.i] of load shedding is given by
[C.sub.i] = [V.sub.i]/[E.sub.i][l.sub.i] ... ... ... ... ... ...
... (1)
Where [l.sub.i] is the quantum of electricity not supplied due to
outages. Summing across sectors, the total cost of load shedding is
given by
C = [[summation].sup.n.sub.i] = 1 [v.sub.i]/[E.sub.i][l.sub.i] ...
... ... ... ... ... ... (2)
Where n is the number of sectors.
This approach can be applied on the production sectors of the
economy, viz, agriculture, industry and commerce, but not to domestic
consumption of electricity.
The reasons why this approach leads to a high estimate of the cost
of Load shedding are as follows:
(i) It does not distinguish between the average and marginal
productivity of the electricity input, that is, there could be some
economies of scale in the use of energy.
(ii) It assumes that output lost is proportional to the extent of
electricity not supplied and the firms do not make adjustments to
recover at least part of the output.
As opposed to the above, an approach that yields a low estimate is
one which focuses only on the wage cost, on the assumption that the idle
factor during outages is labour. As such, in this case
[C.sub.i] = [w.sub.i]/[E.sub.i][l.sub.i] ... ... ... ... ... ...
... (3)
Where [W.sub.i] is the wage bill.
The adjusted Value added Approach This approach postulates the
marginal cost of unsupplied electricity is different from the average
cost as given in (1) above. Accordingly,
[partial derivative][V.sub.i]/[partial derivative][E.sub.i] =
[beta][V.sub.i]/[E.sub.i] [beta] > 0 ... ... ... ... ... ... ... (4)
[beta] is estimated on the basis of the historical relationship
between value added and electricity consumption. Generally, it is
observed that [beta] < 1.
However, the value added approaches suffer from the defect that
they do not allow for spoilage costs arising from damage to materials
that takes place at the time when the outage occurs, especially if there
is no prior notice.
Marginal Cost of Unsupplied Electricity
It has been argued by Bental (1982) that by observing firms'
behaviour with respect to the acquisition of own generating power, the
marginal cost of unsupplied electric energy may be inferred. A
competitive risk-neutral firm equates, at the margin, the cost of
generating a kwh on its own to the expected gain due to that kwh. This
expected gain is also the expected loss from the marginal kwh which is
not supplied by the utility. Therefore, the marginal cost of generating
its own power may serve as an estimate of the marginal outage cost.
The cost to a firm of generating its own power consists of the two
elements. The first part is the yearly capacity cost of the generator.
This can be represented as follows:
K(c) = annual capital cost (depreciation + interest cost) of a
generator with capacity in kva
In addition,
VC = variable cost per Kwh, consisting mainly of fuel cost
l = hours of outages
The marginal cost, MC of self-generation per Kwh is given by
MC = [partial derivative]K(c)/[partial derivative]c + vc ... ...
... ... ... ... (5)
On the assumption that the MC is constant, the total cost, TC, of
Load Shedding is given by
TC = MC.l ... ... ... ... ... ... ... (6)
This approach may not lead to proper estimates in the following
cases:
(i) Presence of economies/ diseconomies of scale in the capital
cost of generators such that [partial derivative]K(c)/[partial
derivative](c) is not constant.
(ii) Imperfections in the capital market whereby firms, especially
the smaller ones, are unable to borrow for acquisition of a generator.
(iii) In Pakistan previous surveys of firms, for example by the
Institute of Public Policy (2009), indicated that not all units have
self-generation. This implies that the marginal cost of outages is lower
than the marginal cost of a generator. For such units, this method
cannot, therefore be applied.
The Value of Leisure Approach
Munasinghe (1980) has proposed a novel approach for evaluating the
cost of outages to residential consumers, as the value of leisure
foregone. According to this approach, the principal outage cost imposed
on a household is the loss of leisure during the evening hours when
electricity is essential. During the day time there is sufficient slack
in the execution of household activities that are interrupted by the
outage, such as cooking or cleaning, to permit rescheduling of these
activities without causing much inconvenience.
As such, the monetary value of this lost leisure is equal to income
earning rate on the basis of consumers' labour-leisure choice.
Munasinghe accordingly computes the cost per Kwh of unsupplied
electricity as
C = y/k ... ... ... ... ... ... ... ... (6)
Where y is the hourly income and k the normal level of electricity
consumed per hour in the absence of outages. Therefore, the total cost
of outages to residential consumer is, C, where
C = [y/k] x l ... ... ... ... ... ... ... ... (7)
A principal practical advantage of this method of estimating outage
costs for residential consumers is that it relies on the relatively
easy-to-obtain data. But for proper application of this method it is
essential to have the levels of electricity consumption by households at
different income levels.
Other problems with this approach include the following:
(i) It assumes that the income earner in the household has flexible
working hours so that he/she can effectively exercise his/her
labour-leisure choice. This may be true in the case of self-employed
persons. But for wage earners who work fixed hours, the marginal value
of leisure is unlikely to be equal to the income rate per hour. As such,
some authors have preferred to apply this approach by assuming that the
value of leisure is only a fraction of income.
(ii) It ignores the presence of household economic activities like
cottage industry or sewing/embroidery work by women, especially in lower
income households. This is sometimes the case in Pakistan. Such,
activities may not readily be rescheduled in the presence of outages,
especially if they are of long durations. As such, in these cases the
cost of outages must include the value of lost output.
(iii) Outages, especially when accompanied with voltage
fluctuations, can damage home-based appliances like TV, refrigerator,
air-conditioner, freezer, etc. Cost has to be incurred to repair the
damage. These are equivalent to spoilage costs and should be included in
the cost of load shedding.
The Consumer Surplus Approach
This is relatively popular approach and has been applied by Sanghvi
(1982). The demand curve for electricity captures the willingness to pay
for the service and the consumer surplus of electricity supply is
represented by the area between the demand and supply curves. The loss
of consumer surplus due to supply interruptions is represented by the
shaded area, ABE, in Figure 1 below.
[FIGURE 1 OMITTED]
The prime magnitude required for application of this approach is
the price elasticity of demand, which is not possible to measure in the
presence of outages. Also, given a non-linear schedule of power tariffs,
as is the case with residential consumers in Pakistan, the magnitude of
the consumer surplus lost due to outages becomes difficult to quantity.
Further, if AB is large then the consumer may be able to reduce the loss
by investing in self-generation. This becomes more attractive the larger
the amount of electricity not supplied.
The Contingent Valuation (WTP) Approach
This approach involves asking consumers their willingness to pay
for more reliable supplies of power. For example, the question could be
as follows:
If the incidence of outages is reduced to half its present level,
how much more would you be willing to pay on your monthly electricity
bill?
An alternative approach is to ask the following question:
If level of outages were to double, what reduction in your monthly
electricity bill would you consider to be fair?
The contingent valuation approach is prone to giving biased
estimates as it is based on subjective responses. It is likely that in
response to the first question the consumer understates his willingness
to pay for improved service, while he may overstate the compensation
that he would like to receive for deterioration in the reliability of
supply.
The Survey Based Approach
The most comprehensive approach to quantify the cost of outages is
to undertake a random survey of affected consumers. This enables
explicit and direct determination of different components of outage
costs including the spoilage cost, idle factor cost and adjustment cots.
However, the survey based approach is more costly than approaches
which rely largely on secondary data. Also, the possibility of a bias
cannot be ruled out by the respondents who may exaggerate the costs in
order to attract greater attention to the problem of load shedding.
We apply each of the above approaches to quantification of outage
costs to domestic consumers in light of the data obtained from the
survey of 500 households in Pakistan.
Table 5 gives the sample distribution by city the sample was
distributed among cities on the basis of share of city in provincial
population. 57 percent of the sample household units are in the province
of Punjab while about 22 percent are in Sindh. From the remaining 33
percent, 15 percent are in Khyber Pakhtunkhwa (KPK) and 6 percent in
Balochistan.
The distribution of sample households by income group is given in
Figure 2. About 21 percent of the households have permanent monthly
income, proxied by monthly consumption expenditure, of upto Rs 15000, 36
percent have income between Rs 15000 to Rs 35000, 35 percent have income
between Rs 35000 to Rs 70000 while 8 percent have income above 70000 per
month. The overall average monthly income of sample households is Rs
38429.
Value of Leisure Approach
Munasinghe (1980) argued that the outage cost corresponds to the
value of leisure, which he proxies by income.
The estimated outage cost per kwh for domestic consumers based on
this approach is derived from the Survey as Rs 91 per kwh in Table 6.
The Munasinghe approach yields very high estimates.
There is another way of examining the validity of assumptions made
by Munasinghe. Respondents were asked which activities are disrupted
most in the household by load shedding. The frequency of different
responses is given in Table 7.
Leisure is reported by only two percent of the sample households as
the activity most disturbed by load shedding. Other activities are of
greater importance to households, including cooling/heating, studies of
children and preparation for work/school reported 24 percent, 18 percent
and 17 percent respectively as the principal activity affected by
outages. Therefore, the Munasinghe hypothesis that leisure is the
activity most disrupted is not borne out by the data obtained from
households in Pakistan.
It is our view that the Munasinghe approach has a developed country
bias. It cannot be applied in the context of low-to-middle income
countries like Pakistan. A significant and new finding is the impact of
outages on children, either in terms of the ability to undertake studies
(homework) or in preparation to go to school.
Generator Cost Approach
This approach is based on the assumption that the principal form of
adjustment to outages by households is the acquisition of a generator
and/or a UPS (Uninterrupted Power Supply). As such, the cost of
self-generation corresponds to the outage cost.
The question that arises is if a household does not have a
generator/UPS then is the outage cost zero? Clearly, this is not the
case.
It is likely that there are outage costs, especially in terms of
the monetised value of the utility lost due to disturbance to some
household activities, but these costs may not be large enough to justify
the resort to self-generation.
Table 8 gives the percentage of households by level of consumption
expenditure with a generator and/or UPS. Overall, 28 percent of the
households have a generator and 30 percent have UPS. Poorer households
generally are unable to self-generate electricity. However, majority of
the households in the upper most income group have made arrangements for
alternative sources of power at the time of load shedding.
Given the high percentage of households which do not have
self-generation the issue is one of quantifying the cost of outages in
the case of such households.
Willingness to Pay
The willingness to pay approach provides the basis for determining
the subjective valuation by households of the cost of outages to them.
There is, of course, the likelihood of a 'free rider' problem
here. A household may understate its willingness to pay on the
expectation that other households may reveal a high enough WTP to
justify investment in improving the reliability of the power system.
Table 9 indicates the outage cost per hour as implied by the WTP.
This can be estimated as follows:
SOCKW = (WTP/100) AEB/ENS ... ... ... ... ... ... (1)
Where,
SOCKW = subjective valuation by household of the outage cost per
kwh
WTP = % higher tariff that the household is willing to pay for
improved reliability of power supply (with minimal outages)
AEB = Annual electricity bill paid to the DISCO/KESC
ENS = electricity not supplied in the outages.
It is interesting to note that while the subjective valuation of
the outage cost per hour is somewhat low at below Rs 11 per kwh, it is
higher for households belonging to the 'middle class'.
4. METHODOLOGY FOR QUANTIFICATION OF OUTAGE COST
The methodology for quantification of outage cost to domestic
consumers is qualitatively different from that used in the case of
industrial and commercial consumers. The basic reason for this is that
there is no notion of 'output' in the case of a household, (2)
which is more of a consuming unit. As such, outages impact the level of
utility/quality of life of a household.
The exposure to outages daily is given by DLOUT where
D = [[summation].sup.n.sub.i] = 1 [n.sub.i][d.sub.i] ... ... ...
... ... ... ... (1)
Where [n.sub.i] = number of outages of duration [d.sup.i], i = 1
,.... n.
The normal level of electricity consumption per hour is given
e=([Kwh.sub.1] + [Kwh.sub.2])/8760-365D ... ... ... ... ... ... ...
(2)
Where,
[Kwh.sub.1] = electricity purchased from the distribution company
during summer months
[Kwh.sub.2] = electricity purchased from the distribution company
during winter months.
The normal consumption of electricity during times when there are
no outages depends upon the number of electrical appliances at home. As
such,
e = [[beta].sub.o] +
[[summation].sup.m.sub.j=1][[[beta].sub.j][A.sub.j] ... ... ... ... ...
...(3)
Where, [b.sub.j] * = electricity consumption by appliance j, where
j = 1,2,3,...., m.
[A.sub.j] = number of appliances j
[[beta].sub.o] = basic electricity consumption (e.g. for lighting).
Depending upon the nature of use of particular appliances the share
of electricity consumed in different activities like heating/cooling,
household functions, entertainment/leisure is derived. That is
[[summation].sup.r.sub.k] = 1[W.sub.j] = 1 ... ... ... ... ... ...
... (4)
Where [W.sub.k] = share in electricity consumption of activity k, k
= 1,2,...., r.
If a sampled household has a generator then
[P.sup.1.sub.k] = 1 if activity k can be performed during the
outage.
[P.sup.1.sub.k] = 0 if activity k cannot be performed during the
outage.
Then the extent of substitution, S, by the generator of public
supply during outages is given by [S.sub.1] where
[S.sub.1] = [[summation].sup.r.sub.k] = 1 [W.sub.k] [P.sup.1.sub.k]
... ... ... ... ... ... ... (5)
Similarly, the extent of substitution by a household which has a
UPS can be derived
[S.sub.2] = [[summation].sup.r.sub.k=1] [W.sub.k][P.sup.2.sub.k]
... ... ... ... ... ... ... (6)
It may, of course, be noted that in the case of household which has
neither a generator nor UPS, [S.sub.1] = 0, [S.sub.2] = 0.
For a household which has a generator the costs of operation have
been obtained
as
[G.sub.c] = K(i + [delta]) + 12f + 4(m + 0) - T ... ... ... ... (7)
Where, K = capital cost, I = annual interest rate, [delta] = annual
rate of depreciation, f = monthly fuel cost, m = quarterly maintenance
costs, o = quarterly other costs, T= savings in terms of payment to the
utility.
Similarly, the cost of a UPS can be derived as [G.sub.u]. In this
case T = 0 because the UPS stores electricity obtained at the time when
there are no outages.
There are also other costs arising from the outages, including
spoilage cost, SPC, damage to appliances, DAC and miscellaneous costs,
MC.
The last part of the methodology relates to the valuation of costs
arising from disturbance of activities which cannot be performed or only
partially performed during the outages either because of the absence of
self-generation or because of only partial substitution by
generator/UPS.
These costs are subjective in nature in terms of a loss of utility
and are, therefore, not observed. We use the willingness-to-pay (WTP) as
a measure of the subjective costs and apply this magnitude to the part
of the electricity consumption which is not substituted by
self-generation during outages. As such,
MUTL = WTP ([B.sub.1] + [B.sub.2])(1 - [S.sub.1] - [S.sub.2]) ...
... ... ... (8)
Where,
WTP = extent of higher tariff that household is willing to pay for
better quality of service (with minimal outages).
B1 = electricity bill of the distribution company during summer
months.
B2 = electricity bill of the distribution company during winter
months.
The overall outage costs to the household, OTC, is given by
OTC = [G.sub.c] + [G.sub.u] + SPC + DAC + MC + MUTL ... ... ... (9)
In the case of a household with no self-generation capacity
OTC = SPC + DAC +MC + MUTL
Where, MUTL = WTP([B.sub.1] + [B.sub.2])
This methodology is new and has not been used yet in other studies.
5. RESULTS
The objective of this section is to present the estimated
magnitudes of different types of costs associated with outages. As
identified in previous section, these include direct cost which consist
of spoilage costs and indirect or adjustments costs which include
generators costs and UPS costs.
Total Outage Costs
Table 10 shows that the total outage cost on average to each
residential consumer is almost 31,000 Rs per annum. The variation in
outage costs is not very large among Provinces, ranging from about Rs
29,200 per consumer in Punjab to Rs 34,100 in K-PK.
Outage costs rise sharply by consumption (income) level of a
consumer. For households with monthly consumption expenditure of up to
Rs 15000, the outage cost annually is Rs 8800. For the highest
expenditure group of households the cost rises to Rs 75200.
Overall, for the sample as a whole, the largest component of outage
costs is self-generation costs at 56 percent. Monetisation of utility
loss and other costs (spoilage costs, income foregone in household
economic activity, etc. each account for 22 percent.
For lower income households, the share of monetisation of utility
loss is higher at 44 percent because a low proportion of such households
have either a generator or an UPS. As opposed to this, the share of
self-generation costs for the highest expenditure households is high at
74 percent.
The burden of outage costs as a percentage of total consumption
expenditure by a household is given in Table 11. It appears that the
highest burden is on the 'middle class' living in the cities
of Pakistan. It is 7 percent for such households as compared to 6.2
percent for low income households and 5.8 percent for the richest
households.
Table 12 indicates the total outage cost per kwh for residential
consumers on average is close to Rs 24 (25 cents) per Kwh.
The highest outage cost per Kwh is observed in Sindh at Rs 40 (42
cents) per Kwh, while the lowest cost is in Punjab at Rs 18 (19 cents)
per Kwh. The outage cost per Kwh is the highest for the 'middle
class' at Rs 27 (28 cents) - Rs 29 (30 cents).
Blowing-up of the sample to arrive at a national estimate requires,
first, estimation of the number of urban households in the country.
According to the PES the population of Pakistan in 2011-12 is 180.7
million, out of which 37.4 percent is located in the urban areas. The
average household size is given in the latest HIES of the PBS at 6.19.
This implies that there are 10.9 million urban households in the
country.
Second, there is a need to determine the distribution of urban
households by level of monthly consumption expenditure. This has also
been derived from the HIES and is presented in Table 13. Overall, the
total outage cost to residential consumers in the urban areas of
Pakistan is Rs 195.8 Billion in 2011-12.
6. CONCLUSIONS AND POLICY IMPLICATIONS
We have highlighted in the previous section the principal findings
on the incidence of outages and cost of load shedding in the residential
sector. In this concluding section we derive the key policy
implications.
The estimated impact of outages on households is as follows:
(i) Outages on the average occur almost five times a day for 17
percent of the time. The highest incidence is in Punjab at 1683 hours
annually, 16 percent above the national average. The lowest incidence is
in Sindh at 23 percent below the national average.
(ii) Outages are disruptive most of heating/cooling, household
activities, preparation for work/study (especially by children) and any
home-based economic activity.
(iii) The outage cost per kwh works out as Rs 24(25c).
Table 14 presents the total cost of electricity consumption to
household at different levels of total consumption expenditure (proxy
for income). Overall, this is estimated at close to 17 percent. A
striking finding is that the cost is the lowest for the upper most
income group.
In the pre-load shedding period, in 2005-06, according to the HIES,
the share of electricity cost in total consumption expenditure was 5
percent on average for urban households. Following the high levels of
load shedding this share has jumped up by over three times.
It is clear that the high share of expenditure on electricity is
cutting into consumption of food, clothing and basic services (like
education and health), especially by the low income groups. As, such an
indirect impact of the high level of load shedding in the country is the
reduction in nutrition levels, particularly of children. Along with
impact on preparation for school and homework, the impact of outages on
children needs to be more strongly highlighted.
Overall, limits of affordability to power tariffs have been reached
by bulk of the households and the scope for further enhancement in
tariffs is very limited. The recent increase in tariffs will put a large
burden, especially on the middle class.
The prevalence of self-generation is relatively low among
residential consumers. 28 percent have generators and 30 percent have
UPS. Resort to self-generation is the highest in Sindh and KPK and among
consumers in the highest income category.
The average capacity of generators in use is under 3.5 KVA. The
proposal for eliminating the GST on small generators and UPS is
justified in this case also, as for commercial consumers.
Based on responses by the sample households, the following
proposals are presented for reducing the level of outage costs:
(i) The majority, 65 percent, of respondents prefer, given the
total duration of load shedding, shorter though more frequent outages.
Higher duration of a typical outage is one of the main reasons why
outages costs are higher in Karachi, despite lower incidence of outages.
(ii) Bulk of the load shedding is in the morning from 6:00 am to
9:00 am. This creates disturbance in preparation for work/school and
heating during winters. Over 43 percent of sample households report that
changing load shedding times to later in the day would be less
disruptive, especially to low income households.
(iii) The worst time in year for load shedding is summer and worst
day are Sunday, Monday and Friday. To the extent there is scope, the
pattern of load shedding needs to be adjusted accordingly.
(iv) There has been a clear vote of no-confidence against the
services provided by the power sector. 43 percent rate the quality of
services as 'very low' and 35 percent as 'low'.
Distribution companies, in particular, will have to work very hard to
rehabilitate their image.
(v) A series of recommendations have been made for reducing the
costs of load shedding, as follows,
Construct New Dams 43%
Build New Power Plants 27%
Import Electricity 22%
Minimise Electricity Theft 17%
Stop Corruption 17%
Use Coal 14%
Gas Pipeline From Iran 15%
Subsidy 13%
Reduce Price 10%
Solar Energy 8%
Therefore the largest responses relate to enhancement in
electricity supply and to improved management of power sector. Overall,
power outages have become a major source of inconvenience and cost to
domestic consumers in Pakistan.
Comments
Pakistan went through an extraordinary period of having surplus
electricity from the late 1990s to 2004-05 but since then, the country
has been facing an acute shortage of electricity. This widening demand
supply gap has resulted in regular load shedding of eight to ten hours
in urban areas and eighteen to twenty hours in rural areas. To analyse
the impact of energy crises on domestic consumer the study uses data
from the survey of 500 households from 13 major cities in Pakistan. New
methodology is developed for quantification of outage cost to domestic
consumers. The authors deserve compliments over conducting a rigorous
work on cost of power load shedding.
I have a few short comments on this paper.
(1) It would be appropriate if study construct consumption
expenditure groups on the basis of per capita household expenditure
rather than overall household expenditure because it masks significant
disparities in expenditure across households because household size is
not taken into account.
(2) The study concentrates outage cost incurred by using UPS 30
percent and generator 28 percent of households in 13 major cities of
Pakistan. This survey had a large share of households who are using
generator which looked some sample biasness. The authors had not
discussed other 42 percent households who are not using UPS and
generator. The high share of UPS and Generators may inflate power outage
cost.
(3) It is observed that these 42 percent households used candles/
lantern/emergency lights as alternative for lighting purposes i.e.
studying, other household chores. It is a big share of households which
must be included in power outages cost. The study needs some discussion
about it:
(4) KP and Balochistan share is given 33 percent which is 21
percent (15+6).
(5) A proper citation style should follow such as APA.
Overall this paper had great contribution in quantification of
outage cost to domestic consumers.
Rashida Haq
Pakistan Institute of Development Economics, Islamabad.
REFERENCES
Bental, B. and S. A. Ravid (1982) A Simple Method for Evaluating
the Marginal Cost of Unsupplied Electricity. The Bell Journal of
Economics 13:1, 249-253.
Munasinghe, M. (1980) The Costs Incurred by Residential Electricity
Consumers Due to Power Failures. Journal of Consumer Research 6,
361-369.
Pasha, Hafiz A., Aisha Ghaus and Salman Malik (1989) The Economic
Cost of Power Outages in the Industrial Sector of Pakistan. Energy
Economics 11:4, 301-318.
Sanghvi, A. P. (1982) Economic Cost of Electricity Supply
Interruptions: US and Foreign Experience. Energy Economics.
(1) For check this if missing footnote..
(2) With the exception of households which engage in some economic
activity at home.
* The [[beta].sub.j] is estimated by OLS regression across the
sample households with electricity consumption per hour, which varies
with ownership of different types of appliances.
Hafiz A. Pasha and Wasim Saleem
Hafiz A. Pasha <hafiz-pasha@gmail.com> is Dean and Professor
of Economics, Beaconhouse National University, Lahore. Wasim Saleem
<wasimsaleem3344@yahoo.com> is Lecturer, Beaconhouse National
University, Lahore.
Authors' Note: The authors thank USAID for support to the
research. Any defects that remain are solely the responsibility of the
authors.
Table 1
Long-Term Trend in Capacity and Generation of Electricity in
Pakistan 1970-71 to 2011-12
Annual Annual Index of
Installed Growth Electricity Growth Capacity
Capacity Rate Generation Rate Utilisation
(MW) (%) (GWH) (%) (%)
1070 71 1862 7202 81
1980-81 4105 8.2 16062 8.4 82
1990-91 8356 7.4 41042 9.8 102
2000-01 17498 7.7 68117 5.2 81
2011-12 23358 2.7 98664 3.4 88
Source: Handbook of Statistics, SBP, and Pakistan Economic Survey,
MOF, Government or Pakistan.
Table 2
Growth in Electricity Consumption from 2000-01 to 2011-12
(GWH)
Domestic Industrial Commercial
2000-01 22765 14349 1774
2007-08 33704 20129 5572
2011-12 33138 21334 5526
Growth Rate (%)
2000-01 to 2007-08 5.8 5.4 10.5
2007-08 to 2010-11 2.1 0.8 0.5
2001-01 to 2011-12 -0.4 1.5 -0.2
Agricultural Others * Total
2000-01 4924 3773 48583
2007-08 8472 4923 73400
2011-12 8290 4760 73084
Growth Rate (%)
2000-01 to 2007-08 8.1 6.7 6.1
2007-08 to 2010-11 1.9 2.2 1.6
2001-01 to 2011-12 -0.5 -0.7 -0.2
Source: PES.
* mostly government, street lights and traction.
(1) 300 days operation with 16 hours daily.
Table 3
Surplus/Deficit in Demand and Supply during System * Peak Hours
Generation
Capacity Demand Supply-Gap %
2007 15575 17487 -1912 11
2008 14707 19281 -4574 24
2009 16050 20304 -4254 26
2012 16104 22622 -6518 29
Source: NEPRA, State of Industry Report.
* NTDC and KESC combined.
Table 4
Incidence of Load Shedding
No. of Times
there is a
Load Shedding Annual Hours
in a Day of Outages
By Province
Punjab 6 1683
Sindh 3 1123
KPK 4 1216
Balochistan 4 1069
By Income Group
Up to 15000 5 1498
15001-35000 4 1394
35001-70000 5 1430
70001 + 5 1702
Total 5 1453
Table 5
Distribution of Sample by Province and by City
Provinces Cities Numbers Percentage
Punjab Lahore 96 19
Faisalabad 51 10
Sialkot 13 3
Gujranwala 26 5
Multan 38 8
Rawalpindi/Islamabad 61 12
Total 285 57
Sindh Karachi 80 16
Hyderabad 20 4
Sukkur 10 2
Total 110 22
KPK Peshawar 50 10
Mardan 13 3
Abbotabad/Bannu 12 2
Total 75 15
Balochistan Quetta 30 6
Total 30 6
Total 500 100
Table 6
Outage Cost per kwh according to the Value of Leisure Approach *
Electricity
Consumption Outage
Group Income ** per hour *** Cost per
(Rs per Month) per hour (kwh) kwh (Rs)
0-15000 67.5 0.9 75
15001-35000 144.8 1.5 97
35001-70000 295.5 3.3 90
Above 70000 612.6 5.7 107
Total 218.3 2.4 91
* Y = income per hour worked based on 8 hours a day for 22 days a
month.
Kwh = normal power consumption per hour (in public supply).
** Proxied by consumption expenditure, which is assumed to
correspond to permanent income.
*** On the assumption that electricity is consumed 16 hours a day.
The consumption of electricity in the evenings is assumed to be
three times the daily average.
Table 7
Activities most Disturbed by Load Shedding
% of
Sample
Units
Cooling/heating 24.4
Studies (home work) of children 18.2
Preparation for work/school 17.4
Regular household work (cooking, cleaning, etc.) 14.6
Shortage of water 13.0
Income generating activities (home based) 8.2
Social Activities 2.2
Entertainment, leisure 2.0
Total 100.0
Table 8
Sample Households with Generator and/or UPS
% of Sample Households
Level of monthly
consumption expenditure (Rs) With Generator With UPS
0-15000 2 4
15001-35000 17 26
35001-70000 45 47
70001 and above 75 43
Total 28 30
Table 9
Subjective Valuation of the Outage Cost per Hour
Monthly Willingness to Annual
Expenditure Pay Electricity Bill
Group (extra over tariff)
(Rs) (%) (Rs)
0-15000 30.3 15330
15001-35000 28.7 28836
35001-70000 28.3 65094
70001 and above 31.8 130590
Total 29.2 46734
Monthly Electricity not Subjective Valuation
Expenditure Supplied by Household of
Group Outage Cost per Hour
(Rs) (kwh) (Rs per kwh)
0-15000 479 9.70
15001-35000 732 11.31
35001-70000 1599 11.52
70001 and above 4299 9.66
Total 1289 10.59
Table 10
Total Outage Cost per Residential Consumer
(Rs)
Cost of Self-Generation
Monetisation Total
of Utility Generator UPS Other Outage
Loss Cost Cost Costs Cost
By Province
Punjab 7355 11263 3864 6747 29229
Sindh 7626 17562 2054 6075 33317
KPIC 4954 18964 2037 8104 34059
Balochistan 3530 18120 2573 5235 29458
By Income Group (Rs)
0-15000 3828 290 400 4262 8780
15001-35000 5655 6380 2734 6749 21518
35001-70000 9544 22370 4831 7053 43798
70001 and above 8193 50900 4550 4549 75192
Total 6824 14215 3114 6712 30865
Share (%) 22 46 10 22 100
Table 11
Total Outage Cost as Percentage of Total Household Consumption
Expenditure
(000RS)
Annual Annual Consumption Outage Costs % of
Outage Expenditure Consumption
Cost Expenditure
0-15000 8.8 142.5 6.2
15001-35000 21.5 305.9 7.0
35001-70000 43.8 627.6 7.0
70001 and above 75.2 1293.9 5.8
Total 30.9 461.1 6.7
Table 12
Total Outage Cost per kwh to Residential Consumers
(Rs)
Total Electricity Outage Cost
Outage not provided per Kwh (Rs)
Costs (Kwh)
By Location
Punjab 29229 1655 17.66
SindR 33317 830 40.14
KPK 34059 865 39.37
Balochistan 29458 1474 20.00
By Income Group
0-15000 8780 479 19.32
15001-35000 21518 732 29.40
35001-70000 43798 1599 27.39
70001 and above 75192 4299 17.49
Total 30865 1289 23.94 (25 c)
Table 13
National Estimate of Outage Costs to Urban Residential Consumers,
2011-12
Monthly Total Outage
Consumption Number of Cost per Total
Expenditure Households Household Outage Cost
Group (Rs) (000s) (a) (Rs) (Rs billion)
0-15000 5014 8780 44.0
15001-35000 4360 21518 93.8
35001-70000 763 43798 33-4
70001 and above 327 75192 24.6
Total 10464 (b) 195.8
(a) adjusted on the basis of distribution in the HIES, 2010-11.
(b) 10.9 million households in urban areas with 98 percent of
households having access to electricity according to PSLSMS,
2010-11.
Table 14
Total Cost of Electricity Consumption Per Residential Consumer
(Rs in 000)
Annual
Electricity Cost Total
Electricity
Monthly or Total Annual Cost as % Of
Expenditure Public Outage Consumption Consumption
Group(Rs) Supply Cost Expenditures Expenditure
0-15000 15.3 8.8 142.5 16.9
15001-35000 28.8 21.5 305.9 16.4
35001-70000 65.1 43.8 627.6 17.4
70001 and above 130.6 75.2 1293.9 15.9
Total 46.7 30.9 461.1 16.8
Table 15
Present Tariff Structure for the Residential Sector
(Rs)
Actual Proposed
Per kwh Per kwh
Up to 50 units 2.00 2.00
For consumption exceeding 50 units
1-100 units 5.79 5.79
101-200 units 8.11 8.11
201-300 units 8.11 12.09
301-700 units 12.33 16.00
Above 700 units 15.07 18.00
Fig. 2. Distribution of Selected Households by Income Group
Upto 15000 21%
15001-35000 36%
35001-70000 35%
70001+ 8%
Note: Table made from pie chart.
