PART 2
Cost of
Loadshedding to
Small-Scale Industry
Institute of Public Policy
Beaconhouse National University, Lahore
1
CONTENTS
ACRONYMS
iv
CHAPTER 1
INTRODUCTION
CHAPTER 2
THEORETICAL FRAMEWORK
2.1 OUTAGES AND A FIRM’S BEHAVIOUR
2.2 METHODOLOGY FOR QUANTIFICATION OF COST OF OUTAGES
CHAPTER 3
THE SAMPLING FRAMEWORK AND ITS DISTRIBUTION
3.1 SAMPLING FRAMEWORK
3.2 PROFILE OF RESPONDANTS
3.3 CHARACTERISTICS OF SELECTED UNITS
CHAPTER 4
THE INCIDENCE OF LOADSHEDDING AND OUTPUT LOSSES
4.1 INCIDENCE AND PROFILE OF LOADSHEDDING
4.2 EXTENT OF TOTAL TIME LOST
4.3 SEASONALITY OF OUTAGES
4.4 EXTENT OF OUTPUT LOST DURING OUTAGES
CHAPTER 5
ADJUSTMENTS TO LOADSHEDDING
5.1
NUMBER AND TYPES OF ADJUSTMENTS
5.2
EXTENT OF LOSS OF OUTPUT IN OUTAGES
CHAPTER 6
COSTS OF OUTAGES
6.1
TOTAL OUTAGE COSTS
6.2
TYPES OF OUTAGE COSTS
6.3
BURDEN OF OUTAGE COSTS
6.4
OUTAGE COSTS PER KWH
6.5
NATIONAL ESTIMATE OF OUTAGE COSTS
CHAPTER 7
LOAD MANAGEMENT STRATEGY: CONSUMER’S PREFERENCES
7.1
LEVEL OF SATISFACTION WITH CURRENT LEVEL OF SERVICE
7.2
PREFERRED CHANGES IN TIMINGS OF LOADSHEDDING
CHAPTER 8
SUGGESTIONS BY THE SAMPLE UNITS
CHAPTER 9
CONCLUSIONS AND POLICY IMPLICATIONS
9.1
IMPACT OF OUTAGES
9.2
AFFORDABILITY OF POWER TARIFFS
9.3
COMPETITIVE DISADVANTAGE OF SMALL UNITS
9.4
INVESTMENT IN GENERATORS
9.5
LOAD MANAGEMENT STRATEGY
1
2
2
3
8
8
10
11
16
16
18
20
21
23
23
25
27
27
28
29
31
31
33
33
36
41
44
44
45
46
47
48
i
TECHNICAL ANNEXURE
49
LIST OF TABLES
Table 3.1: National Distribution of Small Manufacturing Establishments in the Economic Census,
9
2005, by Province and Industry Group
Table 3.2: Distribution of Sample units by City, Province and Industry
10
Table 3.3: Distribution of Sample Analysed by Cities
10
Table 3.4: Average Employment and Capacity Utilization by Industrial Group, 2012
11
Table 3.5: Average Sale and Operating Expenses of Sample Units, 2012
12
Table 3.6: Average Value Added, Electricity Purchased and Value Added per Kwh of Electricity of
13
Sample Units
Table 3.7: Operating Cost Structure of Sample Units
13
Table 3.8: Reasons Why Production Target was Not Attained
15
Table 4.1: Frequency of Loadshedding in 2012
16
Table 4.2: Percentage Distribution of Average Length of Outages, 2012
17
Table 4.3: Duration of Outages
18
Table 4.4: Proportion of Time Lost
19
Table 4.5: Seasonality in Outages
20
Table 4.6: Nature of Impact of Loadshedding
21
Table 4.7: Ranking of Disruptions Due to Outages
22
Table 5.1: Percentage of Sample Units by Number of Adjustments by Process
23
Table 5.2: Percentage of Sample units Adjusting through Various Mechanisms
24
Table 5.3: Number of Adjustments by Firms with and without Generators
25
Table 5.4: Proportion of output Loss Not Recovered
26
Table 6.1: Total Outage Costs Per Unit
28
Table 6.2: Outage Costs as Percentage of Value Added
29
Table 6.3: Outage Costs Per kwh
31
Table 7.1: Percentage of Time DISCOs Kept to the Announced Loadshedding Schedule
33
Table 7.2: Average Time Required for Adjustment to Changes in Loadshedding Schedule
34
Table 7.3: Level of Satisfaction with Current quality of Service by DISCOs/KESC
35
Table 7.4: Additional Tariff For Better Quality of Service (with No Loadshedding)
36
Table 7.5: Perceived Outage Costs per Kwh as implied by Willingness to Pay
37
Table 7.6: Percentage Willing To Change Work Timing If off Peak Power Tariff Are Reduced
38
Table 7.7: Worst Time of The Year for Loadshedding
39
Table 7.8: Worst Day of The Week for Outages
39
ii
Table 7.9: Information that can be provided by Distribution companies to Units
40
Table 8.1: Suggestions by Sample Units by City
42
Table 9.1: Total Costs of Electricity Consumption as a percentage of the Value of Production
45
Table 9.2: Comparison of the Impact of Loadshedding by Size of Industrial Unit
46
Table 9.3: Benefit-Cost Ratio of Self-Generation
47
Table 9.4: Proposed Tariff Structure on Imported Generators
47
LIST OF BOXES
Box 2.1: A Numerical Example of Quantification of Net Idle Factor Cost
6
Box 3.1: Capacity Utilization and Outages
12
Box 4.1: Incidence of Outages
17
Box 4.2: Production Losses During Outages
19
Box 5.1: Extent of Recovery of Output by Use of Generators
25
LIST OF FIGURES
Figure 2.1: Adjustment by a Firm to Outages
2
Figure 2.2: Flow Chart Showing Costs of Outages
4
Figure 3.1: Sampling Strategy
8
Figure 3.2: National Distribution by Province and Industry Group
9
Figure 3.3: Distribution of Selected Units by Industrial Group and Process
11
Figure 3.4: Actual as % of Target Production in 2012 by Industrial Group
14
Figure 6.1: Distribution of Outage Costs
29
Figure 6.2: Outage Costs as Percentage of Value Added
30
iii
ACRONYMS
CMI
=
Census of Manufacturing Industries
DISCO
=
Distribution Company
KESC
=
Karachi Electric Supply Corporation
K-PK
=
Khyber-Pakhtunkhwa
OLS
=
Ordinary Least Squares
PBS
=
Pakistan Bureau of Statistics
PES
=
Pakistan Economic Survey
WTP
=
Willingness to Pay
iv
CHAPTER 1
INTRODUCTION
This part of the report presents the findings on costs of loadshedding to small-scale industrial
units in Pakistan, quantified on the basis of data obtained from a nationwide survey of such
units.
The report is organized in nine chapters. Chapter 2 presents the methodology used for
qualification of costs due to outages. Chapter 3 describes the survey including the sampling
methodology and assessment of the quality of data collected, given the complex technical
nature of the survey. Subsequent Chapters up to Chapter present the magnitudes of key
parameters like the relevant characteristics of the responding units, incidence of outages, level
and pattern of adjustments and the magnitude of different outages costs. Chapter 8 highlights
the suggestions by sample units for reduction in incidence and costs of outages.
Chapter 9 gives a summary of the principal findings and the major policy implications emerging
from the research. It is clear from the results that small-scale industry has faced severe
disruptions due to the high and growing incidence of loadshedding. As such, the economic
return of reducing outages and of facilitating the process of adjustment to these outages is very
high. This could contribute to some revival of the economy and reduce unemployment.
Thanks are due to the sample units for responding to a questionnaire, which was complex and
difficult to administer. Thanks are also due to the survey team which travelled all over the
country and sometimes found itself in a difficult law and order situation, especially in Karachi.
The main text of the report is presented in a non-technical manner.
Technical analyses are included in various Boxes and Technical Annexes.
Any defects which remain are of course, the responsibility of the authors.
1
CHAPTER 2
THEORETICAL FRAMEWORK
2.1 OUTAGES AND A FIRM’S BEHAVIOUR
The behavior of a firm in the presence of frequent and persistent outages has been modelled in
the Technical Annex. The firm is assumed to be operating in a competitive environment, given
the smallness of its size; and pursues profit maximization. The results derived from this
theoretical framework are as follows:
i.
If outages are seen as, more or less, permanent in nature then the optimal size of the firm
is lower than in the absence of outages. In particular, there is a tendency to shed some
labor.
ii.
The likelihood that the firm will make adjustments to recover some of the lost output
depends on the following:
a) The extent to which the market situation is favorable for the firm
b) The electricity-intensity of the production process; the lower the intensity the
greater the likelihood that the firm will make an adjustment
c) The lower the costs of adjustment.
d) The larger the outage and the level of expectation that this will continue.
Figure 2.1
Adjustment by a Firm to Outages
2
Based on the results from the theoretical analysis we present in Figure 2.1 visually the change
in the equilibrium of a firm in the presence of outages
Type I firm initially experiences outages which reduce production from 𝑄0 − 𝑄1 . At 𝑄1 the gap
between price and marginal cost is AB. AB is larger the bigger the outage. The marginal cost
curve of adjustment by the firm is given by XY. If XY is too high, then the firm makes no
adjustment.
In the case of type II firm, Y lies between A and B. As such, the firm makes an adjustment and
the new output level is 𝑄𝑒 . Type I firm reduces output by (𝑄0 − 𝑄1 ) while the loss of output of
Type II firm is (𝑄0 − 𝑄𝑒 ). Also, the fall in profit of Type I firm due to outages is ABC. In the case
of Type II firm it is BYZC.
The above theoretical framework is used to develop the methodology for quantifying the cost of
outages.
2.2. METHODOLOGY FOR QUANTIFICATION OF COST OF OUTAGES
We have indicated above that in the presence of persistent outages firms will be inclined to
make adjustments. The extent and nature of adjustments will depend on a number of factors
which have also been identified above. The methodology used for quantifying the cost of
outages is based largely on that developed by Pasha, Ghaus and Mallik [1989]. Fig 2.2 presents
a flow chart for identifying the different types of outage costs.
There are two types of costs associated with outages. The first type is direct costs which
consist primarily of the value of lost production and spoilage costs. The second type is
adjustment costs. The particular mechanisms chosen for recovering some or all of the output
lost will be based on cost minimisation considerations. Accordingly, a firm will opt for a type of
adjustment as long as it is cheaper than other options. Therefore, firms may opt for multiple
adjustments, especially when the size of the outage is large.
Direct Costs
In order to derive the magnitude of direct costs, we designate the following:
𝑛𝑖 = number of times of occurrence of outage daily on average of duration i. i = 1, 2, 3, 4, 51
𝜖𝑖 = proportion of output lost during an outage of duration i
𝛾𝑖 = restart time after an outage of duration i.
1
The durations are 0-1/2 hr; ½ hr to 1 hr; 1 hr to 2 hrs; 2 hrs to 3 hrs; 3 hrs and above.
3
Figure 2.2
Flow Chart Showing Costs of Outages
OUTAGES
PLANT
SHUTDOWN
SPOILAGE COST
RESTART TIME
OUTPUT
RECOVERED
OUTPUT LOST
PERMANENTLY
TOTAL DIRECT
COSTS
ADJUSTMENTS
USE OF
GENERATORS
OVER TIME
EXTRA SHIFTS
NET IDLE
FACTOR COST
OTHERS
TOTAL INDIRECT (ADJUSTMENTS COSTS)
TOTAL OUTAGE
COST
4
The total number of outages during the year is given by
NOUT = ∑5𝑖=1 𝑛𝑖 × 365
..…………………………………………………...(2)
The total time lost due to outage is
TOUT = ∑5𝑖=1( 𝑛𝑖 [𝑑𝑖 + 𝛾𝑖 ] × 365)
……..………………………………………………(3)
Where 𝑑𝑖 is the duration of the outage.
The potential extent of output loss due to outages is given by
LOUT = ∑5𝑖=1 𝑛𝑖 [𝑑𝑖 + 𝛾𝑖 ] 𝜖𝑖 × 365
.……………………………………………………(5)
But the firm may not be operating throughout the year and for 24 hours each day. Therefore, if
H is the normal hours worked during the year, the actual output lost is given by
….…………………………………………………(5)
𝐻
ACOUT = 𝐿𝑂𝑈𝑇. 8760
And the value of this loss is as follows
VOUT = ACOUT.V
.……………………………………………………(6)
Where V is the value added by the firm per hour.
However, the firm may recover some of the output lost through adjustments. If 𝜆 is the extent of
output recovered then we have the expression for the net idle factor cost, NIFC, as follows:
NIFC = (1-λ) VOUT
……………………………………………………(7)
Box 2.1 gives a simple numerical example for calculation of the net idle factor cost incurred by a
sample unit due to outages on the basis of data obtained from the survey.
The other part of direct costs is spoilage costs. We represent
𝑆𝑖 = spoilage cost (in rupees) in each outage of duration i
Then the spoilage cost, SPC, is derived as follows:
𝐻
SPC = ∑𝑛𝑖=1 𝑛𝑖 𝑠𝑖 . 365 × 8760
……….…………………………………………….(8)
And the total direct costs of outages are
TDC = NIFC + SPC
…………………………………………………………(9)
5
Box 2.1
A Numerical Example of Quantification of Net Idle Factor Cost
Suppose a firm experiences outages of duration of one hour six times a day (over the 24 hours) and the
restart time after the outage is half an hour.
Then
NOUT = 6 × 365 = 2190
TOUT = 6 × 365× 1.5 =3285
If the proportion of output lost is 50%. Then
LOUT = 3285 × 0.5 = 1642.5
The firm normally operates one shift (of 8 hours) for 300 days. Then
H = 8 × 300 = 2400
And, ACOUT = 1642.5 ×
2400
8760
= 450
If the value added per hour is Rs 1000, then
VOUT = 450 × 1000 = 450,000Rs
and if the proportion of output recovered through adjustments is 60%, then λ = 0.6 and NIFC is given by
NIFC = 450,000 × (1-0.6) = 180,000 Rs
Adjustment Costs
Generators Cost
A key response by industrial units, which is being observed in Pakistan, is the resort to own
sources of energy supply through investment in generators. This is motivated by the high and
rising incidence of outages since 2007 and the growing recognition that power loadshedding
(along with gas shortages) could persist over the next many years.
In practice, the extent of substitution of the conventional power source (through DISCOs)
depends on the energy-intensity of operations (as derived in the theoretical framework above),
on the possibility of making other cheaper adjustments and the cost of capital for acquisition of
generators. The latter is likely to be relatively high for small-scale units.
The survey of units has indicated, first, whether a unit has a generator or not, second, the
capital cost of the generator, third, monthly running cost of fuel for operating the generator and
fourth, other costs (including labor, repairs and maintenance cost, etc.) on a quarterly basis. As
such, we designate the following:
𝐾𝑔 = capital cost of generator
foc = fuel operating cost per month
opc = other operating costs quarterly
This leads to the estimate of the overall annual generator cost, GENCO, as follows:
GENCO = (𝜏 + 𝛿)𝐾𝑔 + 12(foc) +4(opc)
……...……………………………………………(10)
6
Where, 𝜏 is the cost of capital and 𝛿 is the rate of depreciation. The combined value of 𝜏 𝑎𝑛𝑑 𝛿
is taken as 0.32.
However, the use of generators implies savings in costs of power supply from the local DISCO.
Therefore, the net cost, NGENCO, is given by
NGENCO = GENCO – k (TOUT) × (ADJG) ×tf .
𝐻
………..………………………………(11)
8760
Where
K = electricity consumption per hour in Kwh
TOUT = total hours lost as derived in equation (3)
ADJG = extent of adjustment by use of generators
tf = tariff per Kwh of the DISCO.
Other Adjustments
Other adjustments include the following and are more in the nature of short-run adjustments
when loadshedding is seen as temporary in nature:
i.
More intensive utilization of existing plant and machinery during times when there is no
loadshedding
ii.
Overtime or additional shifts to make up for at least part of the output loss
iii.
Changes in working days or timings.
The survey reveals that the majority of firms have not made significant adjustments of the above
type and the costs associated with these adjustments are not large. The methodology used for
quantifying these costs has been taken from Pasha, Ghaus and Mallik [1989]. They are
represented by OTC.
Overall, the total adjustment cost, TAJCO, is derived as
TAJCO = NGENCO + OTC
……………………………………………………….(12)
And the total outage out cost, TOUTCO, as follows:
TOUTCO = TDC + TAJCO
……………………………………………………….(13)
The magnitudes of the different components of outage costs are presented in Chapter 6, by
location, industrial classification and type of process of the sample units.
7
CHAPTER 3
THE SAMPLING FRAMEWORK AND ITS DISTRIBUTION
3.1 SAMPLING FRAMEWORK
The primary instrument of data collection was a survey on a pre-designed and tested
questionnaire of a stratified (by city, firm and industry group size) national random sample of
industrial units (see Figure 3.1).
Figure 3.1
Sampling Strategy
Primary Sampling Unit
Industrial Units in Cities
Secondary Sampling Unit
Small Scale Units in Cities
Tertiary Sampling Unit
Various Small Scale Units in Industry Groups
Individual industrial units in various Industry
Groups
The population of the industrial units was obtained from the Economic Census 2005, published
by the Pakistan Bureau of Statistics (PBS). This source was preferred to the Census of
Manufacturing Industries (CMI), which only covers large and medium-scale units (with
employment of 10 workers and above) while the focus of this study is on the small-scale
industrial sector. The national distribution of small manufacturing establishments by province
and industry group is presented in Table 3.1 and Figure 3.2. The derived sample distribution by
city and industrial group is presented in Table 3.2.
Once the sample distribution across cities and industrial groups was finalized, the individual
sample units were selected from the population of units obtained from the Provincial Directories
of Industries prepared by the respective Provincial Labour Departments. For each unit, the
Provincial Directories give name, address, year of registration and level of employment. The last
variable enables the selection of small units.
The questionnaire administered on the sample respondants contains five modules: basic
information on production/sales/employment/costs; incidence of outages; costs of outages;
adjustment to outages, and, preferred load management practices. Though the questionnaire
8
was structured, the last question was open-ended asking the respondants to make suggestions
to reduce the costs of loadshedding. This provides the respondant’s perspective on actions to
counter the problem.
Table 3.1
National Distribution of Small Manufacturing Establishments* in the Economic
Census, 2005 by Province and Industry Group
Distribution of Manufacturing Establishment*
Percentage
Punjab
68.4
Sindh
13.9
K-PK
15.9
Balochistan
1.4
Islamabad
0.4
Total
100
Distribution of National Employment in Small-Scale Units (less than 10 employees)
Industry
%
Food, Beverages
20.9
Textiles, Wearing Apparel & Leather
43.4
Wood & Wood Products (including furniture)
11.1
Paper & Paper products
1.7
Chemicals
0.9
Non-Metallic Mineral Products
2.2
Basic Metal Products
0.7
Fabricated Metal Products, Machinery & Equipment
10.0
Others
9.1
Total
100
*with employment below 10 workers.
Figure 3.2
National Distribution by Province and Industry Group
By Province
By Industry Group
Balochistan
1%
KPK
16%
Others
15%
Fabricated
Metal
Products
10%
Sindh
14%
Punjab
69 %
Wood
and
Wood
Products
11 %
Textiles,
Wearing
Apparel
and
Leather
43%
Food,
Beverage
s and
Tobacco
21%
9
Table 3.2.
Distribution of Sample units by City, Province and Industry
Province
Punjab
Sindh
KPK
Balochistan
Cities
Food
Beverages
and
Tobacco
Lahore
23
Textiles,
Wearing
Apparel
and
Leather
46
Faisalabad
14
Sialkot
Wood &
Wood
Products
Fabricated
Metal
Products
Others
Total
14
12
19
114
29
9
7
12
72
6
12
4
3
5
30
Gujranwala
5
10
3
2
4
24
Multan
6
12
4
3
5
30
Rawalpindi/Islamabad
8
14
4
5
8
39
Total
62
123
38
33
54
310
Karachi
13
26
8
7
12
66
Hyderabad
4
8
2
2
4
20
Sukkur
3
6
2
1
3
14
Total
20
40
12
10
18
100
Peshawar
13
29
10
7
11
70
Total
13
29
10
7
11
70
Quetta
5
8
-
-
7
20
Total
Total
5
8
0
0
7
20
100
200
60
50
90
500
The survey was successfully administered on
500 units as targeted. Following the process of
edit and consistency checking of the completed
questionnaires, 412 units, over 82 percent of
the sample, have been included in the analysis.
3.2 PROFILE OF RESPONDANTS
Distribution of selected units for analysis by city
is given in Table 3.3. 63 percent of the sample
units are in the province of Punjab, given the
concentration of small-scale industry in Punjab,
while about 20 percent are in Sindh. From the
Table 3.3.
Distribution of Sample Analysed by Cities
Cities
Lahore
Faisalabad
Gujranwala
Multan
Sialkot
Rawalpindi / Islamabad
Karachi
Hyderabad
Sukkur
Peshawar
Quetta
Total
Numbers
104
61
16
29
23
27
54
16
11
58
13
412
%
25.2
14.8
3.9
7.0
5.6
6.6
13.1
3.9
2.7
14.1
3.1
100.0
remaining 17 percent, 14 percent are in Khyber-Pakhtunkhwa (K-PK) and 3 percent in
Balochistan. The distribution by industry group and nature of production process is given in
Figure 3.3. The presence of continuous-process technology in small units is low.
10
Figure 3.3
Distribution of Selected Units by Industrial Group and Process
Industry Group
Nature of Production Process
Food
beverages
and
Tobacco
19%
Other
industries
18%
9%
Fabricated
Metal
Products
10%
Textile
wearing
Apparel
and
leather
42%
Wood and
Wood
products
11%
91%
Continuous Process
Batch-Process
3.3 CHARACTERISTICS OF SELECTED UNITS
Basic Information
Respondent units from among
the
batch-making
industries
generally operated a one shift
production cycle. The Second
Table 3.4.
Average Employment and Capacity Utilization by
Industrial Group, 2012
Employment
(No)
Capacity
Utilization
(%)
Food Beverages and Tobacco
7.1
82.3
Textile Wearing Apparel and Leather
8.1
80.7
Wood and Wood products
6.7
76.7
Fabricated Metal Products
6.4
76.6
Other industries
7.0
81.6
Total
7.4
80.3
Industries
shift was generally operated by
industrial
textile,
units
wearing
belonging
apparel
to
and
leather and food, beverages and
tobacco
industries
group.
Continuous process industries,
on an average, worked 293 days
a year. 15 percent of sample units worked 315 days while another 20% of sample units worked
330-335 days a year. The sample units on an average employed just over 7 persons and
utilized 80 percent of their capacity (see Table 3.4). Econometric analysis of the determinants of
the rate capacity utilization has been undertaken in Box 3.1. The results indicate that the
incidence of outages has a significant negative impact on the rate of capacity utilization.
11
Box 3.1
Capacity Utilization and Outages
The following variables are designated for each unit:
CAPU = Rate of capacity utilization (%)
DIND1 = 1 for food, beverages and tobacco
= 0 otherwise
DIND2 = 1 for textiles, apparel and leather
= 0 otherwise
DIND3 = 1 for wood, and wood products
= 0 otherwise
DIND4 = 1 for fabricated, metal products
= 0 otherwise
PTOUT= proportion of time lost due to outages (%).
The results a of the OLS regression with CAPU as the dependent variable are as follows:
CAPU =
84.34
-5.19DIND3
-4.38DIND4
-0.06PTOUT
(58.3)*
(-3.18)*
(-2.55)*
(-1.93)**
R2 =0.06, SEE =8.43, Degrees of Freedom = 394, F = 4.6*
*Significant at 5% level
** Significant at 10% level
The results indicate a lower rate of capacity utilization in the wood and wood products and the fabricated
metal products industry. Also, the incidence of outages has a significant negative impact on the
rate of capacity utilization.
a only the significant variables are highlighted.
Sales and Value Added
The average sales of
the
respondent
Table 3.5
Average Sale and Operating Expenses of
Sample Units 2012
(Rs in Thousands)
units
chosen for analysis
in
2012 is projected at Rs
5.5 million (see Table
3.5)
demonstrating
growth of
almost
Their
Total
Food beverages and Tobacco
6888
4246
Operating
Expenses
as % of
Sales
62
Textile wearing Apparel and leather
5877
3352
66
Wood and Wood products
6497
4336
67
Fabricated Metal Products
5151
3488
68
Others industries
4431
2904
65
Total
5472
3565
65
a
15
percent over the 2011
level.
Industrial Group
Total
Operating
Expenses
operating
expenses average Rs
3.6 million, implying an
operating profit of Rs
1.9 million. It appears that only 14 percent of the sample units are export-oriented, mostly
belonging to textiles, wearing apparel and leather industry.
12
The
average
value
added by sample units
in 2012 is projected at
Table 3.6
Average Value Added, Electricity Purchased and Value Added
per Kwh of Electricity of Sample Units
Value Added
(Thousands)
Electricity
Consumed
(Kwh)
3139
22730
Value
Added
Per
Kwh
(Rs.)
138.1
1930
19801
97.5
2426
12598
192.6
Fabricated Metal Products
2053
19190
107.0
Other industries
2094
19169
109.2
Total
2259
19384
116.5
Rs 2.2 billion, highest
being in food, beverage
Industrial Group
and tobacco industry
followed by wood and
wood
products
Table
units
3.6).
over
sample
have,
average,
(see
on
an
purchased
19
thousand
Food beverages and Tobacco
Textile wearing Apparel
leather
Wood and Wood products
and
kilowatt hours (kwh) of electricity annually from the public distribution companies. The average
value added per kwh is Rs 117. This is the first estimate of the outage cost per kwh. As
highlighted earlier, it overstates the magnitude because it does not incorporate the impact of
adjustments.
Operating Costs
Turning next to operating costs, as mentioned above, average annual operating cost of sample
units is Rs 3.6 million. Out of this, the highest proportion, (56 percent), is spent on purchase of
raw materials, followed by wages (25 percent) (See Table 3.7). Electricity costs purchased from
the distribution companies and self-generation combined account for 15 percent of the costs.
Table 3.7
Operating Cost Structure of Sample Units
(%)
Total
Operating
Cost
(Rs. In
Thousands)
Wages/
Salaries
Raw
Material
Repairs/
Maintenance
Cost of
Electricity
Cost of Self
Generation
Others
Food beverages and
Tobacco
Textile
wearing
Apparel and leather
Wood and Wood
products
Fabricated
Metal
Products
Others industries
4246
24.9
55.6
3.0
10.8
4.5
1.2
3352
24.8
55.2
3.0
10.1
4.4
2.5
4336
21.8
62.2
3.4
7.4
3.7
1.4
3488
20.6
60.5
3.6
9.4
3.5
2.5
2904
27.4
51.1
4.1
11.3
4.0
2.1
Total
3565
24.6
55.8
3.3
10.1
4.2
2.0
Industrial Group
(Percentage of Operating Cost)
13
Attainment of Production targets
Sample units, on an average, have been able to achieve 78 percent of their production target,
as shown in figure 3.4. The highest rate of target achievement is by food, beverages and
tobacco industry at 85%, followed by wood and wood product industry at 78 percent.
Figure 3.4
Actual as % of Target Production in 2012 by Industrial Group
Industrial Group
(%)
85.2
78.8
77.4
78.5
76.9
73.0
Food beverages
and Tobacco
Textile wearing
Apparel and
leather
Wood and Wood Fabricated Metal Others industries
products
Products
Total
By City
84.1
79.5
81.1
78.6
84.7
82.1
69.9
76.4
78.8
71.3
71.5
78.5
Interestingly, sample units in Punjab were able to achieve a higher proportion of the target
compared to the other cities, possibly because the targets were already modified somewhat to
allow for the presence of loadshedding.
14
When enquired as to why the production target was not attained, the principal reasons given are
high level of power outages, law and order situation (especially in Karachi) and market related
factors. (See Table 3.8).
Table 3.8
Reasons Why Production Target was Not Attained
(%)
Power outages
24
Textiles
wearing
Apparel
and
Leather
19
Law and order
25
19
16
19
20
High inflation & market
competition
Shortage or high cost or raw
materials
Other reasons
18
20
20
25
19
14
20
20
17
28
18
22
25
19
13
Total
100
100
100
100
100
Reason for Not Attaining
Targets
Food
Beverages
and
Tobacco
Wood
and
Wood
Products
Fabricated
Metal
Products
Other
Industries
18
21
20
15
CHAPTER 4
THE INCIDENCE OF LOADSHEDDING AND OUTPUT LOSSES
This Chapter quantifies the incidence of loadshedding and the resulting output losses as
revealed by the national sample of small-scale industries units.
4.1 INCIDENCE AND PROFILE OF LOADSHEDDING
The costs of loadshedding will, to a large extent,
Table 4.1
Frequency of Loadshedding in 2012
depend on the frequency and duration of
outages. The incidence of loadshedding is given
in Table 4.1. Overall, the average number of
outages in Pakistan in 2012 is estimated at
14112. Highest number of times outages have
occurred in Lahore at 1874, followed by Sukkur
at 1795, Gujranwala at 1683, and Multan at
1561. Clearly, the average incidence is lower in
Sindh at of 1267 times a year at about 10%
below the national average.
Industry-wise,
the
highest
incidence
was
experienced by the textile, wearing apparel and
leather
industry
(1495)
followed
by
other
industries (1398) and food, beverages and
tobacco industries (1365). Box 4.1 gives the
results of econometric analysis of the incidence
of outages. The results confirm that this is
By Province/City1
Location
Punjab
Lahore
Faisalabad
Other Cities
Sindh
Karachi
Other Cities
KPK
Balochistan
Total
By Industrial Group
Industrial Group
Food beverages and Tobacco
Textile wearing Apparel and leather
Wood and Wood products
Fabricated Metal Products
Others industries
Total
By Process
Nature of Production
Continuous Process
Batch-Process
Total
Average
1499
1874
1047
1379
1267
1095
1596
1239
1348
1411
1365
1495
1289
1296
1398
1411
1956
1355
1411
1
significantly higher in the cities of Punjab and
Balochistan. Also, the incidence of outages is
Other cities are the smaller cities. They have been
combined in view of the smallness of samples in each
location.
higher in continuous- process units, although outage costs are likely to be higher for such units.
The distribution of outages by duration is given in Table 4.2. Generally, the outage duration is 1
to 3 hours a day. The highest number of outages occurs for 1 to 2 hours a day at 51 percent,
followed by outages of 2-3 hours a day at 32 percent. 11 percent of outages have duration of
half to one hour while 5 percent of outages last for over four hours. There is a divergence in the
provincial patterns. In Punjab, outages mostly last for 1-2 and 2-3 hours. In Karachi almost half
of the outages last for over two hours. This pattern of outages is likely to have significant
implications for the costs of outages as will be discussed in Chapter 6.
2
from January to September 2012. The annual incidence was estimated by multiplying by 1.33.
16
Box 4.1
Incidence of Outages
The following variables are designated for each unit:
DLOCi
=1 for the i-th city
= 0 otherwise
DPROD
= 1 if continuous-process unit
= 0 otherwise.
TOUT
= Total incidence of outages (in hrs)
The results a of the OLS regression with TOUT as the dependent variable are as follows:
TOUT =1983
+290DLAH
+676.6MUL
+ 374.0DPES +431.3RWP
+771.3DSIA
(16.3)* (2.09)* (3.6)*
(2.40)*
(2.22)*
(3.79)*
1490.1DQUE
+1070.1DGUJ +316.1DPROD
(5.85)*
(4.6)*
(2.16)*
R2 =0.17, SEE =811, Degree of Freedom = 407, F = 6.89*
*Significant at 5% level
The results indicate that the incidence of outages is significantly higher in the cities of Punjab, Peshawer
and Quetta. Also, the incidence of outages is higher in continuous-process units, although outage costs
are likely to be higher for such units.
a
only the significant variables are highlighted.
Table 4.2
Percentage Distribution Average Length of Outages, 2012
(%)
By City
Location
Punjab
Lahore
Faisalabad
Other Cities
Sindh
Karachi
Other Cities
KPK
Balochistan
Total
By Industrial Group
Food beverages and Tobacco
Textile wearing Apparel and leather
Wood and Wood products
Fabricated Metal Products
Others industries
Total
By Process
Continuous Process
Batch-Process
Total
Less than ½ hr
1/2 – 1hr
1-2 hrs
2-3 hrs
More than 4 hrs
0
0
0
1
3
4
1
0
0
1
15
33
3
3
8
8
10
2
0
11
53
60
44
31
54
39
92
51
0
51
29
5
51
30
35
49
15
28
92
32
2
2
2
2
0
0
0
20
8
5
0
1
2
3
0
1
12
9
4
18
16
11
45
55
63
52
41
51
37
31
26
23
38
32
6
4
4
5
5
5
3
1
1
19
10
11
54
51
51
24
33
32
1
5
5
17
The overall duration of outages, which includes
both the time lost due to an outage and the restart
time (time lost in restarting work following an
outage), is presented in Table 4.3. The total hours,
on an average, lost per annum due to loadshedding
are estimated at 2623 for the sample units. The
highest number of hours lost is in Quetta. Overall in
Punjab, the average number of hours lost per
annum is 2666, in Sindh 2200, K-PK 2777 and
Balochistan, 3733. These durations are for 24hrs a
day for 365 days a year. Clearly, the actual total
time lost depends on working hours during the
year.
Other industries, textiles and wearing apparel and
leather industry take the brunt, losing respectively
2789 and 2727 hours. Again, the incidence is
higher in continuous process (2995 hours) than in
batch-making (2583) industries.
Table 4.3
Duration of Outages
(Outage + Restart Time) [Hours]
By City
Cities
Average
Punjab
Lahore
Faisalabad
Other Cities
Sindh
Karachi
Other Cities
KPK
Balochistan
Total
By Industrial Group
Industrial Group
Food beverages and Tobacco
Textile wearing Apparel and
leather
Wood and Wood products
Fabricated Metal Products
Others industries
Total
By Process
Nature of Production
Continuous Process
Batch-Process
Total
2666
2740
2372
2769
2200
2128
2325
2777
3733
2623
2488
2727
2354
2438
2789
2623
2996
2585
2623
Box 4.2 undertakes econometric analysis of the extent of output losses during outages. The
results confirm that production losses are lower in some cities of Punjab and Peshawer and
Quetta. Also, we have the important finding that outages of longer duration imply higher losses.
Further, the higher the percentage of time the distribution companies keep to the preannounced loadshedding schedule, units are better prepared at these times and suffer lower
losses.
4.2 EXTENT OF TOTAL TIME LOST
The proportion of production time lost is given in Table 4.4. Overall, small scale units in
Pakistan, on an average, are likely to lose 30% of their production time in 2012 due to
loadshedding. The highest, 43%, is lost in Quetta, and the least, 24 percent, in Karachi. Other
industries, and textiles, wearing and apparel industries lose over 30 percent of their production
time, while the loss for continuous process industries is over one-thirds.
18
Table 4.4
Proportion of Time Lost
during outages
(%)
By City
Cities
Average
Punjab
Lahore
Faisalabad
Other Cities
Sindh
Karachi
Other Cities
KPK
Balochistan
Total
30
31
27
32
25
24
27
32
43
30
By Industrial Group
Food beverages and Tobacco
28
Textile wearing Apparel and leather
31
Wood and Wood products
27
Fabricated Metal Products
28
Others industries
32
Total
30
By Process
Continuous Process
34
Batch-Process
29
Total
30
Box 4.2
Production Losses During Outages
The following variables are designated for each unit:
LOSSOUT
= proportion of output lost during outages (prior to recovery)
LOUTN
= proportion of outages of duration of more than 2 hrs
ANALD = percentage of time DISCO/ KESC kept to announced loadshedding
The results a of the OLS regression with LOSSOUT as the dependent variable are as follows:
LOSSOUT =
0.64 - 0.16DLAH
-0.36MUL
- 0.21DPES
+0.10RWP
-0.12DSIA
(24.94)* (-5.91)*
(-6.84)*
(-7.4)*
(2.75)*
(-3.30)*
-0.21DSUK
-0.21DFB
-0.18DQUE
+0.3x10-3LOUTN
-0.06DIND1
(-4.30)*
(-7.03)*
(-3.78)*
(2.05)*
(-2.53)*
-0.04DIND2
-0.06DIND4
-0.75x10-3ANALD
(-2.01)*
(-1.98)*
(-2.23)*
R2 =0.43, SEE =0.13, Degree of Freedom = 348, F = 14.4*
*Significant at 5% level
The results indicate that production losses are lower in some cities of Punjab, Peshawer and Quetta.
Also, we have the important finding that outages of longer duration imply higher losses. Further, the
higher the percentage of time the distribution companies keep to the announced loadshedding schedule,
units are better prepared at these times and suffer lower losses.
a only the significant variables are highlighted.
19
4.3 SEASONALITY OF OUTAGES
A significant seasonality in the incidence of loadshedding emerges from the data (see Table
4.5). The peak loadshedding months are July (accounting for almost 16% of the hours of
loadshedding) closely followed by June. August and May also emerge as high incidence
months, each accounting for over 14% of loadshedding hours. The pattern appears to be similar
for all four provinces and industrial groups.
Table 4.5
Seasonality in Outages
(% of Outage)
By City
Cities
March
April
May
June
July
August
September
Punjab
13
13
14
16
16
15
13
Lahore
13
14
15
15
15
14
12
Faisalabad
14
14
14
14
14
14
14
Other Cities
12
13
14
16
17
15
13
Sindh
13
14
14
15
16
14
13
Karachi
14
14
14
16
15
13
14
Other Cities
13
14
14
15
16
14
13
KPK
10
11
14
17
18
16
12
Balochistan
14
14
15
15
15
13
14
Total
13
13
15
16
16
15
13
March
April
May
June
July
August
September
Food beverages and Tobacco
13
14
15
16
16
14
13
Textile wearing Apparel and leather
13
14
15
16
16
15
13
Wood and Wood products
12
13
14
16
16
15
13
Fabricated Metal Products
13
13
14
16
16
15
13
Others industries
13
13
14
15
16
15
14
Total
13
13
15
16
16
15
13
March
April
May
June
July
August
September
Continuous Process
13
14
15
15
16
14
13
Batch-Process
13
13
14
16
16
15
13
Total
13
13
15
16
16
15
13
By Industrial Group
Industrial Group
By Process
Nature of Production
20
4.4 EXTENT OF OUTPUT LOST DURING OUTAGES
Loadshedding leads to a complete shutdown for 31 percent of sample units, with highest
proportion being in Sindh. However, for 61 percent of the firm it results in partial shutdown. (see
Table 4.6).
Table 4.6
Nature of Impact of Loadshedding
(%)
Complete
Shutdown
Partial
Shutdown
No Impact
By City
Punjab
28
65
8
Lahore
20
80
0
Faisalabad
10
82
8
other Cities
53
48
45
Sindh
51
41
9
Karachi
59
31
9
other Cities
56
65
18
KPK
19
74
7
Baluchistan
38
62
0
Total
31
61
8
By Industrial Group
Food beverages and Tobacco
32
53
15
Textile wearing Apparel and leather
30
65
5
Wood and Wood products
33
52
15
Fabricated Metal Products
30
68
3
Others industries
35
63
3
Total
31
61
8
Continuous Process
39
61
0
Batch-Process
30
61
8
Total
31
61
8
By Process
In the questionnaire the respondants were asked to rank ‘what is most disruptive about an
outage’. For 40% of the respondants loss of/inconvenience to customers was the most
disruptive consequence of loadshedding, for 24% it was equipment shut down and for, 20%, idle
labor (see Table 4.7). Losses of product, breakdown of production process and spoilage costs
were cited as other disruptions.
21
Table 4.7
Ranking of Disruptions Due to Outages
(%)
Equipment
Shut down
Labor will
be idle
Product
will be
lost
Loss of /
Inconvenien
ce to
Customers
Break
down of
producti
on
process
High
spoilage
cost of
raw
material
42
40
34
48
21
24
15
60
38
40
7
14
2
1
0
0
0
10
8
6
0
1
0
0
1
0
4
0
0
0
63
25
15
23
20
13
7
14
3
Total
By City
Punjab
Lahore
Faisalabad
Other Cities
Sindh
Karachi
Other Cities
KPK
Balochistan
Total
19
11
25
24
53
61
37
9
15
24
24
17
38
22
15
9
26
12
23
20
8
16
2
4
10
6
19
9
15
9
By Industrial Group
Food beverages
and Tobacco
23
15
11
42
8
1
19
Textile wearing
Apparel and leather
24
18
9
40
8
1
42
28
30
7
35
0
0
11
15
23
13
50
0
0
10
27
24
5
39
5
0
18
Total
24
20
9
40
6
0
Continuous Process
37
8
5
32
18
0
9
Batch-Process
23
22
9
41
5
1
91
Total
24
20
9
40
6
0
Wood and Wood
products
Fabricated Metal
Products
Others industries
By Process
22
CHAPTER 5
ADJUSTMENTS TO LOADSHEDDING
This chapter focuses on the types of adjustments that small firms make to outages in Pakistan.
The extent of output that is not recovered following the adjustments is also quantified.
5.1 NUMBER AND TYPES OF ADJUSTMENTS
Table 5.1 presents the estimates
of frequency of different types of
adjustments by small units. It
Table 5.1
Percentage of Sample Units by Number of Adjustments
by Process
appears that almost one fourths
of the firms in the sample are
Continuous
Process
BatchMaking
Total
unable to make any form of
No Adjustment
5
24
22
adjustment.
46%
make
one
One Adjustment
50
45
46
adjustment,
21%
make
two
Two Adjustments
29
20
21
Three or More Adjustments
16
11
11
Total
100
100
100
types of adjustment while 11%
are implementing three or more
types of adjustments. Continuous process units are more likely to make adjustments in the face
of potentially higher losses.
The frequency of different types of adjustments is given in Table 5.2. The highest frequency of
adjustment is in the form of self-generation. For the national sample, this is 68%. It is the
highest in Punjab, follow by K-PK, and the lowest in Balochistan. On average generators are
able to substitute for 48% of the public source. Econometric analysis has been undertaken in
Box 5.1 of the extent of recovery of output lost by use of generators. The results indicate, first,
that higher incidence of outages leads to greater resort to generators, second, units with lower
electricity-intensity are more likely to use generators and, third, continuous- process units tend
to have a higher presence of generators.
Beyond the use of generators, the next most frequent form of adjustment is the working of
overtime by 29% of the units, as shown in Table 5.2. Approximately 11% of the firms have opted
for changing shift timings. The labor-related adjustments are more commonly observed in
Punjab.
23
Table 5.2.
Percentage of Sample units Adjusting through Various Mechanisms
(%)
Buying or
Operating
Existing
Generator
Working
Overtime
Increasing
Intensity
of
Machinery
Operation
Changing
Shift
Timings
Changing
Working
Days
Working
Additional
Shifts
Punjab
18.8
23.8
8.5
41.9
6.5
0.4
Lahore
10.6
17.3
16.3
40.4
14.4
1.0
Faisalabad
24.6
37.7
1.6
34.4
1.6
0.0
Other Cities
24.2
22.1
4.2
48.4
1.1
0.0
Sindh
53.1
14.8
9.9
21.0
0.0
1.2
Karachi
61.1
9.3
5.6
24.1
0.0
0.0
Other Cities
37.0
25.9
18.5
14.8
0.0
3.7
KPK
Baluchistan
Total
8.6
15.4
24.0
60.3
38.5
40.3
10.3
7.7
5.8
0.0
0.0
0.5
Food beverages and
Tobacco
Textile wearing Apparel
and leather
Wood
and
Wood
products
Fabricated
Metal
Products
Others industries
21.2
16.9
32.1
47.1
32.1
31.0
45.1
45.1
31.9
27.9
36.3
23.2
11.0
15.2
13.3
12.8
8.9
30.9
8.1
8.8
3.8
6.2
5.7
0.1
14.6
14.0
19.0
6.2
16.9
15.0
Total
68.2
28.9
7.8
11.4
3.2
11.4
Continuous Process
11.4
12.6
15.6
21.3
14.9
15.4
Batch-Process
88.6
87.4
84.4
78.7
85.1
84.6
Total
68.2
28.9
7.8
11.4
11.4
3.2
By City
12.1
8.6
23.1
15.4
20.4
9.0
By Industrial Group
By Process
24
Box 5.1
Extent of Recovery of Output by Use of Generators
The following variables are designated for each unit:
BEXGP
= Extent of adjustment by use of Generator
DOUT
= Total incidence of outages and restart times (in hours) annually
TPE
= Ratio of Value Added to Electricity purchased (Rs per kwh)
ANALD = Per cent of time DISCO/KESC kept to announced loadshedding
DPROD
= 1 if continuous-process unit
= 0 otherwise.
The results a of the OLS regression with BEXGP as the dependent variable are as follows:
BEXGP =
10.57 + 0.15x10-2 DOUT +
73.64 TPE +
0.08 ANALD
+8.49 DPROD
(3.76)*
(1.89)**
(2.21)*
(2.17)*
(2.72)*
R2 =0.05, SEE = 17.97, Degree of Freedom = 396, F = 3.75*
*Significant at 5% level
** Significant at 10% level
The results indicate, first, higher incidence of outages leads to greater resort to generators, second, units
with lower electricity-intensity are more likely to use generators and, third, continuous-process units tend
to have a higher presence of generators.
a only
the significant variables are highlighted.
The question is whether self-generation is the sole adjustment mechanism or if it accompanied
by other adjustments. This analysis is presented in Table 5.3. It appears that almost 70% of the
units without generators make no adjustments at all while 26% of the units with generators
make another adjustment.
Table 5.3
Number of Adjustments by Firms with and without Generators
(%)
Numbers of Adjustments
None
Units without Generators
70
Units with Generators*
-
One
17
59
Two
10
26
Three or more
3
15
Total
100
100
*including the one adjustments of use of generator.
5.2 EXTENT OF LOSS OF OUTPUT IN OUTAGES
Table 5.4 highlights the extent of the permanent loss of output which is not recovered through
the various adjustments. Overall, it is 9% nationally, 8% in Punjab, 9% in Sindh, and K-PK and
12% in Balochistan. These losses are a key indicator of the magnitude of net idle factor costs.
25
Table 5.4
Proportion of output Loss Not Recovered
(%)
By City
Location
Average
Punjab
Lahore
Faisalabad
Other cities
8
9
6
8
9
Sindh
Karachi
Other cities
9
9
KPK
Balochistan
9
12
Total
9
By Industrial Group
Industrial Group
Food beverages and Tobacco
Average
7
Textile wearing Apparel and leather
8
Wood and Wood products
8
Fabricated Metal Products
9
Others industries
Total
11
9
By Process
Nature of Production
Continuous Process
Batch-Process
Total
Average
8
9
9
26
CHAPTER 6
COSTS OF OUTAGES
The objective of this chapter is to present the estimated magnitudes of different types of costs
associated with outages. As identified in chapter 2, these include direct costs which consist of
net idle factor costs and spoilage costs and indirect or adjustments costs which include
generator costs and costs of other types of adjustments like overtime, additional shifts, etc.
Section 1 of the chapter presents the total outage costs by location (province), industrial
classification and process used by sample units. Section 2 derives the cost per kwh of load
shedding. Finally, by blowing up the sample, the magnitude of outage costs to the small-scale
industrial sector of Pakistan is derived.
6.1 TOTAL OUTAGE COSTS
Given the high frequency of load shedding in Pakistan today it appears that the total outage cost
even to small-scale industrial units of the country is high in absolute terms. Table 6.1 shows that
the outage cost approaches to Rs. 300,000 per unit on average in the sample units or close to
$3092 per annum3.
The variation in outage costs annually per unit is substantial depending upon the nature of the
process, that is, batch-making or continuous process. The latter type of units are especially
vulnerable to disruptions from outages. As such, the average cost for a continuous process unit
is Rs. 426,000 or $4392 per annum. This is 52% higher than the cost incurred due to load
shedding by batch-making units.
Among industries, the highest outage cost for small-scale units is in the food, beverages and
tobacco industry at Rs 369,000 or $3804 per annum, almost 26% above the average. Outage
costs are also somewhat higher in the case of textiles and apparel industry and lower in the
wood and wood products and fabricated metal products industries.
Turning to location, the outage cost per unit is substantially higher in Sindh province, especially
in the city of Karachi, at over Rs 500,000 or $ 5155 annually. Units in Punjab have lower outage
costs on average of Rs 235,000 or $ 2422 annually. The question is why in presence of
apparently higher incidence of outages in Punjab (See Table….) the total outage cost is lower.
This is due the higher magnitude of net idle factor costs due to a lower proportion of recovery of
3
At the exchange rate of Rs 97= 1 US $
27
output in the presence of a troubled law and order situation4. Also, the generator costs are
higher because of greater electricity-intensity of operations by units in Sindh5. Further, there is
evidence that outages that occur in Karachi are of a longer duration each time and
unanticipated outages occur more frequently.
Table 6.1
Total Outage Costs Per Unit
(000 Rs)
LOCATION
Punjab
Sindh
K-PK + Baluchistan
INDUSTRY
Food, beverages and tobacco
Textiles and wearing Apparel
and Leather
Wood and Wood Products
Fabricated Metal Products
Other Industries
PROCESS
Continuous Process
Batch-Making
TOTAL
(%)
Net Idle
Factor Cots
Spoilage
Costs
Generator
Costs
Other
Costs
Total
Outage
Costs
145
305
171
40
10
41
45
178
44
5
11
9
235
504
265
252
41
69
7
369
139
39
103
8
289
190
160
206
28
19
29
28
38
41
6
10
5
252
227
281
165
183
181
(62)
76
30
34
(12)
179
60
71
(24)
6
7
7
(2)
426
280
293
(100)
6.2 TYPES OF OUTAGE COSTS
Table 6.1 also clearly establishes the dominance of net idle factor costs at 62% in total outage
costs. This reflects the constraints which small units face in making adjustments, especially in
the presence of difficult law and order situation in some parts of the country, like Karachi, which
reduces the ability to make labor- related adjustments. Consequently, the loss of output and the
implied net idle factor costs are relatively high.
4
As shown in Table 54, while the proportion of lost output, after allowing for adjustments, is 5% in Punjab, it is
almost double, at over 9% in Sindh.
28
The second major cost item is generator costs, with a share in total outage costs of 24%. This is
a reflection of the fact that almost 68 % of the sample units have installed generators and this
has become the primary form of adjustment to outages.
Spoilage costs constitute 12% of the outage costs. As expected, these costs are relatively high
in continuous –process units. Other adjustment costs are minor and account for only 2% of
outage costs.
Figure 6.1
Distribution of Outage Costs
2%
12%
24%
62%
others
idle factor costs
generator costs
spoilage costs
6.3 BURDEN OF OUTAGE COSTS
The burden of outage costs as a percentage of the value added is given in Table 6.2. It appears
that the burden is quite high at almost 13% of the value added. Much of this is a decline in
profits. It is the highest at over 17% in the case of continuous process units, in the textiles and
wearing apparel industry and in Sindh (especially Karachi).
Table 6.2
Outage Costs as Percentage of Value Added
(000 Rs)
LOCATION
Punjab
Sindh
K-PK + Baluchistan
INDUSTRY
Food, beverages and tobacco
Textiles and wearing Apparel and Leather
Wood and Wood Products
Fabricated Metal Products
Other Industries
Sample
Size (No)
Total Outage
Cost
Value
Added
Outage Cost
as % of
Value
Added
260
81
71
235
504
265
1978
3392
2245
11.9
14.9
11.8
79
172
46
40
75
369
289
252
227
281
3153
1971
2529
2027
2198
11.7
14.7
10.0
11.2
12.8
29
PROCESS
Continuous Process
Batch-Making
TOTAL
38
374
426
280
293
2460
2291
2307
17.3
12.2
12.7
Figure 6.2
Outage Costs as Percentage of Value Added
By Province
20
15
10
5
14.9
11.9
11.8
0
Punjab
Sindh
Punjab
k-Pk + Balochistan
Sindh
k-Pk + Balochistan
By Process
20
15
10
17.3
12.2
5
0
Continuous Process
Batch Making
Continuous Process
Batch Making
By Industry
20
15
10
5
11.7
14.7
10
11.2
12.8
Wood & Wood
Products
Fabricated Metal
Products
Others
0
Food, Beverages & Textiles, Apparel &
Tobacco
Leather
Food, Beverages & Tobacco
Textiles, Apparel & Leather
Fabricated Metal Products
Others
Wood & Wood Products
30
6.4 OUTAGE COSTS PER KWH
Table 6.3 indicates that the outage cost per Kwh in the case of small-scale industrial units is
about Rs 50 per Kwh. At about 52 cents per kwh, this is similar to earlier estimates for Pakistan
as highlighted in Part I of the report. In line with the above results, the outage cost per kwh is
the highest in Sindh. Although the overall outage cost is the highest for continuous process
units, it is lower per kwh because of a higher quantity of electricity not provided.
Table 6.3
Outage Costs Per kwh
Location
Punjab
Sindh
K-PK + Baluchistan
Industry
Food, beverages and tobacco
Textiles and wearing Apparel
and Leather
Wood and Wood Products
Fabricated Metal Products
Other Industries
Process
Continuous Process
Batch-Making
Total
Total Outage Cost
(000 Rs)
Electricity not
Provided during
Outages
(000 kwh)
Outage Cost per kwh
(Rs)
235
504
265
5.5
7.3
5.0
42.7
69.0
53.0
369
289
6.0
6.2
61.5
46.6
252
227
281
3.7
6.6
5.3
68.1
34.4
53.0
426
280
293
9.8
5.4
5.8
43.5
51.9
50.5
Overall, the survey of small-scale industrial units reveals that the absolute magnitude of outage
costs is high. There have been significant output losses, as reflected by the large net idle factor
costs. The use of generators has enabled significant recovery of output but at a cost more than
twice the tariff paid for supply from a DISCO. Losses in profitability have been proportionally
greater approaching 36% or more of the profit that could have been made in the absence of
outages.
6.5 NATIONAL ESTIMATE OF OUTAGE COSTS
For the 412 sample of small-scale industrial units the total outage cost is estimated at Rs 121
million on a value added of Rs 950 million. The only contempory information available as of
2011-12 on small-scale industry is the value added in the latest Pakistan Economic Survey.
31
This is estimated at Rs 653 billion. We use the value added as the blow up factor from the
sample to the national estimate as follows:National Estimate of Outage Costs (= (.121/0.950) *
653 Billion) equals Rs = 83.2 Billion Rs
Therefore, the bottom line of the research is that in 2011-12, the total cost of outages to
the small-scale industrial sector of Pakistan is projected at Rs 83 billion. This is equivalent
to $885 million, at the exchange rate of Rs 97 per US dollar.
32
CHAPTER 7
LOAD MANAGEMENT STRATEGY: CONSUMER’S PREFERENCES
The questionnaire contains a module to solicit consumer preferences regarding timing of
loadshedding which can reduce the costs and disruptions due to the outages. These can
provide guidance to the load management strategy by DISCOs, the formulation of which should
be a priority since loadshedding is likely to persist over the next few years.
7.1 LEVEL OF SATISFACTION WITH
CURRENT LEVEL OF SERVICE
Only 28 percent of sample firms
indicated that DISCOs kept to the
announced loadshedding schedule (see
Table 7.1). The percentage is generally
Table 7.1
Percentage of Time DISCOs Kept to the Announced
Loadshedding Schedule
City
Average
Punjab
35.8
Lahore
35.8
Faisalabad
44.3
higher for cities in Punjab. Performance
Other Cities
30
of DISCOs in Sindh and Balochistan in
Sindh
7.8
this respect is particularly weak, with
Karachi
6.9
other Cities
9.5
KPK
26.7
effectively no prior scheduling.This has
had a significant impact on the costs of
Balochistan
2.4
loadshedding as highlighted in Chapter
Total
28
6. The time required for small-scale
By Industrial Group
units to adjust to changes in the
Industrial Group
loadshedding schedule is 1.9 hours on
Food beverages and Tobacco
25.8
Textile wearing Apparel and leather
30.3
an average. The time is much higher for
Wood and Wood products
26.6
continuous process industry (2.6 hours)
Fabricated Metal Products
25.9
than for batch making units (1.8 hours).
Others Industries
26.8
Total
28.0
(See Table 7.2).
The survey teams enquired from the
respondents if they were satisfied with
Nature of Production
Continuous Process
21.4
Batch-Process
28.6
Total
28.0
the current level of service by the
DISCOs/KESC. More than half of the respondents ranked their satisfaction level as very low
while one-third ranked it as low (see Table 7.3).
33
Table 7.2
Average Time Required for Adjustment to Changes in Loadshedding Schedule
(hrs)
By City
City
Average
Punjab
2.2
Lahore
3.1
Faisalabad
1.8
Other Cities
1.5
Sindh
0.9
Karachi
0.6
Other Cities
1.6
KPK
1.8
Balochistan
1.7
Total
1.9
By Industrial Group
Industrial Group
Food beverages and Tobacco
2.1
Textile wearing Apparel and leather
1.8
Wood and Wood products
1.3
Fabricated Metal Products
1.8
Others industries
2.2
Total
1.9
By Process
Nature of Production
Continuous Process
2.6
Batch-Process
1.8
Total
1.9
34
Table 7.3
Level of Satisfaction with Current quality of Service by DISCOs/KESC
(%)
z
Very high
High
Medium
Low
Very Low
By City
Punjab
1
0
6
35
58
Lahore
0
0
7
53
40
Faisalabad
0
0
0
16
84
Other Cities
2
0
8
27
62
Sindh
5
7
14
31
43
Karachi
2
11
15
35
37
other Cities
11
0
11
22
56
KPK
0
3
41
28
28
Balochistan
0
0
0
8
92
Total
1
2
12
32
52
By Industrial Group
Food
beverages
and
Tobacco
Textile wearing Apparel and
leather
Wood and Wood products
3
4
11
34
48
1
1
10
39
50
7
4
15
9
65
Fabricated Metal Products
0
3
28
15
55
Others industries
0
1
8
39
52
Total
2
2
12
32
52
By Process
Continuous Process
0
0
5
26
68
Batch-Process
2
2
13
33
51
Total
2
2
12
32
52
The sample firms were also asked how much higher tariff they are willing to pay for better
quality and reliability of power service–essentially with no loadshedding. This provides the first
estimate of the respondent’s perception of the cost of loadshedding. On an average,
35
respondents are willing to pay an extra 30% for uninterrupted power supply as revealed by
Table 7.4. The premium for better service is highest around 48% in Karachi.
Fabricated metal product and textile, wearing apparel and leather industry it appears, have
indicated their willingness to pay
the highest additional premium for
better quality service.
Table 7.4
Additional Tariff For Better Quality of Service (with No
Loadshedding)
(%)
By City
Translated
into
the
subjective
valuation of the outage cost per
Location
Average
Punjab
26.2
hour, the average for the sample
Lahore
27.8
units is 21 Rs per Kwh.
Faisalabad
17.8
highlighted in Part 1 of the report,
Other Cities
29.8
there is a tendency for the WTP to
Sindh
42.5
be understated. The willingness to
Karachi
46.9
pay is highest, at 52 Rs per kwh in
Other Cities
33.7
Karachi.
KPK
35.9
Balochistan
4.0
Total
30.0
As
7.2 PREFERRED CHANGES IN
TIMINGS OF LOADSHEDDING
One-thirds of the sample units are
willing to change work timings if
off-
peak
power
tariffs
are
reduced. The willingness appears
to
be
higher
in
the
smaller
provinces of Balochistan, K-PK
and Sindh. (See Table 7.5). Also,
food,
beverages and tobacco,
By Industrial Group
Food beverages and Tobacco
25.1
Textile wearing Apparel and leather
34.0
Wood and Wood products
30.6
Fabricated Metal Products
34.4
Others industries
23.5
Total
30.0
By Process
Continuous Process
29.7
wood and wood products are
Batch-Process
30.1
industries which are more willing
Total
30.0
to change work timings if off peak
tariffs are reduced.
36
Table 7.5
Perceived Outage Costs per Kwh as implied by Willingness To Pay
Rs
Location
Average
Punjab
16
Lahore
18
Faisalabad
10
Other Cities
16
Sindh
42
Karachi
52
Other Cities
22
KPK
21
Balochistan
11
Total
21
By Industrial Group
Food beverages and Tobacco
14
Textile wearing Apparel and leather
23
Wood and Wood products
26
Fabricated Metal Products
29
Others industries
17
Total
21
By Process
Continuous Process
17
Batch-Process
22
Total
21
Over 93 percent of the sample firms reported summertime as the worst season for loadshedding
(see Table 7.7). Winter time is the second worst season for loadshedding. Interestingly, textiles,
wearing apparel and leather and wood and wood products are dominant industries categorizing
wintertime as bad season primarily because they are unable to meet their orders, especially
international orders.This is principally the case with export-oriented industries.
37
Table 7.6
Percentage Willing To Change Work Timing If off Peak Power Tariffs Are Reduced
(%)
Yes
No
Total
Punjab
27
73
100
Lahore
25
75
100
Faisalabad
34
66
100
Other Cities
23
77
100
Sindh
30
70
100
Karachi
13
87
100
Other Cities
63
37
100
KPK
62
38
100
Balochistan
100
0
100
Total
34
66
100
Food beverages and Tobacco
47
53
100
Textile wearing Apparel and leather
27
73
100
Wood and Wood products
43
57
100
Fabricated Metal Products
30
70
100
Others industries
36
64
100
Total
34
66
100
Continuous Process
45
55
100
Batch-Process
33
67
100
Total
34
66
100
By City
By Industrial Group
By Process
38
Table 7.7
Worst Time of The Year for Loadshedding
Summer
Punjab
Lahore
Faisalabad
Other Cities
Sindh
Karachi
Other Cities
KPK
Balochistan
Total
Spring
By City
92
87
98
94
93
91
96
98
100
93
2
3
2
1
2
2
4
0
0
2
Rank
Winter
Fall
Total
6
11
0
5
4
6
0
2
0
5
0
0
0
0
1
2
0
0
0
0
100
100
100
100
100
100
100
100
100
100
By Industrial Group
Food beverages and Tobacco
96
3
1
0
100
Textile wearing Apparel and leather
Wood and Wood products
Fabricated Metal Products
Others industries
Total
91
89
93
97
93
1
4
0
1
2
8
7
8
0
5
0
0
0
1
0
100
100
100
100
100
15.8
3.7
4.9
0.0
.3
.2
100
100
100
Continuous Process
Batch-Process
Total
By Process
78.9
5.3
94.7
1.3
93.2
1.7
The questionnaire also contained a question
regarding the worst day of the week for
outages. While 30 percent of the respondents
Table 7.8
The Worst Day of The Week for Outages
Frequency
Percentage
Sunday
52
12.6
said all days are bad, over one-thirds said
Monday
141
34.2
Monday is the worst day. Sunday was the
Tuesday
13
3.2
worst day for about 13 percent of the
Wednesday
11
2.7
Thursday
19
4.6
respondents. The principal reasons cited for
Friday
27
6.6
this is related to the fact that Monday is the
Saturday
23
5.6
start of a work week and an outage disturbs
All days
126
30.6
the working environment. Sunday, of course,
Total
412
100.0
is family/rest day and loadshedding disturbs it. Around 21 percent of the respondents indicate
that will be helpful if the power companies provided more information relating to the scheduling
of the outage; methods to lower the electricity- intensity of operations; methods to minimize loss,
39
and; how to work more efficiently under the
circumstances (see Table 7.9). Clearly,
these should be focused upon in the load
management strategy of the distribution
companies.
Table 7.9
Information that can be provided by
Distribution companies to Units
Percentage
Work more efficiently
33.0
Information about outage
42.0
Time table for load shedding
45.5
Awareness about outage required
14.8
Consumption minimize method
9.1
Minimize line losses
4.5
Others
2.3
40
CHAPTER 8
SUGGESTIONS BY THE SAMPLE UNITS
The questionnaire at the end solicited the respondent’s views/ suggestions to help handle the
loadshedding problem in the country. Specifically, the open ended question asked for
“suggestions to reduce the costs of loadshedding to the small scale industry”. A number of
interesting suggestions emanate from the survey responses. These can be categorized as
relating to the following:

Enhancing the supply of electricity

Alternative sources of energy/ fuel use

Improving governance or management

Changes in pricing policy
Enhancing the Supply of Electricity: The highest numbers of respondents, 45% are of the
view that new dams, including Kala Bagh Dam, should be constructed to permanently enhance
the production of electricity in the country at lower cost (see Table 8.1). This suggestion
dominates the response not only from the sample units located in Punjab, but is also significant
in the case of Peshawar and Karachi. Over 30% of respondents are also of the view that
electricity should be imported. Building new power plants, installing gas pipeline from Iran to
avoid gas shortages are the other significant suggestions to enhance the supply of electricity
and curb power shortages in Pakistan. Responses are more or less, similar across industry
groups. (See Table 8.2)
Alternative sources of Energy Fuel for Energy: A number of suggestions have been given
regarding resort to alternative energy and fuel sources by the respondents. One quarter of the
respondents suggested the use of different methods of electricity generation, while 19 percent
specifically suggested the use of coal for electricity generation. Close to 9 percent of the sample
units suggested introduction of solar energy systems (particularly by the food, and beverage
industry), while there was also some mention of bio- gas and wind energy.
Improving Governance/Management. The most dominant recommendation in this category is
to curb corruption, with over a quarter of respondents emphasizing it. Minimization of electricity
theft has been suggested of 24 percent of the respondents. Only 3 percent of the sample units
proposed Privatization of the DISCOs.
41
Table 8.1
Suggestions by Sample Units by City
(% of Respondents)
Reasons
Lahore
Enhancing Supply of Electricity
Gas Pipe line from Iran to
11
avoid gas shortage
Faisalabad
Gujranwala
5
44
Sialkot
Rawalpindi /
Islamabad
Karachi
Hyderabad
Sukkur
Peshawar
Quetta
Total
7
17
7
31
0
0
9
31
13
Multan
Import Electricity
16
75
25
45
52
22
31
13
45
19
8
33
Construct
new
Dams
(including Kala Bagh Dam)
49
48
63
34
83
67
24
0
9
53
31
45
Use rental power system
3
0
0
0
4
0
4
0
0
5
15
3
Build new power plants
24
7
13
45
22
33
9
6
45
31
15
22
39
25
3
26
30
17
19
18
10
46
19
5
13
14
57
48
30
13
18
24
23
25
0
0
0
0
0
0
0
0
0
2
0
0
18
2
6
24
0
4
2
0
9
3
15
9
Introduce Wind Energy
0
0
0
0
0
0
0
0
0
2
0
0
Use of Petrol
0
0
0
0
0
0
0
0
0
0
8
0
Privatize Electric department
1
0
0
0
0
0
9
0
0
3
31
3
Need Honest Employees
1
0
0
0
0
0
0
6
0
3
8
1
Minimize electric theft
19
36
19
3
30
4
44
25
18
21
23
24
Stop Corruption
21
31
31
7
17
15
20
81
9
38
8
25
Minimize line losses
1
0
0
0
0
0
0
56
0
0
0
2
1
0
0
0
0
0
0
19
0
0
0
1
4
0
0
0
0
0
0
0
0
0
0
1
Government give subsidy on
electricity
8
38
69
28
22
11
28
6
9
7
85
22
Reduce price at source
10
7
0
0
35
26
26
13
0
3
23
12
Total
25
15
4
7
6
7
13
4
3
14
3
100
Alternative Energy Fuel Sources
Use
Coal
for
electric
8
generation
Use different method of
30
electric generation
Bio Gas system
Introduce
System
solar
energy
Governance/Management
Give awareness to people
use of electricity and avoid un
nec
Knowledge about minimize
usage
Pricing Policy
42
Minimizing line losses and the need for honest employees were also emphasized by the sample
units.
Other suggestions relate to educating the electricity consumers, regarding efficient energy use,
and proper use of electricity so that unnecessary consumption is avoided.
Pricing Policy Around one-fifth of the sample units requested for subsidy for electricity from
the government, while 12% of the respondents suggested that the price (at source) should be
reduced through economizing on costs.
To conclude, the top five suggestions emanating from the respondents of the survey are as
following:
First: Construct Dams
Second: Import Electricity
Third: Stop Corruption
Fourth: Use Different Methods of Electricity Generation
Fifth: Minimize Electricity theft.
43
CHAPTER 9
CONCLUSIONS AND POLICY IMPLICATIONS
We have highlighted in previous Chapters the principal findings on the incidence and costs of
power loadshedding to small-scale industrial units of Pakistan. In this concluding Chapter we
derive the key policy implications, starting with estimates of the multi-dimensional impact of
outages on small firms in the manufacturing sector.
9.1 IMPACT OF OUTAGES
The estimated impact of outages on small-scale units from the survey are summarised below:
(i) Outages, on the average, occur for 30 per cent of the time available during normal working
hours. During these outages about 60 per cent of the output is lost, prior to adjustments.
Following adjustments, the permanent loss of output due to outages is 9 per cent. This
implies an over 36 per cent reduction in profitability. The outage cost per kwh works out as
Rs 51 per kwh (53 cents per kwh). This is similar to the magnitude derived by earlier
studies in Pakistan.
(ii) Total employment in the small-scale industrial sector, according to the Labor Force Survey
for 2010-11 by the PBS, was about 3.7million. With a lower output of 9 per cent and an
employment elasticity of 0.8, the employment level in small-scale manufacturing in the
absence of outages would have been higher by about 266,000.
(iii) The impact on exports is fortunately not very large because only a minor proportion of small
industrial units are engaged directly in exporting.
(iv) According to the Pakistan Economic Survey, the annual growth rate of value added in
small-scale manufacturing has been assumed to be as high as 7.5 per cent during the last
five years, when the overall GDP growth rate has on average been only 3 per cent. The
survey reveals that the growth rate of this sector is likely to be between 3.5 per cent and
4.5 per cent. With a share of 5% of the sector in the GDP, it appears that the GDP of
Pakistan in 2011-12 has probably been overstated by about Rs 128 billion. Accordingly, the
revised estimate of the size of the small-scale manufacturing is Rs 525 billion as compared
to the estimate in PES of Rs 653 billion.
(v) Within small-scale industry, the cost of outages appears to be the highest in Sindh
province; in textiles, apparel and leather industry and in continuous process units as shown
in Table 9.1. Gives the relatively low level of output, the outage cost as a percentage of the
value of production emerges the highest in Balochistan.
44
9.2 AFFORDABILITY OF POWER TARIFFS
The total costs of electricity consumption, that is, the costs of public supply and of outage
costs, as a percentage of the value of production are given in Table 9.1. On average, these
costs aggregate to 12 per cent, with a share of outage costs in these costs of 44 per cent.
According to the Census of Manufacturing (CMI) of 2005-06, the share of electricity cost in
value of production, for industry as a whole, was about 3 per cent only, during the period
when there were no outages. As such, the costs of energy have gone up sharply both due to
escalation in tariffs and in the presence of outages.
It is clear that small-scale units now have very limited affordability of further enhancement in
tariffs. Already, profits are down by over 36 per cent in relation to the potential profits in the
absence of outages, a further reduction in profits could lead to the closure of a large number
of small units and displacement of many more workers.
Table 9.1
Total Costs of Electricity Consumption as a percentage of the Value of Production
(000 Rs)
Value of
Production
Electric Cost
A.
B.
C.
LOCATION
Punjab
Lahore
Faisalabad
Other Cities
Sindh
Karachi
Other Cities
K-PK
Balochistan
INDUSTRY
Food, Beverages &
Tobacco
Textiles & Apparel
Wood & Wood Product
Fabricated Metal Products
Others
PROCESS
Continuous Process
Batch-Making
TOTAL
Electricity Cost as Per
Cent of Value of
Production
of public
supply
Total Cost
of Outages
Total
Cost
(at current
prices)
312
399
259
252
602
590
626
307
217
227
245
195
227
519
418
721
321
88
539
644
454
479
1121
1008
1347
628
305
4359
3649
5354
4497
8927
6749
13285
6333
1178
12.4
17.6
8.5
10.7
12.6
14.9
10.1
9.9
25.9
461
376
837
7224
11.6
381
269
306
322
294
246
224
267
675
515
530
589
4922
6592
4621
4448
13.7
7.8
11.5
13.2
609
341
366
414
281
293
1023
622
659
6256
5351
5435
16.3
11.6
12.1
45
9.3 COMPETITIVE DISADVANTAGE OF SMALL UNITS
The high incidence of outages has placed small-scale units at an even greater competitive
disadvantage with respect to large-scale units in the same industry. This is highlighted in Table
9.2 which compares the impact of laodshedding by size of unit. While the relevant magnitudes
for small-scale units are taken from the survey carried out as part of this study, the
corresponding numbers for large-scale units are extracted from IPP (2009).
Table 9.2
Comparison of the Impact of Loadshedding by Size of Industrial Unit
Cost of Loadshedding
Per cent of time lost due to loadshedding a (%)
Per cent of output lost that is recovered (%)
Per cent of output lost (%)
Cost of loadshedding as per cent of Value Added (%)
Fall in profitability (%)
Incidence of Loadshedding and Adjustments thereof
Annual hours of Loadshedding (hrs)
Per cent of units with Generators (%)
Per cent of units making some adjustment (%)
a including restart times
b n.e. = not estimated
c excluding multiplier effects
*from IPP Annual Review of 2009
Large-Scale
Industrial Sector
(2008)*
Small-Scale
Industrial
Sector
(2012)
20
60
6
9
n.e. b
30
60
9
13
36
2023
75
84
2623
68
78
It appears that not only is the incidence of outages higher in the case of small units but also the
extent of recovery of output through adjustments is less. Consequently, the burden of outage
costs as a percentage of value added is higher.
This is probably one of the reasons why a large proportion of public limited companies continue
to enjoy high level of corporate profits, at a time when the economy is growing slowly. This has
resulted in a boom in the stock market.
The equity implications of displacement of production by small-scale units are serious. Not only
does this imply less inclusive growth but also the emergence of quasi-monopolistic elements in
the economy. Perhaps the time has come to consider ‘life-line’ charges for small-scale industrial
units of the type offered to domestic consumers.
46
9.4 INVESTMENT IN GENERATORS
The
cost-benefit
ratio
of
investment
in
Table 9.3
Benefit-Cost Ratio* of Self-Generation
Benefit-Cost
Ratio
generators is high. For small-scale units, this
ratio is high at 3.7 as given in Table 9.3. But
despite this high ratio, one-thirds of the units
have not invested in self-generation and the
extent of substitution of the conventional
power source by those who have generators is
only about one half. Therefore, the scope for
self-generation is about three times the
INDUSTRY
Food, beverages and tobacco
Textiles and Apparel
Wood and Wood Products
Fabricated Metal Products
Other Industries
PROCESS
Continuous-Process
Batch-Making
Total
*
2.2
4.6
4.8
3.0
2.3
4.3
3.6
3.7
present level.
This probably a reflection of inability of small firms to self-finance the acquisition of generators
(with appropriate capacity) and/or because of imperfections in the capital market, whereby small
firms have limited access to bank credit and face high interest rates in the informal sector. As
such, there is a strong case for increasing the access to institutional credit through the offering
of a special package for purchase of small generators. International agencies could provide
special support in this area.
It needs to be emphasised that this is not an efficient solution. There are significant economies
of scale in the use of generators. A multitude of small generators would imply higher costs of
adjustment to outages. But this
outcome is better than the high
idle factor costs that would result
in the absence of such an
adjustment.
At the minimum, it is suggested
that the present tariff structure on
imported generators be reduced.
Table 9.4
Proposed Tariff Structure on Imported Generators
(Percent)
Description
Generating Sets
HS Code
(diesel or semi-diesel
engines) with:
8502.1110
Capacity < 5 KVA
8502.1120
5 KVA to 20 KVA
8502.1130
20 KVA to 50 KVA
Source: Customs Tariff,2012-13, FBR
Existing
Tariff
Proposed
Tariff
0
20
20
0
5
10
The precise nature of the proposal is given in table 9.4.
It appears that in the presence of extremely high and persistent levels of loadshedding and
inadequate response by the power sector, second best solutions may have to be adopted to
reduce the associated outage costs.
47
9.5 LOAD MANAGEMENT STRATEGY
The survey of small-scale industrial units has provided valuable insights on the optimal load
management strategy to minimize the costs of outages as follows:
a) Priority must be given for uninterrupted supply to continuous process units where outage
costs are higher.
b) For the same level of outages (in terms of time lost), outages of shorter duration impose
lower costs and long outages should be avoided.
c) There is a case for reducing off-peak tariffs to facilitate changes in timing daily of
production activities, especially by firms which have low labor costs in relation to costs of
power consumption.
d) Seasonal variation in tariffs may also be contemplated so that lower tariffs are charged in
the winter season, when electricity demand currently is relatively low.
e)
Distribution companies may consider the setting up of extension services, especially for
small firms, to enable them to find ways to conserve energy and to make the right kinds of
adjustments to outages.
In addition, the respondants have made a host of suggestions, which have been listed in
Chapter 7.
In conclusion, the survey of small-scale units has revealed the difficult situation that these
entities face in the presence of the high level of loadshedding. As such, along with the strategy
to reduce outage costs in the face of a given quantum of loadshedding, efforts have to be made
to reduce sharply the level of loadshedding. The returns to the economy of reducing outages
are very large. If the marginal cost of providing electricity through the public system is Rs 12 per
kwh and the saving in outage cost is Rs 51 per kwh, as estimated from the survey, then the
benefit cost ratio is as high as 4.2.
Measures of the type recommended above will have to be adopted on a top priority basis if the
interests of thousands of small entrepreneurs and millions of workers are to be protected. Their
voices are seldom heard currently in the corridors of power.
48
TECHNICAL ANNEXURE
FIRM BEHAVIOR IN THE PRESENCE OF OUTAGES
We make the assumption that the firm is ‘small’ and competitive Input and Output Markets. We
designate the following variables:
p = exogenously given price of output
w = wage rate
r = cost of capital
The firm is a profit maximising agent
We also have variables related to outages as follows:
𝜀 = proportion of time lost due to outages
𝜃 = proportion recovery through adjustments
CA = Cost of Adjustments
m = consumption of electricity per unit of time
𝜋 = pf(K,L) [1 – 𝜀 + 𝜃𝜀 ] – wL – rK- CA (𝜃 ∈ 𝑚)
B
𝜕𝐹
𝜕𝜋
𝜕𝐾
=p
𝜕𝜋
𝜕𝐿
=p
𝜕𝐾
………………………………………………………………………..(1)
.B – r = 0
𝜕𝐹
𝜕𝐾
………………………………………………………………………….(2)
.B – w = 0
q = f(K,L)B
𝜕𝜋
𝜕𝜃
= p.q.𝜀 -
= pq -
𝜕𝐴𝐶
𝜕𝐾𝑊
Therefore,
𝜕𝐴𝐶
𝜕𝐾𝑊
. (m 𝜀) = 0
.𝑚 = 0
…………………………………………………...(3)
𝑞
𝜕𝐴𝐶
𝑚
𝜕𝐾𝑊
p.[ ] -
=0
As such, the probability of
i.
ii.
iii.
𝜕𝜋
𝜕𝜃𝜃=0
> 0 depends on the following factors:
How high p is. That is the extent to which the market situation is favorable for the firm
The electricity intensity of the firm, the lower the intensity the greater the likelihood of the
firm making an adjustment
The lower the marginal costs of adjustments by the firm to recover output lost.
It is also likely that in the long run the firm uses less capital and less labor if B < 1.
49