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IMPACT OF LOAD
SHEDDING
ON SMALL SCALE ENTERPRISES
by
Alfred Mwila, Goodson Sinyenga, Simweemba Buumba,
Rodgers Muyangwa, Namakando Mukelabai, Cletus
Sikwanda, Besa Chimbaka, Gerson Banda, Chenela Nkowani
and Benny K Bwalya.
Working Paper 1
June 2017
Source: Energy savers Inc. Website, Zambia
Energy Regulation Board (ERB)
Plot 9330, Off Alick Nhata Road
Lusaka, Zambia
Downloadable at http://www.erb.org.zm
1
Disclaimer
The Energy Regulation Board (ERB), consistent with its’ mandate of regulating the energy sector
in Zambia, does carry out specialized studies that encourage the exchange of ideas about energy
regulatory impact analysis and development issues in general. This particular study was jointly
undertaken with the Central Statistical Office (CSO). However, the findings, interpretations and
conclusions expressed in this paper are entirely those of the authors. They do not necessarily
represent the views of the ERB, CSO, allied institutions or the Government. Thus, the study carries
the names of authors and should be cited accordingly.
The study team members for the Load shedding study comprised the following: Mr.
Alfred Mwila, Director – Economic Regulation, ERB; Mr. Goodson Sinyenga, Deputy
Director – Economics and Financial Statistics, CSO; Mr. Simweemba Buumba, Senior
Manager-Research and Pricing, ERB; Mr. Rodgers Muyangwa, Manager-Electricity;
ERB, Mr. Namakando Mukelabai, Statistician, ERB; Mr. Cletus Sikwanda, Economist-
Research, ERB; Mr. Besa Chimbaka, Economic Analyst-Electricity, ERB; Mr. Gerson
Banda, Senior Statistician, CSO; and Ms Chenela Nkowani, Programmer, CSO; and
Benny K. Bwalya, Financial Analyst - Electricity.
2
ENERGY REGULATION BOARD
IMPACT OF LOAD SHEDDING ON
SMALL SCALE ENTERPRISES
by
Alfred Mwila, Goodson Sinyenga, Simweemba Buumba, Rodgers Muyangwa,
Namakando Mukelabai, Cletus Sikwanda, Besa Chimbaka, Gerson Banda,
Chenela Nkowani and Benny K. Bwalya.
Working Paper 1
June 2017
Energy Regulation Board (ERB)
Plot 9330, Off Alick Nhata Road
Lusaka, Zambia
Downloadable at http://www.erb.org.zm
i
Table of Contents
Abbreviations .............................................................................................................................................. iv
Executive Summary....................................................................................................................................... v
Chapter 1: Introduction................................................................................................................................1
1.1 Introduction ...................................................................................................................................1
1.2 Problem statement and justification.....................................................................................6
1.3 Study Objectives...........................................................................................................................7
1.3.1 General Objective .................................................................................................................... 7
1.3.2 Specific objectives ...................................................................................................................7
1.4 Structure of the paper.................................................................................................................7
Chapter 2: Theoretical Framework ...........................................................................................................9
2.1 Cost of unserved energy ...........................................................................................................10
2.2 Estimating the cost of power rationing..............................................................................10
CHAPTER 3: METHODOLOGY ...................................................................................................................15
3.1 Approach and Methodology..................................................................................................15
3.2 Sample Survey Coverage and Target population ...........................................................15
3.3 Sampling Design.........................................................................................................................16
3.3.1 Sampling frame...........................................................................................................................16
3.3.2 Sample Size Determination and Allocation ......................................................................16
3.3.3 Sample weights and Sampling..............................................................................................16
3.4 Data analysis and techniques.................................................................................................17
3.5 Limitations ....................................................................................................................................17
CHAPTER 4: RESULTS AND DISCUSSION OF FINDINGS ...................................................................19
4.1 Distribution of small scale enterprises by sector ............................................................19
4.2 Number of workers employed by small scale enterprises...........................................21
4.3 Wage bill ........................................................................................................................................22
4.4 Business working hours ...........................................................................................................22
ii
4.5 Load shedding Experience.......................................................................................................23
4.6 Electricity expenses by small scale enterprises................................................................25
4.7 Business Annual Turnover........................................................................................................26
4.8 The impact of load shedding on turnover..........................................................................27
4.9 The Impact of load shedding on labour costs..................................................................28
4.10 Equipment damage and maintenance attributed to load shedding.......................30
4.11 The cost of restarting operations as a result of load shedding...................................31
4.12 Demand side management strategies................................................................................32
4.12.1 Use of generators....................................................................................................................32
4.12.2 Use of uninterruptible power supply...............................................................................33
4.12.3 Surge protectors......................................................................................................................33
4.12.4 Back up data systems.............................................................................................................34
4.12.5 Use of back up batteries.......................................................................................................34
4.12.6 Security enhancement..........................................................................................................35
4.12.7 Reduction in labour................................................................................................................35
4.12.8 Reduction in the working hours........................................................................................36
4.12.9 Relocation of business...........................................................................................................36
4.12.10 Shutdown of business operations................................................................................37
4.12.11 Change of operations hours...........................................................................................38
4.12.12 Other measures taken.......................................................................................................38
4.13 Challenges due to load shedding..........................................................................................39
4.14 The impact of load shedding on small scale enterprises..............................................40
Chapter 5: Conclusion and recommendations...................................................................................42
References ..............................................................................................................................................46
iii
Abbreviations
CSO Central Statistical Office
ERB Energy Regulation Board
GDP Gross Domestic Product
GWh Giga-Watt hour (1,000 MWh)
IPP Independent Power Producer
SBR Statistical Business Register
SMEs Small and Medium Sized Enterprises
ZESCO ZESCO Limited
Source: SMEs Website, Zambia
iv
Executive Summary
Background
The Electricity Supply Industry (ESI) in Zambia is dominated by hydro generation which in
2015 accounted for 94.1% of national installed capacity. The balance of 5.9% was from
alternative sources such as Diesel, Heavy Fuel Oil (HFO) and Solar Photovoltaic (PV)
generation plants. In 2015, Zambia experienced a drastic reduction in electricity supply
which was attributed to the reduced generation by ZESCO Limited (ZESCO) due to the low
water levels in the reserves caused by poor rainfall in the 2014/15 rainy season. The
power deficit in 2015 ranged from 560 to 1000 MW. By July 2015, ZESCO had increased the
extent of load shedding to at least eight (8) hours a day for the majority of its household,
commercial and industrial consumers. One of the measures of load management undertaken
by ZESCO was load shedding. The load shedding affected the most business operations and
financial viability. From literature, it has been investigated that small enterprises are the
most likely to be adversely affected by measures such as load shedding. This is because,
small enterprises are less resilient and most of them are not insured or have limited capacity
to invest in alternative energy sources (Kazungu, Moshi, & Mchopa, 2014). Given the
importance of small enterprises in the economy, it is critical that the impact of load shedding
is studied and understood. For example, according to Nuwagaba, (2015), considering
the data on Small and Medium Sized Enterprises (SMEs) for the period 1993-2006, SMEs
had created total employment of 214,527 in different sectors of the economy. Agriculture
sector provided 36.7 percent followed by manufacturing with 34.3 percent.
Objective of the study
The objective of this study was to ascertain the extent of the impact of load shedding on
small enterprise business operations and financial performance of smallscale enterprises in
2015.
Methodology
The generic approach to estimating the impact of load shedding or unserved energy is the
Cost of Unserved Energy (COUE). The COUE is defined as the value in monetary terms (e.g.
Kwacha per kWh) that is placed on a unit of electricity not supplied.
This study used the Direct Assessment Method (DAM), a derivative of the COUE, to estimate
the impact. The DAM estimates the cost of power outages by allowing electricity consumers
to express their losses in monetary terms (Kaseke & Hosking, 2012). The approach is based
on the principle that the lost production, materials and time in each productive sector, or
lost goods during an outage (load shedding), can be estimated directly, and this can be
aggregated to a total (ibid, 2012). The approach relies on the individual respondent’s
self-assessment method of valuing the cost of electricity outage.
The scope of the study was limited to four (4) cities, namely, Lusaka, Kitwe, Ndola, and
Livingstone. The four districts were purposively sampled owing to their relatively higher
v
concentration of economic activities. The study employed both qualitative and quantitative
techniques to select samples from the Central Statistical Office (CSO) sampling frame.
The small enterprises sample was limited to establishments whose annual turnover did
not exceed ZMW 250,000.00 as per CSO definition. Sampling weights were used to
correct for differential representation of the sample due to the disproportionate allocation
of the sample and this made it possible to make reference to the rest of the population in
the survey areas.
General Characteristics of small enterprises
The general demographic and characteristics of the sample were as follows:
• Sixty-four (64) percent of the enterprises were formalized by a way of registration with
one of the one of the local authorities such as Zambia Revenue Authority (ZRA), National
Pension Authority (NAPSA), The Patents and Companies Registration Authority (PACRA)
and the Local Council.
• Twenty-Seven (27) percent of enterprises had social security schemes or did make
contributions on behalf of their employees to NAPSA, Local Superannuation Fund
(LASF), Public Service Pension Fund (PSPF) and some any other social security system.
• The period of establishment for small enterprises ranged from 1922 to 2016.
• In 2014 and 2015, only 17.8% and 18.2% of the small enterprises were insured
respectively.
• The highest ranking operational constraint was electricity which was reported by 35.8%
of the enterprises in the survey. This was followed by Finance at 27.7%.
• In terms of the legal status of the establishment, the majority (62%) were individual
proprietorship followed by private limited companies (23.1%) and partnerships (7.2%).
• The wholesale and retail trade had the highest number of small scale enterprises at 7,533
(48.9%), followed by accommodation and food services and other services activities
at 2,771 (18.0%) and 1,868 (12.1%) respectively.
• In the survey areas, small scale enterprises provided employment to a total of 174,028
employees in 2015 and on average 11 employees per enterprise. In terms of sex,
70,644 (41%) were female and 103,383 (59%) were male.
• The annual wage bill for the establishments ranged from K136 to K466,869.45 in 2015.
The average wage bill for each enterprise was K 13,474.33
• The average number of working hours per day for small scale enterprises was 11 hours.
Meanwhile, the average number of operating days per week was 6.
vi
• A total of 12,781 (83.1%) of the small scale enterprises, in the survey areas, experienced
load shedding in 2015.
• Overall, on average the total number of hours of load shedding per day increased from
1 hour per day in 2014 to 4 hours per day in 2015, representing a percentage increase
of 300%.
• Load shedding schedules were not strictly followed by ZESCO as reported by 51%
of the enterprises. Meanwhile, the common source of information for load shedding
schedules was the Short Messaging System (SMS).
• A total of 2,125 (17%) indicated that they did not receive information on
ZESCO load shedding schedules.
• Electricity expenses ranged from K50 to K 14,083 per month in 2015. The
average electricity expenditure was K754.61.
• The majority (85%) of the small scale enterprises on average indicated that
electricity bills constituted up to 25 % of their total annual business expenses.
• The annual turnover, defined as total sales of the business in a year, ranged from
K 2,200 to K 10,999.99. The average annual turnover was K407,527.
Impact of Load Shedding
• The reported loss in turnover as a result of load shedding ranged from K0 to
K759,000. On average the reported loss in turnover was K 19,251.16.
• In the survey, 22.8% of establishments reported cases of idle labour while 9.8%
reported incurring overtime labour costs due to load shedding. Idle labour costs on
average ranged from K0 to K8,333.33. The average idle labour costs per firm were
K130.80. Equally, at firm level overtime labour costs on average ranged from K0 to
K5,000. The average overtime labour costs per firm was K30.49
• In the survey, 29.9% reported damaged equipment due to load shedding.
Furthermore, only 11.7% reported that such equipment was insured. The cost of
damaged equipment ranged from K2 to K56,000. The average cost of damaged
equipment was K3,112.80.
• A total of K 3,754,390.00 was spent in the restarting of operations by small scale
enterprises in 2015 in the four districts as a result of load shedding.
Measure to mitigate load shedding
• In the study, 55% reported employing strategies to mitigate against load shedding.
vii
However, there was no reported shutdown of operations and very few reallocations
of businesses (0.2%).
• A total of 3,511 (29.9%) of small scale enterprises used generators as an
alternative source of energy, while 8,251 (70.1%) did not.
• Only 276 (2.4%) used uninterruptible power supply (UPS) as an alternative
energy source to mitigate against the impact of load shedding.
• Only 246 (2.1%) indicated that they used surge protectors in the four districts.
• Only 813 (6.9%) of the small scale enterprises invested in back up data systems.
• Only 803 (6.9%) used back up batteries in order to mitigate against the loss of power.
• In the survey, 803 (6.9%) small enterprises reported that they had enhanced their
security features.
• A total of 1,013 (8%) out of 12,452 small scale enterprises indicated that they
reduced the labour hours compared to 11,439 (92%) who did not.
• A total of 876 (7.6%) indicated that they reduced business working hours due to
load shedding.
• A total of 808 (6.5%) of the small scale enterprises indicated that they changed their
business operating hours in order to accommodate the load shedding schedule.
Economic impact of load shedding
The study established that a total cost of K623,871,514.50 was incurred as a result of load
shedding by small scale enterprises translating into US$ 0.95/kWhlos (kilowatt hour lost).
In terms of kilowatt hour lost per district, Kitwe district had the highest loss at US$ 1.94/
kWhlos, followed by Lusaka at US$ 0.97/kWhlos. Livingstone district was third at US$ 0.53/
kWhlos, while Ndola was the lowest at US$ 0.51/kWhlos.
Conclusion
This study has established that the incidence of load shedding in 2015 led to adverse
disruptions in the operations of most small enterprises in the survey areas. Furthermore,
most small enterprises had inadequate response strategies as they could not use alternative
sources of energy. Most small enterprises resorted to reducing their work outputs resulting
in reduced turnover whilst incurring additional costs such as idle labor and overtime. Some
enterprises suffered losses due to equipment damage and high replacement costs.
The study estimate of US$ 0.95/kWh for each unsupplied electricity unit confirms the
proposition that small enterprises were adversely affected by load shedding and that there
is an inverse relationship between load shedding and small enterprise productivity as
well as general business performance.
viiiChapter 1
Introduction
1.0 Introduction
Zambia is a landlocked country in Southern Africa with a total surface area of 752,618 square
kilometers and had a population of approximately 15,473,905 in 2015 (Central Intelligence
Agency, 2017). Zambia has had one of the world’s fastest growing economies for the past
ten years, with real Gross Domestic Product (GDP) growth averaging roughly 6.7% per
annum, though growth slowed in 2015 to 2.9%, due to falling copper prices, reduced power
generation, and depreciation of the kwacha. Zambia’s lack of economic diversification and
dependency on copper as its sole major export makes it vulnerable to fluctuations in the
world commodities market and prices turned downward in 2015 due to declining demand
from China.
According to the Central Statistical Office (2016), by 2015, GDP at current prices was estimated
at K183,381.1 million compared to K167, 052.5 million in 2014. The results show that the
Wholesale and retail trade industry had the highest contribution of 22 percent to GDP in
both years. This was followed by Mining and quarrying industry at 14.6 percent in 2014 and
12.7 percent in 2015. The share of Agriculture, forestry and fishing reduced from 6.8 percent
in 2014 to 5.0 percent in 2015. The contribution of electricity generation to GDP increased to
4% in 2015 compared to 3% in 2014.
In 2015, the economy experienced a deterioration in its terms of trade owing to a decline
in exports. The Kwacha depreciated significantly against major international currencies.
The economy witnessed double digit inflation, for the first time since 2010. The Kwacha
depreciated by 25.0%, from K6.47/US$ to K8.09/US$ between January and August 2015, while
between September and December 2015, the Kwacha depreciated by 6.3%, from K10.20/US$
to K10.84/US$. Notably, the Kwacha depreciated significantly by 48.57% between August
and October 2015, owing to the highest trade imbalance recorded in October, 2015 and
the collapse of the copper prices which is Zambia’s main export. Inflation averaged 10.04%,
rising from 7.7% in January to 21.1% in December, mainly driven by the depreciation of the
exchange rate. In line with inflationary pressures, interest rates remained relatively high with
the commercial banks’ lending rates increasing to 23.9 percent at end of December 2015
from 20.5 percent at end of December 2014 (Ministry of Finance, 2016).
In 2015, the Electricity Supply Industry (ESI) in Zambia was dominated by hydro generation
which accounted for 2,269 MW (94.1%) of national installed capacity and the balance of 5.9
percent was from diesel (92 MW) , Heavy Fuel Oil (50 MW), and Solar Photovoltaic (0.06 MW)
generation plants. Figure 1 shows Zambia’s installed generation capacity by technology in
2015.
1Figure 1: Zambia’s installed generation capacity – 2015
The key players in the sector were ZESCO, a vertically integrated power Utility, which
generates, transmits, distributes and supplies electricity. It is a public utility, with the
Government of the Republic of Zambia being a sole shareholder. Other players included:
• The Copperbelt Energy Corporation (CEC) which operates and maintains a network
mainly comprising generation, transmission and distribution assets that supplies
power to Zambia’s mining companies based on the Copperbelt province.
• Lunsemfwa Hydro Power Company Limited an Independent Power Producer (IPP)
that supplies power solely to ZESCO with a total installed capacity of 56 MW
hydro power stations.
• Kariba North Bank Extension Power Corporation Limited, a wholly owned subsidiary
of ZESCO that owns and operates a 360 MW hydro power plant.
• North Western Energy Corporation Limited (NWEC) is licensed to distribute electricity
in the North-Western Province of Zambia. NWEC distributes electricity to non-mining
customers in Lumwana (Barrick), Kabitaka and Kalumbila sites. Power is supplied
by ZESCO at various substations established by NWEC.
• Ndola Energy Company Limited (NECL) an IPP that supplies power to its sole
customer, ZESCO, under a Power Purchase Agreement (PPA). The company
2owned and operated a 50 MW HFO power plant, which is planned to increase by a
further 55 MW HFO power plant beyond 2015.
• Zengamina Power Limited a private company that owns and operates an off-grid
mini hydro power plant with an installed capacity of 0.75 MW situated in Ikelenge,
North-Western Province.
The total generation sent out from both ZESCO and IPPs power plants declined by 7.0 percent
(1,013 GWh) in 2015. Electricity sent out reduced from 14,453 GWh in 2014 to 13,440 GWh
in 2015. The reduction in electricity generation was attributed to poor rainfall experienced
during the 2014/2015 rainy season which resulted in low water levels, thereby impacting
negatively on the capacity to generate power from hydro power plants.
In order to address the imbalance in electricity generation sent out, ZESCO undertook load
management measures which included load shedding. Load shedding is defined as an
intentionally engineered electrical power shutdown where electricity delivery is stopped
for non-overlapping periods of time over different parts of the distribution region1.
There are several factors that can cause load shedding besides insufficient generation
capacity. These factors include inadequate transmission and distribution infrastructure for
the delivery of sufficient power to the area where it is needed. The process is usually done
in stages and depending on the deficit, the utility company might decide to switch off
some segments of the customers during this process. Load shedding is a measure of last
resort to prevent the collapse of the entire power system. When the demand, or load, from
customers is greater than the available supply, the electricity system becomes unbalanced,
which can consequently result in country-wide power trips (a blackout) that could take days
2
to restore .
Particularly in 2015, ZESCO had increased the extent of load shedding from an average of
one (1) hour to between four (4) and eight (8) hours a day for the majority of its household,
commercial and industrial consumers. The power deficit in 2015 ranged from 560 to
1000 MW, and a load shedding schedule for different regions around the country was
developed by the Utility.
1
http://www.gutenberg.us/articles/loadshedding
2
http://www.eskom.co.za/documents/LoadSheddingFAQ.pdf
3In order to mitigate against the power deficit, the Government instituted the following
measures amongst others:
i. Facilitation of the importation of emergency power from various sources within the
region;
ii. Announcement of a ban on local manufacturing and importation of incandescent
bulbs and inefficient lighting devices in January 2016 through Statutory Instrument
(SI) No.74 of 2016;
iii. The Government through the Industrial Development Corporation (IDC) in 2015,
commenced the procurement for the development of two solar power plants of 50
MW each to be awarded to two different developers; and
iv. Further, a new Lunzua power plant, owned by ZESCO and situated in Northern
Province, was constructed and commissioned with a rated capacity of 14.8 MW
adding to the existing capacity of 0.75 MW.
Electricity is a prerequisite for proper functioning of nearly all sub-sectors of the economy.
It is an essential service whose availability and quality determines success or failure of
development endeavors. This argument is valid particularly when we consider supply of
energy to small and large firms/businesses dealing with service provision and manufacturing,
where power is used as an input in the operations/production process rather than a final
consumption service. Hence, a temporary stoppage of power can lead to relative chaos.
While a loss of power in smaller scale settings may not be life threatening but can result
in lost data, missed deadlines, decrease in productivity or loss of revenue (Kazungu,
Moshi, & Mchopa, 2014).
Research on the effect of electricity power outage on Small and Medium Enterprises
(SMEs) in Ghana posited that, the electricity crises in the country costed SMEs over
US$686.4 million of annual sales. Based on previous research findings using a population of
over 4 million SMEs in Ghana with a sample size of 1,250, micro businesses were the most
affected by the electricity problems, recording a loss of around US$2.2 million daily,
which represented over 50% of their daily sales. The impact of power outages is dependent
on the firm’s ability to respond to any shocks, small scale enterprise have little room to
respond compared to medium scale firms (Solomon & Yao, 2015)
In a study carried out in Tanzania in 2014 using a survey research design, Kazungu,
Moshi and Mchopa found that SMEs experience various challenges with power rationing
being one of them. The study found that there was a strong positive correlation
between power rationing and decline in productivity. The study established productivity
loss was highest among SMEs that depended highly on electricity for their business
operations. Specifically, business declined between 50 percent and 60 percent for SMEs
dealing in photocopying and printing, stationery, hair dressing, barbershop and grain
4milling (Kazungu, Moshi, & Mchopa, 2014). The occurrence of power rationing deprives SMEs
electricity for running their operations effectively and as a result, production is hampered
as there is no power to drive the business.
In the case of Zambia, as a result of long hours of load shedding, there was an outcry by
ZESCO’s customers concerning the negative impacts of load shedding on their routine and
core business activities. In particular, some businesses especially small ones, indicated
that they had to lay off workers while others had to close as they could not generate
sufficient revenues due to reduced production, to meet the business expenses. Furthermore,
some small scale enterprises complained of damaged materials and equipment. It was
therefore likely that such impacts would adversely affect the country’s GDP. Sing’andu
(2009) in a study to assess the impact of ZESCO’s power rationing on firm productivity
and profitability of selected manufacturing industries in Lusaka district, established that
power rationing eventually leads to a decline in production and consequently SMEs fail to
reach their projected sales volume. Reduced sales volume translates into reduced business
income because SMEs are unable to meet customer demand.
According to Nuwagaba (2015), SMEs are instrumental for the development of an economy
through, for example, employment creation, increased tax base for the country, and
improved incomes for the low earners among other benefits. Additionally, based on the
1996 baseline survey, SMEs employed 18 percent of the labour force of which 47% were
women in Zambia (ibid, 2015). Therefore, load shedding for such a strategic sector can
have devastating effects on the economy.
Firms suffer three kinds of damages in the case of an outage. First, they produce less, without
electricity, many production processes stop, some production is lost, for example unsaved
computer files, and it takes time to start up production again. Second, extra costs may be
incurred such as paying overtime pay to workers. Third, some goods and inputs may be
damaged, for example hot steel in a steel plant may cool down and have to be reheated. The
damage caused by an electricity interruption in a firm is equal to the value it would normally
have added during that period (Kaseke & Hosking, 2012).
There is no doubt that small scale enterprises are instrumental in the development of
the economy through employment creation amongst others. Additionally, they also
contribute to the treasury of the economy through tax payments. According to Andrew et
al., (2014) there were around 90 million micro, small and medium scale enterprises (MSMEs)
in developing countries and emerging markets and the density of formal MSMEs in low and
middle income countries is rising.
There is no consensus on the definition of an SME, as various countries have different
definitions depending on the phase of economic development and their prevailing social
conditions. Enterprises differ in their levels of capitalization, sales and employment. Hence,
definitions which employ measures of size (number of employees, turnover, profitability,
5net worth, etc.) when applied to one country could lead to all firms being classified as
small, while the same size definition when applied to another country could lead to a
different result (Kanlisi , Amenga, Akomeah , Amoako , & Narh, 2014).
In this study, the definition adopted for a small scale enterprise was based on the
Central Statistical Office (CSO) classification of business enterprises, a small scale business
enterprise refers to a business whose annual turnover does not exceed ZMW 250,000.00.
In terms of the nature of business, most SMEs are engaged in the production of goods
and services with the primary objective of generating employment and income to persons
concerned. The range of products and services that most SMEs are involved in include
textile products, carpentry & other wood products, light engineering and metal fabrication,
food processing, leather products, handicrafts and ceramics. The services sector include
restaurants and food preparation, hair salons and barbershops, passenger and goods
transport, building construction, telecommunication services, business centre services and
cleaning services. The trading sector is largely concentrated in consumable products,
industrial products, and agricultural inputs and produce (Ministry of Commerce, Trade
and Industry, 2007). The business is characterised by the use of low technology, relying
largely on social networks and inter- firm cooperation, and are oriented towards the local
and less affluent segments of the market (Ibid, 2007).
1.1 Problem statement and justification
The persistent and long hours of load shedding experienced in 2015 by small and
medium enterprises in Zambia adversely affected their business operations and
financial viability. The objective of this study was to ascertain the extent to which
load shedding affected small scale enterprises in Zambia. The study is critical
because small scale enterprises are instrumental in the development of an economy
given their contribution to employment creation, increase tax base and improved
incomes especially for low income earners. Therefore, load shedding for such a strategic
sector can have devastating effects on the economy. Therefore, it becomes imperative to
understand the financial and operational impact of load shedding on small scale
enterprises in Zambia, who are presumed to be the most affected. Understanding the
impact of load shedding can provide information for the Government to make a case for
power investment planning and power diversification. It can also provide information to
consumers to devise mitigation measures such as insurance, investment in back-up systems
and diverse energy sources. For electricity utilities, this would help in managing load
shedding through enhanced communication mechanisms, while for regulators this would help
enhance regulatory tools such as Key Performance Indicators Framework, Tariff
Determination and the development of Regulatory Framework for Alternative Energy.
This study will undertake in-depth analysis of critical aspects that affect small scale enterprises
business operations affected by load shedding such as cost of material lost, the labour cost, cost of
equipment damage and maintenance and cost of restarting the business activities.The study will
also investigate the loss in turnover due to load shedding including the different
coping strategies put in place.
61.2 Study Objectives
1.2.1 General Objective
The overall objective of the study was to ascertain the operational and financial impact of
load shedding on small scale enterprises in Zambia during the load shedding experienced
in 2015.
1.2.2 Specific objectives
The specific objectives are as follows:
i. Ascertain the loss the loss in turnover and associated costs due to load shedding;
ii. Ascertain the cost of material lost due to load shedding;
iii. Ascertain the impact of load shedding on labour costs;
iv. Ascertain the cost of equipment damage and maintenance attributed to load
shedding;
v. Ascertain the cost of restarting operations as a result of load shedding; and
vi. Ascertain the measures put in place by enterprises to tackle load shedding.
1.3 Structure of the paper
This paper is structured as follows; chapter 1 provides the background, justification,
problem statement and objectives of the study. The rest of the paper shall proceed as follows:
Chapter 2 discusses the theoretical framework with regards to estimating the costs of
load shedding. Chapter 3 discusses the methodology employed in the study to estimate
the impact of load shedding on small scale enterprises business operations. Chapter 4
discusses the research findings while chapter 5 concludes and makes recommendations
based on the findings of the study.
78 Source: SMES Poultry Website, Zambia
Chapter 2
Theoretical Framework
2.1 Cost of unserved energy
The generic approach used to estimate the cost of power outage is the estimation of
the cost of unserved energy (COUE). COUE is the value of production lost for each unit of
power outage (Terry, 2001). It is also the monetary value placed on a unit of
electricity not supplied as a result of unplanned outages (Minnaar, 2015). It is estimated
by:
GDP
COUE = ----------------------------------
Energy consumed
Using this approach, the Indaba Agricultural Policy Research Institute established that the
COUE in the agriculture sector in 2014 at ZMW 1.38/kWh, while for all sectors it was at
ZMW 15.53/kWh for Zambia. This implies that the agricultural sector was paying an implicit
price of ZMW 1.38 per unit of electricity. In 2015, based on an electricity shortfall of
2,100,000,000 kWh, the value of lost opportunity for all sectors was estimated to be ZMW
32,496,100,813 (that is, 18.8% of the GDP) in Zambia. For the agricultural sector, assuming
an 8.7% contribution to GDP in 2015, the estimated cost of the power shortfall in 2015
translated to ZMW 2,827,160,771 (1.6% of the GDP).
In 2015, Nyamazana (2015) estimated the COUE in Zambia for the years 2012 to 2015 as
depicted in table 1.
Table 1: Cost of unserved energy for Zambia – 2012 to 2015
2012 2013 2014 20153
Total Electricity Cons (kWh Millions) 10,317 10,846 10,721 11,450
GDP (ZMW Millions) 128,370 144,722 165,901 183,381
GDP (US$ Millions) 24,939 26,821 27,066 21,249
COUE: ZMW/kWh 12.44 13.34 15.48 16.02
COUE: US$/kWh 0.194 0.185 0.163 1.856
3 2015 figures are author’s computations using average exchange rate of US$1 to ZMW 8.63
9In 2015, the COUE was estimated at 1.86 US cents per kWh given the deficit of 2,100 GWh
and GDP of US$ 21,249,258,400.93. The loss was equivalent to US$ 3.9 billion or 18% of
GDP.
2.2 Estimating the cost of power rationing
At firm level, there are several approaches that have been used in literature to estimate
the cost of power rationing on different customers or sectors within the economy.
These approaches differ depending on the level of complexity and data requirements.
Some of these methods include the following Contingent valuation; Production function;
Captive generation method; and Direct Assessment Methodology (DAM).
2.2.1 Contingent valuation
According to (Samboko, et al., 2016) the contingent valuation approach is used
where consumers are asked to provide estimates of how much compensation they
would be willing to accept for a given period without power, or how much they are
willing to pay to avoid a power outage.
For example, a question could be phased 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 would be 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? (Institute of Public Policy, 2013).
However, this method is prone to giving biased estimates as it is based on subjective
responses. It is likely that in response to the first question, the consumers understate their
willingness to pay for improved service, while they may overstate the compensation that
they would like to receive for deterioration in the reliability of supply. Using this
approach to determine the welfare costs of electricity outages in Uganda, Kateregga
(2009) found that the costs associated with load shedding varies according to the
incidences, particularly to the time during the day, morning or evening, and the duration
of the outage.
2.2.2 Production function
The production function approach achieves the same objective by providing estimates of
the input cost effect and the output loss from switching to alternative power sources. This
is usually done using panel data from firms on inputs and outputs (Samboko, et
al., 2016). The production function approach requires detailed data on individual firms,
however, that may not be easy to collect.
102.2.3 Captive generation
The captive generation method or the indirect method estimates the costs associated with
load shedding from the actions taken by consumers to mitigate outages by acquiring
generators or captive power units and diesel pumps. This method dates as far back as the
World War One when it was used by the US Navigation Army (1917) and was adopted by
British and other European countries in the 1930s as a way of consolidating their industry
production and estimating the negative effects of power outages (Nyasha , 2014).
Captive generation method is based on observed market behavior, for instance consumer’s
expenditures on generators and use of interruptible power supply contracts Firms are
assumed to be operating to maximize profits, while households are assumed to maximize
utility. A firm or household, faced with frequent power outages, will act to insure itself
against the damage caused, by acquiring backup generating units (ibid, 2014). The gain
from insurance against outages consists of the continued production or the continued
leisure that the self-generated electricity makes possible, and the avoided damage to
equipment that otherwise would have been caused by power outage (Opcit, 2014).
The expected gain from the marginal self-generation kilo watt hour (kWh) is also the
expected loss from the marginal kWh that comes as a result of an outage (Nyasha , 2014).
This method is easy to apply and can provide accurate estimates of the costs to firms as
data on the size of device generating units, the cost of the backup systems and the output
of the system, in the form of power (kWh) generated, can be easily traced to the suppliers
of the devices (Nyasha , 2014). The same information about the output can be traced to
the load that is powered by the device. The units required for these devices are known
internationally, e.g. the cooker consumes 60 AMPs on average and lights 10 AMPs (ibid,
2014).
However, critics argue that the use of the backup generation method to estimate cost
depends on whether the backup power supplies are for emergency or optional standby
(Caves et al. 1992). Where captive generation is used as (normal) emergency backup power,
the method may overestimate cost. On the other hand, Tiwari (2000) argues that power
outage costs are far greater than the backup generation costs, as there are indirect costs
other than direct costs that must still be added.
Further, the method assumes a perfectly competitive market for generators, risk neutrality,
and a production technology in which electricity enters smoothly. The existence of risk
aversion, externalities (which bring about environmental regulation), and technologies in
which relatively small generators, are of no use, would yield misleading estimates of the
marginal outage cost (Nyasha , 2014).
112.2.4 Direct Assessment Method
The Direct Assessment Method (DAM) is an economic appraisal tool that estimates the
cost of power outages by allowing electricity consumers to express their losses
in monetary terms (Kaseke & Hosking, 2012). The approach is based on the principle that
the lost production, materials and time in each productive sector, or lost goods during an
outage (load shedding), can be estimated directly, and this can be aggregated to a total
(ibid, 2012). The approach relies on the individual respondent’s self-assessment method of
valuing the cost of electricity outage.
In order to estimate the cost of load shedding by the DAM, it is important that total
value lost by consumers due to load shedding is ascertained by summing up all the
direct costs experienced during load shedding. The direct costs incurred by firms go
beyond production loss or output loss. In addition to output loss cost, other direct costs
such as materials destruction cost; in stock, labour cost; payment of idle labourers and cost
of overtime and bonuses to meet production and orders, damage to equipment cost, restart
cost, as well as time or opportunity cost per load shedding are part of the load shedding
cost. The total direct cost relationship is captured in the formula below:
TDCi = OLi + MCi + LCi + EDCi + MCi + RCi ………………………………………equation 1
Where: TDCi is the total direct cost for the ith consumer; OLi is cost of lost output; MDCi
is the material destruction cost; LCi is labour cost; EDCi is the equipment damage and MCi is
the maintenance cost as a result of load shedding; and RCi is restart cost.
From equation 1 cost per unit of electricity (kWh) lost can be estimated as:
TDCi
OCi = ---------------------------------------------------------
kWhlosi
Where: OCi is the cost per kWh lost and kWhlosi are the total units of electricity (kWh) lost
or unsupplied due to load shedding.
This method, however, has its own shortcomings. The DAM approach only measures
direct cost of production such as lost output, and not indirect cost such as inconvenience.
In addition, this method does not take into account the fact that foregone production might
be partially made up after the outage and as a result of this, gives an overestimation of
12the cost of electricity outages. Proponents of the method argue that this overestimation
of direct cost compensates for the omission of indirect costs (Borestein, Beshnell, & Wolak,
2002) and (Bose, Shukla, Srivasta, & Yaron, 2006). Self-assessments based on business
surveys may be inclined to strategic misrepresentation (Pasha, Ghaus, & Malik, 1990). The
reported outage cost may be an exaggeration to impress upon the power company the
need for more reliable electricity. Alternatively, the interviewees may be unaware of the
cost or unable to devote the necessary time to complete the questionnaire.
Despite these shortcomings, the flexibility of the DAM and its link to observable market
behavior recommends its use in outage cost research (Pasha, Ghaus, & Malik, 1990). In this
study, in order to arrive at the total cost of load shedding, the DAM was used. This was due
to the nature of the study, research has shown that there is poor record keeping among small
scale enterprise (Bancy, 2007). Thus, DAM was found to be appropriate as the data required
for the study could easily be collected. The other methodologies would require the use of
more detailed data which might have proved difficult to collect given the time frame of the
study.
Table 2 shows variables used for estimating cost of load shedding to small scale
enterprises using the DAM theoretical framework.
Table 2: Variables for estimating cost of load shedding to small scaleenterprise
Independent variable Description
Material Destruction Cost • Average costs of materials lost due to load shedding.
Labour Cost • Labour cost paid due to idle labour as a result of load shedding
• Labour cost paid due to over time as a result of load shedding
Equipment damage cost • Costs associated with equipmentdamage due to load shedding.
Maintenance cost • Costs associated with maintenance of alternative sources of
energy.
Restart costs • Costs associated with re-startingoperations due to loss of power.
1314 Source: SMES Poporn Website, Zambia
CHAPTER 3
METHODOLOGY
3.1 Approach and Methodology
The study employed both qualitative and quantitative survey techniques to collect data
using face to face interviews. The semi structured questionnaire used in the study was
developed by the Energy Regulation Board Zambia (ERB) in consultation with the
Regional Electricity Regulators Association (RERA) of the Southern Africa and the Central
Statistical Office (CSO) Zambia. The final questionnaire consisted of two main parts; the
first part consisted of questions on identification particulars of small scale enterprises
while the other part consisted of questions on the operations of the business. The research
questionnaire was pre-tested on a small group before it was finalised. The research assistants
underwent a training session to acquaint themselves with the questionnaire. Collection of
survey data was over a period of 3 weeks in September 2016.
3.2 Sample Survey Coverage and Target population
The survey on the impact of load shedding covered small scale enterprises found in four
cities namely; Lusaka, Kitwe, Ndola and Livingstone. The four cities were purposively
sampled owing to their relatively higher concentration of economic activities in the
sectors of interests in addition to the ease by which the cities could be accessed with the
available resources.
For the purpose of this study, an enterprise was defined as an undertaking engaged in the
manufacturing or provision of services or any undertaking carrying out business in the
4
field of manufacturing, construction and trading services . Further, based on the CSO
classification of small scale business enterprises, the study was limited to establishments
whose annual turnover did not exceed ZMW 250,000.00 in 2011.
4
http://www.ide.go.jp/English/Publish/Download/Dp/pdf/134.pdf
153.3 Sampling Design
3.3.1 Sampling frame
The 2011 SBR was used as the sampling frame for the survey. The SBR comprises a list of
business establishments in the country classified into three mutually exclusive and
exhaustive categories using respective annual turnover as a measure of size. The
categories are as follows:
i. Large-scale – consists of Business establishments whose annual turnover is ZMW
800, 001 or more.
ii. Medium-scale – consists of Business establishments whose annual turnover is
from ZMW 250, 001 to ZMW 800,000.
iii. Small-scale – consists of Business establishments whose annual turnover is ZMW
250,000 or less.
For this survey, only the small scale part of the SBR was adopted as an intact sampling frame.
3.3.2 Sample Size Determination and Allocation
In this study the sample was drawn from a population of 15,415 small scale enterprises. The
determination of the sample size was mainly guided by the need to strike a balance between
the desired sampling accuracy and its associated cost. For the purpose of this exploratory
survey and due to financial constraints, an error margin of about 9.5 percent was
set as tolerable. Typically the smaller the margin of error with it’s associated Coefficient of
Variation (CV), the larger the sample. Nonetheless, a good sampling design is not only
seen in terms of sampling accuracy but also in terms of how it brings financial and human
resource, and logistics requirements to manageable levels. In the case of this sample design,
a margin of error of 9.5 percent is associated with CVs of less than 20 percent. As a rule of
thumb, any estimate that is associated with the CV of 20 percent and below is acceptable
(Kish, 1965).
Given the above sample specifications, a sample of 696 small scale enterprises was
determined as desirable. This sample took into account a margin of error of 9.5 percent, a
design effect of 1.3 and a non-response rate of 20 percent. Ultimately, the Impact of Load
shedding survey enumerated 600 small scale establishments, representing a response rate
of 86 percent.
3.3.3 Sample weights and Sampling
Sampling weights were required to correct for differential representation of the sample
due to the disproportionate allocation of the sample and to make inference to the rest of
16the population. The weights of the sample are equal to the inverse of the product of the
selection probabilities employed. As stated earlier, the districts were selected purposefully
and all sectors in each district were represented in the sample. Therefore, weights of the
sample in this case were equal to the inverse of the section probability of an entity within a
sector in each district.
The selection probability of an enterprise was calculated as follows:
nei
Pei = ---------------
Nei
Where
Pei = the selection probability of an entity
n = the number of enterprises selected from the ith sector in the district.
ei
Nei = Total number of enterprises listed in the district.
Therefore, the sector specific sample weight was calculated as follows: Wi = 1/ Pei
Wi is called the Probability Proportion to Size sample weight.
In order to reflect growth in the population, the base weight was adjusted using a post
stratification adjustment factor as follows:
Wf = Wi x adj f,
where adj f is obtained by dividing the projected population by the survey population.
3.4 Data analysis and techniques
The data was analysed using Statistical Package for Social Sciences (SPSS) software. Data
entry was done in CSpro which was later exported into SPSS for analysis.
3.5 Limitations
The study faced a number of limitations, firstly the sample used in the study was based on
the CSO’s SBR which was last updated in 2011. Therefore, this presented a challenge as the
sampled respondents had either closed operations or shifted their business operations
elsewhere and could not be located. This observation is in line with Mason (2009) who
observed that the average life cycle of small scale enterprises is around five years or less. In
order to overcome this challenge, sampling with replacement was employed. Additionally,
some addresses on the sampling frame were not clear to locate and even worse, the frame
didn’t have phone numbers to enable enumerators to make prior arrangements and
17assist in locating establishments. This impacted negatively on turn-around time as the
enumerators spent unusually longer time locating establishments.
Further, the nature of the survey and timing of the study also affected the response
rate. The topic the survey was addressing was sensitive and some of the establishments
felt that some of the information, especially financial data, was confidential and hence the
enumerators faced challenges to collect the information. This factor contributed to reduced
turn-around time in data collection. In terms of timing, the survey coincided with the
national elections which in some cases provided challenges of respondent’s cooperation.
18CHAPTER 4
RESULTS AND DISCUSSION OF FINDINGS
4.1 Distribution of small scale enterprises by sector
In 2015, there were a total of 15,415 small scale enterprises, excluding those in
mining or recovery of minerals, of which 9,219 were based in Lusaka, 2,091 in Kitwe,
2,989 in Ndola and 1,116 in Livingstone. The study established that 64.1% of
the enterprises were formalised by a way of registration with one of the
local authorities such as Zambia Revenue Authority (ZRA), National Pension
Authority (NAPSA), PACRA and the Local Council. In addition, 27% of the enterprises
have social security schemes or do make contributions on behalf of their
employees to NAPSA, LASF, PSPF and any other social security system. This
means that any disruption in business operations that affects labour contributions
to social security systems would impact one third of the enterprises. Business
formalisation is more likely to take place in urban areas mainly involving
large firms and those already using proper book keeping (Coolidge & Ilic,
2009). Registration of the business with authorities does indicate evidence of record
keeping.
Figure 2 below shows the distribution of small scale enterprises in Lusaka, Livingstone,
Kitwe and Ndola in 2015.
Figure 2: Number of small scale enterprises by location - 2015
10,000
9,000
8,000
7,000
6,000
5,000
4,000
3,000
2,000
1,000
-‐
Kitwe Ndola Lusaka Livingstone
Number
of
Enterprises 2,091 2,989 9,219 1,116
All enterprises in Kitwe, Livingstone, Lusaka and Ndola were established between 1922 and 2016.
The majority (90.9%) were established after 1991 following the liberalisation of the economy. The
study therefore captured enterprises that
19were mature and could be assumed to be conversant with their operations.
Among the enterprises, the majority were not insured for instance only
17.8% and 18.2% were the only ones insured in 2014 and 2015, respectively. For
those who reported being insured, the common insurance was property
equipment which was reported by 48.4%, then motor vehicles at 46.7%, life at 3.9 %
and other at 0.9%.
The enterprises were requested to rank the major operational constraints and stated as
depicted in figure 3.
Figure 3:
Figure 3: Enterprises
Enterprises operational
operational constraints
constraints --2015
2015
40.00
35.00
30.00
25.00
Percentage
20.00
15.00
10.00
5.00
-
Competiti
Finance Fuel Electricity Labour Security Other
on
Percentage 27.70 1.00 35.80 4.00 20.40 3.50 7.60
The highest ranking operational constraint was electricity which was reported by
The highest ranking operational constraint was electricity which was reported by 35.8%
35.8% of the enterprises. This was followed by Finance at 27.7%, competition,
of the enterprises
security in the were
and other which survey. This was
reported followed
by 20.4%, 3.5%byand
Finance at 27.7%, competition,
7.6% respectively. In a study on
security andinsecurity
Electricity other whichand
wereSMEs,
reported
thebyOverseas
20.4%, 3.5% and 7.6% respectively.
Development Institute (UK)In a
established
study that 49.3%
on Electricity of SMEs
insecurity in SMEs,
and Sub Saharan Africa, identified
the Overseas electricity
Development as a(UK)
Institute major
constraint in their business operations (The Overseas Development Institute, 2014).
23
In terms of the legal status of the establishment, the majority (62%) were
individual proprietorship followed by private limited companies (23.1%) and partnerships
(7.2%). The rest accounted for 7.7%.
Table 3 shows the distribution of small scale enterprises in Lusaka, Kitwe, Ndola and
Livingstone in 2015 by location and economic sector. Wholesale and retail trade had the
highest number of small scale enterprises at 7,533 (48.9%), followed by accommodation and
food services and other services activities at 2,771 (18.0%) and 1,868 (12.1%) respectively.
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