impact of load shedding - erb.org.zm

60
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 IMPACT OF LOAD SHEDDING ON SMALL SCALE ENTERPRISES Source: Energy savers Inc. Website, Zambia

Upload: others

Post on 15-Oct-2021

20 views

Category:

Documents


1 download

TRANSCRIPT

Page 1: IMPACT OF LOAD SHEDDING - erb.org.zm

1

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 RoadLusaka, ZambiaDownloadable at http://www.erb.org.zm

IMPACT OF LOAD SHEDDINGON SMALL SCALE ENTERPRISES

Source: Energy savers Inc. Website, Zam

bia

Page 2: IMPACT OF LOAD SHEDDING - erb.org.zm

2

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.

Page 3: IMPACT OF LOAD SHEDDING - erb.org.zm

i

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, ZambiaDownloadable at http://www.erb.org.zm

         

ENERGY REGULATION BOARD

Page 4: IMPACT OF LOAD SHEDDING - erb.org.zm

ii

Table of ContentsAbbreviations .............................................................................................................................................. 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

2.2 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

...........................................................................................................102.1 Cost of unserved energy

Page 5: IMPACT OF LOAD SHEDDING - erb.org.zm

iii

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

Page 6: IMPACT OF LOAD SHEDDING - erb.org.zm

iv

AbbreviationsCSO 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 W

ebsite, Zambia

Page 7: IMPACT OF LOAD SHEDDING - erb.org.zm

v

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

Page 8: IMPACT OF LOAD SHEDDING - erb.org.zm

vi

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.

Page 9: IMPACT OF LOAD SHEDDING - erb.org.zm

vii

• 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.

Page 10: IMPACT OF LOAD SHEDDING - erb.org.zm

viii

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

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.

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$

Page 11: IMPACT OF LOAD SHEDDING - erb.org.zm

1

Chapter 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.

Page 12: IMPACT OF LOAD SHEDDING - erb.org.zm

2

Figure 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

Page 13: IMPACT OF LOAD SHEDDING - erb.org.zm

3

owned 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 to restore

2.

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

Page 14: IMPACT OF LOAD SHEDDING - erb.org.zm

4

In 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

Page 15: IMPACT OF LOAD SHEDDING - erb.org.zm

5

milling (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,

Page 16: IMPACT OF LOAD SHEDDING - erb.org.zm

6

net 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.

Page 17: IMPACT OF LOAD SHEDDING - erb.org.zm

7

1.2 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.

Page 18: IMPACT OF LOAD SHEDDING - erb.org.zm

8

Source: SMES Poultry W

ebsite, Zambia

Page 19: IMPACT OF LOAD SHEDDING - erb.org.zm

9

Chapter 2

Theoretical Framework

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.

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

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:

Table 1: Cost of unserved energy for Zambia – 2012 to 2015

Page 20: IMPACT OF LOAD SHEDDING - erb.org.zm

10

In 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.

Page 21: IMPACT OF LOAD SHEDDING - erb.org.zm

11

2.2.3 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).

Page 22: IMPACT OF LOAD SHEDDING - erb.org.zm

12

2.2.4 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

Page 23: IMPACT OF LOAD SHEDDING - erb.org.zm

13

the 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 scale enterpriseIndependent 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 equipment damage due to load shedding.

Maintenance cost • Costs associated with maintenance of alternative sources of energy.

Restart costs • Costs associated with re-starting operations due to loss of power.

Page 24: IMPACT OF LOAD SHEDDING - erb.org.zm

14

Source: SMES Poporn W

ebsite, Zambia

Page 25: IMPACT OF LOAD SHEDDING - erb.org.zm

15

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 field of manufacturing, construction and trading services

4. 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

Page 26: IMPACT OF LOAD SHEDDING - erb.org.zm

16

3.3 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

Page 27: IMPACT OF LOAD SHEDDING - erb.org.zm

17

the 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

nei

= the number of enterprises selected from the ith sector in the district.

N ei = 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

Page 28: IMPACT OF LOAD SHEDDING - erb.org.zm

18

assist 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.

Page 29: IMPACT OF LOAD SHEDDING - erb.org.zm

19

CHAPTER 4

RESULTS AND DISCUSSION OF FINDINGS

4.1 Distribution of small scale enterprises by sector

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

22  

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

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.

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

Page 30: IMPACT OF LOAD SHEDDING - erb.org.zm

20

The enterprises were requested to rank the major operational constraints and stated as depicted in figure 3.

Figure 3: Enterprises operational constraints - 2015

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.

23

Figure 3: Enterprises operational constraints - 2015

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%, competition, security and other which were reported by 20.4%, 3.5% and 7.6% respectively. In a study on Electricity insecurity and SMEs, the Overseas Development Institute (UK)

Finance Fuel Electricity Labour Competition Security Other

Percentage 27.70 1.00 35.80 4.00 20.40 3.50 7.60

-

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

Perc

enta

ge

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%.

The highest ranking operational constraint was electricity which was reported by 35.8% of the enterprises. This was followed by Finance at 27.7%, competition, security and other which were reported by 20.4%, 3.5% and 7.6% respectively. In a study on Electricity insecurity and SMEs, the Overseas Development Institute (UK) established that 49.3% of SMEs in Sub Saharan Africa, identified electricity as a major constraint in their business operations (The Overseas Development Institute, 2014).

were 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%.

Page 31: IMPACT OF LOAD SHEDDING - erb.org.zm

21

Table 3: Distribution of small scale enterprises by sector – 2015

Sector / District Kitwe Ndola Lusaka Livingstone TotalPercent

age Share

Agriculture, Forestry and Fishing

- 1 - 11 12 0.1%

Mining Support Services 32 - - - 32 0.2%

Manufacturing 136 195 1,023 72 1,426 9.3%

Repair and Installation of Machinery and Equipment

15 - 56 - 71 0.5%

Water Supply Sewerage Waste Management and Remediation

- - 11 - 110.1%

Construction 28 - 38 - 66 0.4%

Wholesale and Retail Trade 1,088 1,445 4,440 560 7,533 48.9%

Transportation and Storage 26 - 86 25 137 0.9%

Accommodation and Food Services

396 696 1,479 200 2,771 18.0%

Information and Communication

30 26 135 - 191 1.2%

Real Estate Activities - 9 41 - 50 0.3%

Professional Scientific and Technical Activities

- 30 164 - 194 1.3%

Administrative and Sup-port service activities

49 72 176 23 320 2.1%

Education 90 90 342 46 568 3.7%

Human health and So-cial work activities

- - 84 14 98 0.6%

Arts Entertainment and recreation

6 9 52 - 67 0.4%

Other services activities 195 416 1,092 165 1,868 12.1%

Total 2,091 2,989 9,219 1,116 15,415 100.0%

4.2 Number of workers employed by small scale enterprises

Table 4 shows the number of workers employed by small scale enterprises by gender and location. Small scale enterprises provided employment to a total of 174,028 employees in 2015. This implies that each establishment on average had 11 employees. In terms of sex, 70,644 (41%) were female and 103,383 (59%) were male. In terms of location, Lusaka district had the highest number of employees at 113,385 (65%), followed by Kitwe 26,530 (15%) and Ndola 24,101 (14%) in that order. Livingstone had the least number of employees at 10,011 employees reflecting 6%.

Page 32: IMPACT OF LOAD SHEDDING - erb.org.zm

22

Table 4: Number of workers employed by small scale enterprisesFemale employees 2015 Male employees 2015 Total employees 2015

Kitwe 6,794 19,736 26,530

Ndola 9,756 14,344 24,101

Lusaka 51,165 62,220 113,385

Livingstone 2,929 7,082 10,011

Total 70,644 103,383 174,028

4.3 Wage bill

The annual wage bill for the establishments ranged from K136 to K466,869.45 in 2015. The average wage bill for each enterprise was K13,474.33 per month. The wage bill broken down by city for 2015 and 2014 is depicted in figure 4.

Figure 4: Small scale enterprises annual wage bill – 2014 and 2015

The annual wage bill increased from K1,498,983,742 in 2014 to K 1,574,727,600 in 2015 reflecting an increase of 5.1%. In terms of average monthly earnings, an employee earned K754 in 2015. This figure was lower than the average earning of K 2,344 per month which was captured by the CSO’s labour force survey for 2014 for both formal and informal sector (Central Statistical Office, 2015).

4.4 Business working hours

Table 5 shows the average number of working hours per day for small scale enterprises by location in 2015. Lusaka, Ndola and Livingstone districts had the highest number of working hours per day of 11 hours, with Kitwe having the least number of working

24  

1,800,000,000  

1,600,000,000  

1,400,000,000  

1,200,000,000  

1,000,000,000  

800,000,000  

600,000,000  

400,000,000  

200,000,000  

0  

Kiwe Ndola Lusaka Livingstone Total Annual  Wagebill  2014 305,471,077 262,556,339 813,980,069 116,976,256 1,498,983,742 Annual  Wagebill  2015 281,456,809 274,918,877 920,915,623 97,436,291 1,574,727,600

ZMW

 

Page 33: IMPACT OF LOAD SHEDDING - erb.org.zm

23

Table 5: Average number of operating hours per day

District Average operating hours per day 2015

Kitwe 10

Ndola 11

Lusaka 11

Livingstone 11

Total 11

Table 6 shows the average number of operating days per week for the small scale enterprises in the four districts. On average, the small scale establishments operated 6 days in a week as summarized in table 5.

Table 6: Average number of operating days per week - 2015

District Average number of operating days per week

Kitwe 6

Ndola 6

Lusaka 6

Livingstone 6

Total 6

4.5 Load shedding Experience

Table 7 shows the number of small scale enterprises that had experienced load shedding in 2015. The table shows that 12,781 (83.1%) of the small scale enterprises experienced load shedding in 2015, of these, the highest number at 8,343 (65%) were from Lusaka followed by Ndola with 1,874 (13%) and Kitwe with 1,713 (12%). The lowest number was Livingstone with 851 (7%).

hours of 10. Among the enterprises, only 3.7% operated their businesses beyond 22:00 hours implying that any load shedding that was done beyond 22:00 hours did not

have any impact on the operations of the majority of the enterprises.

Page 34: IMPACT OF LOAD SHEDDING - erb.org.zm

24

Table 7: Number of small scale enterprises that had experienced load shedding - 2015Load shedding experience during the year

2015Total

yes noDistrict Kitwe 1,713 378 2,091

Ndola 1,874 1,115 2,989

Lusaka 8,343 843 9,186

Livingstone 851 265 1,116

Total 12,781 2,601 15,382

In terms of the average number of hours of load shedding experienced per day, overall 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% as summarized in Table 8. In terms of the average, enterprises experienced load shedding hours of 3.9 hours from January to June and 6.9 hours from July to December in 2015. Therefore, most load shedding hours were experienced during the period July to December 2015. Around this time it was reported that the power deficit in 2015 ranged from 560 to 1000 MW.

In 2015, the areas that experienced the most load shedding hours on average per day was Lusaka at 5 hours followed by Livingstone at 4 hours. Kitwe and Ndola on average experienced 3 hours per day. This is summarized in table 8.

Table 8: Average number of hours of load shedding per day – 2014 and 2015

DistrictAverage Load shedding hours

per day 2015Average Load shedding hours per day

2014

Kitwe 3 1

Ndola 3 1

Lusaka 5 1

Livingstone 4 0

Total 4 1

As depicted in table 8 the extent of load shedding, in the four cities intensified in 2015 compared to 2014.

Table 9 shows the source of information for load shedding schedules by location in 2015. The table shows that most (5,746) small scale enterprises indicated that the Short Messaging System (SMS) was the major source of information reflecting 45.0% followed by members of the public at 2,425 (19%). Similarly, a total of 724 (6%)

During 2015, load shedding schedules were developed by the Utility advising the public on the time and day when the power outage would be implemented. Enterprises were requested to state if these schedules were strictly followed by ZESCO and 51% stated that they were followed while 49% said otherwise.

Page 35: IMPACT OF LOAD SHEDDING - erb.org.zm

25

indicated that they received this information from other sources which included ZESCO sales points, through own observation and leaflets. A total of 2,125 (17%) indicated that they did not receive information on ZESCO load shedding schedules.

Table 9: Small scale enterprise source of information for ZESCO’s load shedding schedule

District

Major source of ZESCO load shedding schedules

TotalSMS Radio TV Newspaper

Members of the public Other None

Kitwe 560 51 67 167 749 85 34 1,713

Ndola 705 34 60 210 315 32 518 1,874

Lusaka 4,379 221 45 810 1,049 266 1,573 8,343

Livingstone 102 0 96 0 312 341 0 851

Total 5,746 306 268 1,187 2,425 724 2,125 12,781

4.6 Electricity expenses by small scale enterprises

In this study, electricity expenses ranged from K50 to K 14,083 per month. The average electricity expenditure was K754.61. Past studies on Zambia which captured the electricity expenses by small scale enterprises were not immediately available. In this study, the highest expenditure was recorded in October at K10,427,260 while the least was recorded in December at K9,102,827. Table 10 shows the total annual expenditure on electricity by small scale enterprises in 2014 and 2015. The table shows that expenditure on electricity increased by 3.0% from K 114, 897,354 in 2014 to K 118,307,145 in 2015.

Table 10: Small scale enterprises Expenditure on electricity – 2014 and 2015District Electricity Expenses in 2014 Electricity Expenses in 2015

Kitwe 12,228,442.00 12,099,950.00

Ndola 34,036,218.00 37,279,467.00

Lusaka 62,702,574.00 62,469,328.00

Livingstone 5,930,120.00 6,458,400.00

Total 114,897,354.00 118,307,145.00

In terms of the proportion of electricity expenses to the total annual business expenses in 2015, the majority (85%) of the small scale enterprises on average indicated that electricity bills constituted up to 25 % of their total annual business expenses. This was followed by 10% who indicated that electricity bills constituted between 26 to 50 % proportion of the total business expenses, while 5% indicated above 51%. This information is summarized in Table 10.

Page 36: IMPACT OF LOAD SHEDDING - erb.org.zm

26

Table 11: Cost of electricity as a proportion of total annual expenses in 2015Cost of electricity in Kwacha as a proportion of the total

expenses in 2015

Total0 -25 percent 26-50 percent 51-75 percent 76-100 percent

District Kitwe 1799 157 0 0 1,956

Ndola 2044 258 41 0 2,343

Lusaka 6846 906 493 90 8,335

Livingstone 811 0 0 0 811

Total 11,500 1,321 534 90 13,445

4.7 Business Annual Turnover

Table 12: Small scale enterprises turnover – 2014 and 2015District 2015 (Real) 2015 (Nominal) 2014

Kitwe 1,025,962,098.00 1,204,479,503.00 1,443,210,307.00

Ndola 727,120,710.00 853,639,713.00 929,993,511.00

Lusaka 2,614,088,819.00 3,068,940,273.00 3,169,717,225.00

Livingstone 554,396,861.00 650,861,915.00 679,447,150.00

Total 4,921,568,488.00 5,777,921,404.00 6,222,368,193.00

The use of electricity by enterprises is depicted in Table 13. In 2015 enterprises that used electricity in their business accounted for 88.3% (13,542) compared to 85.3% (13,047) in 2014. The highest percentage was recorded in Lusaka city (62.3%) followed by Ndola at 17.4%. Kitwe and Livingstone reported 13.9% and 6.3% respectively. Comparing 2014 and 2015, generally there was a marginal increase in the use of electricity by small scale enterprises in their business operations.

In this study, the annual turnover, defined as total sales of the business in

a year, ranged from K 2,200 to K10,999,989. The average annual turnover was

K407,527, while the highest average turnover per month was recorded in

April at K41, 205.68, while the least turnover was recorded in November K33,

849.50. In 2015, in terms of breakdown by cities the highest total turnover

(K3,068,940,237) was recorded in Lusaka followed by Kitwe at

K1,204,479,503, Ndola at K853,639,713 and Livingstone at K650,861,915.The

performance of turnover in 2015 against 2014 is depicted in table12.

Generally in all the cities, there was a decline in nominal turnover, overall it declined

by 7.1%. Similarly, in real terms, the annual turnover declined from K6, 222,368,

193 in 2014 to K4, 921,568,488 in 2015 reflecting a reduction of 20.9%.

Page 37: IMPACT OF LOAD SHEDDING - erb.org.zm

27

Table 13: Electricity use by establishments for business operations

Did your establishment use electricity in its operations?

2014 2015

Yes No Yes No

District

Kitwe 1,870 203 1,888 203

Ndola 2,308 681 2,360 629

Lusaka 8,039 1,073 8,443 698

Livingstone 830 286 851 265

Total 13,047 2,243 13,542 1,795

In terms of internet connectivity, Table 14 depicts the number of small scale enterprises that had internet connectivity. Out of the 15,340 enterprises only 4,298 (28%) had internet connectivity.

Table 14: Internet connectivity by small scale enterprises - 2015Does this establishment have internet connectivity?

Totalyes No

District Kitwe 811 1,262 2,073

Ndola 740 2,249 2,989

Lusaka 2,397 6,765 9,162

Livingstone 350 766 1,116

Total 4,298 11,042 15,340

4.8 The impact of load shedding on turnover

In order to determine the impact of the load shedding on the business turnover,

small scale enterprises were asked to quantify the loss in turnover in monetary

terms. Load shedding leads to the disruption of business operations that results in

loss of turnover. 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 K19,251.16.

Table 15 shows district wise reported loss in turnover and reported loss turnover as

proportion of 2014 turnover. The total reported annual loss in turnover from the

four cities in 2015 was K456, 255,094.50 reflecting 7.33% of the total annual

turnover. Lusaka district recorded the highest loss in turnover of K 296,035,506

followed by Kitwe at K86,972,776 and Ndola at K 63,718,087.50. Livingstone had

the lowest loss in turnover at K 9,528,725.

Page 38: IMPACT OF LOAD SHEDDING - erb.org.zm

28

Table 15: Impact of load shedding on turnover - 2015

DistrictReported Loss in turnover

as a result of load shedding

Kitwe 86,972,776 1,443,210,307.00 6.03%

Ndola 63,718,087.50 929,993,511.00 6.85%

Lusaka 296,035,506 3,169,717,225.00 9.34%

Livingstone 9,528,725 679,447,150.00 1.40%

Total 456,255,094.50 6,222,368,193.00 7.33%

4.9 The Impact of load shedding on labour costs

The costs arising from idle labour were defined as the amount of money paid to a worker

Table 16: Labour costs due to load shedding by district

District Idle labor cost (ZMW) Overtime cost (ZMW) Total

Kitwe 3,773,150 1,427,900 5,201,050

Ndola 4,753,629 408,000 5,161,629

Lusaka 13,138,908 3,805,450 16,944,358

Livingstone 2,529,000 0 2,529,000

Total 24,194,687 5,641,350 29,836,037

Source: SMES W

elders Website, Zam

bia

2014 (Turnover) Percentage of reported losses to Turnover

In some cases, due to load shedding, some establishments experienced idle

labour while some incurred overtime labour costs as they were forced to

operate longer hours than usual. It was found that 22.8 percent of enterprises

reported cases of idle labour, labour while 9.8% reported incurring overtime labour

costs due to load shedding.

otherwise not working due to load shedding. Meanwhile, overtime labour

cost was defined as the amount of money paid to a worker for extra hours

worked after load shedding. At firm level idle labour costs on average ranged

from K0.00 to K8,333.33 per month. The average idle labour cost per firm was

K 130.80 per month. Equally, at firm level overtime labour costs on average

ranged from K0 to K5, 000 per month. The average overtime labour cost per firm

was K30.49 per month. Table 16 shows the costs associated with idle labour and

overtime in each of the four districts.

Page 39: IMPACT OF LOAD SHEDDING - erb.org.zm

29

Source: SMES W

elders Website, Zam

bia

Page 40: IMPACT OF LOAD SHEDDING - erb.org.zm

30

Lusaka experienced the highest costs of idle labour and overtime costs at K13,138,908 and K3,805,450 respectively. Ndola recorded the second highest costs in terms of idle labour cost of K4,753,629, while on the other hand, its overtime costs were the second lowest after Livingstone at K408,000. Kitwe reported idle labour costs of K3,773,150 and over time costs of K1,427,900. Livingstone recorded K2,529,000 idle labour cost and did not experience any overtime costs. Notably, some small scale enterprises adapted to the load shedding schedules and staff was meant to operate during the period when there was power. In addition, some small scale enterprises employed other alternative sources of energy.

4.10 Equipment damage and maintenance attributed to load shedding

A number of small scale enterprises lost equipment due to load shedding. Some of the equipment was completely damaged. Table 17 shows costs associated with equipment damage and maintenance costs of as a result of load shedding in 2015.

Table 17: Cost of equipment damage and maintenance costs due to load shedding

District Cost of equipment Maintenance costs Total

Kitwe 3,394,610.00 5,629,350.00 9,023,960.00

Ndola 3,111,700.00 10,171,582.00 13,283,282.00

Lusaka 9,666,138.00 91,740,149.00 101,406,287.00

Livingstone 839,000.00 4,986,250.00 5,825,250.00

Total 17,011,448.00 112,527,331.00 129,538,779.00

A total of K17,011,448 was spent on equipment damage as a result of load shedding by small scale enterprises. Of this amount, Lusaka city recorded the highest total cost of K9,666,138 followed by Kitwe city at K3,394,610, while Ndola city reported K3,111,700 and Livingstone with K839,000. In terms of maintenance costs, a total of K112,527,331 was spent in the four cities. Lusaka city recorded the highest at K91,740,149 followed by Ndola at K10,171,582, while Kitwe reported K5,629,350 and Livingstone with K4,986,250.

In order to mitigate the impact of load shedding on the business, some small scale enterprise opted to purchase or hire alternative energy sources such as genset, solar panels, and invertors among others. Table 18 shows the summary of the costs associated with purchasing and hiring per month.

It was established that 29.9% of the small scale enterprises equipment was

damaged due to load shedding. Furthermore, only 11.7% of such equipment was insured. The cost of damaged equipment ranged from K2 to K56,000. The average cost of damaged equipment was K 3,112.80.

Page 41: IMPACT OF LOAD SHEDDING - erb.org.zm

31

Table 18: Purchases and hiring costs for alternative energy sourcesDistrict Purchase Costs Hiring Costs TotalKitwe 3,308,900 408,000 3,716,900

Ndola 6,419,672 171,300 6,590,972

Lusaka 70,991,261 1,881,360 72,872,621

Livingstone 3,630,550 588,000 4,218,550

Total 84,350,383 3,048,660 87,399,043

A total of K87,399,043 was spent on purchasing and hiring alternative energy sources by small scale enterprises in the four districts. Of this amount, K84, 350,383 was spent on purchasing of alternative energy sources, while K3,048,660 was spent on hiring.

Lusaka district reported the highest total cost at K72,872,621 followed by Ndola at K6,590,972 and Livingstone at K4,218,550. Kitwe had the lowest cost at K3,716,900.

4.11 The cost of restarting operations as a result of load shedding

Business owners are likely to suffer from costs incurred due to the restarting of an industrial process. For instance, some operations like bakeries incur costs when operations are restarted. Further, internet café business might also incur restart costs as work might be lost due to loss of power. The costs of restarting operations ranged from K0 to K 35,000. On average the reported costs due to restarting of operations was K 4,663.84. Figure 4 shows the costs due to the restarting of business operations by small scale enterprises.

Figure 5 : Costs of restarting operations due to load shedding

25  

4,000,000.00

3,500,000.00

3,000,000.00

2,500,000.00

2,000,000.00

1,500,000.00

1,000,000.00

500,000.00

-

Kitwe Ndola Lusaka Livingstone Total restart cost 1,288,960.00 236,300.00 2,139,580.00 89,550.00 3,754,390.00

ZMW

Page 42: IMPACT OF LOAD SHEDDING - erb.org.zm

32

A total of K3,754,390.00 was spent on restarting of operations by small scale enterprises in 2015 in the four cities as a result of load shedding. Lusaka recorded the highest cost of restarting operations at K2,139,580 (57%) followed by Kitwe at K1,288,960 (34.3%%). Ndola and Livingstone recorded K236,300 (6.3%) and K839,000 (2.4%) respectively.

4.12 Measures put in place by enterprises to tackle reduced load.

In order to cope with the increased load shedding experienced in 2015, small scale enterprises adopted several strategies with the objective of mitigating against the impact of load shedding on their businesses. One of the objectives of the study was to establish the different coping mechanisms used by small scale enterprises to mitigate against load shedding. This section outlines the various coping mechanisms employed by small scale enterprises.

4.12.1 Demand side management strategies

In the study, 55% reported employing strategies to mitigate against load shedding. However, there was no reported shutdown of operations and very few reallocations of businesses (0.2%). Table 16 depicts the number of small scale enterprises that used various strategies to cope with the reduced load by city.

Source: Energy savers Inc. Website, Zam

bia

Page 43: IMPACT OF LOAD SHEDDING - erb.org.zm

33

Table 19: Demand side management strategiesDid you employ demand side management strat-egies during load shedding experienced in 2015 such as use of LED bulbs?

TotalYes No

District Kitwe 1,161 552 1,713

Ndola 879 978 1,857

Lusaka 4,407 3,784 8,191

Livingstone 485 366 851

Total 6,932 5,680 12,612

4.12.2 Use of generators

Figure 5 shows the number of small scale enterprises that used generators as an alternative source of energy during load shedding in 2015. 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. In a similar study, Andrew, Emily, Alberto and Juan (2014) established that due to electricity insecurity, 33% of SMEs in developing countries used a generator as the main practice to mitigate the impact of electricity insecurity.

Figure 6: Number of small scale enterprises using generators - 2015

4.12.3 Use of uninterruptible power supply

Table 20 shows that out of 11, 698 respondents, only 276 (2.4%) indicated that they used uninterruptible power supply (UPS) as an alternative energy source to mitigate against the impact of load shedding.

26  

9,000  

8,000  

7,000  

6,000  

5,000  

4,000  

3,000  

2,000  

1,000  

-­‐  

Kitwe Ndola Lusaka Livingstone Total Yes 488 542 2,089 392 3,511 No 1,157 1,100 5,570 424 8,251

Num

ber  

Page 44: IMPACT OF LOAD SHEDDING - erb.org.zm

34

Table 20 : Number of small scale enterprises using uninterruptible power supply - 2015Use UPS

TotalYes No

District Kitwe 68 1,577 1,645

Ndola 0 1,642 1,642

Lusaka 208 7,422 7,630

Livingstone 0 781 781

Total 276 11,422 11,698

4.12.4 Surge protectors

Table 21 shows the number of small scale enterprises using surge protectors as a measure to protect electrical devices from damage among the small scale enterprises in 2015. The Table shows that only 249 (2.1%) indicated that they used surge protectors in the four districts.

Table 21: Number of small scale enterprises using surge protectorsYes Surge protectors

TotalNo

District Kitwe 68 1,577 1,645

Ndola 17 1,625 1,642

Lusaka 164 7,466 7,630

Livingstone 0 781 781

Total 249 11,449 11,698

4.12.5 Back up data systems

Table 22 shows the use of back up data systems among small scale enterprises in 2015. In order to save data that could result from damaged electrical equipment due to load shedding some small scale enterprises invested in back up data systems. Table 18 shows that 813 (6.9%) of the small scale enterprises invested in back up data systems.

Table 22: Number of small scale enterprises using back up data systemsBack up data systems

TotalYes No

District Kitwe 18 1,627 1,645

Ndola 290 1,352 1,642

Lusaka 505 7,125 7,630

Livingstone 0 781 781

Total 813 10,885 11,698

Page 45: IMPACT OF LOAD SHEDDING - erb.org.zm

35

4.12.6 Use of back up batteries

Table 23 shows the use of back up batteries as a mitigation measure against load shedding among small scale enterprises in the four districts. The table shows that out of a total of 11,698 small scale enterprises, 803 (6.9%) indicated that they used back up batteries in order to mitigate against the loss of power.

Table 23 : Number of small scale enterprises using back up batteriesBack up batteries

TotalYes No

District Kitwe 34 1,611 1,645

Ndola 273 1,369 1,642

Lusaka 496 7,134 7,630

Livingstone 0 781 781

Total 803 10,895 11,698

4.12.7 Security enhancement

Load shedding brings about security concerns for small scale enterprises. In order to cope with theft threats, small scale enterprises enhanced their security features of their business. Table 24 shows that 803 (6.9%) small enterprises reported that they had enhanced their security features in the four districts.

Table 24: Security enhancementSecurity enhancement

TotalYes No

Dis-trict

Kitwe 51 1,594 1,645

Ndola 389 1,253 1,642

Lusaka 349 7,281 7,630

Livingstone 14 767 781

Total 803 10,895 11,698

4.12.8 Reduction in labour

Small scale enterprises indicated that one way of coping with load shedding was through the reduction in labour. Table 25 shows that 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.

Page 46: IMPACT OF LOAD SHEDDING - erb.org.zm

36

Table 25 : Number of small scale enterprise that reduced labour hours due to load shedding - 2015

Did the establishment reduce on labour to either cope or remedy the load shedding situation?

Total

Yes No

District Kitwe 250 1,463 1,713

Ndola 136 1,704 1,840

Lusaka 574 7,474 8,048

Livingstone 53 798 851

Total 1,013 11,439 12,452

due to load shedding. A total of 876 (7.6%) indicated that they reduced business working hours due load shedding.

Table 26: number of small scale enterprises that reduced working hours due to load shedding - 2015

Did the establishment reduce on working hours to either cope or remedy the load shedding

situation? Total

Yes No

District Kitwe 73 1,640 1,713

Ndola 155 1,685 1,840

Lusaka 593 7,426 8,019

Livingstone 55 796 851

Total 876 11,547 12,423

4.12.10 Relocation of business

Table 27 shows the number of small scale enterprises that relocated their business due to load shedding. The table shows that only 21 (0.2%) small scale enterprises out of 12,402 that had responded indicated that they relocated their business to a different location due to load shedding. These enterprises were all from Lusaka.

4.12.9 Reduction in the working hours

Table 26 shows the number of small scale enterprises that had reduced their working hours

As stated earlier, each establishment on average had 11 employees, however, during the period of load shedding some enterprises (8%) laid off their work force. Specifically, it was reported that on average 15 workers were laid off by each establishment at an associated average cost of K14,883.42. This cost relates to redundancy costs.

Page 47: IMPACT OF LOAD SHEDDING - erb.org.zm

37

Table 27 : Number of small scale enterprises that relocated due to load shedding - 2015Did the establishment Relocate to either

cope or remedy the load shedding situation? Total

Yes No

District Kitwe 0 1,713 1,713

Ndola 0 1,840 1,840

Lusaka 21 7,998 8,019

Livingstone 0 851 851

Total 21 12,402 12,423

each establishment.

4.12.11 Shutdown of business operations

Table 28 shows that none of the small scale enterprise had reported shutting down their business operations due to load shedding.

Table 28: Number of small scale enterprise that had shutdown business operations due to load shedding - 2015

Did the establishment Shutdown to either cope or remedy the load shedding situation?

TotalNo

District Kitwe 1,713 1,713

Ndola 1,840 1,840

Lusaka 8,019 8,019

Livingstone 851 851

Total 12,423 12,423

4.12.12 Change of operations hours

ZESCO designed load shedding schedules in order to assist its clients to plan. The respondents were asked to indicate if they switched the operating hours in order to suit the load shedding schedule. Table 29 shows that 808 (6.5%) of the small scale enterprises from three districts indicated that they changed their business operating hours in order to accommodate the load shedding schedule. However, none of the small scale enterprises in Livingstone had indicated change of operating hours.

In terms of costs associated with relocation of the businesses, it was reported that k18,580.52 per

a total of K 390,191 was spent on relocation of business reflecting an average cost of

Page 48: IMPACT OF LOAD SHEDDING - erb.org.zm

38

Table 29 : Switching of working hoursDid the establishment switch working hours as a

measure to either cope or remedy the load shedding situation?

TotalYes No

Dis-trict

Kitwe 152 1,561 1,713

Ndola 182 1,658 1,840

Lusaka 474 7,545 8,019

Living-stone

0 851 851

Total 808 11,615 12,423

4.12.13 Other measures taken

Table 30 shows other measures taken by small scale enterprises to mitigate against the impact of load shedding which included the use of power banks, candles, charcoal, use of solar and rechargeable lamps, use of torch and additional casual labour for security and doing laundry in some instances.

Other measures taken to mitigate against the impact of load shedding

TotalYes No

District Kitwe 405 1240 1645

Ndola 640 1002 1642

Lusaka 2236 5372 7608

Livingstone 290 491 781

Total 3,571 8,105 11,676

With regards to costs, a total of K4, 178,750 was spent on other measures translating into average costs of K1, 170.19.

Furthermore among those who reported being load shedded, a total of 3,814 (30.1%) reported having lost material. The details on materials lost include the following: beers, foodstuffs, paint, among. Figure 7 shows the number of small scale enterprises that indicated loss of materials in 2015 per district.

A total of K 1,956,735 was spent on the switching of working hours as a measure to cope with load shedding reflecting an average cost of K2,421.70 per establishment.

Table 30: Number of small scale enterprises that used other measures

Page 49: IMPACT OF LOAD SHEDDING - erb.org.zm

39

27  

10,000  9,000  8,000  7,000  6,000  5,000  4,000  3,000  2,000  1,000  

-­‐  

90.0%

80.0%

70.0%

60.0%

50.0%

40.0%

30.0%

                         

  Kitwe Ndola Lusaka Livingstone Total

Yes 699 595 2,321 199 3,814 No 1,014 1,262 5,937 652 8,865

 

Perc

enta

ge

Figure 7: Number of enterprises that lost materials due to load shedding

The total cost of material lost ranged from K0 to K70,000. The average cost of material lost was K304 per firm. Cumulatively all enterprises reported a total cost of material lost as K 4,487,214. Of the enterprises that reported material lost, only 3.2% of the establishments reported having insured the materials that were lost due to load shedding.

4.13 Challenges due to load shedding

Figure 8 : Challenges faced due to load shedding by small scale enterprises

45

Figure 8 : Challenges faced due to load shedding by small scale enterprises

4.14 The impact of load shedding on small scale enterprises As stated in chapter two of this study report, the impact of load shedding on small scale enterprises will be estimated using the Direct Assessment Method (DAM).

The total direct cost relationship is restated in the formula below:

𝐓𝐓𝐓𝐓𝐓𝐓𝐢𝐢 = 𝐎𝐎𝐎𝐎𝐢𝐢 + 𝐌𝐌𝐓𝐓𝐢𝐢 + 𝐎𝐎𝐓𝐓𝐢𝐢 + 𝐄𝐄𝐓𝐓𝐓𝐓𝐢𝐢 + 𝐑𝐑𝐓𝐓𝐢𝐢 Where:

TDCi is the total direct cost for the ith consumer;

OLi is cost of lost output (turnover);

MDCi is the material destruction cost;

LCi is labour cost;

EDCi is the equipment damage;

MCi is the maintenance cost as a result of load shedding; and RCi is restart cost.

Theft Phonedisruption

InternetDisruption Water Supply Storage

Disruption Other None

Percentage 20.5% 17.6% 17.9% 85.5% 40.6% 19.9% 18.1%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

Per

cen

tage

During load shedding, establishments faced several challenges as depicted in figure 8. Water supply disruption (85.5%) was reportedly the major challenge followed by storage disruption (40.6%) and theft cases (20.5%) in that order. On the other hand 19.9% reported other operational challenges which included delayed finishing of works for those in tailoring business and reduction in business sales among others. While 18.1% reported they did not experience challenges due to load shedding.

Page 50: IMPACT OF LOAD SHEDDING - erb.org.zm

40

4.14 The impact of load shedding on small scale enterprises

As stated in chapter two of this study report, the impact of load shedding on small scale enterprises will be estimated using the Direct Assessment Method (DAM).

The total direct cost relationship is restated in the formula below:

TDCi = OLi + MCi + LCi + EDCi + RCi

Where:

TDCi is the total direct cost for the ith consumer;

OLi is cost of lost output (turnover);

MDCi is the material destruction cost; LCi is labour cost;

EDCi is the equipment damage;

MCi is the maintenance cost as a result of load shedding; and RCi is restart cost.

The cost per unit of electricity (kWh) lost is the estimated as follows:

OCi = TDCi

-----------

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 computed as the product of normal energy consumption per hour multiplied by the total number of load shedding hours in a year. Table 32 shows the variables used in the DAM.

Source: SMES W

ebsite, Zambia

Page 51: IMPACT OF LOAD SHEDDING - erb.org.zm

41

Table 31: Variables used in the DAM

No. Symbol Variable Cost

1. OLi Cost of lost output (turnover) 456,255,094.50

2. MDCi Material destruction costs; 4,487,214.00

3. LCi Labour costs (idle and overtime costs) 29,836,037.00

4. EDCi Equipment damage 17,011,448.00

5. MCi Maintenance cost 112,527,331.00

6. RCi Restart costs. 3,754,390.00

7. TDCi Total direct cost for the ith consumer 623,871,514.5

8. kWhlosi Total units of electricity (kWh) lost or unsupplied due to load shedding

65,927,681.82

OCi Cost per kWh lost (ZMW) 9.46

The district with the highest loss was Kitwe with 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.

Table 32: The cost of load shedding on small scale enterprisesDistrict Cost per

kWh US$cost per kWh

(ZMW)Total direct cost kWh lost

Kitwe 1.94 19.43 104,981,846.00 5,403,856.00

Ndola 0.51 5.13 82,621,268.50 16,105,575.00

Lusaka 0.97 9.71 417,605,675.00 43,009,080.30

Livingstone 0.53 5.34 18,662,725.00 3,496,189.09

Overall Cost 0.95 9.46 623,871,514.50 65,927,681.82

The total cost of lost output for all enterprises was K 456,255,094.50 while the cost

of lost material was K4,487,214. Further, labour costs were K29,836,037,

while a total cost of K112,527,331 related to maintenance costs and

K17,011,448.00 to equipment damage costs. The costs of restarting operations

were K3,754,390. The overall total direct costs amounted to K623,871,514.5

(0.3% of GDP) and total units of 65,927,681.82 of unserved energy

translating into ZMW 9.46/kWhlos (kilowatt hour lost).

Page 52: IMPACT OF LOAD SHEDDING - erb.org.zm

42

Chapter 5

Conclusion and recommendations

5 Conclusion

expenses.

Further, some enterprises reported damaged equipment due to load shedding and most of this equipment was not insured. Meanwhile, some enterprises spend resources on restarting of operations as a result of load shedding.

During load shedding some enterprises employed strategies to mitigate against load shedding. However, there was no reported shutdown of operations and very few reallocations of businesses. The key mitigating strategies were: use of generators; use of uninterruptible power supply (UPS); use of surge protectors; use of back up data systems; and use of back up batteries. Further, as a consequence of load shedding, some small

The Zambian Electricity Supply Industry (ESI)’s overreliance on hydro generation suffered a setback in 2015 when there was 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 resultant power deficit of between 560 to 1000 MW resulted in load shedding of up to 8 hours. The load shedding affected the most business operations and financial viability. The small enterprises were the most affected mainly due to their lack of resilience and limited capacity to invest in alternative energy sources. Given the importance of small enterprises in the economy, it was imperative that the impact of load shedding is studied and understood so that corrective steps are undertaken.

Meanwhile, between 2014 and 2015, 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 K19,251.16. Close to a third of the

establishments, experienced cases of idle labour and overtime labour costs due

to load shedding.

The general demographic and characteristics of the sample established that most small enterprises are not yet formalized although they have existed for over 10 years. The small enterprises faced serious electricity constraints. Most enterprise provide employment to an average of 11 employees who are given wages. Overall 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%. Most enterprises were uncertain about the loading schedules because these were not strictly followed by ZESCO. The majority (85%) of the small scale enterprises on average indicated that electricity bills constituted between 0 to 25 % of their total annual business

Page 53: IMPACT OF LOAD SHEDDING - erb.org.zm

43

Source: SMES W

ebsite, Zambia

enterprises enhanced their security features while others reduced labour hours and others changed their business operating hours.

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 hours lost per district, Kitwe 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.

This study has established that the incidence of load shedding in 2015 lead 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 a possible inverse relationship between load shedding and small enterprise productivity as well as general business performance.

Page 54: IMPACT OF LOAD SHEDDING - erb.org.zm

44

Source: zambiatourism

Website, Zam

biaSource: zam

biatourism W

ebsite, Zambia

Page 55: IMPACT OF LOAD SHEDDING - erb.org.zm

45

Recommendations

2. Most small enterprises are not formalized and have limited insurance which is a risk to their business operations and financial viability. It is recommended that Government puts in place initiatives that promote business formalization and insurance schemes. Once formalized, it will be easy for small enterprises to access support such as credit to access alternative sources of energy and insurance schemes.

3. The lack of cost reflective tariffs has discouraged investment in both additional hydroelectricity capacity and alternative sources of electricity. The cost of service study being undertaken by the Energy Regulation Board (ERB) in 2017, must be utilized and its recommendations implemented in full so that a clear cost of power production is known and the migration path to cost reflectivity is clearly understood and known by all concerned.

4. Whilst the 2015 power situation was induced by a natural phenomenon, there is still concern that ZESCO must improve on the quality of service delivery. A case in point is when small enterprises could not plan their business operations because the load shedding schedules that were published by ZESCO were not strictly followed. Additionally, 17% of the enterprises reported that they did not have access to information on the load shedding schedules developed by the Utility. In this regard, ZESCO must increase awareness of information on the load shedding schedules. Further, the ERB must enhance the Key Performance. Indicator Framework (KPI) for ZESCO to cover the aspect of adherence to load shedding schedules.

5. In addition, specific studies must be commissioned to ascertain the comparative cost benefit analysis for hydroelectricity in comparison to alternative sources of electricity.

1. The over dependence on hydro power coupled by the inability by small enterprises to respond to any disaster in the electricity system, makes it inevitable that the country diversifies the energy mix. The country must develop an integrated Resource Management Plan (IRP) that will clearly outline pipeline projects for alternative sources of energy such as solar, coal, geo thermal and wind, which are less prone to disasters such as reduced water levels.

Page 56: IMPACT OF LOAD SHEDDING - erb.org.zm

46

References

Andrew , S., Emily , D., Alberto , L., & Juan, P. (2014). How does electricity insecurity affect businesses in low and middle income countries? Retrieved April 15, 2016, from www.odi.org

Bancy, M. W. (2007). Record Keeping and Growth of Micro and Small Enterprises. Retrieved December 16, 2016, from http://ir- library.ku.ac.ke/bitstream/handle/123456789/7122/Bancy%20Wawira%20Muchira.pdf?sequen ce=1

Borestein, S., Beshnell, J. B., & Wolak, F. A. (2002). Measuring market inefficiencies in California’s restructured wholesale electricity market. . American Economic Review,volume , 92, no.5, pp. 1376–1405. .

Karnataka: Energy policy Rview.

Central Intelligence Agency. (2017). The World Fact Book. Retrieved April 2017, from https://www.cia.gov/library/publications/the-world-factbook/geos/za.html

Central Statistical Office. (2015). Zambia Labour Force Survey Report 2014. Lusaka: Central Statistical Office.

Chisala, C. (2008). Institute of Developing Economies. Retrieved May 15, 2016, from http://www.ide.go.jp/English/Publish/Download/Dp/pdf/134.pdf

Coolidge, J., & Ilic, D. (2009). Tax Compliance Perception and Formalisation of Small Businesses in South Africa. Policy Reserach Working Paper 4992 World Bank, 1.

Institute of Public Policy. (2013). Introduction and Analysis of Secondary Data. Lahore: Institute of Public Policy.

Kanlisi , S. K., Amenga, J. E., Akomeah , D. O., Amoako , R., & Narh , E. (2014). The Socio-Economic Contribution Of Small-Scale Industries to Livelihood of Women in the Shea Butter Industry in the Wa Municipality. European Scientific Journal, 50-62.

Kaseke, N., & Hosking, S. (2012, June). Cost of Load Shedding to Mines in Zimbabwe: Direct Assement Approach. Retrieved April 10, 2016, from http://www.ijmra.us/project%20doc/IJPSS_JUNE2012/IJMRA-PSS967.pdf

Kateregga, E. (2009). The welfare costs of electricity outages: A contingent valuation analysis of households in the suburbs of Kampala, Jinja and Entebbe. Retrieved April 20, 2016, from http://www.academicjournals.org/article/article1379599097_Kateregga.pdf

Kazungu, I., Moshi, J., & Mchopa, A. (2014). POWER RATIONING DILEMMA: A BLOW TO SMALL &

MEDIUM ENTERPRISES (SMEs). International Journal of Economics, Commerce and Management United Kingdom, 1-14.

Kish, L. (1965). Survey Sampling. New York: Wiley.

Bose, R. K., Shukla, M., Srivasta, L., & Yaron, G. (2006). Cost of unserved energy in Karnataka, India.

Page 57: IMPACT OF LOAD SHEDDING - erb.org.zm

47

Mason, M. K. (2009). Research on Small BusinessesMason. Retrieved December 16, 2016, from http://www.moyak.com/papers/small-businessstatistics.html

Ministry of Commerce, Trade and Industry. (2007). Small and Medium Enterprises Survey 2003 – 2004.

Retrieved April 15, 2016, from www.mcti.gov.zm

magazine.

Nuwagaba, A. (2015). Micro Financing Of Small and Medium Enterprises (SMEs) in Zambia. Retrieved April 5, 2016, from http://www.ijbmi.org/papers/Vol%284%298/H0408048056.pdf

Nyamazana. (2015). Zambia Economic Cost of Load shedding. Lusaka: Institute of Economic and Social Research, UNZA.

Nyasha , K. (2014). A Comparative Cost Assessment of Electricity Outages and Generation Expansion in Zimbabwe. Retrieved December 2016, from http://www.garph.co.uk/IJARMSS/Apr2014/1.pdf

Pasha, H. A., Ghaus, A., & Malik, S. (1990). The economic cost of Power outages in the industrial sector of Pakistan. Energy Economics, volume 4, pp. 301-18.

Project Gutenberg . (n.d.). Retrieved April 7, 2016, from http://www.gutenberg.us/articles/loadshedding

Samboko, P., Chapoto, A., Kuteya, A., Kabwe, S., Mukuka, R. M., Mweemba, B., et al. (2016, March). The Impact of Power Rationing on Zambia’s Agricultural Sector. Retrieved April 6, 2016, from http://www.iapri.org.zm/images/WorkingPapers/wp105.pdf

Solomon, K. F., & Yao, L. (2015). Electricity Power Insecurity and SMEs Growth: A case Study of the Cold Store Operators in the Asafo Market Area of the Kumasi Metro in Ghana. Open Journal of Business and Managenment, 3, 312-325.

Retrieved March 2017, from https://assets.publishing.service.gov.uk media/57a089e1ed915d622c000441/61270- Electricity-Insecurity-Briefing-170914.pdf

Yao , L., & Solomon , K. F. (2015,). Electricity Power Insecurity and SMEs Growth: A Case Study of the Cold Store Operators in the Asafo Market Area of the Kumasi Metro in Ghana. Open Journal of Business and Management, 3,312-325.

Zambia Institute for Policy Analysis & Research. (2015). Electricity Sub-Sector Brief Quaterly Report.

Lusaka: Zambia Institute for Policy Analysis & Research.

Ministry of Finance. (2016). 2015 Annual Economic Report. Lusaka: Ministry of Finance.

Minnaar, U. (2015). Cost of unserved energy. Johannesburg: SAIEE, Wattnow

Terry. (2001). Cost of unserved energy, World bank. Washington DC: Tata Energy Research

Company. The Overseas Development Institute. (2014). Electricity insecurity and SMEs .

Page 58: IMPACT OF LOAD SHEDDING - erb.org.zm

48

Page 59: IMPACT OF LOAD SHEDDING - erb.org.zm

49

Page 60: IMPACT OF LOAD SHEDDING - erb.org.zm

50

Head OfficePlot No. 9330, Mass MediaOff Alick Nkhata Road,P. O. Box 37631, Lusaka, Zambia.Tel: 260-211-258844 - 49 Fax: 260-211-258852

E-mail: [email protected] I Website: www.erb.org.zm I Toll Free Line: 8484

Copperbelt OfficePlot No. 332Independence AvenueP.O. Box 22281Kitwe, ZambiaTel: +260 212 220944Fax: +260 212 220945

Livingstone OfficePlot No. 708Chimwemwe RoadNottie BroadieP.O. Box 60292Livingstone, ZambiaTel: +260 213 321562-3Fax: +260 213 321576

Chinsali OfficePlot No. 76MayadiP.O. Box 480052Chinsali, ZambiaTel: +260 214 565170Fax: +260 214 565171