- A SURVEY REPORT ON IMPACT OF LOAD

SHEDDING IN URBAN AREAS OF

MULTAN -

§ ARIF Masood - [Roll # 50] § Subject: Business Research Methods. § BS (Accounting & Finance) (2006-2010) § Department Of Commerce, BZU Multan.

[IMPACT OF LOAD SHEDDING – SURVEY] [ARIF Masood - 50] [Page-1]

è IMPACT OF LOAD SHEDDING IN URBAN AREAS OF MULTAN- SURVEY REPORT è

è INTRODUCTORY SECTION è

Electricity shortage and Load shedding is a phenomena that is not new to Pakistan but

in past one year or so Pakistan has been hit by worst power shortage crisis of its more

than 50 year’s history. In today world, without electricity and power it’s not just difficult

but almost impossible to survive and prosper. Everyone across Pakistan has been badly

affected by this power and electricity crisis. No matter one belongs to business sector,

industry or is just a domestic user of electricity is being affected. Like other cities of

Pakistan, Multan also experienced many hours of hours of load shedding in past 4-6

months specially. In fact, it is reported that as compared to other major cities of

Pakistan, Multan faced more hours of load shedding.

We conducted this survey to measure impact of load shedding on life of people

belonging to different walks of life in urban areas of Multan. Aim was to ascertain how

people feel about load shedding and how it has affected them. Apart from that study

focused on how different people have taken actions to cope with this heavy load

shedding and what actions they think, they can take to help country in overcoming load

shedding problem. Study conducted was applied in nature and it was important to find

out various factors that relate to load shedding in current state of affairs. It will help not

only Government in understanding people, so that it can manage this crisis in better.

WAPDA and power supplying agencies can take help from this study and can bring

improvement in scheduled hours of load shedding, so that people are least affected. It

will also be helpful to know what actions people are ready to take on personal level to

cope with load shedding. Government can assist people in this regard by help of

information found during this study.

Ø Hypotheses

Apart from collecting information about different variables, study will be to check below

stated hypothesis;

“As income level increases, number of bearable hours decrease”

In next section you will read about data collection techniques and methodology

adopted in this study. Section 3 will deal with statistical findings; where as

Section 4 will handle statistical analysis. Afterwards will be conclusion,

Limitations and recommendations along with references.

WHAT is ahead!

[IMPACT OF LOAD SHEDDING – SURVEY] [ARIF Masood - 50] [Page-2]

è METHODOLOGY SECTION è

Population Boundaries: Residents of Urban areas constitute the whole population of

this study.

Survey Instrument: Primary data was collected during this study and no help was

taken from secondary data since no secondary data in form of literature was available.

To collect primary data a questionnaire constituting around 19 questions was designed.

Questionnaire basically asked for following information:

• Demographic information • Effects of load shedding • Actions taken in response of load shedding • Actions that people are willing to take at personal level to overcome load shedding

Sample size and Sampling Technique: The questionnaires were distributed to 58

correspondents who filled it by themselves. Convenient sampling technique was used to

select correspondents, but while using convenient sampling technique it was kept in

mind that there should not be any concentration. Therefore intentional efforts were

made to make it sure that there is no much difference in proportion of correspondents

from different locations of Multan.

Analysis Technique: SPSS version 12 was used to input data and further analyze it

statistically.

Important Variables of Study: Current and bearable hours of load shedding are 2

important variables of study. Apart from that income level and category are also

important in this study. Hours of Load shedding are an independent variable on which

action taken by correspondent depends therefore action take in response is a

dependent variable.

Independent Variable Intervening Variable Dependent Variable

As income level increases, number of bearable hour’s decreases, but this could be intervened

by category variable, for example a low income level person belonging to business category can

have less bearable hours as compared to high income level student or job holder. So, there is

negative relationship between income variable and bearable hour’s variable, but this extent of

this relationship can be disturbed by intervening variable of category.

Income Level Category You Belong to.. Bearable Hours

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è RESULTS & ANALYSIS SECTION è

Ø Frequencies OF Age & Gender Group

Age Group

Age Range Frequency Percent

15-20

15 25.9

20-25 31 53.4

25-30 9 15.5

30+ 3 5.2

Total 58 100.0

Gender Group

Gender Frequency Percent

Female 36 62.1

Male 22 37.9

Total 58 100.0

Male

38%

Female

62%

Gender Percentage

0

5

10

15

20

25

30

35

15-20 20-25 25-30 30+

Age RANGE Frequency

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Ø Cross Table (Contingency Table) OF Age & Gender Group

• In age range of [15-20], 10 were female and 5 were male. • In age range of [20-25], 18 were female and 13 were male. • In age range of [25-30], 6 were female and 2 were male. • In age of [30+], 2 were female and 1 was male. • Overall 36 were female and 22 were male.

Cross Table (Contingency Table) OF Gender and Age Group

Gender Group

Total Female Male

Age Group

15-20 10 5 15

20-25 18 13 31

25-30 6 3 9

30+ 2 1 3

Total 36 22 58

0

5

10

15

20

25

30

35

15-20 20-25 25-30 30+

Age and Gender

Fre

qu

en

cy

Female

Male

[IMPACT OF LOAD SHEDDING – SURVEY] [ARIF Masood - 50] [Page-5]

Ø Frequencies & Percentages for Category

Category wise Distribution

Category Frequency Percent

Business 3 5.2

Student 41 70.7

Household 2 3.4

JOB holder 12 20.7

Total 58 100.0

Business

5%

Student

71%

Household

3%

Jobholder

21%

Category wise Distribution

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Ø Frequencies & Percentages for Location

[Multan MAP – LOCATION wise correspondents distribution]

Correspondents Location

Location Frequency Percent Bosan road Side 24 41.4

Cantt 8 13.8

Mumtazabad 7 12.1

newMultan 13 22.4

others 6 10.3

Total 58 100.0

CANTT, MDA zone

(13.8%)

BOSAN road zone

(41.4%)

Mumtaz’abad Side

(12.1%)

New Multan

Side (22.4%)

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Ø Frequencies and Histogram of Income level

Income Level Frequency Percent 1 Below 3000 28 48.3

2 3000-8000 18 31.0

3 8000-15000 3 5.2

4 15000-25000 5 8.6

5 25000+ 4 6.9

Total 58 100.0

0 1 2 3 4 5 6

INCOME [POCKET money] range

0

5

10

15

20

25

30

Frequency

Mean = 1.95

Std. Dev. = 1.234

N = 58

Histogram for Income Level

Mean of income Level of total

of 58 correspondents is [1.95].

[IMPACT OF LOAD SHEDDING – SURVEY] [ARIF Masood - 50] [Page-8]

Ø Cross Table for Experience of Load Shedding and Affection of Load

shedding

Experiencing Load Shedding

Total No Yes

Affection of Load Shedding

No 8 1 9

Yes 2 47 49

Total 10 48 58

47 out of 58 replied in yes to both questions, where as 8 replied in NO to both questions. Replies of only 3 were contradictory in nature to both questions. It shows that both variables are strongly and positively co-related.

Ø Hours of Load shedding - Frequencies

Hours of Load shedding Frequency Percent

Below 2 Hours 18 31.0

2-4 Hours 13 22.4

4-8 Hours 18 31.0

8+ hours 9 15.5

Total 58 100.0

Bar Chart for Hours of Load shedding (Percentage)

0

5

10

15

20

25

30

35

Pe

rcen

tage

below 2 2-4 hours 4-8 hours 8+ hours

Hours Range

Hours Of Load Shedding

[IMPACT OF LOAD SHEDDING – SURVEY] [ARIF Masood - 50] [Page-9]

Ø Bearable Hours

Number of Bearable Hours Frequency Percent 0 Hour 13 22.4

below 2 Hours 34 58.6

2-4 Hours 8 13.8

4-8 Hours 2 3.4

more than 8 hours 1 1.7

Total 58 100.0

For majority of correspondents, that is around 81%, the hours of load shedding were 0 hours or below 2 hours.

Ø Adopted For Alternative

Adopted For Alternative

Total No Yes

Which Alternative

Generator 0 6(18.8%) 6

UPS 0 13(40.6%) 13

Emergency Light

0 13(40.6%) 13

Need ONE 22(84.6%) 0 22

Don’t NEED 4(15.4%) 0 4

Total 26 32 58

40.6

000

18.8 40.6%

84.6% 15.4%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Yes

No UPS

Generator

Emergency Light

don’t Need

Need ONE

0 20 40 60 80

0 hour

below 2

2-4 hours

4-8 hours

8+ hours

Bearable hours

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Ø Affect with respect to Timing and Season Identifying impact with respect to TIMING

Frequency Percent

Day Timing 27 46.6

Night Timing 31 53.4

Total 58 100.0

Identifying impact with respect to SEASON

season Frequency Percent

Summer 56 96.6

Winter 2 3.4

Total 58 100.0

Day Timing47%

Night Timing53%

Winter3%

Summer97%

[IMPACT OF LOAD SHEDDING – SURVEY] [ARIF Masood - 50] [Page-11]

Ø Affected Activities Correspondents were asked which of their activities are affected and they were given option of selecting more than 1 option. Data shows following results. Activities Affected Frequency Percentage

Domestic\Household 27 46.6%

Study\Biz 37 63.8%

Entertainment 22 37.9%

Rest\Shedule 21 36.2%

OTHER 2 3.4%

0

20

40

60

80

100

Domestic Study\BIZ Rest\Shedule Other

Affected Activities

Above attached area chart shows that for majority of correspondents’ study\business and rest\schedules activities were most affected.

[IMPACT OF LOAD SHEDDING – SURVEY] [ARIF Masood - 50] [Page-12]

Ø Solutions that can be taken at personal level Solutions at personal Level

Frequency Percentage

Efficient Equipment 28 48.3%

By reducing wrong

use

36 62.1%

Innovative Solutions 19 32.8%

Other 3 5.2%

Ø Degree of Problem Correspondents were asked weather they found load shedding problem as important as poverty, inflation and load shedding. Following results were recorded. Problem importance

Response Frequency Percent Yes, so much Important 33 56.9

To Some Extent 23 39.7

Not, so important 2 3.4

Total 58 100.0

[IMPACT OF LOAD SHEDDING – SURVEY] [ARIF Masood - 50] [Page-13]

Analysis for testing Hypothesis that is “As income level increases,

number of bearable hours decrease”

INCOME range * Bearable Hours Cross tabulation

Bearable Hours Total

0 Hour below 2 Hours 2-4 Hours 4-8 Hours

more than 8 hours

INCOME range Below 3000 Count 6 19 2 0 1 28

% within INCOME range 21.4% 67.9% 7.1% .0% 3.6% 100.0%

3000-8000 Count 4 7 5 2 0 18

% within INCOME range 22.2% 38.9% 27.8% 11.1% .0% 100.0%

8000-15000 Count 0 3 0 0 0 3

% within INCOME range .0% 100.0% .0% .0% .0% 100.0%

15000-25000 Count 2 3 0 0 0 5

% within INCOME range 40.0% 60.0% .0% .0% .0% 100.0%

25000+ Count 1 3 0 0 0 4

% within INCOME range 25.0% 75.0% .0% .0% .0% 100.0%

Total Count 13 35 7 2 1 58

% within INCOME range 22.4% 60.3% 12.1% 3.4% 1.7% 100.0%

Truth of hypothesis can not be completely claimed as earlier while moving from below 3000 income

level to 3000-8000 an increase in graph can be observed. This disruption is actually because of

category variable and that is one variable that put limitations on this study.

1.25

1.88

1 1.060.8

0

0.5

1

1.5

2

hours

below 3000 3000-8000 8000-15000 15000-25000 25000+

“If we exclude all students from study to observe relation between real income level and

bearable hours, we will find the results to be completely supporting the above stated hypothesis.”

[IMPACT OF LOAD SHEDDING – SURVEY] [ARIF Masood - 50] [Page-14]

Ø Limitations of Study

There are number of factor that put limitations on results and scope of this study and

research report. A few of them are listed below;

• Sample size was too small, which was not capable of representing a large part of population.

• There was too much concentration of students; which also became a reason for disturbing real income level.

• Sampling technique used in study was not one of effective techniques.

Ø Conclusion & Recommendations

Even though due to number of reasons and limitation this study may not be considered

accurate and effective in its results, but there are still number of suggestions that can be

given under this study. Study shows there should be better scheduling of load shedding

timing and things should be scheduled in such a way that the activities that are affected

most are not affected that much. Further scheduling should be done according to the

timings important to different consumers of electricity. Since consumers are interested

in reducing extra usage and also using efficient equipment, therefore Government

should take steps in this direction to facilitate the users.

[IMPACT OF LOAD SHEDDING – SURVEY] [ARIF Masood - 50] [Page-15]

Ø References:

• “Power shutdowns bring life to a halt” (2008, October 19). The NEWS.

• “Load shedding – how it has affected people in metro areas of South Africa” (2008, February

22). TNS research surveys CO.

• “Handling Multiple response” (2006, May 2). SPSS log.

• “Power load shedding increases in Pakistan” (2008, March 12). Pakistan Times.

• “Load shedding hits agriculture sector” (2008, November 30). The NATION.