Survey Report - [impact Of Load Shedding On Life In Urban Areas Of Multan]

- 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 IN URBAN AREAS OF MULTAN- SURVEY REPORT

•

•

INTRODUCTORY SECTION •

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” WHAT is ahead! 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.

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

[Page-1]

•

METHODOLOGY SECTION •

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.

Income Level Independent Variable

Category You Belong to..

Intervening Variable

Bearable Hours 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.

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

[Page-2]

•

RESULTS & ANALYSIS SECTION •

Ø Frequencies OF Age & Gender Group Gender Group

Gender Female Male Total

Gender Percentage

Frequency 36 22 58

Percent 62.1 37.9 100.0

Male 38%

Female 62%

35

Age Group Age Range 15-20 20-25 25-30 30+ Total

Frequency

Percent

30

15

25.9

31 9 3

53.4 15.5 5.2

20

58

100.0

15

25

10 5 0 15-20

20-25

25-30

Age RANGE Frequency

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

[Page-3]

30+

Ø

Cross Table (Contingency Table) OF Age & Gender Group

Cross Table (Contingency Table) OF Gender and Age Group

15-20 20-25 25-30 30+ Total

Frequency

Age Group

Gender Group Female Male 10 5 18 13 6 3 2 1 36 22

Total 15 31 9 3 58

35 30 25 20 15 10 5 0

Female Male

15-20

20-25

25-30

30+

Age and Gender

• • • • •

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.

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

[Page-4]

Ø Frequencies & Percentages for Category Category wise Distribution

Category Business

Frequency

Student Household JOB holder Total

Percent

3 41 2

5.2 70.7 3.4

12 58

20.7 100.0

Category wise Distribution Business 5% Jobholder 21%

Household 3%

Student 71%

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

[Page-5]

Ø Frequencies & Percentages for Location Correspondents Location Location Bosan road Side

Frequency 24

Percent 41.4

Cantt

8

13.8

Mumtazabad

7

12.1

newMultan

13

22.4

others

6

10.3

Total

58

100.0

[Multan MAP – LOCATION wise correspondents distribution]

BOSAN road zone (41.4%)

New Multan Side (22.4%)

CANTT, MDA zone (13.8%)

Mumtaz’abad Side (12.1%)

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

[Page-6]

Ø Frequencies and Histogram of Income level

Mean of income Level of total of 58 correspondents is [1.95].

Income Level Frequency 28

Percent 48.3

1

Below 3000

2

3000-8000

18

31.0

3

8000-15000

3

5.2

4

15000-25000

5

8.6

5

25000+

4

6.9

58

100.0

Total

Histogram for Income Level 30

25

20

15 Frequency

10

Std. Dev. = 1.234 Mean = 1.95

5

N = 58

0 0

1

2

3

4

5

6

INCOME [POCKET money] range

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

[Page-7]

Ø

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

Experiencing Load Shedding No Affection of Load Shedding Total

Yes

Total

No

8

1

9

Yes

2

47

49

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 Below 2 Hours 2-4 Hours 4-8 Hours 8+ hours Total

Frequency 18 13 18 9 58

Percent 31.0 22.4 31.0 15.5 100.0

Bar Chart for Hours of Load shedding (Percentage) 35

Percentage

30 25 20 15

Hours Of Load Shedding

10 5 0 below 2

2-4 hours

4-8 hours

8+ hours

Hours Range

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

[Page-8]

Ø Bearable Hours Bearable hours

Number of Bearable Hours 0 Hour

Frequency 13

Percent 22.4

8+ hours

below 2 Hours

34

58.6

4-8 hours

2-4 Hours

8

13.8

2-4 hours

4-8 Hours

2

3.4

more than 8 hours

1

1.7

58

100.0

Total

below 2 0 hour 0

20

40

60

80

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 No Which Alternative

Generator

0

Yes 6(18.8%)

Total

UPS

0

13(40.6%)

13

6

Emergency Light Need ONE

0

13(40.6%)

13

22(84.6%)

0

22

Don’t NEED

4(15.4%)

0

4

26

32

58

Total

No 0

15.4%

84.6%

UPS Generator Emergency Light don’t Need

Yes

40.6

0%

10%

20%

18.8

30%

40%

50%

40.6%

60%

70%

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

80%

Need ONE

90%

100%

[Page-9]

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

Day Timing Night Timing

Frequency 27

Total

Percent 46.6

31

53.4

58

100.0

Day Timing 47%

Night Timing 53%

Identifying impact with respect to SEASON

season Summer Winter Total

Frequency 56 2 58

Percent 96.6 3.4 100.0

Winter 3%

Summer 97%

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

[Page-10]

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%

100 80 60 Affected Activities 40 20 0 Domestic

Study\BIZ

Rest\Shedule

Other

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-11]

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 Yes, so much Important

33

Percent 56.9

To Some Extent

23

39.7

Not, so important

2

3.4

58

100.0

Total

Frequency

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

[Page-12]

Analysis for testing Hypothesis that is “As income level increases, number of bearable hours decrease”

INCOME range * Bearable Hours Cross tabulation Bearable Hours

INCOME range

Below 3000

Count % within INCOME range

3000-8000

28

21.4%

67.9%

7.1%

.0%

3.6%

100.0%

4

7

5

2

0

18

22.2%

38.9%

27.8%

11.1%

.0%

100.0%

0

3

0

0

0

3

.0%

100.0%

.0%

.0%

.0%

100.0%

2

3

0

0

0

5

40.0%

60.0%

.0%

.0%

.0%

100.0%

Count % within INCOME range

25000+

4-8 Hours 0

Count % within INCOME range

15000-25000

2-4 Hours 2

Count % within INCOME range

Total

Count % within INCOME range

Total more than 8 hours 1

Count % within INCOME range

8000-15000

0 Hour 6

below 2 Hours 19

1

3

0

0

0

4

25.0%

75.0%

.0%

.0%

.0%

100.0%

13

35

7

2

1

58

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.

2 1.88

1.5 hours

1

1.25 1

1.06

0.8

0.5 0 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-13]

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-14]

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.

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

[Page-15]