DATA ANALYSIS AND INTERPRETATION - Shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/33833/6/... · 2018-07-02 · DATA ANALYSIS AND INTERPRETATION 6.1. Status of MSW in Erode

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CHAPTER – VI

DATA ANALYSIS AND INTERPRETATION

6.1. Status of MSW in Erode Corporation

The Erode Corporation is a special grade municipality. The

population in the year 2011 is around 4,98,121. The garbage collection per

day comes about 250 metric ton to 275 metric ton. Sixty Municipal wards

have been formed in the town. Garbage is being collected daily at the door

steps in man-hand pushcarts in entire 60 wards by the 857 sanitary

workers.

The municipal sanitary workers are utilized for door to door garbage

collection. The garbages are being dumped in the dumper bins and

dustbins. Dumper placers, lorries and tractors are carrying them to the

municipals compost yard. The compost yard for Erode Municipality is

situated at Vendipalayam 15 km from municipal office.

About 110MT to 135MT of municipal garbage is dumped in the

compost yard for the past 25 years without any treatment and segregation.

Now it is proposed to compost the segregated degradable waste and to

make manure from already half-composted with the involvement of private

agency IWMUST. The solid wastes generated from houses, commercial

establishments, roads, streets, drainages, vegetable markets, turmeric

markets, hospitals and other establishments were collected, transported and

disposed in three stages.

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Primary Collection

The solid wastes are classified as bio-degradable and non-

degradable in the source itself and the same has been collected daily in the

door steps through 275 pushcarts and 54 tricycles. The collected wastes are

temporarily stored in the 200 dumper bins and 40 H.D.P.E.bins.

Secondary Collection

The solid waste collected through pushcarts is temporarily stored in

16 dumper bins and H.D.P.E bins and transported to compost yard through

13 tractors, 8 dumper bin placers and two automatic refuse collectors in a

segregated manner.

Tertiary stage

The compost yard of Erode municipality is located at Vendipalayam

and have an extent of 19.42 acres, which is 7kms far away from municipal

office. This land is used as compost yard since 1946. The segregated

wastes transported through dumper bin placers, automatic refuse collector

are unloaded separately in the yard. To make natural manure from

degradable wastes, the works are in progress of produce more manure.

Staff welfare measures taken by Erode Corporation

Sanitary workers are provided with the safety materials like hand

gloves, face masks, reflected stripped overcoat and raincoats at the cost of

Rs.9.60 lakhs for protection and hygiene. In addition Gun Boots and

Helmets are given to the lorry workers and drain cleaners.


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Compost Yards

In the study area, the MSW is disposed as open land fills in the three

points of which the larger yard is located at Vendipalayam around 4 Km

from Erode city. Another disposal yard is located at Semur at the Western

part and the third yard is located at the bank of river Cauvery at

Vairapalayam, the eastern part of the study area as seen in the figure 6.1.

Figure 6.1 Location of Dump Yards

Table – 6.1

MSW Generation, Collection and Disposal

S. No. Municipality

Municipal Area

(Sq.Km)

Municipal Population

(Lakhs)

Total MSW

generated daily (MT)

Per capita generation

daily (gm)

Total MSW

collected (daily in

MT)

Collection efficiency

(%)

1. V.Chatram 33.40 1.14 38.00 500 34 89

2. Periasemur 23.54 1.20 34.00 700 29 85

3. Surampatti 27.04 1.28 17.00 400 15 88

4. Kasipalayam 25.54 1.23 34.00 550 26 95

5. Erode 8.44 1.24 35.00 600 27 85


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From the Table 6.1 and Figure 6.2, it is clear that Veerappanchatram

municipality is larger in land area (33.40 sq. km) with the population of

1.14. The total MSW generated every day is 38 MT, where the per capita

generation was 500 gm / day. The collection efficiency was 89%.

Verappanchatram municipality is rich in weaving industries and bleaching

factories. There are many government offices and educational institutions

in this municipality.

Periasemur has witnessed strong economic growth over the last

decade. With a rapidly growing industrial activities and an ever rising

demand for quality garments, the town has been in the limelight of the

textile trade. In this region, Civil society groups have been instrumental in

bringing attention to environmental problem and urging governmental

action. Declining yields and concerns over health hazards arising from

industrial pollution have driven the Kalingarayan Farmers Association to

advocate the government action to reduce pollution of the Kalingarayan

canal. In addition to the farmers associations NGO’s such as Green World

in Creating a “Green and Clean Erode” have been growing in prominence

in recent years.

Periasemur municipality generates 34 MT of solid waste. All the

wards in the town are covered under door to door collection of solid waste.

The solid waste generated in the town is collected and dumped in the

compost yard (Puramboke Land) of area 1 acre located near burial ground


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at Jeevanagar in ward 17. Currently 34 MT of solid waste is generated with

a highest per capita waste generation of 700 gram per day when compared

with the other municipalities. In future, there will be corresponding

addition of 30% to 40% in solid waste generation. Hence, there is a need

for scientific management of solid waste to cope with a future demand.

Surampatti municipality generates 17 MT of solid waste. All the

wards in the town are covered under door to door collection of solid waste

and 8 wards in the town are privatized for solid waste collection. The town

doesn’t have land for dumping the collected waste. The existing dumping

yard at Koundachipalayam is under litigation. At present, the garbage

collected is dumped in the compost yard located at B.P.Agraharam. It has

been estimated that solid waste generation per day shall be around 34MTs.

One third out of the same shall comprise biodegradable waste and the

manure generation would be 1/3rd of the same. Thus the estimated saleable

manure generation per day would be 4MTs.

Kasipalayam municipality generates 34MT of solid wastes. The

solid waste is collected from the houses at the door steps, with the available

42 number of pushcarts. The solid waste generated in the town is collected

and dumped in the compost yard of area 1.50 acres, located at Muthusamy

colony, Chinnasadayampalayam, 3 Km from the town centre. The

collection efficiency was the highest in Kasipalayam municipality (95%).


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Figure 6.2

MSW Generation, Collection and Disposal

a)

33.4

23.5427.04

25.54

8.44

1.14 1.2 1.28 1.23 1.24

0

5

10

15

20

25

30

35

40

V.chatram Periasemur Surampatti Kasipalayam Erode

Municipality

Mun

icip

al A

rea

(Sq.

Km

)

b)

38 34 17 34 35

500

700

400

550600

0

100

200

300

400

500

600

700

800


Municipality

Total MSW generated daily (MT)

Per capita generation daily (gm)

c)

34 29

1526 27

89 85 88 8595

0102030405060708090

100


Municipality

Total MSW collected (daily in MT)

Collection eff iciency (%)


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Of the municipal waste, 40% - 60% of the waste is bio-degradable.

Of this, on conversion nearly 30% by weight forms the manure. As the

manure generated from municipal waste is found to be richer in NPK

values to the one available in the market, better yield of agricultural

products can be expected.

Integrated solid waste management is the application of suitable

techniques, technologies and management programs covering all types of

solid wastes to achieve the twin objectives of waste reduction and effective

waste management.

Figure – 6.3 Functional Elements of a Municipal Solid Waste Management System

The activities associated with the management of MSW from the

point of generation to final disposal can be grouped into six functional

elements (ie) a) waste generation b) waste handling and sorting, storing c)

collection d) sorting, processing and transformation e) transfer and

transport f) disposal.

WASTE GENERATION

Waste Handling, Sorting, Storing and Processing

Collection

Transfer and Transport

Sorting, Processing and Transformation of Solid Waste

Disposal


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The inter-relationship between the functional elements is given in

the above Figure.

The status of solid waste generation, collection and disposal, the

whole process of solid waste disposal management is shown in figures.

6.2 Pattern of solid waste generation in Erode Corporation

The pattern of solid waste generation in Erode is similar to the

pattern of urban solid waste generation in India. The data on the MSW

generation maintained by the Urban Local Bodies (ULB) is based on the

number of trips made by the waste transportation vehicles or

approximation on other basis. Generally, there is no practice of weighing

the MSW at any stage, giving the available data little authenticity. The

main issues associated with MSW in Erode are: inefficient, inadequate and

ad hoc primary collection of system, which results in the dumping of solid

wastes into water bodies, road side etc; lack of proper technical expertise in

SWM; lack of proper financial base for the urban local bodies as they

depend too much on government grants; absence of engineered landfills

and crude waste dumping in open dumps resulting in ground water

contamination and breeding of mosquitoes, flies, rodents and pests and lack

of proper private sector participation in the MSW system. The main

objective of the programme was to strengthen the managerial capacity and

responsibility of the community and local governments in planning,

implementation and maintenance of SWM facilities. The Table 6.2 shows

the various sources of waste generators in Erode.


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Table – 6.2 Pattern of Solid Waste Generation

S.No. Source % to Total

1 Household Waste 49

2 Hostels, Marriage halls, Institutions 17

3 Shops and Markets 16

4 Street Sweepings 9

5 Construction 6

6 Slaughter house, Hospitals 3

Total 100

Source: Municipal authority

It could be seen from the above table that Maximum amount of solid

waste comes from domestic waste and it is followed by Hotels, marriage

halls and institution, and other contributors followed by shops and markets

etc. The following figure represents the data given in the Table 6.2.

Figure: 6.4 Pattern of Solid Waste Generation

17%

9%6% 3%

16%

49%

Household Waste Hostels, Marriage halls, Institutions

Shops and Markets Street Sw eepings

Construction Slaughter house, Hospitals


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6.3. Composition of MSW in Erode Corporation

The composition of waste in terms of its physical characteristics will

give a clear idea regarding the consumption pattern and waste disposal in

an area. It is also important for reduction, reuse and recycling of waste.

Higher income and economic growth will also affect the composition of

wastes. Wealthier individuals consume more packaged products, which

results in a higher percentage of inorganic materials – metals, plastics,

glass, and textiles etc. in the waste stream. Large amount of wastes with a

higher content of inorganic materials could have a significant impact on

human health and the environment. Developed countries, such as the US

and Japan have rates of waste generation larger than other countries.

European countries generate between 70% and 80% of those of the US

(Field, 1995).

Various studies have shown that the municipal solid waste in Erode

contains a high biodegradable content. The following Tables 6.3 and 6.4

showed the physical composition and the chemical composition of solid

waste in Erode.


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Table – 6.3

Physical Composition of MSW in Various Zones of Erode Corporation

S. No. Composition V.

Chatram Peria semur

Suram patti

Kasi palayam Erode

1 Compostable Matter (in %) 10 12 17 8 11.75

2 Wooden Matter (in %) 20 14 16 12 11.5

3 Rubber & Leather (in %) 10 14 13 13 12.5

4 Plastics (in %) 20 17 16 13 13.5

5 Metal (in %) 20 20 18 18 19

6 Glasses (in %) 10 13 10 8 8.25

7 Brick & Stones (in %) 10 10 10 16 11.5

8 Ash & Fine Earth (in %) - - - 12 12

Total 100 100 100 100 100


Table – 6.4

Chemical Composition of MSW in Various Zones of Erode

Corporation

S. No. Composition V.

Chatram Peria semur

Suram patti

Kasi palayam Erode

1 Moisture (%) 26.98 19.52 21.03 25.81 23.34

2 Organic Matter (%) 25.14 26.89 25.14 39.07 29.06

3 Nitrogen Vs Total Nitrogen (%) 0.71 0.66 0.64 0.56 0.64

4 Phosphorous (%) 0.63 0.82 0.69 0.61 0.69

5 Potassium (%) 0.83 0.69 0.72 0.78 0.76

6 C/N Ratio (%) 30.94 21.13 23.68 22.45 24.55

7 Calorific value (Kcal/Kg) 43.59 44.73 49.07 53.90 47.82



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The bio degradable component of the solid waste stream is

considerably high. It is followed by plastic, rubber, glass and metal.

Surampatti municipality releases more of compostable matter than other

municipalities. Wooden and plastics are more in the wastes from

Veerappanchatram municipality. The proportion of glasses is more in

Periasemur whereas the construction wastes are more in Kasipalayam

municipality.

Financial aspects

Developing nations spend between 20 and 40% of municipal

revenues on SWM (Thomas – Hope 1998). In India, it is estimated that

between 10 to 40 per cent of the total municipal budget is used for SWM

(Bhide, 1990).

Table – 6.5

Capital Investment need for SWM in Erode Corporation

S.No. Municipality Estimated Cost (Rs. In lakhs)

1. Veerappanchatram 1022.00

2. Periasemur 1108.00

3. Surampatti 1097.00

4. Kasipalayam 1665.00

5. Erode Town 1223.00 Source: Municipal authority

As per the above Table, each municipality SWM is to be upgraded.

As every municipality in need of compost yard, Erode corporation is under


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the process of identifying and acquiring a regional compost yard. Therefore

the total investment required for improvement in SWM is presented in

Table.

Table – 6.6

Priority Projects of SWM suggested under CDP

Estimated cost (in lakhs) S.

No. Projects V.

chatram Peria semur Surampatti Kasi

palayam Erode Town

1. Cost of landfill site & compost yard 535.00 754.00 800.00 1100.00 900.00

2. Landfill site and compost yard development 310.00 200.00 150.00 250.00 300.00

3. Vehicles and equipment for primary and secondary collection

177.00 154.00 147.00 315.00 290.00

Total 1022 1108 1097 1665 1490


It was learnt from the above table that the cost of land fill site and

compost yard ranges from 535 lakhs in Veerappanchatram municipality to

1100 lakhs in Kasipalayam municipality. The cost of landfill site and

compost yard maintenance and development was 310 lakhs in

Veerappanchatram municipality, 200 lakhs in Periasemur 150.00 lakhs in

Surampatti and 250 lakhs in Kasipalayam municipalities.

The municipality proposed to purchase small vehicle with

containers for door to door collection, community bins with 1100 litres

capacity, Dumper placer vehicles in addition to purchase land for

additional compost yard and IEC activities. The investments on these


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vehicles for primary and secondary collection were 177, 154, 147 and 315

lakhs in Veerappanchatram, Periasemur, Surampatti and Kasipalayam

municipalities respectively.

ii) Administrative setup of MSWM

The administrative setup of MSWM in every municipality is shown

in figure – 6.5.

Figure 6.5 The hierarchy of the staff

The Municipal Secretary is at the top of the MSW system. There is

an engineering wing and a finance wing to look into the technical aspects

and to meet the expenses of the MSW management system. The Health

officer is a medical doctor who is assisted by the health inspectors and

junior health inspectors. The sanitation workers are responsible for the

collection and disposal of the solid waste.

Municipal Secretary

Engineering wing Health officer

Finance wing

Health inspector

Sanitation inspector

Sanitation workers


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iii) Vehicles used in the municipal solid waste management.

The details of the vehicles used by the Municipality are given in the

following Table 6.7.

Table 6.7

Vehicles employed for waste collection and transportation in different zones

Zone Tipper lorry Tractor Dumper

Placer Pushcarts Dumper placer

containers Veerappanchatram 2 4 4 21 56

Periasemur 2 2 4 26 65

Surampatti 2 3 3 35 48

Kasipalayam 3 4 4 42 50

Erode Town 4 3 5 45 67

Total 13 16 20 169 286 Source: Municipality authority

There are two tipper lorries and four tractors, four dumper placers,

twenty one pushcarts and fifty six containers are used to collect waste from

various collections points and transferring to the compost yards in

Veerappanchatram municipality. Periasemur municipality has more of

vehicles than Veerappanchatram municipality whereas Surampatti

municipality employed three tipper lorries, four tractors and dumper places,

fourty two push carts and fifty containers to collect and transport the

wastes. Kasipalayam municipality has three tipper lorries four tractors, four

dumper placers, fourty two push carts and fifty dumpter placer containers

for effective solid waste management.


130

6.4. IWMUST and MSW

Integrated waste management and urban (IWMUST) services

company (Tamilnadu) is a private organization which is actively engaged

in MSW in Erode Corporation. It collects the waste brought by the

municipal vehicles and converting the waste and manure. It was estimated

that nearby 30-40% of waste is converted into manure. They feet that

seperation of plastics, metals and glasses from waste becomes a great

challenge which needs more of man power also. The IWMUST units

collect waste from households, hospitals, shops and industries and hand it

over to the municipal disposal system. The collection charges for the

households ranges between Rs.30 andRs.50 and for the other sectors it

depends n the volume of waste collected.

6.5. Micro Analysis of the Impacts of Urban Solid Waste Management

A micro analysis of the variables taken helps to highlight i) the

socio-economic characteristics of the sample units ii) various impacts of

improper solid waste management iii) present status of sold waste

management in Erode and iv) willingness to pay for improved solid waste

management system.

6.5.1. Socio-economic characteristics

Socio-economic characteristics of the study area were analysed by

considering i) gender ii) education iii) occupation iv) house ownership v)

average monthly income vi) monthly expenditure of the respondents.


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i) Gender of the Respondents

Gender plays an important role in managing solid waste in the study

area. Erode district gives equal important for feminine gender on par with

masculine gender because of the great social reformer Thanthai Periyar.

The following table gives the details of gender-wise representation in

managing solid waste.

Table: 6.8 Gender of the Respondents

S.No. Gender Percentage (%)

1. Male 63.4

2. Female 36.6

Total 100 63% of the respondents were male and 35.6% were females.

Figure 6.6 Gender of the Respondents

0

10

20

30

40

50

60

70

Perc

enta

ge (%

)

Male Female

Gender

ii) Educational Level of the Respondents

Education shapes the personality and sharpens the mind of an

individual. For the purpose of this study, educational qualification of the


132

respondents has been classified into four strata viz., No formal education,

primary level educated respondents, secondary level and higher secondary

level. The details of the respondents education and their style of managing

solid waste is furnished in the following table.

Table – 6.9 Educational Level of the Respondents

S.No. Level of education Percentage (%)

1. No formal education 3.4

2. Primary level 35.4

3. Secondary level 44.8

4. Higher education 16.4

Total 100 Source: Primary data

It was learnt from the above table that 44.8% of the respondents

have attained secondary education while those with higher education were

16.4%. Only 3.4% of the respondents were without any formal education.

Figure 6.7 Educational Level of the Respondents

3.4

35.4

44.8

16.4

0

5

10

15

20

25

30

35

40

45

Perc

enta

ge (%

)

No formaleducation

Primary level Secondary level Higher education

Level of Education


133

iii) House Ownership of the Respondents

Shelter is the one of the basic needs of mankind. In this study shelter

occupied by the respondents was studied under two strata viz., owned

house and rented house. The distribution of sample respondent according

to ownership of house and managing solid waste are furnished in the

following table.

Table: 6.10 House Ownership of the Respondents

S.No. Ownership Percentage (%) 1. Owned 92.1 2. Rented 7.9 Total 100

Source: Primary data

It is divulged from the above table that a good majority of the

respondents (92.1%) possess their own house. On the other hand 7.9%

respondents living in the rental houses. From the analysis, it is concluded

that a good majority of the respondent disposing the solid waste are living

in own houses.

Figure – 6.8 House Ownership of the Respondents

92.1

7.9

0102030405060708090

100

Perc

enta

ge (%

)

Ow ned Rented

Ownership


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iv) Occupational Status of the Respondents

Occupation is a status symbolfor an individual. The society respects

and recognises the common man based on the occupation status. For the

purpose of the present study, occupation status of the respondents has been

classified into six categories namely Govt employee, employee of private

sector, business, agricultural sector and others (meanial job, street vendors,

cheap jacks). The details of occupational status and the style of managing

solid waste is presented in the following table.

Table 6.11 Occupational Status of the Respondents

S.No. Occupation Percentage (%)

1. Govt. Job 16.4

2. Private job 29.8

3. Business 16.7

4. Agriculture and related activities 31.6

6. Others 5.5


It is known from the above table that 31.6% of the selected sample

respondents engaged in agriculture and related activities. It is followed by

29.8% working in private sector organisation. 16.7% of the respondents

were engaged in their own businesses, and 16.4% of the respondents were

incumbants govt. employees. On the other hand 5.5% of the respondents

were doing other jobs.


135

Figure 6.9 Occupational Status of the Respondents

16.4

29.8

16.7

31.6

5.5

0

5

10

15

20

25

30

35P

erce

ntag

e (%

)

Govt. Job Private job Business Agriculture andrelated activities

Others

Occupation

v) Average monthly income of the respondents

Income is the base to fullfill the needs of the individual and family

members. The quantum of income generated by the individual shows the

skill and talent. For the purpose of this study, Income of the respondent

was studied under four categories, Below 1,000, 1000-5,000, 5,000-10,000

and above 10,000. The distribution of sample respondent according to

income generating capacity as shown in the following table 6.12.

Table: 6.12 Average monthly income of the respondents

S.No. Income Range (in Rs.) Percentage (%)

1. Less than 1,000 14.8

2. 1,000-5,000 61.0

3. 5,000-10,000 19.4

4. Above 10,000 4.8



136

It is limelighted from the above table that 75.8% of the respondents

have be earned average monthly income of below Rs.5,000. A meagre

percentage of (4.8%) the respondents have earned above Rs.10,000 per

month. On the other hand 19.4% of the respondents have earned Rs.5,000-

10,000 per month.

Figure 6.10 Average monthly income of the respondents

14.8

61

19.4

4.8

0

10

20

30

40

50

60

70

Perc

enta

ge (%

)

Less than 1000 1000-5000 5000-10000 Above 100000

Income Range (in Rs.)

vi) Average Monthly Expenditure of the respondents

The expenditure pattern of the respondents was studied. For this

purpose the common expenses incurred for maintaining the family are

classified into seven major classifications, monthly average expenses for

food, cloth, utilities, education, health, housing expense and transport. The

details of expenditure pattern is shown in the following table 6.13.


137

Table 6.13 Average monthly expenditure of the respondents

S.No. Particulars Monthly Expenditure (in Rs.)

1. Food 1500

2. Clothing 400

3. Utilities 400

4. Education 800

5. Health 400

6. Housing 750

7. Transport 1000


It is identified from the above table that maximum income was

devoted to food (Rs.1,500). It is followed by transport cost and education

for the children. The least cost was Rs.400 equally for clothing utilities.

Figure 6.11 Average monthly expenditure of the respondents

3049.17

886.59 894.2

1662.38

846.02 784.15

1047.65 1045

0

500

1000

1500

2000

2500

3000

3500

Perc

enta

ge (%

)

Food Utilities Health Transport

Monthly Expenditure


138

6.6. Impacts of MSWM

The impacts of MSWM include health impacts to the population,

economic impacts, environmental impacts and social impacts. The

following section deals with the impacts of improper solid waste

management in the municipality.

6.7. Impacts on health

A complex relationship exists between a person’s health and

immediate environment. Diseases can spread through air, water, food, soil,

through environmental factors and lifestyle.

i) Signs and symptoms

The table 6.14 illustrates the signs and symptoms experienced by the

respondents.

Table: 6.14 Signs and symptoms of the Respondents

Sl.No. Signs and symptoms %age affected

1 Diarrhea 41.7

2 Persistent headache 18.2

3 Fever 33.6

4 Cough and cold 31.3

5 Eye irritation 29.4

6 Skin infection 34.6 Source: Primary data

Among the different signs and symptoms identified, 41.7% were

affected with diarrhea, 34.6% with skin infection and 33.6% with fever.

18.2% had persistent headache.


139

Figure 6.12 Signs and symptoms of the Respondents

41.7

18.2

33.6 31.3

29.4

34.6

0

5

10

15

20

25

30

35

40

45Pe

rcen

tage

(%)

Diarrhea Persistentheadache

Fever Cough andcold

Eyeirritation

Skininfection

Signs and Symptoms

iii) Perceived causes of various signs and symptoms of the diseases

The table 6.16 shows the respondent’s perceived causes of disease

symptoms.

Table 6.15 Perceived causes of disease signs and symptoms of the

Respondents

Signs and symptoms Physical Environment

Lifestyle risks**

Non- environment***

Don’t know

Diarrhea 91.4 2.4 5.4 0.8

Persistent headache 49.6 38.2 1.08 1.4

Fever 61.7 18.2 18.3 1.8

Cough and cold 86.3 7.7 4.3 1.7

Eye irritation 77.8 6.2 11.5 4.5

Skin infection 89.2 3.4 4.8 2.6 Source: Primary data * air, water, food and soil, **alcohol, drugs, stress, lack of exercise, ***complications due to other diseases.


140

The respondents mainly attribute physical environmental factors as

the cause for disease symptoms. 91.4% and 89.2% of the respondents

attribute physical environmental factors as the cause for diarrhea and skin

infection respectively. 38.2% considered lifestyle risks as the cause for

persistent head aches.

Figure 6.13 Perceived causes of various signs and symptoms of the

diseases

2.4

38.2

18.2

7.7 6.2 3.45.41.08

18.3

4.311.5

4.80.8 1.4 1.8 1.7 4.5 2.6

77.8

89.286.3

61.7

91.4

49.6

0

10

20

30

40

50

60

70

80

90

100

Diarrhea Persistentheadache

Fever Cough andcold

Eye irritation Skin infection

Signs and Symptoms

Perc

enta

ge (%

)

Physical Environment Lifestyle risksNon-environment Don’t know

iii) Occurrence of diseases

Table 6.16 consider the occurrence of diseases among the

respondents.


141

Table: 6.16 Occurrence of diseases

S.No. Name of disease % affected 1. Cholera 42.1 2. Jaundice 21.8 3. Typhoid 30.6 4. Intestinal parasitism 20.6 5. Fever 33.6 6. Acute respiratory infection 44.4 7. Chicken guinea 29.3 8. Dengue 20.1 9. More than one disease 33.5

10. No. diseases 5.2 Source: Primary data

44.4% of the respondents were affected by acute respiratory

infection followed by cholera at 42.7%. Chicken guinea has affected 29.3%

of the respondents 33.5% of the respondents were affected by more than

one disease 5.2% were not affected by the given diseases.

Figure 6.14 Occurrence of diseases

42.1

21.8

30.6

20.6

33.6

44.4

29.3

20.1

33.5

5.2

0

5

10

15

20

25

30

35

40

45

Perc

enta

ge (%

)

Chol er a Jaundi ce T yphoi d Int est i nal

par asi t i sm

Fever Acute

r es pi r ator y

i nf ec t i on

Chi ck en

gui nea

Dengue M or e than one

di sease

No. di seases

Name of Disease


142

vi) Perceived causes of disease

The table 6.17 shows the respondent’s perceived causes for diseases.

Table 6.17 Perceived causes of disease

Disease Physical Environment*

Lifestyle Risks**

Non- environment***

Don’t know

Dengue 85.6 0.4 5.4 8.6

Cholera 83.5 0.5 6.2 9.8

Jaundice 81.8 1.2 9.3 7.7

Typhoid 78.9 1.7 7.5 11.9

Intestinal parasitism 82.6 0.9 3.5 13

Fever 91.6 0.1 2.7 5.6

Acute respiratory infection

80.2 5.7 9.6 4.5

Chicken guinea 94.4 0.3 1.7 3.6

Source: Primary data *air, water, food and soil; **alcohol, drugs, stress, lack of exercise, ***complications due to other diseases.

The respondents mainly attribute physical environmental factors as

the cause for the given diseases. 94.4% considers physical environment

factors as the cause for chicken guinea. 91.6% of the respondents attribute

physical environmental factors as the cause for rate fever.


143

Figure 6.15 Perceived causes of disease

80.2

94.4

0.4 0.5 1.2 1.7 0.9 0.1

5.7

0.3

5.4 6.29.3

7.53.5 2.7

9.6

1.7

8.6 9.87.7

11.9 13

5.6 4.5 3.6

91.6

82.681.878.9

83.585.6

0

10

20

30

40

50

60

70

80

90

100

Dengue Cholera Jaundice Typhoid Int est inalparasit ism

Fever Acut erespirat ory

inf ect ion

Chicken guinea

Name of Disease

Perc

enta

ge (%

)Physical Environment Lifestyle Risks

Non-environment Don’t know

v) Diseases to Children

The accumulation of solid waste creates diseases which is not

cleared immediately. In this study, an attempt was made whether the

children are affected by the disease. For this purpose a dychotomous test

was employed and the result of the test is shown in the following table.

Table: 6.18 Diseases to Children

S.No. Response Percentage (%)

1. Yes 72.1

2. No 27.9


It was learnt from the above that 72.1% of the respondents agreed

that children are getting affected by the diseases.


144

Figure 6.16 Diseases to Children

72.1

27.9

0

10

20

30

40

50

60

70

80Pe

rcen

tage

(%)

Yes No

Response

vi) Seasonal Occurrence of the Diseases

The change in the climate creates the disease especially, during

rainy season and winter season diseases spread fastly and human beings are

easily affected by various diseases like fever, donsil, asthma, and

pneumonia and plaques. An attempt was made to identify the occurrence of

the disease during change in season with the help of a dychotomy test. The

details are furnished in the following table 6.19.

Table 6.19 Seasonal Occurrence of Diseases


1. Yes 64.3

2. No 35.7

Total 100



145

It is identified from the above table that during change in the season,

diseases occured 64.3% of the respondents agreed that the occurrence of

the diseases as seasonal.

Figure 6.17 Seasonal Occurrence of the Diseases

64.3

35.7

0

10

20

30

40

50

60

70

Perc

enta

ge (%

)

Yes No

Disease

vii) Season prone to diseases

Table 6.20 shows the season in which there is a great chance for

diseases to occur was studied by selecting three seasons.

Table 6.20 Seasons more prone to diseases

S.No. Season Percentage (%)

1. Monsoon 47.9

2. Winter 20.9

3. Summer 31.2



146

It could be seen from the above table that 48% of the respondents

opined that the diseases occurred mainly during monsoon rain season

followed by summer season.

Figure 6.18 Seasons more prone to diseases

47.9

20.9

31.2

0

5

10

15

20

25

30

35

40

45

50

Perc

enta

ge (%

)

Monsoon Winter Summer

Season

viii) Average outpatient expenses

Due to seasonal diseases, the respondents have to spent a sizable

income towards treatment of self and the dependents. The following table

6.21 shows the average expenses incurred for treatment of their ill-health

outpatient.

Table 6.21 Average outpatient expenses

S.No. Average expenses Percentage (%)

1. Less than Rs.500 96.1

2. Above Rs.500 3.9



147

It is lime lighted from the above table 6.21 that a good majority

(96.1%) of the respondents were spent less than Rs.500 during monsoon

season and a meagre percentage of the respondents have spent above

Rs.500 towards medical treatment.

Figure 6.19 Average outpatient expenses

4%

96%

Less than 500

Above 500

ix) Average inpatient expenses

The average inpatient expenditure incurred for the selected sample

respondents for study fall under three categories namely less than Rs.5,000,

Rs.5,000-Rs.10,000 and above Rs.10,000. The details of average

expenditure for inpatient are given in the following able 6.22.


148

Table 6.22 Average inpatient expenses

S.No. Average expenses Percentage (%)

1. Less than Rs.5,000 75

2. Rs.5,000 – Rs.10,000 14.3

3. Above Rs.10,000 10.7


It is examined from the above table 6.22 that a good majority of the

respondents spent lessthan Rs.5000 for taking medical treatment as

inpatient, 14.3% of the respondents spent Rs.5000-10,000 towards medical

treatment as inpatient. On the other hand 10.7% of the respondents have

spent above Rs.10,000 for the medical treatment.

Figure 6.20 Average inpatient expenses

75

14.310.7

0

10

20

30

40

50

60

70

80

Perc

enta

ge (%

)

Less than 500 500-1000 Above 1000

Average Expenses


149

x) Frequency of occurrence of diseases

The Table 6.23 consider whether the frequency of the occurrence of

epidemics has increased over the last few years in the municipality.

Table: 6.23 Frequency of occurrence of diseases

S.No. Response Percentage (%) 1. Yes 86 2. No 14 Total 100


It could be seen from the above table that 86% of the respondents

agreed that the occurrence of epidemics in the municipality has increased

the last few years and 14% of the respondents replied negatively.

Figure 6.21 Frequency of occurrence of diseases

14%

86%

YesNo


150

xi) Reasons for increase in the occurrence of diseases

An attempt was made to identify the reasons for increase in the

occurrence of diseases due to municipal waste. For this purpose the reasons

were studied such as solid waste pollution and other pollutions. The details

are furnished in the following table.

Table 6.24 Reasons for increase in the occurrence of diseases

S.No. Reasons Percentage (%)

1. Solid waste pollution 78.6

2. Other pollution 11.8

3. Don’t know 9.6

Total 100


It could be observed from the above table that around 78.6% of the

respondents expressed solid waste pollution is the main reason for the

increase in the occurrence of diseases. 11.8% replied other types of

pollution such as liquid form and semi-solid form were the main reason.

On the other hand, as the reason 9.6% of them replied that they were not

aware of the reasons.


151

Figure 6.22 Reasons for increase in the occurrence of diseases

78.6

11.8 9.6

0

10

20

30

40

50

60

70

80

Perc

enta

ge (%

)

Solid waste pollution Other pollution Don’t know

Reasons

xii) Possession of insurance

Insurance is an improvement source to manage the risk of the

individual. In this study an attempt was made whether the respondents are

aware of mediclaim insurance. For this purpose, the respondents were

asked to express the possession of insurance. The details of having

insurance furnished in the following table.

Table 6.25 Possession of insurance

S.No. Reasons Percentage (%)

1. Having Insurance 37.1

2. Does not having Insurance 62.9



152

It is divulged from the above table that majority (62.9%) of the

respondents do not having insurance policy. On the other hand, only 37.1%

of the respondent having insurance policy. From the analysis it is inferred

that a good majority of the respondents were not having any insurance

policy due to ignorance and lack of awareness.

Figure 6.23 Possession of insurance

62.9%

37.1%

YesNo

6.8. Economic impact

The economic impacts of SWM include the reduction of land value

is the area. The following section deals with the opinion of the respondents

regarding the residential land value in the municipality.

i) Impact on land value due to solid waste pollution

The value of land has reduced in and around 10 km due to dumping

of solid waste by the respective municipality. The utility of irrigated land

also gradually diminishing due to deleterious effect of solid waste in the


153

land. The demands for the agriculture products from this area are not

having fair market. Hence an attempt was made to learn the economic

impact of solid waste on the land. A dychotomy test was employed and

results are given in the following table.

Table: 6.26 Land Value


1. Yes 11.2

2. No 88.8

Total 100


It is identified from the above table that a good majority (88.8%) of

the respondents were opined that pollution of solid waste does not affect

the land value, due to lack of awareness and non-availability of land nearer

to the Erode city.

Figure 6.24 Land Value

11.2%

88.8%

Yes

No


154

iii) Land value in the absence of solid waste pollution

Table: 6.27 consider the whether residential land value in the

municipal area will increase in the absence of solid waste pollution.

Table 6.27 Land Value in the Absence of Solid Waste Pollution

S.No. Response Percentage (%) 1. Yes 31.1 2. No 68.9 Total 100


68.9% of the respondents are of the opinion that the land value will

not increase in the absence of solid waste pollution.

Figure 6.25 Land Value in the Absence of Solid Waste Pollution

68.9%

31.1%

YesNo

iv) Change of Residence

Due to solid waste, more pollution was occured in the study area. In

this connection, the respondents were asked, whether they are willing to


155

change their residential place or not? The detailed opinion was gathered

and furnished in the following table.

Table 6.28 Change of residence

S.No. Response Percentage (%) 1. Yes 48.1 2. No 61.9 Total 100


It is witnessed from the above table that a good majority (61.9%) of

the respondents were not willing to change their residence from city to

semi-urban area, even though, it is with full of pollution due to solid waste

and other liquid wastes. Whereas 48.1% of the respondents were willing to

change their residence in a non-pollution area.

Figure 6.26 Change of Residence

48.1

61.9

0

10

20

30

40

50

60

70

Perc

enta

ge (%

)

Yes No

Change of Residence


156

i) Source of Drinking Water

Water is the one of the physical needs of human beings. The sources

of the drinking water available in the study area was studied under three

sources viz., Municipal water, well water and bore well water. The details

of usage and drinking water from various sources are furnished in the

following table 6.29.

Table 6.29 Source of drinking water

S.No. Source Percentage (%)

1. Municipal water 73.2

2. Well water 8.1

3. Bore well 18.7


It could be seen from the above table that a good majority (73.2%)

of the respondents depend mainly on the municipal water for drinking

purpose. It is followed by 18.7% of the respondent depending upon bore

well source. On the other hand, 8.1% of the respondents are depending

upon deep wells. From the analysis it is witnessed that a good majority of

the respondents depending on municipal water for drinking purpose.


157

Figure 6.27 Source of Drinking Water

73.2

8.1

18.7

0

10

20

30

40

50

60

70

80

Perc

enta

ge (%

)

Municipal water Well water Bore well

Source

ii) Quality of Water

Quality of water in the study area, Erode city is surrounded with end

number of chemical industries, leather industries and textile processing

industries. These industries disposing their solid and liquid wastes directly

in the Cauvery river. Hence the Cauvery river is highly polluted by these

industries. Even though the corporation authorities took many remedial

measures, but unable to implement the pollution rules strictly. Hence the

quality of water collected from Cauvery river possessing the chemicals of

waste disposal from industry is highly contaminated. The corporation

authorities took several process to purify the water but they are unable to

get the clean drinking water. In this study the respondents were asked to

rate the quality of drinking water supplied by Erode corporation. The

detailed opinion was collected and presented in the following table 6.30.


158

Table 6.30 Quality of Water

S.No. Source Percentage (%)

1. Yes 86.7

2. No 13.3

Total 100


It could be observed from the above table that a good majority

(86.7%) of the respondents express their displeasure and revealed that the

quality of corporation water is highly contaminated and lost its original

quality, and the remaining 13.3% of the respondents accepted the existing

quality. From the analysis it is inferred that a good majority of the

respondents expressed that the quality of corporation water is not good and

highly contaminated.

Figure 6.28 Quality of Water

13.3%

86.7%

YesNo


159

iii) Reasons for Poor Quality of Water

An attempt was made in the study to identify the poor quality of

water supplied by Erode corporation. For this purpose the reasons for

polluting the water was studied under three strata viz., solid waste

pollution, chemical, leather ad textile industrial pollution and hospital

waste disposal of both solid and liquid form. The details are furnished in

the following table.

Table 6.31 Reasons for Poor Quality of Water

S.No. Reason Percentage (%)

1. Solid waste pollution 70.2

2. Other types of pollution 24.3

3. Don’t know 5.5


It is examined from the above table that majority (70.2%) of the

respondents have revealed that the quality of drinking water has detoriated

due to solid waste pollution. 24.3% of the respondents opined that the

water has contaminated due to liquid form of chemicals disposed from

leather and textile processing industries. On the other hand meagre (5.5%)

respondents reveal that hospital related waste disposal was the reason for

high level of contamination. From the analysis it was concluded that solid

waste pollution is the main reason for poor quality of drinking water

supplied by Erode corporation.


160

Figure 6.29 Reasons for Poor Quality of Water

70.2

24.3

5.5

0

10

20

30

40

50

60

70

80Pe

rcen

tage

(%)

Solid waste pollution Other types of pollution Don’t know

Reasons

i) Impact of Solid water in the Society

The evil effect of solid waste was studied by selecting four major

issues such as fast growth of mosquito and flies, air pollution, dirty

surrounding and bad smell. The details of facilities affected by solid waste

to the common citizen in Erode corporation is furnished in the following

table.

Table: 6.32 Impact of Solid Waste in the Society

S.No. Problems Percentage (%) 1. Mosquito, flies 61 2. Air pollution 1.8 3. Dirty surroundings 2.8 4. Bad smell 3.1 5. All the problems 29 6. None 2.3 Total 100



161

It is witnessed from the above table that 61% of the respondents

were complained against solid waste that resulted in fast growth mosquito

of and flies affecting the good health of the people. It is followed by 32%

of the respondents expressed as bad smell. 4% of the respondents revealed

that the good air gets polluted. On the other hand a meagre percent of the

respondents expressed as dirty surroundings. From the analysis, it is

concluded that a good majority of the respondents expressed mosquito and

flies are the major problem due to accumulation of solid waste.

Figure 6.30 Impact of Solid Waste in the Society

61

1.82.8 3.1

29

2.3

0

10

20

30

40

50

60

70

Perc

enta

ge (%

)

Mosquito, flies Air pollution Dirty surroundings Bad smell All the problems None

Problems

6.9. Present status of Solid Waste Management

i) Method of solid waste disposal

The method of disposing the solid waste was studied in this chapter.

For this purpose two methods namely collecting the waste and placed


162

directly to the corporation waste bin and other form is burning directly.

The details are furnished in the following table.

Table 6.33 Method of solid waste disposal

S.No. Method Percentage (%)

1. Municipal waste bin 77.2

2. Burning 22.8

Total 100


It was learned from above table that a good majority (77.2%) of the

respondents safely placing the solid waste in the corporation waste bin.

While 22% of the respondents simply burnt out the solid waste. From the

analysis it is found that a good majority of the respondents disposing the

solid waste directly in the corporations waste bin.

Figure 6.31 Solid waste Disposal

23%

77%

Municipal waste bin

Burning


163

iii) Frequency of Collecting the Solid Waste

The corporation authorities have appointed the employees and

collect the solid waste in the Erode city. The present study aims at

measuring the frequency of collecting the solid waste in the study area. For

this purpose the frequency of collecting the solid waste has been studied

under three classifications viz., Daily, once in three days and once in a

week. The details are displayed in the following table.

Table 6.34 Frequency of solid Waste Collection

S.No. Frequency Percentage (%)

1. Daily 3.3

2. Once in three days 33.5

3. Once in a week 63.2

Total 100


It is disheartening to know that a good majority of the respondents

(63.2%) revealed that the solid waste were collected once in a week, it is

followed by 33.5% of the respondents expressed once in three days. On the

other hand, a few percentage of the respondents said that solid wastes were

collected daily. From the analysis it is concluded that majority of the

respondents expressed that the solid waste were disposed once in a week.


164

Figure 6.32 Frequency of solid Waste Collection

3.3

33.5

63.2

0

10

20

30

40

50

60

70Pe

rcen

tage

(%)

Daily Once in three days Once in a week

Frequency

iv) Rating on Managing Solid Waste

The respondents were asked to express their opinion on the

functioning of Erode Corporation in managing solid waste. For this

purpose three points scale scoring was employed. These scales are very

good, good and bad. The detailed opinion of the respondents on solid waste

management is shown in the following table.

Table 6.35 Rating on Managing Solid Waste

S.No. Response Percentage(%)

1. Very good 2.4

2. Good 30.9

3. Bad 66.7

Total 100



165

It is found from the above table that a majority (66.7%) of the

respondents expressed the style of managing the solid waste as bad, 30.9%

of the respondents said as good. Only a meagre percentage of respondents

compliment as very good. From the analysis it is inferred that majority of

the respondents opined that the style of managing solid waste is bad by the

Erode Corporation.

Figure 6.33 Rating on Managing Solid Waste

2.4

30.9

66.7

0

10

20

30

40

50

60

70

Perc

enta

ge (%

)

Very good Good Bad

Rating of SWM

6.10. Valuation for an improvement of the Solid Waste Management

iii) Solid waste pollution and environmental degradation

An attempt was made in this study to identify the environmental

degradation due to solid waste pollution. For this purpose a dichotomy test

was employed and the result is furnished in the following table.


166

Table 6.36 Role of Solid Waste Pollution


1. Yes 91.1

2. No 8.98


It could be identified in the above table that a very good majority

(91.1%) of the respondents expressed that the environment is drastically

decreased due to solid waste pollution where as (8.9%) the respondents

revealed that the environment degradation is not affected due to solid waste

pollution. From the analysis, it is concluded that majority of the

respondents revealed that the environment is much degraded due to solid

waste pollution.

Figure 6.34 Role of Solid Waste Pollution

91%

9%

YesNo


167

6.11. WTP for improved solid waste management programme

The valuation process consisted of giving the respondents two

hypothetical solid waste management projects. The characteristics and

advantages of the projects were explained to the respondents clearly.

i) Project description

The municipality is planning to have two different solid waste

management programmes that will take into consideration different aspects

of efficient solid waste management starting from generation of wastes to

final disposal. The project can also be done by a private agency. The

second project can also be done by a private agency. The second project

will be having additional advantages when compared with the first project

and the cost will be high. Contribution from the public in the form of user

charges is required.

ii) The first project

It will cost around Rs.6 crore and the key characteristics of the

project are:

1. a new collection system that ensures 100% collection of solid

wastes.

2. Construction of a controlled landfill in the present site with a large

life span.

3. Avoiding contamination of ground water.


168

The valuation exercise used the bidding format and gave Rs.30 per

month as the starting point. If the willingness to pay was more than Rs.30

the amount was raised in the subsequent question and then the respondent

was asked to give the final amount. Similarly, if the willingness to pay was

less than Rs.30, a lesser amount was asked in the subsequent question and

finally the respondents were asked to give their final amount.

iii) Amount willing to pay for the first project by the municipality

Table 6.37 shows the distribution of the various amounts willing to

be paid by the respondents for the first project done by the municipality.

Table 6.37 Amount Willing to Pay

S.No. Amount Percentage (%) 1. More than Rs.30 34.1 2. Amount of Rs.30 16.4 3. Less than Rs.30 40.6 4. None 8.9 Total 100


The above table shows that 34% of the respondents were

willing to pay more than Rs.30 and about 16.4% were willing to pay

Rs.30. On the other hand, 40.6% of the respondents were ready to pay

less than Rs.30 and 8.9% were unwilling to pay any amount for the

project.


169

Figure 7.35 Amount Willing to Pay

34.1

16.4

40.6

8.9

0

5

10

15

20

25

30

35

40

45Pe

rcen

tage

(%)

More than Rs.30 Amount of Rs.30 Less than Rs.30 None

Amount

iv) Reasons for willing to pay for the first project by the municipality

The respondents willing to pay to clear the solid waste in the study

area was studied under two main reasons. There are health concerns and

disamenity. The details are furnished in the following table.

Table 6.38 Reasons for Willing to Pay

S.No. Reason Percentage (%)

1. Health concerns 72.5

2. Disamenity concerns 27.5


It is noted from the above table that 72.5% cited health concerns as

the reason for willing to pay for the project while 27.5% of the respondents

gave disamenity concerns as the reason.


170

Figure 6.36 Reasons for Willing to Pay

72.5

22.4

5.1

0

10

20

30

40

50

60

70

80Pe

rcen

tage

(%)

Health concerns Disamenity concerns Not indicated

Reason

6.12. Statistical Analysis

Regression methods such as linear, logistic, and ordinal regression

are useful tools to analyze the relationship between multiple independent

variables and dependent variable. The regression methods are capable of

allowing researchers to identify independent variables related to dependent

variable. These methods also permit the researcher to estimate the

magnitude of the effect of the independent variables on the dependent

variable. The application of linear, logistic, and ordinal regression methods

depends largely on the measurement scale of the dependent variables and

the validity of the model assumptions. To study the effects of independent

variables on all levels of the ordered categorical dependent, an ordinal

regression method can be appropriately chosen to obtain the valid results.


171

6.12.1.Regression analysis for the first municipal project: PLUM

(Polychromous Universal Model)

The SPSS ordinal Regression procedure, or PLUM (Polychromous

Universal Model), is an extension of the general linear model to ordinal

categorical data (SPSS, Inc 2002). It can specify five link functions as well

as scaling parameters. The model to involves willingness to pay is the

dependant variable and the independent variables are AMI (Average

Monthly Income), Ed (Education), Cd (Children), Gen (Gender), Ea

(environmental ethic) and Hs (House ownership).

i) Model Fitting Information

Table 6.39 give the overall test of the model and test the hypothesis

that at least one of the independent variables (AMI, Ed, Cd, Gen, Ea,Hs)

does not significantly affect the household’s WTP.

Table 6.39 Model Fitting Information

Model -2 Log Likelihood Chi-Square df Sig.

Intercept Only 1497.789

Final 1383.112 114.677 8 000

Link function: Logita.

aThe link function is the function of the probabilities that results in a linear

model in the parameters. The link function specifies what transformation is

applied to the dependent variable or to the cumulative probabilities of the


172

ordinal categories. It is the link between the random component on the left

side of the equation and the systematic component.

The p – value of the Model fitting information table gives as 0.000,

which shows that the overall model is statistically significant or in other

words, the independent variables significantly affects the willingness to

pay of the households at the 0.05 significance level.

ii) Measuring strength of association – Pseudo R-Square

There are several R2 like statistics that can be used to measure the

strength of the association between the dependent variable and the

predictor variables. They are analogies to R-squared in OLS regression not

as useful as the R2 statistic, as their interpretation is not straightforward

(Magidson, 1981). These measures don’t have the percent of variance

explained interpretation and should not be reported in those terms. They

can be taken as additional measures of model effect size, with higher

values being better. Three commonly used statistics are:

iv) Cox and Snell R2 Cox and Snell’s R-Square is an attempt to imitate the

interpretation of multiple R-square based on the likelihood, but its

maximum can be less than 1.0, making it difficult to interpret

R2cs = 1 – <1.0

v) Nagelkerke’s R2: R-square is a modification of the Cox and Snell

coefficient so that it can vary from 0 to 1. Therefore Nagelkerke’s R-


173

square will normally be higher than the Cox and Snell measure but will

tend to run lower than the corresponding OLS R-square

vi) McFadden’s R-square is an information theory measure which is

interpreted as the reduction in entropy that the researcher’s model

achieves compared to the intercept-only model.

Where L(B) is the log-likelihood function for the model with the

estimated parameters and L(Bo) is the log-likelihood with just the

thresholds and n is the number of cases (sum of all weights).

Table 6.40 Pseudo R2

1. Cox and snell 299

2. Nagelkerke 389

3. McFadden 243

Link function Logit

Table 6.40 show that the Pseudo R2 values are having moderate size

effect.

iii) Test of Parallel Lines

This is commonly referred to as the test of parallel lines because the

null hypothesis states that the slope coefficients in the model are the same

across response categories and lines of the same slope are parallel.

Applying the parallel lines of the same slope are parallel. Applying the

parallel lines test if the regression coefficients are not significantly

different across levels of the response variable. Since the ordered logit


174

model estimates one equation over all levels of the response variable, the

test for proportional odds tests, whether our one-equation model is valid.

The assumption is not violated if this test returns a finding of non

significance, meaning there is no significant difference between the model

where the regression lines are constrained to be parallel for each level of

the ordinal dependent compared to the model where the regression lines are

allowed to be estimated without a parallelism constraint.

Table 6.41 Test of Parallel Lines

Model -2 Log Likelihood Chi-Square df Sig.

Null Hypothesis 798.363

General 790.060 8.303 7 307

The null hypothesis states that the location parameters (slope

coefficients) are the same across response categories.

The Table 6.41 given above shows that the assumption is met as the

test shows a level of non-significance.

iv) Parameter Estimates

These are the ordered log-odds (logit) coefficient. Standard

interpretation of the ordered logit coefficient is that for a one unit increase

in the predictor, the response variable level is expected to change by its

respective regression coefficient in the ordered log-odds scale white the

other variables in the model are held constant. Table: 6.42 represent the

parameter estimates.


175

Table: 6.42 Parameter Estimates

95% Confidence

Interval Asdf Asdf Estimate Std. Error Wald df Sig. Lower Bound

Upper Bound

[wtppl=0] -3.821 430 78,875 1 000 -4.664 -2.978

[wtppl=1] -2752 416 43.668 1 000 -3.568 -1.936 Threshold1

[wtppl=2] -.174 400 .190 1 .663 -.957 .609

AMI .000016 .000534 2.331 1 .084 .00013 3.014E-5

Cd -.041 .041 .976 1 .323 -121 040

Gen -.211 .191 1.228 1 .268 -.585 .162

Ea .495 .157 90.343 1 .000 .804 -.187

Hs .676 .188 12.535 1 .000 .297 1.034

[Ed=1] .196 .421 .052 1 .028 -922 .730

[Ed=2] .379 .205 3.394 1 .065 -024 .781

[Ed=3] .479 .189 6.465 1 .011 ..110 .846

Threshold2

[Ed=4] 0a 0

Link function: Logit. aThis parameter is set to zero because it is redundant. Wtpl = Willingness to pay for the 1st project (municipality) 1This represents the response variable in the ordered logistic regression. 2Location refers to the list of independent variable main, nested, and interaction effects in the model.

iv) Summary of the Results

The fitted mode, based on the output is given by,

P(WTP) = O) = exp (-3.821 + 0.000016 AMI + b2 Ed - 0.041 Cd – 0.211

Gen + 0.495Ea + 0.676Hs)/{1 + exp(-3.821 + 0.000016AMI + b2 Ed –

0.041Cd – 0.211 Gen + 0.495 Ea + 0.676 Hs)}

P(WTP = 1) exp (-2.712 +0.000016 AMI + b2 Ed - 0.041 Cd – 0.211 Gen

+ 0.495Ea + 0.676Hs)/{1 + exp(-2.712 + 0.000016AMI + b2Ed – 0.041Cd

– 0.211 Gen + 0.495 Ea + 0.676 Hs)}


176

P(WTP = 2) exp (-0.174 +0.000016 AMI + b2 Ed - 0.041 Cd – 0.211 Gen

+ 0.495Ea+ 0.676Hs)/{1 + exp(-0. + 0.000016AMI + b2Ed – 0.041Cd –

0.211 Gen + 0.495 Ea + 0.676 Hs)}

P(WIP) = 3) =1

Where b2 = 0.196 if Ed = 1; b2 = 0.379 if Ed = 2; and b2 =0.479 if Ed = 3

The results from the analysis shows that the variables Ea, Hs and

Ed are statistically significant at 5 percent level of significance and AMI

is significant at 10 per cent significance level (i.e., the p-values for

the variables Ea, Hs and Education are less than 0.05 and AMI is

less than 0.10).

The parameter estimates table shows that the signs of Children and

Gender are with negative coefficients. This means that the households with

more number of children tend to be unwilling to pay than those with a less

number of children. Then, if respondent is male, the household tends to

have a lower probability of paying. Variables AMI, Ea, Hs and Ed on the

other hand, have positive coefficients. This means that the higher the

average monthly income, the more likely that the household will be willing

to pay. Also, having environmental ethic helps to increase the probability

that the households will be willing to pay for the project. The result shows

that the individuals who owned their houses tend to a higher probability of

paying. Education has also a positive influence on WTP, i.e. higher the

educational attainment higher the willingness to pay for the project.


177

6.12.2. Regression analysis for the first private project

Ordinal Regression for the first project by the private agency is

given below.


Table 6.43 shows the model fitting information for the first project

to be done by the private agency.


Model –2 Log Likelihood Chi-square Df Sig.


Final 1267.338 38.913 8 .000

Link function : Logit

The p-value of the model fitting information table is given as 0.000,



pay of the households.

ii) Pseudo R-Square

Table 6.44 shows the strength of the association of the model.

Table 6.44 Pseudo R-Square

Cox and Snell .711

Nagelkerke .816

McFadden .606


The values of the pseudo R2 show good size effect.


178

iii) Test of Parallel Lines

Table 6.45 shows the test of parallel lines or the model.

Table 6.45 Test of Parallel Lines



General 1406.202 10.712 7 .152



The test finds a non-significant value showing that the assumption

of parallel lines is met.


Table 6.46 shows the parameter estimates for the model.

Table 6.46 Parameter Estimates

95% Confidence Interval Estimate Std.

Error Wald Df Sig. Lower Bound

Upper Bound

[wtppl=0] 1.434 .431 11.073 1 .001 .589 2.278 [wtppl=1] 2.144 .435 24.255 1 .000 1.291 2.997 Threshold [wtppl=2] 2.261 .436 26.854 1 .000 1.406 3.116 AMI .000076 .000 8.851 1 .056 .000 8.613E-5 Cd .075 .049 2.328 1 .127 -.021 .171 Gen -.052 .210 .062 1 .083 -.359 .463 Ea -.163 .166 .956 1 .328 -.488 .163 Hs .984 .188 27.353 1 .000 .615 1.353 [Ed=1] -1.057 .551 3.681 1 .055 -2.137 .023 [Ed=2] -.301 .219 1.890 1 .169 -.731 .128 [Ed=3] -.560 .203 7.579 1 .006 -.959 -.161

Location

[Ed=4] 0a . . 0 . . . Link function: Logit. a. This parameter is set to zero because it is redundant Wtppl = Willingness to pay for the first project (Private)


179

v. Summary of the results

The fitted model, based on the output is given by,

P(WTP = 0) = exp (1.434 + 0.000076 AMI + b2Ed – 0.075Cd – 0.052Gen

– 0.163Ea + 0.984Hs)/{1 + exp(1.434 + 0.000076 AMI + b2Ed – 0.075Cd

– 0.052Gen – 0.163 Ea + 0.984Hs)}



– 0.052Gen – 0.163 Ea + 0.984Hs)}



– 0.052Gen – 0.163 Ea + 0.984Hs)}

P(WTP =3) = 1

Where b2 = –1.057 if Ed = 1; b2 = -0.301 if Ed =2; and b2 = -0.560 if Ed=3

The results from the analysis shows that the variable Hs is

statistically significant at 5 percent level of significance and AMI, Gender

and Education are significant at 10 percent significance level (i.e., the p-

values for the variables Hs is less than 0.05 and AMI, Gender and

Education are less than 0.10).

The parameter estimates table shows that gender, environment ethic

and education are with negative coefficients. If the respondent is a male,

the household tends to have a lower probability of paying. Similarly,


180

households having education and environment ethic tend to have a lower

probability of paying. Variables AMI, Cd and Hs on the other hand, have

positive coefficients. This means that the higher the monthly income, the

more likely that the household will be willing to pay. The individuals who

owned their houses ten to have a higher probability of paying. Having

children has also positive influence on WTP, i.e. having children in the

family, higher will be the probability to pay for the project.

6.12.3. Regression analysis for the second municipal project

Ordinal regression for the second project by the municipality is

given below


Table 6.47 consider the model fitting information for the second

project to be done by the municipality.


Model –2 Log Likelihood Chi-square df Sig.


Final 1922.110 18.223 8 .020


The p-value of the Model fitting information table gives as 0.020





181

ii) Pseudo R-Square

Table 6.48 shows the Pseudo R2 values for the strength of

association


Cox and Snell .677

Nagelkerke .773

McFadden .542

Link function Logit

The values of the pseudo R2 show good size effect.

iii) Test of Parallel lines

Table 6.49 show the test of parallel lines for the model.

Table 6.49 Test of Parallel lines



General 853.159 8.173 7 318



The non-significant value show that the assumption of parallel lines

is met.

iv) Parameter estimates

Table 6.50 show the parameter estimates of the model.


182




Upper Bound

[wtp2=0] -.1.88 .394 .229 1 .632 -.960 .584

[wtppl=1] 2.144 .435 24.255 1 .000 1.291 2.997 Threshold

[wtppl=2] 2.261 .436 26.854 1 .000 1.406 3.116

AMI .000076 .000 8.851 1 .056 .000 8.613E-5

Cd .075 .049 2.328 1 .127 -.021 .171

Gen -.052 .210 .062 1 .083 -.359 .463

Ea -.163 .166 .956 1 .328 -.488 .163

Hs .984 .188 27.353 1 .000 .615 1.353

[Ed=1] -1.057 .551 3.681 1 .055 -2.137 .023

[Ed=2] -.301 .219 1.890 1 .169 -.731 .128

[Ed=3] -.560 .203 7.579 1 .006 -.959 -.161

Location

[Ed=4] 0a . . 0 . . .

Link function: Logit. a This parameter is set to zero because it is redundant Wip2 = Willingness to pay for the second Project (municipality)

v) Summary of the results


P(WTP = 0) = exp (-0.188 + 0.000069 AMI + b2Ed – 0.179Cd – 0.286Gen


– 0.286Gen – 0.201 Ea + 0.4014Hs)}

P(WTP = 1) = exp (0.751 + 0.000069AMI + b2Ed – 0.075Cd – 0.179Gen –

0.286Gen + 0.201Ea + 0.401Hs)/{1 + exp(0.751 + 0.000069AMI + b2Ed –

0.179Cd – 0.286Gen – 0.201 Ea + 0.401Hs)}


183

P(WTP = 2) = exp (1.42 + 0.000069 AMI + b2Ed – 0.179Cd – 0.286Gen –

0.201Ea + 0.401Hs)/{1 + exp(1.42 + 0.000069AMI + b2Ed – 0.179Cd –

0.286Gen – 0.201 Ea + 0.4014Hs)}

P(WTP =3) = 1


i. The results from the analysis show that the variables Cd, Ea and Hs

are statically significant at 5 percent level of significance.

ii. The parameter estimates table shows that Gender and education are

with negative coefficients. If the respondent is a male, the household

tends to have a lower probability of paying. Similarly, households

having education also tend to have a lower probability of paying.

Variables AMI, Cd, Ea and Hs on the other hand, have positive

coefficients. This means that the higher the monthly income, the

more likely that the household will be willing to pay. The

individuals who owned their houses tend to have a higher

probability of paying. Having children and environment ethic has

also a positive influence on WTP for the project.




Final 1434.719 42.411 8 .000

Link function: Logit.


184

The P – value of the Model fitting information table gives as 0.000,




i) Pseudo R-Square

Table 6.52 shows the PseudoR2 values for the variables.


Cox and Snell 574

Nagelkerke .655

McFadden .409

Link function: Logit.

The values of the pseudo R2 values for the variables.

ii) Test of Parallel lines

Table 6.53 consider the test of parallel lines for the model.




General 1413.735 722 7 936


coefficients) are the same across response categories

The finding of a non-significant value in this case, shows, that

assumption of parallel lines is met.


185


Table 6.54 give the parameter estimates for the model.




Upper Bound

[wtp2=0] 2.461 .418 34.599 1 .000 1.641 3.281

[wtppl=1] 3.562 .429 68.907 1 .000 2.721 4.403 Threshold

[wtppl=2] 3.884 .433 80.495 1 .000 3.036 4.733

AMI .000031 .000012 5.738 1 .017 3.082E-5 .000

Cd .002 .046 .003 1 .957 -.093 .088

Gen .329 .197 2.789 1 .005 -.057 .716

Ea .468 .151 9.541 1 .002 .171 .764

Hs .789 .184 .18.397 1 .000 .428 1.149

[Ed=1] -.942 .527 3.195 1 .074 -1.976 .091

[Ed=2] -.182 .218 .700 1 .403 -.609 .245

[Ed=3] .095 .196 .236 1 .672 -.289 .479

Location

[Ed=4] 0a . . 0 . . .

Link function: Logit. a This parameter is set to zero because it is redundant Wip2P = Willingness to pay for the second Project (Private)

v) Summary of the results



– 0.468Ea + 0.789Hs)/{1 + exp(2.461 + 0.000031AMI + b2Ed – 0.002Cd –

0.329Gen – 0.468Ea + 0.789Hs)}

P(WTP = 1) = exp (3.562 + 0.000031AMI + b2Ed – 0.002Cd – 0.329Gen +

0.468Ea + 0.789Hs)/{1 + exp(3.562 + 0.000031AMI + b2Ed – 0.002Cd –

0.002329Gen – 0.468 Ea + 0.789Hs)}


186

P(WTP = 2) = exp (3.884 + 0.000031AMI + b2Ed – 0.179002Cd –

0.329Gen – 0.468Ea + 0.789Hs)/{1 + exp(.3.884+ 0.000031AMI + b2Ed –

0.002Cd – 0.329Gen – 0.468Ea + 0.789Hs)}

P(WTP =3) = 1


i. The results from the analysis shows that the variables AMI, Gender,

Ea and Hs are statistically significant at 5 percent level of significance

and Ed is significant at 10 percent significance level (i.e. the p-values

for the variables AMI, Gender, Ea and Hs are less than 0.05 and Ed is

less than 0.10.

ii. The parameter estimates table shows that the variables AMI, Gen, Cd,

Ea and Hs, have positive coefficients. This means that the higher the

monthly income, the more likely that the household will be willing to

pay. If the respondent is a male, the household tends to have a higher

probability of paying. The individuals who owned their houses tend to

have a higher probability of paying. Having children and environment

ethic has also a positive influence on WTP for the project. Education

is with a negative coefficient i.e households having tend to have a

lower probability of paying for the project.


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DATA ANALYSIS AND INTERPRETATION - Shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/33833/6/... · 2018-07-02 · DATA ANALYSIS AND INTERPRETATION 6.1. Status of MSW in Erode