Determinant of Waste management services Demand in Nsukka

public spending on poverty

ABSTRACT

This research work investigated household preference and willingness to pay for waste management services in Nsukka urban. The study employed a descriptive survey methodology where questionnaire was employed as the research instrument to collect the relevant data. The population of the study is made up of the households in the Nsukka urban (comprising of six town) where simple random sampling techniques was employed to sample 25 households from each of the six town in Nsukka urban with total sample of 150 households. The estimate the household preference and willingness to pay for waste management services, the research employed binary modelling using probit model to estimate the influence of both cultural & demographic factors and economic factors on household willingness to pay for waste management service. The result revealed that demographic factors such age, household size and education have positive significant role on the probability of demanding for waste management in Nsukka urban in Enugu state. The implication is that family with more member tends to demand for more waste management service since they tend to generate more waste over time. Also, economic factors such, income level of the households, awareness of the household about the environment impact of waste management service and cost of waste management service play positive significant impact on the probability of household willingness to pay for waste management service. The research therefore recommend among others that government should subsidies the price of waste management service as to encourage more household to queue in as a way of ensuring a clean environment.

 

 

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  1. SECTION ONE
    • Background to the Study

Waste is directly linked to human development, both technologically and socially. The composition of different wastes has varied over time and location, with industrial development and innovation being directly linked to waste materials. Some components of waste have economic value and can be recycled once they are properly recovered. Waste management agencies have placed an increasing focus on reducing waste so that there is less to cope with. This can be done on an industrial level by developing more efficient processes, reducing packaging, and so forth. Individual consumers can also make commitment to generate less waste.

Waste is said to include refuse from households, non-hazardous solid waste from companies and commercial establishments, refuse from market waste, and street sweepings (Cointreaus-Levine, S 1994 and Alabi, 2004). Broadly, Household wastes also known as residential or domestic wastes are made up of wastes products that are as a result of household activities. These according to (CASSAD, 1998) include preparation of food, sweeping, cleaning, fuel burning and gardening wastes, old clothing, old furnishings retired appliances, packaging and reading materials.

Attempts have been made by scholars, researchers, consultants and government to determine the actual amount of waste being generated in Nigeria in general (Agbola, 2001). In a survey carried out by (CASSAD, 1998) on waste generation in Nigeria. The study shows that the volume of wastes generated by all the states increased over the period between 1994 and 1996. It was estimated that by the year 2010, Nigeria will generate about 3.53 million tonnes of solid waste, based on a per capita solid waste generation of 20kg per year (Agbola, 2001). Nigerian urban areas have been said to be some of the dirtiest, the most unsanitized and the least aesthetically pleasing in the world (Alabi, 2004).

This is because some individuals are naturally dirty, this evidence is seen everyday by way of indiscriminate disposal of garbage into drainages and the highways. About 75 percent of solid waste collected in most Nigerian urban cities is disposed in open. This method which is rampant is not hygienic as it marginalizes the urban environment as a result of the negative externalities it generates (Yusuf, Ojo, & kk, 2007 and Adinnu, 1994). Agreeing with this assertion, (CASSAD, 1998), stated that the decomposition of wastes on open dumping grounds emit intolerable smells and attract potential diseases. The economic importance of waste management on the quality of life cannot be over-emphasized. Wastes that are not well managed can affect the environment in terms of the contamination of the atmosphere, soil and water. This can cause severe problems for humans and animals population. It can also affect human health in particular by causing convulsion, dermatitis, irritation of nose/throat, anaemia, skin burns, chest pains, blood disorders, stomach aches, vomiting diarrhoea and lung cancer which may lead to death (Alabi, 2004).  It is worthy to note that it breed flies (which carry germs on their bodies), mosquitoes, and rats which aids salmonella, leptospirosis and other diseases they cause by biting and spoiling millions of tons of food. Lastly, is the social effect where flood may occur as a result of dumping of refuse in drainage especially during the raining season; an example of this is the recent flood which happened in late July 2010 in Osogbo metropolis. Lives and properties worth millions of naira were lost in this July flood (Osun Mail, 2010). Safe and clean environment is an essential requirement for maintaining life on earth and creating human friendly environment is one of the most important issues in the world today (Khtak and Amin 2013). To meet the needs of rapidly growing population, it is obvious that production has to be increased by at least the population growth rate which leads to waste production that is beyond the absorptive capacity of the environment (Tarfasa, 2007, Subha, Ghani et al., 2014). Population growth, urbanization and greater exploitation of resources resulted in an increasing demand for environmental management. Particularly in developing countries urban areas, the people are facing sever challenges due to lack of healthy urban environment (Khtak and Amin 2013).

Inadequate municipal waste management is one of the major drivers to the degrading of environment quality in urban areas (UNPDDESA, 2005, Khattak, Khan et al., 2009, Wilson and Velis, 2014). This solid waste problem is due to high waste generation, inadequate waste collection, and poor disposal habits by the households/individuals, which is as a result of  lack of appropriate planning, inadequate governance, resource constraint and ineffective management solid waste is a major source of concern in many rapidly growing cities in developing countries. According to UNEP (2004), solid waste generation has become an increasing environmental and public health problem everywhere in the world, particularly in developing countries. The fast expansion of urban agricultural and industrial activities stimulated by rapid population growth has produced vast amounts of solid and liquid wastes that pollute the environment and destroy resources.

The changing economic trends and rapid urbanization also complicate solid waste management in developing countries. Consequently, solid waste is not only increasing in quantity but also changing in composition from less organic to more paper, packing wastes, plastics, glass, metal wastes among other types, a fact leading to the low collection rates (Bartone & Bernstein, 1993).

  • Statement of the Problem

Establishing effective waste management should be a priority for emerging cities, given their crucial role in protecting public health and the environment. However, in the past, most attempts by cities to improve solid waste management focused on the different technical means of collection and disposal (Altaf and Deshazo 1996; Medina 2002).

Collection and disposal of waste has always been the responsibility of government authorities in the past (Harris, Allison & Smith, 2001), hence, waste management is a service for which state and local government is responsible (Cointreaus-Livine, 1994). Waste collection and disposal is the constitutional obligation of the local government. This obligation is not achieved or performed, because, no local government area in Nigeria can afford the huge financial, administrative, technical and human resource requirements to effectively carry out this constitutional obligation (Alabi, 2004). The collection of solid wastes in many Nigerian cities has been concluded to be the responsibility of government as part of its obligations to the citizens.

The inability of the government to manage solid waste collection and disposal effectively arose perhaps from the misconception of this task as a public good. Irrespective of the fact that government gave waste collection a priority in their development objectives, their ability to curtail the problems of waste collection deteriorates with time especially in the rural areas or emerging town like Nsukka, due to rising capital costs for plant and equipment, increasing operation and maintenance costs because of the rapid population growth of emerging urban areas with decreasing waste management coverage levels, and with increase in level of waste generated, confronted by increasing public demand for improved services and infrastructure (Salifu, 2001 and Sule, 1981), the need arises for the involvement of the private sector and the civil society in the provision of municipal solids waste service.

It should be noted, however, that it is only in the large urban centres of Nigeria e.g. Lagos, Ibadan, Warri, Suleja amongst others that the activities of formal private sector are recorded (Alabi, 2004). In majority of the secondary cities such as Osogbo, they are neither totally absent or being substituted with the informal refuse collectors such as cart pushers. This therefore gives rise to the need to evaluate the household willingness to pay for improved solid waste disposal services in the study area. Specifically the study examined the general features of the existing waste management, household willingness potential to pay for improved waste disposal.

  • Objectives of the Study

The broad objective of this study is to examine household’s preferences and willingness to pay for waste management services. The specific objectives are;

  1. To examine the extent cultural and demographic factors impact on household’s preferences and willingness to pay for waste management services in Nsukka urban.
  2. To examine the extent economic factors impact on household’s preferences and willingness to pay for waste management services in Nsukka urban.
    • Research Questions

The following research questions guided the study

  1. To what extent does cultural and demographic factors impact on household’s preferences and willingness to pay for waste management services in Nsukka urban?
  2. To what extent does cultural and demographic factors impact on household’s preferences and willingness to pay for waste management services in Nsukka urban?

1.5 Statement of Hypothesis

  1. H0: Cultural and Demographic Factors does not have significant impact on household preference and willingness to pay for waste management service in Nsukka urban.
  2. H0: Economics Factors does not have significant impact on household preference and willingness to pay for waste management service in Nsukka urban.

1.6 Scope of the Study

This research work assessed households’ preferences and willingness to pay for waste management services with particular reference to Nsukka urban, as case study.

 

 

 

  1. SECTION TWO: LITERATURE REVIEW

2.1 Conceptual Literature

According to Tchobanglous (1993) all wastes arising from human and animal activities that are normally solid and are discarded as useless or unwanted are broadly defined as solid waste. It includes municipal garbage, industrial and commercial wastes, sewerage slug, waste of agricultural and animal husbandry, demolition waste and mining residues. Different individuals have defined solid waste differently. Medina (2002) defines MSW as “…the materials discarded in the urban areas for which municipalities are usually held responsible for collection, transport and final disposal. It encompasses household refuse, institutional wastes, street sweepings, commercial wastes, as well as construction and demolition debris. For Cointreau (1982) Solid waste is material for which the primary generator or user abandoning the material within the urban area requires no compensation after abandonment.

Enger and Smith (2006) categorized solid waste in to four broader kinds as mining, agricultural, industrial, and municipal solid waste. Materials no longer used but are disposed because they are broke, spoiled, or have no longer uses are regarded as solid waste. Such waste can emanate households, commercial establishments, institutions, and some industries. Considering the points through which waste emanated, waste can be divided into: domestic waste, commercial waste, industrial waste, institutional waste, street sweepings and constructions waste. According to Cornwell (1998) solid waste can be classified as organic, inorganic, combustible, putrescible and non-putrescible factions. Cornwell regarded waste classifications based on the kinds and heat content of the waste materials as the most useful. Domestic waste or household waste derived from residential neighborhoods is the largest component of solid waste.

Solid Waste Management (SWM) is defined as the control, generation, storage, collection, transfer and transport, processing and disposal of solid waste consistent with the best practices of public health, economics & financial, engineering, administrative, legal and environmental considerations (Othman, 2002). Solid waste management has three main components: collection and transportation; reuse or recycling; and treatment or disposal (SIDA, 2006). US EPA recommends using integrated, hierarchical approach to waste management with four components: source reduction, recycling, combustion, and land filling, to address the increasing volume of municipal solid waste. It ranks source reduction including reuse as the most preferred method, followed by recycling and composting, and lastly, disposal in combustion facilities and landfills.

Developing countries have peculiar solid waste management problems different than those observed in the industrialized countries. Although low-income countries’ solid waste generation rates average only 0.4 to 0.6 kg/person/day as opposed to 0.7 to 1.8 kg/person/day in the industrialized countries, indeed, the very composition of their waste is different than that of ‘developed’ nations. Cointreau (1982), Blight and Mbande (1996), and Arlosoroff (1982) noted developing countries wastes are 2-3 times greater in waste density at the same time 2-3 times greater in moisture content than that of industrialized nations. Developing country wastes also involve large amount of organic waste (vegetable matter, etc.), large quantities of dust, dirt (street sweepings, etc), and smaller particle size on average than in industrialized nations.

Although there might be some potential opportunities which arise from their waste composition, these peculiarities from industrialized nations present additional problems (Cointreau, 1982; Zerbock, 2003). Firstly, a higher solid waste density has many implications for the ‘traditional’ methods of collection and disposal.

2.2 Empirical literature

Several empirical studies on waste management indicate that age, household size, sex, marital status, education and household are among factors affecting willingness to pay for effective waste management. Niringiye and Omor or (2010) found that age of the respondents has a negative and significant effect on waste management in Kampala city in Uganda. According to Yusuf, Salimonu and Ojo (2007) on willingness to pay to improved household  solid waste management in Oyo State, Nigeria. The outcome of the work showed that the maximum individual is willing to pay for improved solid waste management is N1240. Furthermore, it was discovered that other factors such as age, educational level, household size and households monthly expenditure and income affects willingness to pay for waste disposal.

Das and Gogoi (2010) while analyzing the effect of in a municipal solid waste management of Tinsukia Municipality of Assam in India, observed that Cost of waste management is affected by family income positively. The result of the study shows that an increase in household income increases willingness to pay by as much as 13%. Additionally, they found out that willingness and preference to pay for waste disposal also depend the proportion waste the household generate per month. Household with huge waste to dispose have 21% willingness to pay for such services.

Siriwardena and Gunaratne  (2007) and Jamal (2002), Pek et al.,(2008) used the choice experiment and the multinomial logit regression to investigate solid waste management in Malaysia. Their findings were that the level of increase in waste disposal required better quality disposal options. They concluded that sanitary landfill is more preferred in solid waste disposal by the residents. The study of Morrison et al. (2002) on willingness to pay for waste disposal and management showed that, the willingness to pay by the respondents was negative. The respondents believe that the government should take care of the environmental issues. The implicit price obtained revealed that the households were not interested in environment improvement because there is an alternative to dispose wastes.

In his investigation of household preferences for solid waste management in Malaysia, Jamal  (2002)  that  households derive positive utility from the provisions of compulsory recycling facilities for efficient waste disposal  . Birol et al.,(2009) estimated the value of improved wastewater treatment, a case study of river Ganga, in India by using  the conditional logistic model. It was discovered that the coefficients were significant and in accordance to expectation. Treated wastewater quantity and quality were significant factors in the choice of a wastewater treatment programme. These two attributes increase the probability that a wastewater treatment programme is selected. In other words, households value those wastewater treatment programmes that result in higher quality and quantity of wastewater treated.

While considering the attributes of frequency of vat collection, covered vats, covered collection trucks and monthly increase in tax, Sukanya et al., (2008) used the conditional logistic model and the random parameter model to estimate willingness made by the respondents to improvement in solid waste disposal. Their findings were that the poor and the rich have different attribute to payment. Whereas richer households were willing to pay more for higher wastewater treated to a quality, poorer households were rather willing to pay more for higher quantity of wastewater treated.

 

 

 

III. SECTION THREE: RESEARCH METHODOLGY

3.1 Introduction

This section discussed the procedure and methods adopted in carrying out this study. It contains the following sub-headings: design of the study, area of the study, sample and sampling techniques, research instrument, method of analysis and specification of model(s).

 

3.2   Research Design

The study employed the descriptive survey research design. According to Agba (2008) a survey research design is a procedure used in obtaining information from a sample or relevant population that is familiar with the ideas relating to the objectives of the study. In the opinion of Olaitan and Ali (2000), survey design is a kind of research design that studies small or large population by selecting and analyzing the data collected from the group through the use of questionnaire, telephone or personal interview. The design is therefore appropriate for this study as it intends to obtain data using questionnaire from several households in order to ascertain the factors that drive their demand for waste management services.

3.3 Area of the Study

Nsukka urban town which is the headquarter of Nsukka Local Government Area in Enugu State, South-East Nigeria. The Nsukka urban is made up of the following towns  Edem Ani, Ibagwa Ani, Opi, Ehaalumona, Orba, Ede-Oballa and Obimo. The urban town has an estimated population of 309, 448 and an area of 49 130 m² according to the 2006 Nigerian population census (Anonymous, 2015). Nsukka region (which includes Nsukka urban) is located in the northern section of southeastern Nigeria between latitudes 6o30’ and 7o54’ north, and longitudes 6o54’ and 7o54’east. The average households in the Nsukka urban area is estimated at 61,889 based on average 5 household members.

3.4 Sample and Sampling Techniques

The study adopted simple random sampling technique where 25 households were sampled from each of the six town that made up of Nsukka urban making a total sample size of one hundred and fifty (150) respondents. The choice of simple random sampling techniques is to give every household the chance of been included in the sample.

3.5 Instrument for Data Collection

The study made use of a structured questionnaire to collect data from respondents. The questionnaire supplied information on the factors that influence household willingness and preference for waste management service by households. It also elicited information concerning the cultural and demographic and economic factors influencing households’ preferences and willingness to pay for waste management services.

3.6 Method of Data Analysis

The descriptive statistics technique is adopted to show the characteristics of households’ preferences and willingness to pay for waste management services. In furtherance, the probit regression analysis is employed to empirically estimate the impact of cultural and demographic and economic factors influencing household preferences and willingness to pay for waste management services.

3.7 Specification of Models

The probit regression model is used to estimate the impact of impact of cultural and demographic and economic factors influencing household preferences and willingness to pay for waste management services in Nsukka Urban. The choice of probit model in estimating the relationship stem from the fact that the dependent variable takes binary number (0 and 1). According to Pohlman & Leitner (2003); Wollbridge (2013), probability modelling such as Logit, Probit and Tobit predict the dependent variable responses to changes in the independent variable than linear model like OLS. The reason according to Woolbrdge is that OLS estimate of binary dependent variable often give predicted values beyond the range (0,1). In a similar reaction, Pohlman & Leitner (2003) compare the result of OLS with that of Logit for a binary dependent variable and concluded that probability model like Logit and Probit yield a better result than OLS. Secondly, almost all the review empirical studies on household willingness to pay for waste management employed probability modelling like probit to estimate the relationship. As such this research will follow the part of previous researchers like Das and Gogoi (2010); Siriwardena & Gunaratne (2007); Jamal (2002), and  Pek et al.,(2008) to estimate the household preference and willingness to pay for waste management service.

The dependent variable is demand for waste management services by respondents and ranges between 0 and 1 (demand entails willingness and preference to pay for waste management services). Respondents, who are willing and prefer waste management services as means of waste disposal were scored ‘1’ and those who are not willing to pay for waste management services were scored ‘0’.

The observed binary (1, 0) for whether or not household has demanded for waste management service is expressed in the following probit regression model:

Y= bXij + eij

Where:

Y= Demand for waste management services (1=Yes; 0=No)

Xij =Vector of explanatory variables

bij = Vector of parameter estimates

eij =  Stochastic variable

 

Two models were developed in the study. The first model examines the impact of cultural and demographic factors on the preference and willingness to pay for waste management services. The second model assesses the impact of economic factors on the preference and willingness to pay for waste management services.

Model 1: Impact of Cultural and Demographic Factors on Preference and Willingness to pay for Waste Management Services (WMS).

Y= f(X1, X2, X3, X4, X5)

Y= b0 + b1X1 + b2X2+ b3X3 +b4X4 + b5X5 +u

Where:

Y= Demand for waste management services (1=Yes; 0=No)

X1= Age (in years)

X2= Gender (1=Female; 0=Male)

X3= Marital Status (1=Married; 0=Unmarried)

X4= Education (number of years in school for the head of the family)

X5= Family Size (in person)

b1-5 = Coefficient of explanatory variables

u= Error term.

 

Model 2: Impact of Economic Factors on Preference and Willingness to pay for Waste Management Services (WMS)

Y= f(X1, X2, X3)

Y= b0 + b1X1 + b2X2+ b3X3 +u

Where:

Y= Demand for waste management services (1=Yes; 0=No)

X1= Cost of waste management service (1=Affordable; 0=Not affordable)

X2= Household monthly income (N)

X3= House Ownership (1= Owner of house; 0=Non-owner of house)

X4= Awareness about Environmental implication of waste management service (1=Yes; 0=No)

 

b1-4= Coefficient of explanatory variables

u= Error term.

 

 

 

 

 

 

 

 

 

 

  1. SECTION FOUR

4.1 Presentation and Analysis of Result

This section contains the presentation and discussion of results obtained from the survey exercise. The descriptive statistics was used to analyze the socioeconomic and demographic characteristics of the respondents. Furthermore, the probit regression model was adopted to estimate the impact of cultural and demographic, as well as economic factors on households’ preference and willingness to pay for waste management services in Nsukka Urban.

Table 4.1: Socioeconomic and Demographic Distribution of Respondents

 

Frequency

Percentage

Demand for Waste Management services

 

 

Yes

123

82.0%

No

27

18.0%

Total

150

100.0%

 

 

 

Age (in years)

 

 

20-29 years

12

8.0%

30-39years

28

18.7%

40-49years

38

25.3%

50-59years

43

28.7%

Above 60 years

29

19.3%

Total

150

100.0%

Gender

 

 

Female

102

64.0%

Male

48

32.0%

Total

150

100.0%

 

 

 

Occupational Status

 

 

Employed

132

88.0%

Unemployed

18

12.0%

Total

150

100.0%

Family Size

 

 

1-3persons

33

22.0%

4-6persons

88

58.7%

Above 6 persons

29

19.3%

Total

150

100.0%

Source: Author’s Computation from Field Survey

Table 4.2: Economic Factors Influencing Household Willingness to pay for WMS.

 

Frequency

Percentage

 

 

 

Cost of Waste management  Services

 

 

Affordable

140

93.3%

Not affordable

10

6.7%

Total

150

100.0%

 

 

 

Household Monthly Income

 

 

None

0

0

Below N100, 000

105

70.0%

N100, 000 – N200, 000

34

22.7%

Above N200, 000

11

7.3%

Total

150

100.0%

 

 

 

 

 

 

Ownership Status

 

 

Owner

95

63.3%

Rented

55

36.7%

Total

150

100.0%

 

 

 

Awareness about Environmental Information

 

 

Yes

130

86.7%

No

20

13.3%

Total

150

100.0%

Source: Author’s Computation from Field Survey

 

From table 4.1 above, it was revealed that 123(82%) of the respondents, which formed the majority are willing to pay for waste management services, 27(18%) did not prefer and neither willing to pay for waste management services in Nsukka urban.

Based on age distribution, 12(8%) of the respondents are between 20-29years; 28(18.7%) are between 30-39 years; 38(25.3%) are between 40-49 years; 43(28.7%) are between 50-59 years and 29(19.3%) are above 60 years. Based on gender distribution, 102(68%) of the respondents, which constituted the majority are female while the remaining 48(32%) are male. Based on occupational status, 132(88%) of the respondents, which formed the majority are employed while the remaining 18(12%) are unemployed. Based on family size, 33(22%) of the respondents have a household size between 1-3persons; 88(58.7%) have between 4-6persons and 29(19.3%) have above 6 person in their family.

On the economic factors affecting household preference and willingness to pay for waste management service. It was revealed that 140(93.3%), which is the majority, reported that the cost of waste management services is affordable while the remaining 10(6.7%) stated that it is unaffordable. Based on monthly income distribution, 105(70%) of the respondents earn below N100, 000; 34(22.7%) earn between N100, 000- N200, 000 and 11(7.3%) earn above N200, 000. Based on ownership status, 95(63.3%) of the respondents are the owner of the house while the remaining 55(36.7%) are leaving in rented apartment. 130(86.7%) of the respondents stated that they are aware about waste management information in their locality while the other 20(13.3%) are unaware.

Table 4.3:   Probit Regression Result on the Impact of Cultural and Demographic Factors on Household Willingness to Pay for Waste Management in Nsukka Urban (Model 1)

 

Dependent Variable: DEMAND

 

 

 

Method: ML – Binary Probit  (Newton-Raphson / Marquardt steps)

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable

Coefficient

Std. Error

z-Statistic

Prob.

Remark

 

 

 

 

 

 

 

 

 

 

 

 

C

-4.079607

1.234705

-3.304114

0.0010

Significant

AGE

-0.071882

0.021020

-3.419699

0.0006

Significant

GENDER

0.569650

0.376027

1.514918

0.1298

Non- Significant

MARITAL

-0.220111

0.621424

-0.354204

0.7232

Non- Significant

EDUCATION

0.151463

0.065698

10.78332

0.0000

Significant

SIZE

0.614896

0.119603

5.141126

0.0000

Significant

 

 

 

 

 

 

 

 

 

 

 

 

McFadden R-squared

0.588131

 Mean dependent var

0.820000

 

LR statistic

83.17231

 Avg. log likelihood

-0.194152

 

Prob(LR statistic)

0.000000

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Source: Researcher Own Computation (see Appendix)

Table 4.3 presented the impact of cultural and demographic factors on the household Willingness to pay for waste management in Nsukka urban using probit regression analysis. The McFadden R-square of 0.58 implies that cultural and demographic factors such as age, gender, marital status, education and family size explained about 58% variation in demand for waste management service. The log-likelihood ratio statistic of 83.17, with probability of 0.00, is highly significant at 5%. This means that the first model is sufficient to explain the probability of the effect of cultural and demographic factors on demand for waste management.

 

The result showed that age (p<0.05); education (p<0.05) and family size (p<0.05) are the significant cultural and demographic factors that affects the demand for waste management. While factors such as marital status and gender does not play significant role in determining household willingness and preference for waste management service in Nsukka urban. All the explanatory variables incorporated in the first model were positively related to demand for waste management except for age and marital status. For example, that as people get older, there is decrease in the probability of them demanding for waste management services due to the fact they have many individuals who can help them for such services.  Also, as family size becomes larger, the probability of such family to demand for waste management services will increase because the family will tend to generate more waste over time. In furtherance, being educated increases the probability of demanding for waste management services as they can afford the inherent cost and are aware of the health implication of poor waste management. Being married increases the likelihood of demanding for waste management services since more waste will be generated over time and similarly female head of the family tends to increase the probability demanding for waste management service than the family with male head since the male head is not involved in the waste disposal.

The implication of the result above is that demographic factors combine plays significant role in household waste management service preference and willingness to pay.

 

Table 4.4: Probit Regression Result on the Impact of Economic Factors on Household Willingness to Pay for Waste Management in Nsukka Urban (Model 2)

 

Dependent Variable: DEMAND

 

 

 

Coefficient covariance computed using observed Hessian

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable

Coefficient

Std. Error

z-Statistic

Prob.

Remarks

 

 

 

 

 

 

 

 

 

 

 

 

C

-3.379957

0.869059

-3.889214

0.0001

Significant

COST

2.793480

0.618478

4.516702

0.0000

Significant

INCOME

1.27E-05

4.59E-06

2.771841

0.0056

Significant

OWNERSHIP

0.015338

0.298273

0.051423

0.9590

Not-Significant

AWARENESS

1.042378

0.385385

2.704773

0.0068

Significant

 

 

 

 

 

 

 

 

 

 

 

 

McFadden R-squared

0.621358

 Mean dependent var

0.820000

 

LR statistic

45.44581

 Avg. log likelihood

-0.319907

 

Prob(LR statistic)

0.000000

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Source: Researcher Own Computation (see Appendix)

Table 4.4 presented the impact of economic factors on the demand for waste management services using probit regression analysis. The McFadden R-square of 0.62 implies that economic factors such as cost of waste management, income of the household, ownership of the apartment and awareness of the environmental consequence of poor waste management explained about 62% variation in demand for waste management. The log-likelihood ratio statistic of 45, with probability of 0.00, is highly significant at 5%. This means that the second model is sufficient to explain the probability of the effect of economic factors on demand for waste management in Nsukka urban.

 

The result revealed that factors such as cost of waste management in terms of affordability (p<0.05), household average monthly income (p<0.05) and awareness of the environmental factor of waste management are the significant economic determinants of demand for waste management services. While the ownership of the property, whether the household own the house they live or not does not play any significant role in household preference and willingness to pay for waste management services.

Cost of waste management, household income, environmental awareness and ownership positively impacts on demand for waste management. For example, if people can afford the cost of waste management services, the probability of them demanding for such services will increase. Also, as individual’s income rises, there will be an increase in the probability of demand for waste management services. Being aware of the environmental consequences of improper waste management or being aware of waste management service will increases the likelihood of demanding for waste management services. And lastly, owners of house tends to increase the probability of the household preference and willingness to pay for waste management services.

  1. SECTION FIVE

5.1 Conclusion and Recommendation

The research has thus far examined the household preference and willingness to pay for waste management service in Nsukka urban. Specifically, the researchers made considerable effort to examine the impact of demographic and cultural factor plays in waste management service demand as well as how economic factors such as cost of waste management service, household monthly average income, household awareness of the waste management service and health implication of improper waste disposal as well as the ownership status of the house in question where the household resides.

From our discussion in the analysis, it was clear that demographic factors such as household head age, family size and level of education of the household head plays significant role in their preference and willingness to pay for waste management services. The reason for this might not be far-fetched since, educated household tends be aware of the environmental consequences of improper waste management and secondly earn fair income that can demand for such service. A non-educated family income might be relatively low as such preventing them from demanding for the service. For instance Gogoi (2010) argued that family size has positive significant influence of the probability for demand for waste management service. Noting that the larger the family the more waste they generate and the more willing the family will be to demand or ay for waste management service.

Our findings equally reveal that economic factors such as cost of waste management service in terms of affordability, household income and awareness were significant in influencing household preference and willingness to pay for waste management services. This collaborate with the findings of Salimonu and Ojo (2007) who argued that income of the household plays a positive significant role in household willingness to pay for waste management service.

This research therefore recommends that the government should subsidies the price of waste management service as to encourage more household to queue in as a way of ensuring a clean environment. Secondly, the government need to create better awareness on the need and health implication of improper waste disposal, as this will create the awareness on the household mind.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

References

Boniface, OW (2016). Municipal Solid Waste Characterization in Nsukka Urban in South East Nigeria. Transylvanian Special Issue Review, XXIV(7),

Adinnu, IF (1994). Landfill and Urban Residential Rental Values: A Case Study of Achapo landfill facility in Ojo LGA, Lagos State’. Unpublished MURP Dissertation. Centre for Urban and Regional Planning, University of Ibadan.

Agbola, T (2001). Turning Municipal Waste into Compost: The Case of Ibadan’. In Drechsel, P. and D. Kunze (eds), Waste Composting for Urban and Peri- Urban Agriculture Closing the Rural-Urban. Nutrient Cycle in Sub-Saharan Africa, International Waste Management Institute, Food and Agriculture Organization of the United Nation, CABI publishing, OXon, UK, 69-81.

Alabi, M (2004). Waste Products Survey For Identification and Quantification Of Different Wastes Generated In Nigeria. An Unpublished PhD Thesis in the Dept. of Geography, University of Ibadan.

CASSAD (Centre for African Settlement Studies and Development) (1998). Workshop on Turning Waste to Weath-Strategies, Options, Appropriate and Affordable Technology for Waste Management, Training Module Prepared Course Codes CASTWW/98 April

Cointreaus-Levine, S. (1994). Private Sector Participation in Municipal Solid Waste Services in Developing Countries Volume 1. The Formal Sector. Published for Urban. Management Programme by The World Bank, Washington, D.C.

Harris, PJC, M. Allison, G. Smith, HM. Kindness and J Kelley (2001). ‘The Potential Use of Waste –stream Products for Soil Amelioration in Peri-urban Interface Agricultural Production Systems’. In Drechsel, P. and D. Kunze (eds), op.cit., . 1-28.

Osun mail (2010). Flood of Tears in Osogbo. An Authoritative Weekly Newspaper in Osun State. www.osunmails.com/?p=848

Salifu, L (2001). An Integrated Waste Management Strategy for Kumasi’ In: Drechsel, P. and D Kunze (eds), op.cit., pp. 112-114.

Sule, RAO (1981). The Deterioration of the Nigerian Environment Solid Waste Disposal in Metropolitan Lagos, Geojournal, 3: 57-77.

Yusuf, SA, Ojo, OT & KK (2007). Households’ Willingness to pay for improved household solid waste management in Ibadan –North LGA of Oyo State, Nigeria. J. of Environmental Extension University. of Ibadan. Vol: 6 pp 57-63

Arrow, J.K., R. Solow, P.R. Portney, E.E. Leamer, R. Radner, and H. Schumand. 1993. Report of the NOAA Panel on Contingent Valuation. Federal Register 58 (10), 4601–4614.

Bartone, C.L., and J.D. Bernstein. 1993. Improving Municipal Solid Waste Management in Third World Countries. Resources, Conservation and Recycling 8:43–5.

Cameron, T.A., and M.D. James. 1987. Efficient Estimation Methods for Use with Closed-Ended Contingent Valuation Surveys Data. Review of Economics and Statistics 69: 269–76.

Carson, R.T. 2002. Contingent Valuation: A Comprehensive Bibliography and History. Cheltenham, UK: Edward Edgar.

Carson R.T., N. Flores, and N. Meade. 2001. Contingent Valuation: Controversies and Evidence. Environmental and Resource Economics 19 (2): 173–210.

Carson, R.T., W.M. Hanemann, R.J. Kopp, J.A. Krosnick, R.C. Mitchell, S. Presser, P.A. Ruud, and V. Keny Smith, with M. Conaway and K. Martin 1998. Referendum Design and Contingent Valuation: The NOAA Panel’s No-Vote Recommendation. Review of Economics and Statistics 80 (2): 335–38.

Chakrabarti, S., and P. Sarkhel. 2003. Economics of Solid Waste Management: A Survey of Existing Literature. Kolkata, India: Indian Statistical Institute, Economic Research Unit.

Chuen-Khee, P., and J. Othman 2010. Household Demand for Solid Waste Disposal Options in Malaysia. World Academy of Science, Engineering and Technology 66: 1153–58.

Fantu, S. 2007. Household Heads’ Willingness to Pay for Improved Solid Waste Management: The Case of Common Building Residences in Addis Ababa. MA thesis, Department of Economics, Addis Ababa, Ethiopia.

 

 

 

 

 

 

 

 

 

 

 

 

 

APPENDIXES

Questionnaire

Household Preference and Willingness to Pay for Waste Management Service in Nsukka Urban

Dear Respondent,

We are currently carrying out a research study on the above topic. We hereby solicit for your response to all items in this questionnaire. Please be rest assured that all responses will be strictly used for academic purposes

Thank You.

 

……………..

Researchers

 

Please tick one out of the boxes provided for each items as it applies to you.

Preference and Willingness to Pay for Waste Management Services

  1. Do you prefer and willing to pay for waste management service as option for waste disposal in your locality:

Yes               [            ]

No                 [            ]

Cultural and Demographic Factors

  1. Age as at last birthday:

20-29 years                  [            ]

30-39 years                  [            ]

40-49 years                  [            ]

50-59 years                  [            ]

Above 60 years           [            ]

 

  1. Gender:

Female                         [            ]

Male                           [            ]

 

  1. Marital Status:

Married                                   [            ]

Unmarried                               [            ]

 

  1. Number of years in School:

Years                           [            ]

  1. Family Size:

1-3 persons                  [             ]

4-6 persons                  [             ]

Above 6persons          [             ]

Economic Factors

  1. Cost of Waste management Services:

Affordable                  [           ]

Unaffordable              [           ]

 

  1. Household Monthly Income:

No income                                           [           ]

Below N100, 000                                [           ]

N100, 000- N200, 000                        [           ]

Above N200, 000                               [           ]

 

  1. House Ownership:

Owner                         [           ]

Rented            [           ]

 

  1. Awareness about Environmental implication of waste management service Information:

Yes               [            ]

No                   [            ]

 

 

 

REGRESSION RESULT

Demographic Factor Descriptive Statistics

 

 

 

AGE

DEMAND

EDUCATION

GENDER

MARITAL

SIZE

 Mean

 47.76667

 0.820000

 13.81333

 0.680000

 0.920000

 4.920000

 Median

 44.50000

 1.000000

 16.00000

 1.000000

 1.000000

 5.000000

 Maximum

 64.50000

 1.000000

 18.00000

 1.000000

 1.000000

 8.000000

 Minimum

 24.50000

 0.000000

 1.000000

 0.000000

 0.000000

 2.000000

 Std. Dev.

 12.12334

 0.385475

 3.586677

 0.468039

 0.272202

 1.933526

 Skewness

-0.260169

-1.665853

-1.106502

-0.771744

-3.096281

 0.024003

 Kurtosis

 2.111318

 3.775068

 3.722957

 1.595588

 10.58696

 2.421344

 

 

 

 

 

 

 

 Jarque-Bera

 6.628164

 73.13126

 33.87533

 27.21703

 599.4358

 12.07174

 Probability

 0.036367

 0.000000

 0.000000

 0.000001

 0.000000

 0.008685

 

 

 

 

 

 

 

 Sum

 7165.000

 123.0000

 2072.000

 102.0000

 138.0000

 738.0000

 Sum Sq. Dev.

 21899.33

 22.14000

 1916.773

 32.64000

 11.04000

 557.0400

 

 

 

 

 

 

 

 Observations

 150

 150

 150

 150

 150

 150

 

 

 

Dependent Variable: DEMAND

 

 

Method: ML – Binary Probit  (Newton-Raphson / Marquardt steps)

Date: 02/21/18   Time: 09:37

 

 

Sample: 1 150

 

 

 

Included observations: 150

 

 

Convergence achieved after 5 iterations

 

Coefficient covariance computed using observed Hessian

 

 

 

 

 

 

 

 

 

 

Variable

Coefficient

Std. Error

z-Statistic

Prob.

 

 

 

 

 

 

 

 

 

 

C

-4.079607

1.234705

-3.304114

0.0010

AGE

-0.071882

0.021020

-3.419699

0.0006

GENDER

0.569650

0.376027

1.514918

0.1298

MARITAL

-0.220111

0.621424

-0.354204

0.7232

EDUCATION

0.151463

0.065698

10.78332

0.0000

SIZE

0.614896

0.119603

5.141126

0.0000

 

 

 

 

 

 

 

 

 

 

McFadden R-squared

0.588131

 Mean dependent var

0.820000

S.D. dependent var

0.385475

 S.E. of regression

0.227905

Akaike info criterion

0.468305

 Sum squared resid

7.479451

Schwarz criterion

0.588730

 Log likelihood

-29.12287

Hannan-Quinn criter.

0.517230

 Deviance

58.24574

Restr. deviance

141.4180

 Restr. log likelihood

-70.70902

LR statistic

83.17231

 Avg. log likelihood

-0.194152

Prob(LR statistic)

0.000000

 

 

 

 

 

 

 

 

 

 

 

 

 

Obs with Dep=0

27

 Total obs

150

Obs with Dep=1

123

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

COST

DEMAND

INCOME

OWNERSHIP

 Mean

 0.933333

 0.820000

 83666.67

 0.633333

 Median

 1.000000

 1.000000

 50000.00

 1.000000

 Maximum

 1.000000

 1.000000

 200000.0

 1.000000

 Minimum

 0.000000

 0.000000

 50000.00

 0.000000

 Std. Dev.

 0.250279

 0.385475

 52932.99

 0.483509

 Skewness

-3.474396

-1.665853

 1.054958

-0.553372

 Kurtosis

 13.07143

 3.775068

 2.416510

 1.306220

 

 

 

 

 

 Jarque-Bera

 935.7462

 73.13126

 29.95126

 25.58607

 Probability

 0.000000

 0.000000

 0.000000

 0.000003

 

 

 

 

 

 Sum

 140.0000

 123.0000

 12550000

 95.00000

 Sum Sq. Dev.

 9.333333

 22.14000

 4.17E+11

 34.83333

 

 

 

 

 

 Observations

 150

 150

 150

 150

 

 

 

Economic Factors

 

Dependent Variable: DEMAND

 

 

Method: ML – Binary Probit  (Newton-Raphson / Marquardt steps)

Date: 02/21/18   Time: 09:03

 

 

Sample: 1 150

 

 

 

Included observations: 150

 

 

Convergence achieved after 5 iterations

 

Coefficient covariance computed using observed Hessian

 

 

 

 

 

 

 

 

 

 

Variable

Coefficient

Std. Error

z-Statistic

Prob.

 

 

 

 

 

 

 

 

 

 

C

-3.379957

0.869059

-3.889214

0.0001

COST

2.793480

0.618478

4.516702

0.0000

INCOME

1.27E-05

4.59E-06

2.771841

0.0056

OWNERSHIP

0.015338

0.298273

0.051423

0.9590

AWARENESS

1.042378

0.385385

2.704773

0.0068

 

 

 

 

 

 

 

 

 

 

McFadden R-squared

0.621358

 Mean dependent var

0.820000

S.D. dependent var

0.385475

 S.E. of regression

0.316709

Akaike info criterion

0.706482

 Sum squared resid

14.54413

Schwarz criterion

0.806836

 Log likelihood

-47.98612

Hannan-Quinn criter.

0.747252

 Deviance

95.97224

Restr. deviance

141.4180

 Restr. log likelihood

-70.70902

LR statistic

45.44581

 Avg. log likelihood

-0.319907

Prob(LR statistic)

0.000000

 

 

 

 

 

 

 

 

 

 

 

 

 

Obs with Dep=0

27

 Total obs

150

Obs with Dep=1

123

 

 

 

 

 

 

 

 

 

 

 

 

 

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