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Introduction
Wastes are by-products of human consumption which are a rising problem in the urban areas due to high urban population growth rates, consumption habits, low collection rates and waste accumulation(Ekere, Mugisha, & Drake, 2009; Nigatu, Sundaraa, & Bizunesh, 2011). The swiftly increasing urbanization, population growth and changing consumption pattern of people, the volume of global solid waste generation has increased significantly over years(Maskey, 2018; Mengist & Assegid, 2014; Nigatu et al., 2011). For example, in 2012, 1.3 billion tons of solid waste was generated by urban population globally which is about 48% increase over the past 10 years and it is expected to increase to 2.2 billion tons in coming 2025(Hoornweg & Bhada-Tata, 2012; Maskey, 2018). Although cities are generating snowballing volume of solid wastes, the effectiveness of their solid waste collection and disposal systems are declining(Nigatu et al., 2011). In urban centers throughout African regions, less than half of the solid waste produced is collected, and 95 percent is either indiscriminately thrown away at various dumping sites on the periphery of urban centers, or at temporary sites, characteristically empty lots scattered throughout the city(Nigatu et al., 2011). Developing countries faces even bigger challenge as huge amount of investment is required for municipal solid waste management(Maskey, 2018). About 20-50% of municipal budget is spent on municipal solid waste management and in spite of spending almost half of the municipal budget, about 30-60% of the wastes are uncollected and less than 50% of its population is served (World Bank, 2016). The uncollected waste, is dumped extensively in the streets, banks of the river and in drains which contributes to flooding, breeding of insects and rodent vectors leading to spreading of diseases.
Separation and collection of waste is the primary and ultimate step to solve municipal household solid waste problem (Chu et al., 2016). Waste separation at household level can preserve the quality of recyclables, which will improve the accessibility to informal recycling sectors and help in overall reduction of waste for disposal (Maskey, 2018; Nguyen, Zhu, & Le, 2015). To make successful recycling socio-economic condition and specially awareness and willingness of people plays important role. An effective measure to improve the waste management levels and transform municipal solid waste into useful materials is to conduct source separation instead of just burning or burying it. Source separation refers to the separation of municipal solid waste into several categories at the generation source according to the different characteristics of each material before further treatment. The source separation of waste is also enhances waste recycling and reduces disposal amount(Chen et al., 2016).
In many fast-growing cities of developing countries, dealing with household waste has become a vital policy problem which might be ranges from household level waste control to highly and comprehensively integrated municipal and economic-wide waste reduction activities and programs (Tadesse, Ruijs, & Hagos, 2008). The separation of waste at source has been considered as a fundamental condition in closing the loop of materials, which is expected to reverse the negative impacts of solid waste on the environment and the scarcity of natural resources. However, in cities of developing countries, waste separation is currently one of the biggest challenges for sustainable waste management programs. The various programs of waste separation deployed over several decades have only existed in the form of pilot-programs and have generally not been replicable on large scales (Nguyen et al., 2015). The problem of waste management is magnified in cities where a dense concentration of people leads to a substantial amount of waste generation. In developing countries like Ethiopia, this problem is exacerbated by an influx of people moving to urban centers(Cheeve, 2011). In Ethiopia, rapid urbanization coupled with increased urban population in the last decade brought immense pressure on municipal services, mainly in the management of the ever increasing amounts of SW(Mengist & Assegid, 2014). Population concentration in Robe town is snowballing at a rate of 6%, which exceeds the regional average growth of 4.2%(Duguma et al., 2018). This rapid increase of urban population accelerates solid waste generation in the town. In this town, municipal solid waste management as a sector is a highly neglected sector (Duguma et al., 2018), and about 55% of the town residents did not have awareness of rules and regulations of solid waste management and its consequences on environment and health. For this reason, waste disposed illegally by residents on road sides and open space. Households had less awareness and practice about reusing and recycling of wastes and hence furthermost of them did not separately store waste according to their nature and types. Therefore, this study intended to analysis waste separation situation of households and examine factors prompting solid waste separation among households of Robe town and fill the prevailing knowledge and awareness gaps regarding to waste separation.

Methodology
Study Site
Geographically, Robe town is located at 07° 08′ 00″N and 40° 00′ 00″ E and situated in southeast direction about 430km far from Addis Ababa, the capital city of the country and the area of the town is town is 8024 km2. Population size of the town, according to CSA (2007) was about 54,337. After a period of 8 years from the CSA report, the population size of the town has shown a drastic change that now it is estimated to be 73,859. According to the national survey made by CSA in 2007, the total number of households in Robe town is about 13,471. The town is divided in to three administrative kebeles namely, Oda Robe, Beha Biftu, and Chefe Donsa, each consisting of about 5351, 2172, and 5948 households respectively. Generally, the town is growing at an alarming rate with 6% per year, far more than the national average growth rate of 4.2% per annum (Oromia Planning Bureau, 2016). Robe town is administrative center of Bale Zone, is one of the geographically largest; economically significant; and climatically conducive zones in Ethiopia. Currently, the town is serving as a center for numerous governmental and non-governmental institutions. The combined effect of the economic and ecological importance of the town is attracting more and more people from different areas of the country.
Research Approach
The mixed methods research approach with crosss-sectonal suevry design was used in this study. The mixed research approach was used to examine determinants of households’ munipal waste separation in the town to make use of both quantitative and qualitative research approaches on the bases of quantitative and qualitative philosophical perspectives.The thepretical perspective underpinning this study are both positivism and interpretivism. Positivist argues that social world or reality exists independent of the researcher and the properties of the social world can be measured directly through observation like the natural world. The reality in positivism is what is available to our senses and the human sciences deal with facts like the natural sciences and social world is governed by laws like the natural world and these laws/theories must be discovered, tested and refined through scientific inquiry (Creswell, 2009; Creswell & Clark, 2015; Gray, 2017). The process of research in positivism is deductive, focusing on testing theory. In line with positivist point of view in this study factors affecting households’ solid waste separation was examined quantitatively using regression model. On the other interpretivism philosophical perspective claim that reality is socially constructed by individuals and this social construction leads to multiple meanings (Lodico, Spaulding, ; Voegtle, 2006). This theoretical perspective is linked to constructivism, which argue that reality in the social world does do not exist in the external world but they are created by the subjects’ interactions with the world we live and work and realities are multiple(Creswell, 2007; Creswell & Clark, 2015; Gray, 2004). Constructivist argue that knowledge is constructed, therefore, the role of the researcher is studying socially and culturally influenced multiple views of research participants and process of interaction among individuals (Creswell, 2009; Crotty, 1989; Gray, 2004). This view is use d in this research to study the experience, awareness and perception of people toward solid waste separation. Both positivism and constructivism epistemological perspectives will be used, though their assumption contrasting to one other. Crotty (1989), argue that these two different standpoints can be combined and brought together in a single study with an explicitly stated use of each stance. The advantages of combining these two different stands are to address the issues that need counterweighing the limitation of one method by another and to complement the quantitative results of the research with that of a qualitative one.
Sampling Technique
Two stages of sampling techniques were used to select sample households. On the first stage, Robe town was purposively selected. In the second stage, 372 households were selected from 13,471 households of the town by using random sampling method. Using the following formula (Yemane, 1967);
n=N/(1+N(?e)?^2 ) (1)
Where n is the sample size, N is the population size, and e is the level of precision. At 95% confidence level and 5% precision level, the required sample is 372 households. Formal sample survey used to collect primary data for the study. Therefore, a structured and unstructured questionnaire was used to collect primary data from urban households. Observation and interview were also used to collect a complementary data. The formal survey method was used to collect primary data for this study. Precisely structured and unstructured questionnaire, interview and personal observation were used to collect primary data from selected households. Secondary data were collected from books, published and unpublished materials.
Data Analysis Method
Econometric model was used in this study to examine the factors prompting of households’ municipal solid waste separation into degradable and non-degradable. Logit model was used because of its comparative mathematical simplicity and asymptotic characteristics(Maskey, 2018). It has a cumulative probability function with the ability to deal with dependent variable which allows for estimating the probability that an event will occur or not through prediction of a binary dependent outcome from a set of independent variables. The logit model to identify factors influencing households’ willingness to segregate waste can be specified as:
Y=1/(1+?exp?^(-Z) ) (2)
where,
Y = Respondents’ response for willingness to separate waste (Yes = 1, No = 0).
Z = Summation of explanatory variables multiplied by their coefficient, that is (Maskey, 2018).
Z=B_0+B_1 X_1+B_2 x_2+? +B_n x_n+?_i (3)
Where as ?_0 is Constant
?_1…?_n is Coefficient of explanatory variables X_1…X_n
?_i Error term
Since the conditional distribution of the outcome variable follows a binomial distribution with a probability given by the conditional mean Pi, interpretation of the coefficient will be understandable if the logistic model can be written in terms of the odds and log of the odds, as indicated by Gujarati,(2004).The odds ratio to be used can be defined as the ratio of the probability that separate solid waste (Pi) to the probability that he/she will not (1-Pi).Taking the natural logarithm of the odds ratio it will result known as Logit model as indicated below;

Ln(P_i/(1-P_i ))=Lne^(B_0 )+?_(i=1)^m??B_1 X_1 ?=e^z i (4)
To find out the probability of households’ willingness to segregate waste, the parameters from logit model cannot be used to interpret effects of each of the explanatory variable as the model is nonlinear. In this case, marginal effects are calculated to find the relative magnitude of effects of each of the explanatory variable. The effects of the jth explanatory variable can be summarized as below(Maskey, 2018);
1/n ?_(i=1)^n???PY_(i=1) /(?X_ij )=?_j 1/n ?_(i=1)^n??f(?X’?_i ? ?)?, j=2…k (5)
That is, the mean marginal effects over the sample of n individuals. Maximum likelihood method is used to estimate the parameters of the multiple logistic response function. The log-likelihood function is as follows:
logL(?)?= ?_(i=1)^n?Y_i (?X^’?_i ?)- ?_(i=1)^n??log?1+exp?(?X^’?_i ? ?) (6) Table 1: Description of explanatory variables used in regression model, 1= separate; 0 otherwise
Variables Description Measure and nature
Sex Sex of households 1 male; 0 female
Age Age of households Continuous (in years)
Family size Family size of households Continuous (in number)
Education Educational level
of households 0 = no formal education
1 = primary, 2= secondary
3 = diploma,4 = degree
Awareness Awareness of households toward separation of wastes 1= yes
0 = no
Income Average income of households Continuous (in number)
Reuse and recycling Households reuse and recycling 1= yes
0 = no
Municipality supervision Municipal supervision 1= yes
0= no
Waste collection service Have access to waste collection service or not 1 = yes
0 = no

Make compost Whether households make compost or nor 1= yes
0 = no

Result and Discussion
General Background of Respondents
In this subsection, the general background of the respondents has been discussed which might have remarkable contribution of households’ municipal solid waste separation condition. Among sample respondents 65% (242) were females and the rest 35% were male households. This indicates that females are more active in cleaning their home and compound. In other words, the age of 57.3% respondents lies between 44 and 56 age group. An educational level is expected to have positive impact separation of solid wastes. With respect to educational level larger numbers of respondents attended education, which account about 34.1%. The next are respondents who do not have formal education which account about 19.1% and followed by degree holders (18.3%) and secondary school which account about 17.2% respectively.
Table 2: Personal Information of Respondents
Variables Category Frequency Percentages
Sex Female 242 65.1
Male 130 34.9
Total 372 100
Age 18-30 37 9.9
31-43 101 27.2
44-56 213 57.3
57-69 21 5.6
Total 372 100.0
Education No formal education 71 19.1
Primary school 127 34.1
Secondary school 64 17.2
Diploma 42 11.3
Degree and above
68 18.3
Total 372 100.0
House ownership private house 243 65.3
rental house 129 34.7
Total 372 100.0
Family size 1-4 163 44
5-8 134 36
>8 75 20
Total 372 100.0
As illustrated in table 2, 65.3% of respondents live in private house where as 34.7% live in rented house. As far as family size concerned, 44% of respondent have 1-4 family size and 36% of households have 5-8 family member whereas 20% of respondents have greater than 8 families.
Waste Separation Among Households and its Determinants
Properly separating degradable and non-degradable municipal solid waste is one effective practice ad important parts of solid waste management activities. Degradable wastes are materials break down through natural processes by micro-organisms and produce humus. Materials that are non- degradable must be separated from the degradable materials and disposed of in some other manner. Some common non-biodegradable materials are glass, plastics, rubber products, and metals. separation also involves in situ separation of domestic waste is the sorting out of individual waste types into separate storage containers at the point of generation. Separation of waste can save valuable resources in the form of saved hours required to deal with the un-separated waste (Otitoju & Seng, 2014).
Table 3: households separately stored solid waste
Separately stored solid waste Frequency Percent Cumulative Percent
Yes 136 36.6 36.6
No 236 63.4 100
Total 372 100
In Robe town the awareness of households toward reusing and recycling is low. The respondents were asked about the knowledge of reusing and recycling of solid wastes, more than half of respondents (55.6%) had knowledge regarding to the reusing and recycling of solid wastes. Nonetheless, 44.4% of respondents had no knowledge reusing and recycling of wastes. Of respondents who had knowledge about recycling and reusing solid wastes, only 19.8% of them recycle and reuse solid wastes, but about 80.2 % of them do not recycle or reuse solid wastes. The result of this investigation divulges that about household (36.6%) separate separately store solid wastes (see table 3). This result is much lower than the result of the study conducted in Adama town by Nigatu et al, (2011) which disclose that 66% of households in Adama town stored separately degradable solid waste from non-degradable waste. This is due to lack of awareness and training regarding municipal solid management systems. Separation of municipal solid wastes is determined by various variables. Factors determine the households’ municipal solid waste separation presented in the table 4 below.

Table 4 Determinant of waste separation, dependent variable waste separation =1 and= 0 otherwise
Variables Coefficients Standard Error Z-value P-Value Marginal effect
Gender of Households -0.094 2.490 -37.751 0.020** -0.099
Age of Households -0.135 0.040 -3.375 0.001* 0.873
Family Size 1.046 0.291 6.601 0.000* 0.853
Level of Education 1.914 0.488 3.922 0.000* 0.781
Awareness 0.197 0.272 0.375 0.078*** 0.821
Making Compost 7.784 1.570 4.958 0.000* 0.85
Average Income 1.005 0.001 5.000 0.000* 0.005
Reuse – Recycle 6.958 1.729 4.031 0.000* 0.031
Municipality Supervision -1.076 0.971 -1.108 0.058*** 0.341
Waste collection service 5.042 1.052 4.792 0.063*** 0.341
Constant 7.130 2.381 2.995 0.000* 0.485
-2Log likelihood = 112.446
LR chi-square = 358.166
N = 372
Likelihood ratio = 11.083
* Shows significant at 1% level
** shows significant at % level
*** shows significance at 10% level

Waste separation has vital factors influence of households’ waste management system and precondition for reusing and recycling wastes(Ekere et al., 2009). logistic regression used to understand how various household characteristics explain household behavior in solid waste separation(Banga, 2014). Results from the logistic regression analysis (Table 3) show that, variables such as gender, age of households, family size, level of education, awareness of waste management, home ownership, compost making, average income, reuse-recycling, municipality supervision, taking training and participating in cleanup campaign are significantly explained the separation behavior of households. Marginal effects estimate the marginal impact of a variable on the willingness to participate on waste separation at household level, indicating the probability of the dependent variable at the mean value of given explanatory variable, keeping other regressors constant.
The results of logistic regression model divulge that gender is negatively and significantly associated to municipal solid waste separation at the 5% confidence level. This implies that the likelihood of municipal solid waste separation is higher among women rather than men. This is
expected given the role women play in waste household management activities. Women tend to be responsible for waste work within the household and along with children and domestic workers, may sort and sell recyclable materials(Ekere et al., 2009). Studies in Kampala, Pakistan and Bangladesh also indicates that women were more likely involved in municipal solid management. Ekere et al (2009) also found similar results when they studied the separation of crop waste in Uganda(Banga, 2014; Ekere, Mugisha, & Drake, 2009; Maskey, 2018). Age of households had negative effect the waste separation. The result of the studied show that the lower age groper of households was more likely to separates solid wastes by 87.3% that upper age groups and statistically significant at 1% significant level.
Having awareness about waste management was positive affect the households; willingness to separates solid waste and statistically significant at 1% level. This implied that households those who had awareness about waste management were more likely separate compared to those
who had no awareness. The marginal effect indicates that having awareness about solid waste management has increased the likelihoods of waste separation by 82%. Households who make compost are more likely to separate waste than who do not make compost by 85% and statistically significant at 1% level of significance. This could be because these households are using their degradable waste to make compost and for that they might have already been separating their waste(Maskey, 2018).
The results of this study indicate that income level is negatively affect the municipal solid waste separation at the 1% confidence level. The marginal effect result shows that a unit increase in household income would decrease the likelihood for households’ willingness to separate waste by 78%. This implies that households with high incomes are less likely to engage in separating waste. This is probably due to the fact that high income households can afford to pay for waste collection services. They, therefore, see no reason for segregating the waste before its disposal The result of the study conducted by (Banga, 2014; Maskey, 2018) also result of the study conducted by (Ekere, Mugisha, ; Drake, 2009) which indicate that income the positive relationships between income and households’ waste separation.
educational level of households has positive impact on solid waste separating and statistically significant at 1% significance level. The marginal effects reveal that households with higher level of education were more likely to separate solid waste that lower educational level by 78.1% or increase in one educational levels expected to increase the likelihood of household’s participation in waste separation by 78.1% which is consistent with studies that have looked on similar issue such as by Kamara (2006) and Jerkin et al. (2000). The higher levels of education, is expected to make people more knowledgeable and aware on wastes management issues and importance, hence more responsible and ready to adapt to better ways to improve wastes management. But this argument contradicts the result the study conducted by Bonga, 2014 which indicates that the lower education level was more likely to separate solid waste than those with tertiary education. He further concluded that the lower rate of participation in separation activities by those with more education could be because those with higher education are likely to be employed and have better jobs. reusing and recycling activities is important in household behavior toward solid waste separation(Banga, 2014). The result of regression model indicated that reusing and recycling of solid waste are significantly and positively influence the waste separation of households. The marginal effect also indicates that households who reuse and recycle waste were more likely separated waste by 3.1% than who did not reuse and recycle, and significant at 1% significant level.
Probability of Waste Separation Among Female and Male Households
By considering a simple bivariate logistic regression, using subjects’ separation as the
dichotomous criterion variable and their gender as a dichotomous predictor variable and can be coded as gender with 0 = Female, 1 = Male, and not separate 0 = separate =1. Thus, natural log of the odds of having made one or the other decision is;
In(ODDS)=In(?/(1-?))=a+b_x
Whereas ? is the predicted probability of the event which is coded with 1 (separate wastes) rather than with 0 (not separate waste),1-? is the predicted probability of the other decision, and X is the predictor variable, gender. The table 4 illustrate that that the intercept-only model is ln(odds) = 0.551 by exponentiate both sides we get the predicted odds Exp(B) = 1.73. That is, the predicted odds of deciding to separate municipal solid waste is 1.735 Subsequently 136 of households decided to separate solid wastes as 236 reported no to separate, therefore the observed odds are 236/236 = 1.735.

Table 4 Variables in the Equation
B S.E. Wald Df Sig. Exp(B)
Constant 0.551 0.108 26.211 1 0.000 1.735
Chi-square = 11.083

In the Block 1 output it is indicated that the Omnibus Tests of Model Coefficients of Chi-Square of 11.083 with 1 df which is greater than significant level of 0.001 which indicates that the adding the gender variable to the model has not significantly increase the capacity to predict the probability of households to separate or not to separate solid wastes.
Table 5:
B S. E Wald Sig. Exp(B)

Separate solid waste 0.778 0.240 10.536 0.001 2.178
Constant -1.138 0.200 32.379 0.000 0.320

In(ODDS)=-1.188+0.778 gender
This model used to forecast the odds that households of a given gender will decide to separate solid waste. The odds prediction equation is ODDS=e^(a+b_x ) if the household head is woman (gender =0), then ODDS=e^(-1.138+0.778(0))=e^(-1.138) =0.32. That is, a woman is only 0.32 as likely separating waste. For man (gender =1) then ODDS=e^(-1.138+0.778(1))=e^(-0.36)=0. 698. that is man is 0.698 times less that likely separate solid wastes. This odd ratio can be converted to probabilities. For women, ?=ODDS/(1+ODDS)=0.32/1.698 =18.8 and for menODDS/(1+ODDS)=(0.698 )/1.32 = 52.8. therefore, the mode predicts that 18.8% of women and 52.9% of men separate municipal solid wastes. Furthermore, the probability not separates solid waste is 71.2% for women and 47.1% for men. The probability of men to separating solid waste is greater than probability of women separating solid waste.

Conclusion
This study addressed the households’ municipal solid waste separation and factors affecting households’ willingness to separately store municipal waste in Robe town. The study found that gender of households, age, family size, education level, awareness of waste management, compost making, level of income, reuse and recycling and municipal supervision are significantly influence the wastes separation of households. Age, family size, education level, compost making, income, and reusing and recycling are statistically significant at 1% significant level. Municipal supervision and gender are significant at 5% level, whereas awareness if significant at 10% significant level. The result of this study divulges that indicates less than half of household (43%) separate degradable solid waste from non-degradable. This is because of lack of awareness about reusing and recycling of solid waste. The respondents were asked about the knowledge of reusing and recycling of solid wastes, more than half of respondents (55.6%) had knowledge regarding to the reusing and recycling of solid wastes. Nonetheless, 44.4%of respondents had no knowledge reusing and recycling of wastes. Of respondents who had knowledge about recycling and reusing solid wastes, only 19.8% of them recycle and reuse solid wastes but the rest of the 80.2 % of them do not recycle and reuse solid wastes

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