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An analysis of socio-economic and demographic factors affecting household main source of income in Somalia using binary logistic regression approach

This study applied the Binary Logistic Regression model to investigate socio economic and demographic factors affecting household main source of income. The main objective of this study is to determine the socioeconomic and demographic factors that
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    ~23~ International Journal of Statistics and Applied Mathematics 2019; 4(3): 23-31   ISSN: 2456-1452   Maths 2019; 4(3): 23-31 © 2019 Stats & Maths www.mathsjournal.com Received: 10-03-2019 Accepted: 12-04-2019 Mohamed Hussein Abdullahi Senior Statistician, Directorate of National Statistics of Somalia, Mogadishu, Somalia Correspondence   Mohamed Hussein Abdullahi Senior Statistician, Directorate of National Statistics of Somalia, Mogadishu, Somalia An analysis of socio-economic and demographic factors affecting household main source of income in Somalia using binary logistic regression approach Mohamed Hussein Abdullahi Abstract This study applied the Binary Logistic Regression model to investigate socio economic and demographic factors affecting household main source of income. The main objective of this study is to determine the socio-economic and demographic factors that affect household main source of income in Somalia. This study used secondary data of cross sectional data targeting households from household survey in 2016. The results of the four independent variables in the study which are household head education, household head sex, household residential area and age of household head show that the residence area of households is the most important factor which determines the household main source of income. The sex of the household head is revealed to be the second most important variable among the variables that have an effect on household main source of income. It has been found that the male headed households are more likely to have a salaried labour source of income than female headed households. The results show that the opportunity of getting a salaried labour source of income is not the same for  both the educated and the non-educated household heads. The results, also suggest that the age of household head has a negative relationship with the salaried labour source of income. It is found that an increase of one year of the age of the household head will decrease the opportunity of the household of getting a salaried labour source of income. Keywords: Household main source of income, chi square test, logistic regression, odds ratio 1. Introduction A household is defined as “ a group of persons who share the same living accommodation, who  pool some, or all, of their income and wealth and who consume certain types of goods and services collectively, mainly housing and food ”  (UNDESA 2008). Also, this definition mentions that households have a head and share residence, and that both head and the residents of the household have characteristics that influence their livelihood. The most important characteristics are education, age, residential area; rural and urban, and others. These characteristics have a great effect and impact on the source of the household income. Household Main Source of Income is the major concern mostly in developing countries. The statistical reports produced by statistical offices in developing countries and developed countries illustrated employment conditions and the income sources of the households in their respective countries. Also the statistical offices inform government and citizens about livelihood changes. The source of income reflects the livelihood circumstances that surround any society in the world. The household main source of income has an effect on decision making, planning, because without knowing it the government and other institutions, will find it difficult to develop policies towards employment conditions. The income source of the household measures the living standard differences between Male and Female Headed Households, where by the income is one of the monetary dimensions for measuring well-being of households whether in either urban or rural areas or either female or male household headed. Differences among household living standards may be due to the residence of the household, education level of the household head, the sex of the household head or the age of household head. The (UBOS 2014) [12]  has published Average Monthly Household Income by region in either urban or rural areas. Such classification of households reveals the inequalities between urban and rural areas due to their socio-economic differences.    ~24~ International Journal of Statistics and Applied Mathematics (Sami Gungor et al  . n.d., 2015) [9]  found that the probability of a household being poor in Turkey decreased as the education level of the household head increased. (Janet Currie, 1992) [6]  observed that male jobs are paid more than female jobs and that and female jobs are much more likely to  be found in public than the private sector. However, the Households of Somalia face many challenges concerning access to and availability and quality of the required social services, such as health, education, and opportunity of jobs. Social services need to be delivered equitably to all groups of the population, particularly to vulnerable and marginalized groups, in both rural and urban communities. Vulnerability and exclusion of households from generating income will be the cause of lack of ability within families and communities to meet required of living standard. 2. Review of literature Household income sources are one of the key priorities for each country to stimulate the economic growth and the improvement of the living standard. This portion highlights the pertinent empirical literature on the subject investigated. Mathebula et al  . (2017) [7]  have estimated household income diversification in settlement types of the poorest provinces in South Africa  –   the Eastern Cape, Limpopo and KwaZulu- Natal. They have used the number of income sources, the number of income earners and the Shannon Diversity Index to estimate income diversification in the study provinces. They have used 2010/2011 Income and Expenditure Survey from Statistics South Africa and Wave 3 data from the National Income Dynamics Study. They have found that the diverse of income in the provinces recommend that targeted policy initiatives aimed at enhancing household income are important in these provinces. They have shown that the households in the traditional and urban formal areas diversified income sources to a greater extent than households in urban informal and rural formal settlements. The research fails to present whether socio-economic and demographic factors have an effect diversification of income sources. Chamicha (2015) [4] , has studied the relationship between nonfarm activities and rural livelihood in Tanzania. The study investigated the factors that permit the rural household to involve in the nonfarm activities. The findings of the study divided the factors that the household’s decision to engage in nonfarm activities into two factors which are push and pull factors. The findings showed that the income obtained from agriculture activities were used as a startup capital in nonfarm activities and the income obtained from nonfarm activities were used to finance farm activities. The study found that there is a significant share of the income from nonfarm activities to the overall household income, and also found that the income obtained from nonfarm activities were used to acquire different household needs. They have study concluded that rural nonfarm activities are significant livelihood strategy for the rural households. The research fails to indicate whether socio-economic and demographic factors influenced nonfarm activities for the livelihood of rural households. Birthal et al  . (2014) [3]   has examined farm households’ access to different income generating activities, and their impact on income distribution by using data from a nationally representative large scale survey in India. The authors have used a number of methods to measure economic inequalities. They used Gini index which is a widely used measure of inequality because of its certain desirable properties, such as Pigou-Dalton transfer sensitivity, mean-independence, symmetry, population homogeneity, and decomposability. They have found evidence in the analysis that, as against the common perception of agriculture being the dominant source of income for farm households, the households earns close to half of their income from non-farm activities. Also they have found that non- farm income is more significant for the households at lower end of land distribution. They have discovered non farm income sources are accessible to a small  proportion of farm households and have unbalanced effect on income distribution. In addition to that they have found that nonfarm sources are positively correlated with the total income. The research fails to indicate the effect of socio-economic and demographic factors that the households earn close to half of their income from non-farm activities. Fadipe et al  . (2014) [5]  have examined the determinant of income among rural households in Kwara State, Nigeria. They have used primary data collected by using a questionnaire compiled from 90 randomly selected households. They have employed in the data analysis the analytical tools of descriptive statistics and the multiple regression analysis for the study. They have identified that the farm size and access to electricity, level of education of the household head, and sex of the household head were the main determinant of household income in the study area. They have shown that the farm income is the most significant source of income for rural households in the study area by making up 57.9% of total household income. The research fails to show whether socio-economic and demographic factors has influence that the farm income is the most significant source of income for rural households. Talukder (2014) [11]  has investigated the determinants of income and growth in income of rural households in Bangladesh in the post-liberalization era. He has used secondary data sources on both (1985-86) and (2005), by applying the ordinary least square (OLS) of regression models for assessing the determinants of both economic and non-economic characteristics. He has found that household size was the only non- economic factor that was statistically significant and positive determinant of household income in  both 1985-86 and 2005. In addition to that, he found that endowments, household land area were the largest positive determinant while share of income from wage-salary was the largest negative determinant of income-growth in 1985-86. He also found that the endowments, change in share of income from house rent was the largest positive contributor and share of income from rice was the largest negative contributor to growth in 2005. The r  esearch didn’t show whether socio-economic and demographic factors has influence the different income sources. Ali & IsmaeelRamay (2013) [2] , have examined the determinants of income and income gap in urban and rural areas of Pakistan by using province, literacy, education, occupation, age, gender and marital status as predictors at individual level. They have use their study data for the Household Integrated Economic Survey (HIES) 2010-11 dataset. They have estimated predictions by applying the ordinary least square (OLS) method, also used Blinder-Oaxaca decomposition method to analyze the income gap  between urban and rural Pakistan. They have found that literacy, education and occupation as the major determinants of income in Pakistan. They also discovered that reading and writing skill of individuals has been more important as compared to the numeracy skill. In addition to that they have found that the lower levels of education gets high returns in rural areas whereas higher levels of education gets more return in urban areas. Individual characteristics like literacy,    ~25~ International Journal of Statistics and Applied Mathematics education, occupation and marital status have been found as the major determinants of income gap. However, this study will use a secondary data to see if the results will be that the education as the major determinant of income of the households as this research found. Yadollahi et al  . (2013) [13]  have determined the effect of demography variables that is, age, gender, level of education, and occupation, on family economic status in three dimensions, income, expenditure, and ownership of physical asset, by using data of 390 households Kerman city of Iran. They have used to analyze the data Pearson product moment correlation, Chi-square, Spearman rho, and independent sample t-test. They have discovered that the demography variables have effect on family economic status, and the dimensions, but this research will examine the effect of socio-economic and demographic variables on the household main sources of income. 2.1 Conceptual framework The result of Household Main Source of Income, in of terms labour source and other source is generally is a function of a number of variables which require grasping the associations of the variable with the source of household income. Source of income has interrelation with socioeconomic and demographic characteristics of the human being and the economic development. The factors affecting household main source of income can be classified under three broad categories which are demographic & Social, Economic and Education variables as follows: A.   Demographic & Social variables B.   Education variables are: C.   Economic variables However, conceptual frame work chart is presented in Figure 1, for this study. Source:   Author’s Construction   Fig 1:  Conceptual Framework Chart 3. Data Coverage of the Research The Republic of Somalia is situated in the Horn of Africa. Its  boundaries are defined by the Gulf of Aden and Djibouti in the north, Indian Ocean in the east, the Federal Democratic Republic of Ethiopia in the west, and the Kenyan Republic in the south. The study based on the secondary data which covers Somalis living in urban and rural areas. This ad hoc approach was to create the missing sample frame aimed to ensure representativeness of the covered population but has technical limitations which could not allow collecting data from the nomadic population. The Somali High Frequency Survey (SHFS) Survey in 2016 was designed to provide indicators and data needed to measure, monitor and analyse living standards and poverty in Somalia. The survey used a comprehensive questionnaire to collect data on the number of households and persons living in the selected areas in the sample. The survey comprised the following three data collection methods:    A representative household survey (HHS)    A Market Survey (MS) for prices of food and non-food items    A Currency Exchange Rate Survey (CERS) This research has used data from the representative household survey. 4. Research Design The aim of the study is to find out the effect of some socio-economic and demographic variables on the household main source of income. The study has used secondary data which is obtained from the data collected in 2016 by the Directorate of  Notational Statistics’ Somali (DNS) with close collaboration with World Bank and the Survey is called Somali High Frequency Survey (SHFS). Thus, this study is attempting to determine the extent of the relationships between the dependent and independent variables using the data obtained  by the method used by the Directorate of Notational Statistics’ Somali. 5. Variables The independent variables are household head (HHH) education, which is a dummy variable derived to categorize the education variable into educated household head and non-educated household head. Second variable is sex of household head in two categories, male household head and female household head. Third variable is residential area of household which has two categories rural and urban areas and fourth variable is the age of household head which is continuous. The dependent variable is household main source of income which has two categories, salaried labour source of income and other sources of income. Table 1:  Definition and coding of study variables Dependent variable (DV) Variable Type Code label Main source of household income Categorical Salaried Labour Source = 1 Other Sources = 0 Independent Variable (IV) Variable Type Code label Household head education Categorical Educated = 1  Non-educated = 0 Household head sex Categorical Male = 1 Female = 0 Household residential area Categorical Urban = 1 Rural = 0 Age of the household head Continuous N/A    ~26~ International Journal of Statistics and Applied Mathematics 6. Theoretical Model Logistic regression is a popular and useful statistical method in modeling categorical dependent variable and independent variables. The logistic regression is a mathematical modeling approach used to investigate the relationship between the independent variable and dependent variable as dichotomous variables. The essential aim of the study analysis is to describe the way that household main source of income varies by considering household head’s education, household head’s  sex, residential area of household, and the household head’s age. Consider first the case where the response variable   is binary, assuming only two outcome values, 1 or 0. This may be defined as: The distribution of dependent Y is specified by probabilities (=1)= of success and (=0)=(1)  of failure, and its mean is ()= . The n independent observations, the number of successes have the binomial distribution specified  by the index n and parameter π  . The formula is shown below in equation (3.1). So, each binary observation is a binomial variate with n = 1, (Agresti 1996, p.68) [1] . ()=!!()!  (1) −,  y=0,1 ,2, ....., n (3.1)   7. Associated probabilities of the variables Let x 1 , x 2, x 3, x 4   denote the variables of household head’s education le vel, household head’s sex, household residential area, and household head’s age, respectively. Then the associated probabilities of the variables are computed from the equation: (  )= +    +    +    +    1 +    +    +    +      (3.2)  Probabilities of individual categories are then calculated by considering:   =, k=1 and 0, and i=1, 2, 3 and 4 , for one variable holding the other three variables constant. The probability that the household main source of income of the educated household head will be a salaried labour source of income holding household head’s sex, household residential area, and household’s age constant, is then computed for:   = HHH education level and k= Educated, as:   (  )= +    1 +      (3.3)  The probability that the household main source of income of a male headed household will be a salaried labour source of income, holding household head’s education level, household residential area and household head’s age a constant, is computed for:   = HHH sex and k= Male , as (  )= +    1 +      (3.4)  The probability that the main source of income of the household in the urban residential area will be a salaried labour source of income, holding household head’s education level, household head’s sex, and household head’s age  constant, is computed for,   =        =   (  )= +    1 +      (3.5)  Probability that the main source of income of the household head of a specified age will be a salaried labour source of income, holding household head’s education level, household head’s sex and household residential area constant, is computed for,   = HHH age and =     (  )= +    1 +      (3.6)   8. Descriptive Analyses Descriptive statistics as a terminology is defined as Methods of organizing, summarizing, and presenting data. Descriptive statistics as computed quantities are very important in researches before proceeding to the model, because they simply enable presentation of data in a summary way, to allow simpler interpretation of the data. To report summary findings on the socio-economic and demographic profile of households in the research, simple descriptive statistics such as frequencies and percentages were generated. 9. Chi-Square Test of Independence The outcome and independent variables are both categorical variables. In order to test the relationship between the outcome and the individual independent variable, the Pearson chi-square test for independence will be used by examining for a statistically significant relationship between two categorical variables at a time. 10. Binary logistic Regression Binary Logit Model or Logistic Regression model is most useful when the response variable is not continuous but has only two possible outcomes (dichotomous), 1 or 0. This model is typically used when predicting an event which has two possible outcomes. Since the probability of an event must lie between 0 and 1, it is impractical to model probabilities with linear regression techniques, because the linear regression model allows the dependent variable to take values greater than 1 or less than 0. The logistic regression model is a type of generalized linear model that extends the linear regression model by linking the range of real numbers to the 0 or 1 range (Agresti 1996, p.70) [1] . 11. Results 11.1 Household Head’s Education Level distribution The survey collected data on household’s education level. The household heads or representative respondents were asked by the enumerators of Somali High Frequency survey (SHFS) the Y i = 1 if the i th household gets laboursource of inocome0 If the i th household gets other sourcesof income    ~27~ International Journal of Statistics and Applied Mathematics question “what is the education level of household head?” Respondents were supposed to choose one among the listed responses: i)   Complete secondary ii)   Complete primary but in complete secondary iii)   Incomplete primary iv)    No education v)   Other education vi)   University The distribution of the household head’s education level is  presented in Figure 2 below. The results in Figure 2 show the  percentage distribution of household head Education at all levels of education. It is noted that 55.59 % of household heads are non-educated and 44.41% of household heads are educated, computing 10.48% of household heads who have complete secondary education, 13.58% who have incomplete secondary education, and 9.85% who have incomplete  primary education, and 8.9% who have university education, and 1.6% who have other education. Source:  Constructed by author using SHFS data Fig 2:   Percentage distribution of household head’s education level  For the purpose of this study only two categories of education were adopted, educated and non-educated. As detailed above the educated combines the rest of the other education levels in the above list, except no education which is labeled as non-educated.  11.2 Sex of the household head distribution The household heads by sex are presented in Table 2. The results indicate that 51.3% of the households are female headed while 48.7% are male headed. Table 2: Household head’s Sex   Frequency Percent Valid Percent Cumulative Percent Valid Female 1954 51.3 51.3 51.3 Male 1854 48.7 48.7 100.0 Total 3808 100.0 100.0 Source:  Calculated by author using SHFS data However, the pattern of household headship is inconsistent with other previous surveys. The Population Estimation Survey in 2014 showed that the households headed by men were 81.3%, and households headed by women were 18.7%. Furthermore, the distribution of households by sex of head of households of socio-economic survey in 2002 was 87.4% male headed, and 12.6% female headed. 11.3 Household Residential area distribution Figure 3 below displays the distribution of household heads  by type of residential area and sex. The rural communities there are no more difference in the numbers and percentage of households headed by male and female, the difference is 0.97%. While urban communities are almost same to the rural communities, the observed difference in urban areas is 1.65%. In addition to that the counts of female headed household in urban communities has the highest observation at 1,566 female headed households and 1,503 male headed households in the urban, while in the rural 388 are female headed and 351male headed. Source:  Constructed by author using SHFS data Fig 3:  Percentage and counts of household heads by type of residential area and sex   11.4 Distribution of household Main Source of income The survey collected data on household of all income sources. Household heads or representatives of household head were asked by the enumerators of Somali High Frequency survey (SHFS) the question; “What is the main source of income for the household?” Respondents were supposed to select one among the listed responses: i)   Family assistance within country ii)    NGO iii)    None iv)   Other small family business v)   Pension vi)   Remittance from abroad vii)   Revenue from sales of asset viii)   Salaried labour source ix)   Saving, interest or other investment x)   Trade in domestic goods/products xi)   Trade in foreign goods/products (export or import) The distribution of the household main source of income is  presented in Figure 4 below.
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