Use of Multiple Logistic Regression to Estimate the Effect of Socio-Economic Factors on Household Income Sufficiency (original) (raw)
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International Journal of Statistics and Applied Mathematics, 2019
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 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.
IDENTIFYING HOUSEHOLD LEVEL DETERMINANTS OF POVERTY IN ALBANIA USING LOGISTIC REGRESSION MODEL
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The general goal of this paper is to analyze poverty in household per capita consumption as a monetary measurement and based in the data from Albania trying to identify probable determinants that influence in falling in a trap of poverty. Current literature suggests several ways of modeling the determinants of poverty. Usually the regression analysis is used to check in the same time the influence of the different factors. In this paper, binary logistic regression was estimated with economic status (poor-non poor) as dependent variable and a set of characteristics of individual and household as independents variables. The logistic model used shows that probability of being poor is found to be influenced mainly by education and status of employment of household head, the household composition and geographic divisions.
The study endeavors to estimate the food security status and identify the determinants of food security among households in Rangpur City Corporation of Rangpur Division, Bangladesh. It was found that about 65 per cent of the households are food insecure; utilizing expenditure method of estimating food security status. Further analysis utilizing the binary logistic regression method identified income and age of household head as consequential determinants of food security. They impact positively on food security, implicatively insinuating that gainfully employed and older household heads incline to be food secure. Policies that can engender good business environment for the Rangpur City Corporation poor are recommended, among others.
Logistic regression approach for the determinants of acute poverty at household level in Tanzania
2018
The main aim of this article is to establish poverty determinants i.e. the factors that increase the risk of poverty as well as to estimate the extent to which households are threatened by this phenomenon and to estimate and compare poverty spheres in a regional approach by means of the most important poverty indicators by using socioeconomic aspects of poverty with reference to Tanzania. This study used national panel survey data of 2014/15 of Tanzania to find the determinants of poverty. The households, who scored more than 33% of the deprivation score were categorized to be under acute poverty line. The socio-economic and demographic characteristics of the household head and household’s characteristics in general, were used as the predictor variables to associate them with the household poverty status which were either poor or non-poor. The logistic regression technique has been used to determine the likelihood of the household poverty as affected by socio-economic and demographi...
Binary logistic regression to estimate household income efficiency. (south Darfur rural areas-Sudan)
International Journal of Advanced Statistics and Probability, 2016
The main objective behind this study is to find out the main factors that affects the efficiency of household income in Darfur rejoin. The statistical technique of the binary logistic regression has been used to test if there is a significant effect of fife binary explanatory variables against the response variable (income efficiency); sample of size 136 household head is gathered from the relevant population. The outcomes of the study showed that; there is a significant effect of the level of household expenditure on the efficiency of income, beside the size of household also has significant effect on the response variable, the remaining explanatory variables showed no significant effects, those are (household head education level, size of household head own agricultural and numbers of students at school).
Structure of Household Income and Expenditure and Its impact on Poverty Alleviation
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The aim of current study is to find determinants to alleviate poverty using data of the Household Income and Expenditure Survey-2010 conducted by the Bangladesh Bureau of Statistics. According to the destination, Multinomial logit model with nominal response is appropriate for determining objective variables. It is common practice that the categorical response has more than two levels. Here, dependent variable calorie intake has four levels and independent variable, level of education has two categories, household members have four categories, household income and household expenditure both have four categories. Determining the significance of explanatory variables for alleviating poverty, maximum likelihood ratio test, Wald test were used. The findings suggest that the parameters estimates under three logit, household members and household expenditure are significantly associated with the poverty. Thus, it can be concluded that this model may play a vital role in alleviation of pov...
Journal of Contemporary India Studies Space and Society Hiroshima University, 2012
Poverty in Nepal remains forefront in Nepalese development agenda since 1970s. However, there has not been significant improvement in poverty situation and poverty still remains the key research issue in Nepal. This study aims to analyze income and consumption measure of poverty applying an econometric tool taking a case of Baitadi district from far-western rural hills of Nepal. Data obtained from household survey is used for the analysis. A binary logistic regression model is applied for identifying the variables having a significant impact on income and consumption poverty. Both measures of poverty show that it is quite high in the study areas. Most of the factors that determine income and consumption poverty are more or less similar. However, there are still few factors that affect income and consumption poverty in different way. For instance, a chance of household to suffer food insecurity is significantly higher in Melauli, a relatively remote VDC. Subsistence nature of agriculture and absence of well-developed market structure that leads to higher price of food commdities is the main reason for such difference. Similarly, family size, operational landholding and livestock holding are important determinants of food insecurity, whereas, dependency ratio and occupation are important determinants of income poverty. Education of household head and landholding are important determinants for both income and consumption poverty.
Statistical Analysis of Factors Affecting Poverty Status of Rural Residence
American Journal of Theoretical and Applied Statistics
Poverty is one of the serious problem affect the life of peoples in third world countries. Identifying major factors affecting poverty status of a society is important to decide what action should be taken to alleviate the poverty. The aim of this paper is to assess the factors that affect the poverty status of rural Residence in the study area. A cross-sectional study was conducted in five districts of Gamo Gofa zone, Southern Regional State of Ethiopia. From a total of households in these areas, 4092 were selected using stratified random sampling technique. Data were collected with a well designed questionnaire. If the welfare of a household is below the poverty line, the household is categorized as under poverty and if it is above poverty line, then the household is above poverty. Binary logistic regression model was used to analyze the data using the SPSS software. Several risk factors were found to be significant at the level of 5%. Saving culture, access to credit, resource base, land fertility, use of agricultural inputs, use of improved tools, availability of rain, land topography, labor availability and dependency attitude have significant association with the poverty status of a households. Governments and Non-Governmental organization should be aware of the consequences of these factors which can influence the household income and future poverty status.
The study was carried out at Damot Gale district of Wolaita Zone in Southern Nation Nationalities Regional State with the main objectives to describe determinants of rural poverty in the study area .In order to attain this objective the study made use of cross-sectional household survey data collected from 235 sample households .The data collected were analyzed and discussed applying FGT measure of poverty i.e. poverty head count index, poverty gap and severity. Using cost of basic needs approach; the study found that total poverty line of the study area was about 3612.151 birr per year per adult equivalent consumption. Using this poverty line as bench mark the study indicated that 56.17 percent of the households were poor. The result of the logistic regression model revealed that out of 18 variables included in the model, 13 explanatory variables were found to be significant at 1%, 5% and 10% level. Accordingly, family size, household head sex, household age, dependency ratio and marital status were found to have positive association with poverty of the household and statistically significant. Meanwhile Age square, cultivated land size, oxen, access to credit, off farm activity, household health, remittance, and market access were found out to have strong negative association with the households poverty status and statistically significant up to less than 10% level of significance
This study examines the determinants of poverty in Kenya. While most of the studies done on poverty determinants rely on the income, expenditure and consumption data, The data used in this study comes from the Demographic and Health Surveys, (DHS). The principal component analysis was used to create an asset index which gave the social economic status of each household. A Logistic regression was estimated based on this data with the SES (that is poor and non-poor) as the dependent variable and a set of demographic variables as the explanatory variables. The results presented in this paper suggest that the DHS data can be used to determine the correlates of poverty.