Modeling determinants of poverty in Mauritania

Coulombe, Harold; McKay, Andy

doi:10.1016/0305-750x(96)00017-4

Cited by 48 publications

(46 citation statements)

References 11 publications

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“…The household size and being in rural areas significantly increase the likelihood of being poor. Coulombe and Mckay (1996) used multivariate analysis to analyze the determinants of poverty in Mauritania based on household survey data for 1990. They estimated a multinomial logit model for the probability of being poor depending on household specific economic and demographic explanatory variables.…”

Section: Review Of Literaturementioning

confidence: 99%

Determinants of Household Poverty: Empirical Evidence from Pakistan

Majeed¹,

Malik²

2022

pdr

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This study examines household characteristics and personal characteristics of the household head as the determinants of poverty in Pakistan. The study decomposes education of the household into different levels: primary, middle, matriculation, intermediate, bachelors and higher studies and finds evidence that poverty is greatest among the less literate households and declines as education level increases. The role of remittances appeared significant in reducing probability of being poor and this is more striking in rural areas. The probability of being poor reduces in urban area implying that incidence and severity of poverty is more pronounced in rural areas. The variables that are negatively related with the probability of being poor are: experience, age square and agriculture employment status. While the variables that are positively related with the probability of being poor are: household size, age of the household head, maleheaded households and the provincial residence.

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Section: Review Of Literaturementioning

confidence: 99%

Determinants of Household Poverty: Empirical Evidence from Pakistan

Majeed¹,

Malik²

2022

pdr

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“…Multiple regression model which used to be the main tool of analysis in this kind of studies has been criticized for a number of drawbacks and binary probit or logit models have been proposed as alternative and widely used [2,4,7,10,12,13,24,25] . Present research follows this methodology and we have used a logistical regression model explained in the next section.…”

Section: Introductionmentioning

confidence: 99%

The Determinants of Urban Household Poverty in Malaysia

Mok¹,

Gan²,

Sanyal³

2007

J. of Social Sciences

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Since independence in 1950s Malaysia has been recognized as one of the more successful countries in fighting poverty: head count ratio came down to 5.7% by 2004. However the recent process of rapid urbanization has led to an increase of urban poverty aggravated further by the 1997 Asian financial crisis. It is important to understand the nature and scale of urbanization, the various driving forces that affect it and the determinants of urban poverty as linked to this process. Our research identified the determinants of urban poverty in Malaysia using a logistic regression. A sample of 2,403 urban households from the 2004-05 Household Expenditure Survey (HES) had been used in this research. We first estimated the probability of households with specified characteristics to fall below Malaysia's official poverty line. Then we analyzed the sensitivity of the probability estimated to shift of the poverty line over a reasonable range. Results showed that human capital significantly reduced the chance of being poor while migrant workers are more prone to poverty. Household size, race and regions were also important determinants of poverty outcome in urban Malaysia. The findings had important policy implications for Malaysian government which had pledged to reduce overall poverty rate to 2.8% and eradicated hardcore poverty by 2010 under the Ninth Malaysian Plan.

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“…The descriptive statistics are utilized to describe the characteristics of respondents, while the logistic regression is employed to examine the determinant of poverty amongst the poor households. The methods are commonly used in studies related to determinants of poverty (see, among others, Coulombe and McKay 1996;Alisjahbana and Yusuf 2003;Achia and Wangombe 2010;Kristjanson, Mango et al 2010;van Edig and Schwarze 2012;Dartanto and Nurkholis 2013;Selim 2016).…”

Section: Methodsmentioning

confidence: 99%

“…Studies found that lack of educational attainment of household's head increases the probability of being poor in China , Kenya (Mango, Cheng'ole et al 2004), Malawi (Mukherjee and Benson 2003), Mauritania (Coulombe and McKay 1996) and Turkey (Selim 2016). However, educational attainment is not enough to decrease household poverty in Tanzania (Wedgwood 2007).…”

Section: Study Of Baiyengunhi and Frasermentioning

confidence: 99%

“…Broadly speaking, most empirical studies regarding determinants of poverty suggest that socio-economic factors associated with a higher probability of being poor are demographic characteristics of the head of the household including older age, female-headed household (gender), low education and informal occupation (see, among others, Coulombe and McKay 1996;Deininger and Okidi 2003;Mukherjee and Benson 2003;Mango, Cheng'ole et al 2004;Baiyegunhi and Fraser 2010;Davis and Baulch 2011;van Edig and Schwarze 2012;Dartanto and Nurkholis 2013;Selim 2016). Other literature linked household poverty status with characteristics of the household such as large size and lack of assets such as capital, land and house McCulloch and Baulch 2000;Arif 2006;Lawson, McKay et al 2006).…”

Section: Determinants Of Povertymentioning

confidence: 99%

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Untitled

Kasri¹

2017

IJISEF

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Keywords: zakah institution, impact of zakah, poverty alleviation in Indonesia JEL Classification number: I32, I38, Z12Strategies aimed to increase the impacts of zakah in alleviating poverty need to identify factors that are strongly associated with poverty and subsequently amenable to zakah interventions. However, existing empirical studies related to zakah rarely highlight this issue. This study, therefore, examine the determinants of poverty amongst zakah recipients in Indonesia in order to enhance effectiveness of zakah targeting in alleviating poverty in the world's largest Muslim country. To achieve the objective, the study collected primary data of the households receiving zakah assistance in Greater Jakarta Indonesia and utilized a logistic regression model to analyse the determinants of poverty amongst the recipients. The main result suggests that lower age, lower education, less formal occupation, smaller households size and non-economic zakah assistance were related to a higher probability of being poor. The results suggest that these groups of people should be the main target of zakah interventions in Indonesia. The findings should be taken into account by zakah organizations, government and other relevant institutions to enhance the impacts of zakah in alleviating poverty not only in Indonesia but also in other Muslim countries.

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Modeling determinants of poverty in Mauritania

Cited by 48 publications

References 11 publications

Determinants of Household Poverty: Empirical Evidence from Pakistan

Determinants of Household Poverty: Empirical Evidence from Pakistan

The Determinants of Urban Household Poverty in Malaysia

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