Determinants of Poverty in Cameroon: A Binomial and Polychotomous Logit Analysis

Epo, Boniface Ngah

doi:10.2139/ssrn.1424672

Cited by 6 publications

(19 citation statements)

References 26 publications

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“…Yoksulluğu belirleyen faktörlerin araştırıldığı çalışmalarda genel olarak hanehalkına ilişkin gelir-yaşam koşulları, harcama, bütçe, bölge, işgücü, sağlık, vb. anket verileri kullanılarak analizler gerçekleştirmiştir (Dünya Bankası, 2005;Kızılgöl, 2009;Epo, 2010;Achia vd., 2010;Makame & Mzee, 2014;Rodriguez, 2015). Konu ile ilgili dünyadaki bir çok farklı ülke ve Türkiye için çok sayıda deneysel çalışma yapılmıştır.…”

Section: Literatür Taramasıunclassified

“…Diğer aktif olmayanların ise referans gruba göre yoksul olmama olasılığı 1,285 kat fazladır. Literatürdeki çalışmaların, işsizliğin yoksul olma riskini arttırdığı yönündeki bulguları, ulaşılan bu sonucu destekler niteliktedir (Datt & Jolliffe, 1999;Chaudhry & Rahman, 2009 (Epo, 2010;Mok vd., 2007;Dal, 2013).…”

Section: Uygulamaunclassified

“…Sri-Lanka'da eğitim ve yabancı para transferlerinin yoksulluk riskini azaltıcı bir etkiye sahip olduğu belirtilmiştir. Epo (2010) çalışmasında Kamerun'da Hanehalkı Tüketim Anketlerini kullanılarak yoksulluğun nedenlerini incelemiştir. Çalışmada eğitim, hane reisinin yaşı, çalışan yetişkin hane bireylerinin oranı, altyapı imkânlarına ulaşım değişkenleri hane yoksulluk riskini azaltıcı yönde etkiye sahip iken hanelerin kırsal bölgelerde yaşıyor olması yoksulluk riskini arttırmaktadır.…”

Section: Literatür Taramasıunclassified

“…En düşük risk ise girişimci olarak adlandırılan işveren veya kendi hesabına çalışanlardadır. Bu bulgu uluslararası literatür ile de uyumludur (Rodriguez, 2015;Spaho, 2014 (Epo, 2010;Mok vd., 2007;Dal, 2013).…”

Section: Hanehalkıunclassified

“…Analizler sonucunda elde edilen bulgular literatürle uyumlu olup göreli yoksulluğu belirleyen temel faktörler eğitim, yaşanılan bölge, çalışılan sektörler, yaş, işgücüne katılım, hanehalkı büyüklüğü, medeni durum, SGK'ya kayıtlı olarak işgücü piyasasında faaliyet göstermekte olduğu söylenebilir. (Datt & Jolliffe, 1999;Biyase & Zwane, 2015;Smajic & Ermacora, 2007;Geda vd., 2005;Ayalneh vd., 2008;Cafri, 2009;Sekhampu, 2012;Epo, 2010;Mok vd., 2007;Awan vd., 2011, Myftaraj vd., 2014.…”

Section: Sonuçunclassified

See 4 more Smart Citations

Factors Influencing The Household Relative Poverty In Turkey: Logistic Regression Analysis

2020

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This study investigates the factors that determine the relative household poverty which prevents the growth of social welfare in Turkey. For this purpose, Turkish Statistical Institute (Turkstat) (2017) Income and Living Conditions micro dataset was used. The poverty line was calculated by using 60% of the household median income. In the binary logistic regression analysis, poor and non-poor shares of population was determined according to the poverty line and used as the dependent variables. Household demographic characteristics, labour market conditions and geographical characteristics were employed as explanatory variables. The estimation results indicate that education, age, formal employment, and employment in industry sector decreases relative household poverty. On the contrary, unemployment, causal employee, marriage and household size increase the risk of household poverty.

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Section: Literatür Taramasıunclassified

Section: Uygulamaunclassified

Section: Literatür Taramasıunclassified

Section: Hanehalkıunclassified

Section: Sonuçunclassified

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Factors Influencing The Household Relative Poverty In Turkey: Logistic Regression Analysis

2020

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Implications of Child Labour Earnings for Household Well-Being in Cameroon

Sundjo

2020

Child Labor in the Developing World

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Determinants of Household Poverty Status in Kediri City

Khusaini

Prasetyia²,

Rozanti³

2021

JIAE

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Analyses of the causes and the characteristics of poverty at micro levels provide more efficient strategies for the attainment of main Sustainable Development Goals. This study aimed to analyze the extent to which the characteristics of individuals, households, and communities influence the probability of household poverty status. The 2019 Social Welfare Integrated Data and Village Potential Data of Kediri City were analyzed using an ordered logit regression model and then interpreted based on marginal effect calculation. The study found that household headsâ€™ squared-age, household membersâ€™ education, household membersâ€™ occupation, household head gender (female), ownership of assets, access to the internet, access to proper sanitation, and access to financial institutions reduced the probability of households being categorized as very poor and poor. This finding indicated that household productivity influenced by the household headâ€™s characteristics in managing productive assets, supported by access to infrastructure, could increase the household's welfare. However, the household headâ€™s age and marital status, dependency ratio, and access to health facilities increased householdâ€™s probability of being very poor and poor. Policies regarding poverty must be adjusted to the poverty characteristics and status. Improving access, equalizing education, and improving job opportunity and infrastructure management that ensure accessibility and enhancement in service quality need to be made to increase the status of households with the lowest 40% welfare in Kediri City. Policies regarding poverty should be focused more on social programs for very poor and poor households. Meanwhile, those near-poor and vulnerable-to-poor need more empowering programs.

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Determinants of Poverty in Cameroon: A Binomial and Polychotomous Logit Analysis

Cited by 6 publications

References 26 publications

Factors Influencing The Household Relative Poverty In Turkey: Logistic Regression Analysis

Factors Influencing The Household Relative Poverty In Turkey: Logistic Regression Analysis

Implications of Child Labour Earnings for Household Well-Being in Cameroon

Determinants of Household Poverty Status in Kediri City

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