Is there a link between alcohol consumption and the level of poverty?

Jayathilaka, Ruwan; Selvanathan, Saroja; Bandaralage, Jayatilleke

doi:10.1080/00036846.2015.1114574

Cited by 16 publications

(17 citation statements)

References 14 publications

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“…This may be due to historical, economic, political, and social factors that impact the income and asset-generating opportunities of these vulnerable groups. This effect has been widely documented in the literature (Canelas and Salazar 2014;De Silva 2008;Jayathilaka et al 2016;Ravindra Deyshappriya and Minuwanthi 2020).…”

Section: Ethnicitymentioning

confidence: 65%

Determinants of Poverty in Mexico: A Quantile Regression Analysis

et al. 2021

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Most studies on the determinants of poverty do not consider that the relative importance of each of these determinants can vary depending on the degree of poverty suffered by each group of poor people. For Mexico’s case, the studies carried out so far do not contemplate this approach, even though there is wide variation in the degree of poverty among the different groups of the poor. Investigating these differences is important to design better policies for fighting poverty, which consider how each variable that explains poverty affects each group of people who suffer from poverty differently. This article examines the determinants of poverty for Mexican households. Using data from the Mexican National Household Income and Expenditure Survey (ENIGH) 2018, the study estimates a probit model and a quantile regression model to examine the extent to which the determinants of poverty vary across the poverty spectrum. The results from the probit model indicate that households with more than one member, having a female head, or speaker of an indigenous language are more likely to be poor. The results obtained in the quantile regressions indicate that there are significant differences with the results of the simple ordinary least squares model, especially for households in extreme poverty but also for the other income categories analyzed for several of the explanatory variables used in the models. Households in the categories extremely poor and deeply poor are most affected if they are in the southern region or if the household head speaks an indigenous language or is an elderly person. It is observed that achieving a higher educational level is an effective way to increase income across the poverty spectrum.

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Section: Ethnicitymentioning

confidence: 65%

Determinants of Poverty in Mexico: A Quantile Regression Analysis

et al. 2021

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“…De Silva (2008); (Gunatilaka, Wan, & Chatterjee, 2010); Deepawansa, Sooriyarachchi, and Wickremasinghe (2011);Ranathunga. and Gibson (2014) and Jayathilaka et al (2016) all confirm this relationship in the context of Sri Lanka. Similarly, empirical studies by Rodriguez and Smith (1994); Fields, Cichello, Freije, Menéndez, and Newhouse (2003); Rupasingha and Goetz (2007) recognized employment type is a crucial determinant of poverty.…”

Section: Literature Reviewmentioning

confidence: 72%

“…Scholars such as Amarasinghe, Samad, and Anputhas (2005); Gunewardena (2007); Ranathunga (2010); Sinnathurai and Březinová (2012); Ranathunga. and Gibson (2014) and Jayathilaka, Selvanathan, and Bandaralage (2016) have modelled the determinants of poverty in the context of Sri Lanka using Household Income and Expenditure Survey (HIES) data. These studies used now outdated data and survey waves which did not cover both Northern and Eastern provinces due to civil war.…”

Section: Objectives and Structure Of The Studymentioning

confidence: 99%

“…Other important correlates of poverty for studies examining Sri Lanka include local and foreign remittances (De Silva, 2008;Ranathunga. & Gibson, 2014) alcoholism (Jayathilaka et al, 2016) and the condition of the house (De Silva, 2008;Jayathilaka et al, 2016). Among the household level determinants of poverty, age of the head of household has been recognized as one of the key determinants of poverty.…”

Section: Literature Reviewmentioning

confidence: 99%

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Determinants of Poverty: Is Age Non-Linearly Related with Poverty? Evidence from Sri Lanka

Deyshappriya

Minuwanthi

2020

International Journal of Asian Social Science

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The current study aims to recognize the nature of the relationship between age and poverty in the context of Sri Lanka while examining the determinants of income and multidimensional poverty. The study uses Household Income & Expenditure Survey (HIES) (2016) data from Department of Census and Statistics of Sri Lanka and employed Probit regression analysis to accomplish the objectives of the study. The results stress that there is a non-linear relationship between the age of head of household and probabilities of being income and multidimensional poverty in Sri Lanka. Furthermore, the study determines that the probabilities of being income poor and multidimensionally poor decrease with age till 69.23 years and 68.18 years respectively and increase after that. Moreover, other households factors such as size of household, education, ethnicity, employment status, marital status, sector of living, disability nature of the head of household, having agricultural lands and receiving remittances are also recognized as crucial drivers of both income and multidimensional poverty in Sri Lanka. The study strongly recommends implementing appropriate policies and safety net programs which focus on the households which are headed by elderly people. Similarly, level of education, access to agricultural land and access to better employment opportunities should also be enhanced to ensure poverty-free society.Contribution/ Originality: This study is one of very few studies which have investigated association between age and poverty in non-linear setting. The study observed that the probabilities of being income poor and multidimensional poor decrease with age till 69.23 years and 68.18 years respectively and increase after that.

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“…The researchers employed probit model which was first introduced by Chester Ittner Bliss in 1935 and later used in many studies according to the past literature in achieving similar research objectives [ 52 ]. Hence, it is reasonable to assume that probit model is effective in this study and the validity of findings are reliable.…”

Section: Methodsmentioning

confidence: 99%

Do chronic illnesses and poverty go hand in hand?

et al. 2020

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In the global context, the health and quality of life of people are adversely affected by either one or more types of chronic diseases. The chronic pain associated with diagnosed patients may include heavy medical expenditure along with the physical and mental suffering they undergo. Usually, unbearable amounts of medical expenses are incurred, to improve or sustain the health condition of the patient. Consequently, the heavy financial burden tends to push households from a comfortable or secure life, or even from bad to worse, towards the probability of becoming poor. Hence, this study is conducted to identify the impact chronic illnesses have on poverty using data from a national survey referred as the Household Income and Expenditure Survey (HIES), with data gathered by the Department of Census and Statistics (DCS) of Sri Lanka in 2016. As such, this study is the first of its kind in Sri Lanka, declaring the originality of the study based on data collected from the local arena. Accordingly, the study discovered that married females who do not engage in any type of economic activity, in the age category of 40–65, having an educational level of tertiary level or below and living in the urban sector have a higher likelihood of suffering from chronic diseases. Moreover, it was inferred that, if a person is deprived from access to basic education in the level of education, lives in the rural or estate sector, or suffers from a brain disease, cancer, heart disease or kidney disease, he is highly likely to be poor. Some insights concluded from this Sri Lankan case study can also be applied in the context of other developing countries, to minimise chronic illnesses and thereby the probability of falling into poverty.

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Is there a link between alcohol consumption and the level of poverty?

Cited by 16 publications

References 14 publications

Determinants of Poverty in Mexico: A Quantile Regression Analysis

Determinants of Poverty in Mexico: A Quantile Regression Analysis

Determinants of Poverty: Is Age Non-Linearly Related with Poverty? Evidence from Sri Lanka

Do chronic illnesses and poverty go hand in hand?

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