Social capital and its role in poverty reduction: a Canadian-based analysis

Weaver, Robert D.

doi:10.1080/17486831.2012.636257

Cited by 9 publications

(7 citation statements)

References 44 publications

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“…Likewise, the household can enjoy economies of scale in the purchase of goods and services and have a greater incentive to save than a single person, as well as enjoy greater benefits from government social security (Lerman 2002). Evidence in favor of the hypothesis that being single, whether previously married or never married, increases the probability of being poor includes Cotter (2002), D'Ambrosio et al (2011, Lekobane andSeleka (2017), andWeaver (2012). Recent evidence in this same direction is that of Peng et al (2019), who, for the case of Hong Kong, finds that not having a partner (being single, divorced, or separated) significantly increases the probability of being poor.…”

Section: Marital Status Of the Household Headmentioning

confidence: 99%

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: Marital Status Of the Household Headmentioning

confidence: 99%

Determinants of Poverty in Mexico: A Quantile Regression Analysis

et al. 2021

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“…Previous studies have investigated the determinants of poverty and energy poverty separately. Key factors found to have an impact on poverty include the education status of household members (Chen & Wang, 2015; Peng et al, 2019; Rahman, 2013) and health status (Peng et al, 2019; Weaver, 2012). These two combine to show the strength of the household's human capital.…”

Section: Literature Reviewmentioning

confidence: 99%

Are energy‐poor households also poor? Evidence from Ghana

Adusah‐Poku

Adjei‐Mantey

Kwakwa

2021

Poverty & Public Policy

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Poverty and energy poverty are parts of the Sustainable Development Goals (SDGs). The former has been extensively covered in the literature, whereas the latter has not been given the necessary attention until the last decade. In this paper, we use multidimensional measures of energy poverty and poverty to ascertain whether energy‐poor households are also poor, and vice versa. In addition, the study seeks to examine the factors responsible for poverty and energy poverty. This paper uses data sets constructed from the two most recent nationwide household surveys in Ghana (GLSS VI and GLSS VII samples). We find substantial differences in the incidence and index of multidimensional energy poverty and multidimensional poverty by subgroup demographic characteristics. Further, we find that households belong to different categories of poverty and energy poverty status. Our multinomial regression results reveal that household size, marital status, educational background, residential area, age, employment, and gender significantly predict the household's multidimensional poverty and energy poverty status. We recommend massive investment in renewable energy, to make cleaner energy readily available for Ghanaians. In addition, the Ghanaian government should pursue sustainable employment and job creation avenues, directed toward an improved standard of living for the population.

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“…In addition, households headed by females are consistently found to be more likely to be poor than those headed by males (Grootaert and Braithwaite 1998;Cotter 2002;Devicienti et al 2007). Being single, whether formerly married or have never married, also increases the probability of being poor (Cotter 2002;D'Ambrosio et al 2011;Weaver 2012;Lekobane and Seleka 2017).…”

Section: Demographic Characteristicsmentioning

confidence: 99%

Determinants of Poverty and Their Variation Across the Poverty Spectrum: Evidence from Hong Kong, a High-Income Society with a High Poverty Level

et al. 2018

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This study aims to investigate into the determinants of poverty in Hong Kong. Previous research on poverty, which usually adopted a logistic regression model to examine individuals' probabilities of being poor, could not adequately reveal the heterogeneity in experiences among people across the poverty spectrum, therefore has limited policy effort to address diverse needs of individuals struggling with poverty. In the present study, this concern is addressed by using a quantile regression model to examine the differential effects of the determinants of poverty across the poverty spectrum. Data were drawn from the Hong Kong Panel Survey for Poverty Alleviation (n = 1668). Logistic regression indicated that being elderly, being female, not having a partner, from a single-parent household, not being employed, living in public rental housing, have lower educational attainment, and have poor self-rated health, increased the probability of being poor. Informational support was a protective factor of poverty, while several negative life events, such as having family member(s) with disabilities/chronic diseases and having financial burden, were risk factors of poverty. Quantile regression analysis was adopted to further examine the extent to which determinants of poverty unfold across poverty spectrum, which was captured by five groups of "extremely poor", "deeply poor", "at the poverty line", "near poverty" and "marginally poor". Quantile regression indicated that people living across the poverty spectrum were similarly affected by not having a partner, living in single-parent households and not working. However, extremely poor and deeply poor were more adversely affected by old age than those near poverty and marginally poor. It is also discovered that public rental housing buffered the poverty risks more in those who lived in deep poverty than those who were near poverty and marginally poor. University education protected the near poverty and marginally poor to a larger extent than those who were extremely poor and deeply poor. Information support also buffered the poverty risks, and people living across the poverty spectrum were equally benefited from it.

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Social capital and its role in poverty reduction: a Canadian-based analysis

Cited by 9 publications

References 44 publications

Determinants of Poverty in Mexico: A Quantile Regression Analysis

Determinants of Poverty in Mexico: A Quantile Regression Analysis

Are energy‐poor households also poor? Evidence from Ghana

Determinants of Poverty and Their Variation Across the Poverty Spectrum: Evidence from Hong Kong, a High-Income Society with a High Poverty Level

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