Poverty in Europe by gender: The role of education and labour status

Aísa, Rosa; Larramona, Gemma; Pueyo, Fernando

doi:10.1016/j.eap.2019.04.009

Cited by 18 publications

(14 citation statements)

References 7 publications

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“…As it is known, primary school education is the most basic level of education. This result is consistent with the findings of Oxaal (1997), Çağlayan and Sedefoğlu (2017) and Aisa et al (2019), which were previously mentioned in the literature. Because in all these studies, it has been determined that women's poverty only decreases as women's education level rises.…”

Section: Conclusion and Suggestionssupporting

confidence: 94%

“…But for country-based analysis; France, Poland, Slovenia and Finland have a statistically significant relationship and positive sign for primary education and risk of poverty and social exclusion. These results are consistent with Oxaal (1997), Caglayan and Sedefoglu (2017) and Aisa et al (2019) too. On the contrary; Ireland, Greece, Spain, Italy, Lithuania, Hungary and Netherland have a statistically significant relationship and negative sign for primary education and risk of poverty and social exclusion.…”

Section: Conclusion and Suggestionssupporting

confidence: 91%

“…They use Household Budget Survey 2013, which was set up by the Turkish Statistical Institution (TSI) to apply Bayesian probit regression. According to the study, the education level of a bachelor for 4 years, master and Ph.D. is the most efficient variable which causes a decline in the likelihood of living under the poverty line for female-headed households Aisa et al (2019). analyze the determinants of poverty by gender among workers in Europe for 25 European countries.…”

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confidence: 99%

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Evaluation of Factors Which Cause Female Poverty in the Eu by Panel Data Analysis

GÜNAY¹,

Polat

2021

Yönetim Ve Ekonomi Araştırmaları Dergisi

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The purpose of the study is to investigate the impact of women's unemployment rates and education levels on poverty in 27 European Union member countries. Therefore, an econometric model has been applied by using EU-27 countries' 2005EU-27 countries' -2017 data set. The dependent variable representing female poverty is "female at risk of poverty or social exclusion". The independent variables are the unemployment rate of the active female population and % of women with primary school graduation.While investigating the effect, the augmented mean group estimator (AMG) is used as the econometric analysis method for the model. This estimator is also known as developed by Eberhardt and Teal's (2010).The unemployment rate of the active female population has statistically significant effects on % risk of female's social exclusion and poverty for EU-27 countries, but the primary education graduation ratio has not a statistically significant relationship. The unemployment rate has same-direction relationship with the risk of social exclusion and poverty. Based on this finding, reducing unemployment is a more effective way to eliminate women's poverty in European countries than increasing primary school graduation. In this context, it can be argued that increasing female's labor participation rate in business and employment in European Union countries can effectively prevent female poverty among women.

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Section: Conclusion and Suggestionssupporting

confidence: 94%

Section: Conclusion and Suggestionssupporting

confidence: 91%

mentioning

confidence: 99%

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Evaluation of Factors Which Cause Female Poverty in the Eu by Panel Data Analysis

GÜNAY¹,

Polat

2021

Yönetim Ve Ekonomi Araştırmaları Dergisi

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“…In fact, in the European Union there is a strong relation between the average salary and the educational level of the workforce, and the income of those with higher education is approximately 50% higher than those with only secondary education (Eurostat, 2020). Similar findings were also made by Aisa, Larramona, and Pueyo (2019), showing a negative relation between education and poverty, and Eikemo, Bambra, Joyce, and Dahl (2008), who note that people with higher education perform better in health‐related indicators.…”

Section: Methodssupporting

confidence: 80%

Accessibility to primary schools in Portugal: a case of spatial inequity?

Marques

Wolf

Feitosa

2021

Regional Science Policy & Practice

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Social inequalities have shown a tendency for being amplified by the distribution of socio‐economic groups and of spatial amenities and disamenities. Given their commitment to principles of spatial equity, it is important to understand if public services fit into this pattern or, on the contrary, provide similar levels of accessibility to different socio‐economic groups. This paper analyses the relationship between the location of primary schools and the spatial distribution of socio‐economic groups in Portugal and it concludes that there is a systematic tendency for disadvantaged populations to have worse accessibility than advantaged ones, independently of spatial scales and types of territories.

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“…Among the factors significantly affecting income inequality are demographic changes and changes in the structure of society (Chaupain-Guillot and Guillot, 2015). Demographic and socio-economic factors of the individual in particular determine the level of his or her income (Corsi et al, 2016;Aisa et al, 2019;Gradín et al, 2010). Corsi et al (2016) in connection with remuneration, depending on gender, also talks of another factor, which is preference of job positions or employment industries for certain genders on which the wage level is based, i.e.…”

Section: Literature Overviewmentioning

confidence: 99%

Effect of Gender Differences and Other Factors on Remuneration of Employees in EU Countries

Antošová

Hazuchová

Stávková

2021

Danube

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The aim of the paper is to verify gender income differences and to reveal factors influencing differences in remuneration. Gender income differences across the EU range from 6% in Romania to 27% in the Czech Republic. Data from 178,878 employees filtered from the EU-SILC database were used for calculations. Stepwise regression analysis was used to identify factors that affect gender income differences. The greatest effect was shown in the “Absolute Income”, then the “Job Change in the Last Year” factor, but the “Education” and “Age” showed a strongly negative tendency. Extraordinary attention is devoted to the effect of the “Job sector” factor, which also significantly contributes to the difference in income. Different representation of gender appears in different industries, which can be caused by preferences and be a consequence of persisting society-wide stereotypes. Social policies are not in line with changes in society and lag behind the development of society.

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Poverty in Europe by gender: The role of education and labour status

Cited by 18 publications

References 7 publications

Evaluation of Factors Which Cause Female Poverty in the Eu by Panel Data Analysis

Evaluation of Factors Which Cause Female Poverty in the Eu by Panel Data Analysis

Accessibility to primary schools in Portugal: a case of spatial inequity?

Effect of Gender Differences and Other Factors on Remuneration of Employees in EU Countries

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