Page 33 - IJPS-7-2

International Journal of
Population Studies Gender gap in life expectancy in South and East Europe

in 24 South and East European countries are generally education variables may be explained by the presence of
consistent with the established literature on the link between some other included economic or demographic factor in
economic development and gender gap in LEAB, such as the model, such as, for example, the percentage of urban
Fedotenkov and Derkachev (2020) and Schünemann et al. population in the total population. Hence, as a result of
(2016). There is consistence with the findings of Fedotenkov gender constraints and opportunities in terms of access
and Derkachev (2020) that GDP per capita-positive affects to income, resources and services, reduced job creation,
the sex gap in longevity. To be specific and very simple, and downward pressure on real wages, males and females
our results show that a lower economic development level respond to urban poverty in different ways. For that
widens the gender gap in LEAB and that a higher economic reason, unemployment and underemployment are a major
development level leads to a narrowing of the gender gap concern for many urban economies (Nierenberg, 2005).
in LEAB in South and East European countries. Therefore, Conventionally, the movement to cities contributed to
the GMM/DPD model indicates that higher GDP per capita economic growth and global integration, as more people
considered with a 2-year lag narrows the gender gap in found homes close to schools, medical clinics, workplaces,
LEAB. Generally speaking, higher GDP per capita allows and communication networks (Nierenberg, 2005). The
people to build up larger savings over their lifetimes, as persistence of poverty, and social and health inequalities
well as in public and private pension funds. In turn, higher despite the general improvement in all health and social
savings lead to a better quality of life and the ability to afford indicators, proceeds from previous social and political
medical treatment, which can have long-term effects on conditions that, at different levels, are also present in some
people’s health. Specifically, our empirical results show that metropolitan areas or cities in European countries; mainly
the Gini coefficient up to 2-year lag is positively associated in those that have had delayed industrialization and
with gender gap in LEAB, implying that it has time-related urbanization (Santana et al., 2015).
effects on gender gap in LEAB. Greater income inequality The difference in employment rate by sex and the
within countries is associated with a wider gender gap in difference in unemployment rate by sex have not significant
LEAB and that a lower level of Gini index, that is, less income effect on gender gap in LEAB. In general, our results about
inequality within countries leads to a narrowed gender gap the impact of gender differential in employment on gender
in LEAB. Indeed, these empirical results may be explained gap in LEAB are consistent with the theoretical concepts in
in a way that the GDP per capita and Gini index may affect the literature (e.g., Fedotenkov & Derkachev, 2020; Gjonça et
male’s LEAB more than female’s LEAB. al., 1999). For this variable of difference in employment by
Our empirical results for the other economic development sex with a 1-year lag, there is a consensus with the findings
or demographic indicator, that is, the percentage of of Schumacher and Vilpert (2011), Cullen et al. (2015), and
urban population in total population, revealed a negative Botev (2012) that the gender differences in health behaviors
statistically significant effect on the gender gap in LEAB. and in mortality as a result of hazardous occupational and
This finding clearly means that a higher percentage of urban employment activities have been obvious explanations of the
population in total population leads to a narrowing of the gender gap in LEAB, which is more relevant when historically
gender gap in LEAB. In the literature, there are numerous speaking versus when seen from today’s perspective or during
empirical confirmations of such findings such as the studies recent decades the backward effect of these employment
by Borrell et al. (2014); Santana et al. (2015); and Veneri and variables is less pronounced. Furthermore, it can be said that
Ruiz (2013). Herewith, it is worth mentioning the opinion our results about the employment variables may be due to
of Borrell et al. (2014), as argued that when implementing less risky jobs in these sectors for both sexes.
public health policies and investigating the economic, In addition, since it is known that employed persons as
social, political, and health changes occurring in a country, well as the unemployed belong to working age, that is, up
it is important to understand these processes because the to 65 years old and considering the advanced character of
majority of Europeans live in cities. In this regard, it is the epidemiological transition in these countries, it is likely
worth highlighting the observation also by Spijker and to expect that in most part the dynamics regarding gender
Van Wissen (2010) that the mortality-increasing effects differences in LEAB started to occur in the 60s and older life
of urbanization and industrialization succeeded to hide (Trovato & Heyen, 2006; Zarulli et al., 2021). In addition, if
the mortality-reducing effects as a result of a high living we accept the belief that most of the unemployed belong to
standards as well as country-specific factors, for example, a younger age group, due to further education, job search,
dietary habits acted as a confound. or so, then in proving the validity of our results about not

Furthermore, it may be put forward for consideration significant effect of the difference of unemployment by
that the not significant effect for some employment and sex will be mentioned again the findings by Zarulli et al.

Volume 7 Issue 2 (2021) 27 https://doi.org/10.36922/ijps.v7i2.389