Page 31 - IJPS-7-2

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

LEAB gained the most in Türkiye (10 years for males and rate by sex in total population 15+ with a 1-year lag,
9 years for females) and least in Bulgaria (3.5 years for as females – males DIFFEMPL(-1), the difference in
males and 3 years for females) and Montenegro (4 years unemployment rate by sex as percentage of total labor
for males). The largest gender gap in LEAB was found in force, as females – males (DIFFUNEMPL), Gini index
Russia, Ukraine, and Belarus with mostly 10 – 12 years, with a 2-year lag Gini(-2), the urban population as a
then in Croatia, Hungary, Poland, Slovakia, Moldova, and percentage of total population (URBANPOP), GDP per
Romania with mostly between 7 and 9 years, whereas the capita – constant 2015 US$ with a 2-year lag GDPPC(-2),
smallest gap was found in Cyprus, Macedonia, Malta, and health expenditure as percentage of GDP (HEGDP),
Greece, with mostly between 4 and 6 years. Due to the GDP growth rate (GDPGR), life expectancy at birth
wars and military actions in Bosnia and Herzegovina in with a 1-year lag LEAB(-1) and the percentage share of
1992 – 1994 and in Croatia in 1991 in the study period, the completion of the secondary education by females
these couple of years were not taken into account these among the school-aging population aged 15 – 18 with a
years in comparisons and analyses about LEAB. 1-year lag SEDUF(-1). Since the model was assumed to be
The COVID-19 pandemic outbreak from January dynamic, the Dynamic Panel Wizard tool for estimating
2020 onward has apparently altered mortality rates a lot GMM/DPD was applied. Our dependent variable was
and consequently LEAB for both sexes across the whole MGG (gender gap in LEAB), and the first lag of MGG
world. Our study countries have not been any exception (i.e., MGG (-1)) was an explanatory variable. Furthermore,
from this pandemic. Because of this shock to mortality to remove the cross-section fixed effects of the dynamic
rates and LEAB in 2020, this mentioned year was excluded panel model, the first difference transformation was
from the GMM/DPD analyses. In that direction, the data specified. In addition, period-specific (predetermined)
in 2020 about the LEAB show that there was a drastic instruments (i.e., MGG with one lag and the remaining
decrease in LEAB for almost all countries. Countries with exogenous instruments) were specified as well. Finally,
the largest decreases in LEAB between 2019 and 2020 the weighting and coefficient covariance calculation was
were Russia (2.5 years), Macedonia (2.1 years), Albania specified. Therefore, 1-step GMM iteration was selected
(2 years), and Türkiye and Ukraine (1.9 years). Malta was for i.i.d innovations to calculate the Arellano-Bond 1-step
the only country that registered an increase of the total estimator. In this case, fixed weights standard errors from
LEAB of 0.2 years, and Cyprus has stayed at the same level estimation were computed. The coefficient estimates and
between these 2 years, without any changes in the total summary statistics of the output of the GMM/DPD model
LEAB. If we observe the reductions by gender compared are provided in Table 1. The standard errors presented in
for these 2 years (2019 and 2020), the greatest reduction Table 1 are the standard errors of the 1-step Arellano-Bond
in LEAB was observed among males in Albania (2.6 years) estimator. In the literature, there is proof that the standard
and Russia (2.6 years), Macedonia (2.2 years), Türkiye errors for the 1-step estimator are most reliable.
(1.9 years), Ukraine (1.6 years), Belarus (1.6 years), The results of the GMM estimation presented in Table 1
Bulgaria (1.5 years), Italy (1.4 years), Serbia (1.4 years), indicate that the estimated coefficients of Gini index up
Slovenia (1.4 years), and Romania (1.4 years). On the other to 2-year lag and GDP per capita with 2-year lag have a
side, the greatest losses in LEAB in 2020 were observed positive and significant effect on gender gap in LEAB at p <
among females in Russia (2.4 years), Ukraine (2.2 years), 0.01 and p < 0.05, respectively. Nevertheless, the coefficient
Macedonia (2 years), Albania (1.9 years), Türkiye of GDP growth rate also remains positive, and the
(1.9 years), and Belarus (1.5 years). As quite the opposite, corresponding p value is close to the 10 percent significance
for females in Malta and Cyprus, an increase in LEAB was level. The coefficients of URBANPOP and LEAB (-1) both
observed for the period by 0.9 and 0.1 years, respectively have a negative impact on gender gap in LEAB at p < 0.05%
(United Nations, 2022a). and p < 0.01, respectively. The employment variables,
education variable, and health expenditure as percentage
3.2. Applications of dynamic panel model using of GDP have all statistically insignificant coefficients.
GMM: Main findings However, within the model specification, the difference in
GMM/DPD model was applied to find out the relationship unemployment rate by sex and the health expenditure as
between gender gap in LEAB and explanatory variables percentage of GDP has both negative sign and therefore
for 24 South and East European countries from 1991 to insignificant negative impact on the gender gap in LEAB.
2019. The estimation was based on the unbalanced 339 On the contrary, positive sign but with insignificant impact
observations panel of country level data. The analysis fits on gender gap in LEAB was found for the coefficient of
the gender gap in LEAB (MGG, measured by females’ the difference in employment rate by sex with a 1-year lag
LEAB – males’ LEAB) to the difference in employment and for the female share of the completion of secondary

Volume 7 Issue 2 (2021) 25 https://doi.org/10.36922/ijps.v7i2.389