Inequality in enlargement countries

Data from May 2025.

Planned article update: July 2026.

Highlights

Among the enlargement countries, Moldova had the largest difference in life expectancy between women and men in 2023, with women expected to live 8.9 years longer than men. In the EU, this difference was 5.3 years.

In 2022, the gender gap for economic activity was larger in the enlargement countries than in the EU. The widest gap was in Türkiye, with 38.7 percentage points difference in the economic activity rate between men and women. In comparison, it was 10.2 pp in the EU.

In Montenegro and Bosnia and Herzegovina, the unemployment rates for persons who had completed at most lower secondary education (35% (2020) and 26% (2024), respectively) were more than double those for persons with medium or higher education.

This article is part of an online publication and provides information on inequalities by gender, education and income in the enlargement countries and compares this with the corresponding data for the European Union (EU).

For the articles forming this online publication, only data are used which have been submitted to and validated by Eurostat's subject matter units following the same process as for the EU countries. For Georgia, the Republic of Moldova and Ukraine, only data on life expectancy gender gap are transmitted according to this process; therefore, data on the other indicators presented in the article are not yet available. However, data on these countries are also presented in the Statistics Explained articles for ENP-East countries, which are based on data supplied by and under the responsibility of the national statistical authorities of each country on a voluntary basis. These data are not validated by Eurostat’s subject matter units.

Data shown for Georgia exclude the regions of Abkhazia and South Ossetia over which the government of Georgia does not exercise control. The data managed by the National Bureau of Statistics of the Republic of Moldova does not include data from the Transnistrian region over which the government of the Republic of Moldova does not exercise control. Since 2014, data for Ukraine generally exclude the illegally annexed Autonomous Republic of Crimea and the City of Sevastopol and the territories which are not under control of the Ukrainian government. Data on Ukraine from 2022 onwards are limited due to exemption under the martial law from mandatory data submission to the State Statistics Service of Ukraine, effective as of 3 March 2022, following Russia’s war of aggression against Ukraine. This exemption remained in place until the end of the martial law, on 1 July 2025.

This article presents statistics on inequalities between men and women with respect to life expectancy and activity rate, as well as differences in unemployment rates for persons with different education levels and inequalities in the distribution of income.

Life expectancy gender gap

Life expectancy at birth is the average number of years a newborn child can expect to live, assuming current mortality conditions prevail during the rest of their life. The gender gap for life expectancy may reflect disparities between women and men with respect to living conditions and access to health services. The life expectancy gender gap is defined as the number of years that women can expect to live (at birth) minus that of men. Figure 1 presents the life expectancy gender gap for the enlargement countries and the EU over the period from 2013 to 2023.

In Ukraine, Moldova and Georgia, the differences between the life expectancies of women and men were larger than in the EU. In 2023, newborn women were expected to live 9.9 years longer than men in Ukraine (2019; more recent data not available), 8.9 years in Moldova and 7.6 years in Georgia. With a gender gap of 5.7 years in 2016, Kosovo^[1] had the same difference in life expectancy between the sexes as the EU that year. However, this is the only data point available for Kosovo.

In comparison, newborn women in the EU in 2023 were expected to live 5.3 years longer than their male counterparts.

In all other enlargement countries, the life expectancy gender gap was lower than in the EU. In Montenegro (5.1 years), Türkiye (5.0 years) and Serbia (4.8 years), the gap was relatively close to that in the EU. The difference in life expectancy between women and men was even narrower in North Macedonia with 4.4 years (2021; more recent data not available) and Albania with 3.5 years (2022; more recent data not available).

Throughout the period 2013-2023, the life expectancy gender gap narrowed somewhat in most of the enlargement countries. Larger decreases in this gender gap were seen in Georgia (from 8.3 in 2014 to 7.6 in 2023), Türkiye (from 5.7 years in 2013 to 5.0 years in 2023), Albania (from 4.1 years in 2013 to 3.5 years in 2022) and Serbia (from 5.3 years in 2013 to 4.8 years in 2023). However, the trend was fluctuating somewhat for several of these countries. In Ukraine, the gap decreased from 10.1 in 2014 to 9.9 in 2019 (more recent data not available). Small increases were seen in North Macedonia, from 4.1 years in 2013 to 4.4 years in 2021 (more recent data not available) and Montenegro, from 4.9 years in 2013 to 5.1 years in 2023.

The life expectancy gender gap in the EU decreased from 5.8 years in 2013 to 5.3 years in 2023 (provisional data).

Figure 1: Life expectancy gender gap, 2013-2023
Source: Eurostat (demo_mlexpec)

Economic activity gender gap

The economic activity gender gap is calculated as the activity rate of men minus the activity rate of women. The economic activity rate is the percentage of active persons in relation to the total population. The active persons constitute the economically active population, which includes employed and unemployed persons, but excludes persons who are not working and not available or looking for work, such as students, caregivers and pensioners.

Figure 2 shows the economic activity gender gap for the age group 20-64 years old in the enlargement countries and in the EU, for the period 2014-2024. In all the enlargement countries for which these data are available, the economic activity gender gap was wider than in the EU. However, in most countries the gap decreased during the period, albeit with fluctuations around the main trend. Data for Albania, Georgia, Moldova, Ukraine and Kosovo are not available.

The widest gap was recorded in Türkiye, although this was also the country where the gap narrowed the most between 2014 and 2024. In 2014, the activity rate of men was 46.4 percentage points (pp) higher than for women. It shrunk gradually to 38.7 pp in 2024, with just a minor increase in 2020. The gender gap in Türkiye was nearly 4 times higher than the corresponding gap in the EU throughout the period, despite a reduction in the gap of 7.7 pp. In Bosnia and Herzegovina, the gender gap was 26.7 pp in 2021 (the oldest data available) and 26.5 pp in 2024. In North Macedonia, it fluctuated between 27.7 pp in 2014 and 24.3 pp in 2020 (more recent data not available), with a peak at 28.9 pp in 2016.

Montenegro also experienced fluctuations, but at lower levels: the gap increased from 13.8 pp in 2014 to 14.7 pp in 2020 (more recent data not available), with the narrowest gap in economic activity 12.8 pp in 2015 and the widest 16.1 pp in 2018. In Serbia, the gender gap decreased from 17.0 pp in 2014 to 12.4 pp in 2024.

In comparison, the gender gap with respect to economic activity decreased gradually from 12.7 pp in 2014 to 10.2 pp in 2024 in the EU, with no year-on-year increases measured during the period.

Figure 2: Economic activity gender gap, 2014-2024
Source: Eurostat (lfsa_argan)

Unemployment rate by education level

The unemployment rate of people aged 25-64 is explored in Figure 3, with respect to the level of educational attainment. Data for 2014 and 2024 are presented. The levels of education are the three main aggregates of the International standard classification of education (ISCED): low (having completed ‘less than primary’, primary or lower secondary education), medium (upper secondary and post-secondary non tertiary) and high (tertiary) level of education. Data for Montenegro and North Macedonia refer to 2020 instead of 2024.

In general, the graph shows, with few exceptions, an inverse correlation between the level of education and unemployment: the higher the level of education, the lower the unemployment.

In 2024, in Bosnia and Herzegovina, the unemployment rate of persons with low education was 26%, more than double that of persons with medium education (12%) and three times that of persons with high education (8%). Data for Bosnia and Herzegovina are not available for 2014.

In Montenegro, the unemployment rate of persons with low education was 33% in 2014, rising to 35% in 2020 (more recent data not available). In contrast, for persons with medium education the rate decreased from 18% to 16%, roughly half of that of those with low education. For persons with high education, unemployment grew from 9% to 12%. The unemployment rates in Montenegro in 2020 were approximately 3 times higher than those in the EU in 2024, for each level of education.

In North Macedonia, there was a significant drop in the unemployment rates between 2014 and 2020 (more recent data not available). They went down from 31% to 21% for people with low education, almost halved from 25% to 14% for persons with medium education and decreased from 20% to 13% for persons with a high education.

Serbia observed an even larger decrease in the unemployment rates. The unemployment rates in 2014 were already lower than those in North Macedonia; by 2024, the unemployment rates in Serbia were coming close to those in the EU. Between 2014 and 2024, the unemployment rates in Serbia fell from 19% to 13% for persons with low education, from 19% to 8% for persons with medium education and from 15% to 6% for persons with high education.

Türkiye had some of the lowest unemployment rates among the enlargement countries. In 2014, the unemployment rates for persons with low and high education were both 8%, while the rate for medium education was slightly higher at 9%; the unemployment rates for low and medium education were even lower than in the EU that year. The unemployment rates were slightly lower in 2024, with rates of 7% for persons with low education and 8% for persons with medium and high education. Türkiye stands out among the enlargement countries with no significant differences in unemployment rates with respect to education level.

In the EU, the unemployment rates came down from already moderate levels to relatively low levels between 2014 and 2024. The rates fell from 18% to 11% for persons with low education; from 9% to 5% for medium education, and from 6% to 4% for high education. For medium and high education, this was lower than in any of the enlargement countries, although for low education it was higher than in Türkiye.

Data for Moldova, Georgia, Albania, Ukraine and Kosovo were not available.

Figure 4: Unemployment rate by education level, 2014-2024
Source: Eurostat (lfsa_urgaed)

Inequality of income distribution

The income quintile share ratio, also known as S80/S20 ratio, is a measure of inequality in income distribution across the population. It is calculated as the ratio of total income received by the 20% of the population with the highest income (the top quintile) to that received by the 20% with the lowest income (the bottom quintile).

Figure 4 presents the trend in the S80/S20 ratio in the enlargement countries and in the EU over the period 2014-2024. In general, it shows a downward trend in income inequality in the enlargement countries and the EU, although the ratio is falling from a higher level and more swiftly in the enlargement countries. In the EU, the income inequality ratio fell slowly, with only minor fluctuations, from an already low level of 5.22 in 2014 to 4.66 in 2024.

Kosovo reported by far the highest S80/S20 ratio observed, at 15.58 in 2018. This is the only available data point for Kosovo.

In Türkiye, the indicator trended upward but fluctuated considerably, with the inequality ratio growing from 8.35 in 2014 to 9.06 in 2024. With the exception of the single observation for Kosovo, the S80/S20 ratio in Türkiye was the highest recorded among the enlargement countries every year since 2018.

In Serbia, the income distribution inequality ratio almost halved from its peak at 11.02 in 2016, the highest observed among the enlargement countries (except Kosovo in 2018), decreasing rapidly to 5.55 in 2023 (more recent data not available). The ratio initially stood at 9.41 in 2014, at the beginning of the period.

The income inequality decreased in Montenegro, from a S80/S20 ratio of 7.27 in 2014 to 5.58 in 2022 (most recent data available). Income inequality fell also in North Macedonia, from 7.22 in 2014 to 5.92 in 2020 (most recent data available). Data are only available from 2017 to 2021 for Albania, showing a notable reduction in income inequality from 7.47 to 5.72.

Figure 5: Inequality of income distribution, 2014-2024
Source: Eurostat (ilc_di11)

Source data for tables and graphs

* Enlargement countries — Inequality: tables and figures

Data sources

The data in income disparities used in this article are mainly derived from EU statistics on income and living conditions (EU-SILC), which is the main source of statistics that measure income and living conditions in Europe. EU-SILC collects timely and comparable data on income, poverty, social inclusion and living conditions, in both monetary and non-monetary terms.

Among the enlargement countries, Türkiye started the full implementation of EU-SILC in 2006, while North Macedonia launched it in 2010, Montenegro and Serbia in 2013, Albania in 2017, Kosovo in 2018 and Bosnia and Herzegovina in 2022.

The legal basis for this data collection exercise is Regulation (EU) 2019/1700 establishing a common framework for European statistics relating to persons and households, based on data at individual level collected from samples (Integrated European Social Statistics – IESS).

Information concerning the current statistical legislation on income and living conditions can be found in the 'Legislation' section of the dedicated section on 'Income and living conditions' on Eurostat's website.

The main source for European labour force statistics is the European Union labour force survey (EU LFS). This household survey is carried out in all EU Member States in accordance with European legislation and provides figures at least every quarter.

Among the enlargement countries, Bosnia and Herzegovina, Montenegro, North Macedonia, Serbia and Türkiye have implemented the EU-LFS instrument and conduct the labour force survey according to the same guidelines, methodology and standards as the EU countries.

Information concerning the current statistical legislation on statistics on employment and unemployment can be found in the 'Legislation' section of the dedicated section on 'Employment and unemployment (Labour Force Survey – LFS)' on Eurostat's website.

While basic principles and institutional frameworks for producing statistics are already in place, the enlargement countries are expected to increase the volume and quality of their data progressively, and to transmit these data to Eurostat in the context of the EU enlargement process. EU standards in the field of statistics require the existence of a statistical infrastructure based on principles such as professional independence, impartiality, relevance, confidentiality of individual data and easy access to official statistics; they cover methodology, classifications and standards for production.

Eurostat has the responsibility to ensure that statistical production of the enlargement countries complies with the EU acquis in the field of statistics. To do so, Eurostat supports the national statistical offices and other producers of official statistics through a range of initiatives, such as pilot surveys, training courses, traineeships, study visits, workshops and seminars, and participation in meetings within the European Statistical System (ESS). The ultimate goal is the provision of harmonised, high-quality data that conforms to European and international standards.

Context

An important aspect of European policies is the commitment to leaving no one behind in the transformation underway towards a climate-neutral, resource-efficient, modern and just society and economy. Policy initiatives towards equal opportunities, enabling full and fair participation in society for everyone, is a central piece of this transformation. The Commission is committed to implementing policies that empower women and promote equal opportunities. By prioritising gender equality, the Commission seeks to improve the quality of life for European citizens, promote social cohesion and contribute to a more sustainable and inclusive future for all.

This article presents a selection of indicators relevant to analysing inequalities between different sections of society, based on the policy relevance of the indicators as well as the availability of data. The indicators presented highlight in particular differences in the situation between women and men (i.e. gender gaps) with respect to life expectancy and opportunities in the labour market, as well as inequalities between persons with different education levels with respect to unemployment and inequalities in the distribution of income within the enlargement counties and the EU.

The European Pillar of Social Rights lays down the key principles and rights essential for fair and well-functioning labour markets and social protection systems. The open method of coordination (Social OMC) for social protection and social inclusion aims to promote social cohesion and equality through adequate, accessible and financially sustainable social protection systems and social inclusion policies.

Additional information on statistical cooperation with the enlargement countries is provided in the Statistics explained background article Enlargement policy and statistical cooperation.

Footnotes

This designation is without prejudice to positions on status, and is in line with UNSCR 1244/1999 and the ICJ Opinion on the Kosovo Declaration of Independence. ↑