As of 2023, South Korea is the country with the highest gender pay gap among OECD countries, with a **** percent difference between the genders. The gender pay gap displays the difference between the median wages of full-time employed men and full-time employed women.

The gender wage gap for median earnings in Japan was ** percent in 2023. Japan's gender pay gap was almost twice as high as the average of OECD members.

As of 2022, in all OECD countries, the average earnings of women with income from employment were always lower than those of men, regardless of educational attainment. For instance, in Italy, the average earnings of women who graduated with tertiary education represented ** percent of men's average earnings.

In all OECD member countries, people with a tertiary education earned more than those with an upper secondary education. In detail, the gap was highest in the Latin American countries, with earnings reaching as much as *** percent of those with an upper secondary education in 2021, underlining the high level of income inequality in these countries. On the other hand, the gap was smallest in the Scandinavian countries and Australia and New Zealand, reaching *** percent in Sweden.

In all OECD member countries, people with an educational level below upper secondary education earn less than those with an upper secondary education. In detail, the gap was smaller in Romania, where the former group earned ** percent of what the latter did in 2021, whereas people with a primary education or similar earned less than ** percent of what those with an upper secondary education did.

The relatively small panel cointegration literature on the dynamics between FDI and income inequality predominantly finds that FDI will reduce income inequality in the long-run in developed countries. However, we point out an important technical oversight in the literature. Not accounting for cross-section dependence in panel data methodologies may yield unreliable results. Expanding on the work of @herzer/nunnenkamp:13, who pioneered the use of panel cointegration in the European context, we obtain different results when we account for cross-section dependence and employ economic procedures robust to it. Using a panel containing 16 OECD countries (1979-2017), 2 income inequality measures, and 4 FDI measures, we begin by showing strong evidence for the existence of cross-section dependence. Then, using second-generation econometric procedures, we do not find any evidence for a cointegrating relationship between inward FDI and income inequality. We do find evidence that outward FDI is cointegrated with income inequality; however, contrary to the main results of the literature, we find that it widens the income gap in the long-run. Additionally, our results support the view that fiscal policy is an important tool to reduce income inequality.

We study reciprocity in the labor market context. To this end, we conducted a bilateral gift exchange experiment comparing behavior of subjects from five high-income OECD countries, among them two liberal market economies (USA and Israel), two coordinated market economies (Germany and Japan) and a Mediterranean economy (Spain). We conjecture a higher wage dispersion, higher rejection rate and lower effort (per wage offer), and a higher income inequality in liberal than in coordinated market economies. We observe that all subject pools demonstrate increasing effort levels and decreasing rejection rates in wage offers. We also find considerable differences between subject pools in both one-shot and repeated relationships. The most striking difference in behavior are not between subjects from liberal and coordinated market economies, but rather between the subjects of two European countries, Germany and Spain.

Romania was the OECD country with the highest poverty gap in 2021. The poverty gap in the South-Eastern European country was over ** percentage points. The United States followed behind, whereas Belgium had the lowest gap at ** percentage points.

We study reciprocity in the labor market context. To this end, we conducted a bilateral gift exchange experiment comparing behavior of subjects from five high-income OECD countries, among them two liberal market economies (USA and Israel), two coordinated market economies (Germany and Japan) and a Mediterranean economy (Spain). We conjecture a higher wage dispersion, higher rejection rate and lower effort (per wage offer), and a higher income inequality in liberal than in coordinated market economies. We observe that all subject pools demonstrate increasing effort levels and decreasing rejection rates in wage offers. We also find considerable differences between subject pools in both one-shot and repeated relationships. The most striking difference in behavior are not between subjects from liberal and coordinated market economies, but rather between the subjects of two European countries, Germany and Spain.

Of the countries included, South Africa had the highest income inequality, with a Gini coefficient of 0.62. It was also the country with the highest inequality level worldwide. Of the OECD members, Costa Rica had the highest income inequality, whereas Slovakia had the lowest.

In 2024, the gender pay gap for the median wages in Japan was **** percent. Compared to other OECD countries, Japan was one of the countries with the highest gender pay gap.

In 2023, the gender pay gap for the median wages in Japan was 22 percent. Compared to other OECD countries, Japan was one of the countries with the highest gender pay gap during the measured period.

In 2023, the women-to-men earnings ratio in South Korea was approximately **** percent. While this figure has increased in recent years, the gender pay gap remains significant in South Korean society. South Korea's gender pay gap A growing number of South Korean women have entered the workforce in recent years. However, the female labor force participation rate remains significantly lower than the average of countries of the Organization for Economic Cooperation and Development (OECD). As of 2023, South Korea also had the largest gender pay gap among OECD countries. Challenges of work-life balance The struggle to balance childcare responsibilities with career demands disproportionately affects working women. This is especially true in South Korea, where a survey on women's equality has shown that employers do not provide adequate support for women to achieve a healthy work-life balance. One significant obstacle that makes it difficult for South Korean women to advance in their careers is the challenge of returning to work after an extended career break, such as maternity leave.

Norway NO: GDP: USD: Gross National Income: Atlas Method data was reported at 401.390 USD bn in 2017. This records a decrease from the previous number of 429.276 USD bn for 2016. Norway NO: GDP: USD: Gross National Income: Atlas Method data is updated yearly, averaging 109.233 USD bn from Dec 1962 (Median) to 2017, with 56 observations. The data reached an all-time high of 537.021 USD bn in 2014 and a record low of 5.841 USD bn in 1962. Norway NO: GDP: USD: Gross National Income: Atlas Method data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Norway – Table NO.World Bank: Gross Domestic Product: Nominal. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in current U.S. dollars. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States.; ; World Bank national accounts data, and OECD National Accounts data files.; Gap-filled total;

Canada CA: GDP: USD: Gross National Income: Atlas Method data was reported at 2,167.054 USD bn in 2023. This records an increase from the previous number of 2,075.404 USD bn for 2022. Canada CA: GDP: USD: Gross National Income: Atlas Method data is updated yearly, averaging 606.697 USD bn from Dec 1962 (Median) to 2023, with 62 observations. The data reached an all-time high of 2,167.054 USD bn in 2023 and a record low of 44.354 USD bn in 1962. Canada CA: GDP: USD: Gross National Income: Atlas Method data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Gross Domestic Product: Nominal. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in current U.S. dollars. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States.;World Bank national accounts data, and OECD National Accounts data files.;Gap-filled total;

In 2024, the average monthly earnings for male managers in South Korea were approximately 10.89 million South Korean won, while female managers earned around 9.24 million won. Overall, there was a pronounced gender gap in monthly salaries for South Korean employees across all occupation types. The gender pay gap in South Korea Despite a notable increase in women's participation in the labor market, progress in narrowing the gender pay gap has been slow. In 2022, South Korea had the widest gender pay gap among countries in the Organization for Economic Cooperation and Development (OECD), with women earning only about 69 percent of men's wages. This pay gap impacts women across various employment types and all age groups, with those in their 50s being the most affected. Gender inequality and public perception Gender inequality has remained a persistent issue in South Korean society, despite governmental efforts to tackle it. A recent survey revealed that discrimination against women is most widely perceived in the workplace. Interestingly, there were significant differences in how men and women viewed gender inequality in South Korea. Nearly 75 percent of women believed that South Korean society treats women unfairly, while less than 20 percent of men shared this perspective. Conversely, approximately 52 percent of men felt that men were treated unfairly.

Yemen YE: GDP: USD: Gross National Income: Atlas Method data was reported at 28.296 USD bn in 2016. This records a decrease from the previous number of 29.534 USD bn for 2015. Yemen YE: GDP: USD: Gross National Income: Atlas Method data is updated yearly, averaging 11.969 USD bn from Dec 1992 (Median) to 2016, with 25 observations. The data reached an all-time high of 37.724 USD bn in 2014 and a record low of 4.611 USD bn in 1996. Yemen YE: GDP: USD: Gross National Income: Atlas Method data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Yemen – Table YE.World Bank.WDI: Gross Domestic Product: Nominal. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in current U.S. dollars. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States.; ; World Bank national accounts data, and OECD National Accounts data files.; Gap-filled total;

The number of observations and cases by birth cohorts.

United States US: GDP: USD: Gross National Income: Atlas Method data was reported at 18,980.259 USD bn in 2017. This records an increase from the previous number of 18,369.849 USD bn for 2016. United States US: GDP: USD: Gross National Income: Atlas Method data is updated yearly, averaging 5,959.180 USD bn from Dec 1962 (Median) to 2017, with 56 observations. The data reached an all-time high of 18,980.259 USD bn in 2017 and a record low of 612.179 USD bn in 1962. United States US: GDP: USD: Gross National Income: Atlas Method data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Gross Domestic Product: Nominal. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. Data are in current U.S. dollars. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States.; ; World Bank national accounts data, and OECD National Accounts data files.; Gap-filled total;

Comparing the *** selected regions regarding the gini index , South Africa is leading the ranking (**** points) and is followed by Namibia with **** points. At the other end of the spectrum is Slovakia with **** points, indicating a difference of *** points to South Africa. The Gini coefficient here measures the degree of income inequality on a scale from * (=total equality of incomes) to *** (=total inequality).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).