Graph and download economic data for Employed full time: Median usual weekly real earnings: Wage and salary workers: 16 years and over (LES1252881600Q) from Q1 1979 to Q4 2024 about full-time, salaries, workers, earnings, 16 years +, wages, median, real, employment, and USA.

Colombia CO: Labour Utilisation As % of The USA: USA=100 data was reported at 124.435 % in 2022. This records an increase from the previous number of 120.188 % for 2021. Colombia CO: Labour Utilisation As % of The USA: USA=100 data is updated yearly, averaging 102.032 % from Dec 1970 (Median) to 2022, with 53 observations. The data reached an all-time high of 136.653 % in 2012 and a record low of 85.977 % in 1984. Colombia CO: Labour Utilisation As % of The USA: USA=100 data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Colombia – Table CO.OECD.PDB: Labour Utilisation: OECD Member: Annual. For productivity analysis, the underlying concept for labour input is total hours worked by all persons engaged in production (employees and self-employed). These reflect regular hours worked by full-time and part-time workers, paid and unpaid overtime, hours worked in additional jobs, and time not worked because of public holidays, annual paid leave, strikes and labour disputes and other reasons.

The UK's median gender pay gap has substantially reduced from 36.4% in the 1970s to around 18%, yet it remains one of the highest in the EU and OECD. Previously attributed to differences in education and work experience, this explanation is outdated as women now frequently outpace men in education and are less likely to leave the workforce. However, women still earn about 10% less than men, even with similar work and qualifications. Research has shifted focus from productivity differences to the potential role of employer wage-setting practices. This research suggests that women's negotiating power may be undermined by familial responsibilities, leading to lower mobility in the job market and consequently lower wages. The study will explore how employer wage-setting power and job-to-job mobility contribute to the gender pay gap, aiming to inform effective policies.

The collection contains a set of syntax files used to construct an alternative employment concentration index that takes into account commuting costs. The files are based on the Stata language and use the Business Structure Dataset and the UK Longitudinal Household Study to derive the index.

The median gender pay gap has declined dramatically in the UK from 36.4% in the 1970 (O'Reilly, Smith et al. 2015) to around 18% in the most recent data (ONS 2018). Still, by international standards the pay gap is high: the UK has the fourth largest gender pay gap in the EU and the eighth largest of OECD countries (OECD 2019). Researchers and policy makers have focused on gender differences in education and labour market experience as the likely drivers of the pay gap. However, today these explanations no longer stand up to scrutiny. Women are on average better educated than men and they are much less likely to withdraw from the labour market for long periods of time. Nevertheless, women earn on average about 10% less than men even when they work full-time and have similar education and labour market experience. While explanations focusing on women's potential lower productivity as the cause of the gender pay gap have been thoroughly investigated and found inadequate, there is less evidence on the role played by employers. This research will contribute to addressing this gap. The standard economic model of the labour market assumes that wages are determined by the market and that individual employers cannot choose the wages they offer to their employees. A different model assumes that for a variety of reasons competition is not perfect and employers have some discretion over the wages they offer. This wage setting power is likely to be weaker when workers are mobile. Mobile workers will leave an employer offering wages below the market rate. However, if workers are relatively immobile, employers can exploit this 'immobility' by offering them lower wages. If women are more constrained by family responsibilities in the types of jobs that they will take-up or in the amount of time and effort they can devote to job search, they will generally be more immobile and thus at a disadvantage. Women's family responsibilities might be ultimately responsible for the gender pay gap but not because they limit their productivity but rather because they reduce their bargaining power with firms. This research project will examine the role of employer wage-setting power in driving the gender pay gap in two ways. First, using data from the UK's largest longitudinal study, it will investigate the extent to which job-to-job mobility patterns differ between men and women, and whether any differences can explain the observed gender gap in pay progression. Second, it will develop an index of employer wage-setting power based on geographical location, industry and cost of travel and test whether the index can explain gender differences in pay progression. Tackling the gender pay gap is a widely shared goal among policy makers, political parties, women's groups, trade-unions and employer organizations. A better understanding of the factors driving the gap is essential to design effective policies. For example, in April 2017, the UK government has mandated large employers report annually on the pay gap in their organization. If women's lower productivity is to blame for the gender pay gap, such legislation is likely to be ineffective and even counterproductive. On the other hand, mandatory reporting is likely to be more effective if employers' stronger wage-setting power is a significant factor behind the pay gap. More generally, if employers enjoy significant wage setting power relative to some of their employees, this has implications for legislation on anti-discrimination, the minimum wage, trade-unions and family policy.

Graph and download economic data for Real Median Personal Income in the United States (MEPAINUSA672N) from 1974 to 2023 about personal income, personal, median, income, real, and USA.

About 50.4 percent of the household income of private households in the U.S. were earned by the highest quintile in 2023, which are the upper 20 percent of the workers. In contrast to that, in the same year, only 3.5 percent of the household income was earned by the lowest quintile. This relation between the quintiles is indicative of the level of income inequality in the United States. Income inequalityIncome inequality is a big topic for public discussion in the United States. About 65 percent of U.S. Americans think that the gap between the rich and the poor has gotten larger in the past ten years. This impression is backed up by U.S. census data showing that the Gini-coefficient for income distribution in the United States has been increasing constantly over the past decades for individuals and households. The Gini coefficient for individual earnings of full-time, year round workers has increased between 1990 and 2020 from 0.36 to 0.42, for example. This indicates an increase in concentration of income. In general, the Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing. Income distribution is also affected by region. The state of New York had the widest gap between rich and poor people in the United States, with a Gini coefficient of 0.51, as of 2019. In global comparison, South Africa led the ranking of the 20 countries with the biggest inequality in income distribution in 2018. South Africa had a score of 63 points, based on the Gini coefficient. On the other hand, the Gini coefficient stood at 16.6 in Azerbaijan, indicating that income is widely spread among the population and not concentrated on a few rich individuals or families. Slovenia led the ranking of the 20 countries with the greatest income distribution equality in 2018.

In 2024, the employment-to-population ratio worldwide was estimated to be approximately 58 percent, indicating that nearly 60 percent of the global population above 15 years was employed. Among the provided regions, Africa had the highest employment-to-population ratio, at 60 percent, with Europe and Central Asia having the lowest at 55 percent. Global income growth As greater portions of the population hold stable employment over time, income has also grown globally. From 1970 until today, North America has seen the largest increase in net national incomes per capita, but this increase has occurred in other regions as well. In terms of real wages, while they have grown over time, they have experienced a slight decrease in light of the high global inflation rates. Decrease in child labor Even though greater proportions of the population are employed, child labor has decreased over time. In 2000, there were 245 million children working, which has decreased to 160 million by 2020. The majority of working children are in the agricultural sector, especially younger children within the 5-11 and 12-14 age groups.

Nicaragua NI: Primary Education: Teachers: % Female data was reported at 77.021 % in 2010. This records an increase from the previous number of 76.259 % for 2008. Nicaragua NI: Primary Education: Teachers: % Female data is updated yearly, averaging 81.387 % from Dec 1970 (Median) to 2010, with 33 observations. The data reached an all-time high of 88.708 % in 1988 and a record low of 73.546 % in 2006. Nicaragua NI: Primary Education: Teachers: % Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nicaragua – Table NI.World Bank: Education Statistics. Female teachers as a percentage of total primary education teachers includes full-time and part-time teachers.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Wages in China increased to 120698 CNY/Year in 2023 from 114029 CNY/Year in 2022. This dataset provides - China Average Yearly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Luxembourg LU: Primary Education: Teachers: % Female data was reported at 75.842 % in 2015. This records an increase from the previous number of 74.504 % for 2014. Luxembourg LU: Primary Education: Teachers: % Female data is updated yearly, averaging 53.143 % from Dec 1970 (Median) to 2015, with 31 observations. The data reached an all-time high of 75.982 % in 2013 and a record low of 48.156 % in 1989. Luxembourg LU: Primary Education: Teachers: % Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Luxembourg – Table LU.World Bank: Education Statistics. Female teachers as a percentage of total primary education teachers includes full-time and part-time teachers.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Nigeria NG: Primary Education: Teachers: % Female data was reported at 48.200 % in 2010. This records a decrease from the previous number of 48.289 % for 2007. Nigeria NG: Primary Education: Teachers: % Female data is updated yearly, averaging 44.913 % from Dec 1970 (Median) to 2010, with 27 observations. The data reached an all-time high of 50.860 % in 2005 and a record low of 22.390 % in 1971. Nigeria NG: Primary Education: Teachers: % Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank.WDI: Education Statistics. Female teachers as a percentage of total primary education teachers includes full-time and part-time teachers.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Singapore SG: Primary Education: Teachers: % Female data was reported at 81.235 % in 2009. This records an increase from the previous number of 81.031 % for 2008. Singapore SG: Primary Education: Teachers: % Female data is updated yearly, averaging 68.320 % from Dec 1970 (Median) to 2009, with 25 observations. The data reached an all-time high of 81.235 % in 2009 and a record low of 65.399 % in 1972. Singapore SG: Primary Education: Teachers: % Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Singapore – Table SG.World Bank.WDI: Education Statistics. Female teachers as a percentage of total primary education teachers includes full-time and part-time teachers.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).