This table contains figures on the corrected gender pay gap of employees from all jobs of four hours or more per month, of employees living in the Netherlands aged 15 to 64. The reference date is the last Friday in September. For the determination of the number of jobs and the calculation of hourly wages and pay differences, a research file has been compiled based on job information from the System of Social Statistical Files (SSB) and three years of the Labor Force Survey (EBB). A person can have more than one job at the time of the survey and is then counted more than once in the research population. The population is split into government jobs and corporate jobs. These two subpopulations have been studied separately. The sample originates from the biennial survey Monitor Wage Differences between men and women. See sections 3 and 4 for more information about this study. Data available from: 2008 Status of the figures: The figures in this table are final. Changes as of November 30, 2022: A minor error in a hyperlink has been fixed. Changes as of November 29, 2022: Final figures for 2020 have been added. When will new numbers come out? It is not known when new figures will be released.

This dataset was collected as part of a study investigating the gender pay gap in the freelancing sector of Bangladesh, with a particular focus on the online platform, Freelancer.com. The dataset consists of self-reported data from 210 randomly selected freelancers, who were among the top search results with good reviews on the platform. The data were collected directly from the profiles of these freelancers, and the link to each profile is included in the dataset. The dataset provides comprehensive information about each freelancer, including their gender, hourly payment rate, number of reviews, number of recommendations, job completion rate, budget adherence rate, on-time delivery rate, repeat hire rate, payment verification status, total work experience, location, membership type, monthly investment on Freelancer.com, type of work, type of education, institution name, degree name, education level, years of education, and preferred freelancer status. The purpose of this dataset is to provide insights into the relationship between these factors and the hourly earnings of freelancers, with a particular emphasis on exploring any disparities between male and female freelancers. The scope of the dataset extends to the digital gig economy in Bangladesh, and its nature is quantitative. This dataset is intended for use in further research aiming to understand the complexities of the gender pay gap in the freelancing sector, and to devise effective strategies to bridge this gap.

Selection correction methods usually make assumptions about selection itself. In the case of gender wage gap estimation, those assumptions are especially tenuous because of high female nonparticipation and because selection could be different in different parts of the labor market. This paper proposes an estimator for the wage gap that allows for arbitrary and unobserved heterogeneity in selection. It applies to the subpopulation of always employed women, which is similar to men in labor force characteristics. Using CPS data from 1976 to 2005, I show that the gap has narrowed substantially from a ?0.521 to a ?0.263 log wage point differential for this population.

We use a globally unique dataset that scores every individual academic’s holistic research performance in New Zealand to test several common explanations for the gender pay gap in universities. We find a man’s odds of being ranked professor or associate professor are more than double a woman’s with similar recent research score, age, field, and university. We observe a lifetime gender pay gap of ~NZ$400,000, of which research score and age explain less than half. Our ability to examine the full spectrum of research performance allows us to reject the ‘male variability hypothesis’ theory that the preponderance of men amongst the ‘superstars’ explains the lifetime performance pay gap observed. Indeed women whose research career trajectories resemble men’s still get paid less than men. From 2003–12, women at many ranks improved their research scores by more than men, but moved up the academic ranks more slowly. We offer some possible explanations for our findings, and show that the gender gap in universities will never disappear in most academic fields if current hiring practices persist.

Do women elected officials contribute to the creation of public sector workforces that are more representative of the populations they serve? A more representative bureaucracy is expected to produce better outcomes, and thus understanding the role that elected leadership plays in diversifying the bureaucracy is important. Using data from over 5000 Brazilian municipalities from 2001 to 2012, we examine whether the election of women mayors leads to the formation of municipal executive bureaucracies that are more representative in terms of gender. In addition, we test whether the presence of a woman mayor leads to increased wages for women bureaucrats and smaller wage gaps between men and women bureaucrats. We find that while women mayors do not increase women’s numerical representation in the municipal executive bureaucracy, they do contribute to the creation of bureaucracies with fewer gender inequalities. Electing a woman mayor increases the average wages of women bureaucrats and decreases the gender wage gap in the bureaucracy. These findings suggest that women mayors advocate for the promotion of women to leadership positions and reduce the gap between men’s and women’s ranks in the bureaucracy since the salaries of Brazilian civil servants are linked to their positions.

This table shows the pay differences for the average hourly wage between female and male employees in the Netherlands. The differences are shown for a number of characteristics of the job, the company and the employee. Data available from 2008. Status of the figures: All figures in this table are final. Changes as of November 11, 2022: The figures for 2021 have been added. When will new numbers come out? Final figures for 2022 will be added in the fourth quarter of 2023.

Nuclear energy, nuclear power plants. Corruption. Gender equality. Health and healthcare. Management of natural disasters.

Topics: 1. Nuclear power: positive or negative associations with nuclear power; risks and benefits of nuclear power; contact with nuclear power issues in the past (visited a nuclear power plant, lived in the vicinity of a nuclear power plant, worked on nuclear energy issues); preferred sources of information about nuclear power; knowledge test on nuclear energy (EU as the largest nuclear energy producer, nuclear power plants are the only sources of radioactive waste, one third of Europe´s energy is produced by nuclear power plants, construction of new nuclear power plants in the own country); self-rated knowledge about the safety of nuclear power plants; most trusted sources of information on the safety of nuclear power plants; sufficient provision of information about the risks and benefits of different types of energy in school and through the media; assessment of nuclear power plants as a personal threat; assessment of the medial representation of nuclear incidents as exaggerated; attitude towards selected statements: safe operation of nuclear power plants is possible, national legislation ensures nuclear safety, national nuclear safety authority and companies ensure safe operation of nuclear power plants, safe disposal of nuclear waste, security against terrorism and criminal use; attitude towards the following statements on nuclear energy: helps to limit global warming, independence from imported fuels such as gas and oil, guarantees low and stable energy prices; reasons for and against extending the lifetime of nuclear power plants; attitude towards a reduction or increase of nuclear energy as a proportion of all energy sources; assessment of the benefits of an EU legislation on nuclear waste management; preferred site for a nuclear power plant (own country, another EU country outside the EU); preferred decision-making body in adapting and developing the energy supply (citizens, NGOs, or government agencies); interest in information on the safety of nuclear power plants; summarized assessment of the risks and benefits.

1. Corruption: opinion on corruption: is a big problem, assessment of existing corruption in local, regional, national, and EU institutions, successful prosecutions discourage from corruption; assumed spreading of corruption in selected areas; self-experienced corruption in the last 12 months; assessment of the causes for corruption; opinion on the fight against corruption: effective efforts of national governments, lenient court judgments, helpful support by the EU in the fight against corruption in the own country; assessment of the responsibility for preventing and fighting corruption; most trustworthy body for complaints in corruption cases.

2. Gender equality: occurrence of gender inequality; comparison with 10 years ago; opinion on the work of women: usually work less, number of working women in the own country is too low; preferred measures to increase the number of working women; unequal pay as a matter of urgency for the EU; important measures to reduce the gender pay gap; opinion on the employment of women: non-employment leads to isolation, father should give up his job to look after the children if his pay is lower than the one of the mother, childcare facilities costs are often higher than the additional income of the mother, career of the mother must be kept back for raising children, own income is indispensable for women, more men in education, role of men in the household; attitude towards men in child rearing and household; appropriate measures to improve the balance between work and family life; urgency to increase the proportion of women in parliament; most important measures to achieve a higher proportion of women; opinion on women in positions of responsibility: lower career orientation of women, less freedom due to obligations in family life, working life is dominated by men, too low-skilled women; assessment of the urgency to take action against violence against women; assessment of the urgency to take action against a higher risk of poverty for single mothers and older women; assessment of the occurrence of sexism in politics, media, work life, state institutions; areas where gender inequality occurs, and preferred measures at national or EU level; responsibility for gender equality in the state government or the EU; sufficiency of the measures m...

In 2024, the average annual full-time earnings for the top ten percent of earners in the United Kingdom was 72,150 British pounds, compared with 22,763 for the bottom ten percent of earners. As of this year, the average annual earnings for all full-time employees was 37,430 pounds, up from 34,963 pounds in the previous year. Strong wage growth continues in 2025 As of February 2025, wages in the UK were growing by approximately 5.9 percent compared with the previous year, with this falling to 5.6 percent if bonus pay is included. When adjusted for inflation, regular pay without bonuses grew by 2.1 percent, with overall pay including bonus pay rising by 1.9 percent. While UK wages have now outpaced inflation for almost two years, there was a long period between 2021 and 2023 when high inflation in the UK was rising faster than wages, one of the leading reasons behind a severe cost of living crisis at the time. UK's gender pay gap falls in 2024 For several years, the difference between average hourly earnings for men and women has been falling, with the UK's gender pay gap dropping to 13.1 percent in 2024, down from 27.5 percent in 1997. When examined by specific industry sectors, however, the discrepancy between male and female earnings can be much starker. In the financial services sector, for example, the gender pay gap was almost 30 percent, with professional, scientific and technical professions also having a relatively high gender pay gap rate of 20 percent.

In recent years several languages have experienced an increase in gender- inclusive language (GIL) in different forms. In grammatical gender languages (such as French, Italian, German, Spanish), a key aspect of gender-inclusive language is adapting person nouns, which are gender marked, to be inclusive towards all genders. This can occur in two versions: a softer, binary gender approach where women and men are addressed by the respective person noun, or an inclusive gender approach where all genders are addressed. It has repeatedly been found that using GIL (usually tested in a binary form) leads to a higher mental representation of women (Stahlberg et al., 2007). While there are many areas of application for GIL, one that has received a lot of attention and where the potential real-world impact is high is broadly speaking the labour market and occupational interest. Nearly a quarter of the gender pay gap can be attributed to women and men working in different occupations, whereby the occupations that men work in tend to have higher salaries (European Commission, n.d.). What if a part of this gap is due to women’s behaviour in applying for jobs, which may be mitigated by using GIL? Rather than looking at the demand side of the problem, e.g., discrimination against women, this study looks at the supply side, i.e., the role women’s behaviour plays in choosing certain jobs. We focus on gender- inclusive language (GIL) as a potential policy instrument for increasing the quantity as well as quality of female labour supply. Ample research indicates a positive effect of GIL on attitudes and preferences for women toward masculine jobs (Vervecken et al., 2013; Hentschel et al., 2018; Bem & Bem, 1973; Stout & Dasgupta, 2011). There is an underlying assumption that this has real world implications as these attitudes carry over into behaviour. However, the existing research on GIL and job applications does not test actual application behaviour. Looking at the relationship between attitudes and behaviour in other spheres, it is known that attitudes and behaviour correlate, but need not perfectly align. Indeed, the relationship between attitudes and behaviour is not straightforward (Ajzen & Fishbein 1977). Therefore, the link should not be taken for granted in the case of GIL and instead warrants empirical testing.

We plan to test an application effect (RQ1) and a performance effect of the advertisement (RQ2) and a performance effect of the task description (RQ3): do more women apply for a male-stereotyped task when it is advertised in GIL? Second, do women perform better when a male-stereotyped task is advertised in GIL? Third, do women perform better when a male-stereotype task is described in GIL?

The theory of stereotype threat predicts that, in certain situations, someone can think that other people think that others will be better at a specific task, so feel under pressure to perform well, and this performance pressure makes them perform worse. Specifically, there is a widespread stereotype that men are better at maths. Thus, when women are asked to do a maths task, this stereotype can be salient and trigger stereotype threat (Spencer, Steele and Quinn 1999). Therefore, when a woman is looking for tasks to do on a crowd working platform and sees a math-related task, advertised in masculine language, this triggers a stereotype threat. However, when gender-inclusive language is used, there is the potential that this stereotype threat is weakened, as gender-inclusive language has the potential to reduce the stereotypicality associated with a role (Gabriel et al. 2008a).

We run the same experiment in two countries (Germany and Italy) and pool the analyses.

Anica Waldendorf is the main contributor and thus first author. Arnout van de Rijt and Klarita Gërxhani are equal contributors, hence they appear in alphabetical order.

In 2025, there were estimated to be approximately 3.6 billion people employed worldwide, compared to 2.23 billion people in 1991 - an increase of around 1.4 billion people. There was a noticeable fall in global employment between 2019 and 2020, when the number of employed people fell from due to the sudden economic shock caused by the COVID-19 pandemic. Formal vs. Informal employment globally Worldwide, there is a large gap between the informally and formally employed. Most informally employed workers reside in the Global South, especially Africa and Southeast Asia. Moreover, men are slightly more likely to be informally employed than women. The majority of informal work, nearly 90 percent, is within the agricultural sector, with domestic work and construction following behind. Women’s employment As the number of employees has risen globally, so has the number of employed women. Overall, care roles such as nursing and midwifery have the highest shares of female employees globally. Moreover, while the gender pay gap has shrunk over time, it still exists. As of 2024, the uncontrolled gender pay gap was 0.83, meaning women made, on average, 83 cents per every dollar earned by men.

This table contains figures on jobs and hourly wages of employees from all jobs of four hours or more per month, of employees living in the Netherlands aged 15 to 64. The reference date is the last Friday in September. For the determination of the number of jobs and the calculation of hourly wages and pay differences, a research file has been compiled based on job information from the System of Social Statistical Files (SSB) and three years of the Labor Force Survey (EBB). A person can have more than one job at the time of the survey and is then counted more than once in the research population. The population is split into government jobs and corporate jobs. These two subpopulations have been studied separately. The data in the table by government and industry can be further broken down by gender, level of education and whether the person is in charge of the job. The sample originates from the biennial survey Monitor Wage Differences between men and women. See sections 3 and 4 for more information about this study. Data available from: 2008 Status of the figures: The figures in this table are final. Changes as of November 30, 2022: A minor error in a hyperlink has been fixed. Changes as of November 29, 2022: Final figures for 2020 have been added. When will new numbers come out? It is not known when new figures will be released.

According to a survey in 2024, female primary care physicians in the United States had earned an annual compensation of 253 thousand U.S. dollars while male primary care physicians earned 295 thousand dollars. The gender pay gap is even wider among specialist physicians with female specialists earning over 100 thousand U.S. dollars less than male counterparts.

Over this 23-year period, annual wages in Spain fluctuated greatly, ranging from a low of 29,892 euros in 2006 to a high of approximately 33,253 euros in 2009. The average annual wage stood at approximately 31,945 euros in 2023. Compared to other European countries, Spain ranked fairly low in 2023. The annual salary in the Iberian country was similar to salaries in Italy and Slovenia, but remained far from the figures that were registered in France, Ireland, and Germany. Minimum wage Spain's minimum monthly wage was 1,134 euros as of 2024. Unlike the average annual wage, it has been constantly increasing on a nearly continuous basis since 2008, when the minimum wage was 600 euros per month. In 2019, the Socialist government of Spain passed a law by that increased the national minimum wage by 164 euros, therefore making it stand at 900 euros per month and reflecting the largest increase to date. Along with the monthly wage, the national minimum daily wage has also been raised consistently over the past years. In 2024, the gross minimum was 37.8 euros a day, whereas in 2000 it was 20 euros a day. Unequal pay The average salary in Spain diverges considerably according to different factors. For instance, the gender salary gap remains significant in the Mediterranean country, although it has shrunk in recent years. In 2022, the average salary for a male full-time employee was around nine percent higher than his female counterpart. The gender gap is even wider for permanent positions: that year, average annual salaries for women were roughly 6,000 euros less than average salaries for men. The salary gap is also conspicuous when looking at the wage for workers with disabilities, a gap that has increased in recent years. Geographic location is also important; the average net salary in regions such as Extremadura and the Canary Islands was less than 23,100 euros per year in 2022, far from the salary in the Basque Country and Madrid (32,300 and 31,200 euros, respectively).

Annual estimates of paid hours worked and earnings for UK employees by sex, and full-time and part-time, by home-based region to local and unitary authority level.

Kashid, Raigad, Maharashtra, India is around 180 Kms away from Pune, Maharashtra. A survey conducted as part of PhD requirement. The raw data is available at Mendeley. As the name suggests, Annual Income and Gender are Independent Variables. Analysis of variance is carried out and the results are tabulated. In case of Annual Income variable after one way ANOVA, post hoc analysis is done. Similarly, in case of gender further analysis is done with independent t test.

Abstract

The 2018 Dhaka Low Income Area Gender, Inclusion, and Poverty (DIGNITY) survey attempts to fill in the data and knowledge gaps on women's economic empowerment in urban areas, specifically the factors that constrain women in slums and low-income neighborhoods from engaging in the labor market and supplying their labor to wage earning or self-employment. While an array of national-level datasets has collected a wide spectrum of information, they rarely comprise all of the information needed to study the drivers of Female Labor Force Participation (FLFP). This data gap is being filled by the primary data collection of the specialized DIGNITY survey; it is representative of poor urban areas and is specifically designed to address these limitations. The DIGNITY survey collected information from 1,300 urban households living in poor areas of Dhaka in 2018 on a range of issues that affect FLFP as identified through the literature. These range from household composition and demographic characteristics to socioeconomic characteristics such as detailed employment history and income (including locational data and travel details); and from technical and educational attributes to issues of time use, migration history, and attitudes and perceptions.

The DIGNITY survey was designed to shed light on poverty, economic empowerment, and livelihood in urban areas of Bangladesh. It has two main modules: the traditional household module (in which the head of household is interviewed on basic information about the household); and the individual module, in which two respondents from each household are interviewed individually. In the second module, two persons - one male and one female from each household, usually the main couple, are selected for the interview. The survey team deployed one male and one female interviewer for each household, so that the gender of the interviewers matched that of the respondents. Collecting economic data directly from a female and male household member, rather than just the head of the household (who tend to be men in most cases), was a key feature of the DIGNITY survey.

Geographic coverage

The DIGNITY survey is representative of low-income areas and slums of the Dhaka City Corporations (North and South, from here on referred to as Dhaka CCs), and an additional low-income site from the Greater Dhaka Statistical Metropolitan Area (SMA).

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling procedure followed a two-stage stratification design. The major features include the following steps (they are discussed in more detail in a copy of the study's report and the sampling document located in "External Resources"):

FIRST STAGE: Selection of the PSUs

Low-income primary sampling units (PSUs) were defined as nonslum census enumeration areas (EAs), in which the small-sample area estimate of the poverty rate is higher than 8 percent (using the 2011 Bangladesh Poverty Map). The sampling frame for these low-income areas in the Dhaka City Corporations (CCs) and Greater Dhaka is based on the population census of 2011. For the Dhaka CCs, all low-income census EAs formed the sampling frame. In the Greater Dhaka area, the frame was formed by all low-income census EAs in specific thanas (i.e. administrative unit in Bangladesh) where World Bank project were located.

Three strata were used for sampling the low-income EAs. These strata were defined based on the poverty head-count ratios. The first stratum encompasses EAs with a poverty headcount ratio between 8 and 10 percent; the second stratum between 11 and 14 percent; and the third stratum, those exceeding 15 percent.

Slums were defined as informal settlements that were listed in the Bangladesh Bureau of Statistics' slum census from 2013/14. This census was used as sampling frame of the slum areas. Only slums in the Dhaka City Corporations are included. Again, three strata were used to sample the slums. This time the strata were based on the size of the slums. The first stratum comprises slums of 50 to 75 households; the second 76 to 99 households; and the third, 100 or more households. Small slums with fewer than 50 households were not included in the sampling frame. Very small slums were included in the low-income neighborhood selection if they are in a low-income area.

Altogether, the DIGNITY survey collected data from 67 PSUs.

SECOND STAGE: Selection of the Households

In each sampled PSU a complete listing of households was done to form the frame for the second stage of sampling: the selection of households. When the number of households in a PSU was very large, smaller sections of the neighborhood were identified, and one section was randomly selected to be listed. The listing data collected information on the demographics of the household to determine whether a household fell into one of the three categories that were used to stratify the household sample:

i) households with both working-age male and female members; ii) households with only a working-age female; iii) households with only a working-age male.

Households were selected from each stratum with the predetermined ratio of 16:3:1. In some cases there were not enough households in categories (ii) and (iii) to stick to this ratio; in this case all of the households in the category were sampled, and additional households were selected from the first category to bring the total number of households sampled in each PSU to 20.

The total sample consisted of 1,300 households (2,378 individuals).

Sampling deviation

The sampling for 1300 households was planned after the listing exercise. During the field work, about 115 households (8.8 percent) could not be interviewed due to household refusal or absence. These households were replaced with reserved households in the sample.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaires for the survey were developed by the World Bank, with assistance from the survey firm, DATA. Comments were incorporated following the pilot tests and practice session/pretest.

Cleaning operations

Collected data was entered into a computer by using the customized MS Access data input software developed by Data Analysis and Technical Assistance (DATA). Once data entry was completed, two different techniques were employed to check consistency and validity of data as follows:

1. Five (5%) percent of the filled-in questionnaire was checked against entered data to measure the transmission error or typos, and;
2. A logical consistency checking technique was employed to identify inconsistencies using SPSS and or STATA software.