This chart looks at the U.S. states with the largest number of workers per 100k immigrants, specifically looking at the 10 states with the highest percentage of workers per 100k immigrants.

In 2024, about 62.7 percent of the Asian community was employed. The highest employment rate was found among Mexican-Americans, at 64.1 percent, and the lowest employment rate was found among Puerto Ricans, at 55.2 percent. In total, around 60 percent of all working-age Americans were employed at this time.

The federally mandated minimum wage in the United States is 7.25 U.S. dollars per hour, although the minimum wage varies from state to state. As of January 1, 2025, the District of Columbia had the highest minimum wage in the U.S., at 17.5 U.S. dollars per hour. This was followed by Washington, which had 16.66 U.S. dollars per hour as the state minimum wage. Minimum wage workers Minimum wage jobs are traditionally seen as “starter jobs” in the U.S., or first jobs for teenagers and young adults, and the number of people working minimum wage jobs has decreased from almost four million in 1979 to about 247,000 in 2020. However, the number of workers earning less than minimum wage in 2020 was significantly higher, at about 865,000. Minimum wage jobs Minimum wage jobs are primarily found in food preparation and serving occupations, as well as sales jobs (primarily in retail). Because the minimum wage has not kept up with inflation, nor has it been increased since 2009, it is becoming harder and harder live off of a minimum wage wage job, and for those workers to afford essential things like rent.

United States Exports: Machining Centers for Machine Working Hard Materials data was reported at 0.958 USD mn in Jan 2025. This records a decrease from the previous number of 1.324 USD mn for Dec 2024. United States Exports: Machining Centers for Machine Working Hard Materials data is updated monthly, averaging 0.528 USD mn from Jan 2017 (Median) to Jan 2025, with 97 observations. The data reached an all-time high of 3.461 USD mn in Dec 2019 and a record low of 0.059 USD mn in Feb 2017. United States Exports: Machining Centers for Machine Working Hard Materials data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.JA026: Exports: by Commodity: 6 Digit HS Code: HS 72 to 84.

United States Imports: Machining Centers for Machine Working Hard Materials data was reported at 4.126 USD mn in Jan 2025. This records a decrease from the previous number of 5.302 USD mn for Dec 2024. United States Imports: Machining Centers for Machine Working Hard Materials data is updated monthly, averaging 3.410 USD mn from Jan 2017 (Median) to Jan 2025, with 97 observations. The data reached an all-time high of 9.479 USD mn in Jan 2024 and a record low of 0.159 USD mn in Apr 2017. United States Imports: Machining Centers for Machine Working Hard Materials data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.JA135: Imports: by Commodity: 6 Digit HS Code: HS 79 to 84.

In the first quarter of 2024, almost two-thirds percent of the total wealth in the United States was owned by the top 10 percent of earners. In comparison, the lowest 50 percent of earners only owned 2.5 percent of the total wealth. Income inequality in the U.S. Despite the idea that the United States is a country where hard work and pulling yourself up by your bootstraps will inevitably lead to success, this is often not the case. In 2023, 7.4 percent of U.S. households had an annual income under 15,000 U.S. dollars. With such a small percentage of people in the United States owning such a vast majority of the country’s wealth, the gap between the rich and poor in America remains stark. The top one percent The United States follows closely behind China as the country with the most billionaires in the world. Elon Musk alone held around 219 billion U.S. dollars in 2022. Over the past 50 years, the CEO-to-worker compensation ratio has exploded, causing the gap between rich and poor to grow, with some economists theorizing that this gap is the largest it has been since right before the Great Depression.

This Gallup poll aims to collect the opinions of Canadians on issues of importance to the country and to the government. This survey focuses on mostly political topics, such as elections and voting, and the influence of the United States over Canada. Respondents were also asked questions so that they could be grouped according to geographic, demographic, and social variables. Topics of interest include: American investment in Canada, the American lifestyle; Canada's dependence on the United States, the federal election; financial dependence on the United States; government policy; how hard people work; religious services; Sunday school; union membership; and voting behaviour. Basic demographics variables are also included.

An influential body of work has identified a ‘welfare-state paradox’: work–family policies that bring women into the workforce also undermine women’s access to the top jobs. Missing from this literature is a consideration of how welfare-state interventions impact on women’s representation at the board-level specifically, rather than managerial and lucrative positions more generally. This database includes data that contribute to addressing this ‘gap’. It compiles existing secondary data from various sources into a single dataset. Both the raw and 'fuzzy' data used in a fuzzy-set Qualitative Comparative Analysis of 22 industrialised countries are available. Based on these data, analyses reveal how welfare-state interventions combine with gender boardroom quotas and targets in (not) bringing a ‘critical mass’ of women onto private-sector corporate boards. Overall, there is limited evidence in support of a welfare-state paradox; in fact, countries are unlikely to achieve a critical mass of women on boards in the absence of adequate childcare services. Furthermore, ‘hard’, mandatory gender boardroom quotas do not appear necessary for achieving more women on boards; ‘soft’, voluntary recommendations can also work under certain family policy constellations. The deposit additionally includes other data from the project that provide more context on work-family policy constellations, as they show how countries performance across multiple gendered employment outcomes spanning segregation and inequalities in employment participation, intensity and pay, with further differences by class.

While policymakers in the UK and elsewhere have sought to increase women's employment rates by expanding childcare services and other work/family policies, research suggests these measures have the unintentional consequence of reinforcing the segregation of men and women into different 'types' of jobs and sectors (Mandel & Semyonov, 2006). Studies have shown that generous family policies lead employers to discriminate against women when it comes to hiring, training, and promotions, as employers assume that women are more likely to make use of statutory leaves and flexible working. Furthermore, state provision of health, education, and care draws women into stereotypically female service jobs in the public sector and away from (better-paid) jobs in the private sector. Accordingly, research suggests that the more 'women-friendly' a welfare state is, the harder it will be for women - especially if they are highly skilled - to break into male-dominated jobs and sectors, including the most lucrative managerial positions (Mandel, 2012).

Yet, more recent evidence indicates that women's disadvantaged access to better jobs is not inevitable under generous welfare policies. For instance, women's share of senior management positions in Sweden, where women-friendly policies are most developed, now stands at 36%; this compares to a figure of 28% in the UK, where gender employment segregation has historically been lower (Eurostat, 2018). Thus, the aim of this project is to provide a clearer and fuller understanding of how welfare states impact on gender employment segregation by using innovative methods and approaches that have not been used to examine this research puzzle before.

To this aim, the project is organised into three 'work packages' (WPs). WP1 examines how conditions at the country-level mediate the relationship between welfare states and gender segregation in employment across 21 advanced economies. This includes Central and Eastern European countries, which prior research has tended to overlook. The country-level conditions included are cultural norms, regulations regarding women's representation on corporate boards, and labour-market characteristics. Data will be compiled from the International Social Survey Programme, OECD, Eurostat, the Global Media Monitoring Project, the World Bank, and Deloitte's Women in the Boardroom project. WP2 then investigates how the impact of welfare-state policies on a woman's career progression varies according to her socioeconomic position and the specific economic and social context in which she lives, using regional and individual-level data from the European Social Survey. Subsequently, WP3 carries out systematic comparative case studies to explore in depth the underlying mechanisms that explain why certain welfare states and regions exhibit high levels of gender inequality but low levels of class inequality, while in other places, the opposite is true. Data are drawn from the same sources as for WP1 and WP2, as well as academic literature and other relevant sources (e.g. government websites).

The project is important because its findings will inform policymakers about how their policies affect different groups of women and how to overcome the 'inclusion-inequality' dilemma (Pettit & Hook, 2009), i.e. bring more women into the workforce by providing adequate family policies and...

This repository contains data (survey responses) and replication scripts for all analysis (main manuscript and online appendix) of the article "Does Hard Propaganda (Also) Work in Democracies? Evidence from the United States" forthcoming in Perspectives on Politics.

Statistics illustrates market overview of machine-tools; which can carry out different types of machining operations without tool change between such operations, for working wood, cork, bone, hard rubber, hard plastics or similar in the United States from 2007 to 2024.

Statistics illustrates the import value of Machine-tools; which can carry out different types of machining operations without tool change between such operations, for working wood, cork, bone, hard rubber, hard plastics or similar in Northern America from 2007 to 2024 by trade partner.

Statistics illustrates consumption, production, prices, and trade of Machine-tools; n.e.s. in heading no. 8465, for working wood, cork, bone, hard rubber, hard plastics or similar hard materials in United States Virgin Islands from 2007 to 2024.

United States Imports: Saw Machine for Work Wood Cork Bone Hard Rubber Etc data was reported at 44.858 USD mn in Jan 2025. This records a decrease from the previous number of 44.974 USD mn for Dec 2024. United States Imports: Saw Machine for Work Wood Cork Bone Hard Rubber Etc data is updated monthly, averaging 40.163 USD mn from Jan 2002 (Median) to Jan 2025, with 277 observations. The data reached an all-time high of 94.056 USD mn in Oct 2021 and a record low of 16.648 USD mn in Feb 2010. United States Imports: Saw Machine for Work Wood Cork Bone Hard Rubber Etc data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.JA135: Imports: by Commodity: 6 Digit HS Code: HS 79 to 84.

United States Exports: Saw Machine for Work Wood Cork Bone Hard Rubber etc data was reported at 4.706 USD mn in Jan 2025. This records an increase from the previous number of 3.954 USD mn for Dec 2024. United States Exports: Saw Machine for Work Wood Cork Bone Hard Rubber etc data is updated monthly, averaging 5.200 USD mn from Jan 2002 (Median) to Jan 2025, with 277 observations. The data reached an all-time high of 10.698 USD mn in Jun 2022 and a record low of 2.193 USD mn in Apr 2009. United States Exports: Saw Machine for Work Wood Cork Bone Hard Rubber etc data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.JA026: Exports: by Commodity: 6 Digit HS Code: HS 72 to 84.

Statistics illustrates the net export value of Machine-tools; which can carry out different types of machining operations without tool change between such operations, for working wood, cork, bone, hard rubber, hard plastics or similar in Chile from 2007 to 2024 by trade partner.

Statistics illustrates the import volume of Machine-tools; which can carry out different types of machining operations without tool change between such operations, for working wood, cork, bone, hard rubber, hard plastics or similar in Montserrat from 2007 to 2024 by trade partner.