Context:

The Occupational Employment Statistics (OES) and National Compensation Survey (NCS) programs have produced estimates by borrowing from the strength and breadth of each survey to provide more details on occupational wages than either program provides individually. Modeled wage estimates provide annual estimates of average hourly wages for occupations by selected job characteristics and within geographical location. The job characteristics include bargaining status (union and nonunion), part- and full-time work status, incentive- and time-based pay, and work levels by occupation.

Direct estimates are based on survey responses only from the particular geographic area to which the estimate refers. In contrast, modeled wage estimates use survey responses from larger areas to fill in information for smaller areas where the sample size is not sufficient to produce direct estimates. Modeled wage estimates require the assumption that the patterns to responses in the larger area hold in the smaller area.

The sample size for the NCS is not large enough to produce direct estimates by area, occupation, and job characteristic for all of the areas for which the OES publishes estimates by area and occupation. The NCS sample consists of 6 private industry panels with approximately 3,300 establishments sampled per panel, and 1,600 sampled state and local government units. The OES full six-panel sample consists of nearly 1.2 million establishments.

The sample establishments are classified in industry categories based on the North American Industry Classification System (NAICS). Within an establishment, specific job categories are selected to represent broader occupational definitions. Jobs are classified according to the Standard Occupational Classification (SOC) system.

Content:

Summary: Average hourly wage estimates for civilian workers in occupations by job characteristic and work levels. These data are available at the national, state, metropolitan, and nonmetropolitan area levels.

Frequency of Observations: Data are available on an annual basis, typically in May.

Data Characteristics: All hourly wages are published to the nearest cent.

Acknowledgements:

This dataset was taken directly from the Bureau of Labor Statistics and converted to CSV format.

Inspiration:

This dataset contains the estimated wages of civilian workers in the United States. Wage changes in certain industries may be indicators for growth or decline. Which industries have had the greatest increases in wages? Combine this dataset with the Bureau of Labor Statistics Consumer Price Index dataset and find out what kinds of jobs you would need to afford your snacks and instant coffee!

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Public sector employment by industry, UK, published quarterly, seasonally adjusted.

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Industrial product price index (IPPI), by product by North American Product Classification System (NAPCS) 2017 Version 2.0. Monthly data are available from January 1956. The table presents data for the most recent reference period and the last four periods. The base period for the index is (202001=100).

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In 2024, enterprise spending on cloud infrastructure services amounted to *** billion U.S. dollars, a growth of ** billion U.S. dollars compared to the previous year. The growing market for cloud infrastructure services is driven by organizations' demand for modern networking, storage, and databases solutions. Increased spending on cloud services, mainly on platform as a service The platform as a service (PaaS) segment, which includes analytics, database, and internet of things (IoT) has the highest growth rate within the cloud infrastructure services market. The managed private cloud services share declined in comparison. Infrastructure as a service (IaaS) remained relatively steady, with companies like Amazon Web Services and Microsoft dominating the market. However, software as a service (SaaS) is not included, which itself continues to experience growth in end-user spending worldwide. Data center spending declined in 2020 Enterprise spending on data center hardware and software, on the other hand, began to slightly decline after several years of steady growth. Data center hardware and software encompasses spending on servers, networking, storage, and security software. Because data centers store proprietary or sensitive data, sites are secured by specific software. This includes splitting networks into security zones, for example. Other methods for ensuring security are using tools to scan applications and code before deployment to spot malware or vulnerabilities.

Transport Performance and Analytics (TPA) provides projections of workforce at the small area (Travel Zone or TZ) level for the Sydney Greater Metropolitan Area (GMA).

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