Employment data includes information on employment, hours, and earnings of workers on payrolls. It includes employment rates in a geographical area.
Data scientists can also divide this data by industry or by demographic criteria like location, age, gender, and more.
Employment data often comes from information collected by censuses and other surveys distributed by national or local governments. The government census’ household survey collects unemployment data filtered by demographics. The government establishment survey, on the other hand, records non-farm employment numbers and wages.
There are international organizations that track employment data, like the OECD. Further, every nation collects its own employment data: for example, the US collects this data through the Bureau of Labor Statistics which presents the Current Employment Statistics (CES) program, the Quarterly Census of Employment and Wages, and the Current Population Survey. Finally, private companies collect this data. Examples include the companies Claritas, Equifax, Global Insight, Applied Geographic Solutions, and more. Similarly to national and international databases, these private databases collect information on tens of millions of individuals and businesses, including the number of employees and their demographic information, wages, hours, etc.
Employment data includes information on all aspects of employment and labor: unemployment rate, number of employees per employer, number of employees by industry, wages, hours, and even demographics (race, location, gender, age). It also collects employees’ previous experience, their period of employment, contracts, etc.
After employment reports come out, many Wall Street firms publish estimates of job numbers. Businesses use these numbers to make crucial decisions for their company. Investors, bond holders, and other financial firms understand that employment affects economies at all levels and use the numbers as a measure of an economy’s health.
Companies in the currency and bond markets use this data to measure currency values and to anticipate and the effects of changing inflation and interest rates.
Like other types of demographic data, it is difficult to find comprehensive, large-scale, and up-to-date datasets. By the time surveys are completed, other parts of the datasets are already outdated before they could even be used. Additionally, the difference between private and federal databases and survey programs can lead to the misuse of the information.
To test the quality of a dataset, you should assess the diversity of its sources and check that the data is used by other current consumers. You should also be sure to use datasets that update frequently.
It is best you use employment data that other current data consumers use; this increases the likelihood that the information is up-to-date and accurate.
You should also make sure the database contains information from different surveys and censuses, both governmental and private (which update more often). The combination of these sources should provide the most accurate data.
IEEEXplore: Statistical analysis and data processing: A case study of employment effects of minimum wages
ResearchGate: Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania: Reply
The changing nature and organisation of work and its diverse impacts on societies will need to be understood through not just best practice applied to new topics, but also emerging research approaches and themes rooted in data science and artificial intelligence, such as machine learning, robotics and network science. As the future of work quickly becomes the present, there is an urgent need for scholarship that attempts to understand how to make our new world of work sustainable, equitable and just.
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