Table of Contents

    What is employment data?

    Employment Data typically includes labor force statistics and employee information such as name, employer, position, address, number of hours, salary or payroll information, employment rates, and status. It also covers economic data, demographics, interview difficulty, benefits, interviews, number of published salaries, number of published job openings, and number of employees by job title.

    The data includes industry and labor market information that can be further spliced geographically and demographically with attributes such as age, gender, veteran status, and more. 

    Where does the data come from?

    The data is collected from a few sources:

    • Government-led surveys: Much of the employment data comes from government censuses and surveys (such as from the U.S. bureau of labor statistics). The census collects national employment information through household surveys tracking and collecting data demographically. The national and local governments and department of labor conduct many other surveys and collect employment data. The information usually consists of the number of people employed in a household, industry payroll employment, weekly hours, hourly or weekly earnings and statewide/local area unemployment statistics. 
    • International organizations: Many international organizations such as ILO and OECD track employment data globally. There are country-led organizations as well that collect employment data filtering it down demographically. For instance, in the US, the Current Employment Statistics (CES) program, the Quarterly Census of Employment and Wages, and the Current Population Survey is run by the Bureau of Labor Statistics. 
    • Private businesses: Certain companies also collect employment data, and maintain private databases with information on employees, individuals, and businesses, with complete demographic information. These companies could be research firms, insurance providers, credit bureaus, mapping companies, and marketing research firms, to name a few. 

    What types of attributes or columns should be expected when working with this type of data?

    Employment data tends to be exhaustive, covering various facets of employment or labor information, including demographic and partial firmographics. 

    Some attributes are:

    • Personal information: Age, years of experience, wages, hours of work per week, family details such as the number of dependents, number of kids, non-working members, the company of employment, employees’ previous experience, their period of employment, contracts, and job titles. 
    • Demographic information: Location, gender, and age.
    • Firmographic information: Unemployment rate, number of employees per employer, employment opportunity, employment projections, number of employees by industry, number of jobs published, benefits, interviews, number of published salaries, and number of employees by job title.

    What is the data used for?

    This data forms the basis for predicting trends in a market. It is published, and government agencies along with other institutions such as non-profits, banks, and insurance companies use it to analyze and make data-driven decisions. It is also used to forecast property rates.

    Companies operating  in the currency and bond markets use this historical data to measure currency values. They also leverage the data series to predict the effects of changing inflation and interest rates.

    What are common use cases?

    Some common use cases include risk modeling, risk analysis, and fraud prevention. 

    • Risk analysis and KYC: Employment data, such as income levels, can help financial institutions, banks, and other companies assess geographies to understand the employment situation and associated risks. Employment data further enables financial institutions, such as banks or credit card companies to better understand receivables and improve repayment prediction engines. 
    • Risk modeling: Employment data can help identify and cut down default rates, helping redefine lending scores with better data. 
    • Fraud prevention: Employment data can help build indicators that identify fraudulent transactions by connecting unrelated data, without compromising any data privacy regulations.

    You can use the employment data to power a wide variety of machine learning use cases, such as small business loans default risk.

    Explore all the use cases listed on our site and contact us for your specific requirements.

    How do we test the quality of employment data?

    Employment data is of large scale and exhaustive. Government sources usually take time to update, and there is a risk that the data is outdated by the time it is accessible for use. 

    To test the quality of these datasets, you should determine how recent the information is. Since the employment information keeps changing over time, it is prudent to use a combination of federal and private information that is updated regularly by other current consumers. 

    When working with this combination, it is important to ensure the vendor or data aggregator sources are credible. Since this is mostly public information, it is good to compare it with other sources in use.

    And finally, as the data is comprehensive, you need to determine its relevance to your needs. Employment data has many attributes, but a real estate company, for example, may only need portions of unemployment trends to support its business decisions. 

    What are some of the challenges or things to consider when working with this data?

    One of the key challenges with data this size is how recent or up-to-date it is. As the primary sources for this data are government surveys and censuses, the datasets are sometimes outdated before they can be used. Some key challenges in working with employment data are: 

    • Data timeliness and relevance: The data should be as current as possible. One way to get the most current data is to use employment data from different surveys and censuses, both governmental and private. This is especially relevant with all of the labor force changes stemming from coronavirus (COVID-19). 
    • Source credibility: Due to the nature and expanse of the data, it can be difficult to determine its credibility. Verifying your data vendors for relevance and quality will help overcome this challenge. 
    • Compliance: Employment data may contain personally identifiable information (PII) that must comply with the current regulations such as GDPR and CCPA. You also need to be aware that privacy regulations differ across countries and regions. 

    What are similar data types to employment data?

    You can find a variety of examples of B2B and company data in the Explorium Data Gallery

    Sign-up for Explorium’s free-trial to access the data available on the platform.

    Which industries commonly use this type of data?

    Many industries have picked up the use of employment data to power analytics and machine learning models. Some of these industries are financial services, insurance, hi-tech, eCommerce, manufacturing, and retail.