Industry financial and employment data refers to all the financial and employment information within an industry. This may be further divided by industry in a country, providing valuable information about the region’s economy.
Most of this data comes from government or international sources. Examples include the IRS, the Bureau of Labor Statistics, the Office for National Statistics, and the OECD.
Other possible sources of data include company reports or third-party surveys. However, the size of a single industry renders these sources insufficient—even if all companies published yearly reports, as small businesses do not.
This data generally comes in columns organized by year (sometimes subdivided into quarters) and industry.
Financial data includes solvency, liquidity, and profitability ratios. They often include a rate of change from one year to the next.
Employment data, meanwhile, includes number of employees, wages/salaries, hours worked, and type of employment (full-time vs contract vs self-employed, etc.). They also often include a rate of change from year to year.
In both cases, the industries may be compared to a global median.
This data lends itself very well to industry benchmarking, risk management, and industry or market analysis. Therefore, you’ll find marketing teams, bankers, investors, consulting firms, accountants, governments, and researchers make frequent use of this data.
Since government agencies collect almost all of this data, they guarantee a certain high level of quality. However, you can still build your own database with the statistics and reports published by governments and international bureaus. You can also include reports published by industry-specific data vendors such as those you may find at our site.
One thing to note is that different countries may measure employment statistics differently than others. For example, the US and Canada categorize furloughed workers as unemployed whereas other countries do not.
In many ways, the coronavirus crisis has shone a light on how gig economy workers can increasingly penetrate industries across the economy. A necessitated jump in remote working in line with strict public health restrictions has spurred significant change in business processes over the past few months, and these seem likely to persist to a certain extent in the long term. Proof that productivity within project teams can remain high without the need for face-to-face contact, and in some cases without any prebuilt social capital, adds further value to the argument for a more dynamic staffing model. Greater acceptance of remote working also removes prohibitive geographical barriers to a finite talent pool, facilitating and elevating the use of gig economy workers in higher paid roles, such as in the professional services sector.
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