Macro

Salary Data: Leading Indicator of Expenses

When it comes to predicting top-line revenue, there are a lot of datasets to help: credit card, satellite imagery, etc. This data is readily available and is, as a result, incorporated into models used to influence investment decisions. However, when it comes to predicting expenses, there are few datasets available. Predicting expenses is a complex task because: (i) compensation data is rarely disclosed to the public; and (ii) compensation for contingent workers is unavailable (in some cases a company’s workforce can be two thirds contingent workers).

Revelio Labs

11/6/19

When it comes to predicting top-line revenue, there are a lot of datasets to help: credit card, satellite imagery, etc. This data is readily available and is, as a result, incorporated into models used to influence investment decisions. However, when it comes to predicting expenses, there are few datasets available. Predicting expenses is a complex task because: (i) compensation data is rarely disclosed to the public; and (ii) compensation for contingent workers is unavailable (in some cases a company’s workforce can be two thirds contingent workers).

Using Revelio Labs’ employment data, which leverages H1B Visa data and job posting data to predict individual salaries in a company, we were able to add those salaries by company to predict each company’s expenses and found that for many industries salaries comprised ~80% of overall expenses.

Above, Alphabet and Salesforce mimic a similar trend seen across organizations—salary data is a strong leading indicator of expenses.

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