Research and Development (R&D) expenditure is the amount of money a company spends on developing new products and services each year. Academic business researchers have intensively investigated the relationship between a company’s R&D and its market value, and have searched for ways to derive a firm’s optimal R&D spending. A recent innovation in the analysis and measurement of a firm’s R&D has been the development of the concept of Research Quotient (RQ).
A company’s Research Quotient is the percentage increase in the company’s revenue from a 1% increase in its R&D. RQ is a measure of a firm’s ability to generate revenue from its R&D expenditures. RQ is calculated from a formula that combines a company’s measure of capital, labor and R&D. For more details concerning RQ calculation, click on Manuals and Overviews from the WRDS Research Quotient database.
RQ can be used:
- To Link R&D spending to firm growth
- Link R&D spending to market value
- Derive a firm’s optimal R&D spending
The WRDS RQ database includes RQ measures for all companies in the COMPUSTAT database that report R&D expenditures. The data covers 1972 to 2010 and is updated annually. The file allows searching by 4 digit SIC and by GV Key (COMPUSTAT’s unique company identifier).
Table 1 is an example of the output showing some of the default variables.
- “Raw RQ” is the “Research Quotient” that identifies the ability of a firm to generate revenue from its R&D expenditures. The higher the RQ the greater the revenue generated.
- “RSTAR” is a calculation of optimal R&D expenditure.
- “RD Ratio” is the ratio of R&D expenditure to Revenue.
In Table 2, for clarification, I have supplied tickers and names of companies together with a measure of “RQ” that I calculated from the “Raw RQ” supplied by WRDS. This RQ is analogous to the human IQ measure with a mean of 100 and a standard deviation of 15. An RQ with a mean of 100 is often used by academic writers as a way of making the RQ measure more intuitive.
Table 2 ranks the first 20 companies in the U.S. by their RQ in 2010.
There are more than 260 four digit SIC codes represented in the 2010 files, but only 10 codes have more than 35 companies. Table 3 collapses the codes into 2 digits, and ranks the average RQ of the largest 15 industry groups.
About 78% of the companies in the 2010 file were based in the U.S. Figure 1 graphs the countries with 3 or more companies in 2010 by average R&D expenditures and average RQ.
The principal developer of the RQ concept is Anne Marie Knott, Professor of Business at Washington University, St. Louis. In a 2012 article in Harvard Business Review, she estimateds that if the 20 largest US firms had optimized their R&D expenditure in 2010, they would increase their aggregate market capitalization by $1 trillion. (Knott, Anne Marie. “The Trillion-Dollar R&D Fix.” Harvard Business Review (90:5) 2012, pp. 76-82.) This article can be accessed using Business Source Complete.