We test the Cohen & Klepper cost-spreading process share hypotheses using unique data from two national innovation surveys (2009 and 2012). To our knowledge, no other study has the same combination as our dataset, in terms of robust data from a mandatory survey, large sample size, diverse measures for innovation output, and no sample selection bias. We use two direct measures of innovation to test the CK hypothesis: R&D expenditure and the number of innovations. An outcome variable that counts the number of innovations can be easier for respondents to recall from memory and they may reflect the firm’s activities more accurately. Using direct measures of innovation eliminates three forms of bias emanating from patents. Our results show that the CK hypotheses can be supported with the aggregated sample, but the results are weak for separate industries. The count-based process share provides statistically superior results to the expenditure-based process share.