firms, to enhance the likelihood of good matches or as closely-comparable firms to the funded firms as possible.

In terms of appropriate firm-level outcome variables for measuring impact, other studies have examined employment growth and sales growth, and changes in productivity or changes in the efficiency with which firms convert inputs into outputs due to the program.<sup>25</sup> There are also other variables that capture intermediate outcomes along the chain of causality from treatment to outcomes that can be explored, such as patents, achieving the stage of clinical or field trials, or other variables that represent an accepted benchmark of intermediate success. Desirable data on firm characteristics at time of public funding and changes over time to control for in the analysis include firm location, age, size (number of workers and revenues), age of the CEO, education and training details (years of education of the owner, manager, and workforce), total sales, exports, sources of finance, linkages with other local firms/public labs/universities/global firms, and whether the firm benefits from other complementary public programs. Finally, to address the question of whether the additional social benefits of the programs are greater than the cost of the support, it would be desirable to complement this assessment with measures of social benefits to end-use consumers, with households ideally broken down by income groups to the extent that meeting the needs of the poor is an explicit program objective.

A second approach to estimate program impact is an **_experimental_** evaluation with random assignment or a randomized control trial (RCT), which is built into the program design before the program (or the next phase of the program) is implemented. Under RCT, firms in the treatment group receive assistance while those in the control group do not, with random assignment helping ensure that the two groups are similar. Such forward-looking program design with randomization provides the strongest foundation for causal inference.

A question could be raised whether RCT is appropriate to assess competitive matching grant funding where the best projects typically get funded, and whether the guiding principle of competition could be detrimentally affected if only one of every two (or two of every three...) equally-good projects are funded. Also, in a competitive grant scheme with random rejection, it could be claimed that there may not be a straightforward way for rejected submissions to challenge the results if it is not clear if the rejection was based on quality or on random selection. The latter concern can be addressed by making public which submissions exceed an announced threshold for quality, address any challenges on quality, and thereafter conduct a random draw among the most promising quality-based submissions. A public drawing can be open, transparent, and alleviate concerns about corruption. More

> 25 Lerner (1999) assesses the long-run success of firms participating in the US SBIR program by examining the employment and sales growth of 1,435 enterprises over a 10-year period, with 3/5<sup>th</sup> of the enterprises chosen to closely resemble the awardees. He finds that SBIR awardees enjoyed substantially greater employment and sales growth than the matched firms, and were more likely to subsequently receive VC financing. However, the superior performance of awardees was confined to firms in regions with substantial VC activity. And the SBIR awardees receiving larger grants did not perform better than those receiving smaller grants, suggesting that awards played an important role in certifying quality but also that distortions of the award process occur. See also Wallsten (2000), Audretsch (2003), Gans and Stern (2003), Link and Ruhm (2009), and Link and Scott (2010).

34