R&D Spending: Dynamic or Persistent?

Problem definition: Should the management of research and development (R&D) be persistent in its approach to funding R&D or rather allow for quick reaction and dynamism? Academic/practical relevance: Under a persistent policy, allocations remain nearly constant irrespective of circumstances; under a dynamic policy, R&D spending increases (respectively, declines) when opportunities arise (respectively, fail to materialize). Practitioners give conflicting answers as to which policy is preferable, while there is no rigorous academic guidance. Methodology: We use a sample of 3,711 publicly listed companies, observed for seven years (on average) between 1982 and 2003, to compare the outcomes of these R&D allocation policies. We estimate a firm-level dynamic panel data model, via the "system general method of moments" (S-GMM) approach (Arellano and Bond 1991, Blundell and Bond 1998), which combines financial information from the Compustat database with patent data provided by the National Bureau of Economic Research (NBER). Results: We find that a dynamic allocation strategy is associated with worse R&D performance in terms of patent quantity and quality. Our results indicate that the originality of an invention, and also the firm's familiarity with an invention's technological basis, are factors that can mitigate or amplify the harm caused by variability. Finally, we establish that R&D performance suffers from the unpredictable part of dynamic spending; the predictable part has either no effect or a positive one. Managerial implications: There are many reasons why managers may wish to alter the level of R&D spending. Some of these reasons (e.g., pursuing technological opportunities) reflect more positive intentions than do others (e.g., chasing targets for earnings). Whatever the rationale for a change in spending, our paper highlights the possible negative consequences that managers should consider; it also documents the contingencies under which adaptation is especially harmful and identifies policies for mitigating adaptation pains. Thus, we offer managers a framework for conceptualizing principles about how best to invest in R&D. Our paper also issues this warning about the goal of hitting quarterly financial targets: if R&D spending is viewed as discretionary when such targets must be met-which is customary (as documented by Roychowdhury 2006) for some publicly traded companies-then one should expect to observe long-term negative consequences that cannot be reversed simply by later restoring or even increasing R&D investment.