Firms invest in research and development (R&D) to generate knowledge and innovations that improve their future profitability. When making research and development decisions, firms are confronted with the trade-off between the current costs of research and development and the uncertain stream of future payoffs. Furthermore, the knowledge created by research and development investment has public good aspects which, together with imperfections in the market for financing research activity, can lead to inefficiently low levels of firm investment (Hall and Lerner 2010). Many policy initiatives to encourage research and development investment and innovation operate with financial incentives to reduce the cost of innovation, such as direct subsidies or favourable tax treatment of research and development expenses. In order to evaluate the effectiveness of these instruments it is necessary to understand how the financial position of the firm affects its investment decision.
The long-run return to research and development investment
A firm’s research and development investment decision is driven by a comparison of costs and expected long-run returns. Many empirical studies have relied on the knowledge production function model developed by Griliches (1979) to estimate the private and social return to research and development investment as the marginal product of the knowledge stock created by these investments (Hall et al. 2010). Peters et al. (2015) take a different approach and exploit information in the firm’s demand curve for research and development. They develop and estimate a dynamic model of a firm’s research and development investment decision that recognises the intertemporal trade-off between the costs and expected long-run return to the investment. In their framework, the expected return to current research and development investment depends upon the effectiveness of research and development in generating new product or process innovations, the impact of these innovations on future profits, and the persistence of these profit gains. Analysing data on German manufacturing firms, they find that a firm’s age, size, productivity, and industry affiliation impact its expected return to research and development, thus, its decision to invest.
Another likely factor contributing to the expected return to research and development is the firm’s financial position. The path from research and development spending to developing a new product or production process to the conversion into sales and future profits necessitates investments in legal, marketing, design, and testing processes that require financial resources. The substantial uncertainty surrounding the innovation process further suggests that firms that can financially support a wider portfolio of projects may be more successful in generating profitable innovations. Firms in a strong financial position may thus have a better chance of both successfully innovating and exploiting the economic returns to their innovations than those in a weak financial position. As a result, the former would have a higher return to research and development investment and be more likely to invest.
In a new paper (Peters et al. 2016), we study 1,200 German firms in five high-tech manufacturing industries over the time period 1993 to 2008, and show how differences in their financial strength are coupled with differences in the long-run return to research and development. We measure the firms’ financial strength using each firm’s credit rating produced by the company Creditreform, the largest German credit rating agency. The rating primarily ranks the firm’s ability to service its debts fully and on time. It takes into account information on firm characteristics such as age, size, and growth prospects, its past economic decisions, as well as payment history. We assign each firm to one of three financial strength categories, low, medium, and high, based on their credit rating.1 Firms with a favourable rating tend have access to financial resources. Their resources can come from a combination of current cash flows, retained past earnings, and easy access to external funding. A strong financial position enables firms to finance their research and development outlays and to successfully develop and market their innovations. Firms in poor financial condition on the other hand may lack the necessary resources to fully benefit from the innovation process and thus be less likely to invest in research and development.
We observe that among German firms in the high-tech manufacturing industries, the fraction that report positive research and development spending increases with financial strength. For instance, 87.3% of firms in a strong financial position report positive innovation expenditure, whereas only 70.7% of firms in a weak financial position choose to invest. The differences in investment rates across financial categories reflect variation in the level of expected benefits the firm can earn from these investments. Our empirical model allows us to explain this difference with a combination of differences in the rate of and the economic payoffs to innovation.
The financial resources available to the firm are crucial for generating new innovations. Firms that invest in R&D have a higher probability of developing a new product or adopting a new production process than firms that do not, and the chance of successful innovation varies with the firm’s financial strength. Firms with more financial strength are more likely to innovate than those with less strength. As a firm’s financial position improves, the probability of having no innovation, despite positive research and development investment, decreases from 14.4% to 8.3%, on average. This is consistent with a strong financial position enabling the firm to have a large portfolio of investment projects that generate greater success in the creation of new products and adoption of new production processes. Even without formal R&D spending, a strong financial position provides firms with more channels to exploit external knowledge and allow them to develop innovations.
The firm’s financial strength is also correlated with the economic payoffs from these innovations. We find that the productivity gain from innovation is highest for firms in the strongest financial position and declines as financial strength declines. Specifically, German manufacturing firms with high financial strength experience, on average, a productivity increase of 8.6% after the introduction of a new product, 9.0% after a new production process, and 11.5% after the simultaneous introduction of both innovations. These productivity gains are only 0.8%, 0.6%, and 3.8%, respectively, for firms with low financial strength. Innovations developed with limited resources might be of lower quality and limited scope, therefore yielding lower productivity impact. Furthermore, firms with more resources might benefit from a larger number of innovations and, hence, have more ground to exploit economic gain.
The combination of the higher innovation rate and the larger productivity impact of the innovation generate higher expected returns to research and development investment for firms in a stronger financial position. We estimate that the average long-run return from research and development investment across all firms is a 6.6% increase in firm value. However, this return differs substantially across financial strength categories. Firms in the highest category have an average return of 11.6%. The return drops to 5.5 and 2.3% for firms in the medium and low categories, respectively.
Our research documents that firms with better access to financial resources, measured by their credit rating, have higher expected returns to R&D investment and this contributes to higher investment rates. Financial strength contributes to differences in success rates of new product and process innovations, productivity improvement, and the profit impact of the innovations. The effectiveness of R&D promotion policies that provide monetary incentives – such as loan guarantees, tax incentives, or direct subsidies paid to investing firms – are likely to depend on how they affect not only the cost of research and development, but also the long-run expected benefit from research and development.
Griliches, Z (1979), “Issues in Assessing the Contribution of Research and Development to Productivity Growth,” Bell Journal of Economics 10(1): 92-116.
Hall, B, J Mairesse, and P Mohnen (2010), “Measuring the Returns to R&D,” in B H Hall and N Rosenberg (eds.), Handbook of the Economics of Innovation, Elsevier B.V.
Hall, B and J Lerner (2010), “The Financing of R&D and Innovation,” in B H Hall and N Rosenberg (eds.), Handbook of the Economics of Innovation, Elsevier B.V.
Peters, B, M J Roberts, V A Vuong, and H Fryges (2015), "Estimating Dynamic R&D Choice: An Analysis of Costs and Long-Run Benefits," NBER Working Paper No. 19374.
Peter, B, M J Roberts, and V A Vuong (2016), “Dynamic R&D Choice and the Impact of the Firm's Financial Strength,” NBER Working Paper No. 22035.
1 The Creditreform rating is a score between 100 and 600 with 100 being the best rating. We assign firms to the high financial strength category if their rating is 100 to 190. Firms with credit ratings between 191 and 228 are classified in the medium category and firms with ratings higher than 229 are assigned to the low category. These categories correspond to Standard and Poor’s ratings of above BBB, above BB to BBB, and BB and below.