The differential effects of public R&D support on firm R&D: Theory and evidence from multi-country data

This paper aims to evaluate the effects of various forms of public research and development (R&D) support on firms’ incentives to invest in R&D. First, in order to identify potential channels through which public R&D support influences firm R&D, a formal model of firm R&D with public R&D support is developed and analyzed. Four potential channels are identified: the technological-competence-enhancing effect, the demand-creating effect, the R&D-cost-reducing effect and the (project) overlap (or duplication) effect. These multiple channels indicate that it is difficult to evaluate the aggregate effect of public R&D support and that there are differential effects of public R&D support on firm R&D, depending on various firm- or industry-specific characteristics. Second, the differential effects of public R&D support are empirically tested using unique firm-level data for nine industries across six countries. Public support tends to have a complementarity effect on private R&D for firms with low technological competence, for firms in industries with high technological opportunities and for firms facing intense market competition. In contrast, firms with high technological competence and firms that have enjoyed fast demand growth in recent years show a crowding-out effect, and firm size and age do not show any discernible differential effect.     

Advertising, Its Determinants, and Market Structure

This study derives a formal model of firm advertising behavior and applies it to the industry level to figure out the relationship between advertising and market structure. The firm advertising model shows that both consumer preference andfirm-specific advertising competence jointly determineprofit-maximizing advertising intensity. At the industry level, advertising intensity is represented multiplicatively by consumer preference and a measure of market structure, which reflects the joint distribution of the levels of advertising competence and market shares among firms. The new market structure measure suggests that those single-dimensional measures of market structure such as seller concentration and the Herfindahl index are inadequate in explaining interindustry differences in advertising intensity, and that the long-debated advertising-concentration relationship differs depending primarily on the appropriability of advertising. An empirical analysis of 426 five-digit Korean manufacturing industries shows that an inverted U-shaped relationship between the Herfindahl index and industry advertising intensity is observed for consumer goods industries but a lazy J-shaped relationship for producer goods industries.     

Firm Density and Industry R & D Intensity: Theory and Evidence

By deriving a formal model of industry R & D that identifies factors influencing industry R & D intensity, this paper first suggests firm density, defined as the inverse of average firm sales or simply the number of firms divided by industry sales, as a measure of market structure that is appropriate in explaining industry R & D intensity. The model shows that the cost structure of R & D, consumer preference over quality and price, the appropriability of R & D, firm density, and the average level of firm R & D intensity jointly determine industry R & D intensity. In particular, firm density has a positive relationship with industry R & D intensity, implying that firms in higher firm-density industries feel fiercer competitive pressure and thus engage more intensively in R & D. An empirical analysis of panel data on industry R & D activities of Korean manufacturing industries during the period 1991–1996 provides supportive evidence for the predictions of the model including the positive relationship between firm density and industry R & D intensity. The theoretical model and the empirical results are also consistent with the recent survey of U.S. corporate R & D activities by the U.S. Department of Commerce and the National Science Foundation (1999).     

A simple theory and evidence on the determinants of firm R&D

This paper derives a simple, but informative, model of firm R&D to figure out key factors that determine firm R&D effort. The model suggests a demand-pull, technology-push theory of R&D by showing that a firm's profit-maximizing R&D expenditure is determined jointly by both demand-side factors and technology-side factors. The former includes demand size (firm sales) and consumer preference over quality and price and the latter includes R&D cost structure or the production-cost effect of product R&D and firm-specific technological competence. In addition, the model shows that other things being equal, the stock of exogenous technological knowledge, including the firm's previously accumulated technological knowledge, relevant to current R&D which is negatively related with current R&D effort. An empirical analysis of firm R&D intensities and technological capabilities of more than 1600 firms in nine industries across six countries provides supportive evidence for the theory. Further, the theory implies that R&D intensity or the R&D-to-sales ratio is independent of firm size unless firm size affects technological competence and that given consumer preference and R&D cost structure facing all firms in the same industry, the distribution of firm-specific technological competence among firms determines the distribution of firm R&D intensities within the industry.     

Industry R&D intensity distributions: regularities and underlying determinants

This paper aims to examine the nature of the distributions of firm R&D intensities within industries and explore the factors that underlie the industry R&D intensity distributions. In particular, following the seminal study by Cohen and Klepper (1992) and using some new and rich data on firm R&D intensities for seven industries across six countries, this study examines the regularities in the industry R&D intensity distributions and demonstrates, based on a simple model of firm R&D, that the industry R&D intensity distributions are governed by the distributions of technological competence, a measure of firm R&D productivity, which corresponds to the notion of the “unobserved R&D-related capabilities” suggested by Cohen and Klepper (1992). This study found that firm R&D intensities within industries are lognormally distributed, displaying a strikingly regular pattern across industries, that the industry distributions of the levels of technological competence are also lognormal, and that, based on the formal model of firm R&D and the notion of the unobserved R&D-related capabilities, the distribution of firm technological competence within an industry underlies the industry's firm R&D intensity distribution.     

Learning-by-doing in R&D, knowledge threshold, and technological divide

This paper presents a simple R&D-based growth model of the “technological divide,” in which learning-by-doing (investing) in R&D and a threshold level of technological knowledge jointly determine the pattern of economic growth. Specifically, the model generates differences in the growth pattern primarily by modifying the underlying parameters that govern the evolution of economy-wide technological competence or dynamic R&D productivity. The technological divide arises at the threshold level of technological knowledge, which is largely affected by the quality of socio-technological infrastructure. Government policies aimed at enhancing the quality of socio-technological infrastructure can help countries escape from the “technology divide” trap by lowering the knowledge threshold. While the model preserves the spirit of the R&D-based endogenous growth model in the sense of its policy effects and the endogenous evolution of technological competence, the model does not need to reach the scale effect directly, where an increase in the size of an economy generates more rapid growth.     

The Peter Pan syndrome for small and medium-sized enterprises: Evidence from Korean manufacturing firms

This study examines whether government support for small and medium-sized enterprises (SMEs), aimed at stimulating their growth, achieves its intended goal. We argue that government subsidies create the incentive for SMEs to remain small in order to keep receiving such support and thus SMEs are reluctant to grow. We call this phenomenon the Peter Pan syndrome. Using a dataset of Korean manufacturing firms during the period of 2010–2012, we find that the Peter Pan syndrome indeed exists and that the likelihood of the Peter Pan syndrome is conditioned by factors that influence their incentive to remain as SMEs.