co-integration relationship they derive positive knowledge spillovers from G7 to non-G7 countries and “a somewhat puzzling negative effect” from non-G7 to G7 countries. Yasser and Joutz (2006) investigated the case of the U.S., finding significant inter temporal knowledge spillovers with an elasticity at least as large as one. However, they also found that doubling the stock of patents, a proxy for the stock of knowledge, increases long run TFP by a modest 10%.

There is an extensive literature on the existence of knowledge and R&D spillovers within and across countries. Among early contributions, Bernstein and Nadiri (1989) investigated intra-industry R&D spillovers in four American industries (chemicals, petroleum, machinery, and instruments) during 1965-1978. They found that production costs decline as knowledge expands towards externalityreceiving firms, and factor demands change in response to the spillovers. The authors estimated the social and private rates of return to R&D, and found that the social rate exceeded the private rate for the four industries. Coe and Helpman (1995) explored the effects of a country’s R&D capital stock and the R&D capital stocks of its trade partners on a country’s total factor productivity. They employed data for up to 21 OECD countries plus Israel from 1970-1990. The study found large effects of both domestic and foreign R&D capital accumulation on total factor productivity. The effects of foreign R&D capital stocks on domestic productivity seemed to rise with a country’s share of imports over GDP and decline with country size.

Coe and Helpman (1995) emphasized the role of trade in intermediate inputs as a conduit of spillovers. However, a subsequent paper by Keller (2004) that used random measures of bilateral trade to weight the foreign spillover term provided econometric evidence that the import shares are irrelevant for the results in Coe and Helpman (1995), thus shedding some doubt on the trade channel for the transmission of spillovers. Xu and Wang (1999) highlight that technology diffusion in trade models is associated specially with differentiated capital goods trade. Thus, in contrast to Coe and Helpman (1995), who employed overall trade, Xu and Wang (1999) showed that the distinction between capital goods trade and total trade matters. The first one accounts for about 10 percent more of the variation in productivity in domestic industries than Coe and Helpman (1995)’s study, and it also performs better than Keller (2004)’s variable.

There is also an important literature that investigates knowledge spillovers directly, including Jaffe et al. (1993), Branstetter (2001) and Griffith et al. (2006). Jaffe et al. (1993) conduct a detailed analysis to identify where spillovers go. The study involves the comparison of the geographic location of patent citations with that of cited patents. Domestic patents are more likely to be cited by domestic patent applications and more likely to come from the same state. In a related paper, Audrestsch and Feldman (1996) examine the extent to which industrial activity clusters spatially, and the link between geographic concentration and the existence of knowledge spillovers. The study relies on the Small Business Administration’s Innovation Data Base, which compiles data for up to 8,074 commercial innovations introduced in the United States during 1982. The authors employed 4