Most current theories of the cross-country distribution of per-capita income imply that all countries share the same long-run growth rate (of TFP or per-capita GDP). Yet the historical record shows that growth rates can differ substantially across countries for long periods of time. For example, Pritchett (1997) estimates that the proportional gap in percapita GDP between the richest and poorest countries grew more than five-fold from 1870 to 1990, and according to the tables in Maddison (2001) the proportional gap between the richest group of countries and the poorest grew from 3 in 1820 to 19 in 1998.
The “great divergence” between rich and poor countries continued through the end of the twentieth century. Although many studies2 show that a large group of rich and middle-income countries have been converging to parallel growth paths over the past 50 years or so, the gap between these countries as a whole and the very poorest countries as a whole has continued to widen. For example, the proportional gap in per-capita GDP between Mayer-Foulkes’s (2002) richest and poorest convergence groups grew by a factor of 2.6 between 1960 and 1995, and the proportional gap between Maddison’s richest and poorest groups grew by a factor of 1.75 between 1950 and 1998.
Technology appears to be the central factor underlying divergence. Easterly and Levine (2001) estimate that about 60% of the cross-country variation in growth rates of per-capita GDP is attributable to differences in productivity growth, while Klenow and Rodríguez-Clare (1997) estimate that in their sample about 90% of the variation is attributable to differences in productivity growth. Feyrer (2001) finds that although the distribution of capital-output ratios is single-peaked, and the distribution of education levels is almost flat, the distribution of the productivity residual has become increasingly twin-peaked. Although the level of productivity can be affected by many factors other than technology, such as geography and institutions that affect the efficiency of resource allocation, it is hard to see how substantial differences in the growth rate of productivity persisting for such long periods of time can be accounted for by these other factors, which are themselves highly persistent over time. Instead it seems more likely that divergence reflects long-lasting cross-country differences in rates of technological progress.
These facts are especially puzzling when one takes into account the possibility of international technology transfer and the “advantage of backwardness” (Gerschenkron 1952)that it confers on technological laggards. That is, the further a country falls behind the world’s technology leaders the easier it is for that country to progress technologically simply by implementing new technologies that have been discovered elsewhere. Eventually this advantage should be enough to stabilize the proportional gap that separates it from the leaders. This is what happens in neoclassical models that assume technology transfer is instantaneous (Mankiw, Romer and Weil, 1992), and even in models where technologies developed on the frontier are not “appropriate” for poorer countries (Basu and Weil, 1998; Acemoglu and Zilibotti, 2001), in models where technology transfer can be blocked by special interests (Parente and Prescott, 1994, 1999) and in models where a country adopts institutions that impede technology transfer (Acemoglu, Aghion and Zilibotti, 2002).
This paper explores the hypothesis that financial constraints prevent poor countries from taking full advantage of technology transfer and that this is what causes some of them to diverge from the growth rate of the world frontier. It introduces credit constraints into a multi-country version of Schumpeterian growth theory with technology transfer, and shows that the model implies a form of club convergence consistent with the broad facts outlined above. In the theory, countries above some threshold level of financial development will all converge to the same long-run growth rate (but not generally to the same level of per-capita GDP) and those below that threshold will have strictly lower long-run growth rates.
There are three key components to the theory. The first starts with the recognition that technology transfer is costly. The receiving country cannot just take foreign technologies off the shelf and implement them costlessly. Instead, the country must make technology investments of its own to master foreign technologies and adapt them to the local environment, because technological knowledge is often tacit and circumstantially specific. these investments may not involve scientists and high tech labs, and hence would not fit the conventional definition of R&D, nevertheless they play much the same role as R&D in an innovation-based growth model. That is, they generate new technological possibilities in the country where they are conducted, building on knowledge that was created previously elsewhere. As Cohen and Levinthal (1989) and Griffith, Redding and Van Reenen (2001) have argued, each act of technology transfer requires an innovation on the part of the receiving country, and thus R&D or more generally technology investment is a necessary input to the process of technology transfer. Accordingly our theory assigns to R&D the role that Nelson and Phelps (1966) assumed was played by human capital, namely that of determining a country’s “absorptive capacity”.
The second key component is the assumption that as the global technology frontier advances, the size of investment required just in order to keep innovating at the same pace as before rises in proportion. This assumption recognizes the force of increasing complexity, which makes technologies increasingly difficult to master and to adapt to local circumstances. A similar assumption has been shown elsewhere to be helpful in accounting for the fact that productivity growth rates have remained stable in OECD countries over the second half of the 20th Century despite the steady increase in R&D expenditures.
The third key component is an agency problem that limits an innovator’s access to external finance. Specifically we assume that an innovator can defraud her creditors by hiding the results of a successful innovation, at a cost that depends positively on the level of financial development. Because of this, in equilibrium the innovator’s access to external finance will be limited to some multiple of her own wage income, as in the theory of Bernanke and Gertler (1989) modified by Aghion, Banerjee and Piketty (1999). Since wages are limited by domestic productivity, therefore a technological laggard can face a disadvantage of backwardness that counteracts Gerschenkron’s advantage; that is, the further behind the frontier it falls the less its innovators will be able to invest relative to what is required in order to keep innovating at a given rate. The lower the level of financial development in the country the lower will be the (private) cost of fraud, hence the lower will be the credit multiplier and the larger will be the associated disadvantage of backwardness. This is why in our theory the likelihood that a country will converge to the frontier growth rate is an increasing function of its level of financial development.
Our paper relates to several important strands of theory relating growth, convergence and financial market development. There is first the literature on poverty traps and interpersonal convergence or divergence in economies with credit market imperfections, in particular, Banerjee and Newman (1993), Galor and Zeira (1993), Aghion and Bolton (1997) and Piketty (1997). In these models, all agents face the same production technology and, unlike in our model, the same (productivity-adjusted) investment costs, and what generates poverty traps are either non-convexities in production or monitoring, or pecuniary externalities working through factor prices. However, there is no technical progress and therefore no positive long-run growth in these models, which therefore cannot analyze the issue of long term convergence in growth rates. A second strand analyzes the effects of financial constraints and/or financial intermediation on long-term growth. Thus, Greenwood and Jovanovic (1990), Levine (1991), Bencivenga and Smith (1991, 1993), Saint-Paul (1992), Sussman (1993), Harrison, Sussman and Zeira (1999) and Kahn (2001) analyze the effects of financial intermediation on growth in an AK-style model with no distinction being made between investing in technology and investing in physical or human capital accumulation. Whereas King and Levine (1993), de la Fuente and Marin (1996), Galetovic (1996), Blackburn and Hung (1998) and Morales (2003) consider the relationship between finance and growth in the context of innovation-based growth models. De Gregorio (1996) studies the effects on growth of financial constraints that inhibit human capital accumulation. Krebs (2003) shows how imperfect sharing of individual human apital risk can depress long-run growth. However, none of these models analyzes the process of technology transfer that we are focusing on, and therefore none of them is capable of addressing the question of why technology transfer is not sufficient to put all countries on parallel long-run growth paths.Our question is not just why financial constraints make some countries poor but rather why financial constraints inhibit technological transfer and thus lead to an ever-increasing technology gap.
The paper also produces evidence to support its main implications. There is already a substantial body of evidence to the effect that financial development is an important determinant of a country’s short-run growth rate, almost all of which is predicated on the assumption of long-run convergence in growth rates. We extend this analysis to allow for the possibility of different long-run growth rates, using a cross section of 71 countries over the period 1960-1995. Specifically, we estimate the effect of an interaction term between initial percapita GDP (relative to the United States) and financial development in an otherwise standard cross-country growth regression. We interpret a negative coefficient as evidence that low financial development makes convergence less likely. Using a measure of financial development first introduced by Levine, Loayza and Beck (2000) we find that the coefficient is indeed negative, and is large both statistically and economically.
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