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The paper was finished while the author was a Visiting Scholar in the Research Department of the IMF. He has benefited from seminar participants at the IMF Research Department, Cambridge University, Groningen University, University of St. Andrews, Humboldt-University of Berlin, Kiel University, and Munich University. The opinions expressed in the paper are strictly personal and should not be taken as indicative of any official position.
One indicator is that none of the most-well known international datasets used in the empirical growth literature (the Heston-Summers dataset, the Barro-Lee dataset and the World Bank World Development Indicators database) contain any information on product variety over time and/or across countries. Another indicator is that the up-to-date survey of the new growth evidence by Temple (1999) does not contain any work on product variety. The reason for this state of affairs is probably that direct measures of product variety are difficult to obtain and therefore empirical work in this area seems to be a risky business.
Compare Feenstra et. al. (1997) and (1999). In a related paper, Weinhold and Rauch (1997) have constructed a Herfindahl specialization index for 28 different manufacturing industries to analyze the link between openness, specialization and productivity growth. Owen and Wren-Lewis (1993) and Driver and Wren-Lewis (1999) have analyzed the impact of variety and quality upon foreign trade using rough proxies such as cumulated investment and R&D flows.
The model draws on the original theoretical analysis concerning the production of and the demand for ‘variety’ and ‘quality’ by Grossman and Helpman (1991).
Even with no differences across countries in the long-run growth rate, one can explain a large variation in rates of growth with transition dynamics.
In extensive sensitivity analyses of cross-country growth regressions, Levine and Renelt (1992) and Sala-i-Martin (1997) have shown that investment in physical investment is the most robust variable explaining cross-country growth differences. Explaining differences in the level of income across countries by appealing to differences in n and sk, however, obviously begs new questions. Why is it that some countries invest more in physical capital than others and why do individuals in some countries spend more time u to develop new intermediate goods? This model cannot address this question. A more complete model answering this question has to assume utility-maximizing individuals to choose to work in either the final-goods sector or in the intermediate goods sector expanding product variety. In order to simplify the analysis, we will not develop this more complete here.
In the theoretical model above the inputs entered a Cobb-Douglas aggregator function in a symmetric way. For empirical purposes, however, it seems more reasonable to allow the inputs to enter the production function non-symmetrically and the elasticity of substitution to differ from one.
The classification distinguishes about 6400 commodities. Data were collected from the OECD database INTERNATIONAL TRADE BY COMMODITIES STATISTICS - ITCS Classification, Paris 1997. All data are expressed in current U.S. dollars. In principle it would be preferable to use national production data but they are neither available at a sufficiently disaggregated level nor are the available data consistent across countries.
In their extensive discussion of quality and variety, Grossman and Helpman (1991) and Coe and Helpman (1995) and Bayoumi et. al. (1999) have focused on levels of investment in R&D at home and abroad. A clear problem here is that the lag between R&D expenditures and the production of new varieties could be very long. Furthermore, it is also the case that many improvements in quality and variety can be realized without any R&D expenditure being incurred. In particular, increases in variety can occur through imitation, which involves little or no R&D expenditure.
Negative (positive) values for the index indicate lower (higher) product variety than in the United States. The negative numbers are a result of the log transformation in (18).
Industrial coverage of the ITCS classification groups industries into ‘Primary Products’ (textile products, wood products, paper and printing, rubber products, primary metal, leather products and stone, clay and glass) and ‘Secondary Products’ (food products, beverages and tobacco, apparel, chemicals and plastics, fabricated metal products, machinery, electrical products, transportation equipment and instruments).
To save space, the time series for the other OECD countries in the dataset are not reported but are available from the authors upon request.
Canova and Marcet (1995) argue that such a normalization should eliminate a significant part of the cyclical noise in the data.
Yit and IYit were calculated using data from the World Development Indicators 1998 database.
We have included a set of year dummies which should largely take care of the contemporaneous correlation across countries.
The methodology followed by the vast majority of researchers up to 1995, i.e. cross-sectional regression, was based on the hypothesis of a growth process characterized by a smooth path towards a steady state. As Islam (1995) and Caselli et. al. (1996) have demonstrated empirically that this underlying hypothesis is invalid, we have used panel data estimates which are not subject to this restrictive hypothesis on the growth process and allow for heterogeneity in steady state output levels.
Since we include country dummy variables, we cannot include initial per capita GDP which also varies across countries but not over time.
Based upon Monte Carlo evidence, the Im et. al. (1995) unit root test for heterogeneous panels has substantially more power (and better small sample properties) than individual ADF tests.
As the paper measures product variety in traded goods and not only capital goods, an alternative interpretation of this result, however, is the demand theory formulated by Linder (1991), where high income countries have a more advanced and differentiated consumption structure. According to Linder’s (1991) theory, the causal link runs from real income per capita to the degree of product variety. Barker (1977) acknowledges the contribution of Linder (1961) and develops a similar variety hypothesis according to which consumers love variety and therefore exports and imports tend to increase more than proportionally with real income per capita.