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20 Maria Abreu, Henri L.F. de Groot and Raymond J.G.M. Florax(1995), who argue that productivity is a function of both domestic and foreignR&D, with the latter spilling over through trade. Focusing on the 22 OECDcountries that conduct the bulk of the world's R&D, the authors constructmeasures of the domestic R&D stock using accumulated R&D expenditures, andmeasures of the foreign R&D stock using trade-weighted measures of the R&Dstocks of each country's trade partners. In this way the authors give more weightto R&D spillovers from countries that are located relatively close in terms ofbilateral trade (and indirectly in terms of physical distance, since trade is afunction of physical distance). The results indicate that R&D spillovers aresubstantial. In a follow up to this paper, Coe et al. (1997) extend the analysis tostudy R&D spillovers from the OECD countries to a large number of lessdevelopedcountries. The results indicate that R&D spillovers from industrializedcountries to less-developed countries are substantial. Keller (2002) alsoexamines the effect of foreign R&D on domestic productivity, using data disaggregatedby industry for a sample of OECD countries. In order to test whethertechnology diffusion is local or global in scope, the foreign R&D term in themodel is weighted by an exponential distance decay function. Estimation resultsconfirm that technology spillovers are declining with physical distance. Theresults also indicate that sharing a common language facilitates technologydiffusion, and that technology spillovers are becoming less localized over time.Other studies of spillovers within the non-spatial econometrics literaturehave mostly focused on the effects of political instability in neighboringcountries. Ades and Chua (1997) find that regional instability (defined as theaverage number of coups and revolutions in neighboring countries) has a negativeeffect on growth. Murdoch and Sandler (2004) find that a civil war within adistance of 800 km can have a negative effect on growth.One advantage of using spatial econometrics to estimate models oftechnology diffusion and spillovers is that spatial relationships are summarizedin a spatial weights matrix. This allows the estimation of more complex modelsof spatial heterogeneity and spatial dependence. However, there is also a relateddisadvantage, which is that the spatial weights matrix (and by extension therelationships between countries) must be exogenous and in cross-section modelsit should be invariant over time. This precludes using trade, foreign directinvestment or measures of cultural distance as spatial weights. One possiblesolution could be the use of geostatistical models (discussed in Section 3), butmost studies of growth using spatial econometrics have focused on physicaldistance as a measure of the strength of bilateral ties. Interestingly, Keller (2002)uses physical distance to capture bilateral ties in order to avoid focusing on onechannel of technology diffusion. Trade, foreign direct investment, and contactsbetween researchers have all been shown to be a function of physical distance inthe empirical literature. There may thus be an additional methodological reasonfor focusing on bilateral relationships measured in terms of physical distance.
Région et Développement 212.2. Spatial econometrics and growthWe now turn to the literature within spatial econometrics that has studiedthe impact of space on economic growth. In order to analyze the type of methodsand techniques that have been used within this literature, we have compiled a listof all the spatial econometrics studies on growth (both published andunpublished) that could be found using search engines and publication databases(EconLit, SSRN, Econpapers, IDEAS). The studies are listed in chronologicalorder in Table n° 2. We have included each study only once; if a study has beenpublished we no longer consider the original working paper.Figure n° 1 shows the chronological distribution of studies. Over time, thenumber of published and unpublished papers increases exponentially, indicatinga rising interest in applying spatial econometric techniques to the study ofgrowth. It is also interesting to see the geographical scope of the studies. Interms of the countries or regions covered by the data, 68% of all studies useEuropean data (at various levels of aggregation). The second largest category iscomprised by studies that use country data from all continents, 11%. Thegeographical origin of the remaining studies is the US and Canada (8%), LatinAmerica (6%), Asia (5%), and a combination of the above (2%).Figure n°1: Chronological distribution of studies2520Number of studies1510501995 1996 1997 1998 1999 2000 2001 2002 2003 2004*Year* Forecast for 2004 calculated using observations from January-August 2004.A classification of the studies according to the channel through whichlocation affects growth reveals that the vast majority of the studies (63%) fallinto the standard spatial econometrics category (viz., they follow standard spatialeconometric procedures, and the emphasis is on methodology rather than on
Page 2: 14 Maria Abreu, Henri L.F. de Groot
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