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Measuring the technology diffusion from multinational enterprises.

JEL F01 * F20. F23 * O10. O15 * O31 * 047. 057. L24

Many empirical studies (e.g. Borensztein et al., Journal of International Economics, 1998) use FDI to GDP ratio to measure the technology diffusion effect of Multinational Enterprises (MNEs) on host country economic growth. However, this measure cannot extract the technology diffusion effect from the other productivity effects of MNEs. To avoid this problem, Xu (Journal of Development Economics, 2000) measures the technology diffusion from MNEs by technology transfer spending of foreign affiliates to GDP in host countries. However, this measure omits R&D expenditures of foreign affiliates as a component of the technology spending of MNEs' affiliates. The R&D efforts undertaken by the foreign affiliates are considered a main channel of technology diffusion from MNEs (Aitken and Harrison, American Economic Review, 1999).

More specifically, this study has four contributions to the empirical literature. First, we argue there is a missing component of the measure of technology diffusion from MNEs (i.e. foreign affiliates' R&D expenditure). In this study, the technology diffusion effect of MNEs is measured by the following ratio:

DIF = MNE_Tech/GDP. (1)

where DIF is the technology diffusion effect of MNEs. MNE_Tech is the affiliates' spending on royalties, license fees, and R&D, and GDP is the gross domestic product of the host country.

Second, several previous studies use total factor productivity (TFP) rather than labor productivity. TFP exacerbates the measurement problems in cross-country comparisons. Many official estimates used quite different methods to deal with depreciation and aggregation (Sargent and Rodriguez, International Productivity Monitor, 2000). This study will use labor productivity to obviate these measurement problems.

Third, some previous studies use TFP for a country relative to US TFP as a measure of the technology gap. TFP is a poor measure of the technology level for a country (Jones, W.W. Norton & Company Inc, 1998). This study will use the United Nations Development Programme [2001] technology capabilities measure (i.e. receipts of royalties and license fees from abroad per 1,000 people).

Fourth, several previous studies did not take into account the contemporaneous error correlation. This study will examine contemporaneous correlation using seemingly unrelated regression model described in Park (Journal of the American Statistical Association, 1967).

We extend the Xu (Journal of Development Economics, 2000) sample by adding the annual observations from 1995 to 2000. The sample is divided into four groups, classified by the average years of secondary schooling in the male population. More specifically, our panel data model examining the technology diffusion effect of MNEs is:

[] = [N.summation of (J=1)] [[beta].sub.0j][D.sub.jt] + [T.summation of (s=1)] [[beta].sub.0s][] = [[beta].sub.1][DIF_ANU.sub.1,it] + [[beta].sub.2][DIF_AVG.sub.2,it] + [[beta].sub.3][H.sub.3,it] + [[beta].sub.4][RL.sub.4,it] + [] (2)

where [] represents GDP growth per worker of country i at time t, [D.sub.jt] are cross-section dummies, [] are time dummies, H is the average years of secondary schooling in the male population and RL is the receipts of royalties and license fees from abroad per 1,000 people. When j = i, [D.sub.jt] = 1, otherwise [D.sub.jt] = 0. Similarly, when s = t, [] = 1, otherwise [] = 1. The country and time dummies allow the intercept to vary over countries as well as time. This can capture the steady state differences across countries. We link the DIF variable with annual observations and average dummy observations to examine if there is a difference between the results of annual observations and the results of averages observations. To avoid the endogeneity problem, a lagged DIF is used.

The empirical results reveal technology diffusion from MNEs has a positive and significant impact on the productivity growth in both developed and developing countries regardless of the average years of secondary schooling in the male population. These results are consistent with some previous studies (e.g. Lensink and Morrisey, CREDIT Research Paper, 2001). On the other hand, our results contradict other previous studies such, as Xu (2000). The first explanation for this contradiction is these studies lack of accounting for the cross section correlation (Elmawazini, East London Business School Working Paper Series, 2005). The second explanation is our use of more reliable variables and measures. The third and last explanation is the average years of secondary schooling in the male population is a poor proxy for human capabilities in host countries.

Published online: 25 July 2008

K. Elmawazini

Economics Department, Kazakhstan Institute of Management,

Economics and Strategic Research (KIMEP),

4 Abai Ave., office 226, Almaty 050010, Republic of Kazakhstan

K. Elmawazini ([mail])

Economics Department, University of Ottawa, 55, Laurier Avenue East, Desmarais Building,

Room 10101, Ottawa,

Ontario K1N 6N5, Canada

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Title Annotation:ANTHOLOGY
Author:Elmawazini, Khaled
Publication:Atlantic Economic Journal
Geographic Code:1CANA
Date:Sep 1, 2008
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