Abstract
We use a sample of 14 OECD countries and 15 manufacturing industries to test for the effect of trade on productivity. Endogeneity concerns are accounted for using the geographical component of trade as instrument as suggested by Frankel and Romer (Am Econ Rev 89(3):279–399, 1999). We find that trade, measured in terms of the export ratio, increases productivity, even if country-fixed effects such as the quality of institutions are controlled for, though results are less robust for imports. Estimates at the aggregate manufacturing level turn out much larger, emphasizing the role of inter-industry spillovers.
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Notes
An alternative, theoretically maybe more appealing, measure would be total factor productivity. But the use of the variable would have several drawbacks: it would require capital stock data (available only for a much smaller subset of our comprehensive sample) or have to rely on approximations of capital stocks, which is likely to introduce measurement errors. Second, even if capital stock measures were available, the calculation of total factor productivity is problematic: imposing income shares would assume perfect competition, which is certainly violated in a number of industries. On the other hand, estimating the income shares would be aggravated by well known endogeneity problems in estimating production functions.
See, for example, Baier and Bergstrand (2001) for a theoretical derivation of a typical gravity equation.
Rodriguez and Rodrik (2001) have challenged this assumption, arguing that geography may be related to income via (i) its effect on public health, and (ii) the quantity and quality of institutions. This may in fact be a problem in large cross sections including developed and less developed countries, but is less relevant for our sample of fourteen industrialized OECD countries. In addition, we will control for these channels by including country-specific fixed effects.
Since no industry-specific deflators are available we have to use nominal openness rather than ‘real’ openness as advocated by Alcalá and Ciccone (2004). For our sample, which includes mainly industrial countries with a similar level of development, this is no major drawback, since the trade related Balassa–Samuelson effect is usually much less relevant here.
Under normality θ k is equal to \( E[e^{{\vartheta ^{k}_{{ij}} }} ] = e^{{(\hat{\sigma }^{2}_{k} /2)}}, \)where \( \hat{\sigma }^{2}_{k} \) is a consistent estimator of the variance of \( \vartheta ^{k}_{{ij}}. \) To avoid making distributional assumptions we follow the approach suggested by Wooldridge (2003, p. 207ff.) and estimate θ k from a regression of \( Trade^{j}_{{ik}} \) on \( e^{{{\mathbf{a}}{^{\prime}} _{k} {\mathbf{X}}_{{ij}} }} \) through the origin. Since industry dummies are included in all our regressions, however, the correction does not affect the coefficients of the variables of our interest (Trade, Pop, Area) in the main model.
As mentioned above, however, more than 90% of trade is covered by the countries for which bilateral trade data are available.
For exactly identified models, Stock and Yogo provide only critical values for the size criterion (16.38, 8.96, 6.66, and 5.53 for the four quality levels).
The results by Stock and Yogo (2004) are based on the assumption of homoscedasticity and have not yet been extended to more general cases. Hence, the critical values may only be regarded as indicative for the robust F-test.
For inference, we use heteroscedasticity-robust standard errors, which is clearly important in our cross-country and cross-industry sample. But (in contrast to Frankel and Romer) we do not correct the standard errors to account for the fact that ZTrade is generated from the gravity model, since the asymptotic distribution of the test-statistics is not affected by the use of a generated instrument (see Wooldridge 2002).
For reasons of better and more consistent data we use trade in goods (manufacturing, agriculture, mining and quarrying) as explanatory variable in the aggregate models (5). Since manufacturing is by far the most important component of trade in goods this hardly matters for the results.
We also reproduced the Frankel and Romer (1999) estimates using their data and checked whether the coefficient of trade is significantly different for our subsample (by including a dummy for our 14 countries and an interaction of the trade share with that dummy). While the deviation is in fact negative, implying a substantially lower effect of trade on productivity for our sample, the difference is insignificant. In light of the fact that the estimates are rather imprecise, the results are somewhat inconclusive.
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Acknowledgments
This paper has benefited much from the comments and suggestions of two anonymous referees. We also wish to thank David Romer for very helpful comments on an earlier draft.
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Appendix
Appendix
1.1 Data description
All data are averages over the period 1995–2000. Cross-country dimension i comprises 14 countries (AUT, BEL, DEU, DNK, ESP, FIN, FRA, GBR, GRC, ITA, NLD, NOR, SWE, USA); dimension of partner countries j (j ≠ i) comprises the 223 countries contained in the CEPII dataset; for the estimation of the gravity Eq. 8 the dimension j reduces to the 44 partner countries (j ≠ i) for which bilateral trade data by industry are available. Industry dimension k comprises the 15 manufacturing industries shown in Table 6. In addition, some models are estimated for total manufacturing (k = M).
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\( Trade^{j}_{{ik}} \) trade share; \( Trade^{j}_{{ik}} = T^{j}_{{ik}} /PROD_{{ik}}, \)where \( T^{j}_{{ik}} \) is bilateral trade between country i and country j in sector k and PROD ik is the production of country i in sector k. As measures for trade (T), imports (M), exports (X), as well as imports plus exports (MX) are used. Source: OECD Structural Analysis (STAN) Database.
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y ik labour productivity; \( y^{{}}_{{ik}} = VA^{k}_{i} /L^{{}}_{{ik}}, \)where \( VA^{k}_{i} \) is real valued added of country i in sector k in 1995$ (base year 1995, converted into $ with average PPPs exchange rate over the period 1995–2000) and L ik is total employment in industry k of country i. Source: OECD Structural Analysis (STAN) Database.
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Dist ij simple distance between country i and country j. Source: CEPII (Mayer and Zignago (2006)).
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Pop i population of country i in 1000 persons. Source: United Nations: Demographic Yearbook.
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Area i area of country i in square kilometres. Source: CEPII (Mayer and Zignago (2006)).
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LL i dummy variable taking a value of one if country i is landlocked and zero otherwise. Source: CEPII (Mayer and Zignago (2006)).
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CB ij dummy variable taking a value of one if countries i and j share a border. Source: CEPII (Mayer and Zignago (2006)).
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Badinger, H., Breuss, F. Trade and productivity: an industry perspective. Empirica 35, 213–231 (2008). https://doi.org/10.1007/s10663-007-9058-8
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DOI: https://doi.org/10.1007/s10663-007-9058-8