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The long-run effect of foreign direct investment on total factor productivity in developing countries: a panel cointegration analysis

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Abstract

This paper examines the long-run effect of the level of foreign direct investment (FDI) on the level of total factor productivity (TFP) for 49 developing countries for the period 1981–2011 using panel cointegration and causality techniques. It is found that (i) FDI has, on average, a negative long-run effect on TFP in developing countries, (ii) long-run causality runs in only one direction, from FDI to TFP, (iii) in the short run, TFP has a negative effect on FDI, and (iv) the long-run effect of FDI of TFP differs between selected groups of countries: While the estimated long-run FDI–TFP coefficients are always relatively large, negative, and significant for countries with lower levels of human capital, financial development, and trade openness, the estimated effects are relatively small, insignificant, or even significantly positive for subgroups of countries with higher levels of human capital, financial development, and trade openness.

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Notes

  1. Gourinchas and Jeanne (2006) show in a calibrated neoclassical model that the welfare gains from switching from complete financial autarky to perfect capital mobility are negligible compared to the welfare gains from productivity improvements in some countries.

  2. Barro (1999: 135) notes that “[r]ecent theories of endogenous growth allow for a sharper perspective on this residual [TFP]. Specifically, the residual can be clearly interpreted within settings that allow for increasing returns and spillovers or in models in which technological progress is generated by purposeful research. These interpretations provide guidance for explaining the residual in terms of R&D outlays, public policies, and other factors.”

  3. For example, the Monterrey Consensus of the International Conference on Financing for Development notes that “[FDI] is especially important for its potential to transfer knowledge and technology [...]” (UN 2002, p. 9). Similarly, the OECD (2002, p. 95) notes that “many countries view [FDI] as a primary means to acquire technology and knowledge [...].”

  4. The issue of parameter heterogeneity has also been addressed in other studies. Blomström et al. (1994), for example, find that lower-income developing countries do not enjoy substantial growth benefits from FDI, whereas higher-income developing countries do. Nair-Reichert and Weinhold (2001) find in sample of 24 developing countries that FDI has, on average, a positive causal effect on economic growth, but this growth effect is highly heterogeneous. The results by Blonigen and Wang (2005) suggest that FDI affects growth more strongly in less developed countries than in developed countries. Fillat and Wörz (2011) find that the growth effects of FDI are stronger in catching-up economies (such as Hong Kong, Singapore, South Korea, Spain, and Portugal) than in highly industrialized countries (such as Australia, Germany, Ireland, the UK, and the USA). Finally, the results by Mayer-Foulkes and Nunnenkamp (2009) suggest that FDI contributes to convergence in developed countries, but to divergence in underdeveloped countries.

  5. The literature typically distinguishes three types of FDI. Vertical or efficiency-seeking FDI is driven by international factor price differences. It takes place when a firm fragments its production process internationally, locating each stage of production in the country where it can be done at the lowest cost. Horizontal or market-seeking FDI, in contrast, is motivated by the desire to obtain market access and to avoid trade frictions, such as transport costs and import protection in the host country. The decision to engage in horizontal FDI is guided by the proximity-concentration trade-off in which proximity to the host market avoids trade costs but incurs the added fixed cost of building a second production facility. FDI of this type thus occurs when a firm decides to serve foreign markets through local production, rather than through exports, and hence to produce the same product or service in multiple countries. Finally, resource-seeking FDI occurs when firms identify specific host country locations as an attractive source of natural resources at the lowest cost (see, e.g., Herzer 2011).

  6. Dunning (2003, p. 285), for example, notes that “by far the greater part of FDI in developing countries, notably in the larger and faster growing economies, is directed toward accessing local natural resources and/or national or regional markets.”

  7. Herzer (2012, p. 396) argues that “FDI does not contribute to capital formation [...] if FDI takes the form of mergers and acquisitions, and if the proceeds of the sale of the assets are fully consumed.” However, a large fraction of FDI in developing countries is greenfield investment. Moreover, it is reasonable to assume that the proceeds from the sale of domestic firms are in part invested in the domestic market. UNCTAD (2000, p. 168) therefore argues that “over the longer term, there is no reason to expect any difference in the impacts on capital formation of the two modes of entry.”

  8. It is clear that the aggregate elasticity \(\beta \) in Eq. (4) masks differences across different types of FDI. Although these differences are not the subject of this paper, it should be noted that according to conventional wisdom, market-seeking FDI offers more potential for productivity spillovers than resource-seeking and efficiency-seeking FDI (see, e.g., Beugelsdijk et al. 2008; Farole and Winkler 2012). The key argument underlying this conventional wisdom is that firms engaging in efficiency-seeking FDI have little incentive to create vertical linkages with local firms because foreign affiliate production is intended for the global market. In the case of market-seeking FDI, it is argued that such linkages are more likely to occur because foreign affiliate production is intended for the host country market. However, when foreign affiliates primarily export, as is the case with efficiency-seeking FDI, they do not compete with domestic firms, and therefore, they also do not have an incentive to prevent technology diffusion to domestic firms. Thus, it is theoretically unclear whether horizontal FDI is superior to vertical FDI. Unfortunately, there are no adequate data available to provide meaningful estimates of the effects of market-seeking FDI, efficiency-seeking FDI, and resource-seeking FDI on TFP in developing countries (and such an analysis is also beyond the scope of this paper). We thus leave this question for future research.

  9. Although the Penn World Tables (version 8.1) report data on TFP growth and on relative TFP levels (relative to the USA), this database contains no data on the (absolute) level of TFP.

  10. Because TFP is defined as that part of output that is not directly attributable to factor inputs, Eq. (5) is simply a definition of TFP, not a TFP function. Equation (5) thus does not imply that TFP depends negatively on the amount of inputs used and positively on output. Rather, output depends positively on the amount of inputs used and the level of TFP.

  11. Bosworth and Collins (2003, p. 115) note that “[i]n principle, growth accounts can be constructed to yield estimates of TFP that are independent of the functional form and the parameters of the production process. This requires assuming both a sufficient degree of competition so that factor earnings are proportionate to factor productivities, and the availability of accurate data on factor shares of income.”

  12. The coefficient on the first four years is the return to schooling in sub-Saharan Africa (13.4%). The coefficient on the second four years is the world average return to schooling (10.1%). The coefficient on schooling above eight years is the OECD return to schooling (6.8%). All coefficients are taken from Psacharopoulos (1994).

  13. The classification of developing countries follows that of the IMF, for the years 1992–2011 (IMF 2000, 2014)—more than half of the sample period.

  14. The Hadi (1992) procedure measures the distance of data points from the main body of data and then iteratively reduces the sample to exclude distant data points.

  15. The number in parenthesis denotes the order of integration. The order of integration is the number of times a time series must be differenced to make it stationary. Thus, an I(1) variable must be differenced once to make it stationary, or I(0).

  16. This result necessarily also applies to stationary measurement error (see, e.g., Stock 1987 which occurs when the true variable of interest is cointegrated with the observed proxy). In our case, this means that stationary measurement error in our measure of TFP (and/or FDI activity) should not seriously bias our results. If, in contrast, the measurement error is non-stationary, our cointegration tests should fail to reject the null hypothesis of no cointegration between \(\log ({\textit{TFP}}_{ {it}})\) and \(\log ({\textit{fdi}}_{ {it}})\). The reverse is that if cointegration is found between \(\log ({\textit{TFP}}_{ {it}})\) and \(\log ({\textit{fdi}}_{ {it}})\), then it can be concluded that there is no non-stationary measurement error in \(\log ({\textit{TFP}}_{ {it}})\) and/or \(\log ({\textit{fdi}}_{ {it}})\).

  17. An extension of the panel DOLS estimator is the dynamic seemingly unrelated regression (DSUR) estimator proposed by Mark et al. (2005), which was not included in the simulation study by Wagner and Hlouskova (2010). Unfortunately, the DSUR estimator is seriously biased or not applicable when, as in this study, the number of time periods is small relative to the number of cross-sectional units (see, e.g., Mark et al. 2005; Di Iorio and Fachin 2012).

  18. In the following, we always use demeaned data to account for cross-sectional dependence through common time effects.

  19. The countries and the subsamples are listed in Table 9 in the Appendix. The data used to rank the countries according to their human capital, financial market development, and trade openness levels are averaged over the period 1981–2011.

  20. Findlay (1978, p. 2) argues that domestic firms can only benefit from foreign firms if the technology gap between foreign and domestic firms is not “too wide.” Aghion et al. (2008) present a Schumpeterian growth model explaining why more FDI could have positive growth effects only where local production is relatively close to the technological frontier, whereas growth remains unchanged or is reduced when local firms do not have enough absorptive capacity to benefit from technology spillovers.

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Acknowledgements

We are grateful to two anonymous referees and an anonymous associate editor for many helpful suggestions and constructive comments.

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  1. Department of Economics, Helmut-Schmidt-University, Holstenhofweg 85, 22043, Hamburg, Germany

    Dierk Herzer

  2. Helmut-Schmidt-University, Hamburg, Germany

    Julian Donaubauer

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  1. Dierk Herzer
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Correspondence to Dierk Herzer.

Appendix

Appendix

See Tables 7, 8, and 9.

Table 7 Summary of multi-country macroeconomic studies on the impact of FDI on total factor productivity
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Table 8 Countries and summary statistics
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Table 9 Countries in the subsamples
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Herzer, D., Donaubauer, J. The long-run effect of foreign direct investment on total factor productivity in developing countries: a panel cointegration analysis. Empir Econ 54, 309–342 (2018). https://doi.org/10.1007/s00181-016-1206-1

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