Our empirical analysis utilises panel data on bilateral FDI stocks from 34 OECD countries into 45 ACP countries over the period 2000–2017 to consider the role of PTAs in attracting FDI. We control for policies relating to trade, taxes and investment, along with other explanatory variables identified in the literature. We conclude the prevalence of market seeking FDI in the ACP region, with a role for regional integration in accessing surrounding market potential. We find no significant effect of PTAs on FDI in the Caribbean, while in Africa, the effects depend on the presence of a bilateral BIT.
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Appendix Table 6 lists the ACP countries.
UNCTAD World Investment Report, 2018.
Each methodology has its strengths and weaknesses. Single country case studies have the advantage that the data is more likely to have been reported on a consistent basis and changes in some control variables (e.g. tax policy regimes etc.) can be observed and accommodated more readily. But empirical generalisations from such studies are limited to similar contexts. The conclusions of longitudinal studies are potentially more widely generalisable, but the data used is more likely to suffer from inconsistent reporting and researchers must often consign policy differences to host and source country fixed effects.
Wacker (2016) reviews the alternative measures of the activities of multinational corporations and concludes that “foreign direct investment (FDI) stock data is indeed a good proxy for measuring most real economic activities of multinational firms”. Bellak (1998) and Lipsey (2007) also provide detailed discussions of the issues involved in the choice of stocks over flows and the measurement of FDI in general.
The F test on the significance of country fixed effects rejected the null hypothesis of no significant difference across countries (F = 27.46, p = 0.00) at the 5% level of significance, indicating that pooled OLS is not appropriate.
The Breusch Pagan test results for heteroscedasticity (χ2 = 86.25, p = 0.00) rejected the null hypothesis of homoscedasticity at the 5% level of significance. The Woodridge test for autocorrelation indicated the presence of serially correlated residuals (F (1,267) = 78.4, p = 0.00).
A common problem in testing endogeneity is the identification of valid instruments for the endogenous variables. A valid instrument should be highly correlated with the endogenous explanatory variable but not with the error term, and we used a one period lag of the suspect endogenous variables as an instrument to test for possible endogeneity using the Durbin-Wu-Hausman test. The null hypothesis of exogenous variables was not rejected for host GDP or trade openness (see Appendix Table 13). Other variables that may give rise to endogeneity problems are PTA and BIT, but due to the difficulty in obtaining valid instruments for these variables and the inappropriateness of using their lagged forms, we do not test for their exogeneity here.
Given the possibility of a lagged effect of GDP (host or parent) on current FDI, we included a one-year lag of these variables and re-estimated our base model. Neither variable was significant, with very little change in the other coefficients.
In the Pacific, only PNG is signatory to a BIT.
Except that OECD GDP becomes significant.
The splitting of the PTA dummy in this way does affect some coefficients in our two smaller regional samples. Investment risk becomes positive and significant in the Caribbean, but switches to negative and significant in the Pacific. Distance is now also negative and significant in the Caribbean.
Yackee (2009) notes the prevalence of sophisticated investment contracts in the natural resources and infrastructure concession sectors, which provide more deal-specific provisions than the ambiguous one-size-fits all BIT provisions.
We re-estimated our equation after first-differencing the three non-stationary variables, and except for the coefficient size of ACP GDP and OECD GDP, the magnitude and significance of all other variables show very little difference.
A FEM can be estimated in different ways, including within-transformation, between-effects or LSDV approach. The latter was chosen as it allows time invariant variables to be included.
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We thank the Editor, an anonymous referee, Neil Foster-McGregor, Martin Richardson and the participants of 2017 Australian Conference of Economists for their useful comments and suggestions.
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Calculation of surrounding market potential
Our approach follows the Blonigen et al. (2007) measure of surrounding market potential except that we only include other countries within a specific sub-region, rather than all countries everywhere. The sub-regions are defined as the five economic groupings of the African countries (West Africa, Central Africa, Eastern & Southern Africa, Eastern African Countries), the Caribbean and the Pacific. The weights are calculated as a simple inverse function where the shortest bilateral distance within the region is assigned weight of 1, and all other bilateral distances receive a weight that declines as per the equation below:
where distanceij is the distance between country i and j, and the closest country to j in that region is k. This weight is then multiplied by the GDP (PPP) of country i. The inverse distance weighted GDP of all other countries (excluding j) in the sub-region of country j are summed to give the surrounding market potential of country j.
Tests for stationarity
The Im-Pesaran-Shin (IPS) unit root test is used as it allows the autocorrelation coefficient to vary across cross-sections. It calculates a standardised t-bar test statistic based on the averaged augmented Dickey Fuller statistics for panels (Im et al. 2003). The results are summarised in Table A5, where the null hypothesis of a unit root is rejected for all variables except for ACP GDP, OECD GDP and SMP. With the dependent variable (FDI) as a stationary process, the inclusion of these three non-stationary variables does not raise concerns of spurious correlation.Footnote 14 Moreover, two of these non-stationary variables (ACP GDP and SMP) are also cointegrated (see Table A6), and the residuals from the FGLS estimation of eq. 1 are stationary (see Table A7).
Fixed or Random effects? The unobserved country specific factors can be incorporated into the estimation through a fixed effects model (FEM) or a random effects model (REM). In a FEM, these unobserved characteristics are subsumed in the intercept and hence each country has a different intercept, while in a REM they are considered as part of the error term (Baltagi 2008). The time invariant individual specific effects are allowed to be correlated with the regressors in a FEM whereas they are purely random in a REM. The Hausman specification test (χ2 = 72.59, p = 0.00) rejects the null hypothesis that a REM provides consistent estimates and hence, the FEM is selected. Year effects are jointly insignificant (F = 1.35, p = 0.16) at the 5% level and hence a one way FEM is estimated. Additionally, the FEM is an appropriate specification when the focus is on a specific set of countries making inference conditional on these observed countries (Baltagi (2008)).Footnote 15
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Gounder, A., Falvey, R. & Rajaguru, G. The Effects of Preferential Trade Agreements on Foreign Direct Investment: Evidence from the African Caribbean Pacific Region. Open Econ Rev 30, 695–717 (2019). https://doi.org/10.1007/s11079-019-09532-y
- Foreign direct investment
- Preferential trading arrangements
- Regional integration