As discussed earlier, a Hausman-Taylor estimator takes the advantages of both the FE and RE models and addresses endogeneity by using instrumental variables from exogenous variables within the regression. The Breusch and Pagan Lagrangian multiplier test for random effects suggests that a RE model is preferred to an OLS model.29Trade policy variables (Tariff, LPI, and RTA) are treated as endogenous while economic mass, landlocked status, distance, language, and contiguity variables are treated as exogenous. The treatment of these explanatory variables is guided by a test of endogeneity. The results reported in Table 4.7 show the significance and expected signs
2007, Armstrong et al. 2008, Kalirajan 2008).
28 Baier and Bergstrand (2009) also provide an alternative approach that accounts for arbitrary
distributions of multilateral resistance but without the inclusion of fixed effects. Their approach relies on a first-order Taylor series approximation of the outward and inward multilateral resistance terms (Shepherd 2012).
77
of most independent variables. This is also confirmed by the t-statistics for individual independent variables and the F-statistics for joint significance of all variables. Table 4.7 The determinants of global production sharing
Parts and components Final products
(1) (2)
ln(exporter’s economic mass) 1.334*** 1.345*** (0.0186) (0.0222) ln(importer’s economic mass) 0.954*** 1.023***
(0.0248) (0.0239) ln(importer’s tariff) –0.0932 –0.0687 (0.0576) (0.0463) ln(exporter’s LPI) 0.774*** 1.091*** (0.130) (0.107) ln(importer’s LPI) –0.271* 0.223* (0.141) (0.115) RTA 0.232*** 0.138*** (0.0529) (0.0409) LLAC 0.444*** 0.325*** (0.0952) (0.107) LLDC –0.468*** –0.253*** (0.0773) (0.0863) ln(distance) –1.126*** –0.854*** (0.0456) (0.0480) contiguity 1.211*** 1.691*** (0.189) (0.208) language 0.981*** 0.847*** (0.0884) (0.0981) constant –34.42*** –40.97*** (1.103) (1.192) observations 37,878 39,769
Note: Dependent variable is the log of exports of P&Cs and final products that are estimated in separate equations. Year-specific effects are included but not shown here. Robust standard errors are in
parentheses. ***, **, and * indicate significance at 1 per cent, 5 per cent, and 10 per cent levels, respectively.
Source: Author’s estimations.
The coefficients of the mass variables are significant, yielding elasticity of
approximately 0.9 to 1.3, which is generally in line with other studies (Head and Mayer 2014). Distance also has a significant coefficient, yielding almost negative unity
78 literature around –0.9 (Head and Mayer 2014).
For trade policy variables, LPI and RTA have a significant and positive impact on networked trade, as expected. This suggests that improving trade-related logistics and joining trade agreements are supportive of trade associated with production sharing through reducing the costs of services links. Specifically, an improvement in exporters’ logistics performance index by one per cent will raise the exports of P&Cs and final goods by 0.7 per cent and 1 per cent, respectively; other things remain unchanged.30 Similarly, other things remain unchanged; countries forming regional trade agreements will have their P&C exports 23 per cent higher than those that are outside regional trade agreements (or 14 per cent for the case of final exports).31 The impact of tariffs is found to be insignificant for both P&C and final exports. This may be partly explained by the fact that tariffs are only a crude measure of trade protection given that there are also other behind-the-border barriers playing a role, but they are difficult to measure. As for control variables, the negative sign on the LLDC dummy confirms that
landlocked developing countries are disadvantaged in integrating into networked trade. But this is not the case for landlocked developed countries, given the positive sign on LLAC. Shared land borders and common languages promote networked trade as normally found gravity modelling.
These results are consistent with findings from Arvis et al. (2013) that landlocked status and distance are the major sources of trade costs. Nevertheless, logistics performance is found to be at least as important, and more so than tariffs. Saslavsky and Shepherd (2014) also find that logistics performance is particularly important for trade among developing countries in Asia and the Pacific, which is where the emergence of global production sharing has been most prominent.
What do these econometric results suggest? Landlocked developing countries are disadvantaged by their geographical location in integrating into international markets. The growing interconnectedness of different economies through global production sharing creates important opportunities for a developing country like Laos but also new policy challenges. Because these landlocked countries face higher trade costs than their comparable coastal neighbours, partly due to the importance of policy in addressing
30 However, the negative sign on importers’ logistics performance index for the P&C equation appears to be somewhat counter-intuitive.
79
their sources, policy measures can do a great deal in reducing these costs while boosting trade integration.
While infrastructure development forms an important element in enabling landlocked developing countries to better integrate into the international economy, building hard infrastructure alone without changes in policies (soft infrastructure) to improve
administrative efficiency will not necessarily lead to lower transport costs (Kowalski et al. 2015). Logistics services are more important for limiting the costs of being
landlocked than investing massively in infrastructure that neglects the functioning of logistics services (Arvis et al. 2010). This appears to reflect Laos’ situation quite well. As discussed in Chapter 3, Laos is among the world’s bottom 10 in a recent survey on logistics performance despite the efforts to transform itself to be ‘land-linked’. Laos remained behind all other ASEAN members in almost all aspects including efficiency in border clearance, trade and transport infrastructure and logistics competence.
Robustness checks
To test for sensitivity of the findings, an alternative variable for services links and other estimation methods has been experimented with. First, the liner shipping index from the United Nations Economic and Social Commission for Asia and the Pacific was used instead of the logistics performance index. The results in Appendix 4.C were largely the same as those in Table 4.6, with respect to the significance and signs of all variables. However, the magnitude of the liner shipping index was somewhat lower than that of the logistics performance index.
Second, the fixed effects estimator was used with one based on linear least squares estimation and another one on Pseudo-Poisson Maximum Likelihood (PPML)
estimation. These techniques are chosen given their strong theoretical underpinnings, and the fact that they have been extensively employed in recent gravity model literature, as noted earlier. The PPML method is assumed to account for zero trade flows and heterogeneity due to log-linearisation of a gravity equation (Santos Silva and Tenreyro 2006).In this dataset, zero trade flows are only 32 observations (0.029 per cent of 108,838 observations in 2007, 2010, and 2012) for P&C exports, and 3,248
observations (3.08 per cent of 105,622 observations for the three years) for final goods exports. This suggests that non-PPML estimations may not necessarily suffer from zero trade bias. As both models are estimated with an FE technique, they again cannot capture the effects of trade policy variables. However, they are provided for
80
characteristics for the core variables across the two models: linear least squares and PPML with the fixed effects estimation. The magnitude of most coefficients from the Hausman-Taylor model is closer to those of the linear least squares rather than those of the PPML estimator.
4.5 Concluding remarks
This chapter has analysed trends in global production sharing and the extent to which landlocked developing countries have integrated into this type of trade. Despite developing countries’ increasing participation in international production sharing, landlocked developing countries have lagged behind in this respect. Landlocked status indeed raises the costs of international trade, making landlocked economies
disadvantaged.
This observation has led to econometric analysis to probe the determinants of countries’ participation in networked trade. The estimation was based on a gravity model adapted for trade dominated by global production sharing. Landlocked status was found to reduce networked trade. However, improving trade-related logistics and joining regional trade agreements have a positive impact on countries’ participation in global production sharing. This highlights the importance for landlocked countries to overcome their geographical disadvantage by reducing the costs of services links associated with these factors and better integrating into global production sharing.
A couple of caveats are worth noting. First, what we found are the factors determining trade associated with networked trade at the inter-country level. There is a need to further look into participation in global production sharing at the national level; for example, what the private sector views as important for them to tap into international production sharing. This issue is further examined in Chapter 5, which focuses on the manufacturing sector in Laos. Second, this chapter analysed the patterns and
determinants of countries’ participation in global production sharing across the board. Production sharing in textiles and garments, which is driven by buyers, is essentially different from the pattern that exists in producer-driven industries such as automotive, electronics or precision equipment production. Hence, Chapter 6 explores production sharing at the sectoral level.
81