Essentially, we postulate that this method, which attempts to account for the importance of time dynamics in examining the relationship between trade openness and economic growth and accounts for the simultaneity bias in the data, is an important sensitivity analysis. LSDVC is considered an appropriate method for small samples with a small number of cross-section groups and imbalanced panels, which is consistent with the nature of our dataset.
The potential biases in the estimators resulting from endogeneity related to both reverse causality and omitted variable bias are lessened removed by incorporating lag-dependant variable in the growth equation to be estimated. Table 3 presents the results of estimated models using both methods of investigation. We compare the results and discuss the robustness of the variables of interest, in the sections to follow.
In line with the theoretical propositions and previous empirical analysis discussed, we specify the following growth equation to be estimated:. We also control for individual country effects CountryDummy and specific time effect Time. Importantly, Openness variable in this analysis is measured using three different proxies of trade openness that are integrated individually in growth equations to be estimated due to possible multicollinearity issues indicated by the correlation between trade openness measures see correlation matrix subsequently.
Along the lines of catching-up hypothesis, there should be a strong tendency for convergence among industrialising economies including transition economies. Therefore, we anticipated a negative and significant effect of the initial income levels on growth rates in CEE countries.
However, given the non-significant coefficient obtained on the convergence variable in all estimations, when including its lagged values, we proceed by estimating growth equations using log values of GDPpc in period t , to account for the differences in human capital and technological advancement of CEE countries.
The results presented in Table 3 relate to logGDPpc measured as the log of income levels in time t. The tariff rate data are based on World Bank data and computation of effective tariff rate applied. Considering the relatively heterogeneous characteristics of CEE countries related to size, scope and structure of the economies considered on one side, and homogeneous characteristics of trade policy, it is postulated that these specificities of the dataset indicate advantageous framework to analyse the impact of trade policy liberalisation on countries with supposedly different industrial structure and levels of technological proficiency.
In particular, we emphasise the vital importance of establishing econometric framework for analysing patterns of catching up through trade integration with the more advanced EU countries, in an attempt to investigate how homogeneous trade policy regime has affected transition economies depending on their level of development and technological prowess captured by the dummy variable in the extended growth model. The dummy variable is set to be 1 for the less-developed transition economies namely Romania, Bulgaria, Latvia and Lithuania, accounting for the differences in the initial conditions and patterns of industrial catching up.
Moreover, although trade intensity indicators may fail to accurately capture the effect of trade openness or precisely trade policy regime, in our empirical setting, higher trade intensity ratios are effectively capturing individual country ability to rise economic efficiency and boost technology transfer via increased trade integration with technologically innovative EU countries. Therefore, we expect a positive relationship between trade intensity variables and economic growth.
Apart from this, the average tariff rate is incorporated in growth regression to investigate how liberalisation policy has affected growth performance in a general transition and dynamic framework by estimating growth regression relying on LCDVC technique. Not only that these countries have relatively weaker institutional and technical capacity and underdeveloped instruments of trade protection by the means of non-tariff barriers to trade compared to high-income EU industrialised countries, but essentially the integration into EU economic structures implied a homogeneous trade-related regulatory framework including the application of the mutual recognition principle when it comes to non-tariff barriers to trade effective as of In this study, we rely on the average tariff rate as a proxy for trade policy among CEE countries.
Link between trade liberalisation and economic growth: Felix Magg: ynykyvykeb.tk: Books
Considering the graphical presentation of the tariff data, we conclude that CEECs have followed rapid trade liberalisation policies, and homogeneous trade regime has been applied in the context of EU integration. Last but not least, the EU integration process, which implied universal institutional and policy setting and transitory requirements of the EU enlargement, implies systematic reduction in transaction costs often related to national regulatory regimes and rules of doing business.
The impact of different regulation on trade can be examined by assuming minimum or no transaction costs across transition economies. Tables 1 and 2 present the descriptive statistics of variables and the correlation matrix among variables, respectively. The three different specifications of the models relate to three distinct measurements of trade openness variables export, import and total trade to GDP indices, as explained.
We begin the discussion with Openness and the three distinctive measures of trade intensity.
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The results of our empirical investigation support the hypothesis that trade volumes are positively associated with growth performance, which is consistent with earlier empirical findings. This is to say that CEE countries, which are more integrated with the EU economies through trade, are likely to grow faster than other supposedly less-integrated countries. Essentially, CEEC seems to have benefited from increasing trade integration in terms of growth rates.
All openness variables are significant and positive in all models estimated. The coefficient on openness variable measured as the total trade to GDP model 1 is 0. The marginal effect of both exports and imports share to GDP is positive and significant and thus estimated at about 0.
These results are robust to sensitivity analysis applied using LSDVC econometric framework which accounts for the likelihood of reverse causation between growth and trade volumes emphasised in a number of empirical studies. The obtained coefficients of all trade intensity measures are about the same value in the LSDVC estimations, pointing to the consistent and robust estimate of trade effect on economic growth relying on both estimation techniques.
Standard errors are in parentheses. However, this is not to say that these conventional trade openness measures reflect on trade orientation of CEE countries. These measures have important shortcomings discussed in a number of studies indicated previously in the chapter. Therefore, in this chapter, we do not rely on these measures and do not discuss the impact of these variables in economic growth in the context of trade policy impact. Instead, the other trade-related variable, namely the Tariff Rate variable, is used to measure the effect of trade restrictions on economic growth in CEE countries.
Essentially, the results of our analysis do not support the hypothesis that trade barriers are harmless to economic growth. On the contrary, while the results of PCSE estimations point to the insignificant effect of trade barriers on economic growth, although negatively related to economic growth in Models 1 and 3, the obtained coefficients are highly insignificant with the p-value of over 0.
The results of the LSDVC estimations are estimated in an attempt to remove simultaneity bias point to, however, a significant and yet positive effect of trade barriers on economic growth. It is noteworthy to emphasise that the LSDVC estimator has superior properties if compared to static panel estimators when correcting for autocorrelation, and results obtained with respect to dynamic model should implicitly be considered robust to possible simultaneity bias.
Given the inconsistency in the results obtained, it is perhaps safe to conclude that we find no evidence that trade restrictions negatively affected growth performance of transition economies. On the contrary, the results of the dynamic growth model estimations support the hypothesis that trade restrictions may be beneficial to growth performance depending on the symmetries between trading partners.
Given the homogeneous trade policy regime among individual countries in our dataset, we conclude that trade liberalisation across CEE countries has not been positively associated with growth performance of CEECs, and we find some evidence that trade restrictions have seemingly had a positive effect on the growth performance of CEEC countries if accounting for the dynamic relationship between economic growth and trade openness.
Finally, the results obtained with respect to dummy variable capturing the growth performance of less-developed CEE countries relative to other CEECs suggest that these countries have been growing faster than other supposedly more developed CEE in the period under investigation. This result is in accordance with the theoretical proposition and the catching-up hypothesis. All other variables enter the growth regression with expected signs and significance, pointing to the importance of macroeconomic stability in the growth process captured by the government balance variable, as well as domestic investments depicted by Gross Fixed Capital Formation.
In this chapter, we investigate the impact of trade openness on economic growth in CEE countries over the — periods the actual dataset considering the missing observations in the data before for the trade intensity indicators. Essentially, we use a variety of trade openness measures to deepen our understanding on how trade volumes affect growth performance on one side and how effective has been trade liberalisation policy per se in enhancing the growth performance of CEECs.
The results of our empirical investigation provide robust evidence that trade intensity measures are positively associated with economic growth, pointing to the benefits of trade integration through not only exports but also increasing imports from technologically innovative EU countries to less-advanced CEE economies.
The results seem not sensitive to simultaneity issues, robustness check and different model specifications applied. Apart from this, in this chapter, we postulate that trade intensity indicators do not reflect on trade policy regime. Given the particularities of our dataset referring to homogeneous trade policy regime applied among CEE countries in the context of EU integration and the econometric framework used, the effect of trade barriers on economic growth in transition countries proxied by the average tariff rate seems ambiguous.
Although the results obtained with respect to the impact of tariff rate on economic growth are not robust to different methods of estimation and sensitivity analysis, we conclude that tariff barriers have not been negatively associated with economic growth in the selected transition countries.
Surprisingly, unlike the traditional perception on the negative link between trade barriers and economic growth well substantiated in the empirical literature, we find some evidence that trade barriers may be beneficial to growth performance while relying on a dynamic econometric framework and when removing the possible simultaneity bias.
The results of this analysis have important theoretical and policy implications, implying that trade openness and economic growth are not a simple relationship and that its effects depend on differences in the levels of development, size and technological proficiency which seem consistent with the growth and development literature. Help us write another book on this subject and reach those readers.
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Does trade cause growth?
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