In this study, we investigate the effectiveness of BTAs by assessing their impact on the trade flows between the economic sectors of each pair of contracting countries. To this end, we perceive all trade relationships as an international trade network ITN , in which the sectors nodes are linked by their trade volumes. Network theory applied to trade economics has gained traction in recent years as it allows incorporating topological properties into the analysis [ 18 , 19 ].
Noteworthy examples include studies on the formation and structure of economic dependencies [ 20 ], the resilience of the trade system to an outage of an industry or production facility [ 21 , 22 ], and the growth relevant dissemination of knowledge and technology [ 23 ].
In contrast to gravity models often used for related analyses [ 3 , 5 , 6 ] we can thus take higher orders of mutual economic interdependences into account. In the context of the present work, such higher-order dependencies reflect that BTAs might also affect the demand and supply of sectors indirectly linked with the exporting and importing sectors.
The existence of these indirect effects have recently been disclosed by [ 24 ], who demonstrates that countries that are more connected to trade agreement blocs benefit by exporting more than those that are more isolated. Taking into account all direct and indirect input-output linkages within and across two respective countries, we introduce a quantitative framework for measuring the trade interconnectedness TI between two countries in the ITN.
Accounting for all direct and indirect dependencies thus improves on the recently suggested Supply Propagation Connectivity SPC measure of Wenz and Levermann [ 21 ] which is limited to measure direct dependencies only. Furthermore, we assess the impact of BTAs by evaluating the time evolution of the TI, considering both the trend and changes in the magnitude of the TI after the implementation date of a trade agreement.
These methods, along with the description of the utilized data for this study are presented in section 2 of this paper. We analyze the effect of BTAs in general by drawing upon the agreements that took effect between and in section 3. Specifically, we compare the results for the BTAs formed by the US and China and thereby provide quantitative empirical evidence for the suggested strategic differences in negotiating BTAs. A sensitivity study of our results with further detailed discussions on the effect of certain parameters of our analysis is presented in section 4, before this paper concludes with a discussion in section 5.
The present analysis builds upon the EORA multi-regional input-output MRIO database [ 25 , 26 ], which provides multi-regional input-output tables that depict both national and international intermediate trade flows between 26 industrial sectors of countries.
Furthermore, the monetary values of goods that flow into each country's final demand are included. Notably, among the existing MRIO databases, EORA has the broadest near-global coverage of national economies and industrial sectors, while other similar data sets may exhibit greater level of detail but cover much fewer countries, rendering them less appropriate for the purpose of the present study.
Specifically, as compared with national input-output tables, MRIO tables generally have a rather coarse sectoral detail level but cover many countries, which is essential for studying the impacts of BTAs on the interconnectedness of global trade. In EORA, input-output tables are available on a yearly resolution. One trade flow in an input-output matrix depicts the sum of the monetary values of all goods and services that have been exchanged between two industrial sectors as intermediate or as final demand in the respective year.
Our analysis covers the years between and as this data was available at the time of performing the analyses presented in this manuscript. Here, we interpret the input-output tables from the EORA data set as a weighted and directed complex network, which is identified with a time-dependent representation of the ITN for each year [ 19 , 27 ]. In this network, each node represents an industrial sector of one country that is connected via trade links with a weight proportional to the exchanged trade volume.
Moreover, the final demand of each country is included as an absorbing node. It contains information on all agreements that have been either notified to the WTO or for which an early announcement has been made from to today. If a trade agreement is negotiated between exactly two parties, it is referred to as a BTA. Otherwise, a trade agreement with more parties involved is called a multilateral trade agreement.
We also speak of a BTA if one contracting party or both parties consists of a regional trade bloc itself, e.
There exist several types of trade agreements: In a custom union the involved partners agree to pursue only common trade policies with external countries that do not belong to the union. In contrast, free trade agreements allow each partner to pursue their individual trade conditions with any third country. BTAs are often negotiated in the form of free trade agreements, as custom unions include in general more than two partners.
For this study, we analyze the impacts of all BTAs with a date of entry into force between and To analyze the TI between the two partners in such cases, we first aggregate all trade flows within the respective trade bloc while maintaining the homogeneous sectoral structure. We attribute the obtained TI to all countries contained in the regional trade bloc. A complete list of the analyzed trade agreements is provided in Table 1 Appendix. In most traditional network representations of the ITN, the trade volume is represented by weighted and directed links that connect two industrial sectors or, at a coarser resolution, two countries [ 29 , 30 ].
Here, we assess the interconnectedness between two national economies with a newly developed framework that is based upon the interpretation of the ITN as a flow network [ 31 ]. Generally, flow networks encode the probabilities of a random walker to move from one node to another.
Thus, the ITN as a flow network represents the probabilities that a unit good follows certain paths through different industrial sectors down the supply chain. This probabilistic approach becomes necessary as individual supply chains cannot be traced from existing data. The values of p i j o u t p j i i n can be interpreted as the empirical probability of a unit good respectively, of a certain monetary unit to follow the corresponding edge in the ITN from i to j as a random walker.
To measure how likely it is for a random walker on the ITN to start from a sector in one country and eventually end in another country, we define the trade interconnectedness TI between two countries C 1 and C 2 as. As formalized in Equation 2 , the dependency measures allow for the definition of the output TI which describes the probability of a unit good that is supplied from C 1 to end in C 2.
The input TI describes this probability for a flow of successive payments. Here, the paths are defined in the opposite direction, as the payment flows opposite to the supply of materials, goods or services in the trade network. Figure 1. Hypothetical excerpt of the ITN schematically illustrating the contributions to the different directions of TI.
Colored circles indicate different industrial sectors, while solid dashed arrows indicate the flow of goods payments. Here, the unit good starts in China light blue nodes and ends in Vietnam dark blue nodes.
The individual path probabilities that are used to compute TI out are the output dependency values p i j o u t which are illustrated by exemplary values at the links. In this example, paths of length one and two exist between the two countries blue arrows. Supply directions that are not relevant for the supply of China to Vietnam are depicted by gray nodes and arrows.
In B , the paths of payments that contribute to the input TI of China to Vietnam are marked blue, with the payment flow following the opposite direction as compared to the flow of goods in A. The individual path probabilities used to compute the input TI are the input dependency values p i j i n. In contrast, more traditional methods such as as a difference-in-differences approach would only assess the impact of BTAs compared to country pairs without agreement.
Firstly, we investigate if the mean level of TI has changed markedly after the date of entry into force t f of a specific trade agreement. The score z relates the TI values after t f with the previous levels of the variable.
In general, a more sophisticated approach to assess potential changes in the level of a random variable would include an analysis of variance ANOVA , most likely via the Mann-Whitney U test. Secondly, we are interested in the evolution of the annual TI values after the date of entry into force of an agreement.
To assess this trend, we consider two possible models: In the first model, we perform a simple linear regression. Alternatively, in order to better recognize oscillating or saturating behavior of the time series during the considered 6-year period, we additionally perform a two-segment piecewise linear regression [ 32 ]. The form of this segmented linear model is. In contrast to the linear regression, the model in Equation 5 can also account for one local extreme value during the investigated time period, which would be represented by a change in the signs of the slopes between the two segments.
More complex regression models that exhibit multiple break-points cannot be reliably applied due to the coarse annual resolution of the considered data.
Therefore, we do not consider such more general models, emphasizing that we are only interested in the sign and statistical relevance of short-term multi-annual trends after BTA implementation rather than exact functional descriptions of the shape of these trends or explicit quantitative estimates thereof. Since the segmented model contains two additional parameters as compared to the linear regression model, we perform a model selection based upon the Akaike Information Criterion AIC [ 33 ] to avoid overfitting by the statistical model with a higher number of degrees of freedom.
In the case of the segmented model, the considered time series is too short for a similar relevance assessment. Accordingly, if that model is preferred, the additional breakpoint improves the AIC score as compared to the linear regression model. We then consider the slopes of the two segments as relevant. Combining both the trend properties and score parameter z of the time series of TI values, we finally define the impact index of a BTA as follows:.
Other than common characteristics like the total trade volume or the absolute values of imports or exports studied in previous works [ 4 , 5 ], the TI also captures indirect trade effects.
Such indirect effects arise, for instance, if a customer increases its output, being likely to demand more input that is required for the production of its goods. This increase in demand, in turn, affects the business of the supplying industry at the input side. Capturing effects at both, demand and supply side individually, TI can be defined in each direction and thus allows distinguishing between the input TI and output TI of one country to another country as trade partner.
The input TI output TI thereby quantifies the relative importance of one country C 1 as a supplier consumer for the production of another country C 2. Note that the countries' relative economic relevance for each other is not symmetric. Furthermore, the TI is a relative measure in the sense that it is based on the fraction of a country's total trade activities that is accounted for by a specific partner. For instance, in a global setting in which all countries increase their international exports, the TI between two countries decreases if the growth in bilateral trade volume is smaller than the global average growth given that the nation's sectoral structure remains the same.
As explained above, theory suggests that BTAs foster trade activities among the partners, which should result in a stronger TI between the involved countries. To put these results into context, we further assess the relevance of the empirically identified impacts of the BTAs by comparing the estimated BTA impact indices with the corresponding values for those pairs of countries that have not entered a trade agreement until For the latter purpose, we calculate the BTA index for the 15, country pairs that have not signed such an agreement within the study period and assume an arbitrary reference year of Note that this simple analysis does not allow directly drawing a causal link of the BTA implementation resulting in stronger entangled economic ties, since it would also be compatible with the explanation that countries with generally more positive economic development have a higher tendency toward negotiating trade agreements.
Further studies on this aspect would be necessary to further address this point. In general, from the estimated probability distributions, it becomes evident that positive BTA indices are more common than negative values.
These results are consistent with the general increase of international trade volumes in the course of the globalization [ 19 ] that also affects country pairs without a dedicated trade agreement. Figure 2. Light colors show the corresponding results for all 15, pairs of countries that have not negotiated bilateral agreements assuming an arbitrary reference year of Each of the four distributions is normalized to one and thus depicts the relative frequency. Note the logarithmic scaling of the displayed empirical frequencies.
For some countries, especially the US, Australia, India and Columbia, the relative importance of the export linkages to their partners has increased more strongly than the relative importance of the import linkages from their partners. On the other hand, for other countries, such as the Philippines, Algeria, the southern African countries and Uruguay, the import linkages from their partners have gained in importance to a larger extent than their corresponding export linkages.
The only G20 members with non-positive values are Indonesia and China. Figure 3. Global maps of average BTA impact indices. Red values indicate that on average, the relative importances of the partners have increased for the respective countries. The average is taken over all the trade partners with which a specific country has implemented a BTA between and In this context, it is particularly remarkable that China as one of the world's leading economies did not increase the relative importance of its agreement partners for its domestic production.
When examining the composition of the average BTA impact index for China in more detail Figure 4 ], the positive values of the score parameter z see section 2 indicate that for most BTAs, the level of both input and output TI of China to its partners has increased after the date of entry into force.
Bilateral Investment Treaties Other Initiatives. Breadcrumb Trade Agreements. Trade Agreements can create opportunities for Americans and help to grow the U. Subscribe to receive Updates from the Press Office. USTR News. Albania as part of Western Balkans benefits from the diagonal accumulation. FTA with Turkey. The agreements with Liechtenstein and Switzerland entered into force in , and the agreements with Iceland and Norway entered into force in Albania - Country Commercial Guide.
Trade Agreements.
0コメント