How Can Logistics Be Used to Facilitate International Trade?

1. Introduction

Logistics is strongly connected to trade and its contribution to the trade competitiveness of countries is growing, peculiarly since the trade tariffs have largely declined due to the merchandise liberalization process than began afterwards General Agreement on Tariffs and Trade came into forcefulness in 1948. Efficient transport and logistics can either boost merchandise or become a barrier to it, therefore we consider it equally a not-tariff barrier. Nords and Piermartini (2004) debate that the cost of transporting appurtenances to foreign markets presents a good example of non-tariff barriers. Nevertheless, it is not just the transport costs that matter; security, quality of infrastructure, customs procedures and the length of fourth dimension that it takes for goods to be shipped matter as well. Hummels (2001) states that the fourth dimension that it takes for goods to exist shipped and the unpredictability related to time are as well costly to traders. 'International merchandise calls for flows to be organised and synchronised through strategic nodes and networks that facilitate storage, conservation and whatever other value-added service required due to the very characteristics of the goods existence transported' (Puertas et al., 2014, p. 468). The Earth Depository financial institution has adult the logistics functioning index (hereinafter: LPI) as a benchmarking tool to assist countries identify the challenges and opportunities they confront with respect to logistics operation. LPI is used to analyse differences between countries in terms of logistics costs and the quality of the infrastructure for overland and maritime transport (Marti et al., 2014b).

Numerous papers in this field have plant the positive links between trade facilitation and/or logistics performance and trade, such as Wilson et al., 2003; Behar & Manners, 2008; Marti et al., 2014a; Marti et al., 2014b etc. However, their investigations just considered the links between absolute value of logistics functioning index (or its sub-indices) and total/aggregate merchandise (or specific product groups), just there was no investigations how particular LPI sub-alphabetize affect item product groups. This paper investigates the bear upon of the difference of logistics performance sub-indices (hereinafter LPI) values between trading partners on their bilateral trade covering the biennial period from 2010 to 2018. We investigate if the difference in LPI matters and does information technology affect bilateral trade. Our paper extends the existing literature in a manner that nosotros classify trading goods using Broad Economic Categories (BEC) classification, that we and then amass to the 3 basic Arrangement of National Accounts (SNA) classes of goods: intermediate, capital and consumption goods. In that way we also investigate for potential heterogeneous impact of LPI differences with respect to different classes of goods. There is a scarce literature on the product or product groups-specific inquiry in this enquiry field, and in that location is lack of empirical findings most the effects of improvements in merchandise logistics on merchandise in specific product groups. Our supposition is that different logistics functions exercise non matter equally to dissimilar products, for case the perishable nature of nutrient products or the sensitivity of chemical products makes them more vulnerable to delays in trade (Liu & Yue, 2013). Therefore, this paper attempts to detect possible differences in trade of dissimilar classes of goods. In lodge to guess the furnishings of logistics performance on international trade nosotros use augmented gravity model. Equally a proxy variable for logistics performance we utilise LPI, that is, its half-dozen sub-indices, namely 'the efficiency of customs and edge management clearance, the quality of trade and ship infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to rails and trace consignments, and finally, the frequency with which shipments reach consignees inside scheduled or expected delivery times' (Arvis et al., 2018).

Our paper also addresses the gap in the literature investigating how trade patterns vary across different groups of countries. Our focus are European union fellow member countries, where we distinguish between quondam European union fellow member countries (hereinafter EU15) and new European union fellow member countries or and so-called Central and Eastern EU member countries (hereinafter CEMS). We compare furnishings of the difference in LPI on bilateral trade between these two groups of countries and Residue of the World (ROW) countries. At that place is a clear lack of inquiry of the differences in logistics performance within economic integrations and its bear upon on merchandise flows. We emphasise the importance of this research for CEMS countries, well known for relatively complicated transition from planned to market place economic system, outdated ship infrastructure, just as well good geographical position and membership in the European union. According to the Mordor Intelligence, 2018 Report, CEMS, such as Czech Republic, Hungary, Poland, and Slovakia, are amid the fastest growing economies in the Eu. However, their logistics market is nonetheless in babe stage, and undeveloped in comparison with the logistics markets of former EU fellow member countries. CEMS need to address poor infrastructure, especially railways, political corruption, lack of competitiveness etc. Despite the current issues, the CEMS are an attractive location for the investments in logistics. Therefore, from the macroeconomic point of view, it is important to take into consideration the potentials of CEMS logistics market and its affect on international merchandise. On the other paw, we have EU15, European union core group of countries that we basically use as a benchmark for CEMS.

This paper contributes to the existing literature in several ways, covering the connection between logistics performance and international trade in unlike classes of goods. Information technology evaluates the importance of the logistics services in international merchandise studies, and information technology analyses how differences in LPI sub-indices levels between trading countries impact bilateral trade flows, differentiating betwixt two groups of countries within economical integration, namely Eu. As such, our research follows contempo work on the effects of merchandise costs, where logistics functioning plays a significant part (Saslavsky & Shepherd, 2014).

The rest of the paper is structured as follows: Section 2 reviews the literature related to trade logistics enquiry. Section 3 presents the methodology used in the empirical part of the newspaper. Section 4 explains the data and variables included in the analysis. Section five discusses the results and policy implications, and finally, Section 6 presents final remarks.

2. Literature review

Merchandise liberalization and the tariff reductions motivated researchers to start investigating the furnishings of not-tariff barriers on international trade, respectively the furnishings of reductions of transport costs and other barriers to merchandise, and then called 'merchandise facilitation', on international trade (Anderson & van Wincoop, 2004; Baier & Bergstrand, 2001; Brun et al., 2005; Clark et al., 2004; Hummels, 2001, 2007; Limao & Venables, 2001). Every bit many authors note, improvements in trade facilitation leads to international trade growth. In their seminal paper, Wilson et al. (2003) estimate the effects of merchandise facilitation on trade in APEC countries. To measure the effects of merchandise facilitation, they employed the gravity model where port infrastructure, customs environment, regulatory environment, and e-business infrastructure were used as proxy variables for trade facilitation measures. In 2005, aforementioned authors aggrandize their inquiry; they estimate the effects of merchandise facilitation on trade in manufactured products for 75 countries in the flow from 2000 to 2001. The results of both studies suggest that improvements in all four trade facilitation measures lead to increase in international trade. Soloaga et al. (2006) study the furnishings of changes in trade facilitation on merchandise in Mexican main industrial sectors and their results suggest that trade facilitation measures should be taken seriously when creating trade policies since the improvement in merchandise facilitation could increment export by approximately 20% and imports by 11%. Iwanow and Kirkpatrick (2007) investigate the bear on of regulatory quality and trade facilitation on export. The results of their gravity model suggest that all trade facilitation measures, including improved transport and communications infrastructure increase exports. Portugal-Perez and Wilson (2010) use physical infrastructure, data and communications engineering science, border and transport efficiency, and business and regulatory environment every bit trade facilitation proxy variables in social club to detect their effects on merchandise book and the results likewise support aforementioned findings. Co-ordinate to Djankov et al. (2010), each additional day that a product is not being dispatched, reduces trade by more than ane% and that percentage is fifty-fifty college in case of perishable products, such as agronomical products, pregnant that perishable products are more time sensitive. For the first time, in 2007, the World Banking concern published the Logistics Functioning Index which includes all above mentioned trade facilitation measures i.e. customs clearance, transport infrastructure, quality of logistics service, timeliness and the ability to track the shipment. LPI received pregnant attention in the international merchandise literature and public policy discourse and researchers started to utilise information technology as a proxy variable for merchandise facilitation and include information technology in the international trade analysis. Behar and Manners (2008) incorporate LPI 2007 for the kickoff time in their gravity framework in order to investigate the effects of the logistics of source and destination state on bilateral exports and the furnishings of logistics of countries' neighbours on exports. They use amass LPI every bit a proxy variable for logistics of source and destination land and their findings show that logistics positively affect exports, still bordering countries' logistics matters but for the landlocked exporters. Moreover, the results as well show that the destination country'due south neighbours' logistics is negatively related to exports to that country. They explain those findings as a matter of pick. Namely, exporting countries choose betwixt numerous disembark places and mostly send their goods to the 'relatively well-equipped countries before assuasive regional distributors to take over'.

Hertel and Mirza (2009) and Felipe and Kumar (2012) contribute to the literature by including LPI index and its sub-indices every bit trade facilitation measures in social club to approximate the effects of trade facilitation on trade in Asian countries by using gravity model approach. While Hertel and Mirza employ only one LPI sub-index in each regression, Felipe and Kumar incorporate all the LPI sub-indices in one equation. Both analyses conclude that merchandise facilitation positively affect trade and that infrastructure is the most important LPI sub-index. According to Felipe and Kumar' estimation, the gains in trade vary from 28% in case of Azerbaijan to 63% in example of Tajikistan. Their results likewise suggest that from the exporter point of view, infrastructure has the greatest impact on trade while from the importer side, community efficiency has the greatest impact on merchandise.

Puertas et al. (2014) and Marti et al. (2014a, 2014b) too estimate the effects of logistics performance on international trade. All iii studies base of operations research on gravity model and employ LPI as proxy variable for logistics performance and as in Behar and Manners (2008) their results show significant positive effects of logistics functioning of trade, implying that logistics is more important to the exporting than to the importing countries. Furthermore, authors recommend the enhancement of exporter-oriented policies and interventions. Puertas et al. (2014) focus research on 26 European union countries in the catamenia from 2005 to 2010. In the case of European union countries, the competence and quality of logistics services record the highest score, followed by the ability to track and trace consignments and the quality of customs and infrastructure. This results actually show better performance of the Eu private sector in example of trade facilitation since components with the highest touch on are in reliability of the private sector. Marti et al. (2014a, 2014b) estimate the effects of logistics operation on trade flows in emerging countries grouped in five regions, Africa, Eastern Europe, Far Eastward, South America and Center Due east. They control for the trade between different groups of products in accordance to their logistics complexity and their findings show that the more difficult is to transport appurtenances, the more important becomes logistics performance. Similarly, Saslavsky and Shepherd (2014) investigate the effects of logistics performance on trade in parts and components within international product networks and their principal determination is that merchandise in parts and components is more sensitive to logistics performance than merchandise in final goods. Bresslein and Huber (2016) analyse trade patterns of European union countries distinguishing between trade in parts, components and final goods using Eurostat COMEXT database at viii-digit level. Their findings confirm that trade patterns differ for different types of products, namely parts, components, and final goods and that all EU countries are agile through all supply chain, however while developed countries trade mostly with other developed countries, less developed EU countries trade with more adult countries. Latest findings of Gani (2017) and Host et al. (2019) estimate the effects of logistics operation on international trade using cross-country data for a large sample of countries and both hold that logistics performance take statistically significant and positive event on trade flows, peculiarly on exports. In addition, Bugarčić et al. (2020) analyse the impact of logistics operation on trade volume within two groups of countries, Fundamental and Eastern European and Western Balkan countries, and conclude that sub-indices international shipments, logistic quality and competence and tracking and tracing have the highest effects on trade book in observed year 2018.

Finally, the majority of empirical studies agree that logistics performance and merchandise facilitation, in general, play an important role in international merchandise. The findings reveal that logistics and transport is increasingly of import for merchandise across supply chains and therefore is necessary to investigate and better understand how trade patterns vary across different groups of countries within economic integration and how logistics performance and its sub-indices affects merchandise in different groups of products. Participation in regional and global supply chains, particularly for new EU countries is significant for their competitiveness and therefore our aim is to detect the effects of logistics performance on EU trade in specific group of products across and offer suggestions for farther trade and logistics policies.

3. Methodology

In our inquiry, the theoretical framework to investigate the furnishings of logistics service operation on international trade is based on the gravity model theory of international trade. Since the pioneer work of Tinbergen (1962), the gravity equations have been frequently used in many trade related research papers during decades (Anderson & van Wincoop, 2004; Behar & Manners, 2008; Bergstrand, 1985, 1989; Frede & Yetkiner, 2017; Host et al., 2019; Krugman, 1991; Zajc Kejžar et al., 2016). We develop the following structural gravity model to judge the effects of logistics performance differences between trading partners on bilateral trade: (1) $${\mathit{trade}}_{\mathit{ijt}}={\mathrm{\beta }}_0+{\mathrm{\beta }}_1{\mathit{gdp}}_{\mathit{ijt}}+{\mathrm{\beta }}_{\mathrm{ii}}{\mathit{dist}}_{ij}+{\mathrm{\beta }}_{\mathrm{iii}}{\mathit{lpisub}}_{\mathit{ijt}}+{\mathrm{\beta }}_{\mathrm{four}}{\mathit{contig}}_{ij}+{\mathrm{\beta }}_5{\mathit{comlang}}_{ij}+{\sum }_{i=\mathrm{ane}}^m{\delta }_i+{\sum }_{j=1}^k{\gamma }_j+{\mathrm{u}}_{\mathit{ijt}}$$ (ane) where ${\mathit{merchandise}}_{\mathit{ijt}}$ is the value of bilateral trade in U.S. dollars between reporting country i and partner land j in year t (since LPI data is published biennially, t represents years 2010, 2012, 2014, 2022 and 2018). For reporting countries, we accept EU28 member countries (and then i goes from 1 to 28), while j includes EU28 member countries likewise every bit ROW j goes from 1 to 157; for years 2010 and 2022 our sample has 152 partner countries). This difference of v countries is due to the fact that in 2022 5 new countries began publishing LPI scores). ${\mathit{gdp}}_{\mathit{ijt}}$ presents absolute difference of the Gdp (in electric current United states dollars) betwixt country i and country j in year t. ${\mathit{dist}}_{ij}$ represents distance between uppercase cities of trading partners. ${\mathit{contig}}_{ij}$ is a dummy variable with the value one if trading partners share state border, zero if non and ${\mathit{comlang}}_{ij}$ is dummy variable with the value 1 if countries have common official main language, zero if non. We notation that our approach, using differences of trading partners variables as regressors is not new it this blazon of research. Gravity models employed to test Linder's theory/hypothesis utilize absolute deviation between Gdp per capita of the importing and the exporting country as i of the regressors (Arnon & Weinblatt, 1998; Atabay, 2015, Jošić & Metelko, 2018). Furthermore, this arroyo is used likewise in other research fields, similar financial policy, which include bilateral trade data in their analysis (come across Holzner et al., 2018).

Terms ${\sum }_{i=1}^k{\delta }_i$ and ${\sum }_{j=1}^k{\gamma }_j$ stand for exporters and importers fixed furnishings, respectively. Use of exporter and importer fixed effects has go standard since Anderson and van Wincoop (2004) and Baldwin and Taglioni (2006), because it solves potential biases in estimation results due to different price levels between countries and other state-fixed, country-pair fixed and time-fixed characteristics, depending of the type of data (cross-sectional or panel data).

Principal variable(s) of involvement are presented with ${\mathit{lpisub}}_{\mathit{ijt}}$ and is calculated as absolute deviation in 1 of the six LPI sub-indices between trading partners. The half-dozen LPI sub-indices are the following:

1. efficiency of clearance procedure (hereinafter Customs)

2. quality of trade and transport infrastructure (hereinafter Infrastructure)

3. ease of arranging competitively priced shipments (hereinafter International)

4. competence and quality of logistic services (hereinafter Logistics)

5. power to runway and merchandise consignments (hereinafter Tracking)

6. timeliness of shipments with the expected delivery time (hereinafter Timeliness).

The LPI is based on worldwide survey carried out beyond more than 5000 freight forwarders and logistics firms who operate internationally. Each respondent rates their trade logistics experience (in half-dozen above mentioned dimensions/sub-indices) with the eight countries they trade the most. Further details of the construction of each sub-index are available in Arvis et al. (2018). The descriptive statistics of the ${\mathit{lpisub}}_{ij}$ (summary for biennially data from 2010 to 2018) for the full sample is given in Table 2. In Tables 4 and 5 nosotros nowadays results for two sub-samples, for the cases when reporting countries are EU15 countries and partner countries are ROW countries, and when reporting countries are CEMS countries and partner countries are ROW countries.

We guess Model (one) for each LPI sub-index. We couldn't estimate the model with all six LPI sub-indices together due to a loftier caste of correlation between sub-indices, resulting in loftier VIF for some LPI coefficients (higher than 10). In order to detect out whether trade in different product classes is more sensitive to different logistics performance sub-indices, we estimate our gravity model separately for each class of SNA: intermediate, capital and consumption appurtenances.

When choosing the estimator for our model, we had unlike possibilities, similar standard ordinary to the lowest degree squares, fixed furnishings calculator or Poisson estimator. We chose and estimated Model i with Poisson Pseudo-Maximum-Likelihood Estimator (PPML), first introduced in gravity model setting past Silva and Tenreyro (2006). We also used multilateral resistance terms introduced through fixed reporter and partner effects, thus following one of the seminal paper in this field of research, that of Anderson and van Wincoop (2004). That mode we obtain consistent estimates of the gravity model variables, that are robust to different patterns of heteroskedasticity. Furthermore, by using PPML we are able to include nil trade flows, thus avoiding a source of bias. In our information, there are five.2% observations at country pair level with nada trade.

iv. Information

Our data consists of bilateral merchandise information between EU28 fellow member countries and their trading partners, 157 countries in full. We distinguish betwixt 2 groups within European union-28 countries: new EU member countries, that is all countries that became Eu members since 2004 (CEMS) and onetime EU member countries (EU15). Source of bilateral trade data is United nations Comtrade database. We obtained GDP data from World Bank Open Data, while the information for other standard other standard gravity variables nosotros obtained from CEPII database. Data for our master variable of involvement, LPI sub-indices, came from Globe Bank. Table 1 presents descriptive statistics for standard gravity model variables, while as explained in Methodology section, Tabular array ii shows descriptive statistics of absolute differences of LPI sub-indices betwixt trading partners for the full sample.

The effects of logistics performance on international merchandise: EU15 vs CEMS

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17 November 2020

Table one. Summary statistics.

The effects of logistics performance on international trade: EU15 vs CEMS

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17 Nov 2020

Table two. Summary statistics of the variable lpisub for the full sample.

Table 3 shows average trade value between trading dyads for three groups of countries that we define in our paper and from where nosotros can notice the following: 1) EU15 intraregional trade is by far more developed in comparing with CEMS intraregional trade; 2) CEMS trade more than with EU15 (interregional trade) than with other countries in CEMS; 3) EU15 is far more oriented toward merchandise with ROW in comparison with CEMS.

The furnishings of logistics performance on international trade: EU15 vs CEMS

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17 November 2020

Table 3. Summary statistics for average bilateral trade for three groups of countries.

If we compare results of descriptive statistics in Table four with the results in Table five, information technology is clear that EU15 have improve logistics operation with respect to CEMS when comparing both groups with the ROW countries. Nosotros can use this difference equally a proxy variable and debate that it reflects the difference between economic development levels betwixt CEMS and EU15.

The furnishings of logistics operation on international trade: EU15 vs CEMS

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Table 4. Summary statistics of the variable lpisub for EU15-ROW sub-sample.

five. Results and discussion

Our results, ^one presented in Tables 6–viii show that variation in LPI sub-indices help explicate variation in full merchandise, that is, the bigger the difference in LPI sub-indices, the lower the merchandise betwixt trading partners. What is even more of import, we find that there is heterogeneous impact of LPI sub-indices on trade, that is noticeable both in EU15-ROW and CEMS-ROW sub-samples and across all iii classes of goods. Nigh negative and significant touch of an increase in LPI departure between trading partner is evident in the case of trade in intermediate goods between EU15 and ROW. This finding is in line with our expectations since almost two-thirds of global trade is in intermediate goods and trade in intermediate appurtenances is closely connected with regional and global value bondage that shape regional and global merchandise and global economic system.

The effects of logistics performance on international merchandise: EU15 vs CEMS

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17 November 2020

Table 5. Summary statistics of the variable lpisub for CEMS-ROW sub-sample.

The effects of logistics performance on international trade: EU15 vs CEMS

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17 November 2020

Tabular array 6. Estimation results of Model 1 for intermediate appurtenances.

The effects of logistics operation on international trade: EU15 vs CEMS

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17 November 2020

Table 7. Estimation results of Model one for capital goods.

The effects of logistics operation on international trade: EU15 vs CEMS

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17 Nov 2020

Table 8. Estimation results of Model one for consumption goods.

The results testify that in the case of EU15-ROW trade, sub-indices Timelines, Tracking and International seems to accept the highest negative effects on trade. Namely, the score gap in the sub-index the ease of arranging competitively priced shipments between trading partners (International) has the highest negative consequence on trade on average, with its pinnacle in 2016. Furthermore, the score gaps in the power to track and trade consignments (Tracking) and timeliness of shipments with the expected delivery time (Timeliness) also affect trade significantly negative, especially for the flow 2010 to 2014. The score gap in the quality of trade and ship infrastructure is non significant through the whole examined period, which is reverse to the economic literature where transport infrastructure is ane of the most important trade promotors. The situation is slightly different in the instance of trade in intermediate appurtenances between CEMS countries and ROW. The results of merchandise in intermediate goods are in line with the results of the previous works showing that near of sub-indices have significant effect on trade (Felipe & Kumar, 2012; Marti et al., 2014a; Puertas et al., 2014) while the results of merchandise in capital and consumption goods are inconclusive. These results lead us to the conclusion that logistics might exist more important in trade in intermediate appurtenances than in merchandise with upper-case letter and consumption appurtenances.

In the instance of trade in uppercase and consumption goods the results are quite ambiguous. In the example of trade in capital appurtenances, the sub-indices Customs, International, Logistics and Tracking show the highest negative furnishings on trade in example of both EU15 and CEMS merchandise with the ROW. Unlike in the case of trade in intermediate goods, Timeliness is less important when it comes to the merchandise in capital goods, since the nature of trade in capital goods is dissimilar due to several reasons. Supply of uppercase goods is express due to the fact that 10 countries account for eighty% of earth majuscule goods production (Mutreja et al., 2014). Also, need is less elastic when compared with intermediate and consumption appurtenances since buying capital appurtenances is considered as a long-term investment. In the case of trade in consumption goods, in that location is a articulate stardom between estimations results for EU15-ROW vs CEMS-ROW. Namely, in the case of CEMS-ROW, the significance and the signs of the estimated coefficient are in line with our expectations, that is, the larger the LPI gap between trading partners, the lower the trade in consumption goods. On the other side, the significance and the signs of the coefficients for the EU15-ROW instance is counterintuitive; the coefficients are generally not pregnant and are in some cases even positive, especially for the year 2018. We discover the reason for these results in the economic development gap betwixt EU15 and CEMS countries and that trade in consumption goods is mainly demand driven, while LPI sub-indices are supply-side oriented. Furthermore, we tested our model without GDP variable included and we obtained results that show that Gross domestic product differences between EU15 and CEMS with respect to their trading partners from the ROW countries could explicate differences in results when it comes to trade in consumption goods.

Finally, since LPI index and its sub-indices are focused on supply chains, that is, they correspond 'simple comparators of how efficiently supply chains connect firms to markets or logistics performance' (Arvis et al., 2018), nosotros look at the EU15-ROW vs CEMS-ROW departure from regional and global supply chain perspective. We would like to stress out that in our paper nosotros focus just on EU15 and CEMS trade with ROW, fully noting that regional supply chains within EU15 and between EU15 and CEMS take bigger importance to European union as a whole. Since information technology would exist difficult to uncrease effects of LPI differences on intra-Eu trade from other factors that affect strongly merchandise within European union, we focused only on trade of EU members with Tertiary countries (ROW).

When nosotros compare effects of LPI differences on trade in the instance of EU15-ROW vs CEMS-ROW trade, is seems that these differences had more negative touch on EU15-ROW trade in the backwash of the Great Recession and peculiarly for trade in intermediates, where nosotros observe significant negative effects across almost all sub-indices in 2010. As the fourth dimension passed, situation changed and in 2022 we discover that CEMS-ROW trade is highly affected past differences in logistics functioning (Table 6). This can exist attributed to the fact that throughout the observed menses, considerable gap between LPI index and its sub-indices of CEMS countries with respect to EU15 did non diminish. Moreover, based on previous findings (Baldwin & Lopez-Gonzalez, 2015; Lejour et al., 2017) in this field also equally statistical data of average bilateral merchandise flows of EU15-ROW vs CEMS-ROW showed in Tabular array 2, we fence that EU15 countries are more immersed in global supply chains and that this is the reason why they experienced larger negative effects of LPI differences in the backwash of Global Recession as opposed to CEMS countries which are more immersed in regional supply chains and trade with EU15 and other countries of CEMS group of countries. Results for well-nigh contempo available twelvemonth (2018) bear witness that CEMS countries are not converging to EU15 when it comes to merchandise with ROW and that are probably notwithstanding oriented toward regional supply concatenation, that tin can limit their competitiveness in globalized market place of 21th century.

6. Conclusion

The aim of this paper was to investigate the effects of logistics performance, specifically different logistics functions on international trade in different classes of goods and to investigate possible differences within Eu member countries. By incorporating LPI sub-indices, every bit proxy variables for logistics performance in the gravity model, we tried to investigate which of the logistical functions should be treated with priority and how the improvements in trade logistics bear on specific product groups. Furthermore, we were interested about the effects of the divergence in LPI on bilateral trade between ii groups of countries, EU15 and CEMS countries with the rest of the world countries. Nosotros used EU15 as a benchmark for CEMS.

Our results support previously mentioned findings where logistics performance has statistically significant touch on on bilateral trade flows. The results too prove that difference in LPI sub-indices score between trading partners negatively affects trade, notwithstanding this issue is different for unlike classes of appurtenances. Namely, biggest negative effect of divergence in the levels of LPI sub-indices between trading partners is noted in the case of trade in intermediate appurtenances, meaning that intermediate goods are more than sensitive to trade than majuscule or consumption goods, where information technology plays less prominent part. It is also quite interesting to observe that sub-indices like timeliness, tracking and international, which are in the domain of the private sector are more significant for trade in intermediate appurtenances, while customs and infrastructure are more relevant to trade in capital appurtenances. Our findings also evidence that LPI differences betwixt trading partners for both groups of European union countries affect merchandise in intermediate goods more strongly, only information technology varies through unlike years. Global recession from 2008, had negative consequence on global trade that hit harder EU15 countries, which are more oriented to global supply bondage in comparison to CEMS. In the long term, in our instance, from 2010 to 2018, EU15 stabilised trade flows with ROW, while CEMS are withal very much oriented toward regional trade and regional supply chains, with LPI being meaning hurdle in trade with ROW.

Several important policy implications menstruum from our results. We argue that CEMS countries need to put more effort in the development of trade logistics to converge with EU15 in the development of logistics services because it will remove bottlenecks, provide amend ship corridors for trade, assistance reduce trading time, and increase the competitiveness of the shipment prices. In a higher place all, logistics performance is a gather piece of work for both, public and private sector and in lodge to improve logistics performance, countries and integrations must simultaneously piece of work on changes in many areas, namely infrastructure, border procedures and regulatory surround, transport regulation and private sector development. Private sector development should be one of the priorities for CEMS, because that its components affect trade in intermediate appurtenances. That style, CEMS countries will have more chances of increasing participation in global supply chains.

We admit that an important limitation of our research is estimations of our model on cross-section data. Although we had at our disposal biennial console data, we decided to become forth with cross-section estimation since, in our stance, fourth dimension period is also brusque for robust estimation results. As for the time to come inquiry, with more LPI data available over the years, focus should shift on panel data assay. Another possible contribution to this field of enquiry is to downsize the analysis on sectoral or firm level. Finally, more than thorough research of different product groups withing different classes of appurtenances (for example, within intermediate appurtenances) could shed more light into relationship between trade and logistics performance.

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Source: https://www.tandfonline.com/doi/full/10.1080/1331677X.2020.1844582

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