Connecting 2023 to Compete Trade Logistics in the Global Economy The Logistics Performance Index and Its Indicators Connecting to Compete 2023 Trade Logistics in an Uncertain Global Economy The Logistics Performance Index and Its Indicators Jean-François Arvis The World Bank Lauri Ojala Turku School of Economics, University of Turku Ben Shepherd Developing Trade Consultants Daria Ulybina The World Bank Christina Wiederer The World Bank © 2023 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. 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Table of contents Acknowledgements   vi Abbreviations   vii Key messages   ix Policy highlights   xi Executive summary   1 Key changes in global supply chains since 2018 and implications for the 2023 Logistics Performance Index   7 Understanding logistics performance and its determinants is now more important than ever   7 Container shipping disruptions in 2020–22    7 Air freight market disruptions in 2020–22    9 The rapid emergence of e-commerce as an important channel for cross-border trade    9 The 2023 LPI measures structural factors of performance rather than supply chain disruptions   9 The 2023 Logistics Performance Index    11 How to interpret the LPI    11 Features of the 2023 survey     11 Key findings of the 2023 LPI survey    12 The dynamics of LPI scores over 2012–23    15 What is the impact of the supply chain crisis?    15 Logistics performance is determined by more than income    15 Supply chain lead time around the world: Where are the delays?    17 Lead time dispersion and supply chain reliability    17 Dwell time and logistics performance    21 What causes long port delays?    22 Which interventions help reduce these delays?    24 Logistical constraints in landlocked developing countries    24 Connectivity and logistics performance in small maritime economies    25 Conclusions   27 Global supply chains have turned out to be surprisingly resilient during the recent disruptions   27 The top and bottom performers: Performance is steady or improving, but the gap persists   27 Consistency is an important driver of logistics performance    27 C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y iii Table of contents Logistics performance and key performance indicators derived from a Big Data approach    28 Policymaking priorities when managing logistics as a sector of the economy    28 The 2020s is a decade of transformation for global supply chains    29 Notes   31 Appendix 1 2023 LPI results   32 Appendix 2 Lead time data from supply chain tracking datasets    36 Appendix 3 Top and bottom scorers on the LPI, overall and by income group    52 Appendix 4 Description of the new data sources for the LPI 2023    55 Appendix 5 The LPI methodology   62 Appendix 6 Results from the LPI survey question on demand for environmentally sustainable shipping options and on changes in global supply chains since 2019    65 Appendix 7 Respondent demographics   67 Appendix 8 LPI results in research and policymaking literature    69 References   73 Boxes 1.1 Vaccine logistics   8 1.2 The 2023 LPI survey question on supply chain disruptions    10 2.1 The six components of the LPI    12 2.2 How precise are LPI scores?    13 3.1 Measuring performance using tracking indicators: Sources and definitions    19 3.2 India: Boosting performance with supply chain digitalization    22 Figures 1 Distribution of LPI scores, 2012–23    2 2 Dispersion of time spent at port: Long tail distribution of containers in the port of Algiers, Algeria, May–October 2022   4 1.1 Container shipping schedule reliability, average delay for late arrivals, and spot rates, June 2018–22    8 1.2 Global air freight supply, demand, and prices, January 2018–July 2022    9 2.1 Histogram of scores of the 139 countries and four performance groups in the 2023 LPI    14 2.2 LPI component scores, by overall LPI quintile, 2023    15 iv C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Table of contents 2.3 Distribution of LPI scores, 2012–23    16 2.4 Timeliness score, by LPI quintile, 2018 and 2023    16 2.5 Distribution of 2023 LPI scores by income group    16 3.1 Import lead time is the largest driver of variability in international shipping in 2022    20 3.2 Examples of the distribution of import dwell time    20 3.3 Dispersion of mean dwell time across the world    21 3.4 Import and export dwell time of containers, May–October 2022, versus 2023 LPI score, by country    21 3.5 Export dwell time versus import dwell time of container ports    23 3.6 Outliers for import dwell time and comparators, May–October 2022    23 3.7 The association between average connectivity in container shipping and 2023 LPI score quintiles    25 3.8 The association between average inbound connectivity in aviation and postal services and 2023 LPI score quintiles 26 3.9 Most maritime economies have less than 20 shipping connections and depend on transshipment    26 4.1 Demand for environmentally friendly shipping options, by destination LPI score quintile    30 A4.1 Cargo iQ milestones   56 A4.2 Country coverage of Cargo iQ dataset, by World Bank region    56 A4.3 Country coverage of the Universal Postal Union dataset, by World Bank region    58 A4.4 TradeLens data model   59 A4.5 The three phases of container trips    59 A4.6 Country coverage of the MDS Transmodal and MarineTraffic dataset, by World Bank region    60 A7.1 Number of respondents by location and country income group    67 A7.2 Respondents by transport mode and economic activity type    68 A8.1 Use of LPI data in research literature, 2007–22    69 Maps 1 Mean import dwell time of containers around the world, May–October 2022    5 A6.1 How often do shippers ask for environmentally friendly options (e.g., in view of emission levels, choice of routes, vehicles, schedules, etc.) when shipping to…?    65 A6.2 Based on your experience, how have supply chains been affected since the year 2019 when shipping to…?    66 Tables 1 Top 10 and bottom 10 average LPI scores, 2007–23 (1, low, to 5, high)    2 2.1 Bilateral LPI assessments in 2023, by income group    14 A2.1 Lead time data for container shipping, 2022    36 A2.2 Lead time data for aviation, second quarter of 2022    38 A2.3 Lead time data for postal parcels, 2019    41 A2.4 Import delays, May–October 2022    44 A2.5 Import delays, May–October 2022    48 A2.6 Dwell times for landlocked developing countries, 2022 (days)    51 A3.1 Top 12 LPI scorers in 2023 and their top scorer status for 2018, 2016, 2014, and 2012    52 A3.2 Bottom 12 LPI scorers in 2023 and their top scorer status for 2018, 2016, 2014, and 2012    52 A3.3 Top 11 upper-middle-income LPI scorers in 2023 and their top scorer status in 2018, 2016, 2014, and 2012    53 A3.4 Top 13 lower-middle-income LPI scorers in 2023 and their top scorer status in 2018, 2016, 2014, and 2012    53 A3.5 Top 10 low-income LPI scorers in 2023 and their top scorer status in 2018, 2016, 2014, and 2012    54 A4.1 The postal sequence of tracking messages    57 A4.2 List of key performance indicators derived from tracking data    61 A5.1 Methodology for selecting country groups for survey respondents    62 A5.2 Results of principal component analysis for the 2023 international LPI score    63 A5.3 Component loadings for the 2023 international LPI score    64 C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y v Acknowledgements This report was prepared by the World Bank’s the report and for reaching out to forwarding Global Trade and Regional Integration Team in associations, as well as Professor Rosa Guada- the Trade, Investment, and Competition Group, lupe González Ramírez (Universidad de los under the guidance of Mona Haddad (Director) Andes, Chile) and Senior Lecturer Dr. Hans- and Sébastien Dessus (Practice Manager). The Joachim Schramm (Vienna University of Eco- project leaders were Christina Wiederer and nomics and Business, Austria) for their efforts Jean-François Arvis. The other authors were in disseminating the survey. Professor Lauri Ojala (Turku School of Eco- Mohini Datt, Melissa Knutson, Elizabeth nomics, University of Turku, Finland), Ben Price, Nandita Roy, and Chris Wellisz pro- Shepherd (Principal, Developing Trade Con- vided help with dissemination. Daniel Cramer sultants), and Daria Ulybina (World Bank). of BlueTundra.com and Galina Kalvacheva de- The authors thank the data partners of this signed, developed, and maintained the original edition, which for the first time incorporates Logistics Performance Index (LPI) survey and ­actual supply chain tracking data: result websites under the guidance of the core • Cargo iQ, especially Laura Rodriguez. team. A team at the World Bank’s IT Services • MarineTraffic. Department, led by Ritesh Sanan and Misun • MDS Transmodal, especially Antonella Kim, developed the 2023 LPI results website. A Teodoro. team at Communications Development—led by • TradeLens, especially Daniel Wilson. Bruce Ross-Larson—designed, edited, and laid • Universal Postal Union, especially José out the report. Anson, Mauro Boffa, and Fernão de Borba. The LPI survey would not have been pos- The authors also thank Jan Hoffmann of sible without the support and participation of the United Nations Conference on Trade and the International Federation of Freight For- Development, as well as Martin Humphreys, warders Associations (www.fiata.com), espe- Vincent Palmade, and Umar Serajuddin of the cially Stéphane Graber (Director General) and World Bank, the peer reviewers of the report. Amanda Stock (Communications Officer). Other colleagues at the World Bank who pro- National freight forwarding associations and a vided inputs include Abdennour Azeddine, La- large group of small, medium, and large logis- miaa Bennis, Matías Herrera Dappe, Emiliano tics companies worldwide were instrumental Duch, Caroline Freund, Olivier Hartmann, in disseminating the survey. The survey was de- Charles Kunaka, Ryan Kuo, Dunstan Matek- signed with Finland’s Turku School of Econom- enya, Antonio Nucifora, Daniel Saslavsky, and ics, University of Turku (www.utu.fi/en), which Aivin Solatorio. The team also thanks Professor has worked with the World Bank to develop the Gordon Wilmsmeier (Kühne Logistics Univer- concept since 2005. The authors thank the hun- sity, Germany) for comments on an earlier draft dreds of employees of freight forwarding and ex- and Professor Ruth Banomyong (Thammasat press carrier companies around the world who University, Thailand) for providing inputs into responded to the survey. vi C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Abbreviations COVID-19 Coronavirus Disease 2019 EDI Electronic Data Interchange GDP Gross Domestic Product LPI Logistics Performance Index TIR Transports Internationaux Routiers (Routiers, a road transit system) UN United Nations UNLOCODE United Nations Code for Trade and Transport Locations UPU Universal Postal Union C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y vii 2023 LPI scores Interna- Logistics Tracking Interna- Logistics Tracking Infra- tional competence Time- and Infra- tional competence Time- and LPI Customs structure shipments and quality liness tracing LPI Customs structure shipments and quality liness tracing Economy score score score score score score score Economy score score score score score score score Singapore 4.3 4.2 4.6 4.0 4.4 4.3 4.4 Mexico 2.9 2.5 2.8 2.8 3.0 3.5 3.1 Finland 4.2 4.0 4.2 4.1 4.2 4.3 4.2 Namibia 2.9 2.8 2.8 3.0 2.9 2.9 2.8 Denmark 4.1 4.1 4.1 3.6 4.1 4.1 4.3 Argentina 2.8 2.7 2.8 2.7 2.7 3.1 2.9 Germany 4.1 3.9 4.3 3.7 4.2 4.1 4.2 Montenegro 2.8 2.6 2.5 2.8 2.8 3.2 3.2 Netherlands 4.1 3.9 4.2 3.7 4.2 4.0 4.2 Rwanda 2.8 2.5 2.9 2.4 3.0 3.1 3.0 Switzerland 4.1 4.1 4.4 3.6 4.3 4.2 4.2 Serbia 2.8 2.2 2.4 2.9 2.7 3.4 2.9 Austria 4.0 3.7 3.9 3.8 4.0 4.3 4.2 Solomon Islands 2.8 2.4 2.6 2.9 2.9 3.2 2.9 Belgium 4.0 3.9 4.1 3.8 4.2 4.2 4.0 Sri Lanka 2.8 2.5 2.4 2.8 2.7 3.3 3.0 Canada 4.0 4.0 4.3 3.6 4.2 4.1 4.1 Bahamas, The 2.7 2.7 2.5 3.1 2.5 3.0 2.6 Hong Kong SAR, China 4.0 3.8 4.0 4.0 4.0 4.1 4.2 Belarus 2.7 2.6 2.7 2.6 2.6 3.1 2.6 Sweden 4.0 4.0 4.2 3.4 4.2 4.2 4.1 Djibouti 2.7 2.6 2.3 2.5 2.8 3.6 2.7 United Arab Emirates 4.0 3.7 4.1 3.8 4.0 4.2 4.1 El Salvador 2.7 2.4 2.2 2.6 2.7 3.2 2.9 France 3.9 3.7 3.8 3.7 3.8 4.1 4.0 Georgia 2.7 2.6 2.3 2.7 2.6 3.1 2.8 Japan 3.9 3.9 4.2 3.3 4.1 4.0 4.0 Kazakhstan 2.7 2.6 2.5 2.6 2.7 2.9 2.8 Spain 3.9 3.6 3.8 3.7 3.9 4.2 4.1 Papua New Guinea 2.7 2.4 2.4 2.6 2.7 3.3 3.0 Taiwan, China 3.9 3.5 3.8 3.7 3.9 4.2 4.2 Paraguay 2.7 2.4 2.5 2.7 2.6 3.0 2.8 Korea, Rep. 3.8 3.9 4.1 3.4 3.8 3.8 3.8 Ukraine 2.7 2.4 2.4 2.8 2.6 3.1 2.6 United States 3.8 3.7 3.9 3.4 3.9 3.8 4.2 Bangladesh 2.6 2.3 2.3 2.6 2.7 3.0 2.4 Australia 3.7 3.7 4.1 3.1 3.9 3.6 4.1 Congo, Rep. 2.6 2.3 2.1 2.6 2.9 2.9 2.7 China 3.7 3.3 4.0 3.6 3.8 3.7 3.8 Dominican Republic 2.6 2.6 2.7 2.4 2.6 3.1 2.4 Greece 3.7 3.2 3.7 3.8 3.8 3.9 3.9 Guatemala 2.6 2.3 2.4 2.8 2.7 2.6 2.7 Italy 3.7 3.4 3.8 3.4 3.8 3.9 3.9 Guinea-Bissau 2.6 2.7 2.4 2.9 2.9 2.4 2.3 Norway 3.7 3.8 3.9 3.0 3.8 4.0 3.7 Mali 2.6 2.6 2.0 2.6 2.5 3.1 2.7 South Africa 3.7 3.3 3.6 3.6 3.8 3.8 3.8 Nigeria 2.6 2.4 2.4 2.5 2.3 3.1 2.7 United Kingdom 3.7 3.5 3.7 3.5 3.7 3.7 4.0 Russian Federation 2.6 2.4 2.7 2.3 2.6 2.9 2.5 Estonia 3.6 3.2 3.5 3.4 3.7 4.1 3.8 Uzbekistan 2.6 2.6 2.4 2.6 2.6 2.8 2.4 Iceland 3.6 3.7 3.6 3.3 3.5 3.6 3.7 Albania 2.5 2.4 2.7 2.8 2.3 2.5 2.3 Ireland 3.6 3.4 3.5 3.6 3.6 3.7 3.7 Algeria 2.5 2.3 2.1 3.0 2.2 2.6 2.5 Israel 3.6 3.4 3.7 3.5 3.8 3.8 3.7 Armenia 2.5 2.5 2.6 2.2 2.6 2.7 2.3 Luxembourg 3.6 3.6 3.6 3.6 3.9 3.5 3.5 Bhutan 2.5 2.7 2.2 2.3 2.6 2.6 2.3 Malaysia 3.6 3.3 3.6 3.7 3.7 3.7 3.7 Central African Republic 2.5 2.4 2.6 2.1 2.9 2.6 2.4 New Zealand 3.6 3.4 3.8 3.2 3.7 3.8 3.8 Congo, Dem. Rep. 2.5 2.3 2.3 2.5 2.4 2.8 2.5 Poland 3.6 3.4 3.5 3.3 3.6 3.9 3.8 Ghana 2.5 2.7 2.4 2.4 2.5 2.7 2.2 Bahrain 3.5 3.3 3.6 3.1 3.3 4.1 3.4 Grenada 2.5 2.6 2.5 2.6 2.2 3.1 2.3 Latvia 3.5 3.3 3.3 3.2 3.7 4.0 3.6 Guinea 2.5 2.4 2.4 2.2 2.7 2.5 2.7 Qatar 3.5 3.1 3.8 3.1 3.9 3.5 3.6 Jamaica 2.5 2.2 2.4 2.4 2.5 2.9 2.8 Thailand 3.5 3.3 3.7 3.5 3.5 3.5 3.6 Mauritius 2.5 2.4 2.5 1.9 2.5 3.1 2.9 India 3.4 3.0 3.2 3.5 3.5 3.6 3.4 Moldova 2.5 1.9 1.9 2.7 2.8 3.0 2.8 Lithuania 3.4 3.2 3.5 3.4 3.6 3.6 3.1 Mongolia 2.5 2.5 2.3 2.5 2.3 2.7 2.4 Portugal 3.4 3.2 3.6 3.1 3.6 3.6 3.2 Nicaragua 2.5 2.0 1.9 2.8 2.8 2.9 2.4 Saudi Arabia 3.4 3.0 3.6 3.3 3.3 3.6 3.5 Tajikistan 2.5 2.2 2.5 2.5 2.8 2.9 2.0 Türkiye 3.4 3.0 3.4 3.4 3.5 3.6 3.5 Togo 2.5 2.3 2.3 3.0 2.4 2.8 2.3 Croatia 3.3 3.0 3.0 3.6 3.4 3.2 3.4 Trinidad and Tobago 2.5 2.2 2.4 2.5 2.4 2.9 2.5 Czechia 3.3 3.0 3.0 3.4 3.6 3.7 3.2 Zimbabwe 2.5 2.2 2.4 2.5 2.3 2.8 2.7 Malta 3.3 3.4 3.7 3.0 3.4 3.2 3.4 Bolivia 2.4 2.1 2.4 2.5 2.4 2.4 2.5 Oman 3.3 3.0 3.2 3.4 3.2 3.1 3.9 Cambodia 2.4 2.2 2.1 2.3 2.4 2.7 2.8 Philippines 3.3 2.8 3.2 3.1 3.3 3.9 3.3 Gabon 2.4 2.0 2.2 2.6 2.0 3.0 2.5 Slovak Republic 3.3 3.2 3.3 3.0 3.4 3.5 3.3 Guyana 2.4 2.3 2.4 2.1 2.6 2.6 2.2 Slovenia 3.3 3.4 3.6 3.4 3.3 3.3 3.0 Iraq 2.4 2.1 2.2 2.5 2.2 3.0 2.4 Vietnam 3.3 3.1 3.2 3.3 3.2 3.3 3.4 Lao PDR 2.4 2.3 2.3 2.3 2.4 2.8 2.4 Brazil 3.2 2.9 3.2 2.9 3.3 3.5 3.2 Liberia 2.4 2.1 2.4 2.8 2.4 2.3 2.4 Bulgaria 3.2 3.1 3.1 3.0 3.3 3.5 3.3 Sudan 2.4 2.1 2.3 2.4 2.4 2.7 2.3 Cyprus 3.2 2.9 2.8 3.1 3.2 3.5 3.4 Burkina Faso 2.3 2.0 2.3 2.4 2.4 2.4 2.2 Hungary 3.2 2.7 3.1 3.4 3.1 3.6 3.4 Fiji 2.3 2.3 2.2 2.3 2.3 2.3 2.2 Kuwait 3.2 3.2 3.6 3.2 2.9 2.8 3.3 Gambia, The 2.3 1.8 2.3 2.6 2.3 2.6 2.4 Romania 3.2 2.7 2.9 3.4 3.3 3.6 3.5 Iran, Islamic Rep. 2.3 2.2 2.4 2.4 2.1 2.7 2.4 Botswana 3.1 3.0 3.1 3.0 3.4 3.3 3.0 Kyrgyz Republic 2.3 2.2 2.4 2.4 2.2 2.4 2.3 Egypt, Arab Rep. 3.1 2.8 3.0 3.2 2.9 3.6 2.9 Madagascar 2.3 1.8 1.8 2.9 2.2 2.6 2.0 North Macedonia 3.1 3.1 3.0 2.8 3.2 3.5 3.2 Mauritania 2.3 2.1 2.0 2.2 2.5 2.8 2.5 Panama 3.1 3.0 3.3 3.1 3.0 3.4 2.9 Syrian Arab Republic 2.3 2.2 2.2 2.3 2.2 2.5 2.3 Bosnia and Herzegovina 3.0 2.7 2.6 3.1 2.9 3.2 3.2 Venezuela, RB 2.3 2.1 2.4 2.0 2.5 2.5 2.3 Chile 3.0 3.0 2.8 2.7 3.1 3.2 3.0 Cuba 2.2 2.0 2.2 2.1 2.2 2.6 2.4 Indonesia 3.0 2.8 2.9 3.0 2.9 3.3 3.0 Yemen, Rep. 2.2 1.7 1.9 1.7 2.6 2.8 2.3 Peru 3.0 2.6 2.5 3.1 2.7 3.4 3.4 Angola 2.1 1.7 2.1 2.4 2.3 2.1 2.3 Uruguay 3.0 2.9 2.7 2.7 3.1 3.2 3.3 Cameroon 2.1 2.1 2.1 2.2 2.1 2.1 1.8 Antigua and Barbuda 2.9 2.2 2.7 2.9 2.9 3.4 3.2 Haiti 2.1 2.1 1.8 2.3 2.0 2.5 2.1 Benin 2.9 2.7 2.5 2.9 3.0 2.7 3.2 Somalia 2.0 1.5 1.9 2.4 1.8 2.3 1.8 Colombia 2.9 2.5 2.9 3.0 3.1 3.2 3.1 Afghanistan 1.9 2.1 1.7 1.8 2.0 2.3 1.6 Costa Rica 2.9 2.8 2.7 2.8 2.9 3.2 2.9 Libya 1.9 1.9 1.7 2.0 1.9 2.2 1.8 Honduras 2.9 2.8 2.7 3.0 2.7 3.2 2.6 viii C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Key messages • The 2023 edition includes an extended directly affected by the recent crisis, such dataset consisting of (i) the survey-based as the quality of infrastructure or customs. Logistics Performance Index (LPI), The 10 countries with the best logistics per- which results from the traditional LPI formance continued to offer high-caliber survey of logistics professional and (ii) logistics­—­rated 4.1 out of 5 on average com- new key performance indicators (KPI) pared with 4.0 in 2018. The average rating measuring the actual speed of trade of the 10 poorest performers did not fall, around the world. The new KPI are de- despite challenging circumstances, and re- rived from large global tracking datasets mained at 2.1 out of 5, as in 2018. But the (Big Data) covering shipping containers, air 2023 edition included 21 fewer countries, cargo, and parcels. The new KPIs are not yet many of them low-income, than the 2018 included in the construction of the main edition. LPI indicators (country scores and ranks), which remain solely based on the LPI sur- • Mid-level logistics performers are show- vey. The two categories of indicators pro- ing progress. More countries scored vide a complementary yet consistent under- higher in the LPI compared with previous standing of logistics performance. The KPI years. The average overall country score has measure time or count the performance steadily risen over the past decade, with of specific links (e.g. delays at port or air- more countries clustered at an overall score ports), while the survey-based LPI provides of 3 to 4. country-wide assessments of six aspects of logistics performance: trade- and transport-­ • Supply chain reliability is critical. For related infrastructure, customs and bor- containers, the average time across all po- der management, logistics services quality, tential trade routes from entering the port timeliness of shipments, ability to track and of export to exiting the destination port is trace, and the availability of competitively 44 days, with a standard deviation of 10.5 priced international shipments. days. About 60 percent of the time it takes to trade goods internationally is spent at sea. • Logistics services were broadly resilient But the biggest delays occur when contain- for both top performers and bottom per- ers are held up at the origin or destination­ formers in the Logistics Performance —­ at ports, airports, or multimodal facili- Index (LPI), despite a more challeng- ties. Policies targeting these facilities, such ing operating environment. Even with as investing in port productivity, modern- the COVID-19 pandemic–induced dis- izing customs, and new technologies, can ruptions to shipping and the global supply improve reliability. chain crisis, the average overall score in the 2023 LPI was broadly the same as in the last • Performance transcends income. This is survey in 2018. This resilience partly reflects especially apparent with new key perfor- the robustness of the LPI survey, which mance indicators, such as the time contain- captures structural factors that were not ers spent in ports (dwell time). Emerging C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y ix Table of contents economies tend to have shorter delays than in port productivity and digitalization of industrialized economies, possibly because end-to-end supply chains. Middle-income of the lingering effects of the 2021–22 sup- countries with consistent performance ply chain crisis, the effects of Russia’s in- across the six LPI components could out- vasion of Ukraine on logistics in Europe, perform both their peers and more ad- and the leapfrogging of richer economies vanced countries. x C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Policy highlights • Improving customs and infrastructure regimes. The LPI is closely associated with matters most for raising the overall score connectivity indicators such as the number of bottom performers. The performance of maritime or aerial connections. Land- of customs and border agencies, as well as locked developing countries face long delays the quality of trade- and transport-related in transit countries, and small island states infrastructure, is particularly weak in the depend on transshipment and suffer from lowest performing countries. These coun- less frequent connections, which increases tries, many of them in the Middle East and lead time and reduces reliability. North Africa and in Sub-­ Saharan Africa, experience much longer delays than ad- • Environmentally sustainable logistics vanced and emerging economies and many options can lessen the carbon footprint middle-income countries. On average, ex- of supply chains and keep trade moving. port delays are of the same magnitude as im- Environmentally friendly options include port delays but for different reasons: export shifting to less carbon-intensive freight delays are tied more to the quality of service modes, more energy-efficient warehousing, or to economies of scale. or better capacity utilization. Demand for green shipping options is highest (75 per- • Addressing bottlenecks in landlocked cent) for exports to countries in the top developing countries is beyond the scope two performance quintiles and lower for of unilateral interventions and requires exports to countries in the middle (over coordinated interventions across bor- 20 percent) and bottom two (10 percent) ders, such as introducing robust transit quintiles. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y xi Executive summary This seventh edition of Connecting to Compete quality in the country from which these pro- comes as disruptions of global value chains have fessionals operate­ —­ that is, an assessment of revealed the crucial importance of logistics sys- domestic performance­ —­in order to keep the tems. Because of these disruptions, supply chain survey concise and easier to answer. The team resilience and its national security implications also faced difficulties in conducting the survey have emerged as top concerns. These concerns in 2020/21 due to the COVID-19 pandemic, are often linked with supply chain security, eventually postponing the survey to 2022. including cybersecurity­ —­ a key consideration in a highly digitalized and globally connected The LPI measures structural factors service industry. of performance, beyond disruptions This report presents the latest view on trade logistics performance across 139 countries. Lo- The recent supply chain crisis did not substan- gistics is understood as a network of services tially change the relative pattern of LPI scores that support the physical movement of goods, in 2023, except for a slight deterioration of the trade across borders, and commerce within bor- timeliness component. There are several possible ders. It comprises transportation, warehousing, reasons behind this outcome: brokerage, express delivery, terminal operations, • The global scope of the disruptions means and related data and information management. that when everyone is affected, it is difficult Previous editions of this report have relied to assign the impact to individual countries. exclusively on a survey of logistics professionals. • The LPI survey was conducted in late 2022, This edition introduces a new set of key perfor- when disruptions had already greatly dimin- mance indicators, derived from a Big Data ap- ished, possibly creating recency bias among proach, on actual movements of maritime ship- respondents. ping containers, air freight, and postal parcels by • Most LPI components reflect structural fac- trade lane and gateway. These indicators com- tors that are not directly affected by the re- plement the traditional survey-based ­ Logistics cent crisis, such as the quality of infrastruc- Performance Index (LPI), on which LPI scores ture. and ranks are still based. The survey asks a given • Shippers and logistics service providers have country’s partners to assess how easy or difficult generally been able to absorb the disruptions it is to trade in manufactured products trans- well, as indicated by the rebound in GDP ported in unit forms such as shipping contain- growth in most countries.3 ers. The six components of the LPI, unchanged since its launch in 2007, are assessed at the Logistics performance remained country level on a 5-point scale.1 stable or improved, but a gap The 2023 LPI survey was conducted from persisted between the top and September 6 to November 5, 2022. It contains bottom performers 4,090 country assessments by 652 logistics pro- fessionals in 115 countries in all World Bank Overall, the score profile of countries covered regions.[1]2 Unlike previous editions, the 2023 in the LPI has remained stable, despite the survey did not contain questions on logistics more challenging operational environment C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 1 Executive summary Table 1 Top 10 and bottom 10 average LPI scores, 2007–23 (1, low, to 5, high) 2007 2010 2012 2014 2016 2018 2023a Top 10 average 4.1 4.0 4.0 4.0 4.1 4.0 4.1b Bottom 10 average 1.8 2.1 2.0 2.1 1.9 2.1 2.1 Source: 2007, 2010, 2012, 2014, 2016, 2018, and 2023 Logistics Performance Index. a. Data are for 2022.  b. Average is for the top 12 scores due to rounding scores to one decimal point in 2023 rather than two as in previous editions.  since 2018. This reflects logistics service pro- on several continents. They are either fragile viders’ ability to adapt to dramatically chang- economies affected by armed conflict, natural ing circumstances­ —­ but it could also indicate disasters, or political unrest or landlocked coun- the robustness of underlying data across LPI tries challenged by geography or economies of editions (table 1). Average scores among low- scale in connecting to global supply chains. Af- performing countries have increased over time. ghanistan and Libya have the lowest score (1.9), In the 2023 LPI, the top 12 scorers are high- followed by Somalia (2.0), Angola, Cameroon, income economies. Singapore, with a score of 4.3, and Haiti (2.1). is at the top, a position it also held in 2007 and The most frequent LPI score has increased 2012. Of the top 12 scorers, 8 are in Europe (Fin- over the past decade, implying that logistics land, scoring 4.2; Denmark, the Netherlands, and performance overall has improved (figure 1). Switzerland, scoring 4.1; and Austria, Belgium, Between 2018 and 2023, a secondary, smaller Germany, and Sweden, scoring 4.0). They are peak emerged around a score of 3.5, meaning joined by Hong Kong SAR, China; the United that more countries have relatively strong per- Arab Emirates; and Canada. Most of these econo- formance. In addition, the lowest scores have mies have for years been dominant players across tended to increase, particularly in the 2023 LPI, international supply chain networks. but this is due partly to a sample of 139 coun- The bottom 10 scorers are mostly low- and tries compared with 160 in 2018. The 2018 sam- lower-middle-income countries and are located ple included 20 countries with a score of 2.6 or Figure 1 Distribution of LPI scores, 2012–23 Probability density 2012 2014 2016 2018 2023 0.8 0.6 0.4 0.2 0.0 1 2 3 4 5 LPI score Source: 2012, 2014, 2016, 2018, and 2023 Logistics Performance Index. Note: To avoid composition effects, only countries with scores in all years are included in the analysis. 2 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Executive summary below (and an average score of 2.4) that were not that overall performance reflects the ability to included in the 2023 sample. This makes com- perform well across all components­ —­ possibly paring the bottom tail difficult between the two indicating complementarity among them, as years. all stages of the value chain matter. Poor per- Despite this, a considerable gap in per- formance in one component drags down overall formance persists between the top and bot- performance. tom scorers. Although the average score of For countries with low LPI scores, infra- low performers has increased, some countries structure matters most to improving perfor- have stayed at their previous levels. These are mance. But the key to sustained high logistics typically the poor logistics performers­ —­those performance lies in a broader set of interven- with severe logistics constraints (43 countries tions covering policy and private sector devel- in the bottom performance quintile).4 Partial opment. One important objective should be to performers have logistics constraints typically better predict when goods will arrive at their seen in low- and middle-income countries (46 destination, as with supply chain visibility tools countries in the middle quintile and the second that facilitate traceability. quintile from the bottom). Countries in the top two performance groups typically received Measuring the speed of trade: New slightly higher scores in the 2023 LPI than in key performance indicators 2018 and earlier. They include consistent per- formers, countries rated better on logistics per- This edition of Connecting to Compete incorpo- formance than most others in their income rates new key performance indicators, derived group (25 countries in the second quintile from from a Big Data approach, measuring the speed the top)­—­and logistics-friendly countries, the of trade around the world. These indicators are top scorers, most of which are high income (25 based on millions of actual international move- countries in the top quintile). ments of containers, aviation shipments, and postal parcels. Global tracking initiatives­ —­ Strong overall logistics performance including Cargo iQ (supported by the Interna- is driven by good performance tional Air Transport Association), TradeLens, across all six LPI components and the Universal Postal Union­—­made the raw data available to the World Bank. Several trends observed in past LPI reports The key performance indicators comple- still hold. The timeliness component outper- ment the assessment of logistics performance forms the other components in all perfor- provided by the survey-based LPI with more mance quintiles, except the top one, whereas specific measurements: Delays at ports and air- the performance of customs and border agen- ports and international connectivity (for exam- cies underperforms the other components. The ple, the number of international connections by quality of trade and transport infrastructure country and by mode). The new indicators, mea- remains below the overall LPI score in the bot- sured in days or simple counts, are relatable to tom three quintiles. But there is a clear dete- policymakers and practitioners concerned with rioration in timeliness scores in absolute terms the performance of key logistics hubs and gate- in all quintiles due to the effects of the supply ways, such as ports or airports. chain crisis, which were felt most acutely in shipment delays. While absolute scores fell, the A more complex picture of trade relative pattern of performance persisted. bottlenecks The quality of logistics services is on par with overall performance, but the tracking and Understanding performance requires look- tracing component is as good as or better than ing beyond average shipment times. Lead time overall performance in almost all performance (delay) of connections in international supply quintiles. Taken as a whole, the LPI suggests chains is widely dispersed and skewed to the C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 3 Executive summary right of the mean, meaning there are many out- dwell times of three to nine days. Few countries liers with high dwell times.5 The long tail of the have a dwell time of more than 12 days. De- distribution makes lengthy delays likely for the lays can be caused by such factors as low port slowest shipments (see figure 2 for an example handling productivity, city congestion, slow using the port of Algiers in Algeria). It means preparation of trade documents to comply with that they lack of reliability across the supply exchange controls, or abuse of port storage by chain is more important than the average delay importers. Most outliers are in the Middle East at links of the supply chain, especially if trace- and in North, Central, and West Africa. Similar ability along the supply chain and information patterns apply to aviation logistics, with shorter flows are lost. Being unable to locate or pre- delays (typically one-third as long) and substan- dict the movement of containers­ —­ because, for tial overlaps of outliers. example, they are stalled on ships or arbitrarily Dwell time is not clearly associated with in- held up in customs­—­matters a lot to consignees. come (map 1). Countries in Europe and North Trade experiences much more dispersion America do worse on this metric than other in delays when not moving at ports, airports, high-income and emerging economies. Singa- or multimodal facilities than when moving pore, for example, has a dwell time of around on ships. Most time (two-thirds, on average) is three days compared with more than seven for spent in transit. Policies targeting these facili- the United States. The recent turmoil in global ties, such as investing in port productivity or logistics is a first explanation (the data on con- modernizing customs, can improve reliability. tainer movements are for May­ –October 2022). New technologies, such as supply chain visibil- Countries in Northern Europe have been ity platforms, are even more promising. weathering the ripple impact of sanctions on Average delays at ports, at airports, or in Russian shipping. Emerging economies may also postal delivery tend to be negatively correlated benefit from more recent investments in soft with a country’s overall LPI score. Long delays and hard port infrastructure. As for aviation lo- are a sign of performance problems, but short gistics, airport dwell time is shorter than mari- delays do not necessarily indicate high overall time dwell time, typically by a factor of three. logistics performance. Take import dwell time, Airport dwell time follows the same patterns as or the mean time containers stay at ports before maritime dwell time, with substantial overlaps being removed for delivery: Most countries have of outliers for both modes. Figure 2 Dispersion of time spent at port: Long tail distribution of containers in the port of Algiers, Algeria, May–October 2022 Number of consignments 200 Median Mean 150 100 50 0 0 20 40 60 80 100 Import dwell time (days) Source: World Bank estimates based on TradeLens data. Note: Consignments with an import dwell time of more than 100 days are excluded. 4 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Executive summary Map 1 Mean import dwell time of containers around the world, May–October 2022 Source: TradeLens and World Bank. Landlocked developing countries presence of global logistics operators, and expo- and the importance of connectivity sure to best practices. Transport connectivity is driven largely by Landlocked developing countries are logistically economies of scale and the geography of global constrained. They may have short delays at desti- networks. Conversely, countries with few mari- nations (for imports) but longer delays on tran- time connections, such as small maritime econo- sit corridors and at the port of entry. On aver- mies, trade through a chain of container trans- age, the dwell time at the same port of entry is shipment ports. Transshipment hubs have a substantially longer for landlocked developing similar dispersion in port dwell time as destina- countries than for their coastal transit coun- tion ports. Small maritime economies experience tries. Addressing these bottlenecks is beyond not only longer lead time to trade but also less re- the scope of unilateral interventions and requires liable connections, contributing to lower logis- coordinated interventions across borders, such as tics performance. This is due to dependence on robust transit regimes similar to the ones imple- only a few transshipment hubs, which are able to mented in Europe (Transports Internationaux charge markups; the higher cost of going through Routiers and the European transit system). a transshipment hub than exporting from a full- Connectivity, measured as the number of fledged port; and­—­chiefly­—­the extra delays and transport connections, is associated with lo- lower reliability induced by transshipment. gistics performance, irrespective of transport mode. 6 Having more connectivity options is Mounting regulatory and demand positively associated with logistics performance pressure toward environmental through, for example, increasing competition sustainability for logistics services, higher hard and soft trade infrastructure investments (such as ports and As in 2018, the LPI survey links environmen- information technology systems), the growing tal sustainability and logistics performance by C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 5 Executive summary asking how often shippers have asked for environ- quintiles. Despite the variation, demand forces mentally friendly options when sending goods are an important factor pushing logistics opera- to the surveyed countries.7 The wording in this tions in a more sustainable direction (also see question is general because of the numerous ship- map A6.1 in appendix 6). ping options and ways to measure their environ- This trend is in line with the increasing mental impact. Environmentally friendly options number of global and national commitments in logistics range from shorter routes, source of to reduce logistics-related greenhouse gas emis- propulsion, or better capacity utilization to mini- sions and other harmful emissions, for which mize transport emissions. Therefore, the findings targets are becoming ever more challenging in are indicative of the prevalence of such needs all transport modes. This regulatory pressure is among shippers, as encountered by freight for- mounting in air, road, and maritime transport. warding and logistics professionals. It drives the change to more environmentally Almost 75 percent of shippers had asked for friendly logistics processes and equipment, espe- such options “often” or “nearly always” when ex- cially when they can generate economic savings. porting to countries in the top two performance The pressure from demand forces is gaining mo- quintiles.8 The share was slightly over 20 percent mentum, particularly in high-income countries. when exporting to countries in the middle per- For policymakers this means that the search for formance quintile and well below 10 percent implementable “green logistics” policies is be- to countries to the two lowest performance coming more important. 6 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Key changes in global supply chains 1 CHAPTER since 2018 and implications for the 2023 Logistics Performance Index This seventh edition of Connecting to Compete effects of the pandemic. In many countries, the complements the Logistics Performance Index pandemic also affected the availability of truck (LPI) survey results with information derived drivers, train engineers, and port and warehouse from a Big Data approach using technological workers, and it complicated crew changes on advances in tracking shipments across different seagoing vessels. Some countries also applied modes of transport. A deeper understanding of strict zero-COVID policies, with extensive logistics processes at the micro level is impor- local lockdowns. tant in light of changing realities on the ground. Energy and food prices increased because Since its launch in 2007, the LPI has provided of discontinued exports from Belarus, Russia, a simple assessment by professional sources of and Ukraine in the wake of Russia’s invasion of how easy it is to export to a target country in Ukraine. This generated cascading effects such terms of the quality of infrastructure, the qual- as exports bans and overshooting demand. Con- ity and availability of logistics activities, and tainer shipping was affected when most services public sector bottlenecks. The LPI and its com- to and from Russia were discontinued. Russia’s ponents are best interpreted as a snapshot of and Belarus’s trade and transport connections where a country stands on logistics with respect with Europe were largely cut off, including flights to its peers or comparators. As such, it can serve over Russian airspace and container rail services as an entry point to a more comprehensive mea- between Europe and Asia through Russia. surement of a country’s logistics performance. Container shipping disruptions in Understanding logistics 2020–22 performance and its determinants is now more important than ever Container ships carry over half of world trade by value and, until early 2020, offered high reliabil- Measuring logistics performance and under- ity at low freight rates. Since then, freight rates standing its determinants are now more impor- have soared to unprecedented levels, and capac- tant than ever against a background of major ity constraints in seaports, vessels, and container changes in global markets since 2018 due to the availability have become endemic. As a result, COVID-19 pandemic, subsequent shipping and service reliability plummeted to an all-time low air freight disruptions (the latter from restric- toward the end of 2021 (figure 1.1). In summer tions imposed on passenger air transport), and 2022, about 12 percent of the world’s container Russia’s invasion of Ukraine (see also box 1.1 on carrying capacity was onboard vessels outside vaccine logistics). During the COVID-19 pan- seaports waiting to be unloaded.9 Many of these demic, demand for some types of goods, such bottlenecks could be traced to port lockdowns in as electronics and home appliances, rose, while East Asia or productivity constraints on the US production and transport capacity fell. For West Coast, but with global impacts on shipping example, the demand for microchips, a crucial capacity and cascading effects along the supply component in electronics and most manufac- chains. Toward the end of 2022, these problems turing industries, surged, but their supply was eased considerably, and container freight rates hampered by droughts and accidents in sev- are returning to pre–COVID-19 pandemic lev- eral major production sites­—­ in addition to the els as demand drops. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 7 Chapter 1  Key changes in global supply chains since 2018 and implications for the 2023 LPI Box 1.1 Vaccine logistics Vaccines are vital public health products, even The capacity of logistics service providers more so during the global COVID-19 pandemic. is a key determinant of cold chain supply per- But many vaccines have special handling re- formance for vaccines. 3 Targeted investment quirements that require substantial logistics ca- along with strategic planning can mitigate the pacity in sending and receiving countries. For challenges posed by cold chain supply require- instance, some COVID-19 vaccines have cold ments for vaccines.4 —­ chain supply requirements­ high-level logistics The COVID-19 pandemic has resulted in competence that many countries lack. huge losses, including in lives and livelihoods, Logistics bottlenecks can contribute to slow around the world. Ensuring that vaccines are movement of vaccines to and within countries. widely distributed on an ongoing basis is impor- Better logistics performance is associated with tant not only from an equity point of view but also higher vaccination rates, even after per capita in- as part of support to the global economy. Given come and government spending on health are con- the special requirements of vaccines, logistics trolled for.1 A review of a range of studies on logis- service providers can play an important role in tics requirements concluded that an efficient and realizing this vision. —­ resilient supply chain­ which depends on strong —­ logistics­ was vital to ensuring that the COVID-19 Notes 2 vaccines reached their target populations. Effi- 1. Helble and Shepherd 2017. cient cold chain management depends on regula- 2. Fahrni and others 2022. tory requirements, logistics performance, and the 3. Pambudi and others 2021. chemical stability of the goods being moved. 4. Fleming, Okebukola, and Skiba 2021. Figure 1.1 Container shipping schedule reliability, average delay for late arrivals, and spot rates, June 2018–22 Schedule reliability (%) Container shipping spot rate, 40-foot equivalent unit on seven main trades (US$) Average delay for late arrivals (days) 12,000 10 100 Schedule reliability (right axis) 9 90 10,000 8 80 7 70 8,000 Average delay for late arrivals (right axis) 6 60 6,000 5 50 4 40 4,000 3 30 2 20 2,000 1 10 Container shipping spot rate (left axis) 0 0 0 2018 2019 2020 2021 2022 2023 Source: Data on schedule reliability and delays, Sea-Intelligence; data on spot rates, Drewry World Container Composite Index. 8 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Chapter 1  Key changes in global supply chains since 2018 and implications for the 2023 LPI Air freight market disruptions in in goods is expected to grow to US$1– 2020–22 US$2  trillion in merchandise value from its current US$300 billion, resulting in substan- Approximately US$6  trillion worth of tial changes in supply chains.11 E-commerce goods­—­ 35 percent of world trade by value­ —­is was equivalent to 30 percent of global GDP in transported each year as air freight.10 The over- 2019,12 so its role and importance in economic all demand for international air freight has development cannot be overlooked. Most cross- been stable since 2018. Variation in the supply border e-commerce depends on postal-parcel by widebody passenger aircraft, which is offset services provided by members of the Universal only partly by changes in the capacity offered by Postal Union (a specialized UN agency) or the dedicated air freighters, seems to drive air freight networks of global express operators (for exam- pricing. Before the COVID-19 pandemic, about ple, DHL, FedEx, and UPS). half of air freight was carried in scheduled pas- Universal Postal Union members handle senger aircraft. Ad hoc fluctuations in rates can two-thirds of cross-border deliveries of letter- happen due to a sudden local change in demand. parcel items (up to 2 kilograms). Therefore, in- Attracted by higher rates, nonscheduled formation collected by the union is a source of freighter capacity usually takes several weeks to comprehensive data for more than 190 member adjust supply and push prices to a new equilib- countries. It is probably the best unified source rium (figure 1.2). Since January 2022, air freight of information on e-commerce trade.13 prices have declined due to increased passenger widebody aircraft capacity on many routes. This The 2023 LPI measures structural happened first on transatlantic routes and later factors of performance rather than on most Europe–Asia routes. supply chain disruptions The rapid emergence of The recent supply chain crisis did not substan- e-commerce as an important tially change the relative pattern or even absolute channel for cross-border trade scores in the 2023 LPI across countries compared with previous editions, except for a slight deterio- The volume of e-commerce has surged in the ration of the timeliness component since 2018. past decade. By 2030, cross-border e-commerce This may seem odd, given the severity of the Figure 1.2 Global air freight supply, demand, and prices, January 2018–July 2022 Demand or supply (billions of cargo ton-km) Price per kg (US$) 50 5 40 4 Supply (left axis) 30 3 Price (right axis) 20 2 Demand (left axis) 10 1 Before COVID-19 During COVID-19 After COVID-19 0 0 Jan. Apr. July Oct. Jan. Apr. July Oct. Jan. Apr. July Oct. Jan. Apr. July Oct. Jan. Apr. July 2018 2019 2020 2021 2022 Source: Dewulf and Van Broekstaele (2022) based on data from the Freightos Air Index, Cargo iQ (supported by the International Air Transport Association), and WorldACD. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 9 Chapter 1  Key changes in global supply chains since 2018 and implications for the 2023 LPI impacts on container shipping freight and service Further, four of the six LPI components levels, changes in air freight markets, various reflect deep structural factors that are not di- restrictions during the COVID-19 pandemic, rectly affected by the recent supply chain crisis and discontinued trade relations and cargo lanes. (ease of working with customs and other bor- One possible explanation is the global scope of der agencies, infrastructure, logistics services the supply chain crisis. With practically every- quality, and the ability to track and trace ship- body affected by disruptions beyond their control ments). Performance on these metrics may have almost simultaneously, it is difficult to assign the improved due to policy reforms and private impact on individual countries. sector capacity building over time, despite the In addition, the survey data were collected constraints imposed by recent conditions. These when supply chain disruptions had already factors also improve resilience against shocks. greatly diminished. For example, the Global New indicators based on tracking data, such Supply Chain Pressure Index, a composite mea- as the time containers stay in ports or airports, sure of global supply chain disruptions, peaked look at the speed of trade. By nature they are at 4.3 standard deviations above its historical more affected by major disruptions such as the mean at the end of 2021, declined to 2.8 in recent ones than the survey-based LPI. With the March 2022, temporarily increased in April exception of the postal data (which is for 2019), 2022 due primarily to COVID-19 pandemic the data cover the same period as the LPI survey, lockdowns in China and Russia’s invasion of mid-2022. The results are likely to be affected Ukraine, then declined for five months to al- by the tail of the supply chain crisis or Russia’s most normal levels (0.9) in September 2022, invasion of Ukraine (see map 1 in the executive when the LPI survey went live. This may have summary). Unfortunately, precrisis data are not contributed to recency bias among respondents. available for comparison. Box 1.2 The 2023 LPI survey question on supply chain disruptions This year’s survey included a question on dis- to high-income countries appeared to be the ruptions in logistics operations since 2019. The least disrupted, and shipments to low-income effects were far from equal across countries. countries the most (see also map A6.2 in ap- Among respondents dealing with exports to pendix 6). high-income countries, 13 percent reported In summary, respondents in low-scoring en- that operations had suffered major disruptions vironments appear to perceive conditions abroad or had been discontinued. The same was true as much better, whereas those in high-scoring for 59 percent of those exporting to middle- environments perceive them as much worse. income countries and for 75 percent of respon- The order and direction of ratings are consistent dents exporting to low-income countries. When when analyzed by income group (see table 2.1 examined bilaterally by destination, shipments in chapter 2). 10 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 2 CHAPTER The 2023 Logistics Performance Index This chapter focuses on reporting and inter- Second, the presentation of results focuses preting findings from the 2023 Logistics Per- more on groups of countries with broadly simi- formance Index (LPI) survey, taking account of lar performance than on small differences in new realities in the logistics marketplace, policy scores between countries. Where it is impor- environment, and international setting. The tant from a policy perspective to highlight dif- methodology of this part of the report is largely ferences across countries, the analysis focuses unchanged from previous editions. The scores on those differences at the country group level and ranks of the 2023 LPI presented before rather than at the individual country level. the executive summary and in appendix 1 rely This approach still allows for summarizing exclusively on the LPI survey. Box 2.1 summa- broad trends across geographic areas and income rizes the survey-based LPI’s key features. groups, which the following sections do. But it reduces the likelihood of users overinterpreting How to interpret the LPI scores and rankings. The intention is to shift the focus to policy-relevant differences across coun- This edition differs from previous editions of tries from survey-based scores and rankings that Connecting to Compete in how it presents data. can vary due in part to sampling and measure- Previous reports presented LPI scores and ment error and perhaps exceptional situations ranks, along with confidence intervals for both such as the COVID-19 pandemic. (box 2.2). Scores and confidence intervals were As before, the main caveat is that this part rounded to two decimal places, and rankings of the LPI is based on a survey. So, country-level were based on those rounded figures. While raw outcomes can be affected by low numbers of results of this kind are useful for some purposes, respondents, which is the case for some small they risk overinterpretation. LPI scores reflect a and low-income countries. Efforts to collect survey-based quantification of qualitative percep- the maximum amount of information on these tions and are thus subject to concerns about noise. countries do not always pay off. This dynamic Similarly, sampling is nonrandom since re- is an additional reason for presenting results spondents choose whether to participate. Issues by country group rather than individually. As such as these create difficulties when compar- a perception-based indicator, the LPI might ex- ing small changes between countries. An addi- hibit differences from county-level indicators. tional issue relates to year-on-year comparisons, Likewise, the LPI does not measure reforms. which suffer from the limitation that respon- dents grade performance on a qualitative scale Features of the 2023 survey that could suffer from indexing issues. The approach this year differs. First, scores The 2023 LPI survey employed broadly the and confidence intervals are rounded to a single same methodology as the previous six editions , decimal place. The rationale for this change is though with a simplified approach for the ques- that the survey uses whole numbers on a Likert tionnaire. Until 2018, the questionnaire had scale for country ratings, so countries with sim- two parts: international and domestic. In the ilar but not identical response patterns receive international questionnaire, respondents evalu- identical scores. ated six indicators of logistics performance in up C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 11 Chapter 2  The 2023 Logistics Performance Index Box 2.1 The six components of the LPI The World Bank’s Logistics Performance Index (LPI) analyzes coun- target markets. While the pool of participants is not constant over tries through six components: time (due to staffing and organizational changes in the industry), 1. The efficiency of customs and border management participating logistics professionals is central to the quality and clearance. credibility of the LPI, and their involvement and feedback have been 2. The quality of trade- and transport-related infrastructure. essential in developing and refining the survey over time. 3. The ease of arranging competitively priced international shipments. Input and outcome LPI indicators 4. The competence and quality of logistics services. 5. The ability to track and trace consignments. Customs Timeliness 6. The frequency with which shipments reach consignees within the scheduled or expected delivery time. Supply chain Inter- The indicators were chosen based on theoretical and empiri- Infra- national structure service shipments cal research and the practical experience of logistics professionals delivery involved in international freight forwarding. The figure maps the six LPI indicators to two main categories: Services Tracking quality and tracing • Areas for policy regulation, indicating main inputs to the sup- ply chain: customs, infrastructure, and services (indicators Service Areas 1, 2, and 4 above). for delivery policy performance • Supply chain performance outcomes: cost, reliability, and regulations outcomes (inputs) Time, cost, time (indicators 3, 5, and 6 above). reliability The LPI uses standard statistical techniques to aggregate the data into a single indicator, converting qualitative information into quantitative information, before aggregating and weighting (see ap- See the 2023 LPI questionnaire at https://lpi.worldbank.org. 1 pendix 5 for details of the methodology). It relies on an online survey of logistics professionals from multinational freight forwarders and Note the main express carriers. Their views matter because they directly 1. In all editions of the LPI, statistical aggregation has yielded an overall affect the choice of shipping routes and gateways, thereby influenc- score that is close to the simple average of country scores across the six ing firms’ decisions on production location, choice of suppliers, and components. to eight partner countries. In the domestic ques- Key findings of the 2023 LPI survey tionnaire, respondents provided qualitative and quantitative data for the logistics environment Over the past decade, high-income countries in the country where they work. have occupied the top positions in the LPI The 2023 LPI survey used only the interna- rankings (see table A3.1 in appendix 3). Geo- tional part of the survey, so comparisons over graphically, top scorers are concentrated in time reflect solely that part. The domestic part Europe, but East Asia and Pacific, North Amer- was cut for two reasons. First, to counter sur- ica, and the Middle East and North Africa are vey fatigue among respondents. Second, because also represented. There are 12 economies atop most of the data covered by the domestic part the logistics performance leaderboard in 2023, of the survey can be gleaned more easily and all with a score of 4 or higher, compared with accurately from the new supply chain tracking 11 in 2018. These economies have tradition- datasets in chapter 3.14 The 2023 survey, con- ally dominated international supply chain net- ducted from September 6 to November 5, 2022, works, and the composition of the group has included 4,090 assessments of 139 countries by been steady over time. The recent supply chain logistics professionals. crisis has not significantly changed this relative 12 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Chapter 2  The 2023 Logistics Performance Index Box 2.2 How precise are LPI scores? Although the Logistics Performance Index (LPI) bounds for a country’s score (see appendix and its components offer the most comprehen- 5). Confidence intervals must be examined to sive and comparable data on country logistics determine whether a difference between two and trade facilitation environments, they have a scores is statistically significant. An improve- narrow domain of validity because of the limited ment in a country’s performance is considered experience of survey respondents with respect statistically significant only if the lower bound of to the countries they assess and because of the its 2023 score exceeds the upper bound of its high dependence of the logistics of landlocked 2018 score. Because of the LPI’s narrow domain countries and small island states on the logistics of validity and the need for confidence inter- of other countries. vals to account for sampling error, a country’s To account for the sampling error created exact score might be less relevant to policy- by the survey-based dataset, LPI scores are makers than its proximity to others in a wider presented with approximately 80 percent con- performance group or its statistically significant fidence intervals, which yield upper and lower improvement. pattern of results across countries because the • Poor logistics performers. Countries with se- crisis is global in scope. vere logistics constraints, such as the least By contrast, the bottom 10 scorers15 are developed countries (43 countries in the mostly low-income and lower-middle-income bottom quintile). countries, all with an LPI score of 2.2 or lower • Partial performers. Countries with a level of (see table A3.2 in appendix 3). That only 10 logistics constraints most often seen in low- countries meet this criterion is a major change and middle-income countries (46 coun- from 2018, when 22 countries did. It partly re- tries in the middle quintile and the second flects a smaller survey sample (139 countries ver- quintile from the bottom). sus 160 countries), but it could also be linked to • Consistent performers. Countries rated bet- improvements in performance—a point revis- ter on logistics performance than most oth- ited below. Given that four of the six LPI com- ers in their income group (25 countries in ponents reflect deep structural factors that are the second quintile from the top). not directly affected by the supply chain crisis, • Logistics-friendly. Top-performing coun- it is plausible that performance on these metrics tries, most of which are in the high-income has improved due to policy reforms and private group (25 countries in the top quintile). sector capacity building over time, despite the constraints imposed by recent conditions. For The groups track relative performance the most part, the countries in this group are for the set of countries captured in a single fragile economies affected by armed conflict, year of the LPI. As a result, average scores natural disasters, or political unrest or face chal- across groups as well as measures of disper- lenges of geography, such as being landlocked, sion within and across groups can be relevant or diseconomies of scale in connecting to global to understanding how countries compare in supply chains, where countries are too small to a single year. Country scores are bunched at be connected widely. There is more movement the low and middle ranges (corresponding to in and out of the bottom group than in and out the bottom three quintiles)—a key reason for of the top group. the change in reporting practice with this edi- tion (figure 2.1). When countries are grouped Identifying logistics performance groups closely, it is more informative for policymakers LPI scores are broken down into four perfor- to focus on broadly defined country groups mance groups, based on score quintiles:16 than on individual country scores and ranks. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 13 Chapter 2  The 2023 Logistics Performance Index Figure 2.1 Histogram of scores of the 139 countries and four performance groups in the 2023 LPI Percent 20 Poor logistics Partial Consistent Logistics- performers performers performers friendly 15 10 5 0 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6 4.8 5.0 Overall LPI score Source: 2023 Logistics Performance Index. Note: Vertical lines correspond to score cutoffs for the four performance groups identified in the main text. Four groups are displayed because the partial performers group includes two quintiles (the middle and the second from the bottom). Bilateral LPI assessments between highest average score (3.7), followed by the income groups lower-middle-income group (3.1), the upper- Given that the LPI assesses logistics perfor- middle-income group (3.0), and the high- mance by eliciting ratings from profession- income group (2.7). So, respondents’ context als outside the country being scored, break- affects how they score performance abroad.17 ing results out bilaterally (that is, between the These findings are consistent with a model of respondent’s country and the assessed country) perception formation in which respondents provides additional insight. compare performance abroad to performance Respondents from all income groups in their home country. This dynamic is an ad- rated the high-income group the highest, ditional reason for preferring analysis of LPI followed by the upper-middle-income and scores using broad country groups rather than lower middle-income groups, then the low- high-precision scores and limits the extent to income group (table 2.1). Hence, ratings are which differences in score can lead to concrete consistent in a rank order sense across in- policy interpretations. come groups. However, income groups dif- fer noticeably in the average scores they gave Strong overall logistics performance is other groups: the low-income group gave the driven by good performance across all LPI components Performance on LPI components differs by Table 2.1 Bilateral LPI assessments in 2023, by income group overall LPI quintile. The timeliness component outperforms the others in all quintiles except Assessed country the top one (figure 2.2). And the performance Upper middle Lower middle Respondent’s country High income income income Low income Average of customs and border agencies and the quality High income 3.7 2.7 2.4 2.0 2.7 of trade and transport infrastructure are partic- Upper middle income 3.7 3.0 2.6 2.5 3.0 ularly weak in the bottom quintile. The bottom Lower middle income 3.8 3.2 3.1 2.3 3.1 quintile is also characterized by lower quality Low income 4.3 4.2 3.4 2.9 3.7 of logistics services. In the two top-performing quintiles, performance is more consistent across Source: 2023 Logistics Performance Index. the six components. 14 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Chapter 2  The 2023 Logistics Performance Index Figure 2.2 LPI component scores, by overall LPI quintile, 2023 Customs Infrastructure Ease of shipping Quality of Tracking Timeliness Percent arrangements logistics services and tracing 5 4 3 2 1 Bottom quintile Second quintile Third quintile Fourth quintile Top quintile Source: 2023 Logistics Performance Index. The dynamics of LPI scores over supply chain crisis discussed in chapter 1? The 2012–23 data collection for the LPI is part of the answer. Of the six components that respondents rate, Caution should be used in interpreting changes in four relate to deep country characteristics that scores over time. But examining the full distribu- were not affected by the crisis. Difficult supply tion of scores by year can be informative because chain conditions were not related to problems the analysis can focus on such issues as clustering with customs, deficiencies in infrastructure and dispersion at particular points. In general, the quality, a lack of quality logistics services pro- most frequent LPI score has increased over the viders, or difficulties in tracking and tracing past decade, which could signal a trend toward shipments. The crisis was due to a combination rising scores, subject to the caveat that this year’s of supply- and demand-side factors related to the sample is smaller than in previous years and lacks COVID-19 pandemic and efforts to ­control it. some smaller, lower income countries (figure 2.3). But data for one LPI component suggest The change is most pronounced from 2018 to that LPI survey respondents were conscious of 2023, in particular with the emergence of a sec- the new supply chain realities when rating coun- ondary, smaller peak at a score of around 3.5. This tries. Ratings for the timeliness component fell finding is plausible because four of the six LPI in all performance quintiles except the second components relate to deep structural factors that from the top between 2018 and 2023 (figure 4). are not directly affected by the supply chain crisis. Given that a key aspect of the supply chain crisis This change means that the survey sample was delays, this finding suggests that the time- has more countries with strong performance. liness component captures some of the disrup- Perhaps more importantly, the lowest scores tion, subject to the caveat that data collection have tended to increase, particularly from 2016 was undertaken as crisis conditions were easing. to 2023. So, while there is still a considerable range of performance, countries with lower Logistics performance is scores are improving over time. determined by more than income What is the impact of the supply There is a noticeable gap in LPI scores between chain crisis? high- and low-income countries (figure 2.5). High-income countries have a much higher How is it possible to reconcile the apparent median LPI score than low-income coun- increase in LPI scores over time with the recent tries. Moreover, among the 33 top-performing C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 15 Chapter 2  The 2023 Logistics Performance Index Figure 2.3 Distribution of LPI scores, 2012–23 Probability density 2012 2014 2016 2018 2023 0.8 0.6 0.4 0.2 0.0 1 2 3 4 5 LPI score Source: 2012, 2014, 2016, 2018, and 2023 Logistics Performance Index. Note: To avoid composition effects, only countries with scores in all years are included in the analysis. countries in the 2023 LPI, 30 are high income­ Nevertheless, countries can still outper- —­ a finding that has changed little from past form their income group peers despite the per- LPI editions, despite the new reality of global formance gap, as indicated by the dispersion of trade. This point is about the distribution of scores within income groups (see figure 2.5). In scores rather than absolute levels and is thus all groups, there is a wide range of country per- consistent with the idea that the supply chain formance. Clearly, a variety of factors beyond crisis is global in scope rather than affecting just income, from policy to private sector develop- a small number of countries or regions. ment, affect logistics performance. Figure 2.4 Timeliness score, by LPI quintile, 2018 and 2023 Figure 2.5 Distribution of 2023 LPI scores by income group Percentage change 2018 2023 5 LPI score 5 4 4 3 3 2 2 1 Bottom quintile Second quintile Third quintile Fourth quintile Top quintile 1 Source: 2018 and 2023 Logistics Performance Index. Low Lower middle Upper middle High income income income income Source: 2023 Logistics Performance Index. Note: The median is denoted by the bar in the square. The shaded areas are the middle 50 percent of scores. 16 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 3 CHAPTER Supply chain lead time around the world: Where are the delays? The supply chain disruptions of 2021–22 under- central tendency, such as the median (box 3.1). score the importance of reliability, which is cap- The analyses are aggregated at the country level tured as “timeliness of delivery” in the Logistics to be relatable to the LPI survey results. Performance Index (LPI) survey (see chapter 2).18 During this recent crisis, firms and con- Lead time dispersion and supply sumers worldwide experienced goods not arriv- chain reliability ing on time as expected due to disruptions in vessel movements and to shipments staying at Understanding the speed of trade, as well as the hub and gateway facilities for longer than usual. magnitude and nature of delays, requires look- In an environment of low inventory, unex- ing beyond averages. Figure 3.1 breaks down the pectedly long delivery times can translate into lead time of containers from entering the port of human hardship, as with the shortage of baby origin to exiting the port of destination and its formula in the United States19 or fertilizer in variability. Dwell time at hubs and gateways has Sub-Saharan Africa.20 Addressing disruptions considerably more dispersion than is observed such as these cuts across a wide range of policy in international freight transport. On average areas, but focusing on supply chain manage- across all potential routes, a container takes 44 ment highlights the importance of time spent days from entering the port of export to exit- at maritime or aviation hubs and gateways. ing the destination port, with a standard devia- This is one area where investments in produc- tion of 10.5 days. Over 60 percent of this time is tivity, increasing the fluidity of information spent on ships, with the rest split between stays flow, and enhancing logistics service provision at ports of export, import, or transshipment. can contribute to better outcomes. Yet supply chain legs when containers are The 2023 edition of Connecting to Compete not in motion, especially at the port of import, seeks to build understanding of these areas by contribute disproportionately to the variability bringing new information derived from a Big of supply chain lead time. So, while the bulk of Data approach. This chapter provides a global the time required to trade goods internationally comparison of delays at ports and airports based is accounted for by shipping, the largest contrib- on massive numbers of observations represent- utor to low reliability of delivery times is pro- ing a substantial share of, if not all, actual move- cesses in the importing country. ments. The data come from tracking sources in Consistent with this analysis, each link of container shipping, aviation, and postal services. a supply chain is subject to some uncertainty Indicators, expressed as time in days or simple due to factors such as operational constraints, counts, have intuitive meanings and are relat- variations in productivity across operators, and able to policymakers and practitioners con- process unreliability. One way of capturing the cerned with the performance of key logistics uncertainty is through the statistical distribu- hubs or gateways, such as ports and airports. tion of lead time by link. The analysis looks at the composition of total While different links and modes have dis- shipment times and their component parts, as tinguishing features, there are also similarities. well as the reliability of delivery times, mea- Figure 3.2 shows the distribution of dwell time sured using indicators of dispersion around a for Le Havre (a container port) in 2022 and C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 17 Chapter 3  Supply chain lead time around the world: Where are the delays? Box 3.1 Measuring performance using tracking indicators: Sources and definitions Since the first Logistics Performance Index (LPI) in 2007, the eco- an Automatic Identification System data provider (MarineTraffic) system of supply chains and logistics services has changed radi- (box table 1). cally, driven largely by digitalization. Through this process, efficient, These tracking data are exhaustive for long distance interna- timely, and accurate digitized data have been translated into knowl- tional trade and cover container trade, air cargo, and parcels but edge that helps create highly interconnected, transparent, and flex- exclude bulk shipping. While the data are global in scope, they are ible supply chain systems. The shift has improved operational ef- less representative of intraregional trade due to the lack of cover- ficiency and reduced costs across supply chains.39 age of road and rail transport. High-precision tracking systems Digitalizing supply chain operations generates granular high- exist for trucks and freight trains at the country or regional level, frequency datasets by recording data at each step in a supply chain but without a global repository, these modes cannot be analyzed process (box figure). This Big Data approach also brings new busi- in the same way as the others. Yet corridor performance informa- ness opportunities (relevant for the private sector) and analytical tion is available from container tracking data to and from inland applications (relevant for both the private and public sectors), which destinations, which represents the trade of landlocked develop- push technological innovation further. ing countries. The raw data consist of timestamps of events—such as arrival, The key performance indicators focus on the major aspects of departure, loading, and unloading—localized by the United Na- the data that are important from an international trade perspective: tions Code for Trade and Transport Locations (representing ports, dwell time (delays experienced at the same place, such as at ports, airports, and other facilities). Container trips start with an empty airports, and inland facilities), connectivity information (such as the container being sent for stuffing by the exporter and finish with the number of international connections at the origin for a given destina- return of the empty container by the importer. Aviation and postal tion), and trade corridor lead time (time differences between events data have a similar structure, albeit with fewer steps and fewer at different locations) (box table 2; see appendix 4 for information modal options. on the source of data and indicator definitions). To construct a new set of indicators for the 2023 LPI, the World The objective of the 2023 LPI is to provide an example of how Bank collaborated with external data providers. The data consist of these detailed micro-data can be used to measure performance, five high-frequency micro-level datasets: deployment of container complement existing data including the “classic” LPI, offer policy- liner shipping service from MDS Transmodal, air cargo tracking from relevant insights, and give information to operators on their options Cargo iQ (supported by the International Air Transport Associa- for bringing goods to destination. There is scope to extend the ex- tion), flow of international parcels from the Universal Postal Union, ercise to include a broader range of performance indicators and to granular information on consignment activities from TradeLens for inform future research that moves beyond measuring trade times containerized trade, and worldwide container ship port calls from only by averages or medians. (continued) Tracking the supply chain steps Gate out empty Gate in empty • Gate out (empty) Transshipment phase • Arrival/discharge • Gate in (full) • Arrival/discharge • Gate out (full) • Loaded on vessel/ • Loaded/departure Departure • Gate in (empty) Export phase Location is neither Import phase Gate in full origin nor destination Gate out full Origin Destination Country/location of first full event Country/location of last full event Source: World Bank elaboration based on TradeLens dataset. 18 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Chapter 3  Supply chain lead time around the world: Where are the delays? Box 3.1 Measuring performance using tracking indicators: Sources and definitions (continued) Potential interpretation issues with tracking key performance Box table 1 Data sources and partners indicators Data nature and period The key performance indicators provide a wealth of information Source name Description and coverage of observation on supply chain transactions across several modes but are subject MDS Deployed capacity and information Deployed capacity and the list Transmodal on ship parameters and operators of countries that are connected to limits that affect interpretation: servicing countries by regular to each other through direct containerized liner shipping services. liner shipping services (first Although the procedures to input timestamps are rigorously and second quarters of 2022) defined, the process is not fully automated in some countries and Cargo iQ System of shipment planning and Time difference between performance monitoring for air cargo notification for readiness and may depend on practices by local operators, more so for aviation based on definitions of common delivery to consignee/agent and postal data than for maritime data. In agreement with the data business processes and milestones. (four quarters of 2019 and second quarter of 2022) partners, the data do not include countries where there is a strong Universal Data from the Express Mail Service Time difference between suspicion of deficient recording. Postal Union Events message category of the arrival at inward office of Electronic Data Interchange protocol exchange and attempted The postal data date from 2019 (the most recent year available used to track individual express mail and final delivery (2019) at the time of report writing). All other data were collected over six service and parcel items, as well as registered, insured, and express letters. months in mid-2022, when global supply chains were still experienc- TradeLens Blockchain-based data- and document- Timestamps of transport —­ ing severe disruptions­ for instance due to the ongoing effects of sharing platform aiming at simplifying events associated with each and speeding trade workflows for consignment and container the COVID-19 pandemic and Russia’s invasion of Ukraine. participants in the supply chain (May–October 2022) There may be selection bias in the container tracking data ecosystem. The TradeLens dataset used covers about 20 percent of global (TradeLens). Economic operators with more efficient supply chains containerized shipping during the period covered (May–October 2022). use advanced digital tracking solutions. This means that the con- MarineTraffic Port calls for all container Location, arrival, and tainer data, although massive, may underestimate delays. ships based on Automatic departure dates of ships Identification System data. (January–July 2022) The tracking data cover the responsibility of international carri- ers, not logistics by shippers upstream or consignees downstream. Supply chain practices vary across the world. Inefficient practices, Box table 2 Definition of key performance indicators such as early stripping of containers or compulsory warehousing, Indicator Source Definition may be imperfectly reflected in the key performance indicators, Connectivity such that delays may be underestimated. Maritime connectivity MDS Number of partner countries Transmodal accessible through direct service The concepts used locally to measure delays may differ from Aviation connectivity Cargo iQ Number of direct air connections (countries) the definitions used here to ensure global comparability. For in- Postal connectivity Universal Number of international postal stance, in many places, shipments are trucked from port terminals Postal Union connections (countries) to satellite facilities in the same location. The key performance in- Time dicators merge the time spent at all facilities in the same port area, Port dwell time TradeLens Time a container unit spends at not just port terminals. a port (export or import) Consolidated TradeLens Port dwell time plus time spent at inland The port dwell time statistics exclude transshipped containers dwell time multimodal clearance facilities for a container to other destinations for ports of transshipment. Aviation dwell time Cargo iQ Time goods spend at an airport These indicators measure different dimensions than indica- Postal delivery time Universal Delivery time of a postal item from arrival at tors related to port and shipping already available from the World Postal Union country’s postal office of exchange to final (or first unsuccessful attempted) delivery to recipient Bank and the United Nations Conference on Trade and Develop- Corridor lead time TradeLens Lead time of containers from port of ment (UNCTAD). origin to destination, estimated for selected landlocked countries The World Bank publishes the Container Port Performance Turnaround time MarineTraffic Time container ships call at a port, Index, which measures the productivity of terminal handling op- excluding waiting time at anchorage erations.1 Dwell time measures how long containers stay at the port premises, which reflects other factors beyond productivity, includ- ing time to clear and incentives to remove containers fast. source. Here, choosing the number of connections facilitates com- UNCTAD publishes the Liner Shipping Connectivity Index, parisons across modes. which averages several components, including the number of mari- time connections proposed here in the LPI 2023, from the same Note 1. World Bank 2021a. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 19 Chapter 3  Supply chain lead time around the world: Where are the delays? Figure 3.1 Import lead time is the largest chain experience across modes. Major delays driver of variability in international create risks that operators need to manage and shipping in 2022 that policymakers need to be aware of. Some of this variability in dwell times may Percent Export Shipping Transshipment Import 100 be connected with factors under the control of the owner of the goods or the freight forwarder (such as scheduling), but other factors may be 75 out of their control (such as uncertainty as to when goods will be loaded and unloaded or cleared). So, dispersion of lead time reflects the 50 overall reliability of the supply chain for the link under review21 or, if aggregated, for the 25 entire country. Traders facing the possibility of long delays must bear extra costs in establish- ing reliable connections to suppliers and buyers 0 Lead time Variance in foreign markets. From a policy perspective, Source: TradeLens and World Bank modeling. this suggests that interventions targeting supply Note: Based on 2.5 million container movements that are representative of global container shipping trade lanes worldwide between May 2022 and October 2022. chain reliability at trade gateways have the most The model averages mean time and variability over all links in the maritime supply chains (port dwell time and lead time between pairs of ports), weighted by the impact on the costs of trade, though these delays number of observations for each. The estimation assumes that the performance of subsequent legs is statistically independent. constitute a small fraction of the overall supply chain lead time.22 For each key performance indicator, for postal delivery of parcels and express mail the report provides estimates of the mean service courier shipments by air from Singapore and quartiles (first, median, and third). The to Thailand. In both cases, the distribution is interquartile range of lead time (from the first asymmetric relative to a normal distribution quartile to the third) is a robust measure of (or bell curve). In particular, they have long dispersion. With import dwell time, disper- right tails, which means that lengthy lead times sion measured by interquartile range is compa- relative to the average or median are common. rable to the median. In addition to dispersion Major delays are therefore part of the supply of times for deliveries at individual locations, Figure 3.2 Examples of the distribution of import dwell time Container port (Le Havre, May–October 2022) Postal delivery (Singapore–Thailand, 2019) Number of consignments Number of tracked items 1,000 5,000 Mean Median Mean Median 800 4,000 600 3,000 400 2,000 200 1,000 0 0 0 10 20 30 40 0 2 4 6 8 10 Import dwell time (days) Delivery time (days) Source: TradeLens and the Universal Postal Union. Note: The distribution for the container port example excludes 67 outliers, and the distribution for the postal delivery example excludes 55 outliers. 20 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Chapter 3  Supply chain lead time around the world: Where are the delays? there is considerable variation in average dwell Dwell time and logistics times across locations. This feature of the data performance is independent of the mode of transport (figure 3.3). Still, aviation dwell time is notably lower Port dwell time has a subtle connection to logis- than container dwell time.23 Understanding the tics performance. Most countries­—­low, middle, reasons for the dispersion to identify measures and high income­ —­ have similar average dwell that improve performance is an important area times (4–8 days) (figure 3.4). A few outliers for future research. Similarly, analyzing the fac- have a high dwell time with low logistics per- tors that influence the different shapes of the formance. Long delays imply low logistics per- distributions between ports and airports will formance, but low logistics performance does be important for informing policy. not necessarily imply long delays. Figure 3.3 Dispersion of mean dwell time across the world Ports (May–October 2022) Airports (fourth quarter of 2019) Number of ports Number of airports 60 120 50 100 40 80 30 60 20 40 10 20 0 0 0 10 20 30 0 5 10 15 20 25 Mean dwell time (days) Mean dwell time (days) Source: TradeLens and Cargo iQ. Note: Data on ports cover 370 ports, and data on airports cover 470 airports that had at least 120 records. Figure 3.4 Import and export dwell time of containers, May–October 2022, versus 2023 LPI score, by country Imports Exports 2023 LPI score 2023 LPI score 5 5 4 4 3 3 2 2 1 1 0 10 20 30 40 0 5 10 15 20 25 Mean import dwell time (days) Mean export dwell time (days) Source: TradeLens and World Bank. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 21 Chapter 3  Supply chain lead time around the world: Where are the delays? The lack of differentiation between lower- Export dwell time of container ports follows and higher-income countries points to a com- the same dispersion patterns and connection to plex picture. The average dwell time for con- LPI scores as import dwell time­—­but for differ- tainers between May and October 2022 was ent reasons. Overall, export and import dwell 3 days for India and Singapore and 4 for the times are positively associated, but with consid- United Arab Emirates and South Africa but erable dispersion around the average relation- 7 for the United States and 10 for Germany. ship, resulting in a correlation of only 0.1 (figure One explanation could be that the period for 3.5). Exports are less scrutinized by border agen- these estimates coincides with the tail end of cies than imports, but they face a hard schedul- an unprecedented supply chain crisis. Not only ing constraint and depend on the quality and did ships have to wait before being serviced, sophistication of available logistics services. Ex- but containers had to wait for trucks to be re- port containers must reach the port in advance moved. Furthermore, sanctions against Russia to catch the ship they are scheduled to take. The (in response to its invasion of Ukraine), which worse the inland logistics or the lower the fre- disconnected it from most container shipping quency of shipping, the more buffer time the services, explain the high container dwell time exporter includes to avoid missing the shipping in some countries around the Baltic Sea, such connection. For containers, shipping lines and as Finland. terminal operators typically impose deadlines Another possibility is that some emerging of 48 hours ahead of scheduled ship departure. economies invested more recently in modern fa- cilities and technologies, leapfrogging industri- What causes long port delays? alized countries. For example, since 2015, India has invested in soft and hard infrastructure to Few countries other than landlocked ones with connect ports on both coasts to economic poles port delays in the transit country have excessive in the hinterland, including a supply chain vis- import port dwell times (more than 12 days). ibility platform delivered through a public–pri- Most countries with excessive dwell times are vate partnership (box 3.2). The poor perfor- in the Middle East and North Africa and in mance of US ports in terms of productivity has Central and West Africa (figure 3.6). Countries received much scrutiny in recent months, 24 in- with excessive dwell times likely face serious cluding specific productivity constraints, com- constraints in port infrastructure and terminal pounding the factors referred to in the previous productivity, as measured by the World Bank’s paragraph. Finally, many small economies­—­for Container Port Performance Index. 26 Con- example, small island states25­—­see only small trols of import transactions and goods (such volumes that can be handled relatively quickly. as customs and exchange controls) contribute Box 3.2 India: Boosting performance with supply chain digitalization Since 2015, the government of India has invested and offers consignees end-to-end tracking of in trade-related soft and hard infrastructure con- their supply chain. Implementation started in necting port gateways on both coasts to the eco- 2015 on the Indian east coast and was general- nomic poles in the hinterland. Technology has ized in 2020. With the introduction of cargo track- been a critical component of this effort, with ing, dwell time in the eastern port of Visakhapat- implementation under a public-private partner- nam fell from 32.4 days in 2015 to 5.3 days in ship of a supply chain visibility platform,1 which 2019. contributed to remarkable reductions of delays. NICDC Logistics Data Services Limited applies Note radio frequency identification tags to containers 1. See https://nldsl.in/. 22 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Chapter 3  Supply chain lead time around the world: Where are the delays? Figure 3.5 Export dwell time versus import dwell time of container ports Export dwell time (days) 20 15 10 5 0 0 5 10 15 20 25 30 Import dwell time (days) Source: TradeLens and World Bank. Note: Data cover 309 ports with more than 100 observations for both exports and imports for May–October 2022. Figure 3.6 Outliers for import dwell time and comparators, May–October 2022 Ports Countries India Singapore Singapore United States United Arab Emirates United States United Arab Emirates Netherlands Germany … … Lebanon Zimbabwe Benin Finland Benin Sudan Djibouti Albania Jamaica Haiti Nigeria Libya Egypt, Arab Rep. Sudan Nigeria Ghana Syrian Arab Republic Gabon Cameroon Egypt, Arab Rep. Congo, Dem. Rep. Tunisia Angola Algeria Algeria 0 5 10 15 20 0 2 4 6 8 10 Import dwell time (days) Import dwell time (days) Source: World Bank estimate from TradeLens and Cargo iQ data. Note: For ports, outliers are countries with import dwell times of more than 12 days, and for airports, outliers are countries with import dwell times of more than 4 days. to delays, as does abuse of port space as storage in Tunisia, low container handling productivity by importers in some African countries, espe- is the binding constraint.28 cially those where terminal fees are low. 27 The Aviation dwell times exhibit similar pat- removal of units in congested port cities may terns to those of maritime dwell times. Exces- also contribute to delays. Yet each outlier needs sive dwell time for airports is defined as more to be assessed on a case-by-case basis to deter- than 4 days. There is substantial overlap be- mine the main reasons for delays. For instance, tween countries with excessive dwell times in in Algeria, the most important contribution to each mode, which points to serious structural dwell time is the time banks take to validate issues with logistics performance. In line with imports for exchange control purposes prior to expectations, airport delays are one-quarter to submitting an import declaration. By contrast, one-half as long as port delays. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 23 Chapter 3  Supply chain lead time around the world: Where are the delays? Which interventions help reduce logistics sector have permanent institutions, these delays? such as Dinalog in the Netherlands.33 The speed of trade can be boosted by combining Logistical constraints in landlocked policy interventions in the LPI pillars related to developing countries infrastructure, customs, logistics competence, and tracking and tracing. Diagnosis must be Landlocked developing countries are more logis- implemented on a country or even port/cor- tically constrained than their coastal neighbors. ridor basis to identify binding constraints and The development challenges of landlocked devel- prioritize interventions. oping countries have been a constant focus of A combination of reforms to enhance port international organizations and assistance pro- productivity, including private sector participa- grams.34 The key performance indicators in this tion in terminal operations, could improve the report provide information on the time it takes situation in outlier countries. 29 Implementing containers to reach landlocked countries through electronic port community systems also im- transport corridors that link them to ports in proves performance by facilitating the flow of transit countries. Data are available for landlocked information between the numerous participants African, European, and South Asian countries. in port logistics. 30 Many underperforming Relevant key performance indicators for land- countries have yet to modernize customs and locked developing countries include dwell time at border agencies with a focus on automation, risk port of entry, consolidated dwell time adding to management, and integrity.31 port dwell time at inland facilities, and lead time The emerging economies with the shortest on corridors (which combines time in actual mo- delays have gone beyond these packages and tion with, for instance, idle time at land border have implemented bold tracking and tracing so- crossings) (see appendix 2). While covering the lutions. India’s very low dwell time (2.6 days) is bulk of international trade for landlocked devel- one example (see box 3.2). The 2023 LPI data oping countries, these data are less representative partners (see box 1.1 in chapter 1) have proposed for EU countries, where direct trucking is favored similar tracking and tracing solutions. over containerized trade to destination facilities. Measures to speed up the transit of goods Landlocked developing countries face three require adequate private sector capacity to types of delays: handle the logistics of goods beyond the gates, • Longer delays in ports than in correspond- and often in the vicinity, of ports, airports, or ing coastal countries. A first explanation multimodal facilities. This requires integrated might be the additional complexity of or- logistics services (such as third party logistics) ganizing removal of containers from a dis- and proper facilities (such as logistics zones). tance, as opposed to local removal. Often Adequately regulating logistics services and ports in the country of transit offer longer land planning (zoning) is key to promoting free time for containers destined for a land- quality and competition. High-performing locked country.35 countries have also invested in education and • Corridor delays, which reflect the efficiency training, promoting the right skill sets across of the transit system. Nepal and Mali tend jobs (blue collar, technical, and administrative to have the longest corridor delays (over a managerial), as developed in a World Bank tool- week), while in East and Southern African kit applied in several countries.32 landlocked developing countries the delays Finally, private public dialogue is critical tend to be much shorter. for developing a common fact-based diagnosis • Overall dwell time inland, including at the and designing impactful interventions. This destination. could involve ad hoc task forces, which should Improving the connectivity of landlocked include agencies and stakeholders, tasked with a developing countries goes beyond unilateral time-limited mandate. Countries with a strong policy interventions. Central to landlocked 24 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Chapter 3  Supply chain lead time around the world: Where are the delays? connectivity is the design of transit systems.36 international partners (that is, countries) that Transit systems regulate freight services, nota- a country connects to. The United Nations bly trucking, combining quality-oriented regu- Conference on Trade and Development’s Liner lation of entry and operations (typically from the Shipping Connectivity Index combines more perspective of customs security). Transit systems subindicators (such as number of services and also provide for cross-border traceability of ship- shipping alliances) from the same sources.38 ments for customs purposes. Modern transit sys- Connectivity metrics are closely associated tems, such as in the European Union, promote with logistics performance, especially for top regionally integrated markets of authorized performers and countries with high liner ship- operators that meet quality and environmental ping connectivity (figure 3.7). LPI scores are requirements, along with interoperability of fi- more strongly associated with the connectivity- nancial guarantees across borders and digitaliza- related key performance indicators than with tion of transit manifests. The benchmark of an the delay-related key performance indicators. efficient transit system is the Transports Inter- Logistics connectivity enhances logistics nationaux Routiers (International Road Trans- performance through several channels. First, port, or TIR). That system is superseded by the it increases exposure to global operators and European transit system in Western Europe but practices, with positive spillovers on the qual- remains important to countries in Central Asia ity of domestic services. Second, it implies that and the Middle and North Africa. Few regions logistics operators have to deal with a more com- beyond Europe have been able to follow the TIR plex set of operations with more partners, which model, though one exception is the International incentivizes higher productivity and use of tech- Transit of Goods in Central America.37 nology. Third, increased connectivity means more operators and competition. Connectivity and logistics Conversely, countries with limited connec- performance in small maritime tions, such as small island states, require attention. economies Their limited connections means that they de- pend on transshipments to access major markets. The key performance indicators measure con- Viewed across all countries, about 44 percent of nectivity for each mode (container shipping, containers are shipped port-to-port; the majority aviation, and postal) as a simple count of the require transshipment (figure 3.9). Distribution of Figure 3.7 The association between average connectivity in container shipping and 2023 LPI score quintiles Average number of partners 60 40 20 0 Bottom quintile Fourth quintile Third quintile Second quintile Top quintile (lowest performance) (low performance) (average performance) (high performance) (highest performance) Source: World Bank calculations based on data from MDS Transmodal. Note: Connectivity data refer to the second quarter of 2022. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 25 Chapter 3  Supply chain lead time around the world: Where are the delays? Figure 3.8 The association between average inbound connectivity in aviation and postal services and 2023 LPI score quintiles Number of partners Aviation Postal 150 100 50 0 Bottom quintile Fourth quintile Third quintile Second quintile Top quintile (lowest performance) (low performance) (average performance) (high performance) (highest performance) Source: World Bank calculations based on data from Cargo iQ and UPU. Note: Postal connectivity data refer to 2019. Country-level aviation connectivity data refer to the second quarter of 2022. Figure 3.9 Most maritime economies have less than 20 shipping connections and depend on transshipment Distribution of maritime connectivity, Number of transshipments per container trip, second quarter of 2022 May 1 to October 31, 2022 Number of countries 30 Three or more 6% 25 Two 20 12% 15 None 44% 10 One 38% 5 0 0 20 40 60 80 100 Number of partners Source: World Bank calculations based on data from MDS Transmodal and TradeLens. Note: Connectivity is the number of direct international container shipping connections without transshipment. dwell time in transshipment follows the same dis- countries­ —­ depends on factors beyond their persion patterns as those for export and import. policy realm. Competition in shipping mar- Hence, the more dependent a country is on trans- kets, logistics and shipping network structure, shipment, the more it suffers additional delays and and frequency of services are difficult to influ- unreliability, increasing the cost of trade. ence and may not yet have received sufficient The connectivity of island states­ e ven —­ attention from policymakers and international more than that of landlocked developing organizations. 26 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 4 CHAPTER Conclusions Global supply chains have turned The top and bottom performers: out to be surprisingly resilient Performance is steady or improving, during the recent disruptions but the gap persists Given the recent supply chain crisis, the relative Since the 2018 LPI, global logistics networks pattern of LPI scores in 2023 across countries have experienced unprecedented disruptions, has changed little compared with previous edi- and the operational environment in logistics has tions; only scores in the timeliness component grown more complex. Yet logistics performance have deteriorated slightly since 2018. In addi- in 2022, as measured by LPI scores for the 139 tion to the robust nature of the data underlying countries covered, remained stable or improved the LPI (box table 1 in box 3.1 of chapter 3), slightly. At the same time, the gap between the there are several possible explanations. top and bottom performers widened slightly, First, when almost every country is affected as measured by average LPI scores by quintile. by similar disruptions beyond their control al- Thus the fundamental messages of previous edi- most simultaneously, it is difficult to assign the tions hold true. impact to individual countries. Countries in the bottom performance Second, survey data were collected when quintile still need core reforms and modern- supply chain disruptions had already substan- ization, especially in soft infrastructure such tially diminished, as indicated by the Global as customs and border management and opera- Supply Chain Pressure Index,40 for example. tional procedures in ports. Investments in hard The index peaked at the end of 2021 before re- transport infrastructure are also needed­ —­but turning to normal levels in September 2022, they must be aligned with the reforms and in- when the LPI survey went live. This may have vestments in soft infrastructure to improve lo- contributed to recency bias among respondents. gistics performance. Third, most LPI components relate to struc- Countries in the middle performance tural factors that are not directly affected by quintile and the second quintile from the top supply chain disruptions. Trade logistics per- likely face the most challenging policy agenda formance may have improved due to policy re- in view of their available resources. They need to forms and private sector capacity building over reconcile the need for consistency and depth of time, despite the constraints imposed by recent reforms with a set of priorities wider than those conditions. In other words, today’s performance facing top performers, which are farther along, should be higher than what it was five years ago, or countries in the bottom two quintiles, which but the impact of the supply chain crisis may can focus on fewer issues. have prevented some of this development from showing up in the survey data. Consistency is an important driver Fourth, most shippers, logistics service pro- of logistics performance viders, and authorities have absorbed the im- pacts of the recent crisis well. In the big picture, The leading countries in overall logistics perfor- trade logistics operations have been surprisingly mance exhibit strong performance across all six resilient. LPI components. Lower performing countries C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 27 Chapter 4  Conclusions tend to have patchier performance across com- containers are not in motion­ —­ contributes dis- ponents. This distinction highlights the need for proportionately to the variability of lead time reforms in logistics markets to cover a variety of and reduce supply chain reliability. This sup- areas rather than focus on just one. For instance, ports the policy focus on trade facilitation and building physical infrastructure without develop- on soft and hard infrastructure at trade gate- ing service provider capacity would be unlikely to ways and hubs, such as ports and multimodal lead to the expected economic benefits. facilities. These interventions may both reduce Hence, a key lesson of the LPI for low- and trade times and increase supply chain reliability. middle-performing countries is that their re- Delay key performance indicators, such as form agenda needs to encompass not only phys- port dwell time, point to a more complex pic- ical infrastructure but also border procedures ture because they are less strongly associated and private sector development. Information with income group than LPI score is. That in- flow is key to designing effective policy reforms, dustrialized economies often have longer delays which means that both the logistics industry than emerging economies will have to be con- and users of logistics services need a voice in the firmed over time, as it may reflect the magnitude reform process.41 of the supply chain disruptions of 2021–22 in Europe and North America. Logistics performance and key Three groups of countries with outlying key performance indicators derived performance indicators overlap with the bottom from a Big Data approach two LPI score quintiles and require specific pol- icy attention. They include maritime countries Chapter 3 introduced a set of key performance with large dwell times, most of which are located indicators derived from a Big Data approach in the Middle East and North Africa as well as and related to actual movements of interna- Sub-Saharan Africa; landlocked developing tional trade by mode (container, air freight, and countries, which experience additional inland parcels) complement survey-based LPI scores. delays, as well as longer delays than the transit No single indicator can fully explain country- country at the port of entry; and countries with wide logistics performance, but the key per- limited maritime connectivity, which are heavily formance indicators provide partial informa- penalized by delays in multiple transshipments. tion that policymakers and operators can easily The World Bank intends to produce these interpret on such topics as delays for specific new key performance indicators annually and to supply chain links (a port, for example) or the expand the scope of supply chain features that number of connections. they cover. The current report does not exhaust LPI scores are closely associated with the the potential for research on global logistics number of direct international connections based on micro-data. Further research on reli- through shipping, air, or postal networks, espe- ability, value of time, and connections between cially for top logistics performers and countries delays and other outcomes such as port produc- with high liner shipping connectivity. Logistics tivity, international connectivity, and even regu- connectivity enhances logistics performance­ lations should be considered. —­ f or example, by increasing exposure to global operators and practices­ —­ with positive Policymaking priorities when spillovers on the quality of domestic logistics managing logistics as a sector of services through higher productivity and use of the economy latest technology. Increased connectivity usu- ally also means more operators and competition. Improving logistics performance requires coun- Beyond averages, the new data provide de- tries to consider it a cross-cutting policy area. The tailed information on the structure of delays work crosses the administrative boundaries of such as distribution of time spent at ports or air- transportation, commerce, infrastructure, indus- ports. Dwell time at hubs and gateways­—­when try, finance, social issues, and the environment. 28 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Chapter 4  Conclusions And it requires mechanisms that involve the pri- decades. Relying on reliable, affordable, and vate sector and the ability to absorb best practices high-capacity logistics services, globally minded from high-performing countries. manufacturers have expanded their operations The complexity of these issues highlights the in new and existing markets. However, the need for detailed research using the best avail- 2020s are turning into a period of transforma- able data, including the tracking data presented tion for global supply and value chains, which in chapter 3. One question relates to the extent have turned out to be surprisingly resilient dur- to which trade facilitation practices around the ing the recent disruptions. world increase reliability and reduce average First, fundamental trends such as decarbon- lead times. No high-quality quantitative evi- ization, sustainability, and growing digitaliza- dence explains how much unreliable delivery tion predate the recent supply chain turmoil. times contribute to higher trade costs that hold Second, the recent increase in the use of trade back international integration. instruments in geopolitics; manufacturing job Quantifying the social costs and benefits of losses in advanced economies; disruptions to supply chain characteristics—such as length, di- supplies of food, energy, pharmaceuticals, and versity, network characteristics, and resilience— semiconductors; and countries’ failure to align has become important in light of recent disrup- incentives to curb greenhouse gas emissions are tions. Developing policy-relevant tools could affecting the pace and nature of global trade. help decisionmakers identify instances where These events, among others, have raised the pro- policy could play a constructive role in increas- file of supply chain management and logistics ing logistics performance. Academic research and have accelerated the path of transformation. has touched on these areas, but there is a global Businesses and governments are concerned public goods case for developing specific policy- about increasing the resilience and robustness relevant insights that can support updated and of supply chains, in addition to efficiency—in innovative toolkits for policymakers. particular, where goods of primary necessity are Logistics creates new concerns because of its concerned (see box 1.1 in chapter 1). One way environmental footprint. Some logistics regula- to do this is to seek jurisdictions where supply tions apply to movements of goods as well as to chain operations are less exposed to risk. Other facilities and assets. Those regulations may also means are tightening vertical integration (such influence competition at both the national and as buying up suppliers that firms rely on), diver- international levels. Strengthening the legal and sifying the supplier base, and building up inven- regulatory status of logistics as a sector of the tory buffers along the supply chain. economy is likely to be most important in coun- In addition, the regulatory pressure to re- tries in the middle performance quintile and the duce logistics-related harmful emissions ap- second quintile from the top. pears to be the main driver for stakeholders to The need to attract skilled people to logis- switch to more environmentally friendly pro- tics jobs has become acute, especially in devel- cesses or equipment, especially when it can be oped countries—and not just because of the combined with economic savings. But pressure experiences during the COVID-19 pandemic. from demand is growing, especially in high-per- In many parts of the world, there is an almost formance countries (figure 4.1). Hence, imple- endemic lack of truck drivers, warehouse staff, mentable “green logistics” policies have become and seagoing personnel. more important. Efficient management and use of informa- The 2020s is a decade of tion technology solutions in both the private transformation for global supply and public sectors are tools for high-quality lo- chains gistics. Here, the importance of digitalization is growing, boosted by the rapid advancement Effective supply chains have enabled unprec- of software, hardware, and innovation. One ob- edented growth of globalization over the past vious area of development is to increase supply C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 29 Chapter 4  Conclusions Figure 4.1 Demand for environmentally friendly shipping options, by destination LPI score quintile Percent Often or nearly always Sometimes Hardly ever or rarely 100 80 60 40 20 0 Bottom quintile Fourth quintile Third quintile Second quintile Top quintile (lowest performance) (low performance) (average performance) (high performance) (highest performance) Source: 2023 Logistics Performance Index. Note: Refers to the percentage of countries in each quintile reporting the listed average responses, based on how often shippers ask for environmentally friendly options when shipping to destination countries in each group. chain visibility, the benefits of which were made supply chain processes can pose challenges for clear by the recent turmoil. Managing Big Data low- and middle-income countries, where access approaches also brings new business opportu- to technology and reliability of basic infrastruc- nities, as well as analytical applications, which ture (particularly electricity), may constrain the push technological innovation further. More ability to access them. Building capacity, ensur- efficient use of Big Data approaches is an in- ing access to appropriate technologies, and sup- creasingly important policy issue both domesti- porting infrastructure need to remain part of cally and in trade facilitation. Yet digitalizing the policy agenda. 30 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Notes 1 The six LPI components are the efficiency of customs 18 “Timeliness of delivery” is defined in the LPI as “the and border management clearance, the quality of trade frequency with which shipments reach consignees within and transport infrastructure, the ease of arranging the scheduled or expected delivery time.” competitively priced shipments, the competence and 19 See https://www.hhs.gov/formula/index.html. quality of logistics services, the ability to track and trace 20 Shah 2022. consignments, and the frequency of on-time deliveries. 21 Arvis, Marteau, and Raballand 2010. 2 While the Covid-19 induced supply chain crisis had largely subsided by the time of the survey, many of 22 The economic literature has identified time as a major determinant of country-level export performance. Djankov, the respondents’ perceptions were likely to have been Freund, and Pham (2010) focus on time spent at the influenced by their experience over previous months. border, while Hummels and Schaur (2013) examine 3 Global GDP grew by 2.6 percent in 2019, decreased by transport time between exporting and importing countries. 3.1 percent in 2020, and grew by 5.9 percent in 2021. However, there has been no good identification of the role So, global GDP was higher in 2021 than in 2019 (https:// of dispersion in time or reliability. Future research could data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG). examine this question in detail, as existing studies do not 4 The quintiles have different numbers of countries because focus on reliability, even when they use highly detailed of tied scores in some cases. data comparable to those presented here (for example, 5 Lead time refers to the duration of a logistics process, Volpe Martincus, Carballo, and Graziano 2016). irrespective of the location of the initial and end events 23 See https://resilientmaritimelogistics.unctad.org/. defining the process. In contrast, dwell time refers to 24 Lynch 2021. the lead time between the first and last events at the same location in a supply chain and is used mostly in the 25 The practice of stripping containers at ports in some low- and middle-income countries may explain some low dwell context of ports and airports. time. In the context of island states with low shipping 6 Transport connections refer to the number of countries frequencies, consignees are incentivized to move back a country is connected to by one of the three modes containers as soon as possible to avoid demurrage fees. analyzed here (air, shipping, or postal). 26 World Bank 2022. 7 The question in the LPI survey refers only to how often shippers asked for these options, not how often they were 27 Raballand and others 2012. chosen. 28 World Bank 2020b, 2021b. 8 “Shipper” refers to the owner of goods being transported. 29 World Bank 2007. 9 World Bank estimate based on Automatic Identification 30 World Bank 2020a. System data. 31 World Bank 2011. The World Bank has published several 10 IATA 2022. handbooks to support policy reform in these areas (see World Bank 2010, 2011, 2013, 2014). 11 Beretzky and others 2022. 12 UNCTAD 2021a. 32 McKinnon et al. 2017 [add to reference list]. 13 Beretzky and others 2022. 33 See https://www.dinalog.nl/en/. 14 The cross-country datasets measure exports, shipping, 34 See, for example, https://www.un.org/ohrlls/content/ landlocked-developing-countries. and imports. The export and import legs help get logistics information between owners of goods and international 35 The data for European landlocked countries point to the gateways (ports, airport, and land border crossings). same phenomenon. However, it is less representative of the time to trade, unlike in developing countries. Most 15 The analysis is in terms of the top 12 countries and maritime imports for EU landlocked countries are cleared the bottom 10 countries because of tied scores at one at the country of entry and reconsolidated to destination decimal point. rather than containerized to destination. 16 The quintiles have different numbers of countries because of tied scores. 36 Arvis and others 2011. 17 The observed differences do not rise to the level of 37 See https://www.portaltim.sieca.int/TIM/Portal/archivos/ Manual_PortalTIM.pdf. systematic biases. The LPI is based on respondent ratings. It does not weight scores from different income 38 UNCTAD 2021b. groups or geographic regions differently. The survey 39 See, for example, Gupta and others (2020) and engine for the LPI ensures geographic diversity in the Seyedghorban and others (2020). respondent base for the countries assessed (see appendix 40 https://www.newyorkfed.org/research/policy/gscpi#​/overview. 5 for details on the LPI methodology). 41 Users of logistics services are owners of goods and customers of logistics service providers. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 31 APPENDIX 1 2023 LPI results Logistics International competence Tracking and LPI Customs Infrastructure shipments and equality Timeliness tracing Grouped Lower Upper Grouped Grouped Grouped Grouped Grouped Grouped Economy rank Score bound bound Score rank Score rank Score rank Score rank Score rank Score rank Singapore 1 4.3 4.2 4.4 4.2 1 4.6 1 4.0 2 4.4 1 4.3 1 4.4 1 Finland 2 4.2 4.0 4.4 4.0 4 4.2 5 4.1 1 4.2 3 4.3 1 4.2 3 Denmark 3 4.1 4.0 4.2 4.1 2 4.1 9 3.6 14 4.1 9 4.1 10 4.3 2 Germany 3 4.1 4.0 4.2 3.9 7 4.3 3 3.7 8 4.2 3 4.1 10 4.2 3 Netherlands 3 4.1 4.0 4.2 3.9 7 4.2 5 3.7 8 4.2 3 4.0 17 4.2 3 Switzerland 3 4.1 4.0 4.2 4.1 2 4.4 2 3.6 14 4.3 2 4.2 4 4.2 3 Austria 7 4.0 3.8 4.2 3.7 14 3.9 16 3.8 4 4.0 11 4.3 1 4.2 3 Belgium 7 4.0 3.9 4.1 3.9 7 4.1 9 3.8 4 4.2 3 4.2 4 4.0 16 Canada 7 4.0 3.9 4.1 4.0 4 4.3 3 3.6 14 4.2 3 4.1 10 4.1 11 Hong Kong SAR, China 7 4.0 3.9 4.1 3.8 12 4.0 14 4.0 2 4.0 11 4.1 10 4.2 3 Sweden 7 4.0 3.8 4.2 4.0 4 4.2 5 3.4 26 4.2 3 4.2 4 4.1 11 United Arab Emirates 7 4.0 3.9 4.1 3.7 14 4.1 9 3.8 4 4.0 11 4.2 4 4.1 11 France 13 3.9 3.8 4.0 3.7 14 3.8 19 3.7 8 3.8 20 4.1 10 4.0 16 Japan 13 3.9 3.8 4.0 3.9 7 4.2 5 3.3 38 4.1 9 4.0 17 4.0 16 Spain 13 3.9 3.8 4.0 3.6 20 3.8 19 3.7 8 3.9 14 4.2 4 4.1 11 Taiwan, China 13 3.9 3.7 4.1 3.5 22 3.8 19 3.7 8 3.9 14 4.2 4 4.2 3 Korea, Rep. 17 3.8 3.7 3.9 3.9 7 4.1 9 3.4 26 3.8 20 3.8 25 3.8 23 United States 17 3.8 3.7 3.9 3.7 14 3.9 16 3.4 26 3.9 14 3.8 25 4.2 3 Australia 19 3.7 3.5 3.9 3.7 14 4.1 9 3.1 47 3.9 14 3.6 35 4.1 11 China 19 3.7 3.6 3.8 3.3 31 4.0 14 3.6 14 3.8 20 3.7 30 3.8 23 Greece 19 3.7 3.5 3.9 3.2 37 3.7 25 3.8 4 3.8 20 3.9 21 3.9 20 Italy 19 3.7 3.6 3.8 3.4 24 3.8 19 3.4 26 3.8 20 3.9 21 3.9 20 Norway 19 3.7 3.5 3.9 3.8 12 3.9 16 3.0 57 3.8 20 4.0 17 3.7 29 South Africa 19 3.7 3.5 3.9 3.3 31 3.6 30 3.6 14 3.8 20 3.8 25 3.8 23 United Kingdom 19 3.7 3.6 3.8 3.5 22 3.7 25 3.5 22 3.7 28 3.7 30 4.0 16 Estonia 26 3.6 3.3 3.9 3.2 37 3.5 39 3.4 26 3.7 28 4.1 10 3.8 23 Iceland 26 3.6 3.4 3.8 3.7 14 3.6 30 3.3 38 3.5 38 3.6 35 3.7 29 Ireland 26 3.6 3.4 3.8 3.4 24 3.5 39 3.6 14 3.6 33 3.7 30 3.7 29 Israel 26 3.6 3.4 3.8 3.4 24 3.7 25 3.5 22 3.8 20 3.8 25 3.7 29 Luxembourg 26 3.6 3.3 3.9 3.6 20 3.6 30 3.6 14 3.9 14 3.5 46 3.5 37 Malaysia 26 3.6 3.4 3.8 3.3 31 3.6 30 3.7 8 3.7 28 3.7 30 3.7 29 New Zealand 26 3.6 3.4 3.8 3.4 24 3.8 19 3.2 43 3.7 28 3.8 25 3.8 23 Poland 26 3.6 3.5 3.7 3.4 24 3.5 39 3.3 38 3.6 33 3.9 21 3.8 23 Bahrain 34 3.5 3.1 3.9 3.3 31 3.6 30 3.1 47 3.3 46 4.1 10 3.4 41 Latvia 34 3.5 3.1 3.9 3.3 31 3.3 44 3.2 43 3.7 28 4.0 17 3.6 34 Qatar 34 3.5 3.1 3.9 3.1 43 3.8 19 3.1 47 3.9 14 3.5 46 3.6 34 Thailand 34 3.5 3.3 3.7 3.3 31 3.7 25 3.5 22 3.5 38 3.5 46 3.6 34 India 38 3.4 3.3 3.5 3.0 47 3.2 47 3.5 22 3.5 38 3.6 35 3.4 41 Lithuania 38 3.4 3.0 3.8 3.2 37 3.5 39 3.4 26 3.6 33 3.6 35 3.1 62 Portugal 38 3.4 3.1 3.7 3.2 37 3.6 30 3.1 47 3.6 33 3.6 35 3.2 54 Saudi Arabia 38 3.4 3.2 3.6 3.0 47 3.6 30 3.3 38 3.3 46 3.6 35 3.5 37 32 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Appendix 1  2023 LPI results Logistics International competence Tracking and LPI Customs Infrastructure shipments and equality Timeliness tracing Grouped Lower Upper Grouped Grouped Grouped Grouped Grouped Grouped Economy rank Score bound bound Score rank Score rank Score rank Score rank Score rank Score rank Türkiye 38 3.4 3.3 3.5 3.0 47 3.4 43 3.4 26 3.5 38 3.6 35 3.5 37 Croatia 43 3.3 3.0 3.6 3.0 47 3.0 55 3.6 14 3.4 42 3.2 65 3.4 41 Czechia 43 3.3 3.0 3.6 3.0 47 3.0 55 3.4 26 3.6 33 3.7 30 3.2 54 Malta 43 3.3 3.0 3.6 3.4 24 3.7 25 3.0 57 3.4 42 3.2 65 3.4 41 Oman 43 3.3 3.1 3.5 3.0 47 3.2 47 3.4 26 3.2 53 3.1 76 3.9 20 Philippines 43 3.3 3.0 3.6 2.8 59 3.2 47 3.1 47 3.3 46 3.9 21 3.3 49 Slovak Republic 43 3.3 3.0 3.6 3.2 37 3.3 44 3.0 57 3.4 42 3.5 46 3.3 49 Slovenia 43 3.3 3.0 3.6 3.4 24 3.6 30 3.4 26 3.3 46 3.3 59 3.0 65 Vietnam 43 3.3 3.1 3.5 3.1 43 3.2 47 3.3 38 3.2 53 3.3 59 3.4 41 Brazil 51 3.2 3.1 3.3 2.9 56 3.2 47 2.9 68 3.3 46 3.5 46 3.2 54 Bulgaria 51 3.2 3.0 3.4 3.1 43 3.1 52 3.0 57 3.3 46 3.5 46 3.3 49 Cyprus 51 3.2 2.9 3.5 2.9 56 2.8 63 3.1 47 3.2 53 3.5 46 3.4 41 Hungary 51 3.2 2.9 3.5 2.7 65 3.1 52 3.4 26 3.1 57 3.6 35 3.4 41 Kuwait 51 3.2 2.9 3.5 3.2 37 3.6 30 3.2 43 2.9 65 2.8 101 3.3 49 Romania 51 3.2 3.0 3.4 2.7 65 2.9 59 3.4 26 3.3 46 3.6 35 3.5 37 Botswana 57 3.1 2.6 3.6 3.0 47 3.1 52 3.0 57 3.4 42 3.3 59 3.0 65 Egypt, Arab Rep. 57 3.1 2.9 3.3 2.8 59 3.0 55 3.2 43 2.9 65 3.6 35 2.9 72 North Macedonia 57 3.1 2.8 3.4 3.1 43 3.0 55 2.8 75 3.2 53 3.5 46 3.2 54 Panama 57 3.1 2.9 3.3 3.0 47 3.3 44 3.1 47 3.0 61 3.4 55 2.9 72 Bosnia and Herzegovina 61 3.0 2.8 3.2 2.7 65 2.6 76 3.1 47 2.9 65 3.2 65 3.2 54 Chile 61 3.0 2.8 3.2 3.0 47 2.8 63 2.7 85 3.1 57 3.2 65 3.0 65 Indonesia 61 3.0 2.9 3.1 2.8 59 2.9 59 3.0 57 2.9 65 3.3 59 3.0 65 Peru 61 3.0 2.8 3.2 2.6 74 2.5 80 3.1 47 2.7 81 3.4 55 3.4 41 Uruguay 61 3.0 2.7 3.3 2.9 56 2.7 68 2.7 85 3.1 57 3.2 65 3.3 49 Antigua and Barbuda 66 2.9 2.7 3.1 2.2 110 2.7 68 2.9 68 2.9 65 3.4 55 3.2 54 Benin 66 2.9 2.5 3.3 2.7 65 2.5 80 2.9 68 3.0 61 2.7 109 3.2 54 Colombia 66 2.9 2.7 3.1 2.5 84 2.9 59 3.0 57 3.1 57 3.2 65 3.1 62 Costa Rica 66 2.9 2.8 3.0 2.8 59 2.7 68 2.8 75 2.9 65 3.2 65 2.9 72 Honduras 66 2.9 2.7 3.1 2.8 59 2.7 68 3.0 57 2.7 81 3.2 65 2.6 94 Mexico 66 2.9 2.7 3.1 2.5 84 2.8 63 2.8 75 3.0 61 3.5 46 3.1 62 Namibia 66 2.9 2.3 3.5 2.8 59 2.8 63 3.0 57 2.9 65 2.9 93 2.8 80 Argentina 73 2.8 2.6 3.0 2.7 65 2.8 63 2.7 85 2.7 81 3.1 76 2.9 72 Montenegro 73 2.8 2.5 3.1 2.6 74 2.5 80 2.8 75 2.8 76 3.2 65 3.2 54 Rwanda 73 2.8 2.5 3.1 2.5 84 2.9 59 2.4 111 3.0 61 3.1 76 3.0 65 Serbia 73 2.8 2.6 3.0 2.2 110 2.4 89 2.9 68 2.7 81 3.4 55 2.9 72 Solomon Islands 73 2.8 2.4 3.2 2.4 90 2.6 76 2.9 68 2.9 65 3.2 65 2.9 72 Sri Lanka 73 2.8 2.6 3.0 2.5 84 2.4 89 2.8 75 2.7 81 3.3 59 3.0 65 Bahamas, The 79 2.7 2.5 2.9 2.7 65 2.5 80 3.1 47 2.5 103 3.0 87 2.6 94 Belarus 79 2.7 2.4 3.0 2.6 74 2.7 68 2.6 91 2.6 92 3.1 76 2.6 94 Djibouti 79 2.7 2.5 2.9 2.6 74 2.3 108 2.5 102 2.8 76 3.6 35 2.7 87 El Salvador 79 2.7 2.5 2.9 2.4 90 2.2 118 2.6 91 2.7 81 3.2 65 2.9 72 Georgia 79 2.7 2.4 3.0 2.6 74 2.3 108 2.7 85 2.6 92 3.1 76 2.8 80 Kazakhstan 79 2.7 2.5 2.9 2.6 74 2.5 80 2.6 91 2.7 81 2.9 93 2.8 80 Papua New Guinea 79 2.7 2.4 3.0 2.4 90 2.4 89 2.6 91 2.7 81 3.3 59 3.0 65 C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 33 Appendix 1  2023 LPI results Logistics International competence Tracking and LPI Customs Infrastructure shipments and equality Timeliness tracing Grouped Lower Upper Grouped Grouped Grouped Grouped Grouped Grouped Economy rank Score bound bound Score rank Score rank Score rank Score rank Score rank Score rank Paraguay 79 2.7 2.5 2.9 2.4 90 2.5 80 2.7 85 2.6 92 3.0 87 2.8 80 Ukraine 79 2.7 2.4 3.0 2.4 90 2.4 89 2.8 75 2.6 92 3.1 76 2.6 94 Bangladesh 88 2.6 2.3 2.9 2.3 101 2.3 108 2.6 91 2.7 81 3.0 87 2.4 105 Congo, Rep. 88 2.6 2.3 2.9 2.3 101 2.1 125 2.6 91 2.9 65 2.9 93 2.7 87 Dominican Republic 88 2.6 2.3 2.9 2.6 74 2.7 68 2.4 111 2.6 92 3.1 76 2.4 105 Guatemala 88 2.6 2.4 2.8 2.3 101 2.4 89 2.8 75 2.7 81 2.6 116 2.7 87 Guinea-Bissau 88 2.6 2.2 3.0 2.7 65 2.4 89 2.9 68 2.9 65 2.4 129 2.3 117 Mali 88 2.6 2.1 3.1 2.6 74 2.0 130 2.6 91 2.5 103 3.1 76 2.7 87 Nigeria 88 2.6 2.3 2.9 2.4 90 2.4 89 2.5 102 2.3 119 3.1 76 2.7 87 Russian Federation 88 2.6 2.5 2.7 2.4 90 2.7 68 2.3 121 2.6 92 2.9 93 2.5 98 Uzbekistan 88 2.6 2.1 3.1 2.6 74 2.4 89 2.6 91 2.6 92 2.8 101 2.4 105 Albania 97 2.5 2.1 2.9 2.4 90 2.7 68 2.8 75 2.3 119 2.5 124 2.3 117 Algeria 97 2.5 2.1 2.9 2.3 101 2.1 125 3.0 57 2.2 126 2.6 116 2.5 98 Armenia 97 2.5 2.3 2.7 2.5 84 2.6 76 2.2 128 2.6 92 2.7 109 2.3 117 Bhutan 97 2.5 2.3 2.7 2.7 65 2.2 118 2.3 121 2.6 92 2.6 116 2.3 117 Central African Republic 97 2.5 1.9 3.1 2.4 90 2.6 76 2.1 132 2.9 65 2.6 116 2.4 105 Congo, Dem. Rep. 97 2.5 2.2 2.8 2.3 101 2.3 108 2.5 102 2.4 110 2.8 101 2.5 98 Ghana 97 2.5 2.2 2.8 2.7 65 2.4 89 2.4 111 2.5 103 2.7 109 2.2 129 Grenada 97 2.5 2.3 2.7 2.6 74 2.5 80 2.6 91 2.2 126 3.1 76 2.3 117 Guinea 97 2.5 2.3 2.7 2.4 90 2.4 89 2.2 128 2.7 81 2.5 124 2.7 87 Jamaica 97 2.5 2.3 2.7 2.2 110 2.4 89 2.4 111 2.5 103 2.9 93 2.8 80 Mauritius 97 2.5 2.3 2.7 2.4 90 2.5 80 1.9 137 2.5 103 3.1 76 2.9 72 Moldova 97 2.5 2.1 2.9 1.9 133 1.9 132 2.7 85 2.8 76 3.0 87 2.8 80 Mongolia 97 2.5 2.2 2.8 2.5 84 2.3 108 2.5 102 2.3 119 2.7 109 2.4 105 Nicaragua 97 2.5 2.2 2.8 2.0 129 1.9 132 2.8 75 2.8 76 2.9 93 2.4 105 Tajikistan 97 2.5 2.2 2.8 2.2 110 2.5 80 2.5 102 2.8 76 2.9 93 2.0 134 Togo 97 2.5 2.2 2.8 2.3 101 2.3 108 3.0 57 2.4 110 2.8 101 2.3 117 Trinidad and Tobago 97 2.5 2.3 2.7 2.2 110 2.4 89 2.5 102 2.4 110 2.9 93 2.5 98 Zimbabwe 97 2.5 2.3 2.7 2.2 110 2.4 89 2.5 102 2.3 119 2.8 101 2.7 87 Bolivia 115 2.4 2.2 2.6 2.1 120 2.4 89 2.5 102 2.4 110 2.4 129 2.5 98 Cambodia 115 2.4 2.0 2.8 2.2 110 2.1 125 2.3 121 2.4 110 2.7 109 2.8 80 Gabon 115 2.4 2.0 2.8 2.0 129 2.2 118 2.6 91 2.0 135 3.0 87 2.5 98 Guyana 115 2.4 2.2 2.6 2.3 101 2.4 89 2.1 132 2.6 92 2.6 116 2.2 129 Iraq 115 2.4 2.2 2.6 2.1 120 2.2 118 2.5 102 2.2 126 3.0 87 2.4 105 Lao PDR 115 2.4 2.1 2.7 2.3 101 2.3 108 2.3 121 2.4 110 2.8 101 2.4 105 Liberia 115 2.4 1.8 3.0 2.1 120 2.4 89 2.8 75 2.4 110 2.3 133 2.4 105 Sudan 115 2.4 2.2 2.6 2.1 120 2.3 108 2.4 111 2.4 110 2.7 109 2.3 117 Burkina Faso 123 2.3 1.8 2.8 2.0 129 2.3 108 2.4 111 2.4 110 2.4 129 2.2 129 Fiji 123 2.3 2.0 2.6 2.3 101 2.2 118 2.3 121 2.3 119 2.3 133 2.2 129 Gambia, The 123 2.3 2.0 2.6 1.8 135 2.3 108 2.6 91 2.3 119 2.6 116 2.4 105 Iran, Islamic Rep. 123 2.3 2.1 2.5 2.2 110 2.4 89 2.4 111 2.1 133 2.7 109 2.4 105 Kyrgyz Republic 123 2.3 2.1 2.5 2.2 110 2.4 89 2.4 111 2.2 126 2.4 129 2.3 117 Madagascar 123 2.3 2.0 2.6 1.8 135 1.8 136 2.9 68 2.2 126 2.6 116 2.0 134 34 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Appendix 1  2023 LPI results Logistics International competence Tracking and LPI Customs Infrastructure shipments and equality Timeliness tracing Grouped Lower Upper Grouped Grouped Grouped Grouped Grouped Grouped Economy rank Score bound bound Score rank Score rank Score rank Score rank Score rank Score rank Mauritania 123 2.3 1.9 2.7 2.1 120 2.0 130 2.2 128 2.5 103 2.8 101 2.5 98 Syrian Arab Republic 123 2.3 2.1 2.5 2.2 110 2.2 118 2.3 121 2.2 126 2.5 124 2.3 117 Venezuela, RB 123 2.3 2.0 2.6 2.1 120 2.4 89 2.0 135 2.5 103 2.5 124 2.3 117 Cuba 132 2.2 1.8 2.6 2.0 129 2.2 118 2.1 132 2.2 126 2.6 116 2.4 105 Yemen, Rep. 132 2.2 1.8 2.6 1.7 137 1.9 132 1.7 139 2.6 92 2.8 101 2.3 117 Angola 134 2.1 1.8 2.4 1.7 137 2.1 125 2.4 111 2.3 119 2.1 138 2.3 117 Cameroon 134 2.1 1.8 2.4 2.1 120 2.1 125 2.2 128 2.1 133 2.1 138 1.8 136 Haiti 134 2.1 1.8 2.4 2.1 120 1.8 136 2.3 121 2.0 135 2.5 124 2.1 133 Somalia 137 2.0 1.7 2.3 1.5 139 1.9 132 2.4 111 1.8 139 2.3 133 1.8 136 Afghanistan 138 1.9 1.7 2.1 2.1 120 1.7 138 1.8 138 2.0 135 2.3 133 1.6 139 Libya 138 1.9 1.6 2.2 1.9 133 1.7 138 2.0 135 1.9 138 2.2 137 1.8 136 Source: World Bank. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 35 APPENDIX 2 Lead time data from supply chain tracking datasets Table A2.1 Lead time data for container shipping, 2022 Weighted by ship’s twenty-foot Number of Turnaround time at port (days) equivalent unit capacity Number of Number of international Interquartile Economy services alliances connections Median Mean range Median Mean Albania 3 0 7 1.2 1.4 0.5 1.2 1.4 Algeria 25 0 18 2.5 3.1 2.4 2.7 3.2 Angola 11 0 22 2.5 3.1 2.0 3.3 3.8 Antigua and Barbuda 4 0 22 0.5 0.5 0.1 0.5 0.5 Argentina 23 0 21 1.5 1.8 1.0 1.6 1.8 Australia 58 0 36 1.7 2.0 1.1 1.9 2.1 Bahamas, The 17 2 35 1.0 1.3 1.4 1.5 1.7 Bahrain 7 1 10 0.8 2.0 0.5 0.9 1.7 Bangladesh 32 0 12 3.0 2.9 1.0 3.0 3.1 Belgium 114 3 88 1.3 1.6 1.0 1.7 1.9 Benin 16 0 26 1.5 1.5 0.8 1.5 1.5 Brazil 33 0 34 0.8 1.0 0.5 0.9 1.0 Bulgaria 6 0 8 1.0 1.3 0.5 1.0 1.1 Cambodia 12 0 10 0.8 0.9 0.5 0.8 1.0 Cameroon 16 0 26 1.6 1.6 0.8 1.5 1.6 Canada 48 3 41 2.0 2.7 2.6 2.5 3.2 Chile 18 0 18 1.3 1.5 1.0 1.5 1.9 China 590 4 92 0.8 1.1 0.5 1.0 1.4 Colombia 52 1 55 0.6 0.7 0.4 0.8 0.8 Congo, Dem. Rep. 6 0 7 1.8 1.8 1.4 1.0 1.7 Congo, Rep. 15 0 23 1.8 2.4 1.2 1.9 2.1 Costa Rica 27 0 31 0.6 0.7 0.3 0.6 0.7 Croatia 7 2 15 0.8 1.1 0.9 1.7 1.6 Cuba 7 0 15 1.0 1.4 1.2 1.2 1.5 Cyprus 12 0 13 0.6 0.7 0.5 0.7 0.8 Denmark 17 1 20 0.5 0.8 0.5 1.0 1.3 Djibouti 13 1 24 0.8 0.8 0.4 0.8 0.9 Dominican Republic 35 1 51 0.9 1.1 0.6 1.2 1.4 Egypt, Arab Rep. 69 3 46 1.1 1.3 0.7 1.2 1.4 El Salvador 4 0 7 1.2 1.3 0.4 1.2 1.3 Estonia 8 0 11 0.8 1.0 0.8 1.1 1.2 Fiji 14 0 25 1.2 1.4 0.8 1.3 1.4 Finland 30 0 15 1.3 1.4 1.1 1.4 1.6 France 71 4 76 1.1 1.5 1.1 1.7 2.0 Gabon 9 0 16 1.5 1.5 1.0 1.5 1.5 Gambia, The 3 0 3 6.8 6.7 2.7 6.9 6.8 Georgia 5 0 5 1.4 1.7 0.6 1.5 1.6 Germany 119 3 70 1.3 1.7 1.3 2.0 2.4 Ghana 22 0 29 1.1 1.2 0.5 1.1 1.2 Greece 55 3 44 1.2 1.4 0.7 1.3 1.4 Grenada 4 0 17 0.3 0.4 0.2 0.3 0.4 Guatemala 29 0 19 0.6 0.7 0.5 0.7 0.8 36 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Appendix 2  Lead time data from supply chain tracking datasets Weighted by ship’s twenty-foot Number of Turnaround time at port (days) equivalent unit capacity Number of Number of international Interquartile Economy services alliances connections Median Mean range Median Mean Guinea 4 0 10 2.0 2.0 0.8 2.0 2.0 Guinea-Bissau 2 0 4 3.9 3.5 1.1 3.7 3.5 Guyana 9 0 16 1.5 1.4 0.6 1.5 1.4 Haiti 10 0 10 0.8 0.8 0.6 0.8 0.8 Honduras 20 0 13 0.5 0.6 0.3 0.5 0.6 Hong Kong SAR, China 183 4 59 0.6 0.7 0.3 0.7 0.8 Iceland 8 0 10 0.6 0.8 0.9 0.8 0.9 India 117 2 58 0.9 1.1 0.7 1.0 1.1 Indonesia 118 1 17 1.1 1.8 0.9 1.1 1.5 Iran, Islamic Rep. 15 0 11 1.1 1.7 1.0 2.0 2.9 Iraq 10 2 16 1.5 1.6 1.1 1.7 1.9 Ireland 23 0 16 1.2 1.3 1.0 1.1 1.3 Israel 36 2 35 1.2 1.5 1.0 1.3 1.6 Italy 94 4 74 1.0 1.3 1.0 1.5 1.9 Jamaica 33 0 46 1.1 1.4 0.6 1.2 1.6 Japan 206 3 42 0.3 0.5 0.3 0.5 0.6 Korea, Rep. 268 5 78 0.7 1.0 0.6 1.0 1.3 Kuwait 8 0 7 0.8 0.9 0.5 0.8 0.9 Latvia 9 0 10 1.3 1.4 1.1 1.4 1.5 Liberia 3 0 5 2.0 2.3 0.8 2.0 2.3 Libya 16 0 24 2.0 2.4 1.6 2.0 2.4 Lithuania 16 0 23 0.7 0.8 0.5 0.9 1.1 Madagascar 8 0 9 0.9 0.9 0.4 0.9 0.9 Malaysia 208 4 70 1.0 1.2 0.7 1.0 1.4 Malta 22 1 45 1.2 1.3 0.7 1.3 1.4 Mauritania 7 0 5 2.1 2.6 1.3 2.0 2.6 Mauritius 13 0 26 1.3 1.5 0.8 1.1 1.4 Mexico 49 3 46 0.9 1.1 0.6 1.0 1.3 Montenegro 3 0 7 0.4 0.4 0.2 0.4 0.4 Myanmar 12 0 11 2.0 2.0 1.1 2.0 2.0 Namibia 6 0 18 1.3 1.3 0.6 1.2 1.2 Netherlands 137 3 87 0.9 1.3 1.1 1.8 2.0 New Zealand 32 0 33 1.1 1.6 1.0 1.2 1.5 Nicaragua 6 0 8 1.1 1.2 0.7 1.2 1.2 Nigeria 23 0 30 2.9 3.4 2.7 2.9 3.4 Norway 30 0 14 0.3 0.5 0.3 0.3 0.4 Oman 30 3 40 0.8 0.9 0.4 0.9 1.0 Panama 65 4 56 0.9 1.1 0.7 1.0 1.2 Papua New Guinea 19 0 18 1.5 1.9 1.1 1.6 1.8 Paraguay 3 0 2 0.0 0.0 0.0 0.0 0.0 Peru 25 0 31 0.8 0.9 0.5 0.9 0.9 Philippines 66 0 15 1.0 1.3 0.8 1.1 1.3 Poland 29 2 33 0.9 1.4 0.7 2.1 2.4 Portugal 50 1 48 0.8 1.1 0.7 1.0 1.4 Qatar 17 1 25 0.6 0.7 0.5 0.7 0.8 Romania 13 1 20 1.5 2.5 1.6 2.1 2.4 Russian Federation 45 0 34 1.8 2.2 1.5 1.9 2.3 Saudi Arabia 63 3 49 0.8 1.1 0.6 0.9 1.1 C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 37 Appendix 2  Lead time data from supply chain tracking datasets Weighted by ship’s twenty-foot Number of Turnaround time at port (days) equivalent unit capacity Number of Number of international Interquartile Economy services alliances connections Median Mean range Median Mean Singapore 240 5 81 1.0 1.2 0.6 1.2 1.3 Slovenia 14 2 19 0.9 1.3 0.7 2.4 2.2 Solomon Islands 7 0 18 1.6 1.8 1.4 1.6 1.9 Somalia 9 0 14 1.0 1.2 1.3 1.0 1.3 South Africa 26 0 37 2.8 3.3 2.6 3.0 3.5 Spain 144 4 90 0.7 1.0 0.7 1.1 1.4 Sri Lanka 67 3 50 1.0 1.3 0.6 1.0 1.3 Sudan 4 0 1 6.6 6.2 3.4 6.9 6.6 Sweden 30 1 25 0.8 1.0 0.7 1.1 1.3 Syrian Arab Republic 5 0 12 0.9 1.2 0.8 0.9 1.3 Taiwan, China 141 3 61 0.5 0.8 0.5 0.8 1.0 Thailand 89 3 33 0.8 1.0 0.8 1.1 1.4 Togo 25 0 30 1.1 1.4 0.5 1.2 1.4 Trinidad and Tobago 16 0 27 0.8 0.9 0.5 0.8 1.0 Türkiye 109 3 50 0.7 1.0 0.6 1.0 1.2 Ukraine N/A N/A 0 0.9 1.2 0.8 1.2 1.3 United Arab Emirates 85 3 55 1.1 1.6 1.0 1.2 1.6 United Kingdom 133 3 90 0.9 1.2 0.9 1.3 1.8 United States 223 5 102 1.5 2.1 1.4 1.9 2.7 Uruguay 19 0 23 0.9 1.2 0.8 1.0 1.1 Venezuela, RB 6 0 7 1.6 2.0 1.3 1.8 2.2 Vietnam 180 3 34 0.8 0.9 0.5 0.9 1.0 Yemen, Rep. 9 0 8 2.8 3.6 2.1 3.1 3.7 Source: World Bank calculations based on data from MDS Transmodal and MarineTraffic. Note: Data on the number of international connections are for the second quarter of 2022, and data on turnaround time at ports are for June 2022. Table A 2.2 Lead time data for aviation, second quarter of 2022 Aviation import dwell time (time from advisory to the consignee of the freight’s arrival to delivery) Average number of (days) partners (incoming Economy and outgoing) Median Mean Interquartile range Algeria 52.5 10.3 9.9 12.9 Angola 59.5 10.0 9.6 11.8 Argentina 84.5 1.4 1.4 2.6 Armenia 53 2.6 4.0 5.2 Australia 98.5 1.3 0.8 2.0 Austria 122 0.9 0.7 1.7 Bahamas, The 15 4.8 2.4 6.7 Bahrain 71 1.5 1.9 3.0 Bangladesh 73 4.9 3.8 8.0 Belgium 141 0.9 0.9 1.5 Benin 35 4.6 4.7 5.2 Brazil 116.5 2.6 1.7 3.6 Bulgaria 83.5 1.1 1.3 2.3 Cambodia 56.5 3.1 3.5 5.2 Cameroon 54.5 3.7 2.9 4.1 Canada 147 1.8 1.3 2.2 38 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Appendix 2  Lead time data from supply chain tracking datasets Aviation import dwell time (time from advisory to the consignee of the freight’s arrival to delivery) Average number of (days) partners (incoming Economy and outgoing) Median Mean Interquartile range Chile 63.5 2.0 1.5 4.6 China 127 3.4 2.5 4.7 Colombia 80 2.0 1.7 2.8 Congo, Dem. Rep. 42 1.7 1.8 3.9 Congo, Rep. 32 3.3 3.0 4.7 Costa Rica 55 1.8 1.7 2.9 Croatia 68.5 2.2 2.6 3.0 Cuba 31.5 8.3 1.4 10.4 Cyprus 89 1.8 1.3 2.5 Czechia 115.5 1.8 1.8 2.3 Denmark 123 1.3 1.8 2.2 Djibouti 24 5.0 4.9 12.0 Dominican Republic 53 2.7 2.8 5.1 Egypt, Arab Rep. 107.5 8.3 8.7 11.0 El Salvador 21 1.6 1.6 2.2 Estonia 69.5 1.8 1.7 3.0 Finland 104 1.2 1.7 2.1 France 149.5 2.1 1.3 2.8 Gabon 32 12.7 8.2 74.3 Georgia 67.5 2.7 2.9 3.2 Germany 149.5 2.8 1.5 3.3 Ghana 80.5 4.7 5.5 6.1 Greece 111.5 1.9 2.2 2.8 Guatemala 32.5 0.8 1.5 3.6 Guinea 41 0.0 0.0 6.9 Honduras 16.5 2.5 1.8 10.6 Hong Kong SAR, China 135.5 1.6 0.7 2.0 Hungary 101 1.0 1.1 1.6 Iceland 10.5 1.9 1.7 2.1 India 133 3.0 1.9 3.8 Indonesia 104 2.6 2.4 3.7 Iran, Islamic Rep. 77 3.9 2.9 6.1 Iraq 68.5 2.1 2.6 2.5 Ireland 114.5 1.3 1.6 2.2 Israel 100.5 2.7 3.2 4.3 Italy 144.5 3.0 2.6 4.0 Jamaica 27.5 4.8 4.9 7.9 Japan 135 2.6 1.8 3.4 Kazakhstan 53 5.0 2.8 8.5 Korea, Rep. 129 1.4 1.0 2.1 Kuwait 94 2.8 2.7 4.8 Latvia 64.5 3.0 2.1 3.9 Lithuania 75.5 2.9 2.4 2.4 Luxembourg 68 0.8 0.8 1.5 Madagascar 52 2.1 2.6 2.3 Malaysia 111 1.1 0.8 2.1 Mali 50.5 2.7 3.0 4.1 Malta 66 1.8 2.0 3.1 C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 39 Appendix 2  Lead time data from supply chain tracking datasets Aviation import dwell time (time from advisory to the consignee of the freight’s arrival to delivery) Average number of (days) partners (incoming Economy and outgoing) Median Mean Interquartile range Mauritius 63 2.0 2.2 3.8 Mexico 100.5 1.9 1.9 3.3 Myanmar 49 0.9 0.4 1.4 Netherlands 145 1.6 0.8 3.1 New Zealand 74.5 0.8 1.0 1.3 Nigeria 93.5 4.7 5.2 7.8 Norway 108 1.2 1.6 2.3 Oman 82.5 2.2 2.5 3.7 Panama 55 1.9 2.3 2.9 Peru 56 3.6 2.6 10.9 Philippines 92.5 2.9 2.4 5.8 Poland 104 2.1 2.5 2.7 Portugal 110.5 1.9 2.1 3.0 Romania 91.5 1.8 1.9 2.2 Russian Federation 85 2.7 2.5 3.5 Rwanda 31.5 2.6 1.4 3.7 Saudi Arabia 99.5 3.4 2.6 5.4 Singapore 124.5 1.6 0.3 2.5 Slovenia 73 1.9 2.1 3.1 South Africa 132 1.9 1.3 3.1 Spaina 136.5 2.1 1.8 2.9 Sri Lanka 76 2.5 3.0 4.6 Sudan 62 7.9 5.4 5.9 Sweden 116.5 1.7 2.0 2.6 Switzerland 142.5 1.6 1.0 2.2 Taiwan, China 104 1.3 1.3 2.3 Thailand 120 2.1 2.1 3.1 Togo 33.5 4.0 3.8 5.0 Trinidad and Tobago 23 4.7 4.0 8.7 Türkiye 119 3.5 3.0 4.1 United Arab Emirates 136 2.5 1.3 3.4 United Kingdom 152.5 2.0 1.0 3.0 United States 158 4.1 1.2 5.2 Uruguay 39.5 5.1 0.1 9.0 Vietnam 98 2.6 2.4 3.5 Zimbabwe 45.5 4.5 4.6 4.9 Source: Cargo IQ. a. Includes the Canary Islands.  40 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Appendix 2  Lead time data from supply chain tracking datasets Table A 2.3 Lead time data for postal parcels, 2019 Delivery time (days) Average number of Economy partners (countries) Median Mean Interquartile range Afghanistan 42.5 9.7 5.2 11.1 Albania 80.5 1.7 0.9 1.1 Algeria 87 6.7 5.0 5.1 Angola 53.5 13.0 4.9 16.7 Argentina 80.5 27.0 21.1 34.8 Armenia 62 6.3 4.2 5.0 Australia 147 3.7 2.9 3.0 Austria 138 3.8 1.8 3.4 Bahamas, The 25.5 2.2 0.0 0.0 Bahrain 75 7.9 6.7 7.8 Bangladesh 97 6.9 5.2 4.0 Belarus 105 4.1 2.8 4.0 Belgium 107.5 5.6 2.8 5.9 Benin 59.5 3.5 0.2 2.0 Bhutan 25 5.5 2.2 8.0 Bosnia and Herzegovina 76 4.6 3.1 3.8 Botswana 33.5 15.9 12.1 14.9 Brazil 128.5 23.2 19.2 17.9 Bulgaria 107 8.0 2.1 10.2 Burkina Faso 71 3.3 0.1 4.1 Cambodia 57.5 4.0 0.3 4.0 Cameroon 63 10.6 6.0 11.0 Canada 150 4.8 3.2 4.9 Chile 103 8.7 4.6 7.7 China 121.5 5.6 4.1 3.9 Colombia 91 2.4 0.8 2.0 Congo, Dem. Rep. 45 61.2 31.1 109.1 Congo, Rep. 32 16.4 10.0 21.1 Costa Rica 75 10.1 6.2 14.9 Croatia 106.5 2.0 1.1 2.0 Cuba 73 19.4 16.2 17.2 Cyprus 108 2.1 1.2 2.4 Czechia 128.5 4.1 2.3 3.9 Denmark 138 4.7 2.1 5.6 Djibouti 39 3.4 1.0 3.7 Dominican Republic 65 2.0 0.2 0.2 Egypt, Arab Rep. 89 10.2 2.1 13.1 El Salvador 34 4.1 2.1 3.7 Estonia 113 4.5 2.0 5.3 Fiji 59.5 3.8 1.3 2.4 Finland 134 2.5 1.3 2.1 France 141 3.0 2.2 1.3 Gabon 22 11.9 5.0 16.1 Georgia 82 1.8 1.0 1.1 Germany 150.5 1.7 0.9 1.6 Ghana 90.5 2.4 1.0 2.7 Greece 131.5 4.8 3.0 5.0 C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 41 Appendix 2  Lead time data from supply chain tracking datasets Delivery time (days) Average number of Economy partners (countries) Median Mean Interquartile range Guatemala 25 28.7 18.9 31.0 Guinea 32 6.7 2.9 7.0 Guyana 31.5 5.0 0.8 8.1 Haiti 22 11.2 4.9 15.3 Honduras 32 8.5 5.1 9.1 Hong Kong SAR, China 134 1.8 1.2 1.3 Hungary 116.5 2.5 1.5 1.9 Iceland 110.5 2.7 1.3 2.7 India 140 10.4 7.9 8.1 Indonesia 117 13.3 7.2 11.1 Iran, Islamic Rep. 98 4.4 3.0 3.9 Iraq 64.5 14.6 7.9 15.9 Ireland 103 1.6 0.9 1.4 Israel 99 7.1 5.9 7.0 Italy 142.5 4.5 2.1 4.0 Jamaica 80 17.9 9.9 12.2 Japan 140 2.5 1.8 1.5 Kazakhstan 101 8.1 5.9 6.8 Korea, Rep. 120.5 1.8 1.0 2.0 Kuwait 82.5 6.9 3.3 9.2 Kyrgyz Republic 49.5 6.0 3.2 7.3 Lao PDR 50 4.4 2.0 4.1 Latvia 108.5 1.8 1.6 1.9 Liberia 32.5 2.7 0.0 0.2 Libya 42.5 15.2 1.1 10.1 Lithuania 119 5.6 2.2 5.8 Luxembourg 104 2.5 1.2 2.1 Madagascar 36.5 3.9 0.9 6.8 Malaysia 123 5.2 2.9 4.5 Mali 43.5 1.3 0.1 1.2 Malta 105 5.1 1.7 6.0 Mauritania 30 4.8 1.0 3.8 Mauritius 79.5 7.6 4.9 8.1 Mexico 87.5 12.5 7.6 10.9 Moldova 87 2.7 2.0 2.9 Mongolia 62 2.6 0.9 2.8 Montenegro 56 4.7 2.1 5.3 Myanmar 46 1.8 1.0 1.8 Namibia 46 16.0 11.8 17.6 Netherlands 148.5 1.5 0.9 0.7 New Zealand 128.5 2.9 1.8 2.3 Nicaragua 43.5 7.3 5.0 7.3 Nigeria 102.5 6.4 3.2 10.7 North Macedonia 72.5 6.8 4.1 7.8 Norway 139.5 4.9 3.9 5.5 Oman 90 5.6 2.3 6.0 Panama 68 7.7 2.7 5.8 Papua New Guinea 27 8.4 5.0 8.8 42 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Appendix 2  Lead time data from supply chain tracking datasets Delivery time (days) Average number of Economy partners (countries) Median Mean Interquartile range Paraguay 59.5 27.6 18.0 39.9 Peru 97 11.7 7.3 10.0 Philippines 116 18.9 13.7 22.2 Poland 136.5 3.0 1.9 2.1 Portugal 113 13.2 6.8 20.7 Qatar 99.5 5.9 3.1 5.9 Romania 122 2.3 1.1 2.1 Russian Federation 144.5 7.9 5.8 6.6 Rwanda 61.5 5.5 3.0 4.7 Saudi Arabia 116 6.6 4.1 6.2 Serbia 104 9.3 7.7 9.9 Singapore 116 1.9 1.1 1.5 Slovak Republic 110 2.2 1.3 2.0 Slovenia 106.5 3.6 1.9 4.1 Solomon Islands 25.5 7.8 1.0 9.8 South Africa 130 15.9 11.0 13.6 Spain 142 5.8 3.0 5.0 Sri Lanka 87.5 19.0 12.2 27.0 Sudan 42 5.0 1.9 5.4 Sweden 137 2.8 1.9 3.1 Switzerland 145.5 3.0 1.9 3.1 Syrian Arab Republic 44.5 9.6 7.0 13.0 Taiwan, China 103 2.9 2.1 2.8 Tajikistan 29 0.0 0.0 0.0 Thailand 121 2.6 2.1 2.0 Togo 67.5 8.0 3.0 6.2 Trinidad and Tobago 50.5 18.8 14.2 14.1 Türkiye 133.5 9.6 5.4 9.9 Ukraine 129.5 5.0 3.9 3.6 United Arab Emirates 131.5 5.5 1.1 1.5 United Kingdom 139.5 2.4 1.0 2.5 United States 149.5 5.1 3.9 3.8 Uruguay 85.5 9.9 4.7 12.9 Uzbekistan 33.5 5.5 4.0 3.3 Venezuela, RB 37 37.7 21.1 47.6 Vietnam 103.5 8.2 5.0 8.9 Zimbabwe 58 15.2 8.9 16.5 Source: Universal Postal Union. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 43 Appendix 2  Lead time data from supply chain tracking datasets Table A2.4 Import delays, May–October 2022 Consolidated dwell time (days) Port dwell time (days) Number of Economy observations Mean Median Q25 Q75 Mean Median Q25 Q75 Albania 1,039 13.6 6.6 3.7 14.7 13.3 6.4 3.5 14.5 Algeria 2,362 20.9 16.4 10.9 24.6 20.9 16.4 10.9 24.6 American Samoa 20 18.4 17.0 4.9 32.6 18.4 17.0 4.9 32.6 Angola 10,064 6.9 4.8 2.9 8.1 4.4 4.0 2.4 5.5 Argentina 14,350 11.4 9.2 6.4 13.6 11.4 9.2 6.4 13.6 Armenia 12 3.8 3.3 2.7 5.1 3.3 3.3 0.7 5.1 Aruba 927 3.3 2.0 1.0 5.0 3.3 2.0 1.0 5.0 Australia 53,319 3.2 3.0 2.1 4.0 3.2 3.0 2.1 3.9 Austria 227 18.0 13.6 9.1 22.9 14.1 11.0 6.0 18.9 Azerbaijan 48 5.2 4.3 3.5 5.1 4.0 3.6 0.5 5.0 Bahamas, The 25 4.7 3.2 2.1 5.5 4.7 3.2 2.1 5.5 Bahrain 45 6.3 5.5 2.7 9.1 6.0 4.6 2.6 9.1 Bangladesh 14,145 8.1 5.5 3.5 9.4 7.7 5.4 3.5 9.0 Barbados 12 8.7 6.6 5.1 10.8 0.5 0.5 0.5 0.6 Belgium 24,991 10.4 6.6 4.0 12.8 8.3 5.8 3.7 10.1 Belize 1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Benin 5,791 12.4 8.7 5.2 15.0 12.4 8.7 5.2 15.0 Bolivia 273 6.0 3.9 1.4 9.0 6.0 3.9 1.4 9.0 Bonaire, Sint Eustatius and Saba 18 4.0 2.2 1.6 5.7 3.8 1.9 1.5 5.6 Bosnia and Herzegovina 24 12.9 12.4 8.7 17.5 12.9 12.4 8.7 17.5 Botswana 69 13.4 9.0 4.4 18.8 9.9 6.1 3.8 9.2 Brazil 44,205 6.7 4.4 1.9 8.6 6.6 4.4 1.9 8.5 Brunei Darussalam 2,149 2.8 2.3 1.1 4.3 2.8 2.3 1.1 4.3 Bulgaria 3,022 8.6 6.1 3.5 8.9 8.6 6.1 3.5 8.9 Burkina Faso 131 21.4 19.4 12.3 28.5 13.6 12.7 6.9 17.2 Burundi 12 15.1 15.8 9.7 18.4 11.9 9.7 8.6 16.5 Cabo Verde 696 7.9 4.7 2.7 10.7 7.7 4.7 2.7 9.7 Cambodia 7,951 3.6 2.1 1.0 4.1 3.6 2.1 1.0 4.1 Cameroon 6,102 16.4 11.9 7.3 20.2 16.3 11.9 7.3 20.1 Canada 20,359 8.8 5.8 3.2 10.9 6.1 4.2 1.7 7.4 Cayman Islands 27 6.6 4.9 1.9 9.9 0.8 0.8 0.5 1.0 Central African Republic 8 31.3 30.2 26.1 34.0 31.3 30.2 26.1 34.0 Chad 20 15.2 15.5 7.5 18.4 15.2 15.5 7.5 18.4 Chile 20,991 4.1 3.2 1.9 4.5 4.1 3.2 1.9 4.5 China 87,910 5.5 3.7 1.9 6.6 5.5 3.7 1.9 6.6 Colombia 21,401 8.8 7.2 5.1 10.2 8.8 7.2 5.1 10.2 Congo, Dem. Rep. 6,198 18.1 14.7 9.9 22.1 17.5 14.6 9.9 22.0 Congo, Rep. 3,436 9.6 6.6 3.8 11.9 9.3 6.2 3.7 11.7 Costa Rica 9,353 7.6 6.1 3.1 9.4 5.4 3.4 1.9 6.6 Côte d’Ivoire 7,004 10.8 7.9 4.6 13.3 10.7 7.9 4.4 13.3 Croatia 3,879 7.1 4.4 2.9 8.6 6.6 4.3 2.8 7.9 Cuba 24 13.2 13.2 5.8 19.5 13.2 13.2 5.8 19.5 Curaçao 640 7.3 7.0 3.2 10.4 7.3 7.0 3.2 10.4 Cyprus 2,557 3.3 1.8 1.0 4.4 3.3 1.8 1.0 4.4 44 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Appendix 2  Lead time data from supply chain tracking datasets Consolidated dwell time (days) Port dwell time (days) Number of Economy observations Mean Median Q25 Q75 Mean Median Q25 Q75 Czechia 4,891 17.6 15.9 10.7 22.1 11.4 9.9 6.2 14.6 Denmark 13,462 8.4 5.9 2.5 9.3 6.9 5.4 2.3 7.8 Djibouti 7,030 9.0 6.0 3.4 10.4 8.9 5.9 3.4 10.4 Dominican Republic 5,772 7.7 5.5 3.2 9.5 7.5 5.4 3.1 9.3 Ecuador 9,234 7.3 5.8 3.5 9.1 7.3 5.8 3.5 9.0 Egypt, Arab Rep. 17,735 16.9 12.2 6.0 21.7 14.4 9.4 4.9 18.3 El Salvador 6,850 7.2 5.5 3.6 9.0 7.2 5.4 3.6 9.0 Equatorial Guinea 804 6.7 4.5 1.1 10.0 6.7 4.5 1.1 10.0 Estonia 411 4.7 4.0 1.8 5.9 4.7 4.0 1.8 5.9 Eswatini 121 4.4 4.0 3.0 4.4 4.1 3.9 3.0 4.4 Ethiopia 147 11.0 4.6 3.4 9.9 10.9 4.6 3.4 9.5 Faroe Islands 15 1.4 0.5 0.2 1.8 0.8 0.4 0.2 0.8 Fiji 850 2.9 2.4 1.7 3.6 2.9 2.4 1.7 3.6 Finland 3,137 12.5 6.8 3.4 14.7 12.5 6.8 3.4 14.7 France 18,617 8.1 5.7 3.5 9.8 7.9 5.6 3.5 9.5 Gabon 934 11.4 8.8 5.8 13.9 11.4 8.8 5.8 13.9 Gambia, The 2,644 9.7 6.7 3.9 10.7 9.7 6.7 3.9 10.7 Georgia 4,717 4.6 2.5 1.1 5.1 3.6 1.6 0.5 3.7 Germany 51,995 12.1 8.6 5.1 15.3 10.2 7.6 4.7 12.8 Ghana 18,882 7.4 4.6 2.0 9.4 5.7 3.0 1.1 7.5 Greece 8,585 5.2 3.2 1.9 5.5 5.2 3.2 1.9 5.4 Grenada 16 8.9 8.0 5.8 12.0 0.9 0.6 0.6 1.0 Guadeloupe 2,147 5.1 3.3 1.4 6.4 5.1 3.3 1.4 6.4 Guatemala 8,759 8.4 6.8 4.1 10.9 8.4 6.8 4.1 10.9 Guinea 7,892 8.8 6.2 4.0 10.8 8.8 6.2 4.0 10.8 Guyana 871 11.5 8.7 4.1 16.8 11.4 8.7 4.0 16.8 Haiti 610 13.4 10.7 5.8 16.1 13.4 10.7 5.8 16.1 Honduras 6,688 6.9 5.5 2.6 9.3 6.7 5.4 2.4 8.9 Hong Kong SAR, China 13,300 3.1 2.4 0.9 4.1 3.0 2.3 0.9 4.1 Hungary 3,747 14.7 12.3 6.5 20.1 9.9 7.9 4.6 13.5 India 71,765 5.3 2.7 1.2 7.5 2.6 1.5 0.9 3.0 Indonesia 41,619 3.4 2.3 1.2 4.2 3.2 2.2 1.2 4.0 Iraq 1,760 7.0 5.1 3.6 7.8 7.0 5.1 3.6 7.8 Ireland 4,678 9.0 5.7 3.7 10.3 8.9 5.7 3.7 10.2 Israel 13,890 6.8 4.6 2.8 7.5 5.8 4.1 2.5 6.3 Italy 23,629 9.0 6.2 3.7 11.0 8.0 5.9 3.3 9.8 Jamaica 1,536 9.2 7.9 4.7 12.0 9.2 7.9 4.7 12.0 Japan 35,216 7.4 5.5 3.3 8.9 1.0 0.4 0.3 0.6 Jordan 6,741 5.5 3.3 1.6 6.9 5.5 3.3 1.6 6.9 Kenya 21,764 6.9 5.0 2.9 8.8 5.1 3.4 1.9 6.5 Korea, Rep. 35,154 8.5 5.7 2.6 10.5 8.2 5.6 2.6 10.4 Kuwait 7,772 6.0 4.6 3.1 6.9 6.0 4.6 3.1 6.9 Lao PDR 5 6.8 2.9 2.8 3.3 5.0 0.6 0.3 1.1 Latvia 1,365 8.0 5.5 3.3 10.2 8.0 5.5 3.3 10.2 Lebanon 3,195 12.2 9.9 6.4 14.5 12.2 9.9 6.4 14.5 Lesotho 183 5.9 5.1 3.5 6.2 5.0 4.9 3.4 6.0 Liberia 4,175 9.2 6.2 3.1 12.1 9.2 6.2 3.1 12.1 Libya 3,109 13.8 10.2 7.3 15.8 13.8 10.2 7.3 15.8 C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 45 Appendix 2  Lead time data from supply chain tracking datasets Consolidated dwell time (days) Port dwell time (days) Number of Economy observations Mean Median Q25 Q75 Mean Median Q25 Q75 Lithuania 3,059 8.5 5.4 2.4 9.3 8.4 5.4 2.4 9.2 Luxembourg 5 15.2 16.5 12.3 17.3 15.2 16.5 12.3 17.3 Madagascar 4,421 6.3 4.7 1.8 7.9 6.3 4.7 1.8 7.9 Malawi 115 12.7 11.7 7.0 16.3 10.6 9.9 6.3 14.2 Malaysia 39,582 5.8 3.6 1.8 6.6 5.8 3.6 1.8 6.6 Maldives 176 1.5 0.1 0.1 2.1 1.5 0.1 0.1 2.1 Mali 128 22.7 11.2 8.8 23.9 19.7 10.3 8.1 16.5 Malta 91 25.0 20.0 7.1 39.3 25.0 20.0 7.1 39.3 Martinique 1,509 5.4 5.0 2.0 6.3 5.4 5.0 2.0 6.3 Mauritania 3,327 10.7 7.1 4.6 13.0 10.7 7.1 4.6 13.0 Mauritius 8,315 4.3 3.2 2.0 5.0 4.3 3.2 2.0 5.0 Mexico 41,736 8.8 6.2 3.6 10.2 8.6 6.0 3.5 9.9 Moldova 4 10.4 10.5 10.0 10.9 10.4 10.5 10.0 10.9 Mongolia 2 25.1 25.1 20.0 30.3 25.0 25.0 19.9 30.1 Morocco 16,266 10.7 7.2 4.2 12.4 10.2 7.1 4.1 12.2 Mozambique 5,713 7.5 5.6 3.6 9.3 7.4 5.6 3.6 9.2 Myanmar 13,635 8.1 5.0 3.1 9.6 8.1 5.0 3.1 9.6 Namibia 1,097 9.1 6.8 4.1 10.4 9.1 6.8 4.1 10.4 Nepal 2,454 11.6 10.6 6.0 15.5 6.1 3.7 2.0 8.8 Netherlands 72,974 9.4 5.8 3.1 11.2 7.2 5.2 3.0 9.1 New Zealand 24,995 6.5 4.7 2.7 9.3 5.4 3.9 2.2 7.2 Nicaragua 4,077 6.8 5.2 3.4 8.2 6.8 5.2 3.4 8.2 Niger 33 16.6 15.3 7.5 24.0 16.6 15.3 7.5 24.0 Nigeria 26,953 16.2 12.5 7.5 20.2 15.2 11.6 7.0 19.1 North Macedonia 14 13.0 9.0 4.9 12.2 12.8 9.0 4.9 12.2 Norway 4,314 5.0 3.6 1.1 6.1 4.8 3.5 1.1 6.0 Oman 8,864 5.0 3.2 1.6 6.3 5.0 3.1 1.6 6.3 Pakistan 10,834 10.0 6.8 3.7 11.8 6.4 3.7 1.8 7.9 Panama 17,467 6.0 4.5 2.9 7.1 5.0 3.9 2.4 6.1 Papua New Guinea 965 6.8 5.2 3.2 7.5 6.8 5.2 3.2 7.5 Paraguay 739 7.6 6.4 3.9 10.0 7.5 6.3 3.8 10.0 Peru 15,294 2.5 1.8 1.2 2.7 2.5 1.8 1.2 2.7 Philippines 43,236 6.4 5.0 3.0 8.0 6.2 4.9 2.8 8.0 Poland 35,325 11.2 7.5 4.4 13.4 10.3 6.4 4.1 11.8 Portugal 7,805 7.7 5.0 2.8 9.8 6.9 4.8 2.7 8.7 Puerto Rico 2,377 5.8 5.0 3.2 7.1 5.8 5.0 3.2 7.1 Qatar 8,626 4.4 3.0 1.3 5.1 4.3 3.0 1.3 5.1 Réunion 4,786 6.3 5.2 2.3 7.5 6.3 5.2 2.3 7.5 Romania 7,409 10.3 6.6 4.6 11.8 9.6 6.4 4.5 11.2 Rwanda 67 16.5 13.6 8.5 17.6 14.6 11.9 4.7 15.3 Samoa 5 6.0 4.2 4.2 5.2 6.0 4.2 4.2 5.2 Saudi Arabia 25,767 4.3 3.1 1.9 4.4 2.1 0.7 0.5 2.9 Senegal 15,548 8.1 6.4 3.0 9.6 8.0 6.4 3.0 9.6 Serbia 299 10.5 8.0 5.0 13.6 7.6 5.1 3.1 9.6 Seychelles 926 11.7 9.0 5.0 15.2 11.6 9.0 5.0 15.1 Sierra Leone 3,961 9.2 6.2 3.3 11.2 9.2 6.2 3.3 11.2 Singapore 13,621 3.0 1.5 0.8 2.6 3.0 1.5 0.8 2.6 Sint Maarten (Dutch part) 20 8.9 6.7 5.0 12.5 0.8 0.6 0.6 0.9 46 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Appendix 2  Lead time data from supply chain tracking datasets Consolidated dwell time (days) Port dwell time (days) Number of Economy observations Mean Median Q25 Q75 Mean Median Q25 Q75 Slovak Republic 2,981 19.2 16.5 10.2 25.7 10.8 9.9 5.9 14.3 Slovenia 8,125 8.0 5.5 3.2 10.0 7.5 5.3 3.2 9.3 Solomon Islands 98 12.5 9.3 3.3 19.2 11.9 9.0 3.2 19.0 Somalia 3,767 7.3 5.0 3.0 9.0 7.3 5.0 3.0 9.0 South Africa 41,097 5.3 3.7 2.5 5.5 4.0 3.5 2.3 4.9 Spain 39,144 8.5 5.9 3.2 10.9 7.7 5.5 2.9 9.7 Sri Lanka 7,197 5.7 3.6 2.0 5.9 5.7 3.6 2.0 5.9 St. Kitts and Nevis 5 5.0 3.9 3.4 4.0 0.5 0.4 0.4 0.4 St. Lucia 1 9.5 9.5 9.5 9.5 0.5 0.5 0.5 0.5 St. Vincent and the Grenadines 8 4.3 3.6 2.4 5.6 0.8 0.6 0.5 1.0 Sudan 3,540 12.8 6.7 5.5 16.0 12.8 6.7 5.5 16.0 Suriname 729 5.3 3.8 2.0 6.8 5.3 3.8 2.0 6.8 Sweden 12,472 7.6 5.0 3.3 9.1 6.6 4.5 3.1 8.2 Switzerland 330 19.8 17.7 12.4 25.1 12.8 10.0 6.4 16.7 Syrian Arab Republic 160 15.5 12.1 7.5 18.1 15.5 12.1 7.5 18.1 Taiwan, China 11,273 6.8 5.2 3.1 8.8 5.2 3.9 1.8 6.8 Tanzania 11,265 13.9 9.4 4.4 17.8 10.3 5.1 2.8 12.6 Thailand 31,034 5.7 4.3 2.6 7.1 4.4 3.3 1.7 5.4 Timor-Leste 80 4.6 4.1 1.7 5.2 4.0 3.8 1.5 5.2 Togo 7,118 8.1 4.6 3.0 9.0 8.1 4.6 3.0 9.0 Tonga 5 4.4 3.0 3.0 6.1 4.4 3.0 3.0 6.1 Trinidad and Tobago 2,277 9.2 6.7 4.4 10.2 9.1 6.7 4.4 10.2 Tunisia 1,496 18.7 13.4 9.0 23.3 18.7 13.4 9.0 23.3 Türkiye 25,836 8.6 5.7 3.7 10.2 8.6 5.7 3.6 10.1 Turks and Caicos Islands 47 19.2 18.6 11.0 25.9 0.8 0.6 0.5 1.0 Uganda 535 18.2 14.7 9.5 24.1 9.3 8.1 4.9 11.5 United Arab Emirates 47,865 4.5 3.0 1.5 5.9 4.4 3.0 1.5 5.9 United Kingdom 78,224 8.5 5.5 3.3 9.3 7.2 5.0 2.9 8.4 United States 350,868 8.3 5.4 3.2 9.2 7.2 5.1 3.0 8.3 Uruguay 4,819 2.4 1.8 1.2 2.8 2.4 1.8 1.2 2.8 Venezuela, RB 3,861 5.1 3.6 2.4 6.7 5.1 3.6 2.4 6.7 Vietnam 50,207 5.4 3.6 1.8 7.1 5.3 3.6 1.8 7.0 Virgin Islands (U.S.) 7 5.7 4.0 1.1 8.9 0.9 0.9 0.5 1.0 Yemen, Rep. 2,366 4.8 4.1 3.1 6.2 4.8 4.1 3.1 6.2 Zambia 171 13.9 11.7 7.8 16.8 13.6 11.4 7.3 16.8 Zimbabwe 176 12.8 11.8 8.1 15.2 12.8 11.8 8.1 15.2 Source: TradeLens. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 47 Appendix 2  Lead time data from supply chain tracking datasets Table A2.5 Export delays, May–October 2022 Consolidated dwell time (days) Port dwell time (days) Number of Economy observations Mean Median Q25 Q75 Mean Median Q25 Q75 Albania 82 6.9 5.4 3.5 9.1 6.9 5.4 3.5 9.1 Algeria 418 3.4 0.7 0.4 1.9 3.4 0.7 0.4 1.9 Angola 24 10.9 7.0 3.9 15.4 10.9 7.0 3.9 15.4 Argentina 8,804 7.4 6.7 4.7 9.1 7.4 6.7 4.7 9.1 Aruba 9 6.6 6.3 2.2 8.5 6.6 6.3 2.2 8.5 Australia 16,744 5.3 4.6 3.5 6.2 5.3 4.6 3.5 6.2 Austria 164 13.4 10.6 7.2 16.4 9.3 6.9 4.0 10.7 Bahamas, The 25 10.5 9.4 3.7 11.9 10.5 9.4 3.7 11.9 Bahrain 335 4.8 3.8 2.8 6.3 4.8 3.8 2.8 6.3 Bangladesh 17,272 1.7 1.0 0.5 2.0 1.6 0.9 0.5 1.8 Belgium 26,348 7.7 6.3 4.0 9.6 7.3 6.0 3.9 9.0 Benin 491 16.2 13.8 9.8 20.2 16.2 13.8 9.8 20.2 Brazil 44,165 9.6 7.7 5.4 11.8 9.5 7.7 5.4 11.7 Brunei Darussalam 45 1.5 0.9 0.6 2.2 1.5 0.9 0.6 2.2 Bulgaria 1,508 8.4 7.7 5.9 10.1 8.0 7.3 5.7 9.7 Burkina Faso 3 14.2 15.3 8.6 20.4 12.9 13.3 6.6 19.4 Cabo Verde 80 18.2 14.3 6.4 25.5 18.2 14.3 6.4 25.5 Cambodia 10,461 2.4 1.4 1.0 3.0 2.4 1.4 1.0 3.0 Cameroon 1,301 6.7 5.4 3.6 8.2 6.7 5.4 3.6 8.2 Canada 7,550 5.7 5.1 2.3 7.3 4.8 4.7 0.0 6.8 Chile 13,309 4.7 4.2 2.9 5.8 4.7 4.2 2.9 5.8 China 790,942 5.2 4.5 3.1 6.4 4.9 4.3 2.9 6.2 Colombia 7,529 6.3 5.0 3.2 7.8 6.2 5.0 3.1 7.7 Congo, Dem. Rep. 34 21.4 13.2 8.2 22.8 19.6 12.4 7.5 21.9 Congo, Rep. 269 8.5 7.6 6.1 9.9 8.5 7.5 5.9 9.8 Costa Rica 6,046 2.8 2.1 1.3 3.5 2.6 1.9 1.3 3.2 Côte d’Ivoire 2,643 6.4 5.7 4.0 7.6 6.2 5.6 3.9 7.4 Croatia 799 8.8 8.5 6.2 10.7 8.8 8.5 6.2 10.7 Cuba 3 8.9 8.7 6.9 10.8 8.9 8.7 6.9 10.8 Curaçao 8 14.0 12.8 6.5 21.2 14.0 12.8 6.5 21.2 Cyprus 832 1.8 1.4 1.1 2.1 1.8 1.4 1.1 2.1 Czechia 3,222 13.8 12.5 9.5 16.9 7.6 6.7 4.2 9.7 Denmark 12,644 8.1 7.5 4.8 9.7 8.1 7.5 4.7 9.6 Djibouti 2,434 5.3 4.5 2.3 6.6 5.3 4.4 2.3 6.6 Dominican Republic 1,326 9.4 8.1 4.8 11.8 9.3 8.0 4.8 11.7 Ecuador 8,951 3.7 3.1 2.3 4.5 3.6 3.1 2.3 4.4 Egypt, Arab Rep. 18,712 5.6 4.9 3.4 6.7 5.5 4.8 3.4 6.6 El Salvador 544 10.0 8.6 5.9 13.2 10.0 8.6 5.9 13.2 Equatorial Guinea 73 12.9 11.0 6.4 14.8 12.9 11.0 6.4 14.8 Estonia 49 6.2 5.3 4.3 8.3 5.7 5.3 4.3 8.3 Ethiopia 18 11.6 14.0 9.2 15.3 6.4 4.7 3.4 7.9 Fiji 151 3.6 3.2 2.5 4.6 3.6 3.2 2.5 4.6 Finland 6,240 9.4 8.1 6.2 11.1 9.4 8.1 6.2 11.1 France 14,417 9.5 8.0 4.9 12.4 9.2 7.7 4.6 12.0 Gabon 442 6.4 6.1 4.1 [Q?] 9.2 7.7 4.6 12.0 Gambia, The 442 6.4 6.1 4.1 8.2 6.4 6.1 4.1 8.2 48 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Appendix 2  Lead time data from supply chain tracking datasets Consolidated dwell time (days) Port dwell time (days) Number of Economy observations Mean Median Q25 Q75 Mean Median Q25 Q75 Georgia 365 7.9 3.9 2.7 11.2 7.7 3.9 2.7 10.8 Germany 529 4.9 1.6 0.74.2 10.5 7.7 6.0 3.9 9.4 Ghana 1,411 5.3 4.1 3.0 5.8 4.9 3.8 2.7 5.4 Greece 4,624 4.6 3.8 2.8 5.2 4.6 3.8 2.8 5.2 Guadeloupe 101 6.8 4.6 3.1 9.4 6.8 4.6 3.1 9.4 Guatemala 5,328 4.2 3.3 1.9 5.6 4.2 3.3 1.9 5.6 Guinea 314 9.4 8.7 5.5 12.3 9.4 8.7 5.5 12.3 Guyana 250 8.6 7.3 3.6 11.6 8.6 7.3 3.6 11.6 Haiti 47 11.5 10.0 3.0 15.2 11.5 10.0 3.0 15.2 Honduras 4,268 2.8 2.0 1.6 3.1 2.7 2.0 1.6 3.1 Hong Kong SAR, China 5,794 5.0 4.8 3.4 6.3 5.0 4.8 3.4 6.3 Hungary 1,746 13.5 11.5 8.3 16.2 8.8 7.6 4.7 10.3 India 129,906 5.0 4.3 3.0 6.4 4.6 4.1 2.9 5.9 Indonesia 46,046 3.5 3.3 2.2 4.6 3.5 3.3 2.2 4.6 Iraq 4 6.2 6.1 4.8 7.5 6.2 6.1 4.8 7.5 Ireland 3,263 7.6 6.7 4.9 9.4 7.6 6.7 4.9 9.3 Israel 7,059 3.3 2.5 1.9 3.4 3.2 2.5 1.9 3.4 Italy 36,798 7.4 6.2 4.4 9.1 7.0 6.0 4.1 8.5 Jamaica 22 12.8 9.9 5.2 15.7 12.8 9.9 5.2 15.7 Japan 21,019 5.2 4.5 2.4 7.1 1.0 0.4 0.3 0.6 Jordan 2,287 5.3 4.8 3.6 6.6 5.3 4.8 3.6 6.6 Kenya 7,039 5.8 5.3 2.9 7.5 5.8 5.3 2.9 7.4 Korea, Rep. 40,400 3.7 2.8 2.1 3.8 3.6 2.8 2.0 3.8 Kuwait 1,329 4.9 4.5 3.0 6.5 4.9 4.5 3.0 6.5 Latvia 2,342 9.3 8.3 5.3 11.8 9.3 8.3 5.3 11.8 Lebanon 2,386 5.5 4.4 2.9 6.6 5.5 4.4 2.9 6.6 Liberia 243 9.5 8.1 5.7 10.9 9.4 8.0 5.7 10.9 Libya 28 12.7 7.5 4.5 14.9 12.7 7.5 4.5 14.9 Lithuania 1,903 9.2 8.3 5.7 11.9 9.1 8.3 5.6 11.8 Madagascar 1,698 2.8 2.1 1.4 3.0 2.8 2.1 1.4 3.0 Malaysia 32,484 4.4 3.6 2.4 5.5 4.4 3.6 2.4 5.5 Maldives 6 1.5 1.2 1.2 1.3 1.5 1.2 1.2 1.3 Mali 14 15.0 14.0 10.8 16.8 3.6 4.0 2.8 4.4 Malta 59 8.2 6.3 3.8 9.3 8.2 6.3 3.8 9.3 Martinique 139 12.9 10.4 4.4 18.6 12.9 10.4 4.4 18.6 Mauritania 388 15.0 12.8 8.1 19.8 14.9 12.7 8.0 19.8 Mauritius 1,615 3.9 3.3 2.4 4.7 3.9 3.3 2.4 4.7 Mexico 13,672 10.4 8.8 5.8 13.1 10.2 8.7 5.7 12.9 Morocco 4,222 6.4 5.8 3.9 8.1 6.4 5.7 3.9 8.1 Mozambique 345 8.5 7.3 5.8 11.0 8.4 7.3 5.7 11.0 Myanmar 9,036 5.6 5.1 3.6 7.0 5.6 5.1 3.6 7.0 Namibia 1,044 6.8 5.9 4.6 7.7 6.2 5.7 4.4 7.5 Nepal 5 11.5 9.3 9.2 15.7 6.9 7.1 4.5 9.1 Netherlands 35,175 6.5 5.1 3.7 7.0 5.6 4.7 3.4 6.3 New Zealand 27,086 9.1 8.0 5.3 11.4 8.9 7.8 5.1 11.2 Nicaragua 1,285 5.0 4.0 2.4 6.4 5.0 4.0 2.4 6.3 Nigeria 1,128 13.6 11.2 7.7 17.4 13.1 11.0 7.3 16.8 Norway 3,161 6.9 5.4 3.5 8.8 6.9 5.4 3.5 8.8 Oman 4,111 5.1 4.6 2.9 6.9 5.1 4.6 2.9 6.9 C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 49 Appendix 2  Lead time data from supply chain tracking datasets Consolidated dwell time (days) Port dwell time (days) Number of Economy observations Mean Median Q25 Q75 Mean Median Q25 Q75 Pakistan 17,594 5.8 5.1 3.4 7.3 5.7 5.0 3.3 7.0 Panama 4,088 7.4 5.7 3.6 9.2 7.3 5.6 3.5 9.1 Papua New Guinea 587 3.3 2.8 2.1 4.4 3.3 2.8 2.1 4.4 Paraguay 441 10.9 9.5 6.6 12.5 10.9 9.5 6.6 12.5 Peru 12,902 3.9 3.1 2.2 4.8 3.7 2.9 2.1 4.5 Philippines 13,153 4.7 3.3 2.1 5.9 4.6 3.2 2.1 5.9 Poland 17,139 8.1 6.9 5.3 9.7 7.4 6.6 5.0 8.7 Portugal 4,750 5.8 5.3 4.0 6.8 5.6 5.3 4.0 6.7 Puerto Rico 497 7.3 6.0 4.0 9.4 7.2 5.9 4.0 9.3 Qatar 2,104 1.9 1.1 0.6 2.1 1.9 1.1 0.6 2.1 Réunion 154 10.4 8.7 7.1 13.3 10.4 8.7 7.1 13.3 Romania 4,012 5.5 4.9 3.2 7.3 5.3 4.8 3.1 7.1 Saudi Arabia 19,317 5.2 4.5 3.1 6.4 3.1 2.1 0.6 4.4 Senegal 1,196 3.6 3.3 2.3 4.4 3.4 3.1 2.3 4.3 Serbia 4 16.9 17.7 15.2 19.4 13.3 13.2 12.0 14.5 Seychelles 468 7.9 3.0 1.8 9.1 7.7 3.0 1.3 9.0 Sierra Leone 138 8.6 7.2 4.9 11.0 8.6 7.2 4.9 11.0 Singapore 15,384 3.1 2.2 1.5 3.4 3.1 2.2 1.5 3.4 Slovak Republic 1,116 14.0 12.9 9.2 17.0 8.2 6.7 4.6 10.2 Slovenia 4,467 7.1 6.3 5.1 8.2 7.1 6.3 5.0 8.1 Solomon Islands 107 2.7 2.3 1.7 4.4 2.6 2.3 1.7 4.1 Somalia 31 9.0 7.5 6.3 10.4 9.0 7.5 6.3 10.4 South Africa 35,442 5.5 5.3 3.9 6.8 5.5 5.3 3.9 6.8 Spain 37,918 9.8 8.4 5.5 12.2 9.3 8.0 5.3 11.6 Sri Lanka 6,992 4.0 3.5 2.3 5.2 3.9 3.5 2.2 5.2 Sudan 363 10.8 7.1 5.5 13.2 10.8 7.1 5.5 13.2 Suriname 178 9.5 8.8 5.7 13.0 9.5 8.8 5.6 13.0 Sweden 7,074 7.7 6.2 4.7 9.9 7.4 6.1 4.4 9.0 Switzerland 130 16.5 12.6 8.7 19.8 6.3 5.7 4.0 7.1 Syrian Arab Republic 63 5.6 5.0 2.9 7.4 5.6 5.0 2.9 7.4 Taiwan, China 17,613 6.1 5.3 3.8 7.6 5.1 4.5 2.9 6.6 Tanzania 2,410 7.3 5.4 3.8 7.9 7.2 5.4 3.8 7.8 Thailand 48,034 5.8 5.1 3.5 7.4 5.1 4.5 3.0 6.5 Togo 279 17.8 15.6 11.7 21.5 17.8 15.3 11.7 21.0 Trinidad and Tobago 522 11.8 10.4 7.8 14.6 11.8 10.4 7.8 14.5 Tunisia 1,513 4.9 3.0 1.6 6.7 4.9 3.0 1.6 6.7 Türkiye 37,087 8.9 7.8 5.4 11.1 8.9 7.7 5.3 11.1 Uganda 2 15.0 15.0 14.1 15.9 9.2 9.2 6.9 11.5 Ukraine 27 17.5 10.3 6.5 19.5 6.2 5.9 3.7 8.0 United Arab Emirates 24,460 5.5 4.8 3.2 6.9 5.4 4.8 3.1 6.9 United Kingdom 22,041 10.3 8.7 6.2 12.7 9.8 8.3 5.8 12.1 United States 114,211 8.6 6.9 4.5 10.5 8.2 6.8 4.4 10.1 Uruguay 1,749 5.3 4.8 3.2 6.4 5.3 4.8 3.2 6.4 Venezuela, RB 781 13.9 12.5 8.5 16.4 13.8 12.5 8.5 16.2 Vietnam 83,093 4.7 4.1 2.5 6.3 4.0 3.2 1.9 5.4 Yemen, Rep. 73 6.8 6.5 4.5 7.7 2.9 0.0 0.0 5.2 Source: TradeLens. 50 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Appendix 2  Lead time data from supply chain tracking datasets Table A 2.6 Dwell times for landlocked developing countries, 2022 (days) Reference dwell time Inland and destination Country Port dwell time for transit countries dwell time Corridor dwell time Armenia 3.3 3.6 0.4 — Azerbaijan 4 3.6 1.2 — Bolivia 6 4.1 0 — Bosnia and Herzegovina 12.9 6.6 0 — Botswana 9.9 4 3.6 — Burkina Faso 13.6 10.7 7.8 — Burundi 11.9 10.3 3.3 — Chad 15.2 16.3 0 — Ethiopia 10.9 8.9 0.2 — Lao People’s Dem. Rep. 5 4.4 1.8 — Lesotho 5 4 0.9 — Malawi 10.6 7.4 2.1 6.5 Mali 19.7 8 3.1 9.9 Moldova 10.4 9.6 0 — Mongolia 25 5.5 0.1 — Nepal 6.1 2.6 5.5 9.2 Niger 16.6 12.4 0 — North Macedonia 12.8 5.2 0.1 — Paraguay 7.5 11.4 0 — Rwanda 14.6 5.1 2 — Serbia 7.6 5.2 3 — Uganda 9.3 5.1 8.8 4.4 Zambia 13.6 4 0.2 — Zimbabwe 12.8 4 0 —  is not available. — Source: World Bank calculations based on data from TradeLens. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 51 APPENDIX 3 Top and bottom scorers on the LPI, overall and by income group Table A 3.1 Top 12 LPI scorers in 2023 and their top scorer status for 2018, 2016, 2014, and 2012 Economy Top 10 scorer in 2018 Top 10 scorer in 2016 Top 10 scorer in 2014 Top 10 scorer in 2012 Austria Yes Yes No Yes Belgium Yes Yes Yes Yes Canada No No Yes No Germany Yes Yes Yes Yes Denmark Yes No No Yes Finland Yes No No Yes Hong Kong SAR, China Yes Yes No Yes Netherlands Yes Yes Yes Yes Singapore Yes Yes Yes Yes Sweden Yes Yes Yes No Switzerland No Yes No No United Arab Emirates Yes No No No Source: World Bank. Note: Because of tied scores, the top 10 scores were attained by 12 countries. Countries are listed in alphabetical order. Table A 3.2 Bottom 12 LPI scorers in 2023 and their top scorer status for 2018, 2016, 2014, and 2012 Bottom 10 scorer Bottom 10 scorer Bottom 10 scorer Bottom 10 scorer Economy in 2018 in 2016 in 2014 in 2012 Afghanistan Yes No Yes No Angola Yes No No No Cambodia No No No No Cameroon No No No No Cuba No No Yes No Gambia, The No na No No Haiti Yes Yes No Yes Libya Yes No No No Somalia No Yes Yes na Yemen, Rep. No na Yes No Source: World Bank. Note: Countries are listed in alphabetical order. na is not applicable because an LPI score was not calculated for the economy in the year indicated. 52 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Appendix 3  Top and bottom scorers on the LPI, overall and by income group Table A 3.3 Top 11 upper-middle-income LPI scorers in 2023 and their top scorer status in 2018, 2016, 2014, and 2012 Top 10 upper-middle- Top 10 upper-middle- Top 10 upper-middle- Top 10 upper-middle- Economy income scorer in 2018 income scorer in 2016 income scorer in 2014 income scorer in 2012 Bosnia and Herzegovina No No No Yes Botswana No Yes No No Brazil Yes Yes Yes Yes Bulgaria Yes No Yes Yes China Yes Yes Yes Yes Malaysia Yes Yes Yes Yes North Macedonia No No No No Peru No Yes No No South Africa Yes Yes Yes Yes Thailand Yes Yes Yes Yes Türkiye Yes Yes Yes Yes Source: World Bank. Note: Because of tied scores, the top 10 scores were attained by 11 countries. Upper-middle-income status is based on country status in fiscal year 2022/23. Countries are listed in alphabetical order. Table A 3.4 Top 13 lower-middle-income LPI scorers in 2023 and their top scorer status in 2018, 2016, 2014, and 2012 Top 10 lower-middle- Top 10 lower-middle- Top 10 lower-middle- Top 10 lower-middle- Economy income scorer 2018 income scorer 2016 income scorer 2014 income scorer 2012 Benin Yes No No Yes Djibouti No No No No Egypt, Arab Rep. Yes Yes Yes Yes El Salvador No Yes Yes Yes Honduras No No No No India Yes Yes Yes Yes Indonesia Yes Yes Yes Yes Papua New Guinea No No No No Philippines Yes Yes Yes Yes Solomon Islands No No No No Sri Lanka No na No Yes Uzbekistan No No No No Vietnam Yes Yes Yes Yes Source: World Bank. Note: Because of tied scores, the top 10 scores were attained by 13 countries. Lower-middle-income status is based on country status in fiscal year 2022/23. Countries are listed in alphabetical order. na is not applicable because an LPI score was not calculated for the economy in the year indicated. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 53 Appendix 3  Top and bottom scorers on the LPI, overall and by income group Table A 3.5 Top 10 low-income LPI scorers in 2023 and their top scorer status in 2018, 2016, 2014, and 2012 Economy Top 10 LI 2018 Top 10 LI 2016 Top 10 LI 2014 Top 10 LI 2012 Central African Republic No na Yes Yes Congo, Dem. Rep. Yes Yes No No Guinea No Yes Yes Yes Guinea-Bissau Yes Yes Yes Yes Liberia No Yes Yes Yes Mali Yes Yes Yes na Rwanda Yes Yes Yes No Sudan Yes Yes No No Syrian Arab Republic Yes No No Yes Togo Yes Yes Yes Yes Source: World Bank. Note: Low-income status is based on country status in fiscal year 2022/23. Countries are listed in alphabetical order. na is not applicable because an LPI score was not calculated for the economy in the year indicated. 54 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 4 APPENDIX Description of the new data sources for the LPI 2023 This appendix introduces the data sources on the number of services, number of operators, shipment tracking data. To construct new sets number of alliances, and average annual fre- of indicators for the 2023 Logistics Perfor- quency of shipping service, as well as statistics mance Index (LPI), the World Bank collabo- (average, maximum, minimum) on the num- rated with several external data providers. The ber of deployed ships, ship sizes, and ship ages. data comprise the following micro-logistics Under MDS Transmodal’s definition, two high-frequency datasets: deployment of liner economies (or ports) are connected if there is shipping service from MDS Transmodal, air a shipping service between them. As shipping cargo tracking from Cargo iQ (supported by services operate in loops, not point to point like the International Air Transport Association), aviation, connections are counted irrespective of flow of international letters and parcels from the actual port sequence. the Universal Postal Union (UPU), granular high-frequency information on consignment Cargo iQ activities (container data) from TradeLens, and worldwide container ship port calls from The air cargo dataset was provided by Cargo an Automatic Identification System (AIS) data iQ, a nonprofit interest group created in 1997 provider (MarineTraffic). For the first time, LPI by the International Air Transport Association data were not collected entirely in-house. This to develop a system of shipment planning and appendix covers the origin of the data, the coun- performance monitoring for air cargo based on try coverage, and the variables used for the pro- definitions of common business processes and cessing of the key performance indicators. milestones.1 Cargo iQ is a pioneer in digitaliza- tion efforts in the air cargo industry, focusing on MDS Transmodal transparency, visibility, and quality improvement. Cargo iQ brings together more than 60 MDS Transmodal is an independent consul- participants, including forwarders, air carriers, tancy focusing on the international freight ground handling companies, road carriers, and transport sector, including shipping, ports, airports, to define the standards for shared pro- road, rail, logistics, and distribution. It collects cesses and planning to control and evaluate per- and aggregates several types of transport-related formance of cargo shipments. Cargo iQ collects data and maintains databases related to freight more than 110 million data lines a year, 12 mil- transportation. A dataset of aggregates for lion of which are airport-to-airport shipments. country pairs and countries for January–June These records, covering information for about 2022 was derived from MDS Transmodal’s 650 airports in 184 countries and accounting for Containership Databank, which covers ship- 45 percent of global air freight volume, were used ping schedules and volumes offered on liner to construct the aviation pillar of the 2023 LPI. shipping routes. Cargo iQ’s event recording follows a simi- Indicators available as part of the partner- lar Electronic Data Interchange (EDI) protocol ship agreement with MDS Transmodal include as the UPU, with a similar logical ordering of 1. See https://www.cargoiq.org/value-proposition. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 55 Appendix 4  Description of the new data sources for the LPI 2023 Figure A4.1 Cargo iQ milestones DEP ARR RCF NFD DLV Shipment Shipment Cargo received Notification of Cargo delivered departure from arrival at in warehouse at Readiness for to consignee/ origin/last transit/ transit/ delivery of cargo agent departure point destination destination to consignee/ agent Source: Cargo iQ. supply chain events. A shipment, commonly with certain characteristics are excluded from identified through an electronic airway bill the dataset. They are bilateral lanes representing is tracked through the system from the point more than 80 percent of total shipments to tar- of departure of the flight with cargo (DEP in get countries with three or fewer carriers; these figure A4.1) through its arrival (ARR) and excluded 46 countries from the final set of key check-in to a warehouse at a destination airport performance indicators, resulting in 141 coun- (RCF), followed by the advisory to the con- tries in the 2023 LPI aviation pillar. signee of the freight’s arrival (NFD), and the The data from Cargo iQ’s system are based consignee’s final collection of the freight from on a pair of milestones: advisory to the con- the carrier at the destination airport (DLV). signee of the freight’s arrival to the consignee’s For all five milestones, it is the carriers’ re- final collection of the freight from the carrier sponsibility to enter the data in the system in at the destination airport. In other words, the a timely, consistent, and accurate manner. The time elapsed between the two events was com- time differences between the milestones provide puted for each electronic airway bill recorded information on the various aspects of the reli- in the system at a destination country given the ability and performance of individual carriers, validity of the time difference (meaning that freighters, and operators and (at the aggregate both timestamps exist and the time difference level) of airports and countries. between them is positive). The choice of this in- To avoid revealing commercially sensitive dicator was based on two considerations: best information for specific carriers, trade lanes apparent quality of data and country coverage Figure A4.2 Country coverage of Cargo iQ dataset, by World Bank region Percent of countries in the region 80 60 40 20 0 East Asia Europe & Latin America & Middle East North South Sub-Saharan & Pacific Central Asia Caribbean & North Africa America Asia Africa Source: World Bank calculations based on data from Cargo iQ. 56 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Appendix 4  Description of the new data sources for the LPI 2023 and interpretability. This indicator represents Table A 4.1 The postal sequence of tracking messages how fast air cargo shipments move at the desti- Message ID Event description nation, which is the equivalent of import dwell Exporting events time. Future editions of Connecting to Compete A Posting/collection may consider additional delay indicators. B Arrival at outwards office of exchange Low-income countries have the lowest cov- C Departure from outward office of exchange erage: data are available for 25 percent of these Importing events countries. The East Asia and Pacific, South Asia, D Arrival at inward office of exchange and Sub-Saharan Africa regions all have about E Held by import customs 35–40 percent coverage (figure A4.2). Geo- F Departure from inward office of exchange graphical coverage is lower for Cargo iQ than G Arrival at delivery office for the UPU. H Attempted/unsuccessful delivery I Final delivery Universal Postal Union J Arrival at transit office of exchange K Departure from transit office of exchange Most cross-border e-commerce depends on Source: Universal Postal Union. postal parcel services provided by UPU mem- bers or global express operators (for example, DHL, FedEx, and UPS). UPU members han- destination Office of Exchange, in which it dle two-thirds of letter-parcel deliveries (up to 2 departs from the country-of-origin (event C). kilograms) across borders.2 Therefore, informa- After a few potential transiting events (events tion collected by UPU is a source of comprehen- J–K), the item arrives at the destination (event sive data for more than 190 member countries D), where it is unloaded and handed over to the and probably the best unified source of informa- destination post. Event E describes the process tion on e-commerce trade. of separating different items from the bundle UPU maintains technical standards and (receptacle) that they were shipped in, retrieving EDI message specifications used in the exchange the items, and clearing them through customs. of electronic information between postal serv- Finally, the destination Office of Exchange in- ices. To exchange information between mem- ducts it into their domestic network for process- bers’ postal services, UPU maintains EDI data- ing and potential relocation to the delivery of- bases with records on volumes, frequencies, key fice, from which a final delivery to the customer cross-border activities, and other tracking data happens (event I). Unsuccessful deliveries are of postal items. This information is available via recorded using event H. The focus of the LPI the Express Mail Service Events messaging stan- has been on the performance at the destina- dard, which is used to track parcels (packages up tion, making the delay between events D and to 30 kilograms), letters (letter-post items and H/I the primary key performance indicator as- packages up to 2 kilograms), and express mail sessing postal logistics, covering the quality of flows in the UPU network (table A4.1). postal infrastructure and speed of delivery.3 The For an e-commerce item, after a consumer delivery events have also been found to have the places an order, the shipper hands the item over most consistency and country coverage. to the origin post (event A in table A4.1). The The dataset was constructed for the en- post inducts the item into its domestic network, tire calendar year of 2019. The sample com- where it passes through several handling, sort- prised countries with more than 100 inbound ing, and transport processes (event B). At the unique parcel shipments; this included 132 origin Office of Exchange, the item is assigned countries from all World Bank regions and in- to a receptacle for international dispatch to the come groups. After data cleaning, 40 percent of 2. Beretzky and others 2022. 3. Boffa 2015. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 57 Appendix 4  Description of the new data sources for the LPI 2023 Figure A4.3 Country coverage of the Universal Postal Union dataset, by World Bank region Percent of countries in the region 80 60 40 20 0 East Asia Europe & Latin America & Middle East North South Sub-Saharan & Pacific Central Asia Caribbean & North Africa America Asia Africa Source: Universal Postal Union. low-income countries were represented in the Nations Centre for Trade Facilitation and Elec- postal dataset, 50 percent of Sub-Saharan Afri- tronic Business (figure A4.4). can countries were represented, and Europe and TradeLens used a simple, logical data model Central Asia had a representation of 79 percent with three related classes: consignments, trans- (figure A4.3). port equipment, and shipments. The main purpose of this model was to track consign- TradeLens ments, transport equipment (containers), and shipments while managing the identifiers and TradeLens was a highly secure data and docu- relationships between them. The platform al- ment sharing platform aimed at simplifying and lowed a consignment to be in multiple pieces of speeding trade workflows for all participants of transport equipment, along with other consign- the supply chain ecosystem. A collaboration ments. It also allowed transport equipment to between IBM and GTD Solution (a division of be part of multiple consignments. shipping conglomerate Maersk), the platform The dataset extracted by TradeLens for operated between 2018 and the first quarter of the World Bank covers May 1–October 31, 2023. TradeLens used IBM Blockchain Plat- 2022. The sample contained timestamps for 11 form, a permissioned blockchain system that events for four transport modes (ocean, road, offers immutability, privacy, and traceability barge, and rail) and two load statuses (full or of shipping documents. TradeLens brought empty), associated with more than 3 million together more than 1,000 major entities unique tracked consignments and more than involved in the global supply chain, including 30 million observations in total. The dataset more than 200 ports and terminals and more covers more than 11,000 distinct United Na- than 15 customs authorities, and by mid-2022, tions Code for Trade and Transport Loca- it was facilitating the information exchange of tions (UNLOCODE), including destinations, about 60 percent of containerized trade.4 Its origins, and live locations (locations of specific interoperability was supported through the event timestamps). On average, about 9.8 events adaptation of a data model and access con- are associated with each consignment. trol schema that were aligned with the Supply To create the key performance indica- Chain Reference Data Model of the United tors, the World Bank team focused on time 4. See https://www.tradelens.com/network. 58 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Appendix 4  Description of the new data sources for the LPI 2023 Figure A4.4 TradeLens data model Events available Other events BCO/NVO FFW/CHB/ Inland Customs/ Port/ Ocean Port/ Customs/ FFW/CHB/ Inland BCO/NVO 3PL transportation government terminals carriers terminals government 3PL transportation authorities authorities Shipping milestones and shipment data Gate out of full container at import Gate out of empty container from Gate out of empty container from Gate-in of full container at inland Stripping completed in container Gate in full container at terminal Container stuffing completed at Full container loaded on vessel Vessel departure from terminal Full container discharged from Gate out of full container from Export documentation cleared Gate-in of empty container at Gate in of empty container at Customs release inland location Vessel arrival stuffing site stuffing site inland site inland site terminal location export vessel depot depot Export phase Transshipment Import phase phase Source: TradeLens. differences or lead time between subsequent broken into three phases: export on shore, ship- events. Events can happen between different lo- ping and transshipment, and import onshore cations—for instance, on multimodal corridors (figure A4.5). or in shipping. Subsequent events may also occur The tracking data cover the responsibility at the same location, and the time that contain- of international logistics operators, not that of ers stay at the same place is typically referred to shippers upstream or consignees downstream. as dwell time. Data processing consisted of split- Supply chain practices by the latter may vary. ting container trips into a succession of transi- But container data include information on the tions between subsequent events at the same or movement of empty containers, which proxies different locations. Key performance indicators the time taken to stuff export containers or de- were constructed by aggregating the lead time or liver full import containers at the destination. dwell time for UNLOCODE or lead time be- Information on repositioning and return of tween pairs of UNLOCODE. To facilitate in- empty containers may lead to more meaningful terpretation, the global container supply chain is indicators in the future. Figure A4.5 The three phases of container trips Depot Origin Multimodal Port Phase 1 origin multimodal facility export Export on shore Dwell time Dwell time Dwell time Laden containers Port Phase 2 transshipment Maritime Empty Transsipment time Port Multimodal Destination Return Phase 3 import facility depot Import on shore Dwell time Dwell time Dwell time Source: 2023 LPI team. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 59 Appendix 4  Description of the new data sources for the LPI 2023 MarineTraffic The dataset was prepared using Marine­ Traffic data on port calls that covered more than The port call dataset from MarineTraffic is a col- 5,000 container ships calling at more than 800 lection of records, processed from Automatic ports worldwide during the first two quarters of Identification System messages and enriched 2022. Based on estimated time differences be- with proprietary information on ports and ship tween recorded arrivals and departures to port datasets sourced from the International Maritime facilities, an indicator of turnaround time per Organization registry. Ship types ranging from port was constructed. small feeders with capacity up to 1,000 twenty- The data from MDS Transmodal and Mari- foot equivalent units to ultra large container neTraffic cover 52 percent of World Bank mem- vessels with capacity starting at 14,501 twenty- bers (figure A4.6). The United Nations Confer- foot equivalent units. The information available ence on Trade and Development uses the same includes timestamps of port arrivals and depar- sources when producing the Liner Container tures reported through Automatic Identification Shipping Connectivity Index and its own indi- System signals via terrestrial and satellite receivers. cator of turnaround time.5 Figure A4.6 Country coverage of the MDS Transmodal and MarineTraffic dataset, by World Bank region Percent of countries in the region 80 60 40 20 0 East Asia Europe & Latin America & Middle East North South Sub-Saharan & Pacific Central Asia Caribbean & North Africa America Asia Africa Source: World Bank calculations based on data from MDS Transmodal and MarineTraffic. 5. UNCTAD 2021b. 60 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Appendix 4  Description of the new data sources for the LPI 2023 Table A4.2 List of key performance indicators derived from tracking data Source Indicator Definition Period Why it matters MDS Transmodal Number of services Total number of maritime services (operated through liner shipping Second quarter Availability of services and companies on a predefined rotation) between the two countries. of 2022 frequency of connection. Number of alliances Count of the number of alliances per destination country. Second quarter Competition between services. of 2022 Number of partners Count of distinct number of country partners per destination country. Second quarter Shipping connectivity metric. (countries) of 2022 Cargo iQ Number of partners Average number of partner countries First and second Air cargo connectivity metric. (countries) quarters of 2022 Aviation dwell time Time difference between notification of readiness for First and second Efficiency of handling and clearance (days) delivery of cargo and cargo delivered to consignee at quarters of 2022 and notification to consignee. destination country. Median and quartiles are provided. Universal Postal Union Number of partners Average number of country partners. 2019 Postal connectivity. (countries) Postal delivery Median time difference between arrival at inward office of 2019 Efficiency of clearance and postal time (days) exchange and unsuccessful delivery or final delivery to recipient logistics at destination. at the destination country. Median and quartiles are provided. TradeLens Import and export Time spent at the same location (as defined by United Nations Code May 1 to Critical indicator resulting from many factors, dwell time (days) for Trade and Transport Locations) since expedition and before ship October 31, 2022 including goods clearance, removal, and land loading. Two variables are produced for each country: dwell time at services and to some extent terminal and port of departure and consolidated dwell time (including time spent multimodal performance. Export dwell time at intermediate locations). Mean, median, and quartiles are provided. is representative of domestic logistics. The statistics are based on all container trips originating in the country, irrespective of the export and import corridor. Corridors import Estimation of mean time to import for corridors serving landlocked May 1 to Representative of road or rail corridor lead time (days) countries based on lead time between destination and port of import. October 31, 2022 performance excluding multimodal transfer en route which are included in dwell time. Export container Sum of consolidated dwell time and corridor May 1 to Same concept for exports. lead time (days) time for export and stuffing time. October 31, 2022 MarineTraffic Turnaround time Time difference between first instance of arrival and last First and second Proxy of the performance of the ship (days) instance of departure for consecutive repeated port visits (if quarters of 2022 to shore interface (including handling any) calculated for each port call (as defined by United Nations by the terminal operator). Code for Trade and Transport Locations). Aggregated directly from port call time differences to countries over six months. This indicator excludes waiting time at anchorage. Source: World Bank. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 61 APPENDIX 5 The LPI methodology Because logistics has many dimensions, mea- Constructing the international LPI suring and summarizing performance across countries are challenging. Examining the time The main part of the Logistics Performance and costs associated with logistics processes— Index (LPI) survey (questions 4 to 9 in the 2023 port processing, customs clearance, transport, edition) provides the raw data for the interna- and the like—is a good start, and in many tional LPI. Each survey respondent rates up to cases this information is readily available. But eight overseas markets on six core components even when complete, this information cannot of logistics performance. The eight countries are be easily aggregated into a single, consistent, chosen based on the most important export and cross-country dataset, because of structural dif- import markets of the country where the respon- ferences in countries’ supply chains. Even more dent is located, on random selection, and—for important, many critical elements of good landlocked countries—on neighboring coun- logistics—such as process transparency and tries that form part of the land bridge connect- service quality, predictability, and reliability— ing them with international markets (table A5.1). cannot be assessed using only time and cost Respondents take the survey online. The sur- information. vey for this edition was open from September 6 Table A 5.1 Methodology for selecting country groups for survey respondents Respondents from Respondents from Respondents from low‑income countries middle‑income countries high‑income countries Respondents from Five most important export Three most important Two random countries from a list coastal countries partner countries export partner countries of the five most important export + + partner countries and five most Three most important The most important import important import partner countries import partner countries partner country + + Four random countries, one Four random countries, one from each country group: from each country group: a. Africa a. Africa b. East Asia and b. East Asia and Central Asia Central Asia c. Latin America c. Latin America d. Europe less Central d. Europe less Central Asia and OECD Asia and OECD + Two random countries Respondents from Four most important export Three most important from the combined country landlocked countries partner countries export partner countries groups a, b, c, and d + + Two most important import One most important partner countries import partner country + + Two land-bridge countries Two land-bridge countries + Two countries randomly, one from each country group: a. Africa, East Asia and Central Asia, and Latin America b. Europe less Central Asia and OECD Source: 2023 LPI team. 62 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Appendix 5  The LPI methodology to November 5, 2022. The web engine for the • The competence and quality of logistics survey underlying the 2023 LPI was the same services, rated from very low (1) to very high as the engine put in place in 2012 (and used in (5) in survey question 7. subsequent editions). It incorporates the uni- • The ability to track and trace consignments, form sampling randomized approach to gain the rated from very low (1) to very high (5) in most possible responses from underrepresented survey question 8. countries. Because the survey engine relies on • The frequency with which shipments reach a specialized country selection methodology consignees within scheduled or expected for survey respondents based on high trade vol- delivery times, rated from hardly ever (1) to ume between countries, the uniform sampling nearly always (5) in survey question 9. randomized approach can help countries with The overall LPI score is constructed from lower trade volumes rise to the top during coun- these six indicators using principal component try selection. analysis, a standard statistical technique used to The survey engine builds a set of eight coun- reduce the dimensionality of a dataset. In the tries for the survey respondents (see table A5.1). LPI, the inputs for principal component analysis After 200 surveys, the uniform sampling ran- are country scores on questions 4–9, averaged domized approach is introduced into the en- across all respondents providing data on a given gine’s process for country selection. For each overseas market. Scores are normalized by sub- new survey respondent, the approach solicits a tracting the sample mean and dividing by the response from a country chosen at random but standard deviation before conducting the prin- with nonuniform probability—with weights cipal component analysis. The output from the chosen to evolve the sampling toward uniform analysis is a single indicator—the LPI score— probability. Specifically, a country i is chosen which is a weighted average of those scores. The with a probability (N–ni) / 2 N, where ni is the weights are chosen to maximize the percentage sample size of country i so far, and N is the total of variation in the LPI’s original six indicators sample size. As country sample sizes grew above that is accounted for by the summary indicator. 100, the country selection engine excluded The first (principal) eigenvalue of the cor- oversampled countries from the pool to increase relation matrix of the six core indicators is responses from underrepresented countries. greater than 1—and much larger than any other The international LPI is a summary indica- eigenvalue (see the first line of table A5.2). Stan- tor of logistics sector performance, combining dard statistical tests, such as the Kaiser Crite- data on six core performance components into a rion and the eigenvalue scree plot, suggest that single aggregate measure. Some respondents did a single principal component be retained to not provide information for all six components, summarize the underlying data. This principal so interpolation was used to fill in missing val- component is the international LPI score. The ues. The missing values were replaced with the international LPI accounts for 91 percent of the country mean response for each question, ad- variation in the six components. justed by the respondent’s average deviation from the country mean in the answered questions. Table A 5.2 Results of principal component analysis for the 2023 international LPI score The six core components are: • The efficiency of customs and border man- Component Eigenvalue Difference Proportion Cumulative agement clearance, rated from very low (1) 1 5.47139 5.27856 0.9119 0.9119 to very high (5) in survey question 4. 2 0.192832 0.034632 0.0321 0.9440 • The quality of trade and transport infra- 3 0.1582 0.0797762 0.0264 0.9704 structure, rated from very low (1) to very 4 0.0784234 0.0263933 0.0131 0.9835 high (5) in survey question 5. 5 0.0520301 0.00490627 0.0087 0.9921 • The ease of arranging competitively priced 6 0.0471239 na 0.0079 1.0000 shipments, rated from very difficult (1) to Source: 2023 LPI team. very easy (5) in survey question 6. Note: na is not applicable. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 63 Appendix 5  The LPI methodology Table A 5.3 Component loadings for the 2023 international LPI score country’s 2023 LPI score exceeds the upper bound of its 2018 score. Component Weight To calculate the confidence interval, the Customs 0.4105 standard error of LPI scores across all respon- Infrastructure 0.4133 dents is estimated for a country. The upper and International shipments 0.3931 lower bounds of the confidence interval are then Logistics quality and competence 0.4168 Tracking and tracing 0.4133 t(0.1, N–1)S LPI ± , Timeliness 0.4021 N Source: 2023 LPI team. where LPI is a country’s LPI score, N is the Note: na is not applicable. number of survey respondents for that coun- To construct the international LPI score, try, s is the estimated standard error of each normalized scores for each of the six original country’s LPI score, and t is Student’s t-dis- indicators are multiplied by their component tribution. As a result of this approach, confi- loadings (table A5.3) and then summed. The dence intervals and low-high ranges for scores component loadings represent the weight given are larger for small markets with few respon- to each original indicator in constructing the dents, since these estimates are less certain. international LPI score. Since the loadings are The average confidence interval on the 1–5 similar for all six, the international LPI score is scale is 0.25, or about 8 percent of the average close to a simple average of the indicators. Al- country’s LPI score. Hence, caution must be though principal component analysis is rerun taken when interpreting small differences in for each version of the LPI, the weights remain LPI scores. steady from year to year. There is thus a high de- LPI scores have two limitations. First, the gree of comparability across LPI editions. experience of international freight forwarders might not represent the broader logistics envi- Constructing the confidence intervals ronment in poor countries, which often relies on traditional operators. And international and To account for the sampling error created by the traditional operators might differ in their inter- LPI’s survey-based methodology, LPI scores are actions with government agencies—and in their presented with approximate 80 percent confi- service levels. Second, for landlocked countries dence intervals. These intervals make it possible and small island states, the LPI might reflect ac- to provide upper and lower bounds for a coun- cess problems outside the country assessed, such try’s LPI score. To determine whether a differ- as transit difficulties. The low rating of a land- ence between two scores is statistically signifi- locked country might not adequately reflect its cant, confidence intervals must be examined trade facilitation efforts, which depend on the carefully. For example, a statistically significant workings of complex international transit sys- improvement in a country’s performance should tems. Landlocked countries cannot eliminate not be concluded unless the lower bound of the transit inefficiencies with domestic reforms. 64 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y Results from the LPI survey question on demand for environmentally sustainable APPENDIX 6 shipping options and on changes in global supply chains since 2019 Map A6.1 How often do shippers ask for environmentally friendly options (e.g., in view of emission levels, choice of routes, vehicles, schedules, etc.) when shipping to…? Source: 2023 LPI team. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 65 Appendix 6  Results from two LPI survey questions Map A6.2 Based on your experience, how have supply chains been affected since the year 2019 when shipping to…? Source: 2023 LPI team. 66 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 7 APPENDIX Respondent demographics Operators on the ground are best placed to assess Asia, while others were dispersed among East the vital aspects of logistics performance. The Asia and Pacific (13 percent), South Asia Logistics Performance Index (LPI) thus uses (11 percent), and Sub-Saharan Africa (18 per- a structured online survey of logistics profes- cent). The least represented regions are Latin sionals, multinational freight forwarders, and America (9 percent of respondents), the Middle the main global express operators (for example, East and North Africa (9 percent), and North DHL, FedEx, and UPS). The 2023 LPI data are America (3 percent). based on a survey conducted from September 6 to Among the respondents, 36 percent dealt November 5, 2022, answered by 652 respondents. with multimodal transport, 30 percent dealt Among the respondents, 4 percent were in with maritime transport, 17 percent dealt with low-income countries, 39 percent were in lower- road transport, and 12 percent dealt with air middle-income countries, 21 percent were in transport (figure A7.2). While these numbers upper-middle-income countries, and 35 percent are similar to those in 2018, the share of re- were in high-income countries (figure A7.1). spondents dealing with road transport is higher These values are similar to those in previous than in previous years. In 2022, 57 percent of LPI editions, except there are more respondents respondents were in freight forwarding, 12 per- from lower-middle-income countries. cent worked with freight transport, 11 percent About 38 percent of respondents identified worked with customs brokerage, and 8 percent their country of operations as Europe or Central dealt with exports or imports. Figure A7.1 Number of respondents by location and country income group By location By country income group Middle East & North America Low income North Africa 3% 4% 9% Latin America & Caribbean 9% Europe & High income Central Asia 35% 38% Lower middle South Asia income 11% 39% East Asia & Pacific Sub-Saharan Upper middle 13% Africa income 18% 21% Source: 2023 LPI team. C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 67 Appendix 7  Respondent demographics Figure A7.2 Respondents by transport mode and economic activity type By transport mode By economic activity type Express delivery Export/import (shipper) 2% 8% Rail 4% Air Customs transport brokerage 12% 11% Multimodal 36% Freight Freight Road transport forwarding 17% 12% 57% Other Maritime 14% 30% Source: 2023 LPI team. 68 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 8 APPENDIX LPI results in research and policymaking literature Since its launch in 2007, the Logistics Perfor- Figure A8.1 Use of LPI data in research mance Index (LPI) has established itself as a literature, 2007–22 global trade and transport facilitation indicator Number of journal articles and reports for policymakers, academics, logistics practitio- 1,000 ners, consultants, and traders. It is also used by several advocacy groups, such as logistics sector 800 industry associations. More than 1,000 research publications have used LPI data since 2007 (fig- 600 ure A8.1). In addition, hundreds of policymaking reports have relied on LPI data. This excludes numerous textbooks, consultancy reports, and 400 teaching materials and theses at various levels. The LPI has also been used as a compo- 200 nent in various transport and trade indicators, such as the World Economic Forum’s Enabling 0 2007 2010 2015 2020 2022 Trade Index, first published in 2008, and the EU Transport Scoreboard, launched in 2014. Source: Google Scholar. Practically all multilateral agencies, including the African Development, the Asian Develop- ment Bank, and the Inter-American Develop- In about 20 percent of citations, LPI data ment Bank, as well as the United Nations Con- are the main empirical evidence, and in about ference on Trade and Development, the United 30 percent, LPI data are used as a major refer- Nations Economic Commission for Europe, ence. In the remaining 50 percent, LPI data are and the United Nations Economic and Social used as a minor reference. A nonexhaustive list Commission for Asia and the Pacific, have ad- of literature using the LPI since 2018, based on a opted the LPI as a standard element in their literature search in ResearchGate in November trade- and transport-related publications. 2022, is below. LPI indicators are typically cited in research or policy literature that falls roughly evenly into Selected research articles using the two categories: trade economics or trade and LPI since 2018 transport facilitation and supply chain man- agement, transport, and logistics competitive- The following is a nonexhaustive list of litera- ness issues. ture using the LPI since 2018, based on a litera- The division between the two categories is ture search in ResearchGate in November 2022. not clear cut. However, they indicate that the Abdalla, S. S. A., and K. Nakagawa. 2022. “Entrepreneurial Leadership, LPI is widely used for both trade facilitation Supply Chain Innovation, and Adaptability: A Cross-national and policymaking, typically at the macro level Investigation.” Operations Research Forum 3 (1): 23. (the first category), and for more business-ori- Abdulahi, E., and L. Fan. 2020. “Literature Review of Multimodal Transportation Risk Management System. Epitome.” International ented purposes, often at the micro or supply Journal of Multidisciplinary Research 4 (11): 119–127. chain level (the second category). C O N N E C T I N G T O C O M P E T E 2 0 23  T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y 69 Appendix 8  LPI results in research and policymaking literature Acar, M. F., and A. Özer Torgalöz. 2022. “Measuring Foreign Trade- Castro Alegría, R., and E. Pastrana. 2019. “With A Little Help From Logistics Efficiency: A DEA Approach and the Malmquist Index.” New My Friends: Retos y Perspectivas De Colombia En La Alianza Del Perspectives in Operations Research and Management Science Pacífico.” Colombia En Su Ruta, Recorriendo El Camino Hacia 2050, 69–88. 245–276. Fundación Konrad Adenauer Colombia. Akbari, M., H. M. Nguyen, R. McClelland, and K. van Houdt. 2022. Çelebi, D. 2019. “The Role of Logistics Performance in Promoting Trade.” “Design, Implementation and Academic Perspectives on Authentic Maritime Economics & Logistics 21: 307–323. Assessment for Applied Business Higher Education in a Top Chasomeris, M., and S. Gumede. 2022. “Regulation, Governance, and Performing Asian Economy.” Education + Training 64 (1): 69–88. Infrastructure Pricing in South Africa’s Ports Sector.” Regulation and Akdamar, E. 2022. “The Effect of Human Development on the Logistics Finance in the Port Industry, 53–67. Efficiency of the Countries.” Mehmet Akif Ersoy Üniversitesi Iktisadi ve Cho, J., E. K. Hong, J. Yoo, and I. Cheong. 2020. “The Impact of Global Idari Bilimler Fakültesi Dergisi 9 (2): 871–896. Protectionism on Port Logistics Demand.” Sustainability 12 (4): 1444. Alim, M., and S. E. Kesen. 2020. “Smart Warehouses in Logistics 4.0.” In Coffie, I. S., and R. E. Hinson. 2022. “Market Orientation in the Public Logistics 4.0, 186–201. CRC Press. Sector: The Perspective from an Emerging Economy.” New Public Almalki, M., and M. Alkahtani. 2022. “Allocation of Regional Logistics Management in Africa, 17–45. Hubs and Assessing Their Contribution to Saudi Arabia’s Logistics Coskun, H., and M. Civelek. 2020. “Effects of The Sub-Dimensions of Performance Index Ranking.” Sustainability 14 (12): 7474. Logistics Performance Index on foreign Trade Coverage Ratio.” Journal Alnıpak, S., E. Isikli, and S. Apak. 2021. “The Propellants of The of International Trade, Logistics and Law 6 (2): 144–152. Logistics Performance Index: An Empirical Panel Investigation of The Erdebilli, B., and S. G. Aslan Özsahin. 2022. “Uncertainty Management European Region.” International Journal of Logistics Research and with An Autonomous Approach to Fuzzy Set-Covering Facility Location Applications, 1–23. Models.” Journal of Intelligent & Fuzzy Systems 43 (6): 8233–8246. Ambashi, M., S. Buban, H. Phoumin, and R. Shrestha. 2020. Erdinç, Z., and G. Aydınbas. 2021. “An Evaluation on foreign Trade Subregional Development Strategy in ASEAN after COVID-19: and Intelligent Logistics Relation.” International Journal of Current Inclusiveness and Sustainability in the Mekong Subregion. Economic Research 11 (1): 159–182. Research Institute for ASEAN and East Asia. Fahim, P. B. M., J. Rezaei, B. Montreuil, and L. Tavasszy. 2022. “Port An, H., A. Razzaq, A. Nawaz, S. M. Noman, and S. A. R. Khan. 2021. Performance Evaluation and Selection in The Physical Internet.” “Nexus Between Green Logistic Operations and Triple Bottom Line: Transport Policy 124: 83–94. Evidence from Infrastructure-Led Chinese Outward foreign Direct Ghasemi, A., E. Miandoabchi, and S. Soroushnia. 2021. “The Investment in Belt and Road Host Countries.” Environmental Science Attractiveness of Seaport-Based Transport Corridors: An Integrated and Pollution Research 28 (37): 51022–51045. Approach Based on Scenario Planning and Gravity Models.” Maritime Ariansyah, K., E. R. E. Sirait, B. A. Nugroho, and M. Suryanegara. Economics & Logistics 23 (3): 522–547. 2021. “Drivers of and Barriers to E-Commerce Adoption in Indonesia: Goel, R. K., U. Mazhar, and J. W. Saunoris. 2021. “Identifying The Individuals’ Perspectives and the Implications.” Telecommunications Corrupt Cog in The Wheel: Dimensions of Supply Chain Logistics Policy 45 (8): 102219. and Cross-Country Corruption.” Australian Economic Papers 60 (4): Atalan, A. 2020. “Logistics Performance Index of OECD Members.” 693–709. Akademik Arastırmalar ve Çalısmalar Dergisi (AKAD) 12 (23): 608–619. Goel, R., J. Saunoris, and S. Goel. 2020. “Supply Chain Reliability and Atayah, O. F., M. M. Dhiaf, K. Najaf, and G. F. Frederico. 2022. “Impact International Economic Growth: Impacts of Disruptions like COVID-19.” of COVID-19 on Financial Performance of Logistics Firms: Evidence CESifo Working Paper 8294, Center for Economic Studies, Munich, from G-20 Countries.” Journal of Global Operations and Strategic Germany. Sourcing 15 (2): 172–196. Goel, R. K., J. W. Saunoris, and S. S. Goel. 2021. “Supply Chain Awaworyi Churchill, S., K. T. Baako, K. Mintah, and Q. Zhang. 2021. Performance and Economic Growth: The Impact of COVID-19 “Transport Infrastructure and House Prices in the Long Run.” Disruptions.” Journal of Policy Modeling 43 (2): 298–316. Transport Policy 112: 1–12. Haasis, H.-D., and Hapsatou. 2022. “Digital Transformation of Maritime Azhari, B., and T. Taufik. 2021. Strategy Formulation of Smart Logistics Supply Chains Focusing on Ocean Shipping, Port Management, and Development in a National Logistics Company. The 3rd International Hinterland Connection.” Diginomics Research Perspectives, 173–184. Conference on Management of Technology, Innovation, and Project Hofstra, N., M. Vodegel, D. Moeke, M. Tooren, P. Preenen, K. Mennens, (MOTIP 03). and T. Schipper. 2021. “Learning Communities in de Logistiek: De Banomyong, R., D. B. Grant, P. Varadejsatitwong, and P. Julagasigorn. TIP-ontwikkelmethode.” Logistiek Special Edition. 2022. “Developing and Validating a National Logistics Cost in Holl, A., and I. Mariotti. 2022. “An Empirical Study of Drivers for The Thailand.” Transport Policy 124: 5–19. Adoption of Logistics Innovation.” Industry and Innovation 29 (6): Bardal, A., and M. Sigitova. 2020. “Logistics Centres in The Region: The 760–791. Russian Far East.” IOP Conference Series: Materials Science and Humphreys, M., and G. Ashley. 2022. The Container Port Performance Engineering 918 (1): 012035. Index 2021. Washington, DC: World Bank. Beysenbaev, R., and Y. Dus. 2020. “Russia’s National Logistics System: Ittmann, H. W. 2018. “Logistics Performance in South Africa.” Journal of Main Directions of Development.” Logforum 16 (2): 209–218. Transport and Supply Chain Management, 12. Beysenbaev, R., and Y. Dus. 2020. “Proposals for Improving the Logistics Jahanshahee Nezhad, F., M. Taghizadeh-Yazdi, J. Heidary Dahooie, Performance Index.” The Asian Journal of Shipping and Logistics 36 A. Zamani Babgohari, and S. M. Sajadi. 2022. “Designing a New (1): 34–42. Mathematical Model for Optimising a Multi-Product RFID-Based Bilgin, C. 2022. “The Concept of Logistics Performance in International Closed-Loop Food Supply Chain with a Green Entrepreneurial Trade Framework.” Research Anthology on Macroeconomics and the Orientation.” British Food Journal 124 (7): 2114–2148. Achievement of Global Stability, 345–369. IGI Global. Jayathilaka, R., C. Jayawardhana, N. Embogama, S. Jayasooriya, N. Bilgin, T., and K.S. 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Washington, DC: World Bank. 74 C O N N E C T I N G T O C O M P E T E 2 0 23 T R A D E L O G I S T I C S I N A N U N C E R TA I N G L O B A L E C O N O M Y What is the Logistics Performance Index? The LPI is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. The LPI is based on two components: First, a worldwide survey of international logistics operators on the ground (global freight forwarders and express carriers), providing feedback on the logistics “friendliness” of the countries with which they trade. The International LPI 2023 allows for comparisons across 139 countries. Second, this edition introduces indicators derived from global tracking datasets. They measure speed and delays for container, postal and air freight activities. They complement the main indicator but do not enter its score. Hence logistics performance is measured from two different perspectives: one based on the perceptions of international logistics professionals assessing their partner countries, the other one measuring the actual speed of global trade by using supply chain tracking information. This is the seventh edition of Connecting to Compete, a report summarizing the findings from the new dataset for the Logistics Performance Index (LPI) and its component indicators. The 2023 LPI encapsulates the firsthand knowledge of movers of international trade and evidence from supply chain tracking data. This information is relevant for policymakers and the private sector seeking to identify reform priorities for trade and logistics infrastructure. Findings include: • Notwithstanding the pandemic-induced disruptions to shipping and the global supply chain crisis, average overall scores in the LPI 2023 were roughly the same as in the last survey in 2018. • The new indicators point to widespread differences in delays and supply chain reliability across the World. Several countries experience much larger delays than advanced and emerging economies. Binding constraints for low performances may be traced to infrastructure, productivity, or clearance procedures. • The survey confirms growing demand for green logistics options, which lessen the carbon footprint of supply chains and keep trade moving.