# Table of Contents - [Data Downloads — The Atlas of Economic Complexity](#data-downloads-the-atlas-of-economic-complexity) - [The Atlas of Economic Complexity](#the-atlas-of-economic-complexity) - [Glossary — The Atlas of Economic Complexity](#glossary-the-atlas-of-economic-complexity) - [FAQ — The Atlas of Economic Complexity](#faq-the-atlas-of-economic-complexity) - [About — The Atlas of Economic Complexity](#about-the-atlas-of-economic-complexity) - [Trade Data Methodology — The Atlas of Economic Complexity](#trade-data-methodology-the-atlas-of-economic-complexity) --- # Data Downloads — The Atlas of Economic Complexity [![The Atlas of Economic Complexity](https://atlas.hks.harvard.edu/assets/GL_logo_white-HDmHmKd7.png)](https://atlas.hks.harvard.edu/) * [Home](https://atlas.hks.harvard.edu/) * [Explore](https://atlas.hks.harvard.edu/explore/treemap) * [Countries](https://atlas.hks.harvard.edu/countries) * [Data](https://atlas.hks.harvard.edu/data-downloads) * Learn * [About](https://atlas.hks.harvard.edu/about) Atlas Data Downloads ==================== The Atlas provides comprehensive international trade data covering over 6,000 products across 250 countries and territories. Figures shown throughout the Atlas, in visualisations, country profiles, and rankings, may not exactly match those in our bulk data downloads. Download files are refreshed at set intervals throughout the year to incorporate revised country reporting and corrections, which can produce a small gap between the two. The effect on overall trade totals is minimal in both. Below, explore and download curated datasets from the Atlas of Economic Complexity. Use the column filters to discover specific datasets. | Name | Data Type | Classification | Product Level | Years | Complexity Data | | | --- | --- | --- | --- | --- | --- | --- | | Complexity Rankings & Growth Projections | Rankings | HS92HS12SITC | N/A | 1995-2024 | Yes | Download | | Country Trade by Product | Unilateral Trade | HS92 | 1digit | 1995-2024 | Yes | Download | | Country Trade by Product | Unilateral Trade | HS92 | 2digit | 1995-2024 | Yes | Download | | Country Trade by Product | Unilateral Trade | HS92 | 4digit | 1995-2024 | Yes | Download | | Country Trade by Product | Unilateral Trade | HS92 | 6digit | 1995-2024 | No | Download | | Total Trade by Country | Unilateral Trade | HS92 | N/A | 1995-2024 | Yes | Download | | Total Trade by Product | Product Trade | HS92 | 1digit | 1995-2024 | Yes | Download | Total: **71** API Access ---------- Atlas users can query data directly through our GraphQL API. Documentation on access procedures and limitations is available on this [GitHub page](https://github.com/harvard-growth-lab/api-docs/blob/main/atlas.md) . Growth Lab Trade Data Methodology --------------------------------- International trade flows are recorded twice: once by exporters and once by importers. In practice, these records often differ, are incomplete, or are reported using different product classifications. To address these issues, the Growth Lab developed a standardized, data-driven process to produce consistent and comparable bilateral trade estimates. Our methodology combines two core steps: reconciling partner reports and harmonizing product classifications over time. ### Reconciling Exporter and Importer Reports We first adjust reported values to ensure comparability between exporter-reported (FOB) and importer-reported (CIF) data. We then assess the reliability of each country's reporting based on its consistency across trading partners. Using these reliability scores, we combine exporter and importer reports into a single estimated trade value that places greater weight on more reliable sources. ### Harmonizing Product Classifications Countries adopt new trade classification systems at different times, and product codes are regularly revised, split, or merged. To ensure comparability across years, we use data-driven conversion weights that translate trade values between classification vintages while preserving detailed product information. This approach avoids the loss of products and discontinuities that arise from simple one-to-one mappings. By integrating reliability-weighted mirroring with weighted product conversion, we generate bilateral, product-level trade datasets that are consistent across countries and over time. These datasets form the core data underlying the Atlas and improve coverage, reduce reporting discrepancies, and support long-run analysis of global trade patterns. A full description of this methodology is available in our peer-reviewed paper: _Bustos et al. (2026), Tackling Discrepancies in Trade Data: The Harvard Growth Lab International Trade Datasets, Scientific Data._ A visual description of the Growth Lab's trade data methodology is available on our [companion website](https://atlas.hks.harvard.edu/trade-data-methodology) . Data Sources ------------ The Atlas uses data from the following international organizations as inputs into its comprehensive methodology: * **Goods Trade:** United Nations Statistical Division (UN Comtrade) * **Services Trade:** International Monetary Fund, Direction of Trade Statistics (DOTS) * **Economic Indicators:** International Monetary Fund, World Economic Outlook (WEO) * **Price Adjustments:** Federal Reserve Economic Data (FRED), Producer Price Index for Industrial Commodities Atlas trade values are reported in U.S. dollars (USD). Constant-dollar values are adjusted using the FRED Producer Price Index for Industrial Commodities, with the base year set to the most recent Atlas data year. This allows users to interpret values in terms of current purchasing power. For historical analysis (e.g., growth rates and long-run trends), constant-dollar values are recommended. Data Classifications -------------------- ### Product Classifications The Atlas provides trade data using two complementary classification systems: **Harmonized System (HS)** Covers approximately 5,000–5,600 products using 6-digit codes (exact counts vary by revision). Best suited for analyzing contemporary products and industries. HS classifications are available in the Atlas in three revisions: * HS 1992: data available from 1995 onward * HS 2012: data available from 2012 onward * HS 2022: data available from 2022 onward **Standard International Trade Classification (SITC)** Provides a harmonized, long-run dataset spanning multiple classification systems from 1962 to the present. Covers approximately 700 products using up to 4-digit codes. Best suited for long-term historical analysis and trend comparison. SITC data in the Atlas is based upon SITC Rev. 2. ### Services Trade Services exports and imports are reported unilaterally, with partner countries grouped as "Services Partners." Coverage begins in 1980 and varies by country, with data available for approximately 50 to 75 percent of Atlas countries. Country Coverage ---------------- While Atlas Explore includes data for all countries and territories covered by UN Comtrade, Atlas Country Profiles and Rankings are limited to countries that meet minimum coverage and quality standards: * Population of at least 1 million * Average annual trade volume of at least $1 billion * Verified GDP and export data availability * Consistent and reliable trade reporting history The Atlas aims to reflects countries as they exist today, following UN recognition. Map boundaries come from United Nations geospatial data, chosen for its stability over time and its alignment with our trade data sources. When country names, borders, or political status change, the Atlas may take time to reflect those updates as we revise the underlying data and visualisations. Data Update Cycle ----------------- Atlas data updates follow international trade reporting cycles and require substantial processing to ensure quality and consistency. ### Annual Updates Approximately 95 percent of Atlas data is updated once per year, typically between April and June. This reflects country reporting timelines to UN Comtrade, which generally require 12 to 18 months to reach sufficient coverage. For example, most 2024 trade data becomes available in the Atlas between April and June 2026. Annual releases may incorporate late or corrected country reporting which can lead to small revisions in historical data. This process improves long-run accuracy and consistency. ### Interim Updates Throughout the year, we also release interim updates that incorporate newly submitted country data, reflect improvements in data processing methods, and address minor corrections when needed. For real-time information on country-level data availability, see the [UN Comtrade Data Availability Dashboard](https://comtradeplus.un.org/Visualization/DADashboard) . Usage and Citation ------------------ The Atlas of Economic Complexity is a freely available public resource. Its data and tools are widely used in research, policy analysis, and applied projects. We encourage reuse and adaptation, provided that users cite the Atlas and underlying data sources appropriately. ### Citation Guidelines When incorporating Atlas content in your work, please use these citation formats: **For the Atlas Platform:** Growth Lab at Harvard University. "The Atlas of Economic Complexity." Web application. Harvard Kennedy School, 2024. https://atlas.hks.harvard.edu **For Downloaded Datasets:** Each downloadable data file includes specific citation requirements. Example: The Growth Lab at Harvard University. (2026). "Growth Projections and Complexity Rankings" \[Data set\]. [https://doi.org/10.7910/DVN/XTAQMC](https://doi.org/10.7910/DVN/XTAQMC) **For the Original Atlas Book:** Hausmann, R., Hidalgo, C., Bustos, S., Coscia, M., Chung, S., Jimenez, J., Simoes, A., Yildirim, M. (2013). The Atlas of Economic Complexity. Cambridge, MA: MIT Press. Support ------- We provide several resources to help users understand and work with Atlas data: * [Glossary](https://atlas.hks.harvard.edu/glossary) : Definitions of key terms, indicators, and classifications * [FAQ](https://atlas.hks.harvard.edu/faq) : Answers to common questions about data sources, methods, and interpretation * [GitHub Repositories](https://github.com/harvard-growth-lab) : Technical documentation and data access tools For technical questions, research use cases, or data access requests, contact us at [growthlabtools@hks.harvard.edu](mailto:growthlabtools@hks.harvard.edu) . 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to understand the economic dynamics and new growth opportunities for every country worldwide. ![](data:image/svg+xml,%3csvg%20width='39'%20height='30'%20viewBox='0%200%2039%2030'%20fill='none'%20xmlns='http://www.w3.org/2000/svg'%3e%3cpath%20d='M33.8485%2017.8179C33.6645%2017.9676%2033.6268%2018.2518%2033.7648%2018.4528L35.5827%2021.0832C35.6482%2021.183%2035.7472%2021.248%2035.8587%2021.2676C35.9702%2021.2858%2036.0832%2021.2555%2036.1738%2021.1815C36.263%2021.1089%2036.3229%2021%2036.3369%2020.8791C36.3522%2020.7582%2036.3215%2020.6357%2036.2518%2020.539L34.434%2017.9086C34.3685%2017.8119%2034.2695%2017.7484%2034.1594%2017.7317C34.0492%2017.7151%2033.9377%2017.7454%2033.8485%2017.8179Z'%20fill='%23243744'/%3e%3cpath%20d='M33.8491%207.8857C34.0349%208.03721%2034.2975%207.9963%2034.4358%207.79479L36.2574%205.15851C36.3957%204.95852%2036.3579%204.67368%2036.1735%204.52218C35.9878%204.37219%2035.7251%204.4131%2035.5869%204.61309L33.7653%207.24937C33.6996%207.34634%2033.6703%207.46754%2033.6856%207.58724C33.7024%207.70541%2033.7611%207.81298%2033.8491%207.8857Z'%20fill='%23243744'/%3e%3cpath%20d='M38.5167%2012.4091H35.0518C34.7843%2012.4091%2034.5684%2012.607%2034.5684%2012.8522C34.5684%2013.0975%2034.7843%2013.2954%2035.0518%2013.2954H38.5167C38.7842%2013.2954%2039.0002%2013.0975%2039.0002%2012.8522C39.0002%2012.607%2038.7842%2012.4091%2038.5167%2012.4091Z'%20fill='%23243744'/%3e%3cpath%20d='M3.02683%2016.1985H3.19355C3.32181%2017.2092%204.18431%2017.9716%205.21032%2017.9796V27.7809C5.21032%2028.5704%205.84197%2029.25%206.63872%2029.25H10.4574C11.2526%2029.25%2011.8634%2028.5704%2011.8634%2027.7809V17.9828L26.0477%2023.7797C26.1118%2024.8811%2027.0481%2025.7326%2028.159%2025.696C29.27%2025.6594%2030.1485%2024.7506%2030.1389%2023.6476V16.4536V16.4552C32.1092%2016.4058%2033.6818%2014.8062%2033.6818%2012.8485C33.6818%2010.8924%2032.1092%209.29282%2030.1389%209.24348V2.04945C30.1469%200.946463%2029.2684%200.0376336%2028.1574%200.00104411C27.0465%20-0.0339718%2026.1086%200.815955%2026.0445%201.91735L11.9353%207.68366H5.25349C4.23068%207.69639%203.36338%208.4333%203.19347%209.43446H3.02675C1.34664%209.43446%202.39396e-05%2010.8287%202.39396e-05%2012.4967V13.2018C-0.00318254%2013.9992%200.315841%2014.7632%200.883334%2015.3266C1.45244%2015.8885%202.22363%2016.2033%203.02683%2016.1985ZM30.1389%2010.1979C31.5769%2010.2473%2032.7167%2011.4187%2032.7167%2012.848C32.7167%2014.2773%2031.5769%2015.4487%2030.1389%2015.498V10.1979ZM10.9012%2025.5888H9.31732C9.0528%2025.5888%208.81712%2025.4025%208.81712%2025.1383V20.3348C8.82674%2020.069%209.04797%2019.8573%209.31732%2019.8589H10.9012V25.5888ZM10.9012%2018.9041H9.31732C8.51736%2018.9025%207.86486%2019.5407%207.85526%2020.3349V25.1384C7.85526%2025.9279%208.52056%2026.5438%209.31732%2026.5438H10.9012V27.7805C10.9012%2028.0431%2010.7217%2028.2946%2010.4572%2028.2946H6.6385C6.37238%2028.2946%206.17199%2028.0431%206.17199%2027.7805V17.949H10.9011L10.9012%2018.9041ZM12.4242%208.5123L26.0508%202.95135V6.48953C26.0508%206.75374%2026.2656%206.96702%2026.5318%206.96702C26.7979%206.96702%2027.0127%206.75375%2027.0127%206.48953V2.05845C27.0063%201.46955%2027.4728%200.980932%2028.0676%200.955466C28.3625%200.949099%2028.6463%201.0621%2028.8563%201.26901C29.0647%201.47434%2029.1818%201.75764%2029.1769%202.04893V23.6473C29.1818%2023.9402%2029.0647%2024.2219%2028.8563%2024.4288C28.6463%2024.6342%2028.3626%2024.7472%2028.0676%2024.7408C27.4728%2024.7169%2027.0063%2024.2283%2027.0127%2023.6378V9.83199C27.0127%209.56778%2026.7979%209.35449%2026.5318%209.35449C26.2656%209.35449%2026.0508%209.56777%2026.0508%209.83199V22.7447L12.4242%2017.1837V8.5123ZM4.16812%209.78401C4.16812%209.16965%204.63462%208.63804%205.25343%208.63804H11.4624V16.9941H5.25343C4.96326%2016.9989%204.68272%2016.8875%204.47752%2016.6822C4.27232%2016.4785%204.1617%2016.1999%204.16811%2015.9119L4.16812%209.78401ZM0.961844%2012.4961C0.961844%2011.3549%201.87725%2010.3888%203.02666%2010.3888H3.20621V15.2432H3.02666C2.47841%2015.248%201.95096%2015.0347%201.563%2014.6511C1.17504%2014.2676%200.958602%2013.7439%200.961844%2013.2012V12.4961Z'%20fill='%23243744'/%3e%3c/svg%3e)We've upgraded our trade data. Discover the Growth Lab's new cleaning methodology [Learn More](https://atlas.hks.harvard.edu/trade-data-methodology) [Trade Dynamics & Growth Opportunities\ -------------------------------------](https://atlas.hks.harvard.edu/explore/treemap) ![Trade Dynamics & Growth Opportunities](https://atlas.hks.harvard.edu/assets/explore-CPIgVsYs.webp) Build custom data visualizations to reveal 50+ years of global trade flows and opportunities for growth. [Start Exploring](https://atlas.hks.harvard.edu/explore/treemap) [Country Profiles\ ----------------](https://atlas.hks.harvard.edu/countries) ![Country Profiles](https://atlas.hks.harvard.edu/assets/country_profiles-FClv-a6a.webp)[![](https://atlas.hks.harvard.edu/assets/iib-badge-2019-C7A0DgfG.png)](https://www.informationisbeautifulawards.com/) An interactive journey through a country's economic structure and growth patterns to uncover strategies for greater prosperity. [Start Exploring](https://atlas.hks.harvard.edu/countries) [Annual Growth Projections\ -------------------------](https://atlas.hks.harvard.edu/press-release) ![Annual Growth Projections](https://atlas.hks.harvard.edu/assets/annual_growth-D9zS2Uqr.webp) Discover the countries rising and falling in Harvard Growth Lab's annual country growth projections. [Start Exploring](https://atlas.hks.harvard.edu/press-release) [Complexity Rankings\ -------------------](https://atlas.hks.harvard.edu/rankings) ![Complexity Rankings](https://atlas.hks.harvard.edu/assets/ranking-Bl_uvjHe.webp) Explore Harvard Growth Lab's annual ranking of country and product complexity. [Start Exploring](https://atlas.hks.harvard.edu/rankings) ![](https://atlas.hks.harvard.edu/assets/VizHubIcon-DwlpXWcx.svg) [Go to Viz Hub](https://growthlab.app/) The Atlas of Economic Complexity is part of Harvard's Growth Lab Viz Hub, a portfolio of award-winning, interactive visualizations powered by the Growth Lab's research and insights. Check out the Viz Hub to learn more about concepts found in the Atlas along with our other free, online tools and software packages. ![](https://atlas.hks.harvard.edu/assets/metroverse-logo-C5LTRwrF.png) [Go to Metroverse](https://metroverse.hks.harvard.edu/) Metroverse is the Growth Lab's urban economy navigator covering over 1000 cities across the world. By combining cutting-edge research, high-resolution datasets and stunning data visualizations, Metroverse makes visible what a city is good at today to help understand what it can become tomorrow. In the News [![](https://atlas.hks.harvard.edu/assets/new-york-times-iZT0871m.svg)](https://www.nytimes.com/2026/04/13/business/economy/iran-imports-exports-china.html) [How Iran, Suffering Under Sanctions, Diversified Its Economy](https://www.nytimes.com/2026/04/13/business/economy/iran-imports-exports-china.html) [![](https://atlas.hks.harvard.edu/assets/financialtimes_white-1n8dghA2.png)](https://www.ft.com/content/7d51a630-a3de-4cc7-9f5f-0f3e7f0d305a?syn-25a6b1a6=1) [China Shock 2.0: The Flood of High-Tech Goods That Will Change the World](https://www.ft.com/content/7d51a630-a3de-4cc7-9f5f-0f3e7f0d305a?syn-25a6b1a6=1) [![](https://atlas.hks.harvard.edu/assets/Bloomberg_white-COoall9C.png)](https://www.bloomberg.com/opinion/articles/2019-09-18/repo-market-spike-carries-echoes-of-2007-2008-crisis-k0oqoebp) [Markets Are Starting to Play a Haunting 2007 Tune](https://www.bloomberg.com/opinion/articles/2019-09-18/repo-market-spike-carries-echoes-of-2007-2008-crisis-k0oqoebp) [![](https://atlas.hks.harvard.edu/assets/npr_white-BzWFyLyf.png)](https://www.npr.org/sections/money/2018/02/28/589609380/episode-827-three-indicators) [Planet Money: The Three Indicators](https://www.npr.org/sections/money/2018/02/28/589609380/episode-827-three-indicators) "This is like adding the Periodic Table to chemistry. It organizes knowledge in a way that helps everyone: from student to journalist to policy maker to investors to economic experts." ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Lant Pritchett, RISE Research Director, University of Oxford "If you like data visualizations and are interested in analyzing trade flows and the sectoral composition of an economy, you can't beat this website from Harvard." ------------------------------------------------------------------------------------------------------------------------------------------------------------------- The Guardian "...fascinating research done by the Center for International Development at Harvard University, which has a successful record of identifying which countries are positioned to grow." -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Financial Times Atlas in Action =============== The Atlas has reached more than 950,000 people in 228 countries. See the real-world impact of The Atlas as it's used in global trainings, strategic development projects, executive education, and more. **Click on the highlighted countries on the map to learn more about these projects.** > 📢 We deployed our first “learning resource” in the Atlas of Economic Complexity: the Product Space tutorial. > > This feature - devoid of scientific jargon - is designed to give new users a quick, accessible introduction to the Product Space before diving in. > > Our goal is to… [pic.twitter.com/41OhhXodwR](https://t.co/41OhhXodwR) > > — Harvard's Growth Lab (@HarvardGrwthLab) [February 20, 2025](https://twitter.com/HarvardGrwthLab/status/1892582360098500683?ref_src=twsrc%5Etfw) ![Atlas of Economic Complexity Book Cover](https://atlas.hks.harvard.edu/assets/atlas_bookjacket-ChyBi0mx.svg) [Download the 2013 Edition](https://growthlab.hks.harvard.edu/publication/the-atlas-of-economic-complexity-mapping-paths-to-prosperity-2/) [![The Growth 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"GitHub")GitHub](https://github.com/harvard-growth-lab "GitHub") Copyright © 2026 The President and Fellows of Harvard College | [Privacy](https://gdpr.harvard.edu/eeaprivacydisclosures) | [Accessibility](http://accessibility.harvard.edu/) | [Digital Accessibility](https://accessibility.huit.harvard.edu/digital-accessibility-policy) | [Report Copyright](http://www.harvard.edu/reporting-copyright-infringements) --- # Glossary — The Atlas of Economic Complexity [![The Atlas of Economic Complexity](https://atlas.hks.harvard.edu/assets/GL_logo_white-HDmHmKd7.png)](https://atlas.hks.harvard.edu/) * [Home](https://atlas.hks.harvard.edu/) * [Explore](https://atlas.hks.harvard.edu/explore/treemap) * [Countries](https://atlas.hks.harvard.edu/countries) * [Data](https://atlas.hks.harvard.edu/data-downloads) * Learn * [About](https://atlas.hks.harvard.edu/about) Glossary ======== Distance + A measure of a location's ability to enter a specific product. A product's distance (from 0 to 1) looks to capture the extent of a location's existing capabilities to make the product as measured by how closely related a product is to its current exports. A 'nearby' product of a shorter distance requires related capabilities to those that are existing, with greater likelihood of success. Every two products have a notion of distance between them, where products that require similar know-how and capabilities are 'closer' together (i.e. shorter distance, closer to 0), while two products that require completely different capabilities are 'farther' apart (i.e. longer distance, closer to 1). Distance can be thought of as a measure of risk of entering a product, where larger distances express little relatedness to existing know-how and the need to coordinate adding many missing capabilities and inputs in order to enter production, increasing risk. Distance reflects that not every new product has an equal likelihood of success in a location, but is dependent on its similarity to the location's existing capabilities, as reflected in the Product Space. _Technical breakout_: Every two products have a globally defined _proximity_ between them as measured by the probability of co-export, that if a country exports product A, what is the probability they also export product B. The product proximities are fixed globally and measured using 128 countries' export data over 50 years. The _distance_ of a product is then the sum of the proximities connecting that product to all the products that the location is not currently exporting. Formally, for product and country , the distance is: Economic Complexity Index (ECI) + The index is a measure of the knowledge in a society as expressed in the products it makes. The economic complexity of a country is calculated by combining information on the diversity of exports a country produces and their ubiquity, or the number of the countries able to produce them (and those countries' complexity). Countries able to sustain a diverse range of productive know-how, especially sophisticated, unique know-how, are found to be able to produce a wide diversity of goods, including complex products that few other countries can make. To calculate the Economic Complexity Index, we use the information on how diversified and complex a country's export basket is. Countries that are home to a great diversity of productive know-how, particularly complex specialized know-how, are able to produce a great diversity of sophisticated products. The complexity of a country's exports is found to highly predict current income levels, or where complexity exceeds expectations for a country's income level, the country is predicted to experience more rapid growth in the future. ECI therefore provides a useful measure of economic development. _[Technical breakout](https://www.pnas.org/doi/full/10.1073/pnas.0900943106) _: We begin by defining how strongly each country is present in each product using the following index: Where RCA is the revealed comparative advantage (also called Balassa index) computed as: This index is bounded between zero and one and is increasing in the intensity of exports as measured by RCA. Economic complexity is calculated from equations for diversity and ubiquity to express the recursion: where we define Hence, in a vector notation, if to be the vector whose th element is then: where is the matrix whose th element is If we take to infinity, this equation leads to the distribution which remains fixed up to a scalar factor: Therefore, is an eigenvector of . We define Economic Complexity Index as the eigenvector corresponding to the second largest eigenvalue of the matrix. Economic Complexity Growth Projection + A prediction of how much a country will grow based on its current level of Economic Complexity, its Complexity Outlook or connectedness to new complex products in the Product Space, as compared to its current income level in GDP per capita and expected natural resource exports. Economic complexity alone helps explain the lion's share of variance in current income levels. But the value of economic complexity is in its predictive power on future growth, where a simple measure of current complexity and connectedness to new complex products, in relation to current income levels and expected natural resource exports, holds greater accuracy in predicting future growth than any other single economic indicator. To calculate Economic Complexity Growth Projections, we consider four factors as explanatory variables: the Economic Complexity Index; the Complexity Outlook Index; the current level of income; and the expected growth in the value of natural resource exports per capita. In effect, the growth projections show countries grow by expanding the know-how they have that allows them to produce more, and more complex products, depending on the connectedness of know-how and how many other products rely on similar capabilities, as well as the initial economic complexity the country held. Economic Complexity Outlook Index (COI) + A measure of how many complex products are near a country's current set of productive capabilities. The COI captures the ease of diversification for a country, where a high COI reflects an abundance of nearby complex products that rely on similar capabilities or know-how as that present in current production. Complexity outlook captures the connectedness of an economy's existing capabilities to drive easy (or hard) diversification into related complex production, using the Product Space. A low complexity outlook reflects that a country has few products that are a short distance away, so will find it difficult to acquire new know-how and increase their economic complexity. _Technical breakout_: To calculate COI we first need to calculate **distance** of every product to existing production (from 0 to 1). We then sum the 'closeness,' i.e. 1 minus the distance to the products that the country is not currently making, weighted by the level of complexity of these products. Formally, where PCI is the Product Complexity Index of product . The term ensures we only count the products that a country is not currently producing. Intra-region trade + Intra-region trade is the export or import trade inside a region and includes trade between all members of the region. Know-how + The tacit ability to produce a product. Also known as productive capability, know-how refers to the productive knowledge that goes into making products. Opportunity Outlook Gain + Measures how much a location could benefit in opening future diversification opportunities by developing a particular product. Opportunity outlook gain quantifies how a new product can open up links to more, and more complex, products. Opportunity outlook gain classifies the strategic value of a product based on the new paths to diversification in more complex sectors that it opens up. Opportunity outlook gain accounts for the complexity of the products not being produced in a location and the distance or how close to existing capabilities that new product is. _Technical breakout_: Opportunity outlook gain is defined as Where is the Product Complexity Index of product . The term counts only the products that the country is not currently producing. Higher opportunity outlook gain implies that a product is in the vicinity of more products and/or of products that are more complex. Product Complexity Index (PCI) + Ranks the diversity and sophistication of the productive know-how required to produce a product. PCI is calculated based on how many other countries can produce the product and the economic complexity of those countries. In effect, PCI captures the amount and sophistication of know-how required to produce a product. The most complex products (that only a few, highly complex countries can produce) include sophisticated machinery, electronics and chemicals, as compared to the least complex products (that nearly all countries including the least complex can produce) including raw materials and simple agricultural products. Specialized machinery is said to be complex as it requires a range of know-how in manufacturing, including the coordination of a range of highly skilled individuals' know-how. _[Technical breakout](http://www.pnas.org/content/106/26/10570.full) _: PCI is determined by calculating the average diversity of countries that make a specific product, and the average ubiquity of the other products that these countries make. Formally, we can define: Product Space + A visualization that depicts the connectedness between products based on the similarities of the know-how required to produce them. The product space visualizes the paths that countries can take to diversify. Products are linked by their **proximity** to each other, based on the probability of co-export of both of the two products. [The product space](http://science.sciencemag.org/content/317/5837/482) details the connectedness of nearly 900 products, in color-coded sectors, based on real world data on the experience of countries' diversification over the past 50 years. We are able to map a country's location in the product space from its export basket to understand what they are able to make, what products are nearby (at a short **distance**) that depend on similar know-how to that which currently exists, and to define paths to industrial diversification. By using real export data over time, the shape of the product space teaches us how diversification works in practice: countries move from things they know how to do, to things that are nearby or related, or what they call the adjacent possible. The irregularity of the space means that diversification occurs preferentially, where countries in the dense middle of the product space have many nearby opportunities for diversification, as compared to countries at the periphery. Products at the periphery require know-how that is less readily redeployed into many new industries, in cultivating coffee or extracting oil from the ground, while adding know-how to produce men's shirts may open opportunities in several other textiles (women's pants), but shows little relatedness to heavy machinery or chemical products, as fewer countries produce men's shirts and car parts. The product space allows us to predict the evolution of a country's industry, along with recommendations of those products that offer: greater economic complexity (higher wage levels), shorter distance (more existing know-how, reducing risk), and high opportunity outlook gain (opening more adjacent products for continued diversification opportunities). Revealed Comparative Advantage (RCA) + A measure of whether a country is an exporter of a product, based on the relative advantage or disadvantage a country has in the export of a certain good. We use Balassa's definition, which says that a country is an effective exporter of a product if it exports more than its "fair share," or a share that is at least equal to the share of total world trade that the product represents (RCA > 1). For example: In 2010, soybeans represented 0.35% of world trade with exports of $42 billion. Of this total, Brazil exported nearly $11 billion soybeans. Since Brazil's total exports for that year were $140 billion, soybeans accounted for 7.8% of Brazil's exports. By dividing 7.8% / 0.35%, we find Brazil has an RCA of 22 in soybeans, meaning Brazil exports 22 times its "fair share" of soybean exports so we can say that Brazil has a high revealed comparative advantage in soybeans. _Technical breakout_: Formally, if represents the exports of product by country , we can express the RCA that country has in product as We use RCA to determine the intensity of products each country is competitive in. Based on this measure, we construct a country-product matrix using the following index: Entries in this matrix range between zero and one and increase with the value of RCA. Beginning in 2026, this calculation replaces the previous binary approach, which assigned a value of one, when RCA was at least one, and zero otherwise. The updated method provides a more nuanced measure of specialization by reflecting degrees of competitiveness rather than a simple present-or-absent classification. This change affects how we measure both how diversified countries are and how common each product is across exporters. By capturing partial participation, the approach recognizes emerging industries that may have been too small to count under the earlier threshold rule. As a result, the updated calculations now provide a more complete and accurate picture of countries' producabilities and overall economic complexity. Diversity + A measure of how many different types of products a country is able to make. The production of a good requires a specific set of know-how; therefore, a country's total diversity is another way of expressing the amount of collective know-how held within that country. _Technical breakout_: Imagine a matrix, , in which rows represent different countries and columns represent different products. An element of the matrix is equal to 1 if country produces product (with RCA greater than 1), and 0 otherwise. We can measure diversity (and ubiquity) simply by summing over the rows (or columns) of that matrix. Formally, Personbyte + Describes the amount of **know-how** held by one person. Most products today require more productive knowledge to produce than can be mastered by a single individual. To make those products, then, requires that individuals with different know-how interact with one other. Products that require 100 personbytes cannot be made by a micro-entrepreneur working alone, nor a small village that only has a diversity of 50 personbytes. Instead, this product has to be made by an organization with at least 100 individuals (each with a different personbyte), or by a network of organizations that can aggregate these 100 personbytes of knowledge. Proximity + Measures the probability that a country exports product A given that it exports product B, or vice versa. Given that a country makes one product, proximity captures the ease of obtaining the know-how needed to move into another product. Proximity formalizes the intuitive idea that the ability of a country to produce a product can be revealed by looking at which other products it can produce. _Technical breakout_: Our measure of proximity is based on the minimum conditional probability that a country that exports product P will also export product R. Since conditional probabilities are not symmetric, we take the minimum probability of product P, given product R, and vice versa. For example, suppose that 17 countries export wine, 24 export grapes and 11 export both, all with RCA >1. Then, the proximity between the wine and the grapes is 11/24 = 0.46. Note, we use 24 instead of 17 to reduce the likelihood the relationship is false. Ubiquity + Ubiquity measures the number of countries that are able to make a product. _Technical breakout_: Considering the matrix — as described for **diversity** and **RCA** — in which rows represent different countries and columns represent different products, we can measure ubiquity simply by summing over the column of that matrix. 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"GitHub")GitHub](https://github.com/harvard-growth-lab "GitHub") Copyright © 2026 The President and Fellows of Harvard College | [Privacy](https://gdpr.harvard.edu/eeaprivacydisclosures) | [Accessibility](http://accessibility.harvard.edu/) | [Digital Accessibility](https://accessibility.huit.harvard.edu/digital-accessibility-policy) | [Report Copyright](http://www.harvard.edu/reporting-copyright-infringements) --- # FAQ — The Atlas of Economic Complexity [![The Atlas of Economic Complexity](https://atlas.hks.harvard.edu/assets/GL_logo_white-HDmHmKd7.png)](https://atlas.hks.harvard.edu/) * [Home](https://atlas.hks.harvard.edu/) * [Explore](https://atlas.hks.harvard.edu/explore/treemap) * [Countries](https://atlas.hks.harvard.edu/countries) * [Data](https://atlas.hks.harvard.edu/data-downloads) * Learn * [About](https://atlas.hks.harvard.edu/about) Frequently Asked Questions ========================== What is The Atlas of Economic Complexity? + The Atlas is a research and data visualization tool that allows people to learn more about the economic structure of their country, including the growth opportunities that exist in the latent productive capabilities a country has. The Atlas puts the capabilities and know-how of a country at the heart of its growth prospects, where the diversity and complexity of existing capabilities heavily influence how growth happens. What are the Atlas data sources? + Atlas data is compiled from the following international sources: * **Goods Trade (raw data):** United Nations Statistical Division (UN Comtrade) * **Services Trade (raw data):** International Monetary Fund, Direction of Trade Statistics (DOTS) * **Economic Indicators:** International Monetary Fund * **Price Adjustments:** Federal Reserve Economic Data (FRED), Producer Price Index for Industrial Commodities Atlas trade values are reported in U.S. dollars (USD). Constant-dollar values are adjusted using the FRED Producer Price Index for Industrial Commodities, with the base year set to the most recent Atlas data year. This allows users to interpret values in terms of current purchasing power. For historical analysis (e.g., growth rates and long-run trends), constant-dollar values are recommended. Why doesn't Atlas data match UN Comtrade data? + International trade flows are recorded twice: once by exporters and once by importers. In practice, these records often differ, are incomplete, or are reported using different product classifications. To address these issues, the Growth Lab developed a standardized, data-driven process to produce consistent and comparable bilateral trade estimates. Our methodology combines two core steps: reconciling partner reports and harmonizing product classifications over time. **Reconciling Exporter and Importer Reports** We first adjust reported values to ensure comparability between exporter-reported (FOB) and importer-reported (CIF) data. We then assess the reliability of each country's reporting based on its consistency across trading partners. Using these reliability scores, we combine exporter and importer reports into a single estimated trade value that places greater weight on more reliable sources. **Harmonizing Product Classifications** Countries adopt new trade classification systems at different times, and product codes are regularly revised, split, or merged. To ensure comparability across years, we use data-driven conversion weights that translate trade values between classification vintages while preserving detailed product information. This approach avoids the loss of products and discontinuities that arise from simple one-to-one mappings. By integrating reliability-weighted mirroring with weighted product conversion, we generate bilateral, product-level trade datasets that are consistent across countries and over time. These datasets form the core data underlying the Atlas and improve coverage, reduce reporting discrepancies, and support long-run analysis of global trade patterns. A full description of this methodology is available in our peer-reviewed paper: [_Bustos et al. (2026), Tackling Discrepancies in Trade Data: The Harvard Growth Lab International Trade Datasets, Scientific Data._](https://www.nature.com/articles/s41597-025-06488-2) A visual description of the Growth Lab's trade data methodology is available on our [companion website](https://atlas.hks.harvard.edu/trade-data-methodology) . What is the difference between the HS and SITC datasets? + The difference is a tradeoff between time and detail. The SITC data goes back further in time, from 1962 onward, but does not reflect new products that did not exist in 1962 (e.g., cell phones). The HS data is only available back to 1995 but provides a more detailed product set. This is especially important for products introduced in the last two decades, such as high-tech electronic goods. How often is the Atlas data updated? + Atlas data updates follow international trade reporting cycles and require substantial processing to ensure quality and consistency. **Annual Updates** Approximately 95 percent of Atlas data is updated once per year, typically between April and June. This reflects country reporting timelines to UN Comtrade, which generally require 12 to 18 months to reach sufficient coverage. For example, most 2024 trade data becomes available in the Atlas between April and June 2026. Annual releases may incorporate late or corrected country reporting which can lead to small revisions in historical data. This process improves long-run accuracy and consistency. **Interim Updates** Throughout the year, we also release interim updates that incorporate newly submitted country data, reflect improvements in data processing methods, and address minor corrections when needed. For real-time information on country-level data availability, see the [UN Comtrade Data Availability Dashboard](https://comtrade.un.org/data/da) . Why are certain countries named and classified the way they are? + We use the official country names and data provided to UN Comtrade. Why are some countries missing from Country Profiles and the complexity ranking? + While Atlas Explore includes data for all countries and territories, Atlas Country Profiles and Rankings are limited to countries that meet minimum coverage and quality standards: * Population of at least 1 million * Average annual trade volume of at least $1 billion * Verified GDP and export data availability * Consistent and reliable trade reporting history Can I reuse or reprint Atlas data and visualizations? + The Atlas of Economic Complexity is a freely available public resource. Its data and tools are widely used in research, policy analysis, and applied projects. We encourage reuse and adaptation, provided that users cite the Atlas and underlying data sources appropriately. Please refer to our **[Usage and Citation Guidelines](https://atlas.hks.harvard.edu/data-downloads) ** for specific citation requirements for the platform, datasets, and related publications. Where can I learn more about the ideas and methodologies behind the Atlas? + A detailed technical description of the Growth Lab's trade data methodology is available in our peer-reviewed paper: [_Bustos et al. (2026), Tackling Discrepancies in Trade Data: The Harvard Growth Lab International Trade Datasets, Scientific Data._](https://www.nature.com/articles/s41597-025-06488-2) A visual overview of the data pipeline and processing steps is available on our [companion website](https://atlas.hks.harvard.edu/trade-data-methodology) . The intellectual foundations of the Atlas are presented in [**The Atlas of Economic Complexity: Mapping Paths to Prosperity**](https://growthlab.hks.harvard.edu/publications/atlas-economic-complexity-mapping-paths-prosperity-0) , which introduces the core concepts, motivating questions, and analytical framework underlying the platform. These ideas are further developed in the Growth Lab's research on economic complexity and product relatedness, including: * [_The Product Space Conditions the Development of Nations_](http://science.sciencemag.org/content/317/5837/482) * [_The Building Blocks of Economic Complexity_](http://www.pnas.org/content/106/26/10570.full) * [_The Network Structure of Economic Output_](https://link.springer.com/article/10.1007/s10887-011-9071-4) Who should I contact for media, research, or general inquiries about the Atlas? + Please contact the Growth Lab team at [growthlab@hks.harvard.edu](mailto:growthlab@hks.harvard.edu) . ### Explore more Growth Lab work * ![](https://atlas.hks.harvard.edu/assets/VizHubIcon-DwlpXWcx.svg) [#### Visit the Viz Hub, the Growth Lab's portfolio of award-winning, interactive visualizations powered by our research and insights.](https://growthlab.app/) * ![](https://atlas.hks.harvard.edu/assets/GL_logo_black-Dj5kOl8a.png) [#### Read the Growth Lab's latest published 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"GitHub")GitHub](https://github.com/harvard-growth-lab "GitHub") Copyright © 2026 The President and Fellows of Harvard College | [Privacy](https://gdpr.harvard.edu/eeaprivacydisclosures) | [Accessibility](http://accessibility.harvard.edu/) | [Digital Accessibility](https://accessibility.huit.harvard.edu/digital-accessibility-policy) | [Report Copyright](http://www.harvard.edu/reporting-copyright-infringements) --- # About — The Atlas of Economic Complexity [![The Atlas of Economic Complexity](https://atlas.hks.harvard.edu/assets/GL_logo_white-HDmHmKd7.png)](https://atlas.hks.harvard.edu/) * [Home](https://atlas.hks.harvard.edu/) * [Explore](https://atlas.hks.harvard.edu/explore/treemap) * [Countries](https://atlas.hks.harvard.edu/countries) * [Data](https://atlas.hks.harvard.edu/data-downloads) * Learn * [About](https://atlas.hks.harvard.edu/about) About ===== What is The Atlas of Economic Complexity? ----------------------------------------- The Atlas of Economic Complexity is an award-winning data visualization tool that allows people to explore global trade flows across markets, track these dynamics over time and discover new growth opportunities for every country. Built at the Harvard Kennedy School of Government, The Atlas is powered by [Harvard's Growth Lab's](https://growthlab.hks.harvard.edu/) research and is the flagship tool of [The Viz Hub](https://growthlab.app/) , the Growth Lab's portfolio of visualization tools. The Atlas places the industrial capabilities and knowhow of a country at the heart of its growth prospects, where the diversity and complexity of existing capabilities heavily influence how growth happens. The tool combines trade data with synthesized insights from the [Growth Lab's research](https://growthlab.hks.harvard.edu/research) in a way that is accessible and interactive. As a dynamic resource, the tool is continually evolving with new data and features to help answer questions such as: * What does a location import and export? * How has its trade evolved over time? * What are the drivers of export growth? * Which new industries are likely to emerge in a given location? Which are likely to disappear? * What are the GDP growth prospects of a given country in the next 5-10 years, based on its productive capabilities? The original online Atlas was launched in 2013 as a companion tool to the book, [The Atlas of Economic Complexity: Mapping paths to Prosperity](https://growthlab.hks.harvard.edu/publications/atlas-economic-complexity-mapping-paths-prosperity-0) . Today, The Atlas is used worldwide by policymakers, investors, entrepreneurs, and academics as an important resource for understanding a country's economic structure. Our Team -------- Led by [Ricardo Hausmann](https://growthlab.hks.harvard.edu/people/ricardo-hausmann-0) , The Atlas is the result of a multi-year, interdisciplinary collaboration between the Growth Lab's Digital Development & Design Team, researchers, staff, and a rich network of alumni. Today, The Atlas team includes: ### Web Development & Data Visualization [Brendan LeonardSenior Back-End & Data Developer](https://bleonard.dev/) Brendan leads the development of The Atlas data pipeline and API architecture. He works to create technical solutions for new Atlas features implementing research-driven concepts. He is also responsible for managing the cloud-based server and database infrastructure of The Atlas. He holds degrees in government studies and economics from Harvard University and UNC-Chapel Hill. [Ellie JacksonBack-End & Data Developer](https://www.linkedin.com/in/ellie-jackson-6750b539/) Ellie builds, maintains and optimizes the back-end architecture and infrastructure of The Atlas. She also contributes to the acquisition and analysis of various Atlas datasets. Ellie has a master's degree in computational analysis and public policy from the University of Chicago's Harris School. [Nil TuzcuSenior UX/UI & Data Visualization Designer](https://www.niltuzcu.net/) Nil leads The Atlas design vision including the creation of new features, enhancements and the integration of new research. Before joining the Growth Lab, Nil worked as a research fellow at MIT's Department of Urban Studies and as a research associate at Harvard Graduate School of Design. She holds a master's degree from Cornell University and was a SPURS Fellow at MIT (2014-2015). [Robert ChristieFront-End Developer](https://www.bortfolio.net/) Robert is responsible for the front-end development of The Atlas. He architects and implements interactive interfaces and visualizations with an emphasis on cartography. [Tammy ZhangFront-End Developer](https://www.tammy-zhang.com/) Tammy supports the front-end development and maintenance of The Atlas. Previously, she has collaborated with researchers in a variety of fields to communicate their work through interactive data visualization. Tammy holds a bachelor's and master's degree in information science from Cornell University. ### Product Team [Annie WhiteDirector, Software Tools](https://growthlab.hks.harvard.edu/people/annie-white) Annie oversees the strategy and creation of The Atlas, from research to design, development, and launch. Prior to her work at the Growth Lab, Annie was a Director of Digital Product at Sustainalytics, an ESG research and consulting firm. She is interested in the application of software tools to positively impact economic development, equity, and the public interest. [Chuck McKenneyDirector, Communications and Outreach](https://growthlab.hks.harvard.edu/people/chuck-mckenney) Chuck is the Associate Director of Communications & Outreach at Harvard's Growth Lab. Chuck has more than 20 years experience in broadcast, digital and print communications and has worked for academic, media, corporate, and nonprofit organizations. ### Research Team [Muhammed YildirimResearch Director, Academic Research](https://atlas.hks.harvard.edu/about/#) Muhammed is a physicist turned economist from Turkey. He worked on the theory behind The Atlas, determining optimal ways to calculate many of The Atlas variables. He co-developed The Atlas methodology, the data cleaning code and contributed in the preparation of The Atlas book. [Sebastian BustosSenior Research Fellow](https://growthlab.hks.harvard.edu/people/sebastian-bustos) Sebastian is a Research Fellow at the Center for International Development at Harvard University and a Doctoral candidate in Public Policy at Harvard University. His research interests are the development of the private sector and how governments can solve market failures to accelerate the process. [Shreyas Gadgin MathaSenior Computational Social Scientist](https://growthlab.hks.harvard.edu/people/shreyas-matha) Shreyas applies a computational lens to questions in economics. His research interests center around understanding complex socio-economic systems, from labor markets and innovation ecosystems to international trade, using non-traditional data sources such as satellite imagery, textual data, and networks. [Tim ChestonSenior Manager, Applied Research](https://growthlab.hks.harvard.edu/people/tim-cheston) Tim is a Research Fellow at the Center for International Development at Harvard University. His research interests focus on the intersection of social policy and economic policy, including the use of growth diagnostics to unlock structural transformation processes. ### Atlas Alumni [Mali Akmanalp](http://akmanalp.com/) Mali is a software engineer from Turkey. He worked on data updates for the original Atlas, while optimizing its performance and reliability. In the development of Atlas 2.0, Mali built the data ingestion pipeline, the back-end architecture and other infrastructure like the automated deployment setup. [Michele Coscia](http://www.michelecoscia.com/) Michele is an assistant professor from Italy. He teaches network analysis and data visualization at the IT University of Copenhagen. His research interests involve applying network science to economic development, human mobility, and understanding governments. He worked on the visualizations and the network analysis of the original Atlas book. [Steven Geofrey](https://www.fluidencodings.com/) From 2022-2023, Steven led the front-end development of various Atlas initiatives including the rebuilding of the Atlas front-end architecture, user interface and data visualizations. [Quinn Lee](https://atlas.hks.harvard.edu/about/#) Quinn was a developer on the first version of the Atlas as well as on CID's sub-national Atlas projects for Columbia, Mexico, and Peru. [Ben Leichter](https://atlas.hks.harvard.edu/about/#) Ben was an intern on the first version of the Atlas. He was responsible for updating the user interface and creating static and interactive design assets for both the Atlas and CID. [Huy Nguyen](https://www.huy.dev/) Between 2017 - 2019 Huy designed the Atlas front-end architecture, implemented the user interface and visualizations and created a sophisticated build pipeline to bundle assets efficiently. He's passionate about making beautiful, engaging and high-performance web applications using the latest web technologies. [Katherina Nguyen](http://katherinanguyen.com/) Kat led the design vision, developed interactive prototypes and supported launch activities in the development of Atlas 2.0. She also applied her design techniques and processes to support various CID research initiatives. [Greg Shapiro](https://atlas.hks.harvard.edu/about/#) Greg led development of the first version of the Atlas and the launching of localized projects for Mexico, Colombia, and Peru. [Kyle Soeltz](http://www.soeltz.com/) Kyle was a front-end developer for the Atlas, responsible for implementing user interface features, data visualizations, and collaborating with research, design and data counterparts. [Romain Vuillemot](https://romain.vuillemot.net/) Romain is a Data Visualization Researcher from France. He worked on improving the user experience of The Atlas of Economic Complexity. Romain published several research papers to improve The Atlas ranking navigation using novel interaction techniques, and visualization using text-based search. [Gus Wezerek](https://atlas.hks.harvard.edu/about/#) Gus is a visual journalist. He supported the design and development of The Atlas in 2015. Media & Outreach ---------------- The Atlas has reached more than 2 million people and has active users in almost every country worldwide. The Atlas has been featured in various media outlets including the [New York Times](http://www.nytimes.com/interactive/2011/05/15/magazine/art-of-economic-complexity.html) , [Wall Street Journal](https://blogs.wsj.com/indiarealtime/2016/01/01/india-will-be-fastest-growing-economy-for-coming-decade-harvard-researchers-predict/) , [Washington Post](https://www.washingtonpost.com/business/germanys-inconvenient-truth-its-too-complicated/2019/10/14/899ffd72-ee50-11e9-bb7e-d2026ee0c199_story.html) , [Bloomberg](https://www.bloomberg.com/opinion/articles/2019-10-14/german-economic-downturn-is-symptom-of-deeper-growth-problem) , [Financial Times](https://www.ft.com/content/0297ff7c-524e-11e8-b3ee-41e0209208ec) and the [Harvard Gazette](https://news.harvard.edu/gazette/story/2019/09/kennedy-schools-growth-lab-tool-helps-chart-paths-for-economic-growth/) and was short-listed for an [Information is Beautiful Award](https://www.informationisbeautifulawards.com/) in 2018 and 2019. For media inquiries, contact [Chuck McKenney](mailto:chuck_mckenney@hks.harvard.edu) . Harvard's Growth Lab -------------------- Located at the Harvard Kennedy School, the Growth Lab works in the pursuit of inclusive prosperity and a quality of life for everyone that we know is achievable. It places increased economic diversity and complexity at the center of the growth story and uncovers how places move into industries that offer increased productivity. For more information on the Growth Lab, find us on [X](https://x.com/HarvardGrwthLab) or subscribe to our quarterly [newsletter](https://growthlab.hks.harvard.edu/subscribe) . For direct inquiries, contact [Chuck McKenney](mailto:chuck_mckenney@hks.harvard.edu) . Support our Mission ------------------- The Atlas of Economic Complexity is made possible through the support of organizations that align with our mission to advance inclusive growth and prosperity. To learn more about how you can support the Growth Lab and The Atlas, please email Executive Director, [Andrea Carranza](mailto:andrea_carranza@hks.harvard.edu) . Contact Us ---------- Whether you'd like to request an Atlas demo, ask a question about the data, report a problem or learn more about working with us, we're happy to help. Please email us at [growthlabtools@hks.harvard.edu](mailto:growthlabtools@hks.harvard.edu) * What is The Atlas of Economic Complexity? * Our Team * Media & Outreach * Harvard's Growth Lab * Support Our Mission * Contact Us ### Explore more Growth Lab work * ![](https://atlas.hks.harvard.edu/assets/VizHubIcon-DwlpXWcx.svg) [#### Visit the Viz Hub, the Growth Lab's portfolio of award-winning, interactive visualizations powered by our research and insights.](https://growthlab.app/) * ![](https://atlas.hks.harvard.edu/assets/GL_logo_black-Dj5kOl8a.png) [#### Read the Growth Lab's latest published research covering a range of academic papers, books and policy pieces.](https://growthlab.hks.harvard.edu/) [![The Growth Lab](https://atlas.hks.harvard.edu/assets/GL_logo_black-Dj5kOl8a.png)](https://growthlab.hks.harvard.edu/home) 79 JFK St. | Cambridge, MA 02138 growthlab@hks.harvard.edu _Atlas_ * [Home](https://atlas.hks.harvard.edu/) * [Explore](https://atlas.hks.harvard.edu/explore/treemap) 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"GitHub")GitHub](https://github.com/harvard-growth-lab "GitHub") Copyright © 2026 The President and Fellows of Harvard College | [Privacy](https://gdpr.harvard.edu/eeaprivacydisclosures) | [Accessibility](http://accessibility.harvard.edu/) | [Digital Accessibility](https://accessibility.huit.harvard.edu/digital-accessibility-policy) | [Report Copyright](http://www.harvard.edu/reporting-copyright-infringements) --- # Trade Data Methodology — The Atlas of Economic Complexity [![The Atlas of Economic Complexity](https://atlas.hks.harvard.edu/assets/GL_logo_white-HDmHmKd7.png)](https://atlas.hks.harvard.edu/) * [Home](https://atlas.hks.harvard.edu/) * [Explore](https://atlas.hks.harvard.edu/explore/treemap) * [Countries](https://atlas.hks.harvard.edu/countries) * [Data](https://atlas.hks.harvard.edu/data-downloads) * Learn * [About](https://atlas.hks.harvard.edu/about) Your browser does not support the video tag. When Numbers Diverge: A Tale of Two Trading Partners ====================================================== Building Harvard Growth Lab's International Trade Datasets ========================================================== By Sebastián Bustos, Ellie Jackson, David Torun, Brendan Leonard, Nil Tuzcu, Piotr Lukaszuk, Annie White, Ricardo Hausmann & Muhammed A. Yıldırım Trade data powers decisions, from public policy to private investment. But the raw numbers often don't add up. When two countries trade, both report the same transaction, yet their figures often disagree. And over time, product classifications change, making it hard to compare trade data between countries. Here we explain how the Harvard Growth Lab builds a cleaner, more consistent trade dataset by: 1) Reconciling discrepancies between exporters and importers (mirroring); 2) Harmonizing product categories across classification changes using economically relevant weights. The end result is a publicly available trade dataset and a resource for researchers, policymakers, and anyone curious about how the world trades. [Read the Research Paper](https://www.nature.com/articles/s41597-025-06488-2) Explore the methodology Trade Partners ![reporting country](data:image/svg+xml,%3csvg%20width='25'%20height='35'%20viewBox='0%200%2025%2035'%20fill='none'%20xmlns='http://www.w3.org/2000/svg'%3e%3cpath%20d='M0%200V34.375H18.75V28.9062C18.7539%2028.5256%2019.1079%2028.1293%2019.5312%2028.125H25V0H0ZM16.7236%204.29688C17.0374%204.26289%2017.3627%204.43016%2017.5049%204.71191C17.6471%204.99367%2017.5967%205.3709%2017.3828%205.60305L11.9141%2011.8531C11.6579%2012.1422%2011.1747%2012.1928%2010.8643%2011.9629L7.73926%209.61914C7.40473%209.37836%207.32363%208.85215%207.56836%208.52051C7.81309%208.18883%208.33824%208.1132%208.66699%208.36179L11.2183%2010.2783L16.2109%204.56543C16.3396%204.41539%2016.5271%204.31723%2016.7236%204.29688ZM4.6875%2016.0278H20.3125C20.744%2016.0278%2021.0938%2016.3776%2021.0938%2016.8091C21.0938%2017.2406%2020.744%2017.5903%2020.3125%2017.5903H4.6875C4.25602%2017.5903%203.90625%2017.2406%203.90625%2016.8091C3.90625%2016.3776%204.25602%2016.0278%204.6875%2016.0278ZM4.6875%2022.2778H20.3125C20.744%2022.2778%2021.0938%2022.6276%2021.0938%2023.0591C21.0938%2023.4906%2020.744%2023.8403%2020.3125%2023.8403H4.6875C4.25602%2023.8403%203.90625%2023.4906%203.90625%2023.0591C3.90625%2022.6276%204.25602%2022.2778%204.6875%2022.2778ZM4.6875%2028.5278H16.4062C16.8377%2028.5278%2017.1875%2028.8776%2017.1875%2029.3091C17.1875%2029.7406%2016.8377%2030.0903%2016.4062%2030.0903H4.6875C4.25602%2030.0903%203.90625%2029.7406%203.90625%2029.3091C3.90625%2028.8776%204.25602%2028.5278%204.6875%2028.5278ZM20.3125%2029.6875V33.5938L24.2188%2029.6875H20.3125Z'%20fill='black'/%3e%3c/svg%3e)Reporting Country Country A ![intro visualization](https://atlas.hks.harvard.edu/assets/ship-COZZvM3u.svg) ![reporting country](data:image/svg+xml,%3csvg%20width='25'%20height='35'%20viewBox='0%200%2025%2035'%20fill='none'%20xmlns='http://www.w3.org/2000/svg'%3e%3cpath%20d='M0%200V34.375H18.75V28.9062C18.7539%2028.5256%2019.1079%2028.1293%2019.5312%2028.125H25V0H0ZM16.7236%204.29688C17.0374%204.26289%2017.3627%204.43016%2017.5049%204.71191C17.6471%204.99367%2017.5967%205.3709%2017.3828%205.60305L11.9141%2011.8531C11.6579%2012.1422%2011.1747%2012.1928%2010.8643%2011.9629L7.73926%209.61914C7.40473%209.37836%207.32363%208.85215%207.56836%208.52051C7.81309%208.18883%208.33824%208.1132%208.66699%208.36179L11.2183%2010.2783L16.2109%204.56543C16.3396%204.41539%2016.5271%204.31723%2016.7236%204.29688ZM4.6875%2016.0278H20.3125C20.744%2016.0278%2021.0938%2016.3776%2021.0938%2016.8091C21.0938%2017.2406%2020.744%2017.5903%2020.3125%2017.5903H4.6875C4.25602%2017.5903%203.90625%2017.2406%203.90625%2016.8091C3.90625%2016.3776%204.25602%2016.0278%204.6875%2016.0278ZM4.6875%2022.2778H20.3125C20.744%2022.2778%2021.0938%2022.6276%2021.0938%2023.0591C21.0938%2023.4906%2020.744%2023.8403%2020.3125%2023.8403H4.6875C4.25602%2023.8403%203.90625%2023.4906%203.90625%2023.0591C3.90625%2022.6276%204.25602%2022.2778%204.6875%2022.2778ZM4.6875%2028.5278H16.4062C16.8377%2028.5278%2017.1875%2028.8776%2017.1875%2029.3091C17.1875%2029.7406%2016.8377%2030.0903%2016.4062%2030.0903H4.6875C4.25602%2030.0903%203.90625%2029.7406%203.90625%2029.3091C3.90625%2028.8776%204.25602%2028.5278%204.6875%2028.5278ZM20.3125%2029.6875V33.5938L24.2188%2029.6875H20.3125Z'%20fill='black'/%3e%3c/svg%3e)Reporting Country Country B $497M more \= $30M in trade Country A and Country B are trading partners. In theory, their reported trade data should align. In practice, it rarely does. This discrepancy highlights two persistent challenges in international trade data: (1) how countries report trade, and (2) how products are mapped across classification systems. This site explains both issues, and how the Growth Lab handles them. 1\. The Reporting Problem ------------------------- In 2024, both Country A and Country B submitted their bilateral trade statistics to UN Comtrade for the calendar year 2023. Despite their shared transactions, their reported trades with each other did not align. For example, Country A reported that they exported $603 million to Country B. Meanwhile, Country B reported that they imported $1.1 billion from Country A. Both countries are reporting the same flow of goods. Yet Country A reported $497 million less than Country B. If Country B reports imports 82% higher than Country A reports exports, what is the true value of trade transacted between the two countries? How much discrepancy do we expect? Now, scale this example of Country A and Country B to all countries and their trading partners. The discrepancies compound. Trade Data Reporting in an Ideal ScenarioImporter Countries ReportingExporter Countries Reporting Countries appear in the rows as exporters and in the columns as importers. Each square therefore represents trade between a specific exporter-importer pair. Countries are ordered by total trade volume, so the biggest traders appear toward the top-right of the chart. The diagonal is blank because it represents countries trading with themselves, which is excluded intentionally. ### 1.1 How Significant is the Reporting Problem? Let's plot each country-country trading pair on a grid. In an ideal case, all countries report trade with all partners, and exporter and importer values align within a reasonable margin, say 25%. If this were true, every cell would be green, indicating complete reporting and aligned export and import values across all country pairs. In reality, the grid is fragmented, reflecting uneven reporting across countries. Let's consider 2010 global trade data as a representative example. 22% of country pairs reported trade values that aligned, with a discrepancy of less than 25% between the exporter's and importer's values. 23% of country pairs both reported, but with major discrepancies. Each side recorded the same trade relationship, but their values differed by more than 25%. In the remaining 55% of pairs, at least one country did not report any trade. 11% were reported only by importers... ...and 10% only by exporters. For the remaining 34% of country pairs, neither country reported trade data. The resulting grid diverges sharply from the ideal. Much of global trade data is missing, unevenly reported, or inconsistent across countries. Before global trade data can reliably inform policy or economic analysis, these gaps and inconsistencies must be addressed. Whose trade value do we trust? ------------------------------ Country A$603MAverage$851MCountry B$1.1B ### 1.2 How Does the Growth Lab Address the Reporting Problem? Trade is one of the few administrative datasets recorded twice: once by the exporter and once by the importer. Combining these two reports—often called mirroring—sounds straightforward, but in practice the numbers often don't match. One solution might be to take a simple average but that would treat both sides as equally reliable, which is not the case. Instead, we need an approach that gives greater weight to the more trusted source. Another approach might be to rely on external proxies of "quality", such as income level or governance scores. However, these measures tend to be incomplete over time and weak predictors of reporting accuracy. Instead, the Growth Lab evaluates discrepancies across the entire network of trade relationships to infer how consistently each country reports trade, separately from reported trade volumes. A key constraint however is that countries cannot be penalized for discrepancies driven by unreliable partners. A large mismatch with an unreliable partner is less informative than a smaller mismatch with a consistently reliable one. For this reason, reporting reliability is inferred in a network-aware manner, using network metrics described in our published paper. From here, we combine the two reports into a single best estimate using a reliability-weighted blend. More reliable reporters receive greater weight. The result is transparent, reproducible, and adaptive over time, providing a practical way to turn two messy numbers into one trusted measure of actual trade flow. Returning to the Country A and Country B example, suppose Country A is generally more consistent in its reporting, with fewer discrepancies across reliable partners. In that case, Country A's reported value receives greater weight when estimating the true trade flow between the two. Applied to all country pairs, this method reconciles conflicting reports and produces a more accurate and unified view of global trade. ![classification](https://atlas.hks.harvard.edu/assets/classification1-2Nb9mttL.svg) 2\. The Product Concordance Problem ----------------------------------- With reporting discrepancies reconciled, another issue remains: trade products are not always labeled the same way over time. Each year, countries report using a single product classification vintage, but these systems are periodically updated and product codes are split, merged, or renamed. Without a way to map product codes across vintages, we can't make consistent comparisons across years, or align data when countries use different vintages in the same year. Our solution is a method to harmonize product classifications across time. ### 2.1 What is the Product Concordance Problem? International trade data depends on product classification systems that assign codes to goods. These systems change over time as new products emerge and definitions are refined. The World Customs Organization (WCO) maintains the Harmonized System (HS), the global standard for classifying traded products. Every five years, the WCO releases a new HS vintage that adds new products, clarifies existing definitions, and reflects evolving technologies and trade policies. Combined, these adjustments improve how trade is recorded over time. For example, under the HS2007 vintage, apples had a code of 080810, electronic integrated circuits had a code of 854231, and umbrellas had a code of 660199. To illustrate how product concordance works in practice, consider a specific example: how a single HS2007 product code maps back to earlier classification vintages. This example focuses on electronic integrated circuits (product code 854231 in HS2007). With each new vintage, the WCO provides correspondence tables that map product codes between vintages. This diagram illustrates how a single HS2007 code—854231 (Electronic Integrated Circuits)—traces back through earlier classification systems. Moving backward in time, this one code corresponds to four codes in HS2002, which expand to six codes in HS1996, before consolidating into four distinct codes in HS1992: digital monolithic ICs, non-digital monolithic ICs, hybrid ICs, and other electrical parts. However, there is one major issue—these tables only describe the code relationships. They do not specify how to allocate trade values when one code splits into several, or when multiple old codes merge into one. Without allocation weights, UN Comtrade collapses all code relationships into simple one-to-one mappings. This creates three critical problems: 1. Loss of structural detail: One-to-many and many-to-many relationships are forced into one-to-one relationships. 2. Systemic data loss: When a single code maps to multiple targets, only one receives the trade value. The remaining valid codes are dropped entirely. 3. Misrepresented trade values: Reported trade values no longer reflect how trade is actually distributed across products. This problem compounds over time. Converting trade data from 1995 to 2024 using HS1992 requires chaining multiple correspondence tables across successive revisions (HS2022 → HS2017 → HS2012 → HS2007 → HS2002 → HS1996 → HS1992). At each conversion products are lost because one-to-one mappings cannot preserve one-to-many or many-to-many relationships. These losses accumulate with every conversion. As a result, Comtrade's 2023 data converted back to HS1992 contains only around 4,500 six-digit codes, roughly 500 fewer than the 5,040 products defined in HS1992. ![](https://atlas.hks.harvard.edu/assets/2e-CMWfIWUm.png) ### 2.2 How Does the Growth Lab Address the Product Concordance Problem? Our conversion method relies on a key insight: global trade patterns are relatively stable from year to year. As a result, the market composition of each product remains consistent in the years immediately before and after a new vintage is introduced. This stability allows us to construct conversion weights using countries that transition from an older to a newer classification vintage. These countries provide a natural bridge between systems. By comparing their reported trade patterns before and after the transition, we infer how products map across classifications and derive weights for converting data between vintages. How are conversion weights calculated? Using conversion weights, Electronic Integrated Circuits data under HS2007 can be converted back to HS1992. The conversion proceeds sequentially across classification vintages, reallocating trade values from HS2007 to HS2002, then to HS1997, and finally to HS1992. At each step, reported trade values are multiplied by the relevant conversion weights, ensuring that trade is systemically reclassified as it moves backwards through the Harmonized System. Once converted to HS1992, the Electronic Integrated Circuits category reallocates into: * 68% to code 854211 (digital integrated circuits) * 23% to code 854219 (non-digital integrated circuits) * 9% to code 854290 (other electronic integrated circuits) * 0% to code 854800 (parts for integrated circuits) Now these weights reflect actual trade flows and provide a probabilistic allocation that preserves the total trade value. Rather than choosing a single "best match," this approach distributes trade across multiple product codes in proportion to observed market patterns, resulting in more accurate historical comparisons. 3\. The Impact of An Improved Trade Dataset ------------------------------------------- By addressing these two structural limitations of global trade data, the Growth Lab is able to apply these methods directly in its own tools and research. Trade data is first harmonized across vintages using our economically balanced weights. Validating against the International Monetary Fund's Balance of Payments data confirms that this approach accurately reconstructs trade patterns, even for non-reporting countries. Together, these methods recovered $861 billion in trade in 2024 alone and recovered 8 percent of product codes. Applied across all country pairs and six decades of trade data, this methodology underpins the [Atlas of Economic Complexity](https://atlas.hks.harvard.edu/) , the Growth Lab's flagship data tool. Since 2013, the Atlas has supported decision-making by trade ministries, central banks, multinational firms, development agencies and academic researchers. The Atlas of Economic Complexity translates this data into accessible visualizations, while the underlying datasets enable rigorous empirical analysis. Explore The Data ---------------- All components of the Growth Lab's trade data methodology are openly available. You can reproduce the full workflow or adapt individual steps to build your own bilateral trade datasets for any classification vintage and time period. [Read the Research Paper](https://www.nature.com/articles/s41597-025-06488-2) [Explore Mirrored Trade Data](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/5NGVOB) [Download Conversion Weights](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6AADMR) ### Code & Tools [Comtrade Downloader](https://github.com/harvard-growth-lab/comtrade-downloader) [Conversion Weights Generator](https://github.com/harvard-growth-lab/comtrade-conversion-weights) [Mirroring Pipeline](https://github.com/harvard-growth-lab/comtrade-mirroring) ### Support For methodological questions, data access, or research use cases, contact [growthlabtools@hks.harvard.edu](mailto:growthlabtools@hks.harvard.edu) . Recommended Citation -------------------- Please cite the paper, datasets, and code when using these resources. Paper: Bustos, S., Jackson, E., Torun, D., Leonard, B., Tuzcu, N., Lukaszuk, P., White, A., Hausmann, R., Yildirim, M.A. (2025). "Tackling Discrepancies in Trade Data: The Harvard Growth Lab International Trade Datasets." Scientific Data 13, 170 (2026). [https://doi.org/10.1038/s41597-025-06488-2](https://doi.org/10.1038/s41597-025-06488-2) Datasets: Harvard Growth Lab. "Bilateral Trade Data Aggregated by Year," 2025. [https://doi.org/10.7910/DVN/5NGVOB](https://doi.org/10.7910/DVN/5NGVOB) . Harvard Growth Lab. "Weighted Classification Conversion Tables," 2025. [https://doi.org/10.7910/DVN/6AADMR](https://doi.org/10.7910/DVN/6AADMR) . Code: Harvard Growth Lab. Comtrade Downloader (2025). [https://github.com/harvard-growth-lab/comtrade-downloader](https://github.com/harvard-growth-lab/comtrade-downloader) . Harvard Growth Lab. Comtrade Conversion Weights Generator (2025). [https://github.com/harvard-growth-lab/comtrade-conversion-weights](https://github.com/harvard-growth-lab/comtrade-conversion-weights) . Harvard Growth Lab. Comtrade Mirroring Pipeline (2025). [https://github.com/harvard-growth-lab/comtrade-mirroring](https://github.com/harvard-growth-lab/comtrade-mirroring) . Growth Lab's Research Team: Sebastián Bustos, David Torun, Piotr Lukaszuk, Ricardo Hausmann & Muhammed A. Yıldırım Growth Lab's Digital Development & Design Team: Ellie Jackson, Tammy Zhang, Nil Tuzcu, Brendan Leonard, Robert Christie, Annie White [![The Growth Lab](https://atlas.hks.harvard.edu/assets/GL_logo_black-Dj5kOl8a.png)](https://growthlab.hks.harvard.edu/home) 79 JFK St. | Cambridge, MA 02138 growthlab@hks.harvard.edu _Atlas_ * [Home](https://atlas.hks.harvard.edu/) * [Explore](https://atlas.hks.harvard.edu/explore/treemap) * [Countries](https://atlas.hks.harvard.edu/countries) * [Data](https://atlas.hks.harvard.edu/data-downloads) * [Learn](https://atlas.hks.harvard.edu/glossary) * [About](https://atlas.hks.harvard.edu/about) * [Contact Us](mailto:growthlabtools@hks.harvard.edu) * [Newsletter](https://hksexeced.tfaforms.net/f/subscribe?s=a1n6g000000nJnxAAE) * [Work with us](https://growthlab.hks.harvard.edu/jobs-opportunities) #### Connect with us * 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