Glossary

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:

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: 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.

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.

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 is the export or import trade inside a region and includes trade between all members of the region.

The tacit ability to produce a product. Also known as productive capability, know-how refers to the productive knowledge that goes into making products.

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.

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: 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:

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 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).

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.

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,

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.

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 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. Formally,