Product space and regional competitiveness
June 17, 2020
Product space and regional competitiveness
The competitiveness of any subject is one of its most important characteristics. The main goal of this study was to create a picture of the real competitiveness of Kazakhstan and its regions, so that in the future it could help the state and business in making strategic decisions.
The article presents 6 blocks, which reveal the concept of "product space", tools for its calculation and visualization:
"Product space": definition and structure
One of the most popular tools for visualizing relationships between products is product space (hereinafter “space”). It is a network of products connected to each other based on the "similarity of knowledge" necessary for their production.
The space is not linked to a specific country or region. However, a unique product network for a specific country/region can be created by "highlighting" only those products in which the country/region has a comparative advantage.
1. Shared product space
Figure 1 shows the total space that includes 866 products (in the form of circles) of the HS classification (4 digits), having a total of 2532 mutual links.
Figure 1. Product space
The concept of Space was first used in 2007 in the works of Hidalgo, Hausmann, and others. «The Product Space Conditions the Development of Nations».
At the same time, some products were omitted during visualization in order to simplify it and clear the product links from "white noise". This applied to products and links that did not have a high level of significance.
2. Structure of space
The colors of the circles in space represent the industries that the product belongs to. The industry structure was formed according to the General classifier of economic activities, which is the national classifier of the Republic of Kazakhstan. Figure 2 shows the complete legend by type of economic activity at the level of 2 digits of the GCEA.
For convenience, the industries were grouped by sector: green represents agriculture and purple represents mining industry. Manufacturing industries were divided into three main groups: labor-intensive B2C industries ("economy of simple things") in yellow, mechanical engineering industry in blue, and others in red.
Figure 2. Industries
The products themselves are presented at the level of 4 characters of the HS , whereas links between them are defined by the "proximity" indicator of products.
Figure 3. Commodity groups.
The distribution of products in the space is achieved using the force-directed layout. This format distributes products in the space according to the Center-Periphery structure. The center of the space consists mainly of products with the most connections and more complex products such as engineering products (fig. 4), the Periphery consists of products with the least number of connections and simple products such as raw materials (fig. 5).
Figure 4. Product space. Engineering industry (GCEA 26-30)
Figure 5. Product space. Raw materials (GCEA 5-8)
The calculation of competitiveness in the export of products is made on the basis of world trade data (UN Comtrade) and Kazakhstan statistics at the level of detail of commodity groups of 6 characters of the HS (2018).
There are a number of restrictions.
1. Different system for calculating the cost of the product.
Data on world trade flows is collected using 2 cost calculation systems - CIF (cost, insurance, freight - the cost of products including insurance and freight payment), and FOB (free on board - the net value of the product declared by the country of origin).
This creates an asymmetry in world trade data, since the declared imports of all countries do not match the declared exports.
2. Unreliability of the applicant countries.
Deep detailing of trade flows by product implies a large number of inconsistencies unrelated to the cost calculation system. Imperfect data may be due to incorrect calculation and collection of statistics, errors during transit, and so on.
3. Other limitations.
There are various additional factors of data asymmetry. A classic example is the time lag when a country exported products at the end of 2019, but due to long transportation and deferred customs procedures, the import of products was registered in early 2020.
As a result of such asymmetries, the use of a "raw" world trade database can lead to incorrect calculation of competitiveness.
2. Data adjustments
In order to determine the real competitiveness of regions, the methodology developed by the Center for Global Trade Analysis was used to adjust the data.
This methodology includes 4 main steps:
3. Calculation of the index of the identified comparative advantage
The revealed comparative advantage index (RCA) is a localization coefficient and shows how much a country/region exceeds its "fair" share by any indicator.
However, the index authors themselves emphasize that the RCA calculation is subject to modification. In the case of Kazakhstan, the RCA calculation was modified, since the country is an oil exporter, which means that there is a risk of underestimating the RCA for other products due to an inflated volume of total exports.
4. Data visualization
After determining the RCA of countries for each product, data visualization is a "highlight" of those products for which the country/region has RCA>1 in the total space.
A program for creating network graphs was used to visualize the data network.
The visualization process included 3 main steps:
Product space of Kazakhstan
Based on the above calculations and processes, the product space of Kazakhstan was formed. Figure 6 shows the space calculated by without data correction. The size of the circles was adjusted according to the volume of products exported by Kazakhstan in 2018 according to the adjusted database. As can be seen in the figure, the main export product of Kazakhstan was oil (a big purple circle).
Figure 6. Product space. Kazakhstan, 2018 "Raw" database
Figure 7 represents the product space of Kazakhstan on an adjusted database.
Several insights on data adjustment for Kazakhstan:
Figure 7. Product space. Kazakhstan, 2018. Adjusted database
Product space of regions
Similarly to the country’s space, regional product spaces were also formed.
Due to the inferiority of world trade data for 2019, world trade data for 2018 were used to adjust the data, as well as to calculate the RCA of regions.
Data adjustments by region was made universally: all export data was adjusted by 8% due to inability to "mirror" data at the regional level.
As examples of regional product spaces, we suggest considering three regions.
Figure 8 shows the product space of the Akmola region. Most of the products in the space are colored green and yellow - the region's exports mainly consist of simple agriculture products and the "economy of simple things" (in this case, food).
Figure 8. Product space. Akmola region, 2019
Figure 9 gives us a visual representation of the space of the Mangystau region. This region has the smallest set of product groups among the regions of the Republic of Kazakhstan for which it has a comparative advantage. At the same time, part of the product groups is other vehicles (blue), which may be related to the re-export (sending for repair) of specialized machinery and equipment for oil production.
Figure 9. Product space. Mangystau region, 2019
In contrast to the previous cases, figure 10 shows the product space of a more "complex" region – the Karaganda region. In 2018, on the economic complexity index it took 3rd place among the regions of the Republic of Kazakhstan. The figure shows that most of the product groups belong to the metallurgical industry (brown).
Figure 10. Product space. Karaganda region, 2019
Product spaces in other regions are shown in Appendix 1.
Calculating the real competitiveness of regions for products gives two main results – (1) understanding the current situation in the region (how attractive the region is for investment in a particular product based on the current structure of its exports, how strong is the diversification in the region) and (2) determining opportunities for further expansion of the region's product basket, based on the indicator of" proximity " between products.
The basis for identifying opportunities is based on the assumption that a country/region can develop new products at a competitive level only if the country/region already has the competence to create this product. These competencies are visible from the current product basket.
This also implies the impossibility of “leap frog” through space, at least at a competitive level, if the region does not have the competence to create a new product.
Even with all the data clearing and adjustment processes, labor adjustment, this methodology still provides only a superficial view of the competitiveness of a country or region. Why is this happening?
1. The index does not take into account the geographical location of countries
For a more objective assessment, consideration of geographical conditions is essential. Let’s take three countries - Kazakhstan, Vietnam and Belgium - as example. While for Vietnam, exports are not problematic due to access to the sea, and Belgium borders several major developed economies of the world, for Kazakhstan, exports require much more effort due to the lack of access to the sea, a large territory and the absence of major developed markets in the border areas.
So, in Kazakhstan, the cargo can be sent over long distances of 2.5 thousand km within the country, and in Belgium, only 300 km. But for Kazakhstan, this may not be considered an export (Almaty-Uralsk), and for Belgium it will already be an export (Brussels – Paris).
2. The index does not take into account the specifics of products
Each product is unique in its transportation requirements and the specifics of the export. For example, glass products cannot be transported over long distances, and uranium is mostly traded over long distances. Accordingly, the country structure of competition in the market for each product is different. If Kazakhstan competes only with Russia in the glass market, then it is possible to question the correctness of comparing Kazakhstan with all countries of the world in terms of glass exports.
The situation in the uranium market is similar – only a limited number of countries in the world have uranium mines.
At the moment, the Center for Research and Consulting team is in the process of developing a new methodology to bring the competitiveness picture of Kazakhstan and the regions closer to reality. Our goal is to get a tool that will allow us to more accurately evaluate market opportunities for stakeholders, and, accordingly, give better recommendations on their use.
Data – UN Comtrade, the Committee of statistics of MNE of RK, the state revenue committee of MF of RK, OECD, World Bank, IMF
Cover – Unsplash.com website
Author: Aliya Kussainova