Chapter 2: Measuring Readiness for the Future of Production
Definition of Readiness
For the purposes of the Country Readiness Project, “readiness” is the ability to capitalize on future production opportunities, mitigate risks and challenges, and be resilient and agile in responding to unknown future shocks. The assessment measures readiness for the future of production, rather than production performance today. Further, it looks at average readiness of the entire country—the entire country on average, not just the highest performing areas within a country. Results from the quantitative analysis were complemented with qualitative insights from country engagement activities in India, Mexico, Japan, Republic of Korea, Russian Federation and South Africa, and six ASEAN countries.
Methodology and Framework
The inaugural Readiness Assessment was conducted using a framework that was developed through a series of multistakeholder consultations, including with leading experts in government, international organizations and research institutions. The assessment is intended to stimulate discussion and advance further research and will be updated as the future unfolds.
Future of Production Scorecard
While no one can precisely predict the future, we can already see key enablers and factors that are allowing countries to adopt emerging technologies, increase productivity and transform production systems. The framework identifies key levers required to effectively transform production systems in light of rapidly emerging technologies. As shown in Figure 2.1, the assessment measures readiness for the future of production across two different components: Structure of Production and Drivers of Production.
Figure 2.1: Readiness Diagnostic Model Framework
Structure of Production
Production is one of several catalysts for growth that countries can pursue to increase the prosperity of people and achieve other objectives. A country’s Structure of Production depends on several variables, including the strategic decisions a country makes to prioritize sector development across agriculture, mining, industry and services. This structure reflects the complexity and scale of a country’s current production base, as shown in Figure 2.2. The scope of the assessment does not include sectoral mix; therefore, this is not measured as part of the Structure of Production. Countries with a large, more complex Structure of Production today are more ready for the future in that they already have a production base to build upon.
Figure 2.2: Structure of Production: Concepts Measured
- Complexity: Assesses the mix and uniqueness of products a country can make as a result of the amount of useful knowledge embedded in the economy and the ways in which this knowledge is combined. See Box 2.1 for more on Ricardo Haussmann and Cesar A. Hidalgo’s research on economic complexity.
- Scale: Assesses both the total volume of manufacturing output within a country (Manufacturing Value Added) as well as the significance of manufacturing to the economy (Manufacturing Value Added, % of GDP).
Box 2.1: A Closer Look at Economic Complexity
What is economic complexity?
The Economic Complexity Index (ECI) is a measure of the knowledge embedded in a society expressed by the products it makes. Economies with high economic complexity have been able to amass sophisticated capabilities and knowledge to make a diverse and complex set of products. Increases in ECI are associated with improvements in income levels and economic growth.15
How is economic complexity calculated?
The economic complexity of a country or region is calculated based on the diversity and ubiquity of the products it makes, or the number of the economies that are able to produce them. Products that require sophisticated know-how and many capabilities tend to be produced by few economies.
How can economies improve economic complexity?
There is ongoing research on policies that can improve a region’s economic complexity and the growth that accompanies it. Unlike indices that are derived from a set of pre-conceived conditions, the ECI reflects an outcome. Economies can track it and seek to improve it with policies that encourage diversification and a move towards more complex products. These policies are context specific, based on a country’s current product mix, and the capabilities that will help firms ‘jump’ to new products. Examples of these policies may involve upgrading infrastructure, cold chain logistics and customs efficiency to allow a region to move from exporting canned fruit to fresh produce. The creation of special economic zones has allowed countries to attract more foreign direct investment that increases the complexity of their exports by creating microcosms of efficiency. Policies that allow for the import of talent and diffusion of know-how in society also contribute to improvements in ECI.
How are economic complexity scores incorporated into the readiness assessment?
The Economic Complexity Index (ECI) publishes a value for each economy annually. The Readiness Assessment 2018 uses values from the Atlas of Economic Complexity 2016 Global Rankings, which can be found at
http://atlas.cid.harvard.edu/rankings/. All scores are normalized to the 0–10 scale used for all indicators included in the assessment.and 2) uncertainty as to whether an initial market for the first movers exists.
Drivers of Production
The framework’s Drivers of Production are key enablers that position a country to capitalize on emerging technologies and opportunities in the future of production. A consultative process was used to identify six main drivers: Technology & Innovation, Human Capital, Global Trade & Investment, Institutional Framework, Sustainable Resources, and Demand Environment. Each has corresponding categories, sub-categories and indicators that measure key concepts, as shown in Figure 2.3. Countries that perform well across the Drivers of Production are considered more ‘ready’ because the mix of enablers will allow for the adoption and diffusion of technology to accelerate transformation of production systems.
Figure 2.3: Drivers of Production: Concepts Measured
- Technology & Innovation: Assesses the extent to which a country has an advanced, secure and connected ICT infrastructure to support the adoption of new technologies in production. Also measures a country’s ability to foster innovation and commercialize innovations that have potential application in production.
- Human Capital: Assesses a country’s ability to respond to shifts in the production labour market triggered by the Fourth Industrial Revolution by looking at both current labour force capabilities as well as the long-term ability to cultivate the right skills and talent in the future work force.
- Global Trade & Investment: Assesses a country’s participation in international trade to facilitate the exchange of products, knowledge and technology, and to establish global linkages. Also measures the availability of financial resources to invest in production-related development as well as the quality of infrastructure to enable production-related activities.
- Institutional Framework: Assesses how effective government institutions, rules and regulations contribute towards shepherding technological development, novel businesses and advanced manufacturing.
- Sustainable Resources: Assesses the impact of production on the environment, including a country’s use of natural resources and alternative energy sources.
- Demand Environment: Assesses a country’s access to foreign and local demand to scale production. Also measures the sophistication of the consumer base, as this can drive diverse industry activity and new products.
For a detailed description of each driver and analysis of the driver significance in relationship to readiness for the future of production, please refer to Chapter 4 of this report.
The assessment includes 59 indicators that capture pertinent concepts that are fundamental to a country’s readiness for the future of production. These indicators are measured by internationally recognized organizations, including the International Energy Agency (IEA), International Labour Organization (ILO), International Telecommunication Union (ITU), Organization for Economic Co-operation and Development (OECD), United Nations (UN), United Nations Educational, Scientific and Cultural Organization (UNESCO), United Nations Industrial Development Organization (UNIDO), World Bank (WB), World Trade Organization (WTO), and others. The assessment also includes indicators from the World Economic Forum’s Executive Opinion Survey (EOS) that measures the qualitative aspects of various dimensions, or serves as a substitute where a comparable statistical data was not available for a large enough set of countries. See Appendix C for the full indicator list and detailed descriptions.
Box 2.2: Alternative Weighting Schemes Based on Future Scenarios
Given that the assessment is forward looking, the weighting scheme inherently reflects an embedded view of the future. An underlying assumption of the model is that economic complexity is a key measure of readiness, as the ability to make increasingly complex and unique products will be important for future competitiveness in production. Thus, complexity is the core concept at the heart of the weighting scheme used for the assessment. When comparing the relationship between the Drivers of Production and complexity, four drivers stood out for their explanatory power of complexity: Technology & Innovation, Human Capital, Global Trade & Investment, Institutional Framework. These drivers all received the highest weight.
The current weighting scheme reflects one view of the future. Of course, the future is uncertain and hard to predict. Any number of scenarios could unfold and make different drivers more or less important. For example, Sustainable Resources is weighted lower due to its low correlation with Economic Complexity historically. However, sustainable production practices are critical to a sustainable production future and in different scenarios can be weighted much higher. Readers interested in making their own adjustments to weightings given to different drivers can explore the interactive online tool at http://wef.ch/fopreadiness18. For additional perspectives on potential future scenarios for production, please see the World Economic Forum whitepaper Shaping the Future of Production: Four Contrasting Perspectives in 2030 (https://www.weforum.org/whitepapers/shaping-the-future-of-production-four-contrasting-perspectives-in-2030).
Global Mapping of Results
One of the key outputs of the assessment is the global mapping of results. The following section describes the methodology used to develop the global mapping. For a visual explanation of how to read the results, please refer to the How to Read the Country Profiles section.
This inaugural assessment includes 100 countries and economies covering all regions of the world. Country inclusion is largely driven by data availability and the significance of production in these countries. The 100 countries and economies included in the assessment account for over 96% of the global Manufacturing Value Added (MVA).16 Seventy-eight countries have 100% data coverage and 90 countries have at least 98% data coverage. Only Hong Kong SAR has less than 95% data coverage. In cases where data was missing, imputed data was used to calculate overall driver scores. See Appendix C for a list of imputed data and approach by indicator. The World Economic Forum seeks to expand coverage of the assessment as more data becomes available in future years.
Scale and Normalization
All scores for indicators, sub-categories, categories, Drivers of Production, as well as total driver and structure scores are measured on a 0–10 scale, with a maximum value (10) representing the ideal. Individual indicators are normalized using a min-max approach, which converts values for all indicators into unit-less scores ranging from 0 to 10. These normalized scores can then be combined to produce aggregated scores. In the case where a higher value corresponds to a worse outcome (e.g. emissions), the indicators are still normalized so that 10 always corresponds to the ideal outcome.
For each indicator, the ideal value does not necessarily correspond to actual maximum (or minimum) values in the country sample. The target value corresponds to widely accepted policy targets or aspirations and is aligned with the World Economic Forum’s Global Competitiveness Index in cases where indicators are used for both assessments.17 The min and max targets will be kept constant in future iterations of the assessment.
The assessment is weighted at the driver level with categories and sub-categories receiving equal weighting within each dimension. Complexity has a larger weight than Scale within the Structure of Production component. Drivers are also given different weights, derived from their overall significance in relation to economic complexity. See Table 2.1 for a summary of the weighting scheme.
Table 2.1: Readiness Assessment Weighting Scheme
Recognizing that each country has its own unique goals and strategy for production and development, countries do not receive an overall global ranking. Instead, countries are assigned to one of the four archetypes based on their weighted Structure of Production and weighted Driver of Production scores. The lines to divide the four quadrants are drawn using the average Driver of Production score (5.7) and Structure of Production score (5.7) for the Top 75 countries, based on Structure of Production rankings. Fixed lines allow for additional countries to be added in the future without shifting the lines. See Figure 2.4 for a visualization of the archetypes. The two different components reflect the need to both invest in the Drivers of Production to increase capacity to take advantage of future opportunities and develop a strategy to convert capacity into an expanded production base in the future.
Figure 2.4: Country Archetypes
The archetypes provide a unique perspective for benchmarking against countries with a similar outlook for the future of production:
- Leading: Countries with a strong production base today that exhibit a high level of readiness for the future through strong performance across the Drivers of Production component. These countries also have the most current economic value at stake for future disruptions.
- Legacy: Countries with a strong production base today that are at risk for the future due to weaker performance across the Drivers of Production component.
- High-Potential: Countries with a limited production base today that score well across the Drivers of Production component, indicating that capacity exists to increase production in the future depending on priorities within the national economy.
- Nascent: Countries with a limited production base today that exhibit a low level of readiness for the future through weak performance across the Drivers of Production component.
Each country has its own unique strengths and improvement areas and is therefore assigned to an archetype for a variety of reasons. No two countries within an archetype are the same, but general trends can be observed by archetype. Additional cluster analysis within and across archetypes to answer specific research questions can also be conducted using the dataset.
There are several measurement limitations that make this exercise challenging. First, it is inherently difficult to measure or predict uncertainties that come with an unknown future. Furthermore, there is a lack of empirical evidence about the topic, given we are still in the process of understanding the factors and conditions that have the greatest impact on transforming production systems. To address this, the framework will be revisited and updated as the future unfolds.
Secondly, there is a lack of sufficient data for some key concepts. In several cases, the assessment uses proxy indicators where direct measures are not available. For example, more manufacturing specific data would give a better view than national level indicators on topics such as labour force capabilities, emissions and so on. There were also several concepts considered but not included due to lack of data availability for a large set of countries.
Lastly, sectoral strengths and weaknesses in manufacturing are difficult to identify in a holistic assessment. In-depth qualitative analyses have been conducted in select countries to provide a more comprehensive view and complement the quantitative assessment with qualitative insights. However, each country will need to view the assessment in the context of its own sectoral strategy and adapt priorities accordingly.