RESEARCHER OF MONTH- JUNE 2024 | DR. HERICK KAYANGE



RESEARCHER OF MONTH- JUNE 2024

DR.HERICK KAYANGE

RESEARCH TITLE: DYNAMICS AND CONTROLLABILITY OF PRODUCT SPACE NETWORK

 

  1. What is the topic of your research? Why is it important to study the topic?

The research titled "Dynamics and controllability of product space network."  There is a massive difference in income generation from global trade between developed and developing countries. For many years, it was perceived that a country's prosperity depends on the volume of exports in the international trade network.

A study by Hidalgo, et al. [1] stated that a country's prosperity depends on the type of product exported rather than the volume of exports; many poorer countries tend to specialize in exporting commodities with low technological advancement and high labor intensity, making it difficult for them to transition to more advanced products.

A product space network is defined as the network of relatedness of exported products through their proximities. The study of product space networks lacked a dynamic representation of the network.

The study of product space has proven to be a reliable indicator of countries' prosperity. The global product space network can be visualized as a network of all exported products with relative proximity. However, previous studies on product space networks have not considered the impact of exchange rates. The US dollar, being the primary currency used in global trade invoicing, has its exchange rate measured by the United States Dollar Index (USDX). This study aimed to address three significant questions:

  1. What comprehensive mathematical model represents the dynamics of export growth in product space networks as influenced by variations in the United States Dollar Index (USDX)?
  2. What critical transition values indicate a shift from a resilient to a non-resilient state in the product space network?
  3. What are the best optimization strategies for maximizing export volumes within the global product space network
  4. Key Findings and Observations

Key findings from the resilience analysis and other observations include:

The global product space network can be modeled using the Lotka-Volterra model. The developed model portrayed the dynamics of global product space showing its dynamics. Numerical solutions show the exportation dynamics, similar to the trend of global export for 51 years (from 1971 to 2021). The resilience analysis shows that although economic crises continued to face the global economy, their effects were not similar.The findings reveal that the critical transition values (threshold values) generated by the system demonstrated that the energy crisis (1979-1983) had a more significant influence on the resilience of the global product space network compared to other economic shocks. Global resilience was not significantly affected by the Asian financial crisis since it was confined to Asian countries and effectively managed and mitigated by these nations before it could develop to a global level.   The impact of the OPEC oil crisis in 1973, the global financial crisis in 2008, and the COVID-19 pandemic in 2020 on the worldwide economy was effectively managed within one year. Although it is of utmost importance to enhance the exportation of goods within the economic product structure, it is even more critical to uphold the level of diversity in the USDX. The robustness of global trade is more significantly influenced by fluctuations in the US Dollar Index (USDX) than the proximity index.

The analysis of control in USDX-variation and proximity index within the product space network demonstrates a positive correlation with export growth rates. The study categorizes export growth into pre-crisis, crisis, and post-crisis phases, revealing a decline in product exports during significant economic crises, such as the OPEC oil crisis in 1973, the peak of the 1979 Energy crisis, the 2008 financial crisis, and the height of the COVID-19 pandemic. Interestingly, the 1997 Asian financial crisis had minimal impact on the global economy. Understanding these dynamics can help formulate effective strategies to enhance export growth and mitigate the effects of economic downturns. The findings suggest that simultaneously optimizing control in USDX-variation and control proximities between products to their maximum potential would substantially increase the rates at which exports grow among global economies. Policies and strategies for optimizing and controlling USDX-variation and proximities among products have the maximum effect on maximizing exports. After simultaneous optimization of both controls, the product space network can experience maximum exportation of products in the product space network.

Therefore while enhancing the exportation of goods is crucial, maintaining diversity in the USDX is even more critical for the robustness of global trade. By simultaneously optimizing controls over USDX variation and product proximities, the product space network can achieve maximum export growth.

  1. Practical Utilization of Research Results

Understanding the dynamics of global product space and the influence of the US Dollar Index (USDX) can help formulate effective strategies to enhance export growth and mitigate the impacts of economic downturns. Here are keyways the results of this research can be utilized in practice:

  1. Policy and Strategy Formulation:
    • Optimizing USDX Variation and Product Proximities: Simultaneous optimization can substantially increase export growth rates among global economies. Policies focusing on these areas will maximize export potential.
    • Resilient Trade Networks: Insights from the study can help policymakers create more resilient trade networks, informed by international agreements, human capital investments, skills development, and government interventions in monetary policy to improve exchange rates.
  2. Export Capacity Enhancement:
    • Existing Export Framework: Nations should prioritize enhancing their export capacity within their current export framework.
    • Innovative Exportable Commodities: Concurrently, there should be a progressive approach to innovating new exportable commodities based on resource endowments and market introduction.
  3. Mitigating USDX Fluctuations:
    • International Accords: Establishing international agreements to regulate foreign exchange rates vis-à-vis the US dollar is essential.
    • Currency Risk Management: Strategies like forward currency contracts and hedging approaches can manage currency risks effectively.
    • Government Interventions: Interventions in interest rates and controlled open market operations can help stabilize the product space network.
  4. Trade Collaboration and Investment:
    • Agreements and Collaboration: Facilitating trade collaboration through international agreements is crucial.
    • Human Capital and Diversification: Strategic investments in human capital and diversification initiatives will enhance trade capability.
    • Trade Capacity Building: Improving trade infrastructure and capabilities through targeted investments will support sustainable export growth.
  5. Crisis Management and Forecasting:
    • Economic Crisis Response: The methodologies proposed provide valuable insights into managing export dynamics during global crises, such as the economic repercussions of the Russia-Ukraine conflict.
    • Forecasting Export Amounts: These insights are useful for forecasting export amounts and examining network controllability during economic disruptions.

By leveraging these findings, policymakers and trade strategists can develop robust frameworks to enhance global trade resilience, optimize export growth, and mitigate economic shocks.

  1. What are the essential research methods and materials used in your research?

In this study, we modified a new mathematical model to represent a complex network of product exportation influenced by proximities between them and USDX variations by changing a simple Lotka-Volterra model. This model network explained the exportation of 688 types of products with a 688 X 688 proximity matrix, using empirical global trade data spanning 51 years. The model analysis uses the Runge-Kutta fourth-order method, embedded in MATLAB mathematical software. This method efficiently provides numerical solutions for non-linear differential equations that cannot be solved analytically. Considering the complex behavior of the product space network, a Runge-Kutta method was so helpful in getting the desired solution. This method helped analyze the influence of proximity indices and USDX variation in this study. The technique was further used to study the trend of export growth before the economic crisis, during the financial crisis, and after the global economic crisis. The analysis of critical transition values was done using WOLFRAM mathematical software.

In this study, we modified the developed mathematical model to formulate the optimization problem for the growth of export products in the product space network. First, we added control variables in USDX variation and proximity indices to formulate optimization problems. We used Pontryagin's maximum principle to analyze these two structural values and maximize product export in the product space network. We used the result to compare the effectiveness of each control procedure to find the most effective strategy. Finally, we used Pontryagin's principle to analyze the effectiveness of simultaneous optimization of both control variables to maximize exports in the product space network.

Empirical global export and USDX data were used for analysis purposes. Global export data from 1971 to 1999 was derived from the National Bureau of Economic Research (NBER)[1]. Global export data from 2007 to 2021 was sourced from the United Nations COMTRADE[2] website. All global trade data was in the form of Standard International Trade Code level 4 (SITC-4). The data on US dollar indices was obtained from the Marketwatch website. [3].

  1. Is there something else about your doctoral dissertation you would like to share in the press release?

This study was based on the resilience of the global product space network and international trade in general. Extensions can be made by studying the resilience and optimization of product space networks at the country level, and then comparisons within countries should be made. The country-level exchange rate against the US dollar should be used instead of USDX indices. USDX indices are used to measure global currency exchange against the US dollar. Still, the index is based on the US dollar exchange against the six significant currencies: the Euro, British pound, Swiss franc, Japanese yen, Canadian dollar, and Swedish krona. When exchange rates are used instead, studying the exports between two countries will be easier even if their significant exports are not invoiced in the US dollar.

The study of product space is based on the available endowment of resources in the economy. It gradually expands the exportation of new products based on proximity to the available export products. We modeled the dynamics of Global trade based on the Idea of product space using the Idea of the Lotka-Volterra ecological model. Modeling the expansion of exports in global trade may go as far as the ability of the country to introduce products that are exported far from the existing product space network. Therefore, the study can be expanded to include the introduction of production and exportation of products that do not necessarily have high proximity to the existing export products.

 

Paper from this research:

https://www.sciencedirect.com/science/article/pii/S2468227624001376

https://www.researchgate.net/profile/Herick-Kayange/publication/379353150_Analyzing_Global_Trade_Network_Resilience_during_economic_crises_A_Model-Based_exploration_of_countries'_interactions_USDX_Variation_and_number_of_exporting_countries/links/660eb0eeb839e05a20bd4da7/Analyzing-Global-Trade-Network-Resilience-during-economic-crises-A-Model-Based-exploration-of-countries-interactions-USDX-Variation-and-number-of-exporting-countries.pdf

 

contacts

Dr. Herick Kayange

Email: herick.kayange@cbe.ac.tz>

 

[1] NBER: http://www.nber.org/data  

[2] COMTRADE: https://comtrade.un.org/data/  

[3] Market Watch: https://www.marketwatch.com/investing/index/dxy/download-data