CHAPTER ONE

1.1Background of the study

Scholars have long sought to understand why some nations develop and prosper while others languish in poverty. A major school of thought attributes differences in economic growth to varying speeds and patterns of industrialization and the structural economic transitions this entails. Seminal economists like Alexander Gerschenkron (1962) and Walt Rostow (1960) characterized industrialization as a pivotal point in a country’s growth trajectory that requires mobilizing capital and shifting labor out of agriculture. More recently, Dani Rodrik (2016) and Cimoli et al. (2009) emphasize structural transformation towards higher-productivity sectors as the key driver of long-term growth. Countries that industrialized earliest, like the UK in the 19th century, typically saw rapid gains from specializing in labor-intensive manufactures before diversifying (Landes, 1998). Later industrializers like Germany leveraged public support for strategic industries to catch-up more quickly (Chang, 2003). However, premature openness without domestic capability-building hampered structural change in Latin America according to scholars like Prebisch (1950) and Fajnzylber (1983). The latecomer advantage also diminished as technological gaps widened (Abramovitz, 1986).  Meanwhile, the East Asian Tiger economies sustained rapid growth into more complex exports through proactive industrial policies (Amsden, 1989; Wade, 1990). China’s manufacturing-led growth model diverged from other developing nations (Naughton, 2007; Nolan, 2012).

As discussed, the speed and nature of industrialization shapes growth trajectories. However, scholars note aggregate economic measures alone fail to fully capture structural realities driving prosperity. Ricardo Hausmann, Cesar Hidalgo, and others introduced economic complexity metrics derived from networks of related productive knowledge (Hausmann et al., 2014; Hidalgo & Hausmann, 2009). Nations with more interconnected and sophisticated baskets of export capabilities tend to have higher wages, productivity and innovation potential according to this body of work. Tacchella et al. (2012) found complexity better predicted European growth from 1963-2008. Crucially, Caldarelli et al. (2012) demonstrated economic complexity outperformed simple trade or GDP models in forecasting OECD nations’ growth from 1963-2008, indicating it captures deeper developmental dynamics. Structuralist thinkers likewise view economic upgrading as nonlinear, path-dependent processes (Cimoli et al., 2005; Salomon et al., 2020). Related diversification allows progress up a “development ladder” of increasingly complex goods and services (Lin, 2012). Building new capabilities requires scaling existing comparative advantages through coordinated investments and policy support (Stiglitz & Lin, 2020). In this view, differences in the complexity of nations’ production and trade networks – shaped strongly by histories of industrial policies – provide more robust explanations for why some achieve sustained catch-up while others languish (Hausmann et al., 2014).

Moreover, multiple studies have been conducted to explain the determinants of economic complexity and the factors that shape countries’ complexity trajectories over time. (Hausmann et al. 2014) examine technological capabilities as a determinant of economic complexity and found that countries with stronger innovative capacities, as evidenced by patents, scientific publications, and education levels, tend to have higher economic complexity. This suggests knowledge accumulation plays an important role. Research on institutional quality demonstrates that well-functioning legal and political systems improve a nation’s ability to absorb foreign knowledge through trade and investment, facilitating diversification into more complex production (Pugliese et al. 2017). Industrial policy studies provide quantitative evidence linking strategic policy support for targeted industries with faster rates of complexity growth, especially for late industrializers (Cherif & Hasanov 2021). Innovation system research finds interlinkages between innovation performance, export structure, and development leading to self-reinforcing complexity gains over the long run (Crespi et al. 2014).

1.2 Statement of the Problem

The economic complexity framework provides new insights into why some countries develop faster than others. A country’s position in the global product space, mapped according to the similarity and popularity of its exported goods, reveals its stock of productive knowledge (Hidalgo & Hausmann, 2009). More economically complex nations occupying dense areas of the product space tend to enjoy stronger growth, as they can more easily replicate and build upon existing know-how to move into related products. Meanwhile, countries in sparse peripheral positions lack the capabilities to enter nearby opportunities and diversify, constraining their development potential (Hidalgo et al., 2007). Economic complexity analysis shows interacting feedback loops between export sophistication, learning of new technologies, rising productivity and incomes (Cristache et al., 2019). As countries climb the complexity ladder, acquiring skills in more knowledge-intensive industries, their long-term growth prospects strengthen. International trade serves as a key transmission mechanism in this process, allowing countries to specialize, acquire new designs and skills through exchange, and gradually expand into more rewarding economic activities.

Trade can significantly affect various development indicators such as economic growth, health, education, and quality of institutions. Greater trade openness allows countries to specialize in complex goods in which they have a comparative advantage and access wider markets, helping drive economic growth (Frankel & Romer, 1999; Dollar & Kraay, 2003). However, the ability of countries to benefit from trade depends on their productive capabilities and capacity to diversify into more sophisticated exports over time (Hidalgo & Hausmann, 2009; Hausmann et al., 2014). Trade also impacts human development. Access to imports can increase availability of goods that promote health such as medicine, enabling improved health outcomes (Frankel & Romer, 1999; Sachs & Warner, 1995). At the same time, competitive pressures from trade are associated with institutional reforms that strengthen property rights and rule of law, fostering long-run growth (Rodrik, Subramanian & Trebbi, 2004). However, countries may experience transitional costs from trade if they lack capability to quickly switch productive resources to new complex exports (Autor, Dorn & Hanson, 2016).

This study represents the first attempt to empirically examine the relationships between patterns of trade integration and subsequent economic complexity trajectories over long time horizons. Insights from such an analysis hold significance for developing countries seeking to design trade policies that sustainably catalyze structural transformation. Caldarelli et al.’s (2012) research demonstrating complexity’s superior predictive power for long-term growth establishes its theoretical validity as a framework for assessing trade impacts. Their findings validate that complexity captures deeper structural change dynamics shaping prosperity compared to static indicators (GDP). By analyzing how diversity paths within interrelated industries relate to past specialization strategies, lessons may be drawn about policy options supportive of new competitive advantage development through related diversification – a core tenet of complexification theory. Adopting a complexity lens informed by Caldarelli et al.’s findings can offer guidance beyond analyses using aggregate measures by considering influence of integration decisions on long-run structural upgrading opportunities critical for development outcomes. As the first to explore these linkages empirically, this study aims to provide novel insights with implications for maximizing inclusive, sustained transformation through trade.

1.3 Research Questions

1.3.1 Main Research Question

• What is the relationship between trade and  economic complexity?

1.3.2 Specific Research Questions

• Is there linear relationship between trade and economic complexity?
• What role does GDP per capita plays on the relationship between trade and economic complexity?

1.4       Research Objectives

1.4.1 Main Research Objective

• The main objective of this study is to explore of the relationship between trade and economic complexity.

1.4.2 Specific Research Objectives

• To investigate the relationship between trade  and economic complexity.
• To examine the role of  GDP per capita on the relationship between trade and economic complexity