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Does institutional quality foster economic complexity? The fundamental drivers of productive capabilities

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Abstract

This study investigates the role of institutions in shaping international differences in economic complexity—a novel measure of productive capabilities. More specifically, economic complexity corresponds to an enhanced capacity to produce and export a diverse range of sophisticated (high-productivity) products. This paper hypothesizes that there exists a positive association between institutional quality and economic complexity. The underlying intuition is that well-functioning institutions fundamentally drive structural transformation towards productive activities via strengthening incentives for innovative entrepreneurship, fostering human capital accumulation, and deploying human resources in acquiring productive capabilities. Employing data for up to 115 countries, I consistently obtain precise estimates of the positive effect of institutional quality, measured by the Economic Freedom of the World Index, on economic complexity. The main findings advocate for establishing a pro-development institutional environment, which helps attenuating the persistence of underdevelopment by fostering economic complexity.

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Notes

  1. Specifically, the product space represents proximity/relatedness between products, captured by similarities in productive capabilities or knowledge (e.g. technologies, skills, infrastructure, legal systems and tacit knowledge). For instance, the overall affinity between apples and pears would be greater than that between apples and iPhones. The production of less sophisticated products, typically located at the periphery of the product space, is less connected to other high-productivity productive activities due to greater dissimilarities in required productive capabilities. See Hidalgo et al. (2007) for a more detailed discussion.

  2. It is worth noting that ECI is constructed using data on cross-border movements of goods rather than services. Hence, this indicator does not account for the sophistication level of service products. This may underestimate the amount of productive knowledge of several service-based economies, such as Australia (Hidalgo, 2021). Nevertheless, as highlighted by Hidalgo (2021), constructing an internationally comparable index of economic complexity based on trade in services appears to be very difficult due to the scarcity of data. In addition, ECI may fail to account for some products that a country produces but does not export. A more detailed description of ECI is provided by Hidalgo and Hausmann (2009) and Hidalgo (2021).

  3. Using cross-country data, Andersen et al. (2016) document robust evidence of a reduced-form link between UV-R and worldwide differences in income per capita. Accordingly, UV-R exposure is highly predictive of the cross-country variation in the timing of the fertility transition, consistent with a mechanism of transmission proposed by Andersen et al. (2016). In addition, the intensity of UV-R also helps explain subnational comparative development in the United States and China.

  4. A recent study by Casey and Klemp (2021) examines the validity of historical instruments, commonly adopted in the long-run development literature. It empirically establishes that using historical or slowly evolving variables to create plausibly exogenous sources of variation in contemporary variables, including institutions, may provide inconsistent estimates due to potential deviation from the exclusion restriction. Based on replicating influential studies examining the deep origins of economic performance, Casey and Klemp (2021) highlights the relevance of applying the framework of Conley et al. (2012) to formally bound the main coefficient of interest.

  5. According to Ashraf and Galor (2013), genetic diversity corresponds to the likelihood that two people randomly selected from a relevant population are genetically dissimilar to each other, with regard to a given spectrum of genetic traits. The degree of genetic heterogeneity within a country is negatively linked to migratory distance to East Africa. A medium length of genetic diversity is conductive to economic prosperity. In contrast, underdevelopment tends to persist in highly diverse and homogenous societies.

  6. The IV estimates, not reported for brevity, remain unchanged when I use the log of settlers’ mortality rate as an alternative instrument for institutions. Consistent with the results in Table 3, the adoption of two instruments for institutions does not affect the magnitude and statistical precision of the estimated coefficient on EFW.

  7. As highlighted by an Associate Editor, the results would not imply causal inference if I were to incorporate potentially mediating channels in the regression without addressing endogeneity concerns of these variables. It is noteworthy that isolating plausibly exogenous sources of variation of different mechanisms depicted in Fig. 1 is challenging.

  8. According to Dutta et al. (2020), knowledge creation is measured by registered patents, utility model applications, peer-reviewed articles, while knowledge impact is captured by, for example, improvements in labour productivity, entry density of new firms, and firms’ utilization of science and technologies. Moreover, knowledge diffusion is reflected in the international exchange of intellectual properties, ICT services, and high-tech products, among others (Dutta et al., 2020). These factors capture a country’s efforts to acquire productive knowledge. See also Dutta et al. (2020) for a more detailed description.

  9. These estimates are not reported for brevity, but are available upon request.

  10. More specifically, the first-step regression model can be expressed as \(ECI_{i} = \alpha + \beta EFW_{i} + \varphi M_{i} + \rho X_{i} + \delta Continent_{i} + \varepsilon_{i}\), in which \(M_{i}\) stands for possible mediating variables (see also the benchmark model for a description of other variables). Next, the demediated outcome variable is calculated as \(\widetilde{{ECI_{i} }} = ECI_{i} - \hat{\varphi }M_{i}\). The second-stage regression is represented by \(\widetilde{{ECI_{i} }} = \alpha_{2} + \beta_{2} EFW_{i} + \rho_{2} X_{i} + \delta_{2} Continent_{i} + \omega_{i}\). The estimated coefficient on EFW (\(\widehat{{\beta_{2} }}\)) reflects the estimated effect of institutions on economic complexity accounting for potential mediating variables. However, Acharya et al. (2016) emphasize that the standard errors obtained from the second-stage regression can be biased given that the first-stage estimates of \(\hat{\varphi }\) are not accounted for. To address this concern, I derive the standard errors from a bootstrapped procedure using 1000 replications.

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Acknowledgements

I wish to thank Professor Dorian Owen for his generous support from immature ideas to the end of this research. I am also grateful to an Associate Editor (Professor Christopher O'Donnell) and two anonymous reviewers for many helpful suggestions. My gratitude extends to Professors Alfred Haug and David Flath for their constructive comments on an earlier version of this paper. This study benefits from financial support from the University of Otago provided in the form of a Doctoral scholarship. All errors remain my responsibility.

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    Trung V. Vu

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Vu, T.V. Does institutional quality foster economic complexity? The fundamental drivers of productive capabilities. Empir Econ 63, 1571–1604 (2022). https://doi.org/10.1007/s00181-021-02175-4

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