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How do social capabilities shape a country’s comparative advantages? Unpacking industries’ relatedness

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Abstract

This study elaborates on a methodology that combines industry-level data (exports, HS 4-digits) with country-level indicators to determine which social capabilities are important when explaining patterns of comparative advantage (or structural transformation). The indicators used to characterize these capabilities are associated with different dimensions: economic, institutional, and cultural. Through the product space, we estimate a density measure identifying the proximity between non-competitive products and a country’s current export profile, and then unpack the contribution of different relatedness channels to changes in comparative advantage by redefining densities in terms of social affinities between industries. We find that (i) countries can be competitive in certain industries, even if some of these capabilities are not high; (ii) all dimensions, but not all their components, matter in predicting changes in countries’ comparative advantages; (iii) structural transformations take some time to materialize; and (iv) the inclusion of social affinities diminishes the influence of a density variable measuring overall relatedness to predict product takeoff.

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Notes

  1. See, for example, Romalis (2004), Costinot et al. (2012), and other references cited in Bahar et al. (2019).

  2. As stated in Nunn & Trefler (2014) the relationship between the countries’ culture and their comparative advantages has received scant attention in the literature. Two of the few cases are Tabellini (2008) and Costinovis et al. (2017).

  3. As suggested in the literature review of Nunn & Trefler (2014), institutions impinge on comparative advantages through channels that differ from those of endowments. For instance, by dealing with hold-up and underinvestment problems in input–output relationships, by making possible incentive-compatible arrangements for promoting work effort, and by reducing the financing costs in imperfect credit markets when fixed investments are large.

  4. Other papers that attempt unpacking relatedness to identify different mechanisms in specialization or diversification processes (e.g., comparative advantage, regional development, path-breaking development, geographical job locations, bilateral trade) are the following: Boschma & Capone (2015), Zhu et al. (2017), Jara-Figueroa, et al. (2018), Farinha et al. (2019), Jun et al. (2019), Huang & Zhu (2020).

  5. A more detailed description of the concept of social capabilities is presented in Abramovitz (1986, 1995).

  6. This density metric was originally formulated for a background paper of the 2017 World Development Report. ‘Governance and the Law’ (Castañeda & Chávez-Juárez, 2016). Interaction terms between a plain density metric and a country’s socioeconomic indicator (R&D, FDI, etc.), like in Huang & Zhu (2020) and Cortinovis et al. (2017), are used to identify specific factors that mitigate or enhance the effect of relatedness on a specialization process (Hidalgo, 2021). In other words, these terms help to discover mechanisms of path-breaking development and unrelated diversification. In contrast, our approach allows for comparisons between the imputed values of specific social capabilities in pairs of nodes and, thus, we can explore if social affinities—either because the level of the imputed values are low or high in both industries—are conducive to the transmission of other forms of productive capabilities.

  7. The revival of the cultural perspective on development is due to a large extent on the conceptualization and measurement of social capital (e.g., Knack & Keefer, 1997, Tabellini, 2010) and the increase in the number of countries included in recent waves of the World Value Survey (e.g., Petrakis, 2014). Recently, the literature on experimental and behavioral games has also shown that individuals’ culture has an impact on decision-making (e.g., Beugelsdijk & Maseland, 2011). Likewise, at the theoretical level, the influence of social norms and beliefs on behavior is thoroughly explained with different conduits: preferences, cognitive mechanisms, and social coordination (e.g., Basu, 2010 and Castañeda, 2020).

  8. The idea behind averaging the values of RCAc,p, and Ik,p for each period is to avoid noisy information that comes from fluctuating export data. Accordingly, a good p is said to be produced competitively by country c only when its 3-year average has an RCA ≥ 1.

  9. In a few cases of highly skewed distributions, we use the percentile 1 or 99 instead of the minimum and the maximum, respectively. This allows us to avoid collapsed distributions with little useful information. Then, we set observations above the percentile 99 and below the percentile 1 to 1 and 0, respectively.

  10. Note that this adjustment factor varies depending on which one is the incoming node of the edge described in the denominator in Eq. (6). Hence, Eq. (7) generates a directed network.

  11. The reader should be aware that, in this paper, the productive structure and the economic dimension are not the same concepts. The former refers to the country’s export profile (or comparative advantage), while the latter refers to the type of social capability that supports any production process through economic mechanisms: human capital, infrastructure, financing, etc.

  12. Beyond this cut-off point, the proximity coefficients have negligible values, so they are discarded to improve the model’s computational tractability.

  13. As stated above, the proximity and the RCA coefficients in period t are measured with 3-year averages to diminish the effects of noisy information. This implies that the density variables are also measured in periods of three years.

  14. The convention is to remove the remaining cases (RCAc,i,t < 0.5 and \(RCA_{c, i, t + \tau}\) ≤ 1) si they are inconclusive.

  15. Two papers using this type of regressions are the following: Bahar et al. (2014), and Hausmann et al. (2013).

  16. Product opportunity gain is an index that measures the contribution of a certain industry to the complexity of the economy. Therefore, it measures the new capabilities acquired by a country when it can develop a comparative advantage in said industry.

  17. Prepared by The Growth Lab at Harvard University. The Atlas of Economic Complexity. http://www.atlas.cid.harvard.edu.

  18. The Atlas of Economic Complexity uses export statistics from the United Nations COMTRADE database. The data are cleaned by Growth Lab using the Bustos-Yildrim Method. Likewise, the harmonization procedure allows increasing the number of countries for which trade data are available.

  19. Prepared by The World Bank, and available at http://data.worldbank.org/data-catalog/world-development-indicators.

  20. Prepared by The World Bank, and available at http://info.worldbank.org/governance/wgi/index.aspx#home

  21. Available at http://www.worldvaluessurvey.org/wvs.jsp

  22. Available at http://www.systemicpeace.org/inscrdata.html

  23. BACI also uses export statistics from the United Nations COMTRADE database.

  24. A similar IVI has already been used in the literature on economic development, the so-called PRODY (Hausmann et al., 2006).

  25. Data on ‘Health expenditure’ (either as % of GDP or % of government expenditure), and ‘Improved sanitation facilities’ are not reported in the World Development Indicators after 2013. Therefore, to include them in our analysis, we use a non-linear Gaussian process to impute values for all the observations in each of the variables. Refer to Liu et al. (2018) for an explanation of the methodology. We only impute values to country-indicator series that had at least 70% of the data available in the original dataset.

  26. Available at http://www.researchgate.net/publication/269992832_Online_Appendix_to_Welzel’s_CUP_monograph_Freedom_Rising

  27. In an alternative causal interpretation, the attached level of a social capability in a node could be the result of a set of conditions shared by the set of countries that are competitive in the said industry (e.g., having a similar productive structure).

  28. In these box-and-whisker plots, the white line describes the median, the filled-in boxes denote the interquartile range (i.e. between the lower and the upper quartile), and the whiskers extend to cover most of the data. Dots outside the whiskers are considered outliers.

  29. The products with the lowest values for this indicator include ‘lac; gums, resins’, ‘vegetable production’, ‘cotton’, and ‘coffee and tea’, while the highest IVIs are observed in ‘pharmaceutical products’, ‘albuminoidal subs.; modified starches’, and ‘cork and articles of cork’.

  30. However, in this paper we stick to a causal narrative that goes from culture to export specialization when stating that specific cultural values are required for production in complex industries.

  31. In other words, these types of transitions will not be common in countries lacking ‘government effectiveness’ since the social affinity between non-competitive nodes requiring this social capability and their export profile is low.

  32. Although we do not provide a thorough causality analysis, the use of lagged density variables—even with a period of 20 years-– indicates that the social affinities of the past exert an influence on the current evolution of the country’s comparative advantage.

  33. A caveat is in order. With these regressions, we are only formally testing whether social capabilities contribute to the countries’ ‘local development’. Thus, our results do not discard, for example, that government effectiveness can be a relevant factor in other types of structural transformations.

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Funding

This work was supported by a research grant of The World Bank for producing a previous version of this article: Background paper of the World Development Report 2017. Governance and the Law. UPI Number 248827.

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Authors and Affiliations

  1. Centro de Investigación y Docencia Económicas (CIDE, Division of Economics), Mexico City, Mexico

    Gonzalo Castañeda & Luis Castro Peñarrieta

  2. The Alan Turing Institute, London, UK

    Omar A. Guerrero

  3. Colaborative Research Solutions (CORESO), Dully, Switzerland

    Florian Chávez-Juárez

  4. Department of Economics, Universidad Privada Boliviana, La Paz, Bolivia

    Luis Castro Peñarrieta

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  1. Gonzalo Castañeda
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Correspondence to Gonzalo Castañeda.

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Castañeda, G., Castro Peñarrieta, L., Guerrero, O.A. et al. How do social capabilities shape a country’s comparative advantages? Unpacking industries’ relatedness. Rev World Econ (2024). https://doi.org/10.1007/s10290-024-00524-w

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