Social capital as a network measure provides new insights on economic growth

Abstract

Unveiling the main drivers of economic growth is of paramount importance. Previous research recognizes the critical role played by the factors of production: capital and labor. However, the exact mechanisms that underpin Total Factor Productivity (TFP) are not fully understood. An increasing number of studies suggests that the creation and transmission of knowledge, factor supply and economic integration are indeed crucial. Yet, the need for a systematic and unifying framework still exists. Nowadays capital and labor are embedded into a complex network structure through global supply chains and international migration. Recent research has established a link between network centralities and different types of social capital. In this work we employ the OECD's Multi-Regional Input-Output and International Migration datasets to build the network representation for capital and labor of 63 economies during 10 years. We then examine the role of social capital measures as drivers of the TFP adopting an extended Cobb-Douglass production function and addressing potential issues such as multicollinearity, reverse causality and non-linear effects. Our results indicate that social capital in the factors of production networks can significantly drive economic outputs through TFP.

Fig 1. Toy model of the three social capital indicators, where nodes with higher values are colored darker.

In this example all link weights are equal to one. (a) Inwards information capital. (b) Outwards information capital. (c) Inwards Favor capital.

Fig 2. Pairwise distribution matrix for economic output (log(GDP)), capital (log(K)), labor (log(L)) and the developed social capital indicators: Inwards/outwards information capital (Iⁱⁿ/I^out), and favour capital F for the financial, goods and services, and migration networks.

Each observation corresponds to one country and year. Spearman correlations (ρ) are shown in the lower triangular matrix, while the R² of a linear regression model with intercept is shown in the upper triangular matrix.

Fig 3. Composition of the first 4 PCA components accounting for 98% of the variance in the data.

We show the estimated effects of this variables on GDP, which are mostly statistically significant.

Fig 4. Coefficient estimates for model 4 leveraging an Elastic-Net regressor to reduce the multicollinearity effect.

Bootstrap errors are shown in black, demonstrating significance of the effects. All social capital indicators have positive effects but out-information capital in the migration network.

Fig 5. SHAP values for the different features in the Gradient Boosting Regressor.

We observe that capital and country effects are the most important features, however the rest of them also contribute to the model estimates.