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Causality Between International Trade and International Patenting: A Combination of Network Analysis and Granger Causality

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Abstract

This paper combines network analysis with Granger causality tests to examine the possible causal relationship between a country’s position in the international trade and international patenting networks. This combination has not been examined thus far. The first stage applies social network analysis to the international patenting and international trade networks using data for 82 countries for the period 1995–2018. The international patenting network and international trade network evolution shows that the linking frequency of patenting abroad and trade participation increased during the sample period. In the second stage, panel data Granger causality tests were applied to the computed metrics of the two networks. The results of the Granger causality tests suggest two-way causality between a country’s relative position as recipient of foreign patents and imports. This might suggest an endogeneity or feedback effect between incoming foreign patenting and imports. Granger causality suggests causality from the country’s relative position as sender of patents abroad to exports, but not vice versa. This last finding indicates that by recognizing business opportunities in a destination country, patentees decide to register their innovation in the foreign country before proceeding to export.

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Notes

  1. In directed networks, each edge has a direction, pointing from one node to another (here, from the exporting country to the importing country).

  2. Directed ties exhibit directionality, meaning that the relationship between the individuals is uneven.

  3. A weighted network is a network 0where the ties among nodes have weights assigned to them. (Wasserman & Faust, 1994).

  4. Potential connections \(pc=\frac{n*(n-1)}{n}\) 

  5. Vector autoregression (VAR) refers to the number of earlier time periods the model will use. For example, a 5th-order VAR would model each year's wheat price as a linear combination of the last five years of wheat prices. A lag is the value of a variable in a previous time period. In general, a pth-order VAR refers to a VAR model, which includes lags for the last p time periods. A pth-order VAR is denoted as VAR(p) and sometimes called a VAR with p lags.

  6. VAR describes the evolution of a set of k variables, called endogenous variables, over time. Each period of time is numbered, t = 1, …, T. The variables are collected in a vector, yt, which is of length k. The vector is modelled as a linear function of its previous value. The vector's components are referred to as yi,t, meaning the observation at time t of the i th variable (example, a 5th-order VAR).

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Funding

Implementation of Dr. Moussa’s postdoctoral research was co-financed by Greece and the European Union (European Social Fund) through the Operational Program, Human Resources Development, Education and Lifelong Learning, in the framework of the Support of Postgraduate Researchers—B cycle (MIS 5033021) implemented by the State Scholarships Foundation (IKY), contract number: 2019–050-0503–1886.

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  1. Department of Economics, Laboratory of Economic Analysis and Policy, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Bachar Moussa & Nikos C. Varsakelis

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  1. Bachar Moussa
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  2. Nikos C. Varsakelis
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Correspondence to Bachar Moussa.

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Moussa, B., Varsakelis, N.C. Causality Between International Trade and International Patenting: A Combination of Network Analysis and Granger Causality. Atl Econ J 50, 9–26 (2022). https://doi.org/10.1007/s11293-022-09743-8

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