Coalitions in international relations and coordination of agricultural trade policies

Abstract

PurposeThe author attempts to examine the existence and pattern of coalitions in international relations across countries, and investigates whether international relations of coalition partners influence a country's enaction of agricultural non-tariff measures (NTMs).Design/methodology/approachThe author adopts a machine learning technique to identify international relation coalition partnerships and use network analysis to characterize the clustering pattern of coalitions with high-frequent records of global event data. The author then constructs a monthly dataset of agricultural NTMs against China and international relations with China of each importer and its coalition partners, and designs a panel structural vector autoregressive (PSVAR) model to estimate impulse response functions of agricultural NTMs with regard to international relation shocks.FindingsThe author finds countries to establish coalition partnerships. Two major clusters of coalitions are noted, with one composed of coalitions primarily among “North” countries and the other of coalitions among “South” countries. The United States is found to play a pivotal role by connecting the two clusters. The PSVAR estimation reveals reductions of NTMs against China following improved international relations with China of both the importer and its coalition partners. NTM responses are more substantial for measures that are trade restrictive. These results confirm that coalitions in international relations lead to coordination of agricultural NTMs.Originality/valueThe author provides international political insights into agricultural trade policymaking by showing interactions of NTM enaction across countries in the same coalition of international relations. These insights offer useful policy implications to predict and cope with hidden barriers to agricultural trade.

Editorial: Prediction, pattern recognition and machine learning in agricultural economics

“…The author then constructed a monthly dataset of agricultural non-tariff measures (NTMs) against China and international relations with China of each importer and its coalition partners and estimated impulse response functions of agricultural NTMs with regard to international relation shocks. Mao (2023) also identified two major clusters of coalitions, one composed of coalitions primarily among “North” countries and the other of coalitions among “South” countries. The USA is found to play a pivotal role by connecting the two clusters.…”

“…In this special section, Mao (2022) used the LASSO algorithm to identify international relation coalition partnerships and used network analysis to characterize the clustering pattern of coalitions with high-frequency records of global event data. The author then constructed a monthly dataset of agricultural non-tariff measures (NTMs) against China and international relations with China of each importer and its coalition partners and estimated impulse response functions of agricultural NTMs with regard to international relation shocks.…”

Relationship between the evaluation of agricultural scientific and technological innovation capacity and the influencing factors of green agriculture

“…Sun D X and Cheng S T determined that agricultural green technology innovation is the key factor to realize carbon emission reduction [39], Chen F F found that, at present, China has low transformation rate of green technology innovation achievements, and the effect of green energy saving and emission reduction in agriculture is highly difficult to achieve [40]. Mao S P and Yang Y L classified and quantified the agricultural scientific and technological innovation policies issued at the national level since the reform and opening up (1978-2015) from three dimensions, and quantitatively explored its evolution trend and characteristics [41]. Based on the previous research on the progress of agricultural science and technology and green agriculture, this study plans to further deepen the logical relationship.…”