Trade and Transport Margins is the difference between the sales value and th e purchase value of the commodity as compensation for the trader who is the distributor of the commodity. The higher Trade and Transport Margins indicates the longer and less efficient distribution pattern, so that it can harm producers and consumers. Data is taken from the 2020 Poldis survey with strategic commodities of Rice, Eggs, Sugar, and Cooking Oil. The value of the National Trade and Transport Margins in 2020 for rice and sugar has decreased but is not followed by an increasing number of provincial Trade and Transport Margins which have decreased. It is necessary to conduct an analysis to determine the characteristics of commodities in each province, hierarchical cluster analysis by comparing the application of the average, complete, single, ward's, and centroid methods to obtain the best method. Based on the results of hierarchical cluster analysis for all methods, then compared the cophenetic correlation values and the highest correlation on the average method with a correlation of 0.8318939. Then cluster profiling was carried out using the average method, it was concluded that Cluster 1 was dominated by low Trade and Transport Margins for all commodities, so that the distribution pattern of all commodities was efficient in 28 provinces in Indonesia. Cluster 2 has the characteristics of high Trade and Transport Margins of Rice and Cooking Oil as well as eggs and moderate sugar, so the distribution pattern is still not efficient for rice and cooking oil commodities in 4 provinces in Indonesia. Cluster 3 has the characteristics of high Trade and Transport Margins of eggs and sugar as well as medium of rice and cooking oil, so the distribution pattern is still not efficient for eggs and sugar in 2 provinces in Indonesia. Based on the results of the cluster analysis, it is hoped that it can be used as a reference in formulating and establishing policies as an effort to reduce the Trade and Transport Margins value of strategic commodities in provinces in clusters 2 and 3 according to the conditions of commodity characteristics so that the distribution pattern of strategic commodities becomes more efficient.
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