This study assesses the competitiveness of logistics and automotive industry clusters in Java economic corridor, Indonesia. The need for competitive industry clusters in Indonesia has emerged especially after the government launched its Master Plan for Accelerating and Developing Indonesia Economic Development in 2011. Justifications for the competitive industry clusters were largely drawn from the agglomeration literature. Special attention is given to Porter’s (1990) theory of industry clusters. Porter postulates that there are four factors that influence competitiveness of industry clusters: factor (input) conditions, demand conditions, context for firm strategy and rivalry, and related and supporting industries. This study employs both qualitative and quantitative analysis to measure the competitiveness of clusters in Java. The qualitative analysis uses in-depth-interviews to assess the effectiveness of the master plan from the stakeholders’ perspectives. The quantitative analysis combines location quotients (LQ), shift-share analysis, and OLS econometric models to calculate the competitiveness of logistics and automotive industry clusters in the Java economic corridor as well as to determine what factors influence competitiveness.The qualitative analysis concludes that the master plan has poor implementation especially in the lower level of governments. The LQ and shift-share analysis finds some top performing industries in both logistics and automotive clusters in Java, and these industries need to be maintained and develop further. The OLS econometric model runs four models in both clusters. These models find factors that affect logistics clusters are regional GDP, ports, population density, and workforce (factor condition); human development index, poverty rate, economic change, income per capita, and number of unemployed (demand condition); Herfindahl index of logistics firms and competitiveness (firm strategy, structure, and rivalry); and factor supply and cluster share (related and supporting industries). The regression models also found factors that affect competitiveness in automotive clusters are ports, productivity, and university enrollment (factor condition); income per capita and poverty rate (demand condition); Herfindahl index of automotive firms (firm strategy, structure, and rivalry); and cluster employment (related and supporting industries).