Japan, Australia and International Security Burden-Sharing with the United States

Abstract

Because contrary to the claims of some, American leadership is not simply a matter of going it alone and bearing all of the burden ourselves. Real leadership creates the conditions and coalitions for others to step up as well; to work with allies and partners so that they bear their share of the burden and pay their share of the costs and to see that the principles of justice and human dignity are upheld by all. (Obama 2011a). This chapter analyses the politics of ‘burden sharing’ in the US-Japan and the US–Australia alliances in the post-Cold War period. During the Cold War, especially from the 1960s, the US Congress frequently criticised US allies in both Europe and Asia, claiming that they did not pay a ‘fair share’ of defence costs commensurate with their economic size. In the case of the North Atlantic Treaty Organization (NATO), several Congressional initiatives, such as the Mansfield Amendments (1966–73), the Jackson/Nunn Amendment (1974) and the Nunn Amendment (1984), required either a substantial increase in defence efforts by European countries or the reduction of US troop levels in Europe (Lunn 1983: especially chapter 2). Likewise, successive US administrations, as well as Congress, frequently attempted to impose a greater defence burden on Asian allies, especially Japan (Schaller 1997). The burdensharing and ‘free-riding’ debate became particularly intense in the 1980s, when the United States suffered from a severe economic recession caused by both the increase of military expenditure and a growing national budget deficit. At this stage, a Congressional subcommittee on burden sharing was formed to investigate US strategic planning and allied contributions (Weinberger 1987).KeywordsRegional SecurityLiberal Democratic PartyInternational SecurityNorth Atlantic Treaty OrganizationBurden SharingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.