It’s Where You Are That Matters: Network position and central agency termination

Bureaucracy is not immortal and it is theoretically relevant to explain why agencies are terminated or restructured. A group of recent studies has documented the driving forces of agency termination, but few of them pay special attention to the position of agencies in the interagency network and its termination effect. Agencies are not isolated silos, but work together with each other. Some agencies are stars or bridges occupying the core of the network, while others are peripheral in connections with peers. It is interesting to explore to what extent agency position is relevant to agency termination. In this study, we empirically examine whether and how an agency’s network position affects its risk of termination. We expect that agencies central in the network are less likely to be terminated because they are entrenched in interrelated power and interests. We also use the configurational approach to group agencies by their degrees of core and periphery, and we predict that the core-core and core-periphery agencies are less likely to be restructured than the periphery-core and periphery-periphery ones. We hypothesize that the network effect on agency termination would be conditional on agency attributes (i.e., size) and network forms. We test the theoretical hypotheses in the context of central agencies in China, characterized by a unitary, authoritarian system governed by a sole ruling party. The agencies under the control of the State Council (cabinet) undergo critical reshuffle every five years, and their connections with each other vary substantially. It is thus intriguing to examine how the position of an agency would influence its termination probability. The government work report delivered by the Premier to the National People’s Congress every year highlighted the key performance tasks of the central government. These tasks are disaggregated and designated to agencies, individually or collectively, creating an agency-agency task network. We use social network analysis to identify agencies’ connections with each other, and measure network positions by various centrality degree indicators. We control for conventional characteristics of agencies (e.g., age, function, rank, size) and political environment (e.g., Premier turnover, political movement, macroeconomy), and use survival analysis to examine the variations of network position and its impact on agency termination from 2004 to 2022. The findings generally support our hypotheses. We also compare the results with that of other countries, and conclude with implications and future research avenues.