
doi: 10.1111/padm.12834
AbstractCan network administrative organizations (NAOs) improve networks' ability to solve complex social and environmental problems? This is a classical question in collaborative governance. The public management literature examines collaborative outcomes at either the organization or the entire network level, but has not addressed “edge level” outcomes to evaluate structured interactions among network actors. Therefore, we investigate outcomes in an interjurisdictional area that reflect collaborative efforts between local governments. Recently, Guangdong Province in China enacted the River Chief System, an institutional reform that mandates the provincial government to establish an NAO to coordinate intercity rivers' management. To assess how well the reform has worked to reduce pollution, we employ the synthetic control method using monthly water quality data from 14 river monitoring sites in two neighboring cities. Our results indicate that the reform reduced the interjurisdictional river sites' pollution level effectively by 36% in the following year. This preliminary finding contributes to the collaborative governance theory and provides new evidence on whether the NAO model improves the shared outcomes between local governments.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 17 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
