Populism in Taiwan: The Rise of a Populist-Democratic Culture in a Democratising Society

Taiwan's democratic transition has emerged alongside a rise of populism. Based on an analysis of post-electoral survey data, it is shown that populist resentments – embodied in such emotion-laden campaign issues as ethnic identity, national identity and a party's image of interest representation and clean politics – have been the most efficient vote-getting appeals in Taiwan's post-authoritarian electoral competition between two major political parties, the Democratic Progressive Party (DPP) and the nationalist Kuomintang (KMT). In Taiwan's democratic transition, mass demands for the ‘indigenisation’ of politics and the people's worry about an ever-increasing military threat from Mainland China have also popularised as well as polarised these populist appeals. As empirical data show, due to its position as the first Taiwanese party with a lion's share of populist advantages, the DPP was able to win the 2000 and 2004 presidential elections. In sum, Taiwan's electoral politics in the past decade have given rise to a kind of ‘populist-democratic culture’, which inclines Taiwanese politicians to bring up populist issues rather than the rational policy debates of an electoral democracy.     

The Policy of High‐Tech Industry Development: The Case of Location Assessment for Biotech Industry Parks in Taiwan

Biotechnology will be the star industry of the 21^st century, and will also be one of the main focal areas of Taiwan's future industrial development. Many scholars have suggested that the presence of geographical clusters is an important factor that determines an industry's international competitiveness. As an example of the clustering effect, when high‐tech industry was just starting to take off in Taiwan, the establishment of the Hsinchu Science‐Based Industrial Park successfully promoted the upgrading of the industrial structure. The goal of this study is to explore the development of an industrial park location selection model for Taiwan's biotech industry, while considering the influence of the clustering effect, with the ultimate intent of promoting the industry's development. Because clustering factors are certainly not necessarily independent, and may entail a feedback effect, this study uses the analytic network process (ANP) multiple criteria decision‐making (MCDM) model to construct a “biotech park” location selection model, and then determine the optimal location of a biotech park from three alternatives. The results suggest that Taipei City would be the optimal choice for a biotech park. This finding can serve to guide the government's biotech industry development policies.