“软环境建设”是对区域内制度环境进行系统性全方位的优化，进而提升区域吸引力和竞争力的一类特殊政策，该政策具有政策过程面向多元化的政策对象、政策设计需围绕政策对象开展、政策绩效主要由政策对象生产、政府退居服务者角色等一系列特征。这些特征导致“软环境建设”呈现出政策目标复杂、绩效生产间接、政策周期长等政策过程问题，政策实施面临着巨大挑战。但面对相同的“软环境建设”困境，地方政府缘何形成差异化的启动步伐和政策绩效？本文以中国营商环境建设作为“软环境建设”的典型领域，细化研究问题：什么因素影响地方政府采纳营商环境政策？营商环境政策不断迭代对政策绩效会产生何种影响？在探索性案例研究基础上，本文基于“软环境建设”中“政策首次采纳即为第一次政策设计”的特点，将政策采纳与政策执行过程相结合，构建了“软环境建设”的政策过程整体性分析框架，并分别提炼“潜在的政策收益影响政策采纳”和“动态的政策设计影响政策绩效”两个理论命题。在定量分析部分，利用2009-2018年地方政府首次采纳营商环境政策的面板数据，使用时间序列EHA模型检验地方政府营商环境政策采纳的影响因素。其次将动态的政策设计具体化为政策组合规模和政策组合复杂度两个指标，构造了包含198种政策组合的营商环境“政策目标-政策工具”矩阵，分析全国各地级市出台的1528份政策，编码形成44045条政策组合数据，计算生成地级市营商环境政策组合规模和政策组合复杂度面板数据集，并使用面板回归模型检验营商环境动态政策设计对政策绩效的影响。本文实证发现如下：第一，在采纳阶段，城市头部企业重要性显著负向影响营商环境政策采纳概率；而当头部企业重要性相同时，头部企业对供应链上越多企业存在需求，政策采纳概率越高。这表明地方政府对潜在政策收益的分析能够影响政策采纳。第二，在实施阶段，营商环境政策组合规模对城市创业活力和城市创新能力都存在显著正向影响，而政策组合复杂度仅对生产性服务业创业活跃度和每千人实用新型专利授权量有显著负向影响。这表明“动态的政策设计”能够影响政策绩效，但其不同维度特征对政策绩效影响存在差异，对不同政策对象的影响也并不一致。本研究理论贡献如下：建立了“软环境建设”政策过程整体性分析框架，并以营商环境建设作为具体政策领域，定量验证了“潜在的政策收益”对政策采纳的影响和“动态的政策设计”对政策绩效的影响，发展了对“软环境建设”这一新政策现象的学理认知。
Soft environment improvement refers to the systematic and comprehensive optimization of the institutional environment within a region to enhance its attractiveness and competitiveness. This special policy type has rarely been the focus of research. The policy process of soft environment improvement involves diverse policy objects. It requires designs that revolve around these objects, with policy performance mainly generated by these objects, and the government receding to a facilitator‘s role. These characteristics result in complex policy objectives, indirect performance production, and long policy cycle, presenting significant challenges in policy implementation. However, facing similar challenges in soft environment improvement, why do local governments initiate at different paces and achieve differentiated policy performances? This paper takes the improvement of the business environment in China as a typical field of soft environment improvement and further refines the research questions: What factors influence local governments‘ adoption of business environment policies? How does the continuous iteration of business environment policies affect policy performance?Building on two exploratory case studies, this paper combines policy adoption with the execution process to construct an integrative analytical framework for the policy process of soft environment improvement. It is based on the feature that policy adoption is the initial policy design in soft environment improvement. It extracts two theoretical propositions: Potential Policy Benefits Influence Policy Adoption and Dynamic Policy Design Impacts Policy Performance. In the empirical analysis section, this study uses data from 2009 to 2018 on local governments‘ first-time adoption of business environment policies, employing a time-series EHA model to examine the factors influencing the adoption of these policies. Additionally, dynamic policy design is operationalized into two indicators: the size of policy portfolios and the complexity of policy portfolios. A Policy Goal-Policy Tool matrix containing 198 policy combinations was constructed to analyze 1,528 policies issued across various prefecture-level cities, resulting in a dataset of 44,045 policy combinations.Then this study calculate and generate a panel data of the size and complexity of prefecture-level business environment policy portfolios. Further, this study uses a panel regression model to test the effects of dynamic policy design on policy performance (City Entrepreneurial Vitality and City Innovation Capability).The empirical findings are as follows. Firstly, during the adoption phase, the importance of leading enterprises significantly negatively affects the probability of adopting business environment policies; however, When the importance of the leading enterprises is the same, the more enterprises in the supply chain are needed by the leading enterprises, the higher the probability of policy adoption. This result suggests that local governments‘ analysis of potential policy benefits can influence policy adoption. Secondly, during the implementation phase, the size of business environment policy portfolios has a significant positive effect on both city entrepreneurial vitality and city innovation capability, while the complexity of policy portfolios only negatively affects city entrepreneurship activity of the productive service sector and utility model patent authorization per thousand people. Moreover, government organizational capacity can positively modulate the impact of the size of policy portfolios to policy performance. This demonstrates that "dynamic policy design" can influence policy performance, but the impact of its different dimensions varies, and its effects on different policy objects are not consistent.This research contributes to the theoretical understanding by establishing an integrative analysis framework for the policy process of soft environment improvement and empirically verifying the influence of potential policy benefits on policy adoption and the influence of dynamic policy design on policy performance, thereby advancing the academic understanding of this new policy phenomenon and enriching the study of public policy processes in China. Methodologically, the paper describes the time-varying characteristics of policy design more accurately, extends the research scope of policy design in the Chinese context, and clarifies the relationship between dynamic policy design and policy performance. Practically, it provides crucial decision-making references for effectively promoting the adoption and implementation of soft environment improvement.
