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基于搜寻的知识增长模型(或称Idea FloW理论)是增长理论的重要进展。该理论克服了内生增长理论对知识假设的不现实性,给出了知识增长和技术扩散的微观基础,准确地刻画了知识的准公共品性质,还很好地拟合了经验数据。Jovanovic&Rob(1989),Kortum(1997)及Lucas(2009)的原创性工作从不同角度将搜寻引入知识创造与传播过程。Lucas&Moll(2014)在Lucas(2009)基础上考虑个体的动态优化问题,利用Mean Field Games给出了平衡增长路径;Luttmer(2012a),Buera&Oberfield(2016)用不同的方式整合了Kortum(1997)与Lucas(2009)的思路,各自提出统一的理论框架。目前,基于搜寻的知识增长模型已经被用来研究企业动态、国际贸易与技术扩散、组织结构与收入分配以及空间和城市发展等问题,取得了可观的成果。
Search-based knowledge growth model (or Idea FloW theory) is an important advancement of growth theory. This theory overcomes the impracticality of endogenous growth theory to knowledge hypothesis, gives the micro-foundation of knowledge growth and technology diffusion, accurately characterizes the quasi-public goods of knowledge, and fits well the empirical data. The original work of Jovanovic & Rob (1989), Kortum (1997) and Lucas (2009) introduces search from different perspectives into the process of knowledge creation and dissemination. Lucas & Moll (2014) considered individual dynamic optimization problems on the basis of Lucas (2009) and gave a balanced growth path using Mean Field Games. Luttmer (2012a) and Buera & Oberfield (2016) incorporated Kortum (1997) and Lucas (2009), each proposed a unified theoretical framework. Currently, search-based knowledge growth models have been used to study issues such as corporate dynamics, international trade and technology diffusion, organizational structure and income distribution, as well as spatial and urban development and have yielded substantial results.