跨越中等收入陷阱—基于渐进式结构性消费提振视角

An Alternative Way for Crossing the Potential Middle-Income Trap—Research Based on Boosting Consumption Share in Expenditure Structure of China

摘要: 中国如何避免中等收入陷阱？学术界已有不少研究，但依然观点各异。本文将该问题略微调整，由抽象变为具体，认为待解决的问题应是：如何在避免中等收入陷阱过程中少走弯路？本文假定掉入陷阱的最大风险源于宏观消费水准具有不确定性，探析此种情形下的种种可能后果，提出相应对策。借助于对西方及时行乐(instant gratification)消费等理论的批判与吸收，通过建立一个分析模型，实证发现：第一，农村人均可支配收入水平增量与总消费增量之间呈较强正相关关系(对比之下，城镇则有弱负相关性)；第二，大起大落的偶发性消费增减会对跨越陷阱有消极影响；第三，从长期来看，投资对于维持和提高消费量的直接效应较为明显，而其间接效应尤其突出。为此，既需要在宏观调控中保持消费水准连增性，也至少需要维持投资率的适中性，避免经济大起大落。供给侧改革与需求管理尚不宜被认为是简单的矛盾冲突关系。引导投资方向，比简单地抑制投资更为重要。采取渐进式而非激进式提升消费占国民收入的份额方式，更有助于在规避中等收入陷阱过程中少走弯路。

Abstract: How could China avoid the middle-income trap? There are divergent opinions. In this paper, we would like to slightly adjust the problem statement from abstract to concrete, i.e., way of thinking to solve problem should be: How to avoid the middle-income trap with less detours in the process? The problem itself will be clearer by assuming the biggest risk is from uncertainty of consumption in macro level. Through establishment of an inclusive theoretical framework for domestic con-sumption enhancement in expenditure structure, the present paper tries to find advantageous factors for gradually stimulating consumption in China. Based on the hypothesis of consumers’ in-stant gratification, which is from Pro. Laibson in Harvard University, investment and consumption are found not completely a tradeoff in statistic in long-run, though a high investment rate is more likely a "double-edged sword" in short-run. Narrowing the gap between the average personal dis-posable incomes of the urban and rural areas, and ensuring a basic investment rate at least for helping employment of people who have farmer identities more or less, will be more helpful to avoid the potential middle income trap of China, while related investment supervision institution probably including loaning innovation should be seriously established for founding more adequate investment paths besides other institutional innovation. Methods of FMOLS and PC analysis are used in detail for finding these findings.