| DU Yang,ZHANG Xi.China's Natural Rate of Unemployment and Its Application in Macro-regulation Policies[J].The Journal of quantitative and technical economics,2022,(12):26-45 |
| 中国自然失业率及其在调控政策中的应用 |
| China's Natural Rate of Unemployment and Its Application in Macro-regulation Policies |
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| DOI： |
| 中文关键词: 自然失业率 宏观调控政策 三角模型 Kalman滤波 |
| 英文关键词: Natural Rate of Unemployment Macro-regulation Policies Triangle Model Kalman Filter |
| 基金项目:作者感谢国家自然科学基金专项项目“中国人口转变的独特性、经济影响及政策研究”（项目批准号：72141310）的资助。 |
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| 中文摘要: |
| 随着中国劳动力市场统计体系逐步完善，对自然失业率进行常态化估计的条件已经具备。自然失业率不仅是将劳动力市场运行状况与宏观经济指标相互关联的枢纽，通过自然失业率的水平确定周期性失业的规模，也是完善积极就业政策的必要途径。本文以菲利普斯曲线作为理论基础，利用经典的三角模型对2017年1月至2022年4月中国的自然失业率进行了估计。结果表明，中国不变的自然失业率在5.06%左右；利用Kalman滤波估计得到的可变自然失业率，在2022年4月为5.15%左右。根据估计结果，2022年第一季度的周期性失业规模明显增加，宏观经济政策需要根据失业性质的变化积极做出反应，通过扩大有效需求弥补需求缺口，降低失业。 |
| 英文摘要: |
| Rapid growth and structural change have been the features of China's economy for a long time, rather than economic cycle. As a result, in China investigations into the short-term relations among macroeconomic indexes are insufficient, and the system of macro-regulation policies responsible to curb fluctuations is incomplete. Especially, an explicit and close connection has not been established between macro policies and the situation of the labor market, such that the implementation of the “Employment First” Strategy faces practical impediments. This problem is worsening against the background of growing internal and external challenges. Making estimations of the natural rate of unemployment is key to associate macro regulation and labor market monitoring. By identifying the natural and cyclical parts of unemployment, the authority can determine when and how to use policy tools more easily, thereby largely benefiting the maximization of employment and the stabilization of expectations. However, existing studies employed outdated yearly data, providing estimated natural rates of unemployment that are neither precise nor of practical significance. The current improvement of the system of labor market statistics in China included the introduction of the monthly surveyed urban unemployment rate, which allows to perform more up-to-date, frequent, and accurate estimations of China's natural rate of unemployment.Based on the theoretical Phillips Curve and using recent monthly data, this paper estimates China's natural rate of unemployment from January 2017 to April 2022. We establish a triangle model including inflation expectation, demand gap, i.e., the difference between the unemployment rate and the natural rate of unemployment, and supply shocks to explain inflation change. By conducting single-equation regressions, the time-invariant natural rate of unemployment (or Non-Accelerating Inflation Rate of Unemployment, NAIRU) is estimated. Furthermore, the state space model and the Kalman filter are applied to estimate the time-varying NAIRU.The results indicate that the substitutive relation between inflation and unemployment described by the classic Phillips Curve holds in China. The invariant NAIRU during the sample period was around 5.06%, while in April 2022 the time-varying NAIRU was about 5.15%. According to the estimates, cyclical unemployment dramatically expanded in the first quarter of 2022, reaching a scale of 5 million in April 2022. Macro policies are needed to respond actively to the change in the nature of unemployment and reduce it by filling the demand gap.This study contributes to the literature in several ways. First, it takes the lead in using China's monthly data to determine the Phillips Curve and estimate the natural rate of unemployment. Compared to existing studies based on yearly data, our study provides a more relevant analysis on the short-term relation between inflation and unemployment, and more precise estimations of recent NAIRU. Second, unlike the majority of existing studies, which applied the triangle model to China, in our study crude oil price and the USD index are chosen as the supply variables, thereby ensuring a higher validity in the estimations. Third, to obtain the time-varying NAIRU, we make an international comparison among estimations using data with different frequencies, demonstrating how the variance of NAIRU vary between monthly and quarterly settings. Last but not least, this study argues that the natural and cyclical unemployment rates should be taken as important indicators to monitor the labor market and implement macro policies. |
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