财经研究  2018, Vol. 44 Issue (2): 44-57

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#### 文章信息

 财经研究2018年44卷第2期

Yang Pu, Liu Jun, Chang Wei.

The impacts of distortion and relaxation of the hukou system on chinese economy: theory and evidence

Journal of Finance and Economics, 2018, 44(2): 44-57.

### 文章历史

《财经研究》
2018第44卷第2期

1. 华金证券股份有限公司 研究所，上海 200127;
2. 上海财经大学 金融学院，上海 200433

The Impacts of Distortion and Relaxation of the Hukou System on Chinese Economy: Theory and Evidence
Yang Pu1, Liu Jun1, Chang Wei2
1. Research Institute，Huajin Securities Co.，Ltd，Shanghai 200127，China;
2. School of Finance，Shanghai University of Finance and Economics，Shanghai 200433，China
Summary: In order to promote long-term economic development, on the one hand, we could increase the technology level or the quantity and quality of capital and labor input, on the other hand, we could enhance the disposition efficiency by reducing the friction of factor flow. At present, the labor force in China has reached the ceiling, but with the development of the economy, the non-agriculture sectors have the increasing demand for labor force. The hukou system is the main obstacle to labor flow in China, and an increase in labor supply in non-agriculture sectors by a reduction in the friction of labor flow from rural to urban areas through the reform of hukou system is very important for achieving the long-term goal of keeping high-speed economic development. Is the distortion of the hukou system high or low? What is the impact of the relaxation of hukou system on the economy? The answers to these questions are helpful for the governments to evaluate the space and economic benefits of the reform of hukou system, and provide guide for the ongoing reform of hukou system. Based on the two-sector labor flow model of Hansen and Prescott（2002）, this paper divides workers into agriculture and non-agriculture ones, introduces distortion factors, and then constructs a hukou distortion model with two sectors and two classes of workers. Using the macroeconomic data between 1984 and 2013, this paper measures the hukou distortion degree, simulates the change process of economic variables such as employment, wage rates, added value and social capital in 2013 when the hukou system has been gradually relaxed after taking the South Korea as a reference. It gives the answers to the research questions. Moreover, through an extension of the benchmark model, this paper provides a theoretical framework to study the influences of the hukou system, by constructing a provincial heterogeneous hukou distortion model with hukou differences in " agriculture and non-agriculture” sectors and " local and non-local” hukou differences. It comes to the following conclusions: firstly, due to synchronization of economic reform and hukou reform, the hukou distortion degree in China is presented in the inverted U-shape form, and has much room for improvement compared with South Korea. Secondly, if the hukou system is fully relaxed in 2013 after taking South Korea as a reference, the employment in agriculture and non-agriculture sectors will decrease by 58.83% and increase by 26.92% respectively, the wage rate will increase by 19.44% and decrease by 6.77% respectively, and the social added value will increase by 15.33%. The growth of social added value indicates the promotion of social and economic efficiency, and shrinking wage gap between these two sectors indicates that the gap between rural and urban areas has been narrowed. The conclusions of this paper provide important enlightenment for the reform of hukou system in China. It is mainly reflected in the following aspects: firstly, after 2006, the degree of household registration distortion in China has not been improved greatly, and the rising prices in cities are restricting the transfer of labor from rural to urban areas and from less-developed to developed cities. Secondly, a reduction in restrictions on hukou system helps to slow down the trend of labor force flow inversely from first or second tier cities to third or fourth tier cities and from towns to rural areas since 2016. Thirdly, the formulation of hukou system reform policies to reduce the social welfare gap between agriculture and non-agriculture households can solve the problem of labor shortage in non-agriculture sectors effectively. The theoretical contributions of this paper are as follows: firstly, by dividing workers into agriculture and non-agriculture ones, and introducing hukou distortion factor, this paper constructs a hukou distortion model with two sectors and two classes of workers. The model can better measure the hukou distortion degree and evaluate the influence of the hukou system on the economy. Secondly, it establishes a provincial heterogeneous hukou distortion model with ‘agriculture & non-agriculture’ and ‘local & non-local’ differences, and provides a theoretical framework for discussing the impact of these two distortions on the economy. The practical contributions are as follows: firstly, the measurement of the hukou distortion degree and the analysis of the reason for the evolution process are of great significance for the governments to make hukou system reform policies. Secondly, it simulates the changes in economic variables in agriculture and non-agriculture sectors in the process of relaxing the hukou distortion degree and evaluates the economic benefits brought by relaxing hukou system, providing theoretical basis for further hukou system reform in China.
Key words: hukou system    agriculture sector    non-agriculture sector    total factor productivity

①农业户口和非农户口在我国也被称为农村户口和城市户口。

②现有部门间劳动力错配文献，大多只考虑了农业部门和非农部门的差异，而没有考虑工人的户籍差异，如Hansen 和Prescott（2002）、袁志刚和解栋栋（2011）、柏培文（2012）以及盖庆恩等（2013）；绝大多数现有户籍扭曲文献对工人的消费函数进行了一定假设，本文的理论只基于工人的消费约束而没有对工人的消费函数加以假定，使得基于理论的实证分析结果更加稳健。

（一）工人

 ${P_m}{c_u} {\text{≤}} {w_m}(1 - {t_1})$ (1)

 ${P_r}{c_r} {\text{≤}} {w_r}(1 + {t_2})$ (2)

 ${P_m}{c_r} {\text{≤}} (1 - \tau ){w_m}(1 - {t_1})$ (3)

 $(1 - \tau )\frac{{{w_m}}}{{{P_m}}}(1 - {t_1}) = \frac{{{w_r}}}{{{P_r}}}(1 + {t_2})$ (4)

（二）厂商

1. 非农部门

 ${Y_m} = {A_m}{({K_m}^{{\alpha _m}}{L_m}^{1 - {\alpha _m}})^\eta }$ (5)

 $\mathop {\max }\limits_{_{{L_m},{K_m}}} {\pi _m} = {Y_m} - {w_m}{L_m} - (1 + \mu )R{K_m}$ (6)

 ${L_m} = \frac{{1 - {\alpha _m}}}{{{w_m}}}\eta {Y_m}$ (7)
 ${K_m} = \frac{{{\alpha _m}}}{{(1 + \mu )R}}\eta {Y_m}$ (8)

2. 农业部门

 ${Y_r} = {A_r}{(s{L_r})^\phi }$ (9)

 $\mathop {\max }\limits_{{L_r}} {\pi _r} = {Y_r} - {w_r}s{L_r}$ (10)

 $\phi \frac{{{Y_r}}}{{s{L_r}}} = {w_r}$ (11)

（三）市场出清

 $L = {L_m} + L_r^r$ (12)

（四）一般均衡

（五）比较静态分析

 $s = \displaystyle\frac{{\phi {Y_r}({L_u} + {L_r})\displaystyle\frac{{1 + {t_2}}}{{1 - {t_1}}}\displaystyle\frac{{{P_m}}}{{{P_r}}}}}{{(1 - \tau )(1 - {\alpha _m})\eta {Y_m}{L_r} + \phi {Y_r}{L_r}\displaystyle\frac{{1 + {t_2}}}{{1 - {t_1}}}\displaystyle\frac{{{P_m}}}{{{P_r}}}}}$ (13)

 年份 1984 1985 1986 1987 1988 1989 1990 1991 τ 0.391 0.413 0.428 0.442 0.473 0.505 0.469 0.475 年份 1992 1993 1994 1995 1996 1997 1998 1999 τ 0.475 0.474 0.463 0.44 0.417 0.41 0.410 0.410 年份 2000 2001 2002 2003 2004 2005 2006 2007 τ 0.41 0.41 0.409 0.41 0.408 0.395 0.390 0.389 年份 2008 2009 2010 2011 2012 2013 τ 0.389 0.389 0.389 0.39 0.389 0.39

 图 1 户籍扭曲程度和城镇居民与农村居民的人均收入之比的正相关性

 年份 Am Ar wm wr 1+µ 年份 Am Ar wm wr 1+µ 1978 1.097 0.443 1.067 0.288 0.502 1997 3.550 0.950 2.910 0.592 1.190 1979 1.158 0.466 1.102 0.302 0.541 1998 3.710 0.975 3.122 0.607 1.166 1980 1.239 0.453 1.145 0.293 0.592 1999 3.891 0.989 3.371 0.613 1.146 1981 1.258 0.476 1.134 0.306 0.611 2000 4.107 1.006 3.649 0.622 1.139 1982 1.328 0.516 1.184 0.329 0.647 2001 4.318 1.024 3.939 0.633 1.125 1983 1.432 0.555 1.251 0.353 0.703 2002 4.577 1.046 4.305 0.645 1.112 1984 1.544 0.631 1.283 0.403 0.779 2003 4.861 1.081 4.693 0.669 1.101 1985 1.726 0.638 1.406 0.407 0.866 2004 5.055 1.183 4.948 0.738 1.078 1986 1.816 0.657 1.460 0.418 0.899 2005 5.324 1.284 5.311 0.807 1.061 1987 1.970 0.681 1.580 0.432 0.948 2006 5.681 1.396 5.778 0.885 1.057 1988 2.143 0.688 1.723 0.435 0.997 2007 6.181 1.490 6.433 0.952 1.072 1989 2.205 0.692 1.800 0.436 0.994 2008 6.466 1.601 6.914 1.028 1.043 1990 2.077 0.655 1.586 0.399 0.978 2009 6.689 1.712 7.387 1.108 0.990 1991 2.260 0.668 1.740 0.407 1.032 2010 7.055 1.834 8.042 1.195 0.960 1992 2.548 0.704 1.973 0.430 1.120 2011 7.335 1.987 8.572 1.307 0.928 1993 2.803 0.753 2.179 0.462 1.178 2012 7.536 2.128 9.067 1.409 0.884 1994 3.044 0.801 2.387 0.494 1.214 2013 7.703 2.326 9.444 1.560 0.848 1995 3.215 0.861 2.548 0.535 1.212 2014 7.881 2.537 9.842 1.721 0.819 1996 3.365 0.919 2.700 0.573 1.198

 $\tau = 1 - \frac{{{w_r}}}{{{w_m}}}\frac{{1 + {t_2}}}{{1 - {t_1}}}\frac{{{P_m}}}{{{P_r}}} = 1 - \frac{{215}}{{275}} = 0.218$ (14)

 基准模型 t=10% t=30% t=50% t=100% Ym 103.90 106.61 111.44 115.51 122.94 Ym变化率 0% 2.61% 7.26% 11.17% 18.33% Yr 4.71 4.40 3.83 3.32 2.32 Yr变化率 0% −6.56% −18.69% −29.47% −50.82% Y 108.61 111.01 115.27 118.83 125.26 Y变化率 0% 2.21% 6.13% 9.41% 15.33% wm 9.44 9.34 9.17 9.04 8.81 wm变化率 0% −1.07% −2.87% −4.32% −6.77% wr 1.56 1.59 1.64 1.70 1.86 wr变化率 0% 1.68% 5.34% 9.12% 19.44% ${L_r^r}$ 2.42 2.22 1.87 1.56 0.99 ${L_r^r}$ 变化率 0% −8.11% −22.80% −35.38% −58.83% Lm 5.28 5.48 5.83 6.14 6.70 Lm变化率 0% 3.72% 10.42% 16.18% 26.92% Km 279.94 287.25 300.25 311.22 331.25 Km变化率 0% 2.61% 7.25% 11.17% 18.33%

①资本存量的上升是由国外资本的流入、社会闲散资金的流入和社会资本利用率的提升等原因造成的。

（一）工人

 ${P_m}(i){c_u} {\text{≤}} {w_m}(i)(1 - {t_1}(i))$ (15)

 ${P_m}(j){c_u} {\text{≤}} (1 - \varphi (j)){w_m}(j)(1 - {t_1}(j))$ (16)

 $\frac{{{w_m}(j)}}{{{P_m}(j)}}(1 - {t_1}(j))(1 - \varphi (j)) = \frac{{{w_m}(i)}}{{{P_m}(i)}}(1 - {t_1}(i))$ (17)

 ${P_r}(i){c_r} {\text{≤}} {w_r}(i)(1 + {t_2}(i))$ (18)

 ${P_m}(i){c_r} {\text{≤}} (1 - \tau (i)){w_m}(i)(1 - {t_1}(i))$ (19)

 $((1 - \tau (j))\frac{{{w_m}(j)}}{{{P_m}(j)}}(1 - {t_1}(j)) = \frac{{{w_r}(i)}}{{{P_r}(i)}}(1 + {t_2}(i))$ (20)

（二）厂商

1. 非农部门

 ${Y_m}(i) = {A_m}(i){({K_m}{(i)^{{\alpha _m}}}{L_m}{(i)^{1 - {\alpha _m}}})^\eta }$ (21)

 $\mathop {\max }\limits_{_{{L_m}(i),{K_m}(i)}} {\pi _m}(i) = {Y_m}(i) - {w_m}(i){L_m}(i) - (1 + \mu (i))R{K_m}(i)$ (22)

 ${L_m}(i) = \frac{{1 - {\alpha _m}}}{{{w_m}(i)}}\eta {Y_m}(i)$ (23)
 ${K_m}(i) = \frac{{{\alpha _m}}}{{(1 + \mu (i))R}}\eta {Y_m}(i)$ (24)

2. 农业部门

 ${Y_r}(i) = {A_r}(i){(L_r^r(i))^\phi }$ (25)

 $\mathop {\max }\limits_{{L_r}(i)} {\pi _r}(i) = {Y_r}(i) - {w_r}(i)L_r^r(i)$ (26)

 $\phi \frac{{{Y_r}(i)}}{{L_r^r(i)}} = {w_r}(i) \Rightarrow L_r^r(i) = \frac{\phi }{{{w_r}(i)}}{Y_r}(i)$ (27)

（三）市场出清

1. 劳动力市场出清

2. 资本市场出清

 ${K_m} = \sum\limits_{i = 1}^N {{K_m}(i)}$ (28)

* 作者感谢匿名审稿人和编辑的宝贵意见，也感谢上海财经大学田国强教授和IMF人力资源部何晖副教授的悉心指导。当然，文责自负。

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