Housing Prices Threaten Competitiveness: How Do PRC’s Inland-Favoring Land Policies Raise Wages?
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Transcript of Housing Prices Threaten Competitiveness: How Do PRC’s Inland-Favoring Land Policies Raise Wages?
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Housing Prices Threaten Competitiveness How Do Chinas Inland-Favoring Land Policies Raise Wages?
Wenquan Liang, Ming Lu, and Hang Zhang (Shanghai Jiaotong University; Fudan university)
The views expressed in this presentation are the views of the author and do not necessarily reflect the
views or policies of the Asian Development Bank Institute (ADBI), the Asian Development Bank (ADB), its
Board of Directors, or the governments they represent. ADBI does not guarantee the accuracy of the data
included in this paper and accepts no responsibility for any consequences of their use. Terminology used
may not necessarily be consistent with ADB official terms.
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I. Introduction
Since 2003, wage has risen quicklyZhang, 2011
Cai and Du, 2011)
Two kinds of wage growth Productivity-based, not bad
If raised by housing price and land price, the
competitiveness of China's economy will be hurt.
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After 2003, wage rose quickly Zhang, 2011, CER
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Lewis turning point?
Shortage of labor? Wages will grow fast? Population policy adjustment?
When urbanization around 50%; Urban-rural income ratio > 3
The Lewis turning point of labor supply is assumed to be sharp
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Policy turning point
In 2003, land policy changed
Amount: stricter management of the construction land quota
Sale method: listing; bidding; auction
Structure: increasing supply of construction land quota in inland
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Question
Did the housing price affect the wages?
Is there any difference of the mechanism
between the east and the inland?
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Our answer
1. Housing prices significantly raised wages.
2. The impact mainly happened in east provinces due to
the distortion in land supply, especially after 2003.
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The wage-housing price interaction
Wage Housing price Demand effect Cost effect
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Inland provinces share in land supply
0.25
0.3
0.35
0.4
0.45
0.5
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
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The wage-housing price interaction
Avera
ge w
ag
e
Housing price (commercial housing sales/sales area)
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Changes in ratio of housing prices to wages East vs. Inland
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Consequence Too early industrial upgrading
Excessive capital deepening
Wage Housing price Cost effect
Demand effect
Labor Productivity (skill)
Dangerous!!!
If wage growth> productivity growth
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A Spatial Equlibrium Model
Based on the frameworks of Roback (1982) and Moretti (2011)
Each city is competitive economy
-a single tradable good of which the price can be standardized to 1 ;
-a single nontradable good, housing, whose price is determined by the
demand and the supply of housing .
The behaviors of workers and firms determine the population, wage and housing price.
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Workers
Each worker is perfect mobile and provide one unit of labor.
Each workers utility depends on nominal wage, cost of living(housing)
The log indirect utility function of worker in the city :
= (1)
Where is the log nominal wage, is the log value of housing rents, is the share of income spent on housing.
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Question 1
According to (1), the rise of housing price will cause the increase of
wage to keep the spatial equilibrium where every worker gets the same
utility level across cities.
This is our answer to Question 1
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Workers From(1), we cant know the distribution of workers across cities in the spatial
equilibrium. Therefore we introduce an idiosyncratic preference for locations.
The log indirect utility function of worker in the city : = +
where is the preference for city , the larger the value means the more favor to city .
Suppose there are two cities: Inland city and Eastern city , then ~[, ]
where characterize the importance of preference for living in city or .
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In the equilibrium, the marginal workers must be indifference between city and , then we get the following conditions:
=
Then we can know the workers in city and :
= + +
= + +
where the and are the log workers in city and , = + .
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Firms
Production function of firm in the city is
=
1 , + < 1
where is the fixed factor leading to the derived demand for labor slope down.
Suppose the capital is infinitely supply in the given interest rate . Then we get
= 1
1 + ; = ,
where =1
1 +
1
1
1 +
1 + ; = , = , =
So that
= 1
1( )
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Housing
Suppose each worker consume one unit of housing. So the demand function of housing in the city and ,
= +
= +
In our paper, we just assume the supply of housing:
= ; = ,
where the characterize the elasticity of housing supply: the larger the , the smaller the housing elasticity.
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is exogenously determined by characteristics of city which impact the
availability of land from development for two ways:
-(1) geographic characteristics, which make the land in the city undevelopable, result in
the elasticity become smaller. Such characteristics have variation across cities, but not
across time. (Diamond, 2012; Gyourko et al., 2008; Saiz,2010)
-(2) Land regulation can also have a similar effect by further restricting housing supply
(Diamond, 2012; Gyourko et al., 2008)
In China, since 2003 government has reduced construction land supply in the East cities, thus a less elastic housing supply.
The effect on the housing price, wage and population caused by land
regulation difference across cities is what we concern in our paper.
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Equilibrium According to (1)-(3), we get that:
= 1
1 1
+ +
= 1
1 1
+
+
= 1
+
= 1
+
where = 1 + 1 1 ; = ,
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Comparative Static Analysis
What if increase?
> 0;
> 0
> 0
The increase of will lead to the housing price in the city and . By the way, the increment of housing price in the city is larger than the city .
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= 1
1
+ 22 1 < 0
= 11
+ 22 1 > 0
The increase of will lead to an increase of wage in the city , but an decrease in the city .
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Question 2
In China, since 2003, government reduce construction land supply in the East city , which lead to the increase of , then
-raise the housing price in the East city , and lead to the increase of the wage in the East city ;
-raise the housing price in the Inland city , but lead to the decrease of the wage in the Inland city ;
This is our answer to the Question 2
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Endogeneity Housing prices and wages may cause each other
How to identify -- Instrument variable + Border sample
Identification
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Identification
Wage Housing price Cost effect
Demand effect
IV: per capita land supply
in the previous year Construction land quota
Note: No construction land quota data at the city-level, so we use the land supply instead.
X Border
sample
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Approved construction land (from farmland) and
land supply
Province-level grant land
Appro
ve o
f constr
uction land (
from
farm
land)
Unit: Hectare
Note: the part of farmland is mainly controlled by central government; it is exogenous for cites.
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Inland share in approved construction land (from
farmland) and land supply
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11.2
1.4
1.6
1.8
2
2.2
2.4
2.6
2.8
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
The ratio of land supply per capita:
east vs inland
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Regression model
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Level of economic development :per capita GDP
Investment intensity: fixed-asset investment/GDP
Industrial structure :the ratio of tertiary industry output to
secondary industry output
Employment density: number of staffs from secondary and
tertiary industry/ built-up area
Infrastructure: per capita road area
Education: per capita number of teachers in high school
Transportation: per capita number of buses
Environment: per capita green areas
Medical: per capita hospital bed
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All samples2001-2010 286 prefect-level cities (except Lhasa)
Source
1. City statistical yearbook 2001-2010
2. Land resource statistical yearbook 2000-2010
3. Regional development statistical yearbook 2001-2010
DATA
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First
stage
(1) (2) (3)
All sample East Inland
VARIABLES Ln(Housing price) Ln(Housing price) Ln(Housing price)
Per capita grant land -0.00499*** -0.00466*** -0.00537***
(0.000854) (0.00117) (0.00123)
Ln(Per capita GDP) 0.318*** 0.372*** 0.294***
(0.0147) (0.0237) (0.0183)
Ln(Investment intensity) 0.0111 -0.0831*** 0.0833*** (0.0137) (0.0240) (0.0162)
Ln(Industry structure) 0.205*** 0.239*** 0.164***
(0.0110) (0.0222) (0.0127)
Ln(Employment density) 0.0363*** 0.000513 0.0552***
(0.00640) (0.0120) (0.00749)
Ln(Infrastructure) -0.0214* 0.0664*** -0.0655***
(0.0130) (0.0226) (0.0153)
Ln(Transportation) 0.130*** 0.146*** 0.0896***
(0.0114) (0.0188) (0.0138)
Ln(Education) -0.0803*** -0.0170 -0.122***
(0.0264) (0.0469) (0.0310)
Ln(Environment) -0.0185** 0.0323** -0.0294***
(0.00878) (0.0160) (0.0101)
Ln(Medical) -0.0760*** -0.152*** -0.0343*
(0.0159) (0.0297) (0.0184)
Constant 4.156*** 4.536*** 4.053***
(0.245) (0.412) (0.300)
Province dummy Y Y Y
Year dummy Y Y Y
Observations 2,683 959 1,724
R-squared 0.807 0.840 0.740
First-stage F 34.0473 15.9387 18.9669
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2SLS
(1) (2) (3)
All sample East Inland
VARIABLES Lnwage Lnwage Lnwage
Ln(Housing price) 0.353*** 0.742*** -0.150
(0.116) (0.216) (0.165)
Ln(Per capita GDP) 0.129*** -0.0432 0.283***
(0.0372) (0.0800) (0.0488)
Ln(Investment intensity) 0.0300*** 0.0775** 0.0529***
(0.00916) (0.0308) (0.0176)
Ln(Industry structure) -0.0452* -0.138** 0.0418
(0.0241) (0.0556) (0.0271)
Ln(Employment density) -0.0286*** -0.0482*** 0.00808
(0.00630) (0.0103) (0.0113)
Ln(Infrastructure) -0.0219** -0.0546** -0.0506***
(0.00943) (0.0226) (0.0165)
Ln(Transportation) -0.00351 -0.0490 0.0455***
(0.0170) (0.0362) (0.0176)
Ln(Education) 0.00898 -0.0722* -0.00431
(0.0205) (0.0406) (0.0307)
Ln(Environment) 0.0135** 0.00458 -0.00546
(0.00634) (0.0151) (0.00878)
Ln(Medical) 0.0320** 0.127*** -0.0110
(0.0142) (0.0421) (0.0148)
Constant 5.947*** 4.668*** 7.982***
(0.509) (1.003) (0.698)
Province dummy Y Y Y
Year dummy Y Y Y
Observations 2,683 959 1,724
R-squared 0.877 0.790 0.867
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Why some control variables have wrong coefficients? (2SLS)
(1) (2) (3) (4)
VARIABLES Lnwage Lnwage Lnwage Lnwage
Ln(Housing price) 13.31 0.346** 0.0851 0.353***
(72.95) (0.159) (0.192) (0.116)
Ln(Per capita GDP) 0.155*** 0.129***
(0.0400) (0.0372)
Ln(Employment density) -1.874 -0.0290** -0.000330 -0.0286***
(10.65) (0.0115) (0.0135) (0.00630)
Other variables N N Y Y
Province dummy Y Y Y Y
Year dummy Y Y Y Y
Observations 2,734 2,716 2,694 2,683
R-squared 0.876 0.874 0.877
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(1) (2) (3) (4)
East Inland
2001-2003 2004-2010 2001-2003 2004-2010
VARIABLES Lnwage Lnwage Lnwage Lnwage
Ln(Housing price) 11.28 0.583*** -0.169 -0.118
(87.34) (0.162) (0.742) (0.148)
Other variables control control control control
Province dummy Y Y Y Y
Year dummy Y Y Y Y
Observations 291 668 497 1,227
R-squared 0.771 0.612 0.796
Before and after 2003
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East (1) (2) (3) (4) (5) (6)
2001-2002 2003-2004 2002-2003 2004-2005 2003-2004 2005-2006
VARIABLES Lnwage Lnwage Lnwage Lnwage Lnwage Lnwage
Ln(Housing price) 1.596 1.067 4.724 0.972* 1.067 1.513**
(2.687) (0.906) (12.62) (0.571) (0.906) (0.694)
Other variables control control control control control control
Province dummy Y Y Y Y Y Y
Year dummy Y Y Y Y Y Y
Observations 193 179 197 180 179 200
R-squared 0.426 0.393 0.426
Inland (1) (2) (3) (4) (5) (6)
2001-2002 2003-2004 2002-2003 2004-2005 2003-2004 2005-2006
VARIABLES Lnwage Lnwage Lnwage Lnwage Lnwage Lnwage
Ln(Housing price) -0.108 0.108 -0.486 0.200 0.108 -0.00919
(0.326) (0.311) (2.875) (0.304) (0.311) (0.334)
Other variables control control control control control control
Province dummy Y Y Y Y Y Y
Year dummy Y Y Y Y Y Y
Observations 322 345 343 348 345 357
R-squared 0.607 0.599 0.253 0.516 0.599 0.569
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Close to the border
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Close to the boundary
(1) (2) (3) (4) (5) (6) 2001-2010 2001-2003 2004-2010 Right Left Right Left Right Left VARIABLES Lnwage Lnwage Lnwage Lnwage Lnwage Lnwage Ln(Housing price) 0.706 4.482 1.279 0.252 0.545 6.140 (0.552) (10.26) (1.643) (0.255) (0.417) (14.36) Other variables control control control control control control
Province dummy Y Y Y Y Y Y Year dummy Y Y Y Y Y Y Observations 366 326 111 99 255 227 R-squared 0.856 0.405 0.689 0.837
t=1.2
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Bengbu
Puer Honghe
Wenshan
Xishangbanna
Yuxi
Lincang
Shaotong
Dali
Chuxiong
Dehong
Baoshan
Diqing
Nujiang
Lijiang
Kunming
Qujing
Baise
Liuzhou
Nanning
Guigang
Wuzhou
Qinzhou
Laibin
Chongzuo
Guilin
Hezhou
Hechi
Beihai
Fangchenggang
Yulin
Haerbin
Qiqihaer
Mudanjiang
Jiamusi
Daqing
Qitaihe
Suihua
Yichun
Jixi
Heihe
FuyangHuainan
liuan
Hefei
yangzhou
Hegang
Shuangyashan
Daxinganling
Xian
Tongchuan
Baoji
Weinan
Yanan
Hanzhong
Yulin
Ankang
Shangluo
Xianyang
Shenyang
Dalian
Anshan
Fushun
Benxi
Dandong
Jinzhou
Yingkou
Fuxin
Liaoyang
Tieling
Chaoyang Panjin
Huludao
Guiyang
Zunyi
Anshun
Qiannan
Qiandongnan
Tongren
Bijie
Liupanshui
Qianxinan
LasaRikeze
Changdu
Linzhi
Shannan
Naqu
Ali
Fuzhou
Xiamen
Quanzhou
Zhangzhou
Putian
Sanming
Ningde
Longyan
Nanping
Wuhan
Huangshi
Shiyan
Yichang
Xiangfan
ezhou
Jingmen Xiaogan
Jingzhou
Huanggang
Xianning
Suizhou
Enshi
Shennongjia
TianmenQianjiang
Xiantao
Haikou
Sanya
Hainan
Hangzhou Ningbo
Wenzhou
JiaxingHuzhou
Shaoxing
Jinhua
Quzhou
Zhoushan
Taizhou
Lishui
Nanchang
Pingxiang
Jian
Xinyu
Jiujiang
Ganzhou
Jingdezhen
Shangrao
Yingtan
Yichun
Fuzhou
Urumqi
Karamay
HamiTurpan
Boertala
Aletai
Tacheng
Changji
Yili
Bayinguole
Akesu
Kezilesu
Kashgar
Hotan
Changchun
Jilin
Tonghua
Siping
Baicheng
Baishan
Songyuan
Liaoyuan
Yanbian
Wuhu
Maanshan
Huaibei
Tongling
Anqing
Huangshan
Chuzhou
Bozhou
Suzhou
Xuancheng
Chaohu
Chizhou
Yinchuan
Shizuishan
Wuzhong
Guyuan
Zhongwei
Wudu
Zigong
Panzhihua
Luzhou
Deyang
Mianyang
Guangyuan
Suining
Neijiang
Leshan
Yibin
Nanchong
Guangan
Yaan
Aba
Ganzi
Bazhong
Meishan Ziyang
Dazhou
Chengdu
Liangshan
Huhehaote
Baotou
Wuhai
Chifeng
Tongliao
Eerduosi
Hulunbier
Wulanchabu
Bayanzhuoer
Xingan
Xilinguole
Alashan
Langfang
Beijing
Tianjin
Xingtai
Shijiazhuang
Baoding
Handan
Tangshan
Chengde
Cangzhou
Zhangjiakou
Qinhuangdao
Hengshui
Shanghai
Chongqing
Xining
Haidong
Haibei
Huangnan
Hainan
Guoluo
Haixi
Yushu
Changsha
Zhuzhou
Xiangtan
HengyangShaoyang
YueyangChangde
Zhangjiajie
Yiyang
Chenzhou
Yongzhou
Huaihua
Xiangsi
Loudi
Zhengzhou
Kaifeng
Luoyang
Pingdingshan
Anyang
Hebi
XinxiangJiaozuo
Puyang
Xuchang
Luohe
Sanmenxia
Nanyang
Shangqiu
Xinyang
Zhoukou
Zhumadian
Jiyuan
Nanjing
Wuxi
Xuzhou
Changzhou Suzhou
Nantong
Lianyungang
Huaian
Suqian
Yancheng
Zhenjiang
Taizhou
Guangzhou
Shenzhen
Shaoguan
Zhuhai
Aomen
Xianggang
Shantou
Fuoshan
Jiangmen
Zhanjiang
Maoming
Zhaoqing
Huizhou
Meizhou
Shanwei
Heyuan
Yangjiang
Qingyuan
Dongwan
zhongshan
Jieyang
Chaozhou
Yunfu
Jinan
Qingdao
Zibo
Zaozhuang
Dongying Yantai Weihai
Taian
Weifang
Laiwu
Jining
Linyi
Rizhao
Dezhou
Heze
Binzhou
Liaocheng
Linfen
Jinzhong
Yangquan
Datong
Yuncheng
Jincheng
Shuozhou
Lvliang
Changzhi
Xinzhou
Taiyuan
Jiuquan
Taiwan
Liaoning
Hebei=east
Close to the border
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The share of land supply on the
right side of the boundary
2001-2003 2004-2010 t
East (right) 0.791 0.729 1.824
East (except
for LN, and
HB)
0.613 0.535 1.677
LN & HB 0.178 0.194 -0.892
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(1) (2) (3) (4) (5) (6)
2001-2010 2001-2003 2004-2010
Right Left Right Left Right Left VARIABLES Lnwage Lnwage Lnwage Lnwage Lnwage Lnwage
Ln(Housing price) 0.601* 1.452 1.106 0.140 0.396* -6.147
(0.340) (4.838) (1.038) (0.209) (0.236) (44.68)
Other variables control control control control control control
Province dummy Y Y Y Y Y Y
Year dummy Y Y Y Y Y Y
Observations 237 219 72 68 165 151
R-squared 0.878 0.635 0.521 0.748 0.874
Border samples
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Other factors?
1.Did the east experience faster growth in per capita GDP
1.2
1.3
1.4
1.5
1.6
1.7
1.8
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
The ratio of secondary and tertiary industries output in east to that in inland
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2. Did east experience faster growth in minimum wage?
Other factors?
0
100
200
300
400
500
600
700
800
900
2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 2 0 0 6 2 0 0 7 2 0 0 8 2 0 0 9 2 0 1 0
The trend of minimum wage
Eastern cities Inland cities
-
1.05
1.1
1.15
1.2
1.25
1.3
1.35
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
East-inland ratio of average minimum wage
-
(1) (2) (3)
All sample East Inland
VARIABLES Lnwage Lnwage Lnwage
Ln(Housing price) 0.357*** 0.732*** -0.130
(0.113) (0.206) (0.162)
Ln(minimum wage) 0.0461 -0.265* 0.108***
(0.0428) (0.148) (0.0347)
Other variables control control control
Province dummy Y Y Y
Year dummy Y Y Y
Observations 2,681 960 1,721
R-squared 0.882 0.843 0.865
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Robustness checks
Change IV to per capita land supply in current year
-
(1) (2) (3)
All sample East Inland
VARIABLES Lnwage Lnwage Lnwage
Ln(Housing price) 0.160** 0.522*** -0.157 (0.0736) (0.138) (0.119)
Other variables control control control
Province dummy Y Y Y
Year dummy Y Y Y
Observations 2,693 962 1,731
R-squared 0.894 0.854 0.868
IVper capita land supply in current year
(1) (2) (3) (4) 2001-2003 2004-2010 East Inland East Inland VARIABLES Lnwage Lnwage Lnwage Lnwage Ln(Housing price) 3.039 -0.219 0.628*** -0.138
(10.98) (0.849) (0.150) (0.113)
Other variables control control control control
Province dummy Y Y Y Y
Year dummy Y Y Y Y
Observations 294 503 668 1,228 R-squared 0.583 0.752 0.793
-
Robustness Check -2
Another instrument variable
IVland supply/ urban (district) area
-
(1) (2) (3) All sample East Inland VARIABLES Lnwage Lnwage Lnwage Ln(Housing price) 0.380** 0.230* -0.0393 (0.156) (0.130) (0.470) Other variables control control control
Province dummy Y Y Y Year dummy Y Y Y Observations 2,682 959 1,723 R-squared 0.873 0.903 0.878
IV land supply/ urban (district) area
(1) (2) (3) (4)
2001-2003 2004-2010
East Inland East Inland
VARIABLES Lnwage Lnwage Lnwage Lnwage
Ln(Housing price) 8.096 0.0159 0.288** 0.114
(152.3) (0.329) (0.131) (0.220)
Other variables control control control control
Province dummy Y Y Y Y
Year dummy Y Y Y Y
Observations 291 497 668 1,226
R-squared 0.677 0.856 0.813
-
Conclusion
1Housing prices significantly pushed up wages
2After 2003, the misallocation of land supply to the inland raised the housing price in the East, then drove wages up.
Policy implications The allocation of land supply should match with the flow of
population
Allow the transaction of construction land quota among regions
-
Thanks! Comments welcome