housing prices threaten competitiveness: how do prc’s inland-favoring land policies raise wages?

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.

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.

After 2003, wage rose quickly Zhang, 2011, CER

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

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

Question

Did the housing price affect the wages?

Is there any difference of the mechanism

between the east and the inland?

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.

The wage-housing price interaction

Wage Housing price Demand effect Cost effect

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

The wage-housing price interaction

Avera

ge w

ag

e

Housing price (commercial housing sales/sales area)

Changes in ratio of housing prices to wages East vs. Inland

Consequence Too early industrial upgrading

Excessive capital deepening

Wage Housing price Cost effect

Demand effect

Labor Productivity (skill)

Dangerous!!!

If wage growth> productivity growth

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.

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.

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

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 .

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 , = + .

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( )

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.

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.

Equilibrium According to (1)-(3), we get that:

= 1

1 1

+ +

= 1

1 1

+

+

= 1

+

= 1

+

where = 1 + 1 1 ; = ,

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 .

= 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 .

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

Endogeneity Housing prices and wages may cause each other

How to identify -- Instrument variable + Border sample

Identification

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

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.

Inland share in approved construction land (from

farmland) and land supply

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

Regression model

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

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

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

2SLS

(1) (2) (3)


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)


Year dummy Y Y Y


R-squared 0.877 0.790 0.867

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

(1) (2) (3) (4)

East Inland

2001-2003 2004-2010 2001-2003 2004-2010


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


Year dummy Y Y Y Y

Observations 291 668 497 1,227

R-squared 0.771 0.612 0.796

Before and after 2003

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)




Observations 322 345 343 348 345 357

R-squared 0.607 0.599 0.253 0.516 0.599 0.569

Close to the border

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

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

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

(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)




Observations 237 219 72 68 165 151

R-squared 0.878 0.635 0.521 0.748 0.874

Border samples

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

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)



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


Year dummy Y Y Y


R-squared 0.882 0.843 0.865

Robustness checks

Change IV to per capita land supply in current year

(1) (2) (3)



Ln(Housing price) 0.160** 0.522*** -0.157 (0.0736) (0.138) (0.119)

Other variables control control control


Year dummy Y Y Y


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)



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


Ln(Housing price) 8.096 0.0159 0.288** 0.114

(152.3) (0.329) (0.131) (0.220)



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