WP-2020-006
Center for Advanced Economic Study Fukuoka University
(CAES) 8-19-1 Nanakuma, Jonan-ku, Fukuoka,
JAPAN 814-0180 +81-92-871-6631
Junmin Wan and Qiqi Qiu
Fukuoka University, Japan
April 30, 2020
The Impact of Housing Bubbles on Industrial Investments in China
CAES Working Paper Series
The Impact of Housing Bubbles on Industrial Investments in China1
Junmin Wan and Qiqi Qiu2
Graduate School of Economics, Fukuoka University, Japan
April 30, 2020
1 This research was partially supported by the China National Natural Science Foundation - Peking University Data Center for
Management Science (research grant 2016KEY05) and a JSPS KAKENHI Grant (#16K03764). The first author gratefully
acknowledges the support of these funds.
2 The authors thank Hongwei Dai, Masayo Kani, Zhaoxin Niu, Ko Nishihara, Qian Sun, Mitsuo Takase, Takanori Tanaka, Konari
Uchida, Tong Wang, Wako Watanabe, Yuanwei Xu, Junren Yin, Zhongwen Zhang and the participants for their beneficial
comments when the paper was presented at Fukuoka University, Kansai University, Tsinghua University, Conference on “Applied
Finance, Macroeconomic Performance, and Economic Growth” at Zhejiang University of Economics and Finance, the 11th
International Conference on Financial Risk and Corporate Finance Management at Dalian University of Technology, and the 15th
Asia-Pacific Economic Association Annual Meeting at Fukuoka University, as well as the 6th International Conference on the
Chinese Economy: Past, Present and Future at Tsinghua University. The authors give special thanks to Tsutomu Miyagawa for the
constructive comments and to Kazuo Ogawa as well as four anonymous referees for their insightful suggestions. Any remaining
errors here are the authors’ responsibility. Correspondence: Nanakuma 8-19-1, Jonan Ward, Fukuoka City, Fukuoka 8140180,
Japan; (e-mail) Wan: [email protected]; (tel) +81-92-871-6631(ext.4208); (fax) +81-92-864-2904.
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Highlights
1) Housing bubbles in major cities have created a great deal of vacant new housing
(oversupply). We use the matrix of the direct input coefficients of an input-output
table to identify 13 of 36 Chinese industrial sectors as housing-related.
2) We estimate Marginal q values by sector for 2001–2016; sectoral investment is
explained by panel estimations based on Marginal q theory. The Marginal q
investment elasticities of 13 housing-related industries, the remaining 23 industries,
and all industries were 0.2412, 0.8539, and 0.6956 respectively.
3) The housing price Granger explains the producer price index (PPI), and the PPI
Granger the Marginal q values of the 36 industrial sectors. Overcapacity
(overinvestment) was evident in all 13 housing-related industrial sectors, including
metal and cement, during the housing bubble.
2
Abstract
Housing bubbles in major cities have created a great deal of vacant new
housing (oversupply). We use the matrix of the direct input coefficients of an
input-output table to identify 13 of 36 Chinese industrial sectors as housing-related. We
estimate Marginal q values by sector for 2001–2016; sectoral investment is explained
by panel estimations based on Marginal q theory. The Marginal q investment elasticities
of 13 housing-related industries, the remaining 23 industries, and all industries were
0.2412, 0.8539, and 0.6956 respectively. The housing price Granger explains the
producer price index (PPI), and the PPI Granger the Marginal q values of the 36
industrial sectors. Overcapacity (overinvestment) was evident in all 13 housing-related
industrial sectors, including metal and cement, during the housing bubble.
JEL: E22; E32
Keywords: China, housing bubble, industrial sector, input-output table, Marginal q
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1 Introduction
Overcapacity of housing-related industries in China
Overcapacity (overinvestment) in housing-related industries in China has
created many non-performing loans (NPLs) at the individual bank level from the view
of financial sector (Wan 2018b), but we lack direct evidence of the housing-related
industrial sectors that are customers of commercial banks. Financial instability caused
by NPLs has become a top priority (Wan 2018b, Xi, April 26, 2017).3 The cause of
overcapacity must be clarified. Here, we develop a conceptual mechanism and collect
direct evidence of overcapacity within housing-related industries that produce far too
many vacant new buildings nationwide (Wan 2018b, p. 26: “building houses requires
steel and steel requires coal”) during the housing bubble as shown in Figure 1.4
The sectoral profit growths and the sectoral composite prices of outputs
[proxied by the producer price index (PPI)] of excess-capacity industries (such as steel
and coal; Figures 2–3) have fallen faster than those of other industries. Commencing in
2010, the government responded by raising the state capital ratio to address the
overcapacity issues (e.g., coal, Ren et al. 2019; steel and coal, Chen et al. 2018) and
3 For the details see: http://www.xinhuanet.com//politics/2017-04/26/c_1120879349.htm 4 Too many resources were held by housing-related industries; other industries found it difficult to obtain finance. Small- and medium-sized enterprises could not get loans, as described by former Chairman Zhou Xiaochuan of the People’s Bank of China (Wan 2015b, p. 5: “too difficult and expensive to obtain funds, Rongzinan, Rongzigui, in Chinese”).
4
prevent bankruptcies.5 For example, the state capital ratio of Coal Mining and Washing
was 0.86 in 2001, decreased steadily to 0.31 in 2010, but then increased again to attain
0.48 in 2016.6
China has removed 0.17 trillion tonnes of excess steel production capacity
(notably, China produced 0.83 trillion tonnes, 49.1% of all steel worldwide, in 2017),7
0.8 trillion tons of excess coal capacity (China produced 3.52 trillion tonnes, 45.5% of
all coal worldwide, in 2017, of which about 30% was used to make steel for housing
construction),8 and laid off 1.1 million employees between 2013–2017 (Li, March 6,
2018). These cuts significantly raised the prices of both steel and coal (Chen et al.
2018).9 The net profit of the steel industry rose 2.2-fold in 2017, with the top 15 steel
companies worldwide being based in China (Nikkei, May 25, 2018).
Overcapacity and international spillover
The overcapacity of housing-related industries in China significantly impacts
other countries because “overinvestments in housing-related businesses such as
5 President Xi Jinping has stated that China will reduce excess capacity (Xi, September 3, 2016) and that China should work to contain the housing bubble shown in Figure 1 (Xi, December 22, 2016). Additionally, efforts are being made to remove zombie firms contributing to excess capacity in identified industrial sectors (Xi, February 28, 2017; Dai et al. 2019; Shen and Chen 2017). Based on these comments by China’s leader, it is apparent that overcapacity in certain industrial sectors, and housing bubbles, have become key domestic concerns (Nikkei, November 21, 2018). 6 The state capital ratios are based on data of the National Bureau of Statistics of China. See http://data.stats.gov.cn/ 7 For the details, see: https://www.globalnote.jp/post-1402.html. Sourisseau (2018) reported that the steel production growth rate in China was 14.2% during 2000-2013 compared to 6% during 1980-2000, based on data from the World Steel Association. 8 For the details, see: http://www.coalchina.org.cn/detail//18/06/14/00000035/content.html We used Chinese input-output tables for 2012 and 2017 to estimate steel consumption. 9 Coal is also required by the cement industry. China produces over 50% of all cement worldwide, according to the World Cement Association. https://www.worldcementassociation.org/ Also see Selim and Salem (2010) and Hu et al. (2016).
5
construction materials’’ (Wan 2018b, p. 29) may trigger price collapses of materials
such as steel not only in China but also worldwide. In China, the export price of steel is
lower than that of foreign markets, significantly impacting international trade. The
China steel price Granger affects international steel prices (Guo et al. 2019). The
collapse of international steel prices is easily misinterpreted as “dumping”, for example
by the U.S. government.10
To date, no link has been established between collapses in the domestic and
international prices of housing-related materials, and housing bubbles. Here, we
conceptually and empirically examine this issue; we explore the overcapacities of
housing-related industries driven by housing bubbles in China.
Overcapacity caused by overinvestment
Overcapacity reflects an excess of capital stock (a stock variable) caused by
overinvestment (a flow variable). Here, we analyze overcapacity from the perspective of
overinvestment driven by excessive housing construction. A major housing bubble
increases both the demand- and supply-side capacities of steel, coal, and other industries.
On the demand side, rising prices encourage speculative households to buy houses. On
10 The Organization for Economic Co-operation and Development (OECD) has required the Chinese government to monitor the negative impacts of China’s oversupply of iron, steel, coal, and other materials in the international market (Nikkei, May 28, 2016). The argument is that the low price of Chinese steel considerably reduces the price of steel in the U.S., a practice termed “dumping” by the U.S. government (U.S. Department of Commerce, Fact Sheet, November 13, 2017). Thus, the overcapacities of certain Chinese industrial sectors are hotly debated internationally (Liu and Woo 2018; Schnabl 2019).
6
the supply side, a housing bubble encourages developers to build more structures,
increasing the inputs of steel and other raw materials. Consequently, basic material
prices (as indicated by the PPI) rise and raw material industries are strongly incentivized
to expand. Overcapacities in the markets for housing-related materials [steel (Chen et al.
2018; Ren et al. 2018) and coal (Ren et al. 2019)] then develop.
Overinvestment in China
Not only a housing bubble but also other factors may induce overinvestment.
Lin et al. (2015) point out that state ownership of the Big Four banks means that most
loans are granted to inefficient state-owned enterprises (SOEs), implying that fund
misallocation could cause overinvestment in some firms (e.g., SOEs) and simultaneous
underinvestment in others. Chen et al. (2016) and Ding et al. (2019) show that listed
SOEs may enjoy “free cash flow’’ and inefficiently overinvest using only panel firm
data. Kou et al. (2017) quantified industrial policy and used a cost function method to
estimate utilization of industrial capacity; industrial policy created excess capacity
based on an empirical study of 33 industrial sectors of China from 1999 to 2014.
Overinvestment and bubbles in Japan and the U.S.
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Both Japan and the U.S. have experienced stock and land (or housing) bubbles.
Chirinko and Shcaller (2001) show that the bubble in the Japanese stock market had a
significant positive effect on investment, and that the bubble boosted business fixed
investment by 6–9% from 1987 to 1989 by raising the Tobin Average q, as revealed by a
time series of aggregate data. Two other studies by listed firm-level data, Goyal and
Yamada (2004) on Japan and Chirinko and Shcaller (2011) on the U.S., reported similar
findings; the Tobin Average q rises were driven by stock bubbles. No research has yet
explored overcapacity issues of housing-related industries driven by housing bubbles in
China and elsewhere. This is our topic here.
Contribution of this research
Wan (2018b) reported that housing bubbles induced overinvestments in
manufacturing sectors, as revealed by NPLs on bank balance sheets, but we lack the
views of industrial sectors. Our first contribution is that we newly use an input-output
table to identify 13 housing-related industrial sectors among the 36 Chinese sectors, and
we find direct evidence on industrial overinvestments induced by the housing bubble.
Next, we estimate the Marginal q values of all 36 Chinese industrial sectors;
this is a significant contribution. Our approach is unlike the Tobin Average q works of
8
Chirinko and Shcaller (2001, 2011) and Goyal and Yamada (2004). That q value is
applicable only to listed companies, of which China has very few. We analyze both
listed and non-listed companies. Our study differs from that of Ogawa et al. (1994), who
used both sectoral Marginal q and sectoral Average q values to show that a fall in land
prices significantly reduced fixed investment via collateral channels after the land
bubble burst in the 1990s. To the best of our knowledge, no explicit study of
overinvestment by the industrial sector has used Marginal q values to analyze a housing
bubble. We find that the investments of all 36 sectors can be explained by Marginal q
theory, and that the elasticities of housing-related sectors are lower than those of others.
The investment behaviors of industrial sectors are thus well-explained by neoclassical
theory. All 36 sectors should be viewed as market-oriented, at least since the time China
joined the World Trade Organization (WTO) in 2001; neoclassical theory states that
corporate investment behavior is rational in that it is explained by the market.
Additionally, we analyze how a housing bubble affects housing-related
industries; we develop a conceptual framework and run some formal tests. The monthly
ratio of house price:rent, and the bubble test, are used to identify housing bubbles in
major Chinese cities. We find that the housing price bubble Granger explains the PPI,
and the PPI Granger explains the Marginal q.
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Organization of the paper
The remainder of the paper is organized as follows. The research question and
the hypotheses are presented in Section 2. Section 3 identifies housing-related
industries; we use an input-output table to this end. Section 4 describes the data sources,
the empirical specifications, and the estimations. Section 5 summarizes the conclusions.
2 Research question and hypotheses
2.1 Ghost buildings, vacant housing, and the housing bubble
The ratio of investment to gross domestic product in China has increased
continuously since 1952, especially after 2000 (Horioka and Wan, 2007, 2008, Wan
2015a, 2019). Industrial policy has sought to promote industrial investment; however,
ultimately, this triggered overinvestment and/or resource misallocation (Kou et al. 2017).
In this context, “ghost cities’’ have appeared all around China. Zhang et al. (2016)
studied urban vacancies; Yin et al. (2019) analyzed vacant housing in Ordos (Inner
Mongolia), where a major housing bubble burst. In Chengdu (Sichuan province), vacant
houses caused by the ongoing housing bubble were termed “ghost buildings” by
Williams et al. (2019), who identified vacant buildings using the big data of social
10
media.
Wan (2018b, Figures 3 and 10) showed that housing investment correlated
positively with housing prices. Wan (2018b, p.39) identified housing bubbles in major
cities; the vacancies at the end of 2015 could accommodate 30 million persons if one
person was assumed to require 24 square meters. The housing bubble caused excess
demand that in turn triggered oversupply. We clarify the relationship between housing
bubbles and corporate overinvestment using an input-output table and panel data on all
36 industrial sectors and time series data on housing prices and the PPI.
2.2 Excess construction drives input oversupply
Given the observed excessive housing construction, the factor markets
(construction inputs) of steel and cement (about 30% of all steel and about 60% of all
cement are used for housing construction) should be oversupply driven by the factor
demand.11 Following Wan (2020),12 we assume that the housing bubble is an
exogenous shock experienced by factor markets (Figure 4). This perturbs the equilibria
of both quantities and prices (Figure 5); both corporate profits and the Tobin Marginal q
values should be (directly and indirectly) affected. To meet overdemand for steel or
11 We estimated the structure of cement consumption using the Chinese input-output tables for 2012 and 2017. 12 Transmission of factor input quantities and prices in the context of housing bubble or a preference shock is analyzed by adding the input-out table to a neoclassical theory of the general equilibrium framework of Wan (2020). We explicitly model one household sector, two final goods sectors (housing and health), and one intermediate goods sector (steel).
11
cement, increased production capacity, funded by bubble profit, is indispensable;
overinvestment occurs.
2.3 Investments of factor industries and the bubble Marginal q
To clarify the connection between a housing bubble and investment, we
initially identified housing bubbles following Wan (2015a, 2018b). We predicted
bubbles in major Chinese cities from 2004–2017, and then found Grange causality
between the housing price bubble, the PPI, and the Marginal q. To determine the
relationship between the PPI and the Marginal q, we used the PPI as a proxy of output
price when determining Marginal q values. Following Wan (2018a, b), a speculative
housing bubble would have at least some effect (potentially, the principal effect) on
factor industry profits, similar to what was observed in the steel and coal industries.
Thus, the Marginal q values would presumably be affected (even dominated) by the
bubble output prices of (especially) housing-related industries.
2.4 Key hypotheses
The standard theory of investments created by Jorgenson (1963), Tobin (1969),
and Hayashi (1982) indicates that corporate investments should be positively and
12
significantly correlated with the Marginal q values.
Hypothesis 1: Investment is guided by the Marginal q.
This generally means that corporate investment is rational, thus dictated by the
market; but this, in turn, implies other important meanings in China. Whether the
Chinese economy (the so-called “socialized market economy”) is market-oriented
remains hotly debated (see Yu 2019). In the time since China joined the WTO in 2001,
the domestic and global competitiveness of the industrial sector has increased annually.
If the corporate behavior of investors can be explained by the neoclassical theory of the
market economy, all 36 current Chinese industrial sectors should exhibit such behavior.
However, given the unique characteristics of housing factor industries, we explored
their investment behaviors in more detail.
Hypothesis 2: The Marginal q investment elasticities of housing-related sectors would
be lower than those of other sectors.
Housing-related industries such as steel always exhibit unique characteristics
13
(Wu 1998). These are very large-scale endeavors; fixed costs are very high, investment
shot size is very large and the industries must work in standard operating environments.
Such projects are associated with high adjustment costs, some imposed by
environmental assessments or regulations. The higher the adjustment cost, the lower the
Marginal q elasticity of investments (Abel 1980).
3 Identification of housing-related industries
3.1 Direct housing construction inputs of the input-output table
To identify industries heavily involved in housing construction, following
Leontief (1941) and Morishima (1958), we used a matrix of the direct input coefficients
of 23 industrial sectors from 2002-2017 available from the National Bureau of Statistics
of China (NBSC). NBSC reported seven sets of coefficients for the general construction
sector and two sets for the housing construction sector.13 We matched the 23 sectors of
the input-output table to the 36 industrial sectors listed in Marginal q in this paper (not
including construction or housing construction) and estimated the direct input
coefficients to construction and housing construction (Table 1a and b).The coefficients
vary little over time; the housing construction coefficient is very similar to that of
construction.
13 “Construction” includes roads, bridges, and housing.
14
3.2 Direct vs. indirect impacts on factor input sectors
Direct impacts on 13 factor input sectors
Based on the average input coefficients of housing construction by sector
(Table 1a and b), we assume that if the value is greater than 1% [as for Manufacture of
Non-metallic Mineral Products (including cement) and Smelting and Pressing of
Ferrous Metals and Non-ferrous Metals (including iron and steel); 23 and 15%
respectively], those sectors are housing-related industries. Ten sectors (italicized in
Table 1a-b) were identified as housing-related industries. The classification of the 23
industrial sectors of the input-output table differs slightly from the 36 industrial sectors
with Marginal q values; we found that the 10 sectors of the input-output table
corresponded to 13 of the 36 sectors (Table 1c). We term the impact of housing
construction on these 13 sectors “direct”; they lie upstream of housing construction.
Indirect impacts on the remaining 23 sectors
When the direct inputs of steel or cement are examined using other
input-output tables, coal is the major input. Thus, we term the impact of housing
construction on coal “indirect”; the impact lies upstream of steel and cement, which are
15
driven by housing construction. The remaining 23 sectors of Table 1c could be
considered as indirect inputs to housing construction.
4 Marginal q values and investments of the 36 industrial sectors
4.1 Data
Ratio of house price to rent
We collected data on monthly housing prices and residential rental prices for
36 major cities from December 2004 to December 2017; we used the “China Monthly
Economic Indicators” of the NBSC.
Panel data on the 36 industrial sectors
We collected panel data from the National Data provided by NBSC
(http://data.stats.gov.cn/). The principal economic indicators of Industrial Enterprises
(above a designated size) by industry sector, thus 36 sectors, were downloaded. The
aggregation and statistical methods changed after 2000; therefore, we used only the
2000–2016 data.
4.2 Estimations of investments, depreciation rates, and interest payments
16
We lacked data on investments, and interest and depreciation rates; we
estimated these based on the original values of fixed assets and interest expenditures by
industry.
Estimation of investment
The investment and the book value of the fixed assets of industry m at time t
are given by , respectively; thus:
(1)
Estimation of depreciation rate
Following Qiu and Wan (2019), we obtained depreciation rates by industry.
The depreciation rate of industry m at time t is represented by as follows:
(2)
where the fixed asset total at t − 1 is represented by We used a depreciation
rate of 0.074 averaged across all industrial sectors, as estimated by Qiu and Wan (2019),
which is very close to the value of 0.077 used in the Japanese study of Ogawa et al.
(1994).
17
Estimation of interest payment
Wan (2019) found that, since 2011, outstanding corporate deposits on the
balance sheet of the banking sector have overtaken household deposits, leaving some
firms with negative interest payments even if the firm has debt, as the deposit may
exceed the debt. We had data only on final interest payments, thus interest paid on net
debts. Thus, we developed a coefficient to estimate interest paid on outstanding debt.
Briefly, the ratio of the sum of corporate and household deposits in the banking sector
to corporate deposits held by that sector, based on macro data from 2001 to 2016,
served as a multiplier of interest payment by industrial sector. In order, the annual
adjustment ratios from 2001 to 2016 were: 2.43 (2001), 2.45, 2.43, 2.41, 2.47, 2.43,
2.24, 2.38, 2.20, 2.24, 1.86, 1.90, 1.90, 2.21, 1.95, and 1.94 (2016). The annual interest
rate was:
. (3)
4.3 Estimation of Marginal q
Following Jorgenson (1963), Tobin (1969), Abel (1980), Hayashi (1982), and
Ogawa (2003), we considered the following investment framework:
(4)
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, j= 1, 2, 3, 4…,
where Tobin’s Marginal q, interest rate, discount factor, price of investment,
depreciation rate, and rate of profit at time t are represented by , , , , and
, respectively. The investment equation is given an increasing function with q by
, (5)
where investment, capital stock, and Marginal q in industry m at time t are represented
by , , and , respectively.
Because this paper is the first to estimate Marginal q of industrial sector using
macro data for China, and the number of observations is also very limited, we prefer a
simple specification based on Ogawa (2003) rather than Abel and Blanchard (1986) to
obtain a simple result, while the approach of Abel and Blanchard (1986) for China is
left for next research. By adding assumptions on expectations (Ogawa 2003, Eq. (3),
(4)), i.e., the discount rate and the profit rate follow random walk independently,
(6)
, (7)
where and is stationary white noise, respectively. Then it is shown by
Ogawa (2003, Eq.(5)) that the Marginal q by industrial sector can be simply written
19
as,14
. (8)
The rate of profit of industry m at time t is defined as:
, (9)
where the total profit before tax and the fixed assets of industry m at time t are
represented by and , respectively. We estimate . To control for
potential multicollinearity and endogeneity issues, we use and to estimate
Marginal q by industrial sector. The estimated Marginal q values of the 36 sectors are
listed in Table 2a–d, and Marginal q values of some industrial sectors are illustrated in
Figure 6.
4.3 Empirical specifications
Bubble test
Following Mackinnon (1996), Phillips et al. (2015) and Wan (2015a, 2018b),
we performed unit root and bubble tests using the monthly series of the house price:rent
ratios of 36 major Chinese cities.
Granger causality among housing price, PPI, and Marginal q
14 The method of constructing the Marginal q here is also close to Gugler et al. (2004).
20
Housing price bubbles are regional issues; bubbles differ considerably among
regions and from one year to the next. Housing prices in a bubble should impact the
industrial PPI. However, we lacked housing price data by industrial sector. We only had
data on the housing price, PPI, and Marginal q, for all 36 sectors nationwide. Hence,
Granger causality testing of housing prices and PPI (Figure 7), and PPI and Marginal q
(Figure 8), were performed using the method of Toda and Yamamoto (1995).
Investment and Marginal q
We considered the following empirical investment function based on Abel
(1980), Chirinko (1993) and Ogawa et al. (1994, 2019):
, (10)
where the investment ratio and Marginal q of industry m at time t are represented by
and respectively. The coefficient of is , and , and
are a constant term, the industry-specific effect, the time effect, and the random error
term, respectively. Hypothesis 1 could be tested by specification of Eq. (10). Following
Mussa (1977), Abel (1980) and Chirinko (1993, Eq.(13)), the adjustment costs are
assumed to be,
(11)
21
where α and is parameter of quadratic function, respectively. Then the empirical
investment equation can be expressed by
, (12)
as presented in Chirinko (1993, Eq. (17)). Higher adjustment cost (e.g. α) will lower the
speed of investment response (coefficient of , ) by the adjustment cost
theory. Eq. (12) is the structural form of testing Hypothesis 2.
4.4 Investment by housing-related industries and other industries
We analyzed transmission from the housing market to its industrial factor
markets. We assumed that the investments for all industrial sectors were dependent on
the Marginal q values. Thus, as the Marginal q is somewhat affected by the PPI, and PPI
is induced by housing prices in a bubble, overcapacity or overinvestment issues will
arise in industrial sectors (i.e., Housing Price Bubble → PPI, PPI → Marginal q,
Marginal q → Investment) as shown in Figures 4 and 5.
4.5 Estimations
Bubble test
Tables 3a and b show the estimations of the unit root and bubble tests for the
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monthly series of house price:rent ratios in 36 major Chinese cities. Both unit roots and
bubbles are apparent, confirming the housing bubble issue raised by Wan (2015a,
2018b).
Granger causality between housing price and the PPI
The estimations are summarized in Table 4. In terms of the hypothesis that
housing price does not influence the PPI in the sense of Granger causality, the null
hypothesis is rejected. In terms of the hypothesis that PPI does not exhibit Granger
causality in terms of housing price, the test is unable to reject the null hypothesis. Thus,
we conclude that the impact of housing price on the PPI is stronger than that of the PPI
on housing price. These results are consistent with the prediction of section 2.2 and the
transmission shown in Figures 4, 5, and 7.
Granger causality between PPI and Marginal q
The results are shown in Table 5. In terms of the hypothesis that PPI is not the
Granger cause of Marginal q, the test rejects the null hypothesis. In terms of the
hypothesis that Marginal q is not the Granger cause of PPI, the test does not reject the
null hypothesis. These results are consistent with the prediction of section 2.3 as shown
23
in Figure 8.
Investments of 36 industries, 13 housing-related and 23 others
Figure 9 shows the transition of Marginal q and the ratio of investment to real
capital stock. The principal statistics are summarized in Table 6 and the results are
shown in Table 7. In the first and the second columns of Table 7, the Marginal q
significantly impacts investment regardless of sector size (proxied by the ratio of total
assets to the total value of fixed assets). Thus, investment behavior of 36 industries is
explained by the Tobin Marginal q theory. The investment elasticity of Marginal q is
0.6956. These results support the prediction of Hypothesis 1.
In the third and fourth columns of Table 7, the Marginal q coefficients are
significantly positive. The investment elasticity of Marginal q is 0.2412 for the 13
housing-related industries. The asset size coefficients are also significantly positive.
Thirteen factors including steel and cement are identified as direct inputs to housing
construction (Smelting and Processing of Non-ferrous Metals, Manufacture of
Non-metallic Mineral Products, the 13 italicized items in Table 1a-b); all exhibit
overcapacity driven by overinvestment, because their downstream industries (housing
construction) are overproduced. These are “direct effects” driven by housing oversupply.
24
In the fifth and sixth columns of Table 7, the Marginal q coefficients of the remaining
23 sectors are also significantly positive; the elasticity is 0.8539.
In the seventh, eighth and ninth columns of Table 7, all the coefficients of
are significantly positive, and the coefficient for 13 housing-related
industries is significantly smaller than that of the remaining 23 industries. These results
are based on structural model of adjustment cost theory in Eq. (12), and are similar to
the reduced form in Eq. (10). The elasticities of Marginal q on investment based on Eq.
(12) are almost the same as the ones based on Eq. (10). We used the estimated
coefficient based on Eq. (12) to draw the adjustment cost function of Eq. (11) as
illustrated in Figure 10. It is obvious that adjustment cost increases with investment for
the 13 housing-related industries more sharply than that for the remaining 23 ones.
Hence, these results support the prediction of Hypothesis 2.
Elasticity of Marginal q on investment in the U.S., Japan, and China
We calculated the elasticity of Marginal q on investment of manufacturing
companies for both the U.S. and Japan. For the U.S. during 1977-1996 based on Gugler
et al. (2004) and for Japan during 1972-1990 based on Ogawa et al. (2019), the
elasticity is 0.2556 and 0.3544, respectively. The elasticity (0.6956) in China is larger
25
than those of both the U.S. and Japan. At least two factors may contribute to this
difference. The first one would be that our Marginal q is based on profit before tax
(because of lacking data on tax), and the second one would be that our estimation is
based on macro data while Gugler et al. (2004) and Ogawa et al. (2019) are all from
micro data.
Overinvestments, Marginal q, and their implications
The Marginal q values of the coal and other industries were abnormally high in
the housing bubble era, as shown in Table 2a–d, but decreased sharply (to below 1 for
some industrial sectors such as coal) after the government shrank the bubble (Figure 6).
In the bubble era, a high Marginal q reflected a small capital stock; the firm required
more investment. If the Marginal q was lower than 1, this implies that that the industry
engaged in past overinvestment. The PPI created an abnormally high Marginal q during
the housing bubble. Thus, a Marginal q lower than 1 after bubble control policies were
in place would robustly identify overinvestment in industrial sectors. Thus, housing
construction exerted ``indirect impact’’ on coal driven by steel and cement (coal is the
upstream industry of steel and cement; steel and cement are upstream industries of
housing construction; i.e., coal→steel and cement→housing construction).
26
These results directly support the principal finding of Wan (2018b), who
argued that overinvestments in housing-related industries induced by housing bubbles
significantly increased the numbers of NPLs held by Chinese banks. The results are
consistent with those of Chen et al. (2016) and Ding et al. (2019) who used a cash flow
approach to identify bubbles caused by “free cash flow’’. Our results are not only
consistent with those of Kou et al. (2017) in terms of the cost function and industrial
policy approach but also with the policy of “Five Measures to Exclude Excessive
Industrial Capacity’’ proposed by the National Development and Reform Commission
of China (NDRC, Jan. 13, 2016) and the 2015 Annual Report of the NBSC, which
revealed that housing oversupply/overinventory reduced investments in steel, coal, and
other housing-related industries.15
Given that physical and human capital are limited, overinvestment in housing
and related sectors reflects underinvestment in other sectors such as health or education.
Our findings are consistent with the non-linear impact of housing price on private
investment described by Li et al. (2018) using provincial panel data. Housing prices
below a threshold encourage private investment but high prices inhibit investment.
5 Conclusions
15 See Xinhuanet (Jan. 20, 2016) for details.
27
We identified housing bubbles in several major Chinese cities via the bubble
test and by calculating the ratio of house prices to rent; the housing bubble has created a
massive amount of vacant new housing, thus housing oversupply. To identify the
material inputs of housing-related industries, we used a matrix of the direct input
coefficients of the input-output table and found that 13 of the 36 industrial sectors were
housing-related. We then estimated Marginal q values by sector from 2001–2016 and
performed panel estimations. The investments of all 36 industrial sectors are explained
by the Tobin Marginal q theory. The Marginal q investment elasticities of the 13
housing-related industries, the remaining 23 industries, and all 36 industries were
0.2412, 0.8539, and 0.6956 respectively. Finally, we found that the housing price
Granger explains the PPI and the PPI Granger the Marginal q values of all 36 industrial
sectors. Hence, overcapacity caused by overinvestment is evident in at least 13
housing-related industries (including metal and cement), caused by the housing bubble.
The implications of our findings follow. The Tobin Marginal q theory is the
dominant neoclassical framework used to analyze real corporate investment behavior.
The investments of the 36 manufacturing sectors (excluding housing construction) can
be explained by Marginal q theory; all sectors seem to have invested by reference to the
market. When the upstream demand market of housing construction is driven by a price
28
bubble, the overcapacity and overinvestment issues of housing-related industries such as
steel, cement, and coal directly or indirectly reflect the distorted housing market.
Therefore, rectifying the housing market by soft landing or shrinking bubbles [Wan
(2018a, b)] is urgent; this would help solve the overcapacity issues of housing-related
industries. Another implication is that most of China’s economy has gradually changed
to a market basis.
We will build a theoretical decision model of investment within bubble
scenarios and perform more detailed analyses using firm- or loan-level data with a focus
on activities upstream of the housing market.
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Table 1a: Matrix of direct input coefficents of industrial sectors, 2002-2017
Input OutputConstruction Housing Construction
2002 2005 2007 2010 2012 2015 2017 Avg. 2012 2017 Avg.
Mining and Washing of Coal 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%Extraction of Petroleum and Natural Gas 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%Mining and Processing of Ferrous Metal Ores and Non-Ferrous Metal Ores 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Mining and Processing of Non-metal Ores 2% 2% 1% 1% 1% 1% 1% 1% 1% 1% 1%Manufacture of Food from Agricultural Products, Foods, Liquor Beverages, Refined Tea and Tobacco
0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Manufacture of Textile 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%Manufacture of Textile Fabrics Wearing Apparel , Accessories, Leather Fur Feather , Related Products and Footwear
0% 0% 0% 0% 0% 1% 0% 0% 0% 0% 0%
Processing of Timber Manufacture of Wood Bamboo Rattan Palm, Straw Products and Furniture
3% 2% 2% 2% 2% 3% 2% 2% 1% 2% 1%
Manufacture of Paper, Paper Products, Printing Reproduction of Recording Media, Articles for Culture Education Art and Carfts, Sport and Entertainment Activities
0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Processing of Petroleum and Coking of Nuclear Fuel 3% 2% 2% 1% 1% 1% 1% 2% 1% 1% 1%
Table 1b: Matrix of direct input coefficents of industrial sectors, 2002-2017 (cont.)
Manufacture of Raw Chemical Materials and Chemical Products 4% 3% 4% 3% 4% 1% 3% 4% 2% 3% 3%
Manufacture of Non-metallic Mineral Products 11% 21% 21% 23% 19% 19% 19% 19% 24% 22% 23%
Smelting and Pressing of Ferrous Metals and Non-ferrous Metals 12% 7% 16% 12% 16% 12% 11% 12% 17% 12% 15%
Manufacture of Metal Products 5% 4% 4% 3% 4% 5% 5% 4% 4% 5% 4%
Manufacture of General Purpose Machinery and Special Purpose Machinery 4% 3% 3% 3% 1% 1% 1% 2% 1% 1% 1%
Manufacture of Automobiles, Railway Vessel Aerospaceand Other Transport Equipments 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Manufacture of Electrical Machinery and Equipment 3% 3% 4% 4% 4% 4% 3% 4% 4% 4% 4%
Manufacture of Communication Equipment Computers and Other Electronic Equipment
0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Manufacture of Measuring Instruments and Machinery 1% 1% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Utiliztion of Waste Resources 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0%Production and Supply of Electric Power and Heat Power 1% 2% 1% 1% 1% 1% 1% 1% 1% 1% 1%
Production and Supply of Gas 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Production and Supply of Water 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Source: Authors' estimations based on data from the National Data by National Bureau of Statistics of China. http://data.stats.gov.cn/
Table 1c: Matching 23 industrial sectors of input-outpt table with 36 industrial sectors
23 industrial sectors of input-output table 36 industrial sectorsMining and Washing of Coal Mining and Washing of CoalExtraction of Petroleum and Natural Gas Extraction of Petroleum and Natural GasMining and Processing of Ferrous Metal Ores and Non-Ferrous Metal Ores
Mining and Processing of Ferrous Metal Ores Mining and Processing of Non-Ferrous Metal Ores
Mining and Processing of Non-metal Ores Mining and Processing of Non-metal Ores
Manufacture of Food from Agricultural Products, Foods, Liquor Beverages, Refined Tea and Tobacco
Processing of Food from Agricultural ProductsManufacture of Foods Manufacture of Liquor Beverages and Refined TeaManufacture of Tobacco
Manufacture of Textile Manufacture of Textile
Manufacture of Textile Fabrics Wearing Apparel , Accessories, Leather Fur Feather , Related Products and Footwear
Manufacture of Textile Fabrics Wearing Apparel and AccessoriesManufacture of Leather Fur Feather and Related Products and Footwear
Processing of Timber Manufacture of Wood Bamboo Rattan Palm, Straw Products and Furniture
Processing of Timber Manufacture of Wood Bamboo Rattan Palm and Straw ProductsManufacture of Furniture
Manufacture of Paper, Paper Products, Printing Reproduction of Recording Media, Articles for Culture Education Art and Carfts, Sport and Entertainment Activities
Manufacture of Paper and Paper Products Printing Reproduction of Recording MediaManufacture of Articles for Culture Education Art and Carfts, Sport and Entertainment Activities
Processing of Petroleum and Coking of Nuclear Fuel Processing of Petroleum and Coking of Nuclear Fuel
Manufacture of Raw Chemical Materials and Chemical Products
Manufacture of Raw Chemical Materials and Chemical Products Manufacture of Medicines Manufacture of Chemical Fibers Manufacture of Rubber and Plastics Products
Manufacture of Non-metallic Mineral Products Manufacture of Non-metallic Mineral ProductsSmelting and Pressing of Ferrous Metals and Non-ferrous Metals
Smelting and Pressing of Ferrous Metals Smelting and Pressing of Non-ferrous Metals
Manufacture of Metal Products Manufacture of Metal ProductsManufacture of General Purpose Machinery and Special Purpose Machinery
Manufacture of General Purpose Machinery Manufacture of Special Purpose Machinery
Manufacture of Automobiles, Railway Vessel Aerospaceand Other Transport Equipments
Manufacture of Automobiles, Railway Vessel Aerospaceand Other Transport Equipments
Manufacture of Electrical Machinery and Equipment Manufacture of Electrical Machinery and Equipment
Manufacture of Communication Equipment Computers and Other Electronic Equipment
Manufacture of Communication Equipment Computers and Other Electronic Equipment
Manufacture of Measuring Instruments and Machinery Manufacture of Measuring Instruments and Machinery
Utiliztion of Waste Resources Utiliztion of Waste ResourcesProduction and Supply of Electric Power and Heat Power
Production and Supply of Electric Power and Heat Power
Production and Supply of Gas Production and Supply of GasProduction and Supply of Water Production and Supply of Water
Source: Information from National Bureau of Statistics of China. http://data.stats.gov.cn/
Table 2a: Marginal q of 36 industrial sectors, 2001-2016
Year National Total
Mining and Washing of
Coal
Extraction of Petroleum
and Natural Gas
Mining and Processing of
Ferrous Metal Ores
Mining and Processing of Non-Ferrous Metal Ores
Mining and Processing
of Non-metal Ores
Processing of Food from Agricultural
Products
Manufacture of Foods
Manufacture of Liquor
Beverages and Refined Tea
Manufacture of Tobacco
2001 0.7268 0.1746 2.8868 0.6566 0.9264 0.3516 0.6680 0.8203 0.7942 2.29132002 0.9450 0.3645 2.8787 0.8354 1.1374 0.4514 0.9049 1.0992 1.0379 3.05892003 1.2068 0.5071 3.4715 1.7095 1.7427 0.6060 1.2204 1.2654 1.1755 3.82932004 1.7282 1.2875 5.2922 7.0221 4.0397 0.9165 1.7351 1.6289 1.3123 5.69032005 1.9399 1.8702 7.9303 5.8690 7.7674 2.1105 2.5305 2.2556 2.1131 6.76152006 1.7763 1.5364 7.0963 4.3460 9.0975 2.4391 2.4991 2.0898 2.0800 5.98442007 1.9332 1.6518 5.2536 6.1421 7.8010 2.4407 3.0824 2.4610 2.5590 7.26292008 1.3402 2.2658 4.1733 6.8459 4.2923 2.5420 2.4830 1.9003 2.0095 4.52182009 2.9779 4.2563 3.6040 6.6183 5.9316 4.9867 5.5608 5.3547 4.8488 11.58432010 1.5628 2.1477 1.6467 4.3196 3.4620 2.4975 2.8160 2.5991 2.4960 4.57552011 1.2956 1.9132 2.0154 3.0485 3.1599 2.3392 2.3178 2.2621 2.3329 3.38432012 2.1370 2.3808 2.7810 4.5547 4.9309 4.1102 4.1224 4.0820 4.3995 6.61502013 1.5577 1.0797 2.0450 3.0362 2.7197 2.7949 2.8273 3.0549 3.0848 6.66352014 1.6926 0.6177 2.0900 2.3585 2.5548 2.7870 2.7115 3.3923 3.1247 7.75722015 2.1187 0.2344 0.5244 1.9045 2.4372 3.6059 3.5453 4.6259 4.3694 10.60722016 1.5144 0.4581 -0.2929 1.0177 1.6488 2.2254 2.4774 3.1521 2.9368 5.5997Avg. 1.6533 1.4216 3.3373 3.7678 3.9781 2.3253 2.5939 2.6277 2.5422 6.0117
Source: Authors' estimations based on data from the National Data by National Bureau of Statistics of China. http://data.stats.gov.cn/
Table 2b: Marginal q of 36 industrial sectors, 2001-2016 (cont.)
Year Manufacture of Textile
Manufacture of Textile Fabrics
Wearing Apparel and Accessories
Manufacture of Leather Fur
Feather and Related Products and
Footwear
Processing of Timber
Manufacture of Wood Bamboo Rattan Palm and Straw Products
Manufacture of Furniture
Manufacture of Paper and
Paper Products
Printing Reproduction of Recording
Media
Manufacture of Articles for
Culture Education Art
and Carfts, Sport and
Entertainment Activities
2001 0.4770 1.7116 1.3115 0.5449 1.2391 0.4597 1.1291 1.42322002 0.7303 1.9794 1.9587 0.6751 1.4613 0.7411 1.1705 1.88262003 0.8687 2.0332 2.3763 0.8730 1.7801 0.7854 1.4223 1.77992004 0.9197 2.3391 2.8512 1.3984 2.5776 1.0405 1.6575 2.00052005 1.3925 3.0132 3.6518 1.9009 2.6182 1.1721 1.6471 2.25012006 1.3153 2.7733 3.2025 1.9586 2.4984 1.0604 1.4721 1.75142007 1.4755 2.8675 3.7170 2.5735 2.2189 1.2811 1.7053 1.86132008 1.1426 2.3585 3.0223 2.3064 1.6331 0.9706 1.4272 1.25812009 2.8197 5.5614 7.3891 4.7114 4.4540 2.3003 3.3974 3.62832010 1.6821 2.9439 4.0789 2.5135 2.4859 1.1917 1.6795 1.95412011 1.4260 2.4090 3.3829 2.1057 2.1154 0.9066 1.4281 1.53172012 2.3190 5.0656 6.2437 4.0040 3.7301 1.4952 3.1518 9.62392013 1.8746 3.0008 3.8700 3.1028 2.5446 1.0532 2.4119 3.63752014 2.1459 3.3779 4.2435 3.2730 2.8848 1.1121 2.7451 4.02572015 2.9540 4.4821 5.8379 4.0523 3.9681 1.6960 3.6444 5.33822016 2.1014 2.9855 3.9223 2.7811 2.8329 1.2627 2.3705 3.6359Avg. 1.6028 3.0564 3.8162 2.4234 2.5652 1.1580 2.0287 2.9739Source: Authors' estimations based on data from the National Data by National Bureau of Statistics of China. http://data.stats.gov.cn/
Table 2c: Marginal q of 36 industrial sectors, 2001-2016 (cont.)
Year
Processing of
Petroleum and Coking of Nuclear
Fuel
Manufacture of Raw
Chemical Materials
and Chemical Products
Manufacture of Medicines
Manufacture of Chemical Fibers
Manufacture of Rubber and
Plastics Products
Manufacture of Non-metallic Mineral
Products
Smelting and
Pressing of Ferrous Metals
Smelting and
Pressing of Non-
ferrous Metals
Manufacture of Metal Products
Manufacture of General
Purpose Machinery
2001 -0.0485 0.3377 1.4439 0.1855 0.8225 0.3736 0.3863 0.4484 0.9768 0.69662002 0.2179 0.6460 1.7134 0.3519 1.1228 0.5237 0.6125 0.5640 1.2970 1.21672003 0.5205 0.9889 1.7855 0.6703 1.2162 0.8896 1.1695 0.9308 1.6216 1.71562004 1.3748 1.9245 1.7963 0.5820 1.5987 1.2955 2.0334 1.7976 2.4301 2.70752005 -0.5424 2.1461 2.0307 0.5213 1.7195 1.1823 1.7621 2.1701 2.9902 3.22592006 -0.9616 1.6214 1.4858 0.5541 1.5378 1.2387 1.4841 2.9910 2.5591 3.04092007 0.5093 1.9730 1.8963 1.1124 1.8960 1.7096 1.6918 3.1322 2.7011 3.32172008 -1.5052 1.2755 1.6283 0.3667 1.4029 1.5130 0.8004 1.2711 2.2337 2.56742009 2.8790 2.8580 4.1140 1.8562 3.7650 3.5991 1.4195 2.5346 4.4395 5.07102010 1.1590 1.5574 1.9590 1.5831 1.9831 1.8386 0.7414 1.5067 2.4036 2.60122011 0.2852 1.3358 1.6558 1.2235 1.5551 1.5652 0.5695 1.3364 1.8785 1.92492012 0.3271 1.9743 2.8965 1.3331 2.7041 2.3316 0.7540 1.8599 3.6035 2.96232013 0.4624 1.3825 2.1266 0.8857 2.2206 1.7745 0.5764 1.2175 2.2224 2.46582014 0.0687 1.4231 2.2766 1.0823 2.2889 1.9333 0.5766 1.1929 2.4203 2.68792015 0.8380 1.9118 3.1940 1.5497 3.1557 2.3330 0.2584 1.3312 2.7635 3.49432016 1.4674 1.3554 2.2448 1.3053 2.2019 1.7043 0.5293 1.2030 2.3165 2.3381Avg. 0.4407 1.5445 2.1405 0.9477 1.9494 1.6128 0.9603 1.5930 2.4286 2.6274
Source: Authors' estimations based on data from the National Data by National Bureau of Statistics of China. http://data.stats.gov.cn/
Table 2d: Marginal q of 36 industrial sectors, 2001-2016 (cont.)
Year
Manufacture of Special Purpose
Machinery
Manufacture of Automobiles, Railway Vessel Aerospaceand
Other Transport Equipments
Manufacture of Electrical
Machinery and Equipment
Manufacture of Communication Equipment Computers and Other
Electronic Equipment
Manufacture of Measuring Instruments
and Machinery
Utiliztion of Waste
Resources
Production and Supply of Electric
Power and Heat
Power
Production and Supply
of Gas
Production and Supply of Water
2001 0.6511 0.9671 1.3737 2.2023 1.5494 0.3914 -0.0184 0.0790 2002 1.2158 1.7025 1.7906 2.1732 1.8133 0.4278 -0.0286 0.0432 2003 1.5686 2.4353 2.1381 2.3296 2.6390 0.4442 0.1536 0.0114 2004 1.9326 2.4396 2.9217 3.0442 2.8594 14.0942 0.5385 0.3075 0.0472 2005 2.4322 1.8925 3.3505 2.5520 3.8961 5.5056 0.6196 0.3184 -0.0094 2006 2.5149 1.9678 2.9856 2.2390 3.4523 5.1757 0.6328 0.4320 0.1226 2007 3.1932 2.5691 3.5592 2.3075 3.7512 3.5779 0.5737 0.8112 0.1231 2008 2.4793 1.9415 3.1626 1.4889 2.7648 4.2889 0.0915 0.8888 0.0734 2009 4.9537 5.1436 6.7285 3.4329 6.3418 6.5255 0.4677 2.6358 0.1437 2010 2.7041 2.7717 3.1783 2.1534 3.2600 3.4808 0.2502 1.2348 0.1211 2011 2.1523 2.1893 2.2566 1.2776 2.4075 3.6615 0.1859 1.0893 0.1104 2012 3.3239 3.2996 3.6290 2.8435 3.8251 4.3289 0.4487 1.7644 0.2028 2013 2.3479 2.7327 2.5985 2.1604 3.3636 3.4129 0.4342 1.2157 0.1993 2014 2.3331 3.5123 3.0764 2.6201 3.9144 4.0532 0.5215 1.5782 0.3154 2015 2.8624 4.4805 4.4945 3.7967 5.1927 5.1116 0.7804 2.0361 0.5385 2016 1.9758 3.1564 3.3701 2.8407 3.7291 2.8101 0.4157 1.1725 0.3664 Avg. 2.4151 2.7001 3.1634 2.4664 3.4225 5.0790 0.4515 0.9744 0.1555
Source: Authors' estimations based on data from the National Data by National Bureau of Statistics of China. http://data.stats.gov.cn/
Table 3: Bubble test for monthly ratio of housing price to rent in 36 major cities
A: Unit root testPrice-renting ratio (Dec. 2004 – Dec. 2017, 154 observations after adjustments)
Null Hypothesis: The series has a unit root
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -1.328 0.616
*MacKinnon (1996) one-sided p-values.
B: The SADF test and the GSADF test of the price-renting ratio
Null hypothesis: The series has a unit root
Price-renting ratio (Dec. 2004 – Dec. 2017, 157 observations after adjustments)
SADF GSADF
Test statistics 6.648 6.660
p-value 0.000 0.000Right-tailed test.
Note: Critical values of tests are obtained by Monte Carlo simulation with 1,000 replications.The smallest window has 24 observations. The author’s calculations.
Table 4: Granger causality tests for housing pice and PPI for industrial sectors, 2001-2016
H0: Housing price does not Granger cause PPI H0: PPI does not Granger cause Housing price
statistical value p-value statistical value p-value16.62 0.0000 *** 1.73 0.1882
Note:***, ** and * denote rejection of the null hypothesis at the 1%, 5% and 10% significance level, repectively.
Table 5: Granger causality tests for PPI and Marginal q for industrial sectors, 2001-2016
H0: PPI does not Granger cause Marginal q H0: Margimal q does not Granger cause PPIstatistical value p-value statistical value p-value
5.52 0.0189 *** 2.02 0.1544
Note:***, ** and * denote rejection of the null hypothesis at the 1%, 5% and 10% significance level, repectively.
Table 6: Summary statistics of 36 industrial sectors,2001-2016
Variable Obs Median Mean Std. Dev. Min Max
(36 industrial sectors)
Investment(t) / Capital Stock(t-1) 573 0.1962 0.2215 0.2303 -0.2047 3.5904
Marginal q (t) 573 2.1105 2.3553 1.7535 -1.5052 14.0942
Total Assets(t-1) / Total Value of Fixed Assets(t-1) 573 2.7857 2.8811 0.8695 1.2734 6.8588
Year 573 2009 2008.534 4.6013 2001 2016
(13 industrial sectors)
Investment(t) / Capital Stock(t-1) 208 0.2025 0.2094 0.1220 -0.1495 0.5679
Marginal q (t) 208 1.8897 1.8885 1.2211 -1.5052 6.7285
Total Assets(t-1) / Total Value of Fixed Assets(t-1) 208 2.5041 2.7267 0.7752 1.4206 4.9187
(23 industrial sectors)
Investment(t) / Capital Stock(t-1) 365 0.1929 0.2283 0.2734 -0.2047 3.5904
Marginal q (t) 365 2.2448 2.6213 1.9463 -0.2929 14.0942
Total Assets(t-1) / Total Value of Fixed Assets(t-1) 365 2.8421 2.9566 0.9093 1.2734 6.8588
Source: See the text.
Table 7: Determinants of investments in 36 industrial sectors, 2001-2016
(36) (13) (23)
Marginal q (t) 0.0724 ** 0.0654 ** 0.0283 *** 0.0267 *** 0.0812 ** 0.0744 **
(0.0312) (0.0265) (0.0077) (0.0069) (0.0370) (0.0319)
0.0697 ** 0.0302 *** 0.0782 **
(0.0288) (0.0076) (0.0345)
0.0982 0.1390 *** 0.0826
(0.0725) (0.0445) (0.0743)
Year -0.0060 -0.0101 -0.0008 -0.0062 ** -0.0078 -0.0116
(0.0041) (0.0061) (0.0011) (0.0024) (0.0056) (0.0077)
Constant 12.1943 20.1502 1.6966 12.2343 ** 15.7673 22.9968 0.1256 *** 0.1822 *** 0.0996 **
(8.1930) (12.0737) (2.1803) (4.7484) (11.1386) (15.1936) (0.0395) (0.0068) (0.0567)
Observations 573 573 208 208 365 365 573 208 365R-squared 0.1867 0.2006 0.0413 0.0718 0.2244 0.2338 0.1909 0.0511 0.2259
36 13 23
Note: Robust standard errors in parentheses (FE), *** p<0.01, ** p<0.05, * p<0.1.
36 13 13 23 23
[Marginal q (t) - 1]*Price Index for Investment inFixed Assets
Total Assets(t-1) / Total Valueof Fixed Assets(t-1)
Number of industiral sector 36
(36) (13) (23)
reduced form of adjustment cost model (Eq. (10))(industrial sectors)
(Panel estimation with fixed effect and robust standard errors (FE) )
Dependent Variable = Investment(t)/Capital Stock (t-1)
structural formof adjustment cost model (Eq. (12))
(industrial sectors )
Independent Variables
Figure 1: Monthly housing prices and ratio of price to rent during Dec. 2004-Dec. 2017
Source: The data are obtained from the China Monthly Economic Indicators by National Bureau of Statistics from Jan. 2005 to Jan.2018.
Figure 2: PPI by industrial sector during 2004-2016 (previous year=100)
Source: The data from the National Data by National Bureau of Statistics of China. http://data.stats.gov.cn/
Figure 3: Ratio of total profit to fixed asset by industrial sector during 2000-2016 (%)
Source: The data from the National Data by National Bureau of Statistics of China. http://data.stats.gov.cn/
Housing market
Housing related industries
vs. the left ones
Bubble?
Price bubble induces over supply of housing
Producer price commoving with
change of housing related factor
market
H Impact ?
Factor demand of housing construction by
input-output table
Profit, Tobin’s q of housing related factor market and the left industries
q and investment equation?
Difference may exist between housing related industries and
the left ones
H Impact ?
Producer price may affect profit of factor market
Figure 4: Transmission from housing bubble to investments of housing-related industries
Source: Drawn by the authors.
Figure 5: Demand and supply of housing-related factor market (input i of housing construction )
Source: Drawn by the authors.
Quantity of input i of housing construction
S: Supply of input i Price of input i
of housing construction S’
D: Demand of input i D’
E
E’
p
p’
Quantity 1 Quantity 2
Figure 6: Marginal q by industrial sector during 2001-2016
Source: Authors' estimations based on data from the National Data by National Bureau of Statistics of China. http://data.stats.gov.cn/
Figure 7: Growth rate of ratio of housing price to rent vs. PPI during 2001-2016
Source: Authors' estimations based on data from the National Data by National Bureau of Statistics of China. http://data.stats.gov.cn/
Figure 8: PPI vs. Marginal q during 2001-2016
Source: Authors' estimations based on data from the National Data by National Bureau of Statistics of China. http://data.stats.gov.cn/
Figure 9: Marginal q vs. ratio of investment to real capital stock during 2001-2016
Source: Authors' estimations based on data from the National Data by National Bureau of Statistics of China. http://data.stats.gov.cn/
0
2
4
6
8
10
12
14
16
18
20
-0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3
G(I, K, τ) of 36 industries
G(I, K, τ) of the 13 housing‐related industries
G(I, K, τ) of the remaining 23 industries
I/K
Figure 10: Implicit adjustment cost function of the 13 housing-related industries and the remaining 23 industries in China, 2001-2016
Source: See the text.