Proceedings of 24th International Business Research Conference
12 - 13 December 2013, Planet Hollywood, Las Vegas, USA, ISBN: 978-1-922069-37-5
Dose Chinese Economy Suffer From Dutch Disease: from the
View of Real Estate Boom
Mohsen Bahmani-Oskooee*, Yanhui Fan** and Dan Xi**
This paper extends the concept of Dutch disease as it is associated with the
real estate boom in China. The adverse effects resulting from the real
appreciation of RMB are examined through an empirical analysis of China
during the period 2000-2011. The currency appreciation in this paper is
associated with real estate boom rather than excessive natural resource
exploitation as showing in its original concept. We argue that the negative
effect of the boom of real estate sector on the manufactory industry can also
be considered as a typical “Dutch disease”. Given that variables in a Specific
model could be integrated of combination of order one or zero, Pesaran’s
bounds testing approach is applied for cointegration test. It is found that the
real estate boom leads to a real appreciation of RMB and has negative effects
on exports. Particularly, our empirical findings indicate that the real estate
boom has significant negative impacts on the labor-intensive and capitalintensive exports, but not for the technology-intensive exports. The policy
implication of this work is: the government should provide more support to the
manufacturing firm’s innovation and the R&D investment in order to
restructure national comparative advantages. Policies should concern more
about the competitive firms of a particular sector and their innovations rather
than all firms and their final products as a whole.
JEL Codes: F34, G21 and G24
1. Introduction
This paper discusses potential Dutch disease in real estate market of China. Dutch disease is
a process by which a nature resource leads, in the long run, to a decline in production of
other industries and therefore the overall welfare. In the original Dutch disease model
presented by Corden and Neary (1982), there are two causes leading to disease: the
spending effect or wealth effect takes place when increased domestic income from the
booming sector leads to higher aggregate demand, higher inflation, and therefore higher
wages in the economy, which increases the cost of manufacturing sectors and reduces their
competiveness in world market. The resource reallocation effect occurs when a booming
sector attracts capital and labor from the other sectors. It tends to reduce production in the
rest of the economy especially the nontradables sector, thus increase the price of
nontradables relative to that of tradables which is fixed in the world market. Both effects lead
to a real exchange rate appreciation measured by the relative price of nontradables to
tradables. Simply speaking, what we called ―disease‖ is noted by an appreciation of the real
exchange rate and a factor reallocation between sectors.
*
Mohsen Bahmani-Oskooee , The Center for Research on International Economics, Department of Economics,
The University of Wisconsin-Milwaukee, Milwaukee, WI 53201, United States. Email: bahmani@uwm.edu
**
Yanhui Fan , School of Banking and Finance, University of International Business and Economics, Beijing,
China, 100029. Email: fyhbnu@126.com
**
Dan Xi , School of Banking and Finance, University of International Business and Economics, Beijing, China,
100029. Email: danxi83@gmail.com
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Proceedings of 24th International Business Research Conference
12 - 13 December 2013, Planet Hollywood, Las Vegas, USA, ISBN: 978-1-922069-37-5
The theory of Dutch disease analyses the way a boom industry affects other parts of the
economy, especially the parts affected adversely. There is an extensive literature in this field.
Since Nagasaka firstly developed the concept ―Dutch disease‖ in 1977, it has been expanded
in different areas and interpreted in different ways. Based on Nagasaka’s study, there are a
number of works (see Buiter and Purvis (1980, Corden and Neary (1982), Bruno and Sachs
(1982)) aiming at analyzing the mechanism of Dutch disease—the appreciation of currency
caused by the rise of revenues from the boom sectors harms the economy’s exports of the
manufacturing sectors which leads to de-industrialization.
Since then people shifted their attention to prove the existence of Dutch disease empirically
(see Ye (2008), Ismail (2008), Ismail (2010), Algieri (2011), Beverelli,et al (2011)). Later on,
some people tried to explain ―nature resource curse‖ paradox - countries with abundance of
nature resources tend to have less economy growth (see Auty (1993), Sachs and Warner
(2001)). There is a huge literature about the ―resource curse‖, comprehensive surveyed in
van der Ploeg(2011). Recent studies carried out by Van der Ploeg (2011) and Frankel (2010).
Gylfason (2001) also showed that the curse went through lower human capital level in
economies with rich capital resource. There are some other studies attempting to extend the
literature in ways beyond its origin in nature resources. They suggest that besides the
discovery of nature resources, there are some other favorable shocks that result in a decline
in manufacturing production. These shorcks could be the presence of sustained capital
inflow, sharp increase in remittance, or international aid.
More specically, Williamson (1995) explored the ―Dutch disease‖ caused by international FDI.
Lartey (2011) found that the inflow of FDI leads currency appreciation in countries with
greater financial openness. Acosta, et al. (2009), Tressel (2006), Rajan and Subramanian
(2011) indicated that international aid and remittance are additional resources of Dutch
disease. Jackline (1998) examined the effects of the oil-boom in the Gulf States in the
framework of a Dutch disease model and showed that immigration may offset the effects of
Dutch disease in the Gulf States. Using data for Canadian provinces, Michel and Serge
(2012) tested the existence of mitigating effect of immigration through the increase in the size
of non-tradable sectors triggered by the positive shock in booming regions (can’t understand).
Ranis (2007) announced a particular Dutch disease in China caused by the large amount of
labor-intensive exports and capital inflow. There is another strand of study concentrating on
the impacts of Dutch disease on entire economy Ismail (2010) found that Dutch disease
affected labor-intensive industries more than capital-intensive industries. Brahmbhatt and
Canuto noted channels through which the disease can affect the economy and summarized
the policy dealing with the disease (too long). Overall, studies in Dutch disease nowadays are
far beyond its original definition established on over exploitation of nature resources.
However, up to date, no literature has ever related Dutch disease with the real estate boom.
Between 2000 and 2011, the specular rise in commodity and housing price has led to a great
development of the real estate sector. This is illustrated by the dramatically increasing
investment in housing market of Global 500. During the same period, the exchange rate of
RMB appreciated by 13% and the manufacturing industries contracted tremendously. While
real estate boom, to a great extent, plays a critical role in stimulating the whole economy after
the financial crisis in 1998, it forces policy makers and economists to concern the possibility
that the Chinese economy is subject to Dutch disease phenomenon. Regarding the
influences of real estate booming in economy, a large mount of studies considered the
relationship between real estate bubbles and capital inflow, credit expanding, and thus the
corresponding financial crisis (see Collyns and Senhadji (2002), Quigley (2001), Renaud
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Proceedings of 24th International Business Research Conference
12 - 13 December 2013, Planet Hollywood, Las Vegas, USA, ISBN: 978-1-922069-37-5
(2000), Barrell,et al. (2010)). Other studies work on the wealth effect and credit effect of
housing expanding (See Case (2000, 2005), Campbell and Cocco (2007), Chen (2006),
Bernanke and Gertler 1989, Goodhart and Hofmann (2007), Igan (2011)). However, no one
has ever tried to consider the consequence of real estate boom from the view of Dutch
disease.
This work aims to investigate whether real estate boom causes an appreciation of domestic
currency and thus a decline of manufacturing industry. We examined the impact of housing
investment on real exchange rate, and exports of manufacturing sectors separately. As it will
then be shown since some variables turns out to be stationary and some others nonstationary, an appropriate approach to deal with this issue will be Pesaran et al.’s bounds
testing approach to cointegration and error-correction modeling rather than other
cointegration techniques. Within this approach, variables could be stationary, non-stationary,
or a combination of both. To this end, we describe the model and explain the method in
Section II. Section III presents our empirical results with a summary and conclusion in
Section IV. The appendix cites sources of the data and provides definition of variables.
2. The Methodology and Model
The objective of this work is to explore the risk of Dutch disease threatening Chinese
economy. Since Dutch disease is evidenced by a real appreciation of domestic currency and
a decline of manufacturing industry due to resource reallocation effects, to reach our goal, it’s
necessary to estimate how the booming of housing influences real exchange rate and exports
of manufacturing sectors. To this end, the models used to describe the determinants of real
exchange rate and exports of manufacturing sectors are formalized in log-linear form by
equation (1) and (2), respectively:
LnRERt  a  bLnGOVEt  cLnOPENt  dLnEXMGt  eLnEMPt  fLnREI t   t
LnEX t     LnTOTt   LnRERt   LnREIt   t
(1)
(2)
Where:
RERt = Index of the real effective exchange rate. A decline reflects a depreciation of domestic
currency.
GOVEt =Government expenditure
OPENt =Openness to trade
EXMGt =excess money growth
EMPt =Exchange market pressure
REI t =Real Estate Investment/GDP
EX t =Exports/GDP
b, c, d, e, f, β, φ, η are the ―underlying‖ parameters to which we shall frequently refer. As
analyzed previously, a posive f and a negative η attached on REI can evidence Dutch
disease caused by real estate booming.
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Proceedings of 24th International Business Research Conference
12 - 13 December 2013, Planet Hollywood, Las Vegas, USA, ISBN: 978-1-922069-37-5
In detail, equation (1) is the real exchange rate function for the ensuring empirical analysis.
The dependent variable RERt is the real effective exchange rate. An increase (decrease) in
RERt indicates real appreciation (depreciation). Closely following Athukorala (2003), in the
model, government expenditure (GOVE), excess growth in money supply (EXMG), and
exchange market pressure (EMP) represent the three options for policy maker to cushion the
real exchange rate against pressure of appreciation1. As suggested by economic theory,
government expenditure is said to have a positive impact on real exchange rate. The variable
EXMG is included in the model to measure the effectiveness of government’s intervention in
preventing real exchange rate appreciation. Athukorala (2003) suggested that EXMG should
be positively related with real exchange rate, but a negative coefficient on EXMG implies the
role of government’s intervention in averting real exchange rate appreciation. Considering
that China was experiencing different exchange rate regimes in our study period, exchange
rate flexibility rather than change in nominal exchange rate is incorporated in the model as a
measure of exchange rate adjustment. Exchange rate flexibility measured by exchange rate
pressure (emp) takes the following form (Combes, et al. (2011)):
emp 
ERt ERt 1
ERt ERt 1  FRt FRt 1
Where ER is the nominal exchange rate of U.S dollar to RMB, and FR is the position of
foreign exchange purchase. The emp index that varies from 0 to 1 is used to describe the
flexibility of exchange rate. Specifically, emp=0 implies the economy is under fixed exchange
rate regime, while emp=1 indicates a free floating exchange rate regime. Combes pointed out
that this index should be negatively related to real exchange rate; namely coefficient e is
expected to be negative.
In additional to the above variables, we use openness to trade (OPEN) as an additional
determinant of real exchange rate. Greater openness to trade tends to lower pressure for the
appreciation of the real exchange rate. Accordingly, the coefficient on OPEN is expected to
be negative. Our new added independent variable is the housing investment (relative to
GDP) which is an indicator of real estate boom. We expect housing investment has a positive
impact on real exchange rate, implying the existence of Dutch disease phenomenon.
Moreover, as an indicator of expanding in housing market, housing investment is introduced
to examine the real estate booming induced appreciation of currency in model (1). The
coefficient f attached on REI is of most interest. As mentioned above, f is supposed to be
positive suggesting the existence of Dutch disease during real estate booming period.
We now proceed to discuss equation (2) which is used to describe another evidence of Dutch
disease—a declining of manufacturing market. To be more policy oriented, we examine the
impact of housing boom on exports of different types of products separately, including laborintensive, capital-intensive, and technology-intensive products. The selected explanatory
variables include the term of trade, the housing investment, the residual of real exchange rate
on the term of trade and housing investment. Again, as aforementioned reason, an adverse
influence of housing investment on exports of manufacturing products indicates the existence
of Dutch disease.
To estimate the models properly, this study uses a systematic approach to investigate the
long-run and short-run impact of housing investment on the real exchange rate and exports of
labor-intensive, capital-intensive, technology-intensive industries respectively. In general,
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Proceedings of 24th International Business Research Conference
12 - 13 December 2013, Planet Hollywood, Las Vegas, USA, ISBN: 978-1-922069-37-5
most macroeconomic time series follow a unit root process, however some series share comovements with other series due to underlying common economic forces or government
interventions. A stationary linear combination of multiple non-stationary variables is referred
as cointegration. Cointegration test is widely used to determine whether there is a long-run
relationship among variables in the equation under consideration2 (Engle & Granger, 1987;
Johansen, 1988). Considering that the ARDL approach can be applied regardless of whether
the underlying regressors are I(0), I(1), or mutually cointegrated, The ARDL approach is
employed to test the cointegration in this work. Once the long-run relationship has been
determined and using the optimal lag order based on Akaike’s information Criterion (AIC),
Schwarz Bayesian Criterion (SBC) and Hannan-Quinn Criterion (HQC), the model is
estimated by means of the ARDL approach to obtain long-run variables coefficients.
The ARDL takes the following form:
p1
p2
i 0
i 0
yt   0   1,i x1,t i    2,i x2,t i 

pk
p1
i 0
j 1
   k ,i xk ,t i    j yt  j  0 yt 1  1 x1,t 1 
 k xk ,t 1  t
(4)
Where ρ = optimal lags based on the AIC, SBC and HQC criteria.
Conintegration among variables in Model (1) and (2) is tested by the Ordinary Least Square
(OLS) method and by calculating F-statistics for the joint significance of the lagged variables.
Pesaran’s study provides two sets of adjusted critical value bounds for all classifications of
the regressors that estimate lower (purely I(0)) and upper (purely I(1)) bounds of significance.
If the computed F-statistic is less than the lower bound critical value, then the null hypothesis
that there exists no long-run relationship among the variables is not rejected, no matter
whether the regressors are I(0) or I(1), or mutually cointegrated. If the obtained F-statistic is
above the upper bound critical value, then the null hypothesis that there exists no long-run
relationship among the variables is rejected, irrespective of whether the regressors are purely
I(0), I(1), or mutually cointegrated. However, the result is inconclusive if the calculated Fstatistic is between the two bound limits.
Thus far, the cointegrating regression (1) and (2) consider only the long-run property of the
model, and does not deal with the short-run dynamics. The ARDL approach also allows us to
fulfill this purpose by using the error correction model (ECM). In equation (2), the coefficients
indicate the short term effect of each variable on consumption. An error correction
model is a dynamic system which indicates the deviation of the current state from its long-run
relationship.
3. The Findings
This section aims to estimate the error correction equation (3) by drawing quarterly data over
2000-2011 period from China. In estimating the error correction equation (3) we must decide
the order of lags imposed on each first-differenced variable. Following the literature we
imposed a maximum of four lags on each variable and used Akaike’s Information Criterion
(AIC) to select the optimum number of lags.
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Proceedings of 24th International Business Research Conference
12 - 13 December 2013, Planet Hollywood, Las Vegas, USA, ISBN: 978-1-922069-37-5
3.1 Impact of Real Estate Boom on Real Exchange Rate
The estimation results of equation (1) are shown in table 1. The short-run dynamic
coefficients are given in Panel A of table 1. As seen, there is at least one significant short-run
coefficient for each variable. All they show is the dynamic adjustment of all variables. The
variable that is of most is the housing investment. Surprisingly, it has a negative and
significant impact on real exchange rate, implying that housing market growing dose not lead
to an appreciation of RMB in the short-run. Do we obtain the same result in the long-run? The
long-run coefficients are shown in Table 1B. These are estimates of 1……k from the ARDL
model. The long-run coefficients are normalized on ln y by dividing them by  0 . This yields a
positive and highly significant long-run coefficient on LnREI of 0.076, which is consistent with
our conjecture that real estate boom leads to an appreciation of exchange rate. This induced
real appreciation illustrates the possibility that the Chinese economy is suffering from Dutch
disease. The adverse effects of this real appreciation resulting from real estate boom on
manufacturing industry will be analyzed in the second part of this section.
Panel C in Table 1 reports the results of the F test along with some other diagnostics. From
Panel C we see that the calculated F-statistic F=5.01 is higher than the upper bound critical
value of 3.61 at 5% lever. Thus, the null hypothesis of no cointegration is rejected, indicating
long-run cointegration relationships among the variables. The error correction term which is
formed by using the long-run coefficient is an additional indicator for cointegration. We then
replace the lagged level variables in equation (3) by the first differenced ECM and estimate
the model one more time. The sign and magnitude of the ECMt-1 term means a lot: the
negative error correction coefficient is another support for cointegration, and the larger the
error correction coefficient is, in absolute value, the faster the variables will return to its longrun equilibrium once shocked. The equilibrium correction coefficient (ecm) estimated at -1.34,
is highly significant and has the correct sign implying an extremely high speed of adjustment
to equilibrium after a shock. The regression for the underlying ARDL equation fits very well
at R2. Lagrange multiplier serial correlation test is a test for autocorrelation in the errors in a
regression model. Residuals from the underlying model are used in a regression analysis,
and then a test statistic is derived from these. The null hypothesis is that there is no serial
correlation of any order. The test statistic is  2 distributed with a critical value 9.48 at the 5%
level of significance. In general, this serial correlation test is statistically more powerful than
the Durbin-Watson test which is only valid for nonstochastic regressors. The computed LM
statistic is 10.19 in Panel C, meaning the model passes the diagnostic tests against serial
correlation at 10% level. In addition, the computed Ramsey Regression Equation
Specification Error Test (RESET, Ramsey, 1969) statistic reported in Panel C of Table 1 is
less than the critical value at the 5% level of significance, indicating that the model does not
suffer from mis-specification. Finally, CUSUM and CUSUMSQ tests proposed by Brown et al
(1975) are applied for parameter constancy. The cumulative sum (CUSUM) and cumulative
sum of squares (CUSUMQ) from a recursive estimation of the model indicate stability in the
coefficients over the sample period3.
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Proceedings of 24th International Business Research Conference
12 - 13 December 2013, Planet Hollywood, Las Vegas, USA, ISBN: 978-1-922069-37-5
Table 1: Full information of estimate of Equation (1)
Short-run
coefficient
estimates
Lag
Δemp
order
0
1
0.139 -0.239*** -0.111
-0.009
-0.042
(0.98)
(0.24)
(0.60)
0.603***
(4.06)
(0.71)
0.135** -0.471** -0.172** -0.201**
(2.93)
(2.03)
2
(2.51)
(2.58)
(2.57)
-0.340*
-0.107*
-0.126
(1.90)
(1.95)
(1.62)
-0.068
-0.089
(1.52)
(1.36)
emp
lrrei
3
Long-run
lreer
coefficient
estimates(ECMt-1=
-1.34 (4.89))
C
ltot
lopen
Exmg
0.216** -0.369*** 0.151
(2.01) (19.33)
Diagnostic Statistics
Δlrrei
F
LM
5.01
10.19
(1.40)
0.279*** 0.076***
(4.94)
(5.94)
RESET CUSUM CUSUM2 Adj. R2
0.92
S
S
0.75
Notes: a. Number inside parentheses is absolute value of t-ratios. ** Significant 5% ***
significant 1%
b. The upper bound critical value of the F statistic at the usual 5% level of significance
is 3.61. This comes from Pesaran et al. (2001, Table CI-Case III, p. 300).
c. LM is the Lagrange multiplier test for serial correlation. It has a  2 distribution with 4
degrees of freedom. The critical value at the 5% level of significance is 9.48.
d. RESET is Ramsey's specification test. It has a  2 distribution with only one degree
of freedom. The critical value at the 5% level of significance is 3.84.
3.2 Impact of real estate boom on manufacturing industries
This section attempts to estimate the impact of housing investment on labor-intensive,
capital-intensive and technology-intensive industries respectively through the ARDL
approach, the results of which are reported in Table 2-4. Again, Panel A in each table reports
the coefficient estimates of all lagged first differenced variables in the ARDL model(short-run
coefficient estimates). The long-run coefficients, however, are reported in Panel B of each.
Clearly, housing investment has a negative and highly significant impact on exports of laborintensive and capital-intensive products in short-run. As can be seen from Panel B in Table 2
and 3, the impact of housing investment on exports is also negative and highly significant in
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Proceedings of 24th International Business Research Conference
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the long-run, indicating that real estate boom hurts labor-intensive and capital intensive
industries in not only short-run but also long-run. As we expected, the results for Capitalintensive industry are no different than that for Labor-intensive industry. These findings
indicate that real estate boom did hurt manufacturing industry through its positive impact on
real exchange rate. Will the same story happen in technology-intensive industry? Panel A
and B in Table 4 show that the influence of housing investment on the export of technologyintensive product is insignificant in either short-run or long-run. It implies real estate boom
does not have a significant adverse effect on technology-intensive industry4. Therefore, to
minimize the negative effect of housing investment, the government should give more
support on technology-intensive industries rather than the manufacturing industry as a whole.
Panel C in each of the following tables report the results of F test and some other diagnostics
statistics. As shown in each table, the calculated F value is greater than its critical value at
5% level, implying the cointegration relationships between variables under consideration. And
this relationship is confirmed by an additional indicator of cointegration which is the negative
and significant coefficient attached on the ECM terms. The Lagrange multiplier (LM) test is
also reported for residual serial correlation. Since the calculated LM statistic for all three type
of industry is less than the critical value of 9.48, it indicates that the residuals of the estimated
ARDL model are free from serial correlation. Moreover, the results of CUSUM and
CUSUMSQ tests are reported. The plot of CUSUM and CUSUSQ statistic stays within a 5%
significant level, therefore the coefficient estimates are said to be stable.
Table2: Full-Information Estimate of Equation (2) in Labor-Intensive Industry
Panel A
Short-run
coefficient
estimates
Lag
orders
Δlrei_gdp
0
-1.122***
(3.44)
0.270
(0.34)
1
0.422
(1.47)
1.960***
(2.87)
2
Long-run
lliexp
coefficient
estimates（
ECMt-1= 0.68 (5.37)
）
-0.316**
(2.01)
lgdpusa
lreer_res
Lrei_gdp
0.620
(1.50)
-1.734***
(7.36)
-1.163***
(7.95)
1.252
(1.70)
C
ltot
-11.626*** -2.727***
(2.88)
(6.52)
Diagnostic
Statistics
-1.180***
(4.36)
F
LM
RESET
CUSUM
CUSUM 2
Adj. R2
5.05
4.40
1.75
S
S
0.53
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Proceedings of 24th International Business Research Conference
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Table 3: Full-information estimate of equation (2) in capital-intensive industry
Short-run
coefficient
estimates
Lag
orders
Δlrei_gdp
-4.140***
(6.98)
0
0.860
(0.69)
0.206*
(1.92)
1
Long-run lciex_gdp
coefficient
estimates（
ECMt-1= -0.56
(6.99)）
C
F
LM
3.86
3.78
-1.016***
(3.27)
-0.811
(1.69)
ltot
lgdpusa lreer_res lrei_gdp
-60.379*** -7.444*** 5.047***
(6.45)
(6.39)
(5.36)
Diagnostic
Statistics
-1.903***
(3.49)
-1.815***
(3.06)
RESET CUSUM CUSUM 2
0.74
S
S
-3.421***
(8.22)
Adj. R2
0.77
Table 4: Full-information estimate of equation (2) in technology-intensive industry
Short-run
coefficient
estimates
Lag
orders
Δlrei_gdp
-1.098***
(3.16)
0
1
Long-run ltiex_gdp
coefficient
estimates（
ECMt-1= -0.39
(4.25)）
Diagnostic
Statistics
-0.526
(1.34)
-0.270**
(2.24)
-1.019***
(2.97)
0.028
(0.18)
0.731***
( 2.77)
C
ltot
10.260
(0.82)
-2.820***
(3.81)
F
LM
RESET
4.01
8.57
4.32
Lgdpusa lreer_res lrei_gdp
-1.351
(1.08)
-4.309***
(5.66)
0.073
(0.18)
CUSUM CUSUM2 Adj. R2
S
S
0.59
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Proceedings of 24th International Business Research Conference
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4. Summary and Conclusions
Many studies have examined the existence of Dutch disease from difference aspects, namely
foreign direct investment, foreign remittance and international aid ect. This work extended the
existing literature by looking into Dutch disease phenomenon in China from the view of recent
real estate boom. By applying the ARDL approach, this study examines the contribution of
real estate boom on real exchange rate appreciation and further analyzes its subsequent
adverse effects on manufacturing industries. The results show that real estate boom indeed
leads to a real exchange rate appreciation and hurts manufacturing industry to a great extent.
The Chinese economy is suffering from Dutch disease. We argue that even though real
estate boom plays a critical important role in stimulating the Chinese economy since financial
crisis in 1998, the negative side (in term of Dutch disease) of the same coin should obtain
more attention. To provide more specific and valuable policy advice, we divided
manufacturing industries into three types: labor-intensive, capital-intensive and technology
intensive industry. Our finding indicates that real estate boom did have an adverse effect on
labor-intensive and capital-intensive industries, but not on technology-intensive industry.
Intuitively, policy makers should provide more support on technology-intensive sectors and
encourage their further development on technology innovation to build up new international
competitiveness, considering that China is losing its comparative advantage from cheap labor
supply.
End Notes
1.
These options are fiscal contraction, sterilizing foreign exchange market interventions and
nominal exchange rate adjustment and represented by government expenditure(relative to
GDP), excess growth in money supply(measured as the difference between growth in M2
and real GDP growth), and the change in nominal exchange rate
2.
The three main methods for testing cointegration are: the Engle-Graner two-step method,
the Johansen procedure, and Philips-Ouliaris Cointegration Test. Even though Johansen’s
test has a lot of advantages over Engle and Granger’s test, a common limitation of these two
techniques is that in practice all variables in equation should be non-stationary. In the
equation under consideration, if some of the variables are I(0) and some of them are I(1), the
approaches discussed above are not applicable.
3.
The figures are not reported in here due to the page limitation. They will be provided upon
request.
4.
Labor-intensive products with less technology innovation are much easier to be substituted
comparing with technology-intensive products, thus rising labor cost and real appreciation
due to real estate boom harm labor intensive industries in a greater extent. Moreover,
mobility of innovation among countries is costly unlike labor. In that sense, policy maker
should pay more attention on the development of new technology innovation to build up new
competitiveness in the world market since China is losing its comparative advantage on
cheap labor gradually.
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Proceedings of 24th International Business Research Conference
12 - 13 December 2013, Planet Hollywood, Las Vegas, USA, ISBN: 978-1-922069-37-5
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