DP2014-29

The Announcement Effects of Regional
Tourism Industrial Policy:
The Case of the Hainan International
Tourism Island Policy in C hina
J ianjun S UN
S u ZHA NG
No buyo shi YAM ORI
July 27, 2014

The Announcement Effects of Regional Tourism Industrial Policy:
The Case of the Hainan International Tourism Island Policy in China

Jianjun SUN
College of Tourism
Hainan University
58 Renmin Ave., Meilan District, Haikou 570228, China
Email: Jianjunsun@hainu.edu.cn
Su ZHANG
School of Economics
Central University of Finance and Economics
39 South College Road, Haidian District, Beijing 100081, China
Email: zhangsu@pku.edu.cn
Nobuyoshi YAMORI (corresponding author)
Research Institute for Economics and Business Administration
Kobe University
2-1, Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan
Email: yamori@rieb.kobe-u.ac.jp

Acknowledgments:
We are grateful to the anonymous referee and participants at the 2014 International
Conference of Tourism for valuable comments. We appreciate Panpan Wei for the help ingathering data. Jianjun SUN’s research is supported financially by a grant-in-aid from the National
Natural Science Foundation of China (Project Number: 71163010). All errors are theresponsibility of the authors.

1

Abstract:
China’s Hainan Island has a rich diversity of attractions and opportunities for nature-based
tourism, outdoor recreation, and sporting activities. Moreover, Hainan Island has a natural andsocioeconomic base and environment, hosting the implementation of a tourism industrial policy.
On December 31, 2009, China’s State Council announced to the world the national policy of theestablishment of Hainan International Tourism Island. Hence, China’s Hainan Island is well suitedfor investigating the announcement effects of regional tourism industrial policy on the market
values of firms in a tourist destination region. Based on a sample of 19 listed firms in Hainan, or
Hainan concept firms, consisting of various industries, we explore the announcement effects of thenational policy of Hainan International Tourism Island using an event-study approach. Twostatistical methods, mean adjusted and market-model adjusted, are employed to calculate the daily
abnormal returns and the cumulative abnormal returns. The results with and without considering
the clustering issue show that, first, the averages of (standardized) accumulative abnormal returns
are not different from zero prior to the announcement; second, the (standardized) accumulative
abnormal returns of Hainan concept stock continuously go up over trading days after theannouncement of a regional tourism industrial policy. As a robustness check, we also investigate
the effect of the policy on Guangdong concept listed firms. We find no significant impacts of thepolicy announcement on the Guangdong concept listed firms. In sum, these findings indicate that
regional tourism industrial policy is valuable not only to the tourism industry in the destination
region but also to other industries in that region.

JEL Classification: O25, R11
Keywords: Announcement effects, Tourism, Regional industrial policy, Tourism industrial
identity, Hainan

2

The Announcement Effects of Regional Tourism Industrial Policy:
The Case of the Hainan International Tourism Island Policy in China

1. Introduction
Government involvement in tourism in destination areas is an important research theme intourism literature (Pastras and Bramwell, 2013). One common form of state involvement is thesetting and implementation of tourism industrial policy for tourist destinations. 1 Capital markets
usually respond to the announcement of a tourism industrial policy. 2 The response of capital
markets to the policy announcement indicates existing and prospective entrepreneurs’ andinvestors’ expectations, which can influence the types and amounts of the flow of resources andthe types of business activity that could thrive in that area. Hence, the response of capital markets
may give us valuable information on whether regional industrial policy is able to motivate market
forces to invest in the destination region. To some extent, it prefigures whether tourism industrial
policy would work well.
The case of Hainan International Tourism Island is well suited for a study on the response of
markets to a regional tourism policy announcement for several reasons. First, Hainan Island issurrounded by the South China Sea and is a clear and separate geographic region. 3 Hence, it is1

Other forms include regulations, mobilizing, guidance, and so on. For the purposes of this study, regional

tourism industrial policy is defined as measures and programs undertaken by governments to shape the sectoral
structure of the economy by channeling resources into a selected “pillar” or “strategic” tourism industry whileideally or purportedly-preserving market competition and firm-level decision autonomy in tourist destination areas.
2

The phrase “announcement effect” is most often used to describe the reactions of capital markets to news that achange will occur at some future date. An event-study approach is commonly used to investigate these effects.
3

See section 2 for an introduction to Hainan International Tourism Island.
3easy to define firms in the relevant region. Second, the region has a large population and abundantnatural resources such as fresh air, uncontaminated water, a pleasant climate, and a favorable
inhabiting environment. The impact of the implementation of a tourism industrial policy may besizable. Third, China, as the second largest economy in the world, is one of the fastest growing
tourism markets and is also one of the leading tourist destinations (Wang and Ap, 2013). 4 It hascreated a favorable macro-environment for development of the tourism industry in Hainan. Finally,
the implementation of a tourism policy involves inter-organizational coordination and also
requires cooperation with other government organizations (Lai et al., 2006). China is an example
par excellence of a country with a top-down political system. It facilitates coordination andcooperation during the implementation of a tourism industrial policy.
The academic literature on tourism policy is broad and fragmented. Scott (2011) classifies theliterature on tourism policy in terms of the definition of policy, policy-making process,
implementation of tourism policy, assessment of tourism policy, and methodology for policy
studies and provides a strategic and detailed view of the literature on policy in tourism up to theend of 2009. 5 We focus on papers in primary scholarly journals from the beginning of 2010 for
the sake of brevity. 6 A strand of literature deals with the implementation of tourism policy.
Krutwaysho and Bramwell (2010) investigates tourism policy implementation within its societal
context including the economy, governance, politics and culture and focuses on studying the4

For example, the UNWTO (2013) shows that Chinese international tourism receipts grew in double digits (25%)

and that China sits in the second place among the 25 largest international tourism earners, behind only Thailand(27%). The fast growth in Chinese tourism markets benefits from higher income, improved internal infrastructure
and other investments, relaxed limitations on foreign travel, tourism policies, and so on.
5

See Scott (2011) for a detailed review of the literature.

6

Ambrosie (2010) suggests that a review of the literature should be undertaken in a variety of ways in the tourism

policy field because reviewing and integrating such an unconsolidated literature is a daunting task.
4interactions using the case of Phuket, Thailand. With the guidance of the synthesis approach,Wang and Ap (2013) develop a conceptual framework describing the factors affecting theimplementation of tourism policy and illustrate the framework with the experience of tourism
policy implementation at the local level in China. Pastras and Bramwell (2013) emphasize the roleof government in coordinating tourism policies among different actors, institutional arrangements,
and administrative levels and explore government involvement in coordinating and steering the
tourism policies involved in Athens’ tourism marketing. A few studies (Bramwell, 2011; Chaperon
and Bramwell, 2013), based on the governance of tourism, the new institutionalism perspective,
and the strategic-relational approach, examine temporal changes in government involvement intourism activities. Different from the preceding studies adopting descriptive research designs forpolicy implementation, Liu et al. (2012) present a holistic and novel hybrid MCDM (multiple
criteria decision-making) method to analyze the improvement strategy that should be pursued aspart of tourism policy implementation in Taiwan.
Another strand of literature is related to the assessment of tourism policy. Logar (2010)
examines the potential use of policy instruments such as economic, regulatory, and institutional
instruments to manage tourism more sustainably in the coastal town of Crikvenica in Croatia.
Employing the Goffman’s frame analysis, Wu et al. (2012) focus on the holiday system reform
process in China. They explore China’s unique Golden Week holiday system and the ongoingdebate over its reform based on online news stories. Aware of the extraordinary performance of theSingapore tourism industry, Meng et al. (2013) develop a CGE (computable general equilibrium)
model based on the Singapore input-output table, gauge the economic impact of Singapore
inbound tourism and the effectiveness of Singapore tourism policies, and obtain the simulated

5

macroeconomic and sectoral effects. Wang and Xu (2014) find that local government involvement
tends to reduce the economic effects of resource-dependent tourism firms and results indiscernible overcrowding at sites and negative ecological consequences.
Using the case of the Hainan International Tourism Island Policy initiative in 2009, we
explore the response of capital markets to the announcement of a regional tourism industrial
policy in China. To the best of our knowledge, our study is similar to two papers in extant
literature: Chen and Bin (2001) examine the announcement effects of federal and state legislation
regarding casinos on the gaming industry using an event-study approach in the United States.
Chen et al. (2007) apply the same approach to explore the impact of the SARS (severe acuterespiratory syndrome) outbreak on hotel stocks in Taiwan. Our study is significantly different fromthese two in at least two crucial respects. First, we investigate the announcement effects ofcomprehensive tourism industry policy on the whole market instead of only exploring a single
sector such as gambling, hotel, airline, or travel agency in a tourist destination region. Second,although an event-study approach is also applied in our study, we take into account the clustering
issue of cross-sectional correlation in event study and introduce a new test statistic developed by
Kolari and Pynnönen (2010) to address the problem.
We focus on the market values of listed firms locating in Hainan, or Hainan concept firms.
Specifically, based on an event-study approach, we utilize the daily return data of Hainan concept
stocks to explore the announcement effects of industrial policy on tourism. We find that, first,
Hainan concept stocks show a different pattern after the announcement of the tourism industrial
policy than prior to it, and the difference is statistically significant; second, the (standardized)
accumulative abnormal returns of Hainan concept stocks continuously go up over trading days

6

after the announcement of the regional tourism industrial policy. Hence, market participants
positively respond to the announcement of a national policy. The preceding results show that thenational announcement of the Hainan International Tourism Island initiative did indeed convey
information useful for the valuation of Hainan concept firms; the national policy recognizes
Hainan Island as a tourism industrial identity, and the value of receiving a tourism industrial
identity is significant not only for tourism-related firms but also for all other firms on Hainan
Island.
The rest of the paper is organized as follows: In the next section, we provide backgroundinformation on Hainan Province and the evolution of the national policy of Hainan International
Tourism Island. Sample selections and methodology design are outlined in section 3. Section 4
presents empirical findings and discussion. Section 5 concludes this paper.

2. Hainan Province and the National Policy of Hainan International Tourism Island
Hainan Province, founded in 1988, is the southernmost province of China. It neighbors thePhilippines to the east, Malaysia and Brunei to the south, Indonesia to the southwest, and Vietnam
to the west. It has an area of 35,400 square kilometers of land and 2.1 million square kilometers ofmarine space. It consists of Hainan Island,
which is the largest island and is separated
Location ofby the Qiongzhou Strait from the Leizhou

Hainan Island

Peninsula of mainland China, and some 200
scatteredislands,but

33,900squarekilometers (95.8%) of its land is Hainan
Figure 1 Location of Hainan Island
7

Island. Hainan Province takes its name from Hainan Island. It enjoys a marine tropical moist
monsoon climate. The coldest months are January and February, when the monthly averagetemperatures drop to around 16ºC, and the hottest months are July and August, when the monthly
average temperatures range from 25 to 29ºC. There are 9 major cities and 10 counties in Hainan
Province. Haikou, on the northern coast of Hainan Island, is the capital, and Sanya is the leading
and well-known holiday resort on the southern coast. It has a population of around 8.7 million, andits population density is low compared to most Chinese coastal provinces.
Primarily known for its stunning coastline and sun-kissed, palm-lined beach, Hainan Islandoffers a wealth of other attractions—from waterfalls, hot springs, an extinct volcano crater, andtropical mangrove and rain forests that are home to more than 4,600 kinds of flora and fauna andover 570 wildlife species to a rich cultural heritage with three important ethnic minorities playing
a significant role in its colorful history. All these attractions are in the context of Hainan’s tropicalclimate, ideal for year-round leisure and business tourism, including meetings, conferences, andcorporate-sponsored incentive trips. Few destinations around the world offer such a variety of
attractions and opportunities for nature-based tourism, outdoor recreation, and sporting activities(WTTC, 2012).
China’s State Council (the Cabinet in China) recognized the promising economic and social
contributions of the tourism industry and chose Hainan Island as a priority area for tourism
development in the national Seventh Five-Year Plan (1986–1990). 7 Yalong Bay in Sanya wasdesignated for the construction of national holiday resorts and the development of starred hotels,
condominiums, golf courses, and so on in 1992. On December 31, 2009, China’s State Council
7

Tourism was included in the national plan for social and economic development in China for the first time in

1986.
8

announced to the world the national policy of the establishment of Hainan International Tourism
Island. 8 This means that Hainan Island was earmarked to become a world-class international
tourism destination on a par with world-renowned destinations such as Hawaii and Bali. The
Hainan provincial government’s focus on tourism development, which started in earnest aroundthe mid-1990s, has been stepped up significantly over the past few years, particularly after thedeclaration of December 31, 2009. Hence, Hainan has benefited from a number of favorable
policy measures aimed at facilitating and accelerating tourism development and investment. These
include optimizing the destination’s appeal through the granting of visa-free status for selectedmarkets and duty-free shopping for mainland Chinese as well as foreigners.
Both Chinese and foreign airlines are vying with each other to gain traffic rights for new
domestic and international services, and cruise lines are queuing up to tap the latent market
demand for Hainan as a port of call or even to base their cruise ships at Sanya’s Phoenix Island
cruise terminal. Due to the national and provincial authorities’ support in building the necessary
infrastructure and in developing new tourism products and attractions, leading international hotel
brands have been quick to partner with large Chinese investors and developers to establish apresence on Hainan Island and secure a slice of the expanding tourism cake. Indeed, no othertourist destination region in the world—whether province or country—has attracted such hugeinterest from so many different players in such a short time (WTTC, 2012).
8

The State Council decree detailing the development of Hainan into an international tourism destination includes

the following 6 specific strategies: developing Hainan into a pilot region for China’s tourism industry reform,building the island into a world-class leisure travel and holiday tourist destination, establishing a demonstration
zone for China’s ecological development, making Hainan an important platform for international economic
cooperation and cultural exchanges, developing resources in the South China Sea, and developing the Hainan
economy into a service-based as well as a modern national tropical agriculture-based economy. For additional
details on the key players and institutional possesses involved in national policy-making in China’s tourism sector
since 1978, see Airey and Chong (2010).
9In sum, the growing importance of tourism to Hainan is indisputable; significant strides inline with plans have already been made to turn Hainan into an international resort destination; anda distinctive and competitive tourism industry identity has been taking form through the national
policy, a series of government efforts, tourism industry planning, investment in tourism, marketing,and promotional activities in Hainan.

3. Sample Selections and Methodology Design
3.1. Sample Selection
As of December 31, 2009, there are 24 Hainan concept firms listed on the Shanghai Stock
Exchange (SHSE) and Shenzhen Stock Exchange (SZSE) in total. 9 First, we drop the firms
issuing class B, H, and red-chip shares in order to avoid the measuring error of the renminbi(RMB) exchange rate. Second, to lower the speculative trading effects especially among smalland medium-cap stocks, 10 the firms traded in second-tier and NASDAQ-style growth enterprisemarkets are deleted. Then 19 firms that only issue class A shares and are traded on the first-tiermarket (“Zhu Ban Shi Change”) of the SHSE and SZSE are kept in our database. 11 As another

9

The Hainan concept listed firms refer to publicly traded companies listed on China’s domestic stock exchanges,

which have their headquarters and make the most earnings in Hainan Province.
10

Allen et al. (2012) argue that small- and medium-cap stocks are more easily manipulated in Chinese stock

markets.
11

In Chinese stock markets, class A shares are designed to serve domestic investors. All shares of Chinese listed

firms with non-RMB denominations are available to global investors, such as class B, H, and red-chip shares.
Three other markets have been established to complement the first-tier market (“Zhu Ban Shi Change”), a secondtier market similar to the NASDAQ (“Er Ban Shi Change”) for small and medium enterprises (opened in 2004), a
third-tier market (“San Ban Shi Chang”) for dealing with de-listing firms and other Over-The-Counter (OTC)transactions (opened in 2001), and a NASDAQ-style growth enterprise market (“Chuang Ye Ban”) for small andmedium private enterprises (opened in 2009).
10

robustness check, we also examine Guangdong concept listed firms. The reasons the Guangdong
concept is chosen are as follows: First, Hainan is the southernmost province in China. Hainan waspart of Guangdong Province for centuries, and it was newly created in 1988. If there exists anotherunspecified event that had an impact on southern regions of China, the Guangdong concept stocks
will also be the most likely to be influenced by the event. Therefore, if we find a significant impact
on the Hainan concept stocks but no significant impact on the Guangdong concept stocks in termsof the announcement of the Hainan tourism policy, it is reasonably concluded that the significant
impact on Hainan concept stocks is attributable to the announcement of the Hainan tourism policy.
Second, as of December 31, 2009, the number of listed Guangdong concept firms was 225 andaccounted for 13.10% (the largest percentage in 31 concept stocks) of the all listed firms. It isclearly evident that the Guangdong concept best represents the whole stock market’s response to
the policy announcement among all the 31 concept stocks in China. Following the same sample
selection methods as for the Hainan concept, 127 publicly traded companies are kept in the dataset.
Table 1 and the appendix present primary financial information on Hainan and Guangdong
concept stocks, respectively. As evident in Table 1, 12 of 19 Hainan concept stocks are listed on
the Shenzhen Stock Exchange as of November 30, 2009. Price per share, shares outstanding, andmarket capitalization are figures on November 30, 2009. The maximum and minimum prices per
share are 17.08 and 5.23 RMB Yuan, respectively. The outstanding shares range from a high of
3,345.19 million to a low of 99.33 million. The firm with code 600221 has the maximal market
capitalization of 19,937.36 million RMB Yuan, which is 19.13 times the minimal number of
1,042.10 million RMB Yuan for a company with code 000567. The last column of industry showsthat the 19 listed firms are distributed across following industries: 4 in real estate, 2 in

11

transportation, 2 in metallurgy, 1 in hotels, 1 in mining, 1 in farming, 1 in publishing, 1 in textiles,
1 in decoration, 1 in retail, 1 in computers and networking, 1 in automobile manufacturing, 1 inmedical manufacturing, and 1 in drink manufacturing.

Table 1 Hainan Concept Stocks Listed in the Shanghai and Shenzhen Stock Exchanges
MarketPrice per Share

SharesCapitalization (inDate of(in RMB

Outstanding

Million RMB

CodeListedBeing Listed

Yuan)(in Millions)

Yuan)

Industry

000503

SZSE

Nov 30 1992

10.52

569.025986.06

Computer and network

000566SZSE

May 25 1994

15.75

207.863273.83

Medical manufacturing

000567SZSE

May 25 1994

8.94

116.571042.1

Real estate

000571SZSE

May 25 1994

6.71

735.514935.24

Mining000572SZSE

Aug 08 1994

6.35

568.913612.56

Automobile manufacturing000613SZSE

Jan 28 1997

6.05

203.031228.32

Hotel

000657SZSE

Dec 05 1996

9.03

144.051300.78

Metallurgy

000691SZSE

Feb 28 1997

5.23

268.481404.14

Real estate

000735SZSE

Jun 11 1997

6.1

879.895367.3

Farming000793

SZSE

Jul 29 1997

6.88

1169.418045.51

Publishing000886SZSE

Jan 23 1998

5.74

941.645404.99

Real estate

000955SZSE

Dec 09 1999

6.64

232.031540.7

Textile600209SHSE

Mar 25 1999

6.63

223.761483.55

Decoration

600221SHSE

Nov 25 1999

5.96

3345.19

19937.36

Transportation

600238SHSE

Jan 20 2000

15.64

155.182427.06

Drink manufacturing

600259SHSE

May 25 2000

17.08

99.33

1696.56

Metallurgy

600515SHSE

Aug 06 2002

7.9

250.191976.54

Retail600759SHSE

Oct 08 1996

7.33

204.151496.41

Real estate600896SHSE

May 03 1996

9.52

581.325534.13

Transportation

Company Market

Note: SZSE and SHSE are short for Shenzhen and Shanghai Stock Exchanges, respectively. Price per share, shares
outstanding, and market capitalization are figures on November 30, 2009. Our data are from the China Stock Market
and the Accounting Research Database.

12

3.2. Methodology Design
We investigate the announcement effects using an event-study approach. 12 Before
implementing this approach, we need to define the event date of interest and the event window,
which is the period over which the stocks’ prices are examined. We define the event date as
December 31, 2009, when the announcement of Hainan International Tourism Island was made by
the State Council of China 13. Following the standard practice, a 15-day event window centered on
the event day is also analyzed. In order to examine the announcement effects, an eight-day event
window covering the announcement date and the seven days after the event day is selected. To
evaluate whether information on the announcement might have been leaked to the markets prior to
the announcement, a seven-day event window covering the seven days preceding theannouncement is also chosen. In other words, we present empirical evidence for three different
windows: [-7, 0), [0, 7], and [-7, 7], where day 0 refers to the announcement date (i.e., December
31, 2009). The parameters of the normal performance are estimated over the estimation window.
Taking into consideration that the estimation window and the event window do not overlap andthat the estimation sample period is typically 120 to 250 trading days in a daily data, we specify an
estimation period over the period from December 1, 2008, to November 30, 2009 (244 trading

12

Using financial market data, an event study measures the impact of a specific event on the value of firms.

Different from the direct productivity-related measures, which may require many months or even years ofobservation, a measure of the event’s effect can be constructed using security prices observed over a relatively
short time period. The event study has many applications. Some examples in accounting and finance include
mergers and acquisitions, earnings announcements, issues of new debt or equity, and announcements ofmacroeconomic variables such as financial deficit (e.g., Spiegel and Yamori, 2004; Yamori, 1999; Yamori andBaba, 2000; Yamori and Kobayashi, 2002, 2007; Yamori and Murakami, 1999). Some examples from the field of
law and economics include assessing the effects of a change in the regulatory environment on the value of a firmand evaluating the damages in legal liabilities.
13

The stock market opens at 9 o’clock in China, and the news release of the Hainan tourism policy occurred at 9

o’clock.
13

days in China).
Four statistical models are commonly used to calculate the daily abnormal returns andcumulative abnormal returns of stocks: raw, mean-adjusted, market-adjusted, and market-modeladjusted (single factor and multifactor market-adjusted) methods. For simplification androbustness, we choose the mean-adjusted and market-adjusted models. 14 Specifically, we haveAR=
Ri ,t − E  Ri ,t  ,
i ,t

(1)

where ARi ,t is the daily abnormal return of stock i in period t , Ri ,t is the observed daily returnin raw stock price, and E  Ri ,t  is the expected return for stock i on date t . For any stock i ,the expected return is defined as follows: Ri
E  Ri ,t  = 
aˆi + βˆi RM ,tmean − adjusted
market − mod el − adjusted,(2)

where Ri is the simple average of stock i over the 244-day estimation period. RM ,t represents
the value-weighted SHSE and SZSE market index returns on day t . The coefficients αˆi and βˆiare ordinary least-squares values from the single-factor market model regression over theestimation period.
The abnormal return observations ARi ,t must be aggregated in order to draw overall
inferences for the Hainan International Tourism Island announcement effects. The aggregation isalong two dimensions, through time and across stocks. Let τ denote the time index in the event� i (τ ,τ ) as the sample cumulative abnormal return (CAR) from τ to τ .
window. Define CAR
1 2
12

The CAR from τ 1 to τ 2 is the sum of the included abnormal returns,

14

Brown and Warner (1980, 1985) show that the mean-adjusted model performs better than other methods. Levy

(2000) and Chan-Lau (2002) indicate further that the choice of model does not significantly affect the results. In
addition, Campbell et al. (1997) argue that the gains from employing a multifactor market-adjusted method forevent studies are limited.
14τ2

� i (τ ,τ ) =CAR∑ �ARi .
1 2

(3)

τ =τ1

� i isAs the estimation window length increases, asymptotically, the variance of CAR

σ i2 (τ 1 ,τ 2 ) =(τ 2 − τ 1 + 1) σ ε2 ,(4)iwhere σ ε2 is the variance of the stochastic disturbance term in a single-factor market model.
i

A usable test statistic for the null hypothesis of no impact of the event for stock i is then thestandard cumulative abnormal return:� i (τ ,τ ) asy
CAR
1 2
�
SCAR
~ N ( 0,1) .
i (τ 1 ,τ 2 ) =s i (τ 1 ,τ 2 )(5)

� i (τ ,τ ) across N stocks,
Then we aggregate CAR
1 2

CAR (τ 1 ,τ 2 ) =

(

1 N �
∑ CARi (τ 1 ,τ 2 )
N i =1(6)) N1 ∑σ (τ ,τ ) .

Var CAR (τ 1 ,τ 2 ) =

N

2

i =1

2
i

1

2

(7)

In practice, the usual sample variance measure of σ ε2 from the single-factor market modeliregression in the estimation window is usually used as an estimator of σ ε2 . Inferences about theicumulative abnormal returns can be drawn using
J1 =CAR (τ 1 ,τ 2 )(Var CAR (τ 1 ,τ 2 )asy)~ N ( 0,1)

(8)

to test the null hypothesis of no announcement effect of Hainan International Tourism Island (i.e.,
the abnormal returns are zero).
The event day (December 31, 2009) is the same for all sample companies. The clustering
problem of cross-sectional correlation exists among abnormal returns. For the clustering, thesimple covariances between the abnormal returns may no longer be usable. Clustering can beaccommodated in two ways. One way is the application of a multivariate regression model withdummy variables for the event date. In general, the approach has two drawbacks - the test statistics
15

frequently have poor finite sample properties except in special cases, and often the test will havelittle power against economically reasonable alternatives (MacKinlay, 1997). A second way is themodified Patell statistic J 2 , which is a new test statistic that modifies the t statistic of Boehmer
et al. (1991) to take into account the clustering issue of cross-sectional correlation. Kolari and
Pynnönen (2010) show that it performs well in competition with others. We utilize the modified
Patell statistic J 2 to correct the potential cross-sectional correlation.
�
Averaging the standardized cumulative abnormal returns SCARi (τ 1 ,τ 2 ) across stocks, we

have
SCAR (τ 1 ,τ 2 ) =1 N �∑ SCARi (τ 1 ,τ 2 ) ,N i =1(9)and its variance is as follows:) N L( L−−24 ) (1 + ( N − 1) r ) ,(Var SCAR (=τ 1 ,τ 2 )

1

(10)

1

where L1 and r denote the length of estimation period and the average cross-sectionalcorrelation between the stocks’ abnormal returns, respectively. Under the null hypothesis of noannouncement effects, this yields the modified Patell statistic
J2 =SCAR (τ 1 ,τ 2 )
L1 − 2(1 + ( N − 1) r )N ( L1 − 4 )asy~ N ( 0,1) ,(11)

with r denoting the average cross-sectional correlation coefficient of abnormal returns in theestimation period.

16

4. Results and Discussion
4.1. Results
Table 2 presents the information on the cumulative abnormal returns (CAR) and statisticalinferences over three event windows, [-7, 0), [0, +7], and [-7, +7]. Two normal return models are
employed: the mean-adjusted and the market-model-adjusted models. For comparison, empiricalresults on Guangdong are also reported.
Panel A of Table 2 shows the empirical results without considering the clustering for Hainan
and Guangdong concept stocks over three windows. The CAR of Hainan concept stocks underthe market-model-adjusted method is -0.0047 during [-7, 0), which is different from the CAR of
0.0204 under the mean-adjusted model. The two values are not significant. The results indicatethat the values of the CAR are not different from zero in the absence of the announcement of the
tourism island policy. It seems likely that the policy information of the international tourism island
has not been leaked to the market prior to the announcement.
During the event windows [0, +7] and [-7, +7], not only do all the CAR s show positive
values (0.0545 during [0, +7] and 0.0749 during [-7, +7] for the mean-adjusted, 0.0672 during [0,+7] and 0.0625 during [-7, +7] for the market-model-adjusted models), but the J 1 statistics
indicate that they are significant at the 5% level for the mean-adjusted and 1% for the marketmodel-adjusted models, respectively. The results suggest that the announcement has a significant
and positive effect on the price of Hainan concept stocks. The findings indicate that theannouncement of a tourism island policy did indeed convey information useful for the valuation of
Hainan concept firms. The regional tourism industrial identity bestowed by the central government
proves to be valuable.

17

As is evident for Guangdong concept stocks, the values of the CAR show the same pattern
as the Hainan counterpart prior to the policy announcement. All the values of the CAR are
negative and insignificant during event windows [0, +7] and [-7, +7]. They range from a high of 0.0100 to a low of -0.0357. The results clearly show that the announcement of the tourism island
did not have any effect on the performance of the Guangdong concept stocks.
Empirical results for inference when considering the clustering are reported in panel B. The
conclusions after correcting the cross-sectional correlation are consistent with those in panel A.
First, the values of the SCAR are not different from zero prior to the announcement. Second, theannouncement has a significant and positive effect on the performance of Hainan concept stocks.
Finally, the hypothesis of no effect on Guangdong concept stocks is statistically accepted. All
the J 2 statistics in panel B are a little smaller than their counterparts in panel A. Even when crosssectional correlation is relatively low, event-date clustering might be serious in terms of overrejecting the null hypothesis of zero average abnormal return when it is true. The J 2 statistic, inessence, aims at addressing the over-rejecting problem (Kolari and Pynnönen, 2010).

18

Table 2 Cumulative Abnormal Returns and Statistical Inferences
Event
Window

Hainan Concept Firms
Mean Adjusted

Guangdong Concept Firms

Market-Model-Adjusted

Mean Adjusted

Market-Model-Adjusted

Panel A: Inference without Clustering[-7, 0)[0, +7][-7, +7]

Average
CAR
0.02040.05450.0749

J1 stat0.97092.42942.4374p value
0.33160.01510.0148Average
CAR-0.00470.06720.0625

J1 stat-0.28533.80792.5861

p value
0.77540.00010.0097

Average
CAR
0.0012-0.0237-0.0225

p value
0.80630.00440.0141

Average
SCAR
0.0001-0.0031-0.0022

J1 stat0.0424-0.7761-0.5377

p value
0.96610.43770.5907

Average
CAR-0.0257-0.0100-0.0357

J1 stat-0.9111-0.3327-0.8654

p value
0.36220.73920.3867p value
0.99980.99630.9974

Average
SCAR-0.0038-0.0014-0.0036

J2 stat-0.0156-0.0057-0.0148

p value
0.98750.99540.9881Panel B: Inference with Clustering[-7, 0)[0, +7][-7, +7]

AverageSCAR
0.6122
1.71201.7200J2 stat0.85352.38662.3978p value
0.40320.01700.0164

Average
SCAR-0.0938
1.08930.9395

J2 stat-0.24512.84492.4537

J2 stat
0.0002-0.0046-0.0032

Notes: For Hainan concept stocks, r takes 0.4831 and 0.0979 for the mean-adjusted and the market-model-adjusted values, respectively, in computing J2. For
Guangdong concept stocks, r takes 0.4748 and 0.0506 for the mean-adjusted and the market-model-adjusted values, respectively, in computing J2.

19

Figure 2 is provided in order to gain a visual sense of the pattern shown by the numbers in
Table 2. It plots the mean cumulative daily abnormal returns over the event-window horizon [-7,+7], where day 0 represents the announcement date of the Hainan International Tourism Island
Policy. The daily CAR s of Hainan concept stocks under mean and market-model-adjusted
methods are marked using a solid line with a filled square and a solid line with a solid triangle,
respectively. The daily CAR s of Guangdong concept stocks under mean and market-modeladjusted methods are marked using a dashed line with an open square and a solid line with anopen triangle, respectively. The four curves almost display the same time series pattern of rising
before descending during the event window [-7, 0). That is, the rising market trend induces thesame ascending trend of Hainan concept stocks. It graphically indicates that the market may not
gradually learn about the forthcoming announcement. It is possible that the policy information on
the tourism island has not been leaked to the market prior to the announcement. The four curves
diverge greatly after the announcement. The daily CAR of Hainan concept stocks clearly shows

20

a steeply ascending trend. In comparison, the daily CAR of Guangdong concept stocks graduallydrifts down over the seven days after the announcement. It strongly echoes the preceding findingsthat the announcement effects are assimilated into the stock prices and that the regional industrial
identity driven by the regional tourism industrial policy is valuable in the tourist destination regionof Hainan Island.
Classical literature on the event-study approach finds that the announcement effects of goodor bad news are usually captured in the returns on event day (t=0) or on day (t=1) in the case thatannouncements are made on event day after the close of the stock market. Moreover, thecumulative abnormal returns are usually relatively stable in the days after the announcement. By
contrast, Figure 2 clearly shows that there are no effects of the policy announcement on event day,but the accumulative abnormal returns continuously go up on trading days 1, 2, 4, and 5. Hence,
the time series pattern of the daily CAR of Hainan concept stocks after the announcement is notconsistent with those in previous literature. Although it may be due to the current structures ofChinese stock markets, further research is needed.

4.2. Discussion: Why Do Markets Positively Respond to the Announcement?
Before concluding the paper, we briefly investigate why markets positively evaluated theannouncement of the regional tourism industrial policy in Hainan. To the extent that we canidentify one or more credible mechanisms, the conclusion that regional tourism industry identity isvaluable as a driving force behind the improvement of socioeconomic conditions may beconsiderably generalized. However, if we cannot find a credible mechanism, it may be difficult toargue that regional tourism industry policy is one exogenous factor resulting in the promotion of21

regional development.
As is already evident, the announcement has conveyed the information on regional tourism
industrial identity of Hainan to stock markets, and stock markets might expect that Hainan wouldgrow as a world-class international tourist destination. Regional industrial identity is an importantcomponent in understanding the spatial arrangements of industrial and regional development(Romanelli and Khessina, 2005).15 Generally speaking, regional industrial identity is grounded in
the configuration of industry clusters that populate a region. Judging from the announcement, we
reasonably infer that in the future, Hainan Island will be a geographically large region with asingle dominant tourism industry, which then fosters other industrial clusters through various
channels. A great deal of literature (e.g., Jaffe et al., 1993; Audretsch and Feldman, 1996; Murtha
et al., 2001) emphasizes the important role of regional industry clusters in stimulating new ideas
and transferring knowledge across organizations. Hence, stock markets expect that the tourism
industrial identity should accelerate the pace of innovation in the Hainan tourism industry. The
dominant tourism industry is likely to create backward and forward linkages when its demand andsupply enable upstream and downstream industries to be established or promoted in Hainan.
Moreover, few would contest that China’s political system is top-down (Li and Zhou, 2005; Wang
and Wall, 2007). The policy decision made by the State Council to develop tourism on Hainan
Island has been passed down to lower levels of government. The top-down decision-making has

15

Two important dimensions of strength and focus affect economic agents’ understandings about Hainan Islandand thus those agents’ decisions to invest in Hainan Island. The strength dimension of the Hainan tourism
industrial identity attracts the resources that flow into Hainan Island, and the focus dimension of the Hainan
tourism industrial identity influences the heterogeneity of resources that flow into Hainan Island. The dimension ofstrength refers to the number and size of audiences that share a common view of the social and industrial activity
in a region, and the dimension of focus is the extent to which the identity closely associates with just one kind of
business activity.
22

provided the ability to impose a series of policies to promote and strengthen the regional tourism
industry identity. For example, to realize the Hainan’s tourism potential, well-planned, coordinated,and carefully implemented tourism development measures, investment in education and training toprovide qualified human resources, development of optimum tourism products, and effective
marketing promotions are required. A top-down political system ensures that these are effectively
enforced.
Existing and prospective entrepreneurs and investors have perceived the hospitable andprofitable environment of the tourism-dominated region of Hainan Island. Market investors haverealized and made optimal use of the aforementioned information in forming their rational
expectations regarding future values of Hainan concept stocks even before seeing any actualchanges in the stocks. In other words, the identity of a regional tourism industry can create apositive value for Hainan’s regional development. Hence, Hainan concept stocks significantly go
up as a whole, and the stock markets positively evaluated the policy announcement of Hainan
International Tourism Island.

5. Conclusions
Hainan Island has a rich diversity of attractions and tourism resources. China’s central
government and the Hainan provincial government recognized the potential economic and social
contributions of the tourism industry and have been making efforts to turn Hainan into aninternational resort destination. On December 31, 2009, China’s State Council announced
ambitious plans for Hainan, “the national policy of Hainan International Tourism Island,” to23

elevate it to a world-class international tourism destination by 2020.
Based on a sample of 19 listed Hainan concept firms, which belong not only to the tourism
industry but also to other various industries, we examine the announcement effects of the national
policy by using an event-study approach. The empirical findings based on inferences without andwith considering the clustering show that, first, the averages of (standardized) accumulative
abnormal returns are not different from zero prior to the announcement; second, the policy
announcement of Hainan International Tourism Island has a significant and positive effect on theprice of Hainan concept stocks. The findings indicate that the stock prices of the listed firms in
Hainan Island are significantly sensitive to the policy announcement of a tourism island, and thepolicy announcement did indeed convey information useful for the valuation of Hainan concept
firms. From the angle of investors’ expectations, these empirical findings indicate that macroregional industrial policy plays a positive role in motivating market forces to invest in thedestination region.
The mechanism by which Chinese capital markets positively respond to the tourism policy
announcement is likely to be as follows. The national announcement of Hainan International
Tourism Island has bestowed on Hainan a tourism industry identity and conveyed the relevant
information to stock markets. Stock markets expect that the tourism industrial identity might
increase the profitability and the pace of innovation in the Hainan tourism industry and that thedominant industrial cluster in tourism might play an important role in creating industrial backward
and forward linkages. Therefore, the regional tourism industrial identity creates significant value
for all industries in Hainan.
In the Hainan Island case, the regional development is directed at the tourism industry by the24policy of the central government. The effects of the tourism policy are likely to be different fromthose through natural selection and evolution such as the Las Vegas identity as a gambling andnightlife town. Hence, further research may compare the two types of identities forming and theirdynamic evolutions and effects.
Finally, it is worth noting that we investigate the announcement effects using an event-study
method. The method rests on one assumption, that markets are efficient in China. Although thelatest literature (Lin et al., 2011; Chong et al., 2012; Mobarek and Fiorante, 2014) shows that
Chinese stock markets are fairly efficient in weak form and that its market efficiency improved
greatly over our sample period, the research on market efficiency is inconclusive, even in many
developed markets.

25

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30

Appendix Guangdong Concept Stocks Listed in the Shanghai and Shenzhen Stock Exchanges
SharesMarket Capitalization

CompanMarketDate ofPrice per Share

Outstanding(in Million RMB

y CodeListedBeing Listed(in RMB Yuan)(in Millions)

Yuan)

Industry

000001SZSE

Apr 03 1991

24.26

2924.1170939.01

Banking

000002SZSE

Jan 29 1991

11.53

9392.09108290.85

Real estate

000005SZSE

Dec 10 1990

6.3

913.745756.58

Real estate

000006SZSE

Apr 27 1992

12.82

497.1

6372.84

Real estate

000007SZSE

Apr 13 1992

7.54

184.971394.64

Hotel

000009SZSE

Jun 25 1991

12.62

988.1212470.07

Comprehensive

000010SZSE

Oct 27 1995

10.98

67.79

744.38Petrochemical

000011SZSE

Mar 30 1992

11.9

139.74

1662.87

Real estate

000012SZSE

Feb 28 1992

20.41

566.8811570.11

Manufacturing

000014SZSE

Jun 02 1992

18.43

201.7

3717.4

Real estate

000016SZSE

Mar 27 1992

7.29

599.914373.33

Manufacturing

000019SZSE

Oct 12 1992

9.76

73.32

715.61

Manufacturing

000020SZSE

Apr 28 1992

12.4

64.68

801.98Manufacturing

000021SZSE

Feb 02 1994

13.46

878.6

11825.91

Computer and network

000022SZSE

May 05 1993

14.53

464.796753.4

Transportation

000023SZSE

Apr 29 1993

11.21

91.83

1029.47

Manufacturing

000024SZSE

Jun 07 1993

31.09

646.43

20097.65

Real estate

000025SZSE

Jun 21 1993

11.12

179.291993.76

Retail000026SZSE

Jun 03 1993

11.34

79.55

902.06

Retail000027SZSE

Sep 03 1993

13.4

598.668022.01

Electricity

000028SZSE

Aug 09 1993

29.02

233.266769.13Wholesale000029SZSE

Sep 15 1993

7.83

891.656981.65

Real estate

000030SZSE

Sep 29 1993

8.13

60.38

490.93

Manufacturing

000031SZSE

Oct 08 1993

13.02

1813.7123614.52

Real estate

000032SZSE

Oct 28 1993

9.3

232.822165.22

Electronic

000033SZSE

Jan 03 1994

7.31

318.942331.43

Hotel

000036SZSE

Jun 17 1994

7.03

1123.89

7900.93

Real estate

000037SZSE

Jul 01 1994

7.95

338.9

2694.22

Electricity

000039SZSE

Apr 08 1994

11.87

1231.314615.5

Manufacturing

000040SZSE

Aug 08 1994

7.91

349.672765.9Real estate000042

SZSE

Sep 21 1994

26.09

239.326243.89

Real estate000043

SZSE

Sep 28 1994

17.93

110.83

1987.12

Real estate000045SZSE

Aug 15 1994

12.12

195.582370.38

Wholesale000046SZSE

Sep 12 1994

16.19

444.527196.75

Real estate000048SZSE

Nov 01 1994

5.27

379.85

2001.8

Food processing

CompanMarketDate ofPrice per Share

SharesMarket Capitalization

Industry31

yListedBeing Listed(in RMB Yuan)

Code

Outstanding(in Million RMB(in Millions)

Yuan)

000049SZSE

Mar 20 1995

11.49

71.3

819.29Manufacturing

000050SZSE

Mar 15 1995

6.54

312.192041.71

Manufacturing

000055SZSE

Apr 15 1996

10.68

217.412321.9Manufacturing

000058SZSE

Dec 26 1996

7.73

538.3

4161.08

Consultative

000060SZSE

Jan 23 1997

27.18

790.9521497.95

Manufacturing

000061SZSE

Jan 10 1997

12.44

476.245924.37

Consultative

000062SZSE

Jan 30 1997

10.55

166.861760.4

Consultative

000063

SZSE

Nov 18 1997

39.29

1467.3157650.49

Manufacturing

000066SZSE

Jun 26 1997

14.43

550.087937.63

Manufacturing

000068SZSE

Jun 11 1997

8.87

474.354207.49

Manufacturing

000069SZSE

Sep 10 1997

21.02

1337.3128110.3

Tourism

000070SZSE

May 11 2000

8.84

237.692101.16

Manufacturing

000078SZSE

Dec 18 1998

19.41

439.278526.14Retail000088SZSE

Jul 28 1997

8.58

406.1

3484.36

Transportation

000089SZSE

Apr 20 1998

7.43

653.134852.72

Transportation

000090SZSE

Jul 21 1999

13.78

435.966007.6

Building

000096SZSE

Jul 24 2000

7.65

276.572115.76

Wholesale000099SZSE

Jul 31 2000

8.2

274.032247.03

Transportation

000100SZSE

Jan 30 2004

4.85

2497.38

12112.28

Manufacturing

000150SZSE

Aug 07 2000

8.33

159.471328.36

Real estate

000409SZSE

Jun 27 1996

8.12

165.951347.51

Mining000429SZSE

Feb 20 1998

5.55

464.282576.73

Transportation

000502

SZSE

Nov 23 1992

10.92

134.5

1468.71

Real estate

000507SZSE

Mar 26 1993

8.76

246.622160.4

Transportation

000513SZSE

Oct 28 1993

39.6

158.426273.6

Medical manufacturing

000522SZSE

Nov 08 1993

14.51

469.036805.62

Medical manufacturing

000523SZSE

Nov 08 1993

7.96

94.15

749.47

Chemical manufacturing

000524

SZSE

Nov 18 1993

8.35

184.591541.35

Hotel

000527SZSE

Nov 12 1993

22.04

2078.38

45807.4

Electronic manufacturing

000529SZSE

Nov 18 1993

8.04

237.66

1910.82

Food processing

000531SZSE

Jan 06 1994

17.57

266.524682.76

Electricity

000532SZSE

Jan 03 1994

13.29

315.2

4188.98

Comprehensive

000533SZSE

Jan 03 1994

10.43

398.384155.09

Electronic manufacturing000534SZSE

Jan 10 1994

9.01

207.46

1869.19

Real estate

000539SZSE

Nov 26 1993

8.12

1986.86

16133.29

Electricity

000541SZSE

Nov 23 1993

9.96

615.286128.15

Electric manufacturing

CompanMarketDate ofPrice per Share

SharesMarket CapitalizationyListedBeing Listed(in RMB Yuan)

Outstanding(in Million RMB

32

IndustryCode(in Millions)

Yuan)

000573SZSE

Aug 15 1994

5.45

535.2

2916.84

Real estate

000576SZSE

Sep 07 1994

6.74

250.521688.48

Papermaking

000601SZSE

Aug 30 1996

7.48

775.515800.84

Electricity

000636SZSE

Nov 29 1996

9.19

548.445040.21

Electronic manufacturing000637SZSE

Nov 14 1996

9.43

254.752402.28

Petrochemical

000651SZSE

Nov 18 1996

27.33

1851.2250593.71

Electronic manufacturing

000659SZSE

Dec 03 1996

7.3

488.693567.44

Plastic manufacturing000685SZSE

Jan 23 1997

28.49

157.264480.35

Water service

000690SZSE

Jan 28 1997

9.7

1127.8510940.16

Electricity

000712SZSE

Apr 15 1997

18.33

190.97

3500.56

Water service

000782

SZSE

Jun 19 1997

6.7

404.492710.07

Chemical manufacturing

000823SZSE

Oct 08 1997

8.1

288.222334.61

Electronic manufacturing

000828SZSE

Jun 17 1997

7.69

636.534894.93

Transportation

000861SZSE

Oct 28 1998

12.68

149.14

1891.08

Consultative

000893

SZSE

Dec 24 1998

22.71

109.892495.5Vegetable oil processing

000973SZSE

May 25 2000

9.4

514.764838.78

Plastic manufacturing

000975SZSE

Jun 08 2000

8.03

386.383102.62

Mining000976SZSE

Jun 01 2000

4.92

408.71

2010.84

Manufacturing000987SZSE

Jul 18 2000

24.67

358.428842.33

Retail000999SZSE

Mar 09 2000

20.6

338

6962.71

Medical manufacturing

600004SHSE

Apr 28 2003

9.89

1015

10038.35

Transportation

600029SHSE

Jul 25 2003

5.99

1500

8985

Transportation

600030

SHSE

Jan 06 2003

29.19

6570.47

191791.95

Finance

600036SHSE

Apr 09 2002

17.29

15658.81

270740.83

Banking

600048SHSE

Jul 31 2006

24.9

3188.05

79382.35

Real estate

600083SHSE

Jun 06 1997

8.27

206.34

1706.44

Electronic manufacturing

600098SHSE

Jul 18 1997

7.98

2059.216432.42

Electricity

600143

SHSE

Jun 23 2004

10.37

659.776841.78

Plastic manufacturing

600162SHSE

Jun 09 1998

9.16

361.7

3313.15

Real estate600183SHSE

Oct 28 1998

9.76

956.669336.98

Electronic manufacturing

600242SHSE

Dec 07 2000

7.93

95.52

757.51

Transportation

600325SHSE

Feb 25 2004

22

817.05

17975

Real estate600332SHSE

Feb 06 2001

12.49

591

7381.59

Medical manufacturing600380

SHSE

Jun 08 2001

9.8

1097.8710759.17

Medical manufacturing600382SHSE

Jan 18 2001

8.09

278.552253.48

Wholesale600383

SHSE

Apr 12 2001

15.48

2181.633771.17

Real estate

SharesMarket Capitalization

CompanyMarketDate ofPrice per Share

Outstanding(in Million RMB

CodeListedBeing Listed(in RMB Yuan)(in Millions)

Yuan)

33

Industry600393SHSE

Mar 19 2001

10.52

289.183042.12

Real estate600428SHSE

Apr 18 2002

11.07

1310.4214506.39

Transportation

600433

SHSE

Jun 19 2003

7.99

160

1278.4Papermaking

600446SHSE

Dec 24 2003

13.12

137.441803.21

Computer and network

600499SHSE

Oct 10 2002

18.5

446.9

8267.67

Manufacturing600518SHSE

Mar 19 2001

10.7

1694.37

18129.76

Manufacturing

600525SHSE

Dec 02 2002

23.56

151.633572.42

Manufacturing600548SHSE

Dec 25 2001

6.39

1433.279158.6

Transportation

600589SHSE

Jun 12 2001

7.71

533.254111.36

Chemical manufacturing600684SHSE

Oct 28 1993

13.88

187.042596.11

Real estate600685SHSE

Oct 28 1993

27.26

337.289194.24

Manufacturing

600728SHSE

Jul 16 1996

10.05

81.12

815.26

Computer and network

600866SHSE

Aug 18 1994

11.88

419.864987.96

Food processing

600868SHSE

Sep 12 1994

3.77

1898.157156.02

Electricity

600872SHSE

Jan 24 1995

11.16

724.228082.25

Food processing

600894SHSE

Mar 28 1996

7.92

762.416038.28

Manufacturing

600978SHSE

Aug 24 2004

7.81

759.415930.97

Manufacturing

600988SHSE

Apr 14 2004

12.36

34.06

420.96

Mining601318SHSE

Mar 01 2007

56.82

3926.59

223108.65

Insurance

601333SHSE

Dec 22 2006

4.86

2747.99

13355.22

Transportation

Note: SZSE and SHSE are short for Shenzhen and Shanghai Stock Exchanges, respectively. Price per share, shares
outstanding, and market capitalization are figures on November 30, 2009. For firms with codes 000068 and 000090, we took
the figures of December 1, 2009, due to missing values. Our data are from the China Stock Market and the Accounting
Research Database.

34