Individual Preferences over Trade Partners in Taiwan
Chun-Fang Chiang*, Jin-Tan Liu, and Tsai-Wei Wen
National Taiwan University
Abstract
This paper investigates how economic factors and political ideology affect individual
attitudes toward free trade agreements with China and the United States in Taiwan.
According to the Stolper-Samuelson Theorem, we expect to see that high-skilled workers
in Taiwan will be more supportive of free trade with China, and less supportive of free
trade with the U.S. Using survey data from Taiwan, we find that high-educated people
are more supportive to free trade with both countries. However, the effect of education is
much stronger with respect to free trade with China, which is consistent with the
Stolper-Samuelson Theorem. We also find that people who identify themselves as
Taiwanese rather than Chinese are 24 percent less likely to support free trade with China,
but only 3 percent less likely to support free trade with the U.S.
_________________________
*Email:[email protected]
1 Introduction
Most economists support the idea of free trade since it promotes efficiency and
improves overall welfare, as long as the gain from trade can be redistributed in a proper
way. Nevertheless, people other than economists have all kinds of concerns about free
trade, and whether its benefits will be broadly distributed through the population. We
investigate how individuals’ trade preferences influenced by their education levels,
industry of employment, and political ideologies. Moreover, we compare trade
preferences with respect to trade with China and the U.S. By investigating who are more
supportive of trade with the U.S. than China, we test trade theories and explore
non-economic factors that contribute to the formation of free trade preference in Taiwan.
The comparison of attitudes toward free trade with China and the U.S in Taiwan
provides us a good opportunity to examine predictions from Stolper-Samuelson Theory.
Taiwan is a small open economy that relies heavily on international trade. Over the past
decade, China and the U.S. have been two of Taiwan’s biggest trade partners; however,
these two countries are different in terms of skill-intensity. Compared with Taiwan, the
U.S. is more skill-abundant and China is relatively skill-scare. According to the
Stolper-Samuelson Theorem, we expect to see that high-skilled workers in Taiwan will
be more supportive for free trade with China, and less supportive to free trade with the
U.S. Regarding the role of non-economic factors, the cross-strait politics allows us to
investigate the role of non-economic factors in trade preferences. Since many people in
Taiwan have political concerns that the economic integration with China would facilitate
further political integration, political ideologies such as party preferences or national
identity may influence attitudes towards free trade with China and the U.S. differently.
Individual preferences about free trade often influence trade policy in a democratic
society, and the literature of individual trade preferences has grown in recent years. Early
empirical studies about trade policy preferences used data from single-country data and
focused on testing the factor-endowment and the specific-sector models. In a
factor-endowment model, workers are assumed to be fully mobile across industries and
therefore their trade preferences should only depend on their endowments, such as human
capital. However, if workers are not fully mobile, as in a specific-sector model,
individuals in a comparative advantage sector would be more pro-trade than others
regardless of their factor type. Beaulieu (1996) and Scheve and Slaughter (2001) found
support for the factor-endowments model. Magee (1978) and Irwin (1994, 1996) found
support for the specific-sector model. And Baldwin and Magee (1998) and Beaulieu and
Magee (2001) found support for both models.
In recent studies, researchers investigates how trade preferences influenced by
economic factors from various approaches. For example, Blonigen (2009) uses
multiple-year data from NAES and finds both human capital endowments and industry
characteristics influence individuals’ support for free trade. Moreover, he finds that
retirement made former protectionists become trade supporters; skill and industry sector
are no longer significant to explaining trade preference after retire.
Mayda and Rodrik (2005) use two cross-country survey data and find that while
education on average has a positive effect on support level of free trade, however,
high-educated people in countries with lower average education are less pro-trade than
others, which is consistent with Stolper-Samuelson Theorem. However, Beaulieu et al.
(2005) use data from countries in Latin America and find that even in relatively
skill-scarce countries, skilled workers are still more likely to support free trade compared
with unskilled workers, which is not consistent with the Stolper-Samuelson Theorem.
Regarding the role of non-economic factors, Mayda and Rodrik (2005) find that
values, national identities and neighborhood attachments play important roles in
explaining the variation of trade preferences. Hooghe, Liesbet and Marks (2004) focus on
the determinants of public opinion on European integration. They find that not only
economic factors such as education, income and professional skill will have an impact on
support for EU integration, but also find that exclusive national identity has a negative
influence on support for EU integration.
In this paper, we examine individual preferences toward trade partners, using
globalization survey conducted in Taiwan during 2009 and 2010. The survey contains
questions about trade policy-related issues, including attitudes toward free trade and free
trade agreements with China and the U.S.. The data allows us to compare individual
attitude when facing different trade partners and thus test the Stolper-Samuelson
Theorem. In Taiwan, high-educated people are relatively well endowed factor owners
when trades with China. However, when trades with the U.S., these high-educated
workers become scarce factors owners and are less likely to support free trade with the
U.S. Since the Stolper-Samuelson Theorem predicts that trade benefits individuals who
own factors with which the national economy is relatively well endowed, we expect to
see that high-skilled workers in Taiwan will be more supportive for free trade with China,
and less supportive to free trade with the U.S
To briefly preview our results, we find that high-educated workers are more
supportive of free trade than low-educated workers. But the magnitude is much larger
when facing a free trade agreement with China rather than the U.S. Our results indicate
that education affects people’s trade preference in two directions. First, people with
higher education level would be more pro-trade since education has a positive effect on
support of free trade with U.S. and China, as well as on the general preference toward
free trade. This may be because the cost is lower when they face competition under free
trade. This is consistent with the finding of Beaulieu et al. (2005), who found that in all
countries in Latin America, skilled workers are more likely to support free trade. Second,
our results suggest that people with higher education are more likely to support a free
trade agreement with a skill-scared country than a skill-abundant country, which can be
explained by the Stolper-Samuelson Theorem:
We also find that political ideologies play important roles in individuals’ trade
preferences. Pan-Green coalition supporters are 30% less likely to support free trade with
China than independents. However, Pan-Green coalition supporters are not significantly
more or less likely to support a free trade deal with the U.S. Regarding individuals’
national identity, individuals with Taiwanese identity are 24 percent less likely to support
free trade with China, and only 3 percent less likely to support free trade with the U.S.
2
Context
The Republic of China (ROC) with the Kuomintang (KMT) government as its head
lost the Chinese civil war in 1949 and retreated to Taiwan. Since then, the People’s
Republic of China (PRC), commonly known simply as “China”, has governed mainland
China and claimed that Taiwan is part of China while Taiwan is governed by the ROC
government, and referred as de fato independent. There had been no formal political or
economic contact between two governments until 1992. In 1992 China and Taiwan began
having political and economic relations. Figure 1 shows the total trade amount between
Taiwan and China and there is a significant growth of trade between China and Taiwan
over the past 20 years.
In the past decade, the U.S. and China have been the two biggest trade partners of
Taiwan. Table 1 shows that in 1990 and 2000, the United States is Taiwan’s biggest trade
partner; and in year 2009, China became Taiwan’s biggest trade partner. Compared with
the United States, Taiwan is a relative skill-scared country and compared with China,
Taiwan becomes a skill-abundant country. Base on UN’s Human Development Index in
year 2007, United States’ HDI was 0.946, China’s HDI was 0.772 and the HDI in Taiwan
was 0.943, calculated by the ROC’s government.1 We conclude that the “Skill-intensity”
in the U.S. is greater than Taiwan and China has the lowest skill-intensity in these three
countries.
From 1948 to 1987, Taiwan was under martial law and turned into democratic
regime in 1987. Since then, there have been two dominant political forces in Taiwan, the
first being the pro-unification Pan-Blue Coalition, including Kuomintang (KMT), People
First Party (PFP), and New Party. The second major force has been the pro-independence
Pan-Green Coalition, including the Democratic Progressive Party (DPP) and Taiwan
Solidarity Union (TSU). The Pan-Blue coalition generally favors eventual re-unification
with China and currently focuses on improving economic ties with China. However, the
Pan-Green coalition has concerns that economic integration with China may hurt Taiwan
economy and eventually lead to political unification with China.
These concerns are reflected in the recent debates regarding the Economic
Cooperation Framework Agreement (ECFA), which aims to reduce tariffs and
commercial barriers between China and Taiwan. People who support ECFA argue that it
would eventually benefit both countries since it will lower the cost of both imports and
exports. However, people who oppose ECFA argue that total free trade with China may
be detrimental to Taiwan economy since low-priced products made in China will threaten
1
As the UN does not recognize Taiwan as a state, the HDI report does not include data for "Taiwan”. The
ROC's government calculated its HDI as of 2007 to be 0.943 based on the following data: life expectancy
of 78.4 years; adult literacy rate of 97.6%; combined gross enrollment rate of 101.9%; and GDP per capita
(PPP) of US$30,352. Taiwanese producers. They also have political concerns that economic integration might
eventually lead to political integration; in the long run, it could degrade Taiwan’s state
sovereignty.
A live television debate on ECFA between President Ying-jeou Ma and the
opposition party chairperson, Ing-wen Tsai was broadcast on April 25, 2010 in Taiwan.
Subsequently, ECFA was signed on June 29, 2010, in Chongqing city, China and
Taiwan's Executive Yuan approved ECFA on July 2. The survey we use in this research
was conducted before June, 2010, which is before the date that the ECFA was officially
signed by both parties.
3 Data -- Globalization Survey
The following analyses of trade policy preferences take the data from the
globalization studies survey conducted by the Institute for Advanced Studies in
Humanities and Social Sciences in National Taiwan University in three waves. The first
wave of survey was conducted in July and August 2009, the second wave was conducted
in November 2009, and the third wave was conducted in May 2010. The data were
collected by telephone interview (Computer Assisted Telephone Interview, CATI),
sampling Taiwanese adults between the ages of 20 and 65. The sample sizes were 1,500,
1,208, and 1,203 respectively. Below we illustrate the key variables in this paper. Table 2
presents the survey’s brief outline.
A. General Trade Preferences
As in the International Social Survey Programme and World Value Survey, the survey
has a question regarding trade protection. The direct translation of the survey question
is: “Some people have suggested placing limits on foreign imports in order to protect
Taiwanese industries and job opportunities. Others say that such limits would raise
consumer prices and hurt Taiwan’s exports. What is your opinion?” Respondents can
choose from one of the following: (1) Favor strictly limits, (2)Favor limits , (3) Favor
open (4) Favor open completely and (5) Don’t Know. We recode it as a dummy variable
that indicates whether the respondent opposes limits on foreign imports.
B. Trade Preferences toward Different Countries
In addition to the question of general trade preference, the survey also asked
respondents their attitudes toward free trade agreements with the U.S. and with China.
The survey question is the follows: “What is your opinion on Taiwan and the U.S.
signing a free trade agreement?” The survey question regarding their attitudes toward a
free trade agreement with China is as follows: “What is your opinion on Taiwan and
China signing the Economic Cooperation Framework Agreement (ECFA, an agreement
which is similar to FTA)?” For both questions, respondents could choose from one of the
following: (1) Strongly Favor, (2) Favor, (3) Oppose (4) Strongly Oppose and (5) Don’t
Know.2 From the answers to these two questions, we generate two dummy variables:
Support free trade with China and Support free trade with the U.S., coding responses 1
for those favoring and 0 for those opposing.
In the first wave survey, there are two survey versions, A and B. In version A, the
question regarding trade with China was asked before the question regarding trade with
the U.S.; in version B, the order was reversed. In the second and third waves, the question
regarding trade with China was asked before the question of the U.S.
C. Education
For each individual in the survey, we group them into three education levels:
primary or middle school education level; high school education level; and some college
or above. We view the high school and college education level individual as
high-skilled workers.
D. Industry level variables
For each individual in the survey, we calculate Reveal Comparative Advantage
2
In the second wave, both questions have more explanations about the free trade agreements.
The questions are “What is your opinion on Taiwan and the U.S. signing a free trade agreement? That is,
both countries can freely import and export products without tariff.” and “What is your opinion on
Taiwan and China signing the Economic Cooperation Framework Agreement (ECFA, an agreement which
is similar to FTA)? That is, both countries can freely import and export products without tariff.” (RCA) index, developed by Balassa (1965) to measure the global competitiveness of
their industry of employment. We also calculate the RCA index of China industries, and
the U.S industries. The RCA index of industry j in country i is calculated as follows:
∑
∑ ∑
∑
Where
is the export of industry j from Country i; ∑
export of industry j; ∑
world exports.
is the world total
is the total exports of Country i; and ∑ ∑
is the total
According to the classification done by the Japan External Trade
Organization, the industry is classified as a comparative-advantage industry if RCA is
greater than 1, and is viewed as a comparative-disadvantage industry if
is less
than 1.
We also measure the relative advantage of industry j in Taiwan by the relative
advantage index as follows:
TW,CN
TW,US
The export and import amounts by industry were taken from Taiwan’s Bureau of
Foreign Trade and from International Trade Center. For individuals who are not working,
these measures are coded as 0.
E. Political Ideology
Based on the survey question “which political party do you support?”, we classify
respondents into three groups: Pan-Blue coalition supporters, Pan-Green party supporters
and Independents. An individual is identified as a Pan-Green coalition supporter if they
announce support for DDP or TSU; and a Pan-Blue coalition supporter if they support
KMT, New Party or the People First Party.
There is another political preference variable we use in this paper, Identify as Taiwanese.
This variable was coded from the survey question, “There are several ways listed below
to identify yourself. Which one do you think is best describes you? (1) I am Taiwanese; (2)
I am both Taiwanese and Chinese; (3) I am Chinese; (4) Other”. We generate a dummy
variable “Identify exclusively as Taiwanese” to indicate if respondents chose (1) I am
Taiwanese, meaning that they identify themselves exclusively as Taiwanese.
F. Ethnicity
Political party preferences and points of view toward cross strait issues are highly
influenced by ethnicity in Taiwan. There are four major ethnic groups: Taiwanese, Haka,
Mainlander and Aborigine. Taiwanese and Haka are early immigrants who came from
mainland China before WWII. Mainlander represents Chinese immigrants around the late
1940s during the Chinese Civil War. In Taiwan, about 70% of the population is
Taiwanese; 12% is Haka, 12% population is Mainlander and the remaining 2% of
population are aborigines.
Mainlanders in general are more likely to be supporters of the Pan-Blue coalition. In
our data, 53% of Mainlanders are Pan-Blue coalition supporters, and only 3.7% of
Mainlanders are Pan-Green coalition supporters. Regarding their attitudes towards trade
with China, 75% of Mainlanders support free trade with China whereas only 45% of
Taiwanese support free trade with China.
Table 3 presents the summary statistics and variable descriptions we use in this
research.
4. Empirical Results
4.1
General attitudes towards trade protection
Before we estimate individuals’ attitudes towards free trade agreements with the
U.S. and China, we first estimate their general preferences over trade protection by the
Ordered Probit model. Table 5 presents the results. The control variables include
individual’s education level, and social background such as gender, age, ethnicity,
income and employment status, and characteristics of their industries of employment.
Results from Table 5 shows that both individuals’ factor endowments and industry
of employment influence their attitudes on trade protection. Individuals with higher
education levels (i.e., college graduates or high school graduates) are more likely to
oppose trade limits. We also find that an individual in a comparative disadvantage
industry are more likely to favor trade restriction compared to those in the non-trade
sectors.
Individual political preferences influence their trade policy preferences significantly.
Results from table 5 shows that a Pan-Green coalition party supporter is more likely to
oppose free trade compared to an individual who is political neutrality, while Pan-Blue
coalition supporters are more likely to support general free trade. Regarding national
identity, we can see that people who has exclusive Taiwanese identity is significantly less
likely to favor free trade.
Results from table 5 show that both economic and political factors matter. However,
since Taiwan has many trade partners with different levels of skill intensity, we cannot
conclude that whether the positive effects of education on pro-trade attitudes support for
the Stolper-Samuelson theorem.
4.2
Attitudes toward Free Trade with China and the U.S.
We are interested in the role of human capital as well as other economic and
political factors in preferences over free trade agreements with the U.S. and China.
Specifically, we would like to know if people with higher education are more likely to
support free trade with China but less likely to support free trade with the U.S., as the
Stolper-Samuelson theorem predicts. The bivariate probit model enables us to estimate
the correlation of unobservable factors of individuals’ trade preferences toward these two
countries. Thus we use bivariate probit model to estimates individuals’ attitudes toward
free trade policies with China and with the U.S. and then examine the differences in their
attitudes. The structure of the model can be expressed as follows:
1 if
0
0 otherwise
(1)
1 if
0
0 otherwise
and
u1
u2 | X ~
(2)
1
0
,
ρ
0
ρ
1
.
is the dummy variable, Support free trade with China, indicating if the
respondent is in favor of ECFA; Y2 is the dummy variable, Support free trade with the
U.S., indicating if the respondent in favor of FTA with the U.S.
Education
The Bivariate Probit Model results are presented in Table 6. The dependent
variables are Support free trade with China, and Support free trade with the United
States in column (1) and column (2) respectively. Column (1) and (2) of Table 6 present
results when we use individual demographic variables as control variables. The results
show that when focusing on individual with some college education, there is a significant
gap in preferences with different trade partners. Individuals with some college education
are 21 percent more likely to support free trade with China, but only 8.2 percent more
likely to support free trade with the U.S.. Similarly, we can find that having a high school
degree is only significant in China regressions with coefficient 0.07, but is insignificant
in the U.S. regressions. The differences in the coefficients of education variables are
tested by the Wald Test and presented at the bottom of Table 6. The coefficient gap of
college graduates between China regressions and the U.S. regressions are significant.
In column 3 and column 4, we include variables at industry levels to control for
potential comparative advantages under free trade for workers in each industry. While
some variables such as RCA index is significant, adding variables at industry levels does
not affect the effects of education variables. These results suggest that people with higher
education are more likely to support free trade than people with lower education.
Furthermore, when facing two free trade partners: the US and China, higher educated
people are more likely to support free trade with China, a skill-scared country. These
results partially support the Stolper-Samuelson Theorem.
Ideology and ethnicity
Regarding the effect of national identity, individuals who identify themselves
exclusively as Taiwanese are 24 percent less likely to support free trade with China than
others. However, when asked about a free trade agreement with the United States, they
are only 3 percent less likely to support free trade with the U.S.. The results also show
that Pan-Blue supporters are more likely to support free trade with China and the U.S.
than independents, but the magnitudes are significantly different. Taking the results from
column (5) and (6), they are 31 percent more likely to support free trade with China but
only 6 percent more likely to support free trade with the United States. These results
consist with the general impression of these two political camps that Pan-Blue is more
China-friendly and Pan-Green opposes the interaction with China.
4.3 Who prefers to trade with the U.S. more than China?
We can use another approach by asking who prefers to have a free trade agreement
with the U.S. more than with China. We know the respondent’s support levels for free
trade agreements with China and the U.S from the survey. Therefore, we know that who
prefers to trade with the U.S. more than China. We can generate a variable “Trade
Partner Preference Difference” as follows:
Using this variable as dependent variable, we apply the ordered probit model to
investigate who prefers the U.S. more than China as a trade partner with Taiwan. The
results are reported in Table 7. The coefficient sign in the table can be explained as
following: if the coefficient is positive and significant, then people would have greater
possibility to support free trade with United States than China. In Table 7, people with
college education level have higher probability to support free trade with China against
the United States (CN>US) or being no support different between China and the United
States (CN=US). An individual with college education level is less likely to support free
trade with the United States, compared with China. These results consist with the
prediction of the Stolper-Samuelson theorem that high-educated people are more likely
to support free trade in a skill-abundant country.
Despite the finding that high-educated people are more likely to support free trade
with a relative skill scarce country, China. We also find that non-economic factors can
also greatly explain individual trade policy preference differences. The Pan-Blue
coalition supporters are more likely to oppose free trade with China and the Pan-Green
coalition supporters prefer free trade with the United States rather than China. Table 7
also shows that individual who identify themselves as Taiwanese only are more likely to
oppose free trade with China than the U.S.
Overall, results from the ordered probit model are consistent with results from the
biprobit model in the previous subsection.
5.
Discussion and Conclusion
Our results indicate that education affects people’s trade preferences in two
directions. First, people with higher education level would be more pro-trade since we
find that education has a positive effect on support of free trade with U.S. and China, as
well as on the general preference toward free trade. This may be because the cost is lower
when they face competition under free trade, and is consistent with the finding of
Beaulieu et al. (2005), who found that in all countries in Latin America, skilled workers
are more likely to support free trade. Second, our results suggest that people with higher
education are more likely to support a free trade agreement with a skill-scared country
than a skill-abundant country. This is consistent with Stolper-Samuelson Theorem: trade
benefits individuals who own factors with which the national economy is relatively well
endowed. Hence, in Taiwan, high-educated people become relatively well endowed
factor owners when trades with China. However, when trades with the U.S., these
high-educated workers become scarce factors owners and are less likely to support free
trade with the U.S.
In addition to education levels, an individual’s political preference, national identity
and ethnicity also play important roles in an individual’s trade preferences formation. We
find that individuals who identify themselves exclusively as Taiwanese and who support
the Pan-Green coalition tend to be less pro-trade. We also find that individuals’ political
preferences partially explain some trade preference gap between China and the United
States. When the trade partner in the survey question is China, Pan-Blue coalition
supporters are significantly more likely to be pro-trade, and Pan-Green coalition
supporters are significantly less likely to support free trade. When the trade partner in
question is the United States, the effects of the party affiliation and national identity
become less significant.
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Table 1: The Trade amount of Taiwan with top six trade partners
Year 1990
Year 2000
Year 2009
Millon
Millon
Millon
Conutry
Conutry
Rank Conutry
Dollar
Dollar
Dollar
1
US
34,357 28.18%
US
59,939 20.79%
CHINA
78,670 20.81%
2
JAPAN
24,335 19.96%
JAPAN
55,156 19.13%
JAPAN
50,721 13.42%
3
HONG KONG
10,002
8.20%
HONG KONG
33,520 11.63%
US
41,706 11.03%
4
GERMANY
5,913
4.85%
KOREA
12,895
4.47%
HONG KONG
30,567
8.09%
5
SINGAPORE
3,609
2.96%
SINGAPORE
10,469
3.63%
KOREA
17,809
4.71%
6
UK
3,132
2.57%
CHINA
10,440
3.62%
SINGAPORE
13,422
3.55%
Data Source: Bureau of Foreign Trade, Taiwan
Table 2: The Globalization Studies Survey Outline
First Wave
Second Wave
Third Wave
Date
2009.07.22-08.03
2009.11.11-11.16
2010.05.17-05.24
Sample size
1525
1208
1203
Table 3 Summary Statistics
Variables
Dependent Variables
Support free trade (=1 or 0)
Support free trade with China (=1 or 0)
Support free trade with the U.S. (=1 or 0)
Male
Work
Age
Education Variables
High school graduates
College graduates
Political Variables
Pan-Green Parties Supporters
Pan-Blue Parties Supporters
National Identity: Taiwanese only
Ethnicity Dummy
Taiwanese
Haka
Mainlander
Aborigine
Description
Obs.
Mean
S.D.
3801
0.4541
0.4980
3463
0.5074
0.5000
3544
0.7690
0.4216
3969
3934
3936
0.4797
0.5933
42.8554
0.4997
0.4913
11.9928
Dummy variable: 1 if individual's education level is high school graduates
Dummy variable: 1 if individual's education level is college graduates
3936
3936
0.3112
0.5086
0.4631
0.5000
Dummy variable
Dummy variable
Dummy variable, 1 if people view themselves as Taiwanese only; 0 if people
view themselves as both Taiwanese and Chinese, or view as Chinese only.
Ethnicity Dummy: classify into four different ethnic groups
3889
3889
0.1700
0.3016
0.3757
0.4590
3893
0.6350
0.4815
3929
3929
3929
3929
0.7526
0.1186
0.1173
0.0115
0.4316
0.3234
0.3219
0.1064
1 if strongly/weakly oppose import limitation; 0 if strongly/ weakly agree
import limitation)
1 if strongly/weakly agree free trade with China; 0 if strongly/ weakly oppose
free trade with China)
1 if strongly/weakly agree free trade with the U.S.; 0 if strongly/ weakly oppose
free trade with the U.S.)
Dummy variable: male=1, female=0
Dummy variable: if individual have a job, work=1
Table 3 Summary Statistics (con't)
Variables
Employment of Sector
Agriculture
Manufacturing
Industry
Services
Retail-Restarant and Hoteling
Public Sector
Industry level variable
Comparative Advantage Sector
Comparative Disadvantage Sector
Import-production Ratio
Export-production Ratio
Trade exposure index
Area Dummy Variable
Taipei& Keelung
Northern Taiwan
Central Taiwan
Southern Taiwan
Kaohsiung& Pingtung
Eastern Taiwan
Description
Industry dummy: depends on what industry individual is in. Classify into 6
Obs.
Mean
S.D.
2658
2658
2658
2658
2658
2658
0.0406
0.2630
0.0767
0.2987
0.2269
0.0941
0.1975
0.4403
0.2662
0.4578
0.4189
0.2920
Dummy variable: 1 if the sector's RCA index >1
Dummy variable: 1 if the sector's RCA index <1
(Import value by sector)/(GDP by sectors)
(Export value by sector)/(GDP by sectors)
(Import + Export value by sector)/(GDP by sectors)
3936
3936
3936
3936
3936
0.1087
0.0716
0.1679
0.5323
0.7002
0.3114
0.2579
0.7360
1.6811
2.2137
Including Taipei city, Taipe County and Keelung city
Including Taoyuan county, Hsinchu city and Hsinchu county
Including Taichung city, Taichung county, Nantou county and Chunghua
Including Tainan city, Taina county, Yunlin county and Chiayi county
Including Kaohsiung city and county, Pingtung county
Including Yilan county, Hualien county and Taitung county
3936
3936
3936
3936
3936
3936
0.3064
0.1451
0.1905
0.1471
0.1623
0.0485
0.4611
0.3522
0.3928
0.3543
0.3688
0.2149
Variables
Table 4: Summary Statistics by Dependent Variables
Support free
Suppport free
trade with the
trade (Mean)
U.S. (Mean)
Support free
trade with China
(Mean)
Male
Female
Work
Without Work
High school graduation
College graduation
Taiwanese
Mainlander
Haka
Aborigine
0.4912
0.4199
0.4811
0.4142
0.463
0.5168
0.4381
0.5511
0.4716
0.3409
0.8046
0.7334
0.779
0.7539
0.7832
0.8027
0.7595
0.8365
0.7661
0.7027
0.5216
0.4934
0.5192
0.49
0.5291
0.5943
0.459
0.7512
0.5572
0.4634
Polictical Variable
Pan-Green supporter
Pan-Blue supporter
Identity as Taiwanese only
0.3061
0.561
0.3922
0.7427
0.8283
0.7445
0.1447
0.7931
0.3646
Employment of Sectors
Agriculture
Manufacturing
Industry
Service
RWRH(retail-wholesale-restaurant-hotel)
Public Sector
0.3855
0.4667
0.4516
0.4813
0.4631
0.5096
0.6849
0.7639
0.7043
0.7665
0.8042
0.8079
0.3733
0.4341
0.4
0.5214
0.5118
0.6443
Living Area
Taipei& Keelung Area
Northern Taiwan Area
Central Taiwan Area
Southern Taiwan Area
Kaohsiung& Pingtung Area
Eastern Taiwan Area
0.4951
0.4524
0.428
0.3553
0.4256
0.375
0.7938
0.7557
0.7467
0.7278
0.7388
0.7583
0.5338
0.5099
0.4758
0.4086
0.4247
0.52
Male
Table 5: General Trade Preference
Dependent Variable: Support level of free trade
(1)
(2)
(3)
0.271*** 0.271*** 0.272***
[0.038]
[0.038]
[0.038]
Education
High School
College
0.443***
[0.064]
0.722***
[0.064]
Industry variables
Comparative Advantage Sectors
Comparative Disadvantage Sectors
0.440***
[0.064]
0.718***
[0.064]
0.440***
[0.064]
0.718***
[0.064]
-0.112
[0.086]
-0.161**
[0.081]
-0.115
[0.085]
-0.162**
[0.081]
Industry RCA index
Import-Production Ratio
0.033
[0.031]
0.024
[0.018]
Export-Production Ratio
Total Trade- Production Ratio
Pan-Green Parties Supporter
Identify as Taiwanese only
Ethnicity
Haka
Mainlander
Aborigine
Survey fixed effects
Other variables control
Observations
0.439***
[0.064]
0.717***
[0.064]
0.034
[0.043]
0.185**
[0.090]
Non-trade Sectors
Political Preference
Pan-Blue Parties Supporter
(4)
0.273***
[0.038]
0.027**
[0.012]
0.028**
[0.012]
0.183***
[0.043]
-0.298***
[0.055]
-0.247***
[0.042]
0.185***
[0.043]
-0.298***
[0.055]
-0.249***
[0.042]
0.184***
[0.043]
-0.298***
[0.055]
-0.250***
[0.042]
0.185***
[0.043]
-0.298***
[0.055]
-0.249***
[0.042]
-0.032
[0.063]
0.093
[0.063]
-0.416**
[0.199]
-0.027
[0.063]
0.089
[0.063]
-0.413**
[0.196]
-0.027
[0.063]
0.089
[0.063]
-0.412**
[0.196]
-0.027
[0.063]
0.089
[0.063]
-0.413**
[0.196]
yes
yes
3510
yes
yes
3510
yes
yes
3510
yes
yes
3510
Note: 1. Robust Standard errors in Brackets. 2. ***significant at 1%, **significant at 5%, *significant at 10%.
3. Support level of free trade gives responsesto the following question: “Some people have suggested
placing limits on foreign imports in order to protect Taiwanese opportunities. Others say that such limits
would raise consumer prices and hurt Taiwan exports. Do you favor or oppose to place limits on imports?”
(1=strongly agree, 2=agree, 3=oppose, 4=strongly oppose.) 4. Survey fixed effects controls different waves
of survey.6. Other control variables including age, employment status, income.
Table 6: Bivariate Probit Model---Free trade with China/U.S.
VARIABLES
Male
Education
High School
College
Political Preference
Pan-Blue Parties Supporter
Pan-Green Parties Supporter
Identify as Taiwanese only
(1)
CN
0.092***
[0.021]
(2)
US
0.069***
[0.015]
(3)
CN
0.095***
[0.021]
(4)
US
0.071***
[0.015]
(5)
CN
0.095***
[0.021]
(6)
US
0.070***
[0.015]
(7)
CN
0.093***
[0.021]
(8)
US
0.069***
[0.015]
0.069**
[0.032]
0.209***
[0.031]
0.009
[0.022]
0.083***
[0.023]
0.066**
[0.032]
0.204***
[0.031]
0.009
[0.022]
0.082***
[0.024]
0.067**
[0.032]
0.205***
[0.031]
0.008
[0.022]
0.081***
[0.024]
0.067**
[0.032]
0.207***
[0.031]
0.008
[0.022]
0.082***
[0.024]
0.312*** 0.059***
[0.021]
[0.017]
-0.320*** -0.011
[0.026]
[0.021]
-0.244*** -0.034**
[0.021]
[0.017]
Industry Variables
Non-trade Sectors
0.314*** 0.059***
[0.021]
[0.017]
-0.322*** -0.012
[0.026]
[0.021]
-0.243*** -0.035**
[0.021]
[0.017]
0.050
[0.041]
0.003
[0.004]
-0.002
[0.002]
0.028
[0.020]
-0.004
[0.010]
Taiwan's RCA in China Market
Taiwan's RCA in U.S. Market
Import-Production Ratio
Export-Production Ratio
0.014
[0.030]
-0.000
[0.003]
-0.001
[0.001]
-0.018
[0.012]
0.011
[0.007]
Total Trade- Production Ratio
0.313*** 0.059***
[0.021]
[0.017]
-0.322*** -0.011
[0.026]
[0.021]
-0.243*** -0.034**
[0.021]
[0.017]
0.049
[0.041]
0.002
[0.004]
-0.002
[0.002]
0.014
[0.030]
0.001
[0.003]
-0.001
[0.001]
0.005
[0.007]
0.002
[0.005]
Relative advantage VS. CN
Relative advantage VS. US
Ethnicity
Haka
Mainlander
Aborigine
Survey fixed effects
Other variables control
-0.016
[0.033]
0.114***
[0.032]
0.106
[0.104]
yes
yes
-0.001
[0.024]
0.034
[0.023]
-0.085
[0.084]
yes
yes
-0.017
[0.033]
0.112***
[0.032]
0.108
[0.104]
yes
yes
-0.001
[0.024]
0.033
[0.023]
-0.086
[0.084]
yes
yes
-0.017
[0.033]
0.111***
[0.032]
0.108
[0.104]
yes
yes
-0.001
[0.024]
0.033
[0.023]
-0.085
[0.084]
yes
yes
0.314*** 0.059***
[0.021]
[0.017]
-0.323*** -0.012
[0.025]
[0.021]
-0.245*** -0.034**
[0.021]
[0.017]
0.019
[0.044]
0.000
[0.004]
-0.004**
[0.002]
0.002
[0.032]
0.000
[0.003]
-0.002
[0.001]
0.178**
[0.074]
-0.113**
[0.049]
0.044
[0.057]
-0.030
[0.035]
-0.015
[0.033]
0.108***
[0.032]
0.106
[0.104]
yes
yes
-0.000
[0.024]
0.033
[0.023]
-0.086
[0.084]
yes
yes
Observations
3019
3019
3019
3019
3019
3019
3019
3019
Wald Test Result: Null Hypothesis: βcn=βus (the coef. of education level in CN = in US regression)
0.108
0.121
0.116
0.122
High School (p-value)
0.026**
0.033**
0.031**
0.031**
College (p-value)
0.000***
0.000***
0.000***
0.000***
Identify as Taiwanese only (p-va
Note: 1. Robust Standard errors in Brackets. 2. ***significant at 1%, **significant at 5%, *significant at 10%. 3. marginal effects are reported. 4. Support
free trade gives responsesto the following question: “What is your opinion on Taiwan and China/the U.S. signing a free trade agreement?” (1=strongly
agree or agree, 0=oppose or strongly oppose.) 5. Other control variables including age cohort, employment status, and income.
Table 7: Ordered Probit Model--- Dependent variable: Trade Partner Preference
Male
Education Level
High School
College
(1)
(2)
(3)
(4)
0.009
[0.045]
0.011
[0.045]
0.010
[0.045]
0.011
[0.045]
-0.076
[0.071]
-0.157**
[0.070]
-0.076
[0.071]
-0.157**
[0.070]
-0.078
[0.071]
-0.160**
[0.070]
-0.076
[0.071]
-0.158**
[0.070]
0.051
[0.104]
0.015
[0.091]
-0.071
[0.046]
0.012
[0.022]
0.078
[0.107]
0.021
[0.091]
0.284
[0.270]
0.030
[0.092]
Industry Employment Sector
Comparative Advantage Sectors
Comparative Disadvantage Sectors
Import-Production Ratio
Export-Production Ratio
Total Trade- Production Ratio
-0.013
[0.016]
Relative advantage VS. CN
-0.290*
[0.170]
0.008
[0.137]
Relative advantage VS. US
Political Preference Attitude
Pan-Blue Parties Supporter
Pan-Green Parties Supporter
Identify as Taiwanese only
Ethnicty
Haka
Mainlander
Aborigine
Survey fixed effects
Other variables control
Constant/cut1
Constant/cut2
Observations
-0.479***
[0.050]
0.565***
[0.071]
0.404***
[0.050]
-0.480***
[0.050]
0.567***
[0.071]
0.403***
[0.050]
-0.480***
[0.050]
0.566***
[0.071]
0.405***
[0.050]
-0.479***
[0.050]
0.564***
[0.071]
0.407***
[0.050]
0.057
[0.072]
-0.124*
[0.065]
-0.135
[0.214]
yes
yes
-1.258***
[0.124]
0.510***
[0.122]
3019
0.055
[0.072]
-0.124*
[0.065]
-0.134
[0.215]
yes
yes
-1.256***
[0.124]
0.514***
[0.122]
3019
0.056
[0.072]
-0.122*
[0.065]
-0.134
[0.215]
yes
yes
-1.260***
[0.124]
0.509***
[0.122]
3019
0.058
[0.071]
-0.123*
[0.065]
-0.127
[0.216]
yes
yes
-1.257***
[0.124]
0.512***
[0.122]
3019
Note: 1. Robust Standard errors in Brackets. 2. ***significant at 1%, **significant at 5%, *significant at 10%. 3. Dependent
variable has three values: 1 if prefer to trade with China more than the U.S., 2 if no difference in support level toward China or
the U.S., 3=prefer to trade with the U.S. more than China. 4. Survey fixed effects controls different waves of survey.6. Other
control variables including age, employment status, income.