Impact Of Trade With China On The Unemployment Rate In Taiwan
By: Isabella Sun
Honors Thesis
Economics Department
University of North Carolina at Chapel Hill
April 2015
Approved:
______________________________
Dr. Steven Rosefielde
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Acknowledgement
I would like to express my sincerest gratitude to my thesis advisor, Professor Steven
Rosefielde and my faculty advisor, Professor Klara Peter for giving me the opportunity to have
such a valuable learning experience. Professor Rosefielde has been incredibly supportive, and I
thank him for his guidance and understanding. I would also like to thank Professor Peter for her
time and patience as well as for pushing me through this process. This has not been an easy
journey, and I truly appreciate the help I have received from them along the way. Without their
guidance, I would not have been able to complete this research. Thank you to Atiyasha Kaur for
being kind enough to take the time to Skype me into classes while I was abroad in Singapore
making it possible for me to complete the honors course sequence. Lastly, I would also like to
thank my family, especially my mother and sisters, for believing in me and for their emotional
support through this daunting process.
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Abstract
The Sunflower Movement protests that occurred in March of 2014 in Taiwan in response
to the passing of a bilateral free trade agreement with Mainland China drew attention to the
concerns over the impact of increased trade on the domestic unemployment rate. This paper
looks at the effect that increased imports and exports to China as well as to the rest of the world
has on the aggregate unemployment rate in Taiwan. Using the official trade statistics and
unemployment rates provided by the Taiwanese government, this research uses a vector
autoregressive model to test that impact and finds that an increased trade with China increases
the aggregate unemployment rate in Taiwan. Trade with the rest of the world not including China
has the opposite effect where an increase in trade here will decrease the unemployment rate.
Looking at the separate effect of imports versus exports on the unemployment rate, the vector
autoregressive model shows that both imports and exports from China increases the aggregate
unemployment rate. Imports and exports from the rest of the world not including China,
however, decrease the aggregate unemployment rate in Taiwan.
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I. Introduction
The impact of international trade on domestic unemployment is one of the major
concerns of more open trade policies. While trade economists generally agree that a more open
economy is good for growth and development of the economy overall, there are still other
potential effects of trade, one of the most common discussions being the impact on employment.
Intuitively, trade can impact unemployment in different ways through imports and exports. An
increase in exports would imply an increase in domestic production. Increased domestic
production would create more opportunities for domestic jobs, and thus should decrease the
unemployment rate. Increased imports on the other hand would imply relatively more production
abroad, and thus would have the opposite effect on jobs, increasing the unemployment rate.
There are economists that believe that the level of trade openness should have no impact
on the unemployment rate. Krugman (1993) wrote that “the level of employment is a
macroeconomic issue, depending in the short run on aggregate demand and depending in the
long run on the natural rate of unemployment, with microeconomic policies like tariffs having
little net effect.” However, the impact of trade agreements on employment is still at the center of
the free trade agreement debate. For example, before the North American Free Trade Agreement
was passed, concern over the possibility of job losses as a resulting effect of the agreement was
the major point of dissent on ratification of the agreement. 112 of the 141 statements made in the
United States House of Representatives and the Senate in opposition to the ratification were
arguments that NAFTA will destroy jobs. 199 out of 219 of the statements made in support of
ratification were arguments that NAFTA will create jobs1 (Davidson, Martin, & Matusz, 1999).
These numbers suggest that the major concern over trade openness is the effect it will have on
1
C. Davidson, L. Martin, and S. Matusz cite this data from Baldwin and Magee (1997).
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jobs. This research will address these concerns by testing the null hypothesis that increased trade
has no impact on the unemployment rate.
The specific focus of this research is on the impact of trade with China on the
unemployment rate in Taiwan. The topic of trade between Taiwan and China is particularly
relevant due to the protests that occurred recently in Taiwan over a free trade agreement with
China. Taiwan has a complicated relationship with Mainland China. Many Taiwanese fear the
People’s Republic of China’s growing political influence, but Taiwan also has extremely close
economic ties to the Mainland. In 2013, the ruling party of Taiwan, passed the Cross-Strait
Service Trade Agreement, a bilateral trade agreement with China. This event sparked the
Sunflower Movement that dominated the headlines in March of that following year in protest of
the FTA2. Such a trade agreement not only makes the entire country more susceptible to China’s
influence, but it also makes Taiwanese firms more vulnerable to foreign competition. For this
reason, many Taiwanese citizens opposed the agreement due to fear that more open trade will
result in the Chinese “stealing” their jobs. The protests that occurred begs the question, what
impact, if any, does trade with China have on the unemployment rate in Taiwan. If more trade
does not actually impact unemployment, then the concerns of the protestors in terms of impacts
on unemployment are unwarranted. If increased trade does have an impact on the unemployment
rate, then the direction of this impact would have important trade policy implications. Another
possibility to take into consideration is that the level of trade overall may be more important than
the amount of trade specifically with China.
2
The protests occurred to a multitude of political and social reasons, not only over economic
reasons and the possibility of job destruction. The major scandal over the passing of this
agreement was that the government did not follow due procedure. For the purposes of this
research though, it is more important to highlight the unemployment concerns related to the trade
agreement.
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This research will contribute to the existing literature by looking specifically at the
relationship between trade and unemployment in the case of Taiwan. Moreover, it looks at the
impact of trade specifically with Mainland China where much of the previous literature is more
concerned with more open trade with the rest of the world as a whole, which this research will
likewise additionally examine. This research finds that there is no connection between Taiwan’s
unemployment rate and trade between Taiwan and China in the long run. However, there is a
statistically significant impact on the unemployment rate in the short run. Interestingly, the
direction of the impact on unemployment is a positive one, indicating that increased trade with
China increases the unemployment rate in Taiwan. Trade with the rest of the world as a whole,
not including China, has the opposite effect where increased trade to all other countries
combined decreases the unemployment rate. This research also examines the separate effects of
imports and exports on the unemployment rate in Taiwan and finds statistically significant
evidence that increased imports and exports both increase the unemployment rate.
In section II, I will present a review of the previous literature on the topic of the labor
market and trade. This research is focused on the unemployment rate, but other relevant literature
has examined other aspects of the labor market such as wage rates. Other indicators of trade have
also been considered in previous literature. Where this research uses data on imports and exports
of goods, other literature has examined the impacts of tariffs or other trade policies for example.
In section III, I present a theoretical model developed by Dutt, Mitra and Ranjan (2009, 2012),
which explains the connection between trade and employment. Section IV of this paper discusses
the data used for the empirical tests, which are presented in section V. The results from the
empirical tests are subsequently presented in section VI with the figures and tables in the
Appendix.
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II. Literature Review
The economic literature on the impact of trade on employment is divided. There have
been previous studies that have found no statistically significant impact of trade on employment,
but there is also a considerable amount of studies that have found a statistically significant
impact of trade on employment. Many empirical studies have found ambiguity in the direction of
this impact or that there is a dynamic impact of trade on employment, as their results show
opposite effects on employment in the short run and the long run. Overall though, the literature
tends to have stronger evidence to support that increased trade reduces the unemployment rate.
Feliciano (2001), Lang (1998) and Attanasio, Goldberg, and Pavcnik (2004) found no
statistically significant impact of trade on employment. Feliciano (2001) looked at the impact of
trade reforms on employment in Mexico by examining a trade liberalization program between
1986 and 1990 through tariff reduction and import licenses. The results of this study were that
the trade reforms reduced the relative wages of workers in the industries that lost protection,
showing that trade does have an impact on the labor market. However, the impact found on
employment was not statistically significant. Attanasio, Goldberg, and Pavcnik (2004) in
examining the case of Colombia also find impacts of tariff reductions on wages, but no
significant impact on unemployment. Lang (1998) similarly measured the impact of trade
liberalization but in the case of New Zealand and also found no statistically significant impact of
tariff reductions on employment.
Though there is literature that suggests no impact of trade on unemployment, there is also
empirical evidence to suggest that increased trade does have some impact on employment.
Felbermayr, Prat, and Schmerer (2011) for example ultimately conclude that a 10 percentage
point increase in total trade openness reduces unemployment by three quarters of one percentage
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point. In much of the empirical studies that find a statistically significant relationship between
trade and employment though, the evidence is ambiguous as to the direction of this impact,
which will be reviewed below.
Davidson and Matusz (2005) and Grossman (1987) find empirical evidence of ambiguous
effects of trade on unemployment. Davidson and Matusz (2005) use data from the United States
to examine the relationship between job losses and the level of net exports, and find strong
evidence of a negative relationship between trade and job losses. They also find evidence to
support a positive relationship between job acquisition rates and trade. The two results lead to an
ambiguous impact on unemployment since trade leads to a higher rate of break-up but also a
higher rate of matching. Grossman (1987) also using evidence from the United States find that
the responsiveness of employment to competition from imports differed across sectors. Moreover
the impacts in different sectors varied in magnitude.
Dutt, Mitra, and Ranjan (2009), and Hasan, Mitra, Ranjan and Ahsan (2012) find
ambiguity in the impact of trade on unemployment in that this impact changes over time. Dutt,
Mitra, and Ranjan (2009) using cross-national panel data examine the effect of various measures
of trade openness across countries on unemployment rates, and their results generally show that
trade openness reduces unemployment. In examining the short run and long run effects of trade
liberalization, they find that permanent trade liberalization shocks increase unemployment in the
short run, but these effects reverse over time and actually reduce unemployment in the long run.
Empirical evidence from India presented in Hasan, Mitra, Ranjan and Ahsan (2012), also shows
a possibility of increased unemployment as a result of trade liberalization, but this effect only
occurs in the short run. They had more robust evidence to suggest that trade liberalization
reduces unemployment especially in states with flexible labor markets.
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III. Review of Theoretical Models
There have been a number of attempts in the literature to create a theoretical model to
demonstrate the relationship between trade and employment. First, this paper will consider a
basic Ricardian trade model with two countries and two sectors and where trade occurs due to
comparative advantage. In autarky, the economies of both countries operate at full employment
and produce goods in both sectors. Once the economies open up and begin to trade, according to
this model, each country will produce exclusively the product in which they have a comparative
advantage. Thus all of the labor resource that had previously been allocated to the sector, in
which the country did not have a comparative advantage in is then perfectly reallocated to the
other sector. There is no unemployment. While this model is elegantly simple in this way, that
there is zero labor market friction is a criticism of this model. One would not expect the economy
to actually operate so flawlessly. Assuming in fact that all job losses are eventually perfectly
offset by new job creation, this does not happen instantly. Moreover, the economy does not
actually operate with perfect information so it takes time for firms to decide to fire workers and
also for laid off workers to find a new job.
A theoretical model presented by Davidson, Martin and Matusz (1999) is one that takes
into account that imperfection and attempts to model these job search frictions. Davidson, Martin
and Matusz (1999), developed this model of international trade where trade occurred not because
of productivity differences, as they assumed production technology across sectors and across
countries were equal, but because of labor market structures. The model and the ultimate
conclusion of the paper is that models of trade that include unemployment behave differently
than the models that assume full employment. The key aspect of this model is the exogenously
given turnover rates that determines comparative advantage. A country with a sector that has
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lower break-up rates of matches between labor and capital required to produce output and a
higher rate of pairing up these factors is more likely to have a comparative advantage in the good
in that sector. They show that the result of opening up economies to trade in this model has
different effects for unemployment in different countries depending on the conditions. Trade
between a small country and a large country that is more abundant in capital and has more
efficient employment search technology, increases aggregate unemployment in the large country.
Helpman and Itskhoki (2010) also present a similar but more complicated theoretical
model of labor and trade where trade is affected by frictions in the labor market, and impacts on
unemployment are subject to the situation. In this model the two countries produces homogenous
goods in one sector and differentiated goods in the other sector. The implications of this model
are that trade can raise the level of unemployment if the relative labor market frictions are low in
the differentiated goods sector, but trade can also lower the level of unemployment if the relative
labor market frictions are high in the differentiated goods sector.
This theoretical model presented below, developed by Dutt, Mitra, and Ranjan (2009,
2012), illustrates a relationship between trade and unemployment. In this model of trade, labor
(L) is the only factor of production. The economy modeled here produces a single final nontradable good (Z), and two intermediary tradable goods (X and Y) with prices px and py. The
production function for good Z is
𝑍=
𝐴𝑋 1−α 𝑌 𝛼
𝛼𝛼 (1−𝛼)1−𝛼
;0<𝛼<1
(1)
The cost of this final good is determined by the prices of the intermediary goods and
given by the equation and set equal to 1 as it is the only tradable good in the economy.
𝑐(𝑝𝑥 , 𝑝𝑦 ) =
𝑝𝑥 1−𝛼 𝑝𝑦 𝛼
𝐴
=1
(2)
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Due to the nature of the production function for this final good, the relative demand for
the two intermediate goods can be expressed as
𝑋𝑑
𝑌𝑑
=
(1−𝛼)𝑝𝑦
𝛼𝑝𝑥
(3)
The production of the intermediary goods requires a match between workers and jobs and
each pair producing only one unit of output. The rate at which these matches are made is
dependent on the amount of job vacancies and the amount of unemployed individuals. Breakups
are caused by exogenous shocks. The number of workers employed in each sector can be
expressed as (1 − 𝑢𝑖 )𝐿𝑖 where 𝑢𝑖 is the unemployment rate in sector i, and 𝐿𝑖 is the total number
of workers affiliated, either searching or employed, in sector i. Aggregate production in each
sector then can be expressed as
𝑋 = ℎ𝑥 (1 − 𝑢𝑥 )𝐿𝑥 ; 𝑌 = ℎ𝑦 (1 − 𝑢𝑦 )𝐿𝑦
(4)
Market tightness is expressed by a value 𝜃𝑖 = 𝑣𝑖 /𝑢𝑖 where 𝑣𝑖 is the vacancy rate in sector
i. The flow of matches in the labor market is given by the following equation where 𝑚𝑖 is a scale
parameter, and 𝛾 is a parameter that expresses the intensity of job vacancies.
𝑀𝑖 (𝑣𝑖 𝐿𝑖 , 𝑢𝑖 𝐿𝑖 ) = 𝑚𝑖 (𝑣𝑖 )𝛾 (𝑢𝑖 )1−𝛾 𝐿𝑖 = 𝑚𝑖 (𝜃𝑖 )𝛾 𝑢𝑖 𝐿𝑖 ; 0 < 𝛾 < 1
(5)
This describes the flow into employment; so next, there must be an equation to describe
the flow into unemployment. Let 𝜆𝑖 be the exogenously given rate at which breakups of matches
occurs in sector i per period.
𝑢̇ 𝑖 = 𝜆𝑖 (1 − 𝑢𝑖 ) − 𝑚𝑖 𝜃𝑖 𝛾 𝑢𝑖
𝑢𝑥 =
𝜆𝑖
𝜆𝑖 +𝑚𝑖 𝜃𝑖 𝛾
Equation (7) gives the constant steady state rate of unemployment.
(6)
(7)
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This model also includes cost of firing of employees for the employers (Fi) and a
recruitment cost (𝛿𝑖 ). Let 𝜌 be the discount factor, Vi be the asset value of a vacant job, and Ji be
the value of an occupied job.
𝜌𝑉𝑖 = −𝛿𝑖 + 𝑚𝑖 𝜃𝑖 𝛾−1 (𝐽𝑖 − 𝑉𝑖 )
(8)
If there is free entry in job creation, then 𝑉𝑖 = 0, and wages in sector i are denoted, wi an
equation for Ji can be written
𝜌𝐽𝑖 = 𝑝𝑖 ℎ𝑖 − 𝑤𝑖 − 𝜆𝑖 (𝐽𝑖 + 𝐹𝑖 )
(9)
Equation (10), the value of occupied jobs, can be derived from equation (8) when free
entry in job creation is assumed.
𝛿
(10)
𝐽𝑖 = 𝑚 𝜃 𝑖𝛾−1
𝑖 𝑖
Solving equations (9) and (10) together results in
𝑝𝑖 ℎ𝑖 − 𝑤𝑖 − 𝜆𝑖 𝐹𝑖 =
𝛿𝑖 (𝜌+𝜆𝑖 )
𝑚𝑖 𝜃𝑖 𝛾−1
(11)
The left hand side of the equation shows the hiring and firing costs as well as the wage,
and the right hand side of the equation shows the value of the match.
Unemployed individuals in this model can benefit from the value of leisure and
unemployment insurance. Let the benefit to unemployed individuals be denoted b. W i is the
present discounted value of employment, and Ui is the present discounted value of
unemployment.
𝜌𝑊𝑖 = 𝑤𝑖 + 𝜆𝑖 (𝑈𝑖 − 𝑊𝑖 )
(12)
𝜌𝑈𝑖 = 𝑏 + 𝑚𝑖 𝜃𝑖 𝛾 (𝑊𝑖 − 𝑈𝑖 )
(13)
Wage in this model is determined through Nash bargaining. The bargaining power of
workers is given by 𝛽.
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𝛽
(14)
𝑊𝑖 − 𝑈𝑖 = 1−𝛽 𝐽
Solving equations (9), (10), (12) (13), and (14) together gives the equation
(15)
𝑤𝑖 = (1 − 𝛽)𝑏 + 𝛽(𝑝𝑖 ℎ𝑖 + 𝛿𝑖 𝜃𝑖 − 𝜆𝑖 𝐹𝑖 )
Finally, using equations (7), (11), and (15) together to obtain an equation that presents the
impact of trade on unemployment.
𝜕𝜃𝑖
𝜕𝑝𝑖
𝜕𝑢𝑖
𝜕𝑝𝑖
=
=
(1−𝛽)ℎ𝑖
−𝛾
(𝜌+𝜆𝑖 )(1−𝛾)𝜃𝑖
𝛿𝑖 (𝛽+
)
𝑚𝑖
−𝜆𝑖 𝑚𝑖 𝛾𝜃𝑖 𝛾−1 𝜕𝜃𝑖
(𝜆𝑖 +𝑚𝑖 𝜃𝑖
𝛾 2
)
𝜕𝑝𝑖
(16)
>0
1 𝜕𝜃𝑖
𝑖 𝜕𝑝𝑖
= −𝛾𝑢𝑖 (1 − 𝑢𝑖 ) 𝜃
<0
(17)
The implications of equation (17) are that an increase in the relative price of the good in
one sector due to increased trade openness will increase the value of the marginal product of
labor for the good in that sector. Changes in the marginal product for a good will obviously
affect firms’ production decision. So, the relative price of the good will impact the level of
exports and imports in different trading sectors. Sectors with comparative advantage will see an
increase in exports whereas sectors that do not have the comparative advantage will have an
increase in imports. This in turn will lead to firms increasing the number of job vacancies in
sectors increasing exports, which results in a lower unemployment rate in that sector as there is a
higher number of vacancies relative to the number of workers searching. Conversely, sectors
where the relative price of the good decreases and thus the marginal product of labor in those
sectors also decrease become import sectors. Firms in that sector will decrease job vacancies,
which will increase the unemployment rate in that sector as there will be a higher number of
workers searching relative to the number of vacancies.
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The effect on aggregate unemployment for the whole economy depends on which sector
had higher unemployment to begin with. The hypothesis of this theoretical model for the impact
of trade on unemployment is thus flexible since it depends on the initial starting point of
unemployment in each sector. Allowing for different results on unemployment could be useful
for explaining different impacts on open trade on unemployment in different countries. It is also
useful for explaining the conflicting results found in the previous literature for the effect of trade
on employment.
IV. Data
This theoretical trade model described in the previous section describes a relationship
between trade and unemployment in individual sectors. The data used in this research though are
aggregated for the economy in Taiwan as a whole. The aggregate unemployment rate in Taiwan
data comes from the Directorate General of Budget, Accounting and Statistics. The
unemployment rate is collected as a part of The Manpower Survey, which is a sampling survey
of households on labor statistics in Taiwan. They survey is taken monthly of a sample of 20,000
households in Taiwan. The DGBAS provides the data on the labor force including the
unemployment rate free on their website for observations monthly from January 1978 to
December 2014.
The data used in the empirical model for this research is total imports and exports without
distinguishing between different sectors. The data for the total value of imports in US dollars and
the data for the total value of exports in US dollars come from the Republic of China’s
Directorate General of Customs and Administration in the Ministry of Finance. This source
provides data on imports from China to Taiwan and from the whole world to Taiwan and exports
from Taiwan to China and from Taiwan to the whole world. The data for monthly imported
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goods from China, not including reimported goods is available from January 1989 to November
2014. The data for exported goods to China is provided monthly from November 1990 to
November 2014, but includes some missing observations. Continuous data for exports is only
available from August 1992 to November 2014. The data for monthly all imported goods from
the whole world are provided monthly from January 1989 to November 2014. Data for exports to
the world is likewise provided monthly from January 1989 to November 2014.
Other exogenous variables used in the empirical model are the GDP per capita, average
wage and a manufacturing production index. GDP per capita is provided by the DGBAS of
Taiwan in US dollars at current prices quarterly from the first quarter of 1961 to the fourth
quarter of 2014. Average monthly earnings in Taiwan data are provided by the DGBAS from
January 1980 to December 2014 and reported in New Taiwan dollars. The manufacturing
production index comes from the Ministry of Economic Affairs and Statistics Department using
2011 as the base year. The data is provided from January 1971 to December 2012.
Since time series regressions requires consecutive, evenly spaced observations, this
research will analyze the time period from August 1992 to November 2014, as this is the only
complete data for all necessary variables. This gives a total of 268 observations.
Summary statistics for all variables are reported in Table 2. From the summary statistics
it is clear that there has been a substantial increase in the level of imports and exports both with
China as well as with the rest of the world as a whole. Average value of imports from China
rose from an average of 0.15 billion dollars in the early 90s to an average of 3.64 billion dollars
in the early 2010s. The average value of imports from China more than doubled every 5 years.
An even larger average increase is seen for the average value of exports to China with an average
of 0.01 billion dollars in the early 90s to an average of 6.85 billion dollars in the early 2010s.
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There is also an increase in the average value of exports and imports from the rest of the world
not including China in this timeframe, however the increase seen in the rest of the world is not as
drastic. Imports from the rest of the world not including china increased from an average of 7.08
billion dollars in the early 90s to an average of 19.23 billion dollars in the early 2010s. Exports to
the rest of the world not including China increased from an average of 7.90 billion dollars in the
early 90s to an average of 18.75 in the early 2010s.
Interestingly, the summary statistics show that there was a decrease in the average
percent change in imports from China and a decrease in the average percent change in exports to
China across this timeframe. Percent change in exports to China started off very high at 84.83
percent and decreased to an average of 0.92 percent in the late 2010s. The standard errors of the
percent change in exports to China also decrease in every subsequent sub-period. The summary
statistics do not show a trend for the average percent change in imports from China in this
timeframe. The average percent change in imports from China was 4.80 percent in the early 90s,
decreased to 3.08 percent in the late 90s, increased to 3.79 percent in the early 2000s, decreased
again to 2.30 percent in the late 2000s, then increased again to 2.49 percent in the early 2010s.
The summary statistics show that the percent change in imports and exports with the rest of the
world not including China similarly does not show any trend between sub-periods. The percent
change in exports to the world not including China was 1.06 percent in the early 90s, increased
to 1.48 percent in the late 90s, decreases to 1.29 percent in the early 2000s, increased 1.99
percent in the late 2000s, then decreased to 0.38 percent in the early 2010s. The percent change
in exports to the rest of the world not including china was 1.26 percent in the early 90s, increased
to 1.36 percent in the late 90s, decreased to 0.85 percent in the early 2000s, increased to 0.96
percent in the late 2000s, then decreased to 0.79 percent in the early 2010s.
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Unlike the average value of imports and exports in every sub-period, the average
unemployment rate did not increase in each subsequent sub-period. In the timeframe overall
there was an increase in the unemployment rate from 1.59 in the early 90s to 4.19 in the early
2010s. The sub-period with the highest average unemployment rate was the early 2000s with
4.66 percent, and this average decreased only slightly to 4.60 percent in the next sub-period, the
late 2000s. Figure 1 shows the average monthly unemployment rate over time. The sudden
increase in the unemployment rate around 2001 is likely due to the September 11 attack in the
United States in 2001. The decrease in tourism that occurred after September 11 had a negative
impact on many economies including Taiwan. The Great Recession observed around the world
in 2008 can explain the next sharp increase in the unemployment rate in Taiwan around 2008.
The Great Recession is also the likely cause of the sharp decreases in the level of trade with
China in 2008 shown in Figure 2 as well as the sharp decrease in the level of trade with the rest
of the world not including China in 2008 shown in Figure 3.
V. Empirical Model
The increasing means of the variables as shown in the summary statistics over time as
well as a visual analysis of the graphs of the variables, figures 1, 2, and 3, suggests the presence
of a deterministic trend, meaning that the expected value of the variable is not independent of the
time variable. If the expected value of a variable 𝑋𝑡 is not independent of the time variable 𝑡,
then 𝑋𝑡 is nonstationary. The Dickey-Fuller test essentially allows us to test the whether
variables are in fact stationary or not.
Moreover, since the relevant variables in this research, the unemployment rate, trade with
China, both seem to have a general upward trend, it begs the question of whether there may be
any long run relationship between the unemployment rate and trade. Generally speaking, a linear
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combination of time series variables will be non-stationary if at least one of them is nonstationary. However, if there is a long run relationship between two time series variables, then it
could be that a linear combination of those time series variables is stationary, so they share a
common stochastic drift. When time series variables share a common stochastic drift, then they
are cointegrated. The Johansen Cointegration test allows us to test for cointegration.
To test short run causality I use a vector autoregressive model and Granger Causality test.
The main empirical model is as follows
4
4
4
𝑈𝑅𝑡 = 𝛼 + ∑ 𝛽𝑡−𝑙 𝑈𝑅𝑡−𝑙 + ∑ 𝛾𝑡−𝑙 𝑋𝑡−𝑙 + ∑ 𝛿𝑡−𝑙 𝑌𝑡−𝑙 + ∑ 𝜇𝑖 𝐴𝑖 + 𝜀𝑡
𝑙=1
𝑙=1
𝑙=1
𝑖
The 𝜀𝑡 is the disturbance term. There are several assumptions on 𝜀𝑡 in a time series
regression. First, it is assumed that the disturbance term has an expected value of 0. Secondly,
the disturbance term is homoscedastic. Thirdly, the disturbance terms have independent
distributions. Fourthly, the disturbance term is distributed independently of the other variables on
the right hand side of the equation. Fifthly, the disturbance term is normally distributed.
∑𝑖 𝜇𝑖 𝐴𝑖 represents the exogenous controls included in the model. This includes the GDP
per capita, average wage, manufacturing production index as well as monthly dummy variables.
The left hand side of the main empirical model is the percent change in the
unemployment rate from the previous period, which in this case is the previous month. I use
percent change in the unemployment rate rather than simply using the unemployment rate
because vector autoregressive models require relevant variables to be stationary. Figure 7 shows
the graph of the percent change in the unemployment rate from the previous month from
September 1992 to November 2014. A quick visual analysis of the graph suggests that the
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percent change in the unemployment rate from the previous month is stationary. This is
confirmed in Table 3, which presents the findings of the Dickey-Fuller unit root test.
On the right hand side of the main empirical model, 𝑋𝑡 is the percent change in the
volume of Taiwan’s trade with China from the previous period. 𝑌𝑡 is the percent change in the
volume of Taiwan’s trade with the rest of the world not including China from the previous
period. These variables similarly are required to be non-stationary. Figures 8 and 9 show the
percent change in total trade with China from the previous month and the percent change in total
trade with the rest of the world not including china from the previous month respectively. Both
figures seem to suggest that the percent change in trade with China and the percent change in
trade with the rest of the world not including China are stationary. The stationarity of these two
time series variables is confirmed with the Dickey-Fuller test shown in table 3.
In addition to the vector autoregressive model, I also use the Granger Causality test. A
variable 𝑋𝑡 “Granger causes” variable 𝑌𝑡 if the lagged values of 𝑋𝑡 influences variable 𝑌𝑡
controlling also for the lagged values of variable 𝑌𝑡 . The Granger Causality test regresses 𝑌𝑡 on
its own lagged values as well as the other lagged values of the other variables on the right hand
side of the of the vector autoregressive model equation as well as the exogenous variables. It
tests the null hypothesis that the coefficients on the lagged values of the independent variable in
question are jointly 0. If the estimated coefficients are found to be statistically significantly
different than zero then we cannot reject that the independent variable in question does not
“Granger cause” 𝑌𝑡 .
VI. Results
Now I use the data described in section IV to run the empirical models presented in the
previous section to test the relationships between trade and the unemployment rate. First, I use
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the Johansen Cointegration Test to test if there is a long run relationship between trade with
China and the unemployment rate in Taiwan, as in these two variables share a common
stochastic drift. The test shows that we cannot reject the null hypothesis that there is no
cointegration among the unemployment rate and trade with China. Thus, there is no long run
causality between trade with China and the unemployment rate. The test also shows that there is
no long run causality between trade with the rest of the world not including China and the
unemployment rate.
Though the data shows no long run relationship between trade and unemployment, the
vector autoregressive models do show a short run relationship. Table 4 shows the results of the
vector autoregressive models with column 1 being the regression of the percent change in the
unemployment rate on percent change in trade with China and percent change in trade with the
rest of the world not including China. Column 2 shows the same regression with trade with
China as the dependent variable, and column 3 shows the regression with trade with the rest of
the world not including China as the dependent variable. The Granger causality and Wald tests
show that both growth in trade with China and growth in trade with the world not including
China have some predictive power for the percent change in the unemployment rate.
The results of the empirical tests show that a 1 percent growth in the volume of trade with
China will increase the percent change in the unemployment rate by 0.151 percentage points
lagged one period. This number is statistically significantly different from 0. Other lagged
periods of the growth in trade with China do not have a statistically significant effect on the
percent change in the unemployment rate. A growth in trade with the rest of the world not
including China lagged one period similarly has a statistically significant impact on the percent
change in the unemployment rate, but it results in a decrease of 0.284 percentage points. Other
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lagged periods of the growth in trade with the rest of the world not including China do not have
any statistically significant impact on the change in unemployment. The Granger Causality test
shows that the estimated coefficients for the lagged values of trade with China are jointly
statistically significantly different than 0. The test also shows that the estimated coefficients for
the lagged values of trade with the rest of the world not including China is also jointly
statistically significantly different than 0. Thus, the empirical model shows that an increase in
trade with China does have an impact on the unemployment rate, and moreover, this impact
suggests an increase in trade with China will increase the unemployment rate.
This effect is not the same for trade with the rest of the world. An increase in trade with
the rest of the world as a whole not including China decreases the unemployment rate. The
theoretical explanation for this outcome as given by the theoretical model presented earlier
would be that unemployment decreased in the sector with comparative advantage trading and
increased in the sector without comparative advantage. The impact on the unemployment rate
overall was an increase in unemployment as a result of increased trade with China suggesting
that the magnitude of the increase unemployment in the sector without comparative advantage
was greater than the magnitude of the decrease in unemployment in the sector with comparative
advantage.
Subsequently, I test the separate impacts of imports and exports from China and from the
rest of the world not including China using a similar vector autoregressive model. The results of
this model are presented in Table 5. Recall that intuitively, holding all else equal, increasing a
country’s exports should decrease the unemployment rate, and increasing imports should
increase the unemployment rate. Theoretically, the country will export in sectors where they hold
a comparative advantage and import in other sectors, and as the previously presented theoretical
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model explains, there would be a decrease in unemployment in the sector with comparative
advantage and an increase in unemployment in the other sector. However, the results of the
empirical model show that this is not the case for Taiwan and China. An increase in the growth
of imports from China by one percent by will increase the percent change in the unemployment
rate .103, and the direction of this impact is positive as expected. An increase in the growth of
exports to China has a surprising effect as it shows an increase in the unemployment rate as well.
The magnitude of this change, 0.005 lagged one period and 0.008 lagged two periods, is much
smaller than the magnitude of the effect from imports, but both are statistically significantly
different than 0.
The results presented in column 1 of Table 5 also show that an increase in imports with
the rest of the world not including China and an increase in exports with the rest of the world not
including China have the same impact on the unemployment rate. In the case of the world as a
whole though, increased trade in both imports and exports decreases the unemployment rate. The
theoretical model does not explain the mechanisms for this outcome. Still, this result is
interesting, and holds important policy implications for Taiwan in terms of trade agreements with
China. Increased trade in any form, either through imports or exports, both would increase the
unemployment rate. However, this is not true for trade with the rest of the world where an
increase in both imports and exports would decrease the unemployment rate in Taiwan.
The simple case explained in the previously presented theoretical model was that
increased exports in one sector where the relative price for the good produced in that sector
increases the marginal product of labor causing firms to increase production and hire more
workers relative to the number of workers searching in that sector results in a decrease in the
unemployment rate, and holding all else equal, would decrease the aggregate unemployment
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rate. Since this does not explain the empirical results where an increase in exports to China
increases the unemployment rate, consider that an increase in exports must be offset by a
decrease in imports and non-tradable goods. If it is the case that the supply curve for exports to
China is more inelastic than the supply curves for non-tradable goods and imports, then the
resulting decrease in the unemployment rate from the increase in exports would be overpowered
by the increase in unemployment from the necessary increase in the other sectors. The relative
elasticity of these supply curves is a possible explanation for why the empirical results show that
an increase in exports to China increases the aggregate unemployment rate in Taiwan. A similar
explanation of elasticity of supply then, can be used to explain the unexpected result of having an
increase in imports from the rest of the world not including China decrease the unemployment
rate. The increase in imports from the rest of the world not including China offset by an increase
in export sectors and an increase in non-tradable goods sectors would explain the increase
decrease in the aggregate unemployment rate if the supply curve for the imports sector was
relatively more inelastic than the other sectors.
VII. Conclusion
The empirical tests show that though there is no long run causality between the
unemployment rate and the volume of trade with China there is a statistically significant short
run impact. This research ultimately finds that an increase in trade with China increases the
unemployment rate in Taiwan. Interestingly, this effect is only for China as an increase in trade
with the rest of the world not including China decreases the unemployment rate. The theoretical
model presented would explain this result through the different sectors holding comparative
advantage in the different countries. It would appear that sectors in which China hold the
comparative advantage are the sectors in Taiwan that have the larger increase in magnitude of
Sun 24
unemployment. The effect of which overpowers the decreasing unemployment in sectors where
Taiwan holds the comparative advantage. Further studies can be conducted on the dynamics of
specific sectors trading in Taiwan.
Another important finding of this research is that the effect of imports and exports from
China on the unemployment rate in Taiwan is the same. The results show that an increase in both
imports and exports from China will increase the unemployment rate. They also show that an
increase in both imports and exports from the world overall not including China will decrease the
unemployment rate. This could potentially be theoretically explained by differences in supply
elasticities in different sectors.
In terms of policy implications, these findings are extremely valuable to ongoing
discussions in Taiwan over larger multilateral trade agreements such as the Trans-Pacific
Partnership and the Regional Comprehensive Economic Partnership. Trade agreement
discussions take into consideration complex social, political as well as economic issues but, if the
impact of trade on the aggregate unemployment rate were the deciding factor, given the
empirical results of this research, Taiwan would be better advised to sign with the TPP, an
agreement with which China has political reasons not to join, rather than the RCEP, which China
has taken a leadership role in.
The results of this research are interesting, but they are just a starting point. The findings
imply that there are structural barriers to competitive efficiency that prevent Taiwan from
perpetually being in a state of full employment. These barriers are difficult to identify, and are
beyond the scope of this paper but would be an interesting topic for further research.
Sun 25
Appendix
Figure 1. Unemployment Rate
Notes: This graph shows the aggregate unemployment rate in Taiwan from August 1992 to
November 2014.
Figure 2. Total Trade China
Notes: This graph shows the total trade of Taiwan with China (imports from Taiwan to China
plus the exports from China to Taiwan) in billion US dollars from August 1992 to November
2014.
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Figure 3. Total Trade with world not including China
Notes: This graph shows the total trade of Taiwan with the rest of the world not including China
in billion US dollars from August 1992 to November 2014.
Figure 4. First difference of unemployment rate
Notes: This graph shows the first difference of the unemployment rate in Taiwan from
September 1992 to November 2014. The first difference of the unemployment rate is the
difference between the unemployment rate in one month and the unemployment rate in the
previous month.
Sun 27
Figure 5. First Difference of trade with China
Notes: This graph shows the first difference of the total trade with China in billion USD from
September 1992 to November 2014. The first difference of the total trade with China is the
difference between the total trade with China in one month and total trade with China in the
previous month.
Figure 6. First Difference of total trade with world not including China
Notes: This graph shows the first difference of the total trade with the world not including China
in billion USD from September 1992 to November 2014. The first difference of the total trade
with the world not including China is the difference between the total trade with the world not
including China in one month and total trade with the world not including China in the previous
month.
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Figure 7. Percent change in unemployment rate
Notes: This graph shows the percent change in the unemployment rate in Taiwan between each
month from September 1992 to November 2014. The percent change in the unemployment rate
is calculated by the difference in the unemployment rate in the current month and the
unemployment rate in the previous month divided by the unemployment rate in the previous
month.
Figure 8. Percent change in trade with China
Notes: This graph shows the percent change in in total trade with China between each month
from September 1992 to November 2014. The percent change in total trade of Taiwan with
China is calculated by the difference in value of total trade with China in the current month and
the value of total trade with China in the previous month divided by the value of total trade with
China in the previous month.
Sun 29
Figure 9. Percent change in trade with world not including China
Notes: This graph shows the percent change in in total trade with the world not including China
between each month from September 1992 to November 2014. The percent change in the total
trade with the world not including China is calculated by the difference in the value of total trade
with the world not including China in the current month and the value of total trade with the
world not including China in the previous month divided by the value of total trade with the
world not including China in the previous month.
Sun 30
Table1. List of Variable Names
Short Name
Unemployment
Rate
Variable Name
Unemployment Rate
Percent
Change
Unemployment
Rate
Percent change in
the Unemployment
Rate
Imports China
Volume of imports
to Taiwan from
China
Percent
Change
Imports China
Percent change in
Imports to Taiwan
from China
Exports China
Volume of exports
to China from
Taiwan
Percent
Change
Exports China
Percent change in
exports to China
from Taiwan
Total Trade
China
Total volume of
imports and exports
Long definition
Sources
Sample of 20,000 households Directorate General of
in Taiwan taken monthly.
Budget, Accounting
Unemployment rate is
and Statistics
calculated by the number of
people in the labor force that
are unemployed divided by the
number of people in the labor
force.
Percent change in the
Directorate General of
unemployment rate is
Budget, Accounting
calculated by the difference in
and Statistics
the unemployment rate in the
current month and the
unemployment rate in the
previous month divided by the
unemployment rate in the
previous month.
Total value of goods imported Directorate General of
into Taiwan from China in US
Customs and
dollars
Administration in the
Ministry of Finance
Percent change in the imports Directorate General of
to Taiwan is calculated by the
Customs and
difference in the value of
Administration in the
imports in the current month
Ministry of Finance
and the value of imports in the
previous month divided by the
value of imports in the
previous month.
Total value of goods exported Directorate General of
from Taiwan to China in US
Customs and
dollars
Administration in the
Ministry of Finance
Percent change in the exports
to China from Taiwan is
Directorate General of
calculated by the difference in
Customs and
the value of exports in the
Administration in the
current month and the value of
Ministry of Finance
exports in the previous month
divided by the value of exports
in the previous month.
Total trade with China is
Directorate General of
calculated by the sum of the
Customs and
Sun 31
combined between
Taiwan and China
Percent
Change Total
Trade China
Imports World
not including
China
Percent
Change
Imports World
not including
China
Exports World
not including
China
Percent
Change
Exports World
not including
China
Total Trade
World not
including
China
value of imported goods from
China and value of exported
goods to China from Taiwan
in US dollars.
Percent change in
Percent change in total trade
the total volume of
of Taiwan with China is
imports and exports calculated by the difference in
combined between
value of total trade in the
Taiwan and China
current month and the value of
total trade in the previous
month divided by the value of
total trade in the previous
month.
Volume of imports
Total value of goods imported
to Taiwan from the
to Taiwan from the world
rest of the world not
subtracting the total value of
including China
goods imported to Taiwan
from China in US dollars.
Percent change in
Percent change in the imports
the volume of
to Taiwan from the world not
imports to Taiwan
including China is calculated
from the rest of the
by the difference in value of
world not including
imports in the current month
China
and the value of imports in the
previous month divided by the
value imports in the previous
month.
Volume of exports
Total value of goods exported
from Taiwan to the
from Taiwan to the rest of the
rest of the world not
world subtracting the total
including China
value of goods exported to
China from Taiwan in US
dollars.
Percent change in
Percent change in the exports
the volume of
to the world not including
exports from Taiwan
China from Taiwan is
to the rest of the
calculated by the difference in
world not including
the value of exports in the
China
current month and the value of
exports in the previous month
divided by the value of exports
in the previous month.
Total volume of
Total trade with the world not
imports and exports
including china is calculated
combined between
by sum of total exports and
Taiwan and the rest
imports to the whole world
Administration in the
Ministry of Finance
Directorate General of
Customs and
Administration in the
Ministry of Finance
Directorate General of
Customs and
Administration in the
Ministry of Finance
Directorate General of
Customs and
Administration in the
Ministry of Finance
Directorate General of
Customs and
Administration in the
Ministry of Finance
Directorate General of
Customs and
Administration in the
Ministry of Finance
Directorate General of
Customs and
Administration in the
Ministry of Finance
Sun 32
Percent
Change Total
Trade World
not including
China
of the world not
including China
Percent change in
total volume of
imports and exports
combined between
Taiwan and the rest
of the world not
including China
GDP per capita
GDP per capita
Average Wage
Average monthly
earnings
MPI
Manufacturing
Production Index
subtracted by the sum of total
imports and exports to China.
Percent change in the total
trade with the world not
including China is calculated
by the difference in the value
of total trade in the current
month and the value of total
trade in the previous month
divided by the value of total
trade in the
Gross domestic product
divided by total population
Average monthly earnings
reported in New Taiwan
dollars
Index of manufacturing
production using 2011 as the
base year
Directorate General of
Customs and
Administration in the
Ministry of Finance
Directorate General of
Budget, Accounting
and Statistics
Directorate General of
Budget, Accounting
and Statistics
Ministry of Economic
Affairs and Statistics
Department
Sun 33
Table 2. Summary Statistics
August 1992December 1995
41
1.59
(0.26)
January 1996 December 2000
60
2.79
(0.30)
January 2001 December 2005
60
4.66
(0.51)
January 2006 December 2010
60
4.60
(0.83)
January 2011 December 2014
48
4.19
(0.19)
All Time
Periods
269
3.68
(1.24)
Percent Change
Unemployment Rate
0.58
1.06
0.33
0.36
-0.42
0.40
Imports China
(11.57)
0.15
(0.08)
(5.65)
0.36
(0.10)
(3.35)
1.03
(0.47)
(2.81)
2.41
(0.51)
(1.95)
3.64
(0.48)
(5.63)
1.51
(1.33)
Percent Change Imports
China
4.80
3.08
3.79
2.30
2.49
3.22
Exports China
(18.23)
0.01
(0.02)
(19.20)
0.15
(0.13)
(19.46)
1.97
(1.29)
(16.16)
5.20
(1.19)
(20.59)
6.85
(0.55)
(18.61)
2.84
(2.80)
Percent Change Exports
China
84.83
5.79
5.67
2.19
0.92
15.94
(237.14)
0.17
(0.09)
5.38
(18.02)
(25.48)
0.51
(0.22)
3.35
(17.90)
(19.68)
3.00
(1.76)
4.61
(17.94)
(17.79)
7.61
(1.67)
2.09
(16.31)
(12.67)
10.49
(0.95)
1.24
(14.16)
(97.04)
4.36
(4.11)
3.28
(16.90)
7.08
9.17
10.65
15.72
19.23
12.41
(1.05)
(1.38)
(2.35)
(2.98)
(1.32)
(4.70)
1.06
1.48
1.29
1.66
0.38
1.22
(10.09)
(14.89)
(14.18)
(14.56)
(10.03)
(13.17)
7.90
10.16
11.25
14.87
18.75
12.62
Observations
Unemployment Rate
Total Trade China
Percent Change Trade China
Imports World not including
China
Percent Change Imports
World not including China
Exports World not including
China
Sun 34
Percent Change Exports
World not including China
Total Trade World not
including China
Percent Change Total Trade
World not including China
(1.20)
(1.34)
(1.43)
(2.11)
(1.34)
(3.91)
1.26
1.36
0.85
0.96
0.79
1.04
(11.87)
(13.97)
(11.30)
(11.12)
(10.77)
(11.82)
14.98
19.33
21.89
30.60
37.97
25.03
(2.21)
(2.66)
(3.71)
(5.01)
(2.41)
(8.56)
1.11
1.33
0.99
1.21
0.52
1.05
(10.49)
3010.88
(220.40)
(13.75)
3464.50
(195.79)
(12.20)
3661.90
(321.63)
(12.09)
4461.85
(290.75)
(9.78)
5423.81
(226.20)
(11.84)
GDP per capita
4011.47
(846.30)
41247.6
Average Wage
32923.61
39523.58
42289.72
43780.48
46044.21
5
(10075.3
(7939.38)
(8819.31)
(9042.69)
(9277.74)
(10933.49)
5)
MPI
42.24
52.08
64.08
82.38
101.65
68.86
(3.28)
(5.82)
(8.27)
(11.10)
(6.69)
(21.66)
Notes: This table shows the means of variables by sub-period with standard deviations reported in parentheses. Percent changes
calculated as percent change from previous period. Imports, Exports and total trade reported in billion USD. Average wage is average
monthly wage in Taiwan reported in NTD.
Sun 35
Table 3. Tests for Stationarity
Variable
Unemployment Rate
Test Statistic
-2.104
First Difference Unemployment Rate
-6.367***
Percent Change Unemployment Rate
-13.291***
Total Trade with China
-0.255
First Difference Total Trade with China
-8.294***
Percent Change Trade with China
-25.624***
Total Trade with World Not Including China
-1.291
First Difference Total Trade with World Not Including China
-8.524***
Percent Change Trade with World Not Including China
-26.079***
Imports China
0.127
First Difference Imports China
-29.140***
Percent Change Imports China
-26.618***
Exports China
-0.496
First Difference Exports China
-27.000***
Percent Change Exports China
-16.755***
Imports World not Including China
-2.768*
First Difference Imports World not Including China
-26.211***
Percent Change Imports World Not Including China
-25.779***
Exports World Not Including China
-2.776*
First Difference Exports World Not Including China
-26.823***
Percent Change Exports World Not Including China
-26.802***
Notes: This table shows the test statistics for the Augmented Dickey-Fuller Test for unit root.
When the test statistic is greater than the critical value, we reject the null hypothesis that there is
the presence of a unit root or is not stationary. 1% critical value is -3.459, 5% critical value is 2.879, and 10% critical value is -2.570. *** indicates statistically significantly stationary at 1%,
** indicates statistically significantly stationary at 5%, *indicates statistically significantly
stationary at 10%.
Sun 36
Table 4. Vector Autoregressive Model of Unemployment, Growth in Trade with China, and
Growth in Trade with the World
(1)
(2)
(3)
Percent Change Trade
Percent Change
Percent Change Trade
with World not
Unemployment Rate
Dependent Variable:
with China
including China
Percent Change
Unemployment
0.082
L1
0.753***
-0.336***
(0.062)
(0.143)
(0.098)
-0.080
L2
-0.458***
-0.110
(0.064)
(0.147)
(0.101)
-0.071
L3
0.487***
0.291***
(0.065)
(0.148)
(0.102)
0.001
L4
0.322**
0.089
(0.062)
(0.142)
(0.098)
Percent Change
Trade with China
0.151***
L1
-0.357***
0.023**
(0.037)
(0.084)
(0.058)
0.018
L2
-0.041
0.150
(0.039)
(0.089)
(0.062)
0.009
L3
0.178**
0.094
(0.039)
(0.089)
(0.062)
0.030
L4
0.051
0.084
(0.037)
(0.085)
(0.058)
Percent Change
Trade with World
Not Including China
-0.284***
L1
-0.200*
-0.582***
(0.051)
(0.118)
(0.081)
-0.131
L2
-0.174
-0.311***
(0.060)
(0.137)
(0.094)
-0.077
L3
-0.131
0.098
(0.060)
(0.137)
(0.094)
-0.040
L4
-0.193
-0.017
(0.054)
(0.124)
(0.085)
Exogenous Variables
0.003**
GDP per capita
-0.008***
-0.005**
(0.001)
(0.003)
(0.002)
-0.140***
MPI
0.338***
0.299***
(0.050)
(0.115)
(0.079)
0.000
Average Wage
-0.001***
-0.001***
(0.000)
(0.000)
(0.000)
Sun 37
February
March
April
May
June
July
August
September
October
November
December
2.779*
(1.522)
-3.645*
(1.919)
-2.635
(2.271)
2.407
(2.206)
4.348**
(2.170)
4.234**
(2.086)
2.842
(1.986)
-4.566**
(2.013)
-1.407
(1.983)
-3.584*
(1.992)
-4.013**
(1.789)
-25.521***
(3.487)
15.487***
(4.400)
-1.634
(5.205)
-12.262**
(5.056)
-22.731***
(4.973)
-7.995*
(4.782)
-10.589***
(4.551)
-14.417***
(4.615)
-17.336***
(4.546)
-17.340***
(4.566)
-18.194***
(4.100)
-11.821***
(2.401)
11.523***
(3.029)
2.075
(3.583)
-7.764**
(3.481)
-14.686**
(3.424)
-7.049**
(3.292)
-6.896***
(3.133)
-9.798**
(3.177)
-7.963***
(3.130)
-10.195***
(3.143)
-8.855***
(2.823)
Notes: The sample is from January 1993 to November 2014. Standard errors are reported in
parentheses. *** Indicates significant at 1%, ** indicates significant at 5%, and * indicates
significant at 10%. MPI is manufacturing production index. Average wage is average monthly
wage in Taiwan reported in NTD. Variables February through December are monthly dummy
variables with January as the base. This table is the result of the vector autoregressive model as
specified in section V above. The left hand side (dependent variable) of each equation is labeled
at the top of each column.
Sun 38
Table 5. Vector Autoregressive Model of Unemployment and Growth in Imports and Exports
(1)
Dependent Variable:
Percent Change
Unemployment Rate
L1
L2
L3
L4
Percent Change Imports
from China
L1
L2
L3
L4
(3)
Percent
Change
Exports to
China
(4)
(5)
Percent Change
Unemployment
Rate
(2)
Percent
Change
Imports from
China
Percent Change Imports
from the World Not
Including China
Percent Change
Exports to the World
not Including China
0.065
(0.062)
-0.026
(0.064)
-0.039
(0.063)
0.009
(0.062)
-0.686***
(0.152)
-0.562***
(0.157)
0.521***
(0.155)
0.154
(0.152)
-4.083***
(1.309)
-5.497***
(1.349)
-2.031
(1.335)
0.011
(1.311)
-0.407***
(0.128)
-0.181
(0.132)
0.303**
(0.131)
0.027
(0.128)
-0.234***
(0.090)
-0.132
(0.093)
0.282***
(0.092)
-0.027
(0.091)
0.103***
(0.034)
0.002
(0.037)
-0.001
(0.037)
0.004
(0.033)
-0.488***
(0.082)
-0.273***
(0.090)
0.119
(0.090)
-0.053
(0.081)
-0.774
(0.710)
-0.417
(0.776)
-0.538
(0.775)
0.258
(0.699)
-0.024
(0.069)
0.022
(0.076)
0.019
(0.076)
-0.101
(0.068)
-0.011
(0.049)
-0.029
(0.054)
-0.028
(0.054)
-0.054
(0.048)
Sun 39
Percent Change Exports
to China
L1
L2
L3
L4
Percent Change Imports
from the World Not
Including China
L1
L2
L3
L4
0.005**
(0.003)
0.008***
(0.003)
-0.003
(0.003)
-0.003
(0.003)
-0.003
(0.007)
0.001
(0.007)
0.006
(0.007)
-0.001
(0.007)
-0.060
(0.057)
-0.039
(0.056)
-0.041
(0.057)
0.418***
(0.056)
-0.007
(0.006)
-0.006
(0.006)
-0.002
(0.006)
-0.005
(0.005)
-0.003
(0.004)
-0.001
(0.004)
-0.001
(0.004)
-0.003
(0.004)
-0.064
(0.043)
-0.068
(0.052)
-0.093**
(0.052)
-0.015
(0.044)
0.034
(0.105)
0.105
(0.127)
0.157
(0.128)
-0.043
(0.107)
-0.274
(0.907)
0.293
(1.098)
0.481
(1.098)
0.483
(0.922)
-0.531***
(0.089)
-0.104
(0.107)
0.225***
(0.108)
0.086
(0.090)
0.169***
(0.063)
0.273***
(0.076)
0.275***
(0.076)
0.052
(0.064)
Sun 40
Percent Change Exports
to the World Not
Including China
L1
L2
L3
L4
Exogenous Variables
GDP per capita
MPI
Average Wage
February
March
April
May
June
July
-0.198***
(0.058)
-0.051
(0.074)
0.095
(0.075)
0.016
(0.059)
0.038
(0.142)
-0.040
(0.181)
-0.171
(0.183)
0.082
(0.145)
-1.245
(1.227)
-1.443
(1.559)
-1.849
(1.574)
-3.247***
(1.253
0.005
(0.120)
-0.094
(0.153)
-0.117
(0.154)
0.093
(0.123)
-0.755***
(0.085)
-0.491***
(0.108)
-0.135
(0.109)
0.077
(0.087)
0.003**
(0.001)
-0.130***
(0.049)
0.000
(0.000)
3.577**
(1.518)
-2.104
(2.004)
-0.584
(2.396)
4.437**
(2.231)
4.544**
(2.249)
4.890**
(2.106)
-0.006**
(0.003)
0.242**
(0.119)
0.001***
(0.000)
-32.853***
(3.712)
19.743***
(4.901)
-1.975
(5.860)
-5.890
(5.457)
-20.775***
(5.500)
-8.531*
(5.150)
0.026
(0.026)
-1.828*
(1.024)
-0.002*
(0.001)
-96.867***
(31.993)
-59.700
(42.234)
-13.594
(50.498)
-38.053
(47.027)
-17.297
(47.399)
3.927
(44.383)
-0.008***
(0.003)
0.403***
(0.100)
-0.001***
(0.000)
-11.787***
(3.131)
6.853*
(4.133)
-3.137
(4.942)
-13.872***
(4.602)
-17.189***
(4.638)
-7.297*
(4.343)
-0.004**
(0.002)
0.215***
(0.071)
-0.001***
(0.000)
-14.686***
(2.212)
10.751***
(2.920)
1.418
(3.491)
-0.872
(3.251)
-10.709***
(3.277)
-4.193
(3.068)
Sun 41
August
September
October
November
December
Number of Observations
3.547*
(1.980)
-2.568
(1.983)
-0.892
(1.972)
-2.344
(1.968)
-2.794
(1.776)
-11.291**
(4.843)
-13.167***
(4.849)
-14.060***
(4.823)
-13.963***
(4.813)
-15.371***
(4.345)
-10.601
(41.734)
-33.714
(41.792)
-49.695*
(41.565)
-69.873**
(41.478)
-73.639**
(37.446)
-11.284***
(4.084)
-15.548***
(4.090)
-12.443***
(4.068)
-15.637***
(4.059)
-11.167***
(3.664)
-4.430
(2.885)
-7.335**
(2.889)
-3.354
(2.874)
-6.241**
(2.868)
-6.969***
(2.589)
263
263
263
263
263
Notes: The sample is from January 1993 to November 2014. Standard errors are reported in parentheses. *** Indicates significant at
1%, ** indicates significant at 5%, and * indicates significant at 10% level. MPI is manufacturing production index. Average wage is
average monthly wage in Taiwan reported in NTD. Variables February through December are monthly dummy variables with January
as the base. This table is the result of the vector autoregressive model as specified in section V above but with additional right hand
side variables for the separate effects of imports versus exports to China and to the rest of the world not including China. The left hand
side (dependent variable) of each equation is labeled at the top of each column.
Sun 42
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