The interrelationship between exchange-rate uncertainty and unemployment for South Korea and Taiwan:...

International Economics 125 (2011), p. 65-82

The inTerrelaTionship beTween exchange-raTe uncerTainTy and

unemploymenT for souTh Korea and Taiwan: evidence from a vecTor

auToregressive approach

Shu-Chen Chang1

Article received on 28th, October 2010Article accepted on 2nd, June 2011

AbstrAct. The goal of this paper is to estimate the effect between exchange-rate uncertainty and unemployment in South Korea and Taiwan. We use a vector autoregressive model to provide an efficient estimation between exchange-rate uncertainty and unemployment. We found that a long-run equilibrium relationship between exchange-rate uncertainty and unemployment exists in both Taiwan and South Korea when exchange-rate uncertainty is generated by two different measures. The exchange-rate uncertainly has a short-run impact on unemployment and vice versa, no matter which measure of uncertainty is used.

JEL Classification: J64; F31.Keywords: Exchange-Rate Uncertainty; Unemployment; GARCH.

résumé. L’objectif de ce papier est d’estimer la relation entre l’incertitude du taux de change et le chômage en Corée du Sud et Taiwan. Nous utilisons un modèle vectoriel auto régressif afin de fournir une estimation efficace entre l’incertitude des taux de change et le chômage. Nous montrons l’existence d’une relation d’équilibre de long terme entre l’incertitude des taux de change et le chômage à Taiwan et en Corée du Sud, et ce pour deux mesures différentes d’incertitude. L’incertitude des taux de change a un impact à court terme sur le chômage et vice versa, quelle que soit la mesure de l’incertitude.

Classification JEL : J64 ; F31.Mots-clefs : Incertitude des taux de change ; chômage ; GARCH.

1. Department of Business Administration, National Formosa University, 64, Wen-Hua Rd, Huwei, Yunlin 632, Taiwan, E-mail: [email protected] Tel: 886-5631-5776.

Shu-Chen Chang / International Economics 125 (2011), p. 65-8266

1. inTroducTion

The uncertainty of exchange rates has been an issue of wide concern in open economies, including developed and developing countries (De Grauwe, 1988). Although long-run fluctuation in exchange rates might be necessary for achieving stability at the macroeconomic level, exchange-rate volatility can theoretically have a negative impact on a nation’s economy in the event of an exogenous shock (De Grauwe, 1988). In addition, short-run fluctuations in exchange rate may also have negative effects at the microeconomic level. Therefore, exchange-rate fluctuation is usually treated as a risk. A higher risk will lead to a higher cost for risk-averse investors, therefore, result in fewer jobs created. As a consequence, fluctuations will induce more uncertainties and transaction costs.

In recent years, substantial empirical evidences about the impact of exchange-rate volatility on international trade were found in many previous studies (Arize, 1995; McKenzie, 1999; Arize, Osang, and Slottje, 2000; Aftab and Aurangzeb, 2002; Cho, Sheldon, and McCorriston, 2002; Doanlar, 2002; Hau, 2002; Kanas, 2002; Arize, Malindretos, and Kasibhatla, 2003; Aurangzeb, Stengos, and Mohammad, 2005). Only a few empirical studies investigated the impact of exchange-rate uncertainty on employment or unemployment (Buscher and Mueller, 1999; Belke and Gros, 2002; Belke and Setzer, 2003; 2004; Belke and Kaas, 2004; Belke, 2005; Demir, 2010). However, they usually relied on the conventional Ordinary Least Squares (OLS) procedure with restrictive lag structure to estimate the parameters of the (un)employment equation. This procedure not only has a potentially spurious regressive problem if the data tends to be non-stationary but also has the disadvantage of losing valuable information because all variables require prior de-trending. Moreover, they mainly focused on European countries and only consider one-way impact in the overall period. In their studies, the reverse effect between exchange-rate uncertainty and (un)employment was not taken into account.

The market structures in developing countries are different from those in developed European countries. The regimes of developing countries are also different from the European Union. Commodities, capital, services, and personnel can circulate among the member nations in the European Union. However, same thing cannot be done in other developing countries which may generate different effects on exchange-rate fluctuation and employment. This paper focuses on two developing countries, South Korea and Taiwan. Since the rapid industrialization and accelerative exports existed in South Korea and Taiwan, both countries repulsed the competition from other developing countries, even resisted the rise of protectionist’s pressures in the developed countries. In economic cooperation between both countries, Taiwan is South Korea’s 5th largest export partner and 8th import partner, and South Korea is Taiwan’s 6th largest export partner and 4th import partner. Thus, South Korea and Taiwan both not only have a high degree of openness to the world market, but also are mutually important trade partners. It is important to Taiwan and South Korea to understand the long- and short-run effect between exchange-rate uncertainty and employment.

Shu-Chen Chang / International Economics 125 (2011), p. 65-82 67

Toward this goal, this paper investigates the relationship between exchange-rate uncertainty and unemployment using two different uncertainty measures: (1) the moving average standard deviation around the predicted value of exchange rate and, (2) the general autoregressive conditional heteroskedusticity (GARCH). The advantage of using the first measure is to avoid the distinction between the predictable and unpredictable values in exchange rate and include a long-run component. The advantage of using the second measure is to generate time-varying variance in exchange rate through a parametric model. Furthermore, this paper compares the results generated from the two different measures for exchange-rate uncertainty.

2. The economeTric meThodology

2.1. Conceptual framework The short-run spikes of exchange-rate uncertainty may have a strong negative impact on investment and job creation due to set-up costs including accessing, capital, hiring, and firing costs. Thus, possible volatility might have impacts on investment and employment. One reason why employment is affected by exchange-rate volatility stems from the assumption that firms can adjust one or more factors of production in response to movements in exchange rates. The effect of such volatility for firms depends on sunk costs including the costs of hiring new labor or firing labor (these costs cannot be regained).

Belke and Göcke (2001) extended Dixit’s (1994) model and used ‘job creation’ instead of ‘investment’ to model the labor market explicitly. The exchange rate et is defined as the home currency price for foreign exchange. An exchange rate ec is assumed as a variable cost, abstracting from hiring, firing, market entry, and market exit costs. For a situation with certainty, a devaluation of home currency (i.e. an increase of et ) above the variable cost rate ec will induce a market entry and lead to some hiring of new staffs. On the contrary, a revaluation below ec will trigger a market exit and cause some firing by firms, as shown in Figure 1.

However, movements in exchange rate may generate uncertainty and cause a waiting state for firms. Under situation with uncertainty, firms would tend to be in favour of a “wait and see” strategy (Pindyck, 1991, p. 1111) since the firm’s employment decision involves considering sunk and variable costs. In considering a non-recurring single stochastic change in the exchange rate, the entry- (or exit-) trigger value under uncertainty is added (or subtracted)

by i1 2f

+^ h.2 Thus, firms stay passive to avoid future losses if exchange rate will be

unfavorable and enter the market if exchange rate will be favorable.

2. i is an interest rate.


Figure 1 – The relation between activity of an exporting firm and exchange rate

State of activity of an exporting �rm

Active

Inactive0 (Exit rate) (Entry rate) Exchange rate

(Variable unit costs)

Band of incationunder uncertainty

ec

etb a

Source from: Belke, Ansgar and Göcke, Matthias (2001).

Thus, uncertainty in exchange rate may influence economic agents in their behavior of employment decisions. This paper tries to develop a structural model based on the idea that uncertainty of future earnings may influence employment in the labor market. Such a model will provide us with some empirical estimates to determine whether an increase in uncertainty can generate an impact on employment.

As described in the above discussion, this paper assumes constant exit rates from the labor force as well as rigidities of wages and product prices in South Korea and Taiwan. In addition, a firm’s decision to employ labor mainly depends on the volatility of exchange rate because it is high relative to the volatility of relative price levels. This paper extends the approaches of previous studies (Belke and Göcke, 2001; Belke and Kaas, 2004; Belke, 2005) which examine the long-run equilibrium unemployment function to investigate unemployment and volatility of exchange rate. Thus, the long-run equilibrium unemployment function can be written as

L h Lt t i t i t

i

n

0 1

1

a a b y= + + +−

=

/ (1)

Note that Lt denotes unemployment; ht denotes exchange-rate uncertainty; 1a is a coefficient of exchange-rate uncertainty which means the effect of exchange-rate uncertainty on unemployment; ib are coefficients of the lagged values of unemployment; ty represents a vector of random errors with zero mean and finite variance.

However, according to the argument in previous studies (Edwards and Ostry, 1990; Hazari, Jayasuriya and Sgro, 1991; Kim, 1999), unemployment or unemployment news may cause exchange-rate uncertainty. Therefore, Equation (1) is not sufficient to incorporate the feedback between unemployment and exchange-rate uncertainty. This paper uses a structural form of simultaneous equation system to incorporate the feedback between these two variables


so that exchange-rate uncertainty and unemployment are allowed to affect each other. In addition, this structural form includes a structural break within sample period.

2.2. Empirical model and methodologyThe structural form of simultaneous equation system for exchange-rate uncertainty and unemployment can be written as a vector autoregressive (VAR) model in matrix representation as follows:

...X X X Dt t p t p t t1 1 x yW H H= + + + + +− − ; 1,...,t N= (2)

XLhtt

t= = G a

a1

2W = = G i

i

i

i

i

1

2

1

2

j

j

i

iH = = G and t

t

t

1

2

yy

y= = G

Note that W and , ...i p1iH =^ h are vector autoregression parameters, and ty represents a vector of random errors with zero mean and finite variance. Thus, this paper puts a specific consideration for the East Asian financial crisis by using a dummy variable Dt which takes the value of one during 1997:Q3 to 1998:Q2 and zero otherwise. x represents a structural effect of East Asian financial crisis.

By subtracting Xt 1− from each side of Equation (2) and then rearranging terms, a equilibrium model can be obtained as follows:

...X X X X Dt t p t p t t t1 1 1 1 1 x fD U C D C D P= + + + + + +− − − + − ; 1,...,t N= (3)

where XLhtt

t= = G a

a1

2U = = G

bb

cci

i

i

i

i

1

2

1

2C = = G and t

t

t

1

2

ff

f= = G

Note that D is first difference operator. U and , ...i p1iC =^ h are vector autoregression parameters to be estimated and tf represents random errors with zero mean and finite variance. Matrix P , which contains the long-run parameters and may potentially be reduced to rank c , can be written as 'abP = , where 'b represents the vector of cointegrating parameters and a is the vector of error-correction coefficients measuring the speed of convergence to the long-run steady state. The appropriate lag length (i.e., p) is selected based on the Schwarz information criterion.

This paper applies the cointegration measure to Equation (3) and examines whether there is a long-run equilibrium relationship between exchange-rate uncertainty and unemployment. Exchange-rate uncertainty and unemployment are regarded as having a long-run equilibrium relationship if they move close together in the long-run, even through they may drift apart in the short-run. If the structural form is to return long-run equilibrium, the movements of these two variables must respond to the deviation from the long-run relationship. Before testing the long-run relationship between these two variables, it is necessary to test time series properties of the variables. Some unit root tests such as Augmented Dickey-Fuller (ADF) test and Dickey-Fuller generalized least square are used to test variables’ stationary.


Assume that the two variables are non-stationary and integrated of the same order. Then Equation (3) can be estimated by the cointegration methodology suggested by Johansen (1988), and Johansen and Juselius (1990) to determine the presence of cointegrating vectors or long-run relations. Johansen’s (1988) and Johansen and Juselius’s (1990) approach provided two statistics, tracem and maxm , to test the rank for P , where

T n1 1trace i

i r

n

1

m c m=− −= +

|^ ^h h/ ; , 1T n1 1max r im c c m+ =− − +|^ ^h h (4)

and im| are the eigenvalues which can be obtained from the estimate of P . T is the number

of usable observations. On one hand, tracem tests the null hypothesis (i.e. there are at most c cointegrating vectors) against the alternative hypothesis (i.e. the number of cointegrating vectors is greater than c ). On the other hand, maxm tests the null hypothesis (i.e. the number of cointegrating vectors is c ) against the alternative hypothesis (i.e. the number of cointegrating vectors is 1c + ). Critical values for maxm and tracem statistics are provided in Osterwald-Lenum (1992).

Error-correction model (ECM) is appropriate to constitute the short-run effect as the presence of a cointegrating relationship. The ECM for exchange-rate uncertainty and unemployment can be described as follows

L L h EC Dt i t i i t i t t t

i

n

i

n

1

11

m x fD U C D W D= + + + + +− − −

==

// (5)

h L h EC Dt i t i i t i t t t

i

n

i

n

1

11

m x fD U C D W D= + + + + +− − −

==

// (6)

where ECt 1− is the error-correction term generated. m denotes the adjustment parameter, which describes the interrelationship in the short-run. The presence of ECt 1− in Equations (5) and (6) reflects whether actual exchange-rate uncertainty and unemployment adjust instantaneously to their long-run determinants.

2.3. How to measure exchange-rate uncertaintyThis paper uses two different measures for exchange-rate uncertainty. The first measure is the moving-average standard deviation of exchange-rate growth (Arize and Shwiff, 1998; Arize et al., 2000; Arize et al., 2003) and the second one is Generalised Autoregressive Conditional Heteroskedasticity (GARCH) suggested by Bollerslev (1986). The advantage of the first measure for exchange-rate uncertainty is that it can include a long-run component. For this reason, we construct a similar measure in our estimations. The exchange-rate uncertainty is calculated as an eight-term moving average standard deviation around the predicted values of the exchange rate and is defined as:

h e e81

t t i t i

i

1 1 2

1

8 221

#= −+ − + −

=

t^e h o/ (7)

where e is the natural logarithm of exchange rate; et represents the predicted values of exchange rate. h t1 represents as exchange-rate uncertainty.


The second measure used GARCH (1,1) to compute each country’s exchange-rate uncertainty for representing exchange-rate uncertainty. The advantage of this measure is that it allows the variance of the unpredictable element3 to vary with time. GARCH (1,1) model is represented as follows

e e Dt n t i t t

i

n

0 1

1

a a b fD D= + + +−

=

/ (8)

h ht t t2 0 1 12

2 2 1c c f c= + +− −

Note that h t2 is the one-period ahead forecast variance based on past information, so it is called the conditional variance of exchange rate; tf represents random errors with zero mean. This paper uses the value of h t2 as exchange-rate uncertainty. Dt is a dummy variable which can be used to remove possible structural break-influence on the conditional mean. This dummy variable takes the value of one during 1997:Q3 to 1998:Q2 and zero otherwise. The GARCH (1,1) model is estimated by the following log likelihood function:

(2 )ln lnH T h h2 21

21

tt

tt

T

t

T

2

2

211

r f= − − −==

b l// (9)

Both of the above two measures were utilized for Taiwan and South Korea to determine whether the relationship between exchange-rate uncertainty and unemployment are consistent across these two measures.

3. esTimaTed resulTs

3.1. Data description and analysisThe empirical analysis here employs quarterly data for the nominal exchange rate and the unemployment in two developing Asian countries, South Korea and Taiwan. The nominal exchange rate denotes domestic currency against U.S. dollars. The sample period adopts quarterly time series data in the period from 1984: Q1 to 2004: Q3 in South Korea and the period from 1984: Q1 to 2004: Q3 in Taiwan because of availability of data. These quarterly data were obtained from AREMOS Statistical Data Bank of the Ministry of Education in Taiwan.

3. The unpredictable element is the lagged squared random error.


These variables are adjusted seasonally because the time series of quarterly frequencies exhibit cyclical movements that recur every quarter. For seasonal adjustment procedures, this paper adopts a X-11 procedure to remove cyclical seasonal movements from a series and extract the underlying trend component of the series. All data with seasonal adjustments are transformed to logarithmic form to achieve stationary in variance. The statistic descriptions for unemployment and exchange-rate uncertainty are listed in table 1.

Table 1 – Statistical description of main variables

Description Country Observations Mean Standard deviation

Minimum Maximum

Lt Unemployment rate (Unit:%)

Taiwan--- 83 0.8706 0.4097 0.3104 1.6653

South Korea---

83 1.1448 0.3301 0.6642 2.1933

h t1 Uncertainty measure from a eight-term

moving average standard

deviation around the predicted

value of exchange rate

Taiwan--- 83 0.0226 0.0106 0.0073 0.0501

South Korea---

83 0.0459 0.0591 0.0070 0.2221

h t2 Uncertainty measure from GARCH (1,1)

model

Taiwan--- 83 0.0006 0.0006 0.0000 0.0045

South Korea---

83 0.0079 0.0271 0.0003 0.1687

The ADF test is often used to test unit-root in time series properties of the variables. However, it is unreliable because of its poor size and power properties. In other words, the ADF test tends to over-reject the null hypothesis when it is true and under-reject the null hypothesis when it is false (Dejong, Nankervis, and Savin, 1992). Elliot, Rothenberg, and Stock (1996) proposed the Dickey-Fuller generalized least square (DF-GLS) to solve the above problem. Therefore, in the unit root test, we employ the ADF test. We use the DF-GDLS test to determine whether the variables are stationary. Moreover, an appropriate lag length for each test is selected based on the Modified Akaike Information Criterion (MAIC) suggested by Ng and Perron (2001).


The null hypothesis for these procedures is that a unit root exists. The results of both procedures are summarized in table 2, which shows that, for the level of the variables, both test could not reject the null hypothesis at the 5% significance level except h t2 in Taiwan. However, with first-difference, each variable clearly rejects the null hypothesis at the same level. All variables should be integrated of the same order due to conducting cointegration test.4 Thus, all variables are integrated of order one through these unit-root tests.

Table 2 – Results of unit-root test

ADF DF-GLS

Lt h t1 h t2 Lt h t1 h t2

Level:

Taiwan 1.7398 –2.4325 –4.5598 * –1.2680 –2.4688 –4.4353 *

South Korea –2.4229 –2.2065 –3.3001 –2.2130 –2.2619 –3.3047

Difference:

Taiwan –4.3282 * –3.5902 * –10.7385 * –4.1907 * –3.5922 * –10.8649 *

South Korea –5.1706 * –4.8593 * –8.0141 * –5.2307 * –4.9170 * –8.1142 *Notes: The results show a constant and linear trend. h t1 is calculated by a moving average standard deviation of exchange rate. h t2 is calculated by a GARCH (1,1) model. The ADF and DF-GLS critical values are adopted from MacKinnon (1996) and Elliott, Rothenberg, and Stock (1996), respectively. Notation ‘*’ denotes significance at the 5% levels.

3.2. Long-run effect Since two variables are integrated of order one, it would be appropriate to examine the existence of linear combinations of integrated variables that are stationary. If the cointegration exists, the correct specification for the model would be the error-correction term. In Equation (3), the error-correction term contains essential information about the long-run relationship between exchange-rate uncertainty and unemployment. This paper uses the approach proposed in Johansen (1988) and Johansen and Juselius’s (1990) to test for the presence or absence of long-run equilibrium in Equation (3). The cointegration results for exchange-rate uncertainty using two measures are reported in table 3, where r denotes the number of cointegrating vectors.

4. According to Dressler’s (2004) study, Engle and Granger’s (1987) method for determining long-run and short-run impact fails if the series considered are not integrated of the same order.


Table 3 – Estimated results of cointegration test

Using moving average standard deviation

Country tracem maxm

: 0H r0 = : 1H r0 # : 0H r0 = : 1H r0 #

: 1H r1 $ : 2H r1 $ : 1H r1 = : 2H r1 =Taiwan 18.4233 * 0.9252 17.4981 * 0.9252

South Korea 53.2721 * 8.1908 45.0813 * 8.1908

Using GARCH (1,1) model

tracem maxm

: 0H r0 = : 1H r0 # : 0H r0 = : 1H r0 #

: 1H r1 $ : 2H r1 $ : 1H r1 = : 2H r1 =Taiwan 19.3173 * 0.0404 19.2768 * 0.0404

South Korea 26.0492 * 5.9759 20.0733 * 5.9759Notes: The critical values of maxm and tracem are adopted from Osterwald-Lenum (1992). Notation ‘*’ denotes significance at the 5% levels. For uncertainty obtained from GARCH (1,1) model, the lag lengths of cointegration test are two and one in Taiwan and South Korea, respectively. For moving average standard deviation, the lag lengths of cointegration test are six and four in Taiwan and South Korea, respectively.

table 3 report the cointegration results using the eight-term moving-average standard deviation of exchange-rate growth and GARCH (1,1), respectively, for measuring exchange-rate uncertainty. For the maxm test results in table 3, in two different measures of uncertainty, the null hypotheses

0r = are rejected at the 5% significance level in favor of 1r = in Taiwan and South Korea. Furthermore, in these different measures of uncertainty, the null hypotheses 1r # cannot be rejected at the same level in favor of 2r = . For the tracem test results in of same table, we obtain similar conclusions because the null hypothesis 0r = is rejected in each country. These findings indicate that a long-run equilibrium relationship between unemployment and exchange-rate uncertainty in two different measures of uncertainty exists in South Korea and Taiwan.

3.3. Robustness and weak exogeneity test for the modelTo increase validness for the estimated results, a diagnostic check on the residuals using two measures for exchange-rate uncertainty is performed and the result is shown in table 4. This table shows that the residuals in two different measures for exchange-rate uncertainty are white noise since the LM test on the first and twelfth order autocorrelation could not reject the null hypothesis of white noise residuals at the 5% significance level. In addition, the residual heteroskedasticity test statistics in two different measures for exchange-rate uncertainty could not reject the null hypothesis of no heteroskedasticity either, indicating that there is no evidence of residual heteroscedasticity effects in the system. So the systems for two countries are free from serial correlation and heteroscedasticity in two different measures for exchange-rate uncertainty.


Table 4 – Residual diagnostics of autoregressive models

Uncertainty measure from moving average standard deviation

Residual serial correlation test Residual heteroskedasticity test

LM (1) LM (12) ARCH (2)

Taiwan 0.5990 (0.4389) 16.1848 (0.1829) 3.3931 (0.1833)

South Korea 0.0043 (0.9476) 9.7917 (0.6342) 1.5551 (0.4595)

Uncertainty measure from GARCH (1,1) model

Residual serial correlation test Residual heteroskedasticity test

LM (1) LM (12) ARCH (2)

Taiwan 0.2162 (0.6419) 14.4135 (0.2750) 4.3564 (0.1132)

South Korea 0.9017 (0.3423) 8.9722 (0.7053) 4.5921 (0.1006)

Notes: Residual serial correlation and heteroskedasticity tests are estimated by the Breusch-Godfrey Lagrange Multiplier test (i.e., LM test) and the autoregressive conditional heteroskedasticity test, respectively. The data inside parentheses of LM and ARCH are lagged numbers.

Before analyzing the short-run effect from the error-correction model (ECM), the weak exogeneity test is performed by imposing a linear restriction on the adjustment coefficient. Given a single cointegrating vector, the weak exogeneity test is conducted by likelihood ratio statistics. The statistical test will be distributed as Chi-squared with one degree of freedom. The results of weak exogeneity are reported in table 5. In this table, the null hypotheses of weak exogeneity for unemployment and exchange-rate uncertainty are both rejected at the 1% significance level, implying that that unemployment and exchange-rate uncertainty are not weak exogeneity. This finding suggests that each of these variables has a long-run relationship.

Table 5 – Weak exogeneity test

South Korea Taiwan

Uncertainty measure from moving average standard deviation

Cointegration restrictions

B(1,1)=1, B(1,2)=0, A(1,1)=0

B(1,1)=1, B(1,2)=0, A(2,1)=0

B(1,1)=1, B(1,2)=0, A(1,1)=0

B(1,1)=1, B(1,2)=0, A(2,1)=0

LR test for weak exogeneity restriction (rank = 1):

Chi-square (1) 14.233 16.061 19.090 19.025

Probability 0.000 0.000 0.000 0.000

Uncertainty measure from GARCH (1,1) model

Cointegration restrictions

B(1,1)=1, B(1,2)=0, A(1,1)=0

B(1,1)=1, B(1,2)=0, A(2,1)=0

B(1,1)=1, B(1,2)=0, A(1,1)=0

B(1,1)=1, B(1,2)=0, A(2,1)=0

LR test for weak exogeneity restriction (rank = 1):

Chi-square (1) 36.430 43.356 17.497 16.569

Probability 0.000 0.000 0.000 0.000


3.4. Short-run effectSince exchange-rate uncertainty and unemployment are not weak exogeneity, a process in the short-run can be described by using two ECMs with a single cointegrating vector. This paper has the following three observations regarding two ECM estimated results as listed in table 6. First, we would like to know whether one-lagged error-correction term (ECt 1− ) shock is significant and how its coefficient influences unemployment and exchange-rate uncertainty. When the uncertainty measure was generated from moving average standard deviation, we found that the effect of one-lagged error-correction term (ECt 1− ) shock on current unemployment in Taiwan is –0.0197 and significant at the 5% significance level, and the same shock in South Korea is 0.0097 but this shock is insignificant at the same level. The effects of ECt 1− shock on current exchange-rate uncertainty in Taiwan and South Korea are 0.0027 and 0.0084, respectively, and these shocks are both significant at the 5% significance level. On the other hand, when the uncertainty measure was generated from GARCH (1,1) model, we found that the effect of ECt 1− on current unemployment in Taiwan and South Korea are –0.0004 and –0.0101, respectively, but these shocks are insignificant at the 5% significance level. The effect of ECt 1− on current exchange-rate uncertainty in Taiwan and South Korea are 0.0001 and 0.0120, respectively, and both shocks are significant at the 5% significance level. Hence, our evidence from two exchange-rate uncertainties shows that exchange-rate uncertainty in both South Korea and Taiwan is corrected (via the ECM) in order to get closer to the long-run equilibrium if there is a short-run disequilibrium, while the other is not. However, unemployment is corrected to get closer to the long-run equilibrium only in an uncertainty measure which was generated from GARCH (1,1) model.

Second, we want to know whether the effect of lagged unemployment shock is significant and how its shock influences unemployment and exchange-rate uncertainty. In same table, when the uncertainty measure was generated from moving average standard deviation, the effect of the lagged unemployment shock on current unemployment and exchange-rate uncertainty is significantly positive in the first and fourth lags at the 5% significance level in South Korea. Although this lagged effect on current exchange-rate uncertainty in the third lag is found to be –0.0812 and significant at the same level in the same country, the influence is relatively smaller than that in the first and fourth lags. In addition, for Taiwan, the effect of the lagged unemployment shock is not only significantly positive on current unemployment in the first lag at the 5% significance level, but also significantly negative on current exchange-rate uncertainty in the second lag at the same level. However, this lagged effect on exchange-rate uncertainty is as small as in South Korea.


Table 6 – Estimated results of two ECMs

South Korea Taiwan South Korea Taiwan

LtD h t1D LtD h t1D LtD h t2D LtD h t2D

ECt 1− 0.0097[ 1.2219]

0.0084 *[ 4.4616]

–0.0197 *[–2.1249]

0.0027 *[ 3.4948]

–0.0101[–0.9108]

0.0120 *[ 6.3387]

–0.0004[–0.3135]

0.0001 *[ 4.2230]

Lt 1D − 0.3680 *[ 3.2931]

0.1046 *[ 3.9731]

0.3266 *[ 2.8081]

–0.0048[–0.4877]

0.2588 *[ 2.3198]

0.0509 *[ 2.6774]

0.3549 *[ 3.1143]

–0.0018 *[–1.9145]

Lt 2D − –0.0965[–0.7982]

–0.0070[–0.2474]

0.1762[ 1.4789]

–0.0231 *[–2.2916]

0.1569[ 1.3532]

0.0002[ 0.1533]

Lt 3D − –0.0860[–0.7057]

–0.0812 *[–2.8271]

0.0510[ 0.4148]

0.0062[ 0.5940]

Lt 4D − 0.2193 *[ 2.0521]

0.1528 *[ 6.0691]

–0.0240[–0.1938]

–0.0051[–0.4915]

Lt 5D − 0.0979[ 0.8129]

–0.0084[–0.8231]

Lt 6D − –0.0917[–0.7791]

–0.0058[–0.5856]

h ht t1 1 2 1D D− −^ h 0.6068[ 1.4373]

–0.1959 *[–1.9699]

0.2738[ 0.1936]

–0.0364[–0.3048]

1.6182 *[ 2.7693]

0.0231[ 0.2322]

14.0874[ 0.9834]

0.0480[ 0.3979]

h ht t1 2 2 2D D− −^ h 0.5795[ 1.3407]

0.0704[ 0.6914]

0.1293[ 0.0966]

0.0423[ 0.3740]

–5.1738[–0.3838]

0.0715[ 0.6302]

h ht t1 3 2 3D D− −^ h –0.1126[–0.2614]

0.0232[ 0.2288]

–0.7197[–0.5388]

0.0665[ 0.5888]

h ht t1 4 2 4D D− −^ h –1.2408 *[–3.0518]

–0.0837[–0.8740]

–1.0745[–0.8172]

0.1513[ 1.3608]

h ht t1 5 2 5D D− −^ h –3.8438 *[–2.9108]

0.1917 *[ 1.7169]

h ht t1 6 2 6D D− −^ h –1.8722[–1.3419]

0.0905[ 0.7669]

C 0.0054[ 0.7500]

0.0000[–0.0316]

0.0040[ 0.5486]

0.0000[ 0.0014]

Dt 0.1847 *[ 4.0866]

0.0471 *[ 4.4273]

–0.0491[–1.3957]

0.0092 *[ 3.1055]

0.1295 *[ 2.6881]

0.0522 *[ 6.3612]

0.0022[ 0.0656]

0.0002[ 0.8410]

R squared− 0.5386 0.5909 0.3604 0.3359 0.4688 0.4716 0.1956 0.2678

Notation ‘*’ denotes significance at the 10% levels. The values in brackets are the t-statistics values. h t1 represents as exchange-rate uncertainty which is generated by using approach of moving-average standard deviation. h t2 represents as exchange-rate uncertainty which is generated by using approach of GARCH(1,1) model.

On the other hand, when the uncertainty was generated from GARCH (1,1) model, among the effects of the lagged unemployment shock on current unemployment and exchange-rate uncertainty, the effects in the first lag shock are 0.0509 and 0.2588, respectively, and these effects are significant at the 5% significance level in South Korea. In addition, for Taiwan, among the effect of the lagged unemployment shock on current unemployment


(exchange-rate uncertainty), the effect of the firs lag shock is 0.3549 (–0.0018) and this shocks is significant at the 10% significance level. Although this lagged effect on exchange-rate uncertainty has a significant impact in Taiwan, it is relatively smaller than that in South Korea when uncertainty generated from GARCH(1,1) model. The results in South Korea are consistent with the previous study that unemployment news has a positive impact on exchange-rate uncertainty (Kim, 1999).

We also want to know whether the effect of lagged exchange-rate uncertainty shock is significant and how its shock influences unemployment and exchange-rate uncertainty. In same table, when the uncertainty was generated from moving average standard deviation, the effect of the lagged exchange-rate uncertainty shock on current unemployment (exchange-rate uncertainty) is –1.2408 (–0.1959) in the fourth (first) lag, and this effect is significant at the 5% significance level in South Korea. Furthermore, for Taiwan, the effect of the lagged exchange-rate uncertainty shock on current unemployment (exchange-rate uncertainty) is –3.8438 (0.1917) in the fifth lag, and this effect is significant at the 10% significance level. The results obtained from Taiwan and South Korea do not confirm the findings of previous studies (Buscher and Mueller, 1999; Belke and Gros, 2002; Belk, 2005) that exchange-rate uncertainty has a positive impact on unemployment, when the uncertainty measure was generated from moving average standard deviation.

On the other hand, when the uncertainty was generated from GARCH (1,1) model, the effect of the lagged exchange-rate uncertainty shock on current unemployment is 1.6182 in the first lag, and this effect is significant at the 5% significance level in South Korea. However, for Taiwan, the effects of the lagged exchange-rate uncertainty shocks on current unemployment and exchange-rate uncertainty are insignificantly at the 5% significance level and in Taiwan. These results in South Korea confirm the findings of previous studies that exchange-rate uncertainty increases unemployment while none of previous studies used the conditional variance approach to estimate the effect.

Finally, we want to find out whether the effect of the structural shock is significant on unemployment and exchange-rate uncertainty. In table 5, when the uncertainty was generated from moving average standard deviation, the effect of the structural shock on current exchange-rate uncertainty is 0.0092, and this effect is significant at the 5% significance level in Taiwan. However, this shock is significant not only on current unemployment, but also on exchange-rate uncertainty at the same level in South Korea. When the uncertainty was generated from GARCH (1,1) model, the effects of the structural shock on current unemployment and exchange-rate uncertainty are 0.0522 and 0.1295, respectively, and these effect are significant at the 5% significance level for South Korea. However, the effect of structural shock on current unemployment and exchange-rate uncertainty is not found in Taiwan.


3.5. Economic and policy implications The result obtained from GARCH (1,1) measure for uncertainty implies that a rise in exchange-rate uncertainty leads to an increase in unemployment in South Korea. Furthermore, a rise in unemployment will lead to an increase in exchange-rate uncertainty in Taiwan and South Korea. Such a result also has the following important economic and policy implications: (1) persistent overshooting effects of exchange-rate uncertainty on unemployment exist only in South Korea; (2) persistent overshooting effects of unemployment on exchange-rate uncertainty exist in both South Korea and Taiwan; (3) the uncertainty of exchange rate should be taken into account when policymakers formulate a short-run labor policy in South Korea. In addition, if the policymakers in South Korea and Taiwan try to formulate a short-run macroeconomic policy to stabilize the exchange-rate uncertainty, the fluctuation in unemployment should be considered.

4. conclusion

The following conclusions can be drawn from our empirical tests. First, exchange-rate uncertainty and unemployment in South Korea and Taiwan are type I (1) series from the unit-root test. Second, we found that, in two different measures for exchange-rate uncertainty, there is a statistically significant long-run relationship between exchange-rate uncertainty and unemployment in Taiwan and South Korea. Third, the results obtained from our error-correction model show that the lagged unemployment has a significant positive (negative) short-run impact on exchange-rate uncertainty in South Korea (Taiwan) no matter which measure of uncertainty is used. However, this effect in Taiwan is relatively smaller than that South Korea. The different finding between South Korea and Taiwan is because exchange-rate policy in Taiwan is managed float of exchange rate so that volatility of exchange rate is often interfered by the government. Consequently, the spread of uncertainty of exchange rate in Taiwan is smaller than that in South Korea. In addition, the lagged exchange-rate uncertainty has a significantly negative short-run impact on unemployment in South Korea and Taiwan when the uncertainty was generated from moving average standard deviation. On the contrary, when the uncertainty was generated from GARCH (1,1), the lagged exchange-rate uncertainty has a significantly positive short-run impact on unemployment in South Korea. The finding from the moving average standard deviation is conflict with the finding from GARCH (1,1) measure of uncertainty. The reason why the results are different is because exchange-rate uncertainty using GARCH (1,1) measure allows the variance to be a function of the previous forecast errors. In addition, previous studies pointed out that the moving average standard deviation procedure lacks a parametric model for time-varying variance of exchange rate. Therefore, the finding from GARCH (1,1) measure for uncertainty is more appropriate to explain the relationship between exchange-rate uncertainty and unemployment as compared to the finding obtained from the moving average standard deviation.


references

Aftab, Z., Aurangzeb, A. 2002. The long-run and short-run impact of exchange-rate devaluation on Pakistan’s trade performance, Pakistan Development Review, 41 (3), 277-286.

Aurangzeb, A., Stengos, T., Asif, A.U. 2005. Short-run and long-run effects of exchange-rate volatility on the volume of exports: “a“ case study for Pakistan, International Journal of Business and Economics, 4 (3), 209-222.

Apergis, N. 2000. Employment news and exchange-rates: Policy implications for the European Union, Applied Economics Letters, 7 (10), 695-698.

Arize, A.C. 1995.The effects of exchange-rate volatility on U.S. Exports: An empirical investigation, Southern Economic Journal, 62 (2), 34-43.

Arize, A.C., Shwiff, S. 1998. Does exchange-rate volatility affect import flows in G-7 countries. Evidence from cointegration models, Applied Economics, 30 (10), 1269-1276.

Arize, A.C., Osang, T., Slottje, D.J. 2000. Exchange-rate volatility and foreign trade: Evidence from thirteen LDC’s, Journal of Business and Economic Statistics, 18 (1), 10-17.

Arize, A.C., Malindretos, J., Kasibhatla, K.M. 2003. Does exchange-rate volatility depress export flows: The case of LDCs, International Advances in Economics Research, 9, 7-19.

Belke, A., Göcke, M. 2001. Exchange-rate uncertainty and ‘Play’-nonlinearity in aggregate employment, International Advances in Economic Research, 7 (1), 38-50.

Belke, A., Gros, D. 2002. Designing EU–US Atlantic monetary relations: Exchange-rate variability and labour markets, The World Economy, 25 (6), 789-813.

Belke, A., Kaas, L. 2004. The impact of exchange-rate volatility on labor markets: Europe versus United States, Journal of Economic Asymmetries, 1 (2), 21-50.

Belke, A., Setzer, R. 2003. Exchange-rate variability and labour market performance in the Visegràd countries, Economics of Planning, 36, 153-175.

Belke, A., Setzer, R. 2004. Exchange-rate volatility and employment growth: Empirical evidence from the CEE economies, IZA Discussion Papers 1038, IZA, Bonn.

Belke, A. 2005. Exchange-rate movements and unemployment in the EU accession countries. Review of Development Economics, 9 (2), 249-263.

Buscher, H.S., Mueller, C. 1999. Exchange-rate volatility effects on the German labour market: A survey of recent results and extensions, IZA Working Paper No. 37, Bonn.

Bollerslev, T. 1986. Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 307-327.

Cho, G., Sheldon, I.M., McCorriston, S. 2002. Exchange rate uncertainty and agricultural trade, American Journal of Agricultural Economics, 84 (4), 931-42.


De Grauwe, P. 1988. Exchange-rate variability and the slowdown in growth of international trade, IMF Staff Papers, 35, 63-84.

Dejong, D.N., Nankervis, J.C., Savin, N.E. 1992. Integration versus trend stationarity in time series, Econometrica, 60, 423-433.

Dixit, A., Pindyck, R.S. 1994, Investment under Uncertainty, Princeton, NY.

Doanlar, M. 2002. Estimating the impact of exchange-rate volatility on exports: Evidence from Asian countries, Applied Economics Letters, 9 (13), 859-863.

Demir, F. 2010. Exchange-rate volatility and employment growth in developing countries: Evidence from Turkey, World Development, 38 (8), 1127-1140.

Dressler, W. 2004. Cognitive anthropology, 139-140 in The Encyclopedia of Social Science Research Methods. Michael Lewis-Beck, Alan Bryman and Tim Futing Liao, Eds. London: Sage Publications.

Edwards, S., Ostry, J.D. 1990. Anticipated protectionist policies, real exchange-rate and the current account: The case of rigid wages, Journal of International Money and Finance, 9, 206-219.

Elliot, G., Rothenberg, T.J., Stock, J.H. 1996. Efficient tests for an autoregressive unit root, Econometrica, 64, 813-836.

Engle, R.F., Granger, C.W.J. 1987. Cointegration and error correction: Representation, estimation, and testing, Econometrica, 55 (2), 251-276.

Hau, H. 2002. Real exchange rate volatility and economic openness: Theory and evidence, Journal of Money, Credit and Banking, 34 (3), 611-630.

Hazari, B.R., Jayasuriya, S., Sgro, P.M. 1991. Tariff, term of trade, unemployment and the real exchange rate, Southern Economic Journal, 721-731.

Johansen, S. 1988. Statistical analysis of cointegration vectors, Journal of Economic Dynamics and Control, 12, 231-254.

Johansen, S., Juselius, K. 1990. Maximum likelihood estimation and inferences on cointegration - with applications to the demand for money, Oxford Bulletin of Economics and Statistics, 52, 169-210.

Kanas, A. 2002. Is exchange-rate volatility influenced by stock return volatility? Evidence from the US, the UK and Japan, Applied Economics Letters, 9 (8), 501-503.

Kim, Suk, J. 1999. Do macro-economic news announcements affect the volatility of foreign exchange rate? Some evidence from Australia, Applied Economics, 31, 1511-1521.

Mackinnon, J.G. 1996. Numerical distribution functions for unit-root and cointegration tests, Journal of Applied Econometrics, 11 (6), 601-618.

McKenzie, M.D. 1999. The impact of exchange rate volatility on international trade flows, Journal of Economic Surveys, 13 (1), 71-106.


Ng, S., Perron, P. 2001. Lag length selection and the construction of unit root test with good size and power, Econometrica, 69, 1519-1554.

Osterwald-Lenum, M.1992. A note with quartiles of the asymptotic distributions of the maximum likelihood cointegration rank test statistics, Oxford Bulletin of Economics and Statistics, 54, 461-472.

Pindyck, R.S. 1991. Irreversibility, uncertainty and investment, Journal of Economic Literature, 29, 1110-1148.

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