KDI Journal of Economic Policy, 2009 II

Vol.31 No.2 / ISSN 1738-656x 韓國開發硏究 KDI Journal of Economic Policy 2009 II Testing for Nonlinear Threshold Cointegration in the Monetary Model of Exchange Rates with a Century of Data... Junsoo Lee Mark C. Strazicich 화페모형에의한환율결정이론의비선형문턱공적분검정 : 100 년간자료를중심으로 이자율기간구조를이용한정책금리변경의효과분석... 송준혁 Analyzing the Effect of Changes in the Benchmark Policy Interest Rate Using a Term Structure Model 한국경기변동의특징및안정성에대한연구... 이재준 Changes in the Business Cycle of the Korean Economy: Evidence and Explanations 직 간접네트워크외부성하에서인터넷포털기업의시장력분석... 진양수 Market Power of Internet Portals with Direct and Indirect Network Externality: 금연법강화가흡연에미치는영향... 김범수 김아람 The Impacts of Smoking Bans on Smoking in Korea 외환위기이후흉악범죄의증가와정부의범죄억지정책... 김두얼 김지은 Growth of Felonies after the 1997 Financial Crisis in Korea 우리나라대중국수출에서의수출집약도및다양도의역할... 이시욱 The Role of Extensive and Intensive Margins in Korean Exports to China 우리나라수출가격에대한환율전가율변화... 이항용 김현욱 Declines in Exchange Rate Pass-through to Export Prices in Korea

韓國開發硏究 KDI Journal of Economic Policy Contents 1 Testing for Nonlinear Threshold Cointegration in the Monetary Model of Exchange Rates with a Century of Data... Junsoo LeeMark C. Strazicich / 1 : 100 이준수 마크스트래지시히 2 이자율기간구조를이용한정책금리변경의효과분석... 송준혁 / 15 Analyzing the Effect of Changes in the Benchmark Policy Interest Rate Using a Term Structure Model Joonhyuk Song 3 한국경기변동의특징및안정성에대한연구... 이재준 / 47 Changes in the Business Cycle of the Korean Economy: Evidence and Explanations Jaejoon Lee 4 직 간접네트워크외부성하에서인터넷포털기업의시장력분석... 진양수 / 87 Market Power of Internet Portals with Direct and Indirect Network Externality: Yangsoo Jin 5 금연법강화가흡연에미치는영향... 김범수 김아람 / 127 The Impacts of Smoking Bans on Smoking in Korea Beomsoo KimAhram Kim 6 외환위기이후흉악범죄의증가와정부의범죄억지정책... 김두얼 김지은 / 155 Growth of Felonies after the 1997 Financial Crisis in Korea Duol KimJee Eun Kim

韓國開發硏究 KDI Journal of Economic Policy Contents 7 우리나라대중국수출에서의수출집약도및다양도의역할... 이시욱 / 195 The Role of Extensive and Intensive Margins in Korean Exports to China Siwook Lee 8 우리나라수출가격에대한환율전가율변화... 이항용 김현욱 / 235 Declines in Exchange Rate Pass-through to Export Prices in Korea Hangyong LeeHyeon-Wook Kim

韓國開發硏究 제 31 권제 2 호 ( 통권제 105 호 ) Testing for Nonlinear Threshold Cointegration in the Monetary Model of Exchange Rates with a Century of Data Junsoo Lee (Professor and Rick and Elaine Horsley Faculty Excellence Fellow, Department of Economics, Finance and Legal Studies, University of Alabama) Mark C. Strazicich (Associate Professor, Department of Economics, Appalachian State University) 화폐모형에의한환율결정이론의비선형문턱공적분검정 : 100 년간자료를중심으로 이준수 ( 앨라배마대학교교수 ) 마크스트래지시히 ( 에팔레치안주립대부교수 ) * 이준수 : (e-mail) jlee@cba.ua.edu, (address) Department of Economic, Finance and Legal Studies, University of Alabama, Tuscaloosa, Alabama 35487-0224, USA 마크스트래지시히 : (e-mail) strazicichmc@appstate.edu, (address) Department of Economics, Appalachian State University, Boone, North Carolina 28608-2051, USA Key Word: Exchange Rates( 환율결정모형 ), Threshold Cointegration( 비선형공적분 ), Monetary Model( 화폐모형 ) JEL code: C22, F31 Received: 2009. 3. 25 Referee Process Started: 2009. 3. 30 Referee Reports Completed: 2009. 6. 16

ABSTRACT The monetary model suggests that nominal exchange rates between two countries will be determined by important macroeconomic variables. The existence of a cointegrating relationship among these fundamental variables is the backbone of the monetary model. In a recent paper, Rapach and Wohar (2002, Journal of International Economics) advance the literature by testing for linear cointegration in the monetary model using a century of data to increase power. They find evidence of cointegration in five or six of ten countries. We extend their work to the nonlinear framework by performing threshold cointegration tests that allow for asymmetric adjustments in two regimes. Asymmetric adjustments in exchange rates can occur, for example, if transactions costs are present or if policy makers react asymmetrically to changing fundamentals. Moreover, whereas Rapach and Wohar (2002) found it necessary to exclude the relative output variable in some cases to maintain the validity of their cointegration tests, we can include this variable as a stationary covariate to increase power. Overall, using their same long-span data, we find more support for cointegration in a nonlinear framework. 환율결정모형의근간이되는이론으로널리알려져온화폐모형은두국가간의환율이각국의통화량과소득수준에의해결정된다고설명하고있다. 그러나이이론이성립하려면이모형에내포된변수간에공적분이성립해야하는데, Rapach and Wohar(2002) 의논문은 10개국가의자료중대여섯개의자료에만 ( 선형 ) 공적분이존재한다는결과를제시하였다. 본논문은그들이사용한 100년간에걸친자료를사용하되, 환율결정과정에서발생할수있는비대칭적 조정과정을감안하여비선형공적분이성립하는가를검증하였다. 또한독립변수가불안정적이아닐경우에는공적분관계를설정하기곤란하다는이유로누락시키는경우가많은데본논문에서사용되는방법론에서는그러한문제가제기되지않는다. 본논문에서는선형공적분검정결과에비해더많은경우에있어서비선형공적분관계가있다는검정결과가산출되었다.

4 韓國開發硏究 / 2009. Ⅱ above papers. To perform our empirical tests, we first consider the ordinary least squares based autoregressive distributed lag (ADL-OLS) threshold cointegration test developed by Li and Lee (2008). We utilize two different threshold effects hypothesized to arise from asymmetric policy responses and/or transactions costs. In particular, we consider threshold models where adjustment to the long-run equilibrium can depend on the level or change in the deviations from the long-run equilibrium. Moreover, in some countries, the nominal exchange rate and the relative money supply series are each I(1) while the deviation in output series is I(0). While RW (2002) omit the output deviation variable in these cases, we want to include this variable as a stationary covariate in our cointegration tests to increase power. In these cases, we utilize the instrumental variables based autoregressive distributed lag (ADL-IV) threshold cointegration test as suggested in Enders, Im, Lee and Strazicich (2009). The ADL-IV threshold cointegration test is well suited to this task, since the test statistics are unaffected by including stationary covariates. 2 Our data set is the same as in RW and consists of over 100 years of annual data on nominal exchange rates (foreign currency per U.S. dollar), national money supplies relative to the U.S. money supply, and real GDPs relative to the U.S. real GDP for fourteen industrialized countries. 3 The nominal exchange rate series come from Taylor (2001). The money supply and real GDP data come from Bordo and Jonung (1998) and Bordo, Bergman, and Jonung (1998), respectively. The specific sample periods for each country are reported in our Tables below. Using a long-span data set has the distinct advantage of potentially more observations in each regime and greater power in inference tests. Overall, we find greater support for cointegration in a nonlinear framework as compared to the linear tests. Combining results, we reject the null of no cointegration (in at least one regime) in 8 of the 10 countries examined. These findings provide new support to the growing number of papers by Taylor and Peel (2000) and others who find more support for the monetary model in a nonlinear framework. The remainder of the paper proceeds as follows. In Section 2, we briefly describe the monetary model and our test methodology. In Section 3, we discuss our empirical findings. We summarize and conclude and Section 4. 2 The presence of stationary covariates can pose a problem in existing tests for nonlinear cointegration; see, for example, the papers by Bec, Ben Salem, and Carrasco (2004), Kapetanios, Shin, and Snell (2006), Kapetanios and Shin (2006), and Spagnolo, Psaradakis, and Sola (2005). In our analysis of the monetary model the output gap can be a stationary variable. While we want to include this fundamental variable in our cointegration test to increase power, including stationary covariates in these existing tests will induce a nuisance parameter problem that makes the test statistic dependent on the unknown parameter indicating the signal-noise ratio. As such, these tests are less suitable in our applications. In work not reported here, we additionally examined the monetary model by using the ECM-IV threshold cointegration test as suggested in Enders, Lee, and Strazicich (2007). While the ECM-IV test has similar features as the ADL-IV test, we obtain more rejections of the null using the ADL-IV tests so omit the ECM-IV test results in this paper. 3 We thank David Rapach for generously providing the data.

Testing for Nonlinear Threshold Cointegration in the Monetary Model of Exchange Rates with a century of Data 5 Ⅱ. Monetary Model and Testing for Threshold Cointegration The monetary model can be described by: e t = β 0 + β 1 (m t * m t ) + β 2 (y t * y t ) + v t, (1) where e denotes the nominal exchange rate (foreign currency per unit of domestic currency), m* denotes the foreign money supply, m denotes the domestic money supply, y* denotes the foreign country output, y denotes the domestic country output, and t is a time subscript. The United States is the domestic country in each case and all variables are in natural logarithms. 4 If e t, (m t * m t ), and (y t * y t ) are each I(1), then the long-run equilibrium condition implies that these variables are cointegrated and v t = e t β 0 β 1 (m t * m t ) β 2 (y t * y t ) will be a stationary process. 5 While the ADL-OLS threshold cointegration test can have greater power than the ADL-IV test, the ADL-OLS based test has nonstandard distributions that depend on the nuisance parameter when stationary covariates are included. In contrast, the ADL-IV threshold cointegration test is invariant to nuisance parameters in such cases. Therefore, in countries where y* - y, e, and m t * m t are nonstationary, we will utilize the ADL-OLS threshold cointegration test. Then, in countries where y* y is stationary, while e and m t * m t are nonstationary, we will utilize the ADL-IV test. The ADL-IV based test is well suited in this case, since the same standard normal critical values can be adopted with stationary covariates in the testing equation. The nonlinear specification of the monetary model in the ADL threshold cointegration test can be described as follows: 6 4 In a strict theoretical framework, the monetary model in (1) predicts that β1 = β1 and β2 < 0. However, imposing these restrictions is not necessary in our tests for threshold cointegration and may lead to bias test statistics if these restrictions do not strictly hold in practice. As such, we prefer to refrain from imposing any restrictions on these coefficients in our tests. We thank an anonymous referee for bringing this to our attention. 5 We note that the model in (1) is one version of the monetary model of exchange rates and there are other versions that have been proposed. In particular, the model in (1) assumes flexible prices and was originally suggested by Frenkel (1976) and Mussa (1976). In this paper, we focus only on the model in (1) since this model has been most often examined in the literature with cointegration tests. 6 We allow for threshold effects in the short-run dynamics of the cointegrating model. As an anonymous referee correctly notes, allowing for threshold effects in the cointegrating vector would be an alternative way to capture different regimes in the long-run dynamics. However, allowing for a threshold effect in the long-run cointegrating vector is beyond the scope of the present paper. Instead, we follow the common approach taken in the literature on threshold unit root and cointegration models and allow for threshold effects only in the short-run dynamics; see, for example, the papers by Balke and Fomby (1997), Enders and Siklos (2001), and Hansen and Seo (2002), among others. Presumably, it can be possible to allow for threshold effects in both the long-run and short-run dynamics, but we leave these issues for future research.

6 韓國開發硏究 / 2009. Ⅱ Δe t = I t [ρ 1 e t -1 +a 1 (m t * m t ) +a 2 (y t * y t ) +b 1 Δ(m t * m t ) +b 2 Δ(y t * y t )] +(1 I t ) [ρ 2 e t -1 +c 1 (m t * m t ) +c 2 (y t * y t ) +d 1 Δ(m t * m t ) +d 2 Δ(y t * y t )] +u t. (2) Lags of Δe t, Δ(m t * m t ), and Δ(y t * y t ) can be included as necessary to correct for serial correlations. There are clear advantages to using ADL models; see Li and Lee (2008), and Enders, Im, Lee, and Strazicich (2009) for more details. Following these methods, we consider two threshold indicators. The first is the so-called threshold autoregressive (TAR) model: I t = 1 if e t -1 τ and I t = 0 if e t -1 < τ, (3) where is the threshold value. The second threshold indicator is the so-called momentum threshold autoregressive (M-TAR) model: I t = 1 if Δe t -1 τ and I t = 0 if Δe t -1 < τ. (4) We test the following null hypothesis in each case: H o : ρ 1 = 0 and ρ 2 = 0 vs. H 1 : ρ 1 < 0 and/or ρ 2 < 0. (5) Thus, under the alternative hypothesis the deviation from the equilibrium will be stationary in at least one regime. We transform the threshold parameter into its percentile and determine this value by minimizing the sum of squared residuals. Specifically, since the threshold parameter cannot be greater or less than the maximum or minimum value of the threshold variable, we first sort the threshold variable e t-1 into e t-1 *, which takes the ordered values of e t-1 from the minimum to maximum value of e t-1. Then, we consider the following transformation scheme: * 1 1/2 * I t = I( e τ) = I( σ * > 1 1/2 t 1 T e t 1 σ T τ ) = I( 1 1/2 * σ T e t 1 > c*), (6) 1 1/2 where c* = σ T τ is the normalized threshold parameter and σ 2 = T -1 E( e t-1 ) 2 *. Next, we let e ( ) t 1 τ = c* be the c-th percentile of the empirical 1 1/2 * distribution of e t-1*, such that P[ σ T e t 1 c*] = P[ 1 1/2 * σ T e t 1 * e ( ) t 1 τ ] = c. As a result, the threshold parameter τ is transformed into a percentile parameter c defined over the interval 0 and 1, and the asymptotic distribution of the corresponding threshold tests will depend only on the percentile parameter c. We can therefore provide critical values based on the percentile parameter defined on the interval between 0 and 1, rather than on a real value that can potentially vary over - to +. We estimate the threshold percentile parameter by a grid search to find the value of e t-1 (or Δe t-1 ) that minimizes the sum of squared residuals from the regression. For the grid search procedure, we use each value of the sorted data of e t-1 (or Δe t-1 ) from the minimum to maximum value, while trimming values at the

Testing for Nonlinear Threshold Cointegration in the Monetary Model of Exchange Rates with a century of Data 7 <Table 1> ADL-OLS Threshold Cointegration Test Results, I t = 1 if Δe t-1 τ and I t = 0 if Δe t-1 < τ Country ρ 1 ρ 2 Wald threshold percentile lag Australia (1880~1995) 0.019 0.320 5.552-0.055 0.147 0 Belgium (1880~1989-0.143-0.037 31.292*** 0.085 0.773 0 Canada (1880~1995) -0.315-0.079 39.505*** 0.000 0.526 0 France (1880~1989) -0.145-0.094 26.854** 0.073 0.688 0 Italy (1880~1995) -0.239-0.124 69.056*** 0.104 0.853 0 Spain (1901~1995) -0.185-0.193 36.351*** 0.061 0.691 0 Switzerland (1880~1995) 0.058-0.049 15.851 0.046 0.854 0 UK (1880~1995) -0.128-0.086 30.427*** 0.082 0.853 0 Note: The Wald statistic tests the null hypothesis of no cointegration in two regimes (ρ1 = ρ2 =0). All models include a constant term without trend. Critical values come from Table 1 in Li and Lee (2008) for the Boswijk version of the ADL-OLS threshold cointegration test with n = 2 conditioning variables. The percentile threshold value was determined by minimizing the sum of squared residuals. *, **, and *** denote rejection of the null of no cointegration at the 10%, 5%, and 1% levels of significance, respectively. lower and upper 10% of the data. Chan (1993) showed that this type of procedure can estimate the threshold consistently under the null and alternative hypotheses. The threshold parameter estimator is super-consistent under the alternative, implying that the estimated value is expected to converge to its true parameter value more quickly under the alternative hypothesis than under the null. In the ADL-OLS test, we utilize the Boswijk (1994) version of the Wald test to test the null hypothesis as recommended by Li and Lee (2008). The critical values come from Table 1 in Li and Lee (2008). We use critical values corresponding to each of the indicator functions defined in (3) and (4), respectively. In the ADL-IV test we utilize the usual t-statistics to test the significance of ρ 1 and ρ 2, since these test statistics have standard distributions and are unaffected by including y*-y as a stationary covariate. To apply the ADL-IV test we utilize the following instruments: w 1t = [I t (e t-1 - e t-m ), (1 I t )(e t-1 - e t-m )] for [I t e t-1, (1 I t ) e t-1 ], and w 2t = [I t (y 2,t-1 - y 2,t-m ), (1 I t )(y 2,t-1 -y 2,t-m )] for [I t y 2,t-1, (1 I t ) y 2,t-1 ] (7) where y 2t denotes the regressors [(y t * y t ), (m t * m t )]. We let w t = (w 1t, w 2t ) for our instrument. The resulting t-test statistics for ρ 1 and ρ 2 will have asymptotic standard normal distributions in each case; see Enders, Lee, and Strazicich (2007) and Enders, Im, Lee, and Strazicich (2009). 7 7 As noted by an anonymous referee, the IV type tests will be biased if the order of integration in the variables is mis-specified, especially if non-stationary variables are incorrectly considered to be stationary since instruments are required on all nonstationary variables. However, using instruments on stationary

Testing for Nonlinear Threshold Cointegration in the Monetary Model of Exchange Rates with a century of Data 3 I. Introduction The monetary model suggests that nominal exchange rates between two countries will be determined by important macroeconomic fundamentals. Two early references to the model are Mussa (1976) and Bilson (1978). While the monetary model is intuitively appealing, empirical support for the model is often difficult to find. Perhaps most critical in this regard are the findings in Meese and Rogoff (1983), where the authors obtain better forecasts of nominal exchange rates in a simple random walk as compared to the monetary model. If the monetary model is valid and the fundamental variables are nonstationary, then a cointegrating relationship must exist. Many empirical studies, however, fail to find support for (linear) cointegration in the monetary model (e.g., Meese, 1986, Baillie and Selover, 1987, and Sarantis, 1994). More recently, Rapach and Wohar (2002, RW) advance the literature by performing (linear) cointegration tests of the monetary model using a century of data. By using long-span data to increase power, RW find greater support for the monetary model than in many previous tests and find evidence of cointegration in 5 or 6 of 10 countries. 1 In this paper, we re-examine the long-span data in RW and perform nonlinear threshold cointegration tests. If the underlying model is nonlinear and linear cointegration tests are adopted, then lower power can result. As such, it is possible that greater support for cointegration will be found when adopting nonlinear tests. In this regard, a growing number of recent studies document evidence of nonlinear dynamics in exchange rates (e.g., Taylor and Peel, 2000, Guerra, 2001, Kilian and Taylor, 2003). Nonlinear dynamics in exchange rate might arise, for example, if reaction to fundamentals and adjustment depends on the magnitude or sign of the deviation from the equilibrium. For instance, Taylor and Peel (2000) find evidence that deviations in exchange rates from the monetary model follow a nonlinear adjustment process. Although Taylor and Peel (2000) note that a tractable way to model nonlinear adjustment is to adopt a threshold model, they adopt an exponential smooth transition autoregressive (ESTAR) model perhaps for convenience of estimation. While these and other recent papers find greater support for the monetary model in nonlinear models, these papers do not provide formal tests for nonlinear cointegration. Analogous to the linear case, if the variables in a nonlinear monetary model are nonstationary and not cointegrated, then spurious estimates can result. It remains to be seen whether nonlinear cointegration holds or not, but this important question was not examined in the 1 Rapach and Wohar (2002) initially consider fourteen countries, but some of the countries contain a mix of I(0) and I(1) variables that cannot be cointegrated, and in one country, The Netherlands, all of the variables in the model are I(0) so cointegration tests are not performed for four of these countries. However, in this paper, we utilize I(0) regressors in our testing scheme rather than discarding them as we shall see more details shortly. Thus, our procedure permits us to overcome a limitation of Rapach and Wohar (2002) in this regard.

8 韓國開發硏究 / 2009. Ⅱ Ⅲ. Empirical Results We now examine the results of testing for threshold cointegration. To be consistent in our comparisons to the linear tests in RW, we utilize their same unit root test results and the same long-span data. In the ADL-OLS tests, we determine the optimal number of lags in the testing regression by employing the Schwarz information criteria (SIC). In the ADL-IV tests, we jointly determine the optimal value of m to construct a proper IV (w t ) and the optimal number of lags to correct for serial correlations. We first search for the optimal lag for a given value of m, for m = 1 and maxm, where maxm is given as T 0.5. We then determine the optimal value of m as the value that minimizes the residual sum of squares (RSS) from the regression using the optimal number of lags. 1. Asymmetric Momentum Threshold Effects We first examine the ADL-OLS threshold cointegration test results with asymmetric effects modeled by the change in deviations from the equilibrium in the monetary model. This is the momentum threshold model, where the speed of adjustment to the equilibrium will depend on whether the change in the deviation is above or below the threshold level (I t = 1 if Δe t-1 τ and I t = 0 if Δe t-1 < τ). To obtain valid ADL-OLS test results, we will consider only threshold cointegration tests for the eight countries where e, m*-m, and y*-y were each identified as I(1) variables in RW. The test results are displayed in Table 1. Looking at the results, we observe that 6 of the 8 countries reject the null of no cointegration in at least one regime (Belgium, Canada, France, Italy, Spain, and the UK) at the 1% level of significance. Moreover, in each country, except Spain, the speed of adjustment to the monetary model equilibrium is fastest when the rate of depreciation is above the threshold level. Given that the threshold level is close to zero in each case, these findings suggest that nominal exchange rates adjust more quickly to the equilibrium predicted by the monetary model when they are depreciating rather than appreciating. For example, in Canada the estimated persistent parameter when the change in the deviation from the equilibrium is above the threshold level (in regime 1) is -0.315, which is clearly stationary. In contrast, the estimated persistent parameter when the change in the deviation from the equilibrium is below the threshold level (in regime 2) is -0.079, implying that nominal exchange rate behave as a random walk. While less extreme, the differences in the estimated persistent parameters are similar in four of the other five countries that reject the null of no cointegration (Belgium, France, Italy, and the UK). One possible explanation for these findings could be that policy makers are more likely to intervene in currency markets when their currency is depreciating than when their currency is variables should not lead to any serious bias.

Testing for Nonlinear Threshold Cointegration in the Monetary Model of Exchange Rates with a century of Data 9 appreciating. This is an example of policy response to different economic conditions; see also Lee (2006) for the case of Korea regarding fiscal policy response to economic cycles. 2. Asymmetric Deviation Threshold Effects We next examine the ADL-OLS threshold cointegration test results with asymmetric threshold effects modeled by the level of the deviations from the equilibrium. This is the autoregressive threshold model, where the speed of adjustment to the equilibrium depends on whether the level of the deviation is above or below the threshold level (I t = 1 if e t-1 τ and I t = 0 if e t-1 < τ). Again, to obtain valid ADL-OLS test results we will consider only threshold cointegration tests for the eight countries where e, m*-m, and y*-y were each identified as I(1) variables in RW. The test results are displayed in Table 2. Looking at the results, we observe that 4 of the 8 countries reject the null of no cointegration in at least one regime (Canada, Italy, Switzerland, and the UK) at the 1% or 5% level of significance. In two of the four countries (Canada and Italy) that reject the null of no cointegration, the difference in the adjustment speeds is similar to that in the momentum models of Table 1. In Canada, the estimated persistent parameter when the deviation from the equilibrium is above the threshold level (in regime 1) is -0.710 while the estimated persistent parameter when the deviation is below the threshold level is -0.310. In Italy, the <Table 2> ADL-OLS Threshold Cointegration Test Results, I t = 1 if e t-1 τ and I t = 0 if e t-1 < τ Country ρ 1 ρ 2 Wald threshold percentile lag Australia (1880~1995) 0.019 0.320 14.136 0.404 0.853 0 Belgium (1880~1989-0.125-0.071 11.246 0.165 0.809 1 Canada (1880~1995) -0.710-0.310 30.634*** -0.038 0.241 1 France (1880~1989) -0.248-0.194 18.065-0.050 0.422 1 Italy (1880~1995) -0.441-0.136 53.553*** 0.342 0.853 1 Spain (1901~1995) -0.336-0.291 21.232-0.060 0.415 1 Switzerland (1880~1995) -0.317-0.317 25.908** -0.054 0.272 1 UK (1880~1995) -0.199-0.327 26.999** 0.022 0.466 1 Note: The Wald statistic tests the null hypothesis of no cointegration in two regimes (ρ1 = ρ2 =0). All models include a constant term without trend. Critical values come from Table 1 in Li and Lee (2008) for the Boswijk version of the ADL-OLS threshold cointegration test with n = 2 conditioning variables. The percentile threshold value was determined by minimizing the sum of squared residuals. *, **, and *** denote rejection of the null of no cointegration at the 10%, 5%, and 1% levels of significance, respectively.

10 韓國開發硏究 / 2009. Ⅱ estimated persistent parameter when the deviation from the equilibrium is above the threshold level (in regime 1) is -0.441, while the estimated persistent parameter when the deviation is below the threshold level is -0.136. In the other two countries (Switzerland and the UK) that reject the null of no cointegration, the results are less clear. In Switzerland, the adjustment speeds are the same in each regime, while in the UK the speed of adjustment to the equilibrium is fastest when the deviation from the equilibrium is below the threshold level rather than above. Given the lack of a consistent pattern in the estimated threshold values and/or the persistent parameters in these four countries, it is more difficult to provide a general explanation using the levels of the deviations from the equilibrium for the threshold indicator as compared to the results in the momentum models. Overall, we conclude that momentum threshold models provide the clearest and most intuitive evidence of nonlinear adjustments in nominal exchange rates to the equilibrium predicted by the monetary model. 3. Allowing For Stationary Output Deviations In the two countries where y*-y is stationary, while e and m*-m are nonstationary (Finland and Portugal; see RW), RW omit y*-y to maintain the validity of their (linear) cointegration tests. In contrast, rather than omit y*-y from our cointegration tests we want to include this fundamental variable as a stationary covariate to increase power. While omitting this stationary variable can be seen as a limitation of the OLS based cointegration tests, this limitation does not occur in the IV based tests. In contrast, the test statistic in the ADL-IV threshold cointegration test that we consider retains an asymptotic standard distribution even when a stationary covariate is included. Our test results are displayed in Table 3. 8 Looking at the results, we observe that the null of no cointegration is rejected in at least one regime for Finland at the 5% level of significance. Moreover, it is clear that adjustment to the equilibrium is faster when the change in the deviation is above the threshold level (in regime 1) than when the change is below the threshold level (in regime 2). In particular, the estimated persistent parameter is -0.410 when the change in the deviation from the equilibrium is above the threshold level. This indicates that the nominal exchange rate is clearly stationary and supports adjustment to the equilibrium predicted by the monetary model. However, when the change in the deviation is below the threshold level, the estimated persistent parameter is 0.05 and implies that the nominal exchange rate will behave as a random walk. Overall, including the results for Finland, we can reject the null of no cointegration in the momentum model in 7 out of 10 countries at the 1% or 5% level of significance. If we combine these results with those for Switzerland in Table 2, we can reject the null hypothesis of no cointegration in at least one regime in 8 of 10 countries. 8 We adopt only the momentum threshold model in this case since this model already gave the greatest number of rejections of the null.

Testing for Nonlinear Threshold Cointegration in the Monetary Model of Exchange Rates with a century of Data 11 <Table 3> ADL-IV Threshold Cointegration Test Results y*-y is treated as I(0), I t = 1 if Δe t-1 τ and I t = 0 if Δe t-1 < τ Country Coeff t ADL-IV t-stat ρ 1 =ρ 2 lag m Finland (1911~1995) ρ 1-0.41-1.90** -1.80* 2 7 ρ 2 0.05 0.38 Portugal (1890-1995) ρ 1-0.03-0.54-0.80 1 8 ρ 2 0.04 0.59 Note: tadl-iv tests the null hypothesis of no cointegration in the regime against the alternative of cointegration. Asymptotic standard normal critical values are used for the ADL-IV test ( 2.326, 1.645, and 1.282 at the 1%, 5%, and 10% levels of significance, respectively). The value of m in the ADL-IV test was chosen from the model with the minimum sum of squared residuals. The percentile threshold value was determined by minimizing the sum of squared residuals. All models include a constant without trend. t-stat tests the null that ρ 1 = ρ 2. *, **, and *** denote rejection of the null of no cointegration at the 10%, 5%, and 1% levels of significance, respectively. Ⅳ. Conclusion In this paper, we adopt nonlinear threshold cointegration tests to test for cointegration in the monetary model of exchange rates. While previous researchers have estimated nonlinear versions of the monetary model, they were unable to test for cointegration in a nonlinear framework due to nuisance parameter problems in the existing tests. In this paper, we strive to make a contribution towards filling this gap in the literature. To compare results, we utilize the same long-span data that was previously adopted by Rapach and Wohar (2002) to test for linear cointegration in the monetary model. Given that adopting linear tests can lead to lower power if the underlying model is nonlinear, we test for nonlinear cointegration to see if greater support for the monetary model will occur. We first adopt the ADL-OLS threshold cointegration test developed by Li and Lee (2008) and consider two different threshold models. Following this, we utilize the ADL-IV threshold cointegration test developed by Enders, Im, Lee, and Strazicich (2009). The ADL-IV threshold cointegration test has the distinct advantage that we can include relative output as a stationary covariate to increase power, while the test statistic maintains a standard distribution. Overall, we find greater support for cointegration in the nonlinear framework as compared to the linear cointegration tests in Rapach and Wohar (2002). Moreover, our findings suggest that adjustment to the long-run equilibrium predicted by the monetary model is faster when nominal exchange rates are depreciating as compared to when than appreciating. Finally, our findings complement the growing number of papers that find greater support for the monetary model in a nonlinear framework and perhaps help to explain why Meese (1986), Baillie and Selover (1987), and Sarantis (1994), among others, fail to find support for cointegration in a linear framework.

12 韓國開發硏究 / 2009. Ⅱ References Baillie, R. T., and D.D. Selover, Cointegration and Models of Exchange Rate Determination, International Journal of Forecasting, 8(1), 1987, pp.43~51. Bec, F., M. Ben Salem, and M. Carrasco, Tests for Unit-Root versus Threshold Specification with an Application to the Purchasing Power Parity Relationship, Journal of Business and Economic Statistics, 22(4), 2004, pp.382~395. Bilson, J., The Monetary Approach to the Exchange Rate: Some Empirical Evidence, IMF Staff Papers 25, 1978, pp.48~75. Bordo, M. D. and L. Jonung, A Return to the Convertibility Principle? Monetary and Fiscal Regimes in Historical Perspective, in A. Leijonhuvhud (ed.), Monetary Theory as a Basis for Monetary Policy, MacMillan, London, 1998. Bordo, M. D., M. Bergman, and L. Jonung, Historical Evidence on Business Cycles: the International Perspective, in J. C. Fuhrer and S. Schuh (eds.), Beyond Shocks: What Causes Business Cycles, Conference Series, Vol. 42, Federal Reserve Bank of Boston, 1998, pp.65~113. Boswijk, H. P., Testing for an Unstable Root in Conditional and Structural Error Correction Models, Journal of Econometrics 63, 1994, pp.37~60. Chan K. S., Consistency and Limiting Distribution of the Least Squares Estimator of a Threshold Autoregressive Model, Annals of Statistics 21, 1993, pp.520~533. Enders, W., J. Lee, and M. C. Strazicich, IV ECM Threshold Cointegration Tests and Nonlinear Monetary Policy in Korea, KDI Journal of Economic Policy (formerly Korea Development Review) 29(2), 2007, pp.135~157. Enders, E., K. Im, J. Lee, and M. C. Strazicich, New Threshold Cointegration Tests and the Taylor Rule, Working Paper, University of Alabama, Department of Economics, Finance and Legal Studies, 2009. Enders, W. and P. L. Siklos, Cointegration and Threshold Adjustment, Journal of Business & Economic Statistics 19, 201, pp.166~176. Engle, R. F. and C. W. J. Granger, Cointegration and Error Correction Representation: Estimation and Testing, Econometrica 94, 1987, pp.1096~1109. Frenkel, Jacob A., A Monetary Approach to the Exchange Rate: Doctrinal Aspects and Empirical Evidence, Scandinavian Journal of Economics 78, 1976, pp.200~224. Hansen, B. and B. Seo, Testing for Two-regime Threshold Cointegration in Vector Error Correction Models, Journal of Econometrics 110, 2002, pp.293~318. Guerra, Roger, Fundamentals and Exchange Rates: How About Nonlinear Adjustment? Working Paper, University of Geneva, Department of Economics, 2001. Kapetanios, G, Y. Shin and A. Snell, Testing For Cointegration In Nonlinear Smooth Transition Error Correction Models, Econometric Theory 22(2), 2006, pp.279~303. Kilian, L. and M. P. Taylor, Why Is It So Difficult to Beat the Random Walk Forecast of Exchange Rates? Journal of International Economics 60, 2003, pp.85~107. Kremers. J. J. M., Ericsson, N. R., and J. J. Dolado, The Power of Cointegration Tests, Oxford Bulletin of Economics and Statistics 54, 1992, pp.325~348. Lee, S. H., An Evaluation of Fiscal Policy Response to Economic Cycles, KDI Journal of

Testing for Nonlinear Threshold Cointegration in the Monetary Model of Exchange Rates with a century of Data 13 Economic Policy (formerly Korea Development Review) 28(2), 2006, pp.1~44. Li, J. and J. Lee, Single-Equation Tests for Threshold Cointegration, Working Paper, University of Alabama, Department of Economics, Finance and Legal Studies, 2008. Meese, R. A., Testing for Bubbles in Exchange Markets: A Case of Sparkling Rates, Journal of Political Economy 94(2), 1986, pp.345~373. Meese, R. A., and K. Rogoff, Empirical Exchange Rate Models of the Seventies, Journal of International Economics 14, 1983, pp.3~24. Mussa, M., The Exchange Rate, the Balance of Payments, and Monetary and Fiscal Policy Under a Regime of Controlled Floating, Scandinavian Journal of Economics 78, 1976, pp.229~248. Rapach, David E. and Mark E. Wohar, Testing the Monetary Model of Exchange Rate Determination: New Evidence from a Century of Data, Journal of International Economics 58, 2002, pp.359~385. Sarantis, N., The Monetary Exchange Rate Model in the Long-run: An Empirical Investigation, Weltwirtschaftliches Archiv 130, 1994, pp.698~711. Spagnolo, F., Z. Psaradakis and M. Sola, Testing the unbiased forward exchange rate hypothesis using a Markov switching model and instrumental variables, Journal of Applied Econometrics, 20(3), 2005, pp.423~437. Taylor, Alan M., A Century of Purchasing-Power Parity, Review of Economics and Statistics 84, 2002, pp.139~150. Taylor, M. P. and D. A. Peel, Nonlinear Adjustment, Long-Run Equilibrium and Exchange Rate Fundamentals, Journal of International Money and Finance 19, 2000, pp.33~53. Zivot, E., The Power of Single Equation Tests for Cointegration When the Cointegrating Vector is Prespecified, Econometric Theory 16, 2000, pp.407~439.

韓國開發硏究제 31 권제 2 호 ( 통권제 105 호 ) 이자율기간구조를이용한정책금리변경의효과분석 송준혁 ( 한국개발연구원부연구위원 ) Analyzing the Effect of Changes in the Benchmark Policy Interest Rate Using a Term Structure Model Joonhyuk Song (Associate Research Fellow, Korea Development Institute) * 송준혁 : (e-mail) jhsong@kdi.re.kr, (address) Korea Development Institute, Hoegiro 49, Dongdaemun-gu, Seoul, Korea Key Word: (Term Structure of Interest Rate), (No Arbitrage), (Monetary Policy) JEL code: E43, E52, E58 Received: 2009. 8. 31 Referee Process Started: 2009. 8. 31 Referee Reports Completed: 2009. 11. 10

ABSTRACT This paper estimates the term structure of interest rates with the setup of 3-factor no arbitrage model and investigates the trend of term premia and the effectiveness of changes in policy interest rates. The term premia are found to be high in a three-year medium term objective, which can be interpreted as reflecting the recognition of investors who expect a higher uncertainty in real activities for the coming three years than for a longer term. Then, in order to look into the effect of policy interest rates after the recent change of benchmark interest rate, this paper analyzes the effects of the changes in short-term interest rates of the financial market on the yield curve of the bond market at time of change. Empirical results show that the discrepancy between call rate, short-term rate in money market, and instantaneous short rate, short-term rate in the bond market, is found to be significantly widened, comparing to the periods before the change in benchmark interest rate. It is not easy to conclude clearly for now whether such a widening gap is caused by the lack of experiences with managing new benchmark interest rate or is just an exceptional case due to the recent turmoil in the global financial market. However, monetary policy needs to be operated in a manner that could reduce the gap to enhance its effectiveness.

이자율기간구조를이용한정책금리변경의효과분석 17 Ⅰ. 서론.,..... (term structure of interest rates) (,, ),.,..,,.. Vasicek(1977)

18 韓國開發硏究 / 2009. Ⅱ., Cox, Ingersoll, and Ross(1985) (general equilibrium). Duffie and Kan(1996), Dai and Singleton(2000), Duarte(2004), Kim and Orphanides(2006),., Bekaert, Cho, and Moreno(2005), Ang, Piazzesi, and Wei (2006), Rudebusch and Wu(2008), Doh (2009). 2000 2009 1 3,.. 7.,,.. (1991) McCulloch 3 1, (2002) Heath-Jarrow-Morton., (2000) CIR, (2001) -.. (2005) (2007). (2005) Nelson-

이자율기간구조를이용한정책금리변경의효과분석 19 Siegel 1, 3. (2007) 2,.,. (2007) 3, (signalling).. 3,..,. Ⅱ. 모형설정. Harrison and Kreps (1979), (riskneutral probability measure) (state price) (equivalent martingale measure)., (complete market). Duffie(2002), Dai and Singleton(2002), Duarte(2004) 3 (3-factor arbitrage-free term structure model), (affine model).

20 韓國開發硏究 / 2009. Ⅱ. 1 (zero-coupon bond),.. : (1),.. (2)., (stochastic discount factor). 1) (3) (3). log ()., (4) (4). 2) (5). (expectations hypothesis) 1). (marginal rate of substitution) (level of marginal utility). Cochrane(2001). 2) Cochrane(2001).

이자율기간구조를이용한정책금리변경의효과분석 21. (risk premium hypothesis). Dai and Singleton(2000) (level), (slope) (curvature)....,,. 3). 4),. (latent factors)., (risk-neutral probability measure) Ornstein- Uhlenbeck., (5), (mean reversion), 3) Fama and Bliss(1987), Mishkin(1990), Fama(1990), Campbell and Shiller(1991), Cochrane and Piazzesi(2005), Hamilton and Kim(2002). 4) Ang and Piazzesi(2003), Diebold, Rudebusch, and Aruoba(2006), Rudebusch and Wu (2008), Bekaert, Cho, and Moreno(2005).

22 韓國開發硏究 / 2009. Ⅱ, (volatility).. (short rate). (6),.... (7) (7) 3, (market price of risk). Duffie and Kan(1996), (physical probability measure) (). (8),.. (9) 또는 또는,, (PDE). (10),.

이자율기간구조를이용한정책금리변경의효과분석 23, () (). exp log (11). Dai and Singleton(2000). 5) (lower-triangular matrix), (diagonal matrix), (term premium).,. Ⅲ. 자료및추정 (12) (conditional expectations).... 1. 분석자료 KIS 3, 6, 9, 1, 2, 3, 5 1, 2000 8 2 2009 8 19 473. 1 5) Dai and Singleton(2000).

24 韓國開發硏究 / 2009. Ⅱ [Figure 1] Trends of Interest Rates(2000. 8. 2~2009. 8. 19) Source: KIS Ratings.. 6),, 1.,. [Figure 1] 7 1 3. 2000. 2006, 2008. 2008 10. (curvature) 6) (2001) 1.

이자율기간구조를이용한정책금리변경의효과분석 25 <Table 1> Summary Statistics for Interest Rates Mean Std. Dev Skewness Kurtosis Autocorrelation Jarque-Bera Normality Test Call Rate 4.0740 (0.1004) 0.8394 (0.1419) -0.9146 (0.1725) 3.8701 (0.5372) 0.9949 (0.0089) 0.0010 y 3M 4.3714 (0.1206) 1.0101 (0.2065) -0.4850 (0.2515) 3.8713 (0.5268) 0.9952 (0.0128) 0.0010 y 6M 4.5181 (0.1209) 1.0146 (0.2047) -0.3455 (0.2668) 3.7983 (0.4992) 0.9926 (0.0147) 0.0010 y 9M 4.6566 (0.1173) 0.9847 (0.1834) -0.1007 (0.2601) 3.5584 (0.4375) 0.9897 (0.0160) 0.0289 y 1Y 4.7659 (0.1163) 0.9769 (0.1718) 0.0593 (0.2417) 3.3482 (0.3928) 0.9870 (0.0166) 0.2123 y 2Y 5.0678 (0.1126) 0.9472 (0.1530) 0.5186 (0.2053) 3.1640 (0.4251) 0.9805 (0.0181) 0.0010 y 3Y 5.2581 (0.1154) 0.9702 (0.1632) 0.6251 (0.2143) 3.2371 (0.4740) 0.9800 (0.0180) 0.0010 y 5Y 5.5049 (0.1190) 1.0020 (0.1706) 0.6470 (0.2150) 3.1564 (0.4950) 0.9787 (0.0174) Note: Standard errors of summary statistics are estimated using GMM and presented in parentheses. 0.0010., 1 Vasicek CIR. <Table 1> 1 7. 3 1 3.. (normality) Jarque-Bera 3.

26 韓國開發硏究 / 2009. Ⅱ [Figure 2] Sample Average Term Structure 1. 1 0.98,.,. [Figure 2] 1. <Table 1>. 7), 70%,. (principal component analysis). 7) (2001), (2007) 3, 2, 5.,

이자율기간구조를이용한정책금리변경의효과분석 27 [Figure 2] Cumulative Explanatory Power of Principal Components, (level), (slope), (curvature).. 8) 52 moving window (eigenvalue),. [Figure 2]. 1 60% 1. 8) Piazzesi(2003) 3 (level), (slope), (curvature), 2 (level) (slope), 1 (level)., Litterman and Scheinkman(1991) (curvature). Cochrane and Piazzesi(2005).

28 韓國開發硏究 / 2009. Ⅱ,. 2 98% 2000 2006., 3.. 2. 모형추정과결과 Duffie and Stanton(2004), Kim and Orphanides(2006) - (state space model). - (Kalman filter).. (measurement equation) 3 5 7. 3M 3, 3 3..,. (state equation). 9). -. <Table 2>. 9) James and Webber(2000), Kim and Orphanides(2006).

이자율기간구조를이용한정책금리변경의효과분석 29 <Table 2> Parameter Estimates Param. Est Std. Err Param. Est Std. Err 0.1271 0.3701-1.6824 4.1058-1.5111 0.5878-0.1384 0.3959 0.5454 0.1855 0.4534 0.2243-2.1355 0.6331-1.9097 0.7417 0.5410 0.1887-0.0098 0.0900 0.0003 0.0002 0.4420 0.1472 0.0037 0.0069 0.3261 0.1375 0.0053 0.0048 4.89e-8 6.53e-5 0.0402 0.0153 3.28e-4 1.53e-5-0.5728 0.4187 1.11e-4 2.16e-5-1.4363 0.6886 3.59e-4 1.72e-5-3.9055 0.4538 5.19e-4 1.99e-5 0.4946 0.1642 6.24e-5 7.87e-5-9.6307 2.8164 1.08e-3 4.89e-5 Log-likelihood 41.8285, 4.02% (2007) 2 4.07%. (mean reversion).,. <Table 3> RMSE(Root Mean Squared Error) MAE(Mean Absolute Error). RMSE 3 2.424%, 9 1.274%, 1.645%

30 韓國開發硏究 / 2009. Ⅱ <Table 3> Root Mean Squared Error 3M 6M 9M 1Y 2Y 3Y 5Y All RMSE (%) 2.424 1.811 1.274 1.351 1.345 1.424 1.645 1.654 MAE (%) 1.298 1.044 0.865 0.817 0.991 1.099 1.212 1.047 Note: RMSE (%) =, MAE (%) =., MAE 3 1.298%, 1 0.817% 1.047%. RMSE MAE. [Figure 3] 1. 2002, 2000 10).. 2003, 2008. 24bp 2004 14bp. [Figure 2] 1 ( ), 2008, 2009 3.5%. 2008 9 Lehman Brothers Merrill Lynch, 10) 2000 10 0.25%p 2004 11 10.

이자율기간구조를이용한정책금리변경의효과분석 31 [Figure 3] Short Rate, Call Rate and Term Premium [Figure 4] Term Premium for All Maturities

32 韓國開發硏究 / 2009. Ⅱ. [Figure 4].,, 3. 1~3., 3.. Ⅳ. 정책금리변경의효과분석.. 2008 10 9 5.25% 25bp 2009 2 12 2.00% 6 325bp. 2009 2 11 5.25% 2008 8 7 8 6, 2009 8 19, [Figure 5]. 2008 8 2009 2, 1%p.. 2009 2 8, 2 8 0.6%p 2009 8 2009 2. 6.

이자율기간구조를이용한정책금리변경의효과분석 33 [Figure 5] Yields and Term Premia, 2008 8., 2009 2,. 2 12,. 2009 8,. 2008 8. 2009 2,. 7

34 韓國開發硏究 / 2009. Ⅱ. 2008 8 7 5% 5.25%.,,.. [Figure 6] 0 10 (cumulative increments).. 2008 8 7, 25bp. 2008 10 9 10 35bp 3 17bp, 14bp., 12bp. 2008 10 9. 2008 8 7 11). [Figure 6].. 6 2009 2 12. <Table 4> 11bp. 11) 2008 4 4.1% 7 5.9%.

이자율기간구조를이용한정책금리변경의효과분석 35 [Figure 6] Cumulative Increments of Call and Short Rates

36 韓國開發硏究 / 2009. Ⅱ <Table 4> Changes in Call Rates (unit: %) Date t Difference -3 (A) -2 (B) -1 (C) 0 (D) D-C C-B B-A 2008. 10. 9 5.23 5.06 5.12 4.98-0.14 0.06-0.17 2008. 10. 27 4.89 4.98 4.98 4.28-0.70 0.00 0.09 2008. 11. 7 4.02 4.04 4.22 3.97-0.25 0.18 0.02 2008. 12. 11 3.71 3.61 3.62 3.16-0.46 0.01-0.10 2009. 1. 9 2.59 2.58 2.98 2.49-0.49 0.40-0.01 2009. 2. 12 2.20 2.07 2.06 2.02-0.04-0.01-0.13 Note: t = -k denotes k days prior to the decrease in policy rate., (autonomously).. 12) 2008 12 11. (2007),,,.... 3 12)., 2008 3..

이자율기간구조를이용한정책금리변경의효과분석 37 3. 3.,. [Figure 7] 3 [Figure 6].... (market segmentation hypothesis).,,...,,.,

38 韓國開發硏究 / 2009. Ⅱ [Figure 7] Cumulative Increments of 3-Year Yields: Actual vs. Estimated

이자율기간구조를이용한정책금리변경의효과분석 39. 2007 11 30 2008 3 7 RP. 13) RP.. [Figure 8] 2001~07, [Figure 9]. 20bp,., RP,. V. 결론..,.. 13), (2007. 12. 4).

40 韓國開發硏究 / 2009. Ⅱ [Figure 8] Cumulative Increments When Target Call is Decreased: 2001~2007

이자율기간구조를이용한정책금리변경의효과분석 41 [Figure 9] Cumulative Increments When Target Call is Increased: 2001~2007

42 韓國開發硏究 / 2009. Ⅱ. 2008, 2008 9. 7 RP 7,. RP.,. (regime switching),... 2003,..

이자율기간구조를이용한정책금리변경의효과분석 43 참고문헌, :,, 13 2, 2000, pp.1~24., -,, 9 2, 2001, pp.265~286.,,, 12 2, 2007, pp.32~93.,,, 12 4, 2007, pp.121~166., :,, 2000, pp.1~47., Heath-Jarrow-Morton,, 8 2, 2002, pp.56~80.,,, 9, 1996, pp.411~424.,,, 11 2, 2005, pp.35~82.,,, 13, 1991, pp.327~355. Ang, Andrew and Monika Piazzesi, A No-Arbitrage Vector Autoregression of Term Structure Dynamics with Macroeconomic and Latent Variables, Journal of Monetary Economics 50, 2003, pp.745~787. Ang, Andrew, Monika Piazzesi and Min Wei, What Does the Yield Curve Tell Us about GDP Growth? Journal of Econometrics 131, 2006, pp.359~403. Bekaert, Geert, Seonghoon Cho, and Antonio Moreno, New-Keynesian Macroeconomics and the Term Structure, NBER WP No.11340, 2005. Campbell, John Y. and Robert J. Shiller, Yield Spreads and Interest Rate Movements: A Bird's Eye View, Review of Economic Studies 58(3), pp.195~228. Cochrane, John H., Asset Pricing, Princeton University Press, 2001. Cochrane, John H. and Monika Piazzesi, Bond Risk Premia, American Economic Review 95(1), 2005, pp.138~160. Cox, John, Jonathan Ingersoll, and Stephen Ross, A Theory of Term Structure of Interest Rates, Econometrica 53, 1985, pp.385~408. Dai, Qiang and Kenneth J. Singleton, Specification Analysis of Affine Term Structure Models, Journal of Finance 55, 2000, pp.531~552.

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이자율기간구조를이용한정책금리변경의효과분석 45 Rudebusch, Glenn D. and Tau Wu, A Macro-Finance Model of the Term Structure, Monetary Policy, and the Economy, Economic Journal 118(530), 2008, pp.906~926. Vasicek, O., An Equilibrium Characterization of the Term Structure, Journal of Financial Economics 5, 1977, pp.177~188.

韓國開發硏究제 31 권제 2 호 ( 통권제 105 호 ) 한국경기변동의특징및안정성에대한연구 이재준 ( 한국개발연구원부연구위원 ) Changes in the Business Cycle of the Korean Economy: Evidence and Explanations Jaejoon Lee (Associate Research Fellow, Korea Development Institute) * 이재준 : (e-mail) jjoonlee@kdi.re.kr, (address) Korea Development Institute, Hoegiro 49, Dongdaemun-gu, Seoul, Korea Key Word: (Business Cycle), (Volatility), - (Trend-Cycle Decomposition) JEL code: E3, C1 Received: 2009. 3. 27 Referee Process Started: 2009. 3. 27 Referee Reports Completed: 2009. 7. 14

ABSTRACT With a relatively simple quantitative method, this study comprehensively analyzes the characteristics related to business cycles represented by macroeconomic variables of Korea since 1970. This empirical analysis deals with roughly following three topics: How to identify cyclical component with respect to trend; with what characteristics and how the economic variables of each sector move with in the phases of business cycle, and; whether there are signs of a structural change in the phases of business cycle. Section 2 discusses how to identify trends and cycle components, the basis assumption for the analysis of business cycle. Like the Korean economy, where a relatively high growth rate has been maintained, it is appropriate to determine its economic recession based on the fall in the growth trend, not in the absolute level of real output. And, it is necessary to apply the concept of growth cycle against a traditional concept of business cycle. Accordingly the setting of growth trend is of preliminary importance in identifying cyclical fluctuations. The analysis of Korea s GDP data since 1970, the decomposition of trends and cycles through the Band-pass filter is found to appropriately identify the actual phases of busyness cycle. Section 3 analyzes what particular relationship various economic variables have with output fluctuations during the phases of economic cycle, using the corss-correlation coefficients and prediction contribution. Section 4 monitors the stability of the phases of Korea s business cycle and quantitatively verifies whether there is a structural break, and then reviews the characteristics of variations in each sector. And, stylized facts observed through these studies are summarized in the conclusion. The macroeconomic stability of Korea, in particular, is found to continue to improve since 1970, except for the financial crisis period. Not only that, it is found that its volatility of economic growth rate as well as inflation have been reduced gradually. Meanwhile, until recently since 2000, the volatility in domestic demand has remained stable, while that in exports and imports has been increased slightly. But, in an over all perspective, Korea s business cycle variation is on the decline due to shorter response period to shocks and the formation of complementary relationship among economic sectors.

ABSTRACT

50 韓國開發硏究 / 2009. Ⅱ Ⅰ. 서론 (shock-based business-cycle theory) 1970. (cyclical fluctuation) (self-sustaining process), (disturbances) (random shocks) 1)...,. (aggressive) (countercyclical policies).,,.,.,., 1) Slutzky(1937). Chatterjee(2000).

한국경기변동의특징및안정성에대한연구 51.,,.,,. (magnitude), (breadth) (persistence). GDP, (recession) NBER GDP 2.., (business cycle) (growth cycle) (Zarnowits(1992); [1993]). (growth trend).,.,. 1970 (stable) (volatility),.

52 韓國開發硏究 / 2009. Ⅱ Ⅱ. 국내경기변동요인의식별 1. 경기변동요인의식별과관련된쟁점 (recession), 1970 1980 1998. (long-run trend). (growth cycle)., -. 2) (trend)., 0 (covariance stationary stochastic process). Nelson and Plosser(1982),,. (unit root), (power).,,.. 2) Zarnowitz(1992), Chapter 7.

한국경기변동의특징및안정성에대한연구 53,. 3)... 4) 2. 선형추세에의한순환변동요인의추출 1970 ( GDP) 5) [Figure 1].. (long-run growth component) (linear time trend) [Figure 2]. GDP 6.8%, 1970 GDP. 1980, 1980 1997,. 7%. Nelson and Kang(1981) (spurious cycle). 3) Jusellius(2007). 4) Lee and Nelson(2007). Kim and Nelson(1999). 5)..,.

54 韓國開發硏究 / 2009. Ⅱ 13 [Figure 1] Gross Domestic Product(constant won, quarterly, natural logs) and Linear Trend 12 Actual Fitted 11 ln y t = 9.80 + 0. 07trend (765.7) (117.9) 10 9 1970Q1 1972Q1 1974Q1 1976Q1 1978Q1 1980Q1 1982Q1 1984Q1 1986Q1 1988Q1 1990Q1 1992Q1 1994Q1 1996Q1 1998Q1 2000Q1 2002Q1 2004Q1 2006Q1 2008Q1 Note: Numbers in parentheses are t-values. 0.2 [Figure 2] Deviations from Linear Trend for GDP 0.1 0-0.1-0.2 1970Q1 1972Q1 1974Q1 1976Q1 1978Q1 1980Q1 1982Q1 1984Q1 1986Q1 1988Q1 1990Q1 1992Q1 1994Q1 1996Q1 1998Q1 2000Q1 2002Q1 2004Q1 2006Q1 2008Q1., GDP, (differencing), GDP 6.6% 6),

한국경기변동의특징및안정성에대한연구 55 [Figure 3] Log Differences of GDP 30 20 10 0-10 -20-30 -40 1970 1975 1980 1985 1990 1995 2000 2005. (recession), 1970 1990. (growth cycle) (irregular noise).,., GDP. 3. 선형필터를이용한순환변동요인의추출,.,., 7) Hodrick-Prescott, Baxter 6) GDP 6.71%, log 6.61%. 7) DeJong and Dave(2005). Hodrick-Prescott

56 韓國開發硏究 / 2009. Ⅱ and King(1999) Band-Pass( B-P) GDP. B-P (high frequency variation) 6 8. [Figure 4]~[Figure 6] GDP,, B-P Hodrick-Prescott. 8) [Figure 4]. B-P., H-P,,., 1970 1980, 1990. 1970 1, 1980, 1990., 1990, 1996 2/4, 1997 2/4., 1996 1/4:7.1% 2/4: 5.7% 3/4: 7.5% 4/4: 6.3% (Figure 3 )., Harvey and Jaeger(1993) Murrary(2003) (spurious). 8),,.

한국경기변동의특징및안정성에대한연구 57 [Figure 4] GDP Growth Rate (Y-on-Y) and Cyclical Component of Composite Coincident Index (unit: %) 108 104 20 15 10 5 0 100 96 92-5 -10 1970 1975 1980 1985 1990 1995 2000 2005 Note: Thin line is the composite coincident index published by the Statistics Korea. [Figure 5] Cyclical Component from B-P Filtering for GDP 6 4 2 0-2 -4-6 -8-10 1970 1975 1980 1985 1990 1995 2000 2005 Note: GDP, constant Won, seasonally adjusted, quarterly, natural logs. [Figure 6] Cyclical Component from H-P Filtering for GDP 8 6 4 2 0-2 -4-6 -8-10 1970 1975 1980 1985 1990 1995 2000 2005 Note: GDP, constant Won, seasonally adjusted, quarterly, natural logs.

58 韓國開發硏究 / 2009. Ⅱ 1997, 1~2. 1996 1/4., B-P, 1997 2/4 1998 1/4. 9) 2000 2/4, 2000 8. B-P 1997 4/4., 2000.,. <Table 1> Business Cycles in Korea, 1972~2005 Full Cycle(date) Duration(month) Trough Peak Trough Expansion Contraction Full Cycle Cycle1 1972. 3 1974. 2 1975. 6 23 16 39 Cycle2 1975. 6 1979. 2 1980. 9 44 19 63 Cycle3 1980. 9 1984. 2 1985. 9 41 19 60 Cycle4 1985. 9 1988. 1 1989. 7 28 18 46 Cycle5 1989. 7 1992. 1 1993. 1 30 12 42 Cycle6 1993. 1 1996. 3 1998. 8 38 29 67 Cycle7 1998. 8 2000. 8 2001. 7 24 11 35 Cycle8 2001. 7 2002. 12 2005. 4 17 28 45 average 31 19 50 Sources: Statistics Korea. 9) 1997 1998..

한국경기변동의특징및안정성에대한연구 59 [Figure 7] Correction for the Currency Crisis Period from B-P Filtering for GDP 20 10 0-10 -20-30 -40 90 92 94 96 98 00 02 04 06 Band Pass Filtered GDP growth(q-q) GDP growth(y-y) Ⅲ. 거시경제변수의경기변동상의일반적특징 1. 자료및분석방법 60. KDI. 1970 1/4,. 10),,,, 5 60., X-12., (GDP 10) (2008).

60 韓國開發硏究 / 2009. Ⅱ,,, ).,.,,., Stock and Watson(1999). 2. 구조적변화에대한검정,, (structural break test). GDP ( ) (autoregressive, AR), 1971 1/4 2008 2/4 GDP 6.7%, 25.9%, -31.1%, 5.9. GDP 2, AR(2) 11)., 0.36 12),., 1998 1/4 31 (Standard Error of Regression) 6 (outlier). 13) 11) AR(5),, (R-square) 0.11. Akaike Information Criteria AR(2). 12) Stock and Watson(2005).

한국경기변동의특징및안정성에대한연구 61. Quant-Andrews 14) (Table 2 ),., GDP. <Table 3> 1. <Table 3>,. 10%.,, 10%. 15),,, 1996 1997., 5%.,,, GDP 5%, 1981., GDP 13) 1998 1/4 outlier AR(2), 0.33, Standard Error of Regression 4.9, Dubin-Watson. 14) Unknown break point test. Quant(1960), Andrews(1993),. 15) AR(2), AR(2)., AR(5) P-value 0.14.

62 韓國開發硏究 / 2009. Ⅱ <Table 2> Test for Structural Break Point intercept lagged dependent variable(-1) lagged dependent variable(-2) all variables break point 1995. 4/4 2000. 2/4 1995. 4/4 1997. 2/4 p-value 0.217 0.206 0.763 0.997 <Table 3> The Results of Structural Change Test Variables 1) 2) 2) Final Consumption (1996Q4) Consumption(nondurable) (1997Q2)* (1996Q2)* Consumption(service) (1996Q4) Consumption(durables) (1995Q4) Consumption(semi-durable) Government Consumption Gross Fixed Capital Formation Construction Investment Construction(Buildings) (1996Q4) (1978Q2) Construction (1977Q4)* (Residential buildings) Construction (Non-residential buildings) (1996Q3) (1979Q4)* Construction(others) (1997Q4) Facilities Investment (1996Q3) (1978Q4)* Facilities(Transport Equipment) (1996Q3)* Facilities(Machinery) (1996Q3)* Inventory/GDP(trend) (1995Q3)* (1979Q2)* Export (1996Q4) (1977Q4)* Export(Goods) (1996Q4) (1976Q2)* Export(Service) Import (1996Q4) Import(Goods) Import(Service) Net Export/GDPtrend (Weight) (1997Q3)* (1997Q3)* Current Account/GDP$trend 3) (Weight) (1997Q3)* Balance of Goods/GDP$trend (Weight) (1997Q3)* Balance of Services/GDP$trend (Weight) (1995Q3) (1987Q2)*

한국경기변동의특징및안정성에대한연구 63 <Table 3> Continued Variables 1) 2) 2) Consumer Price Index (1996Q4)* (1981Q3)* Producer Price Index (1995Q3)* (1981Q2)* GDP Deflator (1995Q3)* (1981Q2)* Number od Employed Agriculture and Forestry Mining and Manufacturing Construction Manufacturing (1997Q2)* Service (1997Q1)* Wholesale and Retail trade, Hotel and Restaurants (1997Q1)* Transport, Post and Telecommunication Financial Institution, Insurance, Real estate and Renting and Leasing, Business Activities Electricity, Gas and Water Supply Working Hours(level) (2006Q3)* Average Weekly Working Hours (1996Q4)* (1988Q3) Unemployment Rate(level) (1997Q2)* Not Economically Active Pop. (1996Q4) (1985Q1)* Employment Rate(level) (1986Q2)* Working Hours(level) (2006Q3)* Unemployment Rate(month-to-month Differences) (1997Q2)* Employment Rate(month-to-month Differences) (1996Q4) Nominal Wage Real Wage Call(level) (1997Q3)* (1998Q1)* Yields of Corporation Bonds(3-year, level) (1998Q1)* Yields on CD(level) (1997Q3) (1998Q1)* KOSPI(level) (2003Q1)* Call(month-to-month Differences) Yields of Corporation Bonds(month-to-month Differences) Yields on CD(month-to-month Differences) (1992Q1) KOSPI (1997Q2)* Reserve Money(nominal) (1978Q3)* Reserve Money(real) (1997Q3)* M2(nominal) (1998Q3)* M2(real) (1999Q1)* Notes: 1) Unless noted otherwise, all variables analyzed using percent change from the previous periods(annual rate) 2) Structural break points are given for variables that are significant at the 10% significance level. * denotes that variables are significant at the 5% significance level. 3) GDP($) is calculated from nominal GDP() / average(/us$)

64 韓國開發硏究 / 2009. Ⅱ.,,,,..,, () 1978,.. 3. 주요거시변수들의경기변동상의특징 B-P,.. 16) 1. (growth cycle). 1.1 7%,. 2.,. 2.1. 2.2,. 2.3. 2.4, 1, 16) (2008).

한국경기변동의특징및안정성에대한연구 65 (counter-cyclical and leading). 2.5 1980,. Ⅳ. 경기순환과정의안정성에대한분석 1. 우리나라경기순환과정의변동성 ( ) (volatility)., 17) (persistence). Kim and Nelson (1999) McConnell and Perez-Quiros (1999), Stock and Watson(2003) 1980 (great moderation). IMF(2007). 18),,, 17) 1980, 1990.,.,. Kose, Prasad, and Terrones(2003) Aizenman and Pinto(2005). 18),.,.

66 韓國開發硏究 / 2009. Ⅱ [Figure 8] Standard Deviation of GDP Growth rate and CPI Inflation (4-year rolling-window) 4.0 3.0 GDP CPI 2.0 1.0 0.0 1974 4 1976 3 1978 2 1980 1 1981 4 1983 3 1985 2 1987 1 1988 4 1990 3 1992 2 1994 1 1995 4 1997 3 1999 2 2001 1 2002 4 2004 3 2006 2 (improved monetary policy), (inventory management), (sectoral shift)., (smaller shocks). 19) [Figure 8] 10 4 (rolling-window). 1980 1997. 4. 10 1998,.. 20) 19) IMF(2007) 2007 Has the World Economy become More Stable?, References.

한국경기변동의특징및안정성에대한연구 67 <Table 4> Standard Deviation of CPI and GDP CPI Inflation CPI-cyc 1) GDP growth rate GDP-cyc 1) 72q1~80q3 1) 2.87 4.90 1.83 2.5 80q3~89q3 1.59 4.07 1.09 1.8 89q3~97q3 0.86 0.90 0.75 2.0 01q1~07q4 0.54 0.38 0.80 0.7 71q1~07q4 2.14 3.30 1.50 2.3 Note: The Cyclical Component is extracted from B-P Filtering which drops data of 12 quarters from the initial data point. Therefore Cyclical component series start at the first quarter of 1973. [Figure 9] Volatility Trend of CPI and Cyclical Component of GDP 3.0 Standard Deviation of Inflation: CPI 72Q1~80Q3 2.0 80Q3~89Q3 1.0 01Q1~07Q4 89Q3~97Q3 0.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Standard Deviation of Output: GDP GAP, 1997 4/4 2000 4/4, (Table 4 ).,. [Figure 9] Taylor Curve GDP. 20),.

68 韓國開發硏究 / 2009. Ⅱ. 1980 GDP 1970., 1970. 21). AR(1),. 2. 변동성감소의원인에대한계량분석,. GDP (exogenous shocks) (propagation).,,. AR(1) (Table 5 ), 0.28, 1980, 0., 2001 21) (frontier line), Juillard and others(2006).

한국경기변동의특징및안정성에대한연구 69 <Table 5> AR(1) Univariate Time Series Model Estimation Results of GDP Growth Rate Q-on-Q % change Autoregressive Intercept (annual rates) Coefficient SER 1972Q1 1980Q3 4.53 0.36 6.60 1980Q3 1989Q3 8.14 0.03 4.49 1989Q3 1997Q3 6.69 0.10 3.01 2001Q1 2007Q4 5.41-0.10 3.17 1971Q1 2007Q4 4.71 0.28 5.65 Note: SER: Standard Error of Regression.. 1980 1970, 1990, 2000.,.. (time-varying parameter), GARCH(generalized autoregressive conditional heteroskedasticity). 22) GDP, 22), ARCH TVP Kim and Nelson(1989).

70 韓國開發硏究 / 2009. Ⅱ, GARCH(1,1), (random walk). Kalman (Maximum Likelihood Estimation) [Figure 10]~[Figure 12] <Table 6>.,., [Figure 11] 1980 0.3, 0.2. 23), [Figure 12],. GARCH, 1970 3.06 1980 1.56, 1990 0.97 2.98. GARCH 1.14. GDP. 3. 부문별변동성특징 GDP. GDP. GDP <Table 7> 1980 1990,. GDP 23) GDP 1980 2.4%, 1.4%.,.

한국경기변동의특징및안정성에대한연구 71 [Figure 10] Time-Varying Parameter Estimation on using GARCH Model 2.00 1.90 1.80 1.70 1.60 1.50 1.40 1.30 1.20 1.10 1.00 1976 1 1978 1 1980 1 1982 1 1984 1 1986 1 1988 1 1990 1 1992 1 1994 1 1996 1 1998 1 2000 1 2002 1 2004 1 2006 1 2008-I [Figure 11] Time-Varying Parameter Estimation on using GARCH Model 0.30 0.25 0.20 1976 1 1978 1 1980 1 1982 1 1984 1 1986 1 1988 1 1990 1 1992 1 1994 1 1996 1 1998 1 2000 1 2002 1 2004 1 2006 1 2008-I [Figure 12] Standard Deviation of GARCH 18 16 14 12 10 8 6 4 2 0 1976 1 1978 1 1980 1 1982 1 1984 1 1986 1 1988 1 1990 1 1992 1 1994 1 1996 1 1998 1 2000 1 2002 1 2004 1 2006 1 2008-I

72 韓國開發硏究 / 2009. Ⅱ <Table 6> Estimation Results of GARCH-Time-Varying Parameter Model estimate 0.0000 0.7250 0.0138 0.0032 0.0001 standard error n.a. 0.0775 0.0022 0.0011 0.0002 Log Likelihood -140.1261 <Table 7> Contribution to Volatility of GDP Growth by component GDP Private Consumption Gov. Consumption Constuction Investment Facilities Investment Export Import Chaninge in Inventories Statistical Discrepancy sum of Covariance 72q180q3 3.25 1.72 0.11 3.95 1.45 0.41 1.70 4.56 2.87-13.53 80q389q3 1.15 0.22 0.08 0.50 0.39 0.54 0.58 2.12 1.63-4.91 89q397q3 0.55 0.25 0.04 0.42 0.50 0.66 0.68 1.12 0.61-3.72 01q107q4 0.61 0.29 0.01 0.17 0.09 2.11 2.14 0.72 0.55-5.47 71q107q4 2.24 1.25 0.06 1.35 0.76 1.02 1.98 2.38 1.48-8.03 Note: Intangible Fixed Assets are excluded as values are zeroes to the second decimal places. <Table 8> Contribution to Volatility of GDP Growth by component (exclusion of Statistical Discrepancy from GDP) GDP Private Consumption Gov. consumption Constuction investment Facilities Investment Export Import Chaninge in Inventories sum of Covariance 72q180q3 7.53 1.71 0.11 3.98 1.46 0.42 1.74 4.63-6.52 80q389q3 3.34 0.23 0.08 0.51 0.39 0.55 0.59 2.11-1.11 89q397q3 1.40 0.25 0.04 0.41 0.49 0.65 0.68 1.11-2.23 01q107q4 0.56 0.29 0.01 0.17 0.09 2.12 2.14 0.72-4.98 71q107q4 4.08 1.24 0.06 1.34 0.76 1.02 1.98 2.39-4.72 Note: Intangible Fixed Assets are excluded as values are zeroes to the second decimal places.

한국경기변동의특징및안정성에대한연구 73,. <Table 8> GDP,., 4.08,. 1970 7.5, 1980 3.3, 1990 1.4, 2001 0.56. GDP. GDP, 24) 1970,,, 2,.. 1980,.,., 1990,,., <Table 8>. 24) GDP,.,.

74 韓國開發硏究 / 2009. Ⅱ,, 0.56,.,, 0,.,., 25).,. GDP.. GDP () GDP., t GDP,, t-1, (volatility). 26),,. 27) (Table 9 ), 25) McConnell and Perez-Quiros(1999). 26) <Table 7> <Table 8>,. 27).

한국경기변동의특징및안정성에대한연구 75 <Table 9> The Variance of Growth Rates of major Expenditure Components of GDP Private Consumption Government Consumption Construction Investment Facilities Investment Export Import 72q180q3 3.93 3.31 259.05 304.20 46.20 85.49 80q389q3 0.64 3.28 19.97 42.66 17.17 15.33 89q397q3 0.77 2.15 9.50 25.67 13.30 7.94 01q107q4 0.99 0.54 6.15 7.01 9.91 11.96 71q107q4 3.62 2.43 79.29 114.99 22.63 38.04 1980,...,, 01Q1~ 07Q4., 1990,. GDP, [Figure 13] [Figure 14], 60%, GDP 50%. 28),. 29) GDP 28) GDP, 2000,. 29),.

76 韓國開發硏究 / 2009. Ⅱ [Figure 13] Standard Deviation of Export Growth Rate and Weight of Export in GDP 10 8 6 4 2 0 W e i g h t (Ex p o r t /G DP, r i g h t ) S t a n da r d De v i a t i o n (l e ft ) (%) 70 60 50 40 30 20 10 0 1971 1 1972 3 1974 1 1975 3 1977 1 1978 3 1980 1 1981 3 1983 1 1984 3 1986 1 1987 3 1989 1 1990 3 1992 1 1993 3 1995 1 1996 3 1998 1 1999 3 2001 1 2002 3 2004 1 2005 3 2007 1 [Figure 14] Standard Deviation of Import Growth Rate and Weight of Import in GDP 14 12 10 8 6 4 2 0 W e i g h t (Ex p o r t /G DP, r i g h t ) S t a n da r d De v i a t i o n (l e ft ) (%) 60 50 40 30 20 10 0 1971 1 1972 3 1974 1 1975 3 1977 1 1978 3 1980 1 1981 3 1983 1 1984 3 1986 1 1987 3 1989 1 1990 3 1992 1 1993 3 1995 1 1996 3 1998 1 1999 3 2001 1 2002 3 2004 1 2005 3 2007 1., <Table 8> 2 1970., 89Q3~97Q3 0.21, 01Q1~ 07Q4-0.20, + -. 30)

한국경기변동의특징및안정성에대한연구 77, 2000, IT 2001,.., 2003,. 2000, 1980.,. GDP [Figure 16]., 1980 31)., GDP,. 30) GDP(Y), (C), (X) Cov(Y, C)>0, Cov(Y, X)>0, Cov(C, X)<0,. GDP,.,., firm level Campbell Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk aggregate level - --. research issue. 31) (great moderation) Kim and Nelson(1999) McConnell and Perez-Quiroz(1999),, 1980.,, Blanchard and Simon(2001).

78 韓國開發硏究 / 2009. Ⅱ [Figure 15] Volatility of GDP Growth Rate of Korea S tandard Deviation Covariance 2.0 1.5 [Figure 16] Volatility of GDP Growth Rate of US 1.0 0.5 0.0 Standard Deviation Covariance -0.5 1960 1 1962 1 1964 1 1966 1 1968 1 1970 1 1972 1 1974 1 1976 1 1978 1 1980 1 1982 1 1984 1 1986 1 1988 1 1990 1 1992 1 1994 1 1996 1 1998 1 2000 1 2002 1 2004 1 2006 1 2008 1, 1980 + -..

한국경기변동의특징및안정성에대한연구 79 Ⅴ. 결론,,.,,.,., 1990. 1997 4/4 ~ 2000 4/4., 1970,.., 2000,,. (voaltility),.,

80 韓國開發硏究 / 2009. Ⅱ. (structural break),., (great moderation),.

한국경기변동의특징및안정성에대한연구 81 참고문헌, 2000,, 23 4,, 2005.,,, 7, 2001., :,, 15 3, 1993.,, KDI, 2007,, 2007.,, 2008-13,, 2008., :, 2007-05,, 2007. Ahmed, S., A. Levin, and B. A. Wilson, Recent U.S. Macroeconomic Stability: Good Policies, Good Practices, or Good Luck? Board of Governors of the Federal Reserve System, 2002. Aizenman, J. and B. Pinto, Managing Volatility and Crises: A Practitiner s Guide Overview, NBER Working Paper 10602, 2005. Andrews, D.W.K., Test for Parameter Instability and Structural Change with Unknown Change Point: A Corrigendum, Econometrica, Vol. 71, Issue 1, 1993. Ball, L. and N. G. Mankiw, A Sticky-price Manifesto, Carnegie-Rochester Conference Series on Public Policy 41, 1994. Baxter, M. and R. G. King, Measuring Busineess Cycles: Approximate Band-Pass Filters for Economics Times Series, Review of Economics and Statistics 81, 1999. Blanchard, O. J. and J. A. Simon, The Long and Large Decline in U.S. Output Volatility, Brookings Papers on Economic Activity, Vol. 2001, No. 1, 2001, pp.135~164. Burns, A. F. and W. C. Mitchell, Measuring Business Cycles, New York: NBER, 1946. Chatterjee, S., From Cycles to Shocks: Progress in Business-Cycle Theory, Business Review, Federal Reserve Bank of Philadelphia, 2000. Cooley, T., Frontiers of Business Cycle Research, Princeton University Press, 1995. DeJong, N. D., Structural Macroeconometrics, Princeton University Press, 2005. DeJong, N. D., H. Dharmarajan, and R. Liesenfeld, On the Structural Stability of U.S. GDP, University of Pittsburgh, 2004.

82 韓國開發硏究 / 2009. Ⅱ Gordon, R. J., Postwar Macroeconomics: The Evolution of Events and Ideas, 1980. Granger, C. W. J., Investgating Causal Relations by Econometric Models and Cross-spectral Methods, Econometrica 34, 1969. Granger, C. W. J., Testing for Causality, a Personal Viewpoint, Journal of Economic Dynamics and Contro, 2, 1980. Hamilton, J. D., What s Real about the Business Cycle? NBER Working Paper No. W11161, 2005. Harvey, A. C. and A. Jaeger, Detrending, Stylized Facts and the Business Cycle, Journal of Econometircs 8, 1993. IMF, The Changing Dynamics of the Global Business Cycle, World Economic Outlook, 2007. Juillard, M. and others, Welfare Based Monetary Policy Rules in an Estimated DSGE Model for the US Economy, ECB Working Paper No. 613, 2006. Jusellius, K., The Cointegrated VAR Model: Methodology and Applicaitons, Oxford University Press, 2007. Kahn, J. A., M. M. McConnell, and G. P. Perez-Quiros, On the Causes of the Increased Stability of the U.S. Economy, Economic Policy Review, May 2002. Kim, C-J and C. R. Nelson, Has the US Economy Become More Stable? A Bayesian Approach Based on a Markov-Switching Model of the Business Cycle, The Review of Economics and Statistics 81, 1999. Kim, C-J and C. R. Nelson, The Time-Varying Parameter Model for Modeling Changing Conditional Variance: The Case of the Lucas Hypothesis, Journal of Business and Economic Statistics, Vol. 7, No. 4, Oct. 1989. Kim, C-J and C. R. Nelson, State Space Models with Markov Switching, 1999. King, R. and M. Watson, The Post-war U.S. Phillips Curve: A Revisionist Econometric History, Carnegie-Rochester Conference Series on Public Policy 41, 1994. Kose, M. A., E. S. Prasad, and M. E. Terrones, Financial Integration and Macroeconomic Volatility, IMF Working Paper 03/50, 2003. Kydland, F. E. and E. C. Prescott, Business Cycles: Real Facts and a Monetary Myth, 1990. Lee, J. and C. R. Nelson, Expectation Horizon and the Phillips Curve: The Solution to An Empirical Puzzle, Journal of Applied Econometrics, 2007. McConnell, M. M. and G. Perez-Quiros, Output Fluctuations in the United States: What Has Changed Since the Early 1980 s? The American Economic Review, Vol. 90, No. 5, 1999, pp.1464~1476. Mitchell, B., Business Cycles: The Problem and Its Setting, New York: NBER, 1927. Mitchell, B., What Happens During Business Cycles, New York: NBER. 1951. Murrary, B., Cyclical Properties of Baxter-King Filterd Time Series, Review of Economics and Statistics 85, 2003.

한국경기변동의특징및안정성에대한연구 83 Nelson, C. R. and C. I. Plosser, Trends and Random Walks in Macroeconomics Time Series, Journal of Monetary Economics 10, 1982. Nelson, C. R. and H-J Kang, Spurious Periodicity in Inappropirately Detrended Time Series, Econometrica 49, 1981. Quant, R. E., Tests of the Hypothesis That a Linear Regression System Obeys Two Separate Regimes, Journal of the American Statistical Association 55, 1960. Slutzky, E., The Summation of Random Causes as the Source of Cyclic Processes, Econometrica 5, 1937. Stock, J. H. and M. W. Watson, Business Cycle Fluctuations in US Macroeconomic Time Series, Handbook of Macroeconomics, Chapter 1, 1999. Stock, J. H. and M. W. Watson, Has the Business Cycle Changed and Why? NBER Working Paper No. 9127, 2002. Stock, J. H. and M. W. Watson, Has the Business Cycle Changed? Evidende and Explanations, Federal Reserve Bank of Kansas City, 2003. Stock, J. H. and M. W. Watson, Undrestanding Changes in International Business Cycle Dynamics, Journal of European Economic Association 3(5), 2005. Walsh, C., Monetary Theory and Policy, The MIT Press, 2003 Zarnowitz, V., Business Cycles: Theory, History, Indicators, and Forecasting, The University of Chicago Press, 1992.

84 韓國開發硏究 / 2009. Ⅱ 부 록 Volatility Decomposition( 경기변동성의분해 ) - (output volatility) (GDP growth) (), GDP.,. ( 1) business cycle: 1972:1~1980:3, 1980:3~1989:3 ( 2) : 1989:3~1997:3, 2001:1~2007:4 ( 3) : 1971:1~2007:4 - t GDP ( ) ( ). : GDP : t-1 : GDP,,,,,,,, -, <Table 7> <Table 8>.

한국경기변동의특징및안정성에대한연구 85 -,. (finite mean and variance).

韓國開發硏究제 31 권제 2 호 ( 통권제 105 호 ) 직간접네트워크외부성하에서인터넷포털기업의시장력분석 진양수 ( 한국개발연구원부연구위원 ) Market Power of Internet Portals with Direct and Indirect Network Externality Yangsoo Jin (Associate Research Fellow, Korea Development Institute) * 진양수 : (e-mail) yjin@kdi.re.kr, (address) Korea Development Institute, Hoegiro 49, Dongdaemun-gu, Seoul, Korea Key Word: (Network Externality), (Market Power), (Internet Service), (Competition Policy) JEL code: L13, L44, L86 Received: 2009. 3. 2 Referee Process Started: 2009. 3. 10 Referee Reports Completed: 2009. 5. 13

ABSTRACT In the internet portal industry, the indirect network externality from portal visitors to advertisers and the direct network externality among portal visitors have important implications for anti-trust policies. This paper examines the existence and the magnitude of the direct/indirect network externality in the Korean internet portal industry and measures its effect on the market power of the internet portals. The results show that the direct/indirect network externality is substantive in the industry hence the market share of a portal in the visitors' side has the leverage effect on its market power in the advertisers side.

직 간접네트워크외부성하에서인터넷포털기업의시장력분석 89 Ⅰ. 서론,.,,.., (market power). 1), (- )., 2). 3) CRk - (HHI),.., 1) (market power), (Perloff, Karp, and Golan[2007], p.1; DOJ and FTC[1997], p.2)., ( [2004], p.7).,, 2 7. 2) Farrell and Shapiro(1990), Willig(1991), Landes and Posner(1981). 3) An undertaking is unlikely to be dominant if it does not have substantial market power. (Office of Fair Trading[1999]; Oftel[2000]). [2004].

90 韓國開發硏究 / 2009. Ⅱ,.,, (indirect network externality). 4) ( ),.,... 5),. A B, A(B). A(B) 4) (), ( ).. (, ), (2008). Argentesi and Filistrucchi(2007).. 5)..

직 간접네트워크외부성하에서인터넷포털기업의시장력분석 91 A(B) B(A) ( )., ().,,,.. 6). 7),,,. ( ). 8),. 9)...,,,. 6) Willig(1991). 7). 8) (2008) Castronova(2005). 9) Evans(2003) (multi-homing), Rochet and Tirole(2006)..

92 韓國開發硏究 / 2009. Ⅱ.,., - (HHI).,.,,.... Ⅱ. 선행연구 (two-sided market).,,,, TV, (Rochet and Tirole[2003, 2006]; Armstrong [2006])., 10) ( ). 11)12) Evans(2003) Wright(2004). 10) Roson(2005), Evans(2003), Rochet and Tirole(2006), Armstrong(2006). 11) (, ) (+).., Armstrong(2006) (). 12) Rochet and Tirole(2006),..

직 간접네트워크외부성하에서인터넷포털기업의시장력분석 93.,,... Rysman(2004).,, ( ). Argentesi and Filistrucchi(2007),., Kaiser(2007) Kaiser and Wright(2006).,. (2002) 21., (2006). (2008). (2008) 6,,,,., (random coefficient model), (empirical distribution)., (2008)

94 韓國開發硏究 / 2009. Ⅱ.. Ⅲ. 국내인터넷포털산업 1. 경쟁구조분석관련문제 (9 ),,,,,,. 13), (search), (communication), (community), (contents), (commerce), 1S-4C. 14) 1S-4C.,,,,,. 1S-4C. 15) 1S-4C,. 1S-4C.. 16) 13) (). 14),,,,,,,,. 15),,,,,. 16).,.

직 간접네트워크외부성하에서인터넷포털기업의시장력분석 95.. (,, ).. 17), A B, A B. <Table 1>, 1S-4C. 18) <Table 1> 6. ( ),. 19)20), 17), A B.., SSNIP,.,. 18), (chosun.com) (google.co.kr), (bugs.co.kr) 6.. 19)., (, ). 20)., (),,,.

<Table 1> Age Distribution of Unique Visitors 1) (unit: %) 96 韓國開發硏究 / 2009. Ⅱ Age 7~12 13~18 19~29 30~39 40~49 50 Total Avg. of 6 Portals[A] 8.2 12.0 25.1 24.7 20.4 9.6 100.0 2 standard deviation 3.8 1.2 5.3 1.6 2.4 1.6 Naver[B] 10.0 (1.8) 12.4 (0.4) 23.5 (-1.7) 23.8 (-0.9) 20.3 (-0.1) 10.1 (0.5) Daum[B] 8.6 (0.4) 12.2 (0.2) 23.9 (-1.2) 24.3 (-0.5) 20.6 (0.3) 10.4 (0.8) Nate[B] 6.1 (-2.1) 11.2 (-0.8) 29.0 (3.9) 24.8 (0.1) 19.1 (-1.3) 9.9 (0.2) Yahoo[B] 10.7 (2.5) 11.7 (-0.2) 22.2 (-3.0) 25.6 (0.9) 21.5 (1.1) 8.3 (-1.3) Paran[B] 6.4 (-1.8) 11.5 (-0.5) 24.6 (-0.5) 25.7 (1.0) 21.8 (1.5) 10.0 (0.4) Empas[B] 7.4 (-0.8) 12.8 (0.9) 27.7 (2.5) 24.2 (-0.5) 18.9 (-1.5) 9.0 (-0.6) google.co.kr[b] 4.5 (-3.7) 9.2 (-2.7) 29.2 (4.1) 27.4 (2.7) 19.7 (-0.7) 9.9 (0.3) chosun.com[b] 4.7 (-3.5) 9.6 (-2.4) 22.6 (-2.5) 28.5 (3.8) 24.2 (3.8) 10.4 (0.8) bugs.co.kr[b] 3.1 (-5.1) 8.8 (-3.2) 35.1 (9.9) 25.4 (0.7) 20.8 (0.4) 6.8 (-2.8) Notes: 1) During March, 2008. Data Source: KoreanClick. 2) B-A s are in the parentheses..,.,,.

직 간접네트워크외부성하에서인터넷포털기업의시장력분석 97.,,.,. (). 21),..,... 22)23) 2. 국내인터넷포털산업의경쟁현황 20. 24). 2008 3 20 21),.. 22), ([2008]). 23), Tanaka vs. University of Southern California(2001) Knderstart vs. Google(2007). NHN() ([2008]). 24) (2008).

98 韓國開發硏究 / 2009. Ⅱ <Table 2> Degree of Competition Ⅰ End-user Market 1) Advertiser Whole Site Search Section Market Unique Visitor Page View Duration Time Unique Visitor Page View (Sales Revenue 2) ) 0.12 0.35 0.38 0.43 0.74 0.57 0.22 0.58 0.62 0.72 0.92 0.79 0.31 0.78 0.77 0.85 0.96 0.87 HHI 650.8 2,217.9 2,334.6 3,012.5 5,883.9 Notes: 1) During March, 2008. 2) During 2006. 3) The end-user market includes 24 major portals but the advertiser market includes only Top 6 portals due to data availability. 2.7, 5.8. 6 (,,,,, ) 67.8 39.9. <Table 2>. (Unique Visitor), (Page View) (Duration Time). 25), 2008 3 HHI 651, 0.12, 0.22, 0.31. HHI 2,218, 2,335. HHI,. HHI 3,013, 5,884. <Table 3>. <Table 2>, 25).,.

직 간접네트워크외부성하에서인터넷포털기업의시장력분석 99 <Table 3> Degree of Competition Ⅱ End-user Market 1) Unique Visitor Page View Duration Time Number of Banners Listed in the Main Page 2) 0.35 0.35 0.38 0.35 0.55 0.60 0.63 0.64 0.65 0.77 0.76 0.79 HHI 1,887.32 2,231.96 2,329.52 Notes: 1) The average share of hourly Unique Visitors (Page Views, Duration Time) during Mar. 11th, 2008 was used for calculation. 2) The average share of monthly banners from November, 2007 to June, 2008 was used for calculation. 3) The end-user market includes 24 major portals but the advertiser market includes only Top 6 portals due to data availability. ().,. 1990., 2000 1,360 ( 2.3%) 2007 1 1 (7 8) 14%. 6.8, 4.6. 26). (2006 6 ).,, 0.57, 0.79, 0.87, (Table 2). 27) 26). 6,. <Table 7>. 27) (2008), p.28. 6

100 韓國開發硏究 / 2009. Ⅱ <Table 3>. Ⅳ. 데이터. (). 28),.,. <Table 4>. 6 1 6 13, 6, 90 6.3. 2008 3 11. 29) [Figure 1]., 20, 30.. [Figure 2] 6. &(). 30) &().. 28) () 15,000,. 29). 2008 3 11. 3. 30) &().

직 간접네트워크외부성하에서인터넷포털기업의시장력분석 101 <Table 4> Summary Statistics: End-user Market Unique Visitor (in thousand) Duration Time (in million minutes) Naver mean 2,428 18.28 std 1,293 10.13 Daum mean 1,412 11.91 std 766 6.56 Nate mean 744 2.97 std 428 1.94 Empas mean 274 1.42 std 148 0.82 Yahoo mean 361 2.34 std 199 1.33 Paran mean 192 0.87 std 112 0.56 Total 2) mean 902 6.30 std 873 7.14 max 2,428 18.28 min 192 0.87 Notes: 1) Each portal s avg. and std. were computed using hourly unique visitors (duration time) during March 11th, 2008. 2) Computed from the avg. s and std. s of above 6 portals.. 3. (Communication),,, (Categorization). (Search),. ()., 20.

102 韓國開發硏究 / 2009. Ⅱ [Figure 1] Hourly Age Distribution of Unique Visitors Share (cumulative) 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 50~ 40~49 30~39 19~29 13~18 Age 7~12 0.2 0.1 0.0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Time [Figure 2] Hourly Unique Visitors by Age Thousand 450 400 350 300 250 200 150 100 50 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Time Age 7~12 13~18 19~29 30~39 40~49 50~ Sum

직 간접네트워크외부성하에서인터넷포털기업의시장력분석 103 <Table 5> Summary Statistics: Portal Characteristics Communication Categorization Search Loading Speed (Seconds) mean 8.833 9.250 9.767 3.383 std 0.606 0.612 0.294 0.469 max 9.500 10.000 10.000 3.840 min 8.000 8.500 9.400 2.790 Notes: 1) The indices for Communication, Categorization, and Search are out of ten. 2) Loading Speed is for March, 2008. <Table 6> Correlations End-user Market Unique Visitor 1) Unique Visitor Share 1)2) Communication 0.293 0.300 Categorization 0.564 0.548 Search 0.505 0.493 Loading Speed 0.089 0.092 # of Unique Visitors (March, 2008) Advertiser Market 0.941 Advertisement Share 2) Advertisement Price 0.837 Duration Time 0.942 # of Banner Slots in Main Page 0.534 Notes: 1) Each portal s average of hourly Unique Visitor (Page Views, Duration Time) shares during Mar. 11th, 2008 was used for computation. 2) The end-user market includes 24 major portals but the advertiser market includes Top 6 portals.. 6.. 6 <Table 5>. ( ) <Table 6>,.

104 韓國開發硏究 / 2009. Ⅱ <Table 7> Summary Statistics: Advertiser Market Naver Daum Nate Empas Yahoo Paran Total 2) mean std mean std mean std mean std mean std mean std mean std # of Advertisements 379 57 320 42 155 30 38 17 127 31 69 39 181 138 Advertisement Price (Thousand Won) 5,139 414 4,522 594 2,415 98 1,496 162 4,172 476 836 168 3,097 1,761 Duration Time (Billion Minutes) 13.20 0.72 8.73 0.60 2.04 0.13 1.14 0.33 1.38 0.35 0.79 0.11 4.55 5.18 # of Banner Slots in Main Page 8 0.71 8 1.49 9 1.36 3 1.19 5 1.06 8 2.39 7 2.06 max 379 5,139 13.20 9 min 38 836 0.79 3 Notes: 1) Each portal s mean and std were computed using monthly number of advertisements (advertisement price, duration time, number of banner slots in main page) from November, 2007 to June, 2008. 2) Computed from the avg. s and std. s of above 6 portals. (). 6 2007 11 2008 6. <Table 7> 6 180 3. <Table 6>.,.

직 간접네트워크외부성하에서인터넷포털기업의시장력분석 105 <Table 6>.. 31).., 2..,. 32). (Nash). 33) Ⅴ. 모형 1. 수요 가. 최종사용자시장 (empirical structural model). - 31) Hotelling, Hotelling ( ) () ( ).. 32) 4).,, TV (-), (+). 33).,..

106 韓國開發硏究 / 2009. Ⅱ..... 34) - (1)., -.,. (- ), (). - (interaction)., (2). - ().,. 35) 34) ( ) 1. 8~9. 35) ()

직 간접네트워크외부성하에서인터넷포털기업의시장력분석 107 1 (Type I extreme value distribution). (1) (mean utility) (3) (1),. -.. (0). 36), -..,. (0). ( ) - (4). -. ( ) ( )., -. -( ). 36) (bugs.co.kr ), (joins.com ), (2008 3, 0.1% ).

108 韓國開發硏究 / 2009. Ⅱ (5). 37) 나. 광고주시장 (). - ( )...,.. 38) - -.,, 1.. ( ) -,.. 37) 230.7(), 247.1(), 112.3(), 144.0(), 189.1(), 150.3()... 38) (,, ).,. (Nevo[2000, 2001] )...

직 간접네트워크외부성하에서인터넷포털기업의시장력분석 109 exp (6) exp, -. 2. 공급.. (). (7) (7) ( ), ( ).. (7).

110 韓國開發硏究 / 2009. Ⅱ Ⅵ. 추정 1. 추정방법론 Generalized Method of Moments(GMM). Berry, Levinsohn, and Pakes(BLP[1995, 2004]). (empirical distribution) (4). exp exp (8) - - BLP fixed point algorithm ln ln. (3) (9),. (9) = 1,,, (3). (9). ( ).,.. (10) 20, (sample analog). (10).

직 간접네트워크외부성하에서인터넷포털기업의시장력분석 111 (8) (). 712, 1318, 1929, 3039, 4049, 50 6 2,. 39),.. (Berry[1994]). (0) (6) ln ln (11). 6 ( ).,,. 40) -. (11). (12), 2 GMM. argmin 39) (Figure 1 Figure 3 ). 40).

112 韓國開發硏究 / 2009. Ⅱ., -.. 2. 내생성과도구변수 (3) (11)., (11) ( ) ( ). ( )... ( ). (3) ( ). () ( ), / (13). (13),...., (3). - (structural disturbance).,..

직 간접네트워크외부성하에서인터넷포털기업의시장력분석 113 (13)., (13) (13).. - - ( ) (11),. 3. 상호작용모수의식별,, (9) (12) GMM., (3) (11). (). [Figure 1],., 20 30 10 4., 30 20.., 10 4, 20 30..,. (10). [Figure 3], 30 20.

114 韓國開發硏究 / 2009. Ⅱ [Figure 3] Hourly Age Distribution of Unique Visitors (Selected Portals) 1.0 Na v e r 1.0 Na t e Share(Cumulative) 0.8 0.6 0.4 0.2 Share(Cumulative) 0.8 0.6 0.4 0.2 0.0 0 2 4 6 8 10 12 14 16 18 20 22 0.0 0 2 4 6 8 10 12 14 16 18 20 22 Time Time Note: Each empty space in above graph indicates the age group (bottom to up): age 7~12. age 13~18, age 19~29, age 30~39, age 40~49, and age 50 or older. 30. Ⅶ. 추정결과 1. 최종사용자시장 <Table 8>. (computational burden) ( ) (6 ). ( 2 ).,., 1030 (, 30 ),

직 간접네트워크외부성하에서인터넷포털기업의시장력분석 115 <Table 8> Estimation Results: End-user Market mean utility Interaction between Age and Portal Characteristics 7~12 13~18 19~29 30~39 40~49 50 Constant -1.219 (0.248) Communication 0.003 (0.083) Categorization -0.310 (0.174) Search -0.010 1.274 0.002 0.080 0.079 0.080 0.705 (0.055) (0.249) (0.108) (0.037) (0.006) (0.012) (0.109) Loading Speed -0.609 (0.032) Monthly Unique Visitors 1.609 (0.089) -0.518 (0.191) 0.080 (0.046) 0.034 (0.012) 0.227 (0.050) 1.097 (0.817) -2.629 (1.075) Objective fnct.: 25.262, Simulation draws: 1,000. Note: Standard errors are in the parentheses..,,,.,.,. 2. 광고주시장 <Table 9>. GMM.,..

116 韓國開發硏究 / 2009. Ⅱ <Table 9> Estimation Results: Advertiser Market OLS Naver 1.450 (1.381) Daum 1.770 (0.966) Nate 1.628 (0.377) Empas 0.699 (0.222) Yahoo 1.805 (0.507) Paran 0.992 (0.251) Advertisement Price 0.029 (0.128) Duration Time 0.961 (0.993) # of Banner Slots on Main Page 0.082 (0.032) Note: Standard errors are in the parentheses. GMM 3.607 (1.940) 3.670 (1.648) 2.393 (0.675) 1.732 (0.502) 3.914 (1.658) 0.964 (0.318) -0.538 (0.456) 1.126 (0.547) 0.147 (0.068) DF: 3 adj. R 2 : 0.989 J stat.: 7.886 obs.: 48 obs.: 48.,. 41).. 41).

직 간접네트워크외부성하에서인터넷포털기업의시장력분석 117... <Table 9> OLS. GMM, OLS.. <Table 10> 2007 11 2008 6. 6 3 3. 42) 1 2.9, 2 2.6, 3 2.2., Willig (1991) (Nash) (+), Willig(1991). 43) 3. 인터넷포털기업의시장력추정 (7) <Table 10> 42) 1, 2, 3. 43) <Table 10> Willig(1991). (7),., <Table 10>,.,.

118 韓國開發硏究 / 2009. Ⅱ <Table 10> Market Power of Portals (unit: Thousand Won) Price (Data) Market Power Estimate Largest Portal 5,139 2,851 Second Largest Portal 4,522 2,627 Third Largest Portal 2,415 2,167 Note: Avg. from November, 2007 to June, 2008. Ⅷ. 네트워크외부성분석 1. 간접네트워크외부성분석,,..,.. 6 1 ( ) 1%p 0.2%p,,,. 44) 1 34.9%, 2 19.8%, 3 9.9%. <Table 11>. 1 1%p 44), 2008 3 11 (5).,,. (7).

직 간접네트워크외부성하에서인터넷포털기업의시장력분석 119 <Table 11> Experiment Ⅰ: Indirect Network Externality Largest Portal Second Largest Portal Third Largest Portal Share of Hourly Unique Visitors (Data, %) 1) 34.91 19.82 9.94 Duration Time 2) (million minutes) Data 13,200 8,730 2,045 Experiment 13,581 8,644 2,006 Difference 381-86 -39 Advertiser Market Share 2) (%) Data 34.61 29.17 14.17 Experiment 35.76 28.59 13.96 Difference(%p) 1.14-0.58-0.21 Advertisement Price 2) (thousand won) Data 5,139 4,522 2,415 Experiment 5,190 4,501 2,410 Difference 51-21 -5 Rate of Difference(%) 1.01-0.49-0.22 Market Power 2) (thousand won) Estimate 2,851 2,627 2,167 Experiment 2,902 2,605 2,162 Rate of Difference(%) 1.79-0.81-0.24 Note: 1) Avg. of hourly Unique Visitor shares during March 11th, 2008. 2) Avg. of monthly numbers from November, 2007 to June 2008. 1.14%p., 0.2%p 0.2%p.,., 1 1%p 1.79%., 0.2%p 0.2%p.,.

120 韓國開發硏究 / 2009. Ⅱ., Evans(2003) Wright(2004).,,., ( ),.. (0).,... 2. 직 간접네트워크외부성동시분석,.., (1) ( ),.. 1

직 간접네트워크외부성하에서인터넷포털기업의시장력분석 121 1% ( ),, (2, 3 ) 1%. 1 31.3, 2 3 29.0, 23.1. <Table 12>. 1 1%, 1.23%p 1.51%p, 2.37%., 2 1%, 0.87%p, 1.13%p, 1.63%., 2,, 1. 3.,,.., Evans(2003) (multihoming). 45).. ( ). Evans(2003) 45) (multi-homing), (single-homing).

122 韓國開發硏究 / 2009. Ⅱ <Table 12> Experiment Ⅱ: Direct and Indirect Network Externality Monthly Unique Visitors 1) (Data, millions) Share of Hourly Unique Visitors 2) (%) Largest Portal Second Largest Portal Third Largest Portal 31.3 29.0 23.1 Data 34.91 19.82 9.94 Experiment 36.15 20.69 10.27 Difference(%p) 1.23 0.87 0.33 Duration Time 3) (million minutes) Data 13,200 8,730 2,044 Experiment 13,675 9,115 2,113 Difference 475 385 69 Advertiser Market Share 3) (%) Data 34.61 29.17 14.17 Experiment 36.12 30.30 14.31 Difference(%p) 1.51 1.13 0.15 Advertisement Price 3) (thousand won) Data 5,139 4,522 2,415 Experiment 5,207 4.565 2,419 Difference 68 43 4 Rate of Difference(%) 1.34 0.96 0.15 Market Power 3) () Estimate 2,851 2,627 2,167 Experiment 2,919 2,669 2,171 Rate of Difference(%) 2.37 1.63 0.17 Note: 1) Monthly Unique Visitors for March, 2008. 2) Avg. of hourly Unique Visitor shares during March 11th, 2008. 3) Avg. of monthly numbers from November, 2007 to June 2008.. Rochet and Tirole(2006). (single-homing),

직 간접네트워크외부성하에서인터넷포털기업의시장력분석 123.,, ( ). 46),. Rochet and Tirole(2006),.,. ( ),. Evans(2003) Rochet and Tirole(2006). Ⅸ. 결론,..,.. 46) Armstrong(2006).

124 韓國開發硏究 / 2009. Ⅱ,.,..,...,.,.,,..

직 간접네트워크외부성하에서인터넷포털기업의시장력분석 125 참고문헌, :, 2008.,,, 10 2, 2002, pp.17~44.,, 2008, 2008.,,, 2008., :, KISDI 04-12,, 2004.,, 2006, 2006, pp.437~452., 2008 2008, 2008. Armstrong, M., Competition in Two-Sided Markets, Rand Journal of Economics, Vol. 37, No. 3, 2006, pp.668~691. Argentesi, E. and L. Filistrucchi, Estimating Market Power in a Two-Sided Market: The Case of Newspapers, Journal of Applied Econometrics 22, 2007, pp.1247~1266. Berry, S., Estimating Discrete-Choice Models of Product Differentiation, Rand Journal of Economics, Vol. 25, No. 2, 1994, pp.242~262. Berry, S., J. Levinsohn, and A. Pakes, Automobile Prices in Market Equilibrium, Econometrica, Vol. 63, No. 4, 1995, pp.841~890. Berry, S., J. Levinsohn, and A. Pakes, Differentiated Products Demand System from a Combination of Micro and Macro Data: The New Car Market, Journal of Political Economy, Vol. 112, No. 1, 2004, pp.68~105. Castronova, E., Synthetic Worlds: The Business and Culture of Online Games, The University of Chicago Press, 2005. DOJ and FTC, Merger Guidelines, 1997. Evans, D., The Antitrust Economics of Multi-Sided Platform Markets, Yale Journal of Regulation 20, 2003, pp.325~381. Farrell, J. and C. Shapiro, Horizontal Merger: An Equilibrium Analysis, The American Economic Review, Vol. 80, 1990, pp.107~126. Kaiser, U., When Pricing below Marginal Cost Pays Off: Optimal Price Choice in a Media Market

126 韓國開發硏究 / 2009. Ⅱ with Upfront Pricing, CIE Discussion Paper, No. 05-49, 2007. Kaiser, U. and U. Wright, Price Structure in Two-Sided Markets: Evidence from the Magazine Industry, International Journal of Industrial Organization, Vol. 24, No. 1, 2006, pp.1~28. Landes, W. M. and R. A. Posner, Market Power in Antitrust Cases, Harvard Law Review, Vol. 95, 1981, pp.937~996. Nevo, A., Mergers with Differentiated Product: The Case of The Ready-to-Eat Cereal Industry, RAND Journal of Economics, Vol. 31, 2000, pp.395~421. Nevo, A., Measuring Market Power in the Ready-to-Eat Cereal Industry, Econometrica, Vol. 69, 2001, pp.307~342. Office of Fair Trading, The Competition Act 1998: Assessment of Market Power, 1999. Oftel, Guidelines on Market Influence Determinations, 2000. Perloff, J. M., L. S. Karp, and A. Golan, Estimating Market Power and Strategies, Cambridge University Press, 2007. Rochet, J. C. and J. Tirole, Platform Competition in Two-Sided Markets, Journal of European Economic Association, Vol. 1, No. 4, 2003, pp.990~1029. Rochet, J. C. and J. Tirole, Two-Sided Markets: A Progress Report, Rand Journal of Economics, Vol. 37, No. 3, 2006, pp.645~667. Roson, R., Two-Sided Markets: A Tentative Survey, Review of Network Economics, Vol. 4., No. 2, 2005, pp.142~160. Rysman, M., Competition between Networks: A Study of the Market for Yellow Pages, Review of Economic Studies, Vol. 71. No. 2, 2004, pp.483~512. Willig, R. D., Merger Analysis, Industrial Organization Theory, and Merger Guidelines, Bailey and Whinston (eds.), Brookings Papers on Economic Activity, Microeconomics, 1991, pp.281~312. Wright, J., One-Sided Logic in Two-Sided Markets, Review of Network Economics, Vol. 3, Issue 1, 2004, pp.44~64.

韓國開發硏究제 31 권제 2 호 ( 통권제 105 호 ) 금연법강화가흡연에미치는영향 김범수 ( 고려대학교정경대학경제학과조교수 ) 김아람 ( 고려대학교정경대학경제학과대학원생 ) The Impacts of Smoking Bans on Smoking in Korea Beomsoo Kim (Assistant Professor, Department of Economics, Korea University) Ahram Kim (Graduate Student, Department of Economics, Korea University) * 김범수 : (e-mail) Kimecon@korea.ac.kr, (address) Department of Economics, Korea University, Anam-dong, Seongbuk-gu, Seoul 136-701, Korea 김아람 : (e-mail) ahram320@hanmail.net, (address) Department of Economics, Korea University, Anam-dong, Seongbuk-gu, Seoul 136-701, Korea Key Word: (Smoking Ban), (Current Smoker), (Cigarettes Per Day) JEL code: J21, I18 Received: 2009. 3. 2 Referee Process Started: 2009. 3. 6 Referee Reports Completed: 2009. 10. 15

ABSTRACT There is a growing concern about potential harmful effect of second-hand or environmental tobacco smoking. As a result, smoking bans in workplace become more prevalent worldwide. In Korea, workplace smoking ban policy become more restrictive in 2003 when National health enhancing law was amended. The new law requires all office buildings larger than 3,000 square meters (multi-purpose buildings larger than 2,000 square meters) should be smoke free. Therefore, a lot of indoor office became non smoking area. Previous studies in other counties often found contradicting answers for the effects of workplace smoking ban on smoking behavior. In addition, there was no study in Korea yet that examines the causal impacts of smoking ban on smoking behavior. The situation in Korea might be different from other countries. Using 2001 and 2005 Korea National Health and Nutrition surveys which are representative for population in Korea we try to examine the impacts of law change on current smoker and cigarettes smoked per day. The amended law impacted the whole country at the same time and there was a declining trend in smoking rate even before the legislation update. So, the challenge here is to tease out the true impact only. We compare indoor working occupations which are constrained by the law change with outdoor working occupations which are less impacted. Since the data has been collected before (2001) and after (2005) the law change for treated (indoor working occupations) and control (outdoor working occupations) groups we will use difference in difference method. We restrict our sample to working age (between 20 and 65) since these are the relevant population by the workplace smoking ban policy. We also restrict the sample to indoor occupations (executive or administrative and administrative support) and outdoor occupations (sales and low skilled worker) after dropping unemployed and someone working for military since it is not clear whether these occupations are treated group or control group. This classification was supported when we examined the answers for workplace smoking ban policy existing only in 2005 survey. Sixty eight percent of indoor occupations reported having an office smoking ban policy compared to forty percent of outdoor occupation answering workplace smoking ban policy. The estimated impacts on current smoker are 4.1 percentage point decline and cigarettes per day show statistically significant decline of 2.5 cigarettes per day. Taking into account consumption of average sixteen cigarettes per day among smokers it is sixteen percent decline in smoking rate which is substantial. We tested robustness using the same sample across two surveys and also using tobit model. Our results are robust against both concerns. It is possible that our measure of treated and control group have measurement error which will lead to attenuation bias. However,

ABSTRACT we are finding statistically significant impacts which might be a lower bound of the true estimates. The magnitude of our finding is not much different from previous finding of significant impacts. For cigarettes per day previous estimates varied from 1.37 to 3.9 and for current smoker it showed between 1%p and 7.8%p.

130 韓國開發硏究 / 2009. Ⅱ Ⅰ. 서론 2002, 30%, 10%. (Surgen General) 1988, (Royal College of Physicians), ( (World Health Organization), Tobacco Free Initiative ). 1) 1986 /(National Academy of Science / National Research Council) (Environmental Tobacco Smoke) (Secondhand Smoke). (Environmental Protection Agency) 1992 A (Evans et al. [1999]). 1998 7 (Tobacco Free Initiative). 2001 52.9% OECD 30, 52%. 20.3% 18%., 5.4% OECD, 14.7%. 30.4% (34%), (31.7%) (OECD[2008])., 1986. 1995. 1) http://www.who.int/tobacco/research/cessation/en/

금연법강화가흡연에미친영향 131, 51%, 13% ( [2002])., 59.5%.. ([1999]). 36% ( [2002])., 1995 9 11 6 7 3,000 2,000. 2003. 2003 4 1 6 7 3,000 2,000,,., 2003. (Evans et al.[1999]),.,. 2003

132 韓國開發硏究 / 2009. Ⅱ.,,,.. (control group). (, ). 2003 (treatment group).,.,. 2001 2005. 2003.,,,., (robustness).. Ⅱ. 문헌연구. (2002)

금연법강화가흡연에미친영향 133 5. 199,,.. 97.5%.. 11 128,,. (2001) 1960 1997., 10% 2.7%..,.,. (2001) 1998 60., 1 GDP,.,, 1 GDP 1, 1.,, 15 1 15.

134 韓國開發硏究 / 2009. Ⅱ,.. Petersen et al.(1988) 1,260.. Longo et al.(1998) (Joint commission on accreditation of healthcare organizations) 1993 12 31 5. Biener et al.(1989) 1985 6, 1. Evans et al.(1999) 1991 1993 National Health Interview Survey 5.7%p 2.3. (instrumental variable),.,.....

금연법강화가흡연에미친영향 135 Ⅲ. 한국의금연정책들.,. 1986 1980. 1998, 2000,,,. 2005 246. 2000 TV,,. KBS, SBS, MBC TV,. 2004,,,,,,,. 1998 7 2004 80, 2006 312..,,,,. 1994 7, 2004 12 30 354 500( 29%).. 1990, 1995

136 韓國開發硏究 / 2009. Ⅱ., (2002) (1999). (2002), 51%, 13%. (1999) 59.5%. ([1999]). 2003 7,,. 2),,,.. 2006,.. 2003. 2). 6 ( ) 94 ( ) ( ). < 1999.10.28, 2003.4.1> 1. 3 2 7 ( ). 1. 1. 61

금연법강화가흡연에미친영향 137 Ⅳ. 실증분석 1. 분석자료.,. 1,500,. 15, 20. 3,000 1999, 2003, 2006 4,,. 16 1998 3 2001 2005 2008.. 2003..,,. 2003 2001 2005. 3) 4,000,,,,,. 2000 200 2026. 3) 1,.

138 韓國開發硏究 / 2009. Ⅱ.,,,,. 2. 연구변수가. 종속변수.?. 1, 0. 4),. 60 (5) 60. 5) 나. 독립변수 (Treat) (Y2005). (difference in differences).,,, 2005., 1, 0.,, 3., 998 998 censor(top coding). 998 998 1.75. 6) (2005=100).,. 4) 2001 2005. 2001? 2005. 2001 0. 8,017 17. 5). 6).

금연법강화가흡연에미친영향 139 3. 분석모형 two part model. two part model, (Duan, Manning, Morris, and Newhouse [1983]; Madden[2008]). (participation equation),. Y it = X it b + Y2005 t * Treat i c + Y2005 t d + δ i + Region i + ε it (1) Y it (i) (t) (1=, 0= ), X,,,, (2005 ),, 14.. 2001, 2005. 2005 1, 2001 0. (Treat) (Y2005), c. Y2005t. Treat (δ i ). 13 (region). 7) (Linear Probability Model: LPM) Logit Probit. Y2005*Treat Ai and Norton(2003). (LPM). (conditional 7) 2001 2005 2005, 2001,,.,,,,,,,,,,,, 13.

140 韓國開發硏究 / 2009. Ⅱ use specification). (Y it Y it >0) = X it b+ Y2005 t * Treat i c+ Y2005 t d + δ i + Region i + ε it (2) Y it (Ordinary Linear Square: OLS). 2003 20 65. 8.,,,,,,, /, (, ),.. 8),. 2001 41%, 2005 37%.. 2005 3.? 1, 0. 67.9%, 62%.., 40.6%, 39.6%. (Treat) 1, (Treat) 0. 8),.

금연법강화가흡연에미친영향 141,,. (Treat=1) (Treat=0), (control group) (treatment group). (Treat).,.,., (Moore et al.[1997]). (attenuation bias) (Wooldridge[2002], pp.73~76). (lower bound). Ⅴ. 회귀분석결과 < 1>. 2001 2005 3,948 4,069. 9) 2001, 2005. 2001 40 2005 39, 59% 61%.. ( ) 35% 40%. 242 272 4 12%. 10) 9) 1998,. 10) (2005 ) 2001.

142 韓國開發硏究 / 2009. Ⅱ <Table 1> Sample Characteristics Variable Year 2001 2005 Mean Age 39.82 (10.52) 39.49 (10.7) Monthly Real Income (Year 2005=100) 242.35 271.54 Unit: % Male 59.24 61.06 Education Middle School Graduates or less 23.40 18.53 High School Graduates 41.61 41.33 College Graduates and over 35.01 40.14 Occupations Indoor Occupations Executive, Administrative, etc 10.72 11.22 Administrative Support 9.11 11.41 Outdoor Occupations Sales Occupations 18.93 16.49 Factory Worker 19.95 23.81 All Current Smoker 0.405 0.378 Cigarettes Per Day(smokers Cigarettes Per Day(all only) workers) 16.234 (7.86) 7.183 (9.61) 15.908 (9.05) 6.007 (9.51) Male Current Smoker 0.644 0.571 Cigarettes Per Day(smokers only) 16.645 16.445 (7.77) (8.94) Cigarettes Per Day(all workers) 10.684 (10.12) 9.390 (10.58) Female Current Smoker 0.058 0.075 Cigarettes Per Day(smokers only) 9.543 9.454 (6.02) (7.83) Cigarettes Per Day(all workers) 0.543 (2.63) 0.705 (3.27) Observations 3,948 4,069 Note: Korea National Health and Nutrition Survey wave 2 (2001) and wave 3 (2005) is used. Sample are restricted to worker age between 20 and 65. Occupations like military personal, farmer are dropped. Executive, administrative and administrative support are classified as indoor workers. Sales occupations and factory workers are classified as outdoor workers. Real monthly household income (in 2005 won) in the unit of 10,000 won is used. Sample weights are used for all calculations. Standard deviations are in parenthesis.

금연법강화가흡연에미친영향 143, 10.7% 11.2%, 9.1% 11.4%. 2001 18.9% 2005 16.5% 2.4%p, 20% 23.8% 3.8%p.,, 2001 2005. 2001 40.5% 2005 37.8% 2.6%p. 2001 16.2 2005 15.9, 7 6. 2001 64% 2005 57% 11 9. 5.8% 7.5% 2005 2001. 0.5 0.7. < 2> ( =1, =0) (binary variable) (participation equation). (binary) (LPM). (Y2005*Treat),,,, 14, 12.5%p..,.. 32,. 40 0.8%p. 54%p. 1 (1) 0.01%,. 8,017.,

144 韓國開發硏究 / 2009. Ⅱ <Table 2> Impact of Smoking Bans on Current Smoker Current Smoker (1) (2) (3) (4) (5) Y2005*Treat -0.125 (0.0159) -0.0894 (0.0170) -0.0892 (0.0170) -0.0422 (0.0204) -0.0410 (0.0204) Y2005 0.0119 (0.0115) 0.00185 (0.0116) 0.000373 (0.0116) -0.0168 (0.0123) 0.0457 (0.0133) Male 0.541 (0.00929) 0.551 (0.00952) 0.550 (0.00953) 0.548 (0.00975) 0.609 (0.0128) Age 0.00962 (0.00368) 0.00792 (0.00369) 0.00808 (0.00369) 0.00754 (0.00372) 0.00741 (0.00371) Age2/100-0.0174 (0.00444) -0.0163 (0.00444) -0.0164 (0.00445) -0.0158 (0.00447) -0.0158 (0.00446) Logincome -0.0590 (0.00743) -0.0507 (0.00760) -0.0511 (0.00766) -0.0486 (0.00767) -0.0485 (0.00768) Having Children under 14-0.00625 (0.0112) -0.00477 (0.0113) -0.00538 (0.0113) -0.00618 (0.0113) -0.00670 (0.0113) High School Graduates -0.00502 (0.0148) -0.00482 (0.0148) 0.00142 (0.0152) 0.000773 (0.0151) College Graduates and Over -0.0762 (0.0172) -0.0739 (0.0173) -0.0519 (0.0192) -0.0521 (0.0192) Region Dummy Y Y Y Occupation Dummy Y Y Year*male Y R 2 0.313 0.317 0.319 0.320 0.323 Observations 8,017 8,017 8,017 8,017 8,017 Note: Linear Probability Model is used. Survey wave 2 (2001) and 3 (2005) is used. Less than high school graduates is omitted group. Four occupation (executive, administrative support, sales, factoryworker) is considered. Treat =1 if executive and administrative support and 0 otherwise. Robust standard errors are in parenthesis. Sample weights are used. 8.9%p.,, 95% 7.6%p.

금연법강화가흡연에미친영향 145 13,.. 4.2%p. ek. 4.1%p, 95%. < 3> two part model. < 2>, (OLS).,,. 2.5, 16 16%. 95%. < 4>,. 11) 4.2%p.. 3.2%p, 90%. 2.4, 4.7, 95% 90%. 11),. Evans et al. (1999) extensive margin intensive margin. Tobit 0 censor,.

146 韓國開發硏究 / 2009. Ⅱ <Table 3> Impact of Smoking Ban on Smoking, Two Part Model Y2005*Treat Y2005 Male Age Age2/100 Logincome Having Children under 14 High School Graduates College Graduates and Over Current Smoker -0.0410 (0.0204) 0.0457 (0.0133) 0.609 (0.0128) 0.00741 (0.00371) -0.0158 (0.00446) -0.0485 (0.00768) -0.00670 (0.0113) 0.000773 (0.0151) -0.0521 (0.0192) Cigarettes Per Day Only Smokers -2.476 (0.684) 0.364 (1.033) 7.540 (0.767) 0.387 (0.131) -0.391 (0.161) 0.0241 (0.257) 0.0319 (0.357) -0.989 (0.545) -2.700 (0.615) Region Dummy Y Y Occupation Dummy Y Y Year*male Y Y R 2 0.323 0.091 Observations 8017 3037 Note: OLS is used. Less than high school graduates is omitted group. Four occupation (executive,administrativesupport,sales,factoryworker) is considered. Treat =1 if executive and administrative support and 0 otherwise. Survey wave 2 (2001) and 3 (2005) is used. Strict smoking ban rule is applied in 2003. Robust standard errors are in parenthesis. Sample weights are used.

금연법강화가흡연에미친영향 147 <Table 4> Impacts of Smoking Ban, by Gender Cigarettes per day Cigarettes per day Current smoker Only smokers All adults Y2005*Treat -0.0423 (0.0315) Male -2.376 (0.707) -1.887 (0.637) Mean 0.6016771 16.53405 10.57809 R 2 0.063 0.053 0.0604 Observations 4722 2835 Female 4706 Y2005*Treat -0.0322-4.744-0.485 Mean (0.0179) 0.0671665 (2.747) 9.487763 (0.205) 0.6365444 R 2 0.045 0.116 0.0352 Observations 3,295 202 3,294 Note: See Notes on Table 2 for the first column and Table 3 for the second column. 1.9, 0.5, 95%. Ⅵ. 추정의안정성확인.,.., 2001 2005. 2001 2005 20 65. 2001 62 2005 66 20 65 2005 20

148 韓國開發硏究 / 2009. Ⅱ. 2001 20 61 2005 24 65. < 5>,, 2001 2005.,, 8,017 261 7,756, 3.9%...., 2001 2005 (Treat=1) (Treat=0).., 2001 2003 2005... 4. 2001 2005. 12) 2001 2005 2,862 72 2.5%. (selection, or sorting). 12).,,,,,,,,,.

금연법강화가흡연에미친영향 149 <Table 5> Robustness Check for Different Cohorts Current Y2005*Treat -0.0410 (0.0204) smoker Cigarettes per day Only smokers Age 20~65 in 2001 or 2005-2.476 (0.684) Cigarettes per day All adults -1.331 (0.396) R 2 0.323 0.091 0.263 Observations 8017 3037 8000 Age 20~61 in 2001 and Age 24~65 in 2005 Y2005*Treat -0.0393 (0.0208) -2.365 (0.693) -1.348 (0.410) R 2 0.324 0.089 0.262 Observations 7756 2969 7739 Note: Korea National Health and Nutrition Survey second and third waves are used. OLS is used., (HR [2009]),,,,. 0. 0, 0 0 Tobit (unbiased). < 6> (OLS) -1.331 1.3. Tobit -3.35, 95%. Tobit, -1.03.

150 韓國開發硏究 / 2009. Ⅱ <Table 6> Comparison between OLS and Tobit Smoking Ban OLS -1.331 (0.396) Cigarettes per day All adults Tobit -3.345 (0.955) Observations 8,000 8,000 Note: Marginal effect of tobit model is -1.033 Ⅶ. 결론, 2003 3,000 2,000. 13).,..,,.,. 4.1%p, 2.5. 95%. 13).

금연법강화가흡연에미친영향 151,.,. (attenuation bias). (lower bound)... 2001. 2.5 ( 16% ) Evans et al.(1999) 1.37 3.9, 4.1%p Evans et al.(1999) 1%p 7.8%p, 5.7%p, Longo et al.(1998) 2.7%p.

152 韓國開發硏究 / 2009. Ⅱ 참고문헌,,, 2001.,,,, 2002., 2008, 2008.,, 2003.8.,,, 2001.,, 1999. HR,,, 2009. 5. 15. Ai, Chunrong, and Edward C. Norton, Interaction Terms in Logit and Probit Models, Economics Letters 80, 2003, pp.123~129. Biener, Lois, David Abrams, Michael Follick, and Larry Dean, A Comparative Evaluation of a Restrictive Smoking Policy in a General Hospital, American Journal of Public Health 79(2), 1989, pp.192~195. Duan, N., W. Manning, C. Morris, and J. Newhouse, A Comparison of Alternative Models for the Demand for Medical Care, Journal of Business Economics and Statistics 1(2), April 1983, pp.115~126. Evans, William N. Matthew C. Farrelly, and Edward B. Montgomery, Do Workplace Smoking Bans Reduce Smoking? American Economic Review 89(5), September 1999, pp.729~747. Longo, Daniel, Mary Feldman, Robin Kruse, Ross Brownson, Gregory Petroski and John Hewett, Tobacco Control, 1998. 7, pp.47~55. Madden, David, Sample Selection Versus Two-part Models Revisited: The Case of Female Smoking and Drinking, Journal of Health Economics 27(2), 2008, pp.300~307. Moore, Jeffrey, Linda Stinson, and Edward Welniak, Jr., Income Measurement Error in Surveys: A Review, Proceeding Monograph from the Cognitive Aspects of Survey Methodology II Conference, Charlottesville, VA, 1997. OECD, Health Data, 2008. Petersen, Lyle, Steven Helgerson, Carol Gibbons, Chanelle Calhoun, Katherine Ciacco, and Pitchford Karen, Employee Smoking Behavior Changes and Attiudes Following a Restirictive Policy

금연법강화가흡연에미친영향 153 on Worksite Somiking in a Large Company, Public Hearth Reports 103(2), 1988, pp.115~120. Wooldridge, Jeffrey, Econometric Analysis of Cross Section and Panel Data, MIT Press, 2002, pp.73~76.

韓國開發硏究제 31 권제 2 호 ( 통권제 105 호 ) 외환위기이후흉악범죄의증가와 정부의범죄억지정책 김두얼 ( 한국개발연구원부연구위원 ) 김지은 ( 한국개발연구원연구원 ) Growth of Felonies after the 1997 Financial Crisis in Korea Duol Kim (Associate Research Fellow, Korea Development Institute) Jee Eun Kim (Research Assistant, Korea Development Institute) * 본논문의초고는경제학공동학술대회법경제학회분과, 서울대학교법과대학법과문화포럼및경제학부경제사세미나, 경제사상연구회, 2009 Annual Meeting of Asian Law and Economics Association, KDI Journal of Economic Policy Conference, 2009 KIEA International Conference 에서발표되었다. 유익한논평을해주신김종면, 이인재, 윤용준선생님및세미나참석자여러분께감사드린다. ** 김두얼 : (e-mail) duolkim@kdi.re.kr, (address) Korea Development Institute, 49 Hoegiro, Dongdaemun-gu, Seoul 130-740, Korea 김지은 : (e-mail) mangodream@kdi.re.kr, (address) Korea Development Institute, 49 Hoegiro, Dongdaemun-gu, Seoul 130-740, Korea * Key 한진희 Word: -제1 (Felonies), 저자, 최용석 -공저자 1997 (Financial Crisis of 1997), (Deterrence Policy), (Crime Rate), (Repeated Offender) JEL code: H4, H59, K14, K42, N4 Received: 2009. 5. 28 Referee Process Started: 2009. 5. 28 Referee Reports Completed: 2009. 10. 21

ABSTRACT The Korean economy successfully overcame the macroeconomic downturns driven from the Asian financial crisis in a very short period of time. The economic shock, however, generated a variety of social problems, one of which was the increase in felonies (homicides, robbery, rape, and arson), or degradation of public safety. We argue that the Korean criminal policy has not been effective to ameliorate the rising trends in crime caused by the financial crisis. In order to substantiate this claim, we assess the effectiveness of criminal policy: policing, sentencing, and corrections. First, there has been resource shortage in policing since the 1997 financial crisis. For the past ten years, the investment of human resource and budget in the police has been virtually stagnant, as well as in prosecutors investigation activities. The insufficient resource allocation in policing caused a huge decline in arrest rates and prosecution rates. Second, the Korean judicial system has not increased the severity of punishment. Comparing the pre- and the post-financial crisis period, the average length of prison sentence by the courts has declined. Given the degrading in the quality of crime and the decreasing amount of inputs into the policing and prosecution, the government should have increased the severity of punishment to deter crime. Third, we found that the government hired more officers and allocated larger budget into prison and probation. However, it is difficult to suggest that the increased level of resources in correctional programs have been effective in preventing released prisoners from committing future crimes. This is because the number of repeat offenders convicted of more than a third offense increased dramatically since 1997, pushing felonies upward. In sum, the government organizations failed to respond respectively or to make coordinated actions, eventually causing a dramatic increase in crimes. This research brings explicit policy implications. In order to prevent possible additional degradation of public safety, the government must put more efforts into increasing the effectiveness of policy and to investing more resources into said policies. We also emphasize the importance of the institutional mechanisms which foster policy coordination among the Police, the Prosecutor s Office, the Ministry of Justice, and other relevant government organizations.

ABSTRACT

158 韓國開發硏究 / 2009. Ⅱ Ⅰ. 서론 1997 10,.,,,,,. 1),.,. (2008), (2007), (2008), (2003),. Cook and Zarkin(1985), Raphael and Winter-Ebmer(2001), Donohue and Levitt (2001), Machin and Meghir(2004). Levitt (2004, p.171),.. 1997, 1).

외환위기이후흉악범죄의증가와정부의범죄억지정책 159 (, deterrence)..,.,..,,,,,,.,,,.. (index crime).,.,,,,,. Ⅱ. 범죄억지정책의틀 (security) (deterrence) (prevention) (punishment)..,

160 韓國開發硏究 / 2009. Ⅱ <Table 1> Theoretical Framework of Criminal Deterrence Policy (A) Policy measures and goals of criminal deterrence Policy Measures Policy Instruments Intermediate Variables Policy Goal Police Forces and Expenditure Policing Sentencing Workforce and Expenditure in the Prosecution Office Prosecutor s demanded sentence Sentence by the courts Arrest Rates Control the number of crime and crime rate Correction Workforce Expenditure Repeat Offense Rates (B) Government organizations in charge of policy instruments Criminal Deterrence Policy Policing The Police O Government Organizations The Prosecution Apprehension O O The Court Sentencing O O Correction The Ministry of Justice O. 2) (incapacitation), (rehabilitation).. () (), (), () (Table 1 ).,,. 2) Becker(1968).

외환위기이후흉악범죄의증가와정부의범죄억지정책 161,,.,....,,,. Becker(1968), Ehrlich (1973), Wolpin(1978)..,,.,,,.,..,..,,,,

162 韓國開發硏究 / 2009. Ⅱ, (coordination failure).,,..,. Ⅲ. 흉악범죄의추이.,..,,, 4 () ( ),. 3), 2007 21,000 (Table 2 ). 1.1%, 2.5%,,,, 5.4%, 21.4%, 3), (), (),. (),

외환위기이후흉악범죄의증가와정부의범죄억지정책 163 <Table 2> The Number of Crimes and Trial Cases in 2007 Crime Occurrence and Treatment (Criminal Analysis) The criminal trial (Yearbook of Judicature) Name of Offense Reported case The number of Cases in prosecuted Trial Name of Offense Received case Criminal Offense 845,311 269,521 63,695 Criminal Offense 110,388 Homicide 1,124 674 674 Homicide 762 Robbery 4,470 1,397 1,397 Theft 212,530 22,353 10,736 Robbery and Theft 14,044 Arson 1,694 402 392 Accidental Fire 1,908 405 27 Arson and Accidental Fire 803 Rape 13,634 4,052 2,017 Adultery 613 41 36 Rape and Adultery 2,153 Others 609,338 240,197 48,416 Others 92,626 Special Law Offense 1,120,666 772,952 53,134 Special Law Offense 139,784 Total 1,965,977 1,042,473 116,829 Total 250,172 Felony 20,922 6,525 4,480 Violent Crime 18,973 5,574 3,146 Violent Crime 3,718 Notes: Felony includes homicide, robbery, arson, and rape. Violent crime refers to homicide, arson, accidental fire, rape, and adultery. See the text for detailed discussion on the definition of the violent crime. Source: The Supreme Public Prosecutor s Office, Criminal Analysis 2008, pp.50~55, 343~345; National Court Administration, Yearbook of Judicature 2008, pp.864~873. 8.1%, 65.1%. 21,000 1960 3,000 6 (Figure 1 ). 4) 70%. 10 1960 10 2007 43 4. 4) 1964 1967.

164 韓國開發硏究 / 2009. Ⅱ [Figure 1] Trends in Felonies and Felony Rates(1967~2007) 25000 50 45 20000 40 35 Number of felonies 15000 10000 30 25 20 Felony rates 15 5000 10 5 0 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year 0 Number of felonies Felony rate Notes: Felony rate is computed per 100,000 people. Source: The Supreme Public Prosecutor s Office, Criminal Analysis; Korea National Statistical Office, Korea Statistical Yearbook.. 1960 1970 3,000~5,000 1980 10,000. 1980 1990 15 10,000 1997 2007 21,000.. 5),,,. 1960 2007 2, 4.5, 8, 10, 1997 (Figure 2 ). < 3> 1977 2007 30, 5) 1970 1997.

외환위기이후흉악범죄의증가와정부의범죄억지정책 165 [Figure 2] Trends in Felonies and Felony Rates by Category(1967~2007) (A) Homicide 1200 3.0 1000 2.5 800 2.0 Number of Crimes 600 1.5 Crime rate 400 1.0 200 0.5 0 1965 1970 1975 1980 1985 1990 1995 2000 2005 0.0 Year Number of crimes Crime rate (B) Robbery 8000 18 7000 16 6000 14 Number of crimes 5000 4000 3000 12 10 8 6 Crime rate 2000 4 1000 2 0 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year 0 Number of crimes Crime rate

166 韓國開發硏究 / 2009. Ⅱ (C) Arson 2000 4.0 1800 3.5 Number of crimes 1600 1400 1200 1000 800 3.0 2.5 2.0 1.5 Crime rate 600 400 200 1.0 0.5 0 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year 0.0 Number of crimes Crime rate (D) Rape 16000 30 14000 25 12000 Number of crimes 10000 8000 6000 20 15 10 Crime rate 4000 2000 5 0 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year 0 Number of crimes Crime rate Notes: Crime rate is computed per 100,000 people. Source: The Supreme Public Prosecutor s Office, Criminal Analysis; Korea National Statistical Office, Korea Statistical Yearbook.

외환위기이후흉악범죄의증가와정부의범죄억지정책 167 <Table 3> Proportion of Felonies and the Growth Rate Number of Crimes Proportion(%) 1977 1987 1997 2007 1977~2007 Felony 5,229 9,135 11,914 20,964 Homicide 516 631 815 1,094 Robbery 1,204 3,023 4,425 4,577 Arson 330 558 885 1,690 Rape 3,179 4,923 5,790 13,604 Felony 100.0 100.0 100.0 100.0 Homicide 9.9 6.9 6.8 5.2 Robbery 23.0 33.1 37.1 21.8 Arson 6.3 6.1 7.4 8.1 Rape 60.8 53.9 48.6 64.9 Average Annual Growth Rate (%) Felony 6.2 2.9 6.5 5.2 Homicide 3.4 2.4 3.9 3.2 Robbery 12.1 6.4 1.5 6.7 Arson 4.9 5.9 9.3 6.7 Rape 5.0 2.0 9.6 5.5 Notes: The number of cases is the three-year average before and after the base year. The average for 2007 is the average of 2006 and 2007. The average annual growth rate is computed by average value of annual growth rates. Source: The Supreme Public Prosecutor s Office, Criminal Analysis....

168 韓國開發硏究 / 2009. Ⅱ [Figure 3] Intentional Homicide Rates of the OECD Countries(2000~2004) 6 Intentional Homicides (per 100,000 people) 5 4 3 2 1 0 United States Turkey Switzerland Finland Sweden Slovenia Czech Republic Korea Hngrary United Kingdom Canada Portugal France Poland Nation Belgium Australia New Zealand Italy Spain Germany Iceland Netherands Norway Luxembourg Austria Denmark Greece Notes: Homicide rate is computed per 100,000. The homicide rates of Mexico marks 13 per 100,000 people, the highest among the OECD nations, but excluded in this figure. The average of homicide rates in the OECD countries is 2.16 (1.77 excluding Mexico), median is 1.6. Source: UNDP, Human Development Report 2007/2008, pp.322~325...,, 2000 2005 UNDP, OECD (Figure 3 ). 6), OECD..,, 6) UNDP 1990 Human Development Report, 2006, 2007/8.

외환위기이후흉악범죄의증가와정부의범죄억지정책 169, DNA..,..,..,. 40 2, 8, 1997...,,,.. [Figure 4]. 1970 2007 3.5, () 3.0 4.0..

170 韓國開發硏究 / 2009. Ⅱ 5.0 4.5 4.0 3.5 [Figure 4] Trends in Felony, Violent Crime, and All Crimes(1977~2007) Crime Index 3.0 2.5 2.0 1.5 1.0 0.5 0.0 1975 1980 1985 1990 1995 2000 2005 2010 Year Felony All crimes Violent crime Notes: The average of crimes occurred from 1970 to 1980 is indexed as 1. Source: The Supreme Public Prosecutor s Office, Criminal Analysis.. [Figure 1],. 1997,.. Ⅳ. 정부의범죄억지정책 1. 방범및검거,,,. 7)

외환위기이후흉악범죄의증가와정부의범죄억지정책 171,., 1970 57,000 1990 16, 1996 147,000 (Figure 5(A) ). 1975 1995 20 16 34, 30.,,, 1960 33,000 2007 96,324 3 (Figure 5(B) ). 63,000 90% 57,000 1960 1997, 10 10% 6,695. 8) 1970 1990 1990 6 1996 5, 2000 5. 1995 1995 8 4..,,. 1975 2006 10, 1 5 (Figure 6(A) ).,. (Figure 6(B) )., 1980 7) 2 3, 3. 8), 1998 10 20.

172 韓國開發硏究 / 2009. Ⅱ [Figure 5] Trends in Police (A) Police(1975~2006) 180 40 160 35 140 30 Police (unit=1000) 120 100 80 60 25 20 15 Police per 10,000 people 40 10 20 5 0 1975 1980 1985 1990 1995 2000 2005 Year 0 Police forces Police forces per 10,000 people (B) Police Officers, Combat Police, Administrative Employees(1960~2007) 100 90 80 70 unit= 1000 people 60 50 40 30 20 10 0 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 Year Police officer Combat police Administrative employees Source: The National Police Agency, The Whitepaper of Police; The National Police Agency, Statistical Yearbook of Police; Statistical Korea, Korean Statistical Information Service(KOSIS)

외환위기이후흉악범죄의증가와정부의범죄억지정책 173 [Figure 6] Trends in Police Expenditure(Real Term, 2,000 = 1) (A) Police Expenditure(1975~2007) 6 70 5 60 Police expenditure (Unit=1000 billion won) 4 3 2 50 40 30 20 Expenditure per police (Unit=1 million won) 1 10 0 1975 1980 1985 1990 1995 2000 2005 연도 year Total Expenditure Expendiutre per polic 0 (B) Proportion of Labor Cost in Police Expenditure and Expenditure on Non-Labor Cost per Police(1982~2007) 70 14 Proportion of labor cost in total police expenditure (%) 65 60 55 50 45 12 10 8 6 4 2 Non-labor cost per police (Unit=1 million won) 40 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Year 0 Proportion of labor cost Non-labor expenditure per police Notes: We divided total police expenditure by the number of police officers since the number of total police employees is not available for some years. Labor Cost Raito of labor cost = Total Police Expenditure Source: The National Police Agency, The Whitepaper of Police; The National Police Agency, Statistical Yearbook of Police.

174 韓國開發硏究 / 2009. Ⅱ 1990 50%, 2000 15%p 65%.. 1 1 ( ) 총예산 인건비 경찰청인력 (1). [Figure 6] (B), 1980 1990., 1. 1980 700 2007 1,700, 1988 4,000 2007 8,000 (Figure 7(A) ). 1988 2007 4.5. 1 1990 8 6, 1 1997 (Figure 7(B) ).,, 1. 9),., 1977 1997 96%, 9) 1.

외환위기이후흉악범죄의증가와정부의범죄억지정책 175 [Figure 7] Trends of Workforce and Expenditure in the Prosecution (A) Workforce in the Prosecution 1800 9000 1600 8000 1400 7000 Prosecutor 1200 1000 800 600 6000 5000 4000 3000 Prosecution Employees 400 2000 200 1000 0 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Year 0 Prosecutor Prosecution Employee (Excluding prosecutors) (B) Assistant Employees per Prosecutor and Real Expenditure 9 350 8 300 Assistant employees per prosecutor 7 6 5 4 3 2 250 200 150 100 Expenditure per prosecutor (2000=1) 1 50 0 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Year 0 Assistant employees per prosecutor Expenditure per prosecutor Total Prosecution EmployeesProsecutors Note: Assistants per prosecutor = Prosecutors Source: The Public Prosecutors' Quota Act, Ministry of Justice, Yearbook of Judicial Affairs.

176 韓國開發硏究 / 2009. Ⅱ [Figure 8] Arrest Rate of Offenders(1967~2007) 105 100 Prosecution rate (%) 95 90 85 80 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 Year Note: 1977~1997: The Average: 97.1%, Median 96.6%, 1998~2007: The Average: 91.1%, Median 90.8%. Arrested cases Arrest Rates = Reported cases We excluded the arrest rate in 1993 because the number is 115%. Source: The Supreme Public Prosecutor s Office, Criminal Analysis. 90% 6%p (Figure 8 ).,, 50% 2000 30% (Figure 9(A) )., (Figure 9(B) ).,..

외환위기이후흉악범죄의증가와정부의범죄억지정책 177 [Figure 9] Prosecution Rates and the Number of Prosecuted Offenders (A) All Crimes (1967~2007) 12000 70 10000 60 Prosecuted offenders 8000 6000 4000 50 40 30 20 Prosecution rates (%) 2000 10 0 1965 1970 1975 1980 1985 1990 1995 2000 2005 Year 0 Number of prosecuted offenders Prosecution rates (B) Prosecution Rates by Crime Category 100 90 80 70 Prosecution rates 60 50 40 30 20 10 0 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 Year Homicide Robbery Arson Rape Number of Prosecuted Offenders Note: Prosecution Rates = Number of Arrested Offenders Source: The Supreme Public Prosecutor s Office, Criminal Analysis.

178 韓國開發硏究 / 2009. Ⅱ, 1997...,. 2. 양형,,. 1953. 1995,..,. t 1 (2) : t 1 : i.,.,..,,,,,

외환위기이후흉악범죄의증가와정부의범죄억지정책 179,,. 10),,,., (Table 2 ).,.,.,..,, 1, 1, 3, 5, 10. (2) (3) : :.,,,,. 10), 2001,,., 2001,.,.

180 韓國開發硏究 / 2009. Ⅱ (2). 30. 1970 40% 2000 20% (Figure 10(A) )., 1970 60% 2000 40% (Figure 10(B) )... <Table 4>., 10. <Table 4> 15,. 2000. 2005 2000.,,,., 0, 30..,.,

외환위기이후흉악범죄의증가와정부의범죄억지정책 181 [Figure 10] Sentencing: Imprisonment, Suspended Sentencing, (A) Criminal Offense Monetary Penalty 100000 90000 80000 Number of the convicted 70000 60000 50000 40000 30000 20000 10000 0 1975 1980 1985 1990 1995 2000 2005 2010 Year Monetary penalty Suspended sentencing Dealth penalty, Imprisonment for life,iimprisonment for definite terms 2500 (B) Violent Crimes 2000 Number of the convicted 1500 1000 500 0 1975 1980 1985 1990 1995 2000 2005 2010 Year Monetary penalty Suspended sentencing Dealth penalty, Imprisonment for life,iimprisonment for definite terms Source: The Supreme Public Prosecutor s Office, Criminal Analysis.

182 韓國開發硏究 / 2009. Ⅱ <Table 4> Weight Given to a Sentence Range for Computation of the Average Prison Sentence Suspended Sentencing Fines Less than a year Imprisonment for definite terms 1~3 years 3~5 years 5~10 years More than 10 years Imprisonment for life Death Penalty Weight 0 0 0.5 1.5 4 7.5 15 30 30.,, (4). (4)., (4).,. (5),.,. [Figure 11]. 1...,, 3 5~10 10 (Figure 12 ).

외환위기이후흉악범죄의증가와정부의범죄억지정책 183 [Figure 11] Sentencing of Violent Crimes(1975~2007) 6 5 Average prison sentnece (Year) 4 3 2 1 0 1975 1980 1985 1990 1995 2000 2005 2010 Year Violent crime (imprisonment) Violent crime (All forms of sentence) Source: Ministry of Justice, Yearbook of Judicial Affairs; Ministry of Justice, Yearbook of Judicature. [Figure 12] Proportion of Each Sentence Length at the Criminal Trials 70 60 50 Proportion (%) 40 30 20 10 0 1975 1980 1985 1990 1995 2000 2005 2010 Year Less than 3 years 3-5 years 5-10 years More than 10 years Notes: The number of cases sentenced to Less than 3 years is the sum of Less than 1 year and Less than 3 years. Source: National Court Administration, Yearbook of Judicature.

184 韓國開發硏究 / 2009. Ⅱ 3., 3. 10. [Figure 13],. 1998. 11) 10.,. [Figure 10],,,,. 12) (5),. 3. 교정. 13), 11) 1948 1998 902( 19), 1989 1998, 1998. 12), [Figure 10(A)].. 13), 1.

외환위기이후흉악범죄의증가와정부의범죄억지정책 185 [Figure 13] Number of Cases Sentenced to Imprisonment for Life and Death (A) Death Penalty Penalty(1975~2007) 40 35 30 Number of the convicted 25 20 15 10 5 0 1975 1980 1985 1990 1995 2000 2005 2010 Year Creminal offense Violent crime Special law offense 160 (B) Imprisonment for life 140 Number of the convicted 120 100 80 60 40 20 0 1975 1980 1985 1990 1995 2000 2005 2010 Year Criminal offense Violent crime Special law offense Source: Ministry of Justice, Yearbook of Judicial Affairs, National Court Administration, Yearbook of Judicature.

186 韓國開發硏究 / 2009. Ⅱ..,.,,. 14) (incapacitation), (rehabilitation). Levitt(2004). Dills, Miron, and Summers(2008) Levitt(2004). Donohue and Siegelman(1998).,. 1990 (Figure 14(A) ). (Figure 14(B) ). 1980 7 2000 3. 1 1981 2007 4. 1980,.,, (Figure 14(C), (D) ).. 14) (2007), p.1088.

외환위기이후흉악범죄의증가와정부의범죄억지정책 187 [Figure 14] Trends in Prison Inmates and Resource Input(1981~2006) (A) The Number of Prison Inmates per Prison Officer and the Real Expenditure per Prisoner 8 900 7 800 Number of prisoners per prison officer 6 5 4 3 2 1 700 600 500 400 300 200 100 Expenditure per prisoner (Unit=1billion, at 2000 price) 0 1980 1985 1990 1995 2000 2005 Year 0 Number of prisoners per prison officer Real expenditure per prisoner (B) Average Number of Prison Inmates per Day 80 Average prison inmates per day (Unit=1000 people) 70 60 50 40 30 20 10 0 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 Year Average number of prison inmates per day

188 韓國開發硏究 / 2009. Ⅱ 16 (C) The Workforce of Correctional Facilities 14 12 10 1000 people 8 6 4 2 0 1980 1985 1990 1995 2000 2005 2010 Year Prison officer Correction commissioner (D) Expenditure on Correctional Facilities(At 2000 prices) 900 800 Expenditure on Correctional Facilities (Unit=1 Billion, at 2000 prices) 700 600 500 400 300 200 100 0 1980 1985 1990 1995 2000 2005 2010 Year Source: Ministry of Justice, Yearbook of Judicial Affairs.

외환위기이후흉악범죄의증가와정부의범죄억지정책 189 1989, 1997,., 1997, 150 (Figure 15 )... 1980 9,000, 70~80%. 2000 4,000 3 6,000 (Figure 16 )..... Ⅴ. 맺음말 1997., 10.,.,,

190 韓國開發硏究 / 2009. Ⅱ 400 [Figure 15] Trends in Probationers and Resource Input (A) The Number of Probationers per Probation Officers The number of probationers per probation officers 350 300 250 200 150 100 50 0 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 Year Juvenile All age (B) Probationers and Number of Probation Officiers 180000 1400 160000 1200 140000 120000 1000 Probationer 100000 80000 800 600 Probation officer 60000 400 40000 20000 200 0 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Year 0 Probationer Probation officer Source: Ministry of Justice, Yearbook of Judecial Affairs.

외환위기이후흉악범죄의증가와정부의범죄억지정책 191 [Figure 16] Repeat Offenders among Felony Criminals(1975~2007) 10 9 8 Defendant (Unit=1000 people) 7 6 5 4 3 2 1 0 1975 1980 1985 1990 1995 2000 2005 2010 Year First offense Second offense More than third offense Source: The Supreme Public Prosecutor's Office, Criminal Analysis.,.,,,. 10,,.,.,, 3.

192 韓國開發硏究 / 2009. Ⅱ......

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韓國開發硏究제 31 권제 2 호 ( 통권제 105 호 ) 우리나라대중국수출에서의수출집약도및다양도의역할 이시욱 ( 한국개발연구원부연구위원 ) The Role of Extensive and Intensive Margins in Korean Exports to China Siwook Lee (Associate Research Fellow, Korea Development Institute) * 이시욱 : (e-mail) swlee@kdi.re.kr, (address) Korea Development Institute, Hoegiro 49, Dongdaemun-gu, Seoul, Korea Key Word: (Extensive Margin), (Intensive Margin), (Export Survival) JEL code: F14, F43 Received: 2009. 7. 7 Referee Process Started: 2009. 7. 8 Referee Reports Completed: 2009. 9. 15

ABSTRACT This paper examines relative contributions of extensive margin and intensive margin of Korean exports growth to China after 1990s, based on an analytical approach proposed by the Hummels and Klenow(2005). In this paper, extensive margin is defined as a weighted count of Korean exports categories relative to the rest of world s export categories to China. On the other hand, intensive margin refers to Korean exports to China relative to the rest of the world s exports to China, exclusively in those product categories that Korea exports to China. According to the results of the analysis, the expansion of Korean exports to China was induced mainly by the increase of intensive margin. This result is consistent with Besedeš and Prusa(2007) as well as the Helpman, Melitz and Rubinstein(2007) who suggest that intensive margin is a more important factor than extensive margin for sustaining growth of export in the long term. In addition, this paper shows that the survival rates of exports of parts and components and capital goods is relatively higher in comparison to those of primary and consumption goods. This implies that the expansion of international division of labor under the global production network could substantially affect the survival of exports.

ABSTRACT

198 韓國開發硏究 / 2009. Ⅱ Ⅰ. 문제의제기., WTO, FTA,, 20.?,,.. (variety) (quality).... Hummels and Klenow (2005) (extensive margins) (intensive margins).. Hummels and Klenow(2005), GDP, 60%. Kehoe and Ruhl(2002), Bergin and Glick(2005). Besedeš and Prusa (2007), Helpman, Melitz, and Rubinstein (2007),

우리나라대중국수출에서의수출집약도및다양도의역할 199., 2~3. 1990.., 1992 8. 1990,. 1) 1992 3.3% 2007 10.9%.. 1990,. Besedeš and Prusa(2007),.,.,.,. Hummels and Klenow(2005). 2), 1) IMF, 1992~2007 2.1% 2.7% 0.6%p, 1990. 2) ( ) ( ).,

200 韓國開發硏究 / 2009. Ⅱ 1,,,,., Kaplan and Meier(1958) (survival analysis).,... UN Comtrade., Hummels and Klenow (2005) Kaplan and Meier(1958),.,. Ⅱ. 문헌조사. -,.,,.., Armington (1969).,,.

우리나라대중국수출에서의수출집약도및다양도의역할 201.. Krugman(1980), Helpman (1981), (love of variety) (ideal variety). Krugman(1980),.. 3). 1,,,... Hummels and Klenow(2005),. Hummels and Klenow(2005) (intensive margin) (extensive margin). Hummels and Klenow(2005) GDP, 60%.., Kehoe and Ruhl(2002), Bergin and Glick(2005), Borchert(2007). Bergin and Glick(2005) FTA. NAFTA FTA 3),

202 韓國開發硏究 / 2009. Ⅱ 1989 10% (Least-traded Goods: LTG) 1999 16~42%, EU. 4) Besedeš and Prusa(2007), Helpman, Melitz, and Rubinstein(2007).,. Besedeš and Prusa(2007), (survival),, 2. 5) 1990~2005 Amurgo-Pacheco and Pierola (2008),., (Revealed Comparative Advantage Index), (Export Similarity Index), (Trade specialization index). (2007).,.,. (2005),.. 1980~97 4) LTG SITC 4 digit 1989 10%. 5) Brenton et al.(2009),,,.

우리나라대중국수출에서의수출집약도및다양도의역할 203 Kang(2004)., (2007).,,., (2008). (2008).,. Ⅲ. 실증분석. ( ). (1),... (1). (2) 1. 수출다양도및집약도분석가. 분석방법론 Hummels and Klenow(2005) (2).

204 韓國開發硏究 / 2009. Ⅱ. Hummels and Klenow(2005), (extensive margin).., (2) /., (2)., ( )., (2)...,. Hummels and Klenow(2005) Feenstra(1994). (3),..,.,. (3).

우리나라대중국수출에서의수출집약도및다양도의역할 205.,.,. 6) CIF,. 나. 기초통계자료의구축및요약 UN Comtrade. UN Comtrade 1992~2007 HS 6(Harmonized System 6 digit code). 7) HS 6 UN BEC (Broad Economic Categories classification) <Annex Table 1> 1,,,. 8), BEC Hatzichronoglou (1997). SITC Rev.3 <Annex Table 2>, SITC 5 HS 6. 9), 6). 7) Comtrade database 1992~2006, 2007 Comtrade. 8),. Comtrade 6. 9) OECD Hatzichronoglou(1997) (International Standard Industrial Classification: ISIC) 4,,.. (ISIC 3825).

206 韓國開發硏究 / 2009. Ⅱ,,,,,,. <Table 1>. 1992 26 2007 1,000 40. 3.3% 10.9%., HS 6 1992 2,580 2007 3,792 1.5, 52.4% 78.0%.., <Table 2>. 1992~2007 12.8% 59.5%, 84.3% 32.4%. 2007,, 1 2.5% 0.7%. <Table 1> Chinese Imports from the World and Korea Import in value(billion Dollars) Number of Commodity(HS, 6 digit) Total import (A) Import from Korea (B) B/A Total import (C) Import from Korea (D) D/C 1992 81 3 3.3% 4,926 2,580 52.4% 1995 131 10 7.8% 4,807 3,488 72.6% 2000 207 21 10.1% 4,996 3,736 74.8% 2005 643 76 11.8% 5,042 3,865 76.7% 2007 956 104 10.9% 4,860 3,792 78.0% Source: Author s calculation based on the UN Comtrade database and China Customs Statistics., Hatzichronoglou(1997) SITC 5. Chen, Qu, and Wang(2008).

우리나라대중국수출에서의수출집약도및다양도의역할 207 <Table 2> Composition of Korean Exports to China by Types of Products Primary goods Semi-processed products High tech Others High tech Others 1992 0.7 0.0 0.7 84.3 1.3 83.0 1995 0.6 0.0 0.6 74.9 1.4 73.4 2000 0.5 0.0 0.5 69.9 1.0 68.9 2005 0.4 0.0 0.4 30.1 0.8 29.3 2007 0.7 0.0 0.7 32.4 1.2 31.2 Parts and components and Capital Goods Consumption goods High tech Others High tech Others 1992 12.8 1.1 11.7 2.2 0.0 2.2 1995 20.2 2.3 17.9 4.4 0.1 4.3 2000 26.2 8.8 17.4 3.4 0.0 3.4 2005 67.6 48.1 19.5 2.0 0.1 1.9 2007 59.5 46.8 12.7 2.5 0.3 2.2 (unit: %) Note: This is a share in total exports to China, based on the United Nation s BEC[Broad Economic Categories] classification. Source: Author s calculation based on the UN Comtrade database. 45.7%,., 1.2% 0.3%., 1992 1.1% 2006 44.5% 2006 2/3. 다. 분석결과 1) 상대적시장점유율변화추이 ( ). (2)

208 韓國開發硏究 / 2009. Ⅱ <Table 3> Relative Market Share of Korean Exports to China by Product Types (unit: %) 1992 1995 2000 2005 2007 All Products 3.4 8.5 11.3 13.4 12.2 High tech 0.9 3.8 6.3 18.2 19.3 Others 3.6 9.1 12.4 11.3 9.1 Primary goods 0.4 0.9 0.3 0.3 0.4 Semi-processed products 6.5 15.1 18.2 17.1 15.7 High tech 8.9 26.0 17.1 17.2 17.6 Others 6.4 14.9 18.3 17.1 15.6 Parts and components 1.9 6.3 7.0 17.1 16.8 High tech 0.7 3.5 5.8 17.4 19.4 Others 2.1 7.0 7.6 16.7 11.2 Capital goods 1.1 4.5 6.0 14.6 13.9 High tech 0.5 2.8 5.6 20.8 20.2 Others 1.2 4.9 6.3 7.5 6.6 Consumption goods 1.1 6.5 6.9 7.5 6.3 High tech 0.2 2.1 1.1 1.3 5.9 Others 1.2 6.8 7.4 8.6 6.4 Note: Refer to <Annex table1> and <Annex Table2> for the detailed information on product classification.. 1990. <Table 3> 1990.,., <Annex Table 3> <Annex Table 4> 5. <Annex Table 3>

우리나라대중국수출에서의수출집약도및다양도의역할 209, 1992 TV (HS 854011), (HS 852290), 2007 (HS 854229), (HS 847330).,. <Annex Table 4>, 1992 (HS 847989), (HS 844400), (HS 844711), (HS 844590), 2007 (HS 852520), (HS 901380), (HS 847170).. 2) 제품다양도변화추이 [Figure 1] 1992~2007.,,. 10), 1992~93.. Kehoe and Ruhl(2002). 1992 8, 1992~93., 1993,.,. Hatzichronoglou(1997), 1990 10) 1..

210 韓國開發硏究 / 2009. Ⅱ [Figure 1] Trends in Extensive Margin of Korean Exports to China <Table 4> Extensive Margin of Korean Exports to China by Product Types (unit: %) All Products Semi-Processed products Parts and components and Capital goods Consumption goods High tech Others High tech Others High tech Others High tech Others 1992 73.7 78.5 77.3 80.5 68.9 85.2 53.6 85.2 1995 90.7 89.2 91.9 89.8 83.2 95.8 96.1 92.6 2000 93.8 84.0 89.7 94.5 92.8 97.0 85.5 87.8 2005 96.0 77.2 94.5 95.9 96.1 98.6 91.0 87.3 2007 95.7 91.3 93.2 93.6 95.9 98.0 91.0 87.1 Note: Refer to <Annex table1> and <Annex Table2> for the detailed information on product classification., 1990. 11),, (Table 4 ). 11) 2007 1. 2006 75.9%.

우리나라대중국수출에서의수출집약도및다양도의역할 211 2) 제품집약도변화추이,.. [Figure 2] 1992~2007. 1992 2005.., 1992 2.1% 2007 16.9%. 2001~05.,, 1992~98.,, 1990 [Figure 2] Trends in Intensive Margin of Korean Exports to China

212 韓國開發硏究 / 2009. Ⅱ <Table 5> Intensive Margin of Korean Exports to China by Product Types (unit: %) All Products Semi-Processed Parts and components products and Capital goods Consumption goods High tech Others High tech Others High tech Others High tech Others 1992 1.3 4.7 11.5 8.0 0.8 1.9 0.3 1.4 1995 4.2 10.2 28.3 16.6 3.7 5.9 2.1 7.3 2000 6.8 14.8 19.1 19.3 6.1 7.2 1.2 8.4 2005 19.0 14.6 18.2 17.8 19.3 12.6 1.4 9.8 2007 20.2 9.9 18.9 16.7 20.5 9.2 6.5 7.2 Note: Refer to <Annex table1> and <Annex Table2> for the detailed information on product classification..,, 1990. 2001,. 1992~97,.?..,. 3) 대중국수출경쟁력상승요인 : 품질향상 vs. 물량증가 (3)

우리나라대중국수출에서의수출집약도및다양도의역할 213.,,. [Figure 3] (3) 2000. 12),., 2000 0.94 2006 0.98., 2001., 2005 1 [Figure 3] Trends in Unit Value of Korean Exporting Products to China 12) 2000 Comtrade database.,, (HS 84 85) 1992~99 10~20%., 2000 Comtrade database., 2007. 2000~06.

214 韓國開發硏究 / 2009. Ⅱ [Figure 4] Trends in Quantity of Korean Exporting Products to China,., 2003.,,.. 4) 대중국수출에있어수출다양도와집약도의기여도분해. <Table 6> (1) 2000~06 2.4%,. -2.3%., (2),