Vol.32 No.3 / ISSN x 韓國開發硏究 KDI Journal of Economic Policy 2011 Terms of Trade Shocks and Nontradable Goods Price Inflation Targeting Under a

Vol.32 No.3 / ISSN 1738656x 韓國開發硏究 KDI Journal of Economic Policy 2011 Terms of Trade Shocks and Nontradable Goods Price Inflation Targeting Under a Small Open Economy... Lee, Hangyu 개인 CB 자료를이용한우리나라가계의부채상환위험분석 Strategic Crossfund Subsidization: Evidence from Equity Funds in Korea... Sungbin Cho 우리나라주식형펀드의전략적행동 : 주식형펀드간교차보조를중심으로 The Information Effect of Medical Examination on Individual Health Promotion Behaviors: Evidence from Korea... Jae Young Lim : Dynamics of Asset Returns Considering Asymmetric Volatility Effects: Evidences from Korean Asset Markets... YunYeong Kim Jinsoo Lee 우리나라자산가격변동의기준점효과및전망이론적해석가능성검정 he Effect of Doctor's Payment Method on Patient's Medical Care Use: Revisit of the Patient's Asymmetric Information Problem... Jae Young Lim Changik Jo 환자의의료이용에대한의사의지불방식의효과 : 재방문환자의비대칭적정보의문제

韓國開發硏究 KDI Journal of Economic Policy Contents 1 소규모개방경제하에서의교역조건충격과통화정책... 이한규 / 1 Terms of Trade Shocks and Nontradable Goods Price Inflation Targeting Under a Small Open Economy Lee, Hangyu 2 우리나라주식형펀드의전략적행동 : 주식형펀드간교차보조를중심으로... 조성빈 / 45 Strategic Crossfund Subsidization: Evidence from Equity Funds in Korea Sungbin Cho 3 The Information Effect of Medical Examination on Individual Health Promotion Behaviors: Evidence from Korea... Jae Young Lim / 73 : 임재영 4 우리나라자산가격변동의기준점효과및전망이론적해석가능성검정... 김윤영 이진수 / 93 Dynamics of Asset Returns Considering Asymmetric Volatility Effects: Evidences from Korean Asset Markets YunYeong KimJinsoo Lee 5 The Effect of Doctor's Payment Method on Patient's Medical Care Use: Revisit of the Patient's Asymmetric Information Problem... Jae Young LimChangik Jo / 125 : 임재영 조창익

韓國開發硏究제 33 권제 1 호 ( 통권제 110 호 ) 소규모개방경제하에서의교역조건충격과통화정책 이한규 ( 한국개발연구원부연구위원 ) Terms of Trade Shocks and Nontradable Goods Price Inflation Targeting Under a Small Open Economy Lee, Hangyu (Associate Research Fellow, Korea Development Institute) * 본논문은기발간된 소규모개방경제하에서의교역조건충격과통화정책 ( 정책연구시리즈 200916, 한국개발연구원, 2009) 을보완및수정한것임을밝힌다. ** 이한규 : (email) hglee@kdi.re.kr, (address) Korea Development Institute, 49 Hoegiro, Dongdaemungu, Seoul, Korea Key Word: (Small Open Economy), (Terms of Trade Shocks), JEL Code: F41, E52 (Nontradable Goods), (Monetary Policy) Received: 2010. 8. 4 Referee Process Started: 2010. 8. 13 Referee Reports Completed: 2011. 3. 16

ABSTRACT Terms of trade shocks have been considered one of the main driving forces causing business cycle fluctuations in small open economies. Despite their importance in business cycles of small open economies, it is hard to find a serious study in existing literature investigating their implications on monetary policy under a small open economy. Considering it, this paper studies what form of monetary policy rule is the most adequate for a small open economy where terms of trade shocks are dominant factors in generating its business cycle fluctuations. For this purpose, various implementable monetary policy rules frequently analyzed in existing literature are compared in terms of social welfare levels which they can provide for the economy respectively. Main results of this paper can be summarized as follows. First, for a small open economy where terms of trade shocks are main driving forces of its business cycle fluctuations, the nontradable goods price inflation targeting can provide higher level of social welfare than other traditional monetary policy rules such as the CPI inflation targeting or the fixed exchange rate regime. Second, the social welfare improvement of the nontradable goods price inflation targeting is more apparent when export goods price shocks are more important than import goods price shocks.

소규모개방경제하에서의교역조건충격과통화정책 3 Ⅰ. 서론, (pricetaker).,.., Mendoza(1995) Kose(2002). 1), Mendoza (1995) (real business cycle theory) (quantitative), 50%,, Kose(2002) 90%,., (New Keynesian),. Galí and Monacelli(2005) 1), Neumeyer and Perri(2004), Lubik and Teo(2005).

4 韓國開發硏究 / 2011. Ⅰ. 2),.,,.., (nontradable goods). (tradable goods),.., (quantitatively).,,.,, Galí and Monacelli(2005),. 2) DSGE(Dynamic Stochastic General Equilibrium), Faia and Monacelli(2008), De Paoli(2009) (2008).

소규모개방경제하에서의교역조건충격과통화정책 5,,.,.,.,,.. 3), 2.. 4).,. (solution) (approximation).,.,. 3) Rotemberg and Woodford(1999) Galí(2008). 4).

6 韓國開發硏究 / 2011. Ⅰ Ⅱ. 이론모형 1. 이론모형의주요특징,,.,.,,. Galí and Monacelli(2005),.,,.,..,, (observational equivalence),.,. 5),, 5) KDI.

소규모개방경제하에서의교역조건충격과통화정책 7., (dynamics),,. 6) (Domestic Inflation Targeting),.,.., Calvo(1983),, (incomplete asset market)., (uncontingent bond).,. 7) 2. 이론모형의구성가. 가계부문, (notations). s t t (state), s t t (history of states) s t ={s 0, s 1,,s t}., π(s t ).. 6) (open macroeconomics) Stockman and Tesar(1995) Burstein et al.(2005). 7) (sectorspecific),. Woodford(2003).

8 韓國開發硏究 / 2011. Ⅰ,.,., (uncontingent). 8). (K(s t1 )) (C(s t )), (N(s t )), (I(s t )) (B F (s t )) (lifetime utility). max β, γ γ n.,., 9). P(s t ) S(s t ), W(s t ) Z(s t ). Q * (s t ) 1, (1+i * (s t )) Q * (s t )=1/(1+i * (s t )) 8), (incomplete asset market), Schmitt Grohé and Uribe(2002) Letendre(2000), (unique steady state). (approximation) (numerical method),, SchmittGrohé and Uribe(2002),. 9),.

소규모개방경제하에서의교역조건충격과통화정책 9., Π(s t ), δ, η K..,. q K (s t ) Tobin q,, q K (s t ){1+η K (I(s t )/K(s t1 )δ)}., T N, T N. Y(s t ), Y T(s t ) Y N(s t ) T N. T, N, T N T N. T N (P(s t )), P T (s t ) P N (s t ) T N., χ T N, b T N.

10 韓國開發硏究 / 2011. Ⅰ 나. 최종재 N 생산부문 N, (differentiated goods), i [0,1]. N. ε N N, P N(i:s t )(i [0,1]), N., Y N (i:s t )(i[0,1]) N P N(s t ). Γ(s t,s t+τ ) (stochastic discounting factor),. 11) for 10), Calvo(1983)., N. 1φ N, Calvo. 10) N i, t,. t φ N,. max

소규모개방경제하에서의교역조건충격과통화정책 11 [Figure 1] Structure of Theoretical Model Final Consumption & Investment Final Good : Final Good : Intermediate Good Domestic Intermediate Good Domestic Intermediate Good Capital Labor Capital Labor Perfectly Competitive Market 11),.

12 韓國開發硏究 / 2011. Ⅰ P I N(i,j:s t+τ ) Y I N(i,j:s t+τ ) i j., i, i, i. θ i j. i (optimal reset price) P # N(i:s t ) i j. for P I N(i:s t+τ ) i,. 다. 최종재 T 생산부문 T N, l[0,1]. T.

소규모개방경제하에서의교역조건충격과통화정책 13 T P T (l:s t )(l[0,1]), T, Y T(l:s t )(l[0,1]) T P T (s t ). for, T N., φ T T. max P H * (s t ) P F * (s t ),., l, l, l. T. Y H (l:s t ) Y F (l:s t ) l, ρ, a. l P T # (l:s t ) l.

14 韓國開發硏究 / 2011. Ⅰ 생산부문 라. 최종재 N 을위한중간재 N, N. N. N j., j,. max j, A N (s t ), N N (j:s t ), K N (j:s t ) N j. φ NM j, α.

소규모개방경제하에서의교역조건충격과통화정책 15 j, N i[0,1],. j. MC n N(s t ), N. 12) 마. 최종재 T 를위한중간재생산부문 T, 12) N (),,.

16 韓國開發硏究 / 2011. Ⅰ.,, (pricetaker),,, A T (s t ) T.. 13), T N,., P * H (s t ),, S(s t )P * H (s t ).. T,, Y H(s t ) Y * H (s t ), N T (s t ) K T (s t ). 3. 모형의균형, 실행가능한통화정책및사회후생 가. 모형의균형 2 max., 13).

소규모개방경제하에서의교역조건충격과통화정책 17 (implementable), (metric).,..,,.,, (uncovered interest rate parity)., 1+i(s t ) 1+i * (s t ). 14),,,. 나. 실행가능한통화정책, Ramsey,,,. Ramsey,,. 14),.

18 韓國開發硏究 / 2011. Ⅰ Galí and Monacelli(2005) (CPI Inflation Targeting: CIT), (Domestic Inflation Targeting: DIT), (Exchange Rate Peg).,,.,. (P D (s t )) (ω).,.,,, 1. 15). 다. 사회후생및모형의 2 차근사, Galí and Monacelli(2005).., (metric). SchmittGrohé and Uribe(2007) (lifetime utility),. 15) Galí and Monacelli(2005) (interest rate smoothing).

소규모개방경제하에서의교역조건충격과통화정책 19 s 0, W C (s 0 ) (conditional welfare). 16) (unconditioinal welfare),., Kim and Kim(2003), 1 (firstorder linear approximation). Schmitt Grohé and Uribe(2004) 2 (secondorder approximation). 17), 2,. (optimal reset price) 2., N (infinite sum), 2. 16), s 0 (nonstochastic steady state). 17), 1, 2 (second moment), 2 LQ(linearquadratic). Woodford(2003).

20 韓國開發硏究 / 2011. Ⅰ, (recursive), 2, 2. 2. Ⅲ. 주요통화정책의사회후생비교 1. 모수의설정., (parameter),.,.,,,

소규모개방경제하에서의교역조건충격과통화정책 21.,,. Stockman and Tesar(1995) 0.44, T Chari et al.(2002) 1.5., T 6, (markup rate) 20%. N 10, 10%.,,. (reference),.,, 40~ 60%. 18),,., 60%., T N b T a GDP GDP 60% 30%.,, 18) Burstein et al.(2005, 2007), Mendoza and Uribe(2000) Goldberg and Campa(2006).

22 韓國開發硏究 / 2011. Ⅰ <Table 1> Benchmark Parameters Preference intertemporal elasticity of consumption2 2 elasticity of labor supply1 1 Technology labor share2/3 2/3 Aggregator depreciation rate0.021 0.021 elasticity of substitution between domestic and imported intermediate goods for tradable goods1.5 0.33 elasticity of substitution among intermediate goods for nontradable goods10 0.9 elasticity of substitution between final goods N and T0.44 1.27 elasticity of substitution among differentiated final goods T6 0.83 elasticity of substitution among differentiated final goods N10 0.9 Others frequency of price change: prob. of not reoptimizing price0.66 0.66 time discount factor1% 0.99 sensitivity of policy interest rate to inflation 1.5 Note: 1) a and b are calibrated such that the ratios of tradable goods T and imports to GDP are 60% and 30% respectively at the steady state. 2) η K is calibrated such that the ratio of the standard deviation of investment to that of GDP is around 3. 2/3. 19) ψ π Galí and Monacelli(2005) 1.5.,, VAR(1). 20) log log log log 19),,. Burstein et al.(2005) Lee(2009). 20) (2009).

소규모개방경제하에서의교역조건충격과통화정책 23 0. 21) 2. 기준모수설정하에서의통화정책간사회후생비교 <Table 2> 1,. <Table 2> CPIT, DIT I DIT II. FIX., <Table 2> 100 100, (% deviation), 100. 22), Schmitt Grohé and Uribe(2004) 2. 23),. 826.95 826.92,., 826.13 825.87, 0.1%. 21) 0,. 22), (level). 23) SchmittGrohé and Uribe(2004, 2007).

24 韓國開發硏究 / 2011. Ⅰ <Table 2> Simulation Result with Benchmark Parameters CPIT DIT I DIT II FIX Social Welfare Level Conditional 826.95 826.39 826.13 826.24 Unconditional 826.92 826.28 825.87 826.19 (0.49) (0.80) (0.78) (0.57) Standard Deviation Consumption 0.96 1.63 1.53 0.76 Labor 2.05 3.09 2.69 0.75 (Corr. coefficient between consumption and labor) 0.66 0.89 0.84 0.32 Real Marginal Cost 1.96 1.77 1.80 2.06 Trade Balance 1.25 2.44 1.81 0.40 Real Exchange Rate 0.96 1.12 1.13 0.79 CPI Inflation 0.46 0.64 0.53 0.26 Domestic Goods Price Inf. 0.80 0.91 0.80 0.58 Nontradable Goods Price Inf. 0.68 1.04 0.78 0.23 Correlation Coefficient with GDP Consumption 0.99 0.99 0.98 1.00 Labor 0.55 0.82 0.74 0.37 Investment 0.98 0.98 0.98 1.00 Trade Balance 0.48 0.53 0.65 0.54 Note: 1) The statistics on the table are computed as average of 100 simulations of 100 quarters. 2) The numbers in parentheses in the row of unconditional social welfare level are standard errors of the variable. 3) All variables except for conditional social welfare and unconditional social welfare are defined as % deviations from their steady state level and the trade balance is defined as a ratio to GDP.,,,. <Table 2>

소규모개방경제하에서의교역조건충격과통화정책 25.,, (volatility).,, 1.53%, 0.96% 0.76%.,,. 24),.,., Galí and Monacelli(2005) (intertemporal elasticity of substitution) (elasticity of substitution between domestically produced goods and imported goods) 1,, (optimal monetary policy) (closed economy).. 24) Ramsey Galí and Monacelli(2005) (2008).

26 韓國開發硏究 / 2011. Ⅰ., ( ). Galí and Monacelli(2005), De Paoli(2009), Faia and Monacelli(2008) (2008) Galí and Monacelli(2005) Galí and Monacelli(2005) (inflation gap).,., GDP.,.,, <Table 3>.,., 826.06 825.77.,, 826.99 826.90,.,.,

소규모개방경제하에서의교역조건충격과통화정책 27 <Table 3> Simulation Results with Export/Import Price Shocks Only A. Export Price Shocks Only CPIT DIT I DIT II FIX Social Welfare Level Conditional 826.99 826.31 826.06 826.17 Unconditional 826.90 (0.47) 826.04 (0.83) Standard Deviation 825.77 (0.73) 826.24 (0.42) Consumption 0.74 1.51 1.53 0.63 Labor 2.01 2.85 2.74 0.69 (Corr. coefficient between consumption and labor) 0.69 0.89 0.85 0.68 Real Marginal Cost 1.90 1.64 1.78 2.00 Trade Balance 1.19 2.38 1.85 0.36 Real Exchange Rate 0.79 0.97 1.04 0.63 CPI Inflation 0.44 0.61 0.53 0.20 Domestic Goods Price Inf. 0.75 0.87 0.81 0.58 Nontradable Goods Price Inf. 0.64 1.01 0.80 0.23 Correlation Coefficient with GDP Consumption 0.98 0.99 0.98 1.00 Labor 0.53 0.81 0.75 0.74 Investment 0.97 0.98 0.98 0.99 Trade Balance 0.59 0.56 0.67 0.65 Note: 1) The statistics on the table are computed as average of 100 simulations of 100 quarters. 2) The numbers in parentheses in the row of unconditional social welfare level are standard errors of the variable. 3) All variables except for conditional social welfare and unconditional social welfare are defined as % deviations from their steady state level and the trade balance is defined as a ratio to GDP., 0.82 1.05, 0.90 1.13.,, <Table 3>,

28 韓國開發硏究 / 2011. Ⅰ <Table 3> Continued B. Import Price Shocks Only CPIT DIT I DIT II FIX Social Welfare Level Conditional 825.90 826.01 826.01 826.01 Unconditional 825.93 (0.33) 826.00 (0.09) 826.01 (0.09) 826.08 (0.34) Standard Deviation Consumption 0.63 0.21 0.26 0.42 Labor 0.52 0.13 0.09 0.29 (Corr. coefficient between consumption and labor) 0.98 0.21 0.35 0.95 Real Marginal Cost 0.56 0.47 0.48 0.45 Trade Balance 0.46 0.42 0.29 0.19 Real Exchange Rate 0.56 0.47 0.48 0.45 CPI Inflation 0.17 0.13 0.13 0.17 Domestic Goods Price Inf. 0.32 0.12 0.12 0.07 Nontradable Goods Price Inf. 0.28 0.12 0.08 0.00 Correlation Coefficient with GDP Consumption 1.00 1.00 1.00 1.00 Labor 0.99 0.20 0.35 0.95 Investment 1.00 1.00 1.00 1.00 Trade Balance 0.28 0.10 0.02 0.21 Note: 1) The statistics on the table are computed as average of 100 simulations of 100 quarters. 2) The numbers in parentheses in the row of unconditional social welfare level are standard errors of the variable. 3) All variables except for conditional social welfare and unconditional social welfare are defined as % deviations from their steady state level and the trade balance is defined as a ratio to GDP..,, 0.11..,

소규모개방경제하에서의교역조건충격과통화정책 29,,. (internal transmission mechanism).,, (real marginal cost).,.,.,.. N. (factorneutral). 25),,. 25), 1,.

30 韓國開發硏究 / 2011. Ⅰ,,.,., T T.., 1%, (impulse response function). [Figure 2] 1%. [Figure 2],,., [Figure 2] [Figure 3]., [Figure 3]. [Figure 3], (CPIT) (FIX), 0.1%. <Table 2>. <Table 2>, 1.8%, 1.90% 2.00%,.,, Galí(2008)

소규모개방경제하에서의교역조건충격과통화정책 31 1.0 [Figure 2] Impulse Response Functions of Real Marginal Cost to an 1% Export Price Shock 0.8 0.6 0.4 0.2 0.0 0.2 0 5 10 15 20 25 30 35 40 CPIT DITⅠ DITⅡ FIX 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00 0.02 0.04 0.06 [Figure 3] Differences of Impulse Response Functions of Real Marginal Cost to an 1% Export Price Shock FIX CPIT DITⅠ 0 5 10 15 20 25 30 35 40 (New Keynesian).,.,., (nonsynchronized)

32 韓國開發硏究 / 2011. Ⅰ.,.,,.,,,. 26),,. 27),,,.,. [Figure 4] 1%,. [Figure 4],,,.,,. 26) Galí(2008). 27), Ramsey,.,.

소규모개방경제하에서의교역조건충격과통화정책 33 [Figure 4] Impulse Response Functions of Real Marginal Cost to an 1% Import Price Shock 0.02 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0 5 10 15 20 25 30 35 40 CPIT DITⅠ DITⅡ FIX,.,,.,.. 3. 결과의민감성검증.,.,,

34 韓國開發硏究 / 2011. Ⅰ.,,.,, 2. <Table 4> 0.50 0.95. <Table 4>, ( 0.75) ( 0.50),.,,,., ( 0.95). <Table 4>,,., <Table 5>. (variance), <Table 5>,.,., 0.0002, 0.5 ( 0.0003)

소규모개방경제하에서의교역조건충격과통화정책 35 <Table 4> Sensitivity Analysis: Persistence of Shock A. AR(1) Coefficient of Price Shocks = 0.5 CPIT DIT I DIT II FIX Social Welfare Level Conditional 826.28 825.83 826.02 826.05 Unconditional 826.27 (0.16) 825.75 (0.26) Standard Deviation 825.97 (0.24) 826.08 (0.28) Consumption 0.41 0.74 0.64 0.49 Labor 1.41 1.86 1.64 0.87 (Corr. coefficient between consumption and labor) 0.33 0.75 0.57 0.42 Real Marginal Cost 1.76 1.63 1.74 1.80 Trade Balance 1.01 2.17 1.31 0.65 Real Exchange Rate 0.40 0.47 0.48 0.37 CPI Inflation 0.28 0.43 0.31 0.18 Domestic Goods Price Inf. 0.69 0.65 0.63 0.55 Nontradable Goods Price Inf. 0.43 0.75 0.43 0.12 Correlation Coefficient with GDP Consumption 0.99 0.99 0.99 1.00 Labor 0.22 0.68 0.47 0.45 Investment 0.99 0.99 0.99 1.00 Trade Balance 0.43 0.50 0.61 0.53 Note: 1) The statistics on the table are computed as average of 100 simulations of 100 quarters. 2) The numbers in parentheses in the row of unconditional social welfare level are standard errors of the variable. 3) All variables except for conditional social welfare and unconditional social welfare are defined as % deviations from their steady state level and the trade balance is defined as a ratio to GDP., 0.0004 1.1. 28), 28).

36 韓國開發硏究 / 2011. Ⅰ <Table 4> Continued B. AR(1) Coefficient of Price Shocks = 0.95 CPIT DIT I DIT II FIX Social Welfare Level Conditional 829.69 831.08 829.92 826.81 Unconditional 829.55 (4.27) 830.38 (6.21) Standard Deviation 829.23 (6.49) 826.58 (3.01) Consumption 2.18 2.99 3.05 1.58 Labor 3.25 4.64 4.55 0.74 (Corr. coefficient between consumption and labor) 0.79 0.91 0.92 0.32 Real Marginal Cost 2.50 2.18 2.17 2.81 Trade Balance 1.34 2.12 2.12 0.18 Real Exchange Rate 2.95 3.24 3.21 2.53 CPI Inflation 0.64 0.79 0.70 0.44 Domestic Goods Price Inf. 0.94 1.05 0.96 0.75 Nontradable Goods Price Inf. 0.98 1.27 1.08 0.50 Correlation Coefficient with GDP Consumption 0.93 0.93 0.93 1.00 Labor 0.56 0.73 0.74 0.25 Investment 0.88 0.89 0.91 0.99 Trade Balance 0.56 0.66 0.62 0.07 Note: 1) The statistics on the table are computed as average of 100 simulations of 100 quarters. 2) The numbers in parentheses in the row of unconditional social welfare level are standard errors of the variable. 3) All variables except for conditional social welfare and unconditional social welfare are defined as % deviations from their steady state level and the trade balance is defined as a ratio to GDP... <Table 6>, GDP

소규모개방경제하에서의교역조건충격과통화정책 37 <Table 5> Sensitivity Analysis: Volatility of Shock A. Variance of Price Shocks = 0.0002 CPIT DIT I DIT II FIX Social Welfare Level Conditional 826.61 826.24 826.07 826.14 Unconditional 826.60 (0.40) 826.18 (0.65) Standard Deviation 825.89 (0.64) 826.09 (0.46) Consumption 0.78 1.33 1.24 0.62 Labor 1.68 2.52 2.19 0.61 (Corr. coefficient between consumption and labor) 0.66 0.89 0.84 0.32 Real Marginal Cost 1.60 1.44 1.47 1.68 Trade Balance 1.02 1.99 1.47 0.33 Real Exchange Rate 0.78 0.92 0.92 0.64 CPI Inflation 0.38 0.53 0.43 0.22 Domestic Goods Price Inf. 0.65 0.74 0.65 0.47 Nontradable Goods Price Inf. 0.55 0.84 0.63 0.19 Correlation Coefficient with GDP Consumption 0.99 0.99 0.98 1.00 Labor 0.55 0.82 0.74 0.37 Investment 0.98 0.98 0.98 1.00 Trade Balance 0.48 0.53 0.66 0.54 Note: 1) The statistics on the table are computed as average of 100 simulations of 100 quarters. 2) The numbers in parentheses in the row of unconditional social welfare level are standard errors of the variable. 3) All variables except for conditional social welfare and unconditional social welfare are defined as % deviations from their steady state level and the trade balance is defined as a ratio to GDP.,., GDP,,.

38 韓國開發硏究 / 2011. Ⅰ <Table 5> Continued B. Variance of Price Shocks = 0.0004 CPIT DIT I DIT II FIX Social Welfare Level Conditional 827.29 826.54 826.20 826.34 Unconditional 827.23 (0.65) 826.27 (0.86) 825.81 (0.98) 826.37 (0.63) Standard Deviation Consumption 1.13 1.82 1.83 0.90 Labor 2.42 3.41 3.24 0.88 (Corr. coefficient between consumption and labor) 0.67 0.88 0.85 0.30 Real Marginal Cost 2.28 2.00 2.11 2.42 Trade Balance 1.46 2.84 2.10 0.47 Real Exchange Rate 1.13 1.27 1.33 0.92 CPI Inflation 0.54 0.75 0.62 0.31 Domestic Goods Price Inf. 0.93 1.06 0.93 0.67 Nontradable Goods Price Inf. 0.80 1.21 0.91 0.28 Correlation Coefficient with GDP Consumption 0.99 0.99 0.99 1.00 Labor 0.55 0.81 0.76 0.34 Investment 0.98 0.98 0.98 1.00 Trade Balance 0.48 0.56 0.63 0.52 Note: 1) The statistics on the table are computed as average of 100 simulations of 100 quarters. 2) The numbers in parentheses in the row of unconditional social welfare level are standard errors of the variable. 3) All variables except for conditional social welfare and unconditional social welfare are defined as % deviations from their steady state level and the trade balance is defined as a ratio to GDP.

소규모개방경제하에서의교역조건충격과통화정책 39 <Table 6> Sensitivity Analysis: Degree of Openness A. Tradable Goods/GDP = 0.5 CPIT DIT I DIT II FIX Social Welfare Level Conditional 688.61 688.04 688.02 688.16 Unconditional 688.59 (0.39) 688.01 (0.58) 687.91 (0.55) 688.13 (0.47) Standard Deviation Consumption 1.00 1.52 1.39 0.78 Labor 2.13 2.95 2.61 0.74 (Corr. coefficient between consumption and labor) 0.67 0.86 0.81 0.34 Real Marginal Cost 2.01 1.90 1.90 2.11 Trade Balance 1.10 1.88 1.46 0.36 Real Exchange Rate 0.94 1.05 1.03 0.76 CPI Inflation 0.42 0.53 0.47 0.25 Domestic Goods Price Inf. 0.66 0.73 0.66 0.41 Nontradable Goods Price Inf. 0.58 0.76 0.64 0.23 Correlation Coefficient with GDP Consumption 0.99 0.99 0.98 1.00 Labor 0.56 0.77 0.70 0.38 Investment 0.98 0.98 0.98 1.00 Trade Balance 0.49 0.53 0.61 0.53 Note: 1) The statistics on the table are computed as average of 100 simulations of 100 quarters. 2) The numbers in parentheses in the row of unconditional social welfare level are standard errors of the variable. 3) All variables except for conditional social welfare and unconditional social welfare are defined as % deviations from their steady state level and the trade balance is defined as a ratio to GDP.

40 韓國開發硏究 / 2011. Ⅰ <Table 6> Continued B. Tradable Goods/GDP = 0.7 CPIT DIT I DIT II FIX Social Welfare Level Conditional 853.55 853.62 852.92 852.55 Unconditional 853.51 (0.60) 853.29 (0.88) 852.28 (1.11) 852.57 (0.56) Standard Deviation Consumption 0.89 1.59 1.84 0.77 Labor 1.96 2.98 3.03 0.75 (Corr. coefficient between consumption and labor) 0.63 0.91 0.89 0.28 Real Marginal Cost 1.93 1.58 1.72 2.05 Trade Balance 1.26 2.68 2.09 0.42 Real Exchange Rate 0.98 1.18 1.34 0.82 CPI Inflation 0.52 0.77 0.66 0.28 Domestic Goods Price Inf. 0.97 1.11 1.04 0.80 Nontradable Goods Price Inf. 0.81 1.41 1.06 0.24 Correlation Coefficient with GDP Consumption 0.99 0.99 0.98 1.00 Labor 0.51 0.84 0.81 0.32 Investment 0.98 0.98 0.98 1.00 Trade Balance 0.46 0.55 0.69 0.52 Note: 1) The statistics on the table are computed as average of 100 simulations of 100 quarters. 2) The numbers in parentheses in the row of unconditional social welfare level are standard errors of the variable. 3) All variables except for conditional social welfare and unconditional social welfare are defined as % deviations from their steady state level and the trade balance is defined as a ratio to GDP.

소규모개방경제하에서의교역조건충격과통화정책 41 Ⅳ. 결론..,,.,,,.,.,.,,.,,.,., Ramsey,.

42 韓國開發硏究 / 2011. Ⅰ Ramsey,.

소규모개방경제하에서의교역조건충격과통화정책 43 참고문헌,, 200916,, 2009.,,, 12 2, 2008, pp.151~179. Burstein, A., M. Eichenbaum, and S. Rebelo, Large Devaluations and the Real Exchange Rate, Journal of Political Economy, Vol. 113, No. 4, 2005, pp.742~784. Burstein, A., M. Eichenbaum, and S. Rebelo, Modeling Exchange Rate Passthrough after Large Devaluations, Journal of Monetary Economics, Vol. 54, 2007, pp.364~368. Calvo, G. A., Staggered Prices in a Utilitymaximizing Framework, Journal of Monetary Economics, Vol. 12, 1983, pp.383~398. Chari, V. V., P. J. Kehoe, and E. R. Mcgrattan, Can Sticky Price Models Generate Volatile and Persistent Real Exchange Rates? Review of Economic Studies, Vol. 69, 2002, pp.533~563. Correia, I., J. C. Neves, and S. Rebelo, Business Cycles in a Small Open Economy, European Economic Review, Vol. 39, 1995, pp.1089~1113. De Paoli, B., Monetary Policy and Welfare in a Small Open Economy, Journal of International Economics, Vol. 77, 2009, pp.11~22. Faia, E. and T. Monacelli, Optimal Monetary Policy in a Small Open Economy with Home Bias, Journal of Money, Credit and Banking, Vol. 40, No. 4, 2008. Galí, J., Monetary Policy, Inflation, and the Business Cycle: An Introduction to the New Keynesian Framework, Princeton University Press, 2008. Galí, J. and T. Monacelli, Monetary Policy and Exchange Volatility in a Small Open Economy, Review of Economic Studies, Vol. 72, 2005, pp.707~734. Goldberg, L. S. and J. M. Campa, Distributional Margins, Imported Inputs, and the Sensitivity of the CPI to Exchange Rates, NBER Working Paper #12121, 2006. Kim, J. and S. H. Kim, Spurious Welfare Reversals in International Business Cycle Models, Journal of International Economics, Vol. 60, 2003, pp.471~500. Kose, M. A., Explaining Business Cycles in Small Open Economies How Much Do World Prices Matter? Journal of International Economics, Vol. 56, 2002, pp.299~327. Lee, H., Dynamics of Relative Price of Nontradable Goods and Heterogeneity in Price Stickiness, Working Paper, KDI, 2009. Letendre, MarcAndre, Linear Approximation Methods and International Real Business Cycles with

44 韓國開發硏究 / 2011. Ⅰ Incomplete Asset Markets, Econometric Society World Congress 2000 Contributed Papers #1539, 2000. Lubik, T. A. and W. L. Teo, Do World Shocks Drive Domestic Business Cycles? Some Evidence from Structural Estimation, Working Paper, Johns Hopkins University, 2005. Mendoza, E. G., The Terms of Trade, the Real Exchange Rate, and Economic Fluctuations, International Economic Review, Vol. 36, 1995, pp.101~137. Mendoza, E. G. and M. Uribe, Devaluation Risk and the Businesscycle Implications of Exchange Rate Management, CarnegieRochester Conference Series on Public Policy, Vol. 53, 2000, pp.239~296. Neumeyer, A. and F. Perri, Business Cycles in Emerging Economies: the Role of Interest Rates, Journal of Monetary Economics, Vol. 52, No. 2, 2004, pp.345~380. Rotemberg, J. and M. Woodford, Interest Rate Rules in an Estimated Sticky Price Model, in J. B. Taylor (ed.), Monetary Policy Rules, University of Chicago Press, 1999. SchmittGrohé, S. and M. Uribe, Closing Small Open Economy Models, NBER Working Paper #9270, 2002. SchmittGrohé, S. and M. Uribe, Solving Dynamic General Equilibrium Models Using a Secondorder Approximation to the Policy Function, Journal of Economic Dynamics and Control, Vol. 28, 2004, pp.755~775. SchmittGrohé, S. and M. Uribe, Optimal Simple and Implementable Monetary and Fiscal Rules, Journal of Monetary Economics, Vol. 54, 2007, pp.1702~1725. Stockman, A. C. and L. L. Tesar, Tastes and Technology in a Twocountry Model of the Business Cycle: Explaining International Comovements, American Economic Review, Vol. 85, No. 1, 1995, pp.168~185. Woodford, M., Interest and Prices: Foundations of a Theory of Monetary Policy, Princeton University Press, 2003.

韓國開發硏究제 33 권제 1 호 ( 통권제 110 호 ) 우리나라주식형펀드의전략적행동 : 주식형펀드간교차보조를중심으로조성빈 ( 한국개발연구원연구위원 ) Strategic CrossFund Subsidization: Evidence from Equity Funds in Korea Sungbin Cho (Research Fellow, Korea Development Institute) * 본고는 자산가격변동이민간소비에미치는효과분석 ( 정책연구시리즈 200908, 한국개발연구원, 2009) 으로기발간된원고를수정 보완한것임. * 조성빈 : (email) scho@kdi.re.kr, (address) Korea Development Institute, 49 Hoegiro, Dongdaemungu, Seoul, Korea Key Word: (Strategic Behavior), (Cross Subsidization), (Investor Protection) JEL Code: G23, G29 Received: 2010. 11. 25 Referee Process Started: 2010. 11. 26 Referee Reports Completed: 2011. 3. 23

ABSTRACT This study uses Korea s equity fundrelated data ranging from Jan. 2002 to Apr. 2010 to analyze the existence of crosssubsidization among funds managed by the same management company. The findings are as follows: i) a transfer of performance outcome is confirmed to move from lowfee funds to highfee funds, meaning that management companies tend to maximize their own interest than investors return. And such a tendency has been strengthened since 2008. ii) young funds overperform old funds, iii) funds with high returns in the previous quarter perform better than funds with low return in the same period. These results suggest that in order to protect investors, it is necessary to conduct close monitoring on transactions that might undermine the benefits of investors and comprehensive evaluation on the capability of management companies.

우리나라주식형펀드의전략적행동 : 주식형펀드간교차보조를중심으로 47 Ⅰ. 서론. 2001 155.7 2010 7 327.7. 2001 6.9 2010 7 112.7., 2010 5. () (). 1),.,....,. 2) 1) Khorana, Servaes, and Tufano(2005). 2)., STRATEGIES; Building a Star Fund, at Its Brother's Expense, (New York Times, Jan. 13, 2008).

48 韓國開發硏究 / 2011. Ⅰ..,,... A B. B A. A. A.... 3),,.,..., 3),, 2007. 11. 6.

우리나라주식형펀드의전략적행동 : 주식형펀드간교차보조를중심으로 49... 관련연구, Jensen (1968). Jensen(1968)., Chang and Lewellen(1984), Ippolito(1989) (+), Malkiel(1997), Gruber(1996) (survivorship bias).., Grinblatt and Titman(1992), Hendricks, Patel, and Zeckauser(1993), Brown and Goetzmann (1995), Malkiel(1995), Carhart(1997),. (CAPM) Fama and French(1992, 1993) 3 Carhart(1997) 4. (fund family) (fund complex), 4).. 5) Ippolito(1992), Sirri and Tufano (1998), Chevalier and Ellison(1997) 4) Huij and Verbeek(2007), 1992 80%, 2002 96%. 5)., Gervais, Lynch, and Musto(2005).

50 韓國開發硏究 / 2011. Ⅰ (convex relationship)., Sirri and Tufano(1998), Khorana and Servaes(2007) (spillover effect).,., 2. Massa(2003), Nanda, Wang, and Zheng (2004), Massa(2003) (market segmentation) (fund proliferation) (heterogeneity), (spillover), Nanda, Wang, and Zheng(2004),. Guedj and Papastaikoudi(2005), Gasper, Massa, and Matos(2006). Guedj and Papastaikoudi (2005), (research team,, ),. Gasper, Massa, and Matos(2006),., Evans(2010) (incubation period). Jain and Wu(2000). Huij and Verbeek

우리나라주식형펀드의전략적행동 : 주식형펀드간교차보조를중심으로 51 (2007).. (1997) 1990 9 1993 9 (2000), (2001), (2003), (2003), (2004), (2005), (2005), (2010a, 2010b). (2000) 1999 8. (2001) 1998 12 2001 3,,. (2003),,. (2003) 1998~2001,. (2004) 2003,. (2005) 1999 2003 2. (2005) 2000 7 2003,. (2010a),,. (2010b),.,

52 韓國開發硏究 / 2011. Ⅰ,, 2005. 2002 1 2010 4,. Ⅱ. 자료및방법론 1. 자료.. 6),.,,. 7),, (Wrap account),, (index fund) (active investment).,,., 2002 1 6) 100 60 ( 60% ),. 7),.,,,,,,. (2004).

우리나라주식형펀드의전략적행동 : 주식형펀드간교차보조를중심으로 53 <Table 1> Summary Information on Fund 1) Year Number of Funds Average Net Asset Value Average Fund Age Average Management Fee 2002 146 196.71 30.17 0.634 2003 149 170.33 39.62 0.637 2004 186 160.84 41.28 0.635 2005 243 981.12 36.98 0.653 2006 319 1,084.36 36.79 0.657 2007 467 1,464.18 33.09 0.666 2008 580 810.56 40.54 0.673 2009 594 1,044.58 50.68 0.673 Note: 1) As of the end of each year. 2) In 100 million KRWON. 3) In months. 4) Unit: %s. 2010 4 18.,. <Table 1>,,. <Table 1>,, 2003 2009. 2005~07,. 2008., 2002...

54 韓國開發硏究 / 2011. Ⅰ ( :, :, :, :, : ),. 2. 가설. (cross subsidization)..,.,..,. Jain and Wu(2000),. 8) ( ) (, ). 9) 8),,. 9) (match).,.

우리나라주식형펀드의전략적행동 : 주식형펀드간교차보조를중심으로 55 Gasper, Massa, and Matos(2006). 가. 운용보수수준의차이.,. 10),. (, ),. (opposite trades). 나. 펀드나이에따른차이 Chevalier and Ellison(1997),....... 23 10) (2003) (2010b), (2004) 2003.

56 韓國開發硏究 / 2011. Ⅰ 10 3 23.4% (22.1%). (23.6%) (19.7%)... 11),. 다. 펀드수익률에따른차이.,. 12). 13). 14).,. 3. 분석방법론 Gasper, Massa, and Matos(2006).. 15) Gasper, Massa, and Matos(2006) 11), 3,, 2009. 4. 24. 12) Chevalier and Ellison(1997) Sirri and Tufano(1998). 13),. 14). 15),,.

우리나라주식형펀드의전략적행동 : 주식형펀드간교차보조를중심으로 57 4,.. ( ) ( ), same 1.,,. (+)., Gasper, Massa, and Matos(2006)..,., Gasper, Massa, and Matos(2006).. 16). (counterfactual). (nearestneighbour matching). 17),. 16) Lalonde(1986), Dehejia and Wahba(1998). 17) Dehejia and Wahba(1998) propensity score.

58 韓國開發硏究 / 2011. Ⅰ... Gasper, Massa, and Matos(2006) 4. Probit. 18) ( ),, ( ). Probit...,,,,,,. <Table 2>,,., (panel A). 2002 Young Fund Old Fund (panel B).,, (panel C). 18) ordered probit.

우리나라주식형펀드의전략적행동 : 주식형펀드간교차보조를중심으로 59 <Table 2> Summary Information on Matched Funds 1) Panel A: Management Fee Year High Fee Fund Low Fee Fund Fund Age Net Asset Value Fund Age Net Asset Value 2002 38.72 199.10 38.39 205.37 2003 50.58 179.70 53.76 199.70 2004 60.98 86.79 61.37 108.73 2005 47.79 519.81 49.31 399.56 2006 43.00 790.78 42.82 611.55 2007 41.85 609.01 38.98 516.71 2008 48.00 971.58 48.67 321.86 2009 58.59 1286.34 55.03 572.53 Panel B: Fund Age Year Young Fund Old Fund Management Fee Net Asset Value Management Fee Net Asset Value 2002 0.5104 95.53 0.3675 138.00 2003 0.5349 87.45 0.6768 43.68 2004 0.5766 297.02 0.3963 76.34 2005 0.6776 545.67 0.4732 171.50 2006 0.6763 383.83 0.5650 1032.32 2007 0.6903 155.96 0.6663 1910.18 2008 0.6786 107.04 0.6754 241.72 2009 0.6772 162.90 0.5843 60.99 Year Panel C: Quarterly Return High Return Fund Management Fee Fund Age Net asset Value Low Return Fund Management Fee Fund Age Net Asset Value 2002 0.5623 22.36 152.61 0.5254 20.27 657.01 2003 0.6436 29.56 226.47 0.5502 29.06 113.21 2004 0.4828 38.56 131.19 0.5695 56.07 281.83 2005 0.6048 36.47 1429.73 0.5308 30.58 1009.44 2006 0.6502 22.68 922.18 0.6847 23.49 1766.99 2007 0.6697 34.18 3616.40 0.6581 52.13 877.76 2008 0.6664 36.01 703.55 0.6641 34.73 298.97 2009 0.6334 56.21 611.47 0.7382 52.97 1126.22 Note: 1) As of the end of each year. 2) In months. 3) In 100 million KRWON. 4) Unit: %s.

60 韓國開發硏究 / 2011. Ⅰ Ⅲ. 분석결과 <Table 3>, Panel A..,. 2007, 2008. Panel B,., 2007 2008. 3 Panel C,. (meandifference test).,,.. 19) ( Young Fund, ) ( Old Fund, 19) Gasper, Massa and Matos(2006).,.

우리나라주식형펀드의전략적행동 : 주식형펀드간교차보조를중심으로 61 <Table 3> Mean Difference Test of Matching Sample Panel A: Management Fee 4) Difference in Return(A) 2) Difference in Return(B) 3) AB Whole Period 0.0656 0.0528 2002~2007 0.0507 0.0563 2008~2010 0.0993 0.1158 0.1184** (2.1365) 0.0056 (0.0783) 0.2151** (2.2667) Panel B: Fund Age 5) Difference in Return(A) 2) Difference in Return(B) 3) AB Whole Period 0.5236 0.1661 2002~2007 0.9653 0.0600 2008~2010 0.1087 0.2716 0.3602** (2.4339) 0.9053*** (4.3166) 0.1629 (0.7819) Panel C: Quarterly Return 6) Difference in Return(A) 2) Difference in Return(B) 3) AB Whole Period 0.4046 0.0413 2002~2007 0.6411 0.2245 2008~2010 0.0967 0.1221 0.3633** (2.5173) 0.4166** (2.4304) 0.0255 (0.0986) Note: 1) tvalues in parentheses. with *** p<0.01, ** p<0.05, * p<0.1 2) Difference in Returns when both funds belong to the same Asset Management Firm. 3) Difference in Returns when matched funds belong to different Asset Management Firms. 4) Difference between return from high fee fund and return from low fee fund. 5) Difference between return from young fund and return from old fund. 6) Difference between return from high performing fund and return from low performing fund.

62 韓國開發硏究 / 2011. Ⅰ ), same 1. ( ) () (, ), time. (+). <Table 4>~<Table 6>, <Table 3>. 20)21) Ⅳ. 정책적시사점... 22) ( ).. 20). Gasper, Massa, and Matos(2006). 21). (Appendix Table <A1>~<A3>), KOSPI. 22), 2004. <Table> Number of Disclosures on Changes in Fund Managers Year 2004 2005 2006 2007 2008 2009 Number of Announcements 1,086 2,755 3,545 4,969 5,240 2,449 Note: 1) All funds are included. 2) Figures in 2009 are as of the end of June. Source: Korea Financial Investment Association.

우리나라주식형펀드의전략적행동 : 주식형펀드간교차보조를중심으로 63 <Table 4> Strategic CrossFund Subsidization: Management Fee Constant Same Mgmt Fee (High Fee Fund) Mgmt Fee (Low Fee Fund) Dependent Variable: Difference in Reruns Whole Period 2002~2007 2008~2010 (1) (2) (3) (4) (5) (6) 0.0886 (0.190) 0.1066* (0.057) 0.0578 (0.195) 0.0290 (0.244) 0.0695 (0.212) 0.1826*** (0.066) 0.0128 (0.199) 0.0953 (0.256) 0.2432 (0.290) 0.0181 (0.089) 0.1321 (0.302) 0.5121 (0.454) 0.1983 (0.288) 0.1426 (0.112) 0.1310 (0.302) 0.5166 (0.472) 0.0563 (0.255) 0.2112*** (0.080) 0.1029 (0.265) 0.2155 (0.298) 0.2848 (0.279) 0.2685*** (0.081) 0.1398 (0.261) 0.0901 (0.298) Time Effect No. of Obs. 9,403 9,403 4,420 4,420 4,983 4,983 RSquared 0.001 0.017 0.001 0.018 0.001 0.033 Note: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 <Table 5> Strategic CrossFund Subsidization: Fund Age Constant Same Fund Age (Young Fund) Fund Age (Old Fund) Dependent Variable: Difference in Reruns Whole Period 2002~2007 2008~2010 (1) (2) (3) (4) (5) (6) 0.1950 (0.494) 0.3385** (0.165) 0.1303** (0.059) 0.1535 (0.127) 0.1182 (0.662) 0.2960* (0.163) 0.0652 (0.085) 0.0866 (0.175) 1.0763 (0.784) 0.9014*** (0.208) 0.1255 (0.090) 0.1868 (0.186) 0.6888 (0.998) 0.8763*** (0.206) 0.0380 (0.098) 0.0896 (0.250) 0.3368 (0.823) 0.2160 (0.252) 0.3138*** (0.093) 0.3189 (0.207) 1.7520 (1.093) 0.2972 (0.245) 0.0320 (0.170) 0.3342 (0.207) Time Effect No. of Obs. 6,843 6,843 3,407 3,407 3,436 3,436 RSquared 0.002 0.017 0.006 0.023 0.004 0.023 Note: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

64 韓國開發硏究 / 2011. Ⅰ <Table 6> Strategic CrossFund Subsidization: Return in Previous Quarter Constant Same Past Retum (High Return Fund) Past Retrun (Low Return Fund) Dependent Variable: Difference in Reruns Whole Period 2002~2007 2008~2010 (1) (2) (3) (4) (5) (6) 0.5604*** (0.101) 0.3129** (0.134) 0.0641*** (0.015) 0.0735*** (0.019) 0.9586*** (0.223) 0.4474*** (0.149) 0.0390** (0.016) 0.0630*** (0.020) 0.5557*** (0.137) 0.3921** (0.166) 0.0537*** (0.020) 0.0328 (0.027) 0.4370 (0.271) 0.6001*** (0.201) 0.0535** (0.022) 0.0252 (0.030) 0.5225*** (0.149) 0.0200 (0.225) 0.0568** (0.023) 0.1183*** (0.025) 0.2560 (0.223) 0.1145 (0.219) 0.0113 (0.023) 0.1248*** (0.027) Time Effect No. of Obs. 7,625 7,625 3,716 3,716 3,909 3,909 RSquared 0.008 0.071 0.005 0.052 0.011 0.104 Note: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. 23), 2010 8 9... 24) 23).... (http://er.asiae.co.kr/erview.htm?idxno=2009041509250478675) 24),.

우리나라주식형펀드의전략적행동 : 주식형펀드간교차보조를중심으로 65. 459 ( ).,....,. Nanda, Wang, and Zheng(2004).. 25).. 2009 6 25 2010 10 37 GIPS(Global Investment Performance Standards). 26) GIPS 25).

66 韓國開發硏究 / 2011. Ⅰ (composite). 27)28) Ⅴ. 분석의종합및결론..,. 2002 1 2010 4.,, 2008. 2008.. 8, 26) GIPS CFA(Chartered Financial Analyst), (full disclosure) (fair representation). 27) 300 GIPS. (bias). 28) (2007),,.

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70 韓國開發硏究 / 2011. Ⅰ Jain, P. C. and J. S. Wu, Truth in Mutual Fund Advertising: Evidence on Future Performance and Fund Flows, Journal of Finance, Vol. 55, No. 2, 2000, pp.937~958. Jensen, M. C., The Performance of Mutual Funds in The Period 19451964, Journal of Finance, Vol. 23, No. 2, 1968, pp.389~416. Khorana, A. and H. Servaes, Competition and Conflicts of Interest in the U.S. Mutual Fund Industry, Working Paper, 2007. Khorana, A., H. Servaes, and P. Tufano, Explaining the Size of the Mutual Fund Industry Around the World, Journal of Financial Economics, Vol. 78, 2005, pp.145~185. Khorana, A., H. Servaes, and P. Tufano, Mutual Fund Fees Around the World, Review of Financial Studies, Vol. 22, No. 3, 2008, pp.1279~1310. Lalonde, R., Evaluating the Econometric Evaluations of Training Programs, American Economic Review, Vol. 76, 1986, pp.604~620. Malkiel, B. G., Returns from Investing in Equity Mutual Funds 1971 to 1991, Journal of Finance, Vol. 50, No. 2, 1995, pp.549~572. Massa, M., How Do Family Strategies Affect Fund Performance? When PerformanceMaximization is Not the Only Game in Town, Journal of Financial Economics, Vol. 67, No. 2, 2003, pp.249~304. Nanda, Z. V., Jay Wang, and Lu Zheng, Family Values and the Star Phenomenon: Strategies of Mutual Fund Families, Review of Financial Studies, Vol. 17, 2004, pp.667~698. Sirri, E. R. and P. Tufano, Costly Search and Mutual Fund Flows, Journal of Finance, Vol. 53, No. 5, 1998, pp.1589~1622.

우리나라주식형펀드의전략적행동 : 주식형펀드간교차보조를중심으로 71 <Appendix> <Table A1> Strategic CrossFund Subsidization: Management Fee Constant Same Mgmt Fee (High Fee Fund) Mgmt Fee (Low Fee Fund) Against Preannounced Target Benchmark Portfolio Dependent Variable: Difference in Returns Whole Period 2002~2007 2008~2010 (1) (2) (3) (4) (5) (6) 0.0039 (0.188) 0.0812 (0.057) 0.0565 (0.194) 0.2166 (0.241) 0.0606 (0.211) 0.1276* (0.066) 0.0355 (0.198) 0.1021 (0.254) 0.2992 (0.283) 0.0195 (0.087) 0.2197 (0.297) 0.3989 (0.444) 0.5124 (0.312) 0.0771 (0.110) 0.2150 (0.297) 0.4608 (0.460) 0.2373 (0.254) 0.1706** (0.080) 0.1384 (0.266) 0.5194* (0.300) 0.2138 (0.291) 0.2285*** (0.081) 0.1730 (0.261) 0.3666 (0.298) Time Effect No. of Obs. 9,403 9,403 4,420 4,420 4,983 4,983 RSquared 0.001 0.019 0.000 0.021 0.001 0.041 Note: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

72 韓國開發硏究 / 2011. Ⅰ <Table A2> Strategic CrossFund Subsidization: Fund Age Dependent Variable: Difference in Returns Whole Period 2002~2007 2008~2010 (1) (2) (3) (4) (5) (6) Constant 0.2214 0.1783 1.1094 0.8039 0.4351 1.2384 (0.522) (0.672) (0.773) (0.981) (0.814) (0.922) Same 0.3298** 0.2848* 0.8859*** 0.8649*** 0.2180 0.3000 (0.147) (0.147) (0.206) (0.204) (0.248) (0.241) Fund Age (Young Fund) 0.1305** (0.059) 0.0577 (0.085) 0.1144 (0.089) 0.0296 (0.097) 0.3299*** (0.093) 0.0322 (0.169) Fund Age (Old Fund) 0.1600 (0.129) 0.0966 (0.171) 0.2005 (0.184) 0.1249 (0.246) 0.3514* (0.205) 0.3687* (0.206) Time Effect No. of Obs. 6,843 6,843 3,407 3,407 3,436 3,436 RSquared 0.002 0.017 0.006 0.022 0.004 0.023 Note: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Against Preannounced Target Benchmark Portfolio <Table A3> Strategic CrossFund Subsidization: Return in Previous Quarter Constant Same Past Retum (High Return Fund) Past Retrun (Low Return Fund) Against Preannounced Target Benchmark Portfolio Dependent Variable: Difference in Returns Whole Period 2002~2007 2008~2010 (1) (2) (3) (4) (5) (6) 0.6349*** 1.0183*** 0.6320*** 0.4750* 0.5948*** 1.0717*** (0.097) (0.211) (0.127) (0.255) (0.150) (0.205) 0.2768** (0.132) 0.0732*** (0.015) 0.0876*** (0.018) 0.4190*** (0.145) 0.0473*** (0.016) 0.0788*** (0.020) 0.3556** (0.162) 0.0598*** (0.019) 0.0522** (0.026) 0.5553*** (0.195) 0.0593*** (0.021) 0.0473 (0.029) 0.0453 (0.223) 0.0711*** (0.023) 0.1219*** (0.025) 0.0942 (0.217) 0.0228 (0.023) 0.1283*** (0.027) Time Effect No. of Obs. 7,625 7,625 3,716 3,716 3,909 3,909 RSquared 0.011 0.076 0.007 0.058 0.013 0.108 Note: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

韓國開發硏究 제 33 권제 1 호 ( 통권제 110 호 ) The Information Effect of Medical Examination on Individual Health Promotion Behaviors: Evidence from Korea Jae Young Lim (Associate Professor, Department of Food and Resource Economics, Korea University) 개인의건강증진행위에대한건강검진의정보효과 : 한국의경우를중심으로 임재영 ( 고려대학교식품자원경제학과부교수 ) * 임재영 : (email) jylimecon@korea.ac.kr, (address) Korea University, Anamdong, Seongbukgu, Seoul 136701, Korea Key Word: Information Effect( 정보효과 ), Medical Examination( 건강검진 ), Health Promotion Behaviors ( 건강증진행위 ), Endogeneity( 내생성 ) JEL code: I10, I11 Received: 2010. 10. 14 Referee Process Started: 2010. 10. 18 Referee Reports Completed: 2011. 3. 24

ABSTRACT This paper demonstrates empirically that individuals who monitor indicators of their current cardiovascular health status by undergoing medical examinations are more likely to invest in their own health than those who do not observe such monitoring protocols. Using data from the 2001 National Health and Nutrition Examination Survey of Korea in a structural econometric model, this paper attempts to control the endogeneity problem inherent to the individual decision as to whether to undergo medical examinations, and provides estimation results showing that increased individual health awareness via medical examinations exerts a statistically significant positive effect on health investments. From the policy perspective, the estimation results of this paper may provide a rationale supporting the health policy of free provision of health examination services to the insured via National Health Insurance. 본연구에서는개인이건강검진을통해혈압수치등과같은자신의현재심혈관계통의건강상태에대하여잘알고있을수록, 건강검진을받지않은사람에비해자신의건강을위하여운동과같은건강증진행위에더많은투자를하려한다는것을실증적으로분석하고자한다. 이를위하여본논문에서는 2001 년국민건강영양조사자료를이용하여 bivariate probit 모델의구조적계량경제모형을구축하고, 개인이가입한전국민의료보험의유형, 즉직장혹은지역의료보험가입여부를도구변수로하여건강검진수검여부에대한 개인별의사결정과관련된내생성문제를통제하였다. 추정결과, 개인이건강검진을통해서자신의건강상태에대해더많은정보를보유할수록건강증진을위한투자를더욱더많이한다는것이통계적으로유의하게나타났다. 보건정책적관점에서본논문의실증결과는현재무상으로이루어지고있는국가건강검진서비스제공정책의정책적효과를입증하기위해사용될수있다고판단되어동사업수행의정당성확보에활용될수있을것으로사료된다.

The Information Effect of Medical Examination on Individual Health Promotion Behaviors 75 Ⅰ. Introduction Cardiovascular disease has become the leading cause of death in several countries, and is currently responsible for more than 40% of fatalities in the United States. Developing countries, including India and China, are currently experiencing an epidemic of heart disease and stroke, a trend which is expected to impede economic expansion. This epidemiological transition the shift away from infectious and nutritional deficiency diseases to chronic diseases arises both from an aging population and from a shift in behavioral patterns attendant to urbanization. Sedentary lifestyles, the consumption of a high fat diet, and smoking, for example, all increase the probability that a person will develop a cardiovascular illness. It is, however, possible to prevent cardiovascular (CV) disease. The probability of acute complications is substantially lower in individuals who engage in health maintenance activities, such as exercise 1. However, medical care can do very little to prevent the onset of these conditions, and the capacity for policy to promote CV health is thus quite limited, which suggests that deterrence remains in the hands of the individual (Connolly and Kesson, 1996). Unfortunately, however, many highrisk people still do not take adequate precautions in this regard, despite the great deal of public information disseminated to educate individuals about the need for health maintenance 2. In fact, the population appears to essentially be divided into two main groups those in good health who maintain their health, and those in poor health who take few, if any, precautions. It appears, then, that causality flows in one directionfrom behavior to health outcomeand that health behavior is influenced profoundly by individual preference. Although individuals at higher risk of illness have a strong incentive to maintain their health, they appear to take fewer precautions overall. This pattern indicates that optimizing behavior, in which those who benefit more from an activity are more likely to participate in that activity, is either weak or does not occur. Economic reasoning appears to be inconsistent with observed behavior. Imperfection in the optimization process may help to explain this inconsistency, and correcting these imperfections may require a launching of precautionary efforts across individuals in an optimal manner, which may in the aggregate affect health maintenance. Specifically, individuals at high risk for CV illnesses may make few preventive efforts, as they do not possess complete information regarding their current CV health status. Many of the risk factors associated with behaviorrelated diseases, particularly CV illness, are unobservable; thus, an individual may, at best, 1 According to a previous report (Chae, 1997), exercising one to two times per week reduced the risk of heart attack by 36 percent. The risk fell by 38% for those who exercised three to four times per week. 2 The American Heart Association (1998) has reported that 33% of overweight men and 41% of overweight women are not physically active during their leisure time.

76 韓國開發硏究 / 2011. Ⅰ approximate her 3 return to prevention without medical advice simply by undergoing a medical exam. As a consequence, she may make health investment decisions that would be obviously suboptimal if her health were known. Thus, this study explores the impact of medical exams when they are utilized as brokers of information regarding patients health status. As such, the medical exams should affect a patient s demand for preventive inputs by revealing individualspecific information to her and thus altering her information set in terms of her own health status. Patients value this information, and it improves their ability to select other inputs optimally. This approach differs from those of preceding studies (Kenkel, 1990; Viscusi, 1991; Chen et al., 1995; Hsieh et al., 1997) concerning the behavioral impact of changes in health risk perception that focus on the receipt of general risk information, including product hazard warnings. In the case of behaviorrelated illnesses, such as CV illness, general health risks associated with certain behaviors can be regarded as those which are not individually tailored to the patient s idiosyncratic characteristics, and thus a repeat of these warnings will probably do little to affect a patient s information set. Thus, it appears unlikely that a given patient will alter her behavior upon the receipt of general health information. However, each person receives different information from undergoing a medical exam, and thus an individual s health status knowledge should be expected to exert individual effects on patients preventive efforts. This study contributes to the relevant literature regarding preventive health care, in that it redefines the role of medical exams in the promotion of longterm health. The demand for preventive care arises, in theory, when health inputs reduce either the probability or the consequences of future illness (Ehrlich and Becker, 1972). In specific regard to CV disease, however, an individual s risk of illness is affected less by medical care and more by what the patient does for herself (Newhouse and Friedlander, 1980). Thus, in the case of behaviorrelated illnesses, the link between preventive health care and health promotion depends critically on the information effect of patient s health status knowledge on her health maintenance behaviors. Based on the abovedescribed context, this study sought to empirically evaluate this hypothesis by estimating the effect of medical exams on exercise efforts. However, one more thing that should be taken into consideration is that, as individuals receive information on the basis of their choice to undergo medical exams, it is possible that those who receive information via medical examinations differ from individuals in the uninformed group in some way that is not observed by the researcher. If this is in fact the case, the variable of whether to undergo a medical exam may be picking up the effect of some omitted variable with which it is correlated. Furthermore, the endogeneity of this variable might introduce bias into the estimation results. Thus, it is very important to control the individual decision regarding undergoing medical exams in exploring the information effects of medical examinations on individual health investment. Therefore, this paper might be relevant in that this study attempts to control for 3 In this paper, a consumer of medical service, including a medical exam, is denoted as a female for the sake of convenience.

The Information Effect of Medical Examination on Individual Health Promotion Behaviors 77 the possible endogeneity of individuals choice to undergo medical exams in a robust econometric way, and also in that this paper assesses the information effect of undergoing medical exams on a patient s demand for nonmedical health inputs, such as exercise, with the control of unobserved individual characteristics that might affect her decision to undergo a medical exam. This paper is organized as follows. Section II describes the empirical model. Section III discusses the important characteristics of the data and presents the relevant descriptive statistics. The empirical results are analyzed in section IV. Section V provides the conclusions of this research. Ⅱ. Empirical Model The following estimable model represents the demand for exercise; x b r n + e * i = 0 Z i + b1 H i + b 2 I i + 1 ïì x í ïî x i i = 1 if x = 0 * i > 0 otherwise ( x * i 0) i i (1) * i where x represents the level of exercise selected by individual i and x i is the indicator which represents the existence of regular or strenuous exercise. In this paper, the level of exercise is measured via two considerations: i) does the individual make the decision to engage in any exercise on a regular basis, and ii) does the individual engage in strenuous exercise on a regular basis? If an individual engages in activity at least one time per week and for more than 20 minutes each time, her value of x i equals one, and on the same basis, if an individual engages in strenuous exercise at least one time per week and for more than 30 minutes each time, her x i value equals one. Hence, the variable x i is dichotomous. These two different measurements may capture not only an individual s behavior in engaging in any exercise, but also the degree of difficulty or intensity with which the individual exercises on a regular basis. The Z i is a vector of exogenous sociodemographic variables that influence an individual s health investment, such as exercise decisions, H i is a vector of variables influencing preexam health status, such as the number of chronic diseases during given periods, I i indicates that individual has undergone a medical exam to obtain information regarding her health status prior to making her exercise decisions, n i represents permanent unobservable individual characteristics, and e i represents white noise and is assumed to be a normally distributed error term. Considering that certain factors may complicate the process by which consistent estimates of parameters are acquired, it appears highly likely that the unobservable characteristics included in n may be correlated with some of the observed i

78 韓國開發硏究 / 2011. Ⅰ explanatory variables in the model. For example, a person s preference with regard to exercise is closely associated with her past health investment decisions, and thus with the probability of undergoing a medical exam. In order to control for the effect of unobserved heterogeneity and obtain robust estimates of the parameters of the model, this study utilized maximum likelihood techniques to estimate the relevant equation system. This study estimated an equation explaining a person s decision to undergo medical exams jointly with an equation representing a person s decision to engage in any exercise on a regular basis, and to engage in strenuous exercise if she does exercise. A latent variable was specified for the observed discrete outcomes of the individual s medical exams. An individual s decision to undergo a medical exam ( I i = 1 if she undergoes medical exam, I i = 0 if she does not) is expressed as follows: I d n + h * i = 0 Z i + d1 H i + d 2K i + r 2 ïì I í ïî I i i = 1 if I = 0 * i > 0 otherwise ( I * i 0) i i (2) where K i represents an individual s health insurance type ( K i = 1 if she has an employee insurance program, K i = 0 if she has a selfemployed insurance program). The Korean national health insurance program includes two types of insurance programsan employee insurance program and a selfemployed insurance program. Even if the two different programs have almost identical coverage ranges and coinsurance rates, they differ to some degree in terms of the method by which insurance premiums are charged, and the specific requirements regarding compulsory medical exams for the insured. n i represents permanent unobservable individual characteristics, and hi represents white noise and is also assumed to be a normally distributed error term. In the estimation process above, the estimation of equation (2) requires a variable that affects the decision to undergo a medical exam, but does not directly influence a person s choice to engage in regular exercise or in strenuous exercise. In other words, identification relies on exclusion restrictions. Hence, in this paper, the variable K i is employed to identify equations (1) and (2). In Korea, in order to preserve worker s productivity, the employers strongly require employees to undergo medical exams, and the costs of this are partially subsidized by the Korean central government. Hence, whether a person is enrolled in an employee insurance program may have some impact with regard to an individual s decisions to undergo medical exams. This variable is, however, probably related only indirectly to the exercise decision. As a matter of fact, in the estimation process, whether a person is enrolled in an employee insurance program is only weakly correlated with the exercise decision. Thus, a variable indicating whether a person has an employee insurance program is included in the medical exam equation, but is not included in the other equations. However, in order to confirm the validity of this variable as an instrumental variable (IV), the Weak IV test and Overidentifying restriction test

The Information Effect of Medical Examination on Individual Health Promotion Behaviors 79 (Wooldridge, 2002: Stock and Watson, 2003) would be conducted, and the results would be introduced. Furthermore, as mentioned above, in order to avoid obtaining biased estimates of the parameters of interest, it is necessary to account for unobserved heterogeneity in the estimation process. Hence, in regard to this matter, in the process of estimating equations (1) and (2), I attempted to ameliorate endogeneity bias using the bivariate probit model. The bivariate probit model fits maximumlikelihood two equation probit models, and thus it is utilized in the joint estimation of equations (1) and (2). The bivariate probit model is predicated on the assumption that the error terms in equation (1) and (2) evidence a joint, bivariate normal distribution. This method is an attractive feature of this model, considering the particular situation this paper seeks to analyze: it is fairly simple to imagine that an individual inclination to undergo medical exams is related in some way with the attitude of the person in terms of performing health maintenance protocols, such as exercise, and viceversa. Additionally, considering that such features may constitute a source of endogeneity bias, they may benefit from joint estimation, which establishes the bivariate model in order to ameliorate endogeneity bias (Greene, 1997). Specifically, if the coefficient of correlation ( r ) between the error terms in these two equations does not equal zero, owing to the inherent statistical and structural endogeneity, we are ready to confirm the existence of the endogeneity of an individual s decision to undergo a medical examination. Therefore, after estimating the above two equations using the bivariate probit model, the test result of the hypothesis of r = 0 might be considered evidence of endogeneity, and furthermore if the endogeneity is shown to be extant, the joint estimation of equation (1) and (2) using the bivariate probit model might make produce more robust estimation results. Ⅲ. Data and Descriptive Statistics The data utilized here were obtained from the 2001 National Health and Nutrition Examination Survey (NHANES) of Korea. The 2001 NHANES interviewed 13,200 households in the country, which represented the entire nation. The dataset contains detailed information regarding health and health behaviors, and includes sufficient economic and sociodemographic information to permit the empirical estimation of the model mentioned above. The NHANES was conducted in 1998, 2001, 2005, and 20078. Even if we have more recent data, such as 2005 and 20078, the reason that the 2001 data is utilized is that the method used to define regular and strenuous exercise was considerably different when the 2005 data was collected. Specifically, in the 2001 data, individuals who engaged in activity at least one time per week for more than 20 minutes per time in the past one month were considered to have engaged in regular exercise. Furthermore, with regard to strenuous exercise, the 2001 NHANES asked respondents how frequently each respondent engaged in strenuous exercise, such as running, over the past one month. Individuals who engage in strenuous activity at least one time per week and for more than 30 minutes each time were considered to have engaged in regular

80 韓國開發硏究 / 2011. Ⅰ strenuous exercise. In the 2005 and 20078 data, however, no survey questions asked respondents about whether or not they engaged in regular exercise, which is shown in the 2001 data. Additionally, with regard to strenuous exercise, the 2005 and 20078 data asked how many days the respondent engaged in a high or medium degree of strenuous activity for more than 10 minutes in the most recent week. Additionally, the 2005 and 20078 data asked how many days the respondent walked for more than 10 minutes in the most recent week. Considering that the goal of this paper was to estimate the information effect of medical examination on individual health investments such as regular or strenuous exercise, if the 2005 and 20078 data were employed in this paper, it is highly plausible that the researcher would arbitrarily define the concepts of regular or strenuous exercise, since these activities have yet to be clearly defined 4. In this paper, the age range is restricted to an age range of 1865, because the population below 18 and above 65 are generally dependent family members, whose decisionmaking processes may be affected by other family members. As mentioned above, this study evaluates the effect of information regarding individual health status on two measures of exercise the probability of an individual engaging in any exercise and the probability of an individual engaging in strenuous exercise on a regular basis. The 2001 NHANES attempted to determine whether each respondent engaged in regular physical activity over the past one month. Individuals who engage in activity at least one time per week and for more than 20 minutes each time were considered to have engaged in regular exercise. Furthermore, the NHANES asked how often each respondent engaged in strenuous exercise, such as running, over the past one month. Individuals who engaged in strenuous activity at least one time per week and for more than 30 minutes each time were considered to have engaged in regular strenuous exercise. In addition to these key variables, the NHANES included detailed information regarding the individual s health status. This study utilizes a measure of selfreported health status, as well as the number of chronic diseases a patient experienced over the past oneyear period. The measure of the number of chronic diseases an individual has experienced captures the individual risk of illness. Furthermore, this study attempted to control for other individual healthrelated behaviors such as smoking and drinking, and the respondent s beliefs regarding the utility of undergoing a medical exam. It is reasonable to surmise that these factors may operate as confounding factors in an individual s decision regarding exercise. In the NHANES, each respondent is asked about the degree of caring for her health status and to which the respondent agreed that medical exams provide great utility to the recipient. Table 1 presents descriptive statistics for the subsample who underwent a 4 According to the reviewer s comments, I estimated the information effect of medical examination on the individual probability of engaging in strenuous exercise using the 20078 data and applying identical estimation methods. I defined strenuous exercise by regarding the individuals who engaged in exercise at least one day in the most recent week for more than 10 minutes each time. Even if a different dataset is used, the estimation results are fairly similar to the ones in this paper. The estimation results are available upon request.

The Information Effect of Medical Examination on Individual Health Promotion Behaviors 81 Table 1. Descriptive Statistics for Subsample Variable 5 Choice Variable Underwent Medical Exam Did not undergo Medical Exam Mean St. Dev. Mean St. Dev. Any Exercise 0.373 0.473 0.254 0.421 Any Strenuous Exercise 0.339 0.484 0.230 0.435 SocioDemographic Variables Age 41.084 11.529 37.913 12.203 Sex (0: female, 1: male) 0.527 0.499 0.407 0.491 Marital Status 0.785 0.410 0.691 0.462 Education 5.053 1.123 4.899 1.118 Number of Family member 3.580 1.234 3.624 1.257 Whether to enroll employee health insurance Health Status Variables Subjective Health Status (5: excellent, 4: very good, 3: good, 2: fair, 1: poor) 0.651 0.476 0.402 0.490 3.454 0.808 3.514 0.785 Number of Chronic Diseases 1.075 1.367 0.856 1.255 Economic Variables Living cost per month (Korean 10,000 Won of 2001 price level) Monthly Income (Korean 10,000 Won of 2001 price level) Subjective living level (5: very affordable, 4: affordable, 3: just fit to income, 2: poor, 1: very poor) Control Variables Degree of caring her health (4: never care about, 3: little care about, 2: sometimes care about, 1: always care about) Degree of agreeing to undergo medical exam provides great utility (5: strongly disagree, 4: sometimes disagree, 3: neither disagree nor agree, 2: sometimes agree, 1: strongly agree) 143.344 81.766 127.224 73.352 219.134 127.580 186.147 112.444 2.811 0.600 2.683 0.640 2.075 0.715 2.198 0.740 2.209 0.778 2.018 0.667 5 The parenthesis represents the name of variable used in the estimation process.

82 韓國開發硏究 / 2011. Ⅰ medical exam as compared to those who did not. The subsample of respondents who underwent a medical exam evidenced a high probability of engaging in any exercise and of engaging in strenuous exercise compared to the other group who did not undergo a medical exam. This pattern may reflect the difference between the two groups with regard to overall health status. For example, the subjective health status of those who underwent medical exams was poorer than the other group, and this group was more likely to have experienced chronic disease in the past year. This difference suggests that poorer health status may cause a patient to undergo a medical exam to precisely diagnose her disease before utilizing curative medical services, rather than regularly checking her health status as a preventive strategy. The two groups also evidence different sociodemographic characteristics. Those respondents who underwent medical exams were older, more highly educated, and more likely to be enrolled in an employee insurance program, which is consistent with the characteristics of this type of insurance in Korea, as previously mentioned, and also have higher incomes. This subsample also contains a lower proportion of females. Regarding the difference between the two groups with regard to overall health status, one more important thing should be considered in terms of the plausibility of reverse causality, which suggests that individual decisions about exercise might affect whether the respondent undergoes a medical exam. On one hand, considering that decisions about exercise might represent individual preferences to maintain good health status, individuals who engaged in regular exercise would more frequently undergo medical exams than others. On the other hand, ill health status originating from insufficient exercise might cause losses of income or losses of employment, which should limit access to health services, such as medical exams. Hence, if we fail to control for reverse causality, the estimation results might not be consistent. Therefore, in this regard, the key factor in controlling reverse causality is to consider the dynamic aspects of health status through health investment. Unfortunately, the dataset employed in this study represented merely one wave of crosssectional format. Even if a data limitation exists, the reverse causality problem was controlled in this paper as follows: Firstly, if the receipt of information from a medical examination precedes the exercise decision, we can avoid the plausibility of reverse causality. Fortunately, the NHANES is appropriate to this timing. Each person was initially asked whether she underwent a medical examination within the past two years, and was then asked about her exercise choices over the past one month. Furthermore, the NHANES also include information as to when a respondent underwent a medical exam within the past two years. Therefore, by excluding the observation as to who underwent a medical exam within the past one month, I attempted to make the dataset appropriate to the control of the reverse causality problem. Thus, the sample satisfying this timing scheme contains 6,509 observations. Secondly, I divided the total sample into five subgroups according to the respondents subjectively evaluated health status in order to conduct the sensitivity check. As mentioned above, the individual health status might

The Information Effect of Medical Examination on Individual Health Promotion Behaviors 83 confound the information effect of the medical exam on exercise level. Hence, in this paper, the same estimation model was applied to each subgroup and the estimation results in each subgroup were utilized to confirm the robustness of estimation results derived from the total sample. IV. Estimation Results This section presents the estimation results. Depending on whether or not to treat the endogeneity bias inherent to undergoing medical exam is treated, two different methods are applied in the estimation process. In Model 1, the probabilities of engaging in any regular exercise and in strenuous exercise are estimated independently from the probability of undergoing a medical exam by applying a simple probit model, which suggests that the endogeneity of undergoing a medical exam was not controlled. Model 2, however, attempts to reduce endogeneity bias by applying bivariate probit methods. As mentioned earlier, in the process of applying the bivariate probit method, the two equations were jointly estimated, and the hypothesis of coefficient of correlation ( r ) between the error terms in these two equations being equal to zero was tested in order to recognize the endogeneity bias. 1. The Case of Regular Exercise In the case of regular exercise, the estimation results are shown in Table 2. First of all, with regard to the test for the existence of endogeneity bias, it was shown to be extant. According to the estimation results of Model 2, the Wald test of was rejected with a 5% level of statistical significance, which suggests that the endogeneity problem of individual choice on medical exam does indeed exist; hence, it is natural to surmise that the estimation results of Model 2 are more robust than those of Model 1. In the estimation results of Model 2, it appears that undergoing a medical exam exerts a statistically significant positive effect on the probability of an individual engaging in any regular exercise, which suggests that the information effect of medical exam on the individual decision to exercise does indeed exist. Specifically, based on the estimation results above, we can readily calculate the marginal effect of the variable of whether to undergo a medical exam, which is shown at the third column of Model 2. According to the calculation, individuals who underwent medical exam show higher probability of doing regular exercise by 11.23% than others who didn t. In terms of elasticity, the result above can be reinterpreted that the 1% increase in the rate of undergoing medical exam is shown to raise the probability of doing regular exercise by 0.29%. Several variables representing socialeconomic aspects have been shown to exert significant impacts on the probability of engaging in regular exercise. This probability appears to increase with a person s age, education, and income level.

84 韓國開發硏究 / 2011. Ⅰ Table 2. Estimation Result(I): The Case of Regular Exercise Variable Model 1 (Simple Probit) No endogeneity controlled Model 2 (Bivariate Probit) endogeneity controlled Coefficient s.e. Coefficient s.e. Marginal Effect CONSTANT 1.5471*** 0.3231 1.3971*** 0.3458 Socioeconomic Variables Age 0.0135*** 0.0024 0.0135*** 0.0020 0.0021 Sex 0.0817** 0.0362 0.0832*** 0.0361 0.0157 Marital Status 0.0299 0.0463 0.0300 0.0457 0.0349 Education 0.1472*** 0.0213 0.1474*** 0.0213 0.0264 Number of Family Members 0.0473*** 0.0156 0.0472*** 0.0155 0.0061 Health state Variables Subjective Health Status 0.0815*** 0.0259 0.0815*** 0.0259 0.0346 Number of Chronic Diseases 0.0102 0.0158 0.0102 0.0158 0.0011 Economic Variables Living cost per month 0.0010*** 0.0003 0.0010*** 0.0003 0.0008 Monthly Income 0.0003* 0.0002 0.0003* 0.0002 0.0001 Subjective living level 0.0825*** 0.0322 0.0819*** 0.0319 0.0561 Control Variables Degree of caring her health 0.0377 0.0255 0.0376 0.0254 0.0076 Degree of agreeing to undergo medical exam provides great utility 0.1101*** 0.0318 0.1283*** 0.0347 0.1255 Whether to undergo medical exam Health Exam 0.4280** 0.1874 0.5378*** 0.2060 0.1123 0.1838** 0.1035 N 6,509 6,509 Note: *=statistically significant at the 0.1 level; **=statistically significant at the 0.05 level; ***=statistically significant at the 0.01 level

The Information Effect of Medical Examination on Individual Health Promotion Behaviors 85 Furthermore, according to the calculation of marginal effects of those variables, the increases in the marginal probabilities are 0.0021, 0.0264, and 0.0001, respectively. Additionally, the degree to which one agrees that medical exams provide great utility is demonstrated to exert a significant effect on the probability of engaging in regular exercise. 2. The Case of Strenuous Exercise In the case of engaging in strenuous exercise, as presented in Table 3, the endogeneity bias was still shown to be extant. According to the estimation results of Model 2, the Wald test result of showed that it was rejected with a statistical significance level of 5%, thus suggesting that the estimation results of Model 2 were still more robust than those of Model 1. Concerning the information effect of medical exams on engaging in strenuous exercise, the similar but more strengthened results with controls for endogeneity bias were derived. With no controls for endogeneity bias, it appears that undergoing medical exams exerts a positive effect on the probability of engaging in strenuous exercise, but the statistical significance is not guaranteed. However, controlling for endogeneity bias yields a significant change in the estimation result. With Model 2 using the bivariate probit method, the estimate remains positive but the statistical significance is now guaranteed, which suggests that undergoing a medical exam also exerts a statistically significant effect on an individual s decision to engage in strenuous exercise. Hence, the empirical results presented in this section support the prediction of the information effect of a medical exam. Based on the estimation results above, we calculate the marginal effect of the variable of whether to undergo a medical exam which is shown at the third column of Model 2. According to the calculation, individuals who underwent medical exam show higher probability of doing regular exercise by 11.55% than others who didn t. In terms of elasticity, the result above can be reinterpreted that the 1% increase in the rate of undergoing medical exam is shown to raise the probability of doing regular exercise by 0.31%. Several variables representing socialeconomic aspects have been shown to exert significant impacts on the probability of engaging in strenuous exercise in a fashion similar to that in the case of regular exercise. 3. The Validity of the Instrumental Variable As mentioned above, the estimation of Model 2 using the bivariate probit method requires a variable that influences the decision to undergo a medical exam, but does not directly affect a person s choice to engage in regular exercise or strenuous exercise in order to identify the equation system. In this paper, for that matter, whether or not a person is enrolled in an employee health insurance program is employed as an instrumental variable. The validity of the instrumental variable might be tested by i) instrument relevance and ii) instrument exogeneity (Stock and Watson, 2003). Hence, to assess

86 韓國開發硏究 / 2011. Ⅰ Table 3. Estimation Result (II): The Case of Strenuous Exercise Variable Model 1 (Simple Probit) No endogeneity controlled Model 2 (Bivariate Probit) endogeneity controlled Coefficient s.e. Coefficient s.e. Marginal Effect CONSTANT 1.4957*** 0.3219 1.5469*** 0.3390 Socioeconomic Variables Age 0.0048** 0.0020 0.0048** 0.0020 0.0007 Sex 0.2244*** 0.0356 0.2239*** 0.0356 0.0673 Marital Status 0.0643 0.0456 0.0642 0.0452 0.0486 Education 0.1768*** 0.0215 0.1767*** 0.0213 0.0381 Number of Family Members 0.0266* 0.0514 0.0267* 0.0152 0.0010 Health state Variables Subjective Health Status 0.0867*** 0.0258 0.0868*** 0.0253 0.0377 Number of Chronic Diseases 0.0262 0.0160 0.0261* 0.0158 0.0067 Economic Variables Living cost per month 0.0010*** 0.0003 0.0009*** 0.0003 0.0008 Monthly Income 0.0002 0.0002 0.0001 0.0002 0.0001 Subjective living level 0.0688** 0.0318 0.0690** 0.0317 0.0532 Control Variables Degree of caring her health 0.0830*** 0.0254 0.0830*** 0.0252 0.0237 Degree of agreeing to undergo medical exam provides great utility 0.0775** 0.0315 0.0712** 0.0342 0.1270 Whether to undergo medical exam Health Exam 0.1981 0.1854 0.3108*** 0.0956 0.1155 0.0739** 0.0404 N 6,509 6,509 Note: *=statistically significant at the 0.1 level; **=statistically significant at the 0.05 level; ***=statistically significant at the 0.01 level

The Information Effect of Medical Examination on Individual Health Promotion Behaviors 87 Table 4. Probit Estimation Result of Undergoing Medical Examination Variable Coefficient s.e CONSTANT 6.4512*** 1.1230 Socioeconomic Variables Age 0.0212** 0.0106 Sex 0.1052 0.1954 Marital Status 0.2282 0.2559 Education 0.1801 0.1141 Number of Family Member 0.0839 0.0878 Employee Health Insurance 0.5129*** 0.1240 Health state Variables Subjective Health Status 0.0680 0.1417 Number of Chronic Diseases 0.0210 0.0801 Economic Variables Living cost per month 0.0040** 0.0020 Monthly Income 0.0019* 0.0010 Subjective living level 0.2696* 0.1544 Control Variables Degree of caring her health 0.0494 0.1403 Degree of agreeing to undergo medical exam provides great utility 1.2208*** 0.0702 Likelihood Ratio Test statistic 42.17*** Overidentifying restrictions test statistic 0.002 N 6,509 Note: *=statistically significant at the 0.1 level; **=statistically significant at the 0.05 level; ***=statistically significant at the 0.01 level the relevance of the instrumental variable, two different criteria were employed. The first was whether the instrument variable exerts an impact on an individual s decision regarding medical exams. In this regard, the probit estimation results associated with undergoing medical exams are provided in Table 4.

88 韓國開發硏究 / 2011. Ⅰ The variable expressing whether a person is enrolled in an employee health insurance program was shown to exert a statistically significant effect on the probability of undergoing a medical exam. According to previous researches (Wooldridge, 2002; Stock and Watson, 2003), the tratio or zratio of the instrumental variable must be more than 3.3 for it to work well as an instrumental variable. Given this criterion, the zratio of variable KHI was 4.13, which satisfies this condition. The second one is to utilize the Weak IV test. Stock and Watson (2003) suggested that computing the Fstatistic testing the hypothesis that the coefficients on the instruments are all zero provides a measure of the information content contained in the instrument. In this paper, however, the model specification is not a linear format in which the Fstatistic cannot be calculated. Hence, by performing the likelihood ratio test, I attempted to investigate whether the information content of the instrumental variable was sufficient to explain an individual s decision with regard to medical exams. The test statistic was 42.17 as shown at Table 4, and this is sufficiently large to reject the null hypothesis that the value of likelihood under having the instrumental variable as a covariate is identical to the value under not having the instrumental variable. Regarding the exogeneity of the instrumental variable, the overidentifying restriction test method was employed. In the estimation process, we have a single included endogenous variable and only one instrument. Hence, if the overidentifying restriction test statistic is exactly zero, it is natural to surmise that the coefficients in the equation system are exactly identified. The test statistic was 0.002, as shown in Table 4, and its statistical significance was not guaranteed. Therefore, based on the test results mentioned above, we can conclude that the instrumental variable used in this paper works well. Even if the instrumental variable is shown to satisfy the two statistical tests above, the use of the instrumental variable must be intuitively accepted. Specifically, individuals who enrolled in employee health insurance programs might be more exposed to risky health environments such as job stress or might create a better atmosphere in which more healthrelated information can be collected from colleagues. Furthermore, considering that many firms currently support their workers activities; those workers would tend to spend more time in regular or strenuous exercise. Hence, this instrumental variable might be correlated with omitted variables and thus with the dependent variable, which suggests that the estimation results should not be robust Therefore, concerned the estimation results of this paper, even if several statistical tests were utilized for confirming the wellness of instrumental variable, it is required to contemplate the point mentioned above. 4. Sensitivity Check This paper also involved a sensitivity check to assess whether the estimation results above might be insensitive to individual health status. The total sample was divided into 5 subgroups according to respondents subjectively evaluated health

The Information Effect of Medical Examination on Individual Health Promotion Behaviors 89 Table 5. Sensitivity Check Health Status Regular Exercise Strenuous Exercise Coefficient s.e. Coefficient s.e. Excellent (373) a 0.4708** 0.2027 0.3319** 0.1459 Very Good (3,224) 0.3998** 0.1882 0.3099** 0.1566 Good (2,168) 0.3271* 0.1790 0.2991** 0.1512 Fair (675) 0.2981 0.1875 0.2550* 0.1538 Poor (69) 0.1994 0.1987 0.1889 0.1599 Note: *=statistically significant at the 0.1 level; **=statistically significant at the 0.05 level; ***=statistically significant at the 0.01 level a : the number of sample status and the same estimation model was applied in both casesregular exercise and strenuous exercise. The estimation results shown in Table 5 were obtained via the application of the bivariate probit model, taking into consideration of the endogeneity of individual decisions regarding medical exams. Even if, in the group with comparatively poor health status, the statistical significance of the effect of undergoing medical exams on the probability of doing regular or strenuous exercise was not guaranteed even endogeneity was controlled, the direction of that effect was consistent with the results of this paper. Furthermore, the effect of strenuous exercise was significant in all groups, with the exception of the poor health status group. Considering that the probability of doing strenuous exercise might be influenced more profoundly by individual health status than that of regular exercise, this sensitivity check might help bolster the notion that the reverse causality problems might be not sufficiently serious to render the estimation results in this paper inconsistent. However, the test results shown above might also suggest that the information effect of medical examination is great in individuals with good health status, and conversely suggests that this effect cannot be guaranteed in individuals in poor health. Considering that the object of providing free medical examinations is to improve public health status, the rationale of supporting this policy would be somewhat constrained. In this regard, it might prove beneficial to consider that the reason for undergoing medical examinations can differ between individuals in good health and those in poor health. Whereas, in healthy individuals, the reason for undergoing medical exams is to maintain favorable health status and to detect critical diseases such as cancer in its early stages, in the case of unhealthy people, medical exams are primarily used to find conclusive evidence of a specific disease, as these individuals generally visit a doctor only because they are experiencing some health problem. Even if an information effect of medical exam exists even in those in poor health, those individuals poor health status is clearly not sufficient inducement to engage in regular or strenuous exercise. Furthermore, considering the total sample was divided on the basis of the respondents subjectively evaluated

90 韓國開發硏究 / 2011. Ⅰ health status, some sample selection bias might be inherent to this study. For example while the sample size of the group whose health status was very good was 3,224, only 69 respondents were placed in the poor health group. Therefore, the test results above should be interpreted with great care. V. Conclusion This study attempted to evaluate the information effect of medical examinations on individual health promotion behaviors, most notably exercise. Specifically, this paper empirically demonstrates that individuals who monitor indicators of their current CV health status, such as blood pressure, are more likely to invest in their own health than those who don t engage in such monitoring behaviors. The estimation results provide evidence that individuals who make informed decisions by undergoing medical exams tend to engage in regular and strenuous exercise more frequently than those who do not. Specifically, given control of individual choice as to whether to undergo medical examinations, individuals who undergo medical examinations tend to evidence higher levels of health promotional behavior performance than others who elect not to undergo medical examinations. In terms of policy perspective, policies designed to increase access to medical exams may increase health maintenance behaviors via this information effect. In particular, the Korean national health insurance system provides insured individuals with free access to medical exams. e information effect of this policy. However, with regard to interpreting the results of this paper, the following point should be mentioned. As discussed previously, because the dataset used in this study represented merely one wave of a crosssectional format, we should take into careful consideration of the possibility of reverse causality. Even if the study used several different methods to control or compensate for this problem, in order to make clear the causal pathway of the information effect of medical exam on health promotion behavior, the dynamic aspect of health status should be properly considered. Therefore, in this context, a longitudinal dataset is clearly appropriate, and should be utilized in future of investigating this topic. Furthermore, concerned the interpretation of the results derived in this paper, we should consider the credibility of medical exams. Unfortunately, if the information from medical exams is positively false, the recipient should pay unnecessary monetary and psychological costs. Hence, even if this paper demonstrates the information effect of medical exams on individual health promotion behaviors, we should be careful in interpreting the results.

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韓國開發硏究제 33 권제 1 호 ( 통권제 110 호 ) 우리나라자산가격변동의기준점효과및 전망이론적해석가능성검정 김윤영 ( 단국대학교무역학과부교수 ) 이진수 (KDI 국제정책대학원조교수 ) Dynamics of Asset Returns Considering Asymmetric Volatility Effects: Evidences from Korean Asset Markets YunYeong Kim (Associate Professor, Department of International Trade, Dankook University) Jinsoo Lee (Assistant Professor, KDI School of Public Policy and Management) * 본논문은한국금융연구센터창립기념심포지엄에서발표된바있으며, 한국금융연구센터의재정적지원에감사드린다. 한국금융연구센터창립기념심포지엄과 2009 KDI Journal of Economic Policy Conference 에서토론을해주신백웅기교수님, 이우헌교수님, 윤덕룡박사님과익명의심사자께감 사드린다. 또한본연구를위해자료를정리해준조재한연구조교에게도고마움을표한다. * 김윤영 : (email) yunyeongkim@dankook.ac.kr, (address) 126 Jukjeondong, Yonginsi, Gyeonggido, 448701, Koreag Seoul, Korea 이진수 : (email) jlee@kdischool.ac.kr, (address) 87 Hoegiro, DongdaemunGu, Seoul, 130650, Korea Key Word: (Asset Return), (Anchor Effect), (Prospect Theory), (Volatility) JEL Code: C3, F4 Received: 2010. 10. 4 Referee Process Started: 2010. 10. 13 Referee Reports Completed: 2011. 3. 4

ABSTRACT In this paper, we claim the asymmetric response of asset returns on the past asset returns' signs may be explained from the market behavioral portfolio choice of investors. For this, we admit the anchor and adjustment mechanism of investors which partly explains the momentum in the asset prices. We also claim the prospect theory based on the risk aversions may simultaneously work with the anchor and adjustment effect, whenever the lagged asset return was positive and investors accrued the gain. To identify these effects empirically in a threshold autoregressive model, we suppose the risk aversions inducing the volatility effect is related with the past volatility of asset returns. In application of suggested method to Korean stock and real estate markets, we found these effect exist as expected.

우리나라자산가격변동의기준점효과및전망이론적해석가능성검정 95 Ⅰ. 서론,.,, FRB, ( ),.,. 1) Shiller (2004) 1990.. Tversky and Kahneman(1974) (anchor effect),., (trends). Kahneman and Tversky(1979) (prospect theory),. 2),. (2010) 1) Barberis and Thaler(2003). 2) Shefrin and Statman(1985), Odean(1998) (disposition effect).

96 韓國開發硏究 / 2011. Ⅰ. (). 3),.. Ⅱ. 기준점효과와전망이론을동시에고려한자산가격변동모형 Fama(1965). Kendall(1953) Fama(1965, 1970) AR(1). Δ μαδ (1) Δ,.,, Fama and Schwert(1977). Campbell(1987), Campbell and Shiller(1988), Cutler, Poterba, and Summers (1991), Fama and French(1988, 1989), (priceearnings ratio),., Balvers, Cosimano, and MacDonald(1990), Fama(1990), Schwert (1990). 4) Tversky and Kahneman (1974) (anchor effect) Kahneman and Tversky(1979) (prospect theory) (1) 3).. 4) Kaul(1996).

우리나라자산가격변동의기준점효과및전망이론적해석가능성검정 97. 1. 기준점효과 (Anchor Effect) Tversky and Kahneman(1974),. Shiller(2004, p.149),,.,, (2006),. 5) (learning)... 6) ( ) ( ). Δ γ Δ Δ γ Δ Δ (2) γ γ. γ γ,. 2. 수익변동성을반영한전망이론효과 Kahneman and Tversky(1979),., t1 5) Banerjee(1992), Keynes., Froot, Scharfstein, and Stein(1992),,,. 6) Bayesian.

98 韓國開發硏究 / 2011. Ⅰ ( ) Δ. 7)8). (3) 의확률 의확률 t, t.. <Table 1>.,. <b, 1/2> b 1/2 0 1/2 <Table 1> Investor's Gain or Loss at Time t Due to Her Choice of Action Asset Return at time t1 ( Δ ) Choice of Action Asset Return at time t ( Δ ) Moments of Asset Return at time t mean variance x sell x x 0 hold (2x, 1/2) or (0, 1/2) x x 2 x sell x x 0 hold (0, 1/2) or (2x, 1/2) x x 2 Note: 1) (2x, 1/2) denotes that an investor will receive 2x with the probability of 1/2. 7) (random walk process). 8) 0 (conditional) 0. Z (:, ), P(), Z=z. Z. ( ),.,. Z=z.

우리나라자산가격변동의기준점효과및전망이론적해석가능성검정 99.. 9) Δ t <2x, 1/2> <x, 1> ( ) Δ t <2x, 1/2> <x, 1> ( ), Markowitz(1959) <Table 1> 10). <Table 1> Δ. t... (u 2) (u 1 ) (Figure 1(a) 11) ).,. (u) (μ) (σ 2 ) (additive) u=μλσ 2. 12) λ > 0. (u 2 ) (u 1 ) u 1u 2 = λx 2 > 0,. 13) [Figure 1(b)] 9) Shefrin and Statman(1985), Odean(1998) (disposition effect),..,..,. 10) x. 11),. 12), (multiplicative) ().. 13).

100 韓國開發硏究 / 2011. Ⅰ [Figure 1] Changes in Indifference Curves Due to Investor's Choice of Action (a) Risk Averse Investor (b) Risk Seeking Investor (u 2 ) (u 1 ).. (u) u=μλσ 2. λ < 0. (u 2 ) (u 1 ) u 2u 1 = λx 2 > 0,. (ceteris paribus), [Figure 2] (SS'). 14).,.,?. (9) ( ), ( θ) 14).

우리나라자산가격변동의기준점효과및전망이론적해석가능성검정 101 [Figure 2] Changes in Supply Curve and Equilibrium Price for a Stock (a) Choice of Action (Sell) (b) Choice of Action (Hold) Quantity Quantity. 15), θ. i f i f (4) 3. 추정모형유도. Δ t1 Ω Δ. 16) (4) λ,.. σ t1 (), t 15). 16) (loss cut)..,.

102 韓國開發硏究 / 2011. Ⅰ. λλ σ λ (5) (1) (2) (4). (6) Δ 1, 0., (6) Δ, Δ. <Table 1> λ γ σ 17). (6) (7) (8). (6). (9) Δ μγ Δ (7) <Table 1> γ. Δ Δ (6). (8) 18), C., (9). δ Δ (10) Δ 17),. 18), Fama and Schwert(1977) (treasury bill). Kaul(1996) 3.4.

우리나라자산가격변동의기준점효과및전망이론적해석가능성검정 103 δ γ λ σ, (timevarying) (positive response coefficient). (9) γ λ δ σ, [Figure 3]. σ δ [Figure 4]., ( σ ) onesided rolling regression t1 m ( σˆ ). 19) m ( m ).. (9). Ⅲ. 실증분석 (9) ( ) (, ). 20),,,, / (),,,,. 21) ( 1, 2) ( 3),. 19) onesided rolling regression. t1 t1 ( ). twosided rolling regression t, t. ARCH. (m). 20),. 21) Chen, Roll, and Ross(1983, 1986), Fama and French(1989), Campbell and Ammer(1993), Balke and Wohar(2006), (2002), (2006), (2009), (2009).

104 韓國開發硏究 / 2011. Ⅰ. 19952008, 19912008. (1997. 10~1999. 2), (2002~04), (2007. 8~). 22), () ()., (), / (), (1),,,. <Appendix 1> <Appendix 2>, <Appendix 3>.. 1. 주가의경우 <Table 2> 1 2. 23), 3. 24)25) 22) 1997 10 1999 2 1998 8, 2007 11 2008 9,. (window) m (: 10 5).. 23) 10, 20, 30, 60. 24) 1 2 (omitted variable bias)

우리나라자산가격변동의기준점효과및전망이론적해석가능성검정 105 <Table 2> Regression Results for Daily Korean Stock Market Returns constant 0.011 m = 10 business days m = 20 business days Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 (0.23) P t1 0.035 (1.20) P t1*1 [Pt1>0] 0.041 (0.85) 0.020 (0.40) 0.039 (1.29) 0.002 (0.03) σ m,t1*p t1*1 [Pt1>0] 0.012 (0.58) 0.145 * (1.76) 0.044 (1.48) 0.128 (1.61) 0.029 (1.43) Rate of Change 0.221 in Exchange Rate t1 (6.42) Call Rate t1 0.021 ** *** (2.36) US Stock Market 0.454 Return t1 (17.55) Dummy for Period 1 (1997. 10~1999. 2) Dummy for Period 2 (2002~2004) Dummy for Period 3 (2007. 8~2008) 0.104 *** (0.81) 0.108 (1.26) 0.103 (0.94) 0.011 (0.23) 0.035 (1.20) 0.041 (0.85) 0.007 (0.13) 0.034 (1.13) 0.061 (0.71) 0.007 (0.28) 0.123 (1.49) 0.050 * (1.68) 0.217 *** (2.58) 0.062 *** (2.60) 0.231 *** (6.70) 0.020 ** (2.29) 0.456 *** (17.63) 0.131 (1.01) 0.107 (1.27) 0.089 (0.81) N 3,150 3,150 3,150 3,150 3,150 3,150 Fstatistic 5.65 *** 3.88 *** 40.90 *** 5.65 *** 3.79 *** 41.48 *** adjusted R 2 0.003 0.003 0.102 0.003 0.003 0.104 Note: 1) The numbers in parentheses denote tvalues. 2) ***, **, * denote 1%, 5%, 10% statistical significance, respectively.. 25).

106 韓國開發硏究 / 2011. Ⅰ <Table 2> continued m = 30 business days m = 60 business days Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 constant 0.011 (0.23) 0.018 (0.35) 0.138 * (1.66) 0.011 (0.23) 0.023 (0.46) 0.159 * (1.92) P t1 0.035 (1.20) 0.038 (1.27) 0.045 (1.52) 0.035 (1.20) 0.039 (1.33) 0.039 (1.32) P t1*1 [Pt1>0] 0.041 (0.85) 0.007 (0.08) 0.166 * (1.90) 0.041 (0.85) 0.027 (0.28) 0.092 (0.98) σ m,t1*p t1*1 [Pt1>0] 0.012 (0.45) 0.046 * (1.76) 0.025 (0.82) 0.021 (0.69) Rate of Change 0.223 in Exchange Rate t1 (6.49) *** 0.215 *** (6.25) Call Rate t1 0.020 ** (2.31) 0.021 ** (2.36) US Stock Market 0.455 Return t1 (17.58) *** 0.453 *** (17.51) Dummy for Period 1 (1997. 10~1999. 2) Dummy for Period 2 (2002~2004) Dummy for Period 3 (2007. 8~2008) 0.119 (0.92) 0.106 (1.25) 0.098 (0.89) 0.100 (0.76) 0.106 (1.25) 0.112 (1.02) N 3,150 3,150 3,150 3,150 3,150 3,150 Fstatistic 5.65 *** 3.83 *** 41.03 *** 5.65 *** 3.99 *** 40.70 *** adjusted R 2 0.003 0.003 0.103 0.003 0.003 0.102 Note: 1) The numbers in parentheses denote tvalues. 2) ***, **, * denote 1%, 5%, 10% statistical significance, respectively.

우리나라자산가격변동의기준점효과및전망이론적해석가능성검정 107 <Table 3> Anchor and Volatility Effects in Korean Stock Market m = 10 business days m = 20 business days m = 30 business days m = 60 business days γ 0.044 0.050 * 0.045 0.039 γ 0.084 0.167 ** 0.121 0.053 λ 0.029 0.062 *** 0.046 * 0.021 γ γ 0.128 0.217 *** 0.166 * 0.092 Note: ***, **, * denote 1%, 5%, 10% statistical significance, respectively. <Table 3> 3, 20 30 (+). 20 ( γ = 0.167) 5%, ( λ = 0.062) 1%. 3 δ. 20 δ γ λ (11). δ σ (11) 20 2.69, 2.69 (Figure 3 ). [Figure 4] 20 1995~2008 δ γ λ δ 26). 26) σ., σ γ λ.

108 韓國開發硏究 / 2011. Ⅰ [Figure 3] Relationship between Standard Deviation of Korean Stock Market Returns and their Aggregate Response Coefficient( δ ) (m = 20 Business Days) δ t1 =γ 2 λ 0 σ t1 Low Historical Volatility σ* High Historical Volatility Volatility 0.2 [Figure 4] Historical Trend in Aggregate Response Coefficient( δ ) for Korean Stock Market (m = 20 Business Days) 0.1 Anchor Effect (A) 0 Aggregate Response Coefficient (A+B) 95 96 97 98 99 00 01 02 03 04 05 06 07 08 0.1 0.2 Volatility Effect (B) 0.3 0.4 Period