4. Empirical results

#### 4.1. Descriptive statistics and correlation analysis

Panel A of Table 1 shows the yearly distribution and ownership percentage of companies with NPS shares of more than 5%. In 2000, NPS had more than 5% shares of only one company but it has increased gradually since then and has increased sharply since the 2008 financial crisis. In 2016, NPS owns more than 5% stake in 231 Korean companies with an average stake of 8.46%, with a maximum stake of 13. 5%. Panel B provides yearly statements on NPS's objections to the shareholders' meeting at the shareholders' meeting for reasons of lower dividend. NPS's voting rights were manually extracted from the NPS fund management website since 2005. The number of cases in which NPS exercised its voting rights on the grounds of a lower dividend was only 1 or 2 before 2010 but soared to 16 in 2011, again dropping to 26 in 2014. This seems to be due to the revision of the Enforcement Decree of the Capital Markets Act at the end of 2014 and the legal basis for NPS to invest in the company.

Table 2 presents descriptive statistics for the variables used in this study. Panel A is for a test sample that matches 1: 2 PSM to companies with NPS's over 5% stake (treated group) and to those whose does not (control group). We use this sample to verify the effect of NPS dividend pressure on firm value. The mean value of Q is 1.37 and the median is 1.01. The top 1% of Q is 6.57, indicating that some of the firms in the test sample have a very high Q value. Since the treated group and the control group are matched 1: 2, the NPF has a value of 1 from the 75th percentile. In observations that account for about 2% of the test sample, NPS's have exercised a negative voting right at the shareholders' meeting for reasons of low dividend. Panel B provides descriptive statistics of the variables used in the logistic regression analysis for PSM. Panel A is descriptive statistics of the test sample constructed through PSM, but Panel B is for the entire sample. Korea's listed companies have an average debt ratio of 44% from 2011 to 2016, ROA of 1%, and net profit growth of negative values. The average PER is 14.8x and the average dividend payout ratio is 12. 5%. Table 3 shows the result of Pearson correlation analysis. Q has a positive correlation with NPF. The correlation between Q and the variables representing the dividend level is inconsistent. However, the correlation does not take into account other factors that affect the relationship between the two variables. It is more appropriate to draw conclusions through the regression analysis described below.

which means firm, and NPF is a dummy variable indicating whether the NPS owns more than 5% stake. DIVOUT is the cash dividend rate relative to the net income, DIVTA is the cash dividend rate relative to total assets, and DIVRATE is the cash dividend rate relative to the paid-in capital. SIZE is the natural log of total assets, LEV is the debt ratio, ROA is the returns on assets, CFO is the ratio of operating cash flow to total assets, and σ (CF) and σ (SALES) is volatility. Z is the risk of bankruptcy of Altman's Z-score, OPCYCLE is the natural log of the operating cycle, MB is the market-to-the-book value ratio of equity, TANG is the ratio of tangible assets to total assets, and LOSS is 1 if the company has net loss otherwise 0. In Panel B, NIR is the growth rate of net income, PER is the ratio of stock price to EPS, and the rest is the

Panel B: Number of companies for which NPS exercised a negative voting right at the shareholders' meeting due to a

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Year 2006 2008 2010 2011 2012 2013 2014 2015 2016 2017 Obs 2 1 1 18 1 7 26 16 21 18

Table 1. Distribution of NPS shares (5% or more) and voting exercise (reason for less dividend).

same as Panel A definition.

lower dividend

The definitions of the variables are the same as those in Table 2.

Panel A: Annual distribution of companies with NPS of more than 5% shares

Mean Min Median Max

Year n Ownership owned by NPS

2000 1 6.47 6.47 6.47 6.47 2001 4 6.78 5.37 6.56 8.63 2002 4 7.01 5.37 6.89 8.89 2003 12 6.46 5.04 6.23 8.75 2004 18 7.56 5.18 6.89 12.21 2005 19 8.16 5.73 8.20 11.28 2006 29 7.53 5.02 6.35 15.67 2007 30 7.65 5.06 6.60 15.81 2008 53 7.61 5.01 6.71 15.73 2009 65 6.67 5.01 6.40 12.20 2010 97 6.65 5.00 6.30 9.66 2011 117 7.55 5.00 7.50 11.02 2012 159 7.66 5.02 7.89 11.08 2013 195 8.01 5.03 7.71 13.41 2014 210 8.35 5.00 8. 10 14.82 2015 225 8.65 5.00 8.06 14.20 2016 231 8.46 5.00 8.04 13.50

Panel A of the table is descriptive statistics of variables used in main analysis and Panel B is descriptive statistics of variables used in logistic regression analysis for PSM. Q is Tobin's


Of the original 11,350 firm-year observations from 2011 to 2016, we lose 616 for financial companies and 182 for companies with negative net assets. 1039 observations are companies that have more than 5% shares of NPS and the control group matched using PSM has 2978 observations. In order to exclude the effect of extreme values, 1% of the upper and lower values are winsorized. The final company-year observations, including all financial informa-

Panel A of Table 1 shows the yearly distribution and ownership percentage of companies with NPS shares of more than 5%. In 2000, NPS had more than 5% shares of only one company but it has increased gradually since then and has increased sharply since the 2008 financial crisis. In 2016, NPS owns more than 5% stake in 231 Korean companies with an average stake of 8.46%, with a maximum stake of 13. 5%. Panel B provides yearly statements on NPS's objections to the shareholders' meeting at the shareholders' meeting for reasons of lower dividend. NPS's voting rights were manually extracted from the NPS fund management website since 2005. The number of cases in which NPS exercised its voting rights on the grounds of a lower dividend was only 1 or 2 before 2010 but soared to 16 in 2011, again dropping to 26 in 2014. This seems to be due to the revision of the Enforcement Decree of the Capital Markets Act at

Table 2 presents descriptive statistics for the variables used in this study. Panel A is for a test sample that matches 1: 2 PSM to companies with NPS's over 5% stake (treated group) and to those whose does not (control group). We use this sample to verify the effect of NPS dividend pressure on firm value. The mean value of Q is 1.37 and the median is 1.01. The top 1% of Q is 6.57, indicating that some of the firms in the test sample have a very high Q value. Since the treated group and the control group are matched 1: 2, the NPF has a value of 1 from the 75th percentile. In observations that account for about 2% of the test sample, NPS's have exercised a negative voting right at the shareholders' meeting for reasons of low dividend. Panel B provides descriptive statistics of the variables used in the logistic regression analysis for PSM. Panel A is descriptive statistics of the test sample constructed through PSM, but Panel B is for the entire sample. Korea's listed companies have an average debt ratio of 44% from 2011 to 2016, ROA of 1%, and net profit growth of negative values. The average PER is 14.8x and the average dividend payout ratio is 12. 5%. Table 3 shows the result of Pearson correlation analysis. Q has a positive correlation with NPF. The correlation between Q and the variables representing the dividend level is inconsistent. However, the correlation does not take into account other factors that affect the relationship between the two variables. It is more appro-

tion required for the analysis, are identified as 2194.

4.1. Descriptive statistics and correlation analysis

the end of 2014 and the legal basis for NPS to invest in the company.

priate to draw conclusions through the regression analysis described below.

Panel A of the table is descriptive statistics of variables used in main analysis and Panel B is descriptive statistics of variables used in logistic regression analysis for PSM. Q is Tobin's

4. Empirical results

60 Firm Value - Theory and Empirical Evidence

Table 1. Distribution of NPS shares (5% or more) and voting exercise (reason for less dividend).

which means firm, and NPF is a dummy variable indicating whether the NPS owns more than 5% stake. DIVOUT is the cash dividend rate relative to the net income, DIVTA is the cash dividend rate relative to total assets, and DIVRATE is the cash dividend rate relative to the paid-in capital. SIZE is the natural log of total assets, LEV is the debt ratio, ROA is the returns on assets, CFO is the ratio of operating cash flow to total assets, and σ (CF) and σ (SALES) is volatility. Z is the risk of bankruptcy of Altman's Z-score, OPCYCLE is the natural log of the operating cycle, MB is the market-to-the-book value ratio of equity, TANG is the ratio of tangible assets to total assets, and LOSS is 1 if the company has net loss otherwise 0. In Panel B, NIR is the growth rate of net income, PER is the ratio of stock price to EPS, and the rest is the same as Panel A definition.

The definitions of the variables are the same as those in Table 2.


Table 2. Descriptive statistics.

#### 4.2. Results of PSM and OLS regression

Table 4 shows the results of Eq. (1), a logistic regression for PSM. The dependent variable of the logistic regression model is whether NPS owns more than 5% stake. We used LEV (debt ratio), ROA (return on assets), NIR (growth rate of net income), market (KOSPI or KOSDAQ), PER, and DIVOUT (dividend payout) and yearly dummy as independent variables. The results show that debt ratio, ROA, market, and dividend payout are related to whether NPS owns a large amount of stakes. However, net income growth and PER are not related to NPS holdings more than 5% stake. Panel B shows the results of the PSM, with 1039 observations of firm-year observations that have more than 5% stake in NPS, with 1:2 matching and 2078

(20) LOSS �0.02 �0.59 �0.26 0.02 0.03 �0.21 0.07 0. 10 0.04 1.00

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

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Table 5 shows the results of OLS regression analysis of Eq. (2) that analyzed the effect of NPS's dividend pressure on firm value. The dividend pressure of NPS is measured by the intersection term (DIVi,t∗NPFi,t�1) between the level of cash dividend and whether the NPS invested more than 5%. Columns (1)–(6) show different definitions of cash dividend level, respectively. First, the relationship between cash dividend level (DIV) and firm value has a statistically significant positive value in columns (3), (4), (5), and (6). This suggests that Korean firms tend to have higher firm value as cash dividends are paid more. In addition, in the columns (1), (2), (3), (4), and (5), it is confirmed that the NPF has a statistically significant positive value with the enterprise value. This indicates that a firm with more than 5% stake in NPS has a higher enterprise value. However, the DIVi,t∗NPFi,t�<sup>1</sup> variable, which indicates the dividend pressure of the NPS, is not significantly related to firm value. To summarize, the dividend pressure of the NPS does not directly affect firm value. However, it has been shown that the holding of

controls.

(1) Q 1.00

(11) Q 1.00

(12) ROA 0.15 1.00

Table 3. Pearson correlation.

(13) CFO 0.15 0.47 1.00

(14) σð Þ CFO 0.24 0.08 0.02 1.00

(15) σð Þ SALES 0. 11 0.02 �0.02 0.48 1.00

(16) Z 0.43 0.42 0.29 0.15 0.02 1.00

(17) OPCYCLE �0. 11 �0. 14 �0.22 �0.05 �0. 13 �0. 11 1.00

(18) MB 0.24 �0. 14 �0.01 0.05 0.01 0.05 �0.02 1.00

(19) TANG �0. 12 �0. 10 0.07 �0.18 �0.16 �0.25 0.01 �0.02 1.00

(2) NPF 0.06 1.00

(3) DIVOUT �0.06 �0.01 1.00

(4) ΔDIVOUT �0.02 �0.03 0.59 1.00

(5) DIVTA 0.29 0.05 0.39 0.07 1.00

(6) ΔDIVTA 0. 13 �0.02 0.06 0. 12 0.27 1.00

(7) DIVRATE 0.21 0.24 0.22 0.03 0.61 0. 13 1.00

(8) ΔDIVRATE 0.22 0.08 0.07 0.09 0.34 0.67 0.44 1.00

(9) SIZE �0. 10 0.46 �0.01 0.00 �0. 11 �0.04 0.24 0.06 1.00

(10) LEV �0.04 0.01 �0.15 0.01 �0.37 0.02 �0.25 �0.03 0.33 1.00

(11) (12) (13) (14) (15) (16) (17) (18) (19) (20)

Ask of National Pension Service for Higher Dividend and Firm Value: Evidence from Korea http://dx.doi.org/10.5772/intechopen.75578 63


Table 3. Pearson correlation.

4.2. Results of PSM and OLS regression

Table 2. Descriptive statistics.

Panel B: descriptive statistics of logistic model for PSM

Panel A: descriptive statistics for main analysis

62 Firm Value - Theory and Empirical Evidence

Table 4 shows the results of Eq. (1), a logistic regression for PSM. The dependent variable of the logistic regression model is whether NPS owns more than 5% stake. We used LEV (debt ratio), ROA (return on assets), NIR (growth rate of net income), market (KOSPI or KOSDAQ), PER, and DIVOUT (dividend payout) and yearly dummy as independent variables. The results show that debt ratio, ROA, market, and dividend payout are related to whether NPS owns a large amount of stakes. However, net income growth and PER are not related to NPS

LEV 10,552 0.44 0.21 0.05 0.27 0.44 0.60 0.92 ROA 10,482 0.01 0.12 �0.48 �0.01 0.03 0.06 0.26 NIR 10,480 �0.39 4.67 �26.93 �0.84 �0.17 0.38 16.78 PER 10,223 14.80 106.96 �240.76 �2.79 8.42 19.17 442.48 DIVOUT 10,550 12.50 23.63 �31.82 0.00 0.00 18.46 125.05

Type n mean std a1 a25 a50 a75 a99

Q 3105 1.37 1.37 0.39 0.81 1.03 1.44 6.57 NPF 3117 0.33 0.47 0.00 0.00 0.00 1.00 1.00 Divout 3117 20.52 26.64 �29.98 2.82 14.19 27.59 125.05 Δ Divout 3117 3.37 34.41 �143.57 �2.36 0.00 6.06 134.10 Divta 3117 0.92 1.15 0.00 0.17 0.58 1.22 6.91 Δ Divta 3090 0.01 0.64 �2.15 �0.10 0.00 0.09 2.69 Divrate 3117 0.26 0.34 0.00 0.04 0.15 0.32 1.50 Δ Divrate 3117 0.02 0.12 �0.40 0.00 0.00 0.04 0.50 D\_LOW 3117 0.01 0.11 0.00 0.00 0.00 0.00 1.00 SIZE 3117 26.94 1.63 23.91 25.78 26.72 27.82 31.34 LEV 3117 0.44 0.19 0.07 0.28 0.44 0.59 0.88 ROA 3117 0.05 0.06 �0.11 0.02 0.04 0.07 0.30 CFO 3117 0.07 0.09 �0.15 0.03 0.06 0.11 0.37 σ(CFO) 2947 0.06 0.06 0.01 0.03 0.05 0.08 0.30 σ(SALES) 2947 0.22 0.26 0.01 0.08 0. 14 0.26 1.50 Z 3021 3.53 2.98 0.27 1.89 2.75 4.17 17.53 OPCYCLE 2681 4.56 0.60 2.58 4.24 4.63 4.93 5.83 MB 3022 1.44 3.14 0.06 0.57 0.92 1.51 9.07 TANG 3026 0.33 0.18 0.01 0.20 0.33 0.45 0.80 LOSS 3026 0.08 0.28 0.00 0.00 0.00 0.00 1.00

> holdings more than 5% stake. Panel B shows the results of the PSM, with 1039 observations of firm-year observations that have more than 5% stake in NPS, with 1:2 matching and 2078 controls.

> Table 5 shows the results of OLS regression analysis of Eq. (2) that analyzed the effect of NPS's dividend pressure on firm value. The dividend pressure of NPS is measured by the intersection term (DIVi,t∗NPFi,t�1) between the level of cash dividend and whether the NPS invested more than 5%. Columns (1)–(6) show different definitions of cash dividend level, respectively. First, the relationship between cash dividend level (DIV) and firm value has a statistically significant positive value in columns (3), (4), (5), and (6). This suggests that Korean firms tend to have higher firm value as cash dividends are paid more. In addition, in the columns (1), (2), (3), (4), and (5), it is confirmed that the NPF has a statistically significant positive value with the enterprise value. This indicates that a firm with more than 5% stake in NPS has a higher enterprise value. However, the DIVi,t∗NPFi,t�<sup>1</sup> variable, which indicates the dividend pressure of the NPS, is not significantly related to firm value. To summarize, the dividend pressure of the NPS does not directly affect firm value. However, it has been shown that the holding of


Var. (1) DIVOUT (2) ΔDIVOUT (3) DIVTA (4) ΔDIVTA (5) DIVRATE (6) ΔDIVRATE

(2.43) 2.41 1.80 2.33 3.87 2.92

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(�1.74) �0.41 9.72 7.00 7.08 8.77

(�0.04) �0.25 �1.42 1.63 �1.24 �1.09

(2.06) 2.53 2.66 2.61 2.47 2.41

(�4.76) 13.90 �4.55 �4.72 �7.19 �5.46

(13.62) 13.90 15.28 14.37 15.26 14.25

(2.42) 2.67 1.03 3.16 1.67 2.66

(2.63) 2.66 1.97 2.62 2.05 2.04

(7.60) 7.65 7.99 7.61 7.47 7.42

(�1.25) �0.37 �0.17 �0.57 0. 14 �0.35

(20.91) 20.91 18.72 21.61 19.56 20.98

(�1.81) �1.84 �1.17 �1.58 �1.40 �1.52

(4.24) 4.25 4.18 4.41 4.15 4.24

(0.42) 0.37 0.41 0.48 0.36 0.53

(1.62) 2. 13 1.97 1.81 1.92 1.58

Intercept 1.66\*\*\* 1.65\*\*\* 1.12\*\*\* 1.57\*\*\* 2.66\*\*\* 1.96\*\*\*

DIVi,t 0.00 0.00 0.26\*\*\* 0.27\*\*\* 0.71\*\*\* 1.99\*\*\*

DIVi,t∗NPFi,t�<sup>1</sup> 0.00 0.00 �0.06 0.14 �0.16 �0.39

NPFi,t�<sup>1</sup> 0.14\*\* 0.14\*\*\* 0.19\*\*\* 0.15\*\*\* 0.18\*\*\* 0. 14

SIZEi,t�<sup>1</sup> �0.08\*\*\* �0.09\*\*\* �0.08\*\*\* �0.08\*\*\* �0.13\*\*\* �0.10\*\*\*

LEVi,t�<sup>1</sup> 2.16\*\*\* 2.19\*\*\* 2.38\*\*\* 2.23\*\*\* 2.40\*\*\* 2.20\*\*\*

ROAi,t�<sup>1</sup> 1.13\*\*\* 1.27\*\*\* 0.48 1.47\*\*\* 0.78 1.22\*\*\*

CFOi,t�<sup>1</sup> 0.18\*\*\* 0.82\*\*\* 0.06\*\* 0.79\*\*\* 0.62\*\* 0.62\*\*

<sup>σ</sup>ð Þ CF i,t�<sup>1</sup> 3.35\*\*\* 3.37\*\*\* 3.45\*\*\* 3.31\*\*\* 3.25\*\*\* 3.21\*\*\*

<sup>σ</sup>ð Þ SALES i,t�<sup>1</sup> �0.04 �0.04 �0.02 �0.06 0.01 �0.03

Zi,t�<sup>1</sup> 0.21\*\*\* 0.21\*\*\* 0.19\*\*\* 0.22\*\*\* 0.20\*\*\* 0.21\*\*\*

OPCYCLEi,t�<sup>1</sup> �0.09\* �0.09\* �0.06 �0.08 �0.07 �0.07

MBi,t�<sup>1</sup> 0.01\*\*\* 0.01\*\*\* 0.01\*\*\* 0.01\*\*\* 0.01\*\*\* 0.01\*\*\*

TANGi,t�<sup>1</sup> 0.06 0.06 0.06 0.07 0.06 0.08

LOSSi,t�<sup>1</sup> 0. 13 0.18\*\* 0.15\*\* 0. 14 0.15 0.12

Adj.R<sup>2</sup> 0.32 0.32 0.35 0.34 0.34 0.35 Year/industry dummy Included Included Included Included Included Included Obs. 2615 2615 2615 2615 2615 2615

Table 5. The effect of dividend pressure of NPS on firm value: main analysis.

Table 2.

The upper part of each cell represents the estimation coefficient and the lower part represents the t value. \*, \*\*, and \*\*\* indicate statistical significance at the 10%, 5%, and 1% levels, respectively. The definitions of the variables are the same as those in

Panel A: The results of logistic regression for PSM:

\*, \*\*, and \*\*\* indicate statistical significance at the 10%, 5%, and 1% levels, respectively. The definitions of the variables are the same as those in Table 2.

Table 4. Results for logistic regression for PSM.

large stakes by the NPS positively affects corporate value. There is also a significant relation between dividend levels and firm value. This suggests that if listed companies in Korea are able to pay dividends, raising dividend levels can be a way to increase corporate value.

In robustness analysis, 2SLS and fixed-effect panel analysis are applied to control endogeneity and heterogeneity problems. Tables 6 and 7 are estimated by fixed-effect panel analysis on unbalanced panel data and control industrial effects. In order to control endogeneity, 2SLS estimates the endogenous variables using the instrument variables in the first step. The main test variable of this study is DIVi,t∗NPFi,t�<sup>1</sup> which is composed of the intersection of two variables, and the endogenous variable is DIVi,t. According to the previous study [14], the endogeneity of the cross term (DIVi,t∗NPFi,t�1) is not simply corrected by the intersection term of DIVi,t estimated in the first step and NPFi,t�1. In the first step, not only DIVi,t but also DIVi,t∗NPFi,t�<sup>1</sup> are estimated together and the estimated variables of the two variables (DIVi,t, DIVi,t∗NPFi,t�<sup>1</sup> ) are input in the second step. In this study, I use the dividend level of previous year (DIVi,t�1) as an appropriate instrument variable for the current dividend level (DIVi,t). This is because the dividend level of the previous year is not a direct causal


The upper part of each cell represents the estimation coefficient and the lower part represents the t value. \*, \*\*, and \*\*\* indicate statistical significance at the 10%, 5%, and 1% levels, respectively. The definitions of the variables are the same as those in Table 2.

Table 5. The effect of dividend pressure of NPS on firm value: main analysis.

large stakes by the NPS positively affects corporate value. There is also a significant relation between dividend levels and firm value. This suggests that if listed companies in Korea are able to pay dividends, raising dividend levels can be a way to increase corporate value.

\*, \*\*, and \*\*\* indicate statistical significance at the 10%, 5%, and 1% levels, respectively. The definitions of the variables are

Panel A: The results of logistic regression for PSM:

64 Firm Value - Theory and Empirical Evidence

Year dummy Included

Min Control/Treated Ratio 1 Max Control/Treated Ratio 3 Matched Sets 1039 Matched Obs. (Treated) 1039 Matched Obs. (Control) 2078

Difference statistic Propensity Score

Method Optimal Variable Ratio Matching

Panel B: The result of PSM

the same as those in Table 2.

Table 4. Results for logistic regression for PSM.

NPFi,<sup>t</sup> ¼ β<sup>0</sup> þ β1LEVi,<sup>t</sup> þ β2ROAi,<sup>t</sup> þ β3NIRi,<sup>t</sup> þ β4Marketi,<sup>t</sup> þ β5PERi,<sup>t</sup> þ β6DIVOUTi,<sup>t</sup>

Parameter Estimate Wald Chi2 P-value Intercept �0.30 2.45 0. 12 LEV 0.53\*\*\* 7.44 0.01 ROA 7.31\*\*\* 185.81 <.0001 NIR 0.01 0.50 0.48 Market �2.22\*\*\* 626.78 <.0001 PER �0.00 1.54 0.21 DIVOUT 0.01\*\*\* 17.08 <.0001

In robustness analysis, 2SLS and fixed-effect panel analysis are applied to control endogeneity and heterogeneity problems. Tables 6 and 7 are estimated by fixed-effect panel analysis on unbalanced panel data and control industrial effects. In order to control endogeneity, 2SLS estimates the endogenous variables using the instrument variables in the first step. The main test variable of this study is DIVi,t∗NPFi,t�<sup>1</sup> which is composed of the intersection of two variables, and the endogenous variable is DIVi,t. According to the previous study [14], the endogeneity of the cross term (DIVi,t∗NPFi,t�1) is not simply corrected by the intersection term of DIVi,t estimated in the first step and NPFi,t�1. In the first step, not only DIVi,t but also DIVi,t∗NPFi,t�<sup>1</sup> are estimated together and the estimated variables of the two variables (DIVi,t, DIVi,t∗NPFi,t�<sup>1</sup> ) are input in the second step. In this study, I use the dividend level of previous year (DIVi,t�1) as an appropriate instrument variable for the current dividend level (DIVi,t). This is because the dividend level of the previous year is not a direct causal





5. Conclusion

This study investigated the effect of NPS dividend pressure on firm value. For the purpose of this study, we analyzed the Korean listed companies from 2011 to 2016. The dividend pressure of NPS was measured by using the intersection of the cash dividend level and whether or not NPS had a large amount of share. The level of cash dividend was measured by cash dividend rate relative to total assets, net income, and capital. The change variables of these variables were also

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The results show that the dividend pressure of NPS had no significant effect on firm value. However, there was a significant relation between dividend level and firm value. In addition, firms with large shareholdings of the NPS tended to have high corporate value. As a result, listed companies in Korea could consider increasing dividends as one of the ways to increase corporate value. This study is expected to provide useful information for future decision-making regarding voting rights related to dividends. It will also help guide the dividend policy of companies that are receiving investment from NPS. In recent years, as Korean corporations have increased their reserves, this research is expected to provide useful information to investors and managers who are interested in possible agency problems. This chapter analyzes the impact of cash dividend on corporate value using the external shock of NPS's dividend expansion pressure. Most of the previous studies have analyzed dividend and investment on the same line and analyzed the effect of these factors on firm value. This study solves the problem of bidirectional causality between dividend and firm value by using exogenous factors such as NPS's dividend pressure. As a result, the dividend pressure of the NPS does not have a significant effect on firm value. On the other hand, there is a significant positive relation between dividend level and firm value and whether mass ownership of NPS has a significant positive effect on firm value. This study is meaningful to verify the direct effect of pension funds on behaviorism. In addition, this study has contributed to verifying the direct effect of corporate dividend increase on firm

This study has the following limitations. First, the proxy of the NPSon firm value. This study is meaningful to verify the direct effect of pension funds on behavior holdings of NPS and the cash dividend level. If a company with a large amount of the NPS pays a large dividend, it is difficult to distinguish whether the corporation voluntarily increases the dividend or increases the dividend by the pressure of the national pension. The solution to these limitations is to be

considered. We constructed the test sample using PSM to reduce the self-selection bias.

value using the external shock of NPS's dividend pressure.

This work was not supported by any funding agency.

The author declares no conflict of interest.

handled in future research.

Acknowledgements

Conflict of interest

The upper part of each cell represents the estimation coefficient and the lower part represents the t value. \*, \*\*, and \*\*\* indicate statistical significance at the 10%, 5%, and 1% levels, respectively. The definitions of the variables are the same as those in Table 2.

Table 7. Robustness test using 2SLS and panel analysis (2).

relationship with the current corporate value but is a variable that determines the present dividend level [15, 16].

First, columns (1)–(3) and columns (4)–(6) in Table 6 are the result of using DIVTA and nDIVTA as DIV variables, respectively. Column (1) and (2) are to estimate DIVTA and DIVTA \* NPF as the first steps for 2SLS, and (3) columns show the main analysis results using the variables estimated in columns (1) and (2). Similarly, columns (4) and (5) are to estimate mns (analysis results using the variables estimated in columns (6) is about main analysis. The results of the first stage, column (1) and (2), show that the dividend level of the previous period has a significant positive relationship with the dividend level of the current period at 1% level. In the second step, in contrast to the results in Table 5, the level of dividends does not affect the firm value. On the other hand, the dividend pressure of NPS has a negative effect on the corporate value. The results of column (6) using the variables of dividends does not affect the firm value. On the other hand, the dividend pressure of NPS has a negative effect on the corporate

Table 7 shows the results of the same analysis as Table 6 using the remaining DIV variables. Columns (1)–(4) are the second stages of 2SLS, and definition of DIV variable is different. Only in column (1), where dividend levels are measured by DIVOUT, the dividend pressure of the NPS has been found to have a negative impact on corporate value at the 10% level. However, in the case of the remaining variables, NPS's dividend pressure and corporate value are not related. Dividend level of the current period can be determined endogenously, but it is difficult to find a precedent study that mentions the problem of endogeneity on the change of the dividend level. In the case of changes in the level of dividends, I do not use 2SLS and applied only fixed-effect panel analysis. Although it is not shown in the table, the dividend pressure of the NPS is not related to firm value. In conclusion, in a few analyses, the dividend pressure of the NPS is found to be negative for firm value. However, in order to generalize this, the evidence of empirical analysis is lacking. In this study, I conclude that the dividend pressure of the NPS is irrelevant to firm value.
