# The COVID-19 Pandemic Impact on Corporate Dividend Policy of Sustainable and Responsible Investment in Indonesia: Static and Dynamic Panel Data Model Comparison

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^{5}

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

#### 2.1. Sustainable and Responsible Investment—Indonesian Biodiversity (SRI-KEHATI) Index

#### 2.2. Pecking Order Theory: A Perspective in Crisis Due to COVID-19 Pandemic

#### 2.3. Hypotheses Development

**Hypothesis**

**1**

**(H1).**

**Hypothesis**

**2**

**(H2).**

**Hypothesis**

**3**

**(H3).**

**Hypothesis**

**4**

**(H4).**

**Hypothesis**

**5**

**(H5).**

**Hypothesis**

**6**

**(H6).**

**Hypothesis**

**7**

**(H7).**

**Hypothesis**

**8**

**(H8).**

**Hypothesis**

**9**

**(H9).**

## 3. Method

## 4. Statistical Analysis Instruments: Static Panel Data Regression vs. Dynamic Panel Data Regression; and the Endogeneity Issue

- For: $k=1,2,\dots ,K$; $i=1,2,\dots ,N$; $t=1,2,\dots ,T$;
- ${Y}_{i,t}$: Response variable for the $i$-th cross-section element and the $t$-the time series element;
- ${X}_{k,i,t}$: The value of the k-th exogenous variable for the $i$-th cross-section element and the $t$-the time series element;
- ${\beta}_{k}$: Slope coefficient for the k-th exogenous variable;
- ${\beta}_{0}$: Panel data regression model intercept;
- ${u}_{i,t}$: Error for the $i$-th cross-section element and the $t$-th time series element.

- For: $k=1,2,\dots ,K$; $i=1,2,\dots ,N$; $t=1,2,\dots ,T$;
- ${Y}_{i,t-1}$: Lagged-1 of the endogenous variable for the $i$-th cross section element and the $t$-th time series element;
- $\delta $: slope coefficient of ${Y}_{i,t-1}$;
- ${\mu}_{i}$: the unobserved effect of the $i$-th cross section element without being affected by the time factor, ${\mu}_{i}~IIDN\left(0,{\sigma}_{v}^{2}\right)$;
- ${v}_{i,t}$: error component is general, ${v}_{i,t}~IIDN\left(0,{\sigma}_{v}^{2}\right)$.

## 5. Empirical Results

^{2}value of 75.5% and 16.1%, respectively. The simultaneous tests on the empirical model (6) obtained a p-value of Wald ${\chi}^{2}=0.0000\le 5\%$, while (7) realized a p-value of $F=0.0000\le 5\%$. It was concluded that at least 1 predictor significantly affected the response variable. The partial test with T-tests were jointly carried out with theoretical hypothesis testing with decision-making criteria, namely significant parameters at the 1%, 5%, and 10% levels, as well as the suitability on the direction of the appropriate effect.

## 6. Discussion

_{1}was rejected. Meanwhile, $GD{P}_{i,t}$ has a negative effect on dividend policy, which was robustly proven by the empirical models (6), (7), (8), and (9). Similar results were even obtained when compared with all the infeasible parameter estimations. This is not in line with [11,13,14,19,33]. The crisis caused by the COVID-19 pandemic as proxied by ${D}_{i,t}$ does not have a negative effect on dividend policy because all of the feasible parameter estimations did not prove it, therefore, H

_{2}was rejected. Meanwhile, ${D}_{i,t}$ has a positive effect on dividend policy, which was robustly proven by the empirical models (6), (7), (8), and (9), and even when compared with all of the infeasible parameters estimations. The test results of these 2 proxies have been shown to be consistent, and this is evidenced by the bivariate Pearson correlation in Table 4. GDP and D have a ${r}_{p}\mathrm{value}=-0.999$, implying an almost perfect correlation between the 2 proxies, and they exhibit a robust behavior in measuring this variable. This is in line with [9], noting that due to the pandemic, the company continues to distribute dividends and even increases it, thereby leading to the suspicion that the firm’s dividend policy gives a positive signal respective of the sluggish trading in the capital market during crisis [3,9,15]. This analysis was corroborated by the fact that most of the companies indexed by SRI-KEHATI increased their dividend rate in 2020 compared to 2019. Therefore, a market reaction test was carried out concerning the rate announced in 2020.

**Hypothesis**

**10**

**(H10).**

**Hypothesis**

**11**

**(H11).**

**Hypothesis**

**12**

**(H12).**

**Hypothesis**

**13**

**(H13).**

_{3}is accepted. A similar condition is exhibited by comparing it with the infeasible parameter estimation, however, this proves that profitability is the strongest predictor of dividend policy, and it is also a postulate. These results are in line with [9,20,21,34,35,36,37]. This indicates the higher the company’s level of profitability, the greater the dividend distribution. This condition shows that SRI-KEHATI indexed firms have a good reputation and are able to make huge profits that tend to be stable as well as positively distribute dividends even in crisis conditions [3,9,15,20].

_{4}is accepted. Meanwhile, that proxied by $DP{S}_{i,t-1}$ has a negative effect on dividend policy, and H5 is rejected. The difference is based on the fact that static data regression describes the actual previous year’s dividend while the dynamic type tests the endogenous variable lagged-1. It was suspected that the correlation of ${u}_{i,t}$ with $PY{D}_{i,t}$, would have some problems. However, it is necessary to compare the estimation of the empirical model (7) with LSDV-RSE as shown in Table 7 which uses $PY{D}_{i,t}$, and the empirical model (9) with LSDV-RSE in Table 9 which uses $DP{S}_{i,t-1}$. The empirical models (7) and (9) has a significant (+) slope coefficient, and insignificant parameters, respectively. The comparison of the 2 shows that (7) has better suitability or effect and predictor significance than (9). Therefore, it is strongly suspected that in the empirical model 9, ${u}_{i,t}$ correlates with $DP{S}_{i,t-1}$, resulting in a biased and inconsistent parameter estimation. The empirical model (7) is strongly suspected to be consistent and unbiased because it uses a $PY{D}_{i,t}$ proxy which is different from $DP{S}_{i,t-1}$, and the parameter estimation is BLUE [9]. It was concluded that the previous year’s dividend has a positive effect on its policy, which is robustly proven by empirical models (6) and (7), even when compared with the infeasible parameter estimations in static panel data regression. These results are in line with [9,34,41,59]. This proves the relevance of the dividend signaling theory that the company issues a positive signal to the market based on its performance. This is in accordance with the H

_{1}and H

_{2}tests that the firm maintains the dividend level.

_{6}is rejected. These are in line with [21,23,59]. This describes the condition that companies with high investment opportunities have absolute access to capital and are not dependent on internal funds, besides, they are able to expand more profitable businesses. The result contradicts [9], that investment opportunity has no effect on dividend policy, with the justification that SRI-KEHATI indexed issuers specifically exhibit different behavior.

_{7}is accepted. These results are in line with [22,24,37]. This condition explains that the higher the interest on the debt to cash flow charged the company, tends to suppress the dividend rate. The crisis condition due to the COVID-19 pandemic shows the firm’s low debt presumed, thereby making it possible to distribute dividends positively [3,15,24]. This contradicts [9], that the SRI-KEHATI indexed issuers exhibit different behaviors.

_{8}is accepted. These results are in line with [21,34,37]. This indicates the larger the firm size, the less vulnerable it is to business risks and the more the profitability. This permits the company to positively distribute dividends despite the crisis conditions caused by the pandemic. It was reported that there is a positive and significant correlation between $LNT{A}_{i,t}$ and $EP{S}_{i,t}$, and it further explains the predictor’s behavior towards dividend policy. This result is in contrast to [9], which explains the different behaviors exhibited by SRI-KEHATI indexed issuers.

_{9}is rejected. However, firm age has a negative effect on dividend policy, and this was proven by empirical models (8) and (9). This is in line with [60,61]. Based on the perspective of life cycle theory, when the company is either in the growth or declining phase, it makes business innovations to maintain its conditions. Under these circumstances, it has a high investment opportunity, thereby having a negative effect on dividend policy [60,61,62]. This contradicts [9], that SRI-KEHATI indexed issuers exhibits different behaviors. Meanwhile, the discrepancy in the results of effect direction is due to the fact that firm age has an indirect effect on dividend policy, where age has moderating effect to causality effect of investment opportunity on dividend policy [62,63].

## 7. Conclusions

_{,}although this has been proven not to occur because the $PY{D}_{i,t}$ proxy is different from $DP{S}_{i,t-1}$ [9]. Therefore, we suggest that future research should study these problems mathematically to examine the problem of biased and inconsistency among static and dynamic panel data model. The problem of endogeneity in static panel data regression was solved by employing the dynamic type, thereby producing parameters that match the effect direction and its significance. It has also been proven to have a stronger and more robust statistical power than the static panel data regression, thereby forming parameter estimations that are more consistent, and efficient as well as less biased [26,27,47,55].

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Acknowledgments

## Conflicts of Interest

## References

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Sector | Company Code Defined by IDX | Total |
---|---|---|

Agriculture | DSNG; LSIP | 2 |

Basic and Chemical Industry | INTP; SMGR | 2 |

Consumer Goods Industry | INDF; KLBF; SIDO; UNVR | 4 |

Finance | BBCA; BBNI; BBRI; BBTN; BMRI | 5 |

Infrastructure, Utility, and Transportation | JSMR; PGAS; TLKM | 3 |

Mining | INCO | 1 |

Miscellaneous Industry | ASCII; AUTO | 2 |

Real Estate, Property, and Building Construction | BSDE; PTPP; WIKA | 3 |

Trade and Service | PJAA; UNTR | 2 |

Variable | Proxy | Formulation | Expected Sign |
---|---|---|---|

Dividend Policy | Dividend Per Share (DPS) | $\mathrm{DPS}=\raisebox{1ex}{$\mathrm{Total}\text{}\mathrm{Dividends}$}\!\left/ \!\raisebox{-1ex}{$\mathrm{Outstanding}\text{}\mathrm{Shares}$}\right.$ | ----- |

Crisis due to the COVID-19 pandemic | Gross Domestic Product (GDP) | GDP Annual Growth Rate of Indonesia | + |

Dummy Variable (D) | 1 = A Crisis Occurs Due to the COVID-19 Pandemic 0 = No Crisis | − | |

Profitability | Earnings Per Share (EPS) | $\mathrm{EPS}=\frac{\mathrm{Earnings}\text{}\mathrm{Available}\text{}\mathrm{for}\text{}\mathrm{Common}\text{}\mathrm{Stock}}{\mathrm{Outstanding}\text{}\mathrm{Shares}}$ | + |

Previous Year’s Dividend | Previous Year’s Dividend (PYD) | PYD = total dividends distributed in the previous year | + |

Lagged of DPS (LD) | $\mathrm{LD}={\mathrm{DPS}}_{\mathrm{i},\mathrm{t}-1}$ | + | |

Investment Opportunity | Market Share Price to Book Value ratio (MBR) | $\mathrm{MBR}=\frac{\mathrm{Market}\text{}\mathrm{Price}\text{}\mathrm{per}\text{}\mathrm{Share}}{\mathrm{Book}\text{}\mathrm{Value}\text{}\mathrm{per}\text{}\mathrm{Share}}$ | − |

Financial Leverage | Debt to Equity Ratio (DER) | $\mathrm{DER}=\frac{\mathrm{Total}\text{}\mathrm{Liability}}{\mathrm{Total}\text{}\mathrm{Equity}}$ | − |

Firm Size | Total Assets (LNTA) | $\mathrm{LNTA}=\mathrm{natural}\text{}\mathrm{logarithmic}\text{}\mathrm{transformation}$$\mathrm{of}\text{}\mathrm{total}\text{}\mathrm{assets}$ | + |

Firm Age | Firm Age (AGE) | $\mathrm{AGE}=\mathrm{firm}\text{}\mathrm{age}\text{}$ | + |

DPS | GDP | D | EPS | PYD | MBR | DER | LNTA | AGE | |
---|---|---|---|---|---|---|---|---|---|

Obs. | 168 | 168 | 168 | 168 | 168 | 168 | 168 | 168 | 168 |

Max. | 1350 | 0.0517 | 1 | 3082.5717 | 20,623,565 | 82.4444 | 16.0786 | 21.1366 | 16 |

Min. | 0 | −0.0207 | 0 | −246.1663 | 0 | 0.4768 | 0.0709 | 14.8437 | 162 |

Mean | 183.3331 | 0.0402 | 0.1429 | 406.9035 | 2,991,335.92 | 4.5596 | 2.3189 | 17.9126 | 57.7083 |

Std. Dev. | 266.6905 | 0.0249 | 0.3510 | 476.1099 | 4,147,811.519 | 11.6088 | 2.9021 | 1.6741 | 33.6469 |

Proxy | DPS | GDP | D | EPS | PYD | MBR | DER | LNTA | AGE |
---|---|---|---|---|---|---|---|---|---|

DPS | 1.000 | ||||||||

GDP | 0.030 | 1.000 | |||||||

D | −0.029 | −0.999 *** | 1.000 | ||||||

EPS | 0.824 *** | 0.121 | −0.120 | 1.000 | |||||

PYD | 0.307 *** | −0.087 | 0.089 | 0.294 *** | 1.000 | ||||

MBR | 0.384 *** | 0.015 | −0.015 | 0.176 ** | 0.162 ** | 1.000 | |||

DER | −0.050 | −0.043 | 0.042 | 0.081 | 0.193 *** | 0.007 | 1.000 | ||

LNTA | 0.173 ** | −0.063 | 0.065 | 0.368 *** | 0.624 ** | −0.155 | 0.606 ** | 1.000 | |

AGE | −0.035 | −0.036 | 0.037 | −0.021 | 0.109 ** | 0.155 | 0.410 ** | 0.312 *** | 1.000 |

Empirical Model | Chow Test | Hausman Test | Lagrange Multiplier Test |
---|---|---|---|

$\mathit{F}$ | ${\mathit{\chi}}^{2}$ | ${\overline{\mathit{\chi}}}^{2}$ | |

(6) | 6.84 ** | 2.66 | 62.06 ** |

(7) | 6.86 ** | 19.09 ** | 62.10 ** |

**Table 6.**Classical Assumption Test for Empirical Model (7) with Static Panel Data Regression using Fixed Effect Model.

Multicollinearity Test | |||||||

Predictor | LNTA | AGE | EPS | DER | PYD | MBR | D |

VIF | 6.12 | 5.10 | 2.08 | 2.07 | 1.84 | 1.26 | 1.21 |

Normality Test:Shapiro-Wilk W Test | Autocorrelation Test:Wooldridge Test | Heteroscedasticity Test:Modified Wald Test for GroupwiseHeteroscedasticity | |||||

z | p-value | F | p-value | ${\chi}^{2}$ | p-value | ||

6.541 | 0.0000 | 3.757 | 0.0650 | $1.7\times {10}^{5}$ | 0.0000 |

Proxy | Empirical Model (6) Using REM | Empirical Model (7) Using FEM with LSDV-RSE | |||||
---|---|---|---|---|---|---|---|

PLS | FEM | REM | PLS | FEM | REM | FEM: LSDV-RSE | |

$\mathsf{\alpha}$ | −451.143 ** (202.460) | 289.864 (679.079) | 236.773 (288.514) | 413.443 ** (200.934) | 275.233 (676.605) | 213.130 (286.757) | 275.233 (276.177) |

${\mathrm{GDP}}_{\mathrm{i},\mathrm{t}}$ | −768.109 ** (416.375) | −696.402 ** (397.682) | −525.773 * (327.436) | ----------- | ----------- | ----------- | ----------- |

${\mathrm{D}}_{\mathrm{i},\mathrm{t}}$ | ----------- | ----------- | ----------- | 55.115 ** (29.574) | 51.760 ** (28.595) | 37.898 * (23.266) | 51.760 *** (18.446) |

${\mathrm{EPS}}_{\mathrm{i},\mathrm{t}}$ | 0.460 *** (0.025) | 0.319 *** (0.034) | 0.371 *** (0.030) | 0.460 *** (0.025) | 0.319 *** (0.034) | 0.371 *** (0.030) | 0.319 *** (0.035) |

${\mathrm{PYD}}_{\mathrm{i},\mathrm{t}}$ | 8.84 × 10^{−6} ***(3.64 × 10 ^{−6}) | 1.07 × 10^{−5} **(4.67 × 10 ^{−6}) | 9.69 × 10^{−6} **(4.22 × 10 ^{−6}) | 8.84 × 10^{−6} ***(3.64 × 10 ^{−6}) | 1.07 × 10^{−5} **(4.66 × 10 ^{−6}) | 9.69 × 10^{−6} **(4.22 × 10 ^{−6}) | 1.07 × 10^{−5} **(5.17 × 10 ^{−6}) |

${\mathrm{MBR}}_{\mathrm{i},\mathrm{t}}$ | 4.437 *** (1.060) | −1.472 (3.272) | 3.925 ** (1.738) | 4.436 *** (1.059) | −1.470 (3.269) | 3.922 ** (1.739) | −1.470 *** (0.466) |

${\mathrm{DER}}_{\mathrm{i},\mathrm{t}}$ | −4.885 (5.017) | −19.337 ** (9.523) | −13.150 ** (6.991) | −4.863 (5.016) | −19.472 ** (9.517) | −13.113 ** (6.990) | −19.472 ** (8.949) |

${\mathrm{LNTA}}_{\mathrm{i},\mathrm{t}}$ | −25.487 ** (12.335) | 18.916 (52.331) | −10.953 (17.230) | −25.546 ** (12.333) | 18.941 (52.279) | −11.133 (17.240) | 18.941 (32.220) |

${\mathrm{AGE}}_{\mathrm{i},\mathrm{t}}$ | 0.052 (0.348) | −9.039 (7.356) | 0.051 (0.676) | 0.051 (0.348) | −9.403 (7.394) | 0.050 (0.677) | −9.403 (6.931) |

${R}^{2}$ | 0.766 | 0.169 | 0.755 | 0.766 | 0.161 | 0.755 | 0.161 |

F-statistics (p-value) | 74.73 (0.0000) | 6.84 (0.0000) | ----------- | 74.77 (0.0000) | 6.86 (0.0000) | ----------- | 26.23 (0.0000) |

$\mathrm{Wald}\text{}{\chi}^{2}$ (p-value) | ----------- | ----------- | 205.76 (0.0000) | ----------- | ----------- | 205.81 (0.0000) | ----------- |

Number of Observation | 168 | 168 | 168 | 168 | 168 | 168 | 168 |

Number of Group | ----------- | 24 | 24 | ----------- | 24 | 24 | 24 |

Parameter Estimation Method | Empirical Model (8) | Empirical Model (9) | ||||
---|---|---|---|---|---|---|

Sargan Test | Arellano-Bond Test | Sargan Test | Arellano-Bond Test | |||

Order-1 | Order-2 | Order-1 | Order-2 | |||

${\mathit{\chi}}^{2}$ | z | z | ${\mathit{\chi}}^{2}$ | z | z | |

FD-GMM | 13.370 | 0.783 | −1.446 | 13.476 | 0.784 | −1.445 |

SYS-GMM | 18.916 | −1.214 | −0.101 | 18.891 | −1.216 | −0.132 |

**Table 9.**Parameter Estimations of Dynamic Panel Data Regression, and their Unbiased Tests: Parameter Estimation of Least Dummy Variable-Robust Standard Error and Ordinary Least Square-Robust Standard Error.

Proxy | Empirical Model (8) Using SYS-GMM | Empirical Model (9) Using SYS-GMM | ||||||
---|---|---|---|---|---|---|---|---|

LSDV- RSE | FD- GMM | SYS- GMM | OLS- RSE | LSDV- RSE | FD- GMM | SYS- GMM | OLS- RSE | |

${\mathsf{\alpha}}_{i,t}$ | −31.002 (484.738) | −38.719 (242.610) | 13.376 (79.139) | 15.495 (79.791) | −37.740 (485.775) | −59.751 (246.071) | 20.275 (85.081) | −7.644 (79.602) |

${\mathrm{GDP}}_{\mathrm{i},\mathrm{t}}$ | −667.698 ** (350.874) | −497.137 *** (32.285) | −99.436 *** (53.729) | −475.229 * (341.235) | ----------- | ----------- | ----------- | ----------- |

${\mathrm{D}}_{\mathrm{i},\mathrm{t}}$ | ----------- | ----------- | ----------- | ----------- | 49.260 ** (25.892) | 36.750 *** (2.206) | 41.677 *** (4.580) | 34.911 * (24.295) |

${\mathrm{EPS}}_{\mathrm{i},\mathrm{t}}$ | 0.313 *** (0.038) | 0.348 *** (0.003) | 0.296 *** (0.003) | 0.279 *** (0.054) | 0.313 *** (0.038) | 0.348 *** (0.003) | 0.277 *** (0.002) | 0.279 *** (0.055) |

${\mathrm{DPS}}_{\mathrm{i},\mathrm{t}-1}$ | −0.119 * (0.147) | −0.299 *** (0.006) | −0.023 *** (0.003) | 0.360 ** (0.156) | −0.118 (0.148) | −0.299 *** (0.006) | −0.009 *** (0.003) | 0.361 ** (0.156) |

${\mathrm{MBR}}_{\mathrm{i},\mathrm{t}}$ | −0.434 (0.854) | −4.540 *** (0.229) | 2.575 *** (0.063) | 2.943 * (2.169) | −0.430 (0.856) | −4.540 *** (0.233) | 2.526 *** (0.135) | 2.492 * (2.169) |

${\mathrm{DER}}_{\mathrm{i},\mathrm{t}}$ | −40.124 (31.399) | −28.244 *** (6.944) | −67.187 *** (3.288) | −4.354 ** (2.157) | −40.179 (31.464) | −28.161 *** (7.072) | −60.323 *** (6.588) | −4.342 ** (2.152) |

${\mathrm{LNTA}}_{\mathrm{i},\mathrm{t}}$ | 18.166 (57.450) | 12.008 (14.494) | 21.456 *** (4.578) | 0.615 (4.836) | 17.913 (57.432) | 12.914 (14.672) | 27.535 *** (4.902) | 0.557 (4.830) |

${\mathrm{AGE}}_{\mathrm{i},\mathrm{t}}$ | −1.620 (11.639) | −0.360 (1.148) | −4.881 *** (0.315) | −0.123 (0.152) | −37.740 (11.850) | −0.693 (1.164) | −5.604 *** (0.396) | −0.123 (0.152) |

Number of obs. | 144 | 120 | 144 | 144 | 144 | 120 | 144 | 144 |

Number of groups | 24 | 24 | 24 | ---------- | 24 | 24 | 24 | ---------- |

Number of instruments | ---------- | 22 | 27 | ---------- | ---------- | 22 | 27 | ---------- |

$\mathrm{Wald}\text{}{\chi}^{2}$ (p-value) | ---------- | 681,039.41 (0.0000) | 724,916.32 (0.0000) | ---------- | ---------- | 736,912.31 (0.0000) | 708,557.76 (0.0000) | ---------- |

${R}^{2}$ | 0.346 | ---------- | ---------- | 0.809 | 0.323 | ---------- | ---------- | 0.810 |

F-statistics (p-value) | 54.24 (0.0000) | ---------- | ---------- | 106.97 (0.0000) | 55.18 (0.0000) | ---------- | ---------- | 106.92 (0.0000) |

**Table 10.**Normality Distribution and Significance Tests of Abnormal Return with the IDX Composite Approach.

Period | AAR | Kolmogorov- Smirnov Test | Data Distribution | df. | One-Sample T-Test | Decision | |
---|---|---|---|---|---|---|---|

Exact-Sig | T-Stat | p-Value | |||||

T − 5 | 0.0001 | 0.670 | Normal | 26 | 0.034 | 0.973 | Rejecting H_{10} |

T − 4 | 0.0088 | 0.809 | Normal | 26 | 2.185 | 0.038 | Accepting H_{10} |

T − 3 | 0.0003 | 0.644 | Normal | 26 | 0.064 | 0.950 | Rejecting H_{10} |

T − 2 | 0.0042 | 0.561 | Normal | 26 | 1.164 | 0.255 | Rejecting H_{10} |

T − 1 | −0.0018 | 0.422 | Normal | 26 | −0.405 | 0.689 | Rejecting H_{10} |

T | −0.0026 | 0.445 | Normal | 26 | −0.497 | 0.623 | Rejecting H_{10} |

T + 1 | 0.0058 | 0.238 | Normal | 26 | 1.681 | 0.105 | Rejecting H_{10} |

T + 2 | −0.0018 | 0.559 | Normal | 26 | −0.426 | 0.673 | Rejecting H_{10} |

T + 3 | −0.0033 | 0.996 | Normal | 26 | −0.979 | 0.337 | Rejecting H_{10} |

T + 4 | 0.0046 | 0.217 | Normal | 26 | 0.769 | 0.449 | Rejecting H_{10} |

T + 5 | −0.0025 | 0.740 | Normal | 26 | −0.916 | 0.368 | Rejecting H_{10} |

**Table 11.**Normality Distribution and Significance Tests of Abnormal Return with the SRI-KEHATI Index Composite approach.

Period | AAR | Kolmogorov- Smirnov Test | Data Distribution | df. | One-Sample T-Test | Decision | |
---|---|---|---|---|---|---|---|

Exact-Sig. | T-Stat | p-Value | |||||

T − 5 | −0.0008 | 0.718 | Normal | 26 | −0.183 | 0.856 | Rejecting H_{11} |

T − 4 | 0.0094 | 0.399 | Normal | 26 | 2.491 | 0.019 | Accepting H_{11} |

T − 3 | −0.0016 | 0.968 | Normal | 26 | −0.410 | 0.686 | Rejecting H_{11} |

T − 2 | 0.0049 | 0.256 | Normal | 26 | 1.318 | 0.199 | Rejecting H_{11} |

T − 1 | −0.0014 | 0.365 | Normal | 26 | −0.332 | 0.743 | Rejecting H_{11} |

T | −0.0020 | 0.272 | Normal | 26 | −0.403 | 0.690 | Rejecting H_{11} |

T + 1 | 0.0069 | 0.253 | Normal | 26 | 2.047 | 0.051 | Rejecting H_{11} |

T + 2 | −0.0010 | 0.787 | Normal | 26 | −0.255 | 0.801 | Rejecting H_{11} |

T + 3 | −0.0040 | 0.929 | Normal | 26 | −1.070 | 0.295 | Rejecting H_{11} |

T + 4 | 0.0051 | 0.082 | Normal | 26 | 0.847 | 0.405 | Rejecting H_{11} |

T + 5 | −0.0009 | 0.801 | Normal | 26 | −0.355 | 0.725 | Rejecting H_{11} |

**Table 12.**Normality Distribution and Significance Tests of Cumulative Abnormal Return with the IDX Composite approach.

Period | CAAR | Kolmogorov- Smirnov Test | Data Distribution | df. | One-Sample T-Test | Decision | |
---|---|---|---|---|---|---|---|

Exact-Sig | T-Stat | p-Value | |||||

T − 5 | −0.0008 | 0.718 | Normal | 26 | −0.183 | 0.856 | Rejecting H_{12} |

T − 4 | 0.0087 | 0.246 | Normal | 26 | 1.533 | 0.137 | Rejecting H_{12} |

T − 3 | 0.0070 | 0.937 | Normal | 26 | 1.071 | 0.294 | Rejecting H_{12} |

T − 2 | 0.0119 | 0.815 | Normal | 26 | 1.489 | 0.148 | Rejecting H_{12} |

T − 1 | 0.0105 | 0.971 | Normal | 26 | 1.084 | 0.288 | Rejecting H_{12} |

T | 0.0085 | 0.711 | Normal | 26 | 0.710 | 0.484 | Rejecting H_{12} |

T + 1 | 0.0154 | 0.620 | Normal | 26 | 1.189 | 0.245 | Rejecting H_{12} |

T + 2 | 0.0144 | 0.567 | Normal | 26 | 1.014 | 0.320 | Rejecting H_{12} |

T + 3 | 0.0104 | 0.508 | Normal | 26 | 0.746 | 0.462 | Rejecting H_{12} |

T + 4 | 0.0155 | 0.448 | Normal | 26 | 1.096 | 0.283 | Rejecting H_{12} |

T + 5 | 0.0146 | 0.782 | Normal | 26 | 1.077 | 0.291 | Rejecting H_{12} |

**Table 13.**Normality Distribution and Significance Tests of Cumulative Abnormal Return with the SRI-KEHATI Index Composite approach.

Period | CAAR | Kolmogorov- Smirnov Test | Data Distribution | df. | One-Sample T-Test | Decision | |
---|---|---|---|---|---|---|---|

Exact-Sig. | T-Stat | p-Value | |||||

T − 5 | 0.0001 | 0.670 | Normal | 26 | 0.034 | 0.973 | Rejecting H_{13} |

T − 4 | 0.0089 | 0.259 | Normal | 26 | 1.435 | 0.163 | Rejecting H_{13} |

T − 3 | 0.0092 | 0.975 | Normal | 26 | 1.296 | 0.206 | Rejecting H_{13} |

T − 2 | 0.0134 | 0.659 | Normal | 26 | 1.546 | 0.134 | Rejecting H_{13} |

T − 1 | −0.0021 | 0.460 | Normal | 26 | −0.480 | 0.635 | Rejecting H_{13} |

T | 0.0090 | 0.403 | Normal | 26 | 0.685 | 0.500 | Rejecting H_{13} |

T + 1 | 0.0148 | 0.375 | Normal | 26 | 1.061 | 0.299 | Rejecting H_{13} |

T + 2 | 0.0130 | 0.489 | Normal | 26 | 0.836 | 0.411 | Rejecting H_{13} |

T + 3 | 0.0097 | 0.313 | Normal | 26 | 0.632 | 0.533 | Rejecting H_{13} |

T + 4 | 0.0143 | 0.449 | Normal | 26 | 0.868 | 0.394 | Rejecting H_{13} |

T + 5 | 0.0119 | 0.516 | Normal | 26 | 0.720 | 0.478 | Rejecting H_{13} |

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**MDPI and ACS Style**

Tinungki, G.M.; Hartono, P.G.; Robiyanto, R.; Hartono, A.B.; Jakaria, J.; Simanjuntak, L.R.
The COVID-19 Pandemic Impact on Corporate Dividend Policy of Sustainable and Responsible Investment in Indonesia: Static and Dynamic Panel Data Model Comparison. *Sustainability* **2022**, *14*, 6152.
https://doi.org/10.3390/su14106152

**AMA Style**

Tinungki GM, Hartono PG, Robiyanto R, Hartono AB, Jakaria J, Simanjuntak LR.
The COVID-19 Pandemic Impact on Corporate Dividend Policy of Sustainable and Responsible Investment in Indonesia: Static and Dynamic Panel Data Model Comparison. *Sustainability*. 2022; 14(10):6152.
https://doi.org/10.3390/su14106152

**Chicago/Turabian Style**

Tinungki, Georgina Maria, Powell Gian Hartono, Robiyanto Robiyanto, Agus Budi Hartono, Jakaria Jakaria, and Lydia Rosintan Simanjuntak.
2022. "The COVID-19 Pandemic Impact on Corporate Dividend Policy of Sustainable and Responsible Investment in Indonesia: Static and Dynamic Panel Data Model Comparison" *Sustainability* 14, no. 10: 6152.
https://doi.org/10.3390/su14106152