*4.2. Sectoral Influence on Dividend Policy*

Table 1 shows that the highest Div/TA ratio is of the information technology (IT), IT enabled service (ITES), and telecommunication (ITTEL)-sector (0.0468), followed by the consumer goods and appliances (CONSGDS)-sector (0.0427). The logistics (LOGISTICS), automobile and ancillary (AUTO), and drugs and pharmaceuticals (PHARMA) sectors have a mean Div/TA ratio in the range of 0.025–0.03. The banking (BANKING)-sector shows the lowest ratio (0.0015), followed by the construction and infrastructure (CONSTR)-sector (0.007) and agro-based (AGRO)-sector (0.0106).

The one-way analysis of variance test was used for determining the relation between dividend policy and sectors. These results (not reported here) show that the means of the dividend policy of each of the 16 sectors vary significantly. The *p*-value of 0.000 < ∝ (i.e., 0.05), hence we reject the null hypothesis. This signifies that the dividend policy significantly differs across sectors. Thus, the industrial sector influences the dividend policy in India.

#### *4.3. Discussion on Sectoral Regression*

In this sub-section, we present our panel regression results to detect the sector-wise factors influencing dividend policy. As discussed in the methodology section, the panel diagnostic test results are used to determine the panel data model favorable for each sector. As seen in Appendix A, the *p*-value of the F-statistics for the AGRO sector is 0.016, which is <∝ (i.e., 0.05), hence we reject the null. This recommends that the FEM technique is more suitable than POLS for estimating the factors influencing the dividend policy in the AGRO-sector. The results reveal that for all other sectors, the *p*-value of F-statistics is greater than 0.05. Thus, for the remaining fifteen sectors, the POLS model is suitable. Accordingly, based on the panel diagnostic test results (Baiocchi and Distaso 2003), we report the findings using the FEM regression estimates for the AGRO-sector, and the POLS regression estimate for the remaining 15 sectors.

The overall R<sup>2</sup> is the highest for the AUTO-sector (0.82) and lowest for PHARMA-sector (0.12). For the LOGISTICS, textile (TEXTILE), and AGRO sectors, R2 ranges between 0.61–0.67. The overall R<sup>2</sup> for the power and fuel (POWER), other financial services (FINSER), and ITTEL sectors lies between 0.50–0.55. Also, for the BANKING, other manufacturing (M-OTH), and CONSGDS sectors, it is between 0.44–0.46. For the remaining sectors—CONSTR, trading/services (S-OTHS), engineering (ENGG), entertainment, health, and tourism (MEHH), and mining-based (MINING)—the overall R<sup>2</sup> value ranges between 0.31–0.38. These overall R2 values in the range of 0.31–0.82 suggest a good indication regarding the explanatory power of the individual sector panel regressions.
