Impact Assessment of Industry-wise Investment returns in the Disinvestment Milieu: Experiential Substantiation from CPSEs in India

 

Sudipta Ghosh^1,2*, P. S. Aithal³

¹Post-Doctoral Research Fellow, Faculty of Management and Commerce,

Srinivas University, Mangaluru, Karnataka, India.

²Assistant Professor, Dept. of Commerce (UG and PG), Prabhat Kumar College, Contai, W.B., India.

³Professor, Faculty of Management and Commerce, Srinivas University, Mangaluru, Karnataka, India.

 

ABSTRACT:

Investment return is employed to assess the effectiveness of an investment. Thus, investment return helps to evaluate the return of a particular investment in relation to the price of its investment. The Central Public Sector Enterprises (CPSEs) in India have been recognized to serve the broad macro-economic objectives of fiscal augmentation, self-sufficiency in the production, etc. However, these goals could not be achieved up to the chosen level. Accordingly, from the fiscal year 1991-92, the Govt. of India adopted the mechanism of disinvestment of the CPSEs in order to ensure most favorable exploitation of national capital and to increase fruitful competency of the CPSEs in India. In this context, the main objective of this paper is to examine the behavior of industry-wise investment returns of the Indian CPSEs with a view to assess their impact in the uninterrupted disinvestment milieu during the period 2010-11 to 2019-20. The study employed popular accounting ratios and statistical test to measure the impact of investment returns at industry-wise level of the CPSEs in India. On the whole, findings of the study concluded that majority of the selected industries have not shown any significant impact in investment returns. In terms of significant results, negative impacts in investment returns are more than that of positive impacts in investment returns among the selected industries. Further, majority of the negative impacts in investment returns are observed among the industries under manufacturing sector, while most of the positive impacts in investment returns are observed among the industries under service sector. Secondary data is used in the study. Furthermore, consolidated published fiscal data are applied in the study. Therefore, it is subject to all the limitations that are inherent in the consolidated published data. The current study has examined investment returns of the CPSEs at industry-wise level. Hence, this study could be extended at firm level (i.e., company-wise level) by selecting companies within each industry of the CPSEs.

 

 

 

 

1. INTRODUCTION:

The term ‘investment’ may be defined as an asset through which the value of money grows over time. Thus, capital generated from investment can be utilized for various purposes like meeting deficit in income, reimbursement of loans, acquisition of other assets, etc. Investment return is used to evaluate the efficiency of an investment. Thus, investment return helps to evaluate the return of a particular investment in relation to the cost of its investment.

 

The Central Public Sector Enterprises (CPSEs) in India have been recognized to provide extensive macro-economic objectives of fiscal augmentation, self-sufficiency in the production, etc. The CPSEs are considered as an instrument for structural change of the economy with equality and public impartiality. However, these goals could not be achieved up to the chosen level. Accordingly, from the fiscal year 1991-92, the Govt. of India adopted the mechanism of disinvestment of the CPSEs, in which equity capital of the Govt. is introverted either in fraction or in totality. The primary rationale behind divestment is to ensure most favorable exploitation of national capital and to increase fruitful competency of the CPSEs in India.

 

2. LITERATURE REVIEW:

Some of the recent and notable studies that were conducted in this context of the present study are briefly summarized below:

 

Singh, G. and Paliwal, D. (2010), observed that working performance of the competitive firms with respect to sales and profitability had declined in the post-disinvestment phase. However, the monopoly firms showed efficiency with respect to revenue. Seema, G., Jain, P.K., Yadav, S.S., and Gupta, V.K. (2011), concluded that disinvestment did not brought preferred outcomes in majority of the performance indicators chosen in the study. Rastogi, M.K. and Shukla, S.K. (2013), stated that disinvestment did not produce acceptable results. The study suggested that the Govt. should aim for tactical disinvestment. Pardeshi, B. and Thorat, H. (2014), revealed that the CPSEs produced low fiscal income, low production, etc. One of the primary reasons for disappointing recital of the CPSEs was due to the non-optimum utilization of installed capability. Singh, G. (2015), observed that disinvestment of the CPSEs had enhanced the profitability performance of the loss making CPSEs. The researcher further stated disinvestment could guide to competency of the CPSEs. However, irresponsible disinvestment may not give optimistic outcome to the CPSEs for a longer time. George, E. and Vinod, R. (2016), observed that growth pace of loss creation CPSEs augmented at an escalating rate. On the other hand, growth pace of profit creation CPSEs augmented at a diminishing pace. Singh, A. (2017), concluded that in spite of various measured adopted, improvement in competitiveness and effectiveness of the CPSEs remained a big challenge to the policy makers. Achini, A. and Begum, S. (2018), observed that due to disinvestment, Maharatna companies had notable impact. However, no considerable impact was observed in the Navratna companies. Richard, P.V. and Kalyani, B. (2019), stated that the Indian PSEs may become monetarily sound if they established a high-quality scheme of fiscal running strategy. Singh, R.A. (2020), stated that profit should not be the sole criteria for the assessment of the CPSEs, since they are established by the Govt. for social wellbeing also. Only an alteration in the possession cannot be the lone reply. Choudhary, V.K., Singh, K. and Gupta, V. (2021) observed positive impact on financial performance of the CPSEs in terms of several parameters like liquidity, dividend, etc. However, profitability, operational efficiency and leverage of the CPSEs did not alter considerably.

 

3. RESEARCH OBJECTIVE:

The key purpose of this research article is to study the behavior of industry-wise investment returns of the Indian CPSEs with a view to assess their impact in the uninterrupted disinvestment milieu.

 

4. RESEARCH HYPOTHESIS:

In relation to the objective of the study, the H_A and H₀ is framed below:

Null Hypothesis (H₀): There is no significant change in the behaviour of investment returns.

Alternative Hypothesis (H_A): H₀ is not true.

 

5. RESEARCH DESIGN:

5.1 Sample:

The sample of our study covers all the industries that are operating in India except departmentally run civic organizations, indemnity companies, and banking institutions. The operating number of CPSEs ranges between 220 and 257 during the selected study period. Further, the sample chosen in the study comprises of thirteen industries under manufacturing sector and seven industries under service sector. The industry-wise classification of the selected sample is presented in Table 1 below:

 

Table 1. Industry-wise Classification of the Selected Sample by Sector and Industry

Manufacturing Sector
1	Agro Industry (AI)
2	Coal Industry (CI)
3	Crude Oil Industry (COI)
4	Other Minerals and Metals Industry (OMMI)
5	Steel Industry (SI)
6	Petroleum (Refinery and Marketing) Industry (PRMI)
7	Fertilizers Industry (FI)
8	Chemicals and Pharmaceuticals Industry (CPI)
9	Heavy and Medium Engineering Industry (HMEI)
10	Transportation Vehicle and Equipment Industry (TVEI)
11	Industrial and Consumer Goods Industry (ICGI)
12	Textiles Industry (TI)
13	Power Generation Industry (PGI)
Service Sector
14	Power Transmission Industry (PTI)
15	Trading and Marketing Industry (TMI)
16	Transport and Logistic Services Industry (TLSI)
17	Contract and Construction and Tech. Consultancy Services Industry (CCTCSI)
18	Hotel and Tourist Services Industry (HTSI)
19	Financial Services Industry (FSI)
20	Telecommunication and Information Technology Industry (TITI)

Source: Published Annual Reports of Public Enterprises Survey from 2010-11 to 2019-20. Department of Public Enterprises, Government of India, New Delhi.

 

5.2 Study Period and Data Source:

The period of study ranges from the fiscal year 2010-2011 to the fiscal year 2019-2020, since it covers the uninterrupted disinvestment period.

 

Derived data is used which has been collected from the published yearly reports of the PSE, Govt. of India.

 

5.3 Segmentation of the Period of Study:

To measure the impact of industry-wise investment returns in the CPSEs, the whole period of study (2010-11 to 2019-20) has been segmented into two sub-periods (i) 1^st sub-period: 2010-11 to 2014-15 and (ii) 2^nd sub-period: 2015-16 to 2019-20.

 

5.4 Methodology for Data Analysis:

The accounting ratios that are chosen for examining the investment returns of the CPSEs in India at industry-wise level are stated below:

ROA = Net Profit after Taxes ÷ Total Assets.

ROCE = EBIT ÷ Capital Employed.

ROE = Net Profit after Taxes ÷ Equity of the Shareholders.

 

To examine the behavior of returns on investment in the CPSEs with a view to assess their impact at industry-wise level, paired ‘t’ test is employed in the study. The test statistic is shown below:

 t = (d) ÷ (s ÷ √ n – 1)

 

Where: d denotes average and ‘s’ denotes standard deviation of the differences d_i i.e., d = (Σd_i ÷ n) and s =

√ Σd_i² ÷ n – (Σd_i ÷ n)².

The paired ‘t’ test follows t allocation with (n – 1) d.f.

 

6. EXPERIENTIAL FINDINGS AND ANALYSIS:

6.1 Industry-wise Impact of ROA:

Table 2 shows that on the average, fourteen industries (i.e., nine industries under manufacturing sector and five industries under service sector) have generated positive ROA, while six industries (i.e., four industries under manufacturing sector and two industries under service sector) have generated negative ROA during the whole period. In terms of average performance, CI shows highest ROA (0.19), while CPI and ICGI have shown lowest ROA (-0.12 in each case) during the entire period.

 

From sub-period analysis, it is observed that out of twenty industries, nine industries (i.e., 45% of the total selected industries) have shown significant differences with respect to average performance of ROA between the two sub-periods. Out of these nine industries, five industries under manufacturing sector (i.e., COI, OMMI, SI, TVEI, and PGI) and one industry under service sector (i.e., TMI) show negative impact in investment returns, while one industry under manufacturing sector (i.e., CPI) and two industries under service sector (i.e., TLSI and HTSI) have shown positive impact of the same during the study period. Thus, out of nine industries that have shown significant results, negative impact has been observed in most of the cases. Furthermore, most of the negative impacts are observed under manufacturing sector. Rest of the eleven industries (i.e., 55% of the total selected industries) has shown insignificant results, thereby indicating that there has been no significant change in the behaviour of ROA during the period under study.

 

On the average, CI and CPI shows highest ROA (0.20) and lowest ROA (-0.20) respectively in the 1^st half, while CI and ICGI shows highest ROA (0.18) and lowest ROA (-0.11) respectively in the 2^nd half.

 

 

Table 2. Industry-wise Average ROA during 2010-11 to 2019-20

Selected Industries	Average ROA					Results of Paired – ‘t’ Test		Impact
	Whole Period	1st Sub-Period		2nd Sub-Period
Manufacturing Sector:
AI	-0.05	-0.06		-0.03		Insignificant		No Impact
CI	0.19	0.20		0.18		Insignificant		No Impact
COI	0.08	0.10		0.05		Significant at 5% level		Negative Impact
OMMI	0.12	0.15		0.08		Significant at 5% level		Negative Impact
SI	0.01	0.03		-0.01		Significant at 5% level		Negative Impact
PRMI	0.04	0.03		0.06		Insignificant		No Impact
FI	0.09	0.08		0.09		Insignificant		No Impact
CPI	-0.12	-0.20		-0.03		Significant at 5% level		Positive Impact
HMEI	0.04	0.05		0.04		Insignificant		No Impact
TVEI	0.03	0.03		0.02		Significant at 1% level		Negative Impact
ICGI	-0.12	-0.13		-0.11		Insignificant		No Impact
TI	-0.01	0.02		-0.03		Insignificant		No Impact
PGI	0.05	0.06		0.04		Significant at 1% level		Negative Impact
Service Sector:
PTI	0.04		0.04	0.04	Insignificant		No Impact
TMI	0.001		0.01	-0.01	Significant at 1% level		Negative Impact
TLSI	-0.02		-0.05	0.02	Significant at 1% level		Positive Impact
CCTCSI	0.04		0.04	0.04	Insignificant		No Impact
HTSI	0.05		0.02	0.08	Significant at 1% level		Positive Impact
FSI	0.02		0.02	0.02	Insignificant		No Impact
TITI	-0.07		-0.07	-0.07	Insignificant		No Impact

Source: Author’s Calculation.

 

Table 3. Industry-wise Average ROCE during 2010-11 to 2019-20

Selected Industries	Average ROCE			Results of Paired – ‘t’ Test	Impact
	Whole Period	1st Sub Period	2nd Sub-Period
Manufacturing Sector:
AI	0.01	-0.01	0.03	Insignificant	No Impact
CI	0.35	0.38	0.31	Insignificant	No Impact
COI	0.13	0.18	0.09	Significant at 5% level	Negative Impact
OMMI	0.21	0.26	0.16	Insignificant	No Impact
SI	0.05	0.09	0.01	Insignificant	No Impact
PRMI	0.15	0.13	0.17	Insignificant	No Impact
FI	0.30	0.31	0.29	Insignificant	No Impact
CPI	-0.05	-0.11	0.02	Insignificant	No Impact
HMEI	0.12	0.12	0.12	Insignificant	No Impact
TVEI	0.10	0.15	0.05	Significant at 1% level	Negative Impact
ICGI	0.13	0.23	0.03	Insignificant	No Impact
TI	0.04	0.08	-0.01	Insignificant	No Impact
PGI	0.09	0.10	0.08	Insignificant	No Impact
Service Sector:
PTI	0.09	0.08	0.09	Significant at 5% level	Positive Impact
TMI	0.25	0.33	0.17	Insignificant	No Impact
TLSI	0.07	0.02	0.12	Significant at 1% level	Positive Impact
CCTCSI	0.12	0.11	0.12	Insignificant	No Impact
HTSI	0.22	0.11	0.34	Significant at 1% level	Positive Impact
FSI	0.05	0.06	0.04	Insignificant	No Impact
TITI	-0.09	-0.08	-0.10	Insignificant	No Impact

Source: Author’s Calculation.

 

6.2 Industry-wise Impact of ROCE:

Table 3 reveals that on the average, eighteen industries (i.e., twelve industries under manufacturing sector and six industries under service sector) show positive ROCE and two industries (i.e., one industry under manufacturing sector and one industry under service sector) show negative ROCE during the entire period under study. On the basis of average performance, CI shows highest ROCE (0.35), while TITI shows lowest ROCE (-0.09) during the whole period.

 

According to sub-period analysis, fifteen industries (i.e., 75% of the total selected industries) reveal insignificant results (i.e., no significant impact) with respect to ROCE. Out of twenty industries, five industries (i.e., 25% of the total selected industries) have shown significant differences in average investment returns (i.e., ROCE) between the two sub-periods. Further, out of these five industries, two industries under manufacturing sector (i.e., COI and TVEI) show negative impact in ROCE, while three industries under service sector (i.e., PTI, TLSI, and HTSI) have shown positive impact of the same during the period under study.

 

 

On the basis of average performance, CI and CPI shows highest ROCE (0.38) and lowest ROCE (-0.11) respectively in the 1^st sub-period, while HTSI reveals highest ROCE (0.34) and TITI shows lowest ROCE (-0.10) in the 2^nd sub-period.

 

6.3 Industry-wise Impact of ROE:

Table 4 reveals that on the average, thirteen industries (i.e., nine industries under manufacturing sector and four industries under service sector) show positive ROE, while seven industries (i.e., four industries under manufacturing sector and three industries under service sector) show negative ROE during the whole study period. In terms of average performance, CI shows highest ROE (0.61), while AI shows lowest ROE (-0.43) during the entire study period.

 

Out of twenty industries, nine industries (i.e., 45% of the total selected industries) have shown significant results with respect to average performance of ROE between the two sub-periods. Out of these nine industries, three industries under manufacturing sector (i.e., COI, SI, and TVEI) and two industries under service sector (i.e., CCTCSI and FSI) reveal negative impact in investment returns with respect to return on equity. On the other hand, two industries under manufacturing sector (i.e., CI and ICGI) and two industries under service sector (i.e., TLSI and HTSI) reveal positive impact in ROE during the study period.

 

Rest of the eleven industries (i.e., 55% of the total selected industries) reveals insignificant results. This implies that there has been no significant change in the average performance of ROE during the two sub-periods of the study.

 

In terms of average performance, CI reveals highest ROE (0.52) and TLSI shows lowest ROE (-0.80) in the 1^st sub-period, while CI reveals highest ROE (0.70) and TITI shows lowest ROE (-0.19) in the 2^nd sub-period.

 

Table 4. Industry-wise Average ROE during 2010-11 to 2019-20

Selected Industries	Average ROE			Results of Paired – ‘t’ Test	Impact
	Whole Period	1st Sub-Period	2nd Sub-Period
Manufacturing Sector:
AI	-0.43	-0.68	-0.17	Insignificant	No Impact
CI	0.61	0.52	0.70	Significant at 1% level	Positive Impact
COI	0.12	0.16	0.08	Significant at 5% level	Negative Impact
OMMI	0.14	0.18	0.11	Insignificant	No Impact
SI	0.01	0.07	-0.05	Significant at 5% level	Negative Impact
PRMI	0.14	0.11	0.16	Insignificant	No Impact
FI	0.15	-0.41	0.71	Insignificant	No Impact
CPI	-0.15	-0.18	-0.11	Insignificant	No Impact
HMEI	0.15	0.18	0.11	Insignificant	No Impact
TVEI	0.11	0.19	0.04	Significant at 1% level	Negative Impact
ICGI	-0.17	-0.26	-0.08	Significant at 5% level	Positive Impact
TI	-0.10	-0.04	-0.16	Insignificant	No Impact
PGI	0.10	0.11	0.10	Insignificant	No Impact
Service Sector:
PTI	0.15	0.14	0.16	Insignificant	No Impact
TMI	-0.03	0.05	-0.10	Insignificant	No Impact
TLSI	-0.37	-0.80	0.06	Significant at 5% level	Positive Impact
CCTCSI	0.15	0.18	0.13	Significant at 5% level	Negative Impact
HTSI	0.16	0.07	0.25	Significant at 1% level	Positive Impact
FSI	0.13	0.15	0.12	Significant at 5% level	Negative Impact
TITI	-0.16	-0.14	-0.19	Insignificant	No Impact

Source: Author’s Calculation.

 

7. CONCLUSIONS:

On the whole, findings of the study concluded that majority of the selected industries have not shown any significant impact in investment returns. In terms of average performance, CI has generated highest returns in terms of all the performance parameters during the whole period and the two sub-periods (except ROCE in the 2^nd sub-period). In terms of significant results, negative impacts in investment returns are more than that of positive impacts in investment returns among the selected industries. Further, majority of the negative impacts in investment returns are observed among the industries under manufacturing sector, while most of the positive impacts in investment returns are observed among the industries under service sector.

 

8. LIMITATIONS AND RESEARCH OPPORTUNITY:

Secondary data is used in the study. Furthermore, consolidated published fiscal data are applied in the study. Therefore, it is subject to all the limitations that are inherent in the consolidated published data.

 

The current study has examined returns on investment of the CPSEs at industry-wise level. Hence, this study could be extended at firm level (i.e., company-wise level) by selecting companies within each industry of the CPSEs.

 

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