Diversification and Corporate Performance

Industrial Diversification and Corporate Performance

The results shown in Table 3 demonstrate that the coefficients of EI, HHI, and Segment are significantly negative and that the coefficient of Rs is significantly positive regardless of the proxies of investment efficiency and market value. The results indicate that industrial diversification will hinder the performance of China's listed energy firms for three main reasons.

First, China's energy industry is a national strategic industry. One of the major goals of China's conventional energy companies is to secure the domestic energy supply, while China's renewable energy companies mainly aim to diversify domestic energy structures to mitigate the pressures of climate change. In other words, China's energy companies not only pursue excellent performance, but are also responsible for delivering national strategies for economic development and social progress. Thus, China's energy companies cannot compete freely through industrial diversification to maximize their profits as companies in other industries do. Taking the China National Petroleum Corporation (CNPC) (CNPC is China's largest oil and gas producer and supplier and its headquarter is located in Beijing ,China) as an example, the CNPC insisted on dedicating oil supply to Chinese economic development even though the company's profits have been reduced drastically as a result of low global oil prices since 2014. Furthermore, with such limited profits, the CNPC invested more than 1.3 billion RMB in social welfare in 2015. Thus, national and social responsibilities render China's energy companies significantly different from companies operating in other sectors.

Second, the energy industry is heavily dependent on capital. Energy companies require sufficient capital to maintain production, carry out research, and mitigate the adverse effects of profit fluctuations resulting from huge sunk cost uncertainties. In China's energy industry, companies like CNPC are rare, and most companies lack funds. Together with long construction periods, serious uncertainties regarding returns on investment, and serious external financing constraints, industrial diversification will affect the performance and sustainable development of China's energy companies by dispersing companies' limited resources and by affecting each company's core competitiveness. For example, in China's coal sector, Jizhong Energy Resources Co. Ltd. (JZER) (JZER's headquarters is located in Xingtai City, Heibei Province, China) suffered huge losses from its industrial diversification. When the coal price fell sharply in 2012, JZER diversified its investments into the pharmaceutical, aviation, electricity, chemical, manufacturing, and logistics sectors to alleviate the adverse effects of the depressed coal market. However, due to a lack of professionals, JZER's aviation businesses lost 200, 500, and 300 million RMB.

Third, due to the long-term state-owned monopoly over China's energy industry, the core competitiveness of China's energy companies is still incapable of facilitating industrial diversification. Industrial diversification is likely to lead to failure if new businesses do not focus on their own strengths.

Moreover, in industrial diversified energy companies, the performance of related diversified firms is significantly better than that of unrelated diversified firms (see Columns 5 and 10 in Panels A and B of Table 3). The causes for this result are two-fold. On the one hand, segments of related diversified industrial companies can produce remaining inputs that can be utilized by other segments. According to the theory of scope economics, remaining inputs can be efficiently converted into company joint costs to reduce the costs of separate production in these segments and to improve corporate performance. On the other hand, with single industry risks forbidden, related industrial diversification could save energy and costs in corporate management and may ultimately benefit corporate performance.

We did not find significant differences in the correlation between industrial diversification and corporate performance among conventional and renewable energy firms.

Table 3. Industrial diversification and energy company performance regression results.

Panel A: Independent Variables Are Investment Efficiency Proxies
			ROA					CROA
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
Intercept	-0.0823 ***	-0.0848 ***	-0.0729 ***	-0.0119***	-0.0756 ***	-0.1331 ***	-0.1346 ***	-0.1327 ***	-0.1538***	-0.1320***
	(0.0203)	(0.0203)	(0.0198)	(0.0238)	(0.0223)	(0.0238)	(0.0238)	(0.0235)	(0.0269)	(0.0263)
Ln(asset)	0.0045 ***	0.0046 ***	0.0042 ***	0.0046 ***	0.0032 ***	0.(X)66 ***	0.0067 ***	0.0067 ***	0.0067 ***	0.0057 ***
	(0.0009)	(0.0009)	(0.(XX)9)	(0.0009)	(0.0010)	(0.0011)	(0.0011)	(0.0011)	(0.0011)	(0.0011)
EBIT Sales	0.0011 ***	0.0011 ***	0.0011 ***	0.0011 ***	0.0011 ***	0.0011 ***	0.0011 ***	0.0011 ***	0.0011 ***	0.0011 ***
	(0.0000)	(0.0000)	(O.(XXX))	(O.(XXX))	(0.0000)	(O.(XXX))	(0.0000)	(O.O(XX))	(O.(XXX))	(0.0000)
Exp_Sales	-0.0120 ***	-0.0120 ***	-0.0119***	-0.0121 ***	-0.0122 ***	-0.0130 ***	-0.0130 ***	0.0128 ***	-0.0129 ***	-0.0131 ***
	(0.0033)	(0.0033)	(0.0034)	(0.0033)	(0.0035)	(0.0037)	(0.0037)	(0.0037)	(0.0037)	(0.(X)39)
El	-0.0142 ***					-0.0066
	(0.0033)					(0.0037)
HHI		-0.0274 ***					-0.0141 *
		(0.0060)					(0.0067)
Segment			-0.0050 ***					-0.0033 **
			(0.0013)					(0.0015)
Rs				0.0330 ***					0.0170 *
				(0.0073)					(0.0081)
Related					0.0119 ***					0.0070
					(0.0031)					(0.0037)
N	696	696	696	701	633	582	582	582	587	536
Adj R2	0.6182	0.6197	0.6162	0.6187	0.5991	0.6309	0.6169	0.6301	0.6315	0.6036

 

Table 3. Cont.

Panel A: Independent Variables Are Investment Efficiency Proxies
			TQ					MB2
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
Intercept	5.7707 ***	5.6532 ***	6.0171 ***	4.8970	5.3864 ***	7.3444 ***	7.2883 ***	7.3796 ***	7.1762 ***	7.2040 ***
	(0.5777)	(0.5767)	(0.5649)	(0.6771)	(0.5781)	(1.1968)	(1.1985)	(1.1675)	(1.4036)	(1.2512)
Ln(asset)	-0.2246 ***	-0.21780 ***	-0.2317 w	-0.2217***	-0.2292 ***	-0.2931 ***	-0.2900 ***	-0.2934 w	-0.2932 ***	-0.3021 ***
	(0.0262)	(0.0261)	(0.0259)	(0.0261)	(0.0249)	(0.0542)	(0.0543)	(0.0536)	(0.0542)	(0.0539)
F.BITSales	0.0029 ***	0.0028 ***	0.0031 ***	0.0028	0.0040 ***	0.0121 ***	0.0121 ***	0.0121 ***	0.0120***	0.0146 ***
	(0.0010)	(0.0010)	(0.0010)	(0.0010)	(0.0010)	(0.0020)	(0.0020)	(0.0020)	(0.0020)	(0.0021)
Exp_Sales	0.0475	0.0504	0.0497	0.0444	0.0443	0.4851 ***	0.4862 ”	0.4861 ***	0.4844 ***	0.5305 ***
	(0.0952)	(0.0949)	(0.0954)	(0.0948)	(0.0917)	(0.1972)	(0.1972)	(0.1973)	(0.1965)	(0.1986)
El	-0.3499 ***					-0.078
	(0.0947)					(0.1962)
HHl		-0.7240 ***					-0.1878
		(0.1696)					(0.3525)
Segment			-0.1191 ***					-0.0317
			(0.0370)					(0.0764)
Rs				0.8027 ***					0.1561
				(0.2082)					(0.4317)
Related					0.3280 ***					0.0490
					(0.0805)					(0.1741)
N	696	696	696	701	633	696	696	696	701	633
Adj R2	0.1928	0.198	0.189	0.1932	0.1831	0.1123	0.1125	0.1123	0.1115	0.1178

Notes: *, ** and *** denote significance at 10%, 5% and 1%, respectively.

 

International Diversification and Corporate Performance

Overall, the paper found no significant correlations between the international diversification and performance of China's listed energy companies. However, international diversification can improve the performance of renewable energy firms, but can also damage the performance of conventional energy firms (see the Roverseas coefficient in Table 4).

Among China's conventional energy companies, coal companies have few overseas markets, while oil companies have access to several offshore businesses. In the oil industry, China's oil offshore business is mainly dominated by China's three state-owned oil companies. Recently, a few private oil companies entered overseas oil markets and have limited overseas market shares. China's renewable energy industry is dominated by private companies, which gain much from European, American and Japanese markets.

However, there have recently been great risks associated with the offshore business transactions of China's energy companies. At first, oil exporter geopolitics severely affected Chinese energy company profits. For example, the CNPC almost lost its local capital with civil strife and local wars in Libya, Syria, Sudan, and Iraq, and the nationalization of oil resources in Venezuela damaged the profits of China's oil companies dramatically. Furthermore, China's oil companies lack sufficient information on offshore markets. Incomplete information on legal and fiscal policies and resources can have unfavorable effects on the overseas production and operation of China's oil and gas companies. In addition, the overseas projects of many China's oil companies suffer serious losses from the poor circulation of corporate resources and the sharp decline in global oil prices. For instance, the total profits of one of China's private oil companies, Metro Energy Company Ltd. (Metro's headquarters is located in Hangzhou City, Zhejiang Province, China), are 246 million yuan, which is much less than expected. This has mainly been attributable to a delay of oil and gas projects due to poor cash flows and unfavorable market environments with low global oil price and dollar appreciation. These factors create large fluctuations in the corporate performance of China's conventional energy companies in offshore markets. The increase in the proportion of overseas business will damage the performance of China's conventional energy companies.

For China's renewable energy firms, domestic market capacity is limited due to infrastructure construction imperfections and power grid connections. The offshore market is the main source of income for China's renewable energy firms. Over the last ten years, overseas business for China's renewable energy companies mainly focused on the production of equipment and relevant raw materials for power generation. The extension of the offshore market will improve the performance of China's renewable energy companies by mitigating product overstocking and accelerating capital circulation.

This shows that the effects of international diversification on the performance of China's energy companies are dependent on companies' external market conditions.

 

Robustness Test

In the above analysis, the paper verifies the stability of our results using alternative diversification indicators. However, these results may be disturbed by endogeneity caused by energy companies' industrial diversification decisions as explained in Section 2. For example, the performance of specialized energy companies is worse than that of other companies in the same industry, and companies engage in industrial diversification to identify other opportunities. Then, poor corporate performance is very likely caused by a company's characteristics rather than by industrial diversification. Thus, in this section, the paper aims to eliminate the endogeneity disturbance caused by energy firms' industrial or international diversification decisions by running the same model in Section 4.1 with a subsample of companies reporting a change in the number of segments during the sample period.

Table 4. Regression results and international diversification and energy company performance.  

		ROA			CROA			TQ			M2R
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
Intercept	-0.0393	0.0411	-0.0453	-0.1005 ***	-0.1025 ***	-0.1057 ***	7.6291 ***	7.6422 ***	7.2690 ***	8.6740 ***	8.5482 ***	8.7498 ***
	(0.0242)	(0.0243)	(0.0235)	(0.0292)	(0.0293)	(0.0281)	(0.6031)	(0.6051)	(0.5834)	(1.3211)	(1.3255)	(1.2774)
Ln(asset)	0.0019	0.0019	0.0021	0.0046***	0.0047 ***	0.0048 ***	0.3145***	0.3151 444	0.2972 444	0.3491 444	0.3429 444	0.3522444
	(0.0011)	(0.0011)	(0.0010)	(0.0013)	(0.0013)	(0.0012)	(0.0262)	(0.0263)	(0.0252)	(0.0574)	(0.0576)	(0.0551)
MIT Sain	0.0011 444	0.0011 **	0.0011 ***	0.0011 ***	0.0011 ***	0.0011 ***	0.0030 ***	0.0030 ***	0.0029 ***	0.0120***	0.0120***	0.0120 ***
	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0009)	(0.0009)	(0.000«))	(0.0020)	(0.0020)	(0.0020)
Lxp_Salcs	0.0125 ***	-0.0123 ***	0.0125 ***	0.0126 ***	-0.0125 ***	0.0126***	0.0635	0.0662	0.0651	0.5514 ***	0.5628 ***	0.5433***
	(0.0036)	(0.0036)	(00035)	(0.0038)	(0.0039)	(0.0038)	(0.0884)	(0.0886)	(0.0882)	(0.1937)	(0.1941)	(0.1931)
Market II	0.0019			0.0134			0.1964			-1.5252
	(0.0154)			(0.0164)			(0.3827)			(0.8383)
Market F		0.0031			0.0105			0.1339			-1.1486*
		(0.0104)			(0.0111)			(0.2595)			(0.5683)
Roverseas			0.0159			0.0272 *			0.5043			-1.7309 **
			(0.0131)			(0.0139)			(0.3268)			(0.7156)
Industry	0.0059	0.0071	0.0054	0.0073	0.0092 *	0.0099 **	0.5689 ***	0.5845 ***	0.5634 ***	0.8522 ***	0.9286 ***	0.9098 ***
	(0.0039)	(0.0040)	(0.0035)	(0.0045)	(0.0045)	(0.0040)	(0.0972)	(0.0989)	(0.0876)	(0.2129)	(0.2166)	(0.1918)
Market H • Industry	0.0157			0.0140			0.4461			1.9233
	(0.0189)			(0.0209)			(0.4705)			(1.0308)
Market F * Industry		0.0145			0.0157			0.2942			1.5524 **
		(0.0125)			(0.0137)			(0.3109)			(0.6810)
Roverseas * Industry			0.0264			0.0374 *			1.0473 ***			2.3722 ***
			(0.0158)			(0.0169)			(0.3923)			(0.8589)
N	683	683	683	573	573	573	683	683	683	683	683	683
Adj R2	0.5811	0.5819	0.5819	0.6026	0.6031	0.6056	0.2095	0.2101	0.2123	0.1291	0.1310	0.1341

Notes: *, ** and *** denote significance at 10%, 5% and 1%, respectively.

 

Table 5. Regression results on industrial diversification and energy company performance with company fixed effects.

Panel A: Independent Variables Are Investment Efficiency Proxies
			ROA					CROA
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
Intercept	-0.1230 ***	-0.1143 ***	-0.0945 ***	-0.1585 ***	-0.0985 *	-0.1524 ***	-0.1573 ***	0.1413 ***	-0.1747 ***	-0.1600 ***
	(0.0316)	(0.0313)	(0.0304)	(0.0355)	(0.0327)	(0.0392)	(0.0394)	(0.0388)	(0.0427)	(0.03%)
Ln(asset)	0.0063 ***	0.0058	0.0051 **	0.0062 **	0.0042 ***	0.0069 ***	0.0072 ***	0.1X167 ***	0.0070 ***	0.0069 ***
	(0.0014)	(0.0014)	(0.0014)	(0.0014)	(0.0014)	(0.0018)	(0.0018)	(0.0017)	(0.0018)	(0.0017)
EBIT Sales	0.0011 ***	0.0011 ***	0.0011 ***	0.0014 **	0.0011 ***	0.0011 ***	0.0011 ***	0.0011 ***	0.0011 ***	0.0011 ***
	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)	(0.0000)
Exp Sales	-0.0101 ***	-0.0102 ***	-0.0100 ***	-0.0102 ***	-0.0112 ***	-0.0127 ***	-0.0127 ***	-0.0123 ***	-0.0127 ***	-0.0125 ***
	(0.0037)	(0.0037)	(0.0037)	(0.00)7)	(0.0038)	(0.0042)	(0.0042)	(0.0042)	(0.0042)	(0.0042)
HHI	-0.0309 ***					-0.0076
	(0.0085)					(0.0054)
El		-0.0148 ***					-0.0180 *
		(0.0046)					(0.0100)
Segment			-0.0053					-0.0045 *
			(0.0018)					(0.0021)
Rs				0.0369 **					0.0187
				(0.0101)					(0.0118)
Related					0.0111 ***					0.0087
					(0.0043)					(0.0050)
N	415	415	415	420	420	356	356	356	361	361
Adj R2	0.6825	0.6804	0.679	0.682	0.6569	0.6572	0.6584	0.65%	0.657	0.6584

 

Table 5. Cont.

Panel B: Independent Variables Are Market Value Proxies
			TQ					M2B
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
Intercept	5.4795 **»	5.4795 ***	6.1997 ***	4.6375 ***	5.7246 ***	7.0142 ***	6.8636 ***	6.9093 ***	7.0392 ***	6.6874 ***
Ln(asset)	(0.8092) 0.2087 ***	(0.8092) 0.2087 **	(0.7786) 0.2387 ***	(0.9079) -0.2133 ***	(0.8176) 0.2455 «*	(1.8986) 0.2982 ***	(1.9229) 0.2895 ***	(1.8394) 0.2939 ***	(2.1520) 0.2943 ***	(1.9183) 0.2826 ***
EBIT_Sales	(0.0370) 0.0031 ***	(0.0370) 0.0031 ***	(0.0352) 0.0034 ***	(0.0364) 0.0031 ***	(0.0357) 0.0037	(0.0858) 0.0143 ***	(0.0873) 0.0142 ***	(0.0827) 0.0142 ***	(0.0857) 0.0142 ***	(0.0838) 0.0142 ***
Exp_Sales	(0.0010) 0.1153	(0.0010) 0.1153	(0.0010) 0.117	(0.0010) 0.1113	(0.0010) 0.0940	(0.0023) 0.4769 *	(0.0023) 0.4784 *	(0.0023) 0.4762 *	(0.0023) 0.4771 *	(0.0023) 0.4932 *
HHI	(0.0952) -0.7767 ***	(0.0952)	(0.0958)	(0.0947)	(0.0959)	(0.2266) 0.07472	(0.2267)	(0.2268)	(0.2252)	(0.2249)
El	(0.2168)	0.7767 ***				(0.2774)	0.01676
Segment Rs		(0.2168)	-0.1271 *** (0.0457)	0.9042 ***			(0.5173)	0.02156 (0.1081)	0.09491
Related				(0.2590)	0.2927 ***				(0.6166)	0.0303
N	415	415	415	420	(0.1069) 420	415	415	415	415	(0.2508) 420
Adj R2	0.2167	0.2073	0.2071	0.2137	0.1535	0.1049	0.1047	0.1048	0.1045	0.1051

Notes: * and *** denote significance at 10% and 1%, respectively.

 

The results shown in Table 5 demonstrate that the regression results of the subsample are consistent with those shown in Table 3, though the significance of CROA and M2B coefficients decreases (see Columns 6, 7, 8, 9 and 10 in Panels A and B of Table 5). The test results show that our results are robust and that industrial diversification's reverse effects are not a result of industrial diversification decision endogeneity.