Compensations of Top Executives and M&A Behaviors: An Empirical Study of Listed Companies
Abstract
:1. Introduction
2. Theoretical Analysis and Hypothesis
2.1. Salary-Based Executive Incentives and Corporate M&A
2.2. Equity-Based Executive Incentives and Corporate M&A
3. Data and Summary Statistic
3.1. Sample Selection and Data Sources
3.2. Variable Selection and Definition
3.3. Econometric MODEL
4. Empirical Analysis and Results
4.1. Descriptive Statistics and Correlation Analysis Results for the Variables
4.2. The Influence of Executive Incentives on M&A Propensity
4.3. Influence of Executive Incentives on the Scale of M&A
4.4. Robustness Test
5. Discussion
5.1. Results Analysis
5.2. Implications
5.3. Limitations and Future Research
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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1 | Per the regulations of the CSRC and individual stock exchanges, M&A of listed corporations are subject to a series of administrative or internal procedures from the disclosure of information to completion. The procedures can become even more complicated if the M&A involve major asset reorganization, stock issuance, asset purchase, or reorganization of state-owned assets. |
Variable | Code | Measuring Method |
---|---|---|
Data year | YEAR | The year 2011is used as the base period for two dummy variables. |
Industry | FIN | Per the Guidelines for the Industry Classification of Listed Companies (2012) issued by CSRC, the agricultural, forestry, husbandry, and fishery industries (type A) are used as the base period for 16 dummy variables. The financial (type J) and education (type P) industries are excluded. |
Firm size | FS | The year-end total assets are used as the proxy variable, LN (unit: million RMB), for corporation size. |
Firm age | FA | Ln (from time established to 2016). |
Innovation performance | IPOR | Number of patent applications per million RMB. |
Second offerings | SEOS | Number of follow-on offerings in the past 5 years, including public issuances and nonpublic issuances. |
Refinancing demands | REFD | The cash-flow gap is used as the proxy variable for the standardization of total assets at the end of year (Shyam-Sunder and Myers 1999). Cash-flow gap = increase in long-term investment + increase in fixed asset investment + increase in working capital + dividends − cash flow from operating activities + financing expenses. |
(a) Panel A Regional Distribution of Samples | |||||
Region | No. | % | Region | No. | % |
Anhui | 120 | 4.2 | Liaoning | 66 | 2.3 |
Beijing | 262 | 9.2 | Inner Mongolia | 12 | 0.4 |
Fujian | 102 | 3.6 | Ningxia | 6 | 0.2 |
Gansu | 18 | 0.6 | Qinghai | 6 | 0.2 |
Guangdong | 518 | 18.1 | Shandong | 198 | 6.9 |
Guangxi | 15 | 0.5 | Shanxi | 30 | 1.1 |
Guizhou | 15 | 0.5 | Shannxi | 42 | 1.5 |
Hainan | 21 | 0.7 | Shanghai | 174 | 6.1 |
Hebei | 42 | 1.5 | Sichuan | 102 | 3.6 |
Henan | 114 | 4.0 | Tianjin | 27 | 0.9 |
Heilongjiang | 12 | 0.4 | Tibet | 12 | 0.4 |
Hubei | 93 | 3.3 | Xinjiang | 18 | 0.6 |
Hunan | 89 | 3.1 | Yunnan | 30 | 1.1 |
Jilin | 33 | 1.2 | Zhejiang | 297 | 10.4 |
Jiangsu | 300 | 10.5 | Chongqing | 32 | 1.1 |
Jiangxi | 48 | 1.7 | Total | 2856 | 100.0 |
(b) Panel B Ownership Distribution of Samples | |||||
Ownership | No. | % | |||
State-owned | 827 | 28.86% | |||
97 | 3.38% | ||||
Private | 1909 | 66.61% | |||
others | 23 | 0.80% | |||
total | 2866 | 1 | |||
(c) Panel C Year and Industry Distribution of Samples | |||||
Year | 2011 | 2012 | 2013 | ||
Industry | No. (%) | No. (%) | No. (%) | ||
farming, forestry, husbandry and fishing (A) | 17 (1.7) | 15 (1.6) | 15 (1.6) | ||
Mining (B) | 23 (2.4) | 20 (2.1) | 21 (2.2) | ||
Manufacturing (C) | 649 (68.2) | 663 (69.6) | 662 (69.5) | ||
Electricity/heat/gas water production and supply (D) | 17 (1.7) | 18 (1.9) | 19 (2) | ||
Construction (E) | 20 (2) | 25 (2.6) | 25 (2.6) | ||
Wholesale and retail (F) | 40 (4.2) | 37 (3.9) | 38 (4) | ||
Transportation, storage, and post (G) | 27 (2.8) | 26 (2.7) | 26 (2.7) | ||
Accommodation and catering (H) | 5 (0.5) | 5 (0.5) | 5 (0.5) | ||
Information transmission/software and information technology services (I) | 77 (8) | 68 (7) | 68 (7) | ||
Real estate (K) | 36 (3.7) | 39 (4.1) | 35 (3.7) | ||
Rental and business services (L) | 9 (0.9) | 6 (0.6) | 7 (0.7) | ||
Scientific Research and Technology Services (M) | 6 (0.6) | 6 (0.6) | 6 (0.6) | ||
water conservancy/environment and public facilities management (N) | 3(0.3) | 12 (1.3) | 12 (1.3) | ||
Residential services, repairs and other services (O) | 7 (0.7) | 0 (0) | 0 (0) | ||
Health and social affairs (Q) | 2 (0.2) | 2 (0.2) | 2 (0.2) | ||
Culture, sports and entertainment (R) | 8 (0.8) | 8 (0.8) | 8 (0.8) | ||
Others (S) | 6 (0.6) | 2 (0.2) | 3 (0.3) |
MAE | CE | FS | FA | IPOR | REFD | SEOS | TMTST | TMTSA | |
---|---|---|---|---|---|---|---|---|---|
CE | 0.463 ** | 1 | |||||||
FS | −0.018 | −0.017 | 1 | ||||||
FA | −0.005 | −0.007 | .008 | 1 | |||||
IPOR | −0.030 | −0.054 ** | −0.069 ** | −0.142 ** | 1 | ||||
REFD | −0.018 | −0.078 ** | 0.097 ** | 0.076 ** | −0.102 ** | 1 | |||
SEOS | 0.032 | 0.078 ** | 0.088 ** | 0.236 ** | −0.119 ** | 0.181 ** | 1 | ||
TMTST | −0.016 | −0.008 | −0.136 ** | −0.311 ** | 0.214 ** | −0.214 ** | −0.273 ** | 1 | |
TMTSA | −0.038 * | 0.004 | 0.222 ** | 0.108 ** | −0.089 ** | −0.144 ** | 0.166 ** | −0.221 ** | 1 |
Mean | 0.025 | 0.080 | 11567 | 2.397 | 0.008 | 0.179 | 0.320 | 0.206 | 3.188 |
S. D. | 0.143 | 0.279 | 57489 | 0.512 | 0.016 | 0.159 | 0.591 | 0.243 | 0.608 |
Model 1 | Model 2 | Model 3 | Model 4 | |
---|---|---|---|---|
Control variables | ||||
YEAR | Include | Include | Include | Include |
IND | Include | Include | Include | Include |
FS | 0.000 | 0.000 | 0.000 | 0.000 |
FA | −0.248 ** | −0.227 *** | −0.232 ** | −0.221 *** |
IPOR | −9.147 * | −8.446 * | −8.423 * | −8.487 * |
REFD | −0.954 ** | −1.194 *** | −1.370 *** | −1.420 *** |
SEOS | 0.314 *** | 0.280 *** | 0.259 *** | 0.284 *** |
Predictor variables | ||||
TMTSA | −0.189 * | −0.230 ** | ||
TMTST | 0.388 + | 0.172 + | ||
Model Indices | Wald = 79.17 *** | Wald = 87.19 *** | Wald = 80.31 *** | Wald = 101.72 *** |
Obs | 2823 | 2823 | 2823 | 2823 |
Model 5 | Model 6 | Model 7 | Model 8 | |
---|---|---|---|---|
Control variables | ||||
YEAR | Include | Include | Include | Include |
IND | Include | Include | Include | Include |
FS | 0.000 | 0.000 | 0.000 | 0.000 |
FA | −0.028 | −0.144 | −0.026 | −0.197 |
IPOR | −1.650 * | −0.433 | −1.334 * | −0.203 |
REFD | −0.077 | −0.068 | −0.070 | −0.040 * |
SEOS | 0.055 | 0.074 | 0.057 | 0.008 |
Predictor Variables | ||||
TMTSA | −0.015 ** | −0.016 *** | ||
TMTST | −0.011 | −0.014 | ||
Model Indices | Wald = 61.80 *** | Wald = 67.86 *** | Wald = 57.33 *** | Wald = 55.38 *** |
Obs | 2854 | 2854 | 2854 | 2854 |
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Xue, J.; Fan, H.; Dong, Z. Compensations of Top Executives and M&A Behaviors: An Empirical Study of Listed Companies. Int. J. Financial Stud. 2020, 8, 64. https://doi.org/10.3390/ijfs8040064
Xue J, Fan H, Dong Z. Compensations of Top Executives and M&A Behaviors: An Empirical Study of Listed Companies. International Journal of Financial Studies. 2020; 8(4):64. https://doi.org/10.3390/ijfs8040064
Chicago/Turabian StyleXue, Jiao, Heng Fan, and Zhanxun Dong. 2020. "Compensations of Top Executives and M&A Behaviors: An Empirical Study of Listed Companies" International Journal of Financial Studies 8, no. 4: 64. https://doi.org/10.3390/ijfs8040064