Appendix B: Internet Appendix

Transcription

1 Appendix B: Internet Appendix This appendix reports results of additional tests that may be briefly mentioned but are not included in the main text. Performance Metrics Used in DM and CEO pay Table B1 summarizes the data obtained from Incentive Labs on the extent to which DM and CEO pay is explicitly linked to performance goals. Performance pay is the fraction of total pay that is linked to an explicit performance goal. On average about 25.5% (28.6%) of the total compensation of DMs (CEOs) is explicitly linked to a performance goal. From the column titled Frequency, we find that 76.74% (77.57%) of 675 DM (CEO) pay contracts link a part of pay to an explicit performance goal. Consistent with accounting metrics providing more information about DM performance, we find that while 17.9% of DM pay is linked to an accounting based performance goal, only 5.2% of DM pay is linked to a stock based performance goal. Interestingly we find that this pattern is similar for CEO pay as well. Finally we find that among accounting metrics, profit based metrics are more important for DM pay as compared to sales based metrics. On average, 16.6% of the total compensation received by DMs is linked to profitability targets and only 1.4% is linked to sales based targets. In terms of frequencies, while 68.7% of DMs have a part of their pay linked to a profit goal, only 17.9% have their pay linked to a sales goal. This offers a strong rationale for using ROA as the measure of division performance in our tests. DM pay-for-performance and accounting informativeness In table B2, we analyze the effect of accounting informativeness on the structure of DM pay. We use the volatility of earnings of firms in an industry as our first measure of accounting informativeness and divide our sample into divisions in industries with above and below median earnings volatility and repeat our tests in the two subsamples (column (1) & column (2)). The results indicate that consistent with Risk hypothesis, DM pay for divisional performance is indeed lower (higher) for divisions in industries with high (low) earnings volatility. The DM pay for division performance in industries with more informative accounting profits is approximately twice that in industries with less informative accounting profits. Interestingly, we do not find any significant difference in pay for other divisions performance across the two subsamples. From the row titled Division ROA we find that the coefficients on Division ROA are significantly different across columns (1) and (2). Note that our tests in the row titled Division ROA is equivalent to estimating equation (1) with a full set of interaction terms between High earnings volatility a dummy variable that identifies industries with greater earnings volatility and all the independent variables and testing for significance of the coefficient on High earnings volatility Division ROA In the next two columns, we use our second proxy of accounting informativeness which captures the extent to which accounting profits are related to contemporaneous stock returns. We measure this by regressing stock returns on accounting profits for all single segment firms within an industry (Kothari (2001), Ball et al. (2000) and Bushman et al. (2004)) and classify industries with above median regression coefficient as having more informative accounting profits. We then divide our sample into divisions in industries with 1

2 above and below median earnings regression coefficient and repeat our tests within the two subsamples. Our results indicate that DM pay for divisional performance is greater for divisions in industries with more informative earnings. Here again the coefficient in column (3) is more than twice as large as that in column (4), and the difference is significant at less than ten percent level. In summary, our results in table B2 offer significant support for Risk hypothesis and highlight an important cost of conglomeration. Conglomerates with divisions in industries with less informative accounting profits offer lower pay-for-performance to the DM. To the extent pay-for-performance is useful in providing incentives for the DM, this may be costly for the firm. DM pay-for-performance, asset complementarity and relative accounting informativeness In table B3, we analyze whether DM pay loads more heavily on other division s performance if that division s performance is a relatively less noisy (more precise) signal of managerial effort. Specifically, we use our two measures of earnings informativeness and classify divisions as relatively noisy if their earnings are less informative as compared to the other divisions in the firm. In table B3, we use our measure of asset complementarity and estimate the pay for own and other division s performance of DMs of more and less complementary divisions. In columns (1) to (4) (columns (5) to (8)), we report the results for subsample of divisions in firms with the value of complementarity measure above (below) the median. In column (1) (column (2)), we further split the subsample into divisions in industries with earnings volatility greater (less) than the average earnings volatility of other divisions within the firm, and in column (3) (column (4)), we report the results for subsample of divisions in industries with earnings coefficient less (greater) than the average earnings coefficient of other divisions within the firm. Focusing on columns (1) and (2) (High asset complementarity divisions), we find that DM s pay is more sensitive to other division s ROA when the other division has low volatility relative to the DM s division. The coefficient on Other Division s ROA in column (1) is almost thrice that in column (2) and the difference between these coefficients is statistically significant at 10% level. In the next two columns, we use the value relevance of accounting earnings as our measure of relative earnings informativeness. Again, we find that the coefficient on Other Division s ROA in column (3) is almost twice that of column (4). However, the difference between the coefficients in not statistically significant. Short-term vs Long-term Pay and tournament incentives In table B4, we seek to examine whether the sensitivity of short-term and long-term components of DM pay to other division s performance varies with the extent of tournament incentives (as captured by Pay gap) within the firm. Consistent with the idea that the dark side of tournaments may be relevant in some sub-samples, we find that DMs receive greater short-term pay for other division s performance when tournament incentives are stronger (high-pay gap subsample). However, we do not find such a difference for long-term pay. In table B5, we analyze whether the use of tournament incentives varies with the degree 2

3 of relatedness and the extent of investment opportunities, subsidy etc. Tournaments can weaken (strengthen) the incentives to cooperate (sabotage) (Kale and Reis (2010); Charness et al (2014)). The potential for sabotage may be greater when divisions within a conglomerate are related. To this extent we expect the use of tournament incentives to be less prevalent when divisions within a firm are related. Consistent with this conjecture we find the Pay gap to be substantially lower in firms with related divisions (See columns (1) and (2)). In percentage terms, the Pay gap is is 36%-51% lower in firms with related divisions. We do not find any systematic differences in tournament incentives based on the extent of investment opportunities or divisional subsidies. CEO pay-for-performance in multi-division firms A firm s division manager and its CEO are likely to have different spheres of influence. While the DM s actions are likely to have a direct effect on her division s performance, her actions are likely to affect other divisions performance only in an indirect manner. In contrast, a CEO s actions are likely to affect the performance of all the divisions in the firm. If firms design incentive contracts taking these differences into account, then the pay-for-performance sensitivity of CEOs should be different from that of DMs. To see if this is the case, in table B6, we repeat our estimation with CEO compensation for the firms in our sample as the dependent variable. Consistent with our conjecture, we find that pay-for-performance sensitivity of the CEO is different from that of the DM. Unlike DM pay, we find CEO pay to be equally sensitive to both Division ROA and Other division ROA. The estimates from column (1) indicate that a 1% increase in divisional ROA is associated with a 0.388% increase in CEO pay. This translates into an average increase in CEO compensation of $2.98 for every $1,000 increase in annual divisional profits. DM payfor-performance sensitivity ($0.84 increase in DM pay for every $1,000 increase) is almost 28% of that of the firm s CEO. Thus although the coefficients on Division ROA is similar for both CEOs and DMs, due to differences in the level of DM and CEO pay, the payfor-performance sensitivity for the DM and the CEO are very different in terms of dollar amounts. In columns (2) & (3), we repeat our estimation from column (1) after including firm and industry-year fixed effects. We find the results to be similar to the ones in column (1). 3

4 Table B1: Performance metrics used in Division manager and CEO compensation This table reports the summary statistics for the key performance variables used in divisions manager and CEO compensation based on data obtained from Incentive Labs. The data covers the period DM CEO Variable N Mean Median Frequency Mean Median Frequency Performance pay Accounting pay Stock pay Absolute perf. pay Relative perf. pay Abs accounting pay Abs stock pay Rel accounting pay Rel stock pay Accounting pay (Profitability) Accounting pay (Sales) Variable definitions for Incentive Lab data Performance Pay: The proportion of total pay linked to performance metrics. Accounting Pay proportion: The proportion of total pay linked to accounting based performance metrics. Accounting Pay dummy: A dummy variable that takes the value 1 if the use of an accounting based performance metrics was reported in compensation grants. Stock Pay: The proportion of total pay linked to stock based performance metrics. Absolute performance Pay: The proportion of total pay linked to absolute performance metrics. Relative performance Pay: The proportion of total pay linked to relative performance metrics. Absolute accounting Pay: The proportion of total pay linked to absolute accounting based performance metrics. Absolute stock Pay: The proportion of total pay linked to absolute stock based performance metrics. Relative accounting Pay: The proportion of total pay linked to relative accounting based performance metrics. Relative stock Pay: The proportion of total pay linked to relative stock based performance metrics. Accounting Pay (Profitability) : The proportion of total pay linked to accounting profitability based performance metrics. Accounting Pay (Sales) : The proportion of total pay linked to accounting sales based performance metrics. 4

5 Table B2: DM pay-for-performance and Accounting Informativeness This table reports the results of a panel data regression of DM compensation on division and other divisions ROA. Specifically, we estimate the following panel regression model: y ijt = α + β 1 Division ROA jt + β 2 Other division ROA it + β 3 Log(T otal assets) it+ β 4 Log(Division assets) jt + Time FE+ where the dependent variable y is DM compensation. In column (1) (column (2)), we report the results for subsample of divisions in industries with earnings volatility above (below) the median, and in column (3) (column (4)), we report the results for subsample of divisions in industries with earnings coefficient above (below) the median. Division ROA and Other division ROA are the difference in coefficient estimates for the subsamples. All variables are defined in detail in appendix A. The data covers the period The compensation data is from Execucomp, segment level financial data is from the Compustat Business Segment files and firm-level data is from the Compustat Industrial Annual files. The standard errors are robust to heteroskedasticity and clustered at the firm level. ( ), ( ), ( ) denote statistical significance at 1%, 5%, and 10% levels respectively. High earnings Low earnings High earnings Low earnings Volatility Volaitility Coefficient Coeffcient (1) (2) (3) (4) Division ROA 0.292*** 0.613*** 0.597*** 0.269** (0.087) (0.175) (0.136) (0.120) Other division ROA * (0.112) (0.111) (0.126) (0.143) Log(Division assets) 0.064*** 0.086*** 0.064** 0.086*** (0.023) (0.028) (0.029) (0.024) Log(Total assets) 0.362*** 0.284*** 0.294*** 0.342*** (0.031) (0.025) (0.029) (0.030) Constant 3.231*** 3.433*** 3.727*** 2.748*** (0.309) (0.234) (0.251) (0.246) Observations R-squared Division ROA -.321*.328* (.186) (.185) Other division ROA (.110) (.189) 5

6 Table B3: DM pay-for-performance, Asset Complementarity and Relative Accounting Informativeness This table reports the results of a panel data regression of DM compensation on division and other divisions ROA. Specifically, we estimate the following panel regression model: y ijt = α + β 1 Division ROA jt + β 2 Other division ROA it + β 3 Log(T otal assets) it+ β 4 Log(Division assets) jt + Time FE+ where the dependent variable y is DM compensation. In these tests we further split our data based on the extent of asset complementarity across division and relative accounting informativeness. Columns (1) to (4) (columns(5) to (8)), we report the results for subsample of divisions in firms with the value of complementarity measure above (below) the median. In column (1) (column (2)), we report the results for subsample of divisions in industries with earnings volatility greater (less) than the average earnings volatility of other divisions within the firm, and in column (3) (column (4)), we report the results for subsample of divisions in industries with earnings coefficient less (greater) than the average earnings coefficient of other divisions within the firm. The subsample splits for columns (5) to (8) are defined analogously. Division ROA and Other division ROA are the difference in coefficient estimates for the subsamples. All variables are defined in detail in appendix A. The data covers the period The compensation data is from Execucomp, segment level financial data is from the Compustat Business Segment files and firm-level data is from the Compustat Industrial Annual files. The standard errors are robust to heteroskedasticity and clustered at the firm level. ( ), ( ), ( ) denote statistical significance at 1%, 5%, and 10% levels respectively. 6 High Asset Complementarity Low Asset Complementarity High relative Low relative Low relative High relative High relative Low relative Low relative High relative earnings volatility earnings volatility earnings coefficient earnings coefficient earnings volatility earnings volatility earnings coefficient earnings coefficient (1) (2) (3) (4) (5) (6) (7) (8) Division ROA 0.309* 0.742*** 0.280* 0.989*** ** * (0.167) (0.277) (0.145) (0.341) (0.228) (0.204) (0.213) (0.216) Other division ROA 0.787*** ** (0.182) (0.227) (0.232) (0.264) (0.156) (0.161) (0.218) (0.179) Log(Division assets) * *** 0.096* 0.143*** (0.040) (0.045) (0.040) (0.053) (0.038) (0.051) (0.038) (0.044) Log(Total assets) 0.387*** 0.337*** 0.325*** 0.380*** 0.285*** 0.320*** 0.275*** 0.312*** (0.043) (0.051) (0.043) (0.061) (0.037) (0.046) (0.034) (0.045) Constant 3.060*** 2.837*** 3.049*** 2.974*** 3.722*** 3.673*** 3.883*** 3.616*** (0.330) (0.337) (0.546) (0.297) (0.322) (0.509) (0.444) (0.297) Observations R-squared Division ROA * (.303) (.393) (.301) (.308) Other division ROA.567* (.302) (.391) (.204) (.268)

7 Table B4: Short-term vs Long-term Pay and Tournament Incentives This table reports the results of a panel data regression of DM compensation on division and other divisions ROA. Specifically, we estimate the following panel regression model: y ijt = α + β 1 Division ROA jt + β 2 Other division ROA it + β 3 Log(T otal assets) it+ β 4 Log(Division assets) jt + Time FE where the dependent variable y is DM compensation in columns (1) & (2) and Long-term DM compensation in columns (3) & (4). In columns (1) and (3) (column (2) and (4)), we report the results for subsample of divisions in firms with Log(Pay gap) above (below) the median. All variables are defined in detail in appendix A. Division ROA and Other division ROA are the difference in coefficient estimates for the subsamples. All variables are defined in detail in appendix A. The data covers the period The compensation data is from Execucomp, segment level financial data is from the Compustat Business Segment files and firm-level data is from the Compustat Industrial Annual files. The standard errors are robust to heteroskedasticity and clustered at the firm level. ( ), ( ), ( ) denote statistical significance at 1%, 5%, and 10% levels respectively. Short-term Pay Long-term Pay High Pay gap Low pay gap High Pay gap Low pay gap (1) (2) (3) (4) Division ROA 0.289*** 0.340*** 0.557*** 0.570*** (0.074) (0.095) (0.170) (0.184) Other division ROA 0.246** (0.114) (0.063) (0.320) (0.308) Log(Division assets) 0.053*** 0.043** 0.099*** 0.115** (0.016) (0.017) (0.037) (0.047) Log(Total assets) 0.181*** 0.215*** 0.351*** 0.425*** (0.022) (0.031) (0.043) (0.062) Constant 4.145*** 3.965*** 1.357*** (0.175) (0.163) (0.490) (0.357) Observations R-squared Division ROA (.109) (.241) Other division ROA.240* (.124) (.437) 7

8 Table B5: Tournament Incentives and Division Characteristics This table reports the results of a panel data regression of Log(Pay Gap) on division characteristics as well as division and other divisions ROA. Specifically, we estimate the following panel regression model: y ijt = α + β 1Related/Growth/Subsidy + β 2 Division ROA jt + β 3 Other division ROA it+ β 4 Log(T otal assets) it + β 4 Log(Division assets) jt+ Time FE where the dependent variable y is Log(Pay Gap). All variables are defined in detail in appendix A. The data covers the period The compensation data is from Execucomp, segment level financial data is from the Compustat Business Segment files and firm-level data is from the Compustat Industrial Annual files. The standard errors are robust to heteroskedasticity and clustered at the firm level. ( ), ( ), ( ) denote statistical significance at 1%, 5%, and 10% levels respectively. Relatedness Growth Opportunities Divisional Subsidies (1) (2) (3) (4) (5) (6) Related *** (0.200) High Asset Comp ** (0.156) High Q (0.136) High Sales Growth (0.107) High Subsidy (0.163) Cash In (0.159) Division ROA 0.995*** 0.988*** 0.942*** 0.908** 0.933* 0.877* (0.332) (0.344) (0.342) (0.405) (0.518) (0.515) Other division ROA * (0.263) (0.263) (0.264) (0.276) (0.342) (0.342) Log(Division assets) 0.139** 0.170** 0.161** 0.129* 0.188** 0.187** (0.070) (0.070) (0.074) (0.076) (0.079) (0.079) Log(Total assets) 0.344*** 0.319*** 0.338*** 0.346*** 0.327*** 0.327*** (0.093) (0.093) (0.097) (0.100) (0.107) (0.106) Constant ** * ** ** ** ** (0.785) (0.787) (0.773) (0.779) (0.814) (0.814) Observations R-squared

9 Table B6: CEO pay-for-performance in Multi-division Firms This table reports the results of a panel data regression of CEO compensation on division and other divisions ROA. Specifically, we estimate the following panel regression model: y ijt = α + β 1 Division ROA jt + β 2 Other division ROA it + β 3 Log(T otal assets) it+ β 4 Log(Division assets) jt + Time FE + Firm FE where the dependent variable y is CEO compensation. All variables are defined in detail in appendix A. The data covers the period The compensation data is from Execucomp, segment level financial data is from the Compustat Business Segment files and firm-level data is from the Compustat Industrial Annual files. The standard errors are robust to heteroskedasticity and clustered at the firm level. ( ), ( ), ( ) denote statistical significance at 1%, 5%, and 10% levels respectively. (1) (2) (3) Division ROA 0.388*** 0.347*** 0.379*** (0.097) (0.082) (0.096) Other division ROA 0.376*** 0.420*** 0.397*** (0.114) (0.106) (0.116) Log(Division assets) (0.020) (0.014) (0.019) Log(Total assets) 0.413*** 0.306*** 0.365*** (0.024) (0.066) (0.079) Constant 3.567*** 4.740*** 4.129*** (0.227) (0.448) (0.586) Year FE Yes Yes Yes Firm FE No Yes Yes Industry X Year FE No No Yes Observations R-squared