OLS Regressions

To assess how pilot directors affect corporate investment decisions, this thesis estimates the following models using the ordinary least squares (OLS) method:

Ii,t = ⍺ + βPiloti,t + γ1FirmSizei,t + γ2TobinQi,t + γ3Leveragei,t + γ4Cashi,t + γ5RDi,t + γ6BoardSizei,t + γ7BoardIndepi,t + ⍵Yeart + εi,t,

where i indexes firm and t indexes time. The dependent variable, I, captures a firm’s investment inclination: RawI (the standardized value of investment expenditure), Over_Avg (overinvestment based on the industry average by fiscal years), and Over_Md (overinvestment based on the industry median by fiscal years). We examine the mean and the median because the mean considers the data as a whole but is liable to be affected by outliers while the median is barely affected. Further, the median is more stable but less sensitive to outliers. The main independent variable, Pilot, refers to PilotNum or PilotRatio in different regressions and is measured for firm i over its fiscal year t. Other variables control for firm and industry characteristics that can affect a firm’s investment decisions, as discussed in Section 2.2.2. Year indicates year fixed effects and its coefficient, ⍵, is a dummy.

Table 6 presents the results for the relation between pilot directors and corporate investment activities as estimated via OLS models. Table 6 begins with regressions without year fixed effects in Panel A. We find that the relation between the investment amount and pilot

more pilot directors on a board, the lower the investment expenditure and the lower the probability of a firm overinvesting. Between these two alternative independent variables, we prefer PilotRatio as reported in the even-numbered columns to PilotNum for the following two reasons. First, most regulations of the board voting process are based on the relative ratio instead of the absolute number. Second, ratios have higher volatility and therefore make the effect be more observable.

Next, year fixed effects are added to absorb aggregate time effect. Results are reported in Panel B of Table 6. The coefficient estimates of PilotNum and PilotRatio continue to be negative and become significant at the 5% level with a slightly larger magnitude. As mentioned previously, since pilot directors are bold, aggressive and adventurous, many studies find that they have a tendency to take risks. However, the other side of their personality is also an issue worth attention and investigation: pilots’ cautiousness, meticulousness, and self-discipline may lead them to perform more conservatively.

In regard to the control variables, firms that are larger, with higher Tobin’s Q, lower leverage, and more cash tend to invest more than the optimal expenditure. As can be seen, the estimated coefficients on FirmSize are positive and significant at the 1-5% level and those of TobinQ are also positive, showing that firms with larger size, lower cost of tangible assets,

higher market value, or higher value of intangible assets have more investment opportunities and a slight tendency to overinvest. Higher leverage, which means higher debt-to-equity ratio, is associated with tighter financial constraints and higher default risk for a firm. Thus, overinvestment rarely occurs in firms. Cash holdings are strongly and positively related to overinvestment since they allow firms to obtain immediate funding easily, consistent with the literature. Furthermore, the coefficient estimates of BoardSize are negative and statistically significant at the 1% level since a larger board of directors is expected to have more monitoring function and power. Board independence is also negatively related to overinvestment.

In sum, according to the OLS regressions results and the psychology literature, the net effect of pilot directors’ personality traits on firms’ investment tendency is to reduce overinvestment. This finding suggests that overinvestment is less likely to happen if the board of directors includes one or more pilot directors. In addition, we found that investment expenditure goes up while R&D spending increases. The advanced examination related to R&D spending associated with pilot directors is performed in chapter 4.

Table 6

OLS regressions examining the impacts of pilot directors on the corporate investment decisions

This table presents the results from OLS regressions of investment expenditure on pilot directors.

Columns 1 and 2 report the estimates taking the raw value of investment expenditure as the dependent variable. Columns 3 and 4 report the estimates taking the amount of overinvestment based on the industry average for each fiscal year as the dependent variable. Columns 5 and 6 report the estimates taking the amount of overinvestment based on the industry median for each fiscal year as the dependent variable. PilotNum, which is the independent variable in columns 1, 3 and 5, is the number of pilot directors on the board of directors. PilotRatio, which is the independent variable in columns 2, 4 and 6, is the number of pilot directors divided by the total number of directors on the board. Variable definitions are provided in Table 2. Regressions in Panel A are without year fixed effects. Regressions in Panel B include year fixed effects. The t-statistics are reported in parentheses. ***, ** and * denote significance at the 1%, 5% and 10% level, respectively.

Panel A: Without year fixed effects

(1) (2) (3) (4) (5) (6)

RawI RawI OverI_Avg OverI_Avg OverI_Md OverI_Md

PilotNum -0.025* -0.017* -0.022*

Table 6 (continued)

Panel B: With year fixed effects

(1) (2) (3) (4) (5) (6)

RawI RawI OverI_Avg OverI_Avg OverI_Md OverI_Md

PilotNum -0.030** -0.019* -0.025*

Chapter 4 Robustness Tests

We conduct a set of robustness tests for baseline results and discuss the details of these tests in this chapter.

The baseline tests in this thesis show a negative relation between pilot directors and overinvestment. We then proceed to decompose investment expenditure into CAPX, AQC, and IVCH, which represent capital expenditures, acquisitions, and the increase in investments, respectively. By U.S. and Canadian GAAP definition, an increase in investments represents funds used to increase a company’s long-term investments, such as long-term receivables, or investments in unconsolidated subsidiaries. OLS regressions are conducted to catch a glimpse of which investment element is most affected by pilot directors. The results are delivered in Table 7. Although overCAPX_Avg and overCAPX_Md increase with PilotRatio increases, the estimated coefficients on overIVCH_Avg and overIVCH_Md are negative and statistically significant at the 1% level. In addition, overAQC_Avg and overAQC_Md are also negative though they are not significant. The positive and significant coefficient between pilot director ratio and overinvestment in capital investments implies that pilot directors’ aggressiveness and ambition may drive them to overinvest. Nevertheless, the relation between pilot directors and overinvestment is negative overall. These circumstances imply that the main deduction in section 3.2 derives from the acquisitions and increase in investments rather than capital expenditures. In other words, pilot directors do not restrain the capital expenditures which boosts long-term growth but they are concerned with the amount of increase in investments.

Table 7

Robustness tests for OLS regressions examining the impacts of pilot directors on the different investment expenditure elements with year fixed effects

This table presents the results from OLS regressions regarding to the three elements of investment expenditure on pilot directors. Investment expenditure is decomposed into CAPX, AQC and IVCH, which represent capital expenditures, acquisitions, and the increase in investments, respectively. For better interpretation, we magnify IVCH by ten thousand times. The dependent variables in columns 1 and 2 are the amount of CAPX deducted by the industry average and median by fiscal year, respectively.

Variables are set to zero if negative. The same methodology is applied to AQC in columns 3 and 4, and IVCH in columns 5 and 6, to get the overinvestment amount in CAPX, AQC and IVCH. PilotRatio, the independent variable, is the number of pilot directors divided by the total number of directors on the board. Variable definitions are provided in Table 2. Regressions include year fixed effects. The t-statistics are reported in parentheses. ***, ** and * denote significance at the 1%, 5% and 10% level, respectively. PilotRatio 385.936** 410.860* -128.023 -119.751 -0.489*** -0.489***

(1.99) (1.81) (-0.61) (-0.51) (-3.85) (-3.85)

Next, we aim to capture the net effect of pilot directors on firms’ overall investment efficiency. Investment efficiency is measured by subtracting the industry average/median by fiscal year from the individual firm’s investment expenditure, and then taking the absolute value. It is assumed that the higher the amount of abnormal investment, the worse the investment efficiency. Table 8 shows the OLS results from regressing pilot directors on firms’

investment efficiency, which is measured via InefficI_Avg in Panel A and InefficI_Md in Panel B. Comparing across columns, the results echo the finding in this thesis that pilot directors keep firms from overinvesting and underinvesting. Furthermore, they reduce inefficient investment outcomes as well.

Table 9 presents the OLS results from regressing R&D spending on PilotNum and PilotRatio, respectively. As noted earlier, we also include year fixed effects in the regressions

to account for systematic variations in dependent variables across years. overRD_Avg is the R&D spending above the average measured by fiscal years and industry, defined at the two-digit SIC level. overRD_Md is constructed in the same way, in which the industry median is used to substitute the industry average. The estimated coefficient on both of the independent variables is negative as expected, supporting the results in the Panel B of Table 6 while all of the other controls remain the same. R&D spending and some control variables, such as firm size, leverage, board size and board independence, move in the opposite direction. This may result from such firms’ spending being subject to greater scrutiny by the financial supervision authorities or the firms’ boards of directors. Consistent with the literature, we find that the higher the Tobin’s Q (i.e., more growth opportunities), the higher the R&D spending. Further, firms engage more in R&D when they have a larger amount of cash and cash equivalents.

Table 8

Robustness tests for OLS regressions examining the impacts of pilot directors on the inefficiency of corporate investment policies

This table presents the OLS regressions results of the effect of pilot directors on investment inefficiency. The measures of inefficiency are computed with the industry average and the industry median, respectively. InefficI_Avg, the independent variable in Panel A, is the absolute value of investment spending deducted by the industry average by fiscal year, and therefore represents the amount above or below the average. The similar is applied to the independent variable InefficI_Md in Panel B, which is deducted by the industry median instead. PilotNum, the independent variable in columns 1, 2, 5 and 6, is the number of pilot directors on the board of directors. PilotRatio, the independent variable in columns 3, 4, 7 and 8, is the number of pilot directors divided by the total number of directors on the board. Variable definitions are provided in Table 2. Regressions in odd-numbered columns include year fixed effects while in even-numbered don’t. The t-statistics are reported in parentheses. ***, ** and * denote significance at the 1%, 5% and 10% level, respectively.

Panel A: Measures of inefficiency based on the industry average

(1) (2) (3) (4)

InefficI_Avg InefficI_Avg InefficI_Avg InefficI_Avg

PilotNum -0.019* -0.026**

Table 8 (continued)

Panel B: Measures of inefficiency based on the industry median

(5) (6) (7) (8)

InefficI_Md InefficI_Md InefficI_Md InefficI_Md

PilotNum -0.024* -0.028**

Table 9

Robustness tests for OLS regressions examining the impacts of pilot directors on the R&D expenditure

This table presents the results from OLS regressions of R&D spending on pilot directors. Columns 1 and 2 report the estimates taking the raw value of R&D spending as the dependent variable. Columns 3 and 4 report the estimates taking the amount of R&D spending exceeding the industry average for each fiscal year as the dependent variable. Columns 5 and 6 report the estimates taking the amount of R&D spending exceeding the industry median for each fiscal year as the dependent variable. PilotNum, the independent variable in columns 1, 3 and 5, is the number of pilot directors on the board of directors.

PilotRatio, the independent variable in columns 2, 4 and 6, is the number of pilot directors divided by the total number of directors on the board. Variable definitions are provided in Table 2. Regressions include year fixed effects. The t-statistics are reported in parentheses. ***, ** and * denote significance at the 1%, 5% and 10% level, respectively.

(1) (2) (3) (4) (5) (6)

RD RD overRD_Avg overRD_Avg overRD_Md overRD_Md

PilotNum -0.003** -0.002*** -0.003***

Table 10 displays the OLS results of non-aviation firms’ investment expenditure on pilot directors. Compared with Table 6, these two tables share the same variables but use different samples. We make the results shown in Table 6 more robust by removing firms in the aviation industry to address the concern that firms in this industry are more likely to elect directors with pilot licenses. As can be seen, there is a very similar pattern for the coefficient estimates in Table 6.

All in all, the main finding of this thesis is supported through various robustness tests in this chapter. These tests begin with decomposing the investment expenditure for a micro analysis, and then move on to the measuring investment efficiency of the firms to obtain a macroscopic view. After that, we explore how pilot directors affect corporate investment decisions in intangible assets, R&D spending. Finally, OLS regressions similar to those in section 3.2 are conducted. All variables are the same but in the examined sample, the aviation industry is removed in this robustness test.

Table 10

Robustness tests for OLS regressions examining the impacts of pilot directors on the corporate investment decisions (excluding aviation)

This table presents the results from OLS regressions of non-aviation firms’ investment expenditure on pilot directors. Columns 1 and 2 report the estimates taking the raw value of investment expenditure as the dependent variable. Columns 3 and 4 report the estimates taking the amount of overinvestment based on the industry average for each fiscal year as the dependent variable. Columns 5 and 6 report the estimates taking the amount of overinvestment based on the industry median for each fiscal year as the dependent variable. PilotNum, which is the independent variable in columns 1, 3 and 5, is the number of pilot directors on the board of directors. PilotRatio, which is the independent variable in columns 2, 4 and 6, is the number of pilot directors divided by the total number of directors on the board. Variable definitions are provided in Table 2. Regressions include year fixed effects. The t-statistics are reported in parentheses. ***, ** and * denote significance at the 1%, 5% and 10% level, respectively.

(1) (2) (3) (4) (5) (6)

Raw I Raw I OverI_Avg OverI_Avg OverI_Md OverI_Md

PilotNum -0.030* -0.018* -0.025*

Chapter 5 Discussion and Conclusion

There are two major points in this thesis. First, we propose a sweeping construct of pilot directors’ personality, including audacity and prudence, as an alternative explanation and measurement of risk preference. Second, we clarify the net effect of pilot directors’

comprehensive personality traits on corporate investment. We find that overinvestment is less likely to occur if the board of directors includes one or more pilot directors. Such directors also reduce inefficient investment. Decomposing the investment expenditure shows that the finding above derives from the increase in investments and acquisitions instead of capital expenditures.

Our results highlight the negative relation between number/ratio of pilot directors and corporate overinvestment. However, they do not imply whether pilot directors are conservative in investment decisions in overall. More research is needed to identify situations in which the pilot directors’ personality characteristics may be detrimental to firms’ financial policies.

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