3. Data and Descriptive Statistics
3.3 Other Exogenous Variables Description
, ,
, 1 m t
m t m t
m t
DCA CA NDCA TA −
≡ − . (3.2.4)
Larger discretionary current accruals mean more serious behavior of earnings management. We discard the observations whose discretionary current accruals calculated are more than one. Hence, 830 IPO firms are remained as our sample.
Consequently, we sort these 830 IPO firms into four quartiles by their discretionary current accruals.4 We define the quartile of IPO firms with the lowest discretionary current accruals as “conservative” IPOs, and the quartile of IPO firms with the highest discretionary current accruals as “aggressive” IPOs. Table III, Panel A, reports summary statistics of debt maturity structure in each quartile. It shows that debt maturity rises initially as the discretionary current accruals increase and then drops in the aggressive quartile. Besides, the mean of discretionary current accruals of the 830 IPO firms is 3.4753%, which is significantly larger than that of non-IPO firms, -0.0135%, not reported in the Table III. Accordingly, firms planning to go public obviously have a stronger incentive to manipulate their reported earnings.
3.3 Other Exogenous Variables Description
The variables based on various hypotheses to explain debt maturity structure are included in our empirical model as follows.
(1) Growth option
Previous studies use the ratio of market value of the firm’s assets to the book
4 Because our following empirical research needs to narrow our sample to firms whose IPO dates are between July 1st and December 31st, we simultaneously show these smaller sample characteristics in Table III. The sample contains 404 IPO firms, which are sorted four quartiles, containing 101 firms in each.
value of its assets as the proxy for growth options. The market value of the firm’s assets is an estimated value calculated as the book value of assets plus the market value of equity minus the book value of equity. The formula is as follows:
book value of assets market value of equity book value of equity Growth option
book value of assets
+ −
= .
(3.3.1) Based on Myers (1977) and Barclay and Smith (1995), we expect a negative coefficient for market-to-book ratio.
(2) Firm Size
Firm size is measured as the natural logarithm of the estimated market value of its assets. The formula is as follows:
ln( )
Firm Size= market value of assets . (3.3.2) Following Diamond (1991) and Stohs and Mauer (1996), we expect a positive relation between debt maturity and firm size.
(3) Regulation dummy
To explore the influence of regulation, we develop a dummy variable that is set to one if firms are in the regulated industries and zero otherwise. Regulated industries include railroads, trucking, airlines, telecommunications, and gas and electric utilities.
SIC codes of these industries are 4011, 4210, 4213, 4512, 4812, 4813, and 4900 to 4939. The formula is as follows:
Regulation dummy = 1 if firms are in the regulated industries
= 0 otherwise . (3.3.3) (4) Firm quality
We use firm’s abnormal earnings to proxy for firm quality. It is defined as the difference between next year’s earnings and this year’s earnings scaled by market value of equity in this fiscal year end following Datta, Iskandar-Datta, and Raman (2005). The formula is as follows:
1
t t
t
Earnings Earnings Abnormal Earnings
market value of equity
+ −
= . (3.3.4)
We expect a negative relation between debt maturity and abnormal earnings.
(5) Assets maturity
Stohs and Mauer (1996) argue that the maturity of corporate debt changes positively with that of assets. Hence, we use the firm’s assets maturity to test matching hypothesis. Assets maturity is computed as the value-weighted average of the maturity of current assets and net property, plant and equipment. The maturity of current assets (ACT/COGS) is measured as current assets divided by the cost of goods, and the maturity of net property, plant and equipment (PPEGT/DP) is measured as net property, plant and equipment divided by depreciation expense. The formula is the following equation:
PPEGT PPEGT ACT ACT ASSETS MATURITY
AT DP AT COGS
= × + × , (3.3.5)
where PPEGT is net property, plant and equipment, AT is total assets, DP is depreciation expense, ACT is current assets, and COGS is the cost of goods. We expect a positive relation between debt maturity and assets maturity.
(6) Tax rate
According to taxation hypothesis, tax rate is measured as the ratio of income tax expense to pretax income. The formula is as follows:
income tax expense Tax rate
pretax income
= . (3.3.6)
We predict a negative coefficient for tax rate.
(7) Term structure
To calculate the term structure of interest rates, we collect the month-end yield on six-month government bonds and the month-end yield on ten-year government bonds from the Economic Report of the President. Thus, the yield spread between the month-end yield on ten-year government bonds and six-month government bonds is
used as the proxy for the term structure. We expect a positive relation between debt maturity and term structure.
(8) Leverage
Leverage is considered to have a positive influence on the maturity structure of debt. Generally speaking, firms with larger portion of leverage in its capital structure face more liquidity risk. Thus, according to liquidity risk hypothesis, these firms are supposed to issue more long-term debt. The variable of leverage is measured as the ratio of total long-term debt to the estimated market value of firms’ assets. The formula is as follows:
total long term debt Leverage
market value of assets
= , (3.3.7)
where market value of assets is calculated as the book value of assets plus the market value of equity minus the book value of equity. We expect a positive relation between leverage and debt maturity. Because previous studies argue that leverage is an endogenous variable which is affected by debt maturity structure, we also list other variables used to estimate leverage including profitability, fixed assets ratio and investment tax credits dummy. We measure profitability as the ratio of operating income before depreciation to total assets, and measure fixed assets ratio as the ratio of net property, plant, and equipment to total assets, following Datta, Iskandar-Datta, and Raman (2005). The formulas are as follows:
operating income before depreciation Profitability
total assets
= , (3.3.8)
PPEGT Fixed assets ratio
total assets
= . (3.3.9)
Besides, a dummy variable is set to be equal to one for firms which have investment tax credits and equal to zero otherwise, following Johnson (2003). The formula is as follows:
ITC dummy = 1 if firms have investment tax credits
= 0 otherwise . (3.3.10) The descriptive statistics of exogenous variables are reported in Table III, Panel B.
The matrix of Pearson correlation coefficients which captures the relations of each exogenous variable is presented in Table III, Panel C. We would like to check whether the variable of earnings management, DCA, is an endogenous variable. From Panel C, it is showed that there is no significantly strong correlation between DCA and other exogenous variables.