Global Journal of Management and Business Research, D: Accounting and Auditing, Volume 21 Issue 2

ILCOST = Indirect Labor Cost DLCOST = Direct Labor Cost NUMSETUPS = Number of Setups NUMPARTS = Number of Distinct Parts * indicates significant at the 5% level. ** indicates significant at the 1% level. Estimation results for model 1 are presented in Table 3. The adjusted R2 varies across regressions, ranging from 0.08 to 0.60. Estimation results for the multiple regressions relating overhead costs to unit, batch and product level cost drivers are presented in Table 4. The adjusted R2 are higher for model 2 than for model 1, ranging from 0.09 to 0.78. 4 4 4 A drop in the estimated γ 0 is expected as the portion of the setup and parts impact not captured by the volume variable (DLCOST) becomes imbedded in the intercept in model 1. Results in Table 4 also indicate that all three cost drivers are associated significantly and positively with indirect production labor costs, except for the Paint Shop department regression in which NUMSETUPS is not significant and for the Machine Shop and Component Assembly department regressions where NUMPARTS is significant only at the 12% level. The joint test of γ 2= γ 3=0 is rejected at the 1% level for all seven departments, indicating that indirect production labor costs are associated with cost drivers other than direct labor costs alone. With the exception of Brush & Steel Wool department, the coefficients of NUMSETUPS are generally greater and those for NUMPARTS are generally smaller for the Plant A departments than the corresponding coefficients for the Plant B departments. © 2021 Global Journals 2 Global Journal of Management and Business Research Volume XXI Issue II Version I Year 2021 ( ) D 8 Table 3: Tests of a Labor Based Cost Model (Daily Data) (t-statistics in parentheses) Model 1: ILCOST t = β 0 + β 1 DLCOST t Variable Sheet Metal (n=1365) Machine Shop (n=1423) Brush & Steel Wool (n=1314) Paint Shop (n=1302) Component Assembly (n=1368) Welding (n=1327) Final Assembly (n=1391) Intercept t-stat ( β 0=0) 93.80 (6.44) ** 84.83 (5.14) ** 45.67 (5.74) ** 131.84 (13.26) ** 126.20 (7.23) ** 152.68 (8.54) ** 78.80 (4.30) ** DLCOST t-stat ( β 1=0) 0.50 (45.28) ** 0.27 (32.24) ** 0.20 (16.79) ** 0.14 (14.66) ** 0.12 (13.81) ** 0.14 (20.83) ** 0.08 (8.86) ** Adj. R 2 0.60 0.42 0.18 0.14 0.12 0.25 0.08 Durbin-Watson statistic Before Prais-Winsten Correction After Prais- Winsten Correction 1.48 ** 2.04 1.45 ** 1.99 1.07 ** 2.14 1.62 ** 2.00 1.61 ** 2.02 1.71 * 2.01 1.66 ** 2.05 Cost Hierarchy: Evidence and Implications