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

Component Assembly Variable ILCOST DLCOST NUMSETUPS NUMPARTS ILCOST 1.00 0.34 0.36 0.34 DLCOST 0.28 1.00 0.37 0.43 NUMSETUPS 0.41 0.19 1.00 0.80 NUMPARTS 0.35 0.14 0.77 1.00 Welding Variable ILCOST DLCOST NUMSETUPS NUMPARTS ILCOST 1.00 0.50 0.50 0.51 DLCOST 0.45 1.00 0.67 0.71 NUMSETUPS 0.50 0.51 1.00 0.84 NUMPARTS 0.47 0.56 0.83 1.00 Final Assembly Variable ILCOST DLCOST NUMSETUPS NUMPARTS ILCOST 1.00 0.31 0.09 0.26 DLCOST 0.38 1.00 0.02 @ 0.60 NUMSETUPS 0.26 0.06 * 1.00 0.30 NUMPARTS 0.34 0.48 0.35 1.00 Please see Table 1 for the definition of variables. Pearson correlations are above the diagonal, Spearman correlations are below the diagonal. @ indicates not significant at conventional levels. * indicates significant at 5% level. All other correlations are significant at the 1% level. III. E stimation M odels and R esults Following prior studies, we estimate two cost models. The first one to reflect the existing labor-based cost accounting system at our research site that allocates indirect production labor costs to individual jobs based on direct labor costs separately for each production department and is based on the assumption that for each production department, direct labor cost is the only cost driver. We estimate the second cost model to test the presence of the cost hierarchy. Production managers at our research site indicated that indirect production labor hours arise because of activities such as machine setup, materials movement, and inspection. Therefore, we estimate a multiple regression model of indirect production labor costs and three cost drivers: direct labor costs (unit-related driver), number of setups (batch-related driver) and number of parts (product- sustaining driver) identified for this study. Model 1: ILCOSTt = β 0 + β 1 DLCOSTt + ε t Model 2: ILCOSTt = γ 0 + γ 1 DLCOSTt + γ 2 NUMSETUPSt + γ 3 NUMPARTSt+ ε t The hypothesis that indirect production labor costs are related to cost drivers other than production volume is verified by conducting a joint test of whether the coefficients of both NUMSETUPS and NUMPARTS are zero. Based on our discussion of the production process characteristics, we expect systematic differences in the estimated coefficients of model 2 between the departments in the two plants. For the job shop type production departments in Plant A, we expect the coefficient γ 2 (for setups) to be greater and the coefficient γ 3 (for number of parts) to be smaller than the corresponding estimated coefficients for the assembly line type production departments in Plant B . 3 3 Implicit in this allocation procedure is the assumption that indirect production labor costs vary proportionally with the unit-related measure, direct labor costs (Noreen and Soderstrom 1994). Although our main research questions do not pertain to the issue of whether costs are proportional to the underlying activity (Noreen and Soderstrom, 1994), a straightforward test of proportionality involves estimating the above linear regression model using time-series observations and then testing whether the intercept is zero ( β 0=0). The proportionality assumption in Model 2 is evaluated as before by testing whether γ 0 = 0. Although not discussed in the results section but shown in Table 3, the proportionality assumption ( β 0 = 0) is rejected at the 1% significance level for all seven departments. In each case, the estimated β 0 coefficient is positive, suggesting increasing returns to scale for indirect activities. The estimated γ 0 coefficient in Model 2 (shown in Table 4) is positive for all seven departments, but significant only for four suggesting violation of proportionality assumption and presence of increasing returns to scale for these departments. Tests based on the Box-Cox (1964) transformation (Greene 2011) reject the linear specification, but not the loglinear specification, of © 2021 Global Journals 2 Global Journal of Management and Business Research Volume XXI Issue II Version I Year 2021 ( ) D 6 Cost Hierarchy: Evidence and Implications