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

considerably shorter than a day, there is tight matching of indirect costs with their cost driver levels each day. Therefore, our analysis at the daily level provides the most powerful tests. However, to further assess the robustness of our results, we also aggregated the data at the weekly and monthly levels and re-estimated the models. Anderson and Sedotale (2013) had found that, at their research site, monthly aggregation obscured the link between resource consumption and batch-related activities. Estimation results based on weekly data (not shown here) indicate that DLCOST is significant in all except the Brush & Steel Wool and Welding departments. NUMSETUPS is significant in all the departments of Plant A and two departments in Plant B. However, NUMPARTS is significant in only three departments of Plant B. Estimation results based on monthly level data are displayed in Tables 5 and 6. DLCOST is significant in all except the Brush & Steel Wool department. NUMSETUPS is significant in all the three departments of Plant A but none of the four departments in Plant B, consistent with our expectations that setups are more important in job shop type settings. NUMPARTS is significant in only the Welding department. However, the hypothesis that the coefficients of both NUMSETUPS and NUMPARTS are zero is rejected for all except the Paint Shop and Final Assembly departments. We surmise that this results from the severe multicollinearity between NUMPARTS and NUMSETUPS. Re-estimating the regression after deleting either of these two variables yields a positive and significant coefficient for the other variable for all seven departments. Thus, with aggregated monthly data, direct labor and either one of NUMSETUPS or NUMPARTS continue to account for a large share of the variation in indirect labor costs. Table 5: Tests of a Labor Based Cost Model (Monthly Data) (t-statistics in parentheses) Model 1: ILCOST t = β 0 + β 1 DLCOST t Variable Sheet Metal (n=60) Machine Shop (n=60) Brush & Steel Wool (n=60) Paint Shop (n=60) Component Assembly (n=60) Welding (n=60) Final Assembly (n=60) Intercept t-stat ( β 0=0) 4621.71 (3.94) ** 6060.65 (4.24) ** -408.73 (-0.63) 2549.49 (4.26) ** 1624.25 (1.49) 1544.75 (2.05) ** 448.73 (0.37) DLCOST t-stat ( β 1=0) 0.41 (10.45) ** 0.21 (8.60) ** 0.32 (5.90) ** 0.13 (4.04) ** 0.14 (5.06) ** 0.17 (10.76) ** 0.10 (4.91) ** Adj. R 2 0.65 0.56 0.37 0.21 0.30 0.66 0.29 Durbin-Watson Statistic Before Prais-Winsten Correction After Prais- Winsten Correction 1.42 ** 1.49 1.87 1.92 0.94 ** 1.36 1.29 ** 1.79 1.35 ** 1.78 1.35 ** 1.69 1.63 1.98 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. c) ARMA Models In our earlier models we assumed that time- series effects are captured by a first-order autoregressive process. There are two problems with this assumption. First, it is possible that time- series data over a five-year exhibit non-stationarity. For instance, indirect production labor costs and the three explanatory variables may exhibit an upward trend over time because of an increase in sales over this period. Second, all of these variables may exhibit persistence because they represent committed resources that cannot be adjusted in the short-run (Cooper and Kaplan 1992) and because seasonality in demand patterns for the finished products persists over several days. Non- stationarity and persistence in time-series data increase the probability of spurious correlations between the variables in a regression (Harvey 1981, McCleary and Hay 1981). To detect non-stationarity, we first employ the Dickey-Fuller tests for unit roots (Hamilton 1994, pp.486- 501). This procedure involves estimating the model Yt = α + ρ Yt-1 + ut (where Y is the variable under consideration) by OLS regression using daily data and testing whether ρ = 1. Because the t-statistic obtained under the null hypothesis is not normally distributed, modified critical t-values (T-values) tabulated by Schmidt (1988) are used. The results of the univariate models of the dependent and independent variables indicate that the processes are stationary. Since © 2021 Global Journals 2 Global Journal of Management and Business Research Volume XXI Issue II Version I Year 2021 ( ) D 10 Cost Hierarchy: Evidence and Implications