Product Costing and Pricing: Evidence from a Printing · PDF fileKey Words: Printing, Job...

Job Costing and Pricing: Empirical Evidence from a Printing Company

Rajiv D. Banker*

Hsihui Chang*

Chin S. Ou**

Anne Wu***

* The University of Texas at Dallas, Richardson, Texas, USA

** National Chungcheng University, Chia-yi, Taiwan, ROC

*** National Chengchi University, Taipei, Taiwan, ROC

Last revised: December 19, 1999

The authors thank Shi Chen (President), Yun-Fu Chieng (Vice President, Operations), Andy Hong (Assistant Manager, Marketing), Che-Yu Shen (Manager, Marketing), and Suzan Chang (Assistant Controller) at Shen’s Art Printing Co. Ltd. for providing their time and access to cost and operations data that made this study possible


ABSTRACT: Using job cost data from a leading printing company in Taiwan, this study

empirically examines product pricing behavior both before and after a change in the job order

costing system. As direct material costs are billed separately, and labor costs are fixed, only a

negligible proportion of the job costs is variable at this company. A key finding is that allocated

fixed job costs are significant in explaining variations in short run prices even after controlling

for job volume and quality requirements. The results also indicate that pricing behavior changed

following the change in the job costing system. After the change, prices depend more on the full

costs calculated based on the new system than on the full costs based on the old system, although

neither cost estimate was reported to marketing managers when bidding for jobs.

Key Words: Printing, Job order costing, Product costing, Pricing, Cost distortions Data Availability: The confidentiality agreement with the firm that provided data for this study precludes disseminating detailed data without its consent.

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1. Introduction

Whether product cost information is used by managers in making product pricing

decisions remains a controversial question. Some suggest that market demand and competitive

forces influence prices much more than cost considerations. Economic analysis indicates that a

product's price in the short run should be based on its marginal cost to maximize profits.

Consistent with this prescription, most management accounting textbooks suggest that short run

prices should be based on variable costs. Questionnaire survey studies, however, suggest that

managers rely on full cost rather than variable cost information for pricing decisions. While this

inconsistency between theoretical and survey studies questions the role of product costing

systems in pricing decisions, there is very little hard evidence from firms on whether and to what

extent their actual prices depend on variable or fixed costs. In this study, we collect job cost and

price data from a printing company to assess the relationship between costs and prices.

Specifically, we address the question whether pricing decisions depend on allocated fixed costs,

and whether these decisions change following a change in the job costing system.

Economists have extensively studied how prices are determined under various market

conditions. Under perfect or imperfect competition, equilibrium price in the short run is a

function of marginal cost, but independent of fixed cost. Equilibrium price is a function of

average cost in the long run because all factors of production are assumed to be variable in the

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long run (Pashigian 1998, p.269). Noreen and Soderstrom (1994), however, present empirical

evidence indicating that hospital costs do not vary strictly proportionally in the long run.

Reflecting the long run analysis that treats all costs as variable, Kotler (1994, p.498)

states in his marketing textbook that "while market demand might set a ceiling, cost sets the floor

for the price that the firm can charge for its product." The firm will seek "to charge a price that

both covers all its costs for producing, distributing, and selling the product, and delivers a fair

rate of return for its effort and risk" (Kotler and Armstrong 1999, p.305). For short run

decisions, however, Atkinson, Banker, Kaplan and Young (1997, pp.316-319), Garrison and

Noreen (1997, p.832), Hilton (1997, pp.754-760) and Horngren, Sundem and Stratton (1996,

pp.186-188) all prescribe analyses based on variable costs for the determination of optimal

prices.

Questionnaire surveys by Govindarajan and Anthony (1983) and Shim and Sudit (1994)

indicate that managers at a majority of firms rely on full cost information for pricing decisions.

Govindarajan and Anthony (1983) found in their survey of Fortune 1000 companies that 82% of

the respondents priced their products based on full costs. Only 17% of the respondents indicated

that they rely on variable costs for their product pricing decisions. Shim and Sudit (1995) found

that about 70% of the companies used full cost-based pricing, 12% used variable cost-based

pricing, and 18% used market-based pricing. Consistent with this empirical evidence, Banker

and Hughes (1994) provide an economic model of pricing decision that suggests that optimal

price is a function of the full cost of a product if the assumptions of their model hold.

Balachandran, Balakrishnan and Sivaramakrishnan (1997) also find that a product's full costs

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provide a reasonable and practical way to simplify a firm's capacity planning problem,

particularly when products do not share too many constrained resources.

The apparent prevalence of full cost pricing in practice underscores the importance of

appropriate cost allocation methods in the design of product costing systems. Cooper and

Kaplan (1987, p.206) found that "in nearly all of the companies we visited, management was not

convinced that their full cost systems were adequate for product-related decisions. Management

did not believe that their systems accurately reflected the costs of resources consumed to

manufacture products." Consequently, many firms have changed their product costing systems

in the last decade. An interesting question arises in this context: If managers rely on product cost

information in pricing decisions, then does a change in the costing system result in a change in

the pricing behavior? Specifically, do prices continue to be related to old cost information, or do

prices adapt to the new costing system?

With the accelerating globalization of business, management accounting researchers have

increasingly conducted research in international contexts (Chow, Kato and Shields 1994). To

investigate the relationship between costs and prices, we collected job cost, price and operations

data from a leading printing company in Taiwan that had recently changed its product costing

system. This setting is suitable for our research for several reasons. First, the company's senior

executives were willing to provide its price, cost, and operations data to us. Second, because of

the customized nature of printing orders, managers must set prices in the short run individually

for each order. Therefore, we have many individual observations on job costs and prices for our

empirical analysis. Third, the printing industry is highly competitive, and market forces are

likely to influence pricing decisions. Therefore, the role of costs in pricing decisions remains

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questionable ex ante. Fourth, our research site's job cost structure is dominated by fixed costs

because direct material costs are billed separately to the customer and excluded from job costs,

and contractual restrictions with the labor union render all labor costs fixed. Therefore, allocated

fixed costs play a prominent role in our analysis. Finally, the recent change in the job costing

system allows us to examine pricing decisions both before and after the change.

The remainder of the paper is structured as follows. We describe our research site in

section 2, and its old and new costing systems in section 3. In section 4, we describe the sample

data and variable definitions. We discuss our empirical results in section 5. Finally, we

conclude in section 6 with a summary of our results.

2. The Research Site

Taiwanese printing industry's production value in 1996 was estimated at $2.8 billion,1

growing at an estimated rate of 7% annually in the 1990s (Printing Industry Survey Report,

Taiwan, 1987-1996). There are about 7,800 companies in the printing industry in Taiwan, but

only 1,000 have registered factories and only the three largest companies, Chiu-yu, Flower

Queen, and Shen’s, are publicly held companies (Annual Survey of Printing Industry in Taiwan,

1988-1996). Most companies are family-owned businesses. They are small in size, typically

managed by the owners. Competition between all these firms is very keen, based on

competitors' price, quality, and customer service.

Our research site, Shen's Art Printing Co., Ltd. (hereafter Shen's), is one of the leading

printing firms in Taiwan. In 1996, it had an equity capital of $12 million, annual sales of $24

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million, 157 employees and a market share of nearly 1% in a fragmented industry. Shen's has a

strong reputation for high quality printing. Since 1987, it has used a modern factory with

automatic control of temperature, humidity, and air pollution. It was one of the first firms in

Taiwan to obtain both ISO 9002 and ISO 14000 certifications.

Shen’s specializes in printing high quality photographic and other artistic images. Its

printing products can be broadly classified into three types. The first type is "books", such as fine

art books and cookbooks. The second type is "magazines", principally monthly and quarterly

periodicals. The third type is labeled "others", primarily printing products for commercial

publicity and promotion, such as posters, brochures and calendars.

All printing jobs at Shen's are based on sales orders in accordance with customer

requirements. Production plans are the responsibility of a coordination committee consisting of

representatives of the marketing department, the production department and the production

control department. Based on these plans, daily production schedules for different jobs are

prepared by the production control manager.

We interviewed several of Shen’s senior executives, including the two managers

responsible for its marketing and sales activities. They indicated that the marketing department

considers overall market condition, competition, and historical costs to prepare a standard price

reference table. The table is reviewed and authorized by Shen’s president. The actual prices

approved by a sales manager for a job, however, usually vary from the standard list price,

reflecting current market conditions and special considerations about the customer and job

requirements. Our review of internal company documents revealed that Shen's takes into

1 All amounts in New Taiwan Dollars (NT$) have been transformed and presented here as equivalent U.S. Dollars

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consideration several factors in determining job prices. Prices depend on product type: whether

the job is for books, magazines, or others. Since market demand fluctuates periodically in the

printing industry, prices reflect shifts in demand and competitors’ current prices and expected

reactions. Prices also reflect the quality level required by the customer. Quality level is

measured in terms of machine speed, high quality requires low machine speed.

Job costs are not estimated before bidding for a job. However, actual job costs are

accumulated and measured by the cost accounting system. Shen's controller reviews the job

costs and informs senior executives of key trends each month. Also, the marketing department

has access to historical cost data when setting standard list prices. Therefore, while estimated

job costs are not directly considered for the pricing decisions, we cannot preclude the possibility

that cost information influences these decisions overall.

3. Job Costing System

Shen's cost accounting system is a historical cost system that retrospectively assigns

actual costs to jobs based on realized rather than predetermined cost and operating data. It

accumulates printing operation costs in the eight cost pools listed below:

(1) Direct labor (salary, overtime and bonus for press operators and assistants).

(2) Indirect labor (salary and bonus for printing management staff).

(3) Utilities (electricity and power).

(4) Supplies (printing inks and supplies).

(5) Equipment-related (plant and machinery depreciation, insurance, repairs and maintenance).

($) using the prevalent currency exchange rate of about $ 1 = NT$ 26.

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(6) Quality assurance and production control.

(7) Miscellaneous (books, newspaper, copier, transportation, mail, telephone, travelling).

(8) Others (fees, taxes, inspection, training).

Since Shen's bills customers separately for all actual direct material (paper) and printing

plate preparation costs, its job costing system focuses only on conversion costs. Except for

supplies and utility costs, all other conversion costs are considered to be fixed costs. Variable

costs account for less than 5% of the total printing operation costs at Shen's.

Shen's employs a two-stage cost allocation system that first allocates the costs

accumulated in the eight cost pools to individual operation centers. Each printing machine is

considered to be an operation center for this purpose. A separate overhead rate is then

determined for each operation center to allocate costs to jobs in the second stage.

Specifically, in the first stage, costs in each of the eight cost pools are assigned to each

operation center based on relevant allocation bases listed below:

First stage cost pools Allocation bases

(1) Direct labor costs Directly identified

(2) Indirect labor costs Direct labor hours

(3) Utilities Machine hours

(4) Supplies Color-reams

(5) Equipment-related costs

- Machine depreciation, insurance,

repairs and maintenance Directly identified

- Plant depreciation, insurance,

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repairs and maintenance Working area used

(6) Quality assurance and production control Machine hours

(7) Miscellaneous costs Machine hours

(8) Other costs

- Fees and taxes Working area used

- Employee training Number of employees trained

- All other Machine hours

Thus, all costs accumulated in the first stage are allocated to the different operation centers. The

first stage allocation method was not changed during our sample period. However, in the middle

of the sample period, significant changes were made in the second stage allocation method.

In the second stage of the old costing system, Shen’s assigned the costs accumulated in

each operation center to job orders on the basis of the number of color-reams for that operation

center. Color-ream is commonly used in the printing industry as a measure of volume. A ream

has 500 sheets of paper. The following information is used by the production control department

to compute the number of color-reams for each job: How many pages are printed for the job? Is

printing required on one side or both sides? How many colors are required? How many pages

can be printed on a sheet? The number of color-reams for each job is then computed as (colors *

sides * pages)/(500 * pages per sheet). The overhead rate per color-ream for each operation

center is calculated by dividing its accumulated monthly costs by its total number of color-reams

for that month.

The advantage of using color-reams as a single allocation base is its simplicity.

However, the management at Shen's felt that its old costing system was not able to provide

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reliable information for managerial decisions such as production efficiency evaluation and

product pricing. In order to improve the accuracy of job costs, Shen's revised its second stage

allocation procedure.

Instead of using the number of color-reams as the sole cost driver, Shen's established five

activity cost pools for each operation center that correspond to the five basic activities in the

printing process. These activities are: preparation, imposition, press machine, supplies handling,

and machine waiting. Costs accumulated for each operation center are now traced further to the

five activities for each center. Costs assigned to an operation center in the first stage based on

direct labor or machine hours are apportioned between different activities using the same basis.

Supplies costs are traced to supplies handling activity. Equipment-related and other costs

assigned in the first stage based on working area are apportioned based on machine hours and

employee's training costs are apportioned based on direct labor hours.

A separate cost driver rate is calculated for each of the five activities in each machine

operation center. The preparation activity consists of getting the plate ready for imposition onto

the machine, such as machine cleaning and machine adjustment for plate size. Since preparation

activity costs increase with the number of plates used, Shen's new costing system uses the

number of plates as the cost driver for this activity. The imposition activity is also referred to as

plate fixing. It involves the placement of a plate onto a printing machine so that the printing

activity can be performed. Different jobs consume different amounts of time for imposition due

to differences in plate size and quality requirement. Therefore, the number of imposition work

hours is the cost driver for the imposition activity. Press machine represents the core printing

activity. Its cost driver is the actual machine hours used in printing a job. Supplies handling

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includes primarily the changing of inks and other supplies. The number of color-reams is used

as the cost driver for supplies handling costs because the consumption level of inks depends on

the number of color-reams. Machine waiting is the waiting time associated with individual

operation centers due to machine breakdown, and required repairs caused by the size and

complexity of a printing job. The number of waiting hours is the cost driver for this activity.

Depending on the operation centers visited by a printing job, the appropriate activity cost

driver rates are used to assign activity costs to an individual job based on its consumption of

those activity cost drivers. Thus, the new costing system recognizes the operation centers used

by the job and the activities performed in each operation center during the printing process for

the job. Since the old costing system relied on a volume measure (the number of color-reams) as

the sole second stage allocation basis for each operation center, it was likely to overcost large

volume jobs because several printing operation costs are caused by non-unit related drivers

(Cooper and Kaplan, 1987). In contrast, jobs requiring a higher level of quality were likely to be

undercosted under the old costing system because a higher quality level requires lower machine

speed which, in turn, implies more machine hours and higher costs with the same number of

color-reams. These suggest the following two hypotheses:

H1.1: Ceteris paribus, large volume jobs are overcosted under the old costing system.

H1.2: Ceteris paribus, jobs requiring higher levels of quality are undercosted under the old

costing system.

Since most of the job costs are allocated fixed costs, classical economic analysis suggests

that job prices will be independent of such costs. However, questionnaire survey evidence of

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Govindarajan and Anthony (1983) and Shim and Sudit (1995), and recent economic analysis of

Banker and Hughes (1994) suggests the following two hypotheses:

H2.1: Job costs generated from the old costing system are positively associated with job prices

before the change in the costing system.

H2.2: Job costs generated from the new costing system are positively associated with job prices

after the change in the costing system.

If Shen’s managers rely on full costs when setting prices, then the pattern of job prices

should change after Shen’s changed its costing system. The old costs should be the basis for job

pricing before, and the new costs should be the basis after, the adoption of the new costing

system. Therefore, we test the following hypothesis:

H2.3: After the change in the costing system, job prices depend more on job costs estimated

from the new costing system than on job costs generated from the old costing system.

4. Sample Data and Variable Definitions

We hand-collected the data used in this study from Shen's job cost accounting and

operating records. We refer to the year Shen’s changed its job costing system as Year ∆. There

was no change in the printing capacity or plant machinery configuration during our sample

period. However, since detailed capacity utilization data were not maintained, we collected data

for sample of 300 jobs each from the same period in the second quarter of the preceding and the

subsequent years, Year ∆-1 and Year ∆+1 to control for possible differences in demand across

different seasons. After deleting 34 jobs for which complete operating data were not available,

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our final sample included data for the number of observations (jobs) described below by product

types and sample years.

Product Type Year ∆-1 Year ∆+1

Books 143 141

Magazines 88 75

Others 56 63

Total 287 279

We constructed the following variables for our empirical analysis:

OLDCOST: Cost of a job based on the old costing system, available for both Year ∆-1 and Year

∆+1 samples.

NEWCOST: cost of a job based on the new costing system, available only for the Year ∆+1

sample.

OLDCOST/NEWCOST: Ratio of the cost of a job based on the old costing system to the cost of

the same job based on the new costing system.

PRICE: Actual price of a job, excluding direct charges for materials and plate preparation.

COLOR-REAM: Total number of color-reams for a job.

QUALITY: Level of quality required by a job, measured on a 10-point scale representing

machine speed. A value of 1 represents the highest machine speed and the lowest level of

quality requirement, a value of 10 represents the lowest machine speed and the highest level of

quality requirement.

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Price and cost data presented here have been disguised by a scaling factor.2 Descriptive

statistics of all variables are reported in Table 1. The median values of costs, prices, and color-

reams are much smaller than the mean values in both Year ∆-1 and Year ∆+1 samples, indicating

that the data are skewed to the left. When the data are logarithmically transformed for empirical

estimation, this skewness is alleviated.

The total of old costs over all jobs equals the total of new costs over all jobs for Year

∆+1, and these totals of old and new job costs are almost equal for our sample. We observe,

however, that the means of the ratio of the old cost to the new cost are less than one for each

product type for our sample. It may appear at first glance that all product types were undercosted

on average for our sample. In fact, this is a general result that occurs due to skewed data (Sunder

1983): There is a small number of large jobs that were overcosted by the old system, balanced by

a large number of small jobs that were undercosted. Specifically, the average number of color-

reams is 3,165, 16,022 and 2,542 for the 11, 32 and 16 jobs in our sample for which the old cost

is greater than the new cost for the product types "books, "magazines" and "others", respectively.

In contrast, the average number of color-reams is 389, 2,484 and 77 for the 130, 43 and 47 jobs

for which the old cost is less than the new cost for the product types "books", "magazines" and

"others", respectively.

Averages for old costs and prices per job are lower in Year ∆+1 compared to Year ∆-1

for "books" and "others", but they are higher for "magazines". Comparing prices with old costs

for the two years suggests that "magazines" appear to be more profitable than the other two

product types. The profit margin per job for "magazines" is 37% [$2,233-$1,390)/$2,233] and

2 Data transformation impacts only the estimate of the intercept term in our OLS regression.

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41% [($4,891-$2,885)/$4,891], respectively for Year ∆-1 and Year ∆+1, compared to profit

margins of 26% [($1,220-$968)/$1,220] and 20% [($1,079-$792)/$1,079] for "books", and 25%

[($1,071-$841)/$1,071] and 21% [($775-$579)/$775] for "others" for the two samples. In

contrast, comparing prices with new costs for Year ∆+1, we find that while "magazines" are

more profitable than the other two products, "books" are considerably less profitable. The profit

margin per job for "magazines" is 47.7%[$4,891-$2,556)/$4,891], but only 9.8%[($1,079-

$973)/$1,079] for "books" and 26.8%[$775-$567)/$775] for "others".

Descriptive statistics for the quality level reveal an increase in the quality requirement for

all three product types between the Year ∆-1 and Year ∆+1 samples. The average volume

measure, number of color-reams per job, increased substantially for “magazines” and “others",

but decreased for “books” between the two samples.

5. Estimation Models and Results

To evaluate the two cost distortion hypotheses, we specify the following estimation

model:

ln(OLDCOST/NEWCOST) = β0 + β1lnCOLOR-REAM + β2lnQUALITY + ε (1)

Since old and new cost data are both available only after the change in the costing system, we

estimate Model (1) separately for each of the three product types, using only the Year ∆+1

sample data.

We present the regression results in Table 2. Belsley, Kuh and Welsch's (1980)

eigenvector-based diagnostics indicated that collinearity is not a problem for this model. Panel

A of Table 2 indicates that the coefficient estimates for color-reams are significantly positive for

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all three product types, supporting Hypothesis 1.1 that large volume jobs are overcosted. The

coefficient estimates for the quality requirement level are significantly negative for all three

product types, supporting Hypothesis 1.2 that jobs with higher level of quality requirement are

undercosted under the old costing system. Together, these two sets of results indicate that the

old costing system is significantly different from the new costing system. Since the job costs

estimated by the prevailing costing system change significantly between the Year ∆-1 and

Year ∆+1 samples, it is of interest to examine next if the pricing patterns changed systematically

in response to the costing change.

To test the three hypotheses about the relation between costs and prices, we specify the

following three estimation models:

lnPRICE = α0 + α1lnOLDCOST + ε (2)

lnPRICE = α0 + α1lnNEWCOST + ε (3)

lnPRICE = α0 + α1lnOLDCOST + α2lnNEWCOST + ε (4)

Since NEWCOST data are available only for the Year ∆+1 sample, we estimate Model (2) with

both Year ∆-1 and Year ∆+1 data, but Models (3) and (4) only with the Year ∆+1 data.

We present regression results relating prices to the old costs in Table 3. The coefficients

for OLDCOST are significantly positive for all three product types in Year ∆-1 (in Panel A)

supporting Hypothesis 2.1 that prices depend on costs. These coefficients are significant also in

Year ∆+1 (in Panel B). Coefficient for each product type in Year ∆-1 (ranging between 0.83 and

0.97) is also higher than the corresponding coefficient in Year ∆+1 (ranging between 0.61 and

0.69). Adjusted R2 (ranging between 0.78 and 0.84) measuring the explanatory power of the

regressions in Year ∆-1 are also higher than the corresponding values (between 0.69 and 0.80) in

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Year ∆+1. Thus, it appears that the relation between price and old cost is not as strong in Year

∆+1 as in Year ∆-1.

Regression results relating prices to the new costs are reported in Table 4. The results

indicate that the coefficient for NEWCOST is significantly positive for all three product types,

supporting Hypothesis 2.2 that prices after the cost system change depend on the new costs. The

coefficients range between 0.90 and 0.99, indicating that a 1 percent change in cost results in a

nearly 1 percent change in the price. The adjusted R2 for each product type in Year ∆+1 (ranging

between 0.76 and 0.83) is higher than the corresponding values for the OLDCOST regressions

for Year ∆+1 reported in Panel B of Table 3, indicating that NEWCOST explains a little more of

the variation in Year ∆+1 prices than does OLDCOST.

In Table 5, we also report regression results relating prices to both old and new costs.

Belsley, Kuh and Welsch's (1980) diagnostics indicate collinearity between OLDCOST and

NEWCOST, which is likely to inflate the standard errors. However, the coefficient of

NEWCOST for each product type is significantly greater than the corresponding coefficient of

OLDCOST for all three product types, supporting Hypothesis 2.3 that new costs influence prices

after the cost system change more than the old costs. Collectively, the results reported in Tables

3, 4, and 5 suggest that prices depend to a large extent on the old costs in

Year ∆-1 and on the new costs in Year ∆+1.

As described earlier, our field interviews suggested that the pricing decision is influenced

also by factors such as job volume and quality requirements. Nonfinancial measures have

increasingly been the focus of management accounting research (Ittner, Larcker and Rajan 1997,

Banker, Potter and Srinivasan 2000). Therefore, to control for the potential confounding effects

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of these nonfinancial variables, we also added the number of color-reams (COLOR-REAM) and

quality requirement (QUALITY) as control variables to our earlier estimation models:

lnPRICE = α0 + α1lnOLDCOST + α2lnCOLOR-REAM + α3lnQUALITY + ε (5)

lnPRICE = α0 + α1lnNEWCOST + α2lnCOLOR-REAM + α3lnQUALITY + ε (6)

lnPRICE = α0 + α1lnOLDCOST + α2lnNEWCOST + α3lnCOLOR-REAM + α4lnQUALITY+ ε (7)

Belsley, Kuh and Welsch's (1980) diagnostics indicate collinearity between OLDCOST

and COLOR-REAM in Model (5), and between OLDCOST and NEWCOST in Model (7),

which may bias results against the rejection of the null hypotheses. Regression results relating

prices to old costs and control variables in Model (5) are presented in Table 6. We observe from

Panel A of Table 6 that OLDCOST continues to exhibit significant explanatory power in

Year ∆-1 even after controlling for color-reams and quality requirements. As before, the

sensitivity of prices to old costs is reduced in Year ∆+1, especially in relation to the control

variables which assume relatively greater significance in the Year ∆+1 regressions.

We report Model (6) regression results relating prices to new costs and control variables

in Table 7. Again, consistent with the results reported earlier, we find that the new costs

significantly explain variations in prices in Year ∆+1 even after controlling for job volume and

quality requirements.

In Table 8 we report regression results relating price to the old cost, new costs and

control variables for the Year ∆+1 samples specified in Model (7). The coefficient estimate of

the new cost for each product type (ranging from 0.19 to 0.56) is higher than the corresponding

coefficient of the old cost (ranging from 0.06 to 0.30), suggesting that the new cost has a greater

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influence on prices in Year ∆+1 than the old cost, even after controlling for the potential effect of

job volume and quality requirements.

We conducted several econometric tests of our model specification. White's (1980) test

did not indicate heteroskedasticity for any of the models for either Year ∆-1 or Year ∆+1 sample.

We employed the criteria proposed by Belsley, Kuh and Welsch (1980) to identify influential

observations. Results remain substantially unchanged when the models are reestimated after

removing the identified outliers. We also employed the procedure proposed by Davidson and

MacKinnon (1985) to test whether a linear or a log-linear model is the appropriate specification.

The results indicate that a log-linear model is the appropriate specification for all our models

except in the case of Model (1) for "magazines". However, results estimated from a linear

specification of Model (1) for "magazines" are qualitatively similar to the results reported in

Table 2 for a log-linear model. To further evaluate the robustness of our results to different

functional specifications, we re-estimated all models as rank regressions, maintaining the

assumption of only a monotone relationship between the dependent variable and each of the

independent variables in our models (Iman and Conover, 1979). The results (not reported here)

support the insights obtained from the log-linear models described earlier.

6. Concluding Remarks

In this study, we analyzed cost, price and operations data for 566 jobs from a leading

printing company in Taiwan to evaluate the role of job costs in pricing decisions. We also

examined how pricing decisions were affected by a change in the job costing system in the

middle of our two sample periods.

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Our empirical results suggest that prices of printing jobs depend on their full costs. The

costs generated from the old costing system explain much of the variation in prices in the year

preceding the system change and the costs generated from the new costing system explain much

of the variation in prices in the year following the system change. These results are robust to the

inclusion of additional control variables such as job volume and quality requirements.

The case for improvements in the design of product costing systems is often motivated by

appealing to the potential impact of costs on pricing decisions. However, there has been scant, if

any, empirical evidence on whether, and to what extent, costs influence prices, and whether full

as opposed to variable costs are associated with prices. By documenting the role of full costs in

pricing decisions and the impact of a change in the job costing system on job prices in one

organization, we have sought to stimulate empirical research on this important research question

in management accounting.

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Table 1: Descriptive Statistics on Job Costs, Prices and Contextual Variables Panel A: Year ∆-1 Variables Sample (N) Mean Std Dev. 25% 50% 75%

Books (N=143) $968 $1,023 $390 $612 $1,049 Magazines (N=88) $1,390 $1,389 $484 $970 $1,662

OLDCOST

Others (N=56) $841 $1,198 $129 $239 $831 Books (N=143) $1,220 $1,463 $376 $646 $1,460 Magazines (N=88) $2,233 $2,085 $966 $1,540 $2,748

PRICE

Others (N=56) $1,071 $1,443 $163 $502 $1,097 Books (N=143) 3.727 1.816 3 4 5 Magazines (N=88) 4.318 2.521 2.5 4 5.5

QUALITY

Others (N=56) 6.268 3.435 3.5 7 10 Books (N=143) 643 828 211 363 744 Magazines (N=88) 1,750 2,748 292 833 1,603

COLOR-REAM

Others (N=56) 370 1,187 4 38 181 N = Number of job orders. OLDCOST = Job cost generated from the old costing system. PRICE = Actual price of a job. QUALITY = Quality level with values ranging from 1 being the lowest level of quality to 10

being the highest level of quality. COLOR-REAM = Number of color-reams. Panel B: Year ∆+1 Variables Sample (N) Mean Std Dev. 25% 50% 75%

Books (N=141) $792 $2,386 $99 $335 $589 Magazines (N=75) $2,885 $7,158 $435 $681 $2,983

OLDCOST

Others (N=63) $579 $1,373 $43 $130 $480 Books (N=141) $973 $1,639 $223 $632 $954 Magazines (N=75) $2,556 $4,986 $641 $871 $2,624

NEWCOST

Others (N=63) $567 $948 $111 $258 $601 Books (N=141) 0.5725 0.3755 0.3463 0.5149 0.7576 Magazines (N=75) 0.8765 0.4508 0.5376 0.8626 1.3133

OLDCOST/NEWCOST

Others (N=63) 0.6696 0.5009 0.2402 0.4778 1.0635 Books (N=141) $1,079 $1,871 $223 $546 $1,171 Magazines (N=75) $4,891 $9,358 $1,042 $1,900 $4,186

PRICE

Others (N=63) $775 $1,174 $120 $406 $941 Books (N=141) 6.581 1.825 5 6 8 Magazines (N=75) 5.733 2.029 4 5 7

QUALITY

Others (N=63) 8.174 1.792 7 9 10 Books (N=141) 605 1,484 74 274 545 Magazines (N=75) 8,260 26,683 348 1,025 3,465

COLOR-REAM

Others (N=63) 703 3,909 5 22 86 N = Number of job orders. OLDCOST = Job cost generated from the old costing system. NEWCOST = Job cost generated from the new costing system. OLDCOST/NEWCOST = Ratio of OLDCOST to NEWCOST of a job. PRICE = Actual price of a job. QUALITY = Quality level with values ranging from 1 being the lowest level of quality to 10 being the highest level of quality. COLOR-REAM = Number of color-reams.

23

Table 2: Regression Results Relating Cost Distortions to Contextual Variables (t-values in parentheses)

ln(OLDCOST/NEWCOST) = β0 + β1lnCOLOR-REAM + β2lnQUALITY + ε Variables Coeffi-

cients Predicted

signs Books

(N=141) Magazines

(N=75) Others (N=63)

lnCOLOR-REAM β1 + 0.0769

(2.667)*** 0.0798

(1.871)** 0.1158

(3.112)***

lnQUALITY β2 - -0.4034

(1.933)** -1.1248

(3.857)*** -1.9186

(5.501)***

Adjusted R2 0.120 0.404 0.541 F-statistics 10.534 26.142 37.631 * Significant at 10% level using one-tailed t-test, ** Significant at 5% level using one-tailed t-test, *** Significant at 1% level using one-tailed t-test. Variable definitions appear in Table 1.

24

Table 3: Regression Results Relating Job Prices to Old Costs (t-values in parentheses)

lnPRICE = α0 + α1 lnOLDCOST + ε Panel A: Year ∆-1 Variable Coeffi-

cient Predicted

sign Books

(N=143) Magazines


lnOLDCOST α1 + 0.9687 (22.554)***

0.8332 (19.360)***

0.8292 (17.266)***

Adjusted R2 0.781 0.811 0.843 F-statistics 508.667 374.820 298.124 * Significant at 10% level using one-tailed t-test, ** Significant at 5% level using one-tailed t-test, ** Significant at 1% level using one-tailed t-test. Variable definitions appear in Table 1. Panel B: Year ∆+1 Variable Coeffi-

cient Predicted

sign Books

(N=141) Magazines


lnOLDCOST α1 + 0.6862 (21.200)***

0.6390 (13.019)***

0.6067 (15.828)***

Adjusted R2 0.762 0.694 0.801 F-statistics 449.444 169.4934 250.516 * Significant at 10% level using one-tailed t-test, ** Significant at 5% level using one-tailed t-test, ** Significant at 1% level using one-tailed t-test. Variable definitions appear in Table 1.

25

Table 4: Regression Results Relating Job Prices to New Costs (t-values in parentheses)

lnPRICE = α0 + α1lnNEWCOST + ε Variable Coeffi-

cient Predicted

sign Books

(N=141) Magazines


lnNEWCOST α1 + 0.9049 (23.049)***

0.9858 (15.356)***

0.9478 (17.448)***


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Table 5: Regression Results Relating Job Prices to Both Old and New Costs (t-values in parentheses)

lnPRICE = α0 + α1lnOLDCOST + α2lnNEWCOST + ε Variables Coeffi-

cients Predicted

signs Books

(N=141) Magazines


lnOLDCOST α1 + 0.2953 (4.389)***

0.1879 (1.853)**

0.2603 (3.281)***

lnNEWCOST α2 + 0.5586 (6.413)***

0.7342 (4.905)***

0.5841 (4.797)***

Adjusted R2 0.815 0.768 0.853 F-statistics 310.164 123.543 181.963 * Significant at 10% level using one-tailed t-test, ** Significant at 5% level using one-tailed t-test, ** Significant at 1% level using one-tailed t-test Variable definitions appear in Table 1.

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Table 6: Regression Results Relating Job Prices to Old Costs and Control Variables (t-values in parentheses)

lnPRICE = α0 + α1lnOLDCOST + α2lnCOLOR-REAM + α3lnQUALITY + ε Panel A: Year ∆-1 Variables Coeffi-

cients Predicted

signs Books

(N=143) Magazines


lnOLDCOST α1 + 0.9001 (13.923)***

0.5281 (6.431)***

0.3548 (4.658)***

lnCOLOR-REAM α2 + 0.0566 (1.160)

0.2194 (4.170)***

0.3234 (6.835)***

lnQUALITY α3 + 0.1368 (2.372)***

-0.0240 (0.375)

0.0324 (0.504)

Adjusted R2 0.788 0.842 0.917 F-statistics 176.967 155.826 202.670 * Significant at 10% level using one-tailed t-test, ** Significant at 5% level using one-tailed t-test, ** Significant at 1% level using one-tailed t-test. Variable definitions appear in Table 1. Panel B: Year ∆+1 Variables Coeffi-

cients Predicted

signs Books

(N=141) Magazines


lnOLDCOST α1 + 0.5440 (11.134)***

0.1442 (2.058)***

0.4390 (6.693)***

lnCOLOR-REAM α2 + 0.1620 (4.377)***

0.4742 (9.853)***

0.1783 (3.746)***

lnQUALITY α3 + 0.4872 (2.706)***

0.4848 (2.086)***

0.2921 (0.949)


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Table 7: Regression Results Relating Job Prices to New Costs and Control Variables (t-values in parentheses)

lnPRICE = α0 + α1lnNEWCOST + α2lnCOLOR-REAM + α3lnQUALITY + ε Variables Coeffi-

cients Predicted

signs Books

(N=141) Magazines


lnNEWCOST α1 + 0.7919 (11.339)***

0.2521 (2.407)***

0.7251 (8.186)***

lnCOLOR-REAM α2 + 0.0839 (2.005)***

0.4373 (7.819)***

0.1044 (2.188)**

lnQUALITY α3 + 0.1569 (0.866)

0.3247 (1.498)*

-0.6446 (2.436)***


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Table 8: Regression Results Relating Job Prices to Old Costs, New Costs and Control Variables (t-values in parentheses)

lnPRICE = α0 + α1lnOLDCOST + α2lnNEWCOST + α3lnCOLOR-REAM + α4lnQUALITY + ε Variables Coeffi-

cients Predicted

signs Books

(N=141) Magazines


lnOLDCOST α1 + 0.3023 (4.409)***

0.0645 (0.734)

0.1351 (1.366)*

lnNEWCOST α2 + 0.4657 (4.711)***

0.1903 (1.414)*

0.5638 (3.831)***

lnCOLOR-REAM α3 + 0.0722 (1.835)**

0.4309 (7.589)***

0.1002 (2.109)**

lnQUALITY α4 + 0.2896 (1.677)**

0.3973 (1.663)*

-0.3767 (1.149)

Adjusted R2 0.818 0.869 0.866 F-statistics 158.885 124.150 101.317 * Significant at 10% level using one-tailed t-test, ** Significant at 5% level using one-tailed t-test, *** Significant at 1% level using one-tailed t-test. Variable definitions appear in Table 1.

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