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ABSTRACT

The collapse of many large companies around the world and the current

manufacturing environment where fixed manufacturing cost has gained its

significance have awakened the absorption versus variable costing debate. It has long

been acknowledged that absorption costing allows management to increase or

decrease net income being reported by overproducing or under-producing than the

level of production actually needed for sales. Raising funds in globally connected

capital markets and a higher level of capital intensity as indicated by proportion of

fixed manufacturing overhead to total manufacturing costs may have presented an

incentive and opportunity for management to artificially increase or decrease net

incomes being reported under absorption costing. However, just-in-time (JIT)

production system, which has been widely adopted since the early 1980s, tends to

minimize such opportunity to manage net income being reported. When JIT

philosophy has a greater impact than capital intensity, the two product costing

methods would yield net incomes that are not much different. However, when this is

not the case, product costing choices do have impacts on net incomes being reported

and the quality of net incomes may be questionable. This could threaten creditability

of the company and is harmful to capital market development. It is now, therefore,

interesting and important to examine whether the recent changes in operating and

manufacturing environment have provided enough incentive and opportunities for

management to manage net income being reported under absorption costing by

varying production levels in different periods.

This study aims to examine the effects of product costing choices on

profitability in the current operating environment by 1) examining whether fixed

manufacturing overhead has increasingly become a greater portion of total

manufacturing costs; 2) examining whether JIT has impacts on Stock Exchange of

Thailand iSF T) listed manufacturing companies; 3) examining impacts of product

costing choices on profitability of the sample companies over the period studied in

terms of both direction and significance of inventory adjustments; 4) examining

relative impact of capital intensity and JIT on profitability differentials in aggregate

over the nine-year period studied; and also 5) examining relative impacts of capital

intensity and JIT on profitability differentials reported under the two methods in each

of the 9 years studied to observe trends of the impact each of the two factors over

time. The examination was conducted on manufacturing companies listed in the

Stock Exchange of Thailand in three sectors: Agro & Food Industry, Consumer

Products, and Industrials.

This study has provided many important findings. Firstly, capital intensity of

the SET listed manufacturing companies have not increased significantly over the

period studied. Secondly, it has shown that JIT system has not had consistent impacts

on SET listed manufacturing companies. Thirdly, Average differences between net

incomes reported under the two product costing methods in aggregate for all the three

industries fluctuate in the range between 2.11 and 4.67 million Bahts. Average

differences between net incomes under the two product costing methods as a

percentage of net incomes under absorption costing in aggregate for all the three

industries range from 1.78.% to 6.42%. Average difference between profit margin

ratios under the two product costing methods for all the three industries ranges from

iii

0.12% to 0.32%. Finally, the OLS analysis of panel data shows that the extent of net

income differentials and net income differentials as a percentage of sales are driven

more by the capital intensity than the degree companies embraced JIT philosophy.

iv

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V

TABLE OF CONTENTS

Page

Chapter 1: Introduction

1.1 Background to the Problem

1.2 Purposes of the Study 3

1.3 Hypotheses 4

1.4 Scope of Research 5

1.5 Definition of Technical Terms 6

1.6 Contributions of the Study 7

Chapter 2: Literature Review 9

2.1 Manufacturing Costs and Product Costs 10

2.2 Absorption Costing vs. Variable Costing 11

2.3 Impacts of Product Costing Choices on Net Income Being Reported... 12

2.4 Debate on which method to use 13

2.5 Accounting for Manufacturing Overhead 15

2.6 Possible Solutions 17

2.7 Just in Time System 19

2.8 Prior Research Examining Effects of using Absorption Costing as a

Basis for Product Costing in Externally Reported Financial Statements..... 23

Chapter 3: Research Methodology 26

3.1 Sample Selection and Data Collection 26

3.2 Measurement of Variables 27

3.2.1 Capital Intensity 27

3.2.2 JIT Philosophy 29

3.2.3 Profitability Differentials 30

3.2.4 Profitability Differentials Under the Two Product Costing

Choices 30

3.2.4.1 Directions of Inventory Adjustments 31

vi

3.2.4.2 Significance of Inventory Adjustments 31

3.2.4.3 Impacts of Inventory Adjustments on Profitability 32

3.2.4.4 Profitability Differentials 34

3.3 Hypotheses Development and Testing 35

3.3.1 Fixed Manufacturing Overhead Trend 35

3.3.2 Effect of JIT Philosophy 35

3.3.3 The Impact of Capital Intensity and .111 Philosophy on

Profitability Differentials over Time 36

3.3.4 The Impact of Capital Intensity and JIT Philosophy on

Profitability Differentials in Each Year 38

Chapter 4: Results of the Study 39

4.1 Samples 39

4.2 Descriptive Statistics of Variables of Interest 39

4.2.1 The Proportion of Fixed Manufacturing Costs to Total

Manufacturing Costs 40

4.2.2 Inventory to 42

4.2.3 Directions of Inventory Adjustments 44

4.2.4 Significance of Inventory Adjustments 45

4.2.5 Impacts of Inventory Adjustments on Profitability 46

4.2.6 Average Annual Inventory Adjustment as a Percentage of Net

Income under Absorption Costing 48

4.2.7 Average Annual Inventory Adjustment as a Percentage of

Sales 50

4.2.8 Average Net Income Differential 51

4.2.9 Average Net Income Differential as a Percentage of Net

Incomes Under Absorption Costing 53

4.2.10 Average Net Income Differential as a Percentage of Sales..... 54

4.3 Hypotheses Testing Results 55

4.3.1 Capital Intensity. 55

4.3.2 Inventory to Sales 57

4.3.3 The Impacts of Capital Intensity and the JIT Philosophy over

Vii

Time 59

4.3.4 The Impacts of Capital Intensity and the Jff Philosophy in

Each Year 62

Chapter 5: Conclusions 66

5.1 Summary of the Results and Implications 66

5.2 Limitations and Future Research 68

References 69

Vitae 75

viii

LISTS OF TABLES

Page

Table 4.1: Industries of the Samples 39

Table 4.2: Descriptive Statistics of the Proportion of Fixed Manufacturing

Costs to Total Manufacturing Costs 41

Table 4.3: Descriptive Statistics of Inventory to Sales 44

Table 4.4: Number of Companies Having Negative Annual Stock Adjustment 45

Table 4.5: Descriptive Statistics of Average Annual Inventory Adjustment as a

Percentage of Beginning Inventories 46

Table 4.6: Descriptive Statistics of Average Inventory Adjustments 47


Percentage of Earnings Before Interest and Taxes 49

Table 4.8: Descriptive Statistics of Average Annual Inventory Adjustrner.4, as a

Percentage of Sales 51

Table 4.9: Descriptive Statistics of Net Income Differential 52

Table 4.10: Descriptive Statistics of Net Income Differential as a Percentage of

Net Income under Absorption Costing 54


Sales 55

Table 4.12: Correlations and Paired-Sample T-Test Results of 2-Year Average

Proportion of Fixed Manufacturing Costs to Total Manufacturing Costs of the

Total Sample and That of Each Industry at the Beginning and at the End of the

Period Studied 56


Inventory to Sales of the Total Sample and That of Each Industry at the

Beginning and at the End of the Period Studied 58

Table 4.14: Results of Panel Data Analysis 61

Table 4.15: Coefficients of Independent Variables in Model 1 62



ix

LISTS OF CRAPI-IS

Page

Graph 4.1: "[rends of Avergoe Sales, Average Costs of Goods Sold, Average

Depreciation, and Average 'Iota] Nlanufacturing Costs 42

Graph 4.2: Average Sales of Each Industry 48

Graph 4.3: Coefficients of CI and JIT in Model I Comparison 63

Graph 4.4: Coefficients of CI and JET in Model 2 Comparison 64

Graph 4.5: Coefficients of CI and JIT in Model 3 Comparison 65

CHAPTER 1

INTRODUCTION

1.1 Background to the Problem

Two product costing methods that manufacturing companies generally used

are: absorption costing (or full costing) and variable costing (or direct costing). The

difference between the two methods is that absorption costing treats fixed

manufacturing overhead as part of product cost while variable costing treats it as

period cost. The appropriateness of treating fixed manufacturing overhead as part of

product cost was one of the controversial areas debated extensively in accounting

journals during the 1950s and 1960s (Baxendale, Boyd, & Gupta, 2006). The debates

have been resolved in favor of absorption costing. Absorption costing is used for

external financial reporting in many countries.

However, the collapses of many large companies around the world and the

current manufacturing environment where fixed manufacturing cost has gained its

significance have awakened the absorption versus variable costing debate. It has long

been acknowledged that absorption costing allows management to increase or

decrease net income being reported by overproducing or under-producing than the

level of production actually needed for sales. In the past when direct labor made up a

large portion of total manufacturing costs, managing net income through increasing or

decreasing inventories would not have a significant impact. However, the

manufacturing environment has changed drastically in the last two decades as a result

of advances in technology and global competition (Weygandt, Kimmel, & Kieso,

2

2012). 'Ihe manufacturing process has become increasingly automated. Direct labour

component in the manufacturing cost structure has decreased dramatically while fixed

manufitcturing overhead has become a significant portion of total manufacturing

costs. Raising funds from a large number of investors especially in globally

connected capital markets may provide incentives for management to show that the

financial performance of their companies meet investors' expectations while the

greater portion of fixed manufacturing overhead in total manufacturing costs may

have made opportunities to manage income through production and inventory

decisions to actually affect net incomes being reported as intended. It is now,

therefore, interesting and important to examine whether the recent changes in

operating and manufacturing environment have provided enough incentive and

opportunities for management to manage net income being reported under absorption

costing by varying production levels in different periods. This will be evidenced by

large inventory adjustment in each period and thereby shifting fixed manufacturing

overhead between periods.

However, since the early 1980s, just-in-time (JIT) system has been widely

adopted (Huson & Nanda, 1995) and has been one of improvement programs being

taught in most business school. JIT system and other improvement programs can

vastly affect how companies are managed such that the current business environment

cannot be properly understood without some appreciation of what these improvement

programs attempt to accomplish (Garrison, Noreen, & Brewer, 2008). While JIT

system can be used in both merchandising and manufacturing companies, it has

greater profound effects on the operations of manufacturing companies (Garrison et

al., 2008). In JIT system, inventories are kept to a minimum or even at zero. With

3

little or no inventories, changes in inventories will be small and fixed manufacturing

overhead costs shifted between periods under absorption costing will be trivial.

Under JIT system, the distortions of income that can occur under absorption costing

will largely disappear (Garrison et al., 2008). The two product costing methods

would yield net incomes that are not much different.

From the discussion above, it can be seen that raising funds in globally

connected capital markets and a greater capital intensity as indicated by proportion of

fixed manufacturing overhead to total manufacturing costs may have presented an

incentive and opportunity for management to artificially increase or decrease net

incomes being reported under absorption costing while JIT system tends to minimize

such opportunity to manage net income being reported. However, when this is not the

case, product costing choices do have impacts on net incomes being reported and the

quality of net incomes may be questionable. This could threaten creditability of the

company and is harmful to capital market development.

1.2 Purposes of the Study




manufacturing costs; 2) examining whether JIT system has impacts on Stock

Exchange of Thailand (SET) listed manufacturing companies; 3) examining impacts

of product costing choices on profitability of the sample companies over the period

studied in terms of both direction and significance of inventory adjustments; 4)

4

examining relative impact of capital intensity and the degree to which companies

embraced ilT philosophy on profitability differentials in aggregate over the nine-year

period studied; and also 5) examining relative impacts of capital intensity and the

degree to which companies embraced JIT philosophy on profitability differentials

reported under the two methods in each of the 9 years studied to observe trends of the

impact each of the two factors over time.

1.3 Hypotheses

The introduction of advanced manufacturing technologies has changed the

environment in which companies operate. This should have implications on

accounting methods. It is important to examine the usefulness and impacts of some of

accounting methods. One accounting method that may have been impacted by this

changing operating environment is inventory valuation choices. Therefore, it is

important to examine effects of using absorption costing as the basis for inventory

valuation in financial statements on profitability being reported.

Companies are now increasingly implementing new manufacturing

technology. A paired t-test will be conducted to test whether the average proportion

of fixed manufacturing costs to total manufacturing costs at the beginning of the

period studied is significantly different from that at the end of the period studied at the

five percent level of confidence.

Also, it is expected that ending inventories to sales would decline throughout

the period as a result of J1T philosophy. A paired t-test will conducted to test whether

average ending inventory to sales at the beginning of the period studied is

5

significantly different from that at the end of the period studied at the five percent

level of confidence

Lastly, a greater portion of fixed manufacturing overhead in total

manufacturing costs may have made production and inventory decisions an effective

scheme to manage net incomes being reported under absorption costing. However,

JIT system may have mitigated the impact of the choices of product costing.

Regression analysis was conducted to test relative impacts of the two factors using the

following equation:

Profitability Differential = a + b1 (CI) + b2 (JIT) + Sales + e

Where CI is measured by the proportion of fixed manufacturing costs to total

manufacturing costs. JIT is measured by ending inventory to sales.

It was hypothesized that:

H„: bi = 0

Ha: bi 0

1.4 Scope of Research

The examinations will be conducted on SET listed manufacturing companies

that remained listed throughout the period from 2003 to 2011 in three sectors: Agro &

Food Industry, Consumer Products, and Industrials. Data will be collected from

manufacturing companies that remain Stock Exchange of Thailand listed from 2003

to 2011 so that availability of the data in each year in the period is as much

comparable as possible and the length is long enough to observe potential reversal of

inventory adjustments. It is important to note that the period studied is shorter than

that initially proposed in the research proposal. This was due to the fact that trends of

6

variables studied obtained from the sample companies in the year 2003 to 2011

indicate no further benefits to be gained from collecting additional data in the year

2001 and 2002. It was observed that trends of variables of interest obtained from the

data of nine years are unlikely to be different from those will be obtained if the data

were collected from 2001 to 2011. Furthermore, if data were collected from 2001 to

2011 as initially proposed, usable sample companies would be lower than 100

companies. It was deemed that collecting data of another two years will not change

trends of data observed from what has been obtained from the data collected over the

nine years studied and will only reduce the sample size even further. Therefore, data

collection was stopped when the sample size approached 100 companies.

1.5 Definition of Technical Terms

There are some technical terms encountered in this study. The definitions

shown below are based on those presented in Garrison, Noreen, & Brewer (2008).

Manufacturing costs: Costs that are incurred in the manufacturing processes.

They comprise direct materials, direct labor, and manufacturing overhead.

Direct materials: materials that become an integral part of the finished product

and that can be directly traced to it.

Direct labor: labor costs that can be directly traced to the finished product.

Labor costs that cannot be traced easily are classified as indirect labor, which is part

of manufacturing overhead.

Manufacturing overhead: all costs other than direct materials and direct labor

that are incurred in manufacturing processes.

7

Product costs: Costs that are deemed to be the costs that are involved in

acquiring or making a product. Initially, they are recorded as inventory on the

balance sheet. When the products are sold, costs of the products sold are then

expensed as cost of goods sold.

Period costs: all the costs that are not included in product costs. These costs

are expensed on the income statement in the period in which they are incurred.

Fixed costs: Costs that remain constant in total, regardless of changes in the

level of activity.

Variable costs: Costs that vary in total in direct proportion to changes in the

levels of activity.

Absorption costing: an inventory valuation method that treats both variable

and fixed manufacturing costs as product costs.

Variable costing: an inventory valuation method that treats only variable

manufacturing costs as product costs.

1.6 Contributions of the Study

The results of this study provide several important contributions. Firstly, it

provides evidence as to the trend of fixed manufacturing overhead, inventory levels,

inventory adjustments, and the impacts of product costing choices on profitability

being reported of SET listed manufacturing companies from the year 2003 to the year

2011. Secondly, it sheds light on effects of proportion of fixed manufacturing

overhead to total manufacturing costs and the degree to which companies embraced

JIT philosophy on profitability differentials over the nine-year period. This provides

evidence as to whether increase in fixed manufacturing overhead has made production

8

and inventory decision an effective strategy to influence net income being reported

under absorption costing; or this influence has been mitigated by JIT philosophy

embraced by the companies. Finally, it also sheds light on changes in relative impacts

of the two factors from one year to the next.

The remainder of this paper firstly presents literature review. Then, research

methodology used in this study was outlined, followed by results of the study.

Finally, it discusses the results and implications of the study.

CHAPTER 2

LITERATURE REVIEW

This chapter provides a review of literature related to the study. It firstly

describes the concept of manufacturing costs and the two product costing methods to

gain an understanding of each product costing method and how the difference in the

treatment of fixed manufacturing overhead of both methods can result in different net

incomes measured. In this section, debates on the merits of variable versus absorption

costing in the past are also summarized with a conclusion that absorption costing is

the accepted method for external financial reporting in many countries based

predominantly on matching principle. This is followed by a discussion of alternatives

of capacity that can be used in the allocation of overhead to the product manufactured

under absorption costing, issues surrounding each alternative, and a review of a

variety of actions recently proposed in the literature in an attempt to obtain a more

meaningful and higher quality of net income. Next, a brief description of JIT system

is introduced to shed lights on how JIT system may contribute to the discussion in the

literature. Towards the end of literature review section, prior research examining

effects of product costing based on absorption costing for external reporting purposes

was reviewed.

10

2.1 Manufacturing Costs and Product Costs

Manufacturing refers to activities and processes that convert ra \\ materials into

finished products (Reeve & Warren, 2008; Weygandt et al., 2012). Manufacturing

costs are classified into three components as direct materials. direct Likair, and

manufacturing overhead. For the external financial reporting purpose. costs arc

classified as either product costs or period costs. Product costs may not be expensed

in the period in which they are incurred. This is due to the fact that they are expensed

gradually as units of finished products are sold. In contrast, period costs are deducted

from revenues in the period in which they are incurred. Selling and administrative

expenses, which are non-manufacturing costs, are period costs.

Both direct materials and direct labour costs can be traced directly to specific

product and increase as production volume increases. As such, allocating direct

material and direct labour to products is straight forward. l-lo \\ever, manufacturing

overhead is indirect to products and must be allocated to products on the basis of

some allocation base that reflects the consumption of manufacturing overhead costs

such as direct labour hours, machine hours, direct material costs, and number of

activities performed. To identify allocation bases that reflect consumption of

manufacturing overhead, the relationship between allocation bases and manufacturing

overhead costs must be determined. In order to facilitate management planning and

control, manufacturing overhead costs can be separated into variable and fixed

manufacturing overhead costs. The variable manufacturing overhead costs comprise

costs that vary in proportion to changes in the level of activity. The fixed

manufacturing overhead costs comprise costs that remain the same in total as the level

of activity changes within the relevant range. There is general agreement that variable

tt

manufacturing overhead along with direct material and direct labour should be treated

as product costs. However, it is debatable that fixed manufacturing overhead should

be treated as product costs. The next section describes two product costing methods

representing these divergent ideas of how fixed manufacturing overhead should be

treated in product costing.

2.2 Absorption Costing vs. Variable Costing

There are two commonly used product costing methods: absorption costing

and variable costing. Absorption costing is the accepted alternative used for

determining product costs and cost of goods sold for external financial reporting

purposes. All manufacturing costs, regardless of whether they are variable or fixed

costs, are treated as product costs. Along with direct materials and direct labour costs,

both variable and fixed manufacturing overhead costs are allocated to products

manufactured. These costs are expensed when the products are sold; otherwise, they

are reported as a current asset on the statement of financial positions. Period costs

include only selling and administrative expenses. Therefore, similar to other

manufacturing costs, fixed manufacturing overhead costs are expensed proportionally

to the number of units of products sold.

Under variable costing, only variable manufacturing costs are treated as

product costs. Fixed manufacturing overhead is treated as a period cost, similar to

selling and administrative expenses. Fixed manufacturing overhead is charged off in

its entirety against revenue in the period in which they occurred. The main difference

between absorption costing and variable costing lies in how they treat fixed

12

manufacturing overhead costs. All fixed manufacturing, overhead costs are expensed

immediately. This is in contrast to absorption costing under which fixed

manufacturing overhead costs are charged against revenues gradually as units of

products are sold.

2.3 Impacts of Product Costing Choices on Net Income Being Reported

The differences in the treatment of fixed manufacturing overhead may lead to

different incomes reported under absorption costing and variable costing.

Net income under variable costing is equal to net income under absorption

costing' when units manufactured equal units sold. Net income under absorption

costing will be greater than net income under variable costing when the number of

units manufactured exceeds the number of units sold. This is due to the fact that some

of the products manufactured during the period have not been sold. Part of fixed

manufacturing overhead incurred during the period remains in ending inventories,

which are presented in the statement of financial position. In contrast, net income

under absorption costing will be less than net income under variable costing when the

number of units manufactured is less than the number of units sold. This is due to the

fact that some of the products manufactured in prior periods were sold in the current

period. Fixed manufacturing overhead in the beginning inventories was, therefore,

expensed in the current period in addition to that in the products manufactured and

sold during the period. Therefore, inclusion of fixed manufacturing overhead costs in

1 But in the case when different denominator capacity levels are used in each year, net income

under variable costing may be different from that under absorption costing even when the units manufactured equals the units sold (see, Ajinkya, Atiase, & Bamber, 1986; King, 2006; Sopariwala, 2007, p. 44).

13

the costs of products manufactured may encourage management to build up

inventories, rather than minimizing them as underlies JIT philosophy.

2.4 Debate on which method to use

As indicated, absorption and variable costing can result in different net

incomes being reported. This has created the long debate about the different

treatments of fixed manufacturing overhead under the two methods. Prior research

(e.g., Fess & Ferrera, 1961; Fremgen, 1962; Littleton, 1953; Paton & Littleton, 1940)

has stated that absorption costing allows all production costs to be properly matched

to revenues (Foster & 13axendale, 2008; Pong & Mitchell, 2006). According to

matching principle, sales revenues and the costs of generating that revenues must be

matched to determine net incomes Wong & Mitchell, 2006). All production costs,

both fixed and variable costs, are costs involved in manufacturing products; they,

therefore, should be treated as an asset until the product is sold (Fess & Ferrera, 1961;

Pong & Mitchell, 2006). Brummet (1955, p. 441) indicated that absorption costing

results in a more meaningful income measurement and product costing. It is also

particularly suitable for long-run decisions such as pricing and choosing a product mix

because companies must be able to recover all costs in the long run if they are to survive

(Fremgen, 1964; Horngren, Datar, Foster, Rajan, & Ittner, 2009; Matz, Curry, &

Frank, 1962; Neuner, 1962; Pong & Mitchell, 2006; Reeve & Warren, 2008).

In contrast, advocates of variable costing have provided a number of

arguments in justifying the method. Neilsen (1954) indicated that there was

subjectivity involved in the allocation of fixed manufacturing overhead to products; and

variable costing should be used for external financial reporting. The allocation of fixed

14

manufacturing overhead requires estimation of both fixed manufacturing cost for the

period and allocation base, which may involve measurement of impairment losses and

estimation 01- useful life, residual value of assets, and denominator capacity level

(I lomgren et al., 2_009). This introduces a degree of subjectivity, which compromises

the reliability and comparability of the financial statements (Thomas, 1969, 1974).

Seiler (I 959, p. 63) further substantiated variable costing by arguing that the

value of inventory in balance sheets should represent working capital of the company tied

up in unsold products. Fixed manufacturing costs represent resources that provide

production capacity and remain intact in the short run. Therefore, only variable

manufacturing costs reflect actual working capital tied up in inventory. Seiler (1959, p.

65) indicated that presenting separately the effect of heavy investments in fixed costs and

changes in such controllable costs as materials, labor, and variable manufacturing

expenses make variable costing a better accounting method for external reporting.

Similarly, I lorngren and Sorter (1961) and Sorter and Horngren (1962) argued

that future economic benefits could be gained from future costs that will be saved in

the future, rather than the value obtained through sales. Fixed manufacturing

overhead does not change, irrespective of the production level in each period.

Therefore, only variable production costs shall be treated as an asset until the products

are sold.

Furthermore, when production capacity is not fully utilized, costs of idle

capacity present. It was widely recognized that the costs of idle capacity should not

be attached to products manufactured (See, Clark, 1923, as cited in Aranoff, 2011;

Fess & Ferrara, 1961). However, in traditional cost accounting, manufacturing

overhead costs are allocated to products using predetermined overhead rate, which is

15

calculated by dividing budgeted overhead costs by budgeted activity. This results in

allocating costs of idle capacity to products manufactured (Ajinkya et al., 1986;

Bettinghaus, Debruine, & Sopariwala, 2012). Bettinghaus et al. (2012) (see also,

Bruggen, Krishnan, & Sedatole, 2011) indicates that the accounting system that assign

excess capacity costs to product manufactured, together with an incentive system that

rewards short-term decision-making, induces companies to produce in excess of what

they can sell.

The debates have resolved in favor of absorption costing on the basis of

matching principle as a "conceptually superior" method of valuing inventory and

measuring net income for external financial reporting purposes (Baxendale et al.,

2006; Foster & Baxendale, 2008; Pong & Mitchell, 2006). However, separating fixed

costs from variable costs has made variable costing an appealing method for cost

volume profit analysis, management planning and control, incremental analysis

(NAA, 1961; Dixon, 1966; Rickwood & Piper, 1980; Horngren et al., 2009; Pow:4 &

Mitchell, 2006) and evaluating a company's operating leverage (Sopariwala, 2007).

2.5 Accounting for Manufacturing Overhead

As indicated above, manufacturing overhead costs are indirectly related to

products and are allocated to products manufactured using a predetermined

manufacturing overhead rate or multiple predetermined manufacturing overhead rates,

depending on the allocation methods used.

The calculation of predetermined variable manufacturing overhead cost rate

can be done (Horngren et al., 2009, pp. 288-289) by determining the period to be used

for the budget; selecting and estimating the cost-allocation bases for the production

16

volume budgeted; identify and total all the budgeted variable manufacturing overhead

costs associated with each cost allocation base for the period; and dividing the total

budgeted variable manufacturing overhead costs by the estimated cost-allocation base.

The calculation of predetermined fixed manufacturing overhead cost rate can he done

in a similar manner as outlined for the calculation of predetermined variable

manufacturing overhead cost rate. At the end of the period, in case actual production

is different from the denominator capacity level, over-applied or under-applied

manufacturing overhead will occur. The over-applied or under-applied manufacturing

overhead is transferred to cost of goods sold or allocated among the work-in-process,

finished goods, and cost of goods sold at the end of the period.

However, special considerations must be exercised when choosing the

capacity level used in the calculation of predetermined fixed manufacturing overhead

cost rate. There are four types of capacity levels that can be used in the calculation of

predetermined fixed manufacturing overhead rate, or the denominator capacity level:

expected actual capacity, normal capacity, practical capacity, or theoretical capacity.

The choice of capacity level used as the denominator capacity level can affect (have

implications on) information reported in income statement (see, Ajinkya et al., 1986).

Fixed manufacturing overhead costs represent the costs to acquire production

capacity. Some such as those advocate product costing based on variable costing see

that fixed manufacturing overhead should not be treated as product costs. On the

contrary, absorption costing advocates consider that fixed manufacturing overhead

costs should be treated as product costs. Traditionally, normal capacity is used as the

denominator capacity level in the calculation of fixed manufacturing overhead rate,

resulting in all the costs of capacity acquired being allocated to products. This treats

17

the costs of idle capacity as product costs, which are aggregated in cost of goods sold

and ending inventories in traditional financial statements.

2.6 Possible Solutions

In light of income smoothing among large companies in the last decade, it is

questionable whether absorption costing is still an appropriate method for external

financial reporting purposes. A variety of actions have been proposed in preventing

income smoothing through varying production levels. Recently, there have been calls

(e.g., Aranoff, 2011; Baxendale, et al., 2006; Baxendale & Foster, 2010; Garry, 1991;

Sopariwala, 2009) for accounting rule makers' consideration to mandate variable

costing for external financial reporting purposes. However, Sopariwala (2007)

indicated that this is not sufficient to eliminate all inventory-related incentives to

manipulate earnings. Prior research (Ajinkya et al., 1986; King, 2006; Sopariwala,

2007) has shown that a company can still have different absorption and variable

costing incomes by using different denominator capacity levels even when the

company has level or no inventories. This means that different denominator capacity

levels can be used to manipulate absorption costing income (Sopariwala, 2007). To

remove all inventory-related incentives for earnings manipulation, Sopariwala (2007)

has suggested that theoretical capacity or practical capacity should be used as the

denominator capacity level. This also allows for the separation of the cost of capacity

used and the cost of idle capacity. It has been widely acknowledged that charging the

cost of idle capacity to products is inappropriate (Gantt, 1994; Sopariwala, 2007).

Prior research (Baxendale & Foster, 2010; Sopariwala, 2007, 2009) has suggested that

the cost of capacity supplied should be decomposed into the cost of capacity used and

18

the cost of idle capacity; and the cost of idle capacity should be written off separately.

Sopariwala (2007, 2009) has proposed an absorption costing income statement that

isolates the cost of idle capacity and reports it separately from the cost of producing

products. This differs from traditional absorption costing income statements that treat

the cost of idle capacity as product costs and are aggregated in the cost of goods sold

and ending inventories (Baxendale & Foster, 2010). Sales revenue is matched against

the cost of capacity used to earn the sales revenue in calculating operating income.

The cost of idle capacity is, then, deducted from operating income in determining

absorption costing income, so that the operating income was unaffected by varying

production levels. Hence, the cost of idle capacity is not treated as a product cost and

is not aggregated in the cost of goods sold, which are consistent with Clark (1923, as

cited in Aranoff, 2011), Fess and Ferrara (1961), and McNair and Vangenneersch

(1998).

However, mandating actions suggested in the literature such as variable

costing income statement, the use of theoretical capacity or the disclosure of the cost

of idle capacity for external financial reporting purposes is deemed to be unlikely,

mainly due to competitiveness and self-interest reasons. In addition, the International

Accounting Standard and the U.S. GAAP require product costing and net income

measurement to be based on absorption costing for external financial reporting

purposes. Variable costing may be used for internal reporting, especially when the

production and sales of the company vary considerably from period to period and the

amount of fixed manufacturing overhead is quite large. But if there is little variation

in sales and production from period to period, the choice between absorption costing

and variable costing will not make much difference. In such case, either method will

19

result in approximately the same net income. Furthermore, even if variable costing is

not used for external reporting purposes, it is always possible to identify fixed and

variable costs of the company for management planning, control, and decisions. The

question remains whether it is necessary to disclose such information to shareholders

when management is the one who makes decisions, instead of information for

evaluating overall performance of management. Baxendale and Foster (2010) refine

Sopariwala's (2009) absorption costing income statement by using multiple activity

rates instead of a single overhead rate. However, the complication involved,

investment and time required in obtaining data for preparation, the necessity to

disclose such information to shareholders, and the increased reliability and

comparability of income statement remain issues to be concerned about before the

income statement proposed by Baxendale and Foster (2010) can be adopted.

In summary, with all the recent developments relating to possibility to

mandate the variable costing income statement or the newly proposed income

statements discussed above, it is unlikely to happen. Absorption costing will remain

the mandated method for external reporting in many countries. However, this

assessment will have a greater support, if there is empirical evidence to show that

management has not really exploited the opportunity to manipulate net income by

over-producing or under-producing that the quantity they actually need for sales.

2.7 Just in Time System

Many companies have recognized the need to produce products and services

with greater flexibility, responsiveness, low cost, and high quality (Horngren et al.,

2009, p. 740; Reeve & Warren, 2008, p. 473). JIT system was introduced at the

20

Toyota Motor plant in the mid-1970s (Biggart & Gargeya, 2002). There has been a

lack of consensus concerning what JIT system means (Ramarapu, Mehra, & Frolick,

1995). JIT system has been referred to by many names and can be viewed as either a

philosophy or a disciplined method of production (Biggart & Gargeya, 2002). Many

companies would seem to be implementing various aspects of JIT system on an ad

hoc basis while few are applying JIT system as part of an integrated manufacturing

policy (Voss & Robinson, 1987). Despite this, JIT system is generally referred to as a

manufacturing system for achieving excellence through continuous improvements in

productivity and elimination of waste (Bigart & Grageya, 2002; Fullerton &

McWatters, 2001). JIT system, together with manufacturing automation, allow

companies to enhance flexibility and responsiveness of their manufacturing processes

to simultaneously meet customer demand in a timely manner with high quality

products at the lowest possible total costs (Dugdale, 1990; Horngren et al., 2009). JIT

system focuses on reducing time, cost, and poor quality in both manufacturing and

non-manufacturing processes. But it has a greater effect in manufacturing processes.

This study limited its sample to manufacturing companies. Therefore, this section

only describes JIT system within manufacturing setting (see, Hilton, 2008; Horngren

et al., 2009; Mowen & Hansen, 2007; Reeve & Warren, 2008).

Traditionally, inventories are carried for a variety reasons: to balance ordering

or setup costs and carrying costs; to satisfy customer demand either when demand is

greater than expected or when production is lower due to production inefficiency or

machine breakdowns; to prevent shut downs from late deliveries, defective parts, and

machine breakdowns; to take advantage of quantity discounts (Mowen & Hansen,

2007). In contrast, JIT views inventory as wastes that hide these underlying

21

production problems because inventory is needed as buffers to satisfy sales in case of

machine breakdowns, manufacturing schedule changes, and unexpected rework or to

prevent shutdown in case of late deliveries (Reeve & Warren, 2008). JIT system

focuses on removing these production problems so that inventory can be kept to a

minimum (Reeve & Warren, 2008).

In JIT system, manufacturing activity at any particular workstation is

conducted as soon as the workstation's output is needed by the next workstation

(Hilton, 2008; Horngren et al., 2009). In such a demand-pull production system,

defects arising at one workstation inevitably affect other workstations in the

production line (Horngren et al., 2009). This creates urgency for tracing the problems

to and solving the problems at workstation where the problems are likely to originate

(I lorngren et al., 2009). Therefore, workers in JIT production system were granted

with responsibility and authority to make decisions about operations. They are

trained to be multi-skilled and capable of performing any operation within the

manufacturing cell (Horngren et al., 2009). They learn how to operate several

machines and perform routine equipment maintenance and quality control within their

manufacturing cell, rather than rely on centralized service departments (Reeve &

Warren, 2008).

In order to reduce inventory level, the speed of production needs to be

improved. This can be achieved through reducing setup time — the time required to

get production line ready to start the production of a product (Horngren et al., 2009).

When setup time is long, products will be manufactured in large batches and

inventory level will be high (Reeve & Warren, 2008; Horngren et al., 2009).

Therefore, reducing setup time enables products to be manufactured in smaller

22

batches, which reduces inventory level and enhances the company's responsiveness n)

uncertainty in demand (I lorngren et al., 2009). The speed of production can also be

improved through reducing lead time — the time from when an order is received b\

manufacturing until it becomes a finished product. To reduce lead time, total lead

time must be divided into value added and non-value added time (Reeve & Warren.

2008). Value-added lead time is the time required to actually manufacture a unit of

product while non-value-added lead time is the time that a unit of product sits in

inventories or moves unnecessary (Reeve & Warren, 2008). JIT system aims to

minimize non-value-added lead time by organizing production in manufacturing cells,

which group together all the different types of equipment used to make a given

product and operations are sequenced closely (Reeve & Warren, 2008). Production

manufacturing cells, therefore, can minimize unnecessary movement between

operations and material handling costs, thereby reducing lead time, and work in

process, and production costs (Reeve & Warren, 2008; Horngren et al., 2009).

In JIT system, it is important that raw materials are available when they are

needed for production without carrying inventories (Mowen & Hansen, 2007; Hilton,

2008). This can be done by negotiating long-term contracts with suppliers, which are

selected on the basis of their ability to deliver quality materials when needed (Mowen

& Hansen, 2007).

As such, JIT system enables companies to lower carrying costs of inventory,

improve quality by identifying and prevent the cause of defects and rework, and

reducing lead time. In addition, the use of manufacturing cells also makes some costs

usually classified as indirect costs directly traceable to specific products (Mowen &

Hansen, 2007). For example, the costs of setup, maintenance, and quality inspection

23

become direct costs in JIT system, thereby reducing manufacturing overhead. It is

expected that the greater the degree to which companies embraced JIT philosophy, the

less the level of inventory and manufacturing overhead will be. Since inventories in

companies using JIT system are minimal, changes in inventories from period to

period is unlikely to be significant. Therefore, differences between net income under

absorption costing and net income under variable costing will be immaterial, so does

earnings manipulation by over-producing or under-producing.

2.8 Prior Research Examining Effects of using Absorption Costing as a Basis for

Product Costing in Externally Reported Financial Statements

Accounting literature has widely acknowledged that absorption costing for

product costing provides an opportunity for management to smooth net income by

managing production and inventory dated back when variable versus absorption

costing was extensively debated. However, effects of using absorption costing as a

basis for product costing in the externally reported financial statements on net

incomes being reported may have not been documented. No single prior research

examining the effects in those days has been found. This may be due to the fact that,

during the time, fixed manufacturing overhead was a small portion of total

manufacturing overhead and technology and communication systems were not as

advanced as those today. Additionally, the collapse of many large companies has

recently drawn a greater attention to the quality of net income being reported.

Until recently, Pong and Mitchell (2006) explored the impact of absorption

costing as a basis for product costing on the reported profitability of UK

manufacturing companies from 1988 to 2002 by assessing the sensitivity of these

)4

companies' net incomes to the adoption of variable costing. they found that the

selection of absorption or variable costing as a basis for product costing has a

potential important impact on UK manufacturing companies' profitability. Foster and

Baxendale (2008) revisited the debate and examined whether the current operating

environment has increased the potential for net income smoothing through production

and inventory decisions. However, they did this by examining the effect of capital

intensity on fixed manufacturing overhead in ending inventories of thousands of

actively and inactive publicly held U.S. and Canadian companies over a 47-year

period from the year 1960 to the year 2005. They found that while the sample

companies had become capital intensive at a higher level and ill system might have

had some impacts on them from 1986 to 2005, increased capital intensity has a greater

impact than JIT system. However, it is important to note that management's efforts to

manage earnings through production decisions can be observed clearer by focusing on

their impact on resulting net income, not ending inventories.

This study aims to examine the effects of product costino, choices on



manufacturing costs; 2) examining whether JIT system has impacts on Stock

Exchange of Thailand (SET) listed manufacturing companies; 3) examining impacts

of product costing choices on profitability of the sample companies over the period

studied in terms of both direction and significance of inventory adjustments; 4)

examining relative impact of capital intensity and the degree to which companies

embraced JIT philosophy on profitability differentials in aggregate over the nine-year

period studied; and also 5) examining relative impacts of capital intensity and the

degree to which companies embraced ill philosophy on profitability differentials

reported under the two methods in each of the 9 years studied to observe trends of the

impact each of the two factors over time. This study aims to examine the competing

diverse effects of capital intensity and in' philosophy on profitability reported in the

financial statements, which uses absorption costing as a basis for the preparation.

This, therefore, differs from Pong and Mitchell (2006) that mainly examined whether

net incomes under absorption costing would have been higher or lower than net

incomes under variable costing at different levels of fixed to total costs ratios derived

from existing literature. This study also differs from Foster and Baxendale (2008) in

that it will examine relative impact of the two competing factors directly on

profitability differentials reported under the two methods over the nine-year period

studied and also examine changes in relative impacts of the two factors from one year

to the next.

There is no known similar study in Thailand. There are several studies that

examined management accounting practices including absorption costing such as

Ruttanapon, Komaratat, Cheniam, and Bailes (2000), Ruttanapon (2005), and

Komaratat and Boonyanet (2008). These studies mainly conducted surveys on the

extent to which sample companies in Thailand used various management accounting

techniques.

CHAPTER 3

RESEARCH NlETIIODOLOGY

3.1 Sample Selection and Data Collection




manufacturing costs; 2) examining whether JIT system has effects on Stock Exchange

of Thailand listed manufacturing companies; 3) examining impacts of product costing

choices on profitability of the sample companies over the period studied in terms of

both direction and significance of inventory adjustments; 4) examining relative impact

of capital intensity and the degree to which companies embraced JIT philosophy on

profitability differentials in aggregate over the nine-year period studied; and also 5)

examining, relative impacts of capital intensity and the degree to which companies

embraced JIT philosophy on profitability differentials reported under the two methods

in each of the 9 years studied to observe trends of the impact each of the two factors

over time. Since absorption costing and variable costing are methods of product

costing for manufacturing companies, the samples in this study comprised

manufacturing companies listed in the Stock Exchange of Thailand in three sectors:

Agro & Food Industry, Consumer Products, and Industrials.

At the end of the year 2011, there were 41, 39, and 78 companies in Agro &

Food Industry, Consumer Products, and Industrials sectors, respectively. Only

manufacturing companies in those three sectors that remained listed throughout the

27

period studied were included in the final sample. The final sample companies

comprise 33, 30, and 41 companies in Agro & Food Industry, Consumer Products,

and Industrials sectors, respectively. The data was collected from financial statements

in the SETSMART Advance database developed by the SET.

3.2 Measurement of Variables

3.2.1 Capital Intensity

The capital intensity is assessed by the proportion of fixed manufacturing

costs to total manufacturing costs instead of the proportion of fixed manufacturing

costs to costs of goods sold that was used in Foster and Baxendale (2008). The

proportion of fixed manufacturing costs to total manufacturing costs incurred in the

manufacturing process is deemed to give a clearer indication of capital intensity as it

is not affected by the timing of selling goods like the proportion of fixed

manufacturing costs to costs of goods sold. Costs of goods sold are costs that are

realized as expenses in the period which goods are sold and may be greater or lower

than total manufacturing costs incurred during the period. When costs that are

realized as expenses in the period (i.e., costs of goods sold) is lower (higher) than total

manufacturing costs incurred during the period, the proportion of fixed manufacturing

costs to costs of goods sold would be higher (lower) than the proportion of fixed

manufacturing costs to total manufacturing costs. The extent of the differences

between costs of goods sold and total manufacturing costs has a direct impact on the

differences between the proportion of fixed manufacturing costs to costs of goods sold

and the proportion of fixed manufacturing costs to total manufacturing costs. The

28

proportion of fixed manufacturing costs to costs of goods sold may exhibit an

increasing (or decreasing) trend, which gives the readers an impression that capital

intensity of sample companies is increasing (or decreasing), while in fact the

proportion of Fixed manufacturing costs to total manufacturing costs remains the

same. Also, the proportion of non-current assets to sales may be thought intuitively as

another proxy for capital intensity. However, it is deemed to provide a rougher

estimate of capital intensity than the proportion of fixed manufacturing overhead to

total manufacturing costs because it can be affected by noncurrent assets other than

property, plant, equipments, and other factors affecting sales. Therefore, the

proportion of non-current assets to sales does not provide a measure of capital

intensity as good as the proportion fixed manufacturing overhead to total

manufacturing costs. This study, therefore, measures capital intensity by the

proportion of fixed manufacturing costs to total manufacturing costs.

The data used for the calculation of the proportion of fixed manufacturing

costs to total manufacturing costs are not directly available in external financial

statements. The fixed and variable portion of total manufacturing costs is not

information disclosed in the public domain. Consequently, fixed manufacturing costs

of the samples were estimated. Depreciation relating to manufacturing should be used

as a proxy for fixed manufacturing costs. While depreciation is not the only fixed

manufacturing costs, it is the major component of fixed manufacturing costs and other

fixed manufacturing costs are not generally reported. However, not all companies

reported depreciation specific to manufacturing. Therefore, total depreciation, which

comprises depreciation relating to manufacturing and selling and administrative

activities, is used instead to represent the major portion of fixed manufacturing costs.

29

It is acknowledged that a limitation of this study is that the actual fixed manufacturing

costs of the samples are not known. Total manufacturing costs during the period are

calculated from the following equations:

COGS

And,

COGM

Hence,

COGS

and

TMC

FGB + COGM - FGE

WIPB + TMC -- WIPE

FGB + WIPE + TMC - WIPE - FGE

COGS + WIPE + FGE - WIPE - FGB

Where COGS is the costs of goods sold during the period; WIPE is ending

work-in-process; FGE is ending finished goods; WIPE is beginning work-in-process;

FGE is beginning finished goods; TMC is total manufacturing costs during the period,

which comprise direct material, direct labour, and manufacturing overhead incurred

during the period; and COGM is the costs of goods manufactured during the period.

3.2.2 JIT Philosophy

The degree to which a company embraced JIT philosophy is measured by

ending inventory to sales. Ending inventory may be increasingly held to support

changing sales in each period. To measure whether ending inventory is managed to

be just enough to support sales, ending inventory must be measured relative to sales.

The data collected, therefore, are beginning and ending inventories and sales for each

of the nine years studied. It is important to note that only are finished goods and

work-in-process used to represent inventory in the calculation of ending inventory to

30

sales and inventory turnover. Raw materials are excluded because the level of raw

materials companies hold may he impacted by other factors such as purchasing lead

time and discounts being offered.

3.2.3 Profitability Differentials

The differences between net income under absorption costing and net income

under variable costing can be calculated using a one-line adjustment proposed by

Solomons (1965, p. 111-112) as follows:

NI v,,, NI (BINV t - EIN V ,) * X ,

Y t, t

Where NI v, , is net income before tax of company i at time t under variable

costing; NI A, t, is net income before tax of company i at time t, under absorption

costing; BINV , is beginning inventory based on absorption costing of company i at

time t; E1NV , is ending inventory based on absorption costing of company i at time

t; X , is fixed manufacturing costs of company i at time t; and Y is total

manufacturing costs of company i at time t. NI A, t, BINV ,, and EINV , are all

available in financial statements in the databases. The proportions of fixed

manufacturing costs to total manufacturing costs are estimated as discussed earlier.

3.2.4 Profitability Differentials Under the Two Product Costing Choices

Profitability differentials were examined in terms of both directions and

significance of the inventory adjustments as outlined below.

31

3.2.4.1 Directions of Inventory Adjustments

The prevalence of positive or negative differences between beginning

inventories and ending inventories for each period (i.e., annual inventory adjustment)

can indicate whether net incomes under absorption costing of the companies studied

were lower or higher than those would have been under variable costing for most

companies. If there were more negative adjustments, this indicates that more

companies reported net incomes under absorption costing that were higher than those

that would have been under variable costing. In contrast, if there were with more

positive annual inventory adjustments, this indicates that more companies reported net

incomes under absorption costing that were lower than those that would have been

under variable costing. The number of positive and negative annual inventory

adjustments in each year will be counted and compared.

3.2.4.2 Significance of Inventory Adjustments

To visualize significance of inventory adjustments from one year to the next,

average inventory adjustment as a percentage of beginning inventories will be

calculated for each year as follows:

Average inventory adjustment as a percentage of beginning inventories

(BINV i,t- EINV i,t)

BINV i,t 1=

Where BINV , is beginning inventory based on absorption costing of

company i at time t and EINV , is ending inventory based on absorption costing of

company i at time t.

32

3.2.4.3 Impacts of Inventory Adjustments on Profitability

From the relationship between net income under absorption costing and net

income under variable costing in the one-line adjustment proposed by Solomons

(1965, p. 111-112) illustrated earlier and reiterated in equation (1) below, it can be

observed that inventory adjustments and proportion of fixed manufacturing costs to

total manufacturing costs are the two factors that explain the difference between net

incomes under the two product costing methods. Therefore, inventory adjustment can

provide an indication of the extent to which net income being reported and profit

margin ratio can be influenced by the two product costing choices given the capital

intensity of the company in each year.

From equation (I) below, dividing both sides by earnings before interest and

taxes under absorption costing and sales will result in equation (2) and (3),

respectively.

NI f NI A, (BINV - EINV ,) * X t

Y

NI v,t, t =

NI A, t (BINV 1, 1- EINV * X ...(2)

NI t

NI A, t NI A, , YE,,

NI v, t = NI A, (BINV - ENV * X •-(3)

Sales ,,

Sales , Sales ,

Where BINV , is beginning inventory based on absorption costing of

company i at time t and EINV , is ending inventory based on absorption costing of

company i at time t. NI A is earnings before interest and taxes under absorption

costing of company i at time t. NI v, , is earnings before interest and taxes under

variable costing of company i at time t. Sales , are sales of company i at time t.

33

Hence, the extent to which net income being reported and profit margin ratio

can be influenced by the two product costing choices can be assessed by average

annual inventory adjustments, average inventory adjustment as a percentage of

earnings before interest and taxes, and average inventory adjustment as a percentage

of sales. They are calculated for each year as follows:

Average annual inventory adjustment

" = l(BINV — EINV t) I n

Average inventory adjustment as a percentage of earnings before interest and

I (BINV i,t- EINV i,t) I

NI A,i,t

Average inventory adjustment as a percentage of sales

(BINV i,t- EINV i,t)

Sales i,t 2=

However, it should be noted that average annual inventory adjustment is

deemed to give an unclear indication of its relative impact on profitability differentials

when comparing average annual inventory adjustments of different industries and

over time because the sample companies are of different sizes and their sizes change

over time. A better comparison can be achieved through comparing inventory

adjustment to net income being reported and inventory adjustment to sales. The

greater the ratio of inventory adjustment to net income under absorption costing and

inventory adjustment to sales, the greater the impact of the two product costing

choices on net income being reported and profit margin ratio.

taxes

n

34

3.2.4.4 Profitability Differentials

Impact of product costing choices examined solely from inventory

adjustments in the previous section has not taken the capital intensity into account.

Profitability differentials in this section take both capital intensity and annual

inventory adjustments into account. This allows an examination of relative impacts of

capital intensity and JIT philosophy that may affect the annual inventory adjustment,

which in turn affect profitability differentials. This shed lights on appropriateness of

product costing choices within the current operating context where fixed

manufacturing costs have become a greater portion of total manufacturing costs and

where JIT philosophy has become a prominent management concept. Profitability

differentials are the adjustment portion of the equation (1), (2), and (3) illustrated

earlier, which are reiterated below.

NI v NI A, z, (BINV — El-NV t) * X , t •--(1)

NI v r=

NI A, t (BINV — EINV r) * X ---(2)

NI A,

NI V, t, t =

NI A, i, NI A, I, / Y ;. ,

NI A, t - EINV * X .43)

Sales , Sales Sales

Y t

The adjustment portion in equation (1) is the difference between net incomes

reported under the two methods, while those in equation (2) and (3) are the difference

between net incomes reported under the two methods calculated as a percentage of net

income under the absorption costing and as a percentage of sales, respectively.

35

3.3 Hypotheses Development and Testing

3.3.1 Fixed Manufacturing Overhead Trend

As described before, companies are now increasingly implementing new

manufacturing technology. This has seen an increase in investment in advanced

manufacturing technology and indirect costs supporting automation (Drury, 1989). It

is also expected that fixed manufacturing overhead has become a greater portion of

total manufacturing costs in SET listed manufacturing companies. To assess whether

the sample companies have became more (or less) capital intensive, a paired t-test will

be conducted to test whether the average proportion of fixed manufacturing costs to

total manufacturing costs at the beginning of the period studied is significantly

different from that at the end of the period studied at the five percent level of

confidence when examined in aggregate for the total sample and separately for each

industry_ It was hypothesized that:

Ho: Capital Intensity beginning — Capital Intensity ending — 0

Ha: Capital Intensity beginning — Capital Intensity ending t 0

3.3.2 Effect of JIT Philosophy

In JIT production system, finished products are manufactured when they are

needed. Embracing JIT philosophy at a greater degree should result in a decline in

finished goods and work-in-process level. Hence, it is expected that ending

inventories to sales would decline throughout the period. To assess whether the

sample companies have been increasingly embracing JIT philosophy, a paired t-test

will conducted to test whether average ending inventory to sales at the beginning of

36

the period studied is significantly different from that at the end of the period studied at

the five percent level of confidence when examined in aggregate for the total sample

and separately for each industry. It was hypothesized that:

I l: Inventory to Sales Inventory to Sales beginning ending ---

lia:

Inventory to Sales beginning — Inventory to Sales ending 0

3.3.3 The Impact of Capital Intensity and the Degree to Which Companies

Embraced JIT Philosophy on Profitability Differentials over Time

As indicated earlier, fixed manufacturing overhead has become a greater

portion of total manufacturing costs. And this may have made production and

inventory decisions an effective scheme to manage net incomes being reported under

absorption costing. However, JIT system is prominence management concept in the

past two decades. Embracing JIT philosophy should result in minimal inventory

levels and inventory adjustments, thereby mitigating the impact of the choices of

product costing. Management should be well aware of the effect of high inventory

level and tend to decrease inventory to the levels necessary for sales rather than

building up inventory to manage net incomes. The greater the level of capital

intensity of companies within the same period, the greater the incentive for

management to manage earnings by over-producing or under-producing than the

levels actually needed for sales. This results in greater net income differentials. In

contrast, the greater degree to which companies embraced JIT philosophy into their

operations in each period, the smaller net income differentials will be. Therefore, it is

interesting and timely to examine relative impact of the two factors on profitability

37

differentials over time. To do this, data of nine years were pooled and analyzed by

OLS method using the following regression equation:

Profitability Differential = a + b, (CI) + h2 (JIT) + Sales + e


manufacturing costs. JIT is measured by ending inventory to sales. Profitability

differential is measured by each of the following three proxies:

1) the adjustment portion in Solomons' (1965) equation;

(BINV „ — EINV „ t) * X , 1

Y1,

2) the adjustment in item 1) above divided by absorption costing income as

follows;

(BINV „ — EINV „ * t

NI A, E, t

3) the adjustment in item 1) above divided by sales;

(BINV „ t — ENV * X t

Sales t Y,. r

It was hypothesized that:

Ho: hi = 0

Ha: b, 0

38

3.3.4 The Impact of capital Intensity and the Degree to Which companies

Embraced JIT Philosophy on Profitability Differentials in Each Year

In addition to examine relative impacts of the two factors over time, the

impacts of the two factors on profitability differentials in each of the nine years will

also be examined. This study also examines which of the two factors has a greater

influence on the differences in profitability measures in each period using the same

regression model. The purpose of this analysis is to examine trend of impacts of the

two factors over time. It was hypothesized that:

Ho: = 0

Ha: bi # 0

CHAPTER 4

RESULTS OF THE STUDY

4.1 Samples

At the end of the year 2011, there were 158 SFT listed companies in the three

sectors used as samples of this study: 41, 39, and 78 companies in Agro & Food

Industry, Consumer Products, and Industrials, respectively. Non-manufacturing

companies, those did not remain listed throughout the period studied, and one

company in Industrials that was deemed to be an outlier of the data set were excluded,

leaving 104 companies in the final sample. Table 4.1 shows the number of sample

companies in each industry.

Table 4.1: Industries of the Samples

Industry Number of Companies

Agro & Food Industry 33

Consumer Products 30

Industrials 41

Total 104

4.2 Descriptive Statistics of Variables of Interest

This section provides descriptive statistics of variables used in the examination of this

study, which include the proportion of fixed manufacturing costs to total

manufacturing costs, inventory to sales, direction of inventory adjustment, inventory

adjustment to beginning inventory, inventory adjustment to sales, inventory

40

adjustment to net incomes under absorption costing, net incomes differentials, net

income differentials as a percentage of net incomes under absorption costing, and net

income differentials as a percentage of sales.

4.2.1 The Proportion of Fixed Manufacturing Costs to Total Manufacturing Costs

From Table 4.2, the average proportion of fixed manufacturing costs to total

manufacturing costs of the total sample decreases consistently from 5.38% in the year

2003 to 4.69% in the year 2008. Then, it rises up to 5.81% in the year 2009, but it

declines over the next two years to 4.38% in the year 2011. The trends of medians

resemble to those of average. Similar trends of average proportion of fixed

manufacturing costs to total manufacturing costs can also be observed for Agro &

Food and Industrials industries. Average proportion of fixed manufacturing costs to

total manufacturing costs for Consumer Products varies at a greater extent than the

other two industries over time with a similar rise in the year 2009.

The trend over the nine-year period is in contrary to what has been expected.

This is likely to be due to the fact that the nine-year period is too short to observe

significant changes in manufacturing technology that causes manufacturing overhead

costs to increase and that sample companies may have implemented new

manufacturing technology before the year 2003 which is the beginning of the period

studied.

41

Table 4.2: Descriptive Statistics of the Proportion of Fixed Manufacturing Costs

to Total Manufacturing Costs

Year Average (Median) (%) Standard

Deviation

(%)

Agro & Food

Industry

Consumer Products Industrials Total

2003 4M4 (3.46) 5.27 (4.76) 6.54 (6.14) 5.38 (4.77) 3.56

2004 3.98 (3.34) 5.09 (4.38) 5.46 (5.30) 4.88 (4.29) 3.12

2005 4.29 (3.07) 5.09 (4.32) 4.95 (4.62) 4.78 (4.20) 3.21

2006 4.03 (3.17) 4.83 (4.11) 4.88 (4.03) 4.60 (3.85) 3.10

2007 4.11 (3.01) 5.10 (4.77) 5.00 (4.31) 4.74 (4.05) 3.13

2008 3.87 (3.58) 5.54 (4.29) 4.73 (3.93) 4.69 (3.97) 3.28

2009 4.63 (3.45) 6.53 (5.98) 6.24 (5.13) 5.81 (4.71) 4.21

2010 4.04 (3.59) 5.63 (4.32) 5.21 (4.16) 4.96 (3.94) 3.95

2011 3,40 (3.02) 5.51 (4.48) 4.34 (3.98) 4.38 (3.40) 3.97

Standard Deviation

(%)

3.17 4.13 3.24 3.54

There are two possible reasons for the increase in the proportion of fixed

manufacturing costs to total manufacturing costs (i.e., level of capital intensity) in the

year 2009. Firstly, it may result from the fact that depreciation grows at a greater rate

than total manufacturing costs (and also costs of goods sold and sales), which can

result from greater new investments to tap new opportunities or a simple decline in

sales. Secondly, it can result from greater investments to implement new

manufacturing technology. To examine possible reason for the increase of proportion

of fixed manufacturing costs to total manufacturing costs of the total sample and that

of each industry in the year 2009, average sales, average costs of goods sold, average

depreciation, and average total manufacturing costs were calculated to observe the

42

trends of these variables (as shown in Graph 4.1). It can be observed that average

sales, average costs of goods sold, and average total manufacturing costs all decreased

in the year 2009 while average depreciation grew marginally throughout the nine-year

period studied. Therefore, the increase in the proportions of fixed manufacturing

costs to total manufacturing costs of the total sample and that of each industry in the

year 2009 is likely to result from the former reason.

7,000.00

6,000.00

5,000.00

4,000.00

3,000.00

2,000.00

1,000.00

—4— Sales

—a—Cost of Goods Sold

Depreciation

—4E—Total Manufacturing Costs 1

, A , A A A A

2003 2004 2005 2006 2007 2008 2009 2010 2011

Graph 4.1: Trends of Average Sales, Average Costs of Goods Sold, Average

Depreciation, and Average Total Manufacturing Costs

4.2.2 Inventory to Sales

From Table 4.3, while average inventory to sales of the total sample vary over

the nine-year period. Similar trends can also be observed from the medians of

inventory to sales of the total sample and those of each industry. When examining

average inventory to sales of each industry, average inventory to sales of Agro &

Food Industry and Industrials decrease in the majority of the nine years studied while

43

that of Consumer Products gradually increases in the majority of the nine years

studied.

It is likely to he the case that JIT philosophy has an inconsistent impact on

SET listed manufacturing companies. This is in line with what happened in the U.K.

reported in Pong and Mitchell (2006, p. 141). This may be attributable to the nature

products of the industries. Products of Agro & Foods Industry have shorter lives than

those of other industries. This, therefore, creates pressure onto the Agro & Food

manufacturers to keep inventories just enough to support sales as much as possible.

Similarly, products of Industrials tend to capture a significant amount of working

capital and demand for products is more difficult to forecast than that of Consumer

Products. This also creates a greater pressure on manufacturers in this industry than

manufacturers in Consumer Products to try to lower inventory levels. Although it is

relatively easier to forecast demands for consumer products and make it easier to

implement JIT philosophy in the Consumer Products, functionality of the products,

economies of scale to be achieved in the supply chain, and lost sales opportunity

make it unwise to produce product in a make-to order fashion. Additionally, an easier

to forecast demands for the products contributes to smaller annual inventory

adjustments relative to the other two industries.

44

Table 4.3: Descriptive Statistics of Inventory to Sales

Year Average (Median)(%) Standard

Deviation

(%)

Agro & Food

Industry

Consumer Products Industrials Total

2003 10.32 (7.27) 13.93 (11.24) 9.85 (6.14) 11.17 (7.61) 13.38

2004 10.61 (7.92) 12.92 (10.88) 10.60 (6.52) 11.28 (8.16) 12.33

2005 11.05 (7.48) 14.22 (11.07) 11.86 (8.93) 12.28 (9.15) 14.34

2006 9.69 (7.75) 15.87 (9.40) 11.24 (8.04) 12.08 (8.21) 14.35

2007 9.70 (6.71) 17.53 (11.79) 10.48 (7.68) 12.27 (8.34) 15.55

2008 9.29 (5.43) 16.56 (12.24) 10.42 (7.18) 11.83 (7.64) 13.67

2009 9.34 (6.38) 16.97 (13.55) 12.47 (7.79) 12.78 (8.30) 13.14

2010 7.71 (5.55) 16.15 (13.12) 8.73 (5.13) 10.55 (6.75) 11.32

2011 7.31 (5.73) 17.17 (14.22) 9.07 (6.09) 10.85 (7.53) 11.15

Standard Deviation

(%)

10.67 16.09 12.39 13.28

4.2.3 Directions of Inventory Adjustments

From Table 4.4, there are a greater number of companies having negative

annual stock adjustments in six to seven out of the nine years studied for both the total

sample and each industry. This means that there is a greater number of companies

having negative annual inventory adjustments in the majority of the years studied

regardless of whether it is examined in aggregate or by industry. In other words,

companies reporting net incomes under absorption costing that are higher than those

which would have been under variable costing are more pervasive.

45

Table 4.4: Number of Companies Having Negative Annual Stock Adjustment

Industry Total 2003 2004 2005 2006 2007 2008 2009 2010 2011

A gro & Food 33 21 23 17 20 14* 22 10* 15* 22

Consumer

Products

30 12* 21 19 15 14* 15 10* 21 23

Industrials 41 27 33 21 23 13* 25 9* 26 31

Total 104 60 77 57 58 41* 62 29* 62 76

,ess thanhalt of total number of companies in each category.

4.2.4 Significance of Inventory Adjustments

Average annual inventory adjustment as a percentage of beginning inventories

was calculated to assess the significance of inventory adjustments. From Table 4.5,

ignoring the direction of inventory adjustments, average annual inventory adjustment

as a percentage of beginning inventories of the total sample varies from one year to

the next, ranging between 27.40% and 52.20% over the nine-year period. Hence,

annual inventory adjustments are considered to be large and vary greatly. When

examining average annual inventory adjustment as a percentage of beginning

inventories of each industry, it was found that average annual inventory adjustment as

a percentage of beginning inventories of Agro & Foods Industry varies at a greater

extent than that of the other two industries. This is also consistent with the findings

reported earlier that inventory to sales declines marginally and shows unclear impacts

of JIT philosophy.

46


Percentage of Beginning Inventories


Deviation Agro & Food Consumer

Products

Industrials Total

2003 92.52 (29.76) 24.38 (11.37) 23.58 (19.52) 45.90 (20.59) 144.10

2004 41.74 (24.85) 33.74 (18.28) 49.29 (33.91) 42.41 (27.24) 48.54

2005 37.96 (20.39) 19.90 (18.62) 40.42 (21.64) 33.72 (20.70) 43.63

2006 34.10 (26.03) 23.90 (18.89) 29.49 (11.26) 29.34 (18.88) 33.77

2007 31.44 (25.62) 16.76 (11.70) 31.94 (19.44) 27.40 (17.80) 33.07

2008 41.11 (26.16) 33.46 (18.14) 36.93 (27.45) 37.25 (25.24) 40.06

2009 23.01 (14.94) 37.45 (20.81) 25.45 (22.45) 28.14 (18.96) 57.72

2010 40.36 (29.10) 34.65 (22.15) 26.43 (21.58) 33.22 (24.51) 33.07

2011 78.08 (30.98) 20.14 (12.80) 54.83 (28.53) 52.20 (23.40) 95.63

Standard Deviation

(%)

102.42 45.21 43.74 68.62

4.2.5 Impacts of Inventory Adjustments on Profitability

From Table 4.6, ignoring the direction of inventory adjustments, average

annual inventory adjustment of the total sample and that of each industry are quite

large. However, average annual inventory adjustment of Consumer Products is

smallest in the majority of the years studied. This is in line with what has been shown

by relatively smaller significance of inventory adjustment of the industry. However,

comparing average inventory adjustments across all industries must be exercised with

care as sample companies in each industry are of different sizes as indicated earlier.

47

Table 4.6: Descriptive Statistics of Average Inventory Adjustments

Year Average (Median) ( Million Bahts) Standard

Deviation Agro & Food

Industry

Consumer

Products

Industrials Total

2003 98.54 (53.07) 39.54 (22.18) 46.88 (16.59) 61.15 (25.19) 108.40

2004 122.47 (30.09) 40.83 (23.71) 103.69 (44.06) 91.52 (32.81) 197.37

2005 204.53 (34.63) 43.47 (29.06) 265.85 (39.12) 182.24 (33.40) 914.09

2006 163.44 (45.50) 40.73 (27.30) 181.42 (31.59) 135.13 (32.40) 505.13

2007 117.70 (47.05) 31.08 (26.08) 167.77 (21.60) 112.45 (34.51) 452.32

2008 116.77 (39.77) 77.55 (44.28) 166.81 (24.66) 125.18 (35.26) 308.53

2009 127.83 (33.68) 43.28 (22.23) 128.06 (40.61) 103.53 (29.55) 315.62

2010 187.20 (38.95) 87.16 (22.16) 59.14 (27.84) 107.85 (28.60) 285.32

2011 209.02 (59.73) 48.69 (22.98) 87.98 (51.85) 115.05 (40.23) 211.77

Standard

Deviation

373.10 87.63 582.29 429.66

Not only can the level of inventories a company maintains be affected by the

degree to which that that particular company embraced JIT philosophy, but it is also

inevitably related to the level of sales. Everything else remains the same, companies

with lower level of sales tends to maintain lower level of inventory. With lower level

of inventory, inventory adjustments tend to be small. Therefore, the smaller annual

inventory adjustments of Consumer Products can be the result of either a greater

degree to which the companies in this industry embraced JIT philosophy or its

average sales that may be lower than that of the other two industries.

From Table 4.3 discussed earlier, the inventory to sales indicates that

Consumer Products maintains a gradually increasing level of inventory, while the

other two industries' inventory to sales vary across the nine-year studied with a lower

10,000

8,000

6,000

4,000

2,000

--4—Agro & Foods Industry

—IN— Consumer Products

Industrials

0

2003 2004 2005 2006 2007 2008 2009 2010 2011

48

level towards the end of the period studied. This shows unclear impacts of JIT

philosophy on the three industries. Additionally, when examining average sales of

each industry, average sales of Consumer Products is lower than that of the other two

industries (see Graph 4.2). Therefore, the smaller annual inventory adjustments of

Consumer Products are likely to be driven by relatively lower level of sales of the

industry.

Graph 4.2: Average Sales of Each Industry

4.2.6 Average Annual Inventory Adjustment as a Percentage of Net Income under

Absorption Costing

Ignoring the direction of inventory adjustment, average annual inventory

adjustment as a percentage of net income under absorption costing can indicate the

approximate impact of the two product costing choices as a percentage of net income

being reported. From Table 4.7, it is observed that average annual inventory

adjustment as a percentage of net income under absorption costing of the total sample

and that of each industry vary greatly. Consumer Products has the smallest inventory

adjustment as a percentage of net income under absorption costing in five years, while

49

the other two industries have those in two years. That is, Consumer Products has the

smallest inventory adjustment as a percentage of net income under absorption costing

in the majority of the year studied. However, inventory adjustments as a percentage

of net income under absorption costing over time of the total sample and that of the

Agro & Foods Industry do, in fact, decrease. This is in contrast to what can be

observed from the average annual inventory adjustments. It is important to note that

the variations of average annual inventory adjustment as a percentage of net income

under absorption costing can be driven by those of net income under absorption

costing since the average annual inventory adjustment to beginning inventory does

not vary as much.

Table 4.7: Descriptive Statistics of Average Annual Inventory Adjustment as a _

Percentage of Net Income under Absorption Costing



Industry

Consumer

Products

Industrials Total

2003 280.49 (22.36) 30.22 (13.84) 36.84 (10.86) 112.24 (13.89) 558.60

2004 84.53 (27.32) 80.77 (18.37) 51.33 (23.94) 70.36 (21.23) 174.09

2005 102.79 (28.98) 58.09 (20.37) 98.70 (18.30) 88.28 (20.65) 220.51

2006 76.08 (26.48) 37.70 (28.29) 104.39 (20.67) 76.17 (24.59) 176.40

2007 108.61 (24.83) 58.66 (21.67) 66.19 (17.74) 77.48 (20.81) 145.88

2008 36.88 (22.71) 212.60 (40.51) 74.97 (26.41) 102.58 (26.55) 278.52

2009 30.00 (12.80) 150.09 (23.58) 105.86 (15.67) 94.55 (17.16) 343.67

2010 151.14 (18.27) 78.64 (26.60) 17.84 (8.89) 77.67 (15.86) 328.05

2011 41.68 (21.42) 33.47 (17.35) 75.11 (19.68) 52.49 (19.94) 129.87

Standard Deviation 399.05 247.85 204.95 290.52

50

4.2.7 Average Annual Inventory Adjustment as a Percentage of Sales

Given the level of capital intensity of each company, average annual inventory

adjustment as a percentage of sales indicates the impact of product costing choices on

the profit margin ratio. From Table 4.8, ignoring the direction of inventory

adjustment, it is observed that average annual inventory adjustment as a percentage of

sales of the total sample and that of each industry increase slightly. The average

annual inventory adjustment as a percentage of sales of the total sample ranges from

2.42% to 3.73%, while that of Agro & Foods Industry and that of Industrials range

from 2.46 to 3.53% and from 2.43% to 4.80%, respectively. However, the average

annual inventory adjustment as a percentage of sales for Consumer Products varies at

the greatest extent, comparing to the other two industries. It ranges from 2.15% to

5.67%. The inventory to sales indicates that Consumer Products has maintained a

gradually increasing level of inventory throughout the nine-year period studied. The

larger inventory adjustment as a percentage of sales in later years is likely to result

from higher inventory adjustments in the year 2008 and 2010. Furthermore, it is

observed that the average annual inventory adjustment as a percentage of sales for

Agro & Foods Industry and Industrials do not fluctuate as much as the average annual

inventory adjustment as a percentage of beginning inventories of the two industries.

This implies that inventory adjustments of these two industries vary more with sales

than with the beginning inventories. In other words, inventory adjustments of these

two industries may be affected by fluctuations in sales.

In addition, Consumer Products has the smallest average annual inventory

adjustment as a percentage of sales in four years, while the Agro & Foods Industry

has it in five years. That is, inventory adjustments as a percentage of sales of the

51

Agro & Foods Industry and Consumer Products are relatively smaller in the majority

of the years studied. However, inventory adjustments as a percentage of sales do not

increase as much as average annual inventory adjustments do.


Percentage of Sales



Industry

Consumer

Products

Industrials Total

2003 2.65 (1.34) 2.15 (1.35) 2.43 (0.96) 2.42 (1.29) 2.99

2004 3.53 (1.42) 2.50 (1.71) 3.10 (2.50) 3.06 (2.02) 4.63

2005 2.88 (1.72) 2.40 (1.62) 3.87 (1.24) 3.13 (1.56) 4.26

2006 2.67 (1.88) 3.75 (1.70) 3.46 (0.96) 3.29 (1.53) 5.93

2007 2.56 (1.73) 3.85 (1.20) 3.20 (1.18) 3.18 (1.29) 7.14

2008 2.52 (1.86) 5.67 (2.46) 3.29 (1.33) 3.73 (1.91) 6.89

2009 2.46 (0.88) 3.50 (1.37) 4.80 (1.27) 3.68 (1.26) 7.39

2010 2.87 (1.27) 5.26 (2.03) 2.99 (1.08) 3.60 (1.38) 7.68

2011 2.89 (1.37) 2.73 (2.12) 2.89 (1.65) 2.85 (1.63) 3.72

Standard Deviation 4.04 6.90 6.30 5.85

4.2.8 Average Net Income Differential

Table 4.9 shows descriptive statistics of average net income differential. From

Table 4.9, it can be observed that average net income differential of the total sample

and that of each industry increase over time. Also, average net income differential of

Industrials is the largest and that of Consumer Products is the smallest in the majority

of nine years studied.

52

Table 4.9: Descriptive Statistics of Net Income Differential

Year Average (Median) (Millions Baht) Standard

Deviation

(Millions Baht)

2.50

6.36

Agro & Food

Industry

Consumer

Products

Industrials Total

2003 2.10 (1.14) 1.61 (0.99) 2.50 (1.30) 2.12 (1.17)

2004 3.85 (0.77) 2.58 (1.25) 3.98 (1.80) 3.53 (1.35)

2005 5.95 (1.42) 2.24 (1.52) 5.65 (1.55) 4.76 (1.52) 16.77

2006 4.61 (1.20) 1.80 (1.11) 5.18 (1.26) 4.02 (1.19) 11.13

2007 3.38 (1.37) 1.70 (0.63) 4.66 (0.83) 3.40 (1.01) 9.78

2008 2.66 (1.41) 2.90 (1.56) 4.00 (0.93) 3.26 (1.40) 4.89

2009 2.52 (0.92) 3.20 (0.84) 4.42 (1.53) 3.46 (1.04) 5.73

2010 3.56 (1.06) 4.73 (0.85) 3.52 (1.21) 3.85 (1.05) 8.26

2011 4.52 (1.23) 2.25 (0.89) 3.66 (2.49) 3.50 (1.05) 6.30

Standard Deviation

(Millions Baht)

10.30 4.40 9.99

_

8.88

It was expected that net income differentials be larger in Consumer Products

due to its increasing degree of capital intensity observed in Table 4.2. however, net

income differentials of the Consumer Products is the smallest comparing to that of the

other two industries in the majority of the years studied. This is likely to result from

the fact that the average annual inventory adjustment of Consumer Products is the

smallest comparing to that of the other two industries, which was driven by lower

average sales of the industry. However, the smaller size of average net income

differential in Consumer Products does not necessarily indicate that the impact of

product costing on Consumer Products is relatively smaller than the other two

industries as sample companies are of different sizes.

53

4.2.9 Average Net Income Differential as a Percentage of Net Incomes under

Absorption Costing

Taking into account the effect of company site, the impact of the two product

costing methods was calculated as that as a percentage of net income under absorption

costing. Table 4.10 shows descriptive statistics of average net income differential as a

percentage of net income under absorption costing. From Table 4.10, average

difference between net incomes under the two product costing methods as a

percentage of net income under absorption costing of the total sample ranges from

1.78.% to 6.42%. Except for the year 2008 and 2011, the impact of the two product

costing methods as a percentage of net income under absorption costing varies

slightly. Average difference between net incomes under the two product costing

methods as a percentage of net income under absorption costing of Consumer

Products varies at the greatest extent, followed by that of Agro & Food Industry and

Industrials, respectively. In addition, each industry has the largest average net income

differential as a percentage of net income under absorption costing in three years.

while Agro & Food Industry has the smallest average net income differential as a

percentage of net income under absorption costing in the majority of the nine years

studied.

54


Net Income under Absorption Costing

Year Average (Median) Standard


Industry

Consumer

Products

Industrials Total

2003 6.23 (0.73) 1.36 (0.56) 1.93 (0.54) 3.13 (0.59) 13.68

2004 2.94 (0.78) 8.21 (0.55) 3.06 (1.25) 4.51 (0.83) 19.09

2005 2.91 (0.74) 3.14 (0.76) 3.60 (0.96) 3.25 (0.83) 7.26

2006 2.48 (0.67) 1.83 (0.89) 5.73 (0.77) 3.57 (0.78) 11.17

2007 3.01 (0.56) 2.76 (1.08) 2.57 (1.20) 2.77 (1.03) 4.39

2008 1.16 (0.41) 15.39 (2.18) 4.09 (1.08) 6.42 (0.88) 22.93

2009 0.91 (0.23) 9.30 (1.32) 3.61 (0.97) 4.39 (0.88) 12.23

2010 7.32 (0.50) 4.79 (1.02) 0.97 (0.52) 4.09 (0.55) 19.29

2011 1.05 (0.38) 1.54 (0.63) 2.55 (0.77) 1.78 (0.58) 3.99

Standard Deviation

(%)

- 14.12 19.26 8.76 14.18

4.2.10 Average Net Income Differential as a Percentage of Sales

Taking into account the effect of company size, the impacts of two product

costing methods were calculated as a percentage of sales. This also indicates the

impacts of the two product costing methods on profit margin ratio. Table 4.11 shows

descriptive statistics of average net income differentials as a percentage of sales.

From Table 4.11, average differences between profit margin ratio under the two

product costing methods for all the three industries range from 0.12% to 0.32%.

Comparing average differences between profit margin ratio under the two product

costing methods of each industry, that of Agro & Foods Industry and that of

Industrials vary in a narrower range than that of Consumer Products. It can be

55

observed that the impact of the two product costing methods in Consumer Products is

now the largest and that in Agro & Foods Industry k the smallest in the majority of

nine years studied. The impact of the two product costing methods on profit margin

ratio of the total sample changes slightly over the period studied, except that in year

2008 to 2010.


Sales

Year Average (Median) Standard

Deviation

(%)

Agro & Food

Industry

Consumer

Products

Industrials Total

2003 0.11 (0.04) 0.09 (0.05) 0.16 (0.05) 0.12 (0.05) 0.19

2004 0.15 (0.03) 0.16 (0.05) 0.21 (0.11) 0.18 (0.07) 0.37

2005 0.11 (0.03) 0.14 (0.06) 0.16 (0.09) 0.14 (0.07) 0.20

2006 0.11 (0.03) 0.24 (0.06) 0.15 (0.05) 0.16 (0.05) 0.50

2007 0.08 (0.06) 0.25 (0.06) 0.11 (0.07) 0.14 (0.06) 0.50

2008 0.11 (0.03) 0.48 (0.11) 0.12 (0.05) 0.22(0.05) 0.96

2009 0.21 (0.03) 0.27 (0.09) 0.32 (0.06) 0.27 (0.06) 0.78

2010 0.14 (0.03) 0.44 (0.07) 0.38 (0.05) 0.32 (0.05) 1.38

2011 0.09 (0.03) 0.17 (0.06) 0.12 (0.08) 0.12 (0.05) 0.25

Standard Deviation

(%)

0.35 0.86 0.75 0.68

4.3 Hypotheses Testing Results

4.3.1 Capital Intensity

Table 4.2 has shown that the proportion of fixed manufacturing costs to total

manufacturing costs, a proxy of capital intensity, declines over the nine years studied,

56

which is contrary to what has been expected. In order to determine whether there is

any significant change in capital intensity over the period studied and reduce the

impact of one-year anomalies, averages proportion of fixed manufacturing costs to

total manufacturing costs were calculated for the 2-year period at the beginning of the

period studied (2003 and 2004) and at the end of the period studied (2010 and 2011).

A paired t-test was conducted to test whether the 2-year average proportion of fixed

manufacturing costs to total manufacturing costs at the beginning of the period

studied are significantly different from that at the end of the period studied in

aggregate and by industry. Table 4.12 shows the correlations and paired-sample t-test

results of 2-year average proportion of fixed manufacturing costs to total

manufacturing costs of the total sample and that of each industry at the beginning and

at the end of the period studied.


Proportion of Fixed Manufacturing Costs to Total Manufacturing Costs of the

Total Sample and That of Each Industry at the Beginning and at the End of the

Period Studied

Year Average

Agro & Food

Industry

Consumer

Products

Industrials Total

C12003-2004 4.01% 5.18% 6.00% 5.13%

Cl2o10-20i1 3.72% 5.57% 4.78% 4.67%

Correlation of CI 2003-2004 and Cl2o10-2011 0.846* 0.337 0.560* 0.532*

Cl2oo3-zoo4 - Clzoio-2011 0.28% 0.39% 1.22% 0.46%

T Value 1.008 -0.439 2.698* 1.406

*significantly different at the 5% level of confidence.

57

The correlations between 2003-2004 average capital intensity and 2010-2011

average capital intensity both in aggregate and by industry are all positive and

significant. This indicates that sample companies which have higher/lower capital

intensity continue to have higher/lower capital intensity. In addition, the result shows

that the 2-year average proportion of fixed manufacturing costs to total manufacturing

costs of the total sample at the beginning of the period studied is not significantly

different from that at the end of the period studied at the five percent level of

confidence. When examining the 2-year average proportion of fixed manufacturing

costs to total manufacturing costs of each industry, only Industrials exhibits a

significant decrease in capital intensity. As indicated earlier, this may be attributable

to the fact that the nine-year period is too short to observe significant changes in

manufacturing technology that causes manufacturing overhead costs to increase and

that sample companies may have implemented new manufacturing technology before

the year 2003 which is the beginning of the period studied.

4.3.2 Inventory to Sales

From Table 4.3, inventory to sales of the total sample, Agro & Foods Industry

and Industrials decline, while that of Consumer Products increases over the nine-year

period studied. In order to determine any significant change in the inventory levels

maintained to support sales over the period studied and reduce the impact of one-year

anomalies, average inventory to sales was calculated for the 2-year period at the

beginning of the period studied (2003 and 2004) and at the end of the period studied

(2010 and 2011). A paired t-test was conducted to test whether the 2-year average

58

inventory to sales at the beginning of the period studied are significantly different

from that at the end of the period studied in aggregate and by industry. Table 4.13

shows the correlations and paired-sample t-test results of 2-year average inventory to

sales of the total sample and that of each industry at the beginning and at the end of

the period studied.


Inventory to Sales of the Total Sample and That of Each Industry at the

Beginning and at the End of the Period Studied

Year Average

Agro & Food

Industry

Consumer

Products

Industrials Total

INV/S2o03-2004 10.47% 13.43% 10.22% 1 L22% -

INV/S20102011 7.51% 16.66% 8.90% 10.70%

Correlation of

INV/S2oo3-too4 and IN WS2010-2011

0.725* 0.414* 0.778* 0.583*

INV/S.2003-2004 — INWSzoio-mii 2.96% -3.23% 1.32% 0.5.3%

T Value 1.817 -1.190 1.092 0.494


The correlations between 2003-2004 average inventory to sales and 2010-

2011 average inventory to sales both in aggregate and by industry are all positive and

significant. This indicates that sample companies which maintain higher/lower

inventory levels to support sales continue to maintain higher/lower inventory levels to

support sales. However, the results of paired t-tests show that the 2-year average

inventory to sales of the total sample is not significantly different from that at the end

59

of the period studied at the five percent level of confidence. When examining the 2-

year average inventory to sales of each industry, no industry exhibits a significant

change in inventory levels maintained to support sales at the live percent level of

confidence. As indicated earlier, it shows inconsistent impacts of JIT philosophy.

This is consistent with what happened in the U.K. reported in Pong and Mitchell

(2006, p.141). Although there is a greater pressure on manufacturers in Agro & Food

Industry and Industrials to embraced JIT philosophy, there still is a significant work to

be done.

4.3.3 The Impacts of Capital Intensity and the Degree to Which Companies

Embraced JIT Philosophy over Time

As indicated earlier, capital intensity and JIT system tend to have diverse

impacts on inventory adjustments and profitability differentials. To test the relative

impacts of capital intensity and the degree to which companies embraced JIT

philosophy on profitability differentials under the two product costing approaches

over the nine-year period studied, the following multiple regression analysis was

conducted on a panel of nine-year data of 104 companies.

Profitability Differential = a + 111 (CI) + b2 (JIT) + Sales + e


manufacturing costs; the degree to which companies embraced JIT philosophy is

measured by ending inventory to sales; and Profitability Differential measured by

each of the following three proxies:

1) the adjustment portion in Solomons' (1965) equation, which has been

referred to earlier as net income differential:

60

I (BINV — EINV 01* X,,

V,.,

2) the adjustment in item 1) above divided by absorption costing income,

which has been referred to earlier as net income differential as a

percentage of net incomes under absorption costing:

— EINV 1.1)1* X,.,

NI A, t Y ,, ,

3) the adjustment in item 1) above divided by sales, which has been referred

to earlier as net income differential as a percentage of sales:

(BINV — EINV * X,,

Sales

This results in three equations with the same set of independent variables but

different dependent variables. Table 4.14 shows results of panel data analysis when

net income differentials, net income differentials as a percentage of net incomes under

absorption costing, and net income differentials as a percentage of sales were used as

dependent variables, which will be referred to as Model 1, Model 2, and Model 3,

respectively.

61

Table 4.14: Results of Panel Data Analysis

Coefficient

Independent Variable Model 1 Model 2 Model 3

C -0.289215* -0.015964 -0.005389*

CI 4.747518* 0.760335* 0.082353*

JIT 1.437697* 0.160359* 0.026605*

Income 0.609240* -0.003049 0.000563

AR(1) 0.449454* 0.000292 0.262428*

Adjusted R-squared 0.433970 0.064011 0.410688

Durbin Watson Statistic 2.064493 1.829581 2.347106


The results show that both CI and JIT have significant impact on all three

dependent variables and capital intensity does have a greater impact on profitability

being reported than inventory to sales, which was used as a proxy of the degree to

which companies embraced JIT philosophy. This may be due to the fact that JIT

philosophy has an inconsistent effect on the sample companies. Annual inventory

adjustments still do vary a great deal during the period studied. It can be observed

that adjusted R-square for model 2 is quite low, which indicates that model 2 explains

only 6.40% of the variability in dependent variable can be explained by the estimated

multiple regression equation. This is likely due to the fact that there may be other

variables that affect net incomes used in the calculation of dependent variable in the

model. However, the result is consistent with the rest. The impact of capital intensity

on profitability being reported in every model is around four times as much of that of

the degree to which companies embraced JIT philosophy.

62

4.3.4 The Impacts of Capital Intensity and the Degree to Which Companies

Embraced JIT Philosophy in Each Year

In addition to the analyses conducted on a panel of nine-year data of 104

companies, similar analyses were also conducted using data of 104 companies in each

year. The purpose was to examine whether patterns of changes in coefficients of CI

and JIT can be observed. Table 4.15 show coefficients of independent variables in

each of the three models from year by year analysis.

Table 4.15: Coefficients of Independent Variables in Model 1

Year Coefficients Adjusted R-Square

a CI JIT Income

2003 -0.042 2.378* 0.250 0.333* 0.333

2004 -0.367.* 5.639* 1.919* 0.684* 0.391

2005 -0.949* 7.280* 1.608* 2.378* 0.710

2006 -0.604* 5.402' 1.786* 1.340* 0.644

2007 -0.362* 3.535 0.997* 0.968* 0.422

2008 -0.030 2.122 0.686' 0.371* 0.312

2009 -0.151 4.629* 0.649 0.347* 0.246

2010 -0.444* 7.731* 2.690* 0.330* 0.404

2011 0.068 2.470** 0.061 0.300* 0.249


It should be noted that coefficients of CI and JIT are significantly different

from zero at the 5% level of significance in most years and CI has a greater impact on

net income differentials than JIT in every year as shown in Graph 4.3.

2003 2004 2005 2006 2007 2008 2009 2010 2011

63

Graph 4.3: Coefficients of CI and JIT in Model 1 Comparison

Table 4.16 shows coefficients of independent variables in model 2 from year

by year analysis.


Year Coefficients Adjusted R-Square

a CI JIT Income

2003 0.845 -2.633 3.522 -1.060 -0.021

2004 -0.072 1.817* 0.268 -0.003 0.099

2005 -0.0.17 0.495* 0.174* 0.014 0.140

2006 -0.013 0.753* 0.129 -0.003 0.052

2007 0.010 0.097 0.0109* -0.001 0.137

2008 -0.039 2.021* 0.123 -0.013 0.079

2009 -0.022 0.876* 0.143 -0.007 0.113

2010 -0.008 0.355 0.318 -0.003 0.024

2011 0.010 -0.012 0.086* -0.002 0.037


-4t- CI

-JIT 2004 2005 2006 2007 2008 2009 2010 2011

-1

2

3

64

It should be noted that coefficients of Cl and JIT are significantly different

from zero at the 5% level of significance only in some years and adjusted R-square is

extremely low. The results must be used with care. however, CI has a greater impact

on net income differentials than JIT only in years that coefficient of CI is significantly

different from zero as shown in Graph 4.4.


Table 4.17 shows coefficients of independent variables in model 3 from year

by year analysis.

2003 2004 2005 2006 2007 2008 2009 2010 2011

65


Year Coefficients Adjusted 12-Square

a CI JIT Income

2003 -0.000 0.022* 0.003* -0.000 0.205

2004 -0.002* 0.055* 0.013* 0.000 0.396

2005 -0.001* 0.035* 0.005* 0.000 0.455

2006 -0.002* 0.042* 0.018* 0.000 0.366

2007 -0.002* 0.022* 0.023* 0.000 0.560

2008 -0.007* 0.100* 0.040* 0.001 0.492

2009 -0.005* 0.074* 0.027* 0.000 0.403

2010 -0.011* 0.140* 0.066* 0.001 0.605

2011 -0.001* 0.010* 0.016* 0.000 0.561


Coefficients of CI and JIT are significantly different from -zero At the 5% level

of significance in every year. Consistent with Model 1, CI has a greater impact on net

income margin differentials than JIT in every year as shown in Graph 4.5.


CHAPTER 5

CONCLUSION

5.1 Summary of the Results and Implications

This study has provided many important findings. Firstly, capital intensity of

SET listed manufacturing companies have not increased significantly over the period

studied. As indicated earlier, this may be due to the fact that the nine-year period is

too short to observed significant changes in manufacturing technology or that new

manufacturing technology may have been implemented prior to the beginning of the

period studied. Secondly, it has shown that JIT system has not had consistent impacts

on SET listed manufacturing companies. This is likely to be attributable to nature of

products of the industries as discussed before.

Thirdly, the directions of annual inventory adjustments show that there are a

greater number of companies reporting higher net incomes under absorption costing

than those which would have been under variable costing in the majority of the

periods studied. Average inventory adjustments of the total sample vary at a great

extent, ranging between 27.40% and 52.20% of beginning inventory over the nine-

year period studied. This indicates that inventory adjustment retains its significance.

This study examined the impact of product costing methods from both annual

inventory adjustment and net income differentials. Annual inventory adjustment was

examined in this study because it can provide an indication of the extent to which net

income being reported and profit margin ratio can be influenced by the two product

costing methods given the capital intensity of the company in each year (Pong and

Mitchell, 2006). Therefore, the differences in net incomes under the two product

67

costing methods, resulting from inventory and production decisions, can be examined

from annual inventory adjustments. Average inv entory adjustment consistently

increases from 61.15 million Bahts in the year 2003 to I 15.05 million l3ahts in the

year 2011. However, when taking into account the effect of company size, average

inventory adjustment as a percentage of net incomes under absorption costing and

average inventory adjustment as a percentage of sales do not change much over the

nine years studied. This suggests that recent changes in operating environment have

not elevated earnings management through inventory or production decisions.

Average net income differential, which indicates the actual impact of the two

product costing methods, of the total sample and that of each industry increase over

the nine year studied. When taking into account of the company size, average net

income differential as a percentage of net income under absorption costing and that as

a percentage of sales vary slightly. This indicates that the current operating

environment has not driven net income differentials to he greater than what they have

been in the past.

Lastly, with the insignificant change in capital intensity and the degree to

which companies embrace JIT philosophy, it is unclear whether net income

differential was driven more by which of the two factors. This study, therefore,

examined the relative impact of the two factors. The OLS analysis of panel data

shows that while both capital intensity and the degree companies embraced JIT

philosophy do have significant impacts on profitability being reported, capital

intensity has a greater impact than the degree companies embraced JIT philosophy

over the nine-year period studied. The analysis was also conducted on data for each

year. It also shows that both capital intensity and the degree companies embraced JIT

68

philosophy do have significant impacts on profitability being reported in most years.

Also, capital intensity consistently has a greater impact than the degree companies

embraced JIT philosophy in most years. The result indicates that net income

differential is driven by both capital intensity and the degree to which companies

embrace ET philosophy. However, capital intensity has a greater impact and is

affected by long-term investment decisions. Additionally, there are many other ways

to influence net income being reported. This should deter management from

managing net incomes through inventory and production decisions. The evidence in

this study, therefore, provides further supports for accounting standard setters in

mandating product costing choices on theoretical and logical ground.

5.2 Limitations and Future Research

This study has several limitations. Firstly, it is acknowledged that there are

many other ways to manage net incomes. This study focuses on one possible way to

influence net incomes figures. Secondly, some of the variables used in this study arc

not directly available in the public domain and have been estimated. Lastly, due to

availability of data, the number of years studied may be not large enough to observe

the impact of new manufacturing technology implemented on fixed manufacturing

costs and the impact of JIT system. Future research may be conducted in other

countries with a longer period of time studied where the impact of capital intensity

and JIT system can be clearly observed and see if the findings of this study can be

generalized when using different sets of data.

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lziilf111cl/1141: tfitiittilLan (DBA) Mcquarie Graduate School of Management, Sydney,

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ihzinclatill-917 Advanced MBA The University of Texas at Arlington.

tlittillpIYI (MBA) East Texas State University, Texas, U.S.A.

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qiiitvral: csii-mi(' aqn-wilti-,i

vili,-)EnlwriicIrlw infilisininlifii ficuzlii1117•1r1

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