accounting methods and management decisions; the case of inventory costing & inventory...

Electronic copy available at: http://ssrn.com/abstract=1677327Electronic copy available at: http://ssrn.com/abstract=1677327

Journal of Accounting Research Vol. 18 Supplement 1980

Printed in U.S.A.

Accounting Methods and Management Decisions: The Case

of Inventory Costing and Inventory Policy

GARY C. BIDDLE*

1. Introduction Considerable attention in the accounting literature is devoted to dis-

cussing possible motives for and effects of choices among alternative accounting methods. Although insights have been gained into investor (stock market) reactions to changes in accounting methods, little is known about either management motivations for observed accounting choices or the effects of these choices on subsequent operating decisions. One explanation for the dearth of empirical findings is that few of the proposed theories of accounting choice have offered strong links to real economic incentives. In addition, previous studies have concentrated on firm characteristics existing before and concurrent with the accounting choice. Since managers making accounting choices are likely to consider future conditions as well, this approach may overlook important determinants of accounting choices. This approach also precludes the detection of possible changes in operating policies induced by accounting choices.

A choice among alternative inventory costing methods, especially between the last in, first out (LIFO) and first in, first out (FIFO) cost- flow assumptions, can generate potentially large changes in a firm's cash

* University of Chicago. I gratefully acknowledge the comments of Craig Ansley, Sidney Davidson, Nicholas Dopuch, Allan Drazen, Gary Eppen, Shyam Sunder, Victor Zarnowitz, and Tom Stober. This paper is based on my dissertation (see Biddle [1980]) which was supported, in part, by a grant from Arthur Andersen & Co.

235 Copyright (, Institute of Professional Accounting 1981


236 FINANCIAL AND MANAGERIAL ACCOUNTING: 1980

flows due to its impact on taxable earnings. These cash-flow effects provide economic incentives for choices between these methods. More- over, these cash-flow effects depend, in part, on the behavior of year-end physical inventories. Because year-end inventory levels are subject to management control, the LIFO-FIFO choice can both affect and be affected by subsequent inventory management policies. The LIFO-FIFO choice offers, therefore, the opportunity both to identify factors which influence accounting choices and to examine associations between these choices and subsequent operating decisions.

This study investigates whether associations consistent with LIFO- FIFO tax incentives exist between management choices to adopt or not adopt the LIFO inventory costing method and characteristics of firms' year-end inventories. Both pre- and postchoice characteristics are examined. Because the LIFO-FIFO choice is voluntary, a postchoice association would be consistent with managers both anticipating future inventory characteristics when making a LIFO-FIFO choice and changing inventory management policies in response to that choice. Evidence consistent with the hypothesis that LIFO adoptions are associated with changes in inventory management policies would have important macroeconomic implications. Zarnowitz and Moore [1977] have argued that a failure to recognize the major shift in inventory costing methods which occurred in 1973 and 1974 (primarily FIFO to LIFO) resulted in an underestimation of inventory accumulations by the U.S. Department of Commerce. This underestimation resulted from the different procedures used under LIFO and FIFO to assign costs to inventory units. While this effect of LIFO-FIFO choices can bias macroeconomic mea- surements and forecasts, an associated change in inventory management policies by a large number of firms could directly affect underlying macroeconomic stocks and flows.1

The research design involves comparisons between a treatment group sample of firms which adopted LIFO and a matched pair control sample of firms which continued to use FIFO (or an average cost method). Two primary measures of inventory properties are derived from available financial statement disclosures. One measure approximates physical inventory levels, while the other estimates what the difference would have been each year between each firm's cost-of-goods-sold computed under the LIFO and FIFO alternatives. The empirical results based on these measures indicate statistically significant associations between LIFO adoption decisions and inventory properties and reveal striking cash-flow consequences associated with LIFO-FIFO choices.

'A recent study by Halperin [1979] has also suggested that LIFO adoption may induce an inefficient use of resources as firms expend resources in managing year-end inventory levels. This study provides empirical evidence on whether an association consistent with this argument exists.


INVENTORY COSTING AND INVENTORY POLICY 237

2. Previous Research on Choices among Alternative Accounting Methods

Changes in the accounting methods used by firms can result either from voluntary choices by managers or from actions by rule-making bodies (e.g., the FASB, SEC, and IRS) which induce managers to change methods. Previous studies of management accounting choices have fo- cused on (1) the motives for and determinants of voluntary changes in accounting methods and (2) the effects of both voluntary and rule- induced changes on stock price behavior.

MOTIVES FOR AND DETERMINANTS OF ACCOUNTING CHANGES

Much of the research concerning the motives for accounting changes has been concerned with identifying a behavioral paradigm for management choices. The most popular of these paradigms is the so-called smoothing hypothesis. It suggests that managers will be motivated when choosing among alternative accounting methods to choose that method which results in the "smoothest" income stream.

Gordon [1964] introduced smoothing as an implication of a set of propositions regarding management utility maximization. One of these propositions was that "stockholder satisfaction with a corporation increases with the average rate of growth in the corporation's income and the stability of its income" (Gordon [1964, p. 262], emphasis added). Although Gordon asserted that the smoothing hypothesis was testable, he did not suggest how "smoothness" was to be measured. And apart from the quoted proposition, he did not suggest how or why investors would be affected by, or assess, income stability. Nevertheless, the smoothing hypothesis has generated considerable interest. Gagnon [1967], for example, suggested that (managers believe) investors penalize firms which do not exhibit smooth reported income streams and, as a result, managers are motivated to choose between the purchase and pooling alternatives in accounting for business combinations so as to portray this quality best. His results, however, did not reveal consistent evidence of smoothing behavior. Studies by Copeland [1968], Cushing [1969], and White [1972] reached similar conclusions.2

The smoothing paradigm has been critically evaluated elsewhere,3 and it is sufficient here to suggest that it does not offer a strong theoretical basis for explaining management choices among alternative accounting methods. Although several other behavioral paradigms have been proposed (e.g., signaling, debt covenant restrictions, management compen-

2 A recent study by Hong, Kaplan, and Mandelker [1978] has examined whether stock price reactions to the choice of the pooling method over the purchase method of accounting for business combinations are consistent with managers attempting to influence stock prices. Their results were not consistent with this explanation.

3 See, e.g., Ball and Watts [1972], Gonedes [1972], and Gonedes and Dopuch [1974, pp. 109-10].

238 GARY C. BIDDLE

sation, threats of antitrust litigation, etc.), empirical results have not revealed, thus far, more than second-order effects.4

Another line of research in this area has been concerned with empiri- cally identifying characteristics of firms which are associated with voluntary changes in accounting methods. Representative studies include those by Moore [1973] (changes in management); Bremser [1975] (statistical properties of earnings); Gosman [1973] and Eggleton, Penman, and Twombly [1976] (firm size, industry membership, and auditor). While these studies have identified several firm characteristics associated with certain change decisions, the results seem to have limited generalizability to other choices, firms, and time periods. And because the factors examined have often been suggested by intuitive (and not theoretical) reason- ing, the observed associations provide few insights into management motives for observed accounting changes. For example, even though Eggleton et al. [1976] identified an association between industry membership and LIFO-FIFO choices, the paper reveals little about why managers in certain industries decided to change methods. Moreover, because these studies have often ignored postchange firm characteristics, they have overlooked possibly important determinants and effects of accounting choices.

STOCK PRICE REACTION TO ACCOUNTING CHANGES

The second major theme of research on choices among alternative accounting methods has relied upon notions of capital market efficiency and the capital asset pricing model to examine the effects of accounting changes on stock price behavior.5 Representative studies include those by Ball [1972] (reactions to accounting changes in general); Archibald [1972], Baskin [1972], and Kaplan and Roll [1972] (investment credit and depreciation method choices); Gonedes [1975; 1978] (special and extraordinary items); Harrison [1977] (voluntary versus rule-induced accounting changes); and Sunder [1973 a; 1973b; 1975] (LIFO-FIFO changes). Two primary conclusions emerge: (1) investors interpret financial statement information conditional on the accounting methods used and (2) investors react to the real economic (i.e., cash-flow) implications of accounting changes rather than to their effects on accounting measures. Sunder [1973 a; 1973b; 1975], for example, found that the stocks of firms which adopted LIFO during periods of inflation (and which had generally nondecreasing inventories) exhibited positive excess returns, even though this change would have resulted in lower reported net income than if the firms had remained on FIFO.

4 See, for example, Gonedes [1978] (signaling), Holthausen [1979] (debt covenant restrictions), and Watts and Zimmerman [1978] (management compensation). Many of these paradigms lack, like smoothing, strong links to firm cash-flow implications. An active market for managerial services would induce managers to consider the cash-flow implications of accounting choices (see Fama [1980] and Sunder [1980]).

5 See Sharpe [1964], Lintner [1965], Fama [1970], and Fama and Miller [1972] for a discussion of these concepts.


While examinations of stock prices around dates of accounting changes have revealed much about investor reactions to accounting changes, this approach can only indirectly address issues relating to management motivations for observed changes or their effects on subsequent operating decisions. Although some accounting changes (e.g., LIFO adoptions by firms with increasing inventories during periods of inflation) are consistent with managers having anticipated stock price reactions, others are not (e.g., FIFO adoptions during periods of inflation). Moreover, many accounting choices have no obvious (either theoretical or empirical) stock price implications.6 The market value rule7 does, however, provide at least one explanation of why managers would be interested in the cash- flow implications of accounting choices.

TOWARD AN UNDERSTANDING OF REAL EFFECTS

Recently, accounting researchers have begun to examine the potential effects of accounting choices on management decisions. A typical study is one by Collins [1978]. Instead of examining motives for, determinants of, or investor reactions to accounting changes, Collins examined whether the elimination of an acceptable method for accounting for research and development (R&D) expenditures by the FASB (FASB Statement of Financial Accounting Standards No. 2-FASB 2) affected the subsequent R&D expenditure. decisions of managers induced to adopt new methods. Collins, therefore, attemnpted to identify changes in real resource allocation decisions induced by an accounting change (i.e., its real effects). Although testimony from hearings on preliminary drafts of FASB 2 indicates that some managers felt they would be forced to reduce R&D expenditures in response to the change,8 Collins' results were only weakly consistent with this claim. Vigeland [1978] found no stock price reaction to the adoption of FASB 2, further suggesting that managers did not significantly alter R&D expenditures.9 This absence of significant real effects may be due to FASB 2's lack of obvious cash-flow implications.

6Even though FASB Statement No. 8 (Accounting for the Translation of Foreign Currency Transactions and Foreign Currency Financial Statements-effective January 1, 1976) has generated considerable controversy (see, e.g., Merjos [1977]), it has no obvious cash-flow effects and a study of associated stock price reaction (Dukes [1978]) could not reject the null hypothesis of no effect.

7The market value rule suggests that managers are motivated when making investment, production (accounting method), and financing decisions to maximize current shareholder wealth.

8 In FASB [1974] is found the testimony submitted to the FASB concerning the Exposure Draft of Statement No. 2. An example of this testimony is a letter submitted by Alan Hirasuna of L'Garde Corporation in which he says "It's apparent that the currently proposed requirement will force companies who normally make R&D investments which are large compared to their net worth to slow down their rate of investment" (FASB [1974, p. 569]). More on the alleged effects of FASB 2 can be found in Business Week (July 3, 1979), pp. 46-77.

9 A more recent study by Horwitz and Kolodny [1979] provides statistically significant evidence of an association between the passage of FASB 2 and changes in research and

240 GARY C. BIDDLE

The present study also investigates the possible real effects of an accounting choice on management operating decisions. And like the change determinant studies, this study examines empirical associations between change decisions and firm characteristics. Unlike previous studies, this research examines both pre- and postchange associations for a voluntary accounting choice with obvious cash-flow implications. The following section outlines some testable hypotheses suggested by this setting.

3. The LIFO-FIFO Choice, Tax Incentives, and Inventory Management Decisions-Hypotheses to Be Tested

Although a variety of factors (such as those mentioned above) may influence observed choices between LIFO and FIFO, the tax-related cash-flow implications of this choice will be suggested as a primary motive. This argument is consistent both with the evidence from the stock price reaction studies and with discussions in the financial press of costs and benefits associated with LIFO adoptions.10 A manager contem- plating a LIFO-FIFO choice could (and presumably would) forecast the future cash flows which would be produced by each alternative.

Sunder [1976a; 1976b] has identified four factors which affect (expected) cash-flow differences between LIFO and FIFO: (1) year-end inventory levels, (2) changes in inventory input (purchase or production) prices, (3) marginal corporate income tax rates, and (4) discount rates (where a present value expression is desired). Changing inventory input prices and positive income tax rates are necessary for cash-flow differences to arise between LIFO and FIFO. (The importance of price changes is indicated by the renewed popularity of LIFO which has accompanied recent high rates of inflation.)" While managers cannot easily influence input price changes, income tax rates, or discount rates (which depend on firms' costs of capital), inventory levels are subject to management control. Inventory levels (and policies) could be altered in response to LIFO-FIFO cash-flow incentives.

development expenditures by firms induced to change methods. Horwitz and Kolodny suggest that such an association may be due to managerial compensation contracts, debt covenant restrictions, or perceived market inefficiencies on the part of managers. Because they employ a matched pair sample wherein firms are assigned to the treatment and control groups on the basis of their pre-FASB 2 accounting methods (deferral and expense, respectively), a potential threat to the internal validity of their study is that some systematic difference exists between these groups which has both caused them to choose their respective pre-FASB 2 methods and has caused them to make different post-FASB 2 expenditure decisions (e.g., different modes of ownership/financing). The preliminary draft which is available at this writing does not allow a definitive judgment on this point.

10 Representative articles from the financial press include Coopers & Lybrand [1974], Jannis and Johnson [1975], Keithley and Meek [1977], and Rothschild [1975].

"1 See Biddle [1980, appendix B] for historical trends in the use of alternative inventory costing methods in various industries in the post-WWII period.


Yet LIFO-FIFO tax incentives are not the only factors which affect managers' decisions regarding desired year-end inventory levels. Storage costs, order costs, anticipated price changes, order (or manufacturing) lead times, stockout costs, and sales forecasts can immediately be suggested. These factors, many of which will be related to firms' major lines of activity (industry memberships) may dictate characteristic patterns for year-end inventories which are not readily alterable by managers. Because these patterns may or may not be consistent with LIFO tax advantages, both pre- and postadoption associations between inventory properties and observed LIFO-FIFO choices should be anticipated.

Two hypotheses are suggested by the preceding discussion. The first, which will be called the Anticipations hypothesis, suggests that managers making voluntary accounting method choices will take into consideration anticipated future events and circumstances affecting the expected costs and benefits associated with the alternative methods.12 In choices between LIFO and FIFO, year-end physical inventory levels have been identified as an important determinant of associated future cash flows. Thus, as applied in this study, the Anticipations hypothesis implies systematic differences in the post-LIFO adoption inventory patterns of LIFO adopters and nonadopters which are consistent with LIFO-FIFO cash-flow incentives. Because some factors which affect inventory properties will not be readily alterable by managers, systematic differences may also be observed between the preadoption date inventory patterns of LIFO adopters and nonadopters. While preadoption differences are not directly implied by the Anticipations hypothesis, they would be consistent with managers having based their forecasts of future inventory properties, in part, on their past experiences.

The second hypothesis, which will be called the Incentives hypothesis, suggests that managers will alter operating decisions in response to incentives provided by alternative accounting methods (i.e., that accounting choices may produce real effects). As applied in this study, the Incentives hypothesis suggests that managers will modify their inventory policies in response to the cash-flow incentives provided by LIFO and FIFO. For those firms adopting LIFO, changes should be observed in the properties of year-end inventories which are consistent with increasing LIFO cash-flow advantages. Thus, the Incentives hypothesis also implies systematic differences in the post-LIFO adoption inventory patterns of LIFO adopters and nonadopters which are consistent with LIFO-FIFO cash-flow incentives. To the extent these differences are observed, the Anticipations and Incentives hypotheses both offer explanations: one relating the LIFO-FIFO choice to managers' expectations regarding inventory levels and the other relating subsequent inventory management decisions to the LIFO-FIFO choice. The implications of these hypotheses are summarized in table 1.

12 Managers making rule-induced accounting choices may similarly consider future costs and benefits when more than one acceptable alternative remains.

242 GARY C. BIDDLE

TABLE 1 Implications for Associations Between Costing Method Choice and Year-End Inventory

Behavior

Anticipations Hypothesis. When making the voluntary accounting choice between LIFO and FIFO, managers will take into consideration future patterns of year-end inventory levels. These patterns will depend on a number of factors (in addition to tax incentives available under the LIFO alternative) which may be industry- and firm-specific.

Incentives Hypothesis. Managers will respond to the tax-related incentives provided by the LIFO cost-flow assumption by altering inventory management policies.

Implications. (1) Differences should be observed between the postadoption date inventory patterns of

those firms which adopt LIFO and those that do not which are consistent with increasing the tax-related cash-flow advantages of LIFO.

(2) Changes should be observed in the inventory patterns of those firms which adopt LIFO which are consistent with increasing the tax-related cash-flow advantages of LIFO.

(3) According to the previous implications, differences between the inventory patterns of firms which adopt LIFO and those that do not should increase in a manner consistent with LIFO cash-flow incentives between the pre- and post-LIFO periods.

(4) Because many of the factors which will affect desired year-end inventory levels are related to aspects of firms which are not readily subject to management control (e.g., manufacturing processes, characteristics of factor and product markets, etc.), pre- LIFO adoption inventory patterns should differ between those firms that ultimately adopt LIFO and those that do not.*

* As indicated, this implication represents an extension of the Anticipations hypothesis. The Antici- pations hypothesis itself implies only postadoption date differences.

4. LIFO-FIFO Cash Flows and Properties of Year-end Inventories

This section outlines some testable implications of the Anticipations and Incentives hypotheses. Because desired year-end inventory levels are influenced by a number of factors, the discussion first identifies properties of year-end inventories which influence LIFO-FIFO cash flows. A one- period optimal inventory quantity model is then presented which illustrates how LIFO-FIFO cash-flow incentives will affect inventory management decisions.

INVENTORY PROPERTIES WHICH AFFECT LIFO -

FIFO CASH FLOWS

The cash-flow implications of the LIFO-FIFO choice arise from the fact that these methods are used to compute the cost-of-goods-sold (COGS), which is subtracted from revenues to determine taxable earnings. It can be shown that the difference between LIFO- and FIFO-based measures of COGS equals the difference between the inventory holding gains realized under each method. That method with the smallest holding gain (largest holding loss) realization will produce the largest COGS. Positive holding gains occur when a firm holds inventory units and input prices increase. Negative holding gains (holding losses) occur when units are held and prices decrease. Holding gains (losses) are said to be realized


when these units are sold. Since under the FIFO alternative those units which are held in beginning inventories are assumed to be the first that are sold each period, if a firm "turns over" its physical inventories at least once (i.e., if it sells at least as many units as are in the beginning inventory), then the holding gains on these units will be realized. Since most firms have physical inventories which turn over at least once during an annual accounting period, the use of the FIFO cost-flow assumption means that all of the holding gains (or losses) associated with beginning inventory units will be realized each period. The only way a manager using FIFO could reduce holding gain realizations would be to hold fewer beginning inventory units. To avoid the holding gain realizations when prices are increasing, he must also forego the holding gains. And to increase holding loss realizations when prices are falling, he must incur holding losses.

In contrast, the LIFO cost-flow assumption dictates that those units which are held in the beginning-of-period inventory will be assumed sold only if unit sales exceed unit purchases. As a consequence, if prices are increasing, a manager can indefinitely postpone the realization of holding gains associated with beginning inventory units (and on any units which have been added to inventory in subsequent periods) by purchasing at least as many units as are sold each period. This insures that the COGS under LIFO will not be less than what it would have been under FIFO. Thus, the LIFO cost-flow assumption offers the opportunity to postpone indefinitely the realizations of holding gains on inventories. If prices are decreasing, a manager may be able to realize sufficient LIFO holding losses by drawing inventory levels down to provide a higher COGS on LIFO than on FIFO.

Of course, other factors over which managers may have little control will also influence desired year-end inventory levels. A manager contem- plating a LIFO-FIFO choice would have to assess the cumulative effect of these factors on inventory properties as they affect LIFO-FIFO cash flows.13 The preceding discussion suggests that with increasing inventory

13 One way in which insights could be gained into the determinants of LIFO-FIFO choices (and their effects) would be to examine the underlying factors which affect desired year-end inventory levels. For example, one could investigate whether firms with longer order lag times are less likely to adopt LIFO (ceteris paribus). Aside from the formidable data acquisition problems which would arise in an empirical study of factors like order lag times, it is not clear that a general optimal inventory model exists which would enable all of the relevant factors even to be identified. And without a general inventory model, it is not clear how various factors (once identified) would be combined to predict desired year- end inventory levels. While several recent studies (e.g., Cohen and Pekelman [1978; 1979], Cohen and Prastacos [1978], Prastacos [1978], and Cohen and Halperin [1979]) have examined LIFO inventory systems in the context of optimal order quantity models, only two (Cohen and Pekelman [1979] and Cohen and Halperin [1979]) have considered LIFO tax incentives.

Using a simplified model relating desired year-end inventory levels to known future demand distribution, tax rates, discount rates and inventory input prices, selling prices, holding costs, and stockout costs (each on an annualized basis), Cohen and Pekelman [1979]

244 GARY C. BIDDLE

input prices, a firm with generally increasing and less variable physical inventory levels would find greater advantages to a LIFO adoption than an otherwise identical firm with decreasing or more variable inventories.

A MODEL OF OPTIMAL INVENTORY POLICIES IN THE PRESENCE OF LIFO-FIFO TAX INCENTIVES

The one-period optimal inventory quantity model presented in Appen- dix A illustrates how LIFO-FIFO cash-flow incentives should influence management decisions regarding year-end inventory levels. The model (which parallels and extends a similar presentation by Cohen and Pek- elman [1979]) treats costs, decisions, and outcomes on an annual basis. (This is appropriate since for income tax purposes a firm's costs and inventory flows are aggregated annually.) The decision variable is an "order up to" quantity (i.e., inventory units available for sale), and demand is treated as if it arrives on the last day of the year. In spite of its obvious simplicity, the model provides several testable implications. These are: (1) Under conditions of input price inflation, LIFO inventories will be greater than FIFO inventories. (2) Under conditions of input price deflation, LIFO inventories will be smaller than FIFO inventories. (3) The existence of additional LIFO layers with prices which reflect current price changes will increase the differences (predicted in (1) and (2) above) between the optimal level of inventory available for sale under LIFO and FIFO.

While an extension of this model into a multiperiod setting would result in greater realism, available evidence (see Cohen and Pekelman [1979]) does not indicate major alterations would result in the conclusions reached above. Moreover, in a multiperiod setting, the expected profit expression for the LIFO case becomes quite complex and can only be solved by assuming values for the model parameters and applying a procedure like multidimensional search (see Cohen and Pekelman [1979]).

One additional implication, however, will be offered. Under conditions of input price inflation, the one-period model suggests that managers using LIFO will be motivated to hold larger year-end inventories to avoid the holding gain realizations which would occur if year-end inventories fell below previous year-end levels. In a multiperiod setting, managers

found that the LIFO optimal inventory policy was nonstationary and nonmyopic in a multiperiod setting (Cohen and Pekelman [1978, pp. 16-17]). Cohen and Halperin [1979] considered the influence of intrayear inventory purchase decisions on year-end LIFO tax effects. (The idea is that by timing intrayear purchases differently, the firm may be able to alter the costs assigned to LIFO layers.) Although they used an even simpler model (which considered only tax rates, holding costs, and input prices in a deterministic setting with known subperiod sales and a fixed horizon), a linear programming formulation for one firm over a period of only three years (twelve quarters) involved ninety-five variables and seventy-eight constraints (Cohen and Halperin [1979, p. 15]). In general, the data which would be required to predict desired year-end inventory levels using models such as these are not readily available for an empirical study such as this one.


will be similarly motivated to avoid LIFO layer liquidations. Yet, in a multiperiod setting, an addition to inventories in one period implies that higher levels must be maintained in future periods if liquidations are to be avoided. Additions to inventories in one period can produce, therefore, greater inventory holding costs in future periods. As a result, managers using LIFO in a multiperiod setting face greater incentives both to avoid inventory liquidations and inventory additions than those using FIFO (ceteris paribus). This suggests the following implication. (4) Under conditions of input price inflation, LIFO inventories will be less variable than FIFO inventories.14 Assuming the other implications of the one- period model also apply to the multiperiod setting, faster and less variable rates of inventory growth should be observed for the treatment group firms.

5. Research Design and Methodology

The empirical tests conducted in this study rely on pre- and post-LIFO adoption comparisons between two groups of firms: a treatment group of firms which adopted LIFO and a control group of firms which did not adopt LIFO. Because the firms self-selected into these groups by adopting or not adopting LIFO, random assignment could not be used to provide experimental equivalence. However, each control group firm has been matched with a treatment group counterpart on the basis of sales and industry membership to control for the effects of other factors which might influence associations between inventory properties and LIFO adoption decisions.

While this design is not a true experimental design (due to the absence of random assignment), in the class of (ex post) quasi-experimental designs it offers several advantages which increase the internal validity of the comparisons.15 If, for example, comparisons were made between the pre- and post-LIFO adoption inventory properties of the treatment group firms alone, an important threat to internal validity would be the possibility that some other event besides the LIFO adoption could have produced any observed changes in inventory properties. This is known as the history threat. This design would also be subject to the maturation threat-the possibility that an observed difference may be due to the firm's natural evolution or maturation through time. The use of control group firms reduces the history and maturation threats by allowing relative assessments of treatment group firm characteristics (i.e., relative to the control group firms).

14 This implication is consistent with the analytical results of a recent study by Halperin [1979]. Halperin examined the economic efficiency aspects of the LIFO method and concluded that "the LIFO firm will ... use some of its productive resources to maintain year-end inventory levels as a result of the fact that the year-end inventory maintenance activity increases after-tax profits" (Halperin [1979, p. 65]).

15 For a discussion of experimental design issues and terminology, see Campbell and Stanley [1963].

246 GARY C. BIDDLE

MEASURING PHYSICAL INVENTORY LEVELS AND COGS DIFFERENCES

The Anticipations and Incentives hypotheses suggest associations between LIFO-FIFO choices and firms' year-end physical inventory levels. Yet, because any two firms are likely to hold different types of inventory goods (even if they are in the same industry), and since a given firm will hold different goods at different points in time, actual physical unit counts would not provide meaningful comparisons. Moreover, physical unit counts are rarely disclosed. Instead, firms typically disclose year-end inventory dollar amounts which have been determined by applying cost- flow assumptions to physical inventory counts.

Due to some recent disclosure pronouncements, most firms which have adopted LIFO in fiscal years 1973 and thereafter have continuously disclosed what their year-end inventory dollar amounts would have been if they had instead applied FIFO.16 When a firm uses the FIFO (or an average cost) assumption, its year-end inventory amounts will reflect recent input costs. Since virtually all firms "turn over" their inventories at least once each year, FIFO-based year-end inventory amounts will generally be composed of units purchased during that year.17 If an assumption is made about the rate of inventory procurement (purchase or production) in a year (e.g., uniform), the relationship between FIFO- based year-end inventory amounts and annual inventory dollar purchases reveals the timing of inventory unit purchases. By applying available price indexes to these inventory purchases, a dollar-based measure of year-end physical inventories can be obtained.

This approach was used to obtain an equivalent dollar-unit measure of year-end physical inventory levels."8 Because this measure is denominated in dollars of equivalent purchasing power, it is comparable both across firms and through time. Two versions were derived: one equivalent unit measure is based on monthly price indexes (EUM), while the other is based on annual price indexes (EUA). The following relationships describe their derivation (firm subscripts are omitted):

EUMt = Ut Y, ($EjtFUt Y, Pt, 12-j Ut U Pt, 2-1) i=t F=/

EUAt = $EIt IPt

l See Accounting Principles Board Opinions No. 20 and No. 22, Securities and Ex- change Commission Rule 5-02-6b of Regulation S-X and Accounting Series Release No. 141, and Internal Revenue Service Revenue Ruling 73-66 and Revenue Procedure 73-37.

17 All of the firms employed in this study had inventory turnovers greater than one in all years utilized.

18 Derstine and Huefner [1974] apparently used a similar methodology (they called it "the Dollar-Value LIFO method" [1974, p. 218]) to derive an estimate of the differences between FIFO and LIFO inventory dollar values. Their study, which was designed to assess the effects of inventory costing method choice on the financial ratios of twenty-four companies, did not provide any examples or further description of the conversion methodology. Notice that their estimates were of dollar amounts and not of physical inventory levels.


where EUMX, EUAt = dollar equivalent units of year-end physical inventory (based on monthly and annual price indexes, respectively) in year t

$EItF = FIFO-based year-end dollar inventory amount in year t

Ut = ($Purchasest/12)/Pt = average equivalent unit purchases per month in year t

$Purchasest = COGStF - $BItF + $EItF (= COGStL - $BItL + $EtL)

= dollar inventory purchases in year t COGStF = FIFO-based cost-of-goods-sold in year t

$BItF = $EIt-IF = FIFO-based beginning inventory dollar amount in year t

Pt.! = wholesale price index in month j = 1, ..., 12 of year t

Pt = average industry-specific wholesale price index in year t

12

(= 1/12 Pt j) j=1

and Ilifa-1 (I {a} = a if 0< a <1

l if a c 0.

Ultimately, the tax-related cash-flow implications of inventory properties are realized through their effects on COGS estimates. The EUM measures have been employed, therefore, to derive another measure of firm characteristics as they relate to LIFO-FIFO choices-the difference for each firm, each year, between its COGS based on the exclusive use of LIFO and its COGS based on the exclusive use of FIFO. This COGS Difference has been estimated by calculating the difference in the holding gains realized under these methods (COGSL - COGSF = HGF - HGL). Exclusive use has been assumed because it is difficult to determine to what fraction of its inventories a firm would have chosen to apply alternative inventory costing methods in each year in the past (firms using LIFO rarely employ that method for all of their inventories).

A COGS Difference represents, therefore, the dollar amount by which reported (and taxable) earnings would have differed if the firm had employed LIFO rather than FIFO for all of its inventories, given its pattern of physical inventory levels. As defined, a positive (negative) COGS Difference indicates a potential LIFO (FIFO) cash-flow advan- tage of that amount (absolute value) multiplied by the firm's marginal income tax rate. Whereas, in the case of the equivalent unit physical inventory measures, an inventory model was needed to provide testable implications for the Anticipations and Incentives hypotheses, the COGS Differences can be employed directly; a change or difference in inventory properties which results in larger (smaller) LIFO tax advantages would produce larger (smaller) COGS Differences. Thus, to be consistent with

248 GARY C. BIDDLE

these hypotheses, the treatment group firms should exhibit larger COGS Differences in the post-LIFO periods relative to their control group counterparts and should exhibit relatively larger COGS Differences in the post-LIFO than in the pre-LIFO periods.

EMPIRICAL TEST METHODOLOGIES

Three nonparametric statistical techniques are employed in the subsequent empirical tests of the Anticipations and Incentives hypotheses. Although some direct comparisons are made between the paired treatment and control group firms, the primary empirical tests examine changes between the pre- and post-LIFO periods in the relative attributes of the paired firms. Nonparametric techniques are appealing for these comparisons, since they require few assumptions about the underlying attribute populations. The Wilcoxon matched pairs signed ranks test (see Hollander and Wolfe [1973, pp. 27-33]) utilizes both the signs of the changes and their ranks (i.e., their relative sizes) in testing whether the median change across the firm pairs is significantly different from zero. Although this test is potentially the most powerful of the three employed, it assumes that the changes come from independent, continuous, and symmetric distributions with a common median. While data transformations can be employed to provide changes of the same general mag- nitude, the nonrandom sample selection procedures may result in viola- tions of these assumptions. (The independence assumption, for example, includes size independence across firm pairs.)

The matched pairs sign test examines only the signs of the pre- to post- LIFO changes in testing whether the median change is significantly different from zero (see Hollander and Wolfe [1973, pp. 39-45]). Although the sign test is less powerful than the Wilcoxon test, it does not require size independence across firm pairs and assumes only that the changes come from independent distributions with a common median. Like the Wilcoxon test, the sign test does not require independent pre- and post- LIFO observations. However, since both tests can accommodate only one pre- and one post-LIFO attribute observation (i.e., one change observation) for each firm pair, subsequent tests are based on summary measures (usually averages) over multiple pre- and post-LIFO attribute observations. Both Wilcoxon and sign test results are presented for each empirical comparison below.

Because the use of summary measures in the Wilcoxon and sign tests ignores the information contained in the individual observations, a third nonparametric procedure has been introduced. This procedure (see Noether [1967, pp. 41-43]) assumes a separate distribution for each group of pre- and post-LIFO observations previously summarized and tests whether these distributions are equal. While the Noether technique, like the sign test, does not require size independence across firm pairs, it does require independence for the successive attribute observations of each


firm pair. Since the Noether procedure yields implications almost identical to those provided by the Wilcoxon and sign tests, the results are presented in Appendix B.19

6. Data and Data Collection Procedures

TREATMENT GROUP FIRM SELECTION

The first step in compiling the data was the identification of a treatment group sample of firms which adopted LIFO. Two factors limited the range during which this adoption could have taken place. First, because the IRS ruling (Revenue Ruling 73-66) which permitted the disclosure of FIFO-based inventory amounts for LIFO adopters applied only to fiscal years 1973 and thereafter, footnote disclosures of these amounts were not consistently available in firms' financial statements before 1972.20 Second, to insure a sufficient number of data points in the post-LIFO adoption period, fiscal 1975 was the last adoption date considered.21 These dates encompass 1974, which was a year during which a large number of firms adopted or extended their use of LIFO.

Employing COMPUSTAT primary inventory-costing method codes and the Disclosure Journal (which is a record of 10-K, 10-Q, and 8-K filings with the SEC) a total of 251 firms within a COMPUSTAT longevity subsam- ple22 were found to have adopted LIFO or extended its use during the period 1972-75.23 The following data were then gathered from footnote disclosures in the annual reports of each of these 251 firms: (1) the proportion of inventories which were valued using various inventory- costing methods and (2) estimates of what their inventory valuations

19 A random coefficients regression approach (see Swamy [1970]) was also considered as a means of aggregating the time series of attribute observations across firm pairs. This procedure assumes constant variances and zero covariances from period to period in each component regression as well as the absence of any auto (or serial) correlation of the disturbance terms. While autocorrelations were found to be generally insignificant, a joint likelihood ratio test for constant variances between periods (see Keeping [1962, pp. 214-16]) revealed for the several attribute ratio series (see Section 7) to which it was applied significant departures from this assumption. Some trial random coefficients regres- sions on these series produced generally insignificant results.

20 A firm adopting LIFO in 1972 would have made these disclosures in accordance with APB Opinion No. 20, which deals with accounting changes.

21 At the time of data collection, financial statement disclosures were available only through fiscal 1978.

22 The longevity subsample of COMPUSTAT firms available at the University of Chicago is composed of those firms with certain specified financial statement data consistently available over the period of at least 1950-70. This subsample, which contains 755 firms, was employed to insure generally longer periods of continuous data availability for time-series tests.

23 The Disclosure Journal also identifies changes from LIFO to FIFO. For the period surveyed (1972-75), there were very few changes in that direction.

250 GARY C. BIDDLE

would have been had they not adopted LIFO.24 These data were obtained for the year preceding the year LIFO was adopted or extended (these dates were in some cases modified) and for all subsequent years for which these disclosures were available.

Although 251 firms were found to have extended their use of LIFO sometime during the period 1972-75, several problems became evident. In some cases, LIFO was adopted for only a small portion of inventories, in others, LIFO was already being used to some extent, and in some cases, firms had experienced marked changes in size due to mergers, acquisitions, and divestitures. Previous LIFO use would bias the equivalent unit measures, since they rely on a FIFO cost-flow assumption, and would thus confound time-series tests of inventory properties around LIFO adoption dates.25 Small LIFO extensions may not provide detectable changes in inventory policies or detectable incentives for LIFO adoption, while major changes in firms' sizes (due to mergers, acquisitions, etc.) could bias both time-series and paired comparisons. To reduce these problems the treatment group sample was limited to: (1) firms which converted at least 20 percent of their inventories (based on dollar values) to LIFO in the LIFO adoption year and a proportion equal to at least 30 percent within three years of their LIFO adoption dates26 and (2) firms which had not used LIFO for more than 10 percent of their inventories (based on dollar values) prior to their LIFO adoption dates.

Since no comprehensive record of mergers, acquisitions, and divestitures was readily available for the entire post-WWII period, a year-to- year comparison of sales was used to identify major changes in firms' sizes. To provide a minimum-time series of pre-LIFO adoption data points for all firms over periods in which their sizes did not markedly change, the treatment group sample was further limited to: (3) firms with at least a six-year period prior to their LIFO adoption dates over which sales were less than double and more than half of what they were the previous year.27

24 As suggested in IRS Revenue Ruling 73-66, virtually all of the firms disclosed what their inventory values would have been if FIFO had been employed instead of LIFO.

25 For example, a firm which had previously used LIFO for 40 percent of its inventories and which extended this use to 60 percent would report in the notes to its financial statements after the extension what its inventories would have been if it had valued them using FIFO. In this case, the preextension amounts (based on 40 percent LIFO and 60 percent FIFO) would not be comparable with the postextension amounts (which in the notes would be 100 percent FIFO).

26 In some cases, firms disclosed percentages or used phrases like "substantially all" to describe what portion of their inventories were valued using LIFO. In these cases, judgment was exercised to determine whether the firms had converted a sufficient portion of their inventories to LIFO to be included in the treatment group sample.

27 Six years was chosen to provide a minimum pre-LIFO adoption period equal in length to the longest post-LIFO adoption period in the paired sample. This additional condition reduced the sample by three firms.


SELECTION OF CONTROL GROUP FIRMS

The next major step in the data collection process involved the identification of a matched pair control group sample of firms which were "similar" to their treatment group counterparts, but which had never used the LIFO cost-flow assumption. The selection process for the control sample firms was restricted to the COMPUSTAT files to insure data availability, but was not limited to the longevity subsample due to the limited number of candidate firms in that subsample. For each of the firms in the treatment group sample, an attempt was made to identify a control firm counterpart on the basis of the following criteria: (1) no use of LIFO as indicated by the COMPUSTAT inventory-costing method codes28 for any year for which these data were available; (2) the same four-digit SIC industry classification as the treatment group firm; (3) the same size (measured by net sales in the LIFO adoption year) where there was more than one candidate based on the other criteria; (4) a period of at least six years prior to the treatment group firm's LIFO adoption date over which sales less than doubled and were more than half of what they were the previous year.

In several industries (e.g., chemicals and glass) nearly all of the COM-

PUSTAT firms were either already using LIFO to some extent or simul- taneously adopted LIFO. As a result, even though the control group selection criteria were relaxed slightly in a few cases (see below), control group counterparts were not available for forty-two of the treatment group firms.

The resulting paired samples of 105 treatment and control group firms serve as the basis for subsequent descriptions and analyses. They are presented in table 2 along with their four-digit SIC industry classifications (according to the 1977 COMPUSTAT files). In those twenty-three cases where two SIC codes are indicated, control firms were selected from four- digit SIC industry classifications different from those of their treatment group counterparts. Even here, however, each control group firm has the same two- or three-digit SIC classification as the corresponding treatment group firm (except for Super Value Stores and Stop and Shop Compa- nies). In a few instances, the requirement that a control group firm could not have used LIFO to any extent over the range of available data was also relaxed. These instances are noted in the far right-hand column of table 2. For all of the other control group firms, there was no evidence of LIFO use in the COMPUSTAT files over the entire range of data availability.

Table 2 also indicates the relative sizes of the paired treatment and control group firms and the number of months by which their fiscal year-

28 Starting in 1963 the COMPUSTAT inventory method code indicates all methods disclosed in a firm's annual reports in order of application (i.e., the primary method is reported first, the next most widely employed method second, etc.). Unless indicated otherwise, the control group firms did not utilize LIFO to any extent over the entire range of available data.

252 GARY C. BIDDLE

TABLE 2 Treatment and Control Group Samples

SIC Industry Corresponding FYE Notes

Treatment Classi- Control Sales Differ- (see Group Firm fication* Group Firm Ratio** ence*** below)

(1977GruFimec**blw COMPUSTAT)

Cleveland-Cliffs. Co. 1000 Texas Gulf Inc. 0.21 0 Foote Mineral Co. 1000 Brush Wellman Inc. 1.29 0 Homestake Mining 1041 Dome Mines Ltd. 1.93 0 Halliburton Co. 1600 Dillingham Corp. 4.02 0 McDermott (J. Ray) 1600 Fluor Corp. 0.60 7 Stokely-Van Camp Inc. 2030 Green Giant Co. 1.03 0 Tasty Baking Co. 2050 American Bakeries Co. 0.33 0 Heileman Brewing Inc. 2082 Olympia Brewing 1.04 0 Coca-Cola Co. 2086 Pepsico Inc. 1.21 0 Pepcom Inds. 2086 Royal Crown Cos. Inc. 0.18 0 Armstrong Cork Co. 2270-2200 Stevens (J.P.) & Co. 0.70 2 Masland (C. H.) & Sons 2270-2200 Belding Heminway 1.13 0 Hart Schaffner & Marx 2300 Phillips-Van Hausen 1.59 10 Munsingwear Inc. 2300 Jantzen Inc. 0.91 4 Georgia-Pacific Corp. 2400 Boise Cascade Corp. 1.67 0 Masonite Corp. 2400 Evans Products Co. 0.32 4 Scott Paper Co. 2600 Brown Co. 2.55 5 Union Camp Corp. 2600 Domtar Inc. 1.01 0 Stone Container Corp. 2650 Federal Paper Board 0.60 0 Times Mirror Co. 2711 New York Times Co. 1.88 0 Meredith Corp. 2721 Cadence Ind. Corp. 1.82 6 Donnelley (R. R.)/Sons 2750-2731 McGraw-Hill Inc. 0.89 0 Standard Register Co. 2761-2750 Hall (W. F.) Printing 1.61 9 UARCO Inc. 2761 Reynolds & Reynolds 2.24 0 Celanese Corp. 2800-2870 Intl. Minerals/Chem. 2.25 6 DuPont de Nemours 2800-2841 Unilever NV 0.88 0 Monsanto Co. 2800 Grace (W. R.) & Co. 1.01 0 a Stauffer Chemical Co. 2810-2850 Sherwin-Williams Co. 1.07 4 Chemetron Corp. 2810-2841 Purex Corp. 1.05 6 Reichhold Chemicals 2820 Akzonia 0.64 0 Pfizer Inc. 2830 Merck & Co. 1.16 0 Tampax Inc. 2830 Rorer Group 0.56 0 Pratt & Lambert Inc. 2850 Insilco Corp. 0.20 0 Nalco Chemical Co. 2860 Crompton & Knowles 2.19 0 Ferro Corp. 2890 Avery International 1.09 1 Sun Chemical Corp. 2890 Products Res./Chem. 8.15 3 Shell Oil Co. 2911 Imperial Oil Ltd. 1.89 0 Armstrong Rubber 3000 Amerace Corp. 1.10 3 Cooper Tire & Rubber 3000-3079 Monogram Inds. Inc. 0.80 6 General Tire & Rubber 3000 Uniroyal Inc. 0.75 1 Goodrich (B. F.) & Co. 3000 Dunlop Holding Ltd. 0.95 0 Mansfield Tire/Rubber 3000 Alliance Tire/Rubber 1.86 0 Mohawk Rubber Co. 3000 Aegis Corp. 2.47 0 Rubbermaid Inc. 3000-3069 O'Sullivan Corp. 4.15 0 Brown Group Inc. 3140 Genesco Inc. 0.61 3 Libby-Owens-Ford Co. 3210 Seagrave Corp. 7.17 0 Mo. Portland Cement 3241 General Portland Inc. 0.29 0 Basic Inc. 3290-3250 Spartek Inc. 2.38 2 Bliss & Laughlin Inds. 3310 Hofmann Industries 3.28 8 Copperweld Corp. 3310 NW Steel & Wire Co. 1.04 5 b Lukens Steel Co. 3310 Tubos de Acer. de Mex. 2.66 0 U.S. Reduction 3341-3330 Atlas Cons. Min./Dev. 0.75 1 Wallace-Murray Corp. 3430 Masco Corp. 1.25 0 Allied Products 3449 Phillips Industries 1.51 9 Std. Pressed Steel 3449 Zero Corp. 6.29 9 Wolverine Pentronix 3449 Eastern Co. 0.92 0 Lamson & Sessions Co. 3452-3499 Diebold Inc. 0.65 8 Synalloy Corp. 3499 Automated Bldg. Com. 2.06 8 Combustion Engineering 3510 Foster Wheeler Corp. 1.63 0


Table 2. Continued

SIC Treatment Industry Corresponding Sales FYE Notes Grouparmet Classification* Control Rat o** Differ- (see Group Firm (1977 Group Firm alo ence*** below)

COMPUSTAT)

Clark Equipment Co. 3531 Bucyrus-Erie Co. 5.24 0 Unarco Inds. Inc. 3531 Am. Hoist & Derrick 0.56 1 Dresser Inds. Inc. 3533 Schlumberger Ltd. 1.22 2 Big Three Inds. 3533 Smith International 0.81 0 Monarch Machine Tool 3540 Skil Corp. 0.52 0 Emhart Corp. 3550 Black & Decker Mfg. 0.52 3 Ex-Cell-O Corp. 3550 Midland-Ross Corp. 0.93 1 Leesona Corp. 3550 Selas Corp. of Amer. 3.53 0 Chicago Pneumatic Tool 3560 Stewart-Warner Corp. 1.04 0 Gardner-Denver Co. 3560 Curtiss-Wright Corp. 1.20 0 Ingersoll-Rand Co. 3560-3570 Burroughs Corp. 0.94 0 Parker-Hannifin Corp. 3560-3570 Pitney-Bowes Inc. 0.89 6 Carrier Corp. 3580 Tecumseh Products 1.73 2 Copeland Corp. 3580 GCA Corp. 5.12 0 Fedders Corp. 3580 Vendo Co. 3.74 2 UMC Inds. 3580 Tokheim Corp. 4.69 1 McGraw-Edison Co. 3610 Eltra Corp. 1.19 3 Maytag Co. 3630 Republic Corp. 1.02 5 RCA Corp. 3651-3662 Raytheon Co. 2.38 0 Zenith Radio Corp. 3651-3662 Motorola Inc. 0.67 0 Mallory (P.R.) & Co. 3679 Ampex Corp. 0.91 8 Cummins Engine 3713-3711 American Motors Corp. 0.42 3 Borg-Warner Corp. 3714 Eaton Corp. 1.00 0 Budd Co. 3714 Arvin Inds. Inc. 3.07 0 Buell Inds. Inc. 3714 Aspro Inc. 0.76 3 Federal-Mogul Corp. 3714 Champion Spark Plug 0.84 0 TRW Inc. 3714 Bendix Corp. 1.01 3 Timkin Co. 3714-3728 Rohr Industries 1.58 5 Ametek Inc. 3811 Beckman Instruments 1.16 6 Johnson Controls Inc. 3820-3811 Perkin-Elmer Corp. 0.95 5 Multi-Amp Corp. 3825 Fluke (John) Mfg. Co. 0.16 5 Tektronix Inc. 3825-3823 General Signal Corp. 0.61 7 c Bausch & Lomb Inc. 3830 ITEK Corp. 1.50 0 Johnson & Johnson 3841 Am. Hospital Supply 1.97 0 Eastman Kodak Co. 3861 Minn. Mining & Mfg. 1.56 0 Parker Pen Co. 3950 Binney & Smith Inc. 2.34 10 Fleming Cos. Inc. 5140 Scot Lad Foods 1.85 6 Super Value Stores 5140-5411 Stop & Shop Cos. 1.34 1 Carter Hawley Hale 5311 Gamble-Skogmo 0.75 0 Marshall Field & Co. 5311 Cook United Inc. 1.23 11 K-Mart Corp. 5331-5311 Zayre Corp. 5.17 0 American Stores Co. 5411 Jewel Cos. Inc. 1.05 2 Safeway Stores Inc. 5411 Great Atl./Pac. Tea 1.21 10 Winn-Dixie Stores 5411 Lucky Stores Inc. 0.94 5 Thrifty Corp. 5912 Gray Drug Stores 1.80 4 d Teledyne Inc. 9997 Litton Industries 0.56 5

* When two codes are listed, the first is for the treatment and the second for the control firm. * * Net sales of treatment group firm divided by net sales of control group firm in the LIFO adoption

year. *** Number of months in the same year between fiscal year-ends of treatment and control group

firms. (Based on 1977 FYE.) a W. R. Grace and Company used LIFO as a tertiary inventory costing method in the years 1974-77.

During this period FIFO and Average Cost were the primary and secondary methods. b Northwestern Steel and Wire Company used the LIFO inventory costing method between 1960 and

1963. c General Signal Corporation used the LIFO inventory costing method during the period 1948-61 and

as a tertiary method during the period 1963-69. d Gray Drug Stores employed the LIFO inventory costing method during the period 1963-68.

254 GARY C. BIDDLE

ends differed.29 Forty of the firm pairs had a control group firm with sales greater than or equal to those of its treatment group counterpart in the LIFO-adoption year, while the reverse was true for the remaining sixty- five firm pairs. In only two cases is the treatment group firm less than one-fifth the size and in only six cases more than five times the size of the corresponding control group firm according to this sales ratio measure. Table 2 also reveals that a majority of the matched firm pairs (53 out of 105) have identical fiscal year-end months. Table 3 presents the distribution of LIFO adoption dates for the treatment group firms in the paired sample. The vast majority of these firms (92 out of 105) adopted LIFO in 1974.

DATA AVAILABILITY

To provide for the computation of the EUA, EUM, and COGS Differ- ence series, all available observations of the following data items were obtained for each treatment and control group firm: (1) FIFO-based year- end inventory amounts, (2) FIFO-based COGS, (3) sales, and (4) industry- specific wholesale price indexes.

The inventory, COGS, and sales data were obtained from the COMPU-

STAT files and from available published financial statements (and notes thereto as described above). A wholesale price index ( WPI) series was chosen to match each of the seventy-four four-digit SIC industry classifications represented in the treatment and control group samples based on each firm's 1977 COMPUSTAT code. The industries and the WPI series chosen for each are listed in table 4. The price index observations were obtained from the Data Resources Incorporated (DRI) files at the University of Chicago and from various issues of Wholesale Prices and Price Indexes (U.S. Bureau of Labor Statistics). WPIs were used as a measure of inventory input prices because of their wide industry coverage and because most are available on a monthly basis for the post-WWII period (see table 4). WPIs can be expected to provide a close approxi- mation to inventory input price levels and price changes, especially when inventories include finished and semifinished goods and when purchases take place at close to a uniform rate throughout each year. AR of the WPIs employed use 1967 as their base year (i.e., the price level in 1967 equals 100).

Tables 5 and 6 present the distributions of joint data availability of the EUM, EUA, and COGS Differences series for the paired treatment and control group firms. In each case, the distributions include only those data ranges over which the sales of both the treatment and the control

29 Fiscal year-end month was not used as a selection criterion for control group firms. It is presented in table 2 as a descriptive statistic which is important in determining whether the paired firms would have been reacting to similar economic events in determining their fiscal year-end inventory levels. Similar fiscal year-end months contribute to the internal validity of the empirical tests by reducing the possibility that paired firms were reacting to different forecasts and events.


TABLE 3 LIFO Adoption Dates of Treatment Group Firms

Number of Firms Year Adopting LIFO Percent of Sample

in Each Year

1973 2 1.9 1974 . . . 92 87.6 1975 ..11 10.5

Total . . .. .. .. . 105 100%

firm in each pair were less than double and were more than half of what they were the previous year. Table 5 exhibits the generally longer periods of pre-LIFO data availability obtained when the equivalent unit measure is based on annual (EUA) rather than monthly (EUM) price indexes.30 The difference in data availability between the E UMs and COGS Differ- ences arises from the need for a start-up year in the calculation of the COGS Differences.31

Because during the post-LIFO adoption periods the treatment group firms disclosed both the proportions of their inventories valued using alternative costing methods and their inventory valuations under each, a rough check was available for the COGS Differences calculations. Comparisons for ten randomly selected treatment group firms were made between the COGS Differences obtained from the estimation algorithm and comparable figures derived from the firms' financial statement disclosures. While there were some relatively large deviations in the yearly estimates, over the range of post-LIFO years the sum of the estimates provided by the algorithm were generally within 10 percent of the corresponding figure derived from the financial disclosures (see Biddle [1980, pp. 51-52]).32 This evidence suggests that the algorithms used to estimate the COGS Differences and the E U measures on which they are based provide estimates close to those which would have been obtained from internal documents.

7. Empirical Results

In the discussion which follows, Sample 1 refers to the 105 matched pairs of treatment and control group firms listed in table 2. Sample 2 is that subset of Sample 1 in which (a) the paired treatment and control group firms have identical four-digit (SIC) industry classifications, (b) the treatment group firms have 1974 LIFO adoption dates, and (c) the treatment group firms have sales less than five times and more than one-

30 When calculating the EUAs, the annual price indexes employed were based on an average of the monthly indexes ending in the firm's fiscal year-end month.

31 The EUA estimate was used in the start-up year when available in place of the EUM estimate.

32 This is the appropriate comparison since the empirical tests employing the COGS Differences are based on these sums.

256 GARY C. BIDDLE

TABLE 4 Wholesale Price Indexes Selected for Industries Represented in Treatment and Control

Firm Samples

Four- Data Digit COMPUSTAT W WPI Avail- sic Industry Identi- Industry ability Indus- Name fication Description (Key try Codet below) Code

1000 Metal Mining 1011 NS Iron Ore * 1041 Gold Ores 102 NS Nonferrous Metals * 1600 Construction (Not Bldg.) 1321 Sand, Gravel, Cr. Stone A 2030 Canned-Pres. Fr./Veg. 024 NS Processed Fr./Veg. * 2050 Bakery Products 021 NS Cereal, Bakery Prod. * 2082 Malt Beverages 026 NS Beverages, Bev. Malts * 2086 Bot., Canned Soft Drinks 026 NS Beverages, Bev. Malts * 2200 Textile Mill Products 03 NS Textile Pds. & Apparel * 2270 Floor Covering Mills 132 NS Floor Coverings * 2300 Apparel, Other Fin. Pds. 035 NS Apparel * 2400 Lumber & Wood Products 08 NS Lumber & Wood Products * 2600 Paper & Allied Products 09 NS Paper & Allied Products * 2650 Paperbd. Cont.-Boxes 0915 NS Conv. Paper, Paperbd. * 2711 Newspapers: Pub.-Print C913 NS Paper * 2721 Periodicals: Pub.-Print 0913 NS Paper * 2731 Books: Pub., Printing 0913 NS Paper * 2750 Commercial Printing 0913 NS Paper * 2761 Manifold Business Forms 0913 NS Paper * 2800 Chemicals, Allied Pds. 06 NS Chemicals, Allied Pds. * 2810 Indl. Inorganic Chem. 061 NS Industrial Chemicals * 2820 Plastic Mtrl., Syn. Resin 066 NS Plastic Resins, Mtrls. * 2830 Drugs 063 NS Drugs, Pharmaceuticals * 2841 Soap, Other Detergents 067 NS Other Chem., Allied Pds. * 2850 Paints, Varn., Lacquers 0622 NS Paint Materials * 2860 Indl. Organic Chemicals 061 NS Industrial Chemicals * 2870 Agricultural Chemicals 065 NS Agr. Chem., Chem. Pds. * 2890 Misc. Chemical Pds. 067 NS Other Chem., Allied Pds. * 2911 Petroleum Refining 057 NS Ref. Petroleum Pds. * 3000 Rubber, Misc. Plastic 07 NS Rubber, Plastic Pds. * 3069 Fab. Rubber Pds., Nec. 071 NS Rubber, Rubber Pds. * 3079 Misc. Plastic Pds. 07 NS Rubber, Plastic Pds. * 3140 Footwear ex. Rubber 043 NS Footwear * 3210 Flat Glass 1311 NS Flat Glass * 3241 Cement Hydraulic 132 NS Concrete Ingredients * 3250 Structural Clay Pds. 134 NS Struc. Clay Pds. ex. Ref. * 3290 Abrasive Asbestos 13 NS Nonmetalic Mineral Pds. *

Misc. Min. 3310 Blast. Furn., Steel Works 101 NS Iron, Steel * 3330 Prim-Smelt-Refin. Nonfer. 1022 NS Prim. (Nonfer.) Mtl. A

Mtl. Refin. Shapes 3341 Sec-Smelt-Refin. Nonfer. 1024 NS Sec. (Nonfer.) Mtl. A

Mtl. Refin. Shapes 3430 Heating Eqpt., Plumbing 106 NS Heating Equipment * 3449 Misc. Metal Work 108 NS Misc. Metal Pds. * 3452 Bolts-Nuts-Screws-Riv- 104 NS Hardware *

Washers 3499 Fabr. Metal Pds. Nec. 107 NS Fabr. Struc. Metal Pds. * 3510 Engines, Turbines 119401 NS Gas Engines A 3531 Constr. Mach/Eqpt. (M & E) 112 NS Constr. M & E * 3533 Oil Field M & E 1191 NS Oil Field M & E A 3540 Metalworking M & E 113 NS Metalworking M & E * 3550 Special Industry Mach. 11 NS Mach. & Equipment 1/47-12/60

116 NS Special Ind. M & E 1/61-12/78 3560 General Ind. M & E 114 NS Gen. Purp. M & E * 3570 Off. Comp., Acct. Mach. 119301 Comp., Related Mach. A 3580 Refrig., Service Ind. 1141 NS Pumps, Compressors, A

Mach. Eqpt. 3610 Elec. Trans., Dist. Eqp. 1174 NS Transformers, Pwr Reg. A 3630 Household Appliances 124 NS Household Appliances *


Table 4. Continued

Four- Data Digit COMPUSTAT WPI WPI Avail- sIc Industry Identi- Industry ability Indus- Name fication Description (key try Codet below)

Code 3651 Radio-TV Rec. Sets 117 NS Electr. M & E 1/47-12/54

125 NS Home Electronic E. 1/55-12/78 3662 Radio-TV Trans. Eqp. 117 NS Electr. M & E 1/47-11/68

117827 NS Elec. Hdw/Rad. Hdw 12/68-12/78 3679 Electr. Comp. Nec. 1178 NS Electr. Comp., Acs. * 3711 Motor Veh., Car Bodies 141 NS Motor Veh., Eqpt. * 3713 Truck, Bus Bodies 141102 Motor Trucks A 3714 Motor Veh. Pts., Acs. 141 NS Motor Veh., Eqpt. * 3728 Aircraft Pts., Aux. Eqpt. 10 NS Metals, Metal Pds. * 3811 Engr. Lab, Res. Eqpt. 1178 NS Electr. Comp., Acs. * 3820 Meas., Control Instr. 1178 NS Electr. Comp., Acs. * 3823 Indust. Meas. Instr. 1178 NS Electr. Comp., Acs. * 3825 Elec. Meas., Test Instr. 1178 NS Electr. Comp., Acs. * 3830 Optical Instr., Lenses 154 NS Photo Supply, Eqpt. * 3841 Surg., Med. Instr., App. 117 NS Electr. Mach, Eqpt. * 3861 Photo. Eqpt., Supply 154 NS Photo Eqpt., Supply * 3950 Pens, Pencils, 0th. 119307 NS Other Off., Store Mach. A

Office Matl. 5140 Whsl. Groceries, 01402 NS Farm Foods, Proc. Foods, *

Related Pds. Feeds 5311 Ret. Dept. Stores CDNS Fin. Consumer Durables * 5331 Ret. Variety Stores CFGEFNS Cons. Fin. Gds. ex. Food * 5411 Ret. Grocery Stores FOODNS Cons. Finished Foods * 5912 Ret. Drug, Propriety 063 NS Drugs, Pharm. * 9997 Conglomerates NS All Commodities *

t Identification code in each case is WPI plus the indicated code. * Monthly data over entire period 1/1947-12/1978. A Annual data 1/1947-12/1972, monthly data 1/1973-12/1978.

fifth as large as their control group counterparts in 1974. The sixty-seven firm pairs contained in Sample 2 comprise, therefore, a set which is less subject to several possible threats to the validity of the empirical comparisons.

To simplify the presentation further, some abbreviations are employed. EU refers to the equivalent unit measures of physical inventory levels, with E UM and E UA denoting those calculated using monthly and annual price indexes, respectively. (These measures are denominated in 1967 dollars.) Sales refers to net sales in dollars, while Sales67 indicates net sales expressed in 1967 dollars. COGSDIF (denominated in dollars) stands for the COGS Difference (= COGS' - COGSF). Each of these abbreviations may be accompanied by the subscripts T or C, which refer to the treatment and control group counterparts, respectively. The terms Av- erage and Ave are used interchangeably to refer to both time-series and cross-sectional averages; their contexts should make clear which type of average they denote. Other notation includes log for natural logarithmic transformation,33 l\ for first differencing in time, and SD for standard deviation.

33 The log transformations permit differences between the attributes of paired firms (and differences between attribute ratios in different time periods) to be compared across the firm pairs by abstracting from differences in firm sizes. The log transformation was not applied to the COGSDIF observations because these can assume negative values.

258 GARY C. BIDDLE

TABLE 5 Pre-LIFO Adoption Data Availability

Number of E UMs* E UAs* * COGS Differences* * * Years of Pre-LIFO Number Number Number Adoption of Firm Cum. % of Firm Cum. % of Firm Cum. %

Data Pairs Pairs Pairs

27 6 5.7 15 14.3 0 0.0 26 8 13.3 12 25.7 8 7.6 25 1 14.3 3 28.6 7 14.3 24 1 15.3 7 35.3 1 15.3 23 5 20.1 3 38.2 5 20.1 22 2 22.0 4 42.0 2 22.0 21 3 24.9 1 43.0 3 24.9 20 0 24.9 2 44.9 0 24.9 19 2 26.8 3 47.8 2 26.8 18 4 30.6 3 50.7 4 30.6 17 5 35.4 5 55.5 5 35.4 16 8 43.0 0 55.5 8 43.0 15 1 44.0 1 56.5 1 44.0 14 4 47.8 16 71.7 4 47.8 13 21 67.8 9 80.3 21 67.8 12 10 77.3 3 83.2 10 77.3 11 8 84.9 3 86.1 8 84.9 10 2 86.8 3 89.0 2 86.8 9 5 91.6 3 91.9 5 91.6 8 3 94.5 4 95.7 3 94.5 7 2 96.4 1 96.7 2 96.4 6 4 100.0 4 100.0 4 100.0

Total 105 105 105

* Equivalent unit measures of physical inventory levels based on monthly price indexes. Necessary data include beginning and end of year FIFO-based inventory amounts, FIFO-based COGS, and monthly industry-specific wholesale price indexes.

** Equivalent unit measures of physical inventory levels based on annual price indexes. Necessary data include year-end FIFO-based inventory amounts and annual industry-specific wholesale price indexes.

*** Differences between LIFO-based and FIFO-based costs-of-goods-sold. Necessary data include beginning and end of year equivalent unit measures of physical inventory levels, FIFO-based COGS, and monthly industry-specific wholesale price indexes.

COMPARISONS BETWEEN TREATMENT AND CONTROL GROUP

FIRMS IN THE PRE-LIFO AND POST-LIFO PERIODS

The first empirical evidence which is examined involves comparisons between the paired treatment and control group firms in the years preceding their (treatment firms') LIFO adoption dates and parallel comparisons in the post-LIFO adoption periods. The comparisons are based on a set of five attribute measures suggested by the previous discussion: Average Change in Log EUs is a measure of the rate of growth of physical inventory levels. Standard Deviation of Changes in log EUs is a measure of the variability of growth of physical inventory levels. Ratio of Average E Us to Average Sales67 is a measure of the size of physical inventories which is scaled by sales to control for any sys-


TABLE 6 Post-LIFO Adoption Data Availability

Number of Years E UMs, * E UAs, * * and COGS Differ- of Post-LIFO ences* * * Adoption Data

(Includes Year of Number Cumulative LIFO Adoption) of Firm Percent

Pairs

6 1 1.0% 5 74 71.5 4 25 95.3 3 2 97.2 2 3 100.0

Total 105

* Equivalent unit measures of physical inventory levels based on monthly price indexes. Necessary data include beginning and end of year FIFO-based inventory amounts, FIFO-based COGS, and monthly industry-specific wholesale price indexes.

** Equivalent unit measures of physical inventory levels based on annual price indexes. Necessary data include year- end FIFO-based inventory amounts and annual industry-specific wholesale price indexes.

*** Differences between LIFO-based and FIFO-based costs-of-goods-sold. Necessary data include beginning and end of year equivalent unit measures of physical inventory levels, FIFO-based COGS, and monthly industry-specific wholesale price indexes.

tematic differences in the sizes of paired treatment and control firms. Average COGSDIF (COGSDIF is the difference between LIFO- and FIFO-based COGS) summarizes the interaction of inventory and price- level changes as they affect potential cash-flow differences between the LIFO and FIFO alternatives. As defined, a positive (negative) COGSDIF implies larger potential cash flows under the LIFO (FIFO) alternative. Ratio of Average COGSDIF to Average Sales controls for size differences between paired firms which may bias comparisons based on Average COGSDIFs.

Table 7 presents the results of Wilcoxon and sign test comparisons based on these attribute measures estimated over the respective ranges of data availability of the firm pairs in Sample 1. The first two columns present standard normal deviates (and associated significance levels) for the Wilcoxon and sign tests, respectively, while the next two columns present the rank sums and counts on which the tests are based. The vectors of differences utilized in these tests are composed of treatment firm measures less those of their control group counterparts.

The results in table 7 indicate that the paired treatment and control group firms in Sample 1 experienced similar average rates of inventory growth in the pre-LIFO periods, as indicated by the insignificant Z- statistics. However, after they adopted LIFO, the treatment group firms experienced significantly (statistically) higher average rates of inventory growth. According to the tests based on the second attribute measure,

260 GARY C. BIDDLE

TABLE 7 Wilcoxon and Sign Test Comparisonst between Sample 1 Treatment and Control Group

Firms in Pre-LIFO Periods and in Post-LIFO Periods (Difference = Treatment - Control)

Wilcoxon Sign Test Positive Negative Nub Variable Z* Z* Rank Sum Rank Sum f Fmbe

and Period Statistic Statistic (# Pos. (# Neg. o irm (Signif.) (Signif.) Ranks) Ranks) airs

Physical Inventory Growth [Ave(A log EU)] Pre-LIFO

EUMs ..... -0.85 (.1971) -0.10 (.4602) 2516 (52) 3049 (53) 105 EUAs . -1.21 (.1131) -1.27 (.1020) 2404 (46) 3161 (59) 105

Post-LIFO EUMs .... . 4.08 (.0) 3.81 (.0) 4059 (72) 1506 (33) 105 EUAs . 4.14 (.0) 4.20 (.0) 4076 (74) 1489 (31) 105

Variability of Physical Inventory Growth [SD(A log EU)] Pre-LIFO

EUMs .... . -3.20 (.0006) -2.24 (.0125) 1781 (41) 3784 (64) 105 EUAs . -3.19 (.0007) -2.05 (.0202) 1786 (42) 3779 (63) 105

Post-LIFO EUMs . -1.49 (.0683) -0.88 (.1894) 2317 (48) 3248 (57) 105 EUAs . -0.65 (.2587) -0.10 (.4602) 2580 (52) 2985 (53) 105

Ratio of Average Physical Inventories to Average Sales [Ave(EU)/Ave(Sales67)] Pre-LIFO

EUMs .... . -0.00 (.4980) -0.09 (.4641) 2781 (52) 2784 (53) 105 EUAs . -0.20 (.42210) -0.29 (.3859) 2721 (51) 2844 (54) 105

Post-LIFO EUMs . 2.73 (.0031) 2.05 (.0202) 3638 (63) 1927 (42) 105 EUAs . 2.75 (.0030) 1.85 (.0322) 3643 (62) 1922 (43) 105

Average Difference Between LIFO- and FIFO-based COGS [Ave( COGSDIF)] Pre-LIFO ... . 1.73 (.0414) 2.05 (.0202) 3325 (63) 2240 (42) 105 Post-LIFO .4.41 (.0) 4.39 (.0) 4161 (75) 1404 (30) 105 Ratio of Average COGSDIF to Average Sales [Ave( COGSDIF)/Ave(Sales)] Pre-LIFO ... . -0.39 (.3477) -0.88 (.1894) 2660 (48) 2905 (57) 105 Post-LIFO .. 3.67 (.0001) 3.22 (.0006) 3930 (69) 1635 (36) 105

t Based on all available observations for each firm pair. * Standard normal deviate. (One-tail significance.)

the control group (non-LIFO) firms in Sample 1 exhibited generally more variable rates of inventory growth than their treatment group counterparts, though these differences were more pronounced in the pre- than in the post-LIFO periods.

Comparisons based on the third attribute measure indicate that the paired treatment and control group firms held similar levels of physical inventories relative to sales in the pre-LIFO periods, but that the treatment group firms held significantly larger inventories in the post-LIFO periods. The tests based on the fourth attribute measure (Average COGS- DIF) reveal that the treatment group firms (which actually adopted LIFO) had available to them greater potential LIFO tax benefits in both the pre- and post-LIFO periods. Yet this comparison may be somewhat misleading, since table 2 indicates that the treatment group firms tend to be larger than their control group counterparts. The fifth attribute measure controls for systematic differences in size by scaling the Average COGSDIF measure using Average Sales. The Wilcoxon and sign test comparisons based on this scaled measure indicate that relative to sales, the control group firms had slightly (though not significantly) larger potential LIFO cash-flow benefits available to them in the pre-LIFO


periods. The treatment group firms, however, again exhibit significantly larger potential LIFO cash-flow benefits (Average COGSDIFs) in the post-LIFO periods.

Table 8 presents the same set of comparisons as table 7 for the firm pairs of Sample 2 with the attribute measures estimated over the periods six years prior and up to six years after the LIFO adoption dates (for Sample 2 this means 1968-78).3 These estimation periods were employed to reduce any biases associated with comparing firm pairs using attribute observations from different periods in real time, to provide pre- and post- LIFO period estimates with roughly equal sampling error, and to provide the largest possible sample size.

The results presented in table 8 are almost identical to those in table 7. Again, the control group firms exhibit faster rates of inventory growth in the pre-LIFO periods (with the difference being even more pronounced than in table 7), while the treatment group firms again exhibit significantly faster growth rates in the post-LIFO periods. The variability of inventory growth is again found to be higher in both the pre- and post- LIFO periods for the control group firms, though the differences are generally insignificant in both cases. The paired treatment and control group firms are again found to hold similar levels of physical inventories relative to sales in the pre-LIFO periods, with the treatment firms exhibiting larger inventories in the post-LIFO periods (though the levels of statistical significance are less than were found in table 7). Comparisons based on the Average COGSDIF measures again indicate significantly larger potential LIFO cash-flow benefits for the treatment group firms in both the pre- and post-LIFO periods. Comparisons based on the measures of Average COGSDIF scaled by Average Sales again indicate insignificant differences between the paired treatment and control group firms in the pre-LIFO periods and significantly larger Average COGSDIFs (relative to sales) for the treatment group firms in the post-LIFO periods.

The relationships portrayed in tables 7 and 8 between the paired treatment and control group firms are uniformly consistent with the implications of the Anticipations and Incentives hypotheses. Both hypotheses suggest that the treatment group firms should exhibit faster and less variable rates of inventory growth, larger inventories, and larger COGSDIFs in the post-LIFO periods than their control group counterparts. Only those tests based on the variability of inventory growth fail to exhibit high levels of statistical significance. It was also suggested in connection with these hypotheses that because some firm characteristics are not readily alterable by managers, differences should also be observed in the pre-LIFO periods. The evidence in tables 7 and 8 suggests instead that the inventory attributes of the paired treatment and control group firms did not markedly differ in the pre-LIFO periods.

34 Because the treatment group firms in Sample 2 adopted LIFO in 1974, five years is the maximum post-LIFO period. For attribute measures based on changes, there are five years of pre-LIFO and up to five years of post-LIFO-observations.

262 GARY C. BIDDLE

TABLE 8 Wilcoxon and Sign Test Comparisonst Between Sample 2 Treatment and Control

Group Firms in Pre-LIFO Periods and in Post-LIFO Periods (Difference= Treatment - Control)

Wilcoxon Sign Test Positive Negative Nub Variable Z* Z* Rank Sum Rank Sum oN Fer

and Period Statistic Statistic (# Pos. (# Neg. o irm (Signif.) (Signif.) Ranks) Ranks) airs

Physical Inventory Growth [Ave(A log EU)] Pre-LIFO

EUMs .......... -1.65 (.0496) -1.10 (.1357) 875 (29) 1403 (38) 67 EUAs ........... -1.67 (.0471) -1.10 (.1357) 871 (29) 1407 (38) 67

Post-LIFO EUMs . 3.60 (.0001) 3.05 (.0011) 1716 (46) 562 (21) 67 EUAs ........... 3.56 (.0002) 3.30 (.0005) 1709 (47) 569 (20) 67

Variability of Physical Inventory Growth [SD(A log EU)] Pre-LIFO

EUMs .......... -1.34 (.0896) -1.10 (.1357) 924 (29) 1354 (38) 67 EUAs ........... -1.37 (.1499) -0.86 (.1949) 973 (30) 1305 (37) 67

Post-LIFO EUMs ......... -1.32 (.0937) -1.10 (.1357) 928 (29) 1350 (38) 67 EUAs ........... -0.45 (.3264) -0.12 (.4522) 1067 (33) 1211 (34) 67

Ratio of Average Physical Inventories to Average Sales [Ave(EU)/Ave(Sales67)] Pre-LIFO

EUMs .......... -0.39 (.3470) -0.61 (.2709) 1076 (31) 1202 (36) 67 EUAs ........... -0.37 (.3562) -0.61 (.2709) 1080 (31) 1198 (36) 67

Post-LIFO EUMs .......... 1.38 (.0837) 0.61 (.2709) 1360 (36) 918 (31) 67 EUAs ........... 1.40 (.0809) 0.61 (.2709) 1363 (36) 915 (31) 67

Average Difference Between LIFO- and FIFO-based COGS [Ave(COGSDIF)] Pre-LIFO ........ 1.74 (.0407) 1.83 (.0336) 1418 (41) 860 (26) 67 Post-LIFO . ....... 3.17 (.0008) 3.30 (.0005) 1646 (47) 632 (20) 67 Ratio of Average COGSDIF to Average Sales [Ave( COGSDIF)/Ave(Sales)] Pre-LIFO ......... 0.09 (.4652) -0.61 (.2709) 1153 (31) 1125 (36) 67 Post-LIFO . . 2.37 (.0090) 1.59 (.0559) 1518 (40) 760 (27) 67

t Based on observations from period spanning six years prior to and up to six years after LIFO adoption date.

* Standard normal deviate. (One-tail significance.)

JOINT TESTS OF THE ANTICIPATIONS AND INCENTIVES HYPOTHESES

To control for history threat factors (like, for example, recessions), definitive tests of the Anticipations and Incentives hypotheses must be based on observed changes in treatment firm attributes between the pre- and post-LIFO periods relative to changes in the attributes of their control group counterparts. While the comparisons presented in tables 7 and 8 can provide some evidence on these relative changes, a more direct approach is to compare pre- and post-LIFO ratios of attributes of paired treatment and control group firms. Table 9 presents the results of Wilcoxon and sign test comparisons based on five ratio measures (in each case Treatment over Control) which provide joint35 tests of the Antici- pations and Incentives hypotheses: Average of logs of Ratios of EUs (hereafter Ratio 1) is a measure of the relative sizes of the paired treatment and control firms' physical inventories. Average of Changes in

15 Statistically significant differences (in the right directions) for these ratio measures between the pre- and post-LIFO periods are consistent with both hypotheses.


TABLE 9 Wilcoxon and Sign Test Comparisonst Between Pre-LIFO and Post-LIFO Ratios

Which Relate Jointly to the Anticipation and Incentives Hypotheses (Difference = Pre-LIFO - Post-LIFO)

Wilcoxon Sign Test Positive Negative Variable Z* Z* Rank Sum Rank Sum Number

and Sample Statistic Statistic (# Pos. (# Neg. of Firm (Signif.) (Signif.) Ranks) Ranks) Pairs

1. Average Physical Inventoiy Ratio (Treatment over Control) {Ave[log(EUT/EUc)]} Sample 1

EUMs ....... .... -3.14 (.0008) -2.44 (.0073) 1799 (40) 3766 (65) 105 EUAs ............. -3.22 (.0006) -2.44 (.0073) 1774 (40) 3791 (65) 105

Sample 2 EUMs ........... -1.81 (.0350) -1.34 (.0901) 849 (28) 1429 (39) 67 EUAs ... ... -1.84 (.0331) -1.34 (.0901) 845 (28) 1433 (39) 67

2. Average of Changes in Physical Inventory Ratios (Treatment over Control) {Ave[A log( EUT/EUe)]} Sample 1

EUMs ............ -2.81 (.0025) -2.63 (.0043) 1904 (39) 3661 (66) 105 E UAs ..... .... -2.87 (.0021) -2.63 (.0043) 1886 (39) 3679 (66) 105

Sample 2 EUMs ............ -3.40 (.0003) -2.81 (.0025) 594 (22) 1684 (45) 67 EUAs ... ... -3.41 (.0003) -2.81 (.0025) 593 (22) 1685 (45) 67

3. Ratio of Variabilities of Physical Inventory Growth (Treatment over Control) [SD(A log EUT)/SD(A log EUc)]

Sample 1 EUMs ... ... 0.98 (.1636) 0.68 (.2483) 3089 (56) 2476 (49) 105 EUAs .......... 1.15 (.1259) 1.46 (.0721) 3141 (60) 2424 (45) 105

Sample 2 EUMs ........... 1.26 (.1046) 0.86 (.1949) 1340 (37) 938 (30) 67 EUAs ............ 1.31 (.0948) 1.34 (.0901) 1349 (39) 929 (28) 67

4. Ratio of Average Difference between LIFO- and FIFO-based COGS (Treatment over Control) [Ave( COGSDIFT)/Ave( COGSDIF(.)] A

Sample 1 .-1.95 (.0255) -1.66 (.0485) 2172 (44) 3393 (61) 105 Sample 2 .-1.46 (.0719) -1.34 (.0901) 905 (28) 1373 (39) 67 5. Ratio (Treatment over Control) of Ratios of Average COGSDIF to Average Sales

{[Ave( COGSDIFT)/Ave(SalesT)]/[Ave( COGSDIFc)/Ave(Salesc )]} Sample 1 .-1.81 (.0351) -1.85 (.0322) 2216 (43) 3349 (62) 105 Sample 2 .-1.10 (.1358) -1.10 (.1358) 963 (29) 1315 (38) 67



logs of Ratios of E Us (hereafter Ratio 2) is a measure of the rate of change in Ratio 1. Ratio of Standard Deviations of Changes in logs of E Us (hereafter Ratio 3) is a measure of the relative variabilities of inventory growth. Ratio of Average COGSDIFs (hereafter Ratio 4) is a measure of the relative potential LIFO cash-flow benefits available to the paired treatment and control group counterparts. Ratio of Ratios of Average COGSDIFs to Average Sales (hereafter Ratio 5) is a scaled version of Ratio 4 which controls for differences in paired firm sizes across firm pairs.

The comparisons in table 9 (and those in tables 10 and 11) are based on vectors of pre-LIFO minus post-LIFO differences where the attribute ratio measures have been estimated over the periods spanning six years prior and up to six years after the treatment firms' LIFO adoption dates. Every comparison based on the five attribute ratios considered in table 9 provides results in the directions indicated by the Anticipations and Incentives hypotheses, and all but a few portray highly significant differ-

264 GARY C. BIDDLE

TABLE 10 Wilcoxon and Sign Test Comparisonst Between Pre-LIFO and Post-LIFO Average

Ratios Relating to the Anticipations Hypothesis (Difference = Pre-LIFO - Post-LIFO)

Wilcoxon Sign Test Positive Negative Variable Z* Z* Rank Sum Rank Sum Number

and Sample Statistic Statistic (# Pos. (# Neg. Pa ir (Signif.) (Signif.) Ranks) Ranks) airs

6. Average Sales Ratio (Treatment over Control) {Ave[log(SalesT/Salesc)]} Sample 1 .-2.54 (.0056) -1.46 (.0721) 1989 (45) 3576 (60) 105 Sample 2 .-1.48 (.0694) -0.37 (.3557) 902 (32) 1376 (35) 67 7. Average of Changes in Sales Ratios (Treatment over Control) {Ave[A log(SalesT/Salesc)]} Sample 1 -1.80 (.0361) -2.44 (.0073) 2220 (40) 3345 (65) 105 Sample 2 .-2.09 (.0179) -2.57 (.0051) 803 (23) 1475 (44) 67



TABLE 11 Wilcoxon and Sign Test Comparisonst Between Pre-LIFO and Post-LIFO Average Ratios Relating to the Incentives Hypothesis (Difference = Pre-LIFO - Post-LIFO)

Wilcoxon Sign Test Positive Negative Variable Z* Z* Rank Sum Rank Sum umber

and Sample Statistic Statistic (# Pos. (# Neg. oPairm (Signif.) (Signif.) Ranks) Ranks)

8. Average Ratio of Inventory to Sales Ratios (Ave {log[(EUT/Sales67T)/(EUc/Sales67c)} ) Sample 1

EUMs .... ....... -1.87 (.0306) -1.85 (.0322) 2197 (43) 3368 (62) 105 EUAs .... ... -1.95 (.0257) -1.66 (.0485) 2173 (44) 3392 (61) 105

Sample 2 EUMs ..... ... -0.93 (.1760) -1.34 (.0901) 990 (28) 1288 (39) 67 EUAs -0.99 (.1618) -1.34 (.0901) 981 (28) 1297 (39) 67

9. Average Change in Ratio of Inventory to Sales Ratios (Ave (A log[(EUT/Sales67T)(EUc/Sales67c)]}) Sample 1

EUMs ... . -2.34 (.0098) -2.44 (.0073) 2052 (40) 3513 (65) 105 EUAs . ..... -2.50 (.0063) -2.44 (.0073) 2002 (40) 3563 (65) 105

Sample 2 EUMs . -2.83 (.0023) -2.32 (.0102) 686 (24) 1592 (43) 67 EUAs .......... -2.84 (.0022) -2.08 (.0188) 684 (25) 1594 (42) 67



ences. The comparisons based on Ratio 1 indicate that the treatment group firms held significantly larger physical inventories in the post- LIFO than in the pre-LIFO periods relative to their control group counterparts, while the comparisons based on Ratio 2 suggest significantly higher relative rates of inventory growth. The comparisons based on Ratio 3 suggest that the treatment firms had less variable rates of inventory growth in the post-LIFO than in the pre-LIFO periods relative to their control group counterparts, though the differences are not consistently significant across samples, tests, and EU measures. The comparisons based on Ratio 4 indicate significantly higher Average COGS- DIFs for the treatment group firms in the post- than in the pre-LIFO periods relative to their control group counterparts, though the comparisons based on the scaled Ratio 5 version are only moderately significant for Sample 2 firms. Overall, the results presented in table 9 strongly


suggest that relative to their control group counterparts, the treatment group firms exhibited changes in the patterns of their year-end physical inventories between the pre- and post-LIFO periods consistent with the tax-related cash-flow advantages of the LIFO method.

SOME GRAPHICAL COMPARISONS

Most of the relationships established in tables 7, 8, and 9 are clearly depicted in the following illustrations. Figure 1 traces the cross-sectional averages of physical inventory levels (EUMs) for the treatment and control group firms in Sample 1 relative to their (treatment firms') LIFO adoption dates. Figure 2 provides similar tracings for firms from Sample 2.36 Both figures portray parallel trends in the average physical inventories of the treatment and group firms in the pre-LIFO periods. Since the control group firms hold generally smaller inventories, this is consistent with the somewhat higher rates of inventory growth found for them in the pre-LIFO periods in tables 7 and 8. In the post-LIFO periods, however, figures 1 and 2 both indicate that sharply smaller average inventories were held by the control than by the treatment group firms. The ratio plots in each figure indicate a marked increase in the average levels of physical inventories held by the treatment group firms relative to those held by the control group firms at the time the treatment firms adopted LIFO. In figure 2 (which is based on a fixed sample of firms), the absolute difference between the average inventories held by the two groups continues to increase in each of the post-LIFO years.

The time-series patterns of average physical inventories observed in figures 1 and 2 can be explained, in part, by referring to figures 3 and 4. These trace Average Sales67 for the same respective samples of firms. The sales patterns also reveal parallel averages for the treatment and control groups in the pre-LIFO periods, with marked differences arising in the post-LIFO periods. Beginning in the LIFO adoption years of their treatment group counterparts, the control group firms in each figure exhibit markedly smaller increases in Average Sales67. In contrast, the Average Sales67 of the treatment group firms continue to increase over the post- LIFO periods.37 The ratio plots confirm these patterns. For the fixed sample of firms considered in figure 4, the absolute difference between the Average Sales67 of the treatment and control group firms increases in each of the post-LIFO years. Sales, of course, are an important determinant of desired inventory levels. If the paired treatment and control group firms correctly anticipated different patterns of future sales, the resulting differences in anticipated inventory patterns can largely explain their respective LIFO-FIFO choices in their (treatment firms') LIFO

36 Corresponding time-series plots based on the E UA measures provided almost identical patterns and, as a result, are not presented.

37 The declines in Average EUs and Average Sales67 exhibited by both the treatment and control groups in figures 1 through 4 in the years immediately following the LIFO adoption dates are attributable to the 1974-75 recession.

266 GARY C. BIDDLE

o

.M?

I . I f I . I 1\ 1 [ I l1) 'SC,-tLE a

11 3 Sample Size 0.0

1--- 5-'-- .-,6.- ,-- - - '- -S _(Firm Pairs)

Year Relative 18 * '\. 8 Solid Line: Treatment Group to LIFO -17: ":*Broken Line: Control Group

Adoption Year -16 Dotted Line: Ratio of Averages: 4 (Year 0) i. . 46 (Year O) it 4 ' '-,9 >. (Treatment over Control) 5 0

-1t3 7,t: ,1 -1 2 ' 0X2 1

--11 :8i9

-to: K y 9 _9 ' L 96

99 _7 t. *' otw : 101

-6 : 8 K :L 0 5

S~~~~~~~~~~~~~~ - 3 Att: J. IL)

-2: ~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ sk -11O

0 1

0.0 217.5 35.0 52.5 70.0 87.5 105.0 12215 0 2

1964~~~~~~ 196: Brke ine Coto GroupO 1968 Dote Lie Ratio of

:-------:------ : ------:------- :------- :-------: - --- --------:

0.0 18.8 37.5 56>.3 75.0 93.8 112.5 131.3 1D,0.0

O . 9 1.1 1 15 1. J (RATIO SCALE)

FIG. 1-Time-series plots of Average EUMs of Sample I firms (millions of 1967 dollars).

1.( 0 1. 1 .4 1 . (RASTIO SCALEL-)

0. 0 1 7. 5 35. 0 5 2 " 70. 0 8 7. 5 10" LO. 1 2 2) '5 .1.40;).

1964 1 9 6 5 ; >1 Solid Line: Treatment Group.

t966 *; 8 > ~~Broken Line: Control Grou ' 1968

*S \ Dotted Line: Ratio of 1969 2 s +Aerages (Treatment 1 971 L P^*" *mica tover Control) 1972 ..

1973 LIFO Adoption Year 1974 * I 9 7 .* < 1976 1977 1978 . -, ?

0.0 1 7 . 5 3 5 . 0 J 55 J 70.0 827 . 5 1 0 5 . 0 1 22 . 5 140.0

1.0 1.2) 14 1.6 (RATIO SCALE)

FIG. 2.-Time-series plots of Average EUMs of those Sample 2 firms with data available over the period 1964-78 (millions of 1967 dollars). (Sample size = 42 firm pairs.)

adoption years. Thus, figures 3 and 4 provide evidence clearly consistent with the Anticipations hypothesis.38

Figures 5 and 6 present plots of Average COGSDIFs for treatment and control group firms from Samples 1 and 2, respectively. Prior to the mid- 1960s (approximately year -10 and before in figure 5), the treatment and control groups exhibit Average COGSDIFs close to zero, indicating that for both (on average) there were small cash-flow implications associated

38 A sufficient condition for the sales patterns in figures 3 and 4 to be consistent with the Anticipations hypothesis is that the managers of the paired treatment and control group firms forecast future sales with equal accuracy.

INVENTORY COSTING AND INVENTORY POLICY 267 9 15 .1 1 3 l tSJ (F"A T I 0 'S C L EE

1 10 22' 0 3,30.() 440.0 550.0 J 0 70.0 830() Sample Size _ _ _ _ _._ _ _ _-_-_-_-_-_-_-_-_-_-_-_---- -: (Firm Pairs)

Year Relative 6 : to LIFO Solid Line: Treatment Group

Adoption Year 1: , C Broken Line: Control Group 3-

(Year 0) 1 Dotted Line: Ratio of Averages 45

-tJ ' * > (Treatment over Control) , 6

-12 **, 8A

,9 *;* X X '7 6 -- t3 %' >* of : sr *9~~~~~~~~~~~~~~~~~~~~~~~~~~

_0 ? 10

101

-5 ' 105 -4 ' 105 o

-

3 : X, t ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~1 0l 5

? - S : 1 O 4

L: I. 'g : u.)

4:~~~~~~~~~~.' 0 4 K( .1

.,

06A

'!~~~~~ ~ ~ .I . _5,.: .t

oj *4(]')r. E

SCALE dne,),(!.O

- (,t

FIG. 3.-Time-series plots of Average Sales67 of Sample 1 firms (millions of 1967 dollars).

1.0 1.2 1.4 (RATIO SCALE)

0.0 90.0 180.0 270.0 360.0 4J50.0 540.0 630.0 7-20.0 :-------:---------: --------:--------- - *------- - - ---- - -- --- ------ --

1964 1 9 6 ',a

I

1966 Solid Line: Treatment Group 1967 Broken Line: Control Group 1968 Dotted Line: Ratio of

1970 X Averages (Treatment over. 1971 Control) 19 7 2 L I4 1973 0.go LIFO Adoption Year 1 974 -i

1 5' 7

1 978 * - ~-- -------- -- -- -- - -- - --- -- - --- ---- --- - - ---.- - - -_ _ - _ _ _ ___ __ - -------

0.0 90.0 130.0 270*0 360.0 4'150.0 ( 540.0 630.0 7/20.0

1.0 1.2 1.4 (RA\TIO SCALE)

FIG. 4.-Time-series plots of Average Sales67 of those Sample 2 firms with data available over the period 1964-78 (millions of 1967 dollars). (Sample size = 42 firm pairs.)

with LIFO-FIFO choices. Beginning in the mid-1960s, however, increasing rates of inflation produced consistently positive Average COGSDIFs for both (indicating potential LIFO cash-flow advantages). The Average COGSDIFs for the two groups are of similiar magnitudes until one year prior to the LIFO adoption dates when a persistent difference arises. This difference is consistent with the results of the previous comparisons in suggesting that in the postadoption periods the treatment group firms exhibited inventory patterns more conducive to LIFO cash-flow advantages than their control group counterparts.

While the patterns in figures 5 and 6 are clearly consistent with the implications of the Anticipations and Incentives hypotheses, perhaps the

268 GARY C. BIDDLE

SCALED DY lo** I Sample Size -O 0 0 0,0 S0.O 100.0 10. 0 200.0 250.0 300.0 350.0 (Firi Pairs)

Year Relative - 9 Solid Line: Treatment Group

Adoption Year Broken Line: Control Group 3

(Year 0) -1J 46 -14 5 -~~~~ ~ ~ ~~~ ~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 1 12: -

* 96 9 0

GCLLU :~Y 10* 11.

--d) He : ~~~~~~~~~~~~~~~~~t()J

-50. 0. : .) 10.:5. 0. 5. 10. a)-.O

0 I 0_ _---=-=i- o.

1965~~~~~~~~~~~~~~~~~~~~~~~~~~~(

2:8 (LIFO Adoption Year 1974< )10 1969: _ > Gus 100

4 :- tJ :-- --- - --- - -----____ ____ - -- - -- -_----- -- -- - - -: :

--J(0.0 10.0 10 00 150.( 2.0 2u O 300.0 3JO.(O

FIG. 5.-Time-series plots of Average COGSDIFs of Sample 2 firms (millions of dollars).

S:CALEI)0 BY 1O** 1 5 0 0 () 0 1;) J( ) D00 . 0 1J 5 . 0 20 0 . ( 2) ', J . () 3()0: . () 3JK'. O

1i6 i th Solid Line: Treatment Group 196o7 : Broken Line: Control Group) 196t $ m i (LIFO Adoption Year an 19a74) '.970:

t9,7: : 1 972 =_ -

1 9 7U-ae;

1

(9 -7

(DHi

1 r V7 7 +\ 1 "' 78 . ';: +

- "J 0 0 0) () 0 ) m )+ 1 0 0 0 L '5 0 ( ;90 0 0 '30 .)Adt )(y+( 315 0j

FIG. 6.-Time-series plots of Average COGSDIFs of those Sample 2 firms with data available over the period 1964-78 (millions of dollars). (Sample size = 42 firm pairs.)

most striking aspect of the figures is the sheer size of the tax-related cash- flow consequences of the LIFO-FIFO choice. In both of the figures there is indicated for the treatment group firms an Average COGSDIF of more than $32 million in the LIFO adoption year. Multiplied by an approximate corporate income tax rate of 48 percent, this implies a $15 million average cash-flow difference between the LIFO and FIFO alternatives. For the forty-two treatment group firms in figure 6 there is indicated a $33 million average cash-flow difference over the five-year post-LIFO period. Even more surprising is the fact that the forty-two control group firms depicted in figure 6 voluntarily paid (on average) more than $11 million in additional taxes in 1974 alone than they would have paid if they had adopted LIFO that year for all of their inventories. Over the five years 1974-78 these additional tax payments sum to more than $23 million (on average) for each of the control group firms. Similar amounts are implied for the control group firms in figure 5. The relative sizes of these potential cash-flow differences can be assessed in figures 7 and 8. They plot the

INVENTORY COSTING AND INVENTORY POLICY 269 SCALED' DY t(o** 4 Sample Size

o o~~o /5. 15I0.0 225.0 300. 0 375.(0 4 5 . ) 2 (firr1 liars)

Year Relative 19 Solid Line: Treatnent Group) 14~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ (Year 0) -

14~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

101

-4~~~~~C

/50 0. 0 75, 0 150. 0 1225. 0 300, 0 3 7,:. 0 450. 0 5 Si11~.

FIG. 7.-Time-series plots of average ratios of COGSDIFs to Sales for Sample 1 firms.

SCH"l-El PY .10**4 0. 0 J5.0 1I50.0 2.0 30(1,0 375. 450 _J 550 60.

1966 ~~~~~Solid Line: Treatn-ent Group Broken Line: Control Group

j. u y (LIFO Adoption Year= 1974)

I /4

J. 9~~~~~~~~~~~~4

FIG. 8.-Time-series plots of average ratios of COGSDIFs to Sales for those Sample 2 firms with data available over the period 1964-78. (Sample size = 42 firm pairs.)

average ratios of COGSDIFs to Sales for firms from Samples 1 and 2, respectively. In their LIFO adoption years, the treatment group firms in figure 7 exhibited, on average, COGSDIFs which were more than 4 percent of sales. Their control group counterparts exhibited, on average, COGSDIFs to Sales ratios of more than 3 percent. The corresponding values for the firms in figure 8 are 5 and 4 percent, respectively. The average potential cash-flow differences expressed as percentages of sales would be roughly half of the values indicated in figures 7 and 8. Figures 7 and 8 suggest, therefore, that the cash-flow implications of LIFO-FIFO choices are not insignificant for most firms in relation to their sales.3

3' The large spikes observed in figures 5 and 6 in the Average COGSDIFs for the LIFO adoption years are in large part due to the high rates of inflation experienced in 1974. In 1974 the Wholesale Price Index for All Commodities increased by nearly 19 percent, more than three times its average rate of increase over the previous five years.

270 GARY C. BIDDLE

INDEPENDENT TESTS OF THE ANTICIPATIONS AND INCENTIVES

HYPOTHESES

As outlined in Section 3, the Anticipations and Incentives hypotheses represent conceptually separable explanations for (certain) post-LIFO adoption differences in the inventory properties of the paired treatment and control group firms. Yet, precisely because either hypothesis can be used to explain observed differences, empirical tests based solely on inventory property differences (like those in table 9) cannot be used to address the hypotheses independently. In the tests presented below, sales have been introduced to facilitate this separation. Implicit in these tests is the assumption that when making inventory management decisions and LIFO-FIFO choices, managers will view sales as an exogenously determined variable.

The Anticipations hypothesis suggests that when making their respective LIFO-FIFO choices, the managers of the treatment and control group firms forecast different future inventory patterns. An obvious basis (given figures 3 and 4) for these differing forecasts is that the treatment and control group firms anticipated different patterns of future sales.40 Table 10 presents Wilcoxon and sign test results for pre- versus post- LIFO period comparisons based on two sales ratio (treatment over control) measures: Average of logs of Ratios of Sales (hereafter Ratio 6) is a measure of the relative levels of sales of the paired treatment and control group firms. Average of Changes in logs of Ratios of Sales (hereafter Ratio 7) is a measure of the rate of change in the relationship between the sales of the paired treatment and control group firms.

The tests based on Ratios 6 and 7 indicate that the sales of the treatment group firms were larger and were growing faster in the post- than in the pre-LIFO periods relative to those of their control group counterparts. (Only the sign test based on Ratio 6 for the Sample 2 firms is not statistically significant at the 10 percent level.) The results are consistent with the Anticipations hypothesis in suggesting that at the time they adopted LIFO, the managers of the treatment group firms anticipated future sales more conducive to LIFO cash-flow advantages than did their control group counterparts.

The Incentives hypothesis suggests that the managers of the treatment group firms would have responded to the tax-related incentives provided by the LIFO assumption by altering inventory management policies. The inventory model discussed earlier suggests that (during periods of inflation) they should have responded by increasing their inventories relative to those of their control group counterparts. Table 11 presents the results of Wilcoxon and sign test comparisons based on two inventory ratios (treatment over control) which have been scaled by sales to control for

40 While an examination of inventory levels can also provide evidence on the Anticipa- tions hypothesis, management control of inventory levels means that results consistent with the Anticipations hypothesis could also be explained by appealing to the Incentives hypothesis.


the influence of sales on desired inventory levels (and to control for any systematic differences in size across firm pairs): Average of logs of Ratios of E Us to Sales67 (hereafter Ratio 8) is a measure of the relative sizes of the paired treatment and control firms' physical inventories expressed as proportions of their sales. Average of Changes in logs of Ratios of EUs to Sales67 (hereafter Ratio 9) is a measure of the rate of change in Ratio 8.

The results presented in table 11 based on Ratio 8 indicate that the inventories of the treatment group firms were larger in the post- than in the pre-LIFO periods relative to their control group counterparts, even after controlling for the higher levels of treatment group sales noted in table 10. The results based on Ratio 9 indicate that the inventory-to-sales ratios of the treatment group firms were growing at faster rates in the post- than in the pre-LIFO periods relative to their control group counterparts. All of the tests indicate high levels of statistical significance except those based on Ratio 8 for the Sample 2 firms, and here the significance levels are moderate. These results are consistent with the Incentives hypothesis in suggesting that the managers of the treatment group firms responded to the LIFO cash-flow incentives by increasing their inventories by more, relative to their sales, than their control group counterparts. Notice that the common assumption that inventories increase less than in proportion to sales would bias the tests against the observed results.4"

DISCUSSION

The preceding empirical results provide several checks on the validity of the comparisons. The similarity of the test results based on Samples 1 and 2 suggests that the results (for Sample 1) cannot be attributed to the pairing of firms with different industry classifications, different LIFO adoption dates, or markedly different levels of sales. The similarity of the results based on the alternate E U measures suggests that the results are not sensitive to the use of monthly or annual price indexes in their calculation, and a comparison of tables 7 and 8 suggests that the results are not sensitive to differences in the periods over which the attribute measures are estimated. The Wilcoxon and sign test procedures provide almost identical implications for each comparison, while figures 1 through 8 provide graphical evidence consistent with these implications. In addition, the results of the Noether test comparisons shown in Appendix B confirm (at even higher levels of significance in most cases) the results of the other tests, suggesting that the results are not highly dependent on the choice of statistical procedures.

41 The "square root rule," for example, suggests that inventories will increase in proportion to the square root of sales (see Hadley and Whitin [1963]). Because the sales of the treatment group firms generally increased relative to their control group counterparts in the post-LIFO periods, the square root rule suggests that Ratio 8 would decrease between the pre- and post-LIFO periods.

272 GARY C. BIDDLE

The empirical results have revealed evidence consistent with each of the implications of the Anticipations and Incentives hypotheses except those concerning pre-LIFO differences. Since the arguments suggesting pre-LIFO differences actually represent an extension of the Anticipations hypothesis (see table 1), the absence of confirming evidence in this case does not affect the previous conclusions. Moreover, the generally insignificant pre-LIFO differences found in tables 7 and 8 between the inventory properties of the paired treatment and control group firms suggest that the sample selection procedures succeeded in identifying similar treatment and control group counterparts. The generally insignificant pre-LIFO differences imply that the managers' LIFO-FIFO choices were not greatly influenced by their firms' previous inventory properties.

Implicit in the formulation of the Anticipations and Incentives hypotheses and the derivation of their testable implications is the assumption that managers respond to the cash-flow implications of the LIFO and FIFO alternatives.42 Managers, of course, may respond to other aspects of these methods when making LIFO-FIFO choices and subsequent operating decisions. For example, the decrease (increase) in reported net income which accompanies most LIFO (FIFO) adoptions is often cited as a negative (positive) aspect of these decisions (see, e.g., Coopers & Lybrand [1974] and Jannis and Johnson [1975]). However, the empirical results presented above are uniformly consistent with the implications based on the cash-flow assumption (and uniformly incon- sistent with the reported income argument), indicating more than insignificant explanatory power. Yet, the empirical results also raise some important questions. If managers do recognize the cash-flow implications of the LIFO and FIFO alternatives, why was LIFO not adopted earlier and to a greater extent by the treatment group firms?43 Why did the control group firms each voluntarily pay (on average) the tens of millions of dollars in additional income taxes implied in figures 5 and 6? What are the other factors influencing these LIFO-FIFO choices? Future studies will, I hope, provide some answers.

8. Conclusions

This study has examined associations between properties of year-end inventories and managers' choices among alternative inventory costing methods. Through their role in the determination of taxable earnings, these alternative methods (especially the LIFO and FIFO cost-flow assumptions) can produce large differences in firms' cash flows. Moreover, these cash flows depend, in part, on the levels of year-end inventories. It has been suggested that managers respond to these cash-flow incentives

42 As noted in Section 3, a more general formulation of the Anticipations and Incentives hypotheses would involve managers' responses to the incentives provided by the methods without specifying that they be cash-flow incentives.

43 Only a few of the treatment group firms adopted LIFO for all of their inventories.


when making choices among inventory costing methods and subsequent inventory management decisions. The empirical results are consistent with the following conclusions.

(1) Choices between LIFO and FIFO are influenced by managers' forecasts of associated future cash flows. As noted above, several previous studies of motives for and determinants of accounting choices have concentrated on firm characteristics existing prior to and concurrent with the accounting choices. Because LIFO-FIFO choices are voluntary, it has been argued that managers will anticipate future events in making these choices. While generally insignificant pre-adoption date differences were found between firms that adopted LIFO and those that did not, the empirical results indicate significant differences in the postadoption periods. These results emphasize the importance of considering post- as well as prechoice factors in future studies of motives for and determinants of voluntary accounting choices.

(2) Managers' decisions regarding year-end inventory levels are influenced by the tax-related cash-flow incentives provided by the LIFO and FIFO alternatives. The voluntary nature of the LIFO-FIFO choice implies that managers can jointly consider changes in inventory costing methods and changes in inventory management policies. While this makes it difficult to identify changes in inventory management policies which have been induced by costing method choices, the empirical results provide evidence consistent with these real effects. As implied by a one- period model which considers LIFO-FIFO tax incentives in optimal inventory order quantity decisions, the empirical results suggest that the managers of firms that adopted LIFO chose to hold larger inventories than their control group counterparts.

(3) There are large cash-flow implications associated with choices among alternative inventory costing methods. One of the most striking empirical results is the sheer size of the differences between the cash flows available to firms under the LIFO and FIFO alternatives. Even more surprising is the finding that many firms have voluntarily paid tens of millions of dollars in additional income taxes by continuing to use FIFO rather than switching to LIFO. Although cash-flow incentives have exhibited considerable power in explaining LIFO-FIFO choices and their effects on subsequent management decisions, managers are clearly being influenced by other factors as well.

APPENDIX A

A Model of Optimal Inventory Policies in the Presence of LIFO-FIFO Tax Incentives

The following notations are used:

x = physical units in beginning inventory y = physical inventory units procured (purchased or produced) during

the year

274 GARY C. BIDDLE

z = the decision variable which represents physical inventory units available for sale (z = x + y)

c, = unit cost (purchase or production input price) for all inventory units procured during the year

r = unit selling price (r > cl) h = unit holding cost per year (applied to year-end inventory) s = unit stock-out cost

M = marginal corporate income tax rate (O c M c 1) D = a random variable representing annual demand (sales) of physical

inventory units.

It is assumed that: (Al) Excess demand is lost (i.e., no backlogging is permitted). (A2) Demand is distributed with a known cumulative distribution function F. (A3) The initial physical inventory x is valued at a uniform cost of co (i.e., beginning inventory consists of a single layer).

Given this notation and the foregoing assumptions, it follows that:

Revenues = r[min(z, D)] $Purchases = c1y = c1(z - x) Holding costs = h(z -D)+ Stock-out costs = s(D -z)+ FIFO taxes = Mt(r - co)min(x, D)

+ (r - cl)min[z - x, (D -x)+] LIFO taxes = Mt(r - co)min(x, [D - (z -x)]+)

+ (r - c1)min(z - x, D)). The FIFO tax expression can be simplified using the additional (reason- able) assumption that the firm will sell at least as many physical inventory units as are held in its beginning inventory; that is, (A4) Physical inventory turnover is greater than one (x < D). This allows taxes arising from holding gains realizations (the first term in brackets below) to be separated from those arising from profits on the current year's purchases:

FIFO taxes = Mt(ci - co)x + (r - c1)min(z, D)}.

(Assumption (A4) also implies that F(x) = 0.) The LIFO tax expression can also be rewritten to separate taxes

associated with sales of beginning inventory units from taxes arising from sales of inventory units purchased during the current year:

LIFO taxes = Mt(ci - co)min(x, D - (z - x)]+) + (r - cl)min(z, D)}.

Thus, the only differences between the LIFO and FIFO tax expressions are in the terms related to holding gains realizations on beginning inventory units (the first terms within the brackets).

Expressions for expected after-tax cash flows under FIFO (7rF) and LIFO (7L) as functions of the units available for sale (z) can now be written as follows:44

44 If inventory holding and stock-out costs are assumed to be tax deductible, h and s would become (1 - M)h and (1 - M)s, respectively, in expressions (1) and (2).


7TF(Z) = rE[min(z, D)] - ci(z - x)

- hE[(z - D))+]- sE[(D)- z)+]

- M{(ci - co)x + (r - ci)E[min(z, D)])

= r [1-F(u)] du-cl(z-x) (1)

rZ 00 - h (z - u) dF(u) - s (u - z) dF(u)

- M (cl - co)x + (r - ci) [1 - F(u)] du}

7TL(z) = rE[min(z, D)1 - ci(z - x) - hE[(z - Di)+] - sE[(D - z)+}

- Mt(c, - co)E[min(x, [D - (z -x)]+)]

+ (r - ci)E[min(z, D)]}

=r f [1-F(u)] du-cl(z-x) (2)

- h (z - u) dF(u) - s (u - z) dF(u)

- M{(ci - co)L f (u - z + x) dF(u) + x(I - F(z))1

+ (r-ci) F(u)] du

To obtain the cash-flow-maximizing levels of units available for sale, the derivatives of the expected profit expressions (with respect to z) are set equal to zero:

[dc7rF(z)]/dz = r[l - F(z)] - cl- hF(z)

+ s[l - F(z)] - M(r - cl)[1 - F(z)]

= (1 - M)(r - ci) + s

- [r(l - M) + h + s + Mci]F(z) =045

45 The same expression for [dqrF(z)]/dz is obtained without Assumption (A4). Thus, the optimal order quantity derived for the FIFO case is not dependent on the assumption that physical inventory turnover is greater than one. Assumption (A4) does, however, allow a clearer illustration of the origins of the difference between optimal order policies under FIFO and LIFO.

276 GARY C. BIDDLE

[dtrL(z)]/dz = r[i - F(z)] - c- hF(z)

+ s[l - F(z)] - M(r - c)[t1 - F(z)]

+ M[F(z) - F(z - x)](c-co) (4)

= [dzrF(z)]/dz + M[F(z) - F(z - x)](ci - co)

= 0.

By inspection, (3) and (4) reveal that if c1 = co, M = 0 or x = 0, the cash- flow-maximizing levels of inventories (available for sale) will be the same under LIFO and FIFO. These implications are consistent with earlier observations concerning the conditions under which cash-flow differences will arise.46

The second-order conditions can be verified by examining the second derivatives of the expected cash-flow expressions with respect to z:

[d 2%rF(z)]/dz2 = -rt(z) - hf(z) - sf(z) + Mrf(z) - Mc f(z) (5)

[d27TL(z)]/dz2 =-rf(z) - hf(z) - sf(z) + Mrf(z) - Mcof(z) (6) - M(ci - co)f(z - x)

where f is the density function of D. By inspection, it is clear that (5) is less than or equal to zero, thus assuring that the solution to (3) is a maximum. A sufficient condition for (6) to be less than or equal to zero is for c1 ? co. Thus, increasing input prices assures that the solution to (4) is also a maximum. (It is also clear that c1 can be less than co and (6) still be negative depending on the values of the other parameters.)

Solving expression (3) yields the profit-maximizing level of inventory units available for sale under FIFO. This can be written in closed form as:

ZF = F-%{(l -M)(r - c) + s]/[r(l -M) + h + s + Mc,]). (7)

By inspection, it is clear that if holding costs (h) increase, the optimal inventory level under FIFO will decrease, and if per-unit revenues (r) or stock-out costs (s) increase, the optimal inventory level will increase. Although a closed form expression for optimal available inventory is not possible for the LIFO case (zL), an inspection of expression (4) yields identical conclusions about the effects of h, r, and s on ZL.

A comparison of expressions (3) and (4) also reveals that under conditions of inflation (c1 > co):

[d7rL(z)]/dz > [dqrF(z)]/dz.47

46 It can also be noted that co does not appear in expression (3). This implies that the optimal order policy under FIFO does not depend on either the costs assigned to beginning inventory units or to the change in inventory input prices between the previous and current periods. In a multiperiod setting, Cohen and Pekelman [1979] have shown that the FIFO optimal order quantity likewise does not depend on past inventory costs, but there does arise a term which depends on anticipated inventory input prices in the upcoming period.

47 By assumption (A4), [F(z) - F(z - x)] - 0.


Since under these conditions both expected cash-flow expressions ((1) and (2)) are concave, this implies that ZL > ZF; that is, during inflation the cash-flow-maximizing level of inventory available for sale (and, therefore, of expected year-end inventory) would be higher under LIFO than under FIFO. Under conditions of input price deflation (c1 < co):

[d7 L(z)]/dz < [d7rF(z)]/dz

and, therefore, lower inventories would be held under LIFO than under FIFO; that is, zL < ZF (regardless of whether expression (2) is strictly concave).48 These conclusions are consistent with earlier suggestions (and traditional notions) that a firm using LIFO would have greater incentives to avoid inventory declines during periods of rising input prices and greater incentives to allow inventories to decline during periods of falling input prices.

Under the LIFO assumption in a multiperiod setting, units of beginning inventory (x) may be assigned input prices which prevailed more than one period in the past. The presence of these "old" prices may influence LIFO inventory policies, since they can generate large holding gains realizations if these units are sold. It is possible to illustrate this effect in the present one-period model by noting the effect of changes in co on optimal order quantities. Since co does not appear in the expression for ZF (Expression (7)), it does not affect inventory policies under FIFO. The input price assigned to beginning inventory units does appear, however, in expression (4), indicating that it does affect optimal inventory levels under LIFO. A decrease in co will lead to an increase in d7rL(z)/dz and thus (by concavity) to an increase in ZL. Similarly, an increase in co would lead to a decrease in zL (I.e., if y= drL(z)/dz, then dy/dco < 0 and OZL/aCo < 0.) Thus, if co is "adjusted" to reflect the existence of older LIFO layers and the prices assigned to these layers reflect the current trend in input prices (i.e., the relationship between c1 and co), then the optimal order quantity under LIFO will be adjusted upward in the case of rising and downward in the case of falling input prices relative to the FIFO case.

48 Assuming continuity, drL(z)/dz is globally concave since d'L(z = O)/dz = (1 - M)(r - cl) + s > 0 and limzer d7r(z)/dz = -cl -h < 0. Because with cl < co, drL(z)/dz < dqrF(z)/dz, any maxima of XL must be at a lower z than the maxima of rF.

278 GARY C. BIDDLE

APPENDIX B

Noether Test Comparisons Between Pre-LIFO and Post-LIFO Ratios for Those Sample 2 Firm Pairs with Five Years of Post-LIFO Data

Availability* (Comparison = Pre-LIFO versus Post-LIFO)

Reference to. Corresponding Noether Z*

Variable Wilcoxon and Statistic Sign Test (Significance) Results

log(EUT/EUc) Table 9 EUMs -2.76 (.0029) EUAs -2.97 (.0015)

A log(EUT/EUc) Table 9 EUMs -2.88 (.0020) EUAs -2.59 (.0048)

log(SalesT/Salesc) Table 10 -2.12 (.0170) A log(SalesT/Salesc) Table 10 -2.62 (.0044) log[(EUT/Sales67T)/(EUc/Sales67c)] Table 11

EUMs -1.48 (.0694) EUAs -1.51 (.0655)

A log[(EUT/Sales67T)/(EUc/Sales67c)] Table 11 EUMs -1.45 (.0735) EUAs -1.36 (.0869) * Two-sample test for randomized blocks based on rank sums as proposed by Noether [1967, pp. 41-

43]. The use of a sample with equal numbers of pre- and post-LIFO observations (both five years) provides for a consistent and unbiased test (see Noether [1967, p. 43]). The sample size in each comparison is fifty-one firm pairs.

* * Standard normal deviate. Sign indicates direction of difference; a negative (positive) sign indicates larger post-LIFO (pre-LIFO) observations. (One-tail significance.)

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