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Market structure and price adjustment in Canadian manufacturing industries
J ECO B U S N 1988; 40:335-342
335
Market Structure and Price Adjustment in Canadian Manufacturing Industries Stanley W. Kardasz and Kenneth R. Stollery
This study examines the determinants of the speed of price adjustment for a broad sample of Canadian manufacturing industries using a two-stage procedure. The first stage consists of estimating time series price equations which yield measures of the industry rates of price adjustment. The second stage makes use of a cross-section analysis to examine how the estimated adjustment speeds are related to industry structure. The results indicate that the rate of price adjustment varies directly with concentration, and inversely with the degree of product differentiation and the length of the selling lag.
I. Introduction There has been considerable controversy concerning the relationship between concentration and the speed of adjustment of prices to changes in market conditions. The lag and catch-up version of the admini.~red price hypothesis (APH), the most famous hypothesis dealing with this relationship, states that the rate of price a d j ~ varies inversely with concentration. Following Weiss (1966), econometric tests of the APH have generally taken the form of estimating the following equation (or variants thereof) for a crosssection of industries:
Pt/Pt-t=f(ULCt/ULCt_t, UMCt/UMCt_t, Qt/Qt-t, CR)
(l)
In this equation, P, ULC, UMC, Q, and CR represent price, unit labor costs, unit material costs, output, and concentration, respectively, while t and t - k are time subscripts. 1The API-I predicts opposite signs for the concentration coefficient during lag and catch-up periods. A lag period is one during which unit costs and demand increase rapidly following a period during which they were relatively stable or declining, whereas a catch-up period is one of cost and demand stability .or decline following a period of rapid increase. According to the APH, the coefficient of CR will be negative for lag periods and positive for catch-up periods because of the inverse relation between the rate
The authors are from the Department of Economics, University of Waterloo, Waterloo, Ontario, Canada, N2L 3(31
Address repnnt requests to Stanley W. Kardasz, DeparUnemof Economics, Univenity of Wateaoo, Waterloo, Ontario, Canada, N2L 3GI. 1 The output ratio is incb_w54~__as a demand proxy, a proceO___Jrewhich clearly introducea a simultaneity
prubknn. For simOicityof nemion, the indemy mb~ript i has bern omitted. Journal of Economics and Business © 1988 Temple University
0148-61951881503.50
336
S . W . Kardasz and K. R. Stollery
of price adjustment and concentration. Empirical tests of the APH have been inconclusive. As emphasized by Lustgarten (1975, pp. 194-196), this stems, in no small measure, from the inherent difficulty of determining in an objective way whether a specific time period represents a lag, catch-up, or neutral period. 2 The purpose of this paper is to reexamine the relation between the speed of price adjustment and concentration using a method which avoids this problem. Our approach involves a two-stage procedure, which is similar to that employed by Domberger (1979) for the United Kingdom. The first stage consists of estimating time series price equations for a number of individual industries with a view to determining their rates of price adjustment. The equations are of the form: APe = t3o + ~I ACt + B 2 A P F t + 13~A Yt + ~4APt -
1"
(2)
Ct is a measure of the factors that shift the short-run marginal cost function. Thus, Ct is itself a function of input prices, technology, and the capital stock. PFt, the price of
comparable foreign-produced goods, and Yt, "income," are demand shift variables. Equation (2) is based on a simple partial adjustment model which assumes that P t - Pt - i = X ( P * - P t - l) and that Pt*, the equilibrium price, is a linear function of Ct, P F t , and Yr. 3 Since the actual price approaches its equilibrium value more quickly as the adjustment coefficient (h) increases, we use the estimated values of h(~ or 1 - /~4) to measure the industry rates of price adjustment. The second stage of the analysis consists of cross-section regressions examining how ~ is related to concentration a n d other elements of market structure.
II. Time Series Analysis Data
Equation (2) was estimated using seasonally adjusted quarterly data for the period from 1971:4 to 1982:4 for a broad sample of three-digit Standard Industrial Classification (SIC) industries in the Canadian manufacturing sector. Before discussing the estimation methods and results, it is necessary to describe the data used. Statistics Canada's CANSIM data bank was employed as the primary data source, but data were also obtained from the External Trade, Input-Output, and Prices Divisions of Statistics Canada, and the U.S. Bureau of Labor Statistics. All variables employed are expressed as indexes with 1971 = 100. We measured P by the industry selling price index, which is based on transaction prices. 4 C, the cost index, was calculated as a weighted average of labor and material 2 The disagreement between Means (1972, pp. 295-296) and Stifler and KJndahl (1973, pp. 719-720) on the dating of cyclical turning points provides another illustration of this difficulty. 3 The price equation assumes that Canada is a small country and that domestic and foreign goods are imperfect substitutes. These assumptions imply that PF is exogenous and that the domestic price responds to both domestic market conditions and to PF. Equation (2) assumes that the lag coefficients for the cost and demand-shift variables are identical. The latter include both A y, and ~ F , . As pointed out below, A Yt adds very little to the price equations, When we dropped the simplifying assumption that the lag coefficients on ACt and APF, are equal, we found that none of the results reported in this paper were materiatly affected. The price equation is estimated in its first-difference form to reduce mulficoHinearity. Theoretically,/~o should not appear in (2) but its inclusion bounds R2 between 0 and 1. In the event,/~o is insignificant for all but two ~ in our sample (see Table 1). 4 According to Statim'cs Canada (1970, pp. 15-16), the "objective of the index is to portray movements of prices at which transactions take place, defined in this index to be those ruling at the time that new orders are accepted. List or nominal prices are not normally acceptable, if traditionally they do not reflect the price at which commodities are exchanged."
Market Structure and Price Adjustment
337
costs: C = w(AHE/LPROD) + (1 - w)PMA T, where w represents the share of labor costs in total variable 0abor plus material) costs in 1971. A H E is the average hourly earnings of hourly rated wage-earners. Estimates of LPROD, the trend value of output per man-hour, were obtained by regressing quarterly real shipments per man-hour against time, using a variety of functional forms (e.g., linear, quadratic, and log-linear). Our measure of LPROD is the set of predicted values associated with the regression yielding the best fit for each industry. This procedure was followed to obtain a longer-term productivity variable reflecting the effects of changes in technology and the capital stock. PMA T, the price index of materials, is a weighted average of the prices of the principle intermediate inputs used by an industry for which the required data are available. The weights are based on the Canadian industry-by-industry input-output table for 1971. We measured the prices of intermediate inputs by industry selling price indexes for inputs originating in the manufacturing sector and by commodity price indexes for inputs originating in other sectors. To construct PF, the foreign price index, we employed U.S. price data (adjusted for the exchange rate) for most of the industries in our sample because most of Canada's trade is with the United States and because data for Canada's other trading partners are not readily available. 5 The U.S. commodity producer price indexes corresponding to each SIC industry were identified with the aid of a concordance showing the principal input-output commodities produced by each Canadian industry. For five of the industries in our sample (fruit and vegetable canners, shoe factories, cotton yam and cloth mills, fiber and fdament yams, and knitting mills), PF was measured by the price index of competing imports because for these industries the import share is large relative to the export share. The "income" variable, Y, for each industry is a weighted average of the indexes of real domestic product and real expenditure of the industries and final demand categories which are the principal users of that industry's output. The weights are based on the industry-by-industry input-output table for 1971.
Estimation and Results We were able to measure the variables included in equation (2) for 31 industries, and we obtained satisfactory estimates of the price equations for 28 of these industries. It is well known that autocorrelation in the presence of a lagged dependent variable leads to inconsistent parameter estimates, and that the classical assumption of nonautocorrelation is frequently violated in time series analysis. To obtain consistent estimates, we began by assuming that the error structure was autoregressive and that the highest order was two. A maximum likelihood procedure was then used to estimate the coefficients of the structural equations and the autoregression parameters. To determine whether the error process was AR(2), AR(1), or white noise, we employed Sargan's COMFAC algorithm, which is based on a series of Wald common factor restriction hypotheses (Harvey, 1981, pp. 281287). If these tests indicated that no significant autocorrelation was present, the price equation was reestimated using OLS. One of the equations rejected by the COMFAC tests appeared to have an MA(1) error structure and this was confirmed by a Lagrange multiplier (LM) test. This equation was then reestimated by GLS. The price equations were estimated in order to obtain a measure of the rate of price adjustment, ~, for each industry. Nonetheless, our results are interesting in their own 5 In 1976, for example, the United States accounted for 68.8% of Canada's merchandise imports and 67.3% of its merchandise imports (Economic Council of Canada, 1983, pp. 91-94).
338
S . W . Kardasz and K. R. Stollery right and merit at least a brief discussion. The coefficient o f A Yt proved to be significant for only 4 o f the 28 industries in our sample (biscuits, shoe factories, veneer and plywood, and pulp and paper). Since dropping this variable from the regressions for these industries does not affect the relationship between APt and its other determinants in any material way, only the equations excluding A Yt are reported in Table 1. As this table shows, the coefficients o f both ACt and APt_ 1 are significant in 23 equations while that o f APFt is significant in 18.
III. Cross-Section Analysis Specification and Data The dependent variable in the cross-section analysis is the speed o f price adjustment which is measured by ~, the estimate o f X obtained from the price equations. W e consider three determinants o f X: concentration, the degree o f product differentiation, and the length o f the production lag. Table 1. Industry Price Equations
Constant
AC,
APFt
AP,_ i
~2
Method of estimation
Slaughtering and meat processors
0.738 (1.51)
0.390' (8.06)
0.344" (9,80)
0.206" (4.93)
0.92
AR2
Fish products
1.225 (1.16)
0.219 a (3.02)
0.118 • (2.29)
0.376* (3.46)
0.40
AR1
Fruit and vegetable canners
0.276 (0.63)
0.057" (1.69)
0.062* (2.05)
0.861" (13.88)
0.72
AR1
Dairy products
1.020b (2.39)
0.765* (7.54)
-0.039 (0.84)
0.213" (2.15)
0.79
OLS
Flour and breakfast cereals
4.137 b (2.83)
0.446" (4.13)
-0.205 (0.97)
0.244 ° (1.75)
0.39
ARI
Feeds
0.797 (0.94)
0.147 ~ (2,43)
0.620 ° (8.59)
0.122" (1.68)
.80
AR2
Biscuits
0.805 (0.82)
0.055 (1.19)
0.475" (4.30)
0.407 ~ (3.83)
0.50
AR1
Soft drinks
0.078 (0.11)
0.187" (6.54)
0.134" (2.08)
0.700" (8.68)
0.68
AR1
Shoe factories
0.224 (0.55)
0.136" (9.85)
0.002 (0.21)
0.838 ° (18.26)
0.67
AR2
Cotton yam and cloth mills
0.405 (0.51)
0.132" (2.69)
0.293" (2.43)
0.494" (4.62)
0.53
OLS
Fiber and filament yams
-0.245 (0.43)
0.553' (4.07)
0.048 (0.80)
0.143 (0.99)
0.50
OLS
Kn/m'ng mills (except hosiery)
-0.313 (1.10)
0.469* (4.88)
0.022 (1.02)
0.246" (1.82)
0.69
AR1
Sawmills
- 0.728 (0.77)
0.159 (0.86)
0.521 * (9.85)
0.026 (0.29)
0.73
OLS
Industry
Market Structure and Price Adjustment Veneer mad plywood Hoesehold furniture
1.164 (0.96)
339 - 0.241 (1.12)
0.194
0.350" (2.28)
(0.21)
0.353' (4.03) -0.047 (0.58)
0.545"
0.44
OLS
0.747' (6.98)
0.31
AR1
(4.74)
Pulp m i p,per
-0.266 (0.25)
-0.185 (0.93)
0.875" (5.55)
0.321" (1.96)
0.65
AS2
Alumimun rolling, camin8 and omudin8
-0.193 (0.22)
0.058 (1.20)
0.351" (3.36)
0.472" (3.73)
0.45
OLS
0.205 (0.30)
0.588" (4.72)
0.035 (0.85)
0.152 (1.22)
0.39
OLS
Hardware, tools and cutlery
-0.131 (0.27)
0.471" (3.40)
0.153" (2.79)
0.376" (3.21)
0.71
ARI
l-laatlngequipmem
-0.251 (0.54)
0.317" (3.86)
0.063 (1.22)
0.716" (9.92)
0.67
ARI
Asr~mral ~
-0.244 (0.47)
0.350" (2.40)
0.195" (2.79)
0.524" (4.55)
0.56
MA1
Major appliances
-0.031 (0.07)
0.435" (3.67)
0.091 (1.22)
0.468" (3.64)
0.57
ARI
Electrical industrial equipment
-0.449 (1.02)
0.359"
0.242"
0.5{)4"
0.74
OLS
(3.23)
(3.21)
(5.31)
0.580" (8.17)
0.165"
0.123
0.89
AR1
(3.04)
(1.51)
0.52
AR2
0.777" (10.93)
0.78
ARI
0.46
ARI
0.62
OLS
Wire and wire products
Electric wire and cable
0.380 (1.36)
Clay products Pharnm:eutkak and medicines
0.192
0.313"
0.00
0.654"
(0.3o)
(4.3o)
(0.01)
(8.69)
0.090 (0.38)
0.071" (1.97)
0.093" (2.41)
Stop and cleaning
0.230
0.135"
0.III"
0.553"
CCmlWn~nd~
(0.50)
(2.50)
(I.74)
(4.42)
Ln,~_,_m'ial chemicals
0.734 (0.74)
0.279* (2.45)
(5.11)
~ vm'kble = ~LP,.~ • m,,aa,~atat 5%, me-tafttest. bm ~ at 5%, two.tailteat.
0.369"
O. 118 (l.Ol)
t-valuesin ~ .
Concentration is measured b y the fonr-firm concentration ratio (_CR4) and b y an
adjusted ratio (CR4A) which equals CR4/(I + MS) where M S is the import share. 6 Since X was estimated over the period 1971: 4-1982: 4, w e used 1976 o r circa 1976 values o f C R 4 and C R 4 A , depending on data availability. In our sample o f 28 industries, C R 4 varies between 16% and 9 8 % , and it exceeds 20% in all but three industries. A similar picture emerges i f concentration is measured by C R 4 A . Specifically, this variable 6 Using coafideafial data, Baldwin, Gmecki and McVey (1986) examine how nzemured ccm~n~tratic~in Cazz/m ~ i n d e m ~ i, aTeczed by m ~ m , expom, aad , m x m ~ oet~t. Tmy ce,~tede tbet "corm:e~ for i m p ~ hm a mmh Sterner efrm th,a mrre~i~ for ,eccedary ompet" md t h l - ~ ~ acccent ~ h u virtmdly m effect ce m/nmm of cmcemzem" (p. 529). la add/rice, tbey find tlm the bm/c a s ~ ~!~lying CR4A, nmnely, lhat tbe top four firms in aa industry do not purclmse imports ~ re~de, appem to be jmefied (p. 538). Generally speakS, the Herfiadahl index (][i)did not perfornl Its well as CR4. Givea the available data, it was not Ixxmibleto adjust H for import compet/fion. We included a regimJ dummy variable (eqmd to I for ,,,~_,_,,tri~serving resioeal or local madam, 0 otherwi~) bet it proved to be imignifzumt.
340
S . W . Kardasz and K. R. Stollery
ranges from 13% to 75% and it exceeds 20% in 24 industries. From this evidence, we concluded that our sample consists mainly, if not entirely, of industries that are oligopolistic in nature. In general, the firms in an oligopolistic industry are not identical, and as a result they tend to have differing views about how the price (or, if their products are differentiated, the price structure) should be adjusted following a change in market conditions. These conflicting views have to be reconciled before a new equilibrium can be achieved. Since the number of two-way reconciliations (between each firm and every other firm) increases with the number of firms in the industry, it is reasonable to suppose that the adjustment process will become more difficult and hence more lengthy as the number of firms increases. Thus, making the usual assumption that concentration varies inversely with the number of firms, one would expect ~ to vary directly with concentration. 7 At first glance, this hypothesis appears to be in direct conflict with the administered price hypothesis, but this is not the case. The APH is based on the idea that prices in concentrated industries respond more slowly to changes in market conditions than do prices in competitive industries because oligopolists must cope with the uncertainties arising from oligopolistic interdependence whereas atomistic firms do not. This implies that the APH is applicable to comparisons of competitive industries, on the one hand, and oligopolistic industries, on the other, rather than to samples consisting of industries with varying degrees of oligopolistic interdependence. The speed of price adjustment may depend not only on concentration but also on the degree of product differentiation. Scherer (1980, pp. 200-203) argues that interfirra rivalry becomes multidimensional with product heterogeneity. As a result, product differentiation makes it more difficult for the firms to cooperate and thereby to coordinate their price changes. Scherer's argument suggests that )~ varies inversely with the degree of product differentiation. To test this hypothesis, product differentiation was proxied by the advertising-sales ratio (ASR) in 1965, the only year for which these data are available in Canada. Courts, Godley, and Nordhaus (1978) have pointed out that price changes will lag cost changes when firms use historical cost accounting and that the length of the lag depends on the length of the production period, 0. They define 0 as "the length of time between the first purchase of the input used for the production process and the sale of the finished product" (Coutts, Godley, and Nordhaus (1978, p. 37)). Making a number of assumptions, they show that 0 = 2S/X(1 + or~3)where S is total (materials, goods-inprocess, and finished-good) inventories, X is sales, a is the share of materials in sales, and/3 is the proportion of materials which enter at the beginning of the production process (Coutts, Godley, and Nordhaus (1978, pp. 52-55)). Unfortunately, total inventory data are not available for many of the industries in our sample. As a result, we were forced to use a crude proxy, the 1975-77 average of the ratio of value-added to shipments (VAS), to measure physical production time (i.e., the time between the b e g i n n i n g of the production process and the completion of the finished product), and the 1975-77 average of the ratio of finished-good inventories to shipments (FIS) to measure selling time (i.e., the time between the completion of the finished product and final sale). We expect )~ to vary inversely with both VAS and FIS. s 7 For a further discussion of the relation between ~ and concentration, see Domberger (1983, pp. 52-62). s The sources of the CR4 and ASR data are Statistics Canada, Industrial Organtfation and Concentration in the Manufacturing, Mining and Logging Industries, 1980 (Ottawa, 1983) and Advertising Expenditures in Canada, 1965 (Ottawa, 1968). The MS series was obtained from the Input-Output Division of Statistics Canada, and the VAS and Iris series were taken from the CANSIM data bank.
341
Market Structure and Price Adjustment Table 2. Cross-SectionRegressions Constant
CR 4
0.688 b (4.65)
0.004" (1.79)
0.006" (2.18)
0.605 b (4.04) 0.561b (4.67)
CR 4A
0.004" (2.08)
0.513 b (4.39)
0.006" (2.64)
A SR
VA $
FI$
1~2
--0.034" (2.06)
--0.358 (1.32)
--0.157 • (2.32)
0.39
-0.037" (2.32)
-0.267 (0.98)
-0.149" (2.27)
0.42
- 0.036' (2.16)
- O. 186' (2.85)
0.37
- 0.039" (2.46)
- O. 168" (2.70)
0.42
Depeadmt variable ffi ~ = I - ~f4.Atnolu~ t-values in Imeathem. • Significant at 5%, one-eta test. b Significant at 5%, two-tail test.
Estimation and Results Our measure of X, ~, contains a measurement error. Assuming that this error varies directly with the variance of ~, it is inappropriate to assume homoskedastic errors because, from the price equations, it is clear that the variances of the ~s are not constant across industries. For this reason, the cross-section regressions were estimated using weighted least squares, with the weights reflecting the variances of the ~ . 9 From Table 2, it can be seen that the coefficients of both CR4 and CR4A are positive and significant, while the coefficient of ASR is negative and significant. Taken together, these findings support the view that the ability of oligopolists to coordinate their pricing policies increases with concentration and decreases with the degree of product differentiation. The evidence is less clear regarding the Contts-Godley-Nordhaus hypothesis that the rate of price adjustment varies inversely with the length of the production period. In particular, while the coefficient of FIS is negative and sitmificant in line with their hypothesis• that of VAS is insignificant. This may be because FIS appears to be a better measure of the s e l l i n g l a g t h a n is V A S o f t h e p h y s i c a l p r o d u c t i o n p e r i o d .
Conclusions This paper examines the determinants of the speed of price adjustment using an approach which, in contrast to earlier studies, avoids the problem of having to distinguish between lag and catch-up periods. The principalfindingof this study is that,within oligopolistic industries,the rateof price adjustment variesdirectlywith concentration.It also appears that the speed of adjustment varies inversely with the degree of product differentiation and the length of the s e l l i n g lag. The approach employed in this paper is potentially useful from the policy point of view. Large, positive residuals in a cross-section regression explaining the speed of adjustment may indicate the existence of explicit collusion, and, as a result• they can be used to identify industries which merit timber investigation by the competition authorities. 9 Mo~ ~ y ,
the we~hts equ~ ~ e over tiw m u ~ M ~ m r s of the ~s.
342
S.W. Kardasz and K. R. Stollery
The authors would like to thank Ed Nosal, Richard Bodell, and James Brox for their susgestions. Research for this project was supported financially by the C~,mai~,~Department of Consumer and C o ~ Affairs.
References Baldwin, J. R., Gorecki, P. K., and McVey, J. S. May 1986. Internationaltrade,secondary output and concenu'ationin Canadianmanufacturing ~ , 1979. Applied Economics 18(5):529543. Couus, K. W., Godley, W., and Nordhaus, W. 1978. Industrial Pricing in the United Kingdom. Cambridge: Cambridge University Press. Domberger, S. Mar. 1979. Price adjustment and market structure. Economic Journal 89053):96-108. Domberger, S. 1983. Industrial Structure, Pricing and Inflation. Oxford: Martin Robertson and Company. Economic Council of Canada. 1983. The Bottom Line. Ottawa: Minister of Supply and Services Canada. Harvey, A. C. 1981. The Econometric Analysis o f Time Series. Oxford: Phillip Allan
Publishers. Lustgarten, S. June 1975. A d m ~ inflation: A reappraisal. Economic Inquiry 13(2):191206. Means, G. C. June 1972. The adl~ininter~.pri~ thesis ~ . American Economic Review 62(3):292-306.
Seberer, F. M. 1980. IndusWial Market Structure and Economic Performance. Chicago: Rand McNally Collqe P u b i ~ Company. Statistics Canada. 1970. Industry Selling Price Indexes, 1956-1968. Ottawa: Supply and Services
Canada. Stigler, G. J. and
Kindnhl, J. K. Sept. 1973. Industrial prices, as administered by Dr. Means.
American Economic Review 63(4):717-721. Weiss, L. W. Apr. 1966. Bluffness pricing poficies and it~afion recmnklered. Journal o f Political Economy 74(2): 177-187.
335
Market Structure and Price Adjustment in Canadian Manufacturing Industries Stanley W. Kardasz and Kenneth R. Stollery
This study examines the determinants of the speed of price adjustment for a broad sample of Canadian manufacturing industries using a two-stage procedure. The first stage consists of estimating time series price equations which yield measures of the industry rates of price adjustment. The second stage makes use of a cross-section analysis to examine how the estimated adjustment speeds are related to industry structure. The results indicate that the rate of price adjustment varies directly with concentration, and inversely with the degree of product differentiation and the length of the selling lag.
I. Introduction There has been considerable controversy concerning the relationship between concentration and the speed of adjustment of prices to changes in market conditions. The lag and catch-up version of the admini.~red price hypothesis (APH), the most famous hypothesis dealing with this relationship, states that the rate of price a d j ~ varies inversely with concentration. Following Weiss (1966), econometric tests of the APH have generally taken the form of estimating the following equation (or variants thereof) for a crosssection of industries:
Pt/Pt-t=f(ULCt/ULCt_t, UMCt/UMCt_t, Qt/Qt-t, CR)
(l)
In this equation, P, ULC, UMC, Q, and CR represent price, unit labor costs, unit material costs, output, and concentration, respectively, while t and t - k are time subscripts. 1The API-I predicts opposite signs for the concentration coefficient during lag and catch-up periods. A lag period is one during which unit costs and demand increase rapidly following a period during which they were relatively stable or declining, whereas a catch-up period is one of cost and demand stability .or decline following a period of rapid increase. According to the APH, the coefficient of CR will be negative for lag periods and positive for catch-up periods because of the inverse relation between the rate
The authors are from the Department of Economics, University of Waterloo, Waterloo, Ontario, Canada, N2L 3(31
Address repnnt requests to Stanley W. Kardasz, DeparUnemof Economics, Univenity of Wateaoo, Waterloo, Ontario, Canada, N2L 3GI. 1 The output ratio is incb_w54~__as a demand proxy, a proceO___Jrewhich clearly introducea a simultaneity
prubknn. For simOicityof nemion, the indemy mb~ript i has bern omitted. Journal of Economics and Business © 1988 Temple University
0148-61951881503.50
336
S . W . Kardasz and K. R. Stollery
of price adjustment and concentration. Empirical tests of the APH have been inconclusive. As emphasized by Lustgarten (1975, pp. 194-196), this stems, in no small measure, from the inherent difficulty of determining in an objective way whether a specific time period represents a lag, catch-up, or neutral period. 2 The purpose of this paper is to reexamine the relation between the speed of price adjustment and concentration using a method which avoids this problem. Our approach involves a two-stage procedure, which is similar to that employed by Domberger (1979) for the United Kingdom. The first stage consists of estimating time series price equations for a number of individual industries with a view to determining their rates of price adjustment. The equations are of the form: APe = t3o + ~I ACt + B 2 A P F t + 13~A Yt + ~4APt -
1"
(2)
Ct is a measure of the factors that shift the short-run marginal cost function. Thus, Ct is itself a function of input prices, technology, and the capital stock. PFt, the price of
comparable foreign-produced goods, and Yt, "income," are demand shift variables. Equation (2) is based on a simple partial adjustment model which assumes that P t - Pt - i = X ( P * - P t - l) and that Pt*, the equilibrium price, is a linear function of Ct, P F t , and Yr. 3 Since the actual price approaches its equilibrium value more quickly as the adjustment coefficient (h) increases, we use the estimated values of h(~ or 1 - /~4) to measure the industry rates of price adjustment. The second stage of the analysis consists of cross-section regressions examining how ~ is related to concentration a n d other elements of market structure.
II. Time Series Analysis Data
Equation (2) was estimated using seasonally adjusted quarterly data for the period from 1971:4 to 1982:4 for a broad sample of three-digit Standard Industrial Classification (SIC) industries in the Canadian manufacturing sector. Before discussing the estimation methods and results, it is necessary to describe the data used. Statistics Canada's CANSIM data bank was employed as the primary data source, but data were also obtained from the External Trade, Input-Output, and Prices Divisions of Statistics Canada, and the U.S. Bureau of Labor Statistics. All variables employed are expressed as indexes with 1971 = 100. We measured P by the industry selling price index, which is based on transaction prices. 4 C, the cost index, was calculated as a weighted average of labor and material 2 The disagreement between Means (1972, pp. 295-296) and Stifler and KJndahl (1973, pp. 719-720) on the dating of cyclical turning points provides another illustration of this difficulty. 3 The price equation assumes that Canada is a small country and that domestic and foreign goods are imperfect substitutes. These assumptions imply that PF is exogenous and that the domestic price responds to both domestic market conditions and to PF. Equation (2) assumes that the lag coefficients for the cost and demand-shift variables are identical. The latter include both A y, and ~ F , . As pointed out below, A Yt adds very little to the price equations, When we dropped the simplifying assumption that the lag coefficients on ACt and APF, are equal, we found that none of the results reported in this paper were materiatly affected. The price equation is estimated in its first-difference form to reduce mulficoHinearity. Theoretically,/~o should not appear in (2) but its inclusion bounds R2 between 0 and 1. In the event,/~o is insignificant for all but two ~ in our sample (see Table 1). 4 According to Statim'cs Canada (1970, pp. 15-16), the "objective of the index is to portray movements of prices at which transactions take place, defined in this index to be those ruling at the time that new orders are accepted. List or nominal prices are not normally acceptable, if traditionally they do not reflect the price at which commodities are exchanged."
Market Structure and Price Adjustment
337
costs: C = w(AHE/LPROD) + (1 - w)PMA T, where w represents the share of labor costs in total variable 0abor plus material) costs in 1971. A H E is the average hourly earnings of hourly rated wage-earners. Estimates of LPROD, the trend value of output per man-hour, were obtained by regressing quarterly real shipments per man-hour against time, using a variety of functional forms (e.g., linear, quadratic, and log-linear). Our measure of LPROD is the set of predicted values associated with the regression yielding the best fit for each industry. This procedure was followed to obtain a longer-term productivity variable reflecting the effects of changes in technology and the capital stock. PMA T, the price index of materials, is a weighted average of the prices of the principle intermediate inputs used by an industry for which the required data are available. The weights are based on the Canadian industry-by-industry input-output table for 1971. We measured the prices of intermediate inputs by industry selling price indexes for inputs originating in the manufacturing sector and by commodity price indexes for inputs originating in other sectors. To construct PF, the foreign price index, we employed U.S. price data (adjusted for the exchange rate) for most of the industries in our sample because most of Canada's trade is with the United States and because data for Canada's other trading partners are not readily available. 5 The U.S. commodity producer price indexes corresponding to each SIC industry were identified with the aid of a concordance showing the principal input-output commodities produced by each Canadian industry. For five of the industries in our sample (fruit and vegetable canners, shoe factories, cotton yam and cloth mills, fiber and fdament yams, and knitting mills), PF was measured by the price index of competing imports because for these industries the import share is large relative to the export share. The "income" variable, Y, for each industry is a weighted average of the indexes of real domestic product and real expenditure of the industries and final demand categories which are the principal users of that industry's output. The weights are based on the industry-by-industry input-output table for 1971.
Estimation and Results We were able to measure the variables included in equation (2) for 31 industries, and we obtained satisfactory estimates of the price equations for 28 of these industries. It is well known that autocorrelation in the presence of a lagged dependent variable leads to inconsistent parameter estimates, and that the classical assumption of nonautocorrelation is frequently violated in time series analysis. To obtain consistent estimates, we began by assuming that the error structure was autoregressive and that the highest order was two. A maximum likelihood procedure was then used to estimate the coefficients of the structural equations and the autoregression parameters. To determine whether the error process was AR(2), AR(1), or white noise, we employed Sargan's COMFAC algorithm, which is based on a series of Wald common factor restriction hypotheses (Harvey, 1981, pp. 281287). If these tests indicated that no significant autocorrelation was present, the price equation was reestimated using OLS. One of the equations rejected by the COMFAC tests appeared to have an MA(1) error structure and this was confirmed by a Lagrange multiplier (LM) test. This equation was then reestimated by GLS. The price equations were estimated in order to obtain a measure of the rate of price adjustment, ~, for each industry. Nonetheless, our results are interesting in their own 5 In 1976, for example, the United States accounted for 68.8% of Canada's merchandise imports and 67.3% of its merchandise imports (Economic Council of Canada, 1983, pp. 91-94).
338
S . W . Kardasz and K. R. Stollery right and merit at least a brief discussion. The coefficient o f A Yt proved to be significant for only 4 o f the 28 industries in our sample (biscuits, shoe factories, veneer and plywood, and pulp and paper). Since dropping this variable from the regressions for these industries does not affect the relationship between APt and its other determinants in any material way, only the equations excluding A Yt are reported in Table 1. As this table shows, the coefficients o f both ACt and APt_ 1 are significant in 23 equations while that o f APFt is significant in 18.
III. Cross-Section Analysis Specification and Data The dependent variable in the cross-section analysis is the speed o f price adjustment which is measured by ~, the estimate o f X obtained from the price equations. W e consider three determinants o f X: concentration, the degree o f product differentiation, and the length o f the production lag. Table 1. Industry Price Equations
Constant
AC,
APFt
AP,_ i
~2
Method of estimation
Slaughtering and meat processors
0.738 (1.51)
0.390' (8.06)
0.344" (9,80)
0.206" (4.93)
0.92
AR2
Fish products
1.225 (1.16)
0.219 a (3.02)
0.118 • (2.29)
0.376* (3.46)
0.40
AR1
Fruit and vegetable canners
0.276 (0.63)
0.057" (1.69)
0.062* (2.05)
0.861" (13.88)
0.72
AR1
Dairy products
1.020b (2.39)
0.765* (7.54)
-0.039 (0.84)
0.213" (2.15)
0.79
OLS
Flour and breakfast cereals
4.137 b (2.83)
0.446" (4.13)
-0.205 (0.97)
0.244 ° (1.75)
0.39
ARI
Feeds
0.797 (0.94)
0.147 ~ (2,43)
0.620 ° (8.59)
0.122" (1.68)
.80
AR2
Biscuits
0.805 (0.82)
0.055 (1.19)
0.475" (4.30)
0.407 ~ (3.83)
0.50
AR1
Soft drinks
0.078 (0.11)
0.187" (6.54)
0.134" (2.08)
0.700" (8.68)
0.68
AR1
Shoe factories
0.224 (0.55)
0.136" (9.85)
0.002 (0.21)
0.838 ° (18.26)
0.67
AR2
Cotton yam and cloth mills
0.405 (0.51)
0.132" (2.69)
0.293" (2.43)
0.494" (4.62)
0.53
OLS
Fiber and filament yams
-0.245 (0.43)
0.553' (4.07)
0.048 (0.80)
0.143 (0.99)
0.50
OLS
Kn/m'ng mills (except hosiery)
-0.313 (1.10)
0.469* (4.88)
0.022 (1.02)
0.246" (1.82)
0.69
AR1
Sawmills
- 0.728 (0.77)
0.159 (0.86)
0.521 * (9.85)
0.026 (0.29)
0.73
OLS
Industry
Market Structure and Price Adjustment Veneer mad plywood Hoesehold furniture
1.164 (0.96)
339 - 0.241 (1.12)
0.194
0.350" (2.28)
(0.21)
0.353' (4.03) -0.047 (0.58)
0.545"
0.44
OLS
0.747' (6.98)
0.31
AR1
(4.74)
Pulp m i p,per
-0.266 (0.25)
-0.185 (0.93)
0.875" (5.55)
0.321" (1.96)
0.65
AS2
Alumimun rolling, camin8 and omudin8
-0.193 (0.22)
0.058 (1.20)
0.351" (3.36)
0.472" (3.73)
0.45
OLS
0.205 (0.30)
0.588" (4.72)
0.035 (0.85)
0.152 (1.22)
0.39
OLS
Hardware, tools and cutlery
-0.131 (0.27)
0.471" (3.40)
0.153" (2.79)
0.376" (3.21)
0.71
ARI
l-laatlngequipmem
-0.251 (0.54)
0.317" (3.86)
0.063 (1.22)
0.716" (9.92)
0.67
ARI
Asr~mral ~
-0.244 (0.47)
0.350" (2.40)
0.195" (2.79)
0.524" (4.55)
0.56
MA1
Major appliances
-0.031 (0.07)
0.435" (3.67)
0.091 (1.22)
0.468" (3.64)
0.57
ARI
Electrical industrial equipment
-0.449 (1.02)
0.359"
0.242"
0.5{)4"
0.74
OLS
(3.23)
(3.21)
(5.31)
0.580" (8.17)
0.165"
0.123
0.89
AR1
(3.04)
(1.51)
0.52
AR2
0.777" (10.93)
0.78
ARI
0.46
ARI
0.62
OLS
Wire and wire products
Electric wire and cable
0.380 (1.36)
Clay products Pharnm:eutkak and medicines
0.192
0.313"
0.00
0.654"
(0.3o)
(4.3o)
(0.01)
(8.69)
0.090 (0.38)
0.071" (1.97)
0.093" (2.41)
Stop and cleaning
0.230
0.135"
0.III"
0.553"
CCmlWn~nd~
(0.50)
(2.50)
(I.74)
(4.42)
Ln,~_,_m'ial chemicals
0.734 (0.74)
0.279* (2.45)
(5.11)
~ vm'kble = ~LP,.~ • m,,aa,~atat 5%, me-tafttest. bm ~ at 5%, two.tailteat.
0.369"
O. 118 (l.Ol)
t-valuesin ~ .
Concentration is measured b y the fonr-firm concentration ratio (_CR4) and b y an
adjusted ratio (CR4A) which equals CR4/(I + MS) where M S is the import share. 6 Since X was estimated over the period 1971: 4-1982: 4, w e used 1976 o r circa 1976 values o f C R 4 and C R 4 A , depending on data availability. In our sample o f 28 industries, C R 4 varies between 16% and 9 8 % , and it exceeds 20% in all but three industries. A similar picture emerges i f concentration is measured by C R 4 A . Specifically, this variable 6 Using coafideafial data, Baldwin, Gmecki and McVey (1986) examine how nzemured ccm~n~tratic~in Cazz/m ~ i n d e m ~ i, aTeczed by m ~ m , expom, aad , m x m ~ oet~t. Tmy ce,~tede tbet "corm:e~ for i m p ~ hm a mmh Sterner efrm th,a mrre~i~ for ,eccedary ompet" md t h l - ~ ~ acccent ~ h u virtmdly m effect ce m/nmm of cmcemzem" (p. 529). la add/rice, tbey find tlm the bm/c a s ~ ~!~lying CR4A, nmnely, lhat tbe top four firms in aa industry do not purclmse imports ~ re~de, appem to be jmefied (p. 538). Generally speakS, the Herfiadahl index (][i)did not perfornl Its well as CR4. Givea the available data, it was not Ixxmibleto adjust H for import compet/fion. We included a regimJ dummy variable (eqmd to I for ,,,~_,_,,tri~serving resioeal or local madam, 0 otherwi~) bet it proved to be imignifzumt.
340
S . W . Kardasz and K. R. Stollery
ranges from 13% to 75% and it exceeds 20% in 24 industries. From this evidence, we concluded that our sample consists mainly, if not entirely, of industries that are oligopolistic in nature. In general, the firms in an oligopolistic industry are not identical, and as a result they tend to have differing views about how the price (or, if their products are differentiated, the price structure) should be adjusted following a change in market conditions. These conflicting views have to be reconciled before a new equilibrium can be achieved. Since the number of two-way reconciliations (between each firm and every other firm) increases with the number of firms in the industry, it is reasonable to suppose that the adjustment process will become more difficult and hence more lengthy as the number of firms increases. Thus, making the usual assumption that concentration varies inversely with the number of firms, one would expect ~ to vary directly with concentration. 7 At first glance, this hypothesis appears to be in direct conflict with the administered price hypothesis, but this is not the case. The APH is based on the idea that prices in concentrated industries respond more slowly to changes in market conditions than do prices in competitive industries because oligopolists must cope with the uncertainties arising from oligopolistic interdependence whereas atomistic firms do not. This implies that the APH is applicable to comparisons of competitive industries, on the one hand, and oligopolistic industries, on the other, rather than to samples consisting of industries with varying degrees of oligopolistic interdependence. The speed of price adjustment may depend not only on concentration but also on the degree of product differentiation. Scherer (1980, pp. 200-203) argues that interfirra rivalry becomes multidimensional with product heterogeneity. As a result, product differentiation makes it more difficult for the firms to cooperate and thereby to coordinate their price changes. Scherer's argument suggests that )~ varies inversely with the degree of product differentiation. To test this hypothesis, product differentiation was proxied by the advertising-sales ratio (ASR) in 1965, the only year for which these data are available in Canada. Courts, Godley, and Nordhaus (1978) have pointed out that price changes will lag cost changes when firms use historical cost accounting and that the length of the lag depends on the length of the production period, 0. They define 0 as "the length of time between the first purchase of the input used for the production process and the sale of the finished product" (Coutts, Godley, and Nordhaus (1978, p. 37)). Making a number of assumptions, they show that 0 = 2S/X(1 + or~3)where S is total (materials, goods-inprocess, and finished-good) inventories, X is sales, a is the share of materials in sales, and/3 is the proportion of materials which enter at the beginning of the production process (Coutts, Godley, and Nordhaus (1978, pp. 52-55)). Unfortunately, total inventory data are not available for many of the industries in our sample. As a result, we were forced to use a crude proxy, the 1975-77 average of the ratio of value-added to shipments (VAS), to measure physical production time (i.e., the time between the b e g i n n i n g of the production process and the completion of the finished product), and the 1975-77 average of the ratio of finished-good inventories to shipments (FIS) to measure selling time (i.e., the time between the completion of the finished product and final sale). We expect )~ to vary inversely with both VAS and FIS. s 7 For a further discussion of the relation between ~ and concentration, see Domberger (1983, pp. 52-62). s The sources of the CR4 and ASR data are Statistics Canada, Industrial Organtfation and Concentration in the Manufacturing, Mining and Logging Industries, 1980 (Ottawa, 1983) and Advertising Expenditures in Canada, 1965 (Ottawa, 1968). The MS series was obtained from the Input-Output Division of Statistics Canada, and the VAS and Iris series were taken from the CANSIM data bank.
341
Market Structure and Price Adjustment Table 2. Cross-SectionRegressions Constant
CR 4
0.688 b (4.65)
0.004" (1.79)
0.006" (2.18)
0.605 b (4.04) 0.561b (4.67)
CR 4A
0.004" (2.08)
0.513 b (4.39)
0.006" (2.64)
A SR
VA $
FI$
1~2
--0.034" (2.06)
--0.358 (1.32)
--0.157 • (2.32)
0.39
-0.037" (2.32)
-0.267 (0.98)
-0.149" (2.27)
0.42
- 0.036' (2.16)
- O. 186' (2.85)
0.37
- 0.039" (2.46)
- O. 168" (2.70)
0.42
Depeadmt variable ffi ~ = I - ~f4.Atnolu~ t-values in Imeathem. • Significant at 5%, one-eta test. b Significant at 5%, two-tail test.
Estimation and Results Our measure of X, ~, contains a measurement error. Assuming that this error varies directly with the variance of ~, it is inappropriate to assume homoskedastic errors because, from the price equations, it is clear that the variances of the ~s are not constant across industries. For this reason, the cross-section regressions were estimated using weighted least squares, with the weights reflecting the variances of the ~ . 9 From Table 2, it can be seen that the coefficients of both CR4 and CR4A are positive and significant, while the coefficient of ASR is negative and significant. Taken together, these findings support the view that the ability of oligopolists to coordinate their pricing policies increases with concentration and decreases with the degree of product differentiation. The evidence is less clear regarding the Contts-Godley-Nordhaus hypothesis that the rate of price adjustment varies inversely with the length of the production period. In particular, while the coefficient of FIS is negative and sitmificant in line with their hypothesis• that of VAS is insignificant. This may be because FIS appears to be a better measure of the s e l l i n g l a g t h a n is V A S o f t h e p h y s i c a l p r o d u c t i o n p e r i o d .
Conclusions This paper examines the determinants of the speed of price adjustment using an approach which, in contrast to earlier studies, avoids the problem of having to distinguish between lag and catch-up periods. The principalfindingof this study is that,within oligopolistic industries,the rateof price adjustment variesdirectlywith concentration.It also appears that the speed of adjustment varies inversely with the degree of product differentiation and the length of the s e l l i n g lag. The approach employed in this paper is potentially useful from the policy point of view. Large, positive residuals in a cross-section regression explaining the speed of adjustment may indicate the existence of explicit collusion, and, as a result• they can be used to identify industries which merit timber investigation by the competition authorities. 9 Mo~ ~ y ,
the we~hts equ~ ~ e over tiw m u ~ M ~ m r s of the ~s.
342
S.W. Kardasz and K. R. Stollery
The authors would like to thank Ed Nosal, Richard Bodell, and James Brox for their susgestions. Research for this project was supported financially by the C~,mai~,~Department of Consumer and C o ~ Affairs.
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Seberer, F. M. 1980. IndusWial Market Structure and Economic Performance. Chicago: Rand McNally Collqe P u b i ~ Company. Statistics Canada. 1970. Industry Selling Price Indexes, 1956-1968. Ottawa: Supply and Services
Canada. Stigler, G. J. and
Kindnhl, J. K. Sept. 1973. Industrial prices, as administered by Dr. Means.
American Economic Review 63(4):717-721. Weiss, L. W. Apr. 1966. Bluffness pricing poficies and it~afion recmnklered. Journal o f Political Economy 74(2): 177-187.