Asymmetric price adjustment: cross-industry evidence.1. Introduction A consumer need only go to the gasoline station or the grocery store to ponder the question: Why do retail prices always seem to go up so fast when the price of crude oil or farm products go up, but they are so slow to come down when oil or farm prices go down? This supposed phenomenon is referred to alternatively as asymmetric price adjustment, asymmetric price transmission, and price transmission asymmetry (PTA). The question to consider is as follows: Do firms raise prices faster when their costs go up than they cut prices when their costs go down? The macroeconomic version of this question is the following: Are prices more sticky (slow to change) downward than upward? An assumption of downward price stickiness has been central to Keynesian macroeconomics. Certification of this assumption, and thus support for the ideas of a convex aggregate supply curve and the Phillips curve, has important implications for monetary and fiscal policy. The clear route to examining downward price stickiness is microeconomic price theory. However, price theory from textbook economics cannot easily answer our questions. Profit maximization based upon the optimization criteria that marginal benefit should be equal to marginal cost implies that marginal revenue should adjust to marginal cost immediately, so that price should symmetrically adjust in response to increases or decreases in cost. However, evidence has begun to emerge that this standard story may fail to apply in at least some industries. Asymmetric price adjustment has been widely documented in gasoline and agricultural markets in a number of countries. More general studies of an economy are few and limited in scope. In one of the broadest studies currently available, Peltzman (2000) finds some evidence of asymmetric price adjustment in eight combinations of 15 2-digit Standard Industrial Classification (SIC) subsectors. Peltzman's work is constrained by the need to match a measure of industry price to some measure of industry cost. Peltzman uses a producer price index (PPI) for an input that made up a significant portion of the production of an output to proxy cost for the "matching" output PPI or consumer price index (CPI). Peltzman's matching technique limited his study to 15 SIC subsectors characterized by relatively simple and clear production processes. This paper employs an actual measure of industry cost to investigate asymmetric price adjustment in a wide cross section of the U.S. economy with the aim of answering two very important questions. First, is asymmetric price adjustment a real phenomenon in the overall U.S. economy? Second, in what sectors of the economy is asymmetric price adjustment most prevalent? Can the answers to these questions help us to understand why asymmetric price adjustment exists? An understanding of this relationship is important because there are significant implications for social welfare. The inflated prices and reduced output, even if temporary in nature, that follow from the failure of the market to adjust prices in a timely fashion result in a deadweight loss to society. Additionally, consumer perceptions that they are being taken advantage of by "greedy" corporations that are quick to raise price but slow to lower price fuel the debate about how well or how badly our market system really works. Theoretical work that attempts to explain the asymmetry between changes in prices and changes in costs have been based on menu costs, market power, inventory management, markups over the business cycle, and search costs. Ball and Mankiw (1994) build on the menu cost literature by noting that positive trend inflation automatically reduces a firm's relative price between price adjustments. In their model, it then follows that shocks that increase a firm's desired price result in relatively larger adjustments to price than do shocks that decrease the desired price. Borenstein, Cameron, and Gilbert hypothesize an explanation for asymmetric price transmission in the gasoline market: "Prices are sticky downward because when input prices fall the old output price offers a natural focal point for oligopolistic sellers" (1997, p. 325). Oligopolistic firms maintain prices unless their market share declines. A decline in market share indicates that some firm has cheated. Firms necessarily raise price when costs go up but delay price cuts until there is some signal that some other firms have cut their price. Reagan (1982) develops a model of a monopolist who carries inventory and faces uncertain demand. If actual demand is lower than expected, the firm holds inventory in the expectation that demand will eventually increase. Holding inventory eases downward pressure on price. If actual demand is higher than expected, the firm draws down inventory to zero, and price must rise for the market to clear. According to Borenstein, Cameron, and Gilbert, "Production lags and finite inventories of gasoline imply that negative shocks to the future optimal gasoline consumption path can be accommodated more quickly than positive shocks" (1997, p. 327). Thus, asymmetry between the short-run costs of decreasing versus increasing inventories leads to short-run prices responding more to excess demand than to excess supply. An upward-sloping marginal cost curve implies that marginal costs are procyclical; costs increase as output increases during business booms. If markups are countercyclical, then prices are acyclical. Price rigidity results because an increase (decrease) in cost is offset by a decrease (increase) in markup. Markups may be countercyclical due to procyclical elasticity of demand, as in Blinder et al. (1998), or if price wars in oligopoly are more frequent during booms, as suggested by Rotemberg and Saloner (1986). Rotemberg and Saloner (1986) establish their result with the idea that the gain from cheating (deviating from the collusive price) to a firm in an oligopoly is the largest when demand is high during a boom. Haltiwanger and Harrington (1991) extend the research of Rotemberg and Saloner (1986) by considering a firm's expectation of future demand. They note that the discounted loss from cheating is lowest when demand is expected to fall in a recession, because a recession period is the toughest time for coordination in an oligopoly. Therefore, markups are procyclical instead of countercyclical. This can lead to asymmetric price adjustment. Borenstein, Cameron, and Gilbert provide a third and final alternative explanation of asymmetric price adjustment in the gasoline market. Specifically, "Volatile crude oil prices create a signal-extraction problem for consumers that lowers the expected payoff from search and makes retail outlets less competitive" (1997, p. 329). The temporary increase in the market power of the gasoline retailer accelerates the transmission of increases in crude oil prices while moderating the transmission of decreases. Empirical work on asymmetric price adjustment is relatively abundant but generally focuses on specific subsectors of the economy. Much of the literature examines gasoline and agricultural industries, but there are some papers with a slightly broader focus. Frey and Manera (2007) provided a rather extensive survey of empirical models and findings in their study of asymmetric price transmission. Evidence of asymmetric price adjustment at a more general level is found by Buckle and Carlson (2000) and the aforementioned Peltzman (2000). Buckle and Carlson (2000) test the prediction of Ball and Mankiw (1994) that asymmetric price adjustment should be more pronounced with increases in general inflation. Using an ordered probit model to analyze data from a survey of firms in New Zealand soliciting information about the direction of changes in price and cost, Buckle and Carlson find evidence that inflation does lead to added asymmetric price adjustment. The paper by Peltzman appears to be the only paper that has studied asymmetric price adjustment using actual measures of industry prices and goods across a spectrum of the economy. Peltzman also tests whether inventory management, menu costs, or market power could explain asymmetric price adjustment and found no evidence supporting any of these explanations. Section 2 presents the data and methodology, section 3 presents the results, and section 4 concludes. 2. Data and Methodology The Data The ideal dependent variable would be the actual price of each firm's product, and the ideal independent variable would be the product's true cost. Time series of individual product prices are not available, so this paper follows Peltzman (2000) by using the PPI from the U.S. Bureau of Labor Statistics (BLS) as a proxy for industry price. Industry cost is constructed from costs available from Standard & Poor's (S&P) Compustat financial information database. Adequate coverage of quarterly revenue (R) and cost of goods sold (VC) for the period 1st quarter 1966 to 4th quarter 2006 is available from the Compustat database for over 10,000 publicly traded U.S. firms. In 2006, financial data were collected on 841 6-digit North American Industrial Classification System (NAICS) industries. The data "cost of goods sold" is defined by S&P as 'This item represents all expenses that are directly related to the cost of merchandise purchased or the cost of goods manufactured that are withdrawn from finished goods inventory and sold to customers." This data definition fits well with the economics characterization of variable cost. The lack of availability of data for privately held companies is not a significant limitation for many of the 24 two-digit NAICS sectors of the U.S. economy classified by the U.S. Census Bureau. A comparison of 2002 sales from Compustat to the 2002 Economic Census in Table 1 shows that most, if not all, sales are accounted for by publicly held companies in Mining (sector 21), Utilities (22), Manufacturing (31-33), Retail Trade (45), Transportation and Warehousing (48-19), Information (51), and Finance and Insurance (52). There are differences between the two columns because NAICS sales are identified in Compustat at the firm level; whereas, the Census Bureau identifies NAICS at the product level; the data reflect the broad coverage of Compustat. In some cases, Compustat sales are significantly higher than the Census Value of Shipments. This is a likely result of sales that span other sectors, a potential concern for the matching of Compustat industries to a BLS PPI. To mitigate this concern, industries with an overly broad product definition in Compustat were dropped from the analysis (as detailed later). The main limitation to an industry-level study of pricing is the availability of price data. The BLS has significant historical price data for only six sectors including Mining (21), Manufacturing (31-33), Transportation and Warehousing (48), and Information (51). Thus, the fact that Compustat is limited to publicly held companies is not the binding constraint in a study of industry pricing behavior. Five-hundred and twenty-five of the Compustat six-digit NAICS industries can be cross-matched to one of the 1149 six-digit NAICS PPI series currently available from the BLS. Two industries were dropped because the BLS stopped collecting PPI data prior to 2006. One-hundred and ninety-eight industries were dropped because the BLS has collected only very recent PPI data due to NAICS reclassifications of industries in 1997 and 2002. Two industries were dropped because the BLS collected data only sporadically after 2002. Forty-one industries were dropped due to a too-broad product definition (13 so-called six-digit NAICS industries were actually four-digit NAICS subsectors, and 28 industries were defined as miscellaneous or other). Finally, 13 industries were dropped because there were less than 40 observations of the combination of Compustat and BLS PPI data. Thus, there were 269 industries available for the analysis. The last column of Table 1 shows the distribution of the 269 industries over the 24 NAICS sectors and indicates sectors that could not be included in the analysis due to a lack of PPI data. Industry total revenue is the sum of the N individual firm revenues: [R.sub.t] = [N.summation over (i=1)] [R.sub.i,t]. Industry total variable cost is the sum of the N individual firm cost of goods sold; [VC.sub.t] = [N.summation over (i=1)] V[c.sub.i,t]. Industry average variable cost (AV[C.sub.t]) is derived as [P.sub.t](V[C.sub.t]/[R.sub.t]) = [P.sub.t]([AV[C.sub.t] x [Q.sub.t]/[[P.sub.t] x [Q.sub.t]) = AV[C.sub.t] where [P.sub.t] is the industry PPI from the BLS and [Q.sub.t], is quantity sold. Cost information from company financial reports allows an examination of price asymmetry in a much larger set of industries than in Peltzman (2000) because the analysis is not constrained by a subjective matching of price indices. Additionally, the finer industry-level data of this study should provide a better matching of measures of price and cost than the coarse subsector data of previous research. Empirical Methodology The empirical work follows the methodology of Peltzman (2000), who employs two methods: a distributed lag model and a vector autoregression (VAR) model that incorporates an error-correction process. Frey and Manera (2007, p. 372) refer to this last method as "ECMsw" or "Error Correction Model Estimated with Stock and Watson's method." This simple yet flexible method is best suited to the current application, given the wide variety of industries to be studied. Alternative methods summarized by Frey and Manera have typically been applied to single products or product lines. Letting [p.sub.t] = ln([P.sub.t]) and [c.sub.t], = ln(AV[C.sub.t]), the distributed lag model is given by [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] where D is a dummy variable set to 1 if [[DELTA]c.sub.t-i] [greater than or equal to] 0, [[DELTA]x.sub.t-I] is a vector of exogenous variables, and [[epsilon].sub.t] is an error term. Following Peltzman (2000) to allow a comparison of results, the number of lags is set at N = 4 and M = 3. The exogenous variables include the all-commodities PPI from the BLS and the Federal Reserve's Industrial Production Index to represent potential demand shifters. A direct measure of average cost is employed, so there is no need to control for supply shocks. The VAR model is given by [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] where [v.sub.t-1] = [p.sub.t-1] - [c.sub.t-1] is an error-correction process. The dummy variable is on when the change in average cost is positive, and thus a positive [N.summation over (i=0)] [[eta].sub.t-i] demonstrates positive asymmetric price adjustment. In the data, changes in average cost are about evenly split between positive and negative changes across all 269 industries. The error-correction process allows for entry/exit when prices have deviated too far from their equilibrium level. 3. Results Regressions, using both the lag and VAR specifications, were performed at the NAICS six-digit industry level. Additional regressions were performed at the NAICS three-digit subsector level to replicate the level of aggregation in Peltzman (2000). A subsector PPI was calculated for this additional analysis as a sales-weighted mean of the industry PPIs. There was little difference between the lag and VAR specifications, and thus only the VAR results were used for the analysis. Detailed price asymmetry coefficients at various points for all 269 industries and coefficients on demand shifters and the error-correction process are available from the author upon request. Each demand shifter is expected to have a positive sign; whereas, the error-correction process should have a negative sign. The unreported results show a positive and significant coefficient on at least one observation of the Commodity Price Index or the Industrial Production Index for 179 industries and a negative coefficient on the error-correction process for 191 industries. The VAR specification appears to be well-suited to most of the industries. A positive sign on the error-correction process would implausibly imply that prices do not converge to equilibrium for 78 industries, and thus the coefficient on the error-correction term is set to zero when price asymmetry is calculated for these industries. Table 2 provides a summary of the results at the NAICS three-digit subsector level. A single italicized coefficient is reported for subsectors that have only one industry in the data. Two coefficients are reported for the remaining industries. The first coefficient is the salesweighted mean industry asymmetry (after each of four quarters) from the unreported industry results. The second and italicized coefficient is the aggregated subsector asymmetry that matches the SIC two-digit level of aggregation in Peltzman (2000). For comparison, Table 2 reproduces the Peltzman results (the outdated SIC codes are matched to current NAICS codes). Peltzman (2000) provides evidence on only three of the 23 NAICS sectors that comprise the U.S. economy. The results of Table 2 extend the analysis to 11 sectors. Table 3 provides a brief summary and discussion of the evidence from Table 2 and the unreported industry-level findings. For the 13 subsectors in the three sectors that are common to both this study and Peltzman (2000), Table 2 shows that results for nine of the subsectors (311 Food, 312 Beverage, 315 Apparel, 321 Wood, 324 Petroleum and Coal, 325 Chemical, 327 Nonmetallic Mineral, 331 Primary Metal, and 332 Fabricated Metal) are generally consistent with the Peltzman findings. Both the sales-weighted mean industry and aggregated subsector asymmetry coefficients are similar to Peltzman's coefficients for subsectors 312, 321, 324, 327, and 331. One set of the two types of coefficients provides at least some support for Peltzman's finding of positive price asymmetry in subsectors 311, 315, 325, and 332. There are notable differences between this study and Peltzman for only 4 of the 13 subsectors in common: 313, 314, 316, and 322. The old SIC designations used in Peltzman (2000) lump together Food and Beverage (311 and 312), Apparel and Textile, Textile Product, and Leather (313, 314, 315, and 316), and Primary and Fabricated Metal (331 and 332). With the exception of subsector 322 Paper, many of the differences between this study and Peltzman may be accounted for by the composition of these subsectors. The 29 subsectors from eight sectors that are new to this study show little consistent evidence of the existence of positive price asymmetry. Statistically significant persistent positive price asymmetry is found in only 5 of the 30 subsectors. Another nine subsectors could be said to demonstrate persistent positive price asymmetry if a looser standard of a preponderance of positive signs is adopted, as in Peltzman (2000). The remaining 16 subsectors show little or no convincing evidence of subsector-wide positive price asymmetry. An aggregation of ratio 1 in Table 3 shows that the unreported regressions for the 269 industries in this study resulted in 651 positive and 425 negative price asymmetry coefficients (for all four quarters of the forecast horizon). An aggregation of ratio 2 of Table 3 shows that 115 of the 269 industries have one or more positive price asymmetry coefficients in the forecast horizon that is statistically significant. If there was at least one statistically significant positive coefficient, the industry demonstrated either persistent or short-lived positive price asymmetry in 100 of the 115 cases using the criterion of a preponderance of positive signs, as in Peltzman (2000). There is much less support for the existence of positive price asymmetry in the U.S. economy if a standard of statistical significance is adopted. Across the 42 subsectors in total addressed in this study, only seven subsectors (211 Oil and Gas, 312 Beverage and Tobacco, 321 Wood, 331 Primary Metal, 482 Rail, 483 Water Transportation, and 517 Telecommunication) demonstrate positive price asymmetry that is both economically and statistically significant. Of the 115 industries that have one or more statistically significant positive price asymmetry coefficients, 92 industries had one positive and significant coefficient; 19 had two; three had three; and only one had four. In the end, only 143 of the 651 positive price asymmetry coefficients over the forecast horizon were statistically significant. These results make it difficult to say with any degree of certainty that price asymmetry is pervasive in the U.S. economy. A better conclusion, supported by the evidence of this study and Peltzman (2000), is that positive price asymmetry likely exists in select subsectors of the U.S. economy. Interestingly, two of the subsectors identified by this study, namely Food and Oil and Gas, are currently receiving a great deal of media attention because many people believe that prices for these goods rise faster than they fall with input cost changes. A possible issue with the lack of a finding of price asymmetry in some industries is whether the finding can be alternatively explained by a broad lack of variability in the measure of cost for these industries. The strongest evidence in this study and from Peltzman (2000) is for sectors 31 and 32, along with subsectors 331 and 332. The products of these sectors/subsectors likely have few inputs, and perhaps only one, that make up a large share of the final value of the product. The products of the remaining sectors probably have many inputs. It may be that cost variance is lower for products that have many inputs to production. An Analysis of Variance (ANOVA) of the variability of cost data was employed to address this issue. The mean standard deviation of the cost change in the 135 industries from sectors/subsectors 31, 32, 331, and 332 is 6.7%; whereas, the mean of the other 134 industries is 9.3%. The sectors/subsectors that show convincing evidence of price asymmetry actually have relatively lower cost variability. However, ANOVA results indicate that the difference is not significant. A similar result is found when comparing variability of cost data between industries that exhibit evidence of significant price asymmetry (positive or negative) versus those that do not. The mean standard deviation of the cost change in 148 industries where an instance of significant price asymmetry is found is 8.1%, and the mean of the other 121 industries is 7.7%, and ANOVA indicates that the difference is not significant. Industries that show no evidence of price asymmetry have much the same cost variability as those that do. Thus, the results of Tables 2 and 3 do not appear to be based on heterogeneity in cost variability. A potentially informative finding of this study is that positive price asymmetry exists in selected, but not all, manufacturing subsectors and that price asymmetry cannot be confirmed in some nonmanufacturing sectors. The cross-industry differences in price asymmetry may be informative as to the root cause of price asymmetry. As mentioned previously, menu costs, market power, inventory management, markups over the business cycle, and search costs have all been offered as possible explanations of price asymmetry. Obviously, the availability of broad industry-level data is a key issue for specific tests of these explanations. Both direct and indirect measures of menu costs, inventory management, and search costs are particularly challenging to find. Measures of industry concentration can be used as a proxy for market power. The four-firm concentration ratio (CR4) and the Herfindahl-Hirschman Index (HHI) from the 2002 Economic Census are available for many industries. (1) Goods sensitive to business cycles, such as durable goods, can be classified for the manufacturing sector in accordance with the U.S. Census Bureau Current Industrial Reports. (2) As a very rough proxy for search costs, goods in the manufacturing sector can be classified as experience or search goods in accordance with Nelson (1970). One approach to gauging the way in which price asymmetry differs between industries is to simply regress a measure of the asymmetry on the potential explanatory variables. Given the rather crude proxies for some of these variables, results from such a regression should be interpreted as indicative of a possible explanation, rather than definitive. In any case, Table 4 summarizes the results of just such a regression of the (unreported) measures of industry mean asymmetry after one quarter on HHI, a dummy for durable goods, and an experience goods dummy. The results in Table 4 indicate that price asymmetry are as follows: (i) has little connection to market power (given the small coefficient on HHI and also on unreported CR4 results); (ii) is lower for durable goods; and (iii) is higher for experience goods. These results feed into the following discussion of each potential explanation of price asymmetry. Menu Costs While no industry-level menu cost data are available, the general empirical findings may be a guide for future theoretical work in this area. Much of sector 32, Natural Resource Manufacturing, yields commodity-type products that have frequent price changes with minimal menu costs. On the other hand, products in sector 33, Durable Goods Manufacturing, likely have high menu costs. Contrary to the predictions of the menu cost literature, price asymmetry is more evident in natural resource manufacturing. It should be considered that producers of commodity-type products often have significant long-term relationships with their suppliers. It may be the case in this instance that switching costs play a more important role than menu costs in price asymmetry. Market Power As is evident from Table 4, country-level concentration measures offer little explanation as to why price asymmetry differs between industries. The sign on HHI is contrary to the prediction of a market power explanation of price asymmetry. Market power is likely a much more important issue at the local level, but such an analysis cannot be undertaken across industries. Inventory Management Perhaps the most promising story for this study's results is in inventory management. Inventory can easily be accumulated in sectors 31 and 32, and there is relatively strong evidence of price asymmetry for these sectors from both Peltzman (2000) and this study. It is more difficult to adjust inventory levels for many durable goods, inventory is literally left in the ground in mining industries, and little if any inventory exists in service industries. This may explain why there is little support for price asymmetry in these sectors. The inventory story is, however, not complete. Some nondurables in sector 31 are perishable, which precludes accumulating large inventories. Some low-value and/or small durables are relatively easier to keep in inventory. Markups over the Business Cycle The U.S. Census classifies manufacturing subsectors 321 Wood and 327 Nonmetallic Mineral and sector 33 Durable Goods as durable goods. Subsector 327 and sector 33 exhibit little evidence of persistent price asymmetry. The results of Table 4 indicate that price asymmetry is actually lower for durable goods compared to nondurable goods. The findings of this article call into question the ability of theories based on markups over the business cycle to explain price asymmetry. Search Costs Search goods are subsectors 313 Textile Mills, 314 Textile Product Mills, 315 Apparel, 316 Leather, 337 Furniture, and some industries in 339 Miscellaneous. Experience goods (all other subsectors) are associated with high search costs, given the need to "experience" the good; whereas, search goods are associated with low search costs. Table 2 and the unreported industry-level results do indeed indicate that price asymmetry is less frequently observed in search good subsectors compared to experience good subsectors. The positive, albeit imprecise, coefficient on the experience good dummy in Table 4 also supports higher price asymmetry for experience goods. As cautioned previously, these results must be taken within the context of the overly coarse measure of search cost. A definitive answer as to why price asymmetry arises in some industries and not in others is clearly desirable. Finding the answer in future empirical research will depend on finding the data necessary to address the question. 4. Conclusions The objective of this study was to answer two very important questions. First, is asymmetric price adjustment an economically important phenomenon? Second, in what parts of the economy is asymmetric price adjustment most common? The answers to these questions may also allow us to evaluate why asymmetric price adjustment can take place at all. Given the findings of the previous literature and this study, the answer to the first question appears to be yes. This study confirms the finding of Peltzman (2000) by showing that positive price asymmetry is frequent in nondurable goods and natural resource manufacturing. However, price asymmetry is not clearly evident in mining, durable goods manufacturing, and service sectors. The differing results between sectors may best be explained by theoretical explanations of price asymmetry based on inventory management. Inventory is readily adjusted in nondurable goods and natural resource manufacturing where price asymmetry is evident. On the contrary, mining inventory is simply left in the ground, durable goods inventory may be more difficult and expensive to maintain, and inventory is virtually nonexistent in transportation, information, and other service industries. This study's findings provide little support for the existence of the economy-wide downward price stickiness needed to support a convex aggregate supply curve. However, this study and others show that asymmetric price adjustment does seem to be prevalent in certain subsectors of the economy that have direct and noticeable impacts on consumers. Consumers who fuss about food and energy prices that are quick to go up but slow to come down seem to have good reasons to fuss. Received March 2008; accepted August 2008. References Ball, Laurence, and N. Gregory Mankiw. 1994. Asymmetric price adjustment and economic fluctuations. The Economic Journal 104(423):247-61. Blinder, Alan S., Elier D. Canetti, David E. Lebow, and Jeremy B. Rudd. 1998. Asking about prices: A new approach to understanding price stickiness. New York: Russell Sage Foundation. Borenstein, Severin, A. Colin Cameron, and Richard Gilbert. 1997. Do gasoline prices respond asymmetrically to crude oil price changes? Quarterly Journal of Economics 112(l):305-38. Buckle, Robert A., and John A. Carlson. 2000. Inflation and asymmetric price adjustment. The Review of Economics and Statistics 82(1):157-60. Frey, Giliola, and Matteo Manera. 2007. Econometric models of asymmetric price transmission. Journal of Economic Surveys 21(2):349-4l5. Haltiwanger, John, and Joseph E. Harrington, Jr. 1991. The impact of cyclical demand movements on collusive behavior. Rand Journal of Economics 22(]):89-106. Nelson, Phillip. 1970. Information and consumer behavior. Journal of Political Economy 78(2):311-29. Peltzman, Sam. 2000. Prices rise faster than they fall. Journal of Political Economy 108(3):466-502. Reagan, Patricia B. 1982. Inventory and price behavior. Review of Economic Studies 49(1): 137^42. Rotemberg, Julio J., and Garth Saloner. 1986. A supergame-theoretic model of price wars during booms. American Economic Review 76(3):390-407. Carl R. Gwin, Graziadio School of Business and Management, Pepperdine University, 6100 Center Drive, Los Angeles, CA 90045-1590; phone: 310-568-5553, fax: 310-568-5778; E-mail carl.gwin@pepperdine.edu. The author would like to thank John Pepper (editor), two anonymous referees, David VanHoose, and Charles North for very helpful comments and suggestions.
Table 1. Comparison of Sales from S&P Compustat to Sales from
the Economic Census
2002 Economic 2002
NAICS Census Value Compustat
Sector Sector Description of Shipments Sales
11 Agriculture, Forestry, NA 12,468
Fishing, and Hunting
21 Mining 183,673 201,469
22 Utilities 398,907 891,779
23 Construction 1,206,843 143,322
31 Manufacturing 689,689 815,042
32 Manufacturing 1,287,017 2,557,172
33 Manufacturing 1,938,013 3,154,829
42 Wholesale Trade 4,637,494 570,496
44 Retail Trade 2,271,569 857,219
45 Retail Trade 782,114 619,863
48 Transportation & 307,440 389,420
Warehousing
49 Transportation & 74,713 152,410
Warehousing
51 Information 897,830 1,557,095
52 Finance & Insurance 2,803,855 3,118,493
53 Real Estate & Rental & 335,585 69,489
Leasing
54 Professional, Scientific, 867,813 313,622
& Technical Services
55 Management of Companies 107,631 0
and Enterprises
56 Administrative, Support, 397,368 115,671
Waste Management,
Remediation Services
61 Educational Services 30,691 5233
62 Health Care & Social 1,210,942 123,287
Assistance
71 Arts, Entertainment, & 141,923 17,025
Recreation
72 Accommodation & 449,499 144,949
Foodservices
81 Other Services (except 302,090 12,724
Public Administration)
92 Public Administration NA NA
Percent Number of
NAICS Represented Industries Used
Sector Sector Description in Compustat in Analysis
11 Agriculture, Forestry, 1
Fishing, and Hunting
21 Mining 110 11
22 Utilities 224 New PPI
Data
23 Construction 12 No PPI Data
31 Manufacturing 118 50
32 Manufacturing 199 52
33 Manufacturing 163 128
42 Wholesale Trade 12 New PPI
Data
44 Retail Trade 38 New PPI
Data
45 Retail Trade 79 New PPI
Data
48 Transportation & 127 8
Warehousing
49 Transportation & 204 2
Warehousing
51 Information 173 2
52 Finance & Insurance 111 New PPI
Data
53 Real Estate & Rental & 21 5
Leasing
54 Professional, Scientific, 36 3
& Technical Services
55 Management of Companies 0 No PPI Data
and Enterprises
56 Administrative, Support, 29 2
Waste Management,
Remediation Services
61 Educational Services 17 New PPI
Data
62 Health Care & Social 10 5
Assistance
71 Arts, Entertainment, & 12 Sparse PPI
Recreation Data
72 Accommodation & 32 Sparse PPI
Foodservices Data
81 Other Services (except 4 Sparse PPI
Public Administration) Data
92 Public Administration NA No PPI Data
Table 2. Mean Asymmetry by NAICS Subsector
Sales-Weighted Mean Industry
Asymmetry after Quarter
* Italicized Coefficient Is
Aggregated Subsector
Asymmetry after Quarter
NAICS Subsector Description 1 2
11 Agriculture, Forestry, Fishing, and Hunting
113 Forestry and Logging .08 * .052 *
21 Mining
211 Oil and Gas Extraction .055 .444
.258 (a) * .546 (a) *
212 Mining (except Oil and Gas) -.004 -.005
.564 (a) * 1.057 (a) *
213 Support Activities for .03 * -.05 *
Mining
31 Nondurable Goods Manufacturing
311 Food Manufacturing .124 .144
-.054 * -.085 *
312 Beverage and Tobacco .273 .241
Product Manufacturing .338 (a) * .238 *
313 Textile Mills .039 -.159
.098 * .103 *
314 Textile Product Mills -.132 -.135
-.064 (a) -.026 *
315 Apparel Manufacturing .013 .034
-.13 * .137 (a) *
316 Leather and Allied Product .024 -.061
Manufacturing .046 * .012 *
32 Natural Resource Manufacturing
321 Wood Product Manufacturing .069 .106
.499 (a) * .485 *
322 Paper Manufacturing -.029 .072
-.142 * -.264 *
323 Printing and Related .081 -.033
Support Activities .135 * -.202 (a) *
324 Petroleum and Coal Products .1 .03
Manufacturing .087 (a) * .064 *
325 Chemical Manufacturing .037 -.027
.279 (a) * .291 *
326 Plastics and Rubber .095 .161
Products Manufacturing 1.125 (a) * 1.342 *
327 Nonmetallic Mineral Product .029 -.028
Manufacturing .542 (a) * .359 *
33 Durable Goods Manufacturing
331 Primary Metal Manufacturing .229 .177
.34 (a) * .082 (a) *
332 Fabricated Metal Product .152 .152
Manufacturing .05 * .018 *
333 Machinery Manufacturing .114 .173
.013 * .083 *
334 Computer and Electronic -.277 -.038
Product Manufacturing -.002 * .01 *
335 Electrical Equipment, .041 .044
Appliance, and Component .065 * .067 *
Manufacturing
336 Transportation Equipment -.014 -.022
Manufacturing .514 (a) * .506 *
337 Furniture and Related -.036 -.05
Product Manufacturing .04 (a) * .042 *
339 Miscellaneous Manufacturing -.017 -.052
-.03 * -.028 *
48-49 Transportation and Warehousing
481 Air Transportation -.472 (a) -.214
482 Rail Transportation .396 (a) .469
483 Water Transportation .358 .176
.442 (a) * .256 *
484 Truck Transportation .334 .335
486 Pipeline Transportation .028 .064
-.039 (a) * -.009 *
488 Support Activities for .401 .58
Transportation
492 Couriers and Messengers .06 .194
493 Warehousing and Storage .026 -.005
51 Information
515 Broadcasting -.101 (a) -.007
(except Internet)
517 Telecommunications .095 .216 (a)
53 Real Estate and Rental and Leasing
531 Real Estate .101 -.032
.441 (a) .458 *
532 Rental and Leasing Services .128 .166
.19 * .174 *
54 Professional, Scientific and Technical Services
541 Professional, Scientific, .087 .309
and Technical Services .948 (a) * .159 *
56 Administrative and Support and Waste Management and
Remediation Services
561 Administrative and Support -.017 -.066
Services .03 * .034 *
62 Health Care and Social Assistance
621 Ambulatory Health Care .088 .197
Services 1.522 (a) * 1.185 *
622 Hospitals -.086 .019
-.095 * .026 *
623 Nursing and Residential .263 (a) -.061 (a)
Care Facilities
Sales-Weighted Mean Industry
Asymmetry after Quarter
* Italicized Coefficient Is
Aggregated Subsector
Asymmetry after Quarter
NAICS Subsector Description 3 4
11 Agriculture, Forestry, Fishing, and Hunting
113 Forestry and Logging .101 * .111 *
21 Mining
211 Oil and Gas Extraction .303 .429
.385 * .437 *
212 Mining (except Oil and Gas) -.099 -.044
1.189 * 1.045 (a) *
213 Support Activities for -.178 * -.374 *
Mining
31 Nondurable Goods Manufacturing
311 Food Manufacturing .032 .011
-.128 * -.132 *
312 Beverage and Tobacco .333 .284
Product Manufacturing .193 * .125 *
313 Textile Mills -.216 -.262
-.048 * -.101 *
314 Textile Product Mills -.234 -.336
.041 (a) .038 *
315 Apparel Manufacturing -.008 -.053
.11 * .065 *
316 Leather and Allied Product -.006 .12
Manufacturing .001 * .046 *
32 Natural Resource Manufacturing
321 Wood Product Manufacturing .064 .144
.211 * .306 *
322 Paper Manufacturing -.046 -.125
-.109 * -.057 *
323 Printing and Related -.054 -.156
Support Activities -.222 * -.485 *
324 Petroleum and Coal Products -.03 -.006
Manufacturing .036 * -.032 (a) *
325 Chemical Manufacturing -.048 -.148
.017 (a) * .066 *
326 Plastics and Rubber .081 -.003
Products Manufacturing 1.309 * 1.452 *
327 Nonmetallic Mineral Product .058 -.008
Manufacturing .281 * .073 *
33 Durable Goods Manufacturing
331 Primary Metal Manufacturing .147 .242
-.01 * .145 *
332 Fabricated Metal Product .01 .054
Manufacturing -.012 * -.014 *
333 Machinery Manufacturing .155 .13
.068 * .024 *
334 Computer and Electronic .677 .895
Product Manufacturing .017 * -.004 *
335 Electrical Equipment, .106 .122
Appliance, and Component .138 (a) * .139 *
Manufacturing
336 Transportation Equipment -.061 .032
Manufacturing .673 (a) * .6 *
337 Furniture and Related -.063 -.107
Product Manufacturing .053 * .085 (a) *
339 Miscellaneous Manufacturing -.012 -.036
-.036 * -.011 *
48-49 Transportation and Warehousing
481 Air Transportation .229 (a) .115
482 Rail Transportation .295 .258
483 Water Transportation .025 .090
.098 * .388 (a) *
484 Truck Transportation .338 -.287 (a)
486 Pipeline Transportation -.042 -.048
-.023 * .04 *
488 Support Activities for .166 .007
Transportation
492 Couriers and Messengers .189 -.198 (a)
493 Warehousing and Storage -.087 -.16
51 Information
515 Broadcasting -.029 .025
(except Internet)
517 Telecommunications .13 .22
53 Real Estate and Rental and Leasing
531 Real Estate -.114 -.028
.429 * .284 *
532 Rental and Leasing Services .171 -.031
.294 * .512 *
54 Professional, Scientific and Technical Services
541 Professional, Scientific, .276 .294
and Technical Services .298 * -.095 *
56 Administrative and Support and Waste Management and
Remediation Services
561 Administrative and Support -.011 -.015
Services .025 * -.003 *
62 Health Care and Social Assistance
621 Ambulatory Health Care .541 .335
Services 1.603 (a) * 1.754 (a) *
622 Hospitals .298 .293
.304 (a) * .306 *
623 Nursing and Residential .04 .022
Care Facilities
Peltzman Mean Asymmetry
after Month
NAICS Subsector Description 0 2
11 Agriculture, Forestry, Fishing, and Hunting
113 Forestry and Logging
21 Mining
211 Oil and Gas Extraction
212 Mining (except Oil and Gas)
213 Support Activities for
Mining
31 Nondurable Goods Manufacturing
311 Food Manufacturing 0.168 (a) 0.086
312 Beverage and Tobacco
Product Manufacturing
313 Textile Mills 0.087 0.042
314 Textile Product Mills
315 Apparel Manufacturing
316 Leather and Allied Product
Manufacturing
32 Natural Resource Manufacturing
321 Wood Product Manufacturing 0.222 (a) 0.185
322 Paper Manufacturing -0.124 (a) 0.273 (a)
323 Printing and Related
Support Activities
324 Petroleum and Coal Products 0.008 0.078
Manufacturing
325 Chemical Manufacturing 0.129 0.263 (a)
326 Plastics and Rubber
Products Manufacturing
327 Nonmetallic Mineral Product 0.060 0.106
Manufacturing
33 Durable Goods Manufacturing
331 Primary Metal Manufacturing 0.196 (a) 0.187 (a)
332 Fabricated Metal Product
Manufacturing
333 Machinery Manufacturing
334 Computer and Electronic
Product Manufacturing
335 Electrical Equipment,
Appliance, and Component
Manufacturing
336 Transportation Equipment
Manufacturing
337 Furniture and Related
Product Manufacturing
339 Miscellaneous Manufacturing
48-49 Transportation and Warehousing
481 Air Transportation
482 Rail Transportation
483 Water Transportation
484 Truck Transportation
486 Pipeline Transportation
488 Support Activities for
Transportation
492 Couriers and Messengers
493 Warehousing and Storage
51 Information
515 Broadcasting
(except Internet)
517 Telecommunications
53 Real Estate and Rental and Leasing
531 Real Estate
532 Rental and Leasing Services
54 Professional, Scientific and Technical Services
541 Professional, Scientific,
and Technical Services
56 Administrative and Support and Waste Management and
Remediation Services
561 Administrative and Support
Services
62 Health Care and Social Assistance
621 Ambulatory Health Care
Services
622 Hospitals
623 Nursing and Residential
Care Facilities
Peltzman Mean Asymmetry
after Month
NAICS Subsector Description 4 8
11 Agriculture, Forestry, Fishing, and Hunting
113 Forestry and Logging
21 Mining
211 Oil and Gas Extraction
212 Mining (except Oil and Gas)
213 Support Activities for
Mining
31 Nondurable Goods Manufacturing
311 Food Manufacturing 0.087 0.142
312 Beverage and Tobacco
Product Manufacturing
313 Textile Mills 0.032 0.010
314 Textile Product Mills
315 Apparel Manufacturing
316 Leather and Allied Product
Manufacturing
32 Natural Resource Manufacturing
321 Wood Product Manufacturing 0.124 0.176
322 Paper Manufacturing 0.413 (a) 0.434 (a)
323 Printing and Related
Support Activities
324 Petroleum and Coal Products 0.111 -0.099
Manufacturing
325 Chemical Manufacturing 0.173 0.236
326 Plastics and Rubber
Products Manufacturing
327 Nonmetallic Mineral Product 0.115 -0.027
Manufacturing
33 Durable Goods Manufacturing
331 Primary Metal Manufacturing 0.297 (a) 0.331 (a)
332 Fabricated Metal Product
Manufacturing
333 Machinery Manufacturing
334 Computer and Electronic
Product Manufacturing
335 Electrical Equipment,
Appliance, and Component
Manufacturing
336 Transportation Equipment
Manufacturing
337 Furniture and Related
Product Manufacturing
339 Miscellaneous Manufacturing
48-49 Transportation and Warehousing
481 Air Transportation
482 Rail Transportation
483 Water Transportation
484 Truck Transportation
486 Pipeline Transportation
488 Support Activities for
Transportation
492 Couriers and Messengers
493 Warehousing and Storage
51 Information
515 Broadcasting
(except Internet)
517 Telecommunications
53 Real Estate and Rental and Leasing
531 Real Estate
532 Rental and Leasing Services
54 Professional, Scientific and Technical Services
541 Professional, Scientific,
and Technical Services
56 Administrative and Support and Waste Management and
Remediation Services
561 Administrative and Support
Services
62 Health Care and Social Assistance
621 Ambulatory Health Care
Services
622 Hospitals
623 Nursing and Residential
Care Facilities
(a) [absolute value of t] 2.0.
Table 3. Subsector Summary of Industry-Level Evidence of
Positive Price Asymmetry
Ratio (a)
Evidence of Positive
Subsector 1 2 Price Asymmetry?
11 Agriculture, Forestry, Fishing, and Hunting
113 4/4 0/1 Yes, but imprecise
21 Mining
211 7/8 1/2 Yes
212 15/32 1/8 No
213 1/4 0/1 No
31 Nondurable Goods Manufacturing
311 74/104 14/26 Some
312 13/24 4/5 Yes
313 2/8 0/2 No
314 6/12 0/3 No
315 20/32 3/8 Some
316 14/24 2/6 No
32 Natural Resource Manufacturing
321 9/12 2/3 Yes
322 14/28 4/7 No
323 7/12 0/3 No
324 7/12 2/3 Some
325 46/72 8/18 Some
326 26/32 5/8 Some
327 21/40 3/10 No
33 Durable Goods Manufacturing
331 37/48 7/12 Yes
332 55/84 9/21 Yes, but imprecise
333 80/116 11/29 Yes, but imprecise
334 45/84 7121 No
335 35/52 7/13 Yes, but imprecise
336 22/48 6/12 No
337 4/24 0/6 No
339 27/56 5/14 No
48-49 Transportation and Warehousing
481 2/4 1/1 No
482 4/4 1/1 Yes
483 4/8 1/2 Yes
484 3/4 0/1 Yes, but imprecise
486 3/8 1/2 No
488 4/4 0/1 Yes, but imprecise
492 3/4 0/1 Yes, but imprecise
493 1/4 0/1 No
51 Information
515 1/4 0/1 No
517 4/4 1/1 Yes
53 Real Estate and Rental and Leasing
531 3/12 2/3 No
532 5/8 1/2 Yes, but imprecise
54 Professional, Scientific, and Technical Services
541 10/12 2/3 Yes, but imprecise
56 Administrative and Support and Waste Management and
Remediation Services
561 1/8 0/2 No
62 Health Care and Social Assistance
621 4/8 1/2 Yes, but imprecise
622 5/8 2/2 Some
623 3/4 1/1 Some
Subsector Discussion
11 Agriculture, Forestry, Fishing, and Hunting
113 Table 2 and ratio 1 (a ratio similar to the
"preponderance" of signs measure used in Peltzman
[2000] as an indicator of significance) indicate
persistent positive price asymmetry. However, the
estimates are imprecise.
21 Mining
211 Table 2 and ratio 1 indicate persistent positive
price asymmetry.
212 1. Table 2 and ratio 1 actually show a preponderance
of evidence of negative price asymmetry.
2. The aggregated subsector coefficients are relatively
large and significant. However, these results are
inconsistent with the individual industry results.
Unlike almost all of the 269 total individual industry
regressions, regressions based on the aggregated data
for this subsector show that price increases even when
cost goes down. This likely artifact of the aggregation
process brings into question the appropriateness of
analyzing asymmetric pricing at high levels of
aggregation.
213 Table 2 and ratio 1 actually show a preponderance
of evidence of negative price asymmetry.
31 Nondurable Goods Manufacturing
311 1. The sales-weighted mean industry asymmetries in Table
2 and ratio 1 indicate persistent positive price
asymmetry. However, the estimates are imprecise.
2. Ratios 1 and 2 reflect that the industry results show
some level of significant positive price asymmetry for
about half of the industries. The other half actually
shows evidence of negative price asymmetry. Thus, it is
not surprising that the aggregated subsector
asymmetry coefficients are close to zero.
312 Table 2 and ratio 1 indicate persistent positive price
asymmetry.
313
314
315 Table 2, with a single positive and significant
coefficient, and ratios 1 and 2 may indicate some
degree of short-lived positive price asymmetry.
316
32 Natural Resource Manufacturing
321 Table 2 and ratio 1 indicate persistent positive price
asymmetry.
322
323
324 Table 2, with a single positive and significant
coefficient, and ratios 1 and 2 may indicate some
degree of short-lived positive price asymmetry.
325 The aggregated subsector asymmetries in Table 2 (which
are very similar to the results in Peltzman [2000])
and ratios 1 and 2 indicate persistent positive price
asymmetry for many industries in this subsector.
326 1. See sector 113 discussion.
2. See sector 212 discussion point 2.
327 See sector 212 discussion point 2.
33 Durable Goods Manufacturing
331 Table 2 and ratio 1 indicate persistent positive price
asymmetry.
332 See sector 113 discussion.
333 See sector 113 discussion.
334
335 See sector 113 discussion.
336 See sector 212 discussion point 2.
337 1. Table 2 and ratio 1 actually show a preponderance of
evidence of negative price asymmetry.
2. The aggregated subsector coefficients are positive
and some are significant. However, these results are
totally inconsistent with the individual industry results
in this subsector, which show a prevalence of negative
price asymmetry.
339 Table 2 and ratio 1 actually show a preponderance of
evidence of negative price asymmetry.
48-49 Transportation and Warehousing
481 The evidence indicates negative price asymmetry for the
next two quarters and then positive price asymmetry
afterward.
482 Table 2 and ratio 1 indicate persistent positive price
asymmetry.
483 Table 2 and ratio 1 indicate persistent positive price
asymmetry.
484 The evidence indicates positive price asymmetry for the
next three quarters and then negative price asymmetry
afterward.
486
488 See sector 113 discussion.
492 The evidence indicates positive price asymmetry for the
next three quarters and then negative price asymmetry
afterward.
493
51 Information
515 Table 2 and ratio 1 actually show a preponderance of
evidence of negative price asymmetry.
517 Table 2 and ratio 1 indicate persistent positive price
asymmetry.
53 Real Estate and Rental and Leasing
531 See sector 212 discussion point 2.
532 See sector 113 discussion.
54 Professional, Scientific, and Technical Services
541 See sector 113 discussion.
56 Administrative and Support and Waste Management and
Remediation Services
561
62 Health Care and Social Assistance
621 1. See sector 113 discussion.
2. See sector 212 discussion point 2.
622 The evidence indicates negative price asymmetry for the
next quarter and then positive price asymmetry afterward.
623 The evidence indicates positive price asymmetry for the
next quarter and then negative or close to zero price
asymmetry afterward.
(a) Ratio 1 is the ratio of positive to total industry-level price
asymmetry coefficients for all four quarters of the forecast horizon
(detailed results are available from the author upon request). Note:
Ratio 1 is similar to the "preponderance" of signs measure used in
Peltzman (2000) as an indicator of significance. Ratio 2 is the ratio
of industries that have at least one statistically significant
positive price asymmetry coefficient to the total number of
industries.
Table 4. Determinants of Price Asymmetry
Dependent Variable: Asymmetry in Response to
Unit Cost Change after Quarter 1
HHI -0.00005
(2.73) **
Durable Goods Dummy -0.108
(3.85) **
Experience Goods Dummy 0.076
(1.80)
Constant 0.108
(2.48) *
Observations 220
[R.sup.2] 0.08
Absolute value of t statistic is given in parentheses.
* Significant at 5%.
** Significant at 1%.
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