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Search costs, sticky prices and markupsCarl Gwin a & David D. VanHoose ba Graziadio School of Business and Management, Pepperdine University , 24255 Pacific CoastHighway, Malibu, CA 90263-4100, USAb Hankamer School of Business, Baylor University , Box 8003, Waco, TX 76798-8003, USAPublished online: 05 Mar 2010.

To cite this article: Carl Gwin & David D. VanHoose (2011) Search costs, sticky prices and markups, Applied Economics,43:17, 2219-2228, DOI: 10.1080/00036840903153820

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Applied Economics, 2011, 43, 2219–2228

Search costs, sticky prices and

markups

Carl Gwina and David D. VanHooseb,*

aGraziadio School of Business and Management, Pepperdine University,

24255 Pacific Coast Highway, Malibu, CA 90263-4100, USAbHankamer School of Business, Baylor University, Box 8003, Waco,

TX 76798-8003, USA

This article investigates how search costs, price stickiness and product

durability influence the impact of inflation on firm markups. We provide

evidence that each of these three factors plays an independent role in

influencing the responsiveness of markups to inflation. Although we find

that the direct effect of inflation on markups is negative, offsetting positive

influences of inflation on markups arise in industries that produce durable

experience goods with flexible prices. Thus, our results indicate that

markups of industries producing nondurable search goods with sticky

prices tend to experience unambiguously negative impacts from inflation.

One strand of the literature on responses of firm

markups to inflation suggests that variations in

search costs help to explain diverging responses of

markups to inflation in different industries.

Another strand identifies differences in the extent

to which product prices are sticky as a key factor

influencing differential movements in industry

markups in reaction to inflation. Yet another

thread in the literature proposes a relationship

between search costs and the degree of price

stickiness. Hence, the objective of this article is to

explore the relationship among consumer search

costs, the relative degree of price stickiness and firm

markups.The next section summarizes theoretical predic-

tions about how search costs, price stickiness and

markups should be related. Section II discusses

our data and explains our empirical approach.

Section III presents our empirical findings.

Section IV summarizes our conclusions.

I. Theoretical Relationships among SearchCosts, Price Stickiness and Markups

What are the key determinants of how firm markups

respond to variations in inflation rate? The literature

on this topic reaches conflicting conclusions. On the

one hand, Van Hoomissen (1988), Tommasi (1994)

and Wu and Zhang (2001) suggest that inflation-

induced changes in relative firm size or current prices

being informative about future prices should lead to

higher markups. On the other hand, Benabou (1988,

1992a) andDiamond (1993) have argued that inflation

gives consumers greater incentives to search for old

sticker prices, which increases price dispersion but

reduces market power, thereby reducing markups.

Furthermore, Benabou (1992b) finds evidence sup-

porting a negative relationship between inflation and

markups in the US retail trade sector, and Kaskarelis

(1993) likewise concludes inflation and markups are

negatively related in the UK manufacturing sector.

*Corresponding author. E-mail: David_VanHoose@baylor.edu

Applied Economics ISSN 0003–6846 print/ISSN 1466–4283 online � 2011 Taylor & Francis 2219http://www.informaworld.com

DOI: 10.1080/00036840903153820

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Gwin and Taylor (2004) provide evidence

derived from annual US data that helps to reconcile

these disparate perspectives. Gwin and Taylor find

that in product markets with lower (higher) search

costs, inflation is more likely to generate lower

(higher) markups. Their results suggest that

Diamond and Benabou’s analyses pointing to

lower markups in response to inflation applies in

industries in which consumers face relatively low

search costs. In contrast, their conclusions indicate

that in industries in which consumers face higher

search costs, markups tend to increase, perhaps as a

consequence of the contrary effects identified by

other authors.Another slant on the answer to the question on

how inflation affects markups emphasizes how

cross-industry variations in stickiness of product

and input prices can influence markup adjustments

to variations in the inflation rate. This approach

suggests that in industries with sticky (flexible)

product prices, revenues from sales of output tend

to fall (rise) in relation to input costs in response

to higher inflation, resulting in lower markups.

In an analysis of relative stickiness of product

prices and wages and responsiveness of markups to

inflation in more than 280 industries, Gwin and

VanHoose (2009) offer evidence that in those

industries in which product prices are relatively

sticky (flexible), higher inflation tends to reduce

(increase) markups.The fact that industry data indicates that markups

are related to both search costs and the degree of

price stickiness suggests the likelihood of a systematic

relationship among search costs, price stickiness and

markups. Fishman (1992) offers a theoretical analysis

of how consumer search can generate a staggered-

pricing equilibrium and price dispersion as a conse-

quence of inflation, which suggests that search

technologies available to consumers can influence

the degree of price rigidity. Indeed, Fishman and

Simhon (2005) conclude that lower (higher) search

costs reduce (increase) the price differential sufficient

to induce consumers to shop for lower prices,

consequently leading to more (less) stickiness in

product prices.Taken together, these seemingly disparate contri-

butions suggest a linkage among search costs, price

stickiness and the responsiveness of firms’ markups to

inflation. Specifically, if search costs and price

stickiness have independent influences on the

manner in which inflation affects markets, decreased

(increased) markups should more likely be observed

in industries with lower (higher) search costs and

more (less) price stickiness.

II. Methodology and Data

We follow Gwin and Taylor’s (2004) methodology

for examining the impact of inflation on an annual

series of margins. In this article, however, we consider

a quarterly series of margins. In addition, we expand

the empirical model in Gwin and Taylor to include

consideration of (i) a measure of the degree of price

stickiness in an industry and (ii) a measure of goods

durability. We include a control for durable

goods based on Barsky et al. (2007), who link

goods durability to sticky-price models, and

Domowitz et al. (1988), who note that cyclical

fluctuations in markups differ between durable and

nondurable goods industries.The hypothesized model is

MARGINi,t ¼ �þ �i,t�� þ yt�y þ ð�i,t � si,tÞ�s

þ ð�i,t �Ddurable, iÞ�durable

þ ð�i,t �Dflexible, iÞ�flexible þ �i,t ð1Þ

where

MARGINi,t is the contribution margin for the

i-th selling industry at time t;�i,t is either aggregate inflation at time

t or the industry-specific inflation

measure for industry i at time t;yt is the growth rate of real US

Gross Domestic Product (GDP)

between time t and t – 1;si is an industry-specific measure of

search costs for industries that

purchase from industry i;Ddurable is a dummy variable set to 1 for

durable good industries (as clas-

sified by the US Census Bureau);Dflexible is a dummy variable set to 1 for

flexible-price industries; and�i,t is the error term that varies across

industry and time.

In the absence of sufficient data on buyer search

costs, we proxy search costs based on a classification

of an industry’s goods as either experience goods or

search goods using the classifications from Nelson

(1974). We examine a cross-section of industries

through time, so we utilize panel estimation techni-

ques. Recognizing the potential for covð�i,t,�i,kÞ 6¼ 0,

for t 6¼ k, and hoping to take advantage of the

unobserved heterogeneity across industries, we

rewrite (1) using an error-component, �i,t ¼ �i þ ei,t,

where the error term in (1) can be separated into an

industry-specific error term ð�iÞ and an error term

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that varies by industry and time ðei,tÞ. Consequently,

we estimate the empirical model in the form

MARGINi,t¼ �þ�i,t��þyt�yþð�i,t�Dexperience, iÞ�s

þð�i,t�Ddurable, iÞ�durable

þð�i,t�Dflexible, iÞ�flexibleþ�iþ ei,t

ð2Þ

where Dexperience is a dummy variable set at unity for

experience goods industries.Durable goods are classified for the manufacturing

sector in accordance with the US Census BureauCurrent Industrial Reports, Appendix B. These

classifications are available at http://www.census.gov/indicator/www/m3/.

To accomplish our task of classifying industries as

sticky- or flexible-price industries, we build on thework of Woodford (2003, p. 225), who describes the

evolution of prices over time as

dpt ¼ �p þ �pGAPþ "pðwt � CPIt � �wtÞ þ �Et½dptþ1�

ð3Þ

where dpt is the price inflation, GAP is the output gapcalculated as quadratically detrended log real GDP,

ð �wt þ CPIt � wtÞ is negative percentage deviation ofreal wage from steady state and E [dpt+1] is expected

future price inflation.To implement the Woodford specification, we

employ price data from the US Bureau of Labor

Statistics (BLS) that is available at the industry leveland GDP data from the US Bureau of Economic

Analysis (BEA). We use industry-level Producer PriceIndexes (PPI) from the BLS to proxy industry prices.Because the measures of the percentage deviation of

real wage from steady state would fail to consider theimpact of changes in productivity on industry costs,

we substitute a measure of the percentage change inAverage Variable Cost, dAVCt, for percentage devi-

ation real unit labour cost from steady state. In thiscase, we substitute a measure of dAVCt in place of

ðwt � CPIt � �wtÞ in (3).The BLS does not collect variable cost data, so

following Gwin and VanHoose (2008a, b) we con-

struct industry average variable cost data from costsavailable from Standard & Poor’s (S&P) Compustatfinancial information database. Quarterly revenues

(R) and cost of goods sold (VC) for the period1st Quarter 1966 to 4th Quarter 2006 are avail-

able from the Compustat database for over 10 000publicly traded US firms in 1195 six-digit North

American Industry Classification System (NAICS)industries. An industry’s total revenue is the sum of

the N individual firm revenues: Rt ¼PN

i¼1 Ri,t.Industry total variable cost is the sum of the

N individual firm cost of goods sold: VCt ¼PNi¼1 VCi,t. Industry Average Variable Cost (AVCt)

is derived as PtðVCt=RtÞ ¼ PtðAVCt�QtÞ=ðPt�QtÞ ¼

AVCt where Pt is the industry PPI from the BLS.In 2006, financial data was collected for 841 six-

digit NAICS industries. The data ‘cost of goods sold’is defined by S&P as, ‘This item represents allexpenses that are directly related to the cost of mer-chandise purchased or the cost of goods manufac-tured that are withdrawn from finished goodsinventory and sold to customers.’ This data definitionfits well with the economic characterization of vari-able cost. A total of 525 of the Compustat six-digitNAICS industries can be cross-matched to one of the1149 six-digit NAICS PPI series currently availablefrom the BLS. The BLS has significant historicalprice data for only six economic sectors includingMining; three sectors of Manufacturing; Transporta-tion and Warehousing; and Information. Datalimitations, therefore, reduce the number of industriesthat can be analysed. Two industries were droppedbecause the BLS stopped collecting PPI data prior to2006. Another 198 industries were dropped becausethe BLS has collected only very recent PPI data dueto NAICS reclassification of industries in 1997 and2002. Two additional industries were droppedbecause the BLS collected data only sporadicallyafter 2002. In addition, we dropped 41 industriesbecause their product definitions were overly broad(13 so-called six-digit NAICS industries were actuallyfour-digit NAICS subsectors, and 28 industries weredefined as miscellaneous or others). Finally, wedropped 13 industries because there were fewer than40 observations of the combination of Compustat andBLS PPI data. Of the 269 industries remaining afterthe above steps, 230 could be matched to classifica-tions of durable/nondurable goods, experience/searchgoods, and sticky-/flexible-price industries.

We identify sticky- and flexible-price industries bypartitioning the industry "p coefficients into twodistinct nonoverlapping groups with Stata’s kmedianscluster analysis. In an iterative process, each industryis assigned to the group whose median centre isclosest, and then based on that categorization, newgroup centres are determined. These steps continueuntil no industry changes groups. A median centreprovides a more stable measure of the group centres,a feature particularly useful for this applicationbecause the flexible-price coefficients are relativelylarge and statistically significant whereas the sticky-price coefficients are close to zero and statisticallyinsignificant.

To summarize, our empirical hypotheses, takinginto account both theoretical predictions regardingthe influence of search costs and price stickiness and

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considerations relating to previous observationsregarding durable goods, are as follows:

(1) Industry markups are influenced by inflation.(2) Experience goods have higher search costs than

search goods, so an industry’s markup isrelatively higher in an inflationary environmentif the industry produces experience goods.

(3) An industry’s markup is relatively higher in aninflationary environment if the industry pro-duces durable goods, because inflation may actas a signal to buyers to accelerate the timing oftheir purchases of durable goods.

(4) Industry markups are relatively higher in aninflationary environment for flexible-priceindustries than sticky-price industries.

III. Results

A complete list of the 230 industries along withtheir classifications as experience/search good, dur-able/nondurable good, and sticky-/flexible-price isprovided in the lengthy table in the Appendix.A cursory review of the Appendix table indicatesthat there may be an issue with correlation between thethree classifications of industries. Are sticky-priceindustries significantly more likely to produce durablegoods or, as suggested by Fishman (1992) andFishman and Simhon (2005), experience goodsas compared with flexible-price industries? Thecorrelation matrix reported in Table 1 provides someindication of correlations across these categories, butthe correlations do not appear to be so large that wewill have difficulty in separating out the effect of eachindustry classification. Table 2 provides a simple countof the observations of each type of industry classifica-tion. The counts reported in this table also indicatesome degree of correlation, but not to a degree thatindividual effects cannot be analysed.

Table 3 reports the coefficients estimated fromfixed-effects regressions of Equation 2 using ourquarterly sample of observations on profit margins.Unreported random-effects regressions yield almostidentical results.

We report parameter estimates for five specifica-tions. Specification 1 includes real GDP growth rateand industry inflation only. The impact of inflationon industry markups is negative, consistent withearlier work by authors such as Benabou (1992b) andKaskarelis (1993), but the estimate has low precision.

Empirical precision of the estimated effect ofinflation on markups is improved noticeably by

taking into account measures of search costs (asproxied by experience goods), durable goods andprice flexibility in Specifications 2 through 5 inTable 3. Specification 2 includes a single interactionof inflation with the experience goods industrydummy. As hypothesized, producing items withhigher search costs – as proxied by the experiencegoods measure – results in an offsetting positive effectof inflation on industry markups.

Specification 3 includes an individual interactionof inflation with the durable-goods industry dummy.As suggested by the work of Barsky et al. (2007) andDomowitz et al. (1988), the durability of itemsproduced by industries appears to play an indepen-dent, statistically significant, role in influencing theeffects of inflation on markups. Our statisticallysignificant point estimate indicates that producingdurable goods tends to result in a somewhat offsettingpositive effect of inflation on industry markups.

Specification 4 adds a sole interaction of inflationand the flexible-price industry dummy. As hypothe-sized, markups in industries with flexible prices alsotend to experience partly offsetting positive effects oninflation on markups.

Finally, Specification 5 includes all of the interac-tions. The results, which are little changed from thespecifications including only individual interactions,offer support for the conclusion that markups ofindustries producing durable items with high searchcosts (i.e. experience goods) and flexible prices are lessprone to being depressed by inflation. In contrast,inflation tends to have an unambiguously negativeimpact on markups of industries producing nondur-able goods with low search costs (i.e. search goods)and sticky prices.

Table 2. Summary count of industry classifications

Count Sticky Flexible Experience Search

Durable 141 82 59 134 7Nondurable 89 33 56 70 19

Sticky 115 – – 106 9Flexible 115 – – 98 17

Total 230 115 115 204 26

Table 1. Pairwise correlations (with significance)

Durable Experience Flexible

Durable 1.0000Experience 0.2520 (0.0001) 1.0000Flexible �0.2053 (0.0017) �0.1098 (0.0965) 1.0000

2222 C. Gwin and D. D. VanHoose

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IV. Conclusions

The literature examining the impact of inflation andmarkups offers mixed conclusions, but recent work

has highlighted the potential importance of search

costs encountered by consumers of an industry’s

goods, the durability of the goods an industry

produces, and whether prices are sticky in the

market in which an industry’s goods are sold. Thisarticle finds evidence that each of these three factors

play an independent role in influencing the respon-

siveness of markups to inflation. Although we find

that the direct effect of inflation on markups is

negative, offsetting positive influences of inflation

on markups arise in industries that produce durable

experience goods with flexible prices. Thus, ourresults indicate that industries producing nondurable

search goods with sticky prices observe unambigu-

ously negative impacts of inflation on markups.

References

Barsky, R., House, C. and Kimball, M. (2007) Sticky-pricemodels and durable goods, American EconomicReview, 97, 984–98.

Benabou, R. (1988) Search, price setting and inflation,Review of Economic Studies, 55, 353–76.

Benabou, R. (1992a) Inflation and efficiency in searchmarkets, Review of Economic Studies, 59, 299–329.

Benabou, R. (1992b) Inflation and markups: theories andevidence from the retail trade sector, EuropeanEconomic Review, 36, 566–74.

Diamond, P. (1993) Search, sticky prices, and inflation,Review of Economic Studies, 60, 547–66.

Domowitz, I., Hubbard, R. G. and Petersen, B. (1988)Market structure and cyclical fluctuations in USmanufacturing, Review of Economics and Statistics,70, 55–66.

Fishman, A. (1992) Search technology, staggered price-setting, and price dispersion, American EconomicReview, 82, 287–98.

Fishman, A. and Simhon, A. (2005) Can small menu costsexplain sticky prices?, Economics Letters, 87, 227–30.

Gwin, C. and Taylor, B. (2004) The role of search costs indetermining the relationship between inflation andprofit margins, Journal of Money, Credit, and Banking,36, 139–49.

Gwin, C. and VanHoose, D. (2008a) Alternative mea-sures of marginal cost and inflation in estimationsof new Keynesian inflation dynamics, Journal ofMacroeconomics, 30, 928–40.

Gwin, C. and VanHoose, D. (2008b) Disaggregate evidenceon US price stickiness and implications for sticky-pricemacro models, Economic Inquiry, 46, 561–75.

Gwin, C. and VanHoose, D. (2009) Price and wagestickiness, inflation, and profits, Manuscript,Pepperdine University and Baylor University,September.

Kaskarelis, I. (1993) Inflation and mark-up in UKmanufacturing industry, Oxford Bulletin ofEconomics and Statistics, 55, 391–407.

Nelson, P. (1974) Advertising as information, Journal ofPolitical Economy, 82, 729–54.

Tommasi, M. (1994) The consequences of price instabilityon search markets: toward understanding the effects ofinflation, American Economic Review, 84, 1385–96.

Van Hoomissen, T. (1988) Price dispersion and inflation:evidence from Israel, Journal of Political Economy, 96,1303–14.

Woodford, M. (2003) Interest and Prices, PrincetonUniversity Press, Princeton.

Wu, Y. and Zhang, J. (2001) The effects of inflation on thenumber of firms and firm size, Journal of Money,Credit, and Banking, 33, 251–71.

Table 3. Fixed-effects regression estimates of Equation 2

(1) (2) (3) (4) (5)

Growth of real GDP 0.165 (2.10)* 0.166 (2.11)* 0.170 (2.16)* 0.158 (2.01)* 0.162 (2.06)*Change in log PPI �0.028 (1.20) �0.364 (4.05)** �0.078 (2.63)** �0.242 (3.49)** �0.674 (5.86)**Interaction of industry

inflation and experiencegood dummy

0.360 (3.88)** 0.348 (3.74)**

Interaction of industryinflation and durablegood dummy

0.124 (2.66)** 0.153 (3.21)**

Interaction of industryinflation and flexibleprice dummy

0.241 (3.28)** 0.290 (3.87)**

Constant 29.860 (366.64)** 29.871 (366.64)** 29.841 (365.05)** 29.908 (361.50)** 29.905 (360.88)**Observations 23 712 23 712 23 712 23 712 23 712Number of NAICS

group number230 230 230 230 230

R-squared 0.0005 0.0002 0.0000 0.0020 0.0017

Notes: Absolute value of t-statistics in parentheses.* and ** denote significance at 5 and 1% levels, respectively.

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Appendix

No. NAICS Industry Experience Durable Flexible price

1 311111 Dog and cat food manufacturing 1 0 02 311211 Flour milling 1 0 13 311221 Wet corn milling 1 0 14 311225 Fats and oils refining and blending 1 0 15 311230 Breakfast cereal manufacturing 1 0 06 311312 Cane sugar refining 1 0 17 311313 Beet sugar manufacturing 1 0 08 311320 Confectionery manufacturing from cacao beans 1 0 09 311412 Frozen specialty food manufacturing 1 0 0

10 311421 Fruit and vegetable canning 1 0 111 311422 Specialty canning 1 0 112 311511 Fluid milk manufacturing 1 0 113 311513 Cheese manufacturing 1 0 114 311520 Ice cream and frozen dessert manufacturing 1 0 015 311611 Animal, except poultry, slaughtering 1 0 116 311612 Meat processed from carcasses 1 0 117 311613 Rendering and meat byproduct processing 1 0 118 311615 Poultry processing 1 0 119 311711 Canned and cured fish and seafood, including soups,

stews and chowders1 0 1

20 311712 Fresh and frozen seafood processing 1 0 021 311812 Commercial bakeries 1 0 122 311821 Cookie and cracker manufacturing 1 0 023 311823 Dry macaroni, spaghetti and egg noodle products,

mitse (except canned or frozen)1 0 1

24 311920 Coffee and tea manufacturing 1 0 125 311930 Flavouring syrup and concentrate manufacturing 1 0 026 311991 Perishable prepared food manufacturing 1 0 027 312111 Soft drink manufacturing 1 0 028 312120 Breweries 1 0 029 312130 Wineries 1 0 030 312140 Distilleries 1 0 031 312221 Cigarette manufacturing 1 0 132 313112 Yarn texturizing and twisting mills 0 0 133 313230 Nonwoven fabric mills 0 0 134 314110 Carpet and rug mills 0 0 135 314121 Curtain and drapery mills 0 0 136 314912 Canvas and related product mills 0 0 137 315191 Outerwear knitting mills 0 0 138 315192 Underwear and nightwear knitting mills 0 0 139 315222 Men’s and boys’ suit, coat and overcoat

manufacturing0 0 0

40 315223 Men’s and boys’ shirt, except work shirt,manufacturing

0 0 1

41 315224 Men’s and boys’ pants, except work pants,manufacturing

0 0 1

42 315225 Men’s and boys’ work clothing manufacturing 0 0 143 315232 Women’s and girls’ blouse and shirt manufacturing 0 0 044 315233 Women’s and girls’ dress manufacturing 0 0 045 316110 Leather and hide tanning and finishing 0 0 146 316211 Rubber and plastics footwear manufacturing 0 0 147 316212 House slipper manufacturing 0 0 048 316213 Men’s nonathletic footwear manufacturing 0 0 149 316214 Women’s nonathletic footwear manufacturing 0 0 050 316991 Luggage manufacturing 0 0 051 321113 Sawmills 1 1 152 321991 Manufactured home, mobile home manufacturing 1 1 153 321992 Prefabricated wood building manufacturing 1 1 054 322110 Pulp mills 1 0 155 322130 Paperboard mills 1 0 1

(continued )

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Continued

No. NAICS Industry Experience Durable Flexible price

56 322211 Corrugated and solid fibre box manufacturing 1 0 157 322212 Folding paperboard box manufacturing 1 0 058 322214 Fibre can, tube and drum manufacturing 1 0 159 322215 Nonfolding sanitary food container manufacturing 1 0 160 322232 Envelope manufacturing 1 0 161 323110 Commercial lithographic printing 1 0 062 323111 Commercial gravure printing 1 0 163 323117 Books printing 1 0 164 324110 Petroleum refineries 1 0 165 324122 Asphalt shingle and coating materials manufacturing 1 0 166 324191 Petroleum lubricating oils and greases, made from

refined petroleum1 0 1

67 325131 Inorganic dye and pigment manufacturing 1 0 168 325181 Alkalies and chlorine manufacturing 1 0 169 325182 Carbon black manufacturing 1 0 170 325211 Plastics material and resins manufacturing 1 0 171 325212 Synthetic rubber manufacturing 1 0 172 325222 Noncellulosic organic fibre manufacturing 1 0 073 325311 Nitrogenous fertilizer manufacturing 1 0 174 325320 Pesticide and other agricultural chemical

manufacturing1 0 0

75 325411 Medicinal and botanical manufacturing 1 0 076 325412 Pharmaceutical preparation manufacturing 1 0 077 325413 In vitro diagnostic substance manufacturing 1 0 078 325510 Paint and coating manufacturing 1 0 179 325520 Adhesive manufacturing 1 0 180 325611 Soap and other detergent manufacturing 1 0 081 325612 Polish and other sanitation goods manufacturing 1 0 082 325613 Surface active agent manufacturing 1 0 183 325620 Toilet preparation manufacturing 1 0 084 325910 Printing ink manufacturing 1 0 085 326113 Unlaminated plastics film and sheet, excluding

packaging1 0 1

86 326121 Unlaminated plastics profile shapes 1 0 187 326122 Plastic pipes and pipe fitting manufacturing 1 0 188 326130 Plastic laminates (excluding flexible packaging) 1 0 189 326160 Plastic bottles 1 0 190 326191 Plastic plumbing fixture manufacturing 1 0 091 326211 Tyre manufacturing, except retreading 1 0 192 326291 Rubber product manufacturing for mechanical use 1 0 093 327124 Clay refractory manufacturing 1 1 094 327125 Nonclay refractory manufacturing 1 1 095 327211 Flat glass manufacturing 1 1 096 327213 Glass container manufacturing 1 1 197 327215 Glass product manufacturing made of purchased

glass1 1 0

98 327310 Cement manufacturing 1 1 199 327320 Ready-mix concrete 1 1 0

100 327420 Gypsum product manufacturing 1 1 1101 327992 Ground or treated minerals and earths

manufacturing1 1 1

102 327993 Mineral wool manufacturing 1 1 1103 331111 Iron and steel mills 1 1 1104 331112 Ferroalloy and related product manufacturing 1 1 1105 331210 Iron, steel pipe and tube from purchased steel 1 1 1106 331221 Rolled steel shape manufacturing 1 1 1107 331312 Primary aluminum production 1 1 1108 331315 Aluminum sheet, plate and foil manufacturing 1 1 0109 331316 Aluminum extruded product manufacturing 1 1 1

(continued )

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Continued

No. NAICS Industry Experience Durable Flexible price

110 331411 Primary smelting and refining of copper 1 1 1111 331421 Copper rolling, drawing and extruding 1 1 1112 331511 Iron foundries 1 1 1113 331512 Steel investment foundries 1 1 1114 331522 Nonferrous, except Al, die-casting foundries 1 1 0115 332111 Iron and steel forging 1 1 0116 332116 Metal stamping 1 1 0117 332211 Cutlery and flatware, except precious, manufacturing 1 1 0118 332212 Hand and edge tool manufacturing 1 1 0119 332213 Saw blade and handsaw manufacturing 1 1 0120 332311 Prefabricated metal buildings and components 1 1 1121 332313 Fabricated plate work (stacks and weldments) 1 1 1122 332321 Metal window and door manufacturing 1 1 1123 332322 Sheet metal work manufacturing 1 1 0124 332323 Ornamental and architectural metal work

manufacturing1 1 1

125 332431 Metal can manufacturing 1 1 1126 332510 Hardware manufacturing 1 1 0127 332722 Bolt, nut, screw, rivet and washer manufacturing 1 1 1128 332811 Metal heat treating 1 1 0129 332812 Metal coating and nonprecious engraving 1 1 0130 332813 Electroplating, anodizing and colouring metal 1 1 0131 332912 Fluid power valve and hose fitting manufacturing 1 1 1132 332991 Ball and roller bearing manufacturing 1 1 1133 332993 Ammunition, except small arms, manufacturing 1 1 0134 332994 Small arms manufacturing 1 1 0135 332996 Fabricated pipe and pipe fitting manufacturing 1 1 0136 333111 Farm machinery and equipment manufacturing 1 1 1137 333112 Lawn and garden equipment manufacturing 1 1 0138 333120 Construction machinery manufacturing 1 1 0139 333131 Mining machinery and equipment manufacturing 1 1 0140 333132 Oil and gas field machinery and equipment 1 1 1141 333210 Sawmill and woodworking machinery 1 1 0142 333291 Paper industries machinery manufacturing 1 1 0143 333292 Textile machinery manufacturing 1 1 0144 333293 Printing machinery and equipment manufacturing 1 1 0145 333294 Food product machinery manufacturing 1 1 0146 333295 Semiconductor machinery and parts 1 1 1147 333311 Automatic vending machine manufacturing 1 1 0148 333312 Commercial laundry and drycleaning machinery 1 1 0149 333313 Office machinery manufacturing 1 1 0150 333314 Optical instrument and lens manufacturing 1 1 0151 333415 AC, refrigeration and forced air heating 1 1 1152 333512 Metal cutting machine tool manufacturing 1 1 1153 333611 Turbine and turbine generator set units

manufacturing1 1 0

154 333613 Mechanical power transmission equipmentmanufacturing

1 1 0

155 333911 Pump and pumping equipment manufacturing 1 1 1156 333912 Air and gas compressor manufacturing 1 1 1157 333922 Conveyor and conveying equipment manufacturing 1 1 0158 333924 Industrial truck, trailer and stacker manufacturing 1 1 0159 333991 Power-driven handtool manufacturing 1 1 1160 333992 Welding and soldering equipment manufacturing 1 1 1161 333993 Packaging machinery manufacturing 1 1 0162 333994 Industrial process furnace and oven manufacturing 1 1 0163 333996 Fluid power pump and motor manufacturing 1 1 1164 333997 Scale and balance, except laboratory, manufacturing 1 1 0165 334111 Electronic computer manufacturing 1 1 1

(continued )

2226 C. Gwin and D. D. VanHoose

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No. NAICS Industry Experience Durable Flexible price

166 334112 Computer storage device manufacturing 1 1 1167 334113 Computer terminal manufacturing 1 1 0168 334210 Telephone apparatus manufacturing 1 1 0169 334220 Broadcast and wireless communications equipment

manufacturing1 1 0

170 334310 Audio and video equipment manufacturing 1 1 0171 334411 Electron tube manufacturing 1 1 0172 334413 Semiconductors and related device manufacturing 1 1 1173 334414 Electronic capacitor manufacturing 1 1 1174 334415 Electronic resistor manufacturing 1 1 0175 334416 Electronic coils, transformers and inductors

manufacturing1 1 0

176 334417 Electronic connector manufacturing 1 1 1177 334510 Electromedical apparatus manufacturing 1 1 1178 334511 Search, detection and navigation instruments 1 1 0179 334512 Automatic environmental control manufacturing 1 1 0180 334513 Industrial process variable instruments 1 1 1181 334514 Totalizing fluid meters and counting devices 1 1 1182 334515 Electricity and signal testing instruments 1 1 0183 334516 Analytical laboratory instrument manufacturing 1 1 1184 334517 Irradiation (ionizing radiation) equipment 1 1 0185 334613 Magnetic and optical recording media manufacturing 1 1 0186 335121 Residential electric lighting fixture manufacturing 1 1 0187 335122 Nonresidential electric lighting fixture manufacturing 1 1 1188 335211 Electric housewares and household fan

manufacturing1 1 1

189 335212 Household vacuum cleaner manufacturing 1 1 0190 335311 Electric power and specialty transformer

manufacturing1 1 1

191 335312 Motor and generator manufacturing 1 1 1192 335313 Switchgear and switchboard apparatus

manufacturing1 1 0

193 335314 Relay and industrial control manufacturing 1 1 0194 335911 Storage battery manufacturing 1 1 0195 335912 Primary battery manufacturing 1 1 0196 335931 Current-carrying wiring device manufacturing 1 1 0197 335932 Noncurrent-carrying wiring device manufacturing 1 1 1198 335991 Carbon and graphite product manufacturing 1 1 0199 336110 Automobile, light truck and utility vehicle

manufacturing1 1 1

200 336211 Motor vehicle body manufacturing 1 1 1201 336212 Truck trailer manufacturing 1 1 0202 336213 Motor home manufacturing 1 1 1203 336321 Vehicular lighting equipment manufacturing 1 1 0204 336370 Motor vehicle metal stamping 1 1 1205 336411 Aircraft manufacturing 1 1 0206 336412 Aircraft engine and engine parts manufacturing 1 1 1207 336510 Railroad rolling stock manufacturing 1 1 0208 336611 Ship building and repairing 1 1 0209 336612 Boat building 1 1 0210 336991 Motorcycle, bicycle and parts manufacturing 1 1 0211 337110 Wood kitchen cabinet and countertop manufacturing 0 1 0212 337121 Upholstered household furniture manufacturing 0 1 0213 337122 Nonupholstered wood household furniture

manufacturing0 1 0

214 337211 Wood office furniture manufacturing 0 1 1215 337214 Office furniture, except wood, manufacturing 0 1 1216 337910 Mattress manufacturing 0 1 1217 339111 Laboratory apparatus and furniture manufacturing 1 1 0218 339112 Surgical and medical instrument manufacturing 1 1 0

(continued )

Search costs, sticky prices and markups 2227

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No. NAICS Industry Experience Durable Flexible price

219 339113 Surgical appliance and supplies manufacturing 1 1 0220 339114 Dental equipment and supplies manufacturing 1 1 0221 339115 Ophthalmic goods manufacturing 1 1 0222 339911 Jewelry, except costume, manufacturing 0 1 1223 339920 Sporting and athletic goods manufacturing 1 1 0224 339931 Dolls and stuffed toys 1 1 0225 339932 Game, toy and children’s vehicle manufacturing 1 1 0226 339941 Pen and mechanical pencil manufacturing 1 1 0227 339950 Sign manufacturing 1 1 0228 339991 Gasket, packing and sealing device manufacturing 1 1 1229 339992 Musical instrument manufacturing 1 1 0230 339993 Fastener, button, needle and pin manufacturing 1 1 0

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