B argaining versus posted price competition in customer ... · B argaining versus posted price competition in customer markets Timothy N. Cason , ... (De Meza, 1998). The continuous

International Journal of Industrial Organization21 (2003) 223–251

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B argaining versus posted price competition in customermarkets

a , b c*Timothy N. Cason , Daniel Friedman , Garrett H. MilamaDepartment of Economics, Krannert School of Management, Purdue University,

West Lafayette, IN 47907-1310,USAbDepartment of Economics, 217 Social Sciences I, University of California, Santa Cruz,

CA 95064,USAcDepartment of Economics, Ryerson University, Jorgenson Hall, Toronto, ON, Canada M5B 2K3

Received 8 February 2001; received in revised form 16 October 2001; accepted 10 May 2002

Abstract

We compare posted price and bilateral bargaining (or ‘haggle’) market institutions in 12pairs of laboratory market sessions. Each session runs for 50–75 periods in a customermarket environment, where buyers incur a cost to switch sellers. Costs evolve following arandom walk process. Coasian and New Institutionalist traditions provide competingconjectures on relative market performance. We find that efficiency is lower, sellers pricehigher, and prices are stickier under haggle than under posted offer. 2002 Elsevier Science B.V. All rights reserved.

JEL classification: D4; L13

Keywords: Posted offers; Experiment; Sticky prices; Market power

1 . Introduction

The ancient practice of price negotiation, or haggling, has largely been replacedin industrialized societies by sellers posting prices. Posted prices save time, a

*Corresponding author. Tel.:11-765-494-1737; fax:11-765-494-9658.E-mail addresses: [email protected](T.N. Cason), [email protected](D. Friedman),

[email protected](G.H. Milam).

0167-7187/02/$ – see front matter 2002 Elsevier Science B.V. All rights reserved.PI I : S0167-7187( 02 )00056-5

mailto:[email protected]



224 T.N. Cason et al. / Int. J. Ind. Organ. 21 (2003) 223–251

tremendous advantage in retail markets—imagine having to negotiate individualprices for every item you purchased at the supermarket! However, haggling hasnot entirely disappeared and, beyond time costs, it is not clear which institutionbetter promotes efficiency. Nor is it clear how the two institutions influence thedivision of surplus. Such questions become increasingly important as electroniccommerce transforms transaction costs, including time costs for negotiations,which can be conducted now by ‘shop bots’ (e.g. Eisenberg, 2000).

Laboratory comparison of market institutions has a history going back at least toPlott and Smith (1978). The laboratory is especially appropriate for our questionsbecause it allows control of key features such as time costs that confoundinferences from field data.

The present paper reports a laboratory experiment comparing the efficiency, thesurplus distribution and the price dynamics of two market institutions, the familiarseller posted offer institution and a new bilateral bargaining institution called‘haggle.’ In the haggle institution, as in the continuous double auction, each buyerand seller can continuously improve her own offer or accept a counterparty offer atany moment during a trading period of known duration. In haggle, but not in thecontinuous double auction, each offer is extended only to a single counterparty,not to all possible counterparties. In the posted offer institution, sellers post asingle take-it-or-leave-it price each period, which buyers either accept or reject.

The laboratory environment we use is a ‘customer’ market, a term coined byOkun (1981). Each buyer is attached to a particular seller and must pay a switchcost to transact with another seller. Switch costs are important in labor markets(where they include search, relocation and retraining) and in many intermediateand wholesale markets, and are present even in retail markets (think of theshopper’s extra time cost in an unfamiliar supermarket). Customer markets are thenatural habitat for haggle, since starting negotiations with a new counterpartynormally entails some setup cost. The posted offer institution is also common incustomer markets and has already been studied in the laboratory (e.g. Cason andFriedman, 2002).

No formal models are available for comparing market institutions in customer1markets, but it is not hard to find cogent arguments with conflicting predictions. A

strong oral tradition rooted in Coase (1960) holds that parties will find ways toexhaust mutual gains unless artificial barriers are imposed (De Meza, 1998). Thecontinuous bargaining protocol in haggle imposes fewer barriers than the unilater-al, one-shot, take-it-or-leave-it offer allowed in the posted offer institution, sohaggle should be more efficient. Moreover, haggle permits first degree pricediscrimination and thus should enhance efficiency while allowing sellers to capturea greater share of the surplus.

New institutionalists might make the opposite predictions, based on incomplete

1Formal comparisons in a static setting (i.e. no ongoing attachments or persistent cost shocks)include Julien et al. (2000) and Camera and Delacroix (2001).

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information considerations. The posted offer institution is a solution to the‘fundamental problem of commerce,’ the tendency of buyers and sellers tounderreveal their willingness to transact in order to extract more favorable prices(e.g. Myerson and Satterthwaite, 1983; Milgrom and Roberts, 1992; McKelvey andPage, 2000). Posted offer constrains these tendencies more than haggle and henceshould facilitate more efficient exchange. According to this view, sellers’commitment technology in posted offer should allow them to extract a larger shareof the surplus than in haggle, where constraints and bargaining opportunities aremore symmetric.

The two market institutions are of special interest from an historical orevolutionary perspective. Haggle spans the gap between pre-market gift exchangeand early organized marketplaces. Historically it has tended to be displaced bymore complicated or structured forms of exchange like posted offer (North, 1991),but it still holds a major share in modern economies (e.g. procurement, car dealers,and wholesale transactions) as well as in traditional economies like Morocco(Geertz et al., 1979). In the field, transactors’ time costs are higher for haggle thanfor posted offer. Additionally, the expansion of retail from single proprietor ‘Momand Pop’ outlets into chain stores leads to agency problems that favor postedprices. These factors account for the shift into posted offer in modern economies,particularly for standardized, small ticket items. The use of new informationtechnology such as the Internet and automated ‘price bots’ may overcome theseobstacles. Indeed haggling is reemerging in e-commerce with such sites aswww.BargainandHaggle.comand www.Priceline.com,perhaps because sellersbelieve it will allow them to extract a larger share of the surplus. In the laboratorywe can remove agency problems and equalize the time cost in order to test suchconjectures and thus better understand the forces driving the evolution of market

2institutions.The customer market environment also provides a non-trivial setting to study

price dynamics. Do transaction prices track competitive equilibrium prices whensellers’ production costs change, or are transaction prices sticky? Early work byScitovsky (1952) attributes price stickiness to switch costs based on a modifiedkinked demand model. This and later models do not clearly predict which marketinstitution will have stickier prices. Do sellers price discriminate between attachedcustomers and new customers? Theoretical work by Klemperer (1987) suggeststhat if sellers are unable to discriminate between attached vs. unattachedcustomers, they will compete vigorously early on and will raise prices once theyachieve a base of attached customers. Taylor (1999) examines subscriptionmarkets, such as banking and long-distance telephone service, and shows in avariety of settings that firms will offer lower prices to new customers. Although

2Kirchsteiger et al. (2001) employ a clever alternative laboratory research strategy to investigate theevolution of market institutions, by allowing traders to select their own information conditions andmatching rules.

www.BargainandHaggle.com

www.Priceline.com,


our laboratory environment is not closely adapted to either of these theoreticalanalyses, it does provide evidence regarding the joint dynamics of price andattachment, with and without the ability to price discriminate.

Laboratory researchers have studied the posted offer institution extensively; seeHolt (1995) for a survey of work from Plott and Smith (1978) and Ketcham et al.(1984) up through the early 1990s. These studies confirm that the posted offerinstitution does allow sellers to extract a greater share of the surplus than do moresymmetric trading institutions such as the continuous double auction. Efficiencyalso is lower in posted offer, but both effects tend to decline over time in therepetitive stationary environments used in most of these studies. It is not clearwhether these results extend to customer market environments with switch costs

3and with changing seller costs each period.Laboratory studies of pairwise bargaining (e.g. Roth and Murnighan, 1982) also

have a long history; surveyed in Roth (1995). Almost no studies embed continuousbilateral bargaining in a multilateral market context, as we do in the haggleinstitution. One exception is Hong and Plott (1982). Motivated by a proposal thatbarge operators post prices at the Interstate Commerce Commission, this experi-ment compared bilateral (telephone) negotiations with posted offer trading. Thesame 33 subjects (22 sellers and 11 buyers) used both institutions in four sessions.Hong and Plott find that relative to negotiation, price posting leads to collusivebehavior including higher prices, lower volume, and reduced efficiency. Ourresults are different, not surprisingly given the many environmental and institu-tional differences, such as the number of traders, demand shifts instead ofvariations in costs, no switch cost instead of positive switch cost, and free-formverbal instead of computerized price-only negotiation.

A series of papers by Davis and Holt (1994, 1997, 1998) is also relevant, sincethey take a step toward introducing negotiation in a standard posted offer,multilateral market context. Their posted offer markets featured a single round ofstructured negotiation. Buyers could ‘request’ a discount, and sellers could offer asingle, private discounted price. As in haggle, this allows sellers to pricediscriminate. In order to limit buyers’ incentive to cycle through sellers repeatedly,the authors introduce a small (5 or 10-cent) switching cost. Davis and Holt findthat the opportunity to offer secret discounts increased the variance of marketoutcomes (Davis and Holt, 1994), did not substantially improve market per-formance in a nonstationary environment (Davis and Holt, 1997), and led to thebreakdown of explicit cartel agreements (Davis and Holt, 1998).

3Cason and Friedman (2001) study a posted offer market with switch costs, but not in a customermarket environment. Guided by the static theoretical model of Burdett and Judd (1983), their sellershave identical constant costs and buyers see iid random samples of posted prices. As the sample sizeand cost varies, the observed prices vary rather smoothly between the extreme predictions of Diamond(1971) (monopoly pricing) and Bertrand (1883) (marginal cost pricing), roughly in line with pricedispersion predictions of the theoretical model.


The next section describes our experiment, a balanced panel design with twelvematched pairs of sessions for the two trading institutions. Each session has two orthree runs of 25 trading periods. Sessions differ by switch cost (zero, low or high)and trader experience.

The following section presents the results. Posted offer proves to be (econ-omically and statistically) significantly more efficient than haggle. Decompositionof efficiency losses shows that, consistent with the new institutionalist view, thedominant source of lower efficiency is low volume; that is, traders in haggle morefrequently fail to complete mutually beneficial transactions. Market power,measured as seller markup over the competitive equilibrium price, also variesacross the two institutions. Contrary to the new institutionalist view, markups aresignificantly higher in haggle than in posted offer. The data show consistentlyhigher markups for attached customers than new customers, consistent with theTaylor and Klemperer models, and this effect is stronger in haggle in low switchcost sessions. Prices are also sticky, more so in haggle than in posted offermarkets. Finally, we find that buyers switch to new sellers less frequently inhaggle. The last section summarizes the results, offers some interpretations andconjectures, and suggests new avenues of investigation.

2 . The experiment

The main treatment variable is the market institution. In the posted offertreatment each seller enters a single take-it-or-leave-it price, and each buyerpurchases at most one indivisible unit from one seller at that seller’s posted price.In the haggle treatment each seller posts a ‘list’ price that serves as a starting pointfor negotiations with individual buyers. Each negotiating buyer–seller pair sendsbinding price offers in a negotiation window. Only the two parties to thenegotiation see the window. Either party can send a better offer at any time, andeither party can accept the other’s offer at any time until the period ends. Themessage space is restricted to prices in dollars and cents. Each seller begins at hislist price and cannot withdraw or increase his offer price to any attached buyerwithin the trading period, nor can any buyer reduce her bid price. Additionaldetails can be found in Appendix A, which contains an abridged version of thehaggle experiment instructions. A complete version of the instructions is posted onthe journal web page.

To create a customer market, we introduce attachments and switch costs. Eachbuyer begins each period attached to some seller, whose posted or list price sheobserves costlessly. In order to see other sellers’ prices or initiate negotiations witha new seller she must sink a switch costC > 0. If she does so she observes theoffer or list prices for all sellers (who are not further identified). In the posted offertreatment she can then accept any of these prices; in the haggle treatment she caninitiate negotiations with any one of the sellers. She enters the next period attached


to the seller she purchased from most recently. Thus the switch decision is a moreinclusive version of search. Switch costC is constant across buyers and periods,and is publicly announced before the first period. It is varied across sessions atthree levels: zero (a baseline for comparison), low (20 cents) and high (50 cents,over half of the median surplus in a transaction).

Customer markets feature long term but impermanent attachments. To discour-age permanent attachments, the experiment introduces random production costinnovations. In each session the production costs follow a random walk with amean zero additive innovation to marginal production cost each period. Inparticular, sellerj’s cost in periodt evolves randomly according toc 5 c 1 e ,jt jt21 jt

where the innovationse are the sum of two components, one common to alljt

sellers drawn from U(215, 15) and one independent component drawn fromU(25, 5). The larger correlated component keeps sellers’ costs closer together inlater periods. To sharpen comparisons across institutions and across switch costs,we used the same sequence of random shockse for all sessions with the samejt

experience and cost process conditions. The realized cost sequences are shown infigures posted on the journal web page.

Table 1 summarizes the timelines for each trading period for the two marketinstitutions. In the posted offer treatment the period is over after each buyer haseither rejected all available price(s) or else accepted one of the posted prices shesees (that of ‘her seller’ if she does not switch, or all prices if she chose to switch).

4In the haggle treatment the period is over when the 60-s negotiation period ends.

Table 1Timelines for the each market period of the posted offer and haggle trading institutions

Posted offer Haggle

Sellers learn own production costc Sellers learn own production costc

Sellers post prices Sellers post list prices

Buyers observe own seller’s price Buyers observe own seller’s list price

Buyers may choose to switch (at costC) Buyers initiate negotiations with own seller;may choose to switch to negotiate with anotherseller (at costC) at any time

Buyers accept or reject seller offers Through bilateral negotiation sellers andbuyers may reach transaction terms

Median transaction price announced to all Median transaction price announced to all

Buyers forecast upcoming median price Buyers forecast upcoming median price

4We ran pilot sessions with periods lasting 180, 120, 90, 60 and 45 s. Except in the 45-s pilots wesaw little impact on trade volume; after the first few periods most trades occur in the last 30 s.Therefore in the experiment we let the negotiation period run for 90 or 120 s in the first few periodsand 60 s thereafter.


Then the sellers and buyers view interim screens that summarize their own activityin that period and their own cumulative profit, as well as the median transaction

5price (across all sellers) for that period. Buyers forecast the next period’s mediantransaction price, at the same time that sellers are posting their prices for theperiod. The buyer with smallest absolute forecast error totaled over all periodsearns a modest prize ($5.00).

Each session is divided into runs of 25 periods, and buyers and sellers arerandomly re-matched to new trading partners at the beginning of each run. Eachsession has three sellers who produce to demand (i.e. no inventories) at a uniform(constant) marginal cost each period with no fixed cost. Each seller’s capacity is 3units, so the maximum quantity that sellers can supply in total is 9 units per

6period. Five buyers each demand only 1 unit per period. Buyer values are constantover each session at 300, 275, 250, 225, and 200 cents. At the end of each 25period run, assignments of buyers to these values are rotated and attachments arereinitialized randomly. Fig. 1 displays the supply and demand for an exampleperiod.

Table 2 presents the experimental design, which employs 24 sessions. Thedesign is balanced, with equal numbers of sessions in each trading institution andswitch cost treatment. Identical software was used at all sites. Experienced subjectsparticipated in an earlier session listed in the table, at the same site and in the sametrading institution. We were able to complete more periods in the experiencedsessions because the experienced sessions required a significantly shorter instruc-tional period. Subjects typically earned between $20 and $30 per 2-h session(including instructions). Half of the data (the posted offer treatment) were reportedpreviously in Cason and Friedman (2002). This earlier paper focused on otherresearch issues, such as the market impact of information differences and changesin random cost sequences.

3 . Results

This section begins with an overview of the data, and then compares relativemarket efficiency, market power, and price stickiness. Our design allows for sharpmatched pair hypothesis tests for many of the comparisons because seller costsfollow identical paths in the two market institutions for each treatment. In testing

5Cason and Friedman (2002) report sessions in the posted offer treatment with no feedback and withfull (all transaction prices) feedback. Based their results, as well and Davis and Holt secret pricediscounts work mentioned earlier, we decided that reporting only the median price last periodrepresents the most useful middle ground for our institutional comparison.

6 If buyers try to accept more units than a specific seller has offered, then in the posted offertreatment the buyers who transacted most recently with that seller receive priority. Any remaining unitsare awarded first come first served, and in the haggle treatment all units are always awarded first comefirst served.


Fig. 1. Example supply and demand (period 22, experienced).

for differences across market institutions, we also match experience, switch cost,and experimenter (UCSC versus [Purdue or USC]). In testing for differencesacross any other treatment, we match market institution and the other treatments.To economize on journal space we have posted on the journal website somesupplementary tables reporting additional data analysis details.

Table 2Sessions and runs by experience, switching cost and institutions

Inexperienced subjects Experienced subjects

Switching cost Switching cost

Institution $0.00 $0.20 $0.50 $0.00 $0.20 $0.50

Posted offer 232 232 232 233 233 233Haggle 232 232 232 233 233 233

Notes: A total of 24 sessions are reported.N 3M within a cell indicatesN sessions, each withM25-period runs (The experienced posted offer sessions actually ran for four runs (100 periods), but herewe report only the first three runs in order to be comparable to the haggle sessions). One session in eachcell was conducted at the University of California at Santa Cruz, one session in each posted offer cellwas conducted at the University of Southern California and one session in each haggle cell wasconducted at Purdue University.


Figs. 2 and 3 show prices in a matched pair of final runs, one posted offer andthe other haggle, with 50 cent switch cost and experienced players. Open circlesrepresent unsold offers or list prices and filled circles represent completedtransactions. A horizontal bar in each period shows the competitive equilibrium(CE) price. Transaction prices stay at a fairly constant level in both cases,remaining well above CE prices.

We see a sharper contrast across institutions in Figs. 4 and 5, depicting anothermatched pair, this time middle runs with 20 cent switch costs. The posted prices inFig. 4 track the CE prices fairly well as seller costs rise and subsequently fall. Thehaggle transaction prices in Fig. 5, however, remain well above the CE price in allbut a few periods (32 through 35). At the same time, trading volume falls belowthe CE level, as we document later.

3 .1. Efficiency

Inefficiency, the loss of potential gains from trade, has two main sources in mostmarkets. Volume (V) or undertrading losses occur when a buyer and seller whocould profitably transact in competitive equilibrium fail to do so. Extra-Marginal(EM) losses occur when a buyer or seller transacts who would not do so incompetitive equilibrium, thereby displacing a more profitable trade (e.g. Cason and

7Friedman, 1996). Customer markets open a third source of inefficiency, the8switching costs incurred by buyers.

Table 3 reports mean efficiency losses by source for each session and treatment.Efficiency and efficiency losses are listed as percentages of potential surplus, so incompetitive equilibrium efficiency would be 100 and all three losses would be 0.Efficiency is lower than in many other laboratory markets, ranging from 78 percentin an experienced high switch cost haggle session to 95 percent in an experiencedzero switch cost posted offer session. As in previous experiments (e.g. Ketcham etal., 1984), average efficiency increases with experience in the posted offerinstitution with zero switch costs; with positive switch costs we find slightincreases in three of four session pairs. Notably, average efficiency does notincrease with experience in haggle for any switch costs. More importantly, withthe exception of the inexperienced zero switch cost sessions, efficiency is alwayslower in haggle than in posted offer, usually 5–8 percentage points lower. Thetrading institutions have no consistent difference in switch costs or EM losses, but

7Assigning inefficiency is ambiguous when in the same trading period an extramarginal unit hasdisplaced an inframarginal unit (EM inefficiency) and also another inframarginal unit has failed totransact (V inefficiency); in these cases (relatively rare in the data) we cannot identify whichinframarginal unit to assign to EM and which to V. Here, we resolve the ambiguity by using the averagevalue of the untraded inframarginal units for each source.

8Due to the random variation in seller costs, for the market to be fully efficient buyers need to incurswitch costs from time to time. These ‘efficient switches,’ when they occur, help reduce EM and Vinefficiency.

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Fig. 2. Posted and transaction prices for posted offer session USC503cx (experienced, $0.50 search).

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21 (2003) 223–251233Fig. 3. List and transaction prices for haggle session PU503hx (experienced, $0.50 search).

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Fig. 4. Posted and transaction prices for posted offer session UC204cx (experienced, $0.20 search).

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21 (2003) 223–251235Fig. 5. List and transaction prices for haggle session UC204hx (experienced, $0.20 search).

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Table 3Inefficiency decomposition

Switch Institution Inexperienced Experiencedcost

Mean Switch cost Extra-marginal Volume Mean Switch cost Extra-marginal Volumeefficiency (%) losses (%) losses (%) losses (%) efficiency (%) losses (%) losses (%) losses (%)

Zero Posted offer 84.1 0 6.1 9.8 94.2 0 4.0 1.7(78.3, 89.8) (0, 0) (3.4, 9.0) (18.3, 1.2) (93.6, 94.9) (0, 0) (4.0, 4.0) (2.4, 1.1)

Haggle 88.7 0 4.3 7.0 86.9 0 3.3 9.7(90.9, 87.1) (0, 0) (2.6, 6.6) (6.5, 6.3) (89.2, 84.6) (0, 0) (3.6, 3.0) (7.1, 12.3)

Low Posted offer 86.7 7.5 2.8 2.9 86.3 4.2 6.6 2.8(86.4, 87.0) (7.0, 8.0) (3.0, 2.7) (3.6, 2.3) (87.6, 85.1) (5.0, 3.3) (6.2, 7.0) (1.1, 4.6)

Haggle 82.0 5.0 4.8 8.2 82.0 3.7 6.0 8.3(79.5, 84.6) (6.6, 3.3) (4.9, 4.8) (9.0, 7.3) (82.2, 81.7) (5.4, 2.0) (5.1, 6.9) (7.2, 9.3)

High Posted offer 83.6 6.5 4.3 5.5 85.4 8.4 4.5 1.7(86.6, 81.2) (5.3, 7.5) (2.8, 5.6) (5.3, 5.7) (88.5, 82.4) (6.6, 10.1) (4.8, 4.2) (0.1, 3.3)

Haggle 80.6 6.1 4.2 9.0 79.1 6.2 6.1 8.5(81.4, 80.0) (3.6, 8.2) (5.8, 2.8) (9.2, 8.9) (77.7, 80.6) (7.2, 5.3) (8.1, 4.2) (7.1, 10.0)

Note: Values in parentheses are the UCSC session mean followed by the USC or Purdue University session mean.


there is a difference in average V losses, most striking in experienced sessions:1–3 percent in posted offer versus 8–10 percent in haggle.

These differences in efficiency across trading institutions are statisticallysignificant, even based on a very conservative non-parametric signed rank test onpaired session means. To ensure statistical independence, we used only oneobservation per session (the overall mean) for each (in)efficiency measure. Overallefficiency is lower in haggle for 11 of the 12 pairs of sessions, a significant resultat the 5-percent level (Wilcoxon signed-rank test statistic512; two-tailed P-value50.034). Extra-Marginal inefficiency and switch inefficiency are not sig-nificantly different by trading institution, with Wilcoxon testP-values of 0.97 and0.25, respectively. The source of the efficiency difference is volume inefficiency,which is greater in haggle for 11 of the 12 pairs of sessions (Wilcoxon signed-ranktest statistic512; two-tailed P-value50.034). This confirms that the lower

9,10efficiency in haggle is mainly due to trading volume losses.Table 4 provides a parametric decomposition of the efficiency losses, based on a

tobit model with separate observations for each period. The tobit specificationaccounts for the restriction that efficiencies are bounded above by 1 andinefficiencies are bounded below by 0. To account for the fact that a session’soutcomes are not independent across periods, we employ a random-effects errorstructure, with each session-run as a random effect. The point estimates for thehaggle dummy indicate that efficiency is 5 percentage points lower than withposted prices and volume losses are 5 percentage points higher, while other losses

9For readers who prefer parametric regression analysis to these nonparametric matched-pairs tests,we also report regressions that employ one observation per session for each market performancemeasure, using dummy variables for all treatments as explanatory variables. The results usually concurwith the matched-pairs tests, although the statistical significance of the treatment dummy variables isoften limited, due in part to the small number of degrees of freedom and low power in theseregressions. For example, we estimated (Efficiency Measure)5a 1 b(Institution dummy)1c(20-centswitch cost dummy)1d(50-cent switch cost dummy)1e(experienced dummy)1f(USC site dummy)1g(Purdue site dummy). We estimated separate equations for the four efficiency measures in Table 3, allusing a tobit model to account for the bounds of 0 and 1. Although only marginally insignificant orsignificant, consistent with the matched-pairs tests, the estimatedb coefficients indicate that volumeinefficiency is higher (t51.38) and total efficiency is lower (t51.76) in haggle than in posted offer.Also consistent with the matched-pairs tests, switch inefficiency (t50.25) and extra-marginalinefficiency (t51.02) are not significantly different across trading institutions.

10This failure by subjects to execute mutually-beneficial transactions is not observed in thewell-known bargaining experiment reported in Hoffman and Spitzer (1985). Unlike the presentexperiment, this earlier study involved face-to-face, verbal negotiations, and it did not fix a time limiton the bargaining. Our bargaining protocol is more similar to McKelvey and Page’s (1997) recentexperiment. Like us, they observe frequent bargaining breakdowns. Their experiment includes bothcomplete information and private (payoff) information treatments, and breakdowns occur significantlymore frequently with private information. Our experiment features private information.

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Table 4Tobit regressions for efficiency and inefficiency

Variable Efficiency Volume inefficiency EM inefficiency Switch inefficiency

Coefficient S.E. Coefficient S.E. Coefficient S.E. Coefficient S.E.

(1) Intercept 0.92** 0.022 0.06** 0.019 0.04** 0.008 0.01** 0.030(2) 51 if switch cost5$0.20 20.06* 0.026 20.01 0.020 0.009 0.012 0.05 0.030(3) 51 if switch cost5$0.50 20.08** 0.030 20.009 0.018 0.005 0.013 0.07* 0.030(4) 51 if experienced subjects 0.02 0.028 20.18 0.020 0.007 0.012 20.05 0.008(5) 51 if haggle Institution 20.05* 0.020 0.05* 0.017 0.008 0.010 20.009 0.007(6) Observations 1482 1482 1482 1482(7) Log-likelihood 373.14 1213.74 1880.77 1528.54(8) Restricted log-likelihood 331.01 1144.97 1812.30 1515.99

Note: ** denotes significantly different from zero at 1 percent; * denotes significantly different from zero at 5 percent. Estimates are based on a random-effects errorstructureu 5t 1e , where the termt reflects the session-run specific random effect.it i it i


differ insignificantly. These regression results also indicate that overall efficiencyis 6–8 percent lower in the treatments with positive switch costs, attributable atleast in part to greater switch inefficiency. The point estimate indicates 2 percentgreater efficiency in experienced sessions, but this experience effect is notstatistically significant.

3 .2. Market power

Next we examine relative market power, measured as mean markup oftransaction prices over the competitive equilibrium price. The results are reportedin Table 5. In both institutions, average markup increases with the switch cost and(perhaps surprisingly) with experience; the sole exception is the experienced, highswitch cost sessions. In every treatment, average markup is higher in haggle thanin posted offer, usually by at least 50 percent. Fig. 6 illustrates the higher averagetransaction prices in haggle for the high switch cost treatment. This figure plots thetime series of the difference in mean transaction prices (haggle2posted offer) foreach period, for both the inexperienced (panel A) and experienced (panel B)sessions. Our experimental design permits this direct period-by-period comparison,because we used the same random cost sequence in each session. Of the 50inexperienced periods for this high switch cost treatment, in only one period(period 38) did the mean posted offer transaction price exceed the mean haggletransaction price. In the experienced sessions for this treatment, transaction pricesappear about equal in periods 31–60; but prices are systematically higher in haggle

Table 5Mean markup (transaction price2CE price)

Switch cost Inexperienced Experienced

Zero Posted offer 6.6 8.3(1.6, 10.7) (9.7, 7.0)

Haggle 11.7 12.6(0.6, 23.1) (11.4, 13.9)

Low Posted offer 11.0 18.0(19.1, 3.4) (12.7, 23.8)

Haggle 17.4 22.6(13.7, 21.0) (23.6, 21.6)

High Posted offer 14.6 13.2(8.8, 20.6) (11.9, 14.5)

Haggle 25.6 24.7(25.5, 25.6) (21.9, 27.8)

Note: Values in parentheses are the UCSC session mean followed by the USC or Purdue Universitysession mean.


Fig. 6. Panel A: Difference in mean transaction prices (haggle2posted offer) for inexperienced, highswitch cost sessions. Panel B: Difference in mean transaction prices (haggle2posted offer) forexperienced, high switch cost sessions.


11in earlier and later periods. Another paired signed rank test (again with each pairof matched sessions contributing one statistically independent observation) con-firms that markups are significantly higher in haggle (Wilcoxon sum of signedranks58, n512, P-value50.012), controlling for switch cost, experimenter, and

12experience levels.Table 6 provides parametric decomposition of seller markup, with correction for

autocorrelated errors to account for possible price stickiness as discussed below.The dependent variable is mean transaction price minus competitive equilibriumprice. Both switch cost dummy variables are highly significant, indicating markupsroughly 12 cents higher for positive switch cost treatments. The fact that theinteraction terms are insignificant suggests that the impact of switch costs does notdecline significantly later in the run. The significant haggle dummy confirms thatmarkups are higher in this institution than in posted offer, by an average of 7cents.

These higher observed markups coinciding with greater volume losses underhaggle raise the additional question of whether sellers see a net gain in profits dueto these higher markups. We examined seller profits using the paired test of signedranks, with each session pair contributing one observation, and found that they are

13significantly higher in haggle (sum of signed ranks59, n512, P50.016).

Table 6Price markup regressions (dependent variable5mean transaction price2CE price (in cents))

Variable Coefficient S.E.

(1) Intercept 0.295 3.2(2) 51 if switch cost5$0.20 12.64** 4.1(3) 51 if switch cost5$0.20* periods left 20.18 0.1(4) 51 if switch cost5$0.50 11.84** 4.2(5) 51 if switch cost5$0.50* periods left 20.16 0.1(6) 51 if experienced 1.57 2.2(7) Periods left 0.17* 0.07(8) 51 if haggle institution 7.27** 2.2

Observations: 7322R : 0.643

Note: ** denotes significantly different from zero at 1 percent; * denotes significantly different fromzero at 5 percent (all two-tailed tests). Estimates are corrected for autocorrelation.

11Because of a run of positive common cost shocks, seller costs increase very rapidly in periods26–38 of the experienced sessions. We will later see that transaction prices are ‘stickier’ in haggle, andthis may account for the exceptional price comparison in periods 31–60.

12An OLS regression of markups on the treatment dummy variables—analogous to the efficiencyregressions described in Footnote 9—provides a similar conclusion. Markups are higher in haggle,although only at marginal significance levels for this test (t51.66; two-tailedP-value50.115).

13The OLS regression of seller profits on treatment dummy variables indicates (insignificantly)higher profits in haggle (t51.37; two-tailedP-value50.190).

242T.N

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asonet

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rgan.21 (2003) 223–251

Table 7Median markup by attachment length

Switch Inexperienced Experiencedcost

Previously Attached for one (Attached2 Previously Attached for one (Attached2unattached or more periods unattached) unattached or more periods unattached)

Low Posted offer 7 11 4 14 17 3Haggle 9.5 17 7.5 16 24 6

High Posted offer 9 13 4 7 13 6Haggle 25 26 1 16 22 6


Are haggle markups higher because the institution allows sellers to pricediscriminate between attached buyers and new customers? Table 7 shows medianmarkup for new vs. previously attached buyer–seller pairs in positive switch cost

14sessions. It shows that new customers on average receive a discount in allrelevant treatments, consistent with Taylor (1999). In low switch cost sessions thediscount is larger in haggle than in posted offer, with inexperienced (7.5 vs. 4cents) as well as experienced subjects (6 vs. 3 cents). In high cost sessions it is notclear where the discount is larger.

Do markups for attached customers increase the longer they are attached? Welooked at the data in various ways and found little evidence for a systematicrelationship in either institution.

3 .3. Sticky prices

Cason and Friedman (2002) argue that price stickiness is best measured bycomparing the median change in period-to-period transaction prices to thecorresponding change in competitive equilibrium prices. To maintain comparabili-ty across the two trading institutions, here we look at transaction prices for eachcontinuing buyer–seller pair. Prices are sticky in both trading institutions, since themedian CE price change is always 7–8 cents while the median transaction pricechange is 3–6 cents and is 4–5 in most treatments. This stickiness is lowest withzero switch cost, and stickiness increases with switch cost in all instances exceptfor low switch cost in haggle. Median transaction price changes are generally a bitsmaller (and thus prices stickier) with haggle than with posted offer.

To sharpen the comparison, we classified an individual seller as ‘sticky’ if herabsolute cost changes exceed her absolute price change in at least half of theperiods. Overall, for the posted offer market 24 of 36 sellers were ‘sticky’ vs. 34of 36 under haggle, a very significant difference according to Fisher’s exact test

15(P,0.01).Although transaction prices vary less from period-to-period in haggle than in

posted offer, the list prices posted in haggle exhibit approximately the samevariability as offer prices in the posted offer sessions. For example, sellers’ medianabsolute change in list prices for haggle and in posted prices for posted offer areboth 5 cents. In posted offer 18 of 32 individual sellers have a median absolute

14Since in haggle sellers negotiate individual transaction prices with each buyer, classifyingtransactions into ‘new’ and ‘attached’ categories is straightforward. In posted offer, by contrast, aparticular offer price might be accepted both by new and attached customers, since the institution doesnot permit price discrimination. In these cases the price contributes observations to both the new andattached categories, with the number of observations determined by the number of accepting buyers ofeach type.

15The breakdown by switch costs is 7/12, 7/12, and 10/12 sticky sellers for posted price and 10/12,12/12, and 12/12 for haggle for the zero, 20 cent, and 50 cent switch cost cases, respectively.


Table 8Switch frequencies (percent)

Treatment Inexperienced Experienced

Zero switch cost Posted offer 56 57(59, 52) (44, 70)

Haggle 60 78(75, 42) (97, 59)

Low switch cost Posted offer 18 10(16, 19) (12, 7)

Haggle 12 8(16, 6) (11, 4)

High switch cost Posted offer 7 8(6, 7) (6, 10)

Haggle 6 6(4, 8) (7, 5)

Note: Values in parentheses are the UCSC session mean followed by the USC or Purdue Universitysession mean.

change in posted prices of 5 cents or less, and in haggle 20 of 32 individual sellershave a median absolute change in list prices of 5 cents or less.

3 .4. Buyer behavior

Table 8 indicates that, not surprisingly, switch rates are lower in higher switchcost sessions. More interestingly, switch rates seem lower for haggle when switchcosts are positive—for both inexperienced and experienced sessions and for bothlow and high switch costs. The difference in switch rates is greatest wheninexperienced subjects face low switch costs. More formally, we form matchedpairs of all 25-period runs with positive switch costs in the two trading institutionsand test whether the distribution of switch rate differences is centered on zero. Thetest rejects the null hypothesis that switch rates are the same in the two institutionsin favor of the alternative that switch rates are lower for haggle (Wilcoxon sum of

16signed ranks539, n520, P50.011). This lower observed switch rate isconsistent with the earlier result that seller market power is greater under haggle.

16A more conservative test that treats each pair of sessions (rather than each pair of runs) as the unitof observation marginally fails to reject this null hypothesis of no difference across institutions(P50.117). This could be attributed in part to the small sample size of only eight pairs of sessions withpositive switch costs. Similarly, a tobit regression of each sessions’ switch rate on the treatment dummyvariables does not indicate a significant difference in switch rates across trading institutions. Thisregression estimates six coefficients using only 16 session observations, and only the switch costdummy variable is significantly different from zero.


4 . Discussion

Our laboratory results comparing haggle (bilateral bargaining) markets withposted offer markets can be summarized as follows:

1. Haggle is less efficient at extracting potential gains from trade, mainlybecause mutually beneficial trades are more often missed (‘V losses’), and notbecause of inefficient customer search or extramarginal trades. This result isconsistent with the new institutionalist view and inconsistent with the Coasianview. Unlike most other market institutions studied in the laboratory, efficiencydoes not increase with experience in haggle.

2. Sellers obtain a larger share of realized gains with haggle than with postedoffer. This is contrary to the new institutionalist view that giving sellers thecommitment technology in posted offer should increase their share of the gains. Itis consistent with the view that haggle encourages classic market power with itsdeadweight (V) losses.

3. New buyers receive lower prices, consistent with the work of Taylor and(indirectly) Klemperer. In low switch cost sessions, the effect is stronger inhaggle, as one might expect since haggle but not posted offer allows direct pricediscrimination.

4. Prices are sticky in both institutions, and especially in haggle. Sellers’ chosenprices dampen the fluctuations in their production costs, and reflect only about halfof the resulting fluctuations in competitive equilibrium price.

5. In sessions with positive switch costs, buyers switch sellers less frequentlywith haggle than with posted offer.

One might ask whether the V losses in haggle are due to insufficient time fornegotiation. We do not think so because these losses are not reduced either bylonger negotiation periods in early trials and in pilot experiments, or (as we haveseen in the data reported above) by subjects’ experience. On the other hand, the Vlosses could be due to ‘deadline effects’ in bargaining (Roth et al., 1988). As inprevious bilateral bargaining experiments, our subjects frequently reached agree-ment within the last few seconds of the trading period, no matter how long it isand no matter how experienced the subjects. Perhaps sometimes traders hold outfor their counterparty to concede in the last few seconds and that concession doesnot materialize. We leave to another paper the real time dynamic analysis ofhaggling.

After thinking about our results we noticed a possible explanation for many ofthe performance differences based on a subtle informational difference betweenthe two market institutions. A switching buyer in posted offer sees his actualalternative opportunities because sellers are committed to their posted prices. Bycontrast, a switching buyer in haggle only sees a weak signal of alternativeopportunities because list prices are not committed, and so the switch option is less


valuable. Therefore in haggle buyers are less inclined to switch and sellers arebetter able to exercise market power via larger markups and lower trading volume.Note that these institutional switch value differences are also present in postedoffer and haggle markets in the field. For example, consider a procurement agentcontemplating switching parts suppliers for an assembly plant. This agent couldestimate different values of switching depending on whether she must negotiateterms with a new supplier, or whether she can simply accept or reject non-negotiable seller offers.

More experiments are necessary to test this explanation and other possibletheories. The Coasian versus New Institutionalist theme was secondary in thepresent paper, but some investigators might wish to pursue it further bysimplifying the laboratory environment (e.g. draw private costs and valuesindependently each period rather than random walks, and eliminate attachments)and deriving explicit testable models. A different empirical project would be tointerpolate new market institutions with information and negotiation proceduresbetween haggle and posted offer, and study these new institutions in thelaboratory. For example, subjects could bargain by following a strict two or threeround alternating offers protocol. The impact of differing market informationcould also be studied, for example, by allowing switching buyers to observesellers’ transaction price history and/or their current production costs. Our currentdesign features a competitive equilibrium that tends to favor the buyers in terms ofthe division of surplus. This asymmetry may be behind some of the divergencefrom the CE results that we find. One interesting extension would be to remove orreverse this asymmetry and see if key results—such as the higher prices in hagglecompared to posted offer—continue to hold.

Another useful extension would allow sellers to choose which trading institutionthey wish to employ. Our results suggest that sellers’ profits suffer when theyadopt posted offer rules, but as we discussed in the introduction there exist agencyrationales for adopting this less flexible pricing institution. It would be useful toinclude such agency complications in an experiment that addresses the endogenousdevelopment of market institutions. It would also be valuable to include newinnovations such as bargaining bots and Priceline.com style haggling that offer theopportunity for entirely new trading institutions.

A cknowledgements

We thank the NSF for funding the work under grants SBR-9617917 andSBR-9709874; Brian Eaton for programming assistance, and Alessandra Cassar,Svetlana Pevnitskya and Sujoy Chakravarty for help in running the experimentalsessions. We benefited from the comments of three anonymous referees, FlavioMenezes, John Morgan, Shyam Sunder, and participants at the March 2000 TokyoExperimental Economics Conference, the September 2000 Tucson ESA workshop,


the October 2000 Rio de Janeiro LACEA Conference and the January 2001 NewOrleans ASSA meetings. We retain responsibility for any errors.

A ppendix A. Abridged instructions for haggle market

A.1. GeneralEach time for buying and selling is called a TRADING PERIOD and will

usually last 3 min or less. At the start of each period, sellers POST PRICES, i.e.each seller enters a list price for his or her units. Some of these prices then areshown to each buyer as explained below. Each buyer decides whether to search forother prices, to accept the price he or she is shown, to negotiate a better price withthe seller, or simply not buy this period. Then the trading period is over.

A.2. Sources of profitEach buyer can purchase a single unit of the good each period. All buyers have

a VALUE for the unit shown at all times in the STATUS window on their screen(see Fig. 7a) . . . . Different buyers generally have different values.

Fig. 7.


A buyer with valuev who purchases a unit at pricep earns PROFIT5v 2 p thatperiod. . . . Buyers who don’t buy a unit automatically earn a profit of zero thatperiod.

Each seller can sell up to 3 units of the good each period. All sellers have aCOST for each unit of the good in this experiment that is displayed at all times inthe STATUS on their screen (See Fig. 7b). Always check the cost at the beginningof each period because it can change from period to period as explained [twoparagraphs] below. Different sellers may have different costs.

A seller with costc who sells a unit at pricep earns PROFIT5p 2 c on thatunit. Since sellers can sell to different buyers at different prices, total profit for aperiod is equal to the sum of the profits from each unit sold that period. . . . Sellerswho don’t sell any units automatically earn a profit of zero that period.A sellerdoes not pay the cost of a unit unless she sells that unit. Therefore, profit is simplyzero for any unsold units.

Each period, eachseller’s costis randomly changed from the previous period byan amount between2 e and 1 e. In today’s experiment, part of this randomchange is thesame for all sellers, and part of this random change isspecific toeach individual seller. Thee for today’s experiment is $0.20, but $0.15 of thevolatility is common to all sellers, and the remaining $0.05 is specific to eachseller: Each period the computer randomly draws a number from the list20.15,20.14,. . . , 20.01, 0, 0.01, 0.02,. . . , 0.14, 0.15, and adds this same number tothe previous period’s costfor all sellers. Then, the computer randomly draws anumberseparately for each sellerfrom the list 20.05, 20.04,. . . , 20.01, 0,0.01, 0.02,. . . , 0.04, 0.05, and also adds this to the previous period’s costseparately for each seller. Thus, for example, a seller with a cost of $1.51 in period8 would have a cost of $1.69 in period 9 if the number drawn from the first list is0.14 and the number drawn from the second list is 0.04. Each number on both listsare equally likely, random and INDEPENDENT. This means that the draw doesnot depend on the actions of any buyer or seller, nor on the draws for the othersellers (or buyers) that period or in any other period.

A.3. How to buy or sellEach buyer is the CUSTOMER of a specific seller. In the first period, the

computer randomly assigns all buyers to a seller. Buyers and sellers will berandomly rematched at several points throughout the experiment.

If you are a Seller, you begin each period by setting a LIST PRICE that is thenshown to your customers. You set the list price in the STATUS window (see Fig.7b) by adjusting the scroll bar until the price you want is shown and then clickingon the ‘Change List’ button. This price becomes the maximum price in thenegotiation window (see Fig. 8) shown on your screen and on that of yourcustomers.

After all sellers have set list prices and all buyers have forecast, a pricenegotiation window opens on the screen of each buyer showing his or her seller’s


Fig. 8.

list price and a price scroll bar. A similar window opens on the screen of eachseller for each buyer that sees her list price. Note that sellers can have multipleprice negotiation windows open at the same time if they are negotiating with morethan one buyer.

Buyers and Sellers each have three options when negotiating prices:– Accept the most recent offer of their trading partner by clicking on the

‘Accept $x.xx’ button.– Send a counter offer to their trading partner by adjusting the price scroll bar

and clicking on the ‘Counter $x.xx’ button.– Drop out of the negotiation by clicking on the ‘Quit’ button. Note: buyers

who quit must search for a new seller in order to purchase in that periodBuyers have the additional option of paying a search cost to seek a new trading

partner by clicking on the ‘Search’ button in the status window. Buyers whosearch are shown the list prices of all sellers in the Search window (see Fig. 9).They then may select a new trading partner by highlighting a list price and

Fig. 9.


clicking ‘OK’. Once the buyer selects, a new price negotiation window appears onthe screen of both the buyer and the chosen seller. Price negotiation begins fromthat seller’s list price. Note: Buyers must pay the search cost for each searchwhether they end up purchasing that period or not.

A clock in the STATUS window shows the remaining time in the tradingperiod. If time expires on a negotiation then there is no transaction and no profit onit for either the buyer or the seller. Note that if a buyer switches she incurs the costwhether she completes a negotiation or not. The status window also keeps arunning total of your profits for the session.

Note that in today’s session there are more buyers than any single seller hasunits for. Therefore, some sellers may ‘sell out’ of their units before time expires.If the seller that you (as a buyer) are negotiating with sells her last unit before youare done negotiating, then your negotiation window will immediately switch to themessage, ‘Transaction not in progress,’ and you can no longer negotiate with thisseller for the current period. You may, of course, pay the search fee to search foranother seller to negotiate with. If you ever try to switch to a seller that has nomore units remaining to be sold, you will receive a message that this seller hassold all of her units and you will be prevented from initiating negotiations withher.

A.4. At the end of the period

1. At the end of each trading period, a window will appear giving youinformation about your transactions that period as well as a history for theprevious four periods. . . . Theamount in the cost column includes the cost of anysearches.

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