Ex Post (in)efficient Negotiation and Breakdown of Trade

Rajkamal Iyer and Antoinette Schoar*

Abstract One of the central assumptions of incomplete contract theories is that the contract parties will engage in ex post efficient renegotiation in the case of unforeseen shocks. However if parties to the trade are concerned about reputation or norms against price gauging, efficient renegotiation might break down. To test these ideas, we conduct a field experiment among tailoring stores in Chennai, a city in Southern India. We send trained auditors acting as customers to tailor stores and place an order to be picked up in several days. The customer then returns the next day to ask for an urgent completion of the order within one day, which gives the tailors an opportunity to renegotiate the contract. We find that overall tailors do not use the increase in their bargaining power to ask for a higher price. In 44% of the cases they fill the urgent order but do not ask for any increased pay. In the remaining 56% of the cases trade breaks down, i.e. tailors refuse to fill the order. Instead in these cases they offer customers that they can take back the material for the stitching back to find another tailor. But they never ask for additional compensation. However, when offered a higher price the majority of tailors were happy to fill the urgent order. This result suggests that without the customer offering the mark up tailors forego an efficient renegotiation option and trade breaks down. When comparing customers from out of state versus local ones we still find a strong resistance to ask for higher prices in the case of an urgent order. We can also rule out that in this market the equilibrium behavior of a buyer does not require that they voluntarily offer a premium for urgent services. We find that average customers do not offer a premium and in fact there is breakdown of trade. We conjecture that either norms or reputational concerns prevent tailors from proactively suggesting a mark up for urgent delivery. These results put into questions the notion that ex post renegotiation can easily be achieved.

* Iyer: MIT Sloan School of Management, email: riyer@mit.edu; Schoar: MIT Sloan School of Management, CEPR and NBER, email: aschoar@mit.edu. We thank Sharon Bateau, and especially Sandhya Kumar for outstanding research assistance. The Institute for Financial Markets Research in Chennai, India provided financial support. All errors are our own.

One of the important building blocks of incomplete contracting theories is the assumption that

parties to a contract will engage in ex-post efficient renegotiation in the case of (un) foreseen

shocks. As long as the valuation of the good for a buyer is higher than that of the seller, trade

should generally occur. But the final price is determined by the allocation of bargaining power

and outside valuations between the buyer and seller. While these assumptions have been widely

used in incomplete contacting theory, there is very little empirical evidence on whether agents

engage in renegotiation when their bargaining power increases. In particular several recent

theories have raised doubts on whether ex post renegotiation will always reach efficient

outcomes, see for example Hart and Moore (2008). For example, buyers might not agree to

changes in ex ante agreed upon prices if they feel unfairly treated or “aggrieved”, even if this

makes them worse off ex post. Similarly sellers in a market might not feel at liberty to suggest a

price increase if they fear that this will harm their reputation or violate norms against price

gauging. These forces could lead to ex post deviation from efficient renegotiation or even break

down of ex-post efficient trade.

To test the importance of such considerations we conduct a field study among tailoring stores in

Chennai, a city in Southern India. Auditors acting as customers place tailoring orders to have a

garment stitched and return to pick up the item about a week later. Each tailoring visit varies

across three dimensions: (1) the bargaining power of the customer, (2) the direction of who

initiates the negotiation and (3) the level of reputational concerns for the tailor. We first vary the

bargaining power of the customer by introducing an urgency: The customer returns to the store

the same day and asks for expedited stitching of the garment within one day rather than one

week due to an unforeseen emergency. This presents a reduction in the customer’s bargaining

power and would allow the tailor to extract a larger fraction of the rents from the transaction. The

price increase would especially be warranted if the tailor has to pay the workers to stay overtime

to fulfill the urgent request. In order to isolate the change in bargaining power from a change in

the customer’s valuation of the garment, we compare the “in-between” urgency to a situation

where the customer announces the need for urgent stitching upfront. While in both cases the

tailor knows that the customer places a high value on the fulfillment of the order, the bargaining

power of the tailor is higher in the case of in-between urgency since he already holds the cloth

for the order.

We also want to differentiate whether any possible breakdown of trade is driven by

unwillingness to renegotiate on the side of the tailors or true capacity constraints that make it

impossible for them to fill the order. For example, tailors might already have a very full order

book and cannot accommodate any new orders. For that purpose we introduce an additional

treatment arm where the customers, after being turned down for urgent delivery, offers the tailor

to pay twice the original price for the stitching. If the tailors are truly capacity constraint the

additional money offered by the customer should make no difference in the delivery time.

However, if the tailor is worried that being seen as price gauging when initiating renegotiation,

then offer by the customer could change the outcome.

We find that tailors do not use their increased bargaining power to demand higher price for in-

between urgency as compared to the initially agreed upon price. When a customer makes an

urgency request, tailors do not initiate a renegotiation but either agree to fill the order with no

price increase (46%) of the cases or instead tell the customer that they cannot do it and offer the

customer to take back the material (44%). In a vanishingly 5% does the tailor ever ask for extra

money. In fact, it is important to understand that in the in-between visits the tailor could always

mention that the cloth has already been cut and therefore cannot be returned. However, in

contrast, we find that when offered a higher price by the customer, the majority of the tailors (an

additional 40%) are willing to fill the urgent order. In fact in the majority of cases the tailors do

not accept the entire 100% price increase but ask the customer to pay them about 50% more. We

find a similar increase in the acceptance rates of urgent orders when customer offers extra money

both upfront and in-between. However, the likelihood of either asking for more money or

declining the job is much higher upfront. These results suggest that the initiative to renegotiate to

a higher price has to come from the customer. Tailors rather either give up an order or fill the

urgent order without extracting any rents than renegotiating the deal.

The question that arises is why don’t tailors extract surplus and even allow breakdown of

mutually beneficial renegotiation? Customers would be much better off if the tailor was willing

to do the urgent job against a small upcharge, rather than giving the cloth back to them to try a

different tailor. And tailors themselves are losing money by not renegotiating and finishing the

job since they have already spent significant time taking the measurements of the customer.

One possible explanation is that tailors might fear their customers will perceive any attempt to

renegotiate prices ex post as an attempt to take advantage of the customer and thus violate

business norms or hurt the tailor’s reputation. To test this reputation story we add a treatment

arm where we send customers who are from a different part of India to place the tailoring orders

and who clearly state that they are only in Chennai for a onetime event. This should reduce the

concern for the tailor that renegotiation would have reputational effects among the other

customers of the tailor. We find that the reluctance to initiate a price renegotiation is as high with

out of town customers as with local customers. We, also find that tailors are in fact more likely to

accept the request for urgency from out of town customers, often stating that they have “to show

them the quality of service in Chennai”. This finding suggests that narrow reputation concerns

alone do not explain the results since tailors also seem to worry about the perception of their

larger cultural group (“people from Chennai”) or do not want to violate norms of business

practices. However, the results strongly underscore the unwillingness on the part of the tailor to

initiate renegotiation.

A final question in interpreting our results is whether in equilibrium these reluctance of sellers to

negotiate leads to a distortion in the first best outcome. If negotiation parties in the market know

the norms of behavior, there might not be a distortion from the first best outcome: Sellers might

never start a renegotiation when the buyer has a shock so as not to be seen as price gauging. And

buyers proactively will know to suggest a higher price when they need an urgent delivery. To

understand whether this equilibrium behavior plays a role, we conduct a test of the supply side of

the market. For the purpose of this experiment we partnered with a couple of tailors who assisted

us in auditing actual customer visits. In the first treatment when a customer enters with an urgent

request, the tailor does not initiate the renegotiation but refuses to take on the rushed job. We

find that in none of the cases do customers offer a higher price without being prompted. In a

second treatment, when a new client visits the tailoring shop and expresses urgency the tailor

initially refuses the urgent request but then agrees to do the urgent delivery against an extra

charge (which is approximately 10% of the standard delivery stitching charge). Interestingly, we

find that when the tailor asks for extra money customers do not push back and willingly accept to

pay extra for the urgency and place the order. These results confirm that customers do not know

that they should proactively start negotiating in case of urgency. In addition the customers in our

second treatment do not seem to get upset when asked to pay for the rushed delivery.

Literature Review

Our paper contributes to several strands of the literature on incomplete contracting. Klein,

Crawford, and Alchian (1978) and Williamson (1983,), Hart and Moore (1988) argue that

contract incompleteness in the presence of relationship specific investments leaves contracting

parties open to several risks that could lead to break down trade. Our paper adds to this literature

by empirically examining which contractual features are used by market participants to

overcome frictions. Several theoretical papers have emphasized the role of renegotiations in

overcoming contracting frictions (Tirole, 1986; Aghion et al., 1994). By examining the

renegotiation process and outcomes, we highlight the extent of frictions that arise during

renegotiation.

A growing empirical literature tests the role of reputation and norms in contracting. Crocker and

Reynolds (1993) investigate the procurement contracts used by the U.S military and find that

higher reputation and complexity lead to drafting a more incomplete contract. Banerjee and

Duflo (2000) show that contracts written between firms are associated with the reputation levels

of the firms. McMillan and Woodruff (1999) find that inter-firm trade credit is more likely when

the delivering firm trusts the client. In a similar spirit our paper examines the importance of

reputation in the contracting process.

Similarly there is a large literature using laboratory experiments to test bargaining. These

laboratory experiments highlight that in many bilateral bargaining set-ups a non-negligible

fraction of participants do not care solely about their material payoffs, but have fairness

considerations (Güth et al., 1990; Roth 1995; Camerer and Thaler 1995; Fehr and Schmidt

(1999), Fehr, Hart and Zehnder (2011). While these studies have highlighted the bargaining

behavior of people in controlled laboratory settings, one of the questions raised is how these

results translate in a real marketplace. We build on the findings from these controlled laboratory

settings by testing bargaining behavior in real contracting situations.

Finally, our methodology draws on the existing literature of audit studies even though their

context and the questions differ completely from the current study. These audits focus mainly on

discrimination due to auditor characteristics such as minority or gender status. For some of the

most prevalent studies see Ayres and Siegelman (1995), Newmark et al., (1996) or Bertrand and

Mullainathan (2004). On the methodological front, we expand the approach of audit studies by

engaging in real purchase transactions. A major advantage of our methodology is that it allows

one to actually observe traded prices. In addition it provides an empirical strategy to exogenously

vary the conditions under which contracting occurs, which in turn helps in identifying causal

impact of different factors.

Description of Experimental Set-Up

The field experiment was conducted in Chennai, a city with over 4.5 million inhabitants that is

the largest in the south Indian state of Tamil Nadu. To conduct the study, we hire auditors who

visit tailoring shops and place orders under different scenarios. We chose the tailoring industry

to conduct our study for a number of reasons. First, there are a large number of similar-sized

establishments located in the same region, which minimizes the impact of location specific

shocks on our results. Competitive pressure within the industry has also forced prices to

converge to a similar range for standard stitched items, facilitating comparison of the deals

offered. Finally, we wanted an industry where first time customers could place stitching orders

without a prior history of interactions. This allows us to vary the potential threat of hold-up

between the auditor and tailor, by altering the script of each visit, to test how reputational

concerns, relational contracts affect negotiations.

We hired auditors who were familiar with the process of bargaining and who also had prior

experience placing stitching orders in tailoring shops. We verified that the auditors were not

affiliated with the tailoring industry in order to avoid any potential familiarity with the tailors.

We selected auditors who were between the ages of 25 – 35 from typical middle class

backgrounds. Common profiles included recent graduates, part-time employees, and housewives.

Once the auditors were hired, they were given training to explain the set-up of the experiment,

the details of the tailoring industry, and their particular assignment. The auditors were paid a

fixed fee per visit to the tailoring unit that is above the market rate. They were also told that if

they deviate from the script they would not be hired for further visits. The auditors were given

the information that they are part of a study to understand the market structure and functioning of

the tailoring industry. However, auditors (hence forth referred to as “shoppers”) were not told

what the expected outcome of the study is in order to avoid any “demand effects” in their

behavior. Shoppers were given the name and address of the shops they would visit, cloth to be

stitched for each order, and money to complete the order to credibly signal to the tailor that that

they are genuine customers.

Each tailoring visit varies across three primary dimensions: the type of urgency introduced,

whether an urgency payment is offered and the stated locality of the shopper. There are two

different types of urgency possible in each visit: “upfront,” where the shopper mentions the need

for urgent delivery directly after confirming standard delivery terms, and “in-between,” where

the shopper places an order for standard delivery, then returns a short time later to mention the

urgent delivery need. Similarly, there are two different possible monetary offers made with each

visit. In the first scenario, “no money,” the shopper doesn’t offer any additional money when

stating the urgent delivery requirement, however, any offer made by the tailor will be accepted.

In the second scenario, “double money,” the shopper offers double the initial stitching charge

after mentioning the urgency. For the locality variation, a shopper either introduces him or

herself to the tailor as recently having moved to the specific neighborhood in which the tailoring

shop is located, or as visiting Chennai from out of town to attend a specific event.

Approximately half of the shoppers placed orders for a woman’s blouse, while the other half

ordered a man’s shirt. Each item was stitched to a specific measurement, making it difficult for

the tailor to sell resale the item as a generic commodity, particularly as tailors’ distribution

networks are not geared towards retailing pre-made items. The urgency was kept at a pre-

determined level of 1.5 – 2 days from the time of placing the initial order. For example, the

urgent delivery deadline for an order placed by 11am in the morning would be 6pm on the

following day. These specific urgency levels were determined after conducting a number of

pilot interviews with tailors. For the tailors, the average time to stitch the requested items was 2

hours. Our aim was to mimic a common transaction that was neither odd enough to draw

suspicion nor too easy to stitch so that urgency requirement became negligible.

All shoppers were provided training and a detailed script that specifies the negotiation rules they

were asked to follow while placing the order and collecting the finished product. The visit to

each tailor can be summarized as follows: first, the shopper enters the tailor shop and confirms

she or he is talking to the owner/master tailor and that the shop stitches the particular item

selected for the particular visit. During this time, the shopper mentions the locality from where

she or he comes. After the introduction, the shopper mentions the need to get an item stitched

and inquires about the rate. Once both the stitching rate and delivery days have been provided,

the urgency variations are introduced. In the standard upfront visit, the shopper now informs the

tailor about the urgent delivery requirement. Under the no money variation, the shopper will

wait to see how the tailor responds. If the tailor rejects the urgency, the shopper will exit the

store. However, if the tailor counters the urgency request by asking for an extra fee, the shopper

will accept, provide the measurements for the garment and proceed with the order. The same

applies if the tailor accepts the urgency without any extra payment request. The double money

scenarios vary in that at the time of mentioning the urgency, the shopper offers to pay double the

initial stitching charge (but the shopper waits to see if the tailor voluntarily asks for extra

payment, before offering additional money).

The bargaining process for the in-between visit is exactly the same, except rather than

mentioning the urgency need on the initial visit the shopper will instead place the order for

standard delivery but return to the shop after 45 - 60 minutes to inform the tailor about the urgent

delivery requirement. In addition to our 8 standard treatments, we introduce one additional

variation to directly test to the impact of previous relationships. In this scenario, the shopper

places a stitching order for standard delivery. Once the transaction is completed and the order is

collected, the shopper returns for a second visit during which he places an order with in-between

urgency, without offering extra money.

Note that in all the above mentioned treatments the negotiation can also be terminated or

prolonged at any point by the tailor. There were some cases where the tailor terminated the order

before the shopper introduced the urgency. Generally, this occurred when the tailor didn’t have

the capacity or ability to stitch the item.

To monitor shopper performance and detect deviations from the script, one of the shopper’s

assigned visits is to a tailor who also acts as our representative (the shoppers were never

informed about this). Furthermore, in some of the other visits, our representatives visit the

tailoring unit at the same time as the shopper and observe the bargaining process. Directly after

each visit/renegotiation, the shopper is asked to fill out a detailed exit survey that asks about the

outcome of the negotiation. The shopper also goes back to the tailor to collect delivery at the

agreed-upon time and pays the outstanding part of the bill.

As an additional experimental set-up to understand customer behavior, we also conducted a

number of treatments in which we partnered with a tailor who assisted us in auditing actual

customer visits. For this process, we selected 4 tailors (2 male shops and 2 female shops) at 4

locations throughout Chennai. These locations were selected based on the following

characteristics: middle income neighborhoods, with a regular inflow of customers; physical

location of the shop was near a major transit point such as a bus stand or railway station; and the

standard delivery stitching charge was on par with the average rate charged by tailors across

Chennai.

In each shop, we instructed our auditor to act as one of the tailor’s employees. When a customer

entered the store, the auditor observed the interaction between the client and the tailor. For new

customers, the auditor observes the entire interaction to determine whether urgent delivery is

required. If so, the tailor executes one of the 3 treatments. For the first treatment, the tailor

initially refuses the urgent request but calls back the customer and agrees to do the urgent

delivery with extra money, which is approximately 10% of the standard delivery stitching

charge. In the second treatment, the tailor refuses the urgency and doesn’t call the customer

back. This is done to test whether the customer will respond to the rejection by offering to pay

extra.

Each day, we randomly assigned the order in which the tailor treats new customers with urgency.

Once the treatment is completed, we observe the delivery details for all new customers whose

urgency request was accepted by the tailor.

Methodology of Randomization

The randomization involves matching 44 shoppers – 22 female & 22 male shoppers with 221

tailor shops. Each shopper was assigned to visit around 25 tailors, with each tailor visited an

average of 5 times. In total, there were 8 standard treatments which were categorized by two

variations across three primary variables (type of urgency, customer nativity, and extra payment

offer). Among these standard treatments, the randomization imposed that half of the visits would

introduce upfront urgency while the remaining visits were conducted with in-between urgency.

We then randomly assigned variation across the remaining two variables to test how reputational

concerns and social norms regarding payment affected the transaction. Finally, we added a ninth

treatment group, where the shopper placed an urgent order following the completion of standard

delivery order to determine the impact of repeat business on contract negotiation.

To achieve the variation in visit characteristics while maintaining a similar script across

shoppers, the randomization was executed in the following way: The main dimensions of

variation are: (1) the shopper’s “nativity” is assigned as “within state/neighborhood” and “out of

state,” where the shopper would state that he or she came to Chennai from a nearby state to

attend an event. (2) The shopper’s stated “reason for urgency” is assigned from one of the

following: a relative’s marriage, engagement, other religious ceremony or sudden out of town

travel for official event. The goal was to select commonly used justifications for urgency, yet

maintain enough variation such that tailors aren’t facing repeated interactions with customers

with similar urgency needs, which may raise suspicion. (3) The shopper’s offer for getting

urgent delivery was either no money offered or an offer of double the standard delivery stitching

charge quoted.

The randomization also restricts the assignment of shoppers based on the location of the tailor,

which we refer to as a “location group.” Tailor shops located near to one another (so that tailors

can see who is visiting a neighboring tailor shops) are assigned the same location group number,

and shoppers are not assigned to other tailors in the same location group. The reason for the this

constraint is that it might create awkward interactions for the shoppers if a tailor who is

previously visited sees the same shopper go to a neighboring tailor.

In the second step, shoppers and wholesalers are randomly assigned to one another in a

constrained manner. One shopper and one tailor where randomly drawn from the pool of

available candidates and then checked to ensure that the shopper had not been previously

assigned to visit a different tailor in that same location group or had visited the same shop. The

result of this randomization is that the 44 shoppers were each assigned to visit 22-28 tailors, and

each tailor has a range of treatment types assigned.1

The third step in the randomization is to assign the treatments. Each of the 8 primary treatment

groups is assigned a letter from A through H and each shop is assigned a randomly generated

number. The shops are then sorted in ascending order according to this random number. For

the first visit, a shop is assigned one of the four upfront urgency treatments. The first shop is

assigned, upfront, no payment (“A”), the second upfront double payment (“C”), the third upfront,

no payment, out of state (“E”), and the fourth upfront, double payment, out of state (“G”). The

cycle then repeats itself beginning with the 5th shop on the list. The third visit is also an upfront

urgency visit, except this time the sequence of assignment is shifted down by one, meaning the

1 The randomization program then checks that the shopper has not been previously assigned to visit a different tailor in that same location group (to avoid the same shopper visiting neighboring tailor ), and that the tailor did not have a previously assigned visit by a shopper of that same treatment type.

first four shops would be assigned C, E, G, A, respectively. The fifth visit shifts the sequence

down yet another letter. A similar process is used to assign the in-between visits, which occur on

the 2nd, 4th and 6th visits. By assigning each visit to be either upfront or in-between urgency visit,

based on a random stratification at the level of the tailors, each shopper ends up with a similar

number of upfront and in-between urgency visits and a number of in-between visits, typically 15

-18 visits of each type.

After the initial allocation of the 6 visits, any additional visits were assigned, after checking to

make sure the shop had not already received a visit of the same treatment. Throughout this

process, all characteristics are assigned randomly, in either an unconstrained, constrained or

stratified manner. The only aspect of the randomization that it is not strictly randomly assigned is

the relative timing of the visits, although there is still a great deal of randomly induced variation

in this variable. For the most part, visits to different tailors by the same shopper are made in a

random order, based on the randomly assigned characteristics of the visits.

Data Description

In total, 44 auditors conducted 1085 visits to 221 different tailoring shops. The summary

statistics presented in Panel A of Table 1 show that the average number of visits per tailor was

4.91, with a standard deviation of 1.55. The average order price was 81.17. Similarly, the

average number of visits per auditor was 24.66 with a standard deviation of 1.67. In terms of the

initial stitching price for standard delivery, the average quote per tailor was 81.19, which is not

statistically different from the average quote reported per shopper of 81.71. Panel B reports the

results from the visits conducted when tailor acted as the auditor. In total, we worked with 4

tailors who received an average of 21.5 customers with urgent delivery requirements during the

course of the study. The average order price per tailor was 225 as some of the customers placed

orders of multiple items.

In Table 2 we report the detailed price structure of the bargaining outcomes by segmenting the

visits by the type of item stitched and treatment group. The results for women’s blouses are

shown in Panel A. The overall mean price for standard delivery was Rs. 59.64 and as an initial

check on the validity of the randomization process, we find no statistically significant differences

in the mean figure quoted across the various treatment groups. The extra urgency payment

figures show the amount beyond the standard delivery price that the tailor and shopper agreed

upon to complete the transaction with the urgent deadline. Two different specifications are

reported. The overall means also include observations where the urgency payment is zero (the

tailor accepted the urgency and did not ask for additional money). In order test the impact of the

different treatment variations, we first compare the urgency payment means by holding two of

the three dimensions constant (i.e. compare treatments where the type of urgency and shopper

locality are the same, but differ according to whether the shopper offers extra money). When

comparing the overall mean urgency payment across the type of urgency dimension (upfront vs.

in-between), there is no clear direction of impact. When looking at the locality dimension, the

overall mean urgency payment is generally higher for the out of state treatments as compared to

the local. On the final dimension, the shopper’s offer, the mean urgency payment for tailors

offered the double payment treatment is higher than the comparable no money treatment figures

in all cases.

Panel B examines the results for men’s shirts. The overall mean price for standard delivery was

Rs. 104.20. As with the women’s sample, there is no significant difference among the treatment

means. The extra urgency payment figures follow the same pattern as women’s sample as well.

There is no significant variation among the means along the type of urgency and shopper locality

dimensions. When examining the shopper offer dimension, the overall mean for the double

payment treatment is higher for two of the three comparison groups.

Results

Table 3 shows the results for acceptance rates across treatment groups among the shopper as

auditor subsample. The overall acceptance of the urgent among tailors varies from 42-65%.

When comparing comparable treatment groups across the type of urgency (upfront vs. in-

between) and shopper locality (local vs. out of state) dimensions, we don’t find any significant

difference in the acceptance rate. However, we do find the acceptance rate of tailors who are

offered double the initial stitching charge is higher, with the difference statistically significant at

the 10% level. In order to confirm that the shopper’s offer is indeed inducing the higher overall

acceptance rate, we examine the figures on a more granular level. As shown in Column (3), the

percent of tailors in the 8 primary treatment groups accepting the urgent delivery requirement

before money is offered do not significantly differ, with the acceptance rate hovering between

42-49%. Similarly, the percent of tailors who ask for extra money as soon as the urgency is

mentioned (and consequently before the customer has a chance to offer) is also not dissimilar

across treatment groups. As a result, the gap in acceptance rates induced by the double money

offer indicates a potential friction in the contracting process. Intuitively, all tailors have a

reservation wage at which they are willing to stitch an urgent order and should be indifferent as

to whether they or the customer presents an offer to reach that level. However, there appears to

be a clear preference on the part of the tailors to reject an urgent order rather than demand an

extra payment. When looking at the repeat customer treatment group, the acceptance rate among

tailors presented with in-between urgency, no money offered repeat customers is 16.4% higher

than that among tailors offered similar terms from first time customers. This difference is

significant at the 5% level. Interestingly, the overall acceptance rate for repeat customers is on

par with the double money treatments. A full set of pair-wise means comparisons of the overall

and before extra money offer acceptance rates are shown in Tables 4 and 5, respectively.

The final three columns of Table 3 look at differences in service outcomes across the treatment

groups. Interestingly, we find that large fraction of the tailors return the cloth when they refuse

an urgent order. While in the upfront case, this is not surprising, however in case of the in-

between treatment, the tailor has a choice to say that the cloth is cut and therefore cannot return

it. However, as seen from the results, very few tailors exercise this option. Overall, we find no

significant difference in the percent of tailors returning the cloth after rejecting the urgency

request. The same applies to quality rating of the finished product which was conducted by two

external tailors (one to assess the men’s items and one for the women’s) hired to provide ratings

on a 1 to 5 scale, with 5 indicating an order stitched the highest quality. Likewise, there was

little variation in the mean delay in delivery beyond the agreed upon time, which ranged from

0.15 to 0.35 hours, depending on the treatment group. As a result, it appears that the tailors do

not differentiate between customers once the initial contracting terms have been settled.

To further examine how the various treatment characteristics affect contracting outcomes we

look at two primary dimensions: (1) the acceptance rate of urgent orders and (2) the average

urgency price charged. In Table 6 we first examine whether there are significant differences in

the acceptance rate among tailors when controlling for the various characteristics imbedded in

each customer visit. Columns (1) to (4) of Table 6 regress a dummy variable for whether the

tailor accepts the urgent order on a set of dummies encompassing each of the variations

introduced among the different treatment groups. These dummies include variables for whether

the urgency was introduced on an in-between visit, whether an extra urgency payment was

offered, whether the customer was visiting from out state and whether the customer was placing

a repeat order. We find that whether a customer offers an extra urgency payment has a large

impact on tailor acceptance, with the coefficient varying from 0.10 to 0.13 depending on the

specification. This result is significant at the 1% level and the magnitude of this figure, which is

approximately one-quarter of the overall acceptance rate, provides support to our earlier

assertion that the bargaining process surrounding the urgency payment represents a friction in the

contracting process. If tailors are merely concerned about receiving additional compensation to

complete the urgent order, the tailors could simply ask for an urgency payment themselves in the

absence of a customer offer. However, this appears to not be the case.

In Column (4) we add an additional control variable that interacts the in-between treatment

dummy with the urgency payment dummy, but find that the coefficient on the interaction term is

insignificant. Finally, in Columns (5), (6) and (7), we include fixed effect controls for shop-

specific and shopper-specific effects, respectively. Although the impact on the treatment

coefficients is muted, the r-squared goes up significantly, from less than 0.02 to 0.47. This

indicates that a significant proportion of the variation in acceptance is systematically influenced

by factors specific to each tailor and auditor. Table 6b reports the regressions above excluding

repeat visits. We do not find any change in the results.

Table 7 looks at the impact of the treatment dimensions on the total payment made to the tailor

conditional on accepting an urgent order. We limit the sample to those treatments where urgent

orders were accepted. The reason for this change is that (1) we want to show that there is an

overall effect of our treatments and it is not the case that higher urgency payments are offset by

lower upfront prices (2) by focusing on the total price conditional on acceptance we can see how

payments change conditional on acceptance in the upfront and in between cases. The dependent

variable is the rupee amount of the totally payment (including the urgency payment) agreed upon

between the tailor and customer, while the independent variables are the same as in the

acceptance regressions shown in Table 6. Once we control for shop and shopper fixed effect the

coefficients on the in-between and out of state dummies are both insignificant (columns 5 to 7).

Table 7b reports the regressions excluding repeat visits. We do not find any change in the results.

Table 8 checks whether on average the tailors in the treatment versus control groups differ in the

amount of urgency payment they request. It is important to note that in the cases where the

tailors do not ask for more money (and therefore do not accept to change the order) we code the

non-response as a zero. The goal of this table is just to show that without taking into account the

selection of who offers to change the order, the average urgency payment in the in-between

versus upfront case and the out of state versus in-state cases do not differ. As results in column

(1) to (3) suggest we do not find any significant differences between the different groups.

Table 9 examines the bargaining structure on more detailed level by regressing a dummy

variable indicating whether the tailor asks for an urgency payment before the customer offers on

a host of control variables. In addition to the standard dummies covering the various treatment

dimensions, we also include a dummy variable for tailors who have a high initial markup as

defined earlier. When including controls for shop fixed effects, the coefficients on all of the

treatment dummies are insignificant. It is interesting to note that the observable characteristics

of the customer visit, including the timing of the urgency, the customer’s stated location and

whether the customer had previous placed an order also had no impact on inducing the tailor to

ask for an urgency fee.

While the results above suggest that there is ex-post break down of trade unless a client

voluntarily offers additional money for urgency, the important question that arises is whether this

leads to inefficiencies. If average clients in the market offer tailors additional money when they

have urgency then there are no inefficiencies that arise. To make sure that this is not the case we

conduct additional treatments where the tailor acted as the auditor.

Table 10 presents the results for the subsample when the tailor acted as the auditor. When a new

client visited the tailoring unit with urgency, in one treatment, the tailor initially refuses the

urgent request but calls the customer back and agrees to do the urgent delivery with extra money

(which is approximately 10% of the standard delivery stitching charge). Out of a total of 41 cases

we find that in 36 cases the customer willingly accepts to pay the extra charge and places the

order. In the second treatment, the tailor refuses the urgency and doesn’t call the customer back.

Interestingly, we find that none of the customers (43 cases) offers additional money to the tailor.

Conclusion:

This paper uses a novel audit study methodology to understand how contracting parties will

engage in ex-post efficient renegotiation in the case of (un) foreseen shocks. Are there are any

in-efficiencies that arise? Do we see break down of ex-post efficient trade? We send trained

auditors acting as customers to tailor stores and place an order to be picked up in several days.

The customer then returns the next day to ask for an urgent completion of the order within one

day, which gives the tailors an opportunity to renegotiate the contract. We find that overall

tailors do not use the increase in their bargaining power to ask for a higher price. In fact, tailors

refuse to fill the order. Instead in these cases they offer customers that they can take back the

material for the stitching back to find another tailor.

However, when offered a higher price the majority of tailors were happy to fill the urgent order.

This result suggests that without the customer offering the mark up tailors forego an efficient

renegotiation option and trade breaks down. We can also rule out that in this market the

equilibrium behavior of a buyer does not require that they voluntarily offer a premium for urgent

services. We find that average customers do not offer a premium and in fact there is breakdown

of trade. We conjecture that either norms or reputational concerns prevent tailors from

proactively suggesting a mark up for urgent delivery. These results put into questions the notion

that ex post renegotiation can easily be achieved and suggest that reputational concerns or norms

can lead to inefficiencies in contracting.

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Table 1: Sample Size and Randomization Table 1 reports summary statistics for the visits conducted during the experiment. Panel A presents the results from the first experiment where auditor operated as clients to the tailor shops. Average number of visits refers to the visits received per tailor and conducted per shopper. For each tailor, this figure is analogous to the number of treatments. The average order price per treatment is the mean initial price quoted for standard delivery. Panel B presents the summary statistics from the visits conducted where the tailor acted as the auditor.

Panel A: Auditor as Shopper

Group Obs.Avg no of

visitsMedian no of

visitsAverage price

per orderTailors 221 4.91 5 81.17

(1.56) (25.56)Shoppers 44 24.66 25 81.97

(1.70) (22.91)

Panel B: Auditor as Tailor

Group Obs.

Avg no of urgent

customers

Median no of urgent

customersAverage order price per tailor

Tailors 4 21.5 19 225.68(14.55) (129.85)

Table 2: Payment Descriptives Table 2 reports summary statistics on the price structure of the negotiations for visits conducted in experiment 1, where the auditor assumes the role of the buyer. Panel A reports the figures for a women’s blouse as the item to be stitched, segmented by treatment group. Standard Delivery Price refers to the initial price quoted by the tailor for non-urgent delivery. Overall Extra Urgency Payment is the amount beyond the standard delivery price agreed upon to complete the urgent delivery. Panel B reports the same results for the men’s shirt subsample. Standard Delivery Price is price the tailor would have charged for the requested stitching item. Extra Urgency Payment Requested is the amount beyond the standard delivery price requested by the tailor to complete the urgent order. Extra Urgency Price Accepted is the same figure but the sample is restricted to offers accepted by the customer. Panel A: Women's Blouse

TreatmentObs Mean Std. Dev. Mean Std. Dev.(1) (2) (3) (4) (5)

Upfront urgency, no money, local 76 58.4 10.9 1.9 6.7In-between urgency, no money, local 74 60.8 11.2 0.4 1.9Upfront urgency, double money, local 50 59.4 15.5 5.7 13.1In-between urgency, double money, local 50 60.4 19.1 8.9 16.9Upfront urgency, no money, out of state 75 59.7 11.3 2.6 8.4In-between urgency, no money, out of state 76 58.7 11.2 2.3 7.6Upfront urgency, double money, out of state 38 60.9 13.8 4.5 12.7In-between urgency, double, out of state 38 60.6 14.1 12.5 22.2In-between urgency, no money, repeat visit 61 59.3 12.9 2.1 7.0

Panel B: Men's Shirt

TreatmentObs Mean Std. Dev. Mean Std. Dev.

Upfront urgency, no money, local 77 102.5 20.6 1.7 8.6In-between urgency, no money, local 75 105.0 21.8 0.3 2.5Upfront urgency, double money, local 51 106.1 24.0 2.4 12.0In-between urgency, double money, local 50 104.2 20.1 4.2 14.8Upfront urgency, no money, out of state 73 103.8 19.6 0.1 1.2In-between urgency, no money, out of state 72 104.2 21.4 0.3 1.8Upfront urgency, double money, out of state 35 103.8 17.0 1.5 8.7In-between urgency, double, out of state 40 104.0 17.7 7.4 22.5In-between urgency, no money, repeat visit 74 104.6 22.3 1.4 5.8

Extra Urgency PaymentInitial Price

Extra Urgency PaymentInitial Price

Table 3: Acceptance Rates and Delivery Outcomes Table 3 reports acceptance rates and delivery outcome measures for the treatment groups. Accept Urgency is the percent of tailors who accept the urgent order (after all the treatment interventions have been conducted). Urgency Accepted Before Money is the percent of tailors who accept the urgent order before the shopper has the chance to offer an extra urgency payment. Urgency Accepted After Money is the percent of tailors accepting the urgent order only after the shopper offers an urgency payment, while Asked for Extra Money is the percent of tailors who preempt the shopper’s offer by asking for an extra urgency payment immediately. Percent Returning Cloth When Rejecting Urgency measures the percent of tailors who return the stitching material when rejecting the shopper’s urgency requirement under in-between treatments. Mean Quality Rating represent the mean rating assigned to the finished product as assessed by two external tailors on a scale of 1 (lowest) to 5 (highest). Mean Delay is the number of hours beyond the agreed upon urgent delivery time that the final product was ready to be collected.

TreatmentTotal Obs.

Accept Urgency

Urgency Accepted

Before Money

Urgency Accepted

After Money

Asked For Extra Money

% Returning Cloth When

Rejecting Urgency

Mean Quality Rating

Mean Delay

(Hours)Upfront urgency, no money 153 42% 42% -- 7% 3.38 0.15 In-between urgency, no money 149 44% 44% -- 2% 91% 3.48 0.34 Upfront urgency, double money 101 56% 44% 13% 5% 3.51 0.21 In-between urgency, double money 100 58% 43% 15% 6% 83% 3.24 0.23 Upfront urgency, no money, out of state 148 48% 48% -- 6% 3.51 0.25 In-between urgency, no money, out of state 148 49% 49% -- 5% 89% 3.54 0.24 Upfront urgency, double money, out of state 73 55% 47% 8% 3% 3.67 0.35 In-between urgency, double, out of state 78 64% 46% 18% 8% 100% 3.54 0.23 In-between urgency, no money, repeat visit 135 60% 60% -- 7% 83% 3.39 0.23

Table 4: T-Test of Acceptance of Urgency by Treatment Group Table 4 reports the pairwise differences in treatment means. The variable of interest in the acceptance of the urgent order. For each value reported, the difference is calculated as the treatent mean of the cateogry on the vertical axis minus the treatment mean of the category on the horizontial axis. Standard errors for all pairwise t-tests are reported in parentheses. The symbols ***,**,* indicate significance levels of 1%, 5% and 10%, respectively.

Treatment GroupUpfront, No $

In-bet., No $

Upfront, Double $

In-bet., Double $

Upfront, No $, OoS

In-bet, No $, OoS

Upfront, Double $ OoS

In-bet., Double $ OoS

In-bet., No $ 0.0179(0.31)

Upfront, Double $ 0.146* 0.128*(2.30) (2.00)

In-bet., Double $ 0.162* 0.144* 0.0156(2.54) (2.24) (0.22)

Upfront, No $, Out of State 0.0614 0.0435 -0.0846 -0.100(1.07) (0.75) (-1.31) (-1.55)

In-bet., No $, Out of State 0.0749 0.0570 -0.0711 -0.0868 0.0135(1.30) (0.98) (-1.10) (-1.34) (0.23)

Upfront, Double $, Out of State 0.130 0.112 -0.0164 -0.0321 0.0682 0.0547(1.83) (1.57) (-0.21) (-0.42) (0.95) (0.76)

In-bet., Double $, Out of State 0.223** 0.205** 0.0767 0.0610 0.161* 0.148* 0.0931(3.26) (2.97) (1.03) (0.82) (2.33) (2.13) (1.16)

In-bet., No $, Repeat Visit 0.182** 0.164** 0.164** 0.0200 0.120* 0.107 0.0521 -0.0410(3.12) (2.79) (2.79) (0.31) (2.03) (1.81) (0.72) (-0.59)

Treatment Group

 

Table 5: T-Test of Acceptance of Urgency Before Offer by Treatment Group Table 5 reports the pairwise difference in treatment means. The variable of interest in the acceptance of the urgent order before the shopper offer an extra urgency payment. For each value reported, the difference is calculated as the treatent mean of the cateogry on the vertical axis minus the treatment mean of the category on the horizontial axis. Standard errors for all pairwise t-tests are reported in parentheses. The symbols ***,**,* indicate significance levels of 1%, 5% and 10%, respectively.

Treatment GroupUpfront, No $

In-bet., No $

Upfront, Double $

In-bet., Double $

Upfront, No $, OoS

In-bet, No $, OoS

Upfront, Double $ OoS

In-bet., Double $ OoS

In-bet., No $ 0.0179(0.31)

Upfront, Double $ 0.0173 -0.000598(0.27) (-0.01)

In-bet., Double $ 0.0117 -0.00624 -0.00564(0.18) (-0.10) (-0.08)

Upfront, No $, Out of State 0.0614 0.0435 0.0441 0.0497(1.07) (0.75) (0.68) (0.77)

In-bet., No $, Out of State 0.0749 0.0570 0.0576 0.0632 0.0135(1.30) (0.98) (0.89) (0.98) (0.23)

Upfront, Double $, Out of State 0.0475 0.0295 0.0301 0.0358 -0.0140 -0.0275(0.67) (0.41) (0.39) (0.46) (-0.19) (-0.38)

In-bet., Double $, Out of State 0.0432 0.0253 0.0259 0.0315 -0.0182 -0.0317 -0.00421(0.63) (0.36) (0.34) (0.42) (-0.26) (-0.45) (-0.05)

In-bet., No $, Repeat Visit 0.182** 0.164** 0.164** 0.170** 0.120* 0.107 0.134 0.138(3.12) (2.79) (2.79) (2.61) (2.03) (1.81) (1.87) (1.97)

Treatment Group

 

Table 6: OLS Regressions of Urgent Order Acceptance on Treatment Dummies Table 6 reports summary data from OLS regressions of acceptance of the urgent order on a group of dummies representing the variations in the treatment group. The dependent variable is a dummy that takes a value of 1 if the tailor accepts the stitching order for urgent delivery and zero otherwise. In Bet. Treatment is a dummy that takes the value of one if shopper returned to the tailoring shop to introduce the urgency and zero if the shopper introduced the urgency upfront. Extra Payment is a dummy that takes the value of one if the shopper offers double the initial stitching charge to complete the urgent order and zero otherwise. Out of State is a dummy that takes the value of one if the shopper introduce himself or herself as visiting from out of the area and takes a value of zero if the shopper instead mentions that he or she recently moved to the area. Repeat Visit is a dummy that takes a value of one for visits conducted where the urgency was introduced only after the shopper first completed an order for standard delivery. Initial Delivery Days Stated is the number of days stated by the tailor to complete the order under standard delivery terms. Treatment * Extra is an interaction term between the In Between dummy and the Extra Payment dummy. The final two specifications listed in Columns 6 and 7 include fixed effect controls for 221 different tailoring shops and 44 different shoppers. R-squared and sample size are reported at the bottom of the table and standard errors for all coefficients are reported in parentheses. The symbols ***,**,* indicate significance levels of 1%, 5% and 10%, respectively.

(1) (2) (3) (4) (5) (6) (7)In Bet. Treatment Dummy 0.0570* 0.0279 0.0380 0.0158 0.0253 0.0237 0.0259

(0.0306) (0.0322) (0.0311) (0.0408) (0.0282) (0.0277) (0.0347)

Extra Payment Dummy 0.104*** 0.129*** 0.136*** 0.112** 0.102*** 0.105*** 0.107**(0.0322) (0.0333) (0.0319) (0.0475) (0.0315) (0.0311) (0.0425)

Out of State Dummy 0.0172 0.0461 0.0377 0.0460 0.0541* 0.0525* 0.0616*(0.0308) (0.0323) (0.0312) (0.0324) (0.0313) (0.0311) (0.0316)

Repeat Visit Dummy 0.152*** 0.154*** 0.158*** 0.122** 0.118** 0.125**(0.0522) (0.0494) (0.0537) (0.0501) (0.0478) (0.0492)

Initial Delivery Days Stated -0.0405*** -0.0256***(0.00376) (0.00478)

Treatment * Extra 0.0326 0.0102(0.0666) (0.0579)

Constant 0.442*** 0.420*** 0.698*** 0.426*** 0.430*** 0.311*** 0.500***(0.0292) (0.0302) (0.0392) (0.0327) (0.0266) (0.0638) (0.0840)

Shop Fixed Effects yes yes yesShopper Fixed Effects yes yes

Observations 1,085 1,085 1,048 1,085 1,085 1,085 1,048R-squared 0.012 0.020 0.127 0.020 0.409 0.467 0.487

Acceptance

Table 6B: OLS Regressions of Urgent Order Acceptance excluding the Repeat Treatment Table 6B reports summary data from OLS regressions of acceptance of the urgent order on a group of dummies representing the variations in the treatment groups. We restrict the sample to exclude the repeat visit treatment group. The dependent variable is a dummy that takes a value of 1 if the tailor accepts the stitching order for urgent delivery and zero otherwise. In Bet. Treatment is a dummy that takes the value of one if shopper returned to the tailoring shop to introduce the urgency and zero if the shopper introduced the urgency upfront. Extra Payment is a dummy that takes the value of one if the shopper offers double the initial stitching charge to complete the urgent order and zero otherwise. Out of State is a dummy that takes the value of one if the shopper introduce himself or herself as visiting from out of the area and takes a value of zero if the shopper instead mentions that he or she recently moved to the area. Initial Delivery Days Stated is the number of days stated by the tailor to complete the order under standard delivery terms. Treatment * Extra is an interaction term between the In Between dummy and the Extra Payment dummy. The final two specifications listed in Columns 6 and 7 include fixed effect controls for 221 different tailoring shops and 44 different shoppers. R-squared and sample size are reported at the bottom of the table and standard errors for all coefficients are reported in parentheses. The symbols ***,**,* indicate significance levels of 1%, 5% and 10%, respectively.

(1) (2) (3) (4) (5) (6)In Bet. Treatment Dummy 0.0279 0.0380 0.0158 0.0280 0.0260 0.0250

(0.0322) (0.0311) (0.0408) (0.0283) (0.0278) (0.0348)Extra Payment Dummy 0.129*** 0.136*** 0.112** 0.0931*** 0.0922*** 0.0943**

(0.0333) (0.0319) (0.0475) (0.0316) (0.0313) (0.0430)Out of State Dummy 0.0461 0.0377 0.0460 0.0532* 0.0533* 0.0633*

(0.0323) (0.0312) (0.0323) (0.0319) (0.0320) (0.0327)Initial Delivery Days Stated -0.0406*** -0.0254***

(0.00356) (0.00527)Treatment * Extra 0.0326 0.0132

(0.0666) (0.0581)Constant 0.420*** 0.698*** 0.426*** 0.430*** 0.460*** 0.482***

(0.0302) (0.0387) (0.0327) (0.0269) (0.0752) (0.0927)Shop Fixed Effects yes yes yesShopper Fixed Effects yes yes

Observations 950 919 950 950 950 919R-squared 0.018 0.118 0.018 0.442 0.494 0.512

Acceptance

Table 7: OLS Regressions of Total Amount Paid on Treatment Dummies Table 7 reports summary data from OLS regressions of the total amount paid to complete the urgent order on a group of dummies representing the variations in the treatment groups. The dependent variable is a dummy that takes a value of 1 if the tailor accepts the stitching order for urgent delivery and zero otherwise. In Bet. Treatment is a dummy that takes the value of one if shopper returned to the tailoring shop to introduce the urgency and zero if the shopper introduced the urgency upfront. Extra Payment is a dummy that takes the value of one if the shopper offers double the initial stitching charge to complete the urgent order and zero otherwise. Out of State is a dummy that takes the value of one if the shopper introduce himself or herself as visiting from out of the area and takes a value of zero if the shopper instead mentions that he or she recently moved to the area. Repeat Visit is a dummy that takes a value of one for visits conducted where the urgency was introduced only after the shopper first completed an order for standard delivery. Initial Delivery Days Stated is the number of days stated by the tailor to complete the order under standard delivery terms. Treatment * Extra is an interaction term between the In Between dummy and the Extra Payment dummy. The final two specifications listed in Columns 6 and 7 include fixed effect controls for 221 different tailoring shops and 44 different shoppers. R-squared and sample size are reported at the bottom of the table and standard errors for all coefficients are reported in parentheses. The symbols ***,**,* indicate significance levels of 1%, 5% and 10%, respectively.

(1) (2) (3) (4) (5) (6) (7)In Bet. Treatment Dummy 5.157* 5.177* 5.158* 4.895 1.645 2.854 2.535

(2.868) (3.058) (3.061) (4.125) (2.124) (2.258) (2.886)Extra Payment Dummy 10.64*** 10.62*** 10.69*** 10.27** 8.585*** 8.847*** 9.051***

(2.937) (3.087) (3.093) (4.231) (2.164) (2.170) (2.958)Out of State Dummy -0.351 -0.372 -0.356 -0.380 1.316 1.329 1.742

(2.866) (3.082) (3.085) (3.082) (2.362) (2.312) (2.331)Repeat Visit Dummy -0.0953 0.0870 0.0378 0.761 0.539 1.584

(5.069) (5.072) (5.275) (3.487) (3.881) (3.911)Initial Delivery Days Stated 0.243 1.315**

(0.630) (0.654)Treatment * Extra 0.667 -0.0203

(6.120) (4.145)Constant 78.17*** 78.19*** 76.81*** 78.34*** 80.18*** 79.17*** 71.79***

(2.810) (2.967) (4.565) (3.245) (2.030) (7.751) (8.573)Shop Fixed Effects yes yes yesShopper Fixed Effects yes yes

Observations 573 573 572 573 573 573 572R-squared 0.025 0.025 0.026 0.025 0.762 0.787 0.789

Total Amount Paid

Table 7B: OLS Regressions of Total Amount Paid Excluding the Repeat Treatment Table 7B reports summary data from OLS regressions of the total amount paid to complete the urgent order on a group of dummies representing the variations in the treatment groups. The dependent variable is a dummy that takes a value of 1 if the tailor accepts the stitching order for urgent delivery and zero otherwise. In Bet. Treatment is a dummy that takes the value of one if shopper returned to the tailoring shop to introduce the urgency and zero if the shopper introduced the urgency upfront. Extra Payment is a dummy that takes the value of one if the shopper offers double the initial stitching charge to complete the urgent order and zero otherwise. Out of State is a dummy that takes the value of one if the shopper introduce himself or herself as visiting from out of the area and takes a value of zero if the shopper instead mentions that he or she recently moved to the area. Initial Delivery Days Stated is the number of days stated by the tailor to complete the order under standard delivery terms. Treatment * Extra is an interaction term between the In Between dummy and the Extra Payment dummy. The final two specifications listed in Columns 6 and 7 include fixed effect controls for 221 different tailoring shops and 44 different shoppers. R-squared and sample size are reported at the bottom of the table and standard errors for all coefficients are reported in parentheses. The symbols ***,**,* indicate significance levels of 1%, 5% and 10%, respectively.

Total Amount Paid

(1) (2) (3) (4) (5) (6) In Bet. Treatment Dummy 5.177* 5.196* 0.667 1.851 3.154 1.027 (3.057) (3.059) (6.119) (2.154) (2.300) (4.053) Extra Payment Dummy 10.62*** 10.68*** 10.27** 7.758*** 7.812*** 7.411** (3.086) (3.093) (4.230) (2.247) (2.295) (3.027) Out of State Dummy -0.372 -0.336 -0.380 1.475 1.636 2.105 (3.081) (3.084) (3.081) (2.468) (2.465) (2.483) Initial Delivery Days Stated 0.106 1.065 (0.657) (0.706) Treatment * Extra 4.895 2.517 (4.125) (2.863) Constant 78.19*** 77.54*** 78.34*** 80.23*** 67.47*** 70.66*** (2.966) (4.682) (3.244) (2.076) (9.634) (9.233) Shop Fixed Effects yes yes yes Shopper Fixed Effects yes yes Observations 489 488 489 489 489 488 R-squared 0.030 0.030 0.030 0.780 0.809 0.811

Table 8: Payments for Urgent Delivery Table 8 reports summary data from OLS regressions of the amount of the extra urgency payment accepted on a group of dummies representing the variations in the treatment group. In Bet. Treatment is a dummy that takes the value of one if shopper returned to the tailoring shop to introduce the urgency and zero if the shopper introduced the urgency upfront. Out of State is a dummy that takes the value of one if the shopper introduce himself or herself as visiting from out of the area and takes a value of zero if the shopper instead mentions that he or she recently moved to the area. Repeat Visit is a dummy that takes a value of one for visits conducted where the urgency was introduced only after the shopper first completed an order for standard delivery. R-squared and sample size are reported at the bottom of the table and standard errors for all coefficients are reported in parentheses. The symbols ***,**,* indicate significance levels of 1%, 5% and 10%, respectively.

(1) (2) (3)In Bet. Treatment Dummy 1.028 0.974 0.914

(0.781) (0.776) (0.794)

Out of State Dummy 0.161 0.326 0.329(0.769) (0.711) (0.763)

Repeat Visit Dummy -1.641* -2.496** -2.552**(0.882) (1.139) (1.152)

Constant 2.335*** 2.406*** 3.272(0.601) (0.537) (2.253)

Shop Fixed Effects yes yesShopper Fixed Effects yes

Observations 930 930 930

R-squared 0.004 0.325 0.368

Urgency Payment

Table 9: OLS Regression of Tailors Asking for Urgency Payment Before Customer Offer Table 9 reports results from an OLS regression of whether the tailor asks for an extra payment before the customer offers on a group of dummies representing the variations in the treatment groups. In Bet. Treatment is a dummy that takes the value of one if shopper returned to the tailoring shop to introduce the urgency and zero if the shopper introduced the urgency upfront. Out of State is a dummy that takes the value of one if the shopper introduce himself or herself as visiting from out of the area and takes a value of zero if the shopper instead mentions that he or she recently moved to the area. Repeat Visit is a dummy that takes a value of one for visits conducted where the urgency was introduced only after the shopper first completed an order for standard delivery. Hi Mark Up is a dummy that takes a value of one if the initial price quoted for standard delivery is at least 30% higher than the average of all tailors segregated by stitching item. Fixed effect controls are included for 221 different tailoring shops. R-squared and sample size are reported at the bottom of the table and standard errors for all coefficients are reported in parentheses. The symbols ***,**,* indicate significance levels of 1%, 5% and 10%, respectively.

Ask For Extra Payment Before

Cust OffersIn Bet. Treatment Dummy -0.00561

(0.0158)Out of State Dummy 0.000801

(0.0175)Extra Payment Dummy -0.00639

(0.0161)Repeat Cust. Dummy 0.00864

(0.0270)Hi Mark Dummy -0.0147

(0.0420)Constant 0.0683***

(0.0160)Shop Fixed Effects yes

Observations 930R-squared 0.424

Table 10: Treatment as Tailors Table 10 presents the results from the visits conducted where the tailor acted as the auditor. Refuse, Then Ask for Extra Money refers to the treatment where the shopper initially refuses the customer’s urgent delivery requirement, then accepts. Refuse, Don’t Ask for Extra Money refers to the treatment where the tailors refuses the urgency request and does not counter with an additional money offer. Refuse, Then Accept Order for No Extra refers to the treatment where the tailor initially refuses the urgency, then accepts without asking for an extra urgency payment. Cust. Refuses is the number of observations where the customer rejects the extra urgency payment or doesn’t not counter offer. Cust. Agrees / Counters is the number of observations where the customer either accepts the tailor’s offer for the first treatment group or counters with an urgency payment offer for the second treatment group.

No. Obs

Treatment Total Obs. Cust. Leaves Cust. Agrees Refuse, Then Ask for Extra Money 41 5 36 Refuse, Don’t Ask for Extra Money 43 43 0