The Economics of Relational Contracts: Supporting Empirical Evidence Ricard Gil UC – Santa Cruz...

The Economics of Relational Contracts:Supporting Empirical Evidence

Ricard GilUC – Santa Cruz (visiting LSE Management)

[email protected]

European School of New Institutional Economics 2011

mailto:[email protected]

Outline

1. Introduction

2. Theoretical framework

1. Formal vs. informal contracts

2. Testable implications

3. Empirical challenges and evidence

1. When is a contract enforceable?

• When parties– Understand its terms– Can verify breach– Are able/willing to enforce

• Third party (law & courts) can verify and enforce contractual agreement

• May be costly to make a contract enforceable

Tradeoff b/w formal & informal contracts

• Formal contracts– Enforceable by institutional 3rd parties (courts,

arbitrators)• Strong anti-breach remedies• Costly to include ex-post adjustment • High costs of making contract enforceable

• Informal contracts– Enforceable by the parties and/or market

• Weaker anti-breach remedies• Less costly to include ex-post adjustment• Lower costs of making contract enforceable

2. An illustrative model

• P wants A to provide non-standard performance d– P=employer, A=employee, d=effort

• P has benefit πP(d) & outside option πP

• A has benefit πA(d) & outside option πA

• To make contract on d enforceable, P spends– f(d) if enforcer = court

– i(d) < f(d) if enforcer = P

2.1. Formal contract

• A feasible formal contract sets performance d & price p such that

– PCP: πP(d)-f(d)-p ≥ πP p ≤ πP(d)-f(d)-πP – PCA: πA(d)+p ≥ πA p ≥ πA-πA(d)

πA-πA(d) ≤ p ≤ πP(d)-f(d)-πP πP(d)+πA(d)-f(d) ≥ πP+πA

• The optimal contract dF maximizes the joint surplus πP(d)+πA(d)-f(d) s.t. πP(d)+πA(d)-f(d) ≥ πP+πA

2.2. Informal contract• A feasible informal contract sets performance d,

upfront payment w & bonus b such that– PCP: πP(d)-i(d)-w-b ≥ πP – PCA: πA(d)+w+b ≥ πA

– ICP: -b+(1/r)[πP(d)-i(d)-w-b] ≥ (1/r)πP

– ICA: πA(d)+b+(1/r)[πA(d)+w+b] ≥ πA(d*)+(1/r)πA

• Note: d* = argmax πA(d) = A’s opportunistic action

• Setting b & w so that ICA & PCA bind yields πP(d)+πA(d)-i(d) ≥ πP+πA+r[πA(d*)-πA(d)]

• The optimal contract dI maximizes πP(d)+πA(d)-i(d)

s.t. πP(d)+πA(d)-i(d) ≥ πP+πA+r[πA(d*)-πA(d)]

2.3. Formal & informal contracts may be friends

• Complements or substitutes?

• P & A will add to the informal contract “cheap” provisions that– Constrain A to provide standard performance d, or change A’s

payoff function, reducing her temptation to r[π(d**)-π(d)]– Create the quasi rent w+b when monetary payments are not

practical– These may be suboptimal in a purely formal contract

• Key understanding how the use of formal and/or informal contracting affects the value of fallback option

3. Formal vs. informal contracts

• Informal contract is optimal whenever

πP(dF)+πA(dF)-f(dF) < πP(d’)+πA(d’)-i(d’)

• Informal contract will be used when

πP(dF)-f(dF)-p < πP(d’)-i(d’)-w-b

– r low long-term relationship– i(d) low 2nd party enforcement cheap– f(d) high court enforcement costly

3.1. Informal contracts

•Self-enforcing contracts as long as

ICP: -b+(1/r)[πP(d)-i(d)-w-b] ≥ (1/r)πP

ICA: πA(d)+b+(1/r)[πA(d)+w+b] ≥ πA(d*)+(1/r)πA

•Empirical literature focuses on instances for which IC constraints may not bind

4. Methodological summary: How to assess informal contracts?

• Look for links b/w contract design, outcomes & long-term relationships– Variation in past and future interactions

• Look for relationships w/o a formal contract

4.1. Examples Through Evidence– Outsourcing:

• Corts & Singh JLEO ’04,

• Gil & Marion ’10 • Macchiavello & Morjaria ’11

– Social Networks:• Gil & Hartmann JLEO ’11

• Jackson & Schneider ’11

– Interplay of Formal & Informal Contracts • Gil ’11

Corts & Singh JLEO ’04

• Contracts b/w Oil & gas explorators and contracted drillers– Fixed price: strong incentives & strong holdup risks– Cost +: weak incentives & weak holdup risks

• Past interactions “cost +” contracts today• Effect greater for exploratory wells

– Note: in expl. wells costs predictable & effort important holdup (incentive) problems less (more) severe

• Interpretation– Informal contract on driller’s effort, not on fair bargaining– Cheap informal contracts (i ↓) or closer relationship (r ↓)

parties rely more on informal contract

[Formal vs. informal contracts]






The Role of Repeated Interactions, Self-Enforcing Agreements and Relational [Sub]Contracting:

Evidence from California Highway Procurement Auctions

Ricard Gil and Justin MarionUC - Santa Cruz

• How valuable are supplier relationships? Dependence on continuation value of relationship?

• Auctions for California Department of Transportation (Caltrans) highway construction projects– Universe of 5,120 contracts, May 1996 – December 2005– Winning and losing bids (26,125 total): 1,735 bidders and 2,900 subs

– All project characteristics and address of contractor and subcontractor– Follow prime contractor and subcontractor interactions over time

• Model: Unobservable quality, hold-up temptation– Relational contract lowers cost of employing a subcontractor

• Key contribution: estimate how these effects depend on continuation value of relationship

Measurement

• One way to measure relationship: use prior interactions between firms and suppliers (Corts and Singh, 2004)

• Continuation value? Past interactions may indicate future interactions

• Identification problems

– Prior relationships have value in relationship-specific productivity (Miller, 2008)

• Current cost correlated with measure of relational capital

– Suppose measure future supplier relationship

• Future interactions potentially depend on success of relationship

• Cannot separately identify continuation value from current costs

• Our measure of continuation value: Future Caltrans contracts in same geographic area

Arrival rate of projects exogenous to relationship-specific productivity

Institutional details

• Caltrans awards road construction and repair contracts through sealed-bid first-price auctions

• Potential bidders are notified through newsletter

• Bidders prequalified according to their characteristics and history

• Caltrans engineers provides a list of items required

• Each bid must include list of subcontractors– Each subcontractor included certified for her task– List if greater than $10K or (0.5% of total)– Constraints: Affirmative action, specialty items, caps

• Ex-post changes not unusual (Bajari, Houghton and Tadelis (2007)) due to unforeseen contingencies

• Project work flow important

Predictions

• A contractor will bid lower for a given project when outsourcing to a given subcontractor the lower the coordination costs and the larger the value of future interactions with this subcontractor

• Not here …– A contractor will be more likely to participate in an auction for a given project the

lower the coordination costs and the larger the value of future interactions with her potential subcontractors in the area

– A contractor will be more likely to choose a subcontractor for a given project the lower the coordination costs and the larger the value of future interactions with that subcontractor

• How to test these in the data?– Past interactions proxy for coordination costs– Use contracts in a district within one year in the future to proxy for

continuation value

Specification

• Regress project outcome bid on stock of past interactions Rik and consider interaction with future opportunities Oik in project district

Yik = β0 + β1log(1+Rik)+ β2log(1+Rik)log(1+Oik) + β3log(1+Oik) + +BXik + ρi + uik

• Define stock of relationships Rik for firm i in project k

– All past relationships with listed subcontractors

• Define future opportunities:

– Number of contracts occurring in project district within next year

– Volume of contracts occurring in project district within next year

– Consider those left after current project completion

• Past subcontractor interactions on winning bids correlated with experience, backlog → introduce these variables as covariates

Interpretation of Results

• Stock of relationships are negatively correlated with posted bids

• Posted bids are negatively correlated with value of future contracts

• Interaction of stock of relationships and value of future of contracts are negatively correlated with posted bids

– Direct correlation between posted bids and stock of relationships becomes statistically insignificant

• Stock of relationship matters when future business are at stake

• An increase in one std dev in stock of relationships (+1.228)

– - 0.89% with one std dev below median of future contract volume

– + 1.9% with one std dev above median of future contract volume

– Average winning bid of 3.17 m … savings of between 28K and 60K

[Informal contracts]




With incomplete contracts parties rely on long term relationships to facilitate trade.

Theoretical literature has developed many different models – few empirical papers.

In the context of Kenya rose exports, this paper:1. computes value of the relationship,2. uses a shock to distinguish different models

The Value of Relationships:Evidence from a Supply Shock to Kenya Rose Exporters

Rocco Macchiavello & Ameet MorjariaWarwick University, Harvard Kennedy School

Exports of Roses from Kenya provide an ideal setting:

1. Transactions recorded in Customs data: all exports of flowers from Kenya 09/2004 - 08/2009

2. Use spot market price to compute future rents

3. Short-run shock: post-election violence in January 2008

4. Firm survey designed and conducted by authors through 2 hour face-to-face interviews in Kenya during summer 2008

5. Other sources to complement information

Data

Spot Market (Dutch Auctions) in the Netherlands- Firms have accounts and can export flowers anytime, - No contractual obligation, price determined through bidding,- Quality and Payments enforced by the Auctions.

Direct Relationships with foreign buyers- no formal contract enforcement available- sellers value stable orders and prices, buyers value reliability. Parties work out an informal “marketing plan” at the beginning of the season- save on transport and intermediation

Two Export Channels

1. Flowers are one of main exports from Kenya – the second largest exporter of flowers in the World

2. Industry counts about a hundred large, vertically integrated, exporting firms. All flowers are exported

3. Seasonal Industry

4. Two export channels: Dutch Auctions and Direct Relationships with Foreign Buyers:

- identical logistic & transport, but different incentives

Industry

.51

1.5

2W

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ly P

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s (R

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tive

to S

eas

ona

l Ave

rage

)

0 10 20 30 40 50Week of the Season, 2006-07

Direct Relationships (Estimated) Auctions

Seasonality in Prices

Outline

August 04 Jan / 24-30th / 08

Violence New Season

Future Rents [lower bound ] based on IC constraint

Test 1

Response to Shock

Test 2

Future Outcomes in the Relationship

Test 3

August 07 Dec 07 August 08 Dec 08

Pre-Violence History

Baseline Sample:

1. Following contested presidential elections at the end of 2007

2. Two spikes: - December 31st – January 4th - January 24-30th

3. The violence did not affect all of Kenya

The Ethnic Violence: Timing and Geography

The Geography of Ethnic Violence

The Ethnic Violence did not concern all parts of Kenya where flower firms are located

The violence caused exports to drop mainly because of missing workers (see Ksoll et al. (2010))

-6-4

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(Lo

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-10 -5 0 5 10Weeks from Beginning of Conflict

Conflict No ConflictDifference

The Economics of Ethnic Violence

Outline

August 04 Jan / 24-30th / 08

Violence New Season

Future Rents [lower bound ] based on IC constraint

Test 1



Baseline Sample:

Relationship Value: Different Samples

0.1

.2.3

.4D

ensi

ty

-2 -1 0 1 2 3Relationship Value, in Logs, Season 2006/07

Established Relationships Surviving until 2007/08New Relationships Surviving until 2007/08Relationships that did not Survive until 2007/08

Test 1: Relationship Value and Relationship Age

Outline

August 04 Jan / 24-30th / 08

Violence New Season

Response to Shock

Test 2



Baseline Sample:

Counterfactual: how many flower would have been sold in a relationship had the violence not occurred ?

There is a lot of regularity in shipments within relationships:

Shipments week t predict 85% of variation in shipments in week t + 1 ⇒ Firm’s Response in a Relationship:

R = Shipments During Violence / Predicted Shipments

Response to the Shock: Construction

Test 2: Response to Shock and Relationship Value

Dependent Variable: Response to Shock

[1] [2] [5]

0.066*** 0.077* -0.011

[0.023] [0.048] [0.040]

Firms Fixed Effects yes yes yesRelationship Controls yes yes yesBuyer Fixed Effects no yes noNumber of observations 94 94 95

Relationship Value

Conflict Region No Conflict

Region

Outline

August 04 Jan / 24-30th / 08

Violence New Season

Future Outcomes in the Relationship

Test 3



Baseline Sample:

Test 3: Relationship’s Survival and Response to Shock

Dependent Variable: Relationship Death

[1] [2] [3] [4]

0.057 0.041 0.213* 0.168*

[0.051] [0.061] [0.117] [0.094]

0.007 -0.007

[0.032] [0.032]

-0.130* -0.113*

[0.069] [0.059]

Relationship Controls yes yes yes yesFirm Controls no yes no yesBuyer Fixed Effects no yes no yesNumber of observations 189 189 189 189

Conflict Region

Conflict Region X Response to Shock

Reliability at Time of Violence

Test 1:Corr ( Relationship Value , History ) >> 0

Test 2: Corr ( Response to Shock , Relationship Value) >> 0

Test 3: Corr ( Future Outcomes , Response to Shock) >> 0

Firms value acquiring and maintaining a reputation for being reliable suppliers

Summary

On Social Networks

Agent

Principal1

Principal2

Principal3

T T+1 T+2 T+3 T+4 …

Airing Your Dirty Laundry:Vertical Integration, Reputational Capital and Social Networks

Ricard Gil and Wes HartmannUC – Santa Cruz, Stanford GSB

• Are firms in social networks more willing to outsource (i.e. less likely to vertically integrate)?

• What does the relationship between social networks and vertical integration suggest about the determinants of the boundaries of the firm?

Why Laundry Services and Koreatown?

• Each store makes 2 “make-or-buy decisions”:– Laundry and Dry Cleaning

• Over 80 cleaners within a 2 mile radius of many addresses in Los Angeles

• Useful institutional details for identifying the determinants of the boundaries of the firm

• 2000+ Korean cleaners in southern CA indicate the likely presence of a network

Framework Predictions for Relationship Between SN and VI

Positive Relationship • Reputations with workers

lower the costs of “managed coordination”

• Reputations with creditors lower the capital costs of integrating

Negative Relationship • Reputations with

suppliers lower the costs of “using the market”

• Poor access to formal credit in SN decreases ability to integrate

Definition of the Social Network

• Members:– Koreans in Koreatown

• Non-members:– Koreans elsewhere– Non-Koreans in Koreatown or elsewhere

• Communication and therefore reputation (within laundry industry and society members) should be enhanced in Koreatown

Data Description

• Data from laundry service stores. – 173 stores surveyed. – 138 stores answered.– 4 areas in LA:

• Korea Town (30+18)• China Town/Downtown (30).• Century City (30)• Santa Monica (30).

• Tract level demographic information.

Table 3. Integration Statistics by Social Network Status

Dry Cleaning Laundering

Korea Town

Korean 0.5000 0.3846(0.1000) (0.0973)

Non Korean 0.8462 0.8462(0.1042) (0.1042)

Elsewhere

Korean 0.7353 0.5882(0.0768) (0.0857)

Non Korean 0.7231 0.5231(0.0559) (0.0624)

Difference in Differences -0.3584 -0.5267(0.1728) (0.1776)

The table describes average integration incidence by service (dry cleaningor laundering), location (korea town or elsewhere) and langauge (koreanor non-korean). Standard Errors are in parenthesis.

Social Networks and Economic Performance

• If the social network is beneficial:

– The costs of using the market have been lowered relative to the costs of managing activities within the firm.

– This supports the notion that better outsourcing opportunities may help explain the positive relationship between social networks and vertical integration

– The role of access to credit in a social network is diminished in this developed country context

Do social networks reduce moral hazard?Evidence from the New York City taxi industry

Henry Schneider, Cornell UniversityKirabo Jackson, Northwestern University

Moral Hazard and Social Networks

• Examining NYC yellow taxi leasing market• About 2/3 of drivers lease their taxi (vehicle and

license), 1/3 of drivers own their taxis• Large moral hazard problem in this market

(Schneider 2010)• Lessee-drivers have 62% more accidents, 64% more

driving violations per mile versus owner-drivers• About half due to moral hazard, half due to selection

• In this paper, they examine whether social networks can limit this moral hazard• Using country of birth as measure of social network• Do drivers have better outcomes when they lease from an

owner from the same country (in-network) versus a different country (out-of-network)?

New York City Taxi-Leasing Market

• Typically two drivers per taxi – each drives one 12-hour shift per day

• Number of licenses has essentially been fixed since 1940s• About one-fourth of licenses have specification that owner-must-drive• Various kinds of leasing arrangements (12-hour shifts to 6-month

contracts)

• Here focus on drivers leasing long-term from owner-drivers (~20% of taxis)

• Lessees pay lease fee: capped by taxi regulators at $650/week and therefore face moral hazard

– Lessee-drivers keep all fare revenues and pay some operating costs (Gas, tolls, fines)

– Lessee-drivers also generate costs for owner-drivers (Mechanical failures, accident damage or fines for vehicle safety among others)

– Revenues and costs both increase in driver aggressiveness– Drivers keep full benefit but don’t bear full cost of aggressiveness

– Costs divided this way because costly to monitor driver behavior and contracting on outcomes is also hard

Approach and Possible Selection Issues

• Estimation strategy– All else equal, compare driving outcomes of drivers leasing

in-network vs. drivers leasing out-of-network– Ideally we would randomly assign drivers to in-network

versus out-of-network driving, then measure outcomes– Instead we use three identification strategies

• Within-driver analysis• Instrumental-variable analysis• Country-level analysis

• Selection issues– Owners might have better screening technology for in-

network drivers – in-network drivers are higher quality– Alternatively, owners know that in-network drivers exert

more effort – hence hire lower quality in-network drivers

The Data

• Proprietary data on all NYC yellow taxi drivers• From NYC Taxi and Limousine Commission – the government regulatory

agency• All variables for 2005, some for 2007

• Driving outcomes• Summonses – taxi regulations violations• Convictions – from NYPD, driving violations• Accidents – from NYPD, over $1000/damage or injuries

• Driver characteristics• Name, address, NYC taxi-driving experience, country of birth, English

language test results, driving test results, lease period, taxi owner

• Match to Census data using addresses• Census reports country of birth of all residents in Census tract• Measure network strength as fraction of residents in Census tract from

same country

Market Organized Around Country Networks – Suggesting Benefits

Empirical Approach

• Naive estimation with OLS where Yit is number of bad outcomes (summonses, violations, accidents)

– Since there may be selection into in-network driving based on unobserved driving ability ai, estimate of β is unlikely to represent true causal effect

• “Within-Driver” estimation – Between 2005 and 2007, 58 drivers switched network status– Measure change in outcomes when drivers switch from in-network to out-

of-network leasing (and vice versa)

– Identification assumption is that switching is uncorrelated with changes in unobserved driver ability

Yit in_networkit ln(experienceit ) (ai it )

Yi in_networki ln(experiencei ) i

Empirical Approach (II)

• Instrumental variable approach: instrument for in-network leasing with distance between driver and nearest owner from same country through the following 2SLS system of equations

– Instrument valid if distance to in-network owner is correlated with in-network leasing and uncorrelated with unobserved driver ability

• Country level identification strategy: Aggregate driving outcomes to country level

– Do countries with high rates of in-network leasing have better outcomes?

– Identification assumption is that cross-country differences in in-network driving are uncorrelated with cross-country differences in unobserved ability

in_networki 1 same_blocki 2 ln(experiencei ) 1i

Yi in_networki ln(experiencei ) ai 2i

Yct in_networkct ln(experiencet )c Yctowners ct

In-Network Drivers Have Fewer Summonses

• Dependent variable is number of summonses

• OLS results show that in-network drivers have fewer summons

• Within-driver, IV, country-level isolate causal effort effect• Results are robust to including other controls (e.g., age, education)• In-network effect becomes larger – indicates in-network drivers have lower

ability• Standard deviation of number of summonses is 0.72, so effects are large

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On the Interplay of Formal and Informal Contracts:Evidence from Movies

• How do formal and relational contracting interplay?

• I use unique data set from Spanish movie exhibitor – Ex-ante and ex-post contract terms for 416 movies in 26

theaters from Jan 2001 - Jun 2002 (18,592 obs)– Variation across movies (different informational characteristics),

theaters and weeks since release

• Borrow framework from BGM (2009), adapt model in BGM (1994) to movie industry institutional settings

• Back-of-the-envelope calculation of the effect of relational contracting on movie run length and revenues

Institutional Details

• Movie industry main players– No studios in this paper– Distributor (what movie to distribute)– Exhibitor (on-screen movie run length)

• Governance structure– No formal contract, all decided ex-post– Formal Contract, might be adjusted ex-post

• Revenue sharing contracts– Distributor share varies from 70% to 30%– Sharing terms vary across weeks and movies– Contracts are silent about movie run length (decided by exhibitor)

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Average Share Distributor per Week since Release

30

35

40

45

50

55

60

65

1 2 3 4 5 6 7 8 9 10 11 12Week Since Release

Dis

trib

uto

r S

ha

re

No US Release

US Revenue<$50m

$50m<USRevenue<$100m

US Revenue>$100m

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Ex-post Renegotiation• If no formal contract is used,

– Run length and revenue split determined ex-post for each week and after movie is pulled

• If formal contract is used,– Run length may or may not be extended beyond contract– Contracts are unilaterally terminated by the exhibitor (pull movie)– Distributor never asks for renegotiation (one-sided renegotiation)– Ex-post, exhibitors evaluate net revenues and compare them to costs– Exhibitor asks ex-post for renegotiation for each week independently

• Firms punish through market if opportunistic behavior

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Spy Game

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

1 2 3 4 5 6 7 8

Weeks Since Release

Pearl Harbor

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

1 2 3 4 5 6 7 8

Week Since Release

Ocean's Eleven

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

1 2 3 4 5 6 7 8

Week Since Release

What Women Want

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

1 2 3 4 5 6 7 8

Week Since Release

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Data Description/Stylized Facts

• Data from one Spanish exhibitor – Revenue-sharing “contracts” with 24 different distributors – 416 movies in 26 theaters from 1/2001 to 6/2002 (254 with

previous US run)– 18,592 theater/movie/copy/week obs– Estimated movie revenues by theater from Gil (2004)

• Stylized Facts– Run length decided by exhibitor

– 75% movie runs under formal contract

• 68% of these are renegotiated at least one week

• 21% of these are extended beyond specified

– One-sided renegotiation

– Renegotiation week-by-week and after movie run stopped

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Table 2. OLS Regressions on the Decision of Whether Using Formal Contracts

Dependent Variable: Any Formal Contract?

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

US Release -0.9332 -0.9271 -0.8958 -0.2099 -0.2478(0.2636)*** (0.2657)*** (0.2583)*** (0.0696)*** (0.2423)

Ln[1+US Box Office] 0.0618 0.0613 0.0592 0.0133 0.0168(0.0165)*** (0.0166)*** (0.0160)*** (0.0040)*** (0.0138)

Screens 0.0048 0.0050(0.0038) (0.0038)

Seats per Screen 0.0005 0.0005(0.0002)** (0.0002)**

No. Copy -0.0102(0.0284)

No. Movies 2003-04 0.0042(0.0014)***

Tickets/Movie 2003-04 0.0009(thousands) (0.0004)**

Constant 0.4767 0.4629 0.6327 0.7378 -0.0474(0.1584)*** (0.1441)*** (0.0999)*** (0.0208)*** (0.2784)

FE Movie Copy No Yes Yes Yes YesFE Theater No No Yes Yes YesFE Distributor No No No Yes NoCluster Se Distributor Yes Yes Yes No Yes

Observations 5204 5204 5204 5204 5204R-squared 0.06 0.06 0.08 0.54 0.26

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Value of Relational Contracting (I)

• Back-of-the-envelope calculation of the effect of informal contracting on run length and revenues– Build two different counterfactuals from existing data

• Building counterfactuals– C1:

• Formal contracts: Run length stops at renegotiation• Informal contracts: Run length stops after release

– C2: • Estimate average stopping rule based on revenues per

each theater-week pair (alternative formal contract specifies revenue threshold specific to theater and week)

• Run length stops after revenue goes below specified threshold

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Table 9b. Impact of Relational Contracting on Run Length and Revenues if Using Current Formal Contract

All runs (3,834 obs)Mean Std. Dev.

Counterfactual Run Length 2.04 1.65Observed Run Length 3.97 3.34Counterfactual Revenue 7238.90 7795.21Revenue 14073.31 15518.35Counterfactual Distr Revenue 3927.00 4087.16Distributor Revenue 6393.18 6787.48Counterfactual Exhib Revenue 3311.89 3784.25Exhibitor Revenue 7680.13 8934.82

Table 10. Impact of Relational Contracting on Run Length and Revenues for Alternative Formal Contract

All runs (3,834 obs)Mean Std. Dev.

Counterfactual Run Length 2.25 1.88Observed Run Length 3.97 3.34Revenue 14073.31 15518.35Counterfactual Revenue 7926.64 8162.81Distributor Revenue 6393.18 6787.48Counterfactual Distr Revenue 3882.29 4050.77Exhibitor Revenue 7680.13 8934.82Counterfactual Exhib Revenue 4044.35 4262.83

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Take-aways

• Formal, informal and relational contracts may be complements, not substitutes– Informal contracts do everything a formal contract cannot do– Formal contracts work as limits to reneging temptation– Relational contracts work as enforcement mechanism for the

informal agreement

• Using informal contracting doubles movie run length and box office revenues per run

The Economics of Relational Contracts: Supporting Empirical Evidence Ricard Gil UC – Santa Cruz...

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