Reputational Incentives for Restaurant Hygiene

Ginger Zhe JinUniversity of Maryland

Phillip LeslieStanford University

How does reputation work? Consumers do not know quality ex ante

Consumers learn and form beliefs (=reputation)

Consumer beliefs drive consumer choice in the next period

Reputation motivates sellers to provide high quality

Empirical studies of reputation Demand

• Borenstein & Zimmerman (1988)• Hubbard (2002)• Gompers and Lerner (1998)• Brickley, Coles and Linck (1999)

Price effect• Gorton (1996)• eBay reputation studies

We focus on supply-side effects• Does reputation cause firms to provide high quality? • No need to control for consumer prior belief

Restaurant hygiene

January 16, 1998, LA county restaurant inspectors start

issuing hygiene grade cards

A grade if score of 90 to 100

B grade if score of 80 to 89

C grade if score of 70 to 79

score below 70 actual score shown

Grade cards are prominently displayed in restaurant

windows

Score not shown on grade cards

Question In Jin & Leslie (2003) we show that grade cards

cause restaurants to improve hygiene=> before grade cards there is a shortage

of information to consumers

Before grade cards, about 25% of restaurants had A-grade hygiene

Why have good hygiene if consumers cannot really tell the difference?

Reputational incentives

Some restaurants are able to form reputations for good hygiene

Depends on underlying factors that affect consumer learning

chain affiliation

=> possible free-riding for franchisees

degree of repeat customers in local region

=> regional clustering in hygiene quality

Examples of repeated customers

Brickley & Dark 1987

restaurants close to free way exits have fewer repeated customers

may be a poor measure in LA county

Residential vs. tourist area

Snug Harbor: 93/ 92/ 90

Blue Rose Cafe: 59 / 82/ 72/ 74

Alternative explanations

Regional differences in willingness-to-pay

for hygiene quality

Exogenous restaurant heterogeneity

Manager preferences

Hygiene cost differences

Data and Identification

We observe more information than consumers do

All hygiene inspection outcomes in LA county from

July 1995 to Dec 1998 => hygiene quality

Restaurant name, location, chain affiliation and

owner identity => variations in consumer learning

Grade cards introduced in Jan 1998

Exogenous policy change

Grade cards eliminate informational differences

across restaurants

Basic evidence - chain affiliation

BeforeGC

AfterGC

All restaurants 76.77 89.62

Chains 82.5 92.76

Company-ownedchains

82.94 92.70

Franchised chains 81.84 92.87

Basic evidence - regional clustering

R square(Y=pre GC scores)

Restaurant fixed effects 0.62

City fixed effects 0.20

Zip fixed effects 0.27

Region clustering before GC

Regional clustering after GC

Santa Monica before GCUpper 1/3 Lower 1/3

Regressions for chain affiliation:

Before GC only: (kitchen sink)

Before and after GC:

Kitchen sink regression before GCDep. Var = Score Coeff.Belongs to a chain 2.5698 ***Franchised chain -0.6909 **Chain -# of units in LA 0.0073 ***Chain -% of units in LA 3.2685 **In Zagat 1.9635 **Zagat food score -0.0888 *% of retail employment in zip 2.1805 ***% of white collar employment in zip -0.0195 *% of recreation employment in zip -0.2559 ***% of hotel employment in zip 0.3468 ***% of other employment in zip -0.3285 ***In zips where >15% are chains 1.6512 ***In zips where <5% are chains -3.0807 ***In zips where >50% of chains are franchised -2.6339 ***IN zips where <25% of chains are franchised 1.4001 ***Per capita income by census tract 3.16e-5 ***% of Asian -8.9136 ***% of Hispanic -4.7332 ***% of age>=65 -28.4126 ***Average household size -3.9289 ***% of married 17.6367 ***OBS 82950R square 0.2010

Chain locations in Santa Monica

Full sample with restaurant FE

Dep. Var = Score Coeff.

Belongs to a chain Absorbed

Belongs to a chain* Grade Cards -4.0567 ***

Franchised chain -2.3162 ***

Franchised chain * Grade Cards 1.2332 **

OBS 127,111

# of restaurants 24,304

R2 0.6021

We control for restaurant fixed effects, restaurant characteristics, gradecards * restaurant characteristics, a full set of quarter dummies anddummies for grading regime change. Regional characteristics and fixedeffects are absorbed.

Due to cost differences?

Our solution

is the mean score after grade cards

Assume that two restaurants in the same city with same post-GC score, have the same hygiene cost function

See if the difference in their pre-GC score is related to chain affiliation

Regress sB on sA

Dep. Var = Score before GC Coeff. Coeff.

Belongs to a chain 5.3894 *** 3.8234 ***

Franchised chain -1.7100 *** -0.1636

Mean post GC score 0.4922 *** 0.4868 ***

OBS 77,255 77,255

R2 0.1546 0.2871

City fixed effects No Yes

Restaurant characteristics Yes Yes

Test of regional clustering

Regional effects before GC:

Regional effects after GC:

Before GC:

After GC:

Test 1: Absolute differences in regional effects

Assuming α3=0 implies

If rj=r, then

We reject equality with 99% confidence

Test 2: relative differences in regional effects

• Allowing for α3 ~=0

• If rj=r, then

• Estimate with and without restriction:

• We reject rj=r with 99% confidence

Extensions

The chain effect may be smaller in regions with a high degree of consumer learning pass absolute differences test for chains

fail relative differences test for chains

absolute regional effects change less for chains than for non-chains

Franchisee free-riding may be smaller in regions with a high degree of consumer learning Tests fail to support this hypothesis

Conclusion We analyze reputational incentives by testing supply-side

implications

The results indicate Chain affiliation is an effective source of reputational

incentives A small degree of franchisee free-riding Regional differences in the degree of consumer learning

impact hygiene quality for independent restaurants

Large impact of grade cards suggests low degree of consumer learning for most restaurants