Credence Goods Marco A. Schwarz 1 1 University of Innsbruck June 2, - PowerPoint PPT Presentation

Credence Goods Marco A. Schwarz 1 1 University of Innsbruck June 2, 4, and 5, 2020 These slides are partially based on slides by Rudi Kerschbamer. Thanks! 1/ 88

Outline Credence Goods: Introduction and Theory 1 Characteristics of Credence Goods Basic Model Relaxing Assumptions Credence Goods: Experiments 2 Commissions and Kickbacks 3 Literature 4 2/ 88

What are Credence Goods? Seminal paper (Darby and Karni, 1973): “an individual renting specialized knowledge can evaluate only the results and not the procedure” “Goods and services where an expert knows more about the quality a consumer needs than the consumer himself are called credence goods.” (Dulleck and Kerschbamer, 2006) Other definition (not here): The the attributes of the good remain unobservable even after consumption. Examples of credence goods: health sector, repairs, financial advice, cab rides in an unknown city, construction, ... 3/ 88

Introduction: Motivation Problems (in a vertically differentiated market): Overtreatment: Expert provides more expensive treatment than necessary. Undertreatment: Expert provides insufficient treatment. Overcharging: Expert charges more expensive treatment than provided. Inefficiencies in real-life markets: Healthcare: US: Up to 10% of the 3.3 trillion US$ of yearly health expenditures are estimated to be due to fraud (FBI, 2011). 28% of dentists’ recommendations involve overtreatment (Gottschalk et al., 2020). Fraud in repair services: Cars (Taylor, 1995; Schneider, 2012; Rasch and Waibel, 2018) Computers (Kerschbamer et al., 2016, 2019; Bindra et al., 2020) Phones (Hall et al., 2019) Taxi rides (Balafoutas et al., 2013, 2017) 4/ 88

Basic Model: Players and Actions (based on Dulleck and Kerschbamer, 2006) Customer(s) (“he”) knows he has problem and knows he has a major problem with probability h or a minor problem with probability 1 − h a minor problem can be successfully treated with a minor or a major treatment; a major problem requires a major treatment decides whether to commit to an expert recommendation (visit) and pay price charged (in)sufficient treatment gives net payoff v − p ( − p ) Expert(s) (“she”) p for major ( ¯ sets prices ¯ p and t ) and minor treatment ( ¯ t ) ¯ has costs of ¯ c and ¯ c for providing major and minor c < ¯ treatment performs (costless) diagnosis and charges for recommended treatment → profit = price-cost margin (no visit: zero profit) 5/ 88

Basic Model: Assumptions Customers and experts are homogeneous. Once the customer decided to visit an expert, he commits to be treated (and to pay) that expert and the expert commits to treat the customer and charge one of the posted prices. The diagnosis is costless (or at least observable and verifiable) and perfect. The customer does not know which treatment he received. The expert is not liable for providing an insufficient treatment. It is a one-time interaction. We will relax some of these assumptions later. 6/ 88

Basic Model: Two Versions of Payoffs Version 1 (customer gets v if the problem is successfully treated): customer gets ¯ no visit t t ¯ customer t v − ¯ c v − ¯ c 0 ¯ ¯ needs − ¯ v − ¯ t c c 0 Version 2 (customer suffers if the problem is not successfully treated): customer gets ¯ t t no visit ¯ customer t − ¯ c − ¯ c − ¯ l ¯ − ¯ − ¯ ¯ needs l − ¯ − ¯ t c c l 7/ 88

Basic Model: Game Tree Nature Major problem ( h ) Minor problem ( 1 − h ) Expert sets prices 1 − h h P P Customer 1 − h h No visit Visit Visit No visit Expert provides Expert provides � � � � 0 0 0 0 t t ¯ ¯ t t Expert Expert Expert Expert p p p p p ¯ p ¯ p ¯ p ¯ ¯ ¯ ¯ ¯ � − � � � v − � � � � � � � v − � � � � v − � v − ¯ p p − ¯ p p v − ¯ p p v − ¯ p p ¯ ¯ ¯ ¯ p − ¯ ¯ c p − ¯ c p − ¯ ¯ c p − ¯ c p − ¯ ¯ c p − ¯ c p − ¯ ¯ c p − ¯ c ¯ ¯ ¯ ¯ 8/ 88

Basic Model: Game Tree Efficient treatment and adequate charge Nature Major problem ( h ) Minor problem ( 1 − h ) Expert sets prices 1 − h h P P Customer 1 − h h No visit Visit Visit No visit Expert provides Expert provides � � � � 0 0 0 t t 0 ¯ ¯ t t Expert Expert Expert Expert p p p p p ¯ p ¯ p ¯ p ¯ ¯ ¯ ¯ ¯ � − � � � v − � � � � � � � v − � � � � v − � v − ¯ p p − ¯ p p v − ¯ p p v − ¯ p p ¯ ¯ ¯ ¯ p − ¯ ¯ c p − ¯ p − ¯ ¯ c p − ¯ ¯ c p − ¯ p − ¯ ¯ c c p − ¯ c c p − ¯ c ¯ ¯ ¯ ¯ 9/ 88

Basic Model: Game Tree Undertreatment Nature Major problem ( h ) Minor problem ( 1 − h ) Expert sets prices 1 − h h P P 1 − h h Customer No visit Visit Visit No visit Expert provides Expert provides � � � � 0 0 0 t t 0 ¯ ¯ t t Expert Expert Expert Expert p p p p p ¯ p ¯ p ¯ p ¯ ¯ ¯ ¯ ¯ � − � � � v − p � � � p � � � � v − p � � � � v − p � v − ¯ p − ¯ p v − ¯ p v − ¯ p ¯ ¯ ¯ ¯ p − ¯ ¯ ¯ p − ¯ ¯ ¯ c p − ¯ c p − ¯ c p − ¯ c c p − ¯ c p − ¯ c p − ¯ c ¯ ¯ ¯ ¯ 10/ 88

Basic Model: Game Tree Overtreatment Nature Major problem ( h ) Minor problem ( 1 − h ) Expert sets prices 1 − h h P P 1 − h h Customer No visit Visit Visit No visit Expert provides Expert provides � � � � 0 0 0 t t 0 ¯ ¯ t t Expert Expert Expert Expert p p p p p ¯ p ¯ p ¯ p ¯ ¯ ¯ ¯ ¯ � − � � � v − p � � � p � � � � v − p � � � � v − p � v − ¯ p − ¯ p v − ¯ p v − ¯ p ¯ ¯ ¯ ¯ p − ¯ ¯ ¯ p − ¯ ¯ ¯ c p − ¯ c p − ¯ c p − ¯ c c p − ¯ c p − ¯ c p − ¯ c ¯ ¯ ¯ ¯ 11/ 88

Basic Model: Game Tree Overcharging Nature Major problem ( h ) Minor problem ( 1 − h ) Expert sets prices 1 − h h P P Customer 1 − h h No visit Visit Visit No visit Expert provides Expert provides � � � � 0 0 0 t t 0 ¯ ¯ t t Expert Expert Expert Expert p p p p p ¯ p ¯ p ¯ p ¯ ¯ ¯ ¯ ¯ � − � � � v − � � � � � � � v − � � � � v − � v − ¯ p p − ¯ p p v − ¯ p p v − ¯ p p ¯ ¯ ¯ ¯ p − ¯ ¯ c p − ¯ p − ¯ ¯ c p − ¯ ¯ c p − ¯ p − ¯ ¯ c c p − ¯ c c p − ¯ c ¯ ¯ ¯ ¯ 12/ 88

Basic Model: Game Tree Equilibrium (depending on ¯ c ) Nature Major problem ( h ) Minor problem ( 1 − h ) Expert sets prices h 1 − h P P 1 − h h Customer No visit Visit Visit No visit Expert provides Expert provides � � � � 0 0 0 t t 0 ¯ ¯ t t Expert Expert Expert Expert p p p p p ¯ p ¯ p ¯ p ¯ ¯ ¯ ¯ ¯ � − � � � v − p � � � p � � � � v − p � � � � v − p � v − ¯ p − ¯ p v − ¯ p v − ¯ p ¯ ¯ ¯ ¯ p − ¯ ¯ c p − ¯ ¯ c p − ¯ ¯ c p − ¯ ¯ c p − ¯ c p − ¯ c p − ¯ c p − ¯ c ¯ ¯ ¯ ¯ 13/ 88

Basic Model: Liability and Verifiablility Liability The customer can observe and verify the outcome ex post (but cannot always tell which treatment was provided) and hold the expert accountable. Rules out undertreatment. If the treatment is not verifiable, overcharging dominates overtreatment (for the expert). Verifiability The customer can observe and verify the treatment ex post (but cannot always tell which treatment was necessary). Rules out overcharging. 14/ 88

Basic Model: Liability Nature Major problem ( h ) Minor problem ( 1 − h ) Expert sets prices 1 − h h P P Customer 1 − h h No visit Visit Visit No visit Expert provides Expert provides � � � � 0 0 0 0 t ¯ ¯ t t Expert Expert Expert p p p p ¯ p ¯ p ¯ ¯ ¯ ¯ � � � v − � � � � v − � � � � v − � v − ¯ p p v − ¯ p p v − ¯ p p ¯ ¯ ¯ p − ¯ ¯ c p − ¯ c p − ¯ ¯ c p − ¯ c p − ¯ ¯ c p − ¯ c ¯ ¯ ¯ 15/ 88

Basic Model: Verifiability Nature Major problem ( h ) Minor problem ( 1 − h ) Expert sets prices 1 − h h P P Customer 1 − h h No visit Visit Visit No visit Expert provides Expert provides � � � � 0 0 0 0 t t ¯ ¯ t t Expert Expert Expert Expert p p p ¯ p ¯ ¯ ¯ � − � � � � � � v − � v − ¯ p p v − ¯ p p ¯ ¯ p − ¯ ¯ c p − ¯ c p − ¯ ¯ c p − ¯ c ¯ ¯ 16/ 88

Basic Model: Liability and Verifiability Nature Major problem ( h ) Minor problem ( 1 − h ) Expert sets prices 1 − h h P P Customer 1 − h h No visit Visit Visit No visit Expert provides Expert provides � � � � 0 0 0 0 t ¯ ¯ t t Expert Expert Expert Expert p p ¯ p ¯ ¯ � � � � � v − � v − ¯ p v − ¯ p p ¯ p − ¯ ¯ c p − ¯ ¯ c p − ¯ c ¯ 17/ 88