The document evaluates wholesale and retail mergers in the pharmaceutical sector, emphasizing the differentiated nature of products provided by pharmacies and wholesalers. It discusses the limitations of traditional market share analyses for assessing market power and introduces a stylized model to predict the impact of mergers on pricing and quality at pharmacies. The findings underscore the importance of demand estimation and simulation in understanding consumer welfare effects associated with proposed mergers.

1. E VALUATING WHOLESALE AND RETAIL MERGERS IN
PHARMACEUTICALS
Farasat A.S. Bokhari
Franco Mariuzzo
ESRC Centre for Competition Policy
School of Economics
University of East Anglia
f.bokhari@uea.ac.uk
f.mariuzzo@uea.ac.uk
http://www.uea.ac.uk/economics
OECD’s 13th Global Forum on Competition
for “Competition Issues in the Distribution of Pharmaceuticals”
Paris, France
February 27-28, 2014

2. M OTIVATION
E VALUATING M ERGERS FOR D IFFERENTIATED P RODUCTS
Role of wholesalers and retailers (pharmacies)
Differentiated products
Demand estimation using retail level sales data
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3. M OTIVATION
E VALUATING M ERGERS FOR D IFFERENTIATED P RODUCTS
Role of wholesalers and retailers (pharmacies)
obtain drugs from manufacturers and pass downstream
add services to otherwise similar products
wholesalers: e.g. number and location of warehouses, differences in storage capacities,
delivery frequency to pharmacies
pharmacies: e.g. number of stores per market, location of stores, hours of operation,
queuing time, advice from trained pharmacist, electronic patient records, automatic refill
reminders
Differentiated products
Demand estimation using retail level sales data
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4. M OTIVATION
E VALUATING M ERGERS FOR D IFFERENTIATED P RODUCTS
Role of wholesalers and retailers (pharmacies)
obtain drugs from manufacturers and pass downstream
add services to otherwise similar products
wholesalers: e.g. number and location of warehouses, differences in storage capacities,
delivery frequency to pharmacies
pharmacies: e.g. number of stores per market, location of stores, hours of operation,
queuing time, advice from trained pharmacist, electronic patient records, automatic refill
reminders
Differentiated products
the same drug in two different pharmacies not the same
for non-homogenous products, analyzing pre-merger market shares using
concentration ratios, herfindahl index, etc. are not reliable tools for evaluating preor post-merger market power (price cost margins)
Demand estimation using retail level sales data
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5. M OTIVATION
E VALUATING M ERGERS FOR D IFFERENTIATED P RODUCTS
Role of wholesalers and retailers (pharmacies)
obtain drugs from manufacturers and pass downstream
add services to otherwise similar products
wholesalers: e.g. number and location of warehouses, differences in storage capacities,
delivery frequency to pharmacies
pharmacies: e.g. number of stores per market, location of stores, hours of operation,
queuing time, advice from trained pharmacist, electronic patient records, automatic refill
reminders
Differentiated products
the same drug in two different pharmacies not the same
for non-homogenous products, analyzing pre-merger market shares using
concentration ratios, herfindahl index, etc. are not reliable tools for evaluating preor post-merger market power (price cost margins)
Demand estimation using retail level sales data
provides pre-merger measures of market power
can be used to predict changes in prices and price-cost margins
evaluates changes in consumer welfare due to proposed mergers at the wholesale or
retail level
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6. A S TYLIZED M ODEL
A ND H ORIZONTAL M ERGER P REDICTIONS
Manufacturer sells the same drug to
multiple wholesalers at ex-manufacturer
price pm
Wholesalers allowed a maximum
mark-up over the ex-manufacturer
price, and decide level of discounts to
pharmacies (modeled as homogenous
service/product providers)
Pharmacies choose quantity to obtain
from wholesalers, set price and quality
(R, N) at their pharmacy
Patients choose which pharmacy to visit
based on differences in price, quality
and location of stores (pharmacies are
vertically and horizontally
differentiated)
Some predictions of the model ...
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7. A S TYLIZED M ODEL
A ND H ORIZONTAL M ERGER P REDICTIONS
Merger at Wholesale Level
Merger at Pharmacy Level
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8. A S TYLIZED M ODEL
A ND H ORIZONTAL M ERGER P REDICTIONS
Merger at Wholesale Level
Merger at Pharmacy Level
discounts to pharmacies decrease
pharmacy prices increase (unambiguously)
a one dollar decrease in discounts (typically)
implies a less than dollar increase in
pharmacy prices (pass-through rate less than
one)
when pass-through rate is less than one,
quality at pharmacies also decreases
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9. A S TYLIZED M ODEL
A ND H ORIZONTAL M ERGER P REDICTIONS
Merger at Wholesale Level
discounts to pharmacies decrease
pharmacy prices increase (unambiguously)
a one dollar decrease in discounts (typically)
implies a less than dollar increase in
pharmacy prices (pass-through rate less than
one)
when pass-through rate is less than one,
quality at pharmacies also decreases
Merger at Pharmacy Level
prices increase
quality decreases
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10. A S TYLIZED M ODEL
A ND H ORIZONTAL M ERGER P REDICTIONS
Merger at Wholesale Level
discounts to pharmacies decrease
pharmacy prices increase (unambiguously)
a one dollar decrease in discounts (typically)
implies a less than dollar increase in
pharmacy prices (pass-through rate less than
one)
when pass-through rate is less than one,
quality at pharmacies also decreases
Merger at Pharmacy Level
prices increase
quality decreases
How much the quantity and prices change at the pharmacy level is an empirical issue and
depends on, among other things, consumer demand for pharmacy services
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11. E MPIRICAL S TRATEGY
D EMAND E STIMATION AND M ERGER S IMULATIONS
Data – pharmacy sales data
Estimation – obtain demand parameters
Simulation – predict post-merger prices
Calculation – compute welfare effect
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12. E MPIRICAL S TRATEGY
D EMAND E STIMATION AND M ERGER S IMULATIONS
Data – pharmacy sales data
convert sales of individual drugs to sales of standard units (SU) (using defined daily
dosage of different drugs)
aggregate standard units (quantity and prices) to pharmacy-chain level (K number of
total chains) per market (national or sub-national level and time periods)
obtain observable characteristics of pharmacy-chains per market (e.g. number of
stores, trained pharmacists, average open hours, etc. per city)
Estimation – obtain demand parameters
Simulation – predict post-merger prices
Calculation – compute welfare effect
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13. E MPIRICAL S TRATEGY
D EMAND E STIMATION AND M ERGER S IMULATIONS
Data – pharmacy sales data
Estimation – obtain demand parameters
specify a demand system where demand for SUs from a given pharmacy chain (q) is
a function of own and competitor’s prices (R), quality (N)and other exogenous
demand shifters Z (e.g. demographic differences in cities or trends over time)
qk = Dk (Rk , R−k , Nk , N−k , Z, k ; θk )
standard demand models can be used (logit/nested-logit/random-coefficients-logit or
multi-stage budgeting with AIDS specifications)∗
Simulation – predict post-merger prices
Calculation – compute welfare effect
∗ See accompanying note DAF/COMP/GF(2014)4 for details.
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14. E MPIRICAL S TRATEGY
D EMAND E STIMATION AND M ERGER S IMULATIONS
Data – pharmacy sales data
Estimation – obtain demand parameters
Simulation – predict post-merger prices
use profit maximization conditions for each pharmacy chain to back out effective
marginal costs c for each chain
R=c− O·Ω
−1
q
and
N=
O · Ψ (R − c)
where Ω and Ψ are functions of estimated demand parameters, and O is the K × K joint 1/0 pharmacy
ownership matrix with ones in the leading diagonals and the off-diagonal terms are zero or one if two chains
are co-owned
simulations: change marginal cost from estimated value to higher values (10%,
25%, 50% etc. higher values) and use equations above to obtain predicted values of
pharmacy prices and quality (R and N) for simulated wholesale merger;
alternatively change values of ownership matrix to simulate pharmacy level merger∗
Calculation – compute welfare effect
∗ See accompanying note DAF/COMP/GF(2014)4 for details.
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15. E MPIRICAL S TRATEGY
D EMAND E STIMATION AND M ERGER S IMULATIONS
Data – pharmacy sales data
Estimation – obtain demand parameters
Simulation – predict post-merger prices
Calculation – compute welfare effect
given observed prices/quality pre-merger and predicted post-merger prices and
quality, compute welfare effects
what level of monetary compensation would leave a representative consumer as
well-off at new prices/qualities as she was at the pre-merger prices/qualities?
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16. E MPIRICAL S TRATEGY
D EMAND E STIMATION AND M ERGER S IMULATIONS
Data – pharmacy sales data
Estimation – obtain demand parameters
Simulation – predict post-merger prices
Calculation – compute welfare effect
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17. TAKE AWAY M ESSAGE
H ORIZONTAL M ERGERS IN P HARMA
The final product that reaches a consumer via different routes is highly differentiated due
to the nature of services attached to these products (e.g., frequency of delivery by
wholesalers or advice by pharmacist and physical location of outlets)
Analyses based on pre-merger market shares alone do not provide good measures of
market power (price-cost margins)
Sales data of individual drugs is typically available, and can be aggregated up to sales at
pharmacy-chain level
Standard demand estimation methods and merger simulations from the empirical IO
literature can be adapted to (i) infer price-cost margins at the pharmacy level, (ii) back-out
effective marginal costs for the pharmacies, and (iii) predict changes in retail level prices
and quality due to a proposed merger
These (observed and predicted values) can be used to obtain measures of changes in
consumer welfare – which can then be compared to changes in profits to assess the overall
effect of a proposed merger
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