Measuring consumer switching costs in the television industry
by Oleksandr Shcherbakov
October 4, 2016
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Switching Costs
In many markets, consumers are reluctant to switch
Examples: Banking Telecommunication Electricity Paid-TV
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Consumers repeat purchases from the same provider
Some reasons: Provider consistently oers the best price-and-quality products Exit/termination fee (monetary costs) Installation fee (monetary costs) Too much hassle to switch (utility costs) Brand loyalty (utility costs) Switching costs: additional monetary and utility costs of switching to an
alternative provider
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Anecdotal evidence of switching costs
Survey results for paid-TV: Approx. 80% of satellite subscribers report high levels of satisfaction with their service (Nielsen Media Research survey, 1997). By contrast, a dramatically lower percentage (45%) of cable subscribers are satised. Yet, only 10% of cable TV consumers indicated that they are very likely to switch to satellite service (Chilton Research Services Survey, 1997).
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What are the eects of switching costs on competition?
Quantifying switching costs is important to answer the following questions:
1. Do markets become more competitive due to rms' incentives to invest in their customer base? 2. When do rms start extracting rents (harvesting) from locked-in customers?
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Approach
Step 1. Develop a
dynamic
empirical framework that identies and
estimates customer switching costs using
market-level data.
Identication requires separating the following eects from
aggregate statistics : i. Consumer heterogeneity in preferences ii. Switching costs
Step 2. Simulate counterfactual and compare dierences in the optimal policy of cable TV providers under static and dynamic monopoly and duopoly structures.
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Outline of Talk
1. Overview of Paid-TV Industry 2. Model (dynamic):
i. Flow Utility ii. Dynamic Programming iii. Purchase Probabilities (random logit) and Market Shares 3. Data 4. Identication 5. Estimation algorithm (nested loops) 6. Results
i. Switching Costs ii. Counterfactual simulations 7. Conclusion
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Paid-TV Industry
Before early-1990s, cable TV providers were
local monopolies.
Since then, a Direct Broadcast Satellite (DBS) service was launched and there is new entry into the market. Competition is unusual because cable TV providers set prices and quality locally, whereas DBS providers set them at the national level. Products are vertically dierentiated and more expensive bundles uniformly include all the channels from low quality bundles - used to construct scalar quality measure.
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Users face substantial switching costs
Customers face the following costs when switching providers: Up-front installation fees Equipment purchases Hassle costs (e.g. installation appointments, obtain landlord's permission to install satellite dish, etc.)
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Model
They propose a dynamic model of consumer behaviour in a market with switching costs. Denitions:
t
- time period (one year).
g ∈ {o, c, s}
- outside option (free over-the-air TV), cable and
satellite services
pgjt
- monthly subscription fee
qgjt
- quality of program content measured as a weighted average
total no. of channels (more weight given to more costly channels)
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Consumer's Flow Utility
Consumer's ow utility
Ui (ait , ait−1 ) =
from
service
g
is:
˜ q¯gt ) + ξgt +εigt −ηig · 1(ait−1 6= g ) + |δig (p¯gt , {z }
if ait = c, s,
otherwise
mean utility,δig
εiot
where ηig denote switching costs that are known to consumers ait ∈ {o, c, s} - consumer i 's choice of provider ξgt - unobserved quality shock to econometrician but observed by consumers
(similar to BLP)
εigt - i.i.d. random taste shocks for providers; follows EVT1 distr.
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Dynamic Programming
The Bellman equation for a consumer's dynamic maximisation problem is:
Vi (Ωt , ait−1 , εit ) =
max {Ui (ait , ait−1 ) + βE [Vi (Ωt+1 , ait , εit+1 )|Ωt , ait , εit ]}
ait ∈{o,c,s}
s.t. Ωt denote current service characteristics and any factors aecting future characteristics. It evolves according to a rst-order Markov process.
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Purchase Probabilities
In each year (one time period), each consumer type chooses either cable, satellite or the outside option. The conditional probability of choosing service
g
when they were previously with
Pr(ait = g , ait−1 = k) = Pr =
k
is:
−ηig · 1(g 6= k) + Vig + εigt ≥ −ηil · 1(l 6= k) + Vil + εilt , ∀l 6= g
exp(−ηig · 1(g 6= k) + Vig ) exp(Vi0 ) + exp(−ηic · 1(c 6= k) + Vic ) + exp(−ηis · 1(s 6= k) + Vis )
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Market Shares
Current period
predicted
market shares are given by:
sigt = Pr(ait = g , ait−1 = c) · sict−1 + Pr(ait = g , ait−1 = s) · sist−1 + Pr(ait = g , ait−1 = o) · (1 − sict−1 − sist−1 ) and
aggregate
market shares:
Z sgt =
sgt (p¯ct , q¯ct , ξct , p¯st , q¯st , ξst , sit−1 , ωi ) dGω,sict−1 ,sist−1 (ωi , sict−1 , sist−1 |θ) | {z } sigt
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Heterogeneity Moments
Predicted product-specic market shares for cable TV:
R
!
1 j = arg max {αip pgj 0 t + αiq qgj 0 t } sigt dGω (ωi , sit−1 |θ)
sj|g ,t =
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j 0 ∈Jgt
R
sigt dGω (ωi , sit−1 |θ)
Switching Costs
Data and Instruments
Cable TV:
Exhaustive information (e.g. market size, no. of subscribers, prices, channel lineups) from 1992-2006. Product-specic shares available at market level (a market,
n
is
dened as the no. of homes passed by a cable provider) Satellite TV
Data collected from internet Only total share of satellite rms are available and at a coarser level (typically spans a few markets)
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Instruments for Price and Quality
E [pigt , qigt |ξgt ] 6= 0.
Therefore, need to instrument price and quality.
IV 1. Average prices and quality of other cable systems that belong to the same multiple-system operator (MSO) IV 2. Bargaining power of MSO, proxied by the no. of homes passed and no. of subscribers of the parent company IV 3. MSO average capacity level
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Identication Strategy
Separating the eects of consumer heterogeneity (measured by random coecients) and switching cost parameters from market level data is tough. Therefore, he proposes the following strategy:
Identies
exogeneous shifters
switching cost parameters from
of the
previous period decisions (i.e. if switching costs do not exist, then exog. changes in previous period should not aect current period's decisions) E.g. cost-reducing innovations.
Identies
consumer heterogeneity from variation in observable product
characteristics
across
markets. Product-specic market shares provide
important information on the distribution of preferences.
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Estimation Strategy
The vector of parameter to estimate is:
(α ¯c , α ¯s , α ¯p , α ¯ q , σαc , σαs , σαp , σαq )
Estimation Algorithm: Inner Loop: Solves dynamic programming problem for
each
consumer type
and calculates aggregate market shares. Middle Loop: Solves for
ξct
and
ξst
that match observed market shares to
the ones predicted by the model. Outer Loop: Searches over the parameter vector for values that minimize
GMM objective function
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Solving for
ξct
and
ξst
Match model predictions for aggregate provider-specic shares to the ones observed in the data, i.e. solve the following system of equations:
( sct = sct (p¯ct , q¯ct , ξct , p¯st , q¯st , ξst |θ) sst = sst (p¯ct , q¯ct , ξct , p¯st , q¯st , ξst |θ)
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Moment Conditions
1.
E [ξ˜cnt |zcnt ] = E [ξ˜snt |zsnt ] = 0
2.
E [ucjt |It ] = 0 where
ucjt
(mean independence assumption)
is the measurement and approximation error between the
data and observed predictions of
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product-specic
market shares.
Switching Costs
Larger switching costs in satellite than cable services
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Switching costs have nontrivial eects on mkt eqlbm
DM vs DD - DM oers 51% lower mean utility than DD (competitive
eects due to satellite entry) DM vs SM - SM oers 89% lower utility (cable providers need to
lower prices to attract customers with high switching costs) SD vs DD - SD oers 40% lower utility (if switching costs do not
exist)
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When do rms start harvesting?
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Conclusions
1. Switching costs are signicant and constitute approx. half of the annual variable service costs for each service provider ($190 for cable; $240 for satellite). 2. Professional installation costs only amount to 20% of switching costs, with the remainder attributed to hassle costs. 3. From counterfactual simulations, they nd that due to switching costs, cable rms have incentives to invest in customer base, hence increasing utility.
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