Introduction
Industry and Data
Model
The Costs of Environmental Regulation in a
Concentrated Industry
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Stephen P. Ryan
Future Work
Detail Slides
MIT
Department of Economics
Research Motivation
Introduction
I
Question: How do we measure the costs of a regulation
in an oligopolistic industry?
I
Motivation: EPA is required to estimate costs of
regulation, usually relying on an engineering estimate of
compliance costs
I
I
Problem: This static analysis ignores the influence of
the regulation on entry and investment behavior, and its
subsequent dynamic effect on market power
Goal: measure the welfare effects of an environmental
regulation on a concentrated industry, accounting for
the dynamics of market structure
Research Motivation
Application
Strategy
Major Contributions
Preview Results
Literature Review
Industry and Data
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Research Motivation
Introduction
I
Question: How do we measure the costs of a regulation
in an oligopolistic industry?
I
Motivation: EPA is required to estimate costs of
regulation, usually relying on an engineering estimate of
compliance costs
I
I
Problem: This static analysis ignores the influence of
the regulation on entry and investment behavior, and its
subsequent dynamic effect on market power
Goal: measure the welfare effects of an environmental
regulation on a concentrated industry, accounting for
the dynamics of market structure
Research Motivation
Application
Strategy
Major Contributions
Preview Results
Literature Review
Industry and Data
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Research Motivation
Introduction
I
Question: How do we measure the costs of a regulation
in an oligopolistic industry?
I
Motivation: EPA is required to estimate costs of
regulation, usually relying on an engineering estimate of
compliance costs
I
I
Problem: This static analysis ignores the influence of
the regulation on entry and investment behavior, and its
subsequent dynamic effect on market power
Goal: measure the welfare effects of an environmental
regulation on a concentrated industry, accounting for
the dynamics of market structure
Research Motivation
Application
Strategy
Major Contributions
Preview Results
Literature Review
Industry and Data
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Research Motivation
Introduction
I
Question: How do we measure the costs of a regulation
in an oligopolistic industry?
I
Motivation: EPA is required to estimate costs of
regulation, usually relying on an engineering estimate of
compliance costs
I
I
Problem: This static analysis ignores the influence of
the regulation on entry and investment behavior, and its
subsequent dynamic effect on market power
Goal: measure the welfare effects of an environmental
regulation on a concentrated industry, accounting for
the dynamics of market structure
Research Motivation
Application
Strategy
Major Contributions
Preview Results
Literature Review
Industry and Data
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Application
Introduction
I
Industry: US Portland cement industry
I
I
I
I
I
I
Large fixed costs: $200M+ facilities
Long horizons: 100 year operational lifetimes
Large adjustment costs → lumpy investment
Geographically concentrated
Second largest industrial emitter of SO2 and CO2 , major
source of NOx and particulates
Regulation: 1990 Amendments to Clean Air Act
I
I
I
I
I
Major revision to principal environmental regulation
Requires permits for operation and instituted a new
class of emissions
New plants have additional fixed costs of entry
Surprise legislation
EPA took 12 years to promulgate parts of legislation
pertaining to variable costs
Research Motivation
Application
Strategy
Major Contributions
Preview Results
Literature Review
Industry and Data
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Application
Introduction
I
Industry: US Portland cement industry
I
I
I
I
I
I
Large fixed costs: $200M+ facilities
Long horizons: 100 year operational lifetimes
Large adjustment costs → lumpy investment
Geographically concentrated
Second largest industrial emitter of SO2 and CO2 , major
source of NOx and particulates
Regulation: 1990 Amendments to Clean Air Act
I
I
I
I
I
Major revision to principal environmental regulation
Requires permits for operation and instituted a new
class of emissions
New plants have additional fixed costs of entry
Surprise legislation
EPA took 12 years to promulgate parts of legislation
pertaining to variable costs
Research Motivation
Application
Strategy
Major Contributions
Preview Results
Literature Review
Industry and Data
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Research Strategy
Introduction
Three-pronged approach:
1. Exploiting natural experiment of surprise legislation,
estimate reduced form policy functions that let the data
tell what effects the Amendments had on investment,
entry, and exit behavior
2. Recover the cost structure of the industry before and
after the Amendments by mapping the observed policy
functions into an underlying dynamic model tailored to
Portland cement industry
3. Simulate counterfactual distributions of consumer and
producer surplus to evaluate welfare changes due to
Amendments
Research Motivation
Application
Strategy
Major Contributions
Preview Results
Literature Review
Industry and Data
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Research Strategy
Introduction
Three-pronged approach:
1. Exploiting natural experiment of surprise legislation,
estimate reduced form policy functions that let the data
tell what effects the Amendments had on investment,
entry, and exit behavior
2. Recover the cost structure of the industry before and
after the Amendments by mapping the observed policy
functions into an underlying dynamic model tailored to
Portland cement industry
3. Simulate counterfactual distributions of consumer and
producer surplus to evaluate welfare changes due to
Amendments
Research Motivation
Application
Strategy
Major Contributions
Preview Results
Literature Review
Industry and Data
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Research Strategy
Introduction
Three-pronged approach:
1. Exploiting natural experiment of surprise legislation,
estimate reduced form policy functions that let the data
tell what effects the Amendments had on investment,
entry, and exit behavior
2. Recover the cost structure of the industry before and
after the Amendments by mapping the observed policy
functions into an underlying dynamic model tailored to
Portland cement industry
3. Simulate counterfactual distributions of consumer and
producer surplus to evaluate welfare changes due to
Amendments
Research Motivation
Application
Strategy
Major Contributions
Preview Results
Literature Review
Industry and Data
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Major Contributions
Introduction
I
Extend the analysis of regulation in a concentrated
industry to a strategic, dynamic setting
I
Pose an underlying dynamic model that allows for
flexible investment choices, adjustment costs,
endogenous entry capacity
I
I
Estimate a dynamic model of oligopoly with lumpy
adjustment, recovering full cost structure of industry,
including distribution of sunk entry costs and fixed and
variable costs of investment
Provide a cost estimate of the Clean Air Act in this
industry
Research Motivation
Application
Strategy
Major Contributions
Preview Results
Literature Review
Industry and Data
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Major Contributions
Introduction
I
Extend the analysis of regulation in a concentrated
industry to a strategic, dynamic setting
I
Pose an underlying dynamic model that allows for
flexible investment choices, adjustment costs,
endogenous entry capacity
I
I
Estimate a dynamic model of oligopoly with lumpy
adjustment, recovering full cost structure of industry,
including distribution of sunk entry costs and fixed and
variable costs of investment
Provide a cost estimate of the Clean Air Act in this
industry
Research Motivation
Application
Strategy
Major Contributions
Preview Results
Literature Review
Industry and Data
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Major Contributions
Introduction
I
Extend the analysis of regulation in a concentrated
industry to a strategic, dynamic setting
I
Pose an underlying dynamic model that allows for
flexible investment choices, adjustment costs,
endogenous entry capacity
I
I
Estimate a dynamic model of oligopoly with lumpy
adjustment, recovering full cost structure of industry,
including distribution of sunk entry costs and fixed and
variable costs of investment
Provide a cost estimate of the Clean Air Act in this
industry
Research Motivation
Application
Strategy
Major Contributions
Preview Results
Literature Review
Industry and Data
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Major Contributions
Introduction
I
Extend the analysis of regulation in a concentrated
industry to a strategic, dynamic setting
I
Pose an underlying dynamic model that allows for
flexible investment choices, adjustment costs,
endogenous entry capacity
I
I
Estimate a dynamic model of oligopoly with lumpy
adjustment, recovering full cost structure of industry,
including distribution of sunk entry costs and fixed and
variable costs of investment
Provide a cost estimate of the Clean Air Act in this
industry
Research Motivation
Application
Strategy
Major Contributions
Preview Results
Literature Review
Industry and Data
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Preview of Results
Introduction
I
I
Amendments roughly doubled sunk costs of entry, to
$35M, increasing market power
Consumer welfare decreased 25% due to lower entry
and increased market power (≈ $1.2B)
I
Amendments led to higher investment by incumbents,
but lower overall market capacity
I
Static analysis would ignore the benefits of increased
market power on incumbent firms, obtaining welfare
effect of wrong sign
Research Motivation
Application
Strategy
Major Contributions
Preview Results
Literature Review
Industry and Data
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Preview of Results
Introduction
I
I
Amendments roughly doubled sunk costs of entry, to
$35M, increasing market power
Consumer welfare decreased 25% due to lower entry
and increased market power (≈ $1.2B)
I
Amendments led to higher investment by incumbents,
but lower overall market capacity
I
Static analysis would ignore the benefits of increased
market power on incumbent firms, obtaining welfare
effect of wrong sign
Research Motivation
Application
Strategy
Major Contributions
Preview Results
Literature Review
Industry and Data
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Preview of Results
Introduction
I
I
Amendments roughly doubled sunk costs of entry, to
$35M, increasing market power
Consumer welfare decreased 25% due to lower entry
and increased market power (≈ $1.2B)
I
Amendments led to higher investment by incumbents,
but lower overall market capacity
I
Static analysis would ignore the benefits of increased
market power on incumbent firms, obtaining welfare
effect of wrong sign
Research Motivation
Application
Strategy
Major Contributions
Preview Results
Literature Review
Industry and Data
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Preview of Results
Introduction
I
I
Amendments roughly doubled sunk costs of entry, to
$35M, increasing market power
Consumer welfare decreased 25% due to lower entry
and increased market power (≈ $1.2B)
I
Amendments led to higher investment by incumbents,
but lower overall market capacity
I
Static analysis would ignore the benefits of increased
market power on incumbent firms, obtaining welfare
effect of wrong sign
Research Motivation
Application
Strategy
Major Contributions
Preview Results
Literature Review
Industry and Data
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Related Literature
Introduction
I
Model: Ericson and Pakes (1995), Fershtman and Pakes
(2000), Gowrisankaran and Town (1997), Besanko and
Doraszelski (2004), Doraszelski and Satterthwaite
(2004)
Research Motivation
Application
Strategy
Major Contributions
Preview Results
Literature Review
Industry and Data
Model
Empirical Strategy
I
Econometric technique: Hotz and Miller (1993),
Aguirregabiria and Mira (2002), Pakes, Ostrovsky, and
Berry (2003), Pesendorfer and Schmidt-Dengler (2003),
Jofre-Benet and Pesendorfer (2003), Bajari, Benkard,
and Levin (2004)
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
I
Empirical application: Benkard (2004)
I
Empirical dynamic effects of regulation: Mansur (2003)
Ash Grove Cement Plant in Utah
Introduction
Industry and Data
Plant Photograph
Data Sources
Industry Statistics
Summary Statistics
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Data Sources
Introduction
Industry and Data
I
USGS Minerals Yearbook
I
I
I
I
I
I
Market-level data for prices and quantities
27 markets covering United States 1980-1999
Source of market definitions
517 observations
Instruments on energy prices, labor inputs from Dept.
Energy and County Business Patterns
Portland Cement Association Plant Information Survey
(PIS)
I
I
I
Every plant in United States 1980-1998
Kiln-level data on capacity and production
2233 observations
Plant Photograph
Data Sources
Industry Statistics
Summary Statistics
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Industry Trends
Introduction
Industry and Data
Year
Production
Imports
Consumption
Price
Plants
Capacity
Per Kiln
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
68,242
65,054
57,475
63,884
70,488
70,665
71,473
70,940
69,733
70,025
69,954
66,755
69,585
73,807
77,948
76,906
79,266
82,582
83,931
3,035
2,514
2,231
2,960
6,016
8,939
11,201
12,753
14,124
12,697
10,344
6,548
4,582
5,532
9,074
10,969
11,565
14,523
19,878
70,173
66,092
59,572
65,838
76,186
78,836
82,837
84,204
83,851
82,414
80,964
71,800
76,169
79,701
86,476
86,003
90,355
96,018
103,457
111.90
103.70
95.76
91.01
89.70
84.71
81.48
78.07
75.50
72.04
69.02
66.37
64.25
63.58
68.06
72.56
73.64
74.60
76.45
151
147
143
143
141
136
133
132
127
123
119
119
119
118
118
118
118
118
118
239
267
287
292
297
305
305
314
327
337
345
352
357
363
364
367
376
383
393
Plant Photograph
Data Sources
Industry Statistics
Summary Statistics
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Summary Statistics Plant Data
Introduction
Industry and Data
Variable
Demand Data
MARKETQ
PRICE
PLANTS
WAGE
COAL
ELECTRICITY
POPULATION
GAS
Production Data
QUANTITY
CAPACITY
Investment
INVESTMENT
Minimum
Mean
Maximum
Standard
Deviation
186
36.68
1
20.14
15.88
4.23
689,584
3.7
2,835.84
67.46
4.75
31.72
26.64
5.68
10,224,352
6.21
10,262
138.99
20
44.34
42.33
7.6
33,145,121
24.3
1,565.34
13.68
1.94
4.33
8.13
1.01
7,416,485
2.21
Plant Photograph
Data Sources
Industry Statistics
Summary Statistics
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
177
196
699
797
2348
2678
335
386
-728
2.19
1,140
77.60
Detail Slides
Model of Cement Industry
Introduction
Industry and Data
I
Fully dynamic model of oligopoly based on Ericson and
Pakes (1995)
I
Markets defined by state vector of firm capacities (s)
I
Focus on interactions in independent regional markets,
delineated by geography
I
Discrete time, infinite horizon
I
Long-lived incumbents and short-lived potential entrants
I
Firms maximize profits through production, investment,
entry, and exit
I
Equilibrium is Markov-perfect Nash equilibrium (MPNE)
Model
Product Market
Per-Period Payoffs
Entry and Exit
Value Functions
Equilibrium
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Product Market
Introduction
Industry and Data
I
Homogeneous goods market with CED
ln Q = α0 + α1 ln P
I
Firms choose quantities, have a privately-known shock
to marginal costs (i ), and face increasing costs once
they exceed a capacity bound:
Model
Product Market
Per-Period Payoffs
Entry and Exit
Value Functions
Equilibrium
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
ci (qi ) = qi (MC + i )
"
2 #
qi
qi
−1
≥ν
CAPCOST ·
−ν
si
si
Future Work
Detail Slides
Per-Period Payoff Functions
Introduction
Industry and Data
Investment Costs
Model
Firms have non-convex investment costs:
I (x) = 1(x > 0) ADJPOS + INVPOS · x + INVPOS2 · x 2
+ 1(x < 0) ADJNEG + INVNEG · x + INVNEG2 · x 2
Product Market
Per-Period Payoffs
Entry and Exit
Value Functions
Equilibrium
Empirical Strategy
Empirical Results
Counterfactual
Simulations
I
Firms make optimal investment decisions conditional on
state, s
I
Incur non-convex adjustment costs in investment, x
I
Deterministic investment: new capacity = old capacity
+ adjustment
Conclusion
Future Work
Detail Slides
Entry and Exit
Introduction
Industry and Data
I
Incumbent firms who exit obtain product market profits
and a scrap value, SCRAP
I
Firms make optimal exit decisions conditional on
strategies of competitors: Φ(s)
I
I
I
New entrants pay a privately-known setup fee, SUNKi ,
and investment costs, and become active next period
In equilibrium firms follow cutoff rules for entry:
Θ(s, SUNKi )
Uncertainty in configuration of new period derives from
private information in entry decision, mixed strategies
over exit and investment
Model
Product Market
Per-Period Payoffs
Entry and Exit
Value Functions
Equilibrium
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Value Functions
Introduction
Industry and Data
Model
I
Potential entrant:
Vie (s, SUNKi ) = max
−SUNKi − I (xie ) + βE (V (s 0 )|s)
e
xi
Product Market
Per-Period Payoffs
Entry and Exit
Value Functions
Equilibrium
Empirical Strategy
Empirical Results
I
Incumbent firm’s value depends on its exit choice:
Counterfactual
Simulations
Conclusion
Vi (s, i ) = max [πi (s) + Φ(s) · SCRAP
{xi ,Φ(s)}
+(1 − Φ(s)) I (xi ) + βE (V (s 0 )|s)
Future Work
Detail Slides
Equilibrium
Introduction
Industry and Data
Model
Definition
Markov perfect Nash equilibrium (MPNE) obtains when:
V (σi∗ |s, σ−i ) ≥ V (σi0 |s, σ−i )
Product Market
Per-Period Payoffs
Entry and Exit
Value Functions
Equilibrium
Empirical Strategy
Empirical Results
for optimal strategy σi∗ against any alternative σi0 .
Counterfactual
Simulations
Conclusion
I
I
In equilibrium, every firm follows policies which
maximize its expected discounted stream of revenues
given the strategies of its competitors
Equilibrium restriction is basis for empirical estimator
Future Work
Detail Slides
Linking the Model to the Data
Introduction
Industry and Data
I
I follow Bajari, Benkard, and Levin (2004) and estimate
the parameters of the model in two stages
I
First stage: Flexibly describe what firms will do at every
state vector
I
Second stage: Impose restrictions of MPNE to find
parameters that best explain why firms follow these
equilibrium policies
I
Intuition: Observed policies are result of firms solving a
dynamic programming problem, so I find parameters
which best rationalize these policies as optimal given
the underlying model
Model
Empirical Strategy
Estimator Intuition
Two Types
Production
Parameters
Entry and Exit
Policies
(S, s) Investment
Policy
Simulated Investment
Dynamic Estimator
Constructing the
Estimator
Sunk Entry Costs
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Two Types of Parameters
Introduction
Static Parameters
I
Demand curve
Industry and Data
Model
Empirical Strategy
I
Production costs
Dynamic Parameters
I
Fixed and variable costs of adjustment
I
Scrap value of exiting
I
Distribution of sunk entry costs
Estimator Intuition
Two Types
Production
Parameters
Entry and Exit
Policies
(S, s) Investment
Policy
Simulated Investment
Dynamic Estimator
Constructing the
Estimator
Sunk Entry Costs
Empirical Results
I
I
Model entry and exit policies as probits, as implied by
theoretical model
Characterize investment policy as (S, s) rule, a flexible
method of handling lumpy capacity adjustment
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Production Parameters
Introduction
Industry and Data
Estimating Equation (Production Parameters)
Model
Empirical Strategy
∂πi
= P(Q) + qi P 0 (Q) − MC + i
∂qi
qi
2 · CAPCOST qi
−1
>ν
−ν
= 0.
si
si
si
Estimator Intuition
Two Types
Production
Parameters
Entry and Exit
Policies
(S, s) Investment
Policy
Simulated Investment
Dynamic Estimator
Constructing the
Estimator
Sunk Entry Costs
Firm sets qi such that MR = MC
Empirical Results
I
Solve system of equations at market level in each period
Counterfactual
Simulations
I
Recover production parameters: baseline cost (MC),
capacity cost (CAPCOST), capacity binding level (ν)
I
Conclusion
Future Work
Detail Slides
Entry and Exit
Introduction
Industry and Data
Estimating Equation (Entry and Exit Policy Functions)
Pr (Entry |s) = Φ(γ0 + γ1 SUMCAP + γ2 AFTER1990)
Pr (Exit|s) = Φ(γ0 + γ1 SUMCAP + γ2 AFTER1990
+γ3 CAPACITY + γ4 )
I
I
Estimate entry and exit policies for all states using a
probit
Probits capture mixed strategy, cutoff rule equilibrium
behavior as implied by theoretical model
Model
Empirical Strategy
Estimator Intuition
Two Types
Production
Parameters
Entry and Exit
Policies
(S, s) Investment
Policy
Simulated Investment
Dynamic Estimator
Constructing the
Estimator
Sunk Entry Costs
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Lumpy Investment Policy
Introduction
Estimating Equation (Investment Policy)
Industry and Data
Model
Empirical Strategy
TARGETit = γ40 s1 (CAPACITYit )
+ γ50 s2 (SUMCAP−it ) + γ6 it + uitd
BANDit = TARGETit ± exp γ70 s3 (CAPACITYit )
+γ80 s4 (SUMCAP−it ) + γ9 it + uitb
Estimator Intuition
Two Types
Production
Parameters
Entry and Exit
Policies
(S, s) Investment
Policy
Simulated Investment
Dynamic Estimator
Constructing the
Estimator
Sunk Entry Costs
Empirical Results
Counterfactual
Simulations
I
I
Firms adjust to TARGET when CAPACITY falls outside
either the upper or lower BAND
si (x) are flexible functions of x
Conclusion
Future Work
Detail Slides
Simulated Path of (S, s) Rule
Introduction
Industry and Data
Model
Empirical Strategy
Estimator Intuition
Two Types
Production
Parameters
Entry and Exit
Policies
(S, s) Investment
Policy
Simulated Investment
Dynamic Estimator
Constructing the
Estimator
Sunk Entry Costs
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Dynamic Parameter Estimation
Introduction
Industry and Data
Model
I
Policy functions provide complete description of how a
firm will act for any state vector
I
Demand curve and production parameters tell how
much profit firm derives from product market at each
state
I
Sufficient information to simulate industry evolution
and assign revenues to each path
I
Intuition of dynamic estimator is that expected payoffs
from following observed policies must dominate payoffs
generated by alternative policies
Empirical Strategy
Estimator Intuition
Two Types
Production
Parameters
Entry and Exit
Policies
(S, s) Investment
Policy
Simulated Investment
Dynamic Estimator
Constructing the
Estimator
Sunk Entry Costs
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Linear Basis of Estimator
I
Can expand Vi (·) for incumbent as:
Vi (·) = ui (st ) + E βui (st+1 ) + β 2 ui (st+2 ) + . . .
Introduction
Industry and Data
Model
Empirical Strategy
I
Unknowns in per-period payoffs enter linearly:
ui (s) = πi (s) + Φ · SCRAP
+ 1(x > 0)(ADJPOS + INVPOS · x + INVPOS2 · x 2 )
+ 1(x < 0)(ADJNEG + INVNEG · x + INVNEG2 · x 2 )
= α · ζi (s 0 )
I
Estimator Intuition
Two Types
Production
Parameters
Entry and Exit
Policies
(S, s) Investment
Policy
Simulated Investment
Dynamic Estimator
Constructing the
Estimator
Sunk Entry Costs
Empirical Results
Counterfactual
Simulations
Therefore, rewrite Vi (·) as:
Conclusion
Future Work
"
E
∞
X
t=0
∞
X
β t α · ζi (s 0 ) = E
β t ζi (s 0 ) · α = Wi (s) · α
#
"
#
t=0
Detail Slides
Equilibrium Structure of Estimator
I
Introduction
MPNE requires:
Industry and Data
Wi (s; σi∗ , σ−i ) · α ≥ Wi (s; σi0 , σ−i ) · α
Model
Empirical Strategy
I
Alternative policies constructed by adding noise to
optimal policies
I
Draw nk different alternative policies, compute their
values, and find difference against optimal policy payoff:
gj (α) = Wj (s; σi∗ , σ−i ) − Wj (s; σi0 , σ−i ) · α
I
Estimator finds parameters that rationalize observed
policies as optimal by minimizing profitable deviations:
min
α
nk
1 X
1(gj (α) < 0)gj (α)2
nk
j=1
Estimator Intuition
Two Types
Production
Parameters
Entry and Exit
Policies
(S, s) Investment
Policy
Simulated Investment
Dynamic Estimator
Constructing the
Estimator
Sunk Entry Costs
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Distribution of Entry Costs
Introduction
Industry and Data
I
Entry probit describes when firm enters at a given state
I
Previous estimation sufficient to compute dollar value if
firm did enter
I
The only way a firm does not enter is if it gets a draw
larger than that value
I
Estimator matches probability of entry to probability
draw of sunk entry costs ≤ value of entering:
Pr(Entry|s) = Pr(SUNKi ≤ V e (s))
= G (V e (s); µ, σ 2 )
G (·) is the CDF of sunk entry costs
Model
Empirical Strategy
Estimator Intuition
Two Types
Production
Parameters
Entry and Exit
Policies
(S, s) Investment
Policy
Simulated Investment
Dynamic Estimator
Constructing the
Estimator
Sunk Entry Costs
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Demand Estimates
Introduction
Industry and Data
Table: log q = log p + Intercept + Region
Model
Empirical Strategy
Empirical Results
Variable
logp
Intercept
Coefficient
-1.640
14.900
(Std. Err.)
(0.303)
(1.251)
I
Instruments are cost-side shifters
I
Market fixed effects for heterogeneity in level of demand
I
First-stage F statistic = 30, fail to reject orthogonality
of instruments
I
Robust against specifications with market-specific time
trends, imports in border states
Demand Parameters
Production
Parameters
Implied Markups and
Profits
Entry and Exit
Policies
Investment and Exit
Parameters
Distribution of Entry
Costs
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Production Parameters
Introduction
Industry and Data
Model
Parameter
CAPCOST (×107 )
BINDING LEVEL (ν)
MARGINAL COST
CAPCOST DUMMY (×107 )
BINDING DUMMY
MC DUMMY
Mean
Median
95% Confidence
Interval
1.904
1.903
32.330
-1.379
0.0268
2.4107
1.482
1.900
30.929
-1.378
0.0522
3.247
[1.105, 3.782]
[1.806, 2.016]
[30.761, 37.296]
[-3.056, 0.642]
[-0.131, 0.180]
[-2.23, 4.36]
I
No statistically significant differences in production
costs
I
Binding capacity utilization level roughly 87%
Empirical Strategy
Empirical Results
Demand Parameters
Production
Parameters
Implied Markups and
Profits
Entry and Exit
Policies
Investment and Exit
Parameters
Distribution of Entry
Costs
Counterfactual
Simulations
Conclusion
Future Work
I
Very expensive to produce beyond this
Detail Slides
Implied Markups and Profits
Introduction
Industry and Data
Variable
Price
Revenues
Shock ()
Profit
Margin
Min
Mean
Max
Standard
Deviation
40
9,819
-3,995
-5,041
-0.39
62
41,938
90
13,327
0.31
102
147,071
8,017
60,470
0.60
13
19,389
485
9,372
0.15
Model
Empirical Strategy
Empirical Results
Demand Parameters
Production
Parameters
Implied Markups and
Profits
Entry and Exit
Policies
Investment and Exit
Parameters
Distribution of Entry
Costs
Counterfactual
Simulations
I
I
Implied prices, shocks, revenues, and profits for every
firm at estimated demand and production parameters
Margin is implied profit margin calculated as profits
divided by revenues
Conclusion
Future Work
Detail Slides
Entry and Exit Probit Results
Introduction
Industry and Data
Parameter
Coefficient
Standard
Error
Exit Policy
Constant
CAP
SUMCAP
Late Dummy
-1.306
−1.55 × 10−3
−4.60 × 10−5
4.50 × 10−5
-0.301
0.183
2.81 × 10−3
8.80 × 10−5
1.70 × 10−5
0.081
Entry Policy
Constant
SUMCAP
Late Dummy
-1.68
3.71 × 10−5
-0.491
0.210
3.60 × 10−5
0.242
Model
Empirical Strategy
Sample size for exit policy function = 2233; sample size for entry policy
function = 414.
Empirical Results
Demand Parameters
Production
Parameters
Implied Markups and
Profits
Entry and Exit
Policies
Investment and Exit
Parameters
Distribution of Entry
Costs
Counterfactual
Simulations
Conclusion
I
Both entry and exit less likely after Amendments
I
As operation costs have not changed, must reflect
investment or entry cost shifts
Future Work
Detail Slides
Investment and Exit Parameters
Introduction
Parameter
Early Period
ADJPOS
INVPOS
INVPOS2
ADJNEG
INVNEG
INVNEG2
SCRAP
Late Period
ADJPOS
INVPOS
INVPOS2
ADJNEG
INVNEG
INVNEG2
SCRAP
95% Confidence
Interval
Industry and Data
Median
Standard
Deviation
30,522
131
0.018
22,646
-1,115.78
35.06
84,016
146.72
1.98
0.001
597.99
114.17
4.01
456
[30,491, 30,963]
[125, 131]
[0.018, 0.021]
[21,754, 23,562]
[-1,279, -925]
[28.428, 40.742]
[82,640, 84,109]
Empirical Strategy
27,631
70.20
0.015
22,216
-1,553
55.18
54,801
30.32
0.75
1.2E-5
999
118.88
2.59
424
[27,562, 27,663]
[69.36, 71.67]
[0.015, 0.015]
[20,062, 22,996]
[-1,645, -1,291]
[49.38, 57.25]
[54,423, 55,749]
Model
Empirical Results
Demand Parameters
Production
Parameters
Implied Markups and
Profits
Entry and Exit
Policies
Investment and Exit
Parameters
Distribution of Entry
Costs
Counterfactual
Simulations
Conclusion
Future Work
I
Fixed adjustment costs are economically significant
I
Investment costs have decreased after Amendments
I
Implies there was a shift in entry costs
Detail Slides
Distribution of Sunk Costs of Entry
Introduction
Industry and Data
Model
Parameter
Mean
(000 $)
Standard
Deviation
95% Confidence
Interval
Before Amendments
After Amendments
120,976
162,470
11,603
7,728
[93,321, 132,865]
[145,133, 173,115]
I
34% increase in mean of sunk entry cost distribution
I
Mean sunk cost draw if entered in early period: $17M
I
Mean sunk cost draw if entered in later period: $35M
I
Firms recover their initial outlays within 20 years
Empirical Strategy
Empirical Results
Demand Parameters
Production
Parameters
Implied Markups and
Profits
Entry and Exit
Policies
Investment and Exit
Parameters
Distribution of Entry
Costs
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Policy Experiment
Introduction
Industry and Data
I
I
I compare the distribution of consumer surplus and
producer profits before and after Amendments
Exploit timing in regulation to identify Amendments
shifting sunk cost of entry
I
Benchmark against social planner’s solution
I
I find consumer surplus is lowered due to reduced entry
rates
I
Incumbent firms are better off under the new
regulations due to increased barriers to entry
I
Investment per firm higher under Amendments, total
market capacity lower
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Description
Simulation Results
Conclusion
Future Work
Detail Slides
Simulation Results
Introduction
Industry and Data
Post 1990
Amendments
Counterfactual
Social Planner
New Market
Producer profit
Consumer welfare
Average Capacity Active Firms
Average Market Capacity
Average Market Quantity
Average Market Price
Average Number Active Firms
56,588.09
308,895.53
2,035
5,855
5,158
67.35
3.25
89,478.53
337,020.61
1,950
5,969
5,299
66.92
3.38
-426,805.09
2,279,737.55
3,773
15,094
13,041
43.70
3.96
Incumbent Market
Producer profit
Consumer welfare
Profits of firm 1
Average Capacity Active Firms
Average Market Capacity
Average Market Quantity
Average Market Price
Average Number Active Firms
316,413.99
477,694.68
717,325.06
3,337
6,867
6,069
64.61
2.06
304,151.53
526,479.59
698,447.48
3,324
8,169
7,225
61.15
2.52
-181,123.89
1,656,169.41
304,738.31
4,487.47
13,409
11,794
47.44
2.98
I
Static estimate of costs during this period: $0
I
EPA estimates of future sunk investments ≈ $5-10M
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Description
Simulation Results
Conclusion
Future Work
Detail Slides
Simulation Results
Introduction
Industry and Data
Post 1990
Amendments
Counterfactual
Social Planner
New Market
Producer profit
Consumer welfare
Average Capacity Active Firms
Average Market Capacity
Average Market Quantity
Average Market Price
Average Number Active Firms
56,588.09
308,895.53
2,035
5,855
5,158
67.35
3.25
89,478.53
337,020.61
1,950
5,969
5,299
66.92
3.38
-426,805.09
2,279,737.55
3,773
15,094
13,041
43.70
3.96
Incumbent Market
Producer profit
Consumer welfare
Profits of firm 1
Average Capacity Active Firms
Average Market Capacity
Average Market Quantity
Average Market Price
Average Number Active Firms
316,413.99
477,694.68
717,325.06
3,337
6,867
6,069
64.61
2.06
304,151.53
526,479.59
698,447.48
3,324
8,169
7,225
61.15
2.52
-181,123.89
1,656,169.41
304,738.31
4,487.47
13,409
11,794
47.44
2.98
I
Static estimate of costs during this period: $0
I
EPA estimates of future sunk investments ≈ $5-10M
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Description
Simulation Results
Conclusion
Future Work
Detail Slides
Simulation Results
Introduction
Industry and Data
Post 1990
Amendments
Counterfactual
Social Planner
New Market
Producer profit
Consumer welfare
Average Capacity Active Firms
Average Market Capacity
Average Market Quantity
Average Market Price
Average Number Active Firms
56,588.09
308,895.53
2,035
5,855
5,158
67.35
3.25
89,478.53
337,020.61
1,950
5,969
5,299
66.92
3.38
-426,805.09
2,279,737.55
3,773
15,094
13,041
43.70
3.96
Incumbent Market
Producer profit
Consumer welfare
Profits of firm 1
Average Capacity Active Firms
Average Market Capacity
Average Market Quantity
Average Market Price
Average Number Active Firms
316,413.99
477,694.68
717,325.06
3,337
6,867
6,069
64.61
2.06
304,151.53
526,479.59
698,447.48
3,324
8,169
7,225
61.15
2.52
-181,123.89
1,656,169.41
304,738.31
4,487.47
13,409
11,794
47.44
2.98
I
Static estimate of costs during this period: $0
I
EPA estimates of future sunk investments ≈ $5-10M
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Description
Simulation Results
Conclusion
Future Work
Detail Slides
Conclusion
Introduction
Industry and Data
I
I
The 1990 Amendments to the Clear Air Act changed
the cost structure of the industry, primarily the sunk
cost of entry
Consumer surplus is decreased due to lower entry rates,
resulting in lower quantities and higher prices
I
Producer surplus is higher for incumbent firms
I
Industry has socially-inefficient low capacity
I
Static cost analysis misses almost all of the relevant
economic costs in this industry, underpredicting costs of
regulation and obtaining welfare estimates of wrong
sign on producers
Model
Empirical Strategy
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Future Work
Introduction
Industry and Data
Model
Empirical Strategy
I
I
I
Cost effectiveness: can EPA achieve same reductions in
output using different policy?
Efficiency: does the cost of the Amendments exceed the
benefits of cleaner air
Long-term goal: link model to pollution data to
examine strategic and distributional effects of a trading
permits market
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Future Work
Introduction
Industry and Data
Model
Empirical Strategy
I
I
I
Cost effectiveness: can EPA achieve same reductions in
output using different policy?
Efficiency: does the cost of the Amendments exceed the
benefits of cleaner air
Long-term goal: link model to pollution data to
examine strategic and distributional effects of a trading
permits market
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Future Work
Introduction
Industry and Data
Model
Empirical Strategy
I
I
I
Cost effectiveness: can EPA achieve same reductions in
output using different policy?
Efficiency: does the cost of the Amendments exceed the
benefits of cleaner air
Long-term goal: link model to pollution data to
examine strategic and distributional effects of a trading
permits market
Empirical Results
Counterfactual
Simulations
Conclusion
Future Work
Detail Slides
Entry and Exit Cross-Tabulations
Introduction
Industry and Data
Entry
0
1
Column Count
After
False
214
0.52
15
0.036
229
1990
True
181
0.44
4
0.0097
185
After
False
1,328
0.59
51
0.023
1,379
1990
True
848
0.38
6
0.0027
854
Row Count
395
Model
Empirical Strategy
19
Empirical Results
414
Counterfactual
Simulations
Conclusion
Exit
0
1
Column Count
Future Work
Row Count
2176
57
2,233
I
Upper number within cell is count
I
Lower number within cell is proportion to overall
Detail Slides
Entry and Exit