Defending Against Environmental Insults:
Drugs, Emergencies, Deaths, and the NOx
Emissions Markets
Olivier Deschenes,
UCSB, IZA, and NBER
Michael Greenstone,
MIT and NBER
Joseph Shapiro
MIT
Motivation:
 Evaluations of environmental regulations often ignore
economic costs associated with defensive behavior
 ⇒People may take costly actions to defend against risk or
damage:
 Noise-rated windows and insulation in noisy areas; Burglar alarms
 AC / energy consumption as protection against extreme heat
 ** Medications to protect against from respiratory problems **
 ⇒Defensive expenditures may represent large share of
economic costs associated with damages
 ⇒Difficult to know since there are little comprehensive largescale data available on the value of defensive expenditures
This Paper:
 Study NOx (nitrogen oxide) Budget Trading Program (NBP)
– an emission market – to estimate the importance of
defensive behavior in measures of WTP
 Impact of emissions market on:





Emitted pollution
Ambient pollution
Medication purchases (>>100 million transactions)
Hospital visits
Mortality rates
 Focus on ozone pollution: major US issue
 Asthma affects 8% of population (10% of children), annual
costs ≈ $56 Billion (CDC)
NBP’s Aggregate Impact on NOx Emissions
[Emissions in Thousand Tons]
Note: Graph depicts fitted NOx residuals for Wednesday after partialling out day-of-week
indicators
Approach:
 Comprehensive collection of real-world data on unit-level
emissions, ambient pollution concentrations, medication
purchases, hospital visits, and mortality
 Prior evaluations of emission markets rely on ‘data’ predicted
by chemistry, engineering, and epidemiological models.
 Rely on variation across year, season, and geography in
the implementation of the emissions market
 Leads way to event-study graphs and DDD estimation
 IV estimation of “structural” effect of ozone on health
Outline of Talk
 1. Background on ozone and its effects on health
 2. NOx Budget Trading Program and abatement
technologies
 3. Theoretical framework
 4. Data
 5. Econometric approach
 6. Results
 7. Interpretation and conclusions
Ozone Formation is Complex:
 Ozone = NOx + VOC + Heat
 NOx=NO+NO2
 VOC=Volatile Organic Compounds
(benzene, formaldehyde, …)
Ozone: Mixed Health Effects
 1. Association between daily ozone concentrations and
daily mortality rates (Bell et al 2004; Schwartz et al. 2005)
 EPA benefit-costs analysis driven by this result
 2. Ozone not associated with infant mortality (Currie and
Neidell 2005)
 Controls for zip code fixed effects
 3. Ozone is associated with hospitalizations (Moretti and
Neidell 2011; Lleras-Muney 2010)
 Boats in Los Angeles port / Army relocations

4. Smog alerts reduce outdoor activity (Neidell 2009)
 Zoo and park attendance falls
Outline of Talk
 1. Background on ozone and its effects on health
 2. NOx Budget Trading Program and abatement
technologies
 3. Theoretical framework
 4. Data
 5. Econometric approach
 6. Results
 7. Interpretation and conclusions
Ozone Regulation Timeline:
 Clean Air Act and ozone National Ambient Air Quality
(nonattainment) Standards
 Violations lead to “non-attainment” designation
 Changes from 1971 – 2008
 Current standard: 0.075 ppm 8-hour daily maximum
 NOx cap-and-trade markets:
 1994-2011: RECLAIM (California)
 1999-2002: Ozone Transport Commission market (New England)
 2003-2008: NOx Budget Program (Eastern US)
 2009: CAIR (Eastern US) – covers NOx and SO2 – effectively
replaced NBP
 2010: EPA’s Transport Rule proposed, disputed
NOx Budget Trading Program
(as a solution to ozone problems)
 Many Eastern states argued that they were unfairly subject
to CAA Nonattainment Regulations for ozone due to
emissions from Midwestern states:
Arrows show
normal wind
patterns and
circles are location
and amount of
NOx emissions
NOx Budget Trading Program: Details
 EPA set caps for 19 states and gave option for commandand-control or cap-and-trade
 All opted for cap-and-trade: 8 states in 2003, 11 in 2004
 Operated May 1 – September 30, 2003-2008
 2500+ point sources (electricity generation & industrial
boilers)
 Source of NOx: Utilities 21%, Other Industries 11%;
Transportation 60%; Others 8%
NOx Budget Trading Program: States
Blue: NBP states
Red: Non-participating state
White: border states (excluded)
Rules of the NOx Budget Trading Program
 States chose how to allocate permits
 Grandfathering
 Heat input- based updating
 Output-based updating
 Firms can buy and sell permits
 Market price averaged $2080/ton of NOx
 Each Fall, firms give EPA one permit for each ton of NOx
emitted during the preceding summer
 Permits may be banked 1:1 across years
 30% of firms complied through “non-retrofit” methods
(permits, fuel switch, plant retirement)
How Firms Complied
Technology
Share of units
choosing this
Selective Catalytic Reduction (SCR): ammonia or
urea sprayed into flue gas then absorbed onto
honeycomb-like catalyst (vanadium, tungsten, …)
17
Selective Non-Catalytic Reduction (SNCR):
ammonia added to boiler
4
Combustion Modification (CM)
31
Low NOx Burners (LNB): decrease combustion
temperatures
18
Non-retrofit (permits, fuel switching, retirement)
30
NOx abatement in detail (SCR)
 Clean catalyst from Plant
Crist’s SCR in Florida.
 This plant is not in the
NOx market but has good
promo photos because it’s
a DOE “Clean Coal”
project

SCR catalyst after use.
Gunk is NOx+ammonia.
16
Outline of Talk
 1. Background on ozone and its effects on health
 2. NOx Budget Trading Program and abatement
technologies
 3. Theoretical framework
 4. Data
 5. Econometric approach
 6. Results
 7. Interpretation and conclusions
Model: Grossman (1972) - style model of health production
 Composite good X, leisure L, sickness S
 Sick days S = S(D) depends on dose D of pollution
ingested
 Dose of pollution ingested D depend on ambient pollution C
and defenses A, D = D(C,A)
 ⇒ Sick days depend on C and A:
S = S(C,A)
 Consumer problem:
max U(X,L,S) s.t. I + pW(T-L-S) ≥ X + pAA
Model: Willingness-to-pay
 Now derive marginal willingness to pay for improvements in air
quality (wC) by total differentiation of indirect utility function:
dS   ∂A *   ∂U / ∂S dS 

wC =  pW

−
 +  pA
∂C   λ
dC  
dC 

 Three terms, each valued in dollars:
 Lost time (e.g., hospital visits)
 Investment in defenses (e.g., medications)
 Disutility of sickness (e.g., deaths)
 ⇒ This paper can make progress on estimating all 3 terms
 Caveat: Pollution might enters utility function directly (e.g.,
aesthetics)
Outline of Talk
 1. Background on ozone and its effects on health
 2. NOx Budget Trading Program and abatement
technologies
 3. Theoretical framework
 4. Data
 5. Econometric approach
 6. Results
 7. Interpretation and conclusions
Data Collection: Records Linked by County-Day
4. Medication Data
1. Emission Pollution Data
Marketscan (Medstat) 2001-07
Unit-level daily totals (NOx,
SO2, CO2)
From employees/dependents at 18 large
firms (>22 Mil. person×season×year records)
2. Ambient Pollution Data
Transaction level data on each purchase,
at transacted price (>100 Mil. transactions)
Population-based daily
monitor readings (Ozone,
PM, etc)
For CAA enforcement
3. Weather Data
Population-based daily monitor
readings (Temperature, Rain,
Dewpoint)
5. Hospital Visit Data
Same Marketscan extract as medications
Follow MarketScan methods to extract
emergencies from inpatient & outpatient
events
6. Mortality Data
MCOD Files, 2001-2007
All deaths in US, reports age, county and
cause, and exact date
Accessed through BRDC
Outline of Talk
 1. Background on ozone and its effects on health
 2. NOx Budget Trading Program and abatement
technologies
 3. Theoretical framework
 4. Data
 5. Econometric approach
 6. Results
 7. Interpretation and conclusions
Econometric Approach:
 Unit of observation: county*season*year
 Use DDD model since it closely reflects market design:
 East/West, Pre/Post, Summer/Winter:
Ycst = γ 1 NBPc × SUMMERs × Postt +
γ 2 NBPc × SUMMERs + W β + µ ct + η st + ε cst
'
cst
 c – county; s – season (winter/summer); t – year
 Fixed Effects for: county by year; season by year
 Wcst are controls (eg, weather)
 ⇒ Graphical event-study figures play central role
 Grouped data reflecting different numbers of underlying
observations:
 Regression weights proportional to number of observations
 Weight differs by outcome: (e.g., persons v. pollution
readings)
 Inference: allow arbitrary correlation in errors within MSA
 Some MSAs participated without rest of state (eg,
Birmingham AL & St Louis)
 Paper also shows results clustered at county, and state
Outline of Talk
 1. Background on ozone and its effects on health
 2. NOx Budget Trading Program and abatement
technologies
 3. Theoretical framework
 4. Data
 5. Econometric approach
 6. Results
 7. Interpretation and conclusions
NBP Impact on Emissions
NOx
Effect / Mean
SO2
Effect / Mean
CO2
Effect / Mean
(2)
-0.333***
(0.079)
-0.373
(3)
-0.295***
(0.079)
-0.331
(4)
-0.308***
(0.078)
-0.345
-0.04
(0.040)
-0.015
0.028
(0.056)
0.011
-0.009
(0.052)
-0.003
-2.291
(7.138)
-0.005
18.648*
(10.303)
0.042
-4.725
(10.889)
-0.011
Summer*East
x
x
x
Summer-by-Year FE
x
x
x
Basic Weather Controls
x
Detailed Weather Controls
x
County-by-Year FE
x
x
x
Note: each observation represents a county-year-season. Covariance
matrix allows arbitrary autocorrelation within each MSA. Asterisks
denote p-value < 0.10 (*), <0.05 (**), <0.01 (***).
Note: Size effects are for counties participating in NBP
-8
Ozone 8-Hour Maximum (ppb)
-6
-4
-2
0
2
NBP Impact on Ambient Summer Ozone Pollution
2001
2002
2003
2004
NBP - Non-NBP
2005
2006
95% C.I.
2007
-5
-5
0
Summer Days in Indicated Bin
5
10
15
20
25
30
35
Market Impact: Summer Days in Indicated Bin
0
5
10
15
20
25
30
35
NBP Impact on Distribution of Ambient Summer
Ozone Pollution
0-10 10-20 20-30 30-40 40-50 50-60 60-70*70-80*80-90*90-100 100+
Summer Days in Each Ozone Bin,
Eastern US 2001-2002
0-10 10-20 20-30 30-40 40-50 50-60 60-70*70-80*80-90*90-100 100+
NBP Impact on Summer Days in Each Ozone Bin
NBP Impact on Ambient Summer Ozone Pollution
Ozone 8-Hour Value
Effect / Mean
Ozone Days ≥ 65
Effect / Mean
(2)
-1.334**
(0.597)
-0.027
(3)
-3.435***
(0.912)
-0.069
(4)
-3.273***
(0.740)
-0.066
-4.460**
(2.173)
-0.157
-7.208***
(2.053)
-0.253
-10.701***
(3.331)
-0.376
Summer*East
x
x
x
Summer-by-Year FE
x
x
x
Basic Weather Controls
x
Detailed Weather Controls
x
County-by-Year FE
x
x
x
Note: each observation represents a county-year-season. Regressions are GLS weighted by
number of underlying pollution readings. Mean is for 2001-2002 summers in Eastern US.
Covariance matrix allows arbitrary autocorrelation within each MSA. Asterisks denote pvalue < 0.10 (*), <0.05 (**), <0.01 (***).
Note: Size effects are for counties participating in NBP
NBP Impact on Other Ambient Pollution
 See Table 3
 Estimates for NO2, CO, PM2.5, PM10 (NOx can undergo
reactions to become PM), SO2
 ⇒ No significant effects of NBP on concentrations of
pollutants other than ozone
 ⇒ DDD estimates of health effects likely attributable to
ozone channel
 ⇒ Informative about validity of exclusion restriction (if NBP
used to generate IVs for ozone exposure)
Log Medication Costs
-.04 -.03 -.02 -.01 0
.01
.02
NBP Impact on Log Medication Costs
2001
2002
2003
2004
Diff-in-Diff
2005
95% C.I.
2006
2007
NBP Impact on Log Medication Costs
(2)
-0.018*
(0.010)
(3)
-0.018*
(0.010)
(4)
-0.016**
(0.008)
Respiratory
-0.032*
(0.017)
-0.037**
(0.017)
-0.025*
(0.014)
Cardiovascular
-0.015
(0.010)
-0.015
(0.010)
-0.016**
(0.007)
Gastrointestinal
-0.009
(0.010)
-0.009
(0.010)
-0.011
(0.010)
All Medications
Summer*East
x
x
x
Summer-by-Year FE
x
x
x
Basic Weather Controls
x
Detailed Weather Controls
x
County-by-Year FE
x
x
x
Note: each observation represents a county-year-season. Regressions are GLS
with weight equal to MarketScan population in a given county-year-season.
Covariance matrix allows arbitrary autocorrelation within MSAs. Asterisks denote
p-value < 0.10 (*), <0.05 (**), <0.01 (***).
NBP Impact on Hospital Visit Costs
(2)
-0.688
(17.890)
(3)
-3.082
(17.990)
(4)
-6.611
(19.428)
Respiratory
3.437
(4.235)
3.316
(4.311)
6.066
(3.931)
Cardiovascular
-7.467
(6.853)
-7.619
(6.891)
-6.579
(7.771)
Injury
-4.958
(5.653)
-5.648
(5.675)
-8.463
(5.787)
All Emergencies
FE:
Summer*East
x
x
x
Summer-by-Year FE
x
x
x
Basic Weather Controls
x
Detailed Weather Controls
x
County-by-Year FE
x
x
x
Note: each observation is a county-year-season. Regressions are GLS
with weight equal to MarketScan population in a given county-yearseason. Covariance matrix allows arbitrary autocorrelation within each
MSA. Asterisks denote p-value < 0.10 (*), <0.05 (*
-30
Mortality Rate
-15
0
NBP Impact on Mortality Rate (per 100,000)
2001
2002
2003
2004
Diff-in-Diff
2005
95% C.I.
2006
2007
NBP Impact on Mortality Rate (per 100,000)
(2)
-5.229***
(1.829)
(3)
-5.413***
(1.987)
(4)
-5.271**
(2.154)
Respiratory
-0.896**
(0.396)
-0.884**
(0.396)
-0.753*
(0.404)
Cardiovascular
-0.836*
(0.455)
-1.726*
(0.886)
-1.706*
(0.884)
Neoplasm
-1.959**
(0.984)
-1.355
(0.901)
-0.342
(0.534)
External
-0.475
(0.546)
-0.408
(0.560)
-0.179
(0.490)
All Other
0.182
(0.410)
0.039
(0.410)
0.259
(0.442)
x
x
x
x
x
x
x
All Deaths
Summer*East
Summer-by-Year FE
Basic Weather Controls
Detailed Weather Controls
County-by-Year FE
x
x
x
x
NBP Impact on Age-Specific Mortality Rate (per 100,000)
Cause of Death
Age 0 (Infants)
Summer*Post*NBP
Response Var Mean
Implied 2005 Deaths
Ages 1-64
Summer*Post*NBP
Response Var Mean
Implied 2005 Deaths
Ages 65-74
Summer*Post*NBP
Response Var Mean
Implied 2005 Deaths
Ages 75+
Summer*Post*NBP
Response Var Mean
Implied 2005 Deaths
All
(1)
Respiratory Cardiovascular
(2)
(3)
-4.436
(9.972)
263.31
-78
-1.250
(1.215)
5.12
-22
0.111
(1.170)
5.59
2
-1.112
(1.327)
97.66
-1,295
-0.167
(0.174)
4.14
-194
0.016
(0.369)
21.30
19
-12.801
(7.960)
794.47
-1,135
-1.624
(2.456)
79.93
-144
-7.587
(5.398)
240.75
-789
-42.582** -12.536**
(19.285)
(5.792)
2,768.33
290.89
-3,460
-946
-18.588*
(10.260)
1,131.84
-1,703
Outline of Talk
 1. Background on ozone and its effects on health
 2. NOx Budget Trading Program and abatement
technologies
 3. Theoretical framework
 4. Data
 5. Econometric approach
 6. Results
 7. Interpretation and conclusions
Aggregate Abatement Costs of Market
Total ($)
NBP Abatement Costs, 2003-2007 Total
Upper Bound ($ Billion)
$3.7
Aggregate Health Benefits of Market (Billions $s)
Mortality
(Deaths)
Mortality Medications Total
NBP Health Benefits With Age-Specific VSL and Mortality
Rates
Total Per Year
7,155
$4.3
$0.7
$5.0
Total 2003-2007
32,198
$19.1
$3.3
$22.4
Main Results 1:
 NOx Budget Trading Program decreased NOx emissions by
35%
 Implies reductions in summer ozone concentrations of 7%
 Reduced the number of summer days with harmful ozone by
38%
 No impact on concentrations of other ambient air pollutants
(NO2, SO2, CO, PM2.5 PM10)
Main Results 2:
 Decrease in ozone pollution has large benefits:
 1.6% Reduction in medication purchases: saved $700 million
in defenses annually
• Almost as large as abatement costs associated with NBP
 1.6% Reduction in mortality rate -- prevented 7,000
summertime deaths each year, primarily age 75+ population
• Models lack statistical power to detect long-run changes
in mortality beyond one season
 Reduction in hospital visits expenditures (although statistically
insignificant)
 ⇒Combined health benefits greatly exceed the costs of NBP
Conclusion:
 First study to provide a complete assessment, derived from
real-world data, of the costs and benefits of large marketbased environmental intervention, the NOx Budget Trading
Program
 Research design and rich data sets allows us to construct
credible measures of the willingness to pay for reductions in
ozone air pollution concentrations that accounts for
defensive expenditures, as well as ozone’s direct health
impacts
Conclusion:
 Monetized value of health benefits >> NBP abatement
costs
 Reduction in medication expenditures alone corresponds to
$700 million per year, ≈ upper bound estimate of abatement
costs
 But defensive behavior incomplete: ≈ 90% of benefits from
mortality
 One limitation of this study is that it overlooks non-health
effects which may have their own compensatory responses
 Possible impacts on agricultural yields (ozone is bad)
 Possible impacts on worker productivity