Agriculture, the Acid Rain Program, and the Costs of Reduced SO2
Nicholas J. Sanders∗
College of William & Mary
Alan I. Barreca
Tulane University, IZA, and NBER
June, 2014
Abstract
The Acid Rain Program (ARP) curbed sulfur dioxide (SO2) emissions among heavy polluting
fossil fuel power plants across the Midwestern and Eastern United States through cap-and-trade
regulation. We estimate the impact of this policy on corn and soy yields by comparing changes
in counties near regulated power plants to changes in all other counties. We find the policy
led to a 5.1% decrease in annual crop revenue in treated counties on average, for a total annual
revenue loss of approximately $5.0 billion — a cost approximately double prior estimated annual
abatement costs. We present evidence the ARPs impact operates through reduced airborne
sulfuric acid, which, through rainfall, converts to a useful fertilizer. Reducing SO2 emissions,
while productive for social welfare on aggregate, had meaningful distributional effects on the
agriculture industry. Similar effects may be acute in regions that are highly agrarian or support
a large amount of subsistence farming.
∗
Corresponding Author: Department of Economics, College of William & Mary, [email protected]
1
“From 1985 until 2005 changes in sulfur emission management, primarily due to the
Federal Clean Air Act, have significantly reduced the amount of sulfur released into the
atmosphere. While a good thing for the environment in general, these stricter laws have
created some severe shortages of sulfur for farmers already struggling to grow crops on
marginal lands.”
1
(“Sulfur Deficiency cutting yields in sandy Southeast soils”)
Introduction
Ambient pollution is often a hindrance to economic productivity, reducing health capital of workers
and affecting both short-term productivity and long-run human capital accumulation.1 In contrast,
we provide a case where pollution raises productivity in an important sector of the economy.
Specifically, we use the 1995 United States Acid Rain Program (ARP) to estimate the causal
effects of sulfur dioxide (SO2) emissions on the agricultural sector, with a focus on corn and soy
yields. To our knowledge, this is the first study to quantify the causal effects of the ARP and
related pollution regulation on the agricultural industry.
SO2 emissions from fossil fuel combustion can theoretically impact crop yields through two
primary channels. First, airborne SO2 might directly harm plant leaves — 8 to 24 hours of exposure
to high levels of SO2 can injure sensitive plant species.2 Second, atmospheric SO2 converts to
airborne sulfuric acid, bonds with water particles, and creates “acid rain.” Despite having generally
destructive side effects, this acid rain can transport chemicals that act as a natural fertilizer,
improving crop yields. With two countervailing channels, the net impact of SO2 regulation on
agricultural yields is ambiguous. By focusing on a natural policy experiment, our study helps
resolve both the sign and magnitude of the net effect of airborne SO2 regulation and acid rain
on crop yields in a real-world setting. We find the ARP caused a 13.1% loss in corn yields and a
6.2% loss in soy yields in the average treated county. We find a reduction in cash crop receipts of
approximately $5.0 billion per year, suggesting agriculture incurred program costs nearly double
1
Numerous studies in economics document the adverse health effects of pollution in quasi-experimental settings,
and show the health costs of pollution also manifest in lower levels of labor productivity (Graff Zivin and Neidell, 2012;
Isen, Rossin-Slater, and Walker, 2013) and labor supply (Hanna and Oliva, 2011), and even long term educational
capital accumulation (Currie et al., 2009).
2
Plant Damage from Air Pollution, Department of Crop Sciences, University of Illinois at Urbana-Champaign,
Extension Report No. 1005, July 2002. Available online at http://ipm.illinois.edu/diseases/rpds/1005.pdf
1
current total program cost estimates.3 While the ARP was beneficial for aggregate welfare, these
results highlight that such SO2 regulation has important distributional impacts on a large sector
of the economy.
In 1995, the ARP gave the Environmental Protection Agency (EPA) power to mandate an
SO2 cap-and-trade program on the 110 most-polluting US coal-fired power plants. The program
caused rapid and substantial decreases in emissions in counties surrounding regulated power plants
in a manner that appears unrelated to trends in agricultural yields. Our core strategy estimates
the reduced form effect of the ARP on yields for corn and soy, the two largest field crops by
acreage in the United States. We focuse on the reduced-form effect of the ARP rather than a
direct estimate of marginal units of SO2 for three reasons. First, available SO2 data have limited
coverage. Second, SO2 monitors only measure lower-troposphere levels and therefore miss some
important consequences of SO2 regulation: upper-tropospheric pollution and its role in formation of
acid rain. Third, the reduced form model facilitates a more agnostic view of potential mechanisms
through which the ARP affected crop yields from the start. After substantial investigation into
the mechanism of our reduced form effects, we also estimate the marginal effect of ambient SO2 on
yields in an instrumental variables setting as a robustness check.
We present evidence the ARP affected crop yields directly through atmospheric pollution rather
than other indirect channels, like general availability of capital and labor. We find the policy reduced
both corn and soy yields in counties up to 200 miles away from regulated plants. SO2 has high
atmospheric mobility and, as such, reductions in plant emissions can change atmospheric sulfuric
acid levels far from the source point, beyond the range of any direct local economic effects of
regulation. Yield losses are larger for corn, a crop prone to sulfur deficiency, than for soy, which is
less likely to be sulfur deficient. We also present suggestive evidence losses are largest for counties
downwind of ARP power plants, which indirectly rules out confounding effects that are a function
of distance to power plants but not wind direction. Finally, the effects of the ARP are largest in
counties with soil types more prone to soil nutrient loss and sulfur deficiencies. Though we focus on
corn and soy, given other common field crops, including alfalfa, canola, and wheat, require similar
3
Acid Rain Program Benefits Exceed Expectations, www.epa.gov/capandtrade/documents/benefits.pdf.
2
sulfur soil conditions to maintain high yields, our results may extend to other crops.4
The losses we document are small in comparison to the estimated social gains from the program,
which the EPA puts at over $120 billion per year.5 However, understanding the distributional
impacts of this program is fundamental to the design of more equitable policies. Furthermore, the
results here provide information for coal-reliant countries considering similar pollution abatement
programs. In particular, countries with a large share of the population is both credit constrained and
engaged in subsistence farming may need to subsidize the adoption of sulfur fertilizer to maintain
current yield rates.
In Section 2, we discuss the basic timing and structure of the ARP and its implementation,
along with general trends in crop yields. Section 3 and Section 4 discuss the data and our reducedform methodology, respectively. Section 5 presents reduced-form results, along with estimates on
the ARPs measured impact on pollution levels. Section 6 discusses the ARP’s financial costs to the
agricultural sector. Section 7 concludes. For the interested reader, Appendix B provides discussion
from the agricultural literature regarding the potential effects of sulfur dioxide in crop production,
and Appendix C provides both OLS and IV estimates of the marginal effect of reductions in airborne
SO2 using the ARP as an instrument.
2
Acid Rain Program Timing and US Agriculture
Acid rain concerns in the 1970s spurred the Acid Deposition Act of 1980, a 10-year program
to increase SO2 monitoring, investigate precipitation acidity levels, and establish a network for
studying dry deposition (sulfuric acid deposited in the absence of precipitation). Information from
the Acid Deposition Act let to the Acid Rain Program (ARP), a provision of the Clean Air Act
Amendments of 1990. Phase I of the ARP began in 1995, focused on regulating the 110 power
plants with the highest SO2 emissions. Figure 1 shows the location of Phase I plants, covering 21
states in the Midwest and East United States. In 2000, Phase II further limited emissions of Phase
I plants and added more plants to program, which works through cap-and-trade, where plants can
bank or sell permit allocations. Phase II had a much smaller impact on SO2 emissions than Phase
4
5
“Crop Insights: Sulfur Fertility for Crop Production”, Pioneer Agronomy Sciences
Acid Rain Program Benefits Exceed Expectations, www.epa.gov/capandtrade/documents/benefits.pdf.
3
I, thus we focus on Phase I in our research design.
The EPA distributed allowances based on total historic generation capacity, emissions rates,
and fuel consumption. Each plant reported total emissions to the EPA for verification, holding a
permit for each ton of emissions.6 For plants polluting in excess of held permits, the EPA assigned
a fee of $2,000 (adjusted for inflation) per ton of overage and required eventual accounting for any
overages by purchasing sufficient permits. Plants could hold unused permits, and could transfer
permits across years. As the program moved into Phase II, the EPA further restricted the total
number of available annual pollution permits, with a goal of 8.95 million permitted tons for electric
utilities by 2010.7
The EPA reports the program achieved close to full compliance from all power plants, leading
to a substantial decline in SO2 emissions; in the first 10 years of the program, power sector SO2
emissions dropped by 34 percent from 1990 levels (EPA, 2005).8 Figure 2 illustrates annual SO2
levels, which we calculate using publically available EPA air monitor data. Even before the beginning of the ARP, SO2 pollution was trending downward. However, the introduction of the ARP
coincides with an acute 25% drop in SO2 levels in 1995. A further, though much smaller, decline
in SO2 occurs at beginning of Phase II in 2004.
The ARP changed other airborne pollution levels beyond SO2. As a later part of the ARP,
explicit nitrogen oxide (NOx) regulations began in 2003, and despite no specific ozone (O3) controls,
6
The EPA gave plants a 60-day grace period to buy any additional permits from other firms needed to avoid the
fines.
7
Over much of the program, the nominal cost of an SO2 permit fluctuated between $100 and $200 per ton. Costs
increased in 2004, with price peaking over $1,200 per ton, as firms began banking additional permits in anticipation
of the Clean Air Interstate Rule. A series of lawsuits and additional policies caused prices to fall rapidly, with prices
dropping below $1 in 2012. See Schmalensee and Stavins (2013) for in depth discussion of SO2 markets over time.
8
Between 1990 and 2004, 13 states decreased total plant-measured emissions by over 100,000 tons each. The
largest reductions came from Ohio (over 1 million tons) and Illinois, Indiana, and Missouri (over 500,000 tons each).
The EPA estimates that by 2004, wet sulfate deposition had decreased 19 to 36 percent (with the largest effects
in the Northeast and Midwest) from 1990 levels, while ambient sulfur dioxide dropped between 37 and 54 percent,
and ambient sulfate concentration dropped between 26 and 33 percent. Appendix Figure A-1, taken from the EPA
2004 Acid Rain Progress report, shows the change in annual mean sulfate concentration between the 1989-1991
and 2002-2004 periods (EPA, 2005), where deposition is measured in terms of micrograms per cubic meter. The
EPA Clean Air Status and Trends network (CASTNET) data show annual regional wet and dry sulfur deposition
decreased by over 4 kilograms per hectare (kg/he). Given the short time firms had to comply with Phase I, much of
the reduction came from using sulfur scrubbers, shifting to low-sulfur fuel, or shutting down older and less efficient
boilers. Adoption of low-sulfur coal was another common reduction technique. Deregulation of railroads in the early
1980s led to decreased rail transport prices, which made transporting low-sulfur coal to regulated plants less costly,
and resulted in an unexpectedly low cost of abatement even from the beginning of Phase I (Ellerman and Montero,
1998).
4
the program likely changed O3 levels as well.9 All three pollutants could explain some of the
estimated effect of the ARP: nitrogen plays a role in plant development and O3 can negatively
affect yields by directly damaging plants (Boone, Schlenker, and Siikamaki, 2013). Section 5.5
investigates whether NOx and O3 represent meaningful mechanisms by quantifying the relationship
between exposure to Phase I plants and the respective pollutant.
2.1
The Link Between Airborne SO2, Ground Deposition of Sulfates, and Agriculture
Three chemical compounds are most relevant in our analysis of how the Acid Rain Program may
alter agricultural output. The first is sulfur dioxide, or SO2. The ARP directly regulated SO2 as
the point-source emission from power plant smokestacks and a byproduct of fossil fuel combustion.
The second is sulfuric acid, or H2SO4, sometimes called hydrogen sulfate. H2SO4 is highly corrosive
and the compound responsible for increased acidity of precipitation. The ARP did not regulate
H2SO4 levels per se, but H2SO4 forms with oxidation of SO2, where SO2 combines with oxygen
(forming SO3) and then water (H2O) in the troposphere. By decreasing available atmospheric
SO2, the ARP decreased the formation of atmospheric H2SO4. Airborne H2SO4 then falls to
the ground either in the form of dry deposition or wet deposition. With dry deposition, usually
localized around the area of SO2 origin, H2SO4 falls directly to the ground. With wet deposition,
H2SO4 combines with precipitation, and falls as “acid rain”. Wet deposition can occurs tens to
hundreds of miles away from the initial point of SO2 emissions (Tu et al., 2004), especially with
the high-altitude stacks of power plants.
The third chemical compound is sulfate, or SO4, a salt form of H2SO4. SO4 is a residual
byproduct of dilution of H2SO4 in water, and thus a common byproduct of H2SO4 deposition.
The ARP did not regulate SO4 levels either, though through the chemical process, reductions in
available atmospheric SO2 result in decreased H2SO4 deposition, and thus decreased SO4.
Multiple chemical compounds resulting from airborne SO2 mean the a priori effect of SO2
emissions on crop yields is ambiguous. SO2 might harm agriculture through increased H2SO4,
damaging leaves and raising soil acidity to harmful levels. H2SO4 damage to old growth forests
9
Reductions in airborne SO2 increase availability of sunlight, a necessary component in the formation of O3,
and decreases in NOx could directly change rates of O3 formation depending on local volatile organic compound
conditions.
5
caused by acid rain led to a number of studies in the 1980s on how acid rain might reduce agricultural yields. Study under controlled field conditions found little effect of exposure to acidified
precipitation, with no effect on corn during non-drought years. Unlike the soil in old growth forests
that are highly sensitive to changes in soil pH, agricultural soils are already acidic due to the use
of nitrogen fertilizers — a single fertilizer application is equivalent to 20-25 years of exposure to
acid rain in terms of soil acidity.10 Irving and Miller (1981) tested differential effects from sulfur
deposition via exposure to acid rain (H2SO4) versus exposure to airborne gaseous SO2. When
administered alone, gaseous SO2 caused accelerate aging and increased leaf fall in soy. Exposure to
acid rain alone did not significantly impact yields, but did improve seed growth, which the authors
hypothesized was due to the beneficial effect of additional sulfur and nitrogen absorption. Exposure
to gaseous sulfur dioxide and acid rain simultaneously had no net effect.
Conversely, SO2 could benefit yields through eventual increases of ground level SO4, as sulfur
plays a fundamental role in the crop development process.11 Plants use sulfur in metabolizing
nitrogen, meaning nitrogen fertilizers, a source of major advancements in crop yield over time, are
less effective in the presence of sulfur deficiencies. Sulfur also plays a large role in photosynthesis,
and is a factor in production of chlorophyll. For these reasons, farmers commonly use sulfur
products as fertilizers, including elemental sulfur and sulfate compounds such as calcium sulfate,
and ammonium sulfate. To the extent additional gaseous SO2 translates to additional soil-level
sulfur, reductions in SO2 emissions remove “costless” fertilizer and reduce yields in the absence of
adaptive behavior.12
Indeed, agricultural extension literature highlights the ARP as an explanatory factor for recent
sulfur deficiencies. For example, a North Carolina State University report from the College of
Agricultural and Life Sciences, SoilFacts: Sulfur Fertilization of North Carolina Crops, specifically
notes, “Today [sulfur] deficiency may be more of a concern due to several factors that farmers may
not have considered: 1) tighter air quality standards for atmospheric emissions mean less sulfur
10
articles.chicagotribune.com/1987-12-28/news/8704060486_1_acid-rain-soybeans-crops.
For an extensive review of the agricultural science literature on the role of sulfur and oxides of nitrogen, see EPA
(2008).
12
Sulfur deficiencies are a real concern in modern agriculture. Deficiencies appear as stunted growth and yellowed
leaves due to a lack of chlorophyll coloring (Sawyer, 2004; Stevens et al., 2002).
11
6
falls onto the landscape [. . . ]”.13 This anecdotal evidence suggests farmers may have adapted to
the ARP, using more fertilizer, suggesting our welfare calculations underestimate the costs. We
discuss the potential impacts on fertilizer usage further in Section 6.
To demonstrate the full sequence of events linking the ARP to recent sulfur deficiencies, extensive data on soil-level sulfuric acid deposition would be ideal. Unfortunately, deposition data
are very limited in both their geographic and temporal coverage. Given the data constraints, we
address airborne SO2, the atmospheric catalyst for sulfuric acid, where the data have much better
temporal and geographic coverage.14
2.2
Trends in US Land Productivity Around the ARP
For both corn and soy, advancements in farming methods and technology caused increases in
average yields per acre over recent decades.15 These trends persist across the time frame of our
study. Figure 3 shows crop output for corn (Panel A) and soy (Panel B) in the period 10 years prior
to and 10 years following the ARP. The linear trend line over this period shows annual growth rates
of 1.8% for corn and 1.1% for soy. Our identification strategy mitigates bias from these general
trends by focusing on the changes in yields occurring around 1995 in counties close to regulated
Phase I power plants.
Figure 3 illustrates the substantial variation in annual yields during our study timeframe. Annual yields just prior up to implementation of the ARP were unusual for the US agricultural sector,
with a combination of successive booms and busts on both the supply demand sides. There was
an eastern drought in 1991, an unusually good year for corn in 1992, and a combination of freezes,
unusual rainfall, a Midwestern flood, drought, and insects in 1993, causing unusually low yields
(Kliesen, 1994). Another high-production year followed in 1994, but yields fell again in 1995 due
to heat waves and late planting seasons. Starting in 1996, yields stabilized, followed by a number
13
Extension report E07-50255 , available online at http://www.soil.ncsu.edu/publications/Soilfacts/
AG-439-63W.pdf. In Appendix B, we present additional evidence the agricultural extension literature attributed
reduced sulfate flows decreased to the ARP.
14
Where H2SO4 data are available, we have matched nearby SO2 monitors and found SO2 and sulfuric acid
deposition have a correlation coefficient of approximately 0.7. Appendix Figure A-2 shows sulfur deposition (in
kg/he) versus airborne SO2 (in parts-per-billion), which we measure here using all air monitors within 100 miles of
any ground-deposition measurement station. The scatter plot and fitted line, which uses all data available from all
years, corroborates that changes in airborne SO2 roughly correlate with changes in plant exposure to sulfate.
15
Appendix Figure A-3 shows long-run average corn and soy yield per acre from 1940 through 2010.
7
of consistently high-yield years (Stevens, 1999).
Global commodity prices were volatile over this same period. Appendix Figure A-4 shows the
global price of corn and soy across time (in 2010 dollars), with a large jump around 1995 and an
eventual return to earlier prices afterward. While weather drove early supply losses, there were
also sharp changes in demand on global markets. China left the corn export market in 1994, which
led to speculative price increases, but once domestic production increased in China, speculation
slowed. By early 2000, prices had returned to 1994 levels (Stevens, 1999). Our research design
controls for confounders that affect all regions in a similar fashion over time (e.g., shifts in global
prices).
3
Data
We focus on county-level log yield per acre, using annual crop yield and planted acreage for corn
and soy from the U.S. Department of Agriculture’s National Agricultural Statistical Service. We
calculate yield per acre as total county-wide yield divided by total county planted acres for each
crop.16 Crop yield data are on the county/year level. Following Schlenker and Roberts (2009),
we focus on counties to the east of 100 degree meridian.17 This covers the region affected by the
ARP as Phase I did not regulate any plants west of the 100th degree. Appendix Figure A-5 shows
counties in our reduced form analysis: 1,309 counties with data available on soy yield, and 1,484
counties available with data on corn yield.
In the off chance local weather fluctuations correlate with both ARP timing and proximity to
Phase I plants, we control for both regional temperature and rainfall.18 Following Schlenker and
Roberts (2009), we use weather during the optimal growing season (March-August), and use total
growing degree days rather than mean temperatures.19 We calculate growing degree-days using the
optimal growing temperature ranges for the crops of interest, which is generally 10 to 29 degrees
16
This differs slightly from the yield per acre statistic the data reports, which uses harvested acres as opposed to
planted acres as the denominator. Our results are robust to yield per acre harvested as the outcome.
17
Unlike Schlenker and Roberts (2009), we include Florida, though results are robust to its exclusion.
18
For example, decreased SO2 can result in an increase in ambient solar radiation and temperature due to reduced
reflection of solar radiation by airborne sulfur particles. Sulfur also serves as a source of cloud condensation nuclei,
and reductions in SO2 might influence local rain patterns. If the weather changes as a direct result of the program,
the reduced form effect of the policy will include such effects, making changes in weather endogenous to the policy.
19
Findings in Schlenker and Roberts (2009) suggest weather can have highly non-linear effects on crop output.
8
Celsius.20 To account for the possibility extreme temperatures have negative effects, we linearly
control for the sum of degrees above 29 C as a measure of extreme degree-days.21 Finally, we
control for a quadratic in centimeters of rainfall over the growing season. All weather values are at
the county level as constructed in Schlenker and Roberts (2009).22
We obtain a list of all Phase I and Phase II power plants from the EPA Air Markets Program
Data.23 Section 4 explains how we use these data as a basis for ARP-caused pollution changes. To
demonstrate a link between the ARP and SO2, we use EPA monitor-level pollution data. The EPA
reports daily SO2 averages in parts-per-billion (ppb).24 To assign values to counties, we calculate
the distance between each monitor and each county centroid. Using all monitors within a range of
a maximum distance d of the centroid, we collapse to the county-by-year level using weights equal
to 1/d. We set d = 100, though results are robust to alternate distances. We calculate county level
NOx and O3 levels in the same fashion. In Section 5.5, we use these data to show how the policy
changed monitor-level pollution levels. In Appendix C, we use pollution data to investigate the
marginal effects of SO2 on crop yields.
Panel A of Table 1 shows means and standard deviations (in brackets) for monitor-measured
SO2 and corn and soy output and acreage for the sample counties.25 All results are unweighted
and use data from the period 10 years before and 10 years following the initial enactment of the
ARP (1985-2004). Column 1 shows overall means for the period. Columns 2 and 3 split means
by pre/post ARP enactment. Average contemporaneous SO2 levels across the period are around
6 ppb: 7.4 ppb before the ARP, and down to 4.5 ppb after. Average corn output is 92 bushels
per acre and soy output around 32 bushels per acre, and average agricultural productivity was
20
Each day contributes to the total degree-days a value equal to zero if mean temperature was below 10 C, the
value of the difference between the mean temperature and 10 if the mean temperature is between 10 C and 29 C, and
19 if the mean temperature was above 29. For example, a day with a mean temperature of 20 degrees Celsius would
have a degree-day value of 20 − 10 = 10. To obtain the annual value, we sum daily values across the growing season.
21
We calculate values similarly, where any temperature T below 29 C contributes a value of 0, and any temperature
above 29 C contributes a value of T − 29.
22
We thank Wolfram Schlenker for generously providing the weather data.
23
http://ampd.epa.gov/ampd/.
24
To convert monitor data to annual county level data, we keep monitors with at least 52 readings per year for a
minimum average of one per week. Over 99% of the data has at least 52 daily readings, with over 60% of data having
over 350 readings per year.
25
Pollution data are available for only a subset of our reduced form counties, and as such the number of counties
used to derive SO2 estimates is smaller than the number used for crop estimates.
9
increasing over time even before the EPA passed the ARP.
4
Identification and the Role of the ARP
Our core identification strategy relies on a differences-in-differences approach, where we compare
differences in yields in “treated” counties to “control” counties both “pre” and “post” implementation of the ARP. We define years 1995 and beyond as the “post” period — given the ARP caused
an acute reduction in SO2 in 1995, our base model assumes yield impacts as early as 1995. Our
definition of “treatment” counties accounts for both the number and emissions levels of regulated
Phase I power plants. The ARP resulted in plants with high baseline levels of SO2 reducing emissions by a greater amount than plants with low baseline levels. However, the relationship between
decreases in SO2 and baseline SO2 is not monotonic, as plants could buy and sell pollution permits. Figure 4 illustrates this point by plotting plant-level emissions after the ARP with respect to
pre-ARP emission levels. We plot tons of plant-level SO2 emissions in 1985 (before the ARP) on
the horizontal axis, and tons of plant-level SO2 emissions in 2004 (the last year in our sample) on
the vertical axis. The gray dashed line marks the 45-degree line. We also generate a predicted SO2
level in 2004 based on a linear spline in 1985 emissions, with knots every 50,000 tons (solid line).
More pollution-intensive plants reduced SO2 by a greater amount after the ARP, but for ease of
interpretation we estimate the reduced form focusing on the total number of nearby Phase I power
plants regardless of prior emissions.26 We define plants as “treated” if the plant qualified under
Phase I, and in a robustness check we show a focus on variation in emissions levels across plants
gives similar overall results.
We vary treatment intensity across treated counties by the number of Phase I plants within a
given distance. Airborne SO2 and H2SO4 both travel large distances in the troposphere, meaning
emissions extend far beyond the location of the SO2 point-source. To help motivate the appropriate
distance cutoff, we estimate a semi-parametric model of the link between distance to regulated power
plants and marginal shifts in ambient-level SO2 emissions, allowing the data to determine the likely
extent of the distance effect. We group treated plants by distances of 25 miles from the county
26
Appendix Figure A-6 shows similar graphs comparing 1985 emissions to 1990 and 1995 levels, illustrating there
was little change in emissions before the ARP (1990 levels) and that much of the eventual changes took place in the
first year (1995).
10
centroid: total plants within 0-25 miles, 25-50 miles, and so on, up through distances of 500 miles.
We then estimate the following model, including an additional interaction term for plants at a total
distance over 500 miles:
miles
miles
miles
SO2c,t = Π1 P haseIP lants0-25
+ Π2 P haseIP lants25-50
+ . . . + Π20 P haseIP lants475-500
c,t
c,t
c,t
500
+Π21 P haseIP lantsover
+ ωc,t + γc + λt + φ1t ∗ t + φ2t ∗ t2 + ηc,t .
c,t
(1)
The model includes county fixed effects γ, year fixed effects λ, weather controls ω, and a quadratic
state-specific time trend φ1 and φ2 . Figure 5 plots marginal pollution effects (coefficients Π1 through
Π20 ) with 95% confidence intervals. The coefficient at a given distance implies each additional Phase
I plant correlates with an x part-per-billion change in SO2 for the nearby county after the beginning
of the ARP. Proximity to a Phase I plant results in a decrease in measured SO2 for approximately
175 miles, after which point effects fluctuate around zero. For example, we estimate close to a 0.15
ppb reduction in ambient SO2 for each plant within 75 and 100 miles, and an approximately 0.025
ppb reduction for each plant within 100 and 125 miles.
Given pollution monitors are at ground level, and a portion of SO2 and H2SO4 migration occurs
in the upper troposphere, these pollution analyses potentially underestimate the overall change in
atmospheric pollution and acid rainfall. To investigate this possibility, we repeat equation (1)
using crop yields as the outcome rather than SO2.27 Figure 6 plots coefficients and 95% confidence
intervals. Using log yield as the outcome, a coefficient of x at distance d implies an additional
Phase I plant d miles away correlates with an x percent change in yield per acre after the beginning
of the ARP.
For example, using the estimates for corn (Panel A) the marginal effect of the ARP is a decrease
in yield of 0.5 percent per treated plant for almost all distances from 0 to 175 miles away. By a
distance of over 200 miles, the marginal effect of an additional Phase I plant plateaus around 0.
Comparing Figure 5 to Figure 6 suggests effects may extend 25 miles beyond what SO2 monitors
can detect. We take this as suggestive evidence part of the transport mechanism occurs in the upper
27
We use no weighting structure in SO2 regressions. In crop regressions, we weight by baseline crop acreage for
the first year in the sample (1985). We show our results are robust to a number of alternate weighting structures.
11
troposphere, where airborne sulfuric acid can travel larger distances and interact with rainfall above
the height of pollution monitors. However, differences in sample between our SO2 data and crop
data preclude making strong conclusions.
Panel B shows results for soy, which appears less affected by the ARP than corn overall but with
larger initial effects. This is consistent with soy being less susceptible to sulfur deficiencies.28 Soy
experienced a negative 1 percent change in yields per treated plant 0-25 miles away, but impacts
reach zero per plant by 100 miles. Small negative effects appear for plants at greater distances, but
as a whole results are close to zero.
We use the above semi-parametric results as the baseline for our more simplified reduced form
model. Based on crop yield results, we allow treated plants from up to 200 miles away from the
relevant county to have an affect. For simplicity of interpretation we assign equal weight to any
treated plants within 200 miles of a county. Our core specification is:
miles
ln(yieldc,t ) = β 1 P haseIP lantsXP ost0-200
+ ωc,t + γc + λt + φ1s ∗ t + φ2s ∗ t2 + ηc,t .
c,t
(2)
Following Schlenker and Roberts (2009), we include a vector of weather effects ω, county fixed
effects γ, year fixed effects λ, and state-specific quadratic trend controls φ1s ∗ t and φ2s ∗ t2 .29 Unless
otherwise noted, we weight all regressions using county crop acreage from 1985, the first year in
our sample. We show results are robust to alternative weighting structures, and examine how the
ARP might directly cause changes in planted acreage.
The coefficient of interest is β 1 , the marginal effect of being within 200 miles of an additional
“treated” power plant. We identify β 1 from within county variation in yields for regions near
higher-polluting Phase I plants after the start of the ARP. Equation (2) identifies the reduced
form effect of the policy rather than per-unit effects of ambient SO2. We estimate the effect of
28
North Carolina State University Extension report E07-50255 notes sulfur deficiency “is rare in soybean, even
though it is grown in rotation with corn, which often responds to [sulfur] fertilizer. Soybean is a tap-rooted crop that
may acquire [sulfur] from deeper depths than more shallow-rooted grass crops such as corn.
29
While weather correlates with crop yield, inclusion of weather patterns should have no effect on our estimated
effect of the policy unless also correlated with our measure of treatment (plant emissions). That is, it must be that
changes in ambient emissions correlate with changes in weather patterns. Were that the case, changes in weather
could be endogenous to the policy. As a sensitivity check, we estimate a model without weather controls and find
similar results.
12
monitor-measured SO2 in Appendix C.
Table 1 shows pre- and post-ARP mean SO2 and crop values split by number of treated plants
within 200 miles as a rough measure of treatment intensity. Panel B shows results for counties
with 0 plants, Panel C for counties with 1-7 plants (approximately 50% of counties with non-zero
plant counts), and Panel C for counties with 8 or more plants. Counties with 8 or more plants tend
to have higher yields per acre in the pre-ARP period. For example, yield per acre was 92 bushels
for corn and 33 bushels for soy in counties with 8 or more treatment plants, and respective yields
were only 67 and 24 in counties with 0 treatment plants. Although suggestive of a positive link
between SO2 and crop yields, this cross-sectional comparison is likely biased by omitted factors
(e.g. geography).
Figure 7 shows trends in log yield per acre split by number of treated plants within 200 miles.
As with Table 1, we split into three groups: counties with no treated plants within 200 miles (solid
line), counties within 200 miles of 1-7 treated plants (short dash), and counties within 200 miles of
8 or more treated plants (long dash). We plot residuals net of county fixed effects, weather effects,
and state quadratic trends, making them similar to our primary regression estimation. A visual
examination supports the hypothesis of decreased yield in counties near Phase I plants. Aside from
the overall unusual yield year of 1993 (see Section 2.2), crop trends for all counties are similar
approaching the beginning of the ARP. Starting in 1995, counties near Phase I plants have output
consistently below those further away for both crops.
A potential concern with the difference-in-differences research design is that counties with more
treated plants are potentially different in ways unrelated to SO2 exposure. For such differences
to violate identification in the differences-in-differences model, baseline differences have to interact
with changes in outcomes across time. As a check on this concern, we estimate a model that
includes county-specific linear time trends. We also estimate a model that controls for baseline
economic characteristics interacted with year dummies.
13
4.1
Additional Sources of Variation: Atmospheric Migration of SO2, Prevailing Winds,
and Soil Type
Prevailing wind patterns provide an additional source of variation in how power plant emissions
correlate to county exposure to H2SO4. We exploit this variation to better understand the role of
indirect economic effects that correlate with distance but likely do not correlate with wind direction.
Specifically, we allow the marginal effect of a proximate Phase I plant to vary based on whether
the plant is upwind from the county.30
As an alternate source of variation, we allow differential effects by soil type. Nutrient leaching
(loss of water-soluble soil nutrients caused by rain and irrigation) is a larger problem in soils with
lower clay content (higher sand content), as they are more water permeable. Using data compiled
by Fishback, Horrace, and Kantor (2006) from the State Soil Geographic (STATSGO) Data Base,
we generate an indicator function equal to 1 for all counties in our sample where soil is low in clay.31
We then include two additional interaction terms in (2): SandyXP ost to account for differential,
non-ARP related changes in soil productivity over time, and P haseIP lantsXSandyXP ost to allow
the effect of a Phase I plant to vary by county soil type.
5
5.1
Results
The Effect of the ARP on Agricultural Yield
Table 2 shows results using our main treatment variable, expressed in number of treated Phase I
plants within 200 miles. The outcomes of interest are log yields per acre of corn and soy, respectively;
30
To create a measure of “upwind” plants, we use data on historical wind direction for the 1981-2010 period from
the National Climatic Data Center. These data report average historical wind direction in angle degrees by calendar
day and hour for 262 weather stations across the United States. We find the most common wind direction, in degrees,
for each wind station and assign counties to the nearest weather station. We classify a plant as “upwind” if wind
direction from the plant to the county is within 45 angular degrees (22.5 degrees on either side), extended outward
from the county centroid. We then include an additional interaction with post in (2) to allow the effect of upwind
plants to vary from plants in general. The interaction effect is the additional post-ARP effect caused by a power
plant located specifically upwind.
31
We classify soil as “low” in clay content if it falls below 20% clay. This is based on the commonly used soil
texture triangle — example available at http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/?cid=
nrcs142p2_054167. The Colorado State University extension notes, “Due to the strong physical properties of clay,
a soil with only 20% clay particles behaves as sticky, gummy clayey soil” (http://www.ext.colostate.edu/mg/
gardennotes/214.pdf). General soil data available at http://water.usgs.gov/lookup/getspatial?ussoils. We
are grateful to Fishback, Horrace, and Kantor (2006) for making the data available here: http://www.u.arizona.
edu/~fishback/Published_Research_Datasets.html.
14
a coefficient of x, given two otherwise identical counties, implies a county with one additional treated
Phase I plant within 200 miles has an x percent greater change in yield after the ARP.
Column 1 controls for only county and year fixed effects, Column 2 adds weather controls, and
Column 3 adds state-specific quadratic trends. In all cases, the estimated treatment variable is
negative, and the effect is economically and statistically significant for both crops. Using Column
3, the marginal effect of proximity to one additional treated Phase I plant is a decrease in corn
yield of 0.92% and a decrease in soy yield of 0.44%. The mean corn-producing county with at
least one Phase I plant nearby had 14.3 treated Phase I plants within 200 miles, while the mean
soy-producing county had 14.0. Multiplying marginal effects by mean county exposure gives our
overall estimate of crop reductions in the average treated county: the ARP caused a yield decrease
of 13.1% for corn and 6.2% for soy.
These effects are of reasonable magnitude based on agricultural research on sulfur deficiencies
— insufficient soil sulfur levels at key points in the growth process can cause reductions in yield up
to 75%.32 Smaller marginal effects for soy, coupled with the fact that soy is more resilient to sulfur
deficiency, supports the hypothesis that the ARP is affecting yields via decreased ground-level
sulfate levels.
5.2
Impacts of Wind Direction and Soil Type
Changes to regulation of nearby Phase I plants could cause local economic shocks. Potential
ARP effects correlated with general plant proximity include labor market effects, land development
patterns, and availability of financial capital. Understanding whether these indirect channels are
driving the reduced-form estimates can better establish potential policy solutions to the decreased
yields. We investigate this possibility by exploring differential effects from upwind power plants as
well as by soil types.
Table 3 shows the main effect for the number Phase I plants, as well as the added effect
coming from each plant located upwind (we define “upwind” in Section 4). In all specifications,
32
From an article published in the Southeast Farm Press in August of 2012 (“Sulfur deficiency cutting yields in
sandy Southeast soils”), “Yield losses from sulfur deficiency, especially in corn, can be catastrophic, if the problem
isn’t addressed quickly. Research has shown that for each day sulfur is deficient, past the first 21 days after corn
emerges from the soil, there is a loss of 1-2 bushels per day. If sulfur is deficient when corn is in the silking stage,
yields could be reduced by as much as 75 percent.”
15
decreases in productivity after the ARP are larger for counties with a larger share of plants located
upwind, and the additional effect is statistically significant even after controlling for state trends.
The magnitude is economically significant as well, and suggests regulating upwind plants has a
comparatively greater effect than regulating Phase I plants in other directions. The effect of an
upwind plant is approximately 60% greater for corn and approximately 90% greater for soy.
As an additional exploration of the role of wind dispersion, Appendix Figure A-7 repeats Figure
6, but with exclusive focus on upwind Phase I plants. The graph shows the marginal impact of
an additional treated plant upwind at a particular distance from a county, as compared to a plant
more than 500 miles away. For both crops, non-zero reduced form effects extend greater distances
than when considering treated plants in any direction — upwind treated plants up to 350 miles
away correlate with decreases in corn yield after the ARP, with effects up to 175 miles away for
soy.
Table 4 allows differential effects by soil type, where soil with higher sand content (lower clay
content) is more sensitive to changes in sulfate flows due to higher baseline potential for sulfur
leaching. After controlling for state trends, the negative effect of treating Phase I plants was
around 30% larger and 20% larger for corn and soy, respectively, in counties with soil types more
susceptible to decreases in sulfur flows. The difference for corn is statistically significant at the 5%
level, though for soy the difference is not statistically significant.
In sum, estimates by wind direction and soil type both support the hypothesis that ARPinduced reductions in SO2 translated to decreased sulfur levels in agricultural soil. We next consider
alternative specifications of our basic model, and show results are robust to a number of alternate
fixed effects, sample choices, and weighting methods.
5.3
Robustness of Reduced Form Effects
Table 5 explores sensitivity of our reduced form results. Panel A shows results for corn, and Panel
B shows results for soy. Columns 1 and 2 use alternate weights: Column 1 weights by annual crop
acreage rather than baseline crop acreage, and Column 2 omits weighting. Column 3 replaces statespecific quadratic trends with state-by-year fixed effects, which control for non-pollution state-level
effects by year (e.g., state-level land policies). Column 4 replaces state-specific quadratic trends with
16
more region-specific county-specific linear trends. Column 5 includes year fixed effects interacted
with baseline county economic characteristics of log income per capita, log wage employment, and
log population. Column 6 omits all counties not within 200 miles of at least one treated Phase I
plant, so differences in levels of exposure rather than any comparison to non-exposed counties cause
all variation. Column 7 omits the usually bad crop year of 1993 (see Section 2.2). In each case, the
marginal effect of the ARP on crop yield remains negative for both crops. For both crops, almost
all models are qualitatively similar to our core estimates. All specifications are statistically different
from zero at the 1% level. The specification with the largest deviation from our mean estimates
includes state-by-year fixed effects (Column 4): this restricts variation to number of treated Phase
I plants within 200 miles across counties within a given state, leaving little room for identification.
In Column 8, we shorten the period of analysis to 1990-1999, abstracting from the drought in
1988 and the beginning of Phase II of the ARP in 2000. Results are larger, at 1.30% per treated
Phase I plant for corn and 0.68d% for soy — decreasing effects over the longer timeframe suggest
potential adaptation to sulfur deficiencies over time; perhaps farmers increased the use of sulfur
fertilizer after the ARP (we address this issue below). Column 9 uses the same sample period as
Column 8, but additionally controls for an interaction between Phase II plants within 200 miles and
an indicator for post. Given Phase II did not begin until 2000, we expect little or no economically
or statistically significant pollution effects in the Phase I period. Our findings confirm this result.
The estimate on Phase I plants is unchanged, and the estimate on Phase II plants is statistically
and economically insignificant.
As a final expansion, we explore an alternate instrument based on pre-ARP emissions from Phase
I plants rather than the number of Phase I plants. We use plant emissions data from 1985 and
1990 to construct an average “pre-ARP” level of annual SO2 emissions for each Phase I plant. We
then aggregate average Phase I point-source emissions within a distance of 200 miles to construct
an alternate instrument that is more sensitive to variation in emissions across different Phase I
plants. Column 10 shows the result. The coefficient tells the additional change in post-ARP crop
yield for each 100,000 tons of pre-ARP SO2 emissions from Phase I plants, and is statistically and
economically significant for both crops. An additional 100,000 tons of pre-ARP emissions within
17
200 miles correlates with a 0.77% decrease in corn yield and a 0.40% decrease in soy yield. The
mean treated corn county had 1.3 million tons of per-ARP SO2 emissions within 200 miles, for a
mean treatment effect of -9.6%. The mean treated soy county had 1.3 million tons of per-ARP SO2
emissions within 200 miles, for a mean treatment effect of -4.8%. For both crops, we obtain a very
similar overall result of the program as with our model based on number of Phase I plants, which
we favor for interpretive simplicity.
5.4
How Did the ARP Changed Crop Choice and Crop Revenue?
The agricultural sector could respond to decreased yields in a variety of ways, including substitution to less sulfur-sensitive crops, reallocation of land away from agriculture, and increased use of
fertilizer. Unfortunately, we cannot thoroughly test the crop-substitution response since data by
county and crop type are limited (other than corn and soy). We can investigate whether acreage
of corn and soy changed after the ARP, including whether any counties exit the market for either
crop. Table 6 shows the relationship between our core treatment variable and log planted acres
for corn (Column 1) and soy (Column 2). We omit weighting, as the outcome is the acreage itself.
In both cases, results show no statistically significant change in planted acres for either crop (Appendix Figure A-8 shows crop trends across time by treatment intensity), and no counties in our
sample report zero production or exit the sample across our period. The sign of the coefficients is
suggestive of possible substitution from corn to soy, but given large standard errors we view these
results with caution.
Column 3 shows the change in total crop receipts, where we weight by combined soy and corn
acreage and use all counties with balanced data for either crop.33 We find an economically and
statistically significant effect of the policy; the mean treated county lost 3.5 million in crop receipts
per year. Column 4 uses log of receipts, showing a decrease of 5.1%. Figure 8 shows crop receipts
overall, with a spike in crop receipts in the period from 1995-1997, when global commodity prices
were high (Stevens, 1999), followed by a slump when global prices were low. Such fluctuations in
overall pricing are one reason we show primary results based on crop yields, which are not subject
to factors such as shifts in global pricing. However, the use of a control group in our analysis should
33
We obtain crop receipt data from the Regional Economic Information System.
18
help avoid such concerns, and as such we use crop receipt results, which are statistically significant
at 1%, as the basis for a set of calculations of the cost of the program in Section 6.34
As a final additional robustness check, we test for changes in livestock receipts. Livestock
farming should be subject to the economic effects of the ARP (labor and capital supply), but not
the sulfuric acid reductions. Column 5 repeats Column 4 using the log of all livestock receipts. The
estimate is statistically insignificant, though positive and modest in magnitude — thus, we cannot
rule out the possibility of some substitutions from cropland to livestock use.
5.5
ARP Effects on Monitor-Level Emissions of Other Pollutants
As a further exploration of the mechanisms, we consider how the ARP impacted airborne NOx and
O3, again using our primary measure of regulatory intensity: number of proximate treated Phase I
plants. As with equation (1), we estimate a semi-parametric model to check for patterns between
distance to regulated power plants and marginal shifts in county-level emissions.
Appendix Figure A-10 plots marginal pollution effects with 95% confidence intervals for NOx
(Panel A), and O3 (Panel B), both in parts-per-billion. As before, a coefficient of x at distance d
implies an additional treated Phase I plant at distance d correlates with an x percent change in the
pollution level of a county after the beginning of the ARP.
We find is no consistent relationship between the treatment variable and NOx (Panel A). There
is a positive relationship between the treatment variable and O3 at close distances (Panel B).
Though of different magnitudes, the effect on O3 closely mirrors that of SO2, but with the opposite
sign. A possible explanation for this finding is that airborne SO2 refracts light, decreasing ambient
O3. Thus, our overall reduced form estimates capture some effects of increased O3 on crop yields.
This is not a source of bias for the reduced form effect, but is an important consideration in the
generalization of the effect of SO2 itself, something we address in Appendix C when we control for
multiple pollutants simultaneously.
34
As an additional check, we estimate whether or not the policy caused any changes in local employment rates.
We find no statistically or economically significant change in employment rate, which we define as number of wage
employment jobs divided by total population (both taken from the Regional Economic Information System). Results
are available upon request.
19
6
Quantifying the Additional Costs of the Acid Rain Program
We next provide back-of-the-envelope calculations regarding the welfare costs of the ARP in terms
of crop yields. We first estimate lost revenues by multiplying yield losses by crop market price.35
We base calculations on prices in 1994 because prices were unusual in the other years around the
start of the ARP (see Section 2.2). The National Agricultural Statistics Service reports the 1994
price of corn was $3.50 per bushel (in 2010 dollars). We estimate the ARP reduced mean treated
county corn yield by 13.1%, for a total industry loss of $4.1 billion per year. A price of soy of $8.00
per bushel and a 6.2% reduction in yield implies a total loss in soy revenue of $1.2 billion per year.
As such, the losses total $5.3 billion for these two crops alone.36
As an alternate measure of program costs, we return to our examination of crop receipts by
county from Column 3 of Table 6. Crop receipt data are for all crops, not just corn and soy, but may
provide a more accurate estimate of losses, as other common crops are subject to sulfur deficiency.
Alfalfa and canola, for example, both require more sulfur per acre than corn, and wheat requires
amounts of sulfur similar to soy.37 In levels, we find the mean treated county saw a decrease in crop
receipts of $3.5 million, with total annual losses for all treated counties in our crop data of $4.8
billion, which is quite close to our welfare calculation for just the crops for which we have reliable
yield data.38
These calculations assume no strategic response by farmers. If farmers adapted by using more
fertilizer, our welfare calculations represent a lower bound. There is faint evidence to support
this adaptation mechanism: Appendix Figure A-9 shows national fertilizer usage for sulfur and
35
We assume losses in yields within the U.S. do not affect global prices; to the extent prices rise with lower yields,
then our calculation will be overstated.
36
We derive total values as follows. There are 1,291 affected counties producing corn and 1,176 affected counties
producing soy within 200 miles of a Phase I plant. We use total production by crop in 1994, multiplied by the price
per bushel and the estimated average reduced form decrease. For example, in 1994 all affected counties produced 2.4
billion bushels of soy, with a reduction of 6.2% at a cost of $8.00 per bushel, giving an overall effect of $1.2 billion in
lost revenue.
37
“Crop Insights: Sulfur Fertility for Crop Production”, Pioneer Agronomy Sciences.
38
The marginal effect of an additional Phase I plant within 200 miles upwind of a county meant a decrease in crop
receipts of approximately $254,000 (in 2010 dollars). We calculate mean county effects based on this marginal effect
of $254,000 times the mean county treatment intensity of 13.8 Phase I plants. We calculate total effects using the
same marginal effect, multiplied by treatment intensity for all 1,376 counties with balanced data corn or soy within
200 miles of a Phase I plant.
20
ammonium sulfate (in tons), with a suggestive increase in usage around 2002-2004.39 However,
data are national so formally testing the impacts of the ARP on fertilizer usage by county is
not possible. REIS data include information on county level fertilizer expenditures, though not
specifically by fertilizer type. Using our core model we find no statistically significant change in log
fertilizer expenses. Using non-log values, however, we find an increase in expenditures of $268,000
for the average treated county, approximately 2.7% of the pre-policy mean.
If farmers adapted by shifting land away from agriculture (in ways we cannot detect with our
analysis of corn and soy acres used), our estimates again understate the effect the ARP had on
the agricultural sector, since farmers would remove the least productive lands first. If farmers were
allocating land in a profit-maximizing fashion before, the welfare effect of any such shifts is still
negative — the opportunity to use the land for alternate sources was available before the policy,
but not chosen, and must therefore have been suboptimal.
7
Conclusion
Using proximity to Phase I plants as a source of identification, we show the Acid Rain Program
had negative effects on crop yields for counties up to 200 miles away from regulated point-sources,
with decreases of 13.1% in annual corn output and 6.2% in annual soy output for the mean affected
county. These estimates are robust to a number of alternative specifications, and support the effects
operate though ground sulfur levels since corn is more sensitive to sulfur deficiencies than soy. We
also test the role of pollution reduction allowing effects to vary by primary wind direction (a proxy
for sulfuric acid dispersion), and soil type (a proxy for sensitivity to shifts in sulfuric acid flow).
Larger reductions occur for the counties downwind from regulated plants, and in counties with soil
types more prone to sulfur deficiencies.
Despite the many social and economic benefits of the Clean Air Act Amendments of 1990, the
formation of the Acid Rain Program had the unintended cost of reducing agricultural revenues by
approximately $5 billion per year. True costs of the program may be even higher, as measures
of lost revenue do not account for adjustment costs, like additional fertilizer usage. This result
illustrates a rare situation where pollution positively affects economic productivity, and does so
39
Fertilizer data are from the USDA website (http://www.ers.usda.gov/data/fertilizeruse/).
21
directly rather than through other economic channels. Our results suggest power plant emissions
may provide a “costless” source of fertilizer.
Even with the estimated losses to the agricultural sector, the EPA-estimated $122 billion annual
gains of the ARP greatly exceed the program costs, particularly given the large health benefits
associated with reductions in ambient pollution. Our study focuses on SO2 regulation and the
results cannot be applied to other air pollutants — we anticipate no such effects with particular
matter or carbon monoxide, for example, as SO2 is unique in that the chemical properties are
similar to common fertilizers. Regardless, our results identify a previously unknown distributional
effect of the CAAA and similar air regulations, and suggest governments consider the incidence of
such programs and potential strain it may place in regions where farmers are credit constrained or
have limited access to fertilizers. This is particularly relevant for developing economies dependent
on fossil fuels that have a large agricultural sectors and a substantial amount of subsistence farming.
22
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25
Table 1
Summary Statistics by Acid Rain Program Timing
Overall
Pre-ARP
Post-ARP
Panel A: All Counties (1,484 Corn, 1,309 Soy)
Ambient Airborne SO2
Corn Yield Per Acre
Corn Acres per County
Soy Yield Per Acre
Soy Acres per County
5.94
[2.99]
92.18
[37.01]
44660.09
[52,348.10]
32.00
[9.58]
44909.52
[49,027.76]
7.36
[3.17]
84.97
[34.48]
43964.31
[51,488.24]
30.35
[8.93]
40821.49
[45,487.98]
4.52
[1.95]
99.41
[38.03]
45357.53
[53,188.57]
33.66
[9.91]
49014.11
[52,020.97]
Panel B: Counties Near 0 Treatment Plants (424 Corn, 350 Soy)
Ambient Airborne SO2
Corn Yield Per Acre
Corn Acres per County
Soy Yield Per Acre
Soy Acres per County
4.03
[1.82]
72.91
[33.83]
22125.12
[31,709.65]
25.62
[8.96]
26103.38
[45,802.71]
4.70
[1.82]
67.10
[32.52]
20389.43
[28,848.51]
24.15
[8.44]
19838.52
[28,675.07]
3.33
[1.54]
78.75
[34.13]
23872.85
[34,269.17]
27.05
[9.21]
32154.45
[57,067.22]
Panel C: Counties Near 1-7 Treatment Plants (521 Corn, 481 Soy)
Ambient Airborne SO2
Corn Yield Per Acre
Corn Acres per County
Soy Yield Per Acre
Soy Acres per County
4.56
[2.21]
90.44
[36.95]
48902.80
[58,559.95]
29.70
[9.58]
47655.06
[50,728.06]
5.61
[2.32]
81.63
[34.05]
47073.22
[56,944.50]
28.02
[8.76]
42986.52
[47,013.43]
3.56
[1.52]
99.27
[37.64]
50734.11
[60,083.13]
31.43
[10.08]
52480.77
[53,882.46]
Panel D: Counties Near More Than 8 Treatment Plants (539 Corn, 478 Soy)
Ambient Airborne SO2
Corn Yield Per Acre
Corn Acres per County
Soy Yield Per Acre
Soy Acres per County
6.69
[3.06]
98.01
[36.07]
47482.15
[50,819.01]
34.91
[8.57]
47047.51
[47,639.26]
8.35
[3.09]
91.48
[33.35]
47779.89
[50,637.31]
33.26
[7.97]
43723.50
[46,146.87]
5.04
[1.93]
104.56
[37.47]
47183.75
[51,001.77]
36.55
[8.82]
50347.09
[48,855.67]
Notes: Table provides means and standard deviations (in brackets) for relevant variables included
in the primary regression analysis. Column 1 provides the sample for the full data period from
1985-2004. Column 2 restricts to the ten years prior to the Acid Rain Program (1985-1994).
Column 3 restricts to the 10 years following the Acid Rain Program (1995-2004). We report
sulfur dioxide (SO2) in parts-per-billion (ppb). See Section 3 for further detail.
26
Table 2
Changes in Log Crop Yield After the Acid Rain Program: Marginal Effect of an Additional Phase I Power
Plant Within 200 Miles
(1)
(2)
(3)
Phase I Plants * Post
-0.0044***
(0.0003)
-0.0066***
(0.0003)
-0.0092***
(0.0004)
Mean County Impact
-0.0636
-0.0950
-0.1311
Counties
Observations
1,484
29,680
1,484
29,680
1,484
29,680
Phase I Plants * Post
-0.0015***
(0.0002)
-0.0034***
(0.0002)
-0.0044***
(0.0004)
Mean County Impact
-0.0206
-0.0474
-0.0615
Counties
Observations
1,309
26,180
1,309
26,180
1,309
26,180
X
X
X
X
X
X
X
X
X
Panel A: Corn
Panel B: Soy
County Fixed Effects
Year Fixed Effects
Weather Controls
State Quadratic Trends
Standard errors in parentheses
* p<0.10, ** p<0.05, *** p<0.01
Notes: All regressions weight by baseline crop acreage from 1985, cluster standard errors at the
county level, and control for weather, year fixed effects, county fixed effects, and state-specific
quadratic time trends. Outcome is log yield per acre, and coefficients report the reduced form
effect of an additional Phase I power plant located within 200 miles of a county. We calculate
“Mean County Impact” by multiplying the coefficient by the mean number of Phase I plants
located within 200 miles of any county with at least one plant within 200 miles. See Section 4 for
further detail.
27
Table 3
Changes in Log Crop Yield After the Acid Rain Program: Marginal Effect of an Additional Phase I Power
Plant Within 200 Miles and Variation in Effect by Wind Direction
(1)
(2)
(3)
-0.0025***
(0.0004)
-0.0142***
(0.0018)
-0.0054***
(0.0004)
-0.0096***
(0.0016)
-0.0086***
(0.0004)
-0.0049***
(0.0016)
1,484
29,680
1,484
29,680
1,484
29,680
-0.0007***
(0.0003)
-0.0066***
(0.0013)
-0.0029***
(0.0003)
-0.0042***
(0.0013)
-0.0040***
(0.0004)
-0.0036***
(0.0014)
1,309
26,180
1,309
26,180
1,309
26,180
X
X
X
X
X
X
X
X
X
Panel A: Corn
Phase I Plants * Post
Phase I Plants Upwind * Post
Counties
Observations
Panel B: Soy
Phase I Plants * Post
Phase I Plants Upwind * Post
Counties
Observations
County Fixed Effects
Year Fixed Effects
Weather Controls
State Quadratic Trends
Standard errors in parentheses
* p<0.10, ** p<0.05, *** p<0.01
Notes: All regressions weight by baseline crop acreage from 1985, cluster standard errors at the
county level, and control for weather, year fixed effects, county fixed effects, and state-specific
quadratic time trends. Outcome is log yield per acre, and coefficients report either (a) the
reduced form effect of an additional Phase I power plant located within 200 miles of a county, or
(b) the additional reduced form effect of a Phase I power plant located within 200 miles upwind
of a county. See Section 4 for further detail.
28
Table 4
Changes in Log Crop Yield After the Acid Rain Program: Marginal Effect of an Additional Phase I Power
Plant Within 200 Miles
(1)
(2)
(3)
-0.0047***
(0.0004)
0.0001
(0.0014)
-0.0067***
(0.0003)
-0.0015
(0.0013)
-0.0091***
(0.0004)
-0.0025**
(0.0011)
1,484
29,680
1,484
29,680
1,484
29,680
-0.0015***
(0.0002)
0.0006
(0.0008)
-0.0035***
(0.0002)
0.0008
(0.0009)
-0.0045***
(0.0004)
-0.0008
(0.0008)
1,309
26,180
1,309
26,180
1,309
26,180
X
X
X
X
X
X
X
X
X
Panel A: Corn
Phase I Plants * Post
Phase I Plants * Sandy Soil * Post
Counties
Observations
Panel B: Soy
Phase I Plants * Post
Phase I Plants * Sandy Soil * Post
Counties
Observations
County Fixed Effects
Year Fixed Effects
Weather Controls
State Quadratic Trends
Standard errors in parentheses
* p<0.10, ** p<0.05, *** p<0.01
Notes: All regressions weight by baseline crop acreage from 1985, cluster standard errors at the
county level, and control for weather, year fixed effects, county fixed effects, and state-specific
quadratic time trends. Outcome is log yield per acre, and coefficients report either (a) the
reduced form effect of an additional Phase I power plant located within 200 miles of a county, or
(b) the additional reduced form effect of a Phase I power plant located within 200 miles for
counties with soil that is of higher sand content (clay content of less than 20%). Regressions also
include an interaction between the indicator for higher sand content and an indicator for year
greater than or equal to 1995 . See Section 4 for further detail.
29
30
1,636
26,180
-0.0057***
(0.0005)
1,855
29,680
-0.0092***
(0.0004)
1,309
26,180
-0.0044***
(0.0004)
1,484
29,680
-0.0084***
(0.0005)
(2)
No
Weights
1,309
26,180
-0.0014***
(0.0005)
1,484
29,680
-0.0061***
(0.0006)
(3)
StateXYear
Fixed Effects
1,309
26,180
-0.0061***
(0.0005)
1,484
29,680
-0.0109***
(0.0004)
(4)
County
Linear Trends
1,298
25,960
-0.0049***
(0.0004)
1,470
29,400
-0.0085***
(0.0004)
(5)
Baseline
Interactions
1,298
25,960
-0.0044***
(0.0004)
1,470
29,400
-0.0086***
(0.0004)
(6)
Omit
More Than 200
1,176
23,520
-0.0014***
(0.0003)
1,291
25,820
-0.0044***
(0.0003)
(7)
Omit
1993-1996
1,309
13,090
-0.0068***
(0.0005)
1,484
14,840
-0.0130***
(0.0005)
1990-1999
(8)
1,309
13,090
-0.0012**
(0.0005)
-0.0064***
(0.0005)
1,484
14,840
-0.0035***
(0.0004)
-0.0113***
(0.0005)
(9)
1980-2009
With Phase II
Notes:
Notes: All regressions expand on the regression model in Column 3 of Table 2, using Phase I plants within 200 miles of a county. Column 1 omits years 1993-1996.
Column 2 uses crop acreage by year as weights (rather than baseline 1985 weights), while Column 3 omits weighting. Column 4 replaces quadratic state trends with
state-by-year fixed effects. Column 5 replace quadratic state trends with county linear trends. Column 6 includes interactions between year fixed effects and baseline
county log income per capita, log population, and log county wage employment. Column 7 omits all counties with no plants located within 200 miles of a Phase I
plant. Column 8 restricts the analysis to the 1990-1999 time period. Column 9 uses the same shorter time period, but controls for Phase II plants interacted with post
as well. Column 10 replaces the reduced form measure of treatment with reported pre-ARP annual SO2 Phase I plant emissions within 200 miles. See Section 5 for
further detail.
Standard errors in parentheses
* p<0.10, ** p<0.05, *** p<0.01
Counties
Observations
Phase I SO2 * Post
Phase II Plants * Post
Phase I Plants * Post
Panel B: Soy
Counties
Observations
Phase I SO2 * Post
Phase II Plants * Post
Phase I Plants * Post
Panel A: Corn
(1)
Annual
Weights
Table 5
Changes in Log Crop Yield After the Acid Rain Program: Robustness to Alternate Model Specifications
1,244
24,871
-0.0040***
(0.0004)
1,410
28,196
-0.0077***
(0.0003)
(10)
Alternate
Treatment
Table 6
Shifts in Crop Allocation, Crop Receipts, and Livestock Receipts
(1)
Log Corn Acres
(2)
Log Soy Acres
(3)
Crop Receipts
(4)
Log Crop Receipts
(5)
Log Livestock Receipts
Phase I Plants * Post
-0.0007
(0.0017)
-0.0009
(0.0007)
-254.4339***
(54.1835)
-0.0037***
(0.0005)
-0.0007
(0.0017)
Mean County Impact
-0.0091
-0.0129
-3498.8359
-0.0511
-0.0091
Counties
Observations
1,568
31,369
1,484
29,680
1,569
31,376
1,569
31,376
1,568
31,369
Standard errors in parentheses
* p<0.10, ** p<0.05, *** p<0.01
Notes: All regressions cluster standard errors at the county level, and control for weather, year fixed effects, county
fixed effects, and state-specific quadratic time trends. We vary outcome by column as noted in column headers. We
do not weight regressions using annual planted acreage as the outcome, and restricted to counties with balanced
acreage data for the crop of interest. We weight regressions using farm receipts as the outcome of interest using the
sum of corn acres and soy acres, and restricted to counties with balanced data in either crop. We calculate “Mean
County Impact” by multiplying the coefficient by the mean number of Phase I plants located within 200 miles of
any county with at least one plant within 200 miles. See Section 5 for further detail.
31
8
Figures
Figure 1
Phase I Power Plant Locations
Notes: Power plant location data come from the Environmental Protection Agency. Diamonds
indicate Phase I plants, categorized based on specific, by-plant regulation established in the legislative language of the Acid Rain program. Circles indicate Phase Ii plants. Dashed line indicates
100 degrees longitude.
32
7
6
4
5
Average SO2
8
9
Figure 2
Average Sulfur Dioxide Levels Across Time
1985
1990
1995
2000
2005
Year
Notes: We calculate average sulfur dioxide levels across time using unweighted values from all
EPA pollution monitors active east of 100 degrees longitude using data from the EPA air monitor
database. Dashed line indicates the beginning of the Acid Rain Program.
33
Figure 3
Log Annual Crop Yield Around the Period of the Acid Rain Program
4.2
Log Corn Yield Per Acre
4.4
4.6
4.8
5
Corn
1985
1990
1995
Year
Mean Yield
2000
2005
Linear Fit
3.2
3.3
Log Soy Yield Per Acre
3.4
3.5
3.6
3.7
Soy
1985
1990
1995
Year
Mean Yield
2000
2005
Linear Fit
Notes: We calculate average log annual crop yield using county level annual yield per acre and total
planted acres, available from the United States Department of Agriculture National Agricultural
Statistical Service. Fitted line shows a linear prediction of trend based on crop data.
34
50
Per−Phase I Plant SO2 Emissions
in 2004 (1,000 Tons)
100 150 200 250 300 350
400
Figure 4
SO2 Emissions from Phase I Power Plants in 2004 as Compared to 1985
50
100
150
200
250
300
350
Per−Phase I Plant SO2 Emissions in 1985 (1,000 Tons)
400
Notes: All point source emissions data from the EPA Air Markets Program Data. Horizontal axis
shows reported SO2 emissions for each Phase I plant in 1985. Vertical axis shows reported SO2
emissions for each Phase I plant in 1 2004 (plus). Local polynomial fit uses a bandwidth of 10,000
tons.
35
−.2
−.1
0
.1
Figure 5
Changes in Ambient SO2 After the Acid Rain Program: Marginal Effect of an Additional Phase I Power
Plant Within Designated Miles
50
100
150
200
250
300
Distance
350
400
450
500
Notes: Line shows the marginal effect of an additional Phase I power plant located within 25 mile
distance bins, along with 95% confidence intervals. We derive values from regression equation (1)
in Section 5, which weight by baseline crop acreage in 1985, control for county fixed effects, year
fixed effects, and state quadratic trends, and cluster standard errors by county. We use pollution
at the county level, which we estimate using all balanced pollution monitors within a distance of
100 miles of a county centroid inversely weighted by distance. See Section 5 for details.
36
Figure 6
Changes in Crop Yield After the Acid Rain Program: Marginal Effect of an Additional Phase I Power
Plant Within Designated Miles
−.02
Estimated Marginal Impact by Distance
−.015
−.01
−.005
0
.005
.01
Corn
50
100
150
200
250
300
Distance
350
400
450
500
350
400
450
500
−.02
Estimated Marginal Impact by Distance
−.015
−.01
−.005
0
.005
.01
Soy
50
100
150
200
250
300
Distance
Notes: Line shows the marginal effect of an additional Phase I power plant located within 25 mile
distance bins, along with 95% confidence intervals. We derive values from regression equation (1)
in Section 5, which weight by baseline crop acreage in 1985, control for county fixed effects, year
fixed effects, and state quadratic trends, and cluster standard errors by county. See Section 5 for
details.
37
Figure 7
Average Crop Yield Per Acre Split by Amount of Historic Phase I Sulfur Dioxide Emissions
−.6
−.4
Log Corn Yield Per Acre
−.2
0
.2
.4
Corn
1985
1990
0 Phase I Plants
1995
Year
1−7 Phase I Plants
2000
2005
Over 7 Phase I Plants
−.3
Log Soy Yield Per Acre
−.2
−.1
0
.1
.2
Soy
1985
1990
0 Phase I Plants
1995
Year
1−7 Phase I Plants
2000
2005
Over 7 Phase I Plants
Notes: Notes: We calculate average log annual crop yield using county level annual yield per
acre and total planted acres, available from the United States Department of Agriculture National
Agricultural Statistical Service. We show residuals net of baseline acreage-weighted regressions
controlling for county fixed effects, county-by-year weather, and state quadratic trends. We split
counties by proximity to Phase I power plants: the dashed line shows values for counties within
200 miles of at least one Phase I plant, and the solid line shows values for counties not within 200
of at least one Phase I plant. Section 3 describes data in detail.
38
17.7
Log of Crop Receipts ($2010)
17.8
17.9
18
18.1
Figure 8
County-level Crop Receipts
1985
1990
1995
Year
2000
2005
Notes: Crop receipts data come from the Bureau of Economic Analysis Regional Economic Information System and are in 2010 dollars. Dashed line indicates the beginning of the Acid Rain
Program. See Section 3 for details.
39
Appendix A
Additional Figures
Figure A-1
Changes in Sulfate Deposition as Reported by the Environmental Protection Agency
Notes: Taken from Environmental Protection Agency report ”Atmospheric Deposition of Sulfur
and Nitrogen Compounds”, Chapter 7.
40
0
SO4 Deposition
10
20
30
Figure A-2
Correlation Between Airborne SO2 Readings and Ground Deposition of SO4
0
5
10
Airborne SO2
15
20
Fitted Line
Notes: Sulfate (SO4) deposition data come from the Environmental Protection Agency Clean
Air Status and Trends (CASTNET) system. Airborne sulfur dioxide (SO2) data are from the
Environmental Protection Agency air monitor system. Scatter plot shows the relationship between
airborne SO2 and ground deposition of SO4, as calculated using all SO2 monitors within 100 miles
of a deposition monitor. Solid line is the fitted regression line.
41
Figure A-3
Historical Log Annual Crop Yield
3
3.5
Average Log Yield/Acre
4
4.5
5
Corn
1940
1950
1960
1970
1980
1990
2000
2010
1980
1990
2000
2010
Year
2
2.5
Average Log Yield/Acre
3
3.5
4
Soy
1940
1950
1960
1970
Year
Notes: We calculate average sulfur dioxide levels using all counties in the primary analysis with
balanced pollution data from 1989-2000: Section 3 describes data construction in detail. Dashed
line indicates the beginning of the imposition of the Acid Rain Program. Sulfur dioxide is in
parts-per-billion.
42
500
400
300
200
100
Log Price Per Metric Ton ($2010)
600
Figure A-4
Historic Global Prices for Corn and Soy
1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011
Time
Corn
Soy
Notes: Global price data come from the International Monetary Fund historic primary commodity
data and are inflated to 2010 dollars.
43
Figure A-5
Analysis Counties by Crop
Corn
Soy
Notes: Graphs show all counties with balanced crop data from 1985-2004 east of 100 degrees
longitude. See Section 3 for details.
44
Figure A-6
SO2 Emissions from Phase I Power Plants in Various Years as Compared to 1985 Plant Emission Levels
50
Per−Phase I Plant SO2 Emissions
in 1990 (1,000 Tons)
100 150 200 250 300 350
400
1990
50
100
150
200
250
300
350
Per−Phase I Plant SO2 Emissions in 1985 (1,000 Tons)
400
50
Per−Phase I Plant SO2 Emissions
in 1995 (1,000 Tons)
100 150 200 250 300 350
400
1995
50
100
150
200
250
300
350
Per−Phase I Plant SO2 Emissions in 1985 (1,000 Tons)
400
Notes: All point source emissions data from the EPA Air Markets Program Data. Horizontal axis
shows reported SO2 emissions for each Phase I plant in 1985. Vertical axis shows reported SO2
emissions for each Phase I plant in 1990 and 1995. Local polynomial fit uses a bandwidth of 10,000
tons.
45
Figure A-7
Changes in Crop Yield After the Acid Rain Program: Marginal Effect of an Additional Upwind Phase I
Power Plant Within Designated Miles
−.05
Estimated Marginal Impact by Distance
−.04
−.03
−.02
−.01
0
.01
Corn
50
100
150
200
250
300
Distance
350
400
450
500
350
400
450
500
−.05
Estimated Marginal Impact by Distance
−.04
−.03
−.02
−.01
0
.01
Soy
50
100
150
200
250
300
Distance
Notes: Line shows the marginal effect of an additional upwind Phase I power plant located within 25
mile distance bins, along with 95% confidence intervals. We derive values from regression equation
(1) in Section 5, which weight by baseline crop acreage in 1985, control for county fixed effects,
year fixed effects, and state quadratic trends, and cluster standard errors by county. See Section 5
for details.
46
Figure A-8
Average Planted Acreage by Proximity to Phase I Power Plants
−.1
Average Acres for Corn Production
0
.1
.2
Corn
1985
1990
0 Phase I Plants
1995
Year
1−7 Phase I Plants
2000
2005
Over 7 Phase I Plants
−.3
Average Acres for Soy Production
−.2
−.1
0
.1
.2
Soy
1985
1990
0 Phase I Plants
1995
Year
1−7 Phase I Plants
2000
2005
Over 7 Phase I Plants
Notes: Notes: We calculate average log total planted acres, available from the United States
Department of Agriculture National Agricultural Statistical Service. We show residuals net of
baseline acreage-weighted regressions controlling for county fixed effects, county-by-year weather,
and state quadratic trends. We split counties by proximity to Phase I power plants: the dashed line
shows values for counties within 225 miles of at least one Phase I plant, and the solid line shows
values for counties not within 225 of at least one Phase I plant. Section 3 describes data in detail.
47
50000
Total Sulfuric Acid Fertilizer (Tons)
100000 150000 200000 250000 300000
100000
50000
0
Total Sulfur Fertilizer (Tons)
150000
Figure A-9
Use of Sulfur Fertilizer Across Time
1985
1990
1995
2000
2005
2010
Year
Sulfuric Acid
Sulfur
Notes: Fertilizer use data from National Agricultural Statistics Service at the United States Department of Agriculture. Data show total reported fertilizer use for the United States.
48
Figure A-10
Changes in Additional Pollutants After the Acid Rain Program: Marginal Effect of an Additional Phase I
Power Plant Within Designated Miles
−.5
0
.5
1
1.5
NOx
50
100
150
200
250
300
Distance
350
400
450
500
350
400
450
500
−.0002
−.0001
0
.0001
.0002
.0003
O3
50
100
150
200
250
300
Distance
Notes: Line shows the marginal effect of an additional Phase I power plant located within 25 mile
distance bins, along with 95% confidence intervals. We derive values from regression equation (1)
in Section 5, which weight by baseline crop acreage in 1985, control for county fixed effects, year
fixed effects, and state quadratic trends, and cluster standard errors by county. We use pollution
at the county level, which we estimate using all balanced pollution monitors within a distance of
100 miles of a county centroid inversely weighted by distance. See Section 5 for details.
49
Appendix B
Sulfur, Sulfate, and Agricultural Productivity
Agricultural science suggests both the stock and flow of sulfur are important. Crops obtain sulfur
from the soil, which then needs replenishment to maintain regular growth yields. A standard bushel
of corn, for example, can remove 0.17 pounds of elemental sulfur (approximately 0.50 pounds of
sulfate) per acre.40 Historically productive regions may start with large amounts of ground sulfur,
but absent replenishment, lands could become less productive over time. Irrigation leaches sulfur
as well, and depending on soil type simple water drainage can lower sulfate levels.
Despite its importance in the growth process and continual outflow via plant absorption, sulfur
deficiencies are only recently a concern. Research from the 1970s and 1980s found little benefit to
adding sulfur as fertilizer (Morrison, 2009), By the mid-2000s, experiments with increased application of sulfur fertilizer suggested a newly-found positive relationship between additional sulfur and
yields for most crops studied (Camberato, Maloney, and Casteel, 2012), presenting a shift from prior
findings that sulfur levels were sufficiently high without additional fertilizers (Sawyer et al., 2009).
The shift in baseline sulfur levels is a product of a number of changes to the industry. Adoption of
newer fertilizer and pesticide technologies, both of which have decreased in sulfur content, removed
a common flow of ground sulfur over time. Field burning was a method of returning sulfur-uptake
to the soil, but is now less common.
Most relevant to our analysis, sulfur flow came in the form of acid raid and general sulfuric
deposition, which decreased substantially with the CAAA.41 There was little hypothesized connection between ambient SO2 and ground sulfur during the 1990s, but the agricultural industry
became increasingly aware of the link between the ARP and sulfur deficiencies during the early
2000s. The following quotes (from reports by the Purdue University Department of Agronomy, the
Cornell University Cooperative Extension, and North Carolina State University) show a common
link between the ARP and reduced sulfur:
40
Other sources put sulfur removal at approximately 0.70 pounds per ton of corn silage (Place et al., 2007).
As of yet, there is little work on how the CAAA, and specifically the ARP, affected agricultural productivity. The
EPA considered the effect the program had on agriculture via benefits of ozone reductions, and estimated gains in
crop yields between 1990 and 2010 valued at approximately $7.5 billion due to reductions in O3 (see the Appendix of
EPA (1999)). In a follow-up 2008 report, the EPA further discussed theoretical effects of sulfur and oxides of nitrogen
on plants, but did not expand models to the assessment of the ARP due to a lack of valuation studies linking said
pollutants to the productivity of agricultural land (EPA, 2008).
41
50
Sulfur deficiency of corn and other crops may be becoming more prevalent because less
[sulphur] is deposited from the troposphere to the soil due to reductions in power plant
[sulphur] emissions. (“Sulfur Deficiency in Corn”, 2012)42
Since the Clean Air Act was passed in 1970, emissions of sulfur dioxide have decreased
dramatically resulting in reduced sulfur deposition in many parts of the state. (“Sulfur
for Field Crops”, 2007))43
There are several factors that have resulted in the increasing number of cases where
sulfur is being diagnosed as deficient or limiting in young corn plants. First, there is the
fact that we have had an extended period of frequent and intense rainfall events starting
in the fall of 2002 and continuing through the spring of 2003. Since sulfur is a mobile
nutrient and is water soluble, this sulfur in the upper soil profile (top 2 to 4 inches) has
been leached into the lower rooting zone. The reduction in sulfur emissions brought
about by the clean air act means that these same rainfall events are not replacing the
sulfur leached [. . . ] (“Sulfur Deficiency Symptoms in Emerging Corn, 2003)44
Decreased sulfur flow can be offset by the application of sulfate fertilizer, and awareness regarding sulfur deficiencies likely led some farmers to adapt. A report from the “Corn and Soybean
Digest” in 2009 notes adding sulfur was giving higher yields in some parts of Iowa, and sulfur sales
had jumped 30%.45 Section 6 further expands on potential offsetting expenses of removed sulfur.
42
Camberato, Maloney, and Casteel (2012)
Place et al. (2007)
44
http://www.ces.ncsu.edu/plymouth/cropsci/docs/sulfur.html.
45
“Does Sulfur Pay?”, Corn and Soybean Digest, Feb 1 2009, available online at http://cornandsoybeandigest.
com/does-sulfur-pay.
43
51
Appendix C
The Marginal Effect of Airborne Pollution on Yields
As an expansion, we consider the marginal effect of eliminated SO2. We present OLS estimates
of the observed correlation between ambient SO2 and crop yield, then use an instrument based on
our reduced form model to construct an IV estimate of the marginal effect. For reasons we discuss
below, however, we view interpretation of marginal, per-unit of SO2 effects as suggestive only.
We first estimate the following model:
ln(yieldc,t ) = βSO2c,t + ωc,t + γc + λt + φ1s ∗ time + φ2s ∗ time2 + c,t .
(3)
Two important factors bias this approach. First, omitted variables (e.g. economic activity, other
pollutants) tied to SO2 levels confound estimates. Second, there exists measurement error with
our measure of SO2, potentially biasing estimates downward. To address these concerns, we use
instrumental variables. In our first stage, we use total Phase I plants up to 200 miles from the
county of interest, interacted with an indicator for the beginning of the ARP, as the source of
variation identifying exogenous shocks to SO2 (see Section 4.
SO2c,t = Γc,t P haseIP lantsXP ostc,t + ωc,t + γc + λt + φ1s ∗ t + φ2s ∗ t2 + ηc,t
dc,t + ωc,t + γc + λt + φ1 ∗ t + φ2 ∗ t2 + c,t .
ln(yieldc,t ) = β SO2
s
s
(4)
(5)
We present both our OLS and IV results in Table C-1. Columns 1 and 3 show the OLS correlation
between airborne SO2 in parts-per-billion (ppb) and crop output. The specification follows that of
Column 3 in our reduced form tables, including weather effects, county and year fixed effects, and
state-specific quadratic time trends, weighting by baseline crop acreage and clustering standard
errors at the county level. Corn shows a positive, statistically significant correlation between SO2
and crop yield — an additional ppb of ambient SO2 correlates with a 1.0% increase in yield. For
soy, however, estimates are negative and statistically significant — an additional ppb of ambient
SO2 correlates with a 1.1% decrease in yield.
Columns 2 and 4 of C-1 present the IV relationship. The first-stage regressor P haseIP lantsXP ost
52
describes is number of proximate Phase I plants which we interact with an indicator for post-1995
as in the reduced form.46 We include F-statistics from the first stage (always above 10) regression
in brackets. For each additional Phase I plant nearby, we find an additional 0.07 ppb decrease
in ambient SO2 after the beginning of the ARP. The relationship is statistically significant at 1
percent. In the second stage, an additional ppb of SO2 increases per acre corn yield by 8.5 percent,
a result statistically significant at 1 percent. The relationship for soy yield is economically and
statistically insignificant at 0.1 percent. Both findings support that the net bias in the OLS is
negative.47
Section 5.5 provides suggestive evidence that the ARP caused increases in localized ozone levels,
potentially due to changes in airborne sulfate and light refraction. The basic reduced form estimate
of the policy does not separate the effects of individual pollutants. For the benefit of future policies
that may affect pollutants in isolation, we expand our IV analysis to investigate the relative role
of each. This requires additional instruments for simultaneous identification of changes in both O3
and SO2. We adjust the first stage of our IV to allow for Phase I plants to have different effects by
distance, as we did in Section 5.5. We include the number of Phase I plants at 25-mile intervals,
again up to 200 miles, interacted with an indicator for post-ARP. This gives 9 omitted variables in
the second stage. If the effects of the ARP varied by distance for each pollutant, this can identify
the role of each individually.
Table C-2 shows our multiple pollutant estimates (note sample sizes vary due to varied availability of data by pollutant). Here we report Angrist-Pischke multivariate Fstatistics. For both
pollutants, values are consistently below the 10% maximal relative bias cutoff using the Stock-Yogo
weak ID cutoffs, and we interpret our results with the caveat that first stage identification is weak.
When we instrument separately for both SO2 and O3, SO2 is statistically significant at 1%
and has the expected sign. SO2 has a positive effect on corn yield of 5.3 percent per unit, and O3
46
Sample size varies from that used in the reduced form, as not all counties with crop data have balanced data on
pollution.
47
The ARP had large effects on not just monitor-level (e.g., nonatmospheric), but also atmospheric SO2. The IV
estimate is the reduced form (which includes the effect of both monitor-level and atmospheric level SO2) divided by
the first stage (which includes only changes in monitor-level SO2). If atmospheric SO2 plays a large roll in outcomes
(a likely result given the transportation vector of precipitation), this will artificially inflate per-unit estimates of the
role of SO2.
53
an effect of -1.4 percent per unit, but is significant at only 10%. For soy, SO2 remains positive
at 2.3 percent per unit and is now statistically significant at 1%. Results for O3 are positive but
neither statistically nor economically significant at -0.13 percent per unit. As with our earlier IV
estimates, we view these results as suggestive only, and the specific role of different pollutants in
an open environment remains an important area of study.
54
Table C-1
Ordinary Least Squares and Instrumental Variables Estimates: Airborne Sulfur Dioxide Levels and Crop
Yield
Corn
Soy
OLS
IV
OLS
IV
0.0103***
(0.0021)
0.0988***
(0.0117)
-0.0107***
(0.0019)
-0.007
(0.0089)
First Stage
-
-0.0649***
(0.0042)
-
-0.0560***
(0.0043)
First Stage F-Stat
-
[105.70]
-
[67.48]
1,189
19,020
1,189
19,020
16,760
1,048
16,760
SO2
Counties
Observations
Standard errors in parentheses
* p<0.10, ** p<0.05, *** p<0.01
Notes: This table shows OLS and IV results for the effect of an additional unit of monitor-level
SO2 on crop yield. All regressions weight by baseline crop acreage from 1985, cluster standard
errors at the county level, and control for weather, year fixed effects, county fixed effects, and
state-specific quadratic time trends. Outcome is log yield per acre: coefficients show the
percentage change in yield caused by a reduction of one part-per-billion of ambient SO2 as
measured at the county level. We calculate county pollution values using all pollution monitors
within 100 miles of a county centroid using inverse distance weighting. First stage coefficient
reports the part-per-billion change in SO2 caused by an additional Phase I plant within a distance
of 200 miles.
55
Table C-2
IV With Other Pollutants
(1)
Panel A: Corn
SO2
0.0529***
(0.0134)
-0.0142*
(0.0077)
Ozone
First Stage F Statistic
SO2
O3
[6.64]
[3.42]
Counties
Observations
945
18,900
Panel B: Soy
SO2
0.0233***
(0.0087)
0.0013
(0.0042)
Ozone
First Stage F Statistic
SO2
O3
[7.73]
[4.91]
Counties
Observations
831
16,620
Standard errors in parentheses
* p<0.10, ** p<0.05, *** p<0.01
Notes: This table shows OLS and IV results for the effect of an additional unit of monitor-level
SO2 on crop yield. All regressions weight by baseline crop acreage from 1985, cluster standard
errors at the county level, and control for weather, year fixed effects, county fixed effects, and
state-specific quadratic time trends. Outcome is log yield per acre: coefficients show the
percentage change in yield caused by a reduction of one part-per-billion of ambient SO2 or O3 as
measured at the monitor level. We calculate county pollution values using all pollution monitors
within 100 miles of a county centroid using inverse distance weighting. We base the first stage
regression on a semi-parametric model of proximity to Phase I power plants by 25 mile intervals,
up to a distance of 200 miles. See Section 5.5.
56