Moretti and Neidell 163
boat arrivals and departure to vary depending on how far the SRA is from the port. In all the empirical models in the paper, we cluster all standard errors by date.
26
If there is a homogeneous effect of ozone on health, a necessary assumption for unbiased estimates of 
1
is that covboats
t
, u
azst
⳱0 and covboats
t
dist
s
, u
azst
⳱0. Several factors support the validity of this assumption. One, the supply of commod-
ities is a stochastic process such that variation in the production of goods and ser- vices and their loading and unloading at the port cannot be timed perfectly. For
instance, on any given day the port averages approximately 15 boat arrivals, but the interquartile range of 12 to 17 suggests considerable variation in the number of
arrivals. Two, although one vessel is docked at each berth at the port, there is substantial variation in the tonnage of these boats, an important factor affecting
emissions, particularly NOx Environmental Protection Agency 2000; Gajendran and Clark 2003. In support of this, the average boat tonnage is roughly 17,000, but the
interquartile range is from 9,300 to 22,000. Three, given that these boats travel from great distances, conditions at sea and vessel travel speeds are likely to affect their
exact arrival date.
27
These factors suggest it is reasonable to think of the timing of boat arrivals as virtually random in the short-run. Because of that, we have little
reason to expect that short-run variations in boat movements directly affect health in the short-run, and below we present supporting evidence.
VI. Results
A. Validity of Boat Traffic as an Instrument
To support the validity of our instrument, we begin by demonstrating the virtually random day-to-day fluctuations in boat traffic. In Figure 1, we plot daily boat traffic
for July, 2000, both unadjusted and adjusted for all covariates used in the analysis.
28
Immediately evident is that boat traffic today does not appear to predict boat traffic tomorrow, regardless of whether we adjust for environmental conditions. A positive
departure from the mean is almost always followed by a negative departure from the mean. When we more formally test this by computing partial autocorrelations
using data from all dates, shown in Figure 2, we again find little evidence of a systematic pattern: Once-lagged boat traffic has a statistically insignificant correla-
tion with current boat traffic of only 0.03. These results suggest boat traffic almost perfectly resembles a random walk.
Our instrument may be invalid if people can perfectly observe changes in pollution levels induced by the boats and adjust their exposure accordingly. While people may
have a good sense of seasonal variation in pollution, have reliable information on current weather conditions that may affect pollution, and have easy access to pol-
26. We also estimated models that allowed for arbitrary auto-correlation of four lags, and this had minimal impact on our standard errors.
27. In our sample, 14 percent of vessels have a country of origin in Africa, 19 percent in Asia, 18 percent in Europe, and 47 percent in North America. Nearly 39 percent of the North American boats originate
within the United States, with the remainder almost entirely from Panama and the Bahamas. 28. Results are comparable if we choose other time periods.
164 The Journal of Human Resources
-300 -200
-100 100
200 boat traffic
day
31 30
29 28
27 26
25 24
23 22
21 20
19 18
17 16
15 14
13 12
11 10
9 8
7 6
5 4
3 2
1
raw adjusted
Figure 1 Daily boat traffic in July, 2000
Note: “Raw” plots the total tons of boat traffic by day in July, 2000, demeaned to have a mean of zero. “Adjusted” plots the residuals from regressing boat traffic against maximum temperature, minimum tem-
perature, precipitation, wind speed, humidity, cloud cover, carbon monoxide, nitrogen dioxide, year-month dummies, day of week dummies, and cubic day trend.
lution forecasts, we think it is unlikely they detect daily changes in pollution levels induced specifically by the boats. To probe this, we assess whether pollution fore-
casts—the main source of information available to the public—are based on boats movements. We show in the first two columns of Table 2 estimates of the relationship
between boat traffic and both smog alerts and ozone forecasts. In Column 1, we
Moretti and Neidell 165
Figure 2 Partial autocorrelation of boat traffic
Note: The plotted partial autocorrelations are the coefficients obtained by regressing boat traffic on 40 lags of boat traffic.
regress whether a smog alert was issued anywhere in SCAQMD on our measure of boat traffic and all of the covariates in Equation 3, but only using covariate data for
the SRA of the port. In Column 2, we repeat this regression using the ozone forecast for the SRA of the port as the dependent variable.
29
In both analyses we only use contemporaneous levels and not a five-day average since this more precisely ad-
dresses the question of whether boat traffic is incorporated into air quality forecasts.
30
The results indicate a statistically insignificant coefficient on boat traffic for both measures of air quality information, which supports the notion that boat traffic is
not used in air quality forecasts and hence is unlikely to be related to avoidance behavior.
Our instrument may also be invalid if people possess private information about boats movement and adjust their exposure based on that information. Specifically,
if the information on boats movement induces people to decrease their exposure to ozone by limiting time spent outside and this in turn improves health, we will un-
derestimate the biological effect of ozone on health. We assess this by estimating whether attendance at several outdoor activities is related to boat traffic. If private
information is based on boat traffic, then outdoor activities will decrease when boat
29. We can not use whether an alert was issued in the SRA of the port because this never occurred in the time period studied.
30. By focusing solely on one geographic location, the maximum number of observations is the number of days between April and October multiplied by the number of years of data eight.
166 The
Journal of
Human
Resources
Table 2 Relationship between boat traffic with ozone forecasts and outdoor attendance
1 2
3 4
5 6
Alert Issued
Ozone Forecast
Zoo Attendance
Observatory Attendance
Dodgers Attendance
Angels Attendance
Boat traffic tons1000 0.00001
ⳮ 0.005
ⳮ 1.082
ⳮ 0.352
ⳮ 0.327
1.748 [0.00006]
[0.004] [0.504]
[0.388] [3.143]
[2.891] Maximum temperature
0.00588 0.859
ⳮ 8.083
ⳮ 11.262
ⳮ 28.514
ⳮ 57.273
[0.00181] [0.129]
[14.204] [11.964]
[117.318] [85.574]
Minimum temperature 0.00581
0.820 ⳮ
39.151 10.194
ⳮ 125.882
74.48 [0.00240]
[0.170] [17.875]
[17.976] [137.068]
[131.567] Precipitation
0.00011 ⳮ
0.001 ⳮ
73.266 ⳮ
21.110 ⳮ
53.214 73.28
[0.00040] [0.041]
[18.010] [7.854]
[22.753] [96.141]
Resultant wind speed ⳮ
0.00469 ⳮ
1.045 ⳮ
27.986 35.429
ⳮ 75.297
ⳮ 111.110
[0.00275] [0.201]
[21.494] [23.632]
[167.027] [158.160]
Relative humidity 0.00307
0.129 ⳮ
0.986 ⳮ
10.958 ⳮ
108.655 ⳮ
219.546 [0.00090]
[0.084] [7.428]
[8.216] [86.961]
[68.625] Average cloud cover
ⳮ 0.00169
ⳮ 0.250
ⳮ 14.511
ⳮ 37.436
86.477 ⳮ
221.960 [0.00447]
[0.262] [18.942]
[18.547] [214.933]
[150.598] SUR joint test
2
4 ⳱ 5.52 P–value ⳱ 0.238
Dependent variable mean 0.08
59 4,246
5,469 39,574
25,696 Observations
1,380 1,380
916 837
464 486
Notes: significant at 5 percent, significant at 1 percent. All regressions include carbon monoxide, nitrogen dioxide, year-month dummies, day of week dummies, and cubic day trend. The dependent variable in Column 1 is whether a smog alert was issued anywhere in SCAQMD and in Column 2 is the ozone forecast for the SRA
where the ports reside. The dependent variables for Columns 3–6 are attendance at the four outdoor facilities. “SUR joint test” is a joint test of the boat traffic coefficient on Zoo, Observatory, Dodgers, and Angels attendance.
Moretti and Neidell 167
traffic increases. We use four measures of attendance at outdoor activities in SCAQMD: Two major outdoor attractions, the Los Angeles Zoo and the Griffith
Park Observatory, and two major league baseball teams, the Los Angeles Dodgers and California Angels.
31
Estimates for each venue, shown in Columns 3-6, are sta- tistically insignificant for three of the four venues. Although we find a statistically
significant estimate for attendance at the zoo, this estimate is small in magnitude: A one standard deviation increase in boat traffic is associated with a 1.4 percent in-
crease in attendance. Furthermore, when we estimate these equations simultaneously via seemingly unrelated regression, a joint test of significance reveals a statistically
insignificant association between boat traffic and attendance. These results suggest individuals are unlikely to update their private information about pollution levels
using boat traffic.
B. The Relationship between Boat Movements and Pollution
