Task 4: Analysis of 

Survey Data on Ozone Alert Days

Final Report

Prepared for

Bryan Hubbell

U.S. Environmental Protection Agency

OAQPS, AQSSD, ISEG

(C339-01)

Research Triangle Park, NC 27712

EPA Contract Number 68-D-99-024

RTI Project Number 7647.004.399

September 2003
EPA Contract Number RTI Project Number

68-D-99-024 7647.004.399

Task 4: Analysis of 

Survey Data on Ozone Alert Days

Final Report

September 2003

Prepared for

Bryan Hubbell

U.S. Environmental Protection Agency

OAQPS, AQSSD, ISEG

C339-01

Research Triangle Park, NC 27712

Prepared by

Carol A. Mansfield

Catherine Corey

RTI International*

Health, Social, and Economics Research

Research Triangle Park, NC 27709

*RTI International is a trade name of Research Triangle Institute.
iii

CONTENTS

Section Page

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1

2. Health and Aging Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.1 Background on the Knowledge Networks Panel . . . . . . . . . . . . . . . . . 2-1

2.2 The Survey of Health and Aging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.3 Overall Response Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3

2.4 Ozone Alert Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3

3. County Level Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.1 County Demographic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.2 Determining the Count of “Purple,” “Red,” and “Orange” 

Ozone Days . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.3 Historic Data on Ozone Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2

3.4 Merging the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2

3.5 Summary of Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2

4. Analysis and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R-1
iv

LIST OF FIGURES

Number Page

2-1 Ozone Alert Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4
v

LIST OF TABLES

Number Page

3-1 Summary Statistics for KN Survey Respondents . . . . . . . . . . . . . . . . . . . . . . . 3-3

3-2 Summary Statistics for U.S. Counties (Census 2000) . . . . . . . . . . . . . . . . . . . 3-5

3-3 Ozone Data for Survey Counties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-6

4-1 Ozone Survey Response Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-2

4-2 Model Variable Names and Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-3

4-3 Regression Results of Awareness of Ozone Alert System . . . . . . . . . . . . . . . . 4-4

4-4 Respondents Who Correctly Reported Existence of Ozone Alert System . . . . 4-6

4-5 Likelihood of Modifying Outdoor Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-8

4-6 Likelihood of Modifying Driving Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . 4-10
1-1

SECTION 1

INTRODUCTION

The Air Quality Index (AQI) and the associated ozone alert system is one of a

number of programs that provide information to the public about an environmental threat. 

This information can help the public in two general ways. People can use this information to

decide whether and how much they need to protect themselves by limiting their own

exposure. In addition, the information could encourage people to take whatever steps they

can to reduce the level of the environmental threat for others. For example, in the case of

ozone pollution people could limit time outdoors to reduce their own exposure and limit the

amount they drive during the day to reduce the overall level of ozone pollution in the region.

There is some empirical evidence to support the notion that individuals may take

defensive actions to avoid harmful pollutants. Bresnahan, Dickie and Gerking (1997) find

adults with smog-related health symptoms reduced time spent outdoors when ozone

concentrations were high. Addition behavioral changes such as limiting or changing planned

leisure activities and increased use of home air conditioning were also taken. In addition to

health status, the authors find higher levels of education correlated with averting behaviors

on days of poor air quality. Neidell (2002), using a data set that combines individual child

asthma-related hospitalization data with zip code level population demographics, finds a

negative, significant relationship between health advisories (proxies for avoidance behavior)

and hospitalizations. Families of high socio-economic status display particularly strong

patterns of avoidance behavior.

More generally, there is evidence that the public may respond to safety messages that

affect a variety of health risks. In a controlled evaluation of how variation information

materials communicating the risks from radon influenced people’s perceptions of those risks,

Smith et al. (1990) find that risk communication policies can be effective in modifying risk

perceptions. Defining education and behavioral change is key in measuring the effectiveness

of risk communication materials. In a follow up study, Smith et al. (1995) present results

that track risk perceptions and mitigation decisions of a panel study. They find that
1-2

presenting risk information that emphasizes radon threshold for action increases the

likelihood of mitigating actions being taken. 

In this study, we will use survey data to examine awareness of the ozone alert

program and the factors that influence an individual’s choice to limit time outdoors and the

amount they drive on high ozone days. The data come from a national survey conducted by

RTI and Knowledge Networks (KN) in August 2000. As part of the survey, respondents

were asked a series of questions related to ozone alert days. This data will be combined with

county-level variables to estimate a series of regressions accounting for possible sample

selection bias to help explain awareness of ozone alerts and reported changes in behavior.
2-1

SECTION 2

HEALTH AND AGING SURVEY

The ozone questions were a module in RTI’s Survey of Health and Aging. 

Conducted in August 2000, the panel was based on a sample of adults, aged 18 and older,

selected from a nationally representative Web-enabled panel and surveyed via interactive

TV. 

2.1 Background on the Knowledge Networks Panel

The basis for the Web-enabled panel was a probability-based RDD sample drawn

from all 10-digit telephone numbers in the United States. The survey employed a stratified

random design that incorporates the known probabilities of selection associated with

geographical location, the number of phone lines and people in each household, and whether

or not the phone number was listed. 

Prior to telephone contact, a pre-notification letter was sent to each household whose

randomly sampled telephone number could be matched to a mailing address. For selected

telephone numbers to which an address could not be matched, attempts were made to contact

the household over the phone, obtain an address, and send a letter. Following the prenotification, selected households were contacted via telephone to recruit participants and

obtain information about the households. 

Households that agreed to participate in the panel were then sent equipment to enable

them to hook up the Web-enabled panel. Every participating household received free

hardware, free Web access, free e-mail accounts for each resident over the age of 13,

ongoing technical support, and an incentive program to encourage continued participation. 

These households then completed an initial “demographic characteristic” survey via the

Web. 

2.2 The Survey of Health and Aging

One respondent was randomly selected from each sampled household, and a request

was sent to the individual to ask them to respond to the Health and Aging Survey. An initial
2-2

subsample of 7,493 persons aged 18 years and older was selected. This subsample was

subsequently augmented to include another 1000 persons as part of the nonresponse followup activities, for a total of 8,493 sample members. 

Data collection began on August 3, 2000, and continued through September 13, 2000. 

Telephone follow-up of nonrespondents was conducted for a subsample of 1000 initial

nonrespondents in the panel between August 31 and September 13, 2000.

The survey questions were programmed for administration on interactive TV. To

initiate the survey, e-mail messages were sent to the selected adult panel members. The

e-mail message informed the recipient that could complete the survey. Clicking on a button

within the e-mail started the multimedia questionnaire, which took fifteen minutes to

complete. Self-administration allowed respondents to complete the surveys at their

convenience, in the comfort and privacy of home. Because every household has the same

standardized hardware, survey consistency was assured. Two e-mail reminders were sent to

sampled individuals to encourage their participation.

Telephone follow-up reminders were conducted with a random sample of 1,000

nonrespondents to the initial panel request. 

Of the 7,493 panel members initially selected from the web-enabled panel, 5,868

(78.3 percent) completed the survey via interactive TV. That is, nearly 80 percent of

respondents with working Web-enabled panel systems who were asked to participate in the

Health and Aging Survey completed the survey. 

In addition, telephone data collection was attempted with a stratified random sample

of 1,000 adults in households who had either declined to participate in the panel or who had

not installed their Web-enabled panel box. Altogether, telephone data collection was

conducted with three subsamples of nonrespondents:

 Persons who declined to participate in the panel (n=500);

 Persons who agreed to participate in the panel but did not install their Webenabled panel box (n=500); and

 Panel members who did not respond to the survey (n=1,000). 

Fifteen telephone call-backs, on different days of the week and at different times of the day

were placed to “ring-no-answer” telephone numbers prior to close out. Respondents who
1

The overall weighted response rate accounts for the selection probabilities that were used to select the followup samples. 

2-3

declined to participate in the panel or who had not installed their Web-enabled panel box

were asked to complete the survey by telephone. 

These methods of data collection yielded data from 6,306 eligible respondents. Of

these respondents: 

 5,868—respondents completed the survey via interactive TV;

 238—respondents completed the survey via interactive TV following a telephone

reminder to complete the survey; 

 129—respondents, randomly selected from among households who initially

agreed to participate in the survey panel but had not installed their Web-enabled

panel box, subsequently completed this survey via telephone; and

 71—respondents, randomly selected from among households who initially

declined to participate in the initial Web-enabled panel, subsequently completed

this survey via telephone.

Weights were assigned to all respondents (initial and follow-up) to reflect their overall

probability of being selected for the survey.

2.3 Overall Response Rate

The overall weighted survey response rate1

 was between 43.3 percent and 52.2

percent. The 43.3 percent response rate is a conservative estimate because it assumes that

the pending cases (i.e., all “ring no answer” telephone numbers after 15 callback attempts)

were distributed in proportion to the number of bad numbers obtained, and that this

proportion of telephone numbers was not included in the denominator. The 52.2 percent

response rate is a liberal estimate because it assumes that all pending cases were nonworking numbers, and therefore all removed from the denominator. Given the fact that 15

callbacks were attempted, it is likely that most of the pending were ineligible households. 

Therefore, the higher response rate provides the most reasonable estimate of response rate. 

2.4 Ozone Alert Questions

Out of the panel described above, 6,304 people responded to the ozone alert

questions. We dropped 198 respondents from the data set because their county of residence
2-4

Q14. Ground-level ozone is an air pollution problem in many areas of the country. The

government has developed a ranking system—Code Orange and Code Red—to alert

people on days when ozone levels are high. Are you familiar with this ranking system?

1 (N= ) Yes Go to Q 15

2 (N= ) No Go to Q18

If Q14=1

Q15. To your knowledge, have there been any high ozone alert days (Code Orange or Code

Red) in the area where you live this summer?

1 Yes Go to Q16

2 No Go to Q18

If Q15 = 1

Q16. How did you change your outdoor activities on high ozone alert days?

1 I stayed indoors all or most of those days

2 I limited my time outdoors on all or most of the high ozone alert days

3 I did not change my behavior on those days

If Q15 = 1

Q17. How did you change your driving behavior on high ozone alert days?

1 I did not drive on those days

2 I reduced the amount of driving I did on high ozone alert days

3 I did not change my driving patterns on those days

Figure 2-1. Ozone Alert Questions

could not be identified (those interviewed by telephone), leaving 6,106 observations for

analysis. Each participant provided general demographics information including his/her age,

race, level of education, employment status, and household income. Responses to questions

on current health status and concerns about coping with serious illness in the future were also

included in the data set. Political affiliation and expected voting behavior during the 2000

election were retained as well. Figure 2-1 contains the questions from the survey pertaining

to ozone alerts.
3-1

SECTION 3

COUNTY LEVEL DATA

In addition to the survey data, we collected data for the counties in our sample on

ozone pollution and on county level demographics. 

3.1 County Demographic Data

County-level demographics data was obtained from the Bureau of Census, Census

2000 website (Department of Commerce, 2002). The detailed tables from Summary File 3

provides county level observations on basic demographics including the share of population

residing in urbanized areas, race, household size, employment, educational attainment,

median income, and poverty status. Variables identifying the ability to speak English, mode

of transportation to work, and the number of people who had moved to a new residence in

the past five years were also included since they may reflect certain populations’ awareness

of, or responses to, the ozone alert system. 

3.2 Determining the Count of “Purple,” “Red,” and “Orange” Ozone Days

According to the Environmental Protection Agency (EPA), AQI data for ozone

exceedence days at the county level was not available. As a result, raw ozone data from the

EPA’s Air Quality System (AQS) was used (EPA, 2002). This data set contains hourly

observations for several thousand monitor sites located across the country, predominately in

areas with the potential to experience air quality problems. Observations from May 1, 2000,

through July 31, 2000, were retained since they represent the period prior to administration

of the survey. From the 24-daily observations, the highest daily eight-hour daily maximum

concentration of ozone at each monitoring site was derived. The highest daily value across

all sites in a county was retained and subsequently coded according to the AQI breakpoints

(EPA, 1999). The total count of total “purple,” “red,” and “orange” days for each county

during the summer of 2000 was then determined. 

The count of ozone days are a conservative number since other criteria pollutants

may have had a higher AQI value than ozone on a particular day, driving up the AQI. In

most cases, however, ozone is the primary pollutant contributing to the AQI. In any event,
3-2

ozone value that exceeds 100 on the AQI will always be reported to the public, to alert

sensitive groups to potential health risks. 

3.3 Historic Data on Ozone Levels

The American Lung Association (ALA) publishes an annual document State of the

Air which reports on national air quality. This includes county level data on the total number

of “orange,” “red,” and “purple” days experienced over the entire year. The 2001 version of

the publication provides historical date on the total number of ozone days from 1997-99

(ALA, 2001). The daily color is calculated based upon the highest daily eight-hour daily

maximum concentration of ozone for each ozone-monitoring site. The highest daily eighthour daily maximum concentration in each county was then determined based on the EPAdefined ozone season (including the months of August and September). 

3.4 Merging the Data

The survey and census data was merged with ozone data from 2000 and subsequent

years by the fips code identifier. The 6,106 respondents were mapped to 1,042 different

counties. Of those counties, 478 (46 percent) had complete or partial ozone data, the

remainder did not. As stated previously, monitors are located predominately within

metropolitan areas where the air quality is poorest and sensitive populations must be

informed of potential health effects. We assumed that if air quality monitoring information

was missing the counties did not have high ozone days or if they did that the residents were

not informed through the ozone alert system.

3.5 Summary of Dataset

Table 3-1 contains summary statistics for the demographic characteristics of the

respondents in our sample. Summary statistics for the county level data collected from the

Census are in Table 3-2. Finally Table 3-3 contains the data on ozone alert days in the

counties where our respondents live.
3-3

Table 3-1. Summary Statistics for KN Survey Respondents

Variable Value

Age (mean in years)a 45

Male b 49%

Median HH Income ($1999) $45,000

Educational Attainment c

Less than high school degree 9%

High school degree 31%

Some college 34%

College degree 18%

Graduate degree 8%

Employment Status d

Working full time 56%

Working part time 8%

With a job, on medical leave, vacation or strike 1%

Retired 16%

Homemaker 9%

In school (working, not working) 6%

Unemployed/looking for work 3%

Health Status e

Health excellent 18%

Health very good 37%

Health good 32%

Health fair 10%

Health poor 3%

Household Income ($1999)f

Income $0–$14,999 9%

Income $15,000–$24,999 10%

Income $25,000–$34,999 12%

Income $35,000–$49,999 25%

Income $50,000–$74,999 28%

Income $75,000–$124,999 12%

Income $125,000 and above 4%

(continued)
3-4

Table 3-1. Summary Statistics for KN Survey Respondents (continued)

Variable Value

Marital Status g

Married 63%

Widowed 5%

Divorced 10%

Separated 2%

Never married 19%

Race h

White 81%

Black/African American 11%

American Indian/Alaskan Native 2%

Asian/Pacific Islander 3%

Other 4%

a 53 observations missing b 38 observations missing c 37 observations missing d 49 observations missing e 22 observations missing f 957 observations missing g 29 observations missing h 338 observations missing

N = 6,106 respondents.

Percentage totals may not sum to 100 percent due to nonresponses and rounding.
3-5

Table 3-2. Summary Statistics for U.S. Counties (Census 2000)

Variable Value

Age (years) 36

Median HH Income ($1999) $39,007

County Population in Urban Area 51%

Educational Attainment

No high school degree 19%

High school degree 32%

Some college 28%

College degree 13%

Graduate degree 7%

Employment Status

Unemployed 6%

Language

Households linguistically isolated 2%

Race

White 84%

Black/African American 9%

American Indian/Alaska Native 1%

Asian 2%

Pacific Islander/Hawaiian Native 0.1%

Other 3%

Transportation

Workers who commute alone 82%

Workers who use public transit 2%

Note: Percentage totals may not sum to 100 percent due to nonresponses and rounding.
3-6

Table 3-3. Ozone Data for Survey Counties 

Number of Counties in Dataset N = 1,042 Percent

Survey counties with ozone data for 2000 (May–July ) 478 46.0%

Counties with 1+ orange day(s) 354 74.0%

Counties with 1+ red day(s) 84 17.6%

Counties with 1+ purple days(s) 9 1.9%

Total number of “color” days in those counties in 2000 (May–July)

Total number of orange days 1,506

Total number of red days 171

Total number of purple days 12

Survey counties with ozone data for 1997–1999 438 42.0%

Counties with 1+ orange day(s) 408 93.0%

Counties with 1+ red day(s) 250 57.0%

Counties with 1+ purple days(s) 55 13.0%

Total number of “color” days in those counties 1997–1999

Total number of orange days 10,746

Average annual number of orange days 3,582

Total number of red days 1,401

Average annual number of red days 467

Total number of purple days 212

Average annual number of purple days 71
4-1

SECTION 4

ANALYSIS AND RESULTS

The ozone alert system provides information to individuals about possible threats to

their health. This information can be used for two purposes. On days with high ozone

levels, individuals may take actions to mitigate the impact of ozone on their health. Limiting

time outdoors is one of the most effective methods of reducing exposure to high ozone. In

addition, individuals could also take action to reduce the actual level of ozone pollution, for

example by driving less. However, for the ozone alert system to be effective, people must be

familiar with it. 

Table 4-1 presents the responses to ozone questions (Questions 14-17 in Figure 2-1)

from the Health and Aging Survey. Overall, 33 percent of the sample had heard of the alert

system. Thirty-four percent of the counties represented in the survey experienced at least

one day of code orange (or worse) air quality in 2000, covering 71 percent of the

respondents. Thirty-seven percent of respondents in counties with orange (or worse) air

quality were aware of the ozone alert system, compared with 23 percent of respondents in

counties that did not experience a code orange (or worse) day. Of those who resided in

counties that had experienced a code orange day, 54 percent correctly reported that their

county had experienced a code orange day during that summer. On ozone alert days, 57

percent reported spending less time outdoors, 56 percent reported driving less and 48 percent

reported engaging in both activities. Forty-four percent of respondents reported making

neither behavioral adjustment. 
4-2

Table 4-1. Ozone Survey Response Data

N Percent

Generally aware of ozone alert systema 2,008 33%

Aware of ozone alert system in moderate ozone areasb 1,595 37%

Correctly reported moderate to high ozone alert in their cityc 854 54%

Adjusted outdoor activities on moderate ozone alert daysd 422 57%

Adjusted driving behavior on moderate ozone alert daysd 441 56%

a Aware of alert system, regardless of whether respondent’s county experienced high ozone day in summer

2000. b Those respondents who were aware of ozone alert system in counties that experienced ‘orange’ ozone day in

summer 2000. c Those respondents who were aware of alert system and correctly stated that alert had been issued for orange

or worse ozone days in summer 2000. d Those respondents who adjusted behavior in counties that had experienced moderate or worse ozone days in

summer 2000.

The survey questions generated binary data on awareness and behavioral changes of

respondents. Consequently, our analysis was limited to discrete choice models. A probit

regression model was selected to predict an individual’s choice. The dependent variable in

each model was evaluated against independent variables that included demographic and

county characteristics, as well as environmental factors. Table 4-2 provides the names and

descriptions of the variables used in the analysis. The error term was adjusted for correlation

between observations in the same MSA (Municipal Statistical Area) or county (if the county

did not belong to an MSA) in which the respondent resided, in order to control for

MSA/county-specific effects (using the “cluster” command in STATA).

The first model estimated respondents’ general awareness of the ozone alert system

(survey Question 14). Generally, those with more education, higher incomes and better

health were more likely to be aware of the alert system (see Table 4-3). Awareness is

positively associated with age, suggesting that awareness increases as respondents become

older. Males were less likely to be aware of the alert system, relative to females. 

Black/African-American and white respondents were more likely to be aware of the system,

relative to Asian respondents. Those reporting their current health status to be excellent or

very good were more likely to be aware of the system, relative to those reporting poorer 
4-3

Table 4-2. Model Variable Names and Descriptions

Variable Name Description

age Age of survey respondent

male Gender

black Race is black / African-American

white Race is white

other Race is other

college Highest level of education was some college

bach_grad Highest level of education was bachelor’s or graduate degree

fulltime Employed full time

parttime Employed part time

workother Retired / homemaker / student / employed but on medical leave, vacation or strike

mid_income Household pre-tax income $35,000 - $74,999 ($1999) 

high_income Household pre-tax income $75,000 or greater ($1999)

healthbest Current health status describes as excellent or very good

orange2000 MSA or county (not in MSA) had at least one orange alert day in 2000

red2000 MSA or county (not in MSA) had at least one red alert day in 2000

purple2000 MSA or county (not in MSA) had at least one purple alert day in 2000

orange9799 MSA or county (not in MSA) had at least one orange alert day 1997-1999

red9799 MSA or county (not in MSA) had at least one red alert day 1997-1999

purple9799 MSA or county (not in MSA) had at least one purple alert day 1997-1999

health status. This results may be due to health status being positively, albeit weakly,

correlated with education and household income, both of which have strong associations

with awareness.

Respondents with at least some college education were more likely to be aware of the

system than those with a high school degree or less. This result conforms with findings of

previous studies that suggest a positive relationship between education and awareness of a

variety of environment-related health risks. Those who were employed full time or are

students, homemakers or retired persons are more likely to be aware of the system than those

who are unemployed. These individuals may be better informed through greater access to
4-4

Table 4-3. Regression Results of Awareness of Ozone Alert System (Question 14)

Variable Name

Coefficient

(Standard Error)

age 0.003**

(0.001)

male –0.07**

(0.03)

black 0.18**

(0.09)

white 0.15**

(0.06)

other –0.09

(0.13)

healthbest 0.07*

(0.04)

college 0.08**

(0.04)

bach_grad 0.11**

(0.04)

fulltime 0.15*

(0.09)

parttime 0.07

(0.10)

workother 0.17*

(0.09)

mid_income 0.09**

(0.04)

high_income 0.04

(0.05)

orange2000 0.15*

(0.09)

red9799 0.23**

(0.09)

purple9799 0.25**

(0.11)

_cons –1.33

(0.11)

Log pseudo-likelihood –3,660.6

Pseudo R2 0.03

* significant at p-value = 0.10

** significant at p-value = 0.05

N = 5,958 respondents.
4-5

information, relative to the unemployed. Respondents working part time were not found to

be statistically more or less aware of the alert system, relative to the unemployed. In

addition, those with household incomes between $35,000 and $75,000 were more likely to be

aware of the system, relative to those with incomes below $35,000. This result agrees with

empirical evidence which suggests that, similar to higher levels of education, higher income

is typically associated with greater awareness. However, the coefficient on household

income above $75,000 was not statistically significant, although it was of the expected sign. 

This finding may be due to the relative small size of the sample (only 15 percent of

households in the entire survey had income above $75,000).

Recent and historic environmental conditions may also have impacts on respondents’

awareness of the system. We expect that the occurrence of moderate or high ozone days

would have a positive relationship with awareness, since these conditions trigger the issuance

of warnings. This expectation is supported by our results, which indicated that the

occurrence of a moderate ozone day in the respondent’s county during the summer of 2000

was positively associated with awareness. In addition, the occurrence of historically high

ozone days (1997-1999) was also positively related to awareness. 

The second model estimated whether respondents correctly reported the occurrence

of moderate or worse ozone days in their city during the summer of 2000 (survey Question

15). Gender, race, education, income and historic environmental conditions were significant

predictors of correct response (see Table 4-4). Males were more likely to respond correctly

on whether moderate or worse days had occurred in their city, relative to female respondents. 

Although age was a significant predictor of awareness, it was not found to be a significant

predictor of a correct response. White respondents were less likely to respond correctly,

relative to Asians. Black and other race categories were not statistically significant. Those

with some college education were less likely to respond correctly, relative to those with

lower levels of education. Further analysis would be needed to gain insight into this counterintuitive result. Potentially, less educated individuals reside in areas with higher levels of

pollution and are thus more frequently exposed to ozone alerts and other environment-related

health messages. Employment status was not a significant predictor in this model. 

Consistent with the result above, those with household incomes between $35,000 and

$75,000 were more likely to be aware of the system, relative to those with incomes below

$35,000. This result agrees with empirical evidence which suggests that, similar to higher

levels of education, higher income is typically associated with greater awareness. However, 
4-6

Table 4-4. Respondents Who Correctly Reported Existence of Ozone Alert System

(Question 15)

Variable Name

Coefficient

(Standard Error)

age 0.000

(0.003)

male 0.29**

(0.10)

black 0.18

(0.36)

white -0.52*

(0.27)

other -0.31

(0.50)

college -0.23*

(0.12)

bach_grad -0.19

(0.16)

fulltime 0.29

(0.29)

parttime 0.31

(0.43)

workother 0.18

(0.62)

mid_income 0.18*

(0.11)

high_income -0.03

(0.17)

healthbest 0.14

(0.11)

orange9799 3.53**

(0.349)

_cons -1.87

(0.43)

Log pseduo-likelihood -192.5

Pseudo R2 0.49

* significant at p-value = 0.10

** significant at p-value = 0.05

N = 969 respondents.
4-7

the coefficient on household income above $75,000 was not statistically significant. Current

health status was also not found to be statistically significant.

Historic environmental conditions may also have impacts on correctly reporting on

the alert system. We expect that the occurrence of moderate or worse ozone days in previous

years would have a positive relationship with awareness, since such conditions trigger the

issuance of warnings. This expectation is supported by our results, which indicated that the

occurrence of a moderate ozone day in the respondent’s area between 1997 and 1999 was

positively related to awareness. 

The third model estimated behavioral changes, specifically whether the respondent

stayed indoors more or did not go outside on moderate or high ozone days (survey Question

16). Factors affecting the likelihood of taking averting behavior include age, gender, current

health status, and extremely unhealthy environmental conditions (see Table 4-5). Consistent

with the first model, age is positively associated with taking averting behavior. This may

reflect the heightened sensitivity of older persons to unhealthy environmental conditions,

relative to general population. Males were less likely to adjust their behavior on moderate or

high ozone alert days, relative to females. Whites were less likely than Asians to stay

indoors more on high ozone days, while black and other race respondents were not found to

have statistically different behaviors, relative to Asian respondents. Neither high level of

education, nor work status were found to have a statistically significant effect on averting

behavior. Respondents from high income households (over $75,000) were less likely to take

averting actions, relative to low income households. Intuitively, one would expect wealthier

households would have access to information that would encourage them to stay indoors

more, in order to protect their health. However, there are several potential explanations for

this result. For example, high income households may travel to shopping malls, museums or

other facilities on bad ozone days, as a substitute for time typically spent at the home when

environmental conditions are better. Or, it is possible that those in wealthier households may

have employment schedules or other obligations that do not allow flexibility in terms of

mode of transportation. 

Respondents who reported to be in excellent or very good health were less likely to

take averting actions on moderate to high ozone days, relative to respondents of reporting

fair or poor health status. Ozone poses less of the threat to those in good health, reducing the

benefits to healthy people of staying indoors. Combining information from this response and 
4-8

Table 4-5. Likelihood of Modifying Outdoor Activity (Question 16)

Variable Name

Coefficient

(Standard Error)

age 0.005

(0.003)

male –0.47**

(0.09)

black 0.18

(0.20)

white –0.38**

(0.15)

other 0.29

(0.26)

college 0.05

(0.10)

bach_grad –0.07

(0.11)

fulltime –0.40

(0.32)

parttime –0.37

(0.34)

workother –0.03

(0.31)

mid_income –0.04

(0.10)

high_income –0.28**

(0.13)

healthbest –0.18*

(0.10)

purple2000 0.59**

(0.09)

_cons 0.85

(0.39)

Log pseduo-likelihood –597.2

Pseudo R2 0.10

* significant at p-value = 0.10

** significant at p-value = 0.05

N = 965 respondents.
4-9

the result of Question 14 suggests that those in poor health may adjust their behavior on the

basis of conditions that typically co-occur with high ozone, such extreme temperatures or

smog, rather than as a result of alert system warnings specifically.

The occurrence of high ozone days had a strong positive association with averting

behavior, while moderate alert days were not found to have a statistically significant effect

on averting behavior, suggesting it is when environment-related health risks are at their

highest that individuals will take averting actions, such as remaining indoors or reducing the

time spent outdoors. Historic incidence of moderate and high alert days also did not have a

statistically significant effect on respondents’ behaviors. 

The fourth model also estimates behavioral changes, specifically whether the

respondent drove less or not at all during moderate or high ozone days (survey Question 17). 

Thus, we are interested in determining not only whether people take actions to protect

themselves, but also whether they take measures to reduce the level of environmental threats

for others. Factors that affect the likelihood of taking averting behavior include gender,

current health status, race, and environmental conditions (see Table 4-6). Consistent with

results of the previous model, males were less likely to take averting action on ozone days,

relative to females. Although the coefficient on age remained consistent with earlier results,

it was not a statistically significant determinant in this model. Whites were less likely than

Asians to stay indoors more on high ozone days; black and other race respondents were not

found to be statistically significant. High levels of education and work status also were not

statistically significant determinants of averting behavior. Similar to the previous result,

respondents from high income households were less likely to change their behavior, relative

to low income households. As discussed previously, wealthier households may decide to

substitute for time spent at home on unhealthy air days by traveling to shops or museums, or

they may be more restricted in alternative modes of transportation to work or other activities. 

Respondents who reported to be in excellent or very good health were less likely

to drive less or not at all on moderate to high ozone days, relative to respondents in good, fair

or poor health. Since healthier people may be less likely to feel the adverse effects of poor

environmental conditions, they may have less reason to adjust their behavior in response to

these conditions or be less likely to adjust their behavior to protect the health of others. 

Respondents with poor health may drive less on high ozone days since they are staying

indoors more. 
4-10

Table 4-6. Likelihood of Modifying Driving Behavior (Question 17)

Variable Name

Coefficient

(Standard Error)

age 0.002

(0.003)

male –0.47**

(0.11)

black 0.06

(0.22)

white –0.49**

(0.19)

other 0.51

(0.36)

college 0.05

(0.11)

bach_grad –0.03

(0.121)

fulltime –0.24

(0.35)

parttime –0.06

(0.36)

workother 0.27

(0.35)

mid_income 0.03

(0.10)

high_income –0.38**

(0.15)

healthbest –0.25**

(0.11)

purple2000 0.60**

(0.17)

_cons 0.87

(0.38)

Log pseudo-likelihood –474.6

Pseudo-R2 0.12

* significant at p-value = 0.10

** significant at p-value = 0.05

N = 783 respondents.
4-11

Recent environmental conditions also had effects on averting actions taken by

respondents. The occurrence of high ozone day was positively related to driving less or not

at all, while the occurrence of moderate ozone days was not found to have a statistically

significant effect on averting behavior. This result suggests that it is when environmentrelated health risks are highest that individuals may take averting actions, such as driving less

or not at all, to protect personal health and reduce the health threats to others. Historic

incidence high ozone days did not have a statistically significant effect on respondents’

behaviors. 

Consistent across most models, age and gender are statistically significant predictors

of awareness and the likelihood of taking averting behavior. Race and work status do not

appear to be major predictors of averting behavior. Higher incomes are positively associated

with awareness, but appear to have a negative effect on averting behavior. Health status is

significant across the models, positively relating to awareness, but negatively relating to

behavioral change on moderate or high ozone days. While recent and historic occurrence of

poor environmental quality are key predictors of awareness, it is only the occurrence of high

ozone days during the summer of 2000 that is a statistically significant predictor of averting

behavior.

These results suggest improving awareness of the alert system and its implications for

health effects, should be directed towards the less educated and lower income households

who current report relatively lower levels of general awareness. In addition, education of

higher income households about the benefits to themselves, and their communities, of taking

averting behaviors such as driving less on days with poor air quality may be considered. 
R-1

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