A new measure of the stock and flow of
information and how it affects behavior
Dean R. Lillard1,2,3
1
Ohio State University, 2DIW-Berlin, 3NBER
January 2015
Corresponding author:
Dean R. Lillard
Department of Human Sciences
235B Campbell Hall
1787 Neil Avenue
Columbus, OH 43210-1295
Tel. (614) 292-4561
FAX (614) 688-8133
e-mail: lillard.13@osu. edu
I gratefully acknowledge funding from the National Institute on Aging project “Cross-national
patterns and predictors of life-cycle smoking behavior” (award 1 R01 AG030379-01A2). I thank
the many research assistants who helped me over many years to compile and construct the data I
use to construct the health risk information measures. These include: Eamon Molloy, Anthony
Zhu, Yelena Reznikova, Jeffrey Han, Ashley Tse, Hannah Clark, Jeongmin Shin, Raj Kannapan,
Karen Calabrese, Joshua Kim, and Robert McBride at Cornell University and Temur Akhmedov,
Antonio Armas, Han Cao, Jessica Coleman, Daniel Cull, Jacob Fahringer, Christopher Gaier,
Thomas Gegick, Andi Hila, Tyler Hilston, Mason Kaniewski, Cormac Kelly, Luke Kirrane, Wen
Liu, Srikar Mylavarapu, Jonathan Parrish, Maxwell Qian, Jenna Rizzen, and Shuting Zhao at
Ohio State University.
Abstract
In economic models of behavior, information plays an important role. Sometimes the role lurks
in the background - when usually the model assumes that individuals have some level of
knowledge (full or partial). In this paper I empirically investigate whether and how smoking
behavior changes when people are exposed to information about the risks of smoking. I use
individual survey data that tracks smoking behavior of individuals over their whole life-course from birth until the date of the interview. The data include individuals who started smoking as
early as 1930 - well before scientific evidence had accumulated. To these data I merge newly
developed measures of information about the dangers of smoking. These measures consist of
potential exposure to magazine articles about smoking that vary across individuals, geographic
space, and time. I use those data to create variables that measure each individual’s stock and flow
of information. I explore whether and how individuals changed their smoking behavior as they
accumulated information and as new information was revealed. More generally the paper
highlights conceptual issues for any economic decision that changes with information.
"Smoking causes cancer of the lung"
Science News Letter Vol. 34, October 29, 1938
1. Introduction
In economic models of behavior, information plays an important role. Sometimes the role lurks
in the background - when usually the model assumes that individuals possess full or partial
knowledge relevant to a decision at hand. Sometimes, researchers directly model how
information affects choices. In such models the products people buy or their optimal behavior
hinges critically on the type, level, and completeness of the information they possess that is
relevant to the choice at hand. Studies in this literature have investigated how information affects
consumption of eggs (Brown and Schrader 1990), meat (Kinnucan et al. 1997), packaged food
products and restaurant food (Kozup et al. 2013), breakfast cereal (Ippolito and Mathios 1990),
various types of medical care and services (Pauly and Satterthwaite 1981; Kenkel 1990; Hsieh
and Lin 1997), engagement in precautionary behaviors (Viscusi et al. 1986), and health
behaviors such as drinking or smoking (Schneider et al 1981; Avery et al. 2007). Information
also plays a critical role in the literature that evaluates how people evaluate risks of various types
of activities including exposure to occupation related hazards (Viscusi 1993; Viscusi and Aldy
2003).
The literature characterizes information in various ways. These include data from
responses to consumer knowledge surveys (e.g. Ippolito and Mathios 1990; Kenkel 1990;
Rosenzweig and Schultz 1989), indices of scientific articles on particular topics (Brown and
Schrader 1990; Kinnucan et al. 1997), advertising or articles that appear in print media (Pratt and
Pratt 1995; Teisl et al. 1999; Avery et al. 2007), hospital "health report cards" (Dranove et al.
2003). and product warning labels (Viscusi et al. 1986). Sometimes studies characterize
particular events as indicators of a "shock" to consumers' information sets. Examples include the
publication of the 1964 Surgeon General's Report on Smoking (USDHEW 1964) and publication
of articles in widely circulated magazines that subsequently received great media attention (see
Schneider et al. 1981).
The challenge in all of these studies is to establish that the variation observed in
information as measured translates into changes in consumers' information sets. The
"information shock" measures have the disadvantage that the "shock" occurs once and is
experienced by all consumers at the same time.1 Knowledge surveys have the advantage that
they measure what individual consumers know but have the disadvantage that not only are they
(typically) cross-sectional but that they also represent endogenous behavior on the part of
consumers.2 Consequently, it is difficult to interpret estimated associations between variation in
knowledge and behavior. In exceptional circumstances, external events allow to document that
knowledge changed with particular changes in the information environment. For example,
Ippolito and Mathios (1990) exploit such a situation to show that consumers knew more about
the relationship between dietary fiber and risks of colon cancer because they saw more
advertising of the health benefits of fiber in breakfast cereals. They were able to directly attribute
the increased advertising to a specific regulatory decision of the Food and Drug Administration
in October 1984. But such situations are rare.
Perhaps the strongest measure of information is in cases, as in Viscusi et al. (1986),
where researchers experimentally vary who sees the information. But, aside from the fact that
such measures are typically cross-sectional, the samples tend to be small and therefore less
representative of the general population.
1
Often researchers implicitly assume that the shock affects equally the information set of all consumers. Kenkel (1990) finds that consumers who are female, better educated and with higher income are more likely to correctly identify which symptoms are associated with particular diseases. 2
Finally, a long line of research uses counts of either advertising or articles appearing in
print media to characterize consumers' information sets. In early studies, these measures
fundamentally exploit temporal variation in the advertising or articles that appeared in print
because they do not measure heterogeneity in who actually sees the information (Brown and
Schrader 1990; Kinnucan et al. 1997). More recent research links exposure to individual
differences in media consumption. Avery et al. (2007) use cross-sectional survey data that
includes detailed information on magazines individuals read to create an index of exposure to
smoking cessation product advertising that varies across individuals. Wakefield et al. (2005) use
Nielsen data on television viewing behavior of particular demographic groups to generate
exposure to public service smoking announcements that vary across these groups. But
connecting these improved exposure measures to cross-sectional outcome data limits their
usefulness.
In general, the literature confirms that individuals respond to variation in information.
In this paper my goal is to refine the exposure measure that is based on publication of
articles. I use articles about cigarette smoking that appeared in 120 consumer magazines between
1921 and 2008. I use these data to refine the exposure measure in several ways. First, I use
content analysis to differentiate between articles that had a clear message that smoking damages
health, articles that extolled smoking, and articles that delivered a "neutral" message (that neither
implicated nor absolved smoking for health problems). Second, I develop an exposure measure
varies not only temporally but also geographically (across states).3 I generate geographical
variation using state-specific magazine subscription rates and estimates of the population over
time. For comparison purposes, I also generate exposure measures that only vary over time.
3
I can easily adapt the method to generate variation across particular demographic groups. Because I use longitudinal data, I also create and use measures of exposure to information that
differentiates when the information arrived. I describe the algorithms in more detail below.
I then link exposure measures to longitudinal data on smoking behavior that spans the
same period. I empirically investigate whether and how smoking behavior changed as
information about the risks of smoking unfolded. I use individual survey data that tracks
smoking behavior of individuals over their whole life-course - from birth until the date of the
interview. The data include individuals who started smoking as early as 1921 - well before
scientific evidence had accumulated. I estimate models of the probability an individual starts
smoking, smokes in any given year, and quits smoking (conditional on having started) for the
whole sample, by sex, and by race.
Results confirm that individual smoking behavior varies systematically with information
about the health risks of smoking. Women react to information when they take each smoking
decision. Women with a higher stock of information about the health risks of smoking are less
likely to start, more likely to smoke, and more likely to quit. On average men's initiation
decisions do not vary with exposure to anti-smoking information but men with a higher stock of
anti-smoking information are less likely to smoke and more likely to quit than men who were
exposed to less. Results vary by race. Whites and non black minorities of other races change
their smoking behavior as information changes.
Perhaps unsurprisingly, the addition of geographic variation substantially increases the
predictive power of the information measures. The coefficients on the measures that vary over
geographic space always take a sign that is consistent with economic theory (though not always
statistically different from zero). By contrast measures that vary only over time yield statistically
insignificant or wrong-signed results in the models of initiation and cessation. In addition, the
measures that vary over geography yield coefficients that 27 times larger in absolute value.
To proceed I will next describe the data, how I construct the measures, and the sources of
variation. In section 3 I describe the empirical strategy. In section 4 I present results. I disuss
them in section 5 and discuss limitations of the exposure measures and how one might refine
them. To conclude I summarize and point to future research.
2. Data
The outcome data come from the Panel Study of Income Dynamics (PSID). The PSID began in
1968 with a sample of 5,000 households, representing a disproportionate number of low-income
individuals. The 1986, 1999, 2001, 2003, 2005, 2007, and 2009 questionnaires ask all heads and
wives to report information about their lifetime smoking behavior. A special questionnaire
administered in 1990 asked these questions of PSID household members over age 55.
I construct the lifetime history of smoking of every respondent who answered the
smoking questions in any of the above surveys. The PSID asked each respondent if he or she had
ever smoked, if he or she is a current smoker, the age he or she started, and it asked ex-smokers
to report the age he or she last smoked regularly. To code an indicator of smoking status in each
year, I ignore any temporary quits (because I have no information on them). Instead I assume
that a person smoked from the reported start age until either the survey year or the age a person
reported having quit.
Data constructed in this way from similar retrospective reports have been validated
against contemporaneously measured smoking data [(Kenkel et al (2003), Christopoulou et al
(2011)] and shown to vary systematically in economically predicted ways (Lillard et al. 2013; ).
The PSID has some distinct advantages. One is that it follows individuals over time so I
have up to eight observations on smoking behavior of the same individual. When they are
available, I use multiple reports to reduce measurement error in reported smoking behavior.
Another advantage of the PSID’s longitudinal design is that it follows individuals as they move
between states. I use information in the PSID to impute a state of residence for every PSID
respondent from birth through the last year he or she participated in the survey. The algorithm I
use to impute states of residence is decribed in Lillard and Molloy (2013). This feature is
important because it allows me to match state-specific tax rates and state-specific measures of
information exposure that vary over time.
Health risk information
The basic data consist of counts of articles published between 1921 and 2009 that warn readers
about the health risks of smoking. I use counts of articles published in each of 120 popular
consumer magazines.4 To generate the data on articles, I first searched two electronic databases
(ProQuest and the Historical Reader’s Guide to Periodical Literature) using a keyword search on
“smok* and cancer,” “smok* and health,” “cig* and cancer,” “cig* and health” and similar text
strings. Successive searches produced roughly 5,000 titles of articles published between 1890
and 2009. Two people then independently reviewed all 5,000 titles to identify articles that
potentially warned about the health risks of smoking. This review eliminated roughly 2,500
articles that focused on how health risk information affected financial returns of tobacco
companies, tobacco growing agriculture, and international trade in tobacco. The remaining set
included articles whose titles suggested that the articles discussed content about risks individuals
faced. A team of research assistants collected copies of all articles and read them. Two readers
independently rated the articles as a) “pro-smoking,” b) “neutral,” and c) “anti-smoking” when
4
See the Appendix for a list of magazine titles. they judged that an article conveyed to readers the impression that smoking a) improved, b) did
not affect, or c) degraded the health of smokers. Any disagreement was discussed and resolved.
Figure 1 plots the temporal variation in a simple count of the articles of each type that
appeared between 1919 and 2009.
To generate additional temporal and geographic variation in who saw the articles, I used
information on subscriptions in each state to the magazines in which the articles appeared. In
particular, I compiled data from the Audit Bureau of Circulation on sales of each of those
magazines in every state in each six month period of each year.5 The Audit Bureau of Circulation
is an organization that publishers voluntarily join. Its sole purpose is to audit and verify
circulation figures the publishers provide to them. Their independent auditing provides a
valuable service to publishers because publishers charge advertisers more for space in more
widely circulated magazines. Advertisers therefore demand (and publishers willingly provide) an
independently verified count of circulation. The magazine circulation data vary by month, year,
and state.
I assume that, when a magazine is sold, it is seen by all members of the household in
which the purchaser resides.In fact we only assume a household member potentially sees the
article. From now on I use the terms “exposure” and “potential exposure” interchangeably. To
capture this exposure, we divide estimates of each state’s population from the Current Population
Reports of the Census Bureau by 2.3 (the average household size) and divide the number of
issues sold in each state in each year by that number. The resulting figure is an estimate of the
fraction of each state’s population that read each magazine (in each year). I then multiply the
fraction of each state’s population that read each magazine by the number articles that appeared
in that magazine. This step yields the exposure of a randomly drawn person from a given state to
5
The Audit Bureau of Circulation recently changed its name to the "Alliance for Audited Media." an article that appeared in a given magazine in a given year. Finally, I sum across all magazines
in which an article appeared. The final data proxy for the total potential exposure to anti-smoking
magazine articles in a given state in a given year. Formally our measure is given by:
S
P
⁄ .
Articles type
(1)
where type is alternately all smoking related articles (regardless of their content), "anti smoking"
articles, "neutral" articles, and "pro-smoking articles" s denotes state, t denotes calendar year,
and m denotes each of 120 magazines.
Figure 2 plots the resulting measure for all states between 1919 and 2009. Comparing the
trends in Figure 1 and Figure 2 three points are immediately apparent. First, Figure 1 plots a
simple count of the number of articles that got published while Figure 2 plots a proxy of the
number of articles that were (potentially) seen. Consequently, the range of the data in the two
sets of plots differs markedly. While many "pro-smoking" articles were published in the latter
part of the period, they appeared in magazines that few people read. Second, people saw more
smoking-related articles that struck a "neutral" tone during the middle years of the 20th century during the period when scientific evidence about the health risks of smoking were being fiercely
debated. However, over time, such articles appeared (and were seen) less frequently over time.
And, over time, more articles appeared (and were seen) that unequivocally warned about the
health risks of smoking. Finally and quite importantly, there is not obvious trend in the arrival of
information of any particular type - over time and by state. Although it is difficult to see in the
figures, states differ over time in their exposure to information because the propensity of the
population to subscribe to any given magazine changed.
In the final step, I merge the resulting exposure measure to each PSID respondent by his
or her state of residents in each year of his or her life. I use these data to compute several
alternative measures of exposure to smoking-related information. Here I use: (i) the accumulated
sum of articles a person (potentially) saw since age 10 up to the current year, (ii) the number of
articles in the current year, (iii) the number of articles in the past three years, and (iv) the number
of articles in the past five years. I start counting exposure from age 10, assuming that it is the
earliest age a child can comfortably read.
-Taxes
I use the measure of “full” taxes on cigarettes described in Lillard and Sfekas (2013). This
measure is the sum of the state and federal cigarette tax and the per pack escrow payments that
are required on the 1998 “Master Settlement Agreement” between the four major cigarette
manufactures and the US states. Viscusi and Hersh (2011) and Lillard and Sfekas (2013)
document that this payment is functionally equivalent to a per pack cigarette tax.
-Demographic characteristics
Recall that I construct the life-history of smoking of each PSID respondent, even in years before
the PSID administered a survey. That means I cannot observe time-varying demographic
information on each person. Instead, I use data on their age, sex, race (white, black, and all other
races), highest observed educational attainment, and the average of household income over all
years such data are observed.
From these data, I create three basic samples - those at risk to start smoking, those at risk
to smoke, and those at risk to quit. A person first appears in the "at-risk-to-start" sample in the
year he or she is 13 years old and has never smoked (I drop people who started smoking before
age 13). If a person never starts to smoke (or if the initiation age is 27 or greated), he or she
remains in the sample until he or she is age 26. Smokers remain in the sample until the year they
start. Then they get dropped from the sample. A person is in the "at-risk-to-smoke" sample if he
or she is at least 13 years old. A person is in the "at-risk-to-quit" sample from the year he or she
starts to smoke until either the survey year (current smokers) or the year he or she quits.
Table 1 presents sample means separately for each of these samples. Table 2 presents sample
means for these samples separately for men and women.
3. Empirical strategy
The empirical strategy is simple. I estimate linear probability models of the decision to start,
smoke, and quit as a function of exposure to information about smoking
The basic generic model of smoking behavior is given by:
(2)
where
is a vector of the information variables,
escrow tax on cigarettes in state s in year t.
is the sum of the state, federal and
is a vector of demographic characteristics that
includes age, age, age-squared, an indicator for observations where a person is age 50 or older
(participation and cessation models only), race (2 indicators for black and non-black minorities,
white is reference category), permanent household income, and highest observed education. In
the participation and cessation models it also includes an indicator for ages that are evenly
divisible by 5, the interaction of that indicator and the cigarette tax, and the interaction of the
indicator with the age 50 (see Bar and Lillard 2012). The variable
captures time effects. In the
models that include exposure measures consisting of simple counts of articles that appear each
year it includes a linear and quadratic time trend. In models that include the richer measure of
exposure
controls for year fixed-effects. In the basic results, I also estimate a model that uses
the richer information measures but substitutes a linear and quadratic time trend instead of the
year fixed effects. This specification allows one to compare directly the change in the estimated
relationship when one moves from the simple count to the richer measure of exposure. All
models control for state-fixed effects,
.
The empirical strategy is to start with a simple characterization of information that I then
relax in several ways. I start, in model 1, with a rather naïve specification that characterizes
information using all smoking-related articles that appeared in the current year (that behavior is
measured) - regardless of content. In specification 2 I distinguish between the three types of
articles but still focus only on the information that appeared in the current year. In specification 3
I replace the current year measure with the accumulated exposure to information from age 10 to
the present - again not distinguishing between articles of different content. In specification 4 I
distinguish between the content type of each article summed from age 10 to present. Finally, in
specification 5 I add the exposure in the present year to see if a person is more or less likely to
change his or her smoking behavior in years that more information arrives.
I then estimate specification 5 separately on the sample of men and women in each "atrisk" sample and on white, blacks, and non black minorities in each "at-risk" sample. I also
estimate specifications that vary the number of years over which I measure "current"
information. I allow current information to include information from the current, past 3 and past
5 years.
Because a given individual appears in each sample multiple times, all standard errors are
clusted by person.
4. Results
Table 2 reports coefficient estimates from the five models (plus the supplementary model) for the
pooled sample of men and women who were at risk to take each of the three smoking decisions.
Table 2 Panel A presents results for the initiation models. In the pooled sample of men
and women, men and women who saw more "pro-smoking" articles are less likely to start
smoking. And the absolute value of the coefficient on pro-smoking articles is 24 times bigger
when on uses the state-specific measures compared to the simple count.
Table 2 Panel B presents results for the participation models. Here, the richer measure of
information suggest that people are less likely to smoke when they possess a bigger stock of antismoking information and more likely to smoke when, holding constant that stock, they were
exposed to more articles that were ambiguous about the health risks of smoking. Once again,
though smoking participation is negatively associated with both the simple count and richer
measures of anti-smoing information stock, the absolute value of the coefficient is 27 times
bigger than the simple count coefficient.
Table 2 Panel C presents results for the cessation models. Here, the richer measure of
information suggest that people are more likely to quit smoking when they possess a bigger stock
of anti-smoking information and less likely to smoke when, holding constant that stock, they
were exposed to more articles that were ambiguous about the health risks of smoking.
Table 3 splits the sample by sex and reestimates model 5. In all subsequent models I use
the richer measure of information - that varies by state and year. Here we see that women are
much less likely to start smoking when, from age 10 onwards, they saw more anti-smoking
information and also when they saw more articles that were "neutral" in their content. By
contrast, men's initiation decisions are uncorrelated with all types of information. Both men and
women are less likely to smoke when they were exposed to more anti-smoking information (and
the response of men is about three times bigger than it is for women). Here one also observes
that, holding constant exposure to anti-smoking articles, men and women are more to smoke
when they see more articles that are "neutral" about the risks of smoking. Men and women are
also more likely to quit when they see more anti-smoking articles. While the coefficient is larger
for women than it is for men, the implied effect is statistically the same.
Table 4 presents results for men (Panel A) and women (Panel B) again but distinguishes
between information in the current year, in the past three years, and in the past five years. In
general, holding constant exposure from age 10 to present, a person is more likely to start
smoking in periods when the recent five years witnessed more anti-smoking articles. This result
bears more investigation because it may capture periods near the 1964 Surgeon General's Report
or similar events (when initiation and smoking prevalence were both higher). Exposure to antismoking information from age 10 to present leads to a lower probability of participation and a
higher probability of cessation for men and women regardless of how one measures current
information flows.
Table 5 presents results for the pooled sample of men and women but separated by race.
Panel A presents results for white men and women. They are less likely to start and smoke and
more likely to quit when they see more anti-smoking articles from age 10 to present. One
observes again that, holding constant the stock of anti-smoking articles seen, a person is more
likely to start and smoke when they see more articles published in the past five years. Whites are
more likely start and smoke and less likely to quit when they saw more articles from age 10 to
present that were neutral in content.
Table 5 Panel B presents results for Black men and women. Here results are inconsistent
across types of information. Blacks are more likely to start when, from age 10 to present, they
see more anti-smoking information and less likely to start when they saw more articles that were
neutral in their content they presented. But the probability of smoking participation is higher
when they saw more articles that were neutral in their content they presented. Cessation
decisions of blacks is uncorrelated with information flows.
This lack of consistent results may arise because the information measure is not specific
enough to blacks. A more precise, more race-specific measure, might yield more consistent
results.
Table 5 Panel C presents results for other non black minorities. Here results are
consistent. Non black minorities are less likely to start and smoke and more likely to quit when
they saw more anti-smoking articles from age 10 to present. They are more likely to smoke when
they see articles that were neutral in their content and less likely to quit they see more prosmoking articles from age 10 to present.
6. Conclusion and Future Work
This analysis establishes that smoking decisions vary systematically with the accumulated
amount of information people (potentially) saw from age 10 to present. It is also clear that there
is a complex relationship between current information and smoking decisions, holding
information stock constant. In addition, across the various samples I use, the relationship
between smoking decisions and measures of the different types of information yield results that
vary.
Although these results are provocative, much remains to be done. First, I plan to validate
the measures of exposure using 25 years of data from a consumer survey that tracks the
magazines that individuals read. The data come from the Simmons National Consumer Survey
(NCS). The NCS is one of the two dominant US surveys that firms use to plan their marketing
strategies (the other being an AC Nielsen survey). As such, these data are of high quality and
contained detailed measures on whether or not consumers read each of 182 magazines (and how
often). I will use the NCS data on actual magazines each person read to first show that the statespecific measure of exposure tracks actual exposure.
I will also use the NCS data to predict the demographic characteristics of people who
read each of the magazines common to my list and to the NCS surveys. I will then use the
resulting coefficients as weights that more precisely account for the probability that a given PSID
respondent read each magazine.
This exercise is flawed in two ways. First, it assumes that the determinants of reading a
given magazine do not change over time (which is obviously incorrect). Second, some
magazines that published smoking-related articles are not represented in the NCS - either
because the NCS does not include them or because the magazines no longer exist.
To mitigate these problems I will try one of two strategies. I will restrict the set of
magazines to titles that are represented in the NCS and have existed over the whole sample
period. Second, I will get information on demographic characteristics of readers of each
magazine from other sources (and over time).
Despite these issues, the results suggest that these measures of information and the method that
exploits more variation in exposure to information will serve to explain more variation in behaviors of
interest. The method can certainly be applied to other types of information and to explain other types of
behavior.
References
Anderson C, Burns DM. (2000). Patterns of adolescent smoking initiation rates by ethnicity and
sex. Tob Control. 9(Suppl 2): ii4-ii8.
Avery, R, Kenkel, D, Lillard, DR, Mathios, A. 2007. “Private Profits and Public Health: Does
DTC Advertising of Smoking Cessation Products Encourage Smokers to Quit?” Journal of
Political Economy. Vol. 115 (3): 447-481 (also NBER working paper 11938).
Baltagi, BH and Levin, D. 1986. “Estimating Dynamic Demand for Cigarettes Using Panel Data:
The Effects of Bootlegging, Taxation and Advertising Reconsidered.” Review of Economics and
Statistics, Vol. 68(1): 148-155.
Bar HY, Lillard DR. (2012). Accounting for heaping in retrospectively reported event data - a
mixture-model approach. Stat Med. 31(27): 3347-3365.
Berney LR, Blane DB. Collecting retrospective data: accuracy of recall after 50 years judged
against historical records. Soc Sci Med. 1997(10); 45: 1519-1525.
Bilal U, Fernández E, Beltran P, et al. Validation of a method for reconstructing historical rates
of smoking prevalence Am. J. Epidemiol. doi:10.1093/aje/kwt224.
Birkett NJ. Trends in smoking by birth cohort for births between 1940 and 1975: a reconstructed
cohort analysis of the 1990 Ontario Health Survey. Prev Med. 1997; 26(4): 534-541.
Brenner H. A birth cohort analysis of the smoking epidemic in West Germany. J Epidemiol
Community Health. 1993; 47(1): 54-58.
Brigham J, Lessov-Schlaggar CN, Javitz HS, et al. Reliability of adult retrospective recall of
lifetime tobacco use. Nicotine Tob Res. 2008; 10(2): 287-299.
Brigham J, Lessov-Schlaggar CN, Javitz HS, et al. Validity of recall of tobacco use in two
prospective cohorts. Am J Epidemiol. 2010; 172(7): 828-835.
Bronnum-Hansen H, Juel K. Estimating mortality due to cigarette smoking: two methods, same
result. Epidemiology. 2000; 11(4): 422-426.
Brown, DJ and Schrader, LF. 1990. "Cholesterol Information and Shell Egg Consumption."
American Journal of Agricultural Economics, Vol. 72(3): 548-555.
Burns DM, Lee L, Shen LZ, et al. Cigarette Smoking Behavior in the United States. In: Burns D,
Garfinkel L, Samet J, eds. Changes in Cigarette-Related Disease Risk and Their Implications for
Prevention and Control. Bethesda: Natl Inst Health; 1998: 13-112.
Christopoulou R, Han J, Jaber A, and Lillard, DR. 2011. "Dying for a smoke: how much does
differential mortality of smokers affect estimated life-course smoking prevalence?" Preventive
Medicine, Vol. 52(1): 66-70.
Christopoulou R, Lillard DR. Is smoking behavior culturally determined? Evidence from British
immigrants. NBER Working Paper 19036. 2013.
Christopoulou R, Lillard DR, Balmori De La Miyar JR. Smoking behavior of Mexicans: patterns
by birth-cohort, gender, and education. Int J Public Health. 2013; 58(3): 335-343.
Connor GS, Schoeld-Hurwitz S, Hardt J, et al. The accuracy of self-reported smoking: a
systematic review of the relationship between selfreported and cotinen-assessed smoking status.
Nicotine Tob Res. 2009; 11(1): 12-24.
Mark Coppejans, Donna Gilleskie, Holger Sieg, Koleman Strumpf. 2007. "Consumer Demand
under Price Uncertainty: Empirical Evidence from the Market for Cigarettes." Review of
Economics and Statistics, Vol. 89(3): 510-521.
de Walque D. Education, Information, and Smoking Decisions: Evidence from Smoking
Histories in the United States, 1940-2000. J Human Resources. 2010; 45(3): 682-717.
Douglas S. The duration of the smoking habit. Econ Inq.1998; 36: 49-64.
Douglas S, Hariharan G. The hazard of starting smoking: estimates from a split population
duration model. J Health Econ. 1994; 13(2): 213-230.
Dranove, D., Kessler, D., McClellan, M., and Satterthwaite, M. 2003. "Is More Information
Better? The Effects of "Report Cards" on Health Care Providers." Journal of Political Economy,
Vol. 111(3): 555-588.
Escobedo LG, Peddicord JP. Smoking prevalence in US birth cohorts: the influence of gender
and education. Am J Public Health. 1996; 86(2): 231-236.
Fernandez E, Schia-no A, Borras JM, et al. Prevalence of cigarette smoking by birth cohort
among males and females in Spain, 1910-1990. Eur J Cancer Prev. 2003; 12(1): 57-62.
Forster M, Jones A. The role of tobacco taxes in starting and quitting smoking: duration analysis
of British data. J R Stat Soc Ser A. 2001; 164(3): 517-547.
Federico B, Costa G, Kunst AE. Educational inequalities in initiation, cessation, and prevalence
of smoking among 3 Italian birth cohorts. Am J Public Health. 2007; 97(5): 838-845.
Hamilton, JL. 1972. “The Demand for Cigarettes: Advertising, the Health Scare, and the
Cigarette Advertising Ban.” Review of Economics and Statistics, Vol. 54(4): 401-411.
Harris JE. Cigarette smoking among successive birth cohorts of men and women in the United
States during 1900-80. J Natl Cancer Inst. 1983; 71(3): 473-479.
Hausman JA, Abevaya J, Scott-Morton FM. Misclassication of the dependent variable in a
discrete-response setting. J Econometrics. 1998; 87(2): 239-269.
Hsieh, C. and Lin, S. 1997. "Health Information and the Demand for Preventive Care among the
Elderly in Taiwan." Journal of Human Resources, Vol. 32(2): 308-333.
Ippolito, PM and Ippolito, RA. (1984). "Measuring the Value of Life Saving from Consumer
Reactions to New Information." Journal of Public Economics Vol. 25: 53-81.
Ippolito, PM and Mathios, AD. (1990). "Information, Advertising, and Health Choices: A Study
of the Cereal Market" The Rand Journal of Economics Vol. 21(3): 459-480.
Jaber A, Lillard DR. Tell Me Again? Using Repeated Reports in Panel Data to Reduce Mismatch
Bias (with application to smoking cessation). Ohio StateUniversity. Unpublished manuscript.
Kemm JR. A birth cohort analysis of smoking by adults in Great Britain 1974-1998. J Public
Health Med. 2001; 23(4): 306-311.
Kenkel D. 1990. "Consumer Health Information and the Demand for Medical Care." Review of
Economics and Statistics, Vol. 72(4): 587-595.
Kenkel D, Lillard DR, Liu F. An analysis of life-course smoking behavior in China. Health
Econ. 2009; 18(Suppl 2): S147-S156.
Kenkel D, Lillard DR, Mathios A. Smoke or Fire? Are retrospective smoking data valid?
Addiction. 2003; 98(9): 1307-1313.
Kenkel D, Lillard DR, Mathios A. Accounting for Measurement Error in Retrospective Smoking
Data. Health Econ. 2004; 13(10): 1031-1044.
Kinnucan, HW, Xiao, H., Hsia, C., and Jackson, JD. 1997. "Effects of Health Information and
Generic Advertising on U.S. Meat Demand." American Journal of Agricultural Economics, vol.
79(1): 13-23.
Koenig L, Jacob T, Haber J. Validity of the lifetime drinking history: a comparison of
retrospective and prospective quantity frequency measures. J Stud Alcohol Drugs. 2009; 70(2):
296-303.
Kozup, JC, Creyer, EH, and Burton, S. 2003. "Making Healthful Food Choices: The Influence of
Health Claims and Nutrition Information on Consumers' Evaluations of Packaged Food Products
and Restaurant Menu Items." Journal of Marketing Vol. 67: 19-34.
Krall EA, Valadian I, Dwyer JT, et al. Accuracy of recalled smoking data. Am J Public Health.
1989; 79(2): 200-203.
La Vecchia C, Decarli A, Pagano R. Prevalence of cigarette smoking among subsequent cohorts
of Italian males and females. Prev Med. 1986; 15(6): 606-613.
Lillard, DR and Molloy, E. 2013. “Putting People in Their Place: Reducing Mismatch Bias.”
Working paper. Department of Human Sciences, Ohio State University.
Lillard DR, Molloy E, Sfekas A. Smoking initiation and the iron law of demand. J Health Econ.
2013: 32(1): 114-127.
Lillard DR and Sfekas, A. 2013.. “Just passing through: the effect of the Master Settlement
Agreement on estimated cigarette tax price pass-through.” Applied Economics Letters, 20(4):
353-357
Lopez AD, Collishaw NE, Piha T. A descriptive model of the cigarette epidemic in developed
countries. Tob Control. 1994; 3(3): 242-247.
Marugame T, Kamo K, Sobue T, et al. Trends in smoking by birth cohorts born between 1900
and 1977 in Japan. Prev Med. 2006; 42(2): 120-127.
Menezes AM, Lopes MV, Hallal PC, et al. Prevalence of smoking and incidence of initiation in
the Latin American adult population: the PLATINO study. BMC Public Health. 2009; 9: 151.
Nicolas AL. How important are tobacco prices in the propensity to start and quit smoking? An
analysis of smoking histories from the Spanish National Health Survey. Health Econ. 2002;
11(6): 521-535.
Pauly, MV and Satterthwaite, MA. 1981. "The pricing of primary care physicians' services: a test
of the role of consumer information." The Bell Journal of Economics, Vol. 12(2): 488-506.
Perlman F, Bobak M, Gilmore A, et al. Trends in the prevalence of smoking in Russia during the
transition to a market economy. Tob Control. 2007; 16(5): 299-305.
Pershagen G, Axelson O. A validation of questionnaire information on occupational exposure
and smoking. Scand J Work Environ Health. 1982; 8(1): 24-28.
Peto R, Lopez AD, Boreham J, et al. Mortality from smoking in developed countries, 1950-2000
(online edition). Oxford: Oxford University Press; 2006.
Peto R, Lopez AD, Boreham J, et al. Mortality from tobacco in developed countries: indirect
estimation from national vital statistics. Lancet. 1992; 339(8804): 1268-1278.
Pratt, CA and Pratt, CB. 1995. "Comparative content analysis of food and nutrition
advertisements in Ebony, Essence, and Ladies' Home Journal." Journal of Nutrition Education,
Vol. 27(1): 11-17.
Preston SH, Glei DA, Wilmoth JR. A new method for estimating smoking-attributable mortality
in high income countries. Int J Epidemiol. 2010; 39(2): 430-438.
Preston SH, Wang H. Sex mortality dierences in the United States: the role of cohort smoking
patterns. Demography. 2006; 43(4): 631-646.
Ronneberg A, Lund KE, Hafstad A. Lifetime smoking habits among Norwegian men and women
born between 1890 and 1974. Int J Epidemiol. 1994; 23(2): 267-276.
Rosenzweig, MR and Schultz, TP. 1989. "Schooling, Information andNonmarket Productivity:
Contraceptive Use and its Effectiveness." International Economic Review, Vol. 30(2): 457-477.
Schneider, L, Klein, B. and Murphy, KM. 1981. "Governmental Regulation of Cigarette Health
Information." Journal of Law and Economics, Vol. 24(3): 575-612.
Shipton D, Tappin DM, Vadiveloo T, et al. Reliability of self reported smoking status by
pregnant women for estimating smoking prevalence: a retrospective, cross-sectional study. BMJ.
2009; 339.
Simpura J, Poikolainen K. Accuracy of retrospective measurement of individual alcohol
consumption in men: a reinterview after 18 years. J Stud Alcohol. 1983; 44(5): 911-917.
Teisl, MR, Levy, AS, and Derby, BM. 1999. "The Effects of Education and Information on
Consumer Awareness of Diet-Disease Relationships." Journal of Public Policy and Marketing,
Vol 18(2): 197-207.
Thun M, Peto R, Boreham J, et al. Stages of the cigarette epidemic on entering its second
century. Tob Control. 2012; 21(2): 96-101.
US DHEW. 1964. Smoking and Health: Report of the Advisory Committee of the Surgeon
General of the Public Health Service. U.S. Department of Health Education and Welfare, Public
Health Service Publication No. 1103, Washington, DC.
Viscusi, WK. 1993. The Value of Risks to Life and Health. Journal of Economic Literature.
XXXI: 1912-1946.
Viscusi, WK and Aldy, J. 2003. "The Value of a Statistical Life: A Critical Review of Market
Estimates Throughout the World." Journal of Risk and Uncertainty, Vol. 27(1): 5-76.
Viscus, WK and Hersch, J. 2011. “Tobacco Regulation Through Litigation: The Master
Settlement Agreement.” in Regulation versus Litigation: Perspectives from Economics and Law.
Daniel P Kessler (editor), University of Chicago Press, 71-101.
Viscusi, WK, Magat, WA, and Huber, J. 1986. "Informational regulation of consumer health
risks: an empirical evaluation of hazard warnings." RAND Journal of Economics, Vol. 17(3):
351-365.
Wakefield, Melanie, Glen Szczypka, Yvonne Terry-McElrath, Sherry Emery, Brian Flay, Frank
Chaloupka, and Henry Saffer. (2005). "Mixed Messages on Tobacco: Comparative Exposure to
Public Health, Tobacco Company-and-Pharmaceutical Company-Sponsored Tobacco-Related
Television Caimpaigns in the United States, 1999-2003." Addiction, 100 (12), 1875-1883.
Wang H, Preston SH. Forecasting United States mortality using cohort smoking histories. Proc
Natl Acad Sci USA. 2009; 106(2): 393-398.
Warner KE. Eects of the antismoking campaign: an update. Am J Public Health. 1989; 79(2):
144-151.
Figure 1 Trends in the number of smoking‐related artices published 1919‐2009 Source: ProQuest and the Historical Reader’s Guide to Periodical Literature Figure 2 State‐specific exposure to smoking related articles published 1919‐2009 Source: ProQuest and the Historical Reader’s Guide to Periodical Literature, Audit Bureau of Circulation, and PSID (various years) Table 1 Sample statistics
Variable
Started
Currently smoke
Quit
Articles published in current year
Antismoking, count
Antismoking, state specific exposure
Pro-smoking, count
Pro-smoking, state specific exposure
Neutral, count
Neutral, state specific exposure
Articles published from age 10 to T
Antismoking, count
Antismoking, state specific exposure
Pro-smoking, count
Pro-smoking, state specific exposure
Neutral, count
Neutral, state specific exposure
Demographics
Age
Age 50+
Education
Permanent household income
Female
White
Black
American Indian, Aleut, Eskimo
Asian, Pacific Islander
Hispanic
Other race
Other
Cigarette tax
Year
N (person)
N (person-year)
Starts
Participation
Quits
Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
0.06
(0.24)
0.35
(0.48)
0.02
(0.15)
35.42
0.53
2.10
0.02
3.70
0.05
(23.57)
(0.46)
(2.61)
(0.03)
(4.49)
(0.09)
39.63
0.59
2.30
0.02
3.42
0.04
(23.68)
(0.47)
(2.87)
(0.03)
(4.34)
(0.08)
37.65
0.57
2.27
0.02
3.96
0.05
(22.46)
(0.47)
(2.81)
(0.03)
(4.48)
(0.09)
38.37
3.58
2.21
0.12
4.17
0.38
(24.71)
(3.18)
(2.61)
(0.12)
(4.71)
(0.47)
48.22
9.34
2.67
0.40
4.76
1.32
(27.74)
(8.17)
(2.91)
(0.34)
(4.60)
(1.29)
43.80
7.63
2.53
0.34
5.20
1.26
(24.78)
(5.86)
(2.81)
(0.26)
(4.80)
(1.15)
18.16
(3.96)
12.72
51070.0
0.50
0.58
0.36
0.01
0.01
0.01
0.03
0.86
1972.9
17404
149104
32.91
(16.08)
30.09
(12.02)
0.16
(0.37)
0.08
(0.27)
(2.72)
12.20
(3.04)
11.82
(2.86)
(31502.8) 51013.5 (32700.8) 49281.6 (30965.8)
0.49
0.44
0.64
0.65
0.31
0.30
0.02
0.02
0.01
0.01
0.01
0.01
0.02
0.02
(0.34)
(19.8)
0.86
1977.9
18768
555324
(0.37)
(17.9)
0.81
1975.2
11012
198772
(0.30)
(16.1)
Table 2 Sample statistics, by sex
Men
Starts
Variable
Mean
Women
Participation
Std.
Mean
Std. Dev.
Quits
Mean
Starts
Std. Dev.
Mean
Participation
Std. Dev.
Mean
Std. Dev.
Quits
Mean
Std. Dev.
Started
0.06
(0.24)
0.38
(0.49)
0.98
(0.14)
0.06
(0.24)
0.32
(0.47)
0.98
(0.14)
Currently smoke
0.07
(0.25)
0.04
(0.19)
1.00
(0.03)
0.06
(0.24)
0.03
(0.18)
1.00
(0.04)
Quit
0.02
(0.14)
0.02
(0.15)
0.02
(0.15)
0.02
(0.15)
0.03
(0.16)
0.03
(0.16)
38.10
(23.44)
40.77
(23.72)
36.94
(22.42)
32.70
(23.38)
38.43
(23.58)
38.56
(22.47)
Antismoking, state specific exposure
0.58
(0.47)
0.61
(0.47)
0.56
(0.47)
0.47
(0.45)
0.57
(0.47)
0.59
(0.47)
Pro-smoking, count
2.19
(2.78)
2.31
(2.91)
2.25
(2.76)
2.01
(2.42)
2.28
(2.82)
2.31
(2.87)
Articles published in current year
Antismoking, count
Pro-smoking, state specific exposure
0.02
(0.03)
0.02
(0.03)
0.02
(0.03)
0.02
(0.03)
0.02
(0.03)
0.02
(0.03)
Neutral, count
3.67
(4.54)
3.31
(4.32)
4.05
(4.53)
3.74
(4.43)
3.54
(4.35)
3.85
(4.42)
Neutral, state specific exposure
0.05
(0.09)
0.04
(0.08)
0.05
(0.09)
0.05
(0.08)
0.04
(0.09)
0.05
(0.09)
Articles published from age 10 to T
Antismoking, count
41.39
(24.44)
49.83
(27.70)
42.92
(24.85)
35.31
(24.59)
46.51
(27.69)
44.89
(24.65)
Antismoking, state specific exposure
4.02
(3.27)
9.75
(8.24)
7.43
(5.89)
3.13
(3.02)
8.91
(8.07)
7.89
(5.81)
Pro-smoking, count
2.30
(2.78)
2.69
(2.94)
2.50
(2.76)
2.11
(2.42)
2.65
(2.87)
2.56
(2.86)
Pro-smoking, state specific exposure
0.13
(0.13)
0.40
(0.33)
0.33
(0.25)
0.12
(0.11)
0.40
(0.35)
0.35
(0.27)
Neutral, count
4.16
(4.77)
4.64
(4.57)
5.29
(4.82)
4.17
(4.64)
4.88
(4.63)
5.09
(4.77)
Neutral, state specific exposure
0.40
(0.48)
1.29
(1.25)
1.25
(1.12)
0.37
(0.47)
1.35
(1.32)
1.27
(1.18)
18.14
(3.97)
32.19
(15.34)
30.07
(11.99)
18.18
(3.94)
33.67
(16.80)
30.12
(12.07)
0.14
(0.35)
0.08
(0.27)
0.00
(0.00)
0.18
(0.38)
0.08
(0.27)
13.13
(2.65)
12.47
(3.11)
11.70
(3.05)
12.31
(2.72)
11.91
(2.93)
11.97
(2.58)
56680.6
(34940.2)
55920.7
(35135.9)
50728.1
(32147.6)
45362.0
(26371.9)
45827.3
(29024.3)
47455.4
(29304.5)
0.00
(0.00)
0.00
(0.00)
0.00
(0.00)
1.00
(0.00)
1.00
(0.00)
1.00
(0.00)
Demographics
Age
Age 50+
Education
Permanent household income
Female
White
0.63
0.67
0.65
0.53
0.60
0.64
Black
0.31
0.27
0.29
0.42
0.35
0.32
American Indian, Aleut, Eskimo
0.02
0.02
0.02
0.01
0.02
0.02
Asian, Pacific Islander
0.01
0.01
0.01
0.01
0.01
0.01
Hispanic
0.00
0.00
0.00
0.01
0.01
0.01
Other race
0.03
0.03
0.02
0.02
0.02
0.01
Other
Cigarette tax
Year
N (person)
N (person-year)
0.85
(0.34)
0.86
(0.38)
0.81
(0.30)
0.88
(0.34)
0.86
(0.36)
0.81
(0.31)
1976.0
8888
75193
(18.0)
1979.4
9751
285337
(17.2)
1974.2
5751
110915
(16.4)
1969.8
8516
73911
(21.0)
1976.4
9017
269987
(18.4)
1976.5
5261
87857
(15.6)
Table 2 Effect of Magazine Information on Smoking Behavior
A. Initiation
Article count
Variable
Current year articles
All articles (/10)
1
2
3
State-specific exposure
4
5
0.0004
(0.0003)
Anti-smoking (/10)
Pro-smoking (/10)
Neutral (/10)
1
2
3
4
5
5A
-0.0304
(0.0574)
0.0010
(0.0004)*
-0.0078
(0.0025)**
0.0010
(0.0005)*
-0.0062
(0.0026)*
-0.0325
(0.0601)
-0.2537
(0.6548)
-0.0190
(0.0605)
-0.1902
(0.6553)
-0.0030
(0.0166)
-0.4899
(0.2213)*
-0.0007
(0.0020)
-0.0016
(0.0020)
-0.0069
(0.2325)
-0.0153
(0.2333)
0.1954
(0.0884)*
Articles in years age 10 to T
All articles (/10)
-0.0024
(0.0032)
Anti-smoking (/10)
Pro-smoking (/10)
Neutral (/10)
Full cigarette tax
R2
N(person)
N(person-year)
-0.0064
(0.0036)
0.0011
(0.0004)*
-0.0122
(0.0024)**
0.0008
(0.0005)
-0.0118
(0.0025)**
-0.0036
(0.0042)
-0.2908
(0.0936)**
-0.0035
(0.0042)
-0.2888
(0.0937)**
-0.0086
(0.0038)*
-0.1888
(0.0649)**
0.0008
(0.0020)
0.0015
(0.0020)
0.0014
(0.0217)
0.0022
(0.0218)
0.0444
(0.0195)*
0.0116
0.0109
0.0114
0.0092
0.0092
-0.0144
-0.0144
-0.0141
-0.0145
-0.0146
0.0086
(0.0023)**
(0.0024)**
(0.0023)**
(0.0023)**
(0.0024)**
(0.0033)**
(0.0034)**
(0.0033)**
(0.0033)**
(0.0034)**
(0.0023)**
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
17,404
17,404
17,404
17,404
17,404
17,404
17,404
17,404
17,404
17,404
17,404
149104
149104
149104
149104
149104
149104
149104
149104
149104
149104
149104
0.02
Table 2 Effect of Magazine Information on Smoking Behavior, continued
B. Participation
Article count
Variable
1
2
3
State-specific exposure
4
5
1
2
3
4
5
5A
Current year articles
All articles (/10)
-0.0013
(0.0003)**
0.1193
(0.1105)
Anti-smoking (/10)
0.0003
(0.0004)
0.0017
(0.0004)**
0.0090
(0.1109)
-0.0396
(0.1121)
-0.0285
(0.0162)
Pro-smoking (/10)
-0.0153
(0.0019)**
-0.0086
(0.0020)**
0.2636
(0.5462)
-0.1902
(0.5463)
-1.4467
(0.1629)**
Neutral (/10)
-0.0072
(0.0020)**
-0.0057
(0.0017)**
1.7336
(0.4007)**
1.0893
(0.3921)**
-0.2282
(0.1086)*
Articles in years age 10 to T
All articles (/10)
-0.0067
(0.0062)
Anti-smoking (/10)
Pro-smoking (/10)
Neutral (/10)
Full cigarette tax
-0.0378
(0.0050)**
-0.0354
(0.0049)**
-0.0019
(0.0005)**
-0.0199
-0.0022
(0.0004)**
-0.0193
-0.0622
(0.0082)**
-0.3037
-0.0613
(0.0081)**
-0.2984
-0.0509
(0.0077)**
-0.6622
(0.0020)**
-0.0028
(0.0025)
(0.0017)**
-0.0011
(0.0024)
(0.2363)
0.5147
(0.0508)**
(0.2376)
0.5116
(0.0506)**
(0.1748)**
0.4278
(0.0411)**
-0.0406
(0.0050)**
-0.0395
(0.0053)**
-0.0328
(0.0083)**
-0.0303
(0.0081)**
-0.0333
(0.0050)**
-0.0496
(0.0082)**
-0.0451
(0.0080)**
-0.0478
(0.0084)**
R2
N(person)
0.09
18,768
0.09
18,768
0.09
18,768
0.09
18,768
0.09
18,768
0.10
18,768
0.10
18,768
0.10
18,768
0.10
18,768
0.10
18,768
0.10
18,768
N(person-year)
555,324
555,324
555,324
555,324
555,324
555,324
555,324
555,324
555,324
555,324
555,324
-0.0376
(0.0051)**
-0.0077
(0.0065)
Table 2 Effect of Magazine Information on Smoking Behavior, continued
C. Cessation
Article count
Variable
Current year articles
All articles (/10)
1
2
3
State-specific exposure
4
5
1
2
3
4
5
5A
-0.0007
-0.0522
-0.0490
-0.0176
(0.0002)**
(0.0382)
(0.0410)
(0.0095)
Anti-smoking (/10)
-0.0020
-0.0020
-0.0440
0.0808
0.5175
Pro-smoking (/10)
(0.0003)**
0.0088
(0.0003)**
0.0090
(0.0406)
0.0566
(0.3759)
-0.0747
(0.1306)**
0.0372
(0.0016)**
(0.0016)**
(0.3755)
(0.0968)
(0.0356)
Neutral (/10)
0.0040
0.0039
-0.1498
(0.0011)**
(0.0011)**
(0.0963)
Articles in years age 10 to T-1
All articles (/10)
0.0008
0.0047
(0.0012)
Anti-smoking (/10)
(0.0014)**
-0.0003
0.0002
0.0109
0.0109
0.0032
(0.0003)
(0.0003)
(0.0021)**
(0.0021)**
(0.0019)
Pro-smoking (/10)
-0.0027
-0.0031
-0.0086
-0.0042
-0.1503
Neutral (/10)
(0.0014)*
0.0026
(0.0014)*
0.0012
(0.0406)
-0.0374
(0.0406)
-0.0368
(0.0309)**
-0.0059
Full cigarette tax
0.0210
0.0213
(0.0021)**
(0.0021)**
(0.0011)*
(0.0011)
(0.0102)**
(0.0102)**
(0.0082)
0.0215
0.0200
0.0202
0.0133
0.0130
0.0125
0.0111
0.0111
0.0231
(0.0021)*
(0.0022)**
(0.0022)**
(0.0033)**
(0.0034)**
(0.0033)**
(0.0034)**
(0.0034)**
(0.0022)**
*
R2
0.01
0.01
0.01
0.01
0.02
0.02
0.02
0.02
0.02
0.01
N(person)
11,012
11,012
11,012
11,012
11,012
11,012
11,012
11,012
11,012
11,012
11,012
N(person-year)
198772
198772
198772
198772
198772
198772
198772
198772
198772
198772
198772
0.01
Table 3 Effect of Magazine Information, by Sex
Initiation
Variables
Articles published in current year
Anti-smoking (/10)
Pro-smoking (/10)
Neutral (/10)
Articles published from age 10 to T
Anti-smoking (/10)
Pro-smoking (/10)
Neutral (/10)
Full cigarette tax
2
R
N(person)
N(person-year)
Men
Participation
Cessation
Women
Men
Women
Men
Women
-0.0848
(0.0859)
-0.6667
(0.8378)
-0.1648
(0.3571)
0.1437
(0.0884)
0.2673
(0.9798)
-0.0056
(0.3070)
-0.1369
(0.1500)
-1.2904
(0.7844)
0.6790
(0.5660)
0.2013
(0.1561)
0.2403
(0.7299)
1.3734
(0.5202)**
-0.0595
(0.0529)
0.3309
(0.5256)
-0.1589
(0.1287)
-0.0365
(0.0641)
-0.1772
(0.5399)
0.0409
(0.1471)
0.0114
(0.0063)
-0.1984
(0.1290)
0.0581
(0.0302)
-0.0162
(0.0053)**
0.03
-0.0165
(0.0061)**
-0.2711
(0.1409)
-0.0948
(0.0329)**
-0.0041
(0.0046)
0.03
-0.0941
(0.0114)**
-0.5165
(0.3310)
0.6919
(0.0698)**
-0.0025
(0.0111)
0.14
-0.0354
(0.0115)**
-0.1666
(0.3382)
0.2582
(0.0720)**
-0.0428
(0.0110)**
0.09
0.0089
(0.0028)**
0.0157
(0.0558)
-0.0134
(0.0139)
0.0113
(0.0044)*
0.02
0.0126
(0.0032)**
-0.0253
(0.0599)
-0.0598
(0.0151)**
0.0109
(0.0053)*
0.02
8,888
8,516
9,751
9,017
5,751
5,261
75,193
73,911
285,337
269,987
110,915
87,857
Table 4 Effect of Magazine Information, by recency of information, by sex
Variable
Articles published in recent yrs
Anti-smoking (/10)
Pro-smoking (/10)
Neutral (/10)
Articles published age 10 to T
Anti-smoking (/10)
Pro-smoking (/10)
Neutral (/10)
Full cigarette tax
R2
N(person)
N(person-year)
Current
A. Men
Participation
Past 3 yrs.
Past 5yrs.
0.0060
(0.0023)**
0.1474
(0.0339)**
0.0353
(0.0106)**
-0.0430
(0.1491)
-0.8158
(0.7906)
-0.0285
(0.5587)
-0.0010
(0.0084)
-0.0155
(0.0674)
-0.0180
(0.0350)
0.0158
(0.0065)*
-0.2261
(0.1332)
0.0488
(0.0305)
-0.0138
(0.0053)**
0.0038
(0.0072)
-0.5593
(0.1438)**
-0.0338
(0.0338)
-0.0120
(0.0053)*
-0.0941
(0.0114)**
-0.5216
(0.3314)
0.6917
(0.0698)**
-0.0025
(0.0111)
0.03
8896
75278
0.03
8896
75278
0.14
9751
285499
Current
Inititiation
Past 3 yrs.
Past 5yrs.
-0.0957
(0.0864)
-0.4991
(0.8480)
-0.2201
(0.3605)
-0.0097
(0.0040)*
0.0135
(0.0506)
0.0141
(0.0164)
0.0113
(0.0063)
-0.1973
(0.1288)
0.0582
(0.0302)
-0.0164
(0.0053)**
0.03
8896
75278
Current
Cessation
Past 3 yrs.
Past 5yrs.
0.0504
(0.0050)**
0.5098
(0.0550)**
0.0900
(0.0234)**
-0.0611
(0.0533)
0.3075
(0.5358)
-0.1078
(0.1321)
-0.0066
(0.0023)**
-0.0087
(0.0275)
0.0061
(0.0072)
-0.0024
(0.0015)
0.0108
(0.0207)
0.0012
(0.0051)
-0.0945
(0.0114)**
-0.5302
(0.3366)
0.6954
(0.0698)**
-0.0033
(0.0110)
-0.0987
(0.0117)**
-0.9557
(0.3411)**
0.6764
(0.0702)**
0.0138
(0.0111)
0.0100
(0.0028)**
0.0017
(0.0568)
-0.0158
(0.0141)
0.0102
(0.0044)*
0.0111
(0.0029)**
0.0133
(0.0585)
-0.0181
(0.0145)
0.0114
(0.0045)*
0.0107
(0.0030)**
-0.0016
(0.0607)
-0.0160
(0.0150)
0.0112
(0.0046)*
0.14
9751
285499
0.15
9751
285499
0.02
5811
111037
0.02
5811
111037
0.02
5811
111037
Table 4 Effect of Magazine Information, by recency of information, by sex, continued
Variable
Articles published in recent yrs
Anti-smoking (/10)
Pro-smoking (/10)
Neutral (/10)
Articles published age 10 to T
Anti-smoking (/10)
Pro-smoking (/10)
Neutral (/10)
Full cigarette tax
R2
N(person)
N(person-year)
Current
B. Women
Participation
Past 3 yrs.
Past 5yrs.
0.0195
(0.0024)**
0.1138
(0.0353)**
0.0291
(0.0107)**
0.2361
(0.1547)
0.3998
(0.7417)
1.1206
(0.5099)*
0.0117
(0.0085)
0.0614
(0.0649)
0.0564
(0.0332)
-0.0190
(0.0062)**
-0.3282
(0.1473)*
-0.0987
(0.0336)**
-0.0046
(0.0047)
-0.0445
(0.0072)**
-0.5329
(0.1618)**
-0.1791
(0.0384)**
-0.0065
(0.0045)
-0.0355
(0.0115)**
-0.1661
(0.3382)
0.2584
(0.0720)**
-0.0427
(0.0110)**
0.03
8522
74097
0.03
8522
74097
0.09
9017
270192
Current
Initiation
Past 3 yrs.
Past 5yrs.
0.1626
(0.0889)
0.5372
(0.9960)
0.0962
(0.3117)
0.0106
(0.0039)**
0.0734
(0.0525)
0.0028
(0.0149)
-0.0167
(0.0061)**
-0.2735
(0.1407)
-0.0949
(0.0329)**
-0.0037
(0.0046)
0.03
8522
74097
Current
Cessation
Past 3 yrs.
Past 5yrs.
0.0631
(0.0051)**
0.4942
(0.0538)**
0.1146
(0.0233)**
-0.0589
(0.0649)
-0.2409
(0.5495)
0.0842
(0.1509)
-0.0055
(0.0027)*
-0.0089
(0.0310)
0.0085
(0.0088)
-0.0030
(0.0018)
0.0003
(0.0244)
0.0088
(0.0062)
-0.0351
(0.0115)**
-0.1836
(0.3443)
0.2539
(0.0720)**
-0.0414
(0.0109)**
-0.0430
(0.0117)**
-0.6593
(0.3485)
0.2501
(0.0724)**
-0.0339
(0.0109)**
0.0126
(0.0033)**
-0.0236
(0.0608)
-0.0588
(0.0153)**
0.0103
(0.0054)
0.0134
(0.0033)**
-0.0154
(0.0618)
-0.0605
(0.0155)**
0.0112
(0.0055)*
0.0138
(0.0034)**
-0.0214
(0.0631)
-0.0622
(0.0160)**
0.0118
(0.0056)*
0.09
9017
270192
0.10
9017
270192
0.02
5316
87921
0.02
5316
87921
0.02
5316
87921
Table 5 Effect of Magazine Information, by recency of information, by race
A. White
Current
Inititiation
Past 3 yrs.
Past 5yrs.
Current
-0.0364
(0.0904)
0.3047
(0.9332)
0.3461
(0.3466)
0.0005
(0.0041)
0.0112
(0.0532)
0.0230
(0.0167)
0.0167
(0.0023)**
0.1572
(0.0349)**
0.0307
(0.0105)**
-0.1131
(0.1542)
0.5654
(0.8755)
1.2133
(0.5326)*
-0.0080
(0.0089)
0.0808
(0.0734)
0.0671
(0.0345)
-0.0124
(0.0055)*
-0.4563
(0.1301)**
0.0948
(0.0289)**
-0.0162
(0.0050)**
-0.0130
(0.0056)*
-0.4551
(0.1330)**
0.0851
(0.0291)**
-0.0160
(0.0051)**
-0.0352
(0.0065)**
-0.7884
(0.1437)**
0.0193
(0.0327)
-0.0170
(0.0051)**
-0.0928
(0.0098)**
-0.4617
(0.3042)
0.7304
(0.0658)**
-0.0270
(0.0102)**
R2
N(person)
0.02
10366
0.02
10366
0.02
10366
N(person-year)
86293
86293
86293
Variable
Articles published in recent years
Anti-smoking (/10)
Pro-smoking (/10)
Neutral (/10)
Articles published from age 10 to T
Anti-smoking (/10)
Pro-smoking (/10)
Neutral (/10)
Full cigarette tax
Participation
Past 3 yrs.
Past 5yrs.
Current
Cessation
Past 3 yrs.
Past 5yrs.
0.0599
(0.0050)**
0.7191
(0.0554)**
0.1495
(0.0228)**
-0.0511
(0.0571)
-0.0164
(0.5935)
0.0312
(0.1463)
-0.0070
(0.0025)**
-0.0226
(0.0310)
0.0128
(0.0079)
-0.0020
(0.0016)
-0.0036
(0.0213)
0.0078
(0.0056)
-0.0917
(0.0099)**
-0.4697
(0.3073)
0.7269
(0.0658)**
-0.0242
(0.0101)*
-0.0971
(0.0100)**
-0.9211
(0.3098)**
0.7154
(0.0663)**
-0.0071
(0.0101)
0.0150
(0.0027)**
0.0652
(0.0518)
-0.0586
(0.0134)**
0.0075
(0.0045)
0.0159
(0.0027)**
0.0741
(0.0526)
-0.0610
(0.0136)**
0.0085
(0.0046)
0.0158
(0.0028)**
0.0666
(0.0540)
-0.0619
(0.0140)**
0.0084
(0.0046)
0.10
11289
0.10
11289
0.11
11289
0.02
7051
0.02
7051
0.02
7051
353146
353146
353146
128696
128696
128696
Table 5 Effect of Magazine Information, by recency of information, by race, continued
B. Black
Current
Inititiation
Past 3 yrs.
Past 5yrs.
Current
-0.0285
(0.0967)
-0.1304
(1.0496)
0.3013
(0.3973)
-0.0099
(0.0043)*
0.0583
(0.0578)
0.0397
(0.0181)*
0.0027
(0.0026)
0.0932
(0.0377)*
0.0664
(0.0132)**
0.3522
(0.1877)
0.6152
(0.7627)
-0.0863
(0.6441)
0.0136
(0.0099)
0.0674
(0.0677)
-0.0249
(0.0404)
0.0174
(0.0075)*
0.1007
(0.1544)
-0.0649
(0.0404)
-0.0148
(0.0054)**
0.0239
(0.0077)**
0.0360
(0.1622)
-0.0883
(0.0410)*
-0.0106
(0.0055)
0.0171
(0.0085)*
-0.1202
(0.1773)
-0.2183
(0.0451)**
-0.0100
(0.0054)
0.0312
(0.0176)
-0.7883
(0.4459)
0.3520
(0.0913)**
-0.0399
(0.0153)**
R2
N(person)
0.02
6030
0.02
6030
0.02
6030
N(person-year)
54351
54351
54351
Variable
Articles published in recent years
Anti-smoking (/10)
Pro-smoking (/10)
Neutral (/10)
Articles published from age 10 to T
Anti-smoking (/10)
Pro-smoking (/10)
Neutral (/10)
Full cigarette tax
Participation
Past 3 yrs.
Past 5yrs.
Current
Cessation
Past 3 yrs.
Past 5yrs.
0.0496
(0.0061)**
0.3610
(0.0592)**
0.0602
(0.0284)*
-0.0307
(0.0722)
0.1479
(0.5495)
-0.0293
(0.1447)
-0.0034
(0.0029)
0.0146
(0.0311)
0.0042
(0.0091)
-0.0029
(0.0019)
0.0242
(0.0267)
0.0046
(0.0064)
0.0304
(0.0177)
-0.8376
(0.4620)
0.3598
(0.0908)**
-0.0415
(0.0152)**
0.0233
(0.0181)
-1.2934
(0.4734)**
0.3616
(0.0908)**
-0.0332
(0.0153)*
-0.0015
(0.0042)
0.0324
(0.0796)
-0.0005
(0.0186)
0.0116
(0.0060)
-0.0007
(0.0043)
0.0356
(0.0833)
-0.0022
(0.0192)
0.0126
(0.0061)*
0.0000
(0.0044)
0.0205
(0.0868)
-0.0020
(0.0201)
0.0135
(0.0062)*
0.11
6352
0.11
6352
0.12
6352
0.02
3438
0.02
3438
0.02
3438
172662
172662
172662
60389
60389
60389
Table 5 Effect of Magazine Information, by recency of information, by race, continued
C. Other race
Current
Inititiation
Past 3 yrs.
Past 5yrs.
Current
0.6781
(0.3083)*
1.5552
(2.7051)
-1.7423
(1.0466)
0.0312
(0.0127)*
0.1998
(0.1333)
-0.0316
(0.0504)
0.0227
(0.0066)**
0.2282
(0.0913)*
0.0351
(0.0300)
0.4882
(0.4749)
-4.2113
(2.9068)
-1.0389
(1.9660)
0.0350
(0.0252)
-0.2286
(0.2346)
-0.0872
(0.1259)
-0.0213
(0.0169)
-0.2269
(0.3510)
0.1308
(0.0811)
-0.0236
(0.0168)
-0.0267
(0.0171)
-0.3466
(0.3609)
0.1073
(0.0839)
-0.0188
(0.0172)
-0.0487
(0.0192)*
-0.7536
(0.3839)*
0.0163
(0.0997)
-0.0110
(0.0165)
-0.1277
(0.0376)**
0.3374
(1.0464)
0.4183
(0.1995)*
-0.0177
(0.0302)
R2
N(person)
0.04
1022
0.04
1022
0.04
1022
N(person-year)
8731
8731
8731
Variable
Articles published in recent years
Anti-smoking (/10)
Pro-smoking (/10)
Neutral (/10)
Articles published from age 10 to T
Anti-smoking (/10)
Pro-smoking (/10)
Neutral (/10)
Full cigarette tax
Participation
Past 3 yrs.
Past 5yrs.
Current
Cessation
Past 3 yrs.
Past 5yrs.
0.0753
(0.0136)**
0.3847
(0.1694)*
0.0396
(0.0738)
-0.2939
(0.1994)
2.1251
(1.7887)
-0.9315
(0.6500)
-0.0095
(0.0087)
0.0622
(0.0924)
-0.0187
(0.0344)
-0.0069
(0.0057)
0.1134
(0.0744)
-0.0058
(0.0209)
-0.1311
(0.0378)**
0.3255
(1.0530)
0.4217
(0.1989)*
-0.0209
(0.0304)
-0.1434
(0.0385)**
-0.0753
(1.0544)
0.4256
(0.1989)*
0.0155
(0.0302)
0.0330
(0.0089)**
-0.5872
(0.2021)**
-0.0301
(0.0434)
0.0106
(0.0178)
0.0335
(0.0091)**
-0.5870
(0.2049)**
-0.0313
(0.0440)
0.0110
(0.0181)
0.0347
(0.0094)**
-0.6300
(0.2085)**
-0.0286
(0.0451)
0.0136
(0.0181)
0.12
1127
0.12
1127
0.13
1127
0.04
638
0.04
638
0.04
638
29883
29883
29883
9873
9873
9873
Appendix
120 magazine titles represented in data
America
American Health
American Legion, The
American Mercury
Americas
Atlantic Monthly
Atlantic, The
Better Homes and Gardens
Black Enterprise
Boating
Brides
Business Week
Car and Driver
Century Magazine
Changing times
Colliers
Conde Nasts Traveler
Coronet
Cosmopolitan
Country Living
Current History
Cycle World
Discover
Ebony
Entertainment Weekly
Esquire
Essence
Family Circle
Field & Stream
Flower and Garden
Flying
Forbes
Fortune
Forum and Century
Glamour
Golf Digest
Good Housekeeping
Gourmet
GQ : Gentlemens Quarterly
Harper's Bazaar
Harper's Magazine
Health
Hippocrates
Hot Rod
Inc
Jet
Kiplinger's Personal Finance
Ladies' Home Journal
Life
Literary Digest
Look
Mademoiselle
McCalls
Men's Health
Money
Ms
Muscle & Fitness
Nation's Business
Natural History
New Choices for the Best Years
New Republic, The
New Woman
New York
New Yorker, The
Newsweek
Outdoor Life
Outlook and Independent
Outlook, The
Parenting
Parents
Parent's Magazine
People Weekly
Playboy
Popular Mechanics
Popular Science
Premiere
Prevention
Reader's Digest
Redbook
Reporter
Review of Reviews
Rolling Stone
Runners World
Saturday Evening Post, The
Saturday Review
Saturday Review of Literature
Scholastic
Science
Science Digest
Science Illustrated
Science News
Science News Letter
Scientific American
Scribner's Magazine
Self
Seventeen
Shape
Smithsonian
Sporting News
Sports Afield
Sports Illustrated
Teen
Texas Monthly
Time
Town and Country
Travel
Travel & Leisure
TV Guide
U.S.News & World Report
Us Weekly
Vanity Fair
Vogue
Washingtonian
Woman's Day
Woman's Home Companion
Working Mother
Working Woman
World Press Review
World Tennis
Yankee