Is Time Money? Media Expenditures in Economic and Technological Turbulence

Is Time Money? Media Expenditures inEconomic and Technological Turbulence

Daniel G. McDonald and Benjamin K. Johnson

Recent years have seen changing and shifting technologies as well as an

uncertain economic climate. This research focuses on how audiences have

reacted to these shifts, using a number of different sources of data to test

hypotheses related to spending time and money on media. We suggest that

previous studies examining audience expenditures and diffusion of new tech-

nologies may have overlooked the stressful economic conditions surrounding

diffusion of some of those technologies. We find an increase in entertainment

technology purchases as well as time spent with new and traditional media

during recession years, beyond that indicated by the longer term trends. While

there is a general decrease in coviewing behavior in recent years, the recession

years reversed the trend. Results are discussed in terms of the constancy

hypothesis and our hypothesis that the media provide outlets for reducing

stress during difficult economic times.

The most costly outlay is time.

Antiphon (c. 430 B.C.)

As quoted in Flexner and Flexner (1993)

When it comes to media audiences, time and money are inextricably connected.

From the earliest academic studies of how much individuals’ time and money

are spent with the media (Edwards, 1915; Gulick, 1909), to a ratings industry

set up to determine how much aggregate audience time is being purchased for

advertisers’ money (Beville, 1988), the connection between time and money has

been described, studied, and documented from psychological, sociological, and

economic perspectives (Albarran & Arrese, 2003).

This article focuses on time and money spent during a period of economic and

technological turbulence. We first provide an overview of what is known about

Daniel G. McDonald (Ph.D., University of Wisconsin) is a professor in the School of Communication atThe Ohio State University. His research interests are primarily in the area of mediated communication.

Benjamin K. Johnson (M.A., Michigan State University) is a doctoral student in Communication at The OhioState University. His research interests include selective exposure and computer-mediated communication.

The authors would like to thank Aaron Ibrahim for his thoughts on the early draft of the article.

© 2013 Broadcast Education Association Journal of Broadcasting & Electronic Media 57(3), 2013, pp. 282–299DOI: 10.1080/08838151.2013.816705 ISSN: 0883-8151 print/1550-6878 online

282

McDonald and Johnson/TIME, MONEY, AND TURBULENCE 283

spending money and time on new technologies, and the known links between

the two. We then suggest that economic pressures coincident with diffusion of the

major communication technologies may be responsible for some deviations from

the constancy hypothesis. We then test these ideas with data collected by the U.S.

government’s Bureau of Labor Statistics from 2003 to 2010, and discuss the results

of those analyses in terms of our hypotheses.

Spending Time

Audience time has been of key interest in the field of mass communication since

its earliest days. The standard measures in media effects research, since before the

advent of network radio (Edwards, 1915), through the television era (Bechtel, Ache-

pohl, & Akers, 1972), and continuing with research on the Internet (Valkenburg,

2007) have focused on how much time is spent with a medium, typically either on a

daily or weekly basis (Flanagin, 2005; Jordan, Trentacoste, Henderson, Manganello,

& Fishbein, 2007; Skouteris & McHardy, 2009). While some researchers have gone

to great pains to separate attention from time spent (Chaffee & Schleuder, 1986),

many researchers have used time as a surrogate measure of attention. The use of

time as an indicator of audience attention, though, is complicated by at least two

factors: time-constraint and multitasking.

Time-Constraint

Certain audience experiences are time-constrained while others are constrained

more by processing requirements. Those that are time-constrained require audience

members to devote a specified amount of time to enjoy the entire experience.

Motion pictures and television shows are typically this way. Although the advent

of VCRs, DVDs, and DVRs has given the audience members some control over the

experience, the required time spent while viewing remains relatively close to what

it was 50 years ago. An audience member will need to spend a minimum amount

of time if s/he is going to watch an entire production.

In spite of the technological changes of the past few decades, then, little of the

‘‘in-program’’ time requirement has changed for these media; indeed, despite the

capability for eliminating some of the time requirement, most viewing of television

continues to be real-time viewing, even in homes with a DVR (Nielsen, 2010a).

What has changed, though, is that the technologies currently associated with these

media have decreased waiting time—the time spent waiting for a program or motion

picture to start.

The VCR’s time-shifting function (Krugman & Johnson, 1991) was inherited and

made simpler by the DVR (Nielsen, 2010a). DVDs by mail or streaming over the

Internet allow audience members to avoid theatres or even rental stores altogether

(Hilderbrand, 2010). New technologies have allowed a lag time to replace waiting

284 Journal of Broadcasting & Electronic Media/September 2013

time. We don’t yet know what audience members are doing with the time that has

been ‘‘saved’’ in this way—TV time may simply be spent with other programs or

movies, or could be used in some other, non-media way. Nielsen (2010a) reports

that programs played back on the same day are typically played back in ‘‘real-

time’’ (without fast-forwarding through commercials), and so do not reduce time

spent with a particular program, while many of those played back a few days later

are viewed with fast-forwarding, reducing each media hour to about 47 minutes.

Time spent with print media has been declining for a number of decades, but

involves not only technology alternatives (e.g., online news) but also generational

and cohort change (De Waal, Schonbach, & Lauf, 2005; Kirchhoff, 2011). Because

the print media have always been seen as portable, time spent with them, among

those who continue to read printed material, may have been less impacted by

technological changes. Books, magazines, and newspapers have been affected by

new technologies, through e-readers or Internet versions of publications, but the

new technologies are likely having a different effect on time spent than is the case

with movies or television. Rather than a magazine or book on an airline flight, for

example, we may take an e-reader loaded with 1,000 books. Odds are, we won’t

read all of those books on the flight, because our own processing (reading) speed

remains the same.

Media Multi-tasking

Multi-tasking in general, and media multi-tasking (also referred to as simultaneous

media use) in particular, has been a part of the media experience for many years

(McDonald & Meng, 2009) but has only recently gotten much research attention

(Janusik & Wolvin, 2009; Jeong, Hwang, & Fishbein, 2010; Nielsen, 2010a). While

time spent typically has been seen as an indicator of attention, the very idea of

multitasking makes it clear that attention is not equivalent to time.

In earlier decades, multi-tasking might occur when people would listen to music

while reading a book. Today, an audience member may be purchasing music online

while watching TV, may be using picture-in-picture technology to watch two TV

programs at the same time, may have several Web sites open and be moving

between them, or may be talking on the phone while doing any of a number of other

activities. Traditional TV programs are now encouraging audience involvement in

a series by providing hashtags and other Internet resources for audience members,

telephone numbers for text or call-in votes, and other activities. While such devices

would maintain a consistent amount of time spent viewing, they likely encourage

program engagement or fan behavior, while taking attention away from the program

being viewed, and underscore not only that time and attention are not equivalent,

but also that attention and engagement are separate components of the audience

experience.

Initial research on the Internet and time use (DiMaggio, Hargittai, Neuman, &

Robinson, 2001; Robinson, Kestnbaum, Neustadtl, & Alvarez, 2000) found that


Internet use did not necessarily displace other activities (De Waal et al., 2005).

However, time use data examined over a period of 3 years (1996–1999) indicated

that the Internet was pulling small amounts of time away from television viewing

(McDonald & Dimmick, 2003). Others noted that using the Internet supplements

other activities and serves as a time-saving technology (Robinson et al., 2000).

Mobile media and technologies like the DVR also presumably complement, as

much as displace, other media use. This may result in no net increase in media use,

or an increase in television and other traditional media use due to the enhancing

and time-saving function of newer technology. However, it does not necessarily

follow that spending money on media will remain a constant. New technologies do

require new money.

Spending Money

In the United States, most media are commercial media, so money is necessarily

involved in the audience experience. Audience members either pay directly for

access to the media content (e.g., movies, books, magazines) or pay indirectly (in

the case of sponsored media), providing their time as they watch commercials.

Additionally, and important in the diffusion of new communication technology,

they must pay for the technology to receive the content (e.g., DVR, DVD player,

smart phone).

Sponsored media have received the bulk of the academic research related to

audience spending on mass media. The best known of these is the constancy hypoth-

esis popularized by McCombs and colleagues (McCombs, 1972; Son & McCombs,

1993) and first described by publisher Charles Scripps (see McCombs, 1972). The

constancy hypothesis is that the level of spending on mass media is determined by

the general state of the economy, and that any change in the economy will cause a

parallel change in spending on mass media. An assumption in this research, called

the constancy assumption, is that spending on mass communication will remain at

the same percentage of the gross domestic product over time. Additionally, there

is the assumption of functional equivalence, which suggests that if spending on

one medium goes up (as a percentage of GDP), then spending on another medium

(typically one that serves a similar function) must go down to offset the change

(McCombs, 1972, pg. 10). A number of studies have focused on the constancy

assumption and typically, although not always, found support.

A natural question arises as to how the constancy hypothesis functions during

a period of introduction of new technologies. The assumption of functional equiv-

alence implies that money would need to be diverted from other technologies to

allow for purchase of (or access to) the new technologies. McComb’s original study

(1972) covered the period from 1929 to 1968, a period that included the Great

Depression and much of the diffusion of radio. In the data reported by McCombs,

the first few years of the series (1929–32) indicate a slightly higher spending level

than that seen during the rest of the series.


Similarly, Fullerton’s (1988) study was an attempt to investigate the diffusion of

television under the umbrella of the constancy hypothesis. Fullerton found a short-

term increase in money spent during the early adoption of television, suggesting that

a period of major technological change may provide an exception to the constancy

hypothesis. Wood and O’Hare (1991) also found increasing proportions of media

expenditures from 1979 to 1988, which they attributed to the popularity of diffusing

video technologies (the VCR and various cable TV innovations). Son and McCombs

(1993) focused on the diffusion of videotext, videodisc players, and pay cable as

innovations in the 1980s and also found that there were increasing expenditures on

media in the then-most-recent decades (1970s and 1980s).

Son (1990, reported in Su, 2010) studied that same time period (the early 1980s)

and concluded that the reason for the departure from constancy was the nature of

the technologies that were diffusing. Some technological diffusions are of competing

media—providing content in direct competition with other media—while others

are complementary and add a new dimension to existing media. Son concluded

that adding a new dimension to existing media (new technologies providing a

complementary function) may result in a temporary departure from the principle

of relative constancy.

Spending Time and Money—Exploring theDeviations and Explanations

McCombs (1972) originally suggested that it is the limitation of both time and

money that results in the constancy of spending on media, but few researchers

have studied time in relation to the constancy hypothesis. Su (2010) suggests that

examining time is a logical extension of the traditional study of the constancy

hypothesis, but also suggests that a focus on both time and money would open

a ‘‘new door’’ for traditional research on the constancy hypothesis.

There are two additional wrinkles in our understanding of the relationships among

time, money, and diffusion of new technologies. The first is that most of the diffusion

eras that have been studied in relation to expenditure have been associated with

one major diffusing technology or medium, while the current environment is what

we might call technologically turbulent—multiple technologies providing similar

functions developing, becoming popular, then declining, all within a few months

or years.

The second complication is that earlier technology diffusion often coincided with

major economic setbacks. During the Great Depression (officially August 1929 to

March 1933), the historical record documents first, an increase in motion picture

attendance, followed by much faster diffusion of home radios (Dimmick & Wang,

2005). On the surface, this provides only a slight ‘‘blip’’ in the constancy hypothe-

sis—a short-term spending increase associated with diffusion of a new technology.

As noted above, this could simply be attributable to diffusion of new technology.

However, an explanation as to why people purchase the technologies and how


they are used might need to turn more to social-psychological explanations. If new

technology purchases account for the short-term increase in spending money, why

would they do so during an economic recession, and how might that transfer from

spending money to spending time?

If, during difficult financial times, individuals turn their spending of both time

and money to activities that reinforce their relationships with friends and family,

we might see a similar pattern. For example, purchasing radio receivers during

the depression allowed the family to stay home and enjoy each others’ company.

Had the diffusing technology been the tape recorder, rather than the radio, would

we have seen that same increase? The operant factors in this case are not quality,

portability, or an improvement of an old technology. Radio offered audio, but no

image, while motion pictures, the older technology, offered both image and audio.

What radio did offer was a general entertainment medium for the entire family,

with a focus on choice between alternative programs more than a choice between

activities (Cantril & Allport, 1935; DeBoer, 1940; Eisenberg, 1936).

A number of early studies of television document a positive impact on the time

families spent together, rather than separately (e.g., Riley, Cantwell, & Ruttiger,

1949). Close examination also finds two recessions during early diffusion of tele-

vision—one in 1948–49, and one in 1953–54. Similarly, diffusion of pay cable,

VCRs, and video disc technologies (all related to media use by families) occurred

simultaneously with a recession from July of 1981 to November of 1982, and an

increase in expenditure during that time frame.

Recent research has suggested a number of psychological effects of economic

recessions. The impact on life at home is considerable. Researchers have described

stresses from work brought into the home, less available free time for those work-

ing, increased free time for those left unemployed, intergenerational stress, later

and lower marriage rates, higher divorce rates, marital discord, abandonment, and

mental illness (Cooper, 2011; Cvrcek, 2011; Davalos & French, 2011; Falconier &

Epstein, 2011; Tsai & Chan, 2011).

In particular, Deaton (2012) found sharp increases in worry, stress, and declines

in positive affect between fall 2008 and spring 2009, the beginning of the ‘‘great

recession.’’ Some have suggested that even observing others dealing with recession

events is stressful (Ackerman, Goldstein, Shapiro, & Bargh, 2009; Vohs & Faber,

2007). Similarly, Houdmont, Kerr, and Addley (2012) found that stress induced by

the recent recession resulted in stress, more hazard exposures, and sickness even

among those who held jobs during the recession.

Popular treatments suggest that coping behavior during a recession includes

attempts to overcome the sense of isolation and personal failure by reaching out to

family and others in the social network (e.g., Williams, undated). Ritualized media

use has long been associated with an escape function, but a small stream of research

suggests that it may be a specific strategy for coping with stress. Rosenblatt and

Cunningham’s (1976) study of TV viewing time and family tensions suggested that

TV is used as a coping mechanism in crowded home environments. Anderson and

Collins (1996) and Zillmann (1993) have also suggested that media use may serve as


a coping mechanism for stress and stressful events. Verma and Larson (2002) find

that adolescents report lower worry and stress while watching TV. Lohaus, Ball,

Klein-Hessling, and Wild (2005) find that use of TV and other media serve a coping

function in relieving stress, and Hutchinson, Baldwin, and Oh (2006) found that

both TV and music listening were coping strategies.

This literature suggests to us that the increases in media spending observed during

diffusion of several new technologies may in part be a function of coping with

economic uncertainty; the purchase of new technologies may serve to provide a kind

of coping technology—increasing time with family and friends. In all three earlier

cases (radio, television, and VCRs), the diffusing technologies offered additional

diversions for family and friends.

The present study seeks to clarify whether the ‘‘driver’’ of these deviations from the

constancy hypothesis might be best understood as a drive for family togetherness

rather than an outlay to get the latest equipment. We focus on a combination

of aggregate and individual-level data to better understand how individual and

household factors might affect the diffusion of technology, and affect how we spend

our time and money with technology. To do so, we focus on the recent recession,

the years leading up to it (2003–2007), and one year after the recession had officially

ended (2010). The National Bureau of Economic Research (NBER, 2010) defined the

great recession as the period from December 2007 through June 2009. We thus have

a period of time in which we have innovations of competing and complementary

media technology for the entire period, with major economic changes during a

portion of that time. Using the literature associated with the constancy hypothesis

for our study frame, we suggest the following hypotheses:

During times of innovation, we have seen temporary increases in spending on

media technology. Because there are a number of new technologies diffusing prior

to and during the period examined in the present study, we should see continual

increases in spending on communication technology, but:

H1: During a recession, the percent of consumer spending allocated to commu-

nication technology will increase more quickly than in years prior to the

recession.

Multi-tasking enables a person to increase time spent with media without de-

creasing time spent with other activities. We therefore suggest that:

H2: Because many of the media diffusing during this time period offer increased

opportunities for multi-tasking, we should also see increases in overall time

spent with media.

The question arises as to how people will allocate their time among the new

and traditional media. Given that many of the new media develop as supplemental,

multi-tasking devices, we suspect that use of traditional media may actually increase


because they will be part of entertainment multi-tasking while other technologies

are being used. In previous decades, people might have turned the television off or

left a room to make a phone call, but there is no need to do so if they are texting or

tweeting with friends during a television program. These new technologies should

then also afford more chances for use of traditional media.

H3: Because many of the diffusing media are often relegated to a multitasking

role, we expect that television and other traditional media use will increase

while most new media are diffusing.

Because coping strategies include reliance on social networks, such as family

members and friends, we should see an increase in the use of those networks

during the recession. Of interest for this article is whether some of that increase

results in increased coviewing activity. We suggest that we should see an increase in

coviewing media use during the most recent recession, given that alternative access

to television content (via Internet, smartphones, etc.) contributes to flexibility and

increased availability of typical family content.

H4: During the recession years (2008–2009) we should see an increase in coview-

ing activity in comparison to the non-recession years (2006–2007).

Similarly, because innovations such as DVRs, DVDs by mail, and Internet stream-

ing provide additional control over when TV and movies are viewable, we should

see decreases in time spent waiting for media content.

H5: During the entire innovation period, we should see a decrease in time spent

waiting for media content.

Method

These hypotheses were tested with data from the U.S. Department of Labor Bureau

of Labor Statistics. Aggregate household data from the Consumer Expenditure (CE)

Survey was used to test the hypothesis on money expenditures, and individual-level

data from the American Time Use Survey (ATUS) was used to test the hypotheses

on time expenditures.

Data Sources

Both the CE and ATUS surveys are conducted by the U.S. Census Bureau on

behalf of the Bureau of Labor Statistics. The CE survey uses stratified sampling to

recruit representative samples of American households for both interview and diary


surveys. The interview sample size for 2009, for example, was 35,756 households

(representing a 75.1% response rate), and the diary sample for 2009 was 14,495

households (76.8% response rate) (BLS, 2011a). Aggregate spending data for hy-

pothesis testing were retrieved from the CE online database for the years 1984 to

2010 (BLS, 2011b).

The ATUS uses interviews to construct time diaries of the primary activities that

people expend their time on, along with data on activity location, time of day, du-

ration, and the presence of others (BLS, 2011c). The cooperation rate was over 52%

for all study years, and about 2,200 household representatives are surveyed each

month (BLS, 2011c). A total of 112,038 respondents completed diary interviews from

2003 through 2010. The study oversampled weekend days at a 2.5:1 ratio, so that

the time use analyses in the present study weigh weekend days with a .4 weight.

Computer-assisted telephone interviews were conducted with ATUS respondents.

Cooperation rate was approximately 52% across all the years included in the study.

Both the CE and ATUS datasets are limited by their use of self-report. Respondents

are required to recall not only their annual expenditures or their activities from

the day before, but also the extent of those expenditures or the duration of those

activities. Responses are therefore subject to a variety of self-report biases, from

faulty recall to social desirability.

Measures and Procedure

With regard to money expenditures, the CE survey provided annual household

averages for five categories of goods that cover media spending: (a) telephone

services, (b) entertainment fees and admission, (c) audio and visual equipment and

services, (d) other entertainment supplies, equipment, and services, and (e) reading.

Data from 1984 to 2010 were available for assessing the constancy of media

spending.

The ATUS provided individual-level data from 2003 to 2010 for daily time ex-

penditures. Only primary activities were recorded in the interview diary, so that

secondary activities, i.e., multitasking behaviors, were not measured. This poses an

additional limitation of the data. Activities that are frequently done as secondary

activities (e.g., using media) are underreported in the data, providing conservative

measures of time outlays. We aggregated individuals’ activities to provide aver-

age totals for 9 categories of time use (see Table 1). For the present study, tradi-

tional media time consisted of television, radio, recorded music, movie attendance,

and reading. Television included viewing television, religious television, and home

video including movies. All media is the most inclusive measure of media use,

summing traditional media, new technologies, phone use, and waiting associated

with entertainment and leisure. Each activity reported in the diary format included

information on whether others were present during the activity. These allowed for

the computation of total television coviewing and viewing alone for each person’s

diary day.

Table 1

Average American Household Annual Monetary Expenditures on Media, 2003–2010

Category 2003 2004 2005 2006 2007 2008 2009 2010

Serial Correlation

(2003–2010)

Serial Correlation

(1984–2003)

Telephone 1,138 1,139 1,174 1,174 1,166 1,138 1,185 1,178 r D .586 r D .965***

Entertainment fees

& admission

588 607 659 654 691 622 641 581 r D .036 r D �.016

Entertainment

equipment

869 906 995 978 1,036 1,046 995 954 r D .598 r D .852***

Entertainment

supplies

544 600 551 487 518 484 408 364 r D �.900** r D �.320

Reading 151 150 141 126 124 117 112 100 r D �.987*** r D �.984***

All media 3,289 3,402 3,519 3,420 3,534 3,408 3,341 3,177 r D �.325 r D .686***

All expenditures 48,572 49,904 51,978 52,270 52,120 50,991 50,048 48,109 r D �.102 r D .716***

Media share of all

expenditures

.0677 .0682 .0677 .0654 .0678 .0668 .0668 .0660 r D �.580 r D .084

Note. Spending in 2010 dollars. *p < .05, **p < .01, ***p < .001.

291


Results

An overview of the data from 2003–2010 is provided in Figure 1 and Tables 1

and 2. As indicated in the figure, the period has seen dramatic changes in unem-

ployment, and time spent with media has fluctuated from about 217 minutes to

about 235 minutes per day. At the same time, many new technologies are being

introduced; many of these are diffusing well and appear to be succeeding. A sample

of the technologies and diffusion rate associated with a particular technology is

also provided in the figure to help interpret the context of diffusion. Tables 1

and 2 provide household average annual money expenditures in 2010 dollars and

individual average daily time expenditures. The linear trends (as assessed via serial

correlation) are perhaps most telling here. The spending data include the time period

of 1984–2010 to provide a context for the period of most interest, 2003–2010.

Figure 1

Selected New Media Penetration Rates Plotted Against Average American Daily

Media Use and the U.S. Unemployment Rate, 2003–2010.

Note: Daily media use indicated by lines. Unemployment rate indicated by bars. Datafrom BLS (2011c, 2011d), FCC (2011), Nielsen (2006, 2009, 2010b), Television Bureau ofAdvertising (2011), U.S. Census Bureau (2011).

Table 2

Average American Individual Daily Minute Expenditures on Media, 2003–2010

Category 2003 2004 2005 2006 2007 2008 2009 2010

Serial Correlation

(2003–2010)

Work & education 281 273 281 280 286 282 270 265 r D �.009*

Home & personal care 792 794 796 794 790 785 794 796 r D �.005

Leisure 358 367 354 356 354 362 364 360 r D .002

All media 193 204 191 194 195 204 209 202 r D .023***

Television & other traditional media 169 178 166 169 171 179 182 174 r D .018***

Television 143 149 139 143 145 154 157 152 r D .028***

Viewing alone 75 75 73 79 79 86 87 85 r D .035***

Coviewing 68 74 66 64 65 68 69 67 r D �.003

Attending movies 1.31 1.18 1.21 1.25 1.42 1.20 1.19 0.87 r D �.007*

Entertainment waiting .09 .07 .05 .03 .01 .04 .03 .02 r D �.013***

Note. *p < .05, **p < .01, ***p < .001.

293


As is evident in Table 1, expenditures on the telephone and entertainment equip-

ment have increased slightly in the study period 2003–2010, but not to the signifi-

cant extent found in the entire prior period since 1984. Entertainment fees have

remained constant since 1984. Entertainment supplies, however, have declined

sharply during the study period. Expenditures associated with reading appear to

remain on the same downward trend since 1984. Total expenditures on media and

total household consumer expenditures have both seen insignificant but negative

trends during the study period, but positive correlations over the entire period since

1984. However, the ratio of media to household spending has remained relatively

constant since 1984, despite a slightly negative trend during the study period.

In regard to time spent, there has been a linear increase in time spent with all

media, traditional media, and television (Table 2). Furthermore, time spent viewing

television alone has trended upward since 2003, but coviewing has remained

constant. Additionally, while waiting for entertainment represents a small amount

of time reported in the survey, it has declined significantly over the study period.

Our first hypothesis was that during a recession, the percent of disposable income

spent on communication technology will increase above that spent in years prior to

the recession. These data are available in Table 1. If we group 2008 and 2009 data

as the recession years and all others (1984–2007, 2010) as non-recession years,

a simple ANOVA indicates that there is a significant difference in entertainment

technology purchases during the recent recession years (F1,25) D 9.06, p D .006),

supporting the hypothesis.

Our second hypothesis was that we expected that overall time spent with media

would increase during the entire study period. A regression analysis indicated a

significant increase in time spent with all media is associated with year of data

collection (b D 1.026, t D 3.148, p < .001), indicating an increase of about a

minute per year. A dummy variable coded as 1 for recession years, 0 for all non-

recession years was also significant (b D 8.423, t D 4.544, p D .019), indicating

a significant increase in time spent with media during the recession years beyond

that of the general increasing trend (about 8 minutes added for each recession year).

After controlling for employment status, we found very similar results (b D 9.792,

t D 5.641, p < .001).

Our third hypothesis offers a similar suggestion, with a slightly more conservative

aspect: traditional media use (radio, television, motion pictures, reading) time will

increase as well, because traditional media offer opportunities for multitasking, and

the technologies have developed to make it easier. This hypothesis is also tested

via a regression equation. In this case, the results are very similar, with time spent

with traditional media increasing slightly over half a minute per year (b D .532, t D

1.734, p D .043), and increasing more dramatically during the recession years (b D

8.444, t D 4.8420, p < .001). A regression equation was formulated to control for

employment status, and results were very similar (b D 9.663. t D 5.886, p < .001).

With H4, we expected an increase in coviewing activity during the recession in

comparison to the non-recession years. In this case, there’s a decrease in coviewing

during the study period overall (b D �.408, t D �1.983, p2-tailed D .047), but


a significant increase during the recession (b D 2.856, t D 2.441, p D .015),

supporting the hypothesis. The effect held even after controlling for employment

status (b D 3.049, t D 2.616, p D .004).

Hypothesis 5 suggested that the new technologies should result in a decrease

in time spent waiting for media during the entire study period. Examination of the

data indicated that only small percentages of respondents provided wait time as a

primary activity, and so wait time is likely under-reported in the sample. Of those

reporting wait time, about half also reported attending movies, a percentage much

higher than the general population. As a caution, we therefore selected those who

reported motion picture attendance and compared them to those who reported no

motion picture attendance. Results indicated that, among motion picture attendees,

waiting time decreased slightly as a linear trend during each year of the study

(b D �1.809, t1-tailed D �3.336, p D .001), consistent with H5. When we add

the code for recession years into the equation, there is an increase in time spent

waiting during the recession (b D 4.044, t D 3.318, p D .001). These findings

support the notion that, in general, among those who go to movies, waiting time

has decreased in recent years, while the recession saw an increase in time spent

waiting. Among those who did not report attending movies during this time frame,

waiting time was limited to only a small number of people, and results were only

marginally significant for the study period in general (b D �.006, t1-tailedD �1.880,

p D .030). Results show a similar pattern in regard to the recession years, but were

not significant. We therefore find evidence for H5.

Discussion

The constancy hypothesis is about individual-level behavior and macro-level

effects. Typically, tests of the constancy hypothesis have focused on macro-level

behavior and macro-level data. In the present study, we developed our analyses

from a mix of individual and macro level data. Having access to the individual data

enables us to develop some different measures than have been used previously, but

our results are still subject in some ways to the potential pitfalls of an ecological

fallacy. Still, as Kramer (1983) noted, even if aggregate results do not stand up at

the individual level, they can serve an important function in directing scientific

attention toward explaining the macro phenomenon.

In the current economic climate, it’s difficult to know exactly what is a product

of a short-term change, and what is a long-term trend. At the same time, the present

state provides some unique opportunities to test some micro-level processes that

have macro implications. Two, five, or even 10 minutes per day may not sound

like a major impact on the face of it. Multiplied by millions of people, though, we

see social system impacts that have not only economic implications but also relate

to our understanding of social processes and the role of media in people’s lives.

We find considerable evidence that the economy impacts our use of media

and our spending on media. At one level, the study adds to the weight of the


constancy hypothesis, which stated that any change in the economy will cause a

parallel change in spending on mass media. It adds to the constancy hypothesis by

beginning to disentangle what we might mean by ‘‘a parallel change.’’ Clearly, we

see increases in entertainment technology purchases during recession years, similar

to what has been observed before, but these data enable us to test pre-recession

years against recession years, which has not really been possible before. We are

also able to see that entertainment fees and admissions were constant, entertainment

supplies decreased, and overall media expenditures declined during the recession.

This suggests to us that the ‘‘bump’’ observed in previous studies is associated

with purchase of new technologies, but that all household expenditures have to be

modified to pay for that bump. If that is so, then we need to ask about the value of

the entertainment technologies that are being purchased, and so turn to use of time.

Time spent on media, broadly defined, increased over the study period, and

increased even more during the recession. When we focused on the pre-recession

and recession years, we see increases in time spent with television, both alone and

in coviewing, but the increase in viewing alone is somewhat larger (5.162 minutes

more of viewing alone in the recession years, 3.049 minutes of coviewing, con-

trolling for employment status). For total TV viewing, we see an average increase

of 8.211 minutes per day during the recession, controlling for employment status

(analysis not reported above).

These data are rich sources of additional possibilities, but provide just enough

data to encourage post-hoc explanation and development of hypotheses. We would

have liked to have had better measures of waiting time. These data are not at all

conclusive, but are consistent with our hypotheses. Similarly, had we had measures

of psychological stress, we would have been able to test some of our ideas directly,

rather than in group effects, where differential patterns will likely be evident. The

data here are consistent with the ‘‘stress relief’’ hypothesis, but not conclusive. If

we had had panel data, rather than cross-sections, the ability to test for effects of

the recession, rather than correlations with economic conditions, would have been

immeasurably better. Still, a number of recent reports suggest that recessions have

impact on stress for many more people than those directly affected. Even those who

are secure in their jobs feel stress during a recession (Zivin, Paczowski, & Galea,

2011), so it is difficult to know what sort of measures would be most useful if we

did have them.

We are left with the conclusion that money spent on new technology may simply

be a cost-efficient means of relaxing and obtaining entertainment, with no relation

to the stress associated with the economy, but we don’t think that is the case.

Given these issues, we believe that the data presented here are highly suggestive

that the money is a means to a particular end—during tough economic times, it

is likely that the ‘‘end’’ is feeling better and lowering stress, and the money that

is spent on new technologies is not simply spent because the technology exists.

Logically, much of the money spent on media, especially on media technologies,

can provide a great deal of cost-efficient entertainment—much more than many

other activities. Given the extensive research on the gratifications we obtain from


media, though, it’s not surprising that fulfilling basic stress reduction might be a

major driver behind technology purchases. While our data are not conclusive,

they are strongly suggestive that research on the time and money we spend on

media technologies has only scratched the surface of possible breadth and depth of

motivations.

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Is Time Money? Media Expenditures in Economic and Technological Turbulence

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