Economic Expectations, Optimistic Bias, and Television Viewing During Economic Recession: A Cultivation Study

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2014 41: 180 originally published online 13 April 2012Communication ResearchAmir Hetsroni, Zachary Sheaffer, Uri Ben Zion and Mosi Rosenboim

Economic Recession: A Cultivation StudyEconomic Expectations, Optimistic Bias, and Television Viewing During

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1Ariel University Center of Samaria, Ariel, Israel2Western Galilee College, Beer Sheva, Israel3Ben Gurion University of the Negev, Beer Sheva, Israel4College of Management, Rishon LeZion, Israel

Corresponding Author:Amir Hetsroni, School of Communication, Ariel University Center of Samaria, 31 Erez Street, Ariel 99797, Israel. Email: [email protected]

Economic Expectations, Optimistic Bias, and Television Viewing During Economic Recession: A Cultivation Study

Amir Hetsroni1, Zachary Sheaffer1, Uri Ben Zion2, and Mosi Rosenboim3, 4

Abstract

We examine the relationship between TV viewing and economic expectations during economic recession. A content analysis of 84 hours of local network primetime programming (news and nonnews) identifies a moderate bias toward economic pessimism in the broadcasts. A survey of the adult population (N = 356) points at a significant positive relationship between TV viewing (total viewing and viewing of news programming) and economic pessimism at both the national and the personal levels. A similar relationship exists between TV viewing and optimistic bias—the tendency to be more pessimistic on economic matters at the national than at the personal level. These results remain significant when controlled for demographics, trust in national institutions, evaluation of current economic situation and consumption of media other than TV, and corroborate a second-order cultivation effect in the economic context.

Keywords

television, optimism, pessimism, economy, recession, expectations, optimistic bias, cultivation

Article

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Hetsroni et al. 181

This article examines whether and to what extent people’s expectations about the economy (optimism vs. pessimism) and the optimistic bias that makes personal expectations more optimistic than those made about the national economy are shaped by media consumption habits, notably by the extent of TV viewing. At the personal level, economic optimism is marked by beliefs that one’s savings, financial status, and employment record will improve, whereas economic pessimism is typified by contrasting beliefs. At the national level, eco-nomic optimism is expressed by the belief that the state economy is moving toward pros-perity, whereas economic pessimism is expressed by the belief that recession is underway. Investigating the possible relationship between TV viewing and economic optimism/ pessimism has considerable practical implications in light of the association between certain expectations about the economy and financial behaviors such as increase in savings or the purchase of high-risk shares, and because pessimistic economic expectations are liable to create an atmosphere that may escalate economic downturns (Goidel, Procopio, Terrell, & Wu, 2010). The correlation between TV viewing and economic expectations is equally important from the media effect vantage point as it expands the examination of the cultiva-tion effect into a relevant and hitherto only marginally studied area of economic beliefs. We conduct the first investigation of this kind during economic recession and link public expec-tations with concrete content trends gleaned from content analysis of TV programming.

Economic Expectations and Optimistic BiasIn different settings, our expectations affect the way we act. The economy is not an excep-tion here, as economic expectations affect economic behavior. For instance, when people expect the inflation to rise—they will more likely opt for index adjusted savings options (Skinner, 2007). Furthermore, the citizens’ perceptions and subjective evaluations of the economy are more accurate than actual economic conditions in predicting their economic and political decisions—from buying expensive goods to voting for certain candidates (Sanders, 2000).

Economic expectations relate to the personal (“egotropic”) and the national levels (“sociotropic”; Lewis-Beck & Paldam, 2000). This distinction is important as personal expectations less often predict political actions (e.g., voting for incumbents). Contrastingly, expectations at the national level more frequently predict political decisions (Mutz, 1998, p. 103). According to Aggarwal and Zong (2008), economic expectations at any level can be viewed on a continuum that ranges from pessimism (the economy will deteriorate) to optimism (the economy will get better). Economic optimism is, in fact, a variant of general optimism. Extreme optimism acts in a similar way to overconfidence and induces people to opt for high-risk investments. However, a moderate level of economic optimism is asso-ciated with prudent management of the investment portfolio (Mola & Guidolin, 2009).

While one’s personal economic expectations and his or her corresponding expectations at the national level are statistically related, there exists a conspicuous bias toward opti-mism at the personal level (Mutz, 1998). According to Weinstein (1980, 1989), the reason for this tendency, known as the optimistic bias, is motivational, as overpessimism jeopar-dizes self-efficacy and self-worth. This bent may also stem from a cognitive error, as when




182 Communication Research 41(2)

people think of “others” to whom national-level expectations relate they tend to think about prototypes of high-risk-taking individuals (Spendelow & Jose, 2010). The bottom line is that we believe that we are less likely than other people to experience negative future events, but more likely than other people to experience positive future events. Optimistic bias has been observed in various domains from health beliefs, through career expecta-tions, and up to concerns about the likelihood of victimization (Chapin, 2000; Wei, Lo, & Lu, 2007; Weinstein, 1980). Opinions over the beneficiary/nonbeneficiary role of this bias that varies considerably in size across topics are divided. Some scholars argue that it enhances mental and physical well-being, whereas others suggest that it liable to lead to a failure to take precautions against threats and to behave in an overly risky manner (for review, see Gouveia & Clarke, 2001). The extent of optimistic bias may also play a role in determining its merits, as minor biases may be more helpful whereas major ones are prone to be more harmful (Weinstein & Klein, 1996).

Television Viewing and Economic ExpectationsAs television serves as an important source of information on financial matters and a prom-inent storyteller of economic issues, TV viewing may affect the direction and the intensity of economic expectations (Goidel et al., 2010). However, just a handful of studies endeav-ored to shed light simultaneously on economic media content and on the public’s economic anticipations. The media content investigated in these studies was news. Pruitt, Robert, and George (1988) found that changes in the presentation of economic data in the media are associated with changes in audience expectations regarding the expected unemployment rate. Tims, Freeman, and Fan (1989) showed that changes over time in consumer sentiment are correlated with the tone (positive or negative) of news reports. Evidently, the media’s ability to cultivate a distorted estimation of the economic reality depends chiefly on the accuracy (or lack thereof) of relevant media reports (Mutz, 1998). Opinions over this point are divided. MacKuen, Erikson, and Stimson (1992, p. 604) describe economic news as “the best information about the economy that exists.” However, Fogarty’s (2005) analysis of stories published in the New York Times front-page between the early 1980s and the mid-1990s indicates that news coverage of the economy accentuated negative developments. Soroka (2006) and Ju’s (2008) longitudinal investigations of economy-related items pub-lished in British and South-Korean newspapers demonstrate in this vein that in different parts of the world media reports of economic news are more responsive to negative changes in the economy than to positive changes and that there are more negative reports than positive accounts, regardless of the actual condition of the economy. The reason for the asymmetric coverage might be that negative information is considered of higher news value (Dierkens, 1991), or that people are used to expect “bad news” from the media (Meijer, 2010).

However, not all studies point at a simple negative content trend in economic news. Robinson and Sheehan (1983) contended that in order to provide audiences with unex-pected (and thus eye-catching) news content, the predominant reporting trend in covering the economy is actually random or cyclical, hence it fluctuates frequently from positive to negative and vice versa.




Hetsroni et al. 183

So far, most studies have assumed that people get most of the economic information from news media. However, conventional wisdom has it that in recent years we have become more reliant on nonnews TV offerings (satirical programs like The Daily Show, coaching shows such as Super-nanny, sitcoms like The Middle) as information sources on the economy. A recent study indicates that young Americans use late night talk shows with a noted satirical tone as a gateway for gaining knowledge on different aspects of politics—including the economy (Xenos & Becker, 2009). Such shows are often reproduced locally and broadcast successfully worldwide, however, the economy-related content of such entertainment programming has not hitherto been investigated.

We attend to this lacuna in the literature by juxtaposing citizens’ economic optimism/pessimism with the tone of economic messages spread across entertainment and news pro-gramming and use cultivation as a theoretical framework.

How Does Television Viewing Affect Economic Expectations?The relationship between having various expectations about the economy and different levels of media consumption (predominantly TV viewing) can be accounted for by the cultivation hypothesis. This theory proposes that as television is a noteworthy communica-tion agent and a most effective storyteller, heavy viewing leads to estimates and views that are disproportionately or distortedly represented on the small screen compared with their presence in daily life (Gerbner, Gross, Morgan, Signorielli, & Shanahan, 2002). A meta-analysis of over 80 studies confirmed the existence of a cultivation effect that links heavy exposure to television with distorted reality estimates and views that express insecurity, mistrust and alienation that emanate from the most popular programs (Shanahan & Morgan, 1999, p. 40).

The cultivation effect consists, in fact, of two types. The first refers to the positive rela-tionship between excessive viewing and distorted perception of the world, for example, erroneous assessment of the prevalence of certain occupations in direct correspondence with these occupations’ screen presence (which is radically different from their actual prev-alence in the world) known as a first-order effect. The second is the interrelatedness between excessive viewing and attitudes that directly derive from televised messages, for example, supporting severe punishment to crimes often depicted on television as causing severe damage, termed as second-order effect (Hawkins & Pingree, 1982).

Cultivation occurs when under multiple controls (e.g., demographics and personality traits), heavy TV viewers still provide “cultivated answers” that represent the world of TV content and hold views directly learned from this content more frequently than light view-ers do, and when light viewers provide real-world answers that accurately reflect social circumstances more often than heavy TV viewers do. Though the cultivation effect is small in magnitude (around 1% of the explained variance), it is cross-culturally consistent and robust across different demographic sectors (Shanahan & Morgan, 1999).

Cultivation occurs because people either learn facts actively from TV (Hawkins & Pingree, 1982), or encode information unintentionally while attending to the programs.





Whether the “learning” is intentional or not, we may assume that TV viewers etch informa-tion derived from the programming in their long-term memory. Likewise, reliance on this encoded information is the source of cultivation because heavy viewers are likely to have more information encoded. Accordingly, the cultivation effect is an outcome of a process wherein television viewing enhances construct accessibility. This brings people to adopt social perceptions that correspond with recurring TV images retrieved as exemplars of social reality constructs more frequently when their viewing time increases (Shrum, Burroughs, & Rindfleisch, 2004).

From a cultivation perspective—economic expectations constitute a second-order effect, since they epitomize our opinionated view of the world predicated on what the media tell us. For instance, an expectation that the stock exchange index would rise con-tinuously (expressing economic optimism at the national level) might be partly shaped by exposure to repeated reports on rising stocks. An anticipation to get fired (indicator of economic pessimism at the personal level) might be associated with viewing of dramatic programs, where protagonists, presented as seemingly similar to viewers in their qualifica-tions, lose their job. The direction of the effect of TV viewing on economic expectations should be determined by the tone of televised messages pertaining to the economy, for example, whether it is optimistic or pessimistic. Traditionally, it was thought that popular broadcasting is abundant with optimism. The common happy-ending narrative led research-ers to believe that excessive TV viewing would nurture a rosy worldview among heavy viewers and encourage them to adopt overly optimistic expectations in different aspects of life (Segrin & Nabi, 2002). Most of the recent evidence, however, indicates just the oppo-site. McNaughton, Cassill, and Smith (2002) report that heavy TV viewing predicts severer rating of social problems nationwide. Bruni and Stanca (2008) also link excessive TV viewing with pessimism and suggest that TV viewing serves as a refuge for people who are already unhappy, inducing them to be even less happy. Frey, Benesch, and Stutzer (2007) found that higher levels of TV viewing are negatively related to financial satisfaction, but TV viewing was also found to be positively correlated with material aspirations and higher anxiety levels. This led Frey et al. to suggest that TV viewing cultivates unrealistic aspira-tions that trigger anxiety and that the inevitable shattering of these aspirations results in pessimism. Similarly, Bruni and Stanca (2008) found that TV viewing has a negative impact on individual happiness. It is difficult, however, to decide whether unhappiness is the direct consequence of watching dramatic genres that accentuate mistrust (e.g., detective series) and news that highlight the negative aspects of daily life, or whether previously existing unhappiness brings about watching TV and hence be detached from reality (Robinson & Martin, 2008).

Another explanation why watching TV may induce pessimism is that the typical pro-gramming has changed its tone. Whereas shows of yesteryear adhered to the happy-ending narrative, the current screen is full of reality shows and competition programs that feature economic failures and social losers (e.g., the losing candidates in The Apprentice, the elimi-nated couples in Beauty and the Geek). TV drama has also abandoned the “all ends well” storylines in favor of more complex, often pessimistic, plots. An analysis of medical drama from different decades shows that in the most recent shows doctors have become less




Hetsroni et al. 185

successful in saving their patients’ lives (Jacobs, 2003). Such programs as The Office or The Middle, where plots are concerned with economic matters, feature protagonists who often fail in accomplishing their professional mission.

As for the optimistic bias, only a handful of studies examined how this tendency relates to media consumption and particularly to TV viewing. Chapin (2000) and Wei et al. (2007), who measured the optimistic bias regarding the chance to have a disease (HIV and Flu, respectively), report no association between media consumption measures and optimistic bias, while Chapin (2001), within the context of school-based violence, reports a positive correlation between optimistic bias and Internet use but not between optimistic bias and attending to traditional media. Perhaps because of their inconclusive findings, these studies lack a convincing framework capable of explicating a relationship between media con-sumption and optimistic bias. We suggest that, at least when economic information is con-cerned, media consumption is likely to enhance the effect of this bias because most of the economic content presented in the media is negative in tone (Sanders & Gavin, 2004; Soroka, 2006) and—in most cases—relates less to a specific viewer in person and more to the nation at large or to groups of people. Thus, in comparison with light media consumers, heavy media consumers are prone to attribute an even bleaker base rate to the national economy. Against this backdrop, almost any estimation of one’s own future is bound to be rosier. From a cultivation standpoint, the optimistic bias may be conceived as an extension of a second-order effect because a negative portrayal of the national economy (as shown on television) connotes a message that cultivates overly pessimistic expectations about it. Such message can widen the gap between expectations about the national economy and expectations that the viewers hold about their own economic situation.

Additional Potential Predictors of Economic ExpectationsNaturally, economic expectations are not solely shaped by the media. Our study considers (as control factors) three clusters of variables that may affect economic expectations. They include view of current economic conditions (MacKuen et al., 1992), public trust in national institutions (Chanley, Rudolph, & Rahn, 2000), and demographics.

As future expectations are based on one’s assessment of the current situation—a posi-tive assessment of present economic conditions could be related to optimism regarding the economic future, whereas a negative assessment of current economic conditions could be associated with future pessimism (Kaniel, Massey, & Robinson, 2010). Evidence of such relationship was identified in different aspects of the economy (Skinner, 2007).

As for people’s trust in national institutions, Uslaner (1998) used data from the 1972-1994 General Social Survey (GSS) to show that trust in the government overshadows the influence of media, and particularly TV viewing, on optimism. His findings suggest that when people become aware of the differences between the world portrayal in the media and the actual picture of the world, media consumption loses its potency to make an impact on economic expectations. Jones (2004) also relates to trust in the media (defined in his survey as overall media trust without reference to different types of media), and contends that it is





related to trust in the government. Therefore, when trust in the government is low, trust in the media also tends to decline, and it is difficult for any media content to leave lasting marks on people’s expectations. The pattern of relationship between media consumption, trust in national institutions (e.g., government, media) and economic expectations may be even more complex, as media consumption in general and TV viewing in particular may reduce the trust one has in these institutions (Frey et al., 2007). We will examine the impact of trust in the government and in the media on economic expectations.

Demographic variables as age and gender have shown contradicting effects on eco-nomic expectations (Puri & Robinson, 2007). It is, therefore, difficult to predict whether age and gender would have any consistent effect on economic expectations. The impact of income on economic expectations is also not unequivocal. Theoretically, it makes sense that higher income would be related to economic optimism (Caporale, Yannis, Tsitsianis, & Ya, 2009), but evidence of such relationship is rare. Plausibly, higher income brings with it concerns that moderate or even annul the effect of higher income on optimism (Stutzer, 2004). Marriage, on the other hand, is usually associated with optimistic economic expec-tations, probably because the family serves as a support base that may enhance optimism (Friedman, 2005).

The Context of the StudyThe late-2000s recession, often referred to as the long recession (Crotty, 2008), is a severe ongoing global economic crisis that began in December 2007 and took an acute downturn in September 2008. This recession has affected the entire world economy with higher detri-ment in most developed economies (Rajan, 2010). It has been a major global recession typified by various systemic imbalances and was set off by the outbreak of the late-2000s financial crisis (Imbs, 2010). That crisis developed with notable speed as mortgage-related securities that had spread through the United States and global financial system suddenly collapsed in value (Shiller, 2008). The crisis has destabilized many of the largest financial institutions worldwide and severely damaged a sizable share of the world’s financial system (Obstfeld & Rogoff, 2009). Gradually, the financial crisis has been joined by a developing recession in the nonfinancial sector worldwide (Baccaro et al., 2010).

The collection of data in Israel in early 2009 coincided with the zenith of this global crisis from which the Israeli economy has not been spared. An examination of key Israeli macroeconomic indicators shows an economic decline in 2009 compared with previous years. The annual growth rate for 2009 was negative (–1%) as opposed to approximately 5% annual growth in 2006, 2007, and 2008. The deficit as a percentage of the national product grew from 2% in 2008 to 5.2% in 2009 (Bank of Israel, 2010). Private consump-tion expenditure per capita declined between 2008 and 2009 following 5 years of growth (Central Bureau of Statistics, 2010). According to the Bank of Israel annual report (2010) import and export shrunk sharply in 2009—12.5% and 14%, respectively, commensurate with the decline in world commerce. While in comparison with most developed economies the recession in Israel in 2009 was not as severe, an economic downturn was apparent when this study was conducted.




Hetsroni et al. 187

Previous content analyses of economic news (cf. Soroka, 2006; Ju, 2008) took place during economic prosperity. These analyses mirror a tendency on the part of the media to favor negative coverage. We will examine whether this trend persists, worsens or takes positive turn during economic recession.

Israel is an appropriate location to examine the economic content of TV programming and its impact on people’s evaluations of the economy for two reasons. First, Israel has only two terrestrial stations whose combined share exceeds 60% of the audience (Eurodata TV, 2010). This means that even in a multichannel environment, the majority of Israelis are exposed to a relatively limited number of televised messages that can be easily coded. Second, as the primetime programming in the main Israeli networks consists almost solely of local shows (news and nonnews), the economic information embedded in these pro-grams would likely pertain to Israel.

HypothesesOur study assesses the levels of optimism and pessimism in television programming, the impact of television viewing on economic optimism and pessimism at the national and personal levels, and its effect on the optimistic bias. We control for trust in the government and the media, assessment of the current economic situation, demographics, and consump-tion of media other than TV. From a cultivation standpoint, we expect that heavier TV viewing would be akin to economic expectations as to what the programming projects in this matter. In light of the positive relationship between excessive TV viewing and pessi-mism reported in recent works (Bruni & Stanca, 2008; Frey et al., 2007; Robinson & Martin, 2008) and based on the tendency of TV news to portray the national economy in bleak colors (Sanders & Gavin, 2004), we expect that TV viewing would cultivate eco-nomic pessimism, particularly at the national level. This should expand the optimistic bias. Formally, we hypothesize,

Hypothesis 1 (H1): Heavy TV viewing will be positively associated with higher economic pessimism at the national level.

Hypothesis 2 (H2): Heavy TV viewing will be positively associated with higher economic pessimism at the personal level.

Hypothesis 3 (H3): Heavy TV viewing will be positively associated with greater optimistic bias in economic expectations. In other words, heavy TV viewing will widen the gap between economic expectations at the personal level and economic expectations at the national level.

MethodThe study took place in Israel in January-February 2009. It was comprised of two parts; content analysis from which we determined the levels of optimism and pessimism in TV programming and a structured questionnaire distributed to a sample of Israeli adults (N = 356). The questionnaire addressed statements regarding economic expectations, media





consumption habits, demographic measures, and trust in key national institutions (govern-ment, media).

Content AnalysisSample. The data were obtained from a systematic coding of news items and nonnews

dialogues and talk excerpts about the local (Israeli) economy that appeared over a period of 3 weeks in the primetime lineup (8 p.m. to 11 p.m.) of two TV networks—Channel 2 and Channel 10. These are the most popular TV stations in Israel that attract 60% of the viewers during the primetime hours (Eurodata TV, 2010). Their primetime lineup is divided between newscasts (broadcast daily between 8 p.m. and 9 p.m.) and nonnews programs (dramatic series, reality shows, documentary films, consumer affairs programs, game shows, talent competitions, and skit programs) aired from 9 p.m. to 11 p.m. All economic news items and nonnews economy-related dialogues and talk excerpts broadcast during the last week of January 2009 and the first 2 weeks of February 2009 were sampled. Hence, our database of 126 hours of programming (63 hours from Channel 2 and 63 hours from Channel 10) provides an adequate representation of the most successful TV programs across genres (42 hours of news and 84 hours of nonnews).

Coding categories and reliability. In the news section, the unit of analysis was a discrete item whose main theme is the Israeli economy or one of its sectors/aspects (real estate, fishing and agriculture, stock exchange, banking, currency, finances and inflation, heavy industry, high tech, marketing, economic globalization, welfare and unemployment). Sam-ple items include reporting of the daily trend in the local stock exchange covering an upsurge in unemployment in rural districts (including interviews with employers and employees), and a story about an Israeli startup company sold to Google for US$40 million.

In the nonnews section, the unit of analysis was a dialogue/talk excerpt in which the Israeli economy was a major theme. The opening and ending of a scene or a change of the topic of discussion within the scene marked the beginning and ending of such dialogue. The dialogues addressed, for instance, the cost of living (a single mom’s financial hard-ships), real estate prices (a young couple mortgage shopping), and economic globalization (a textile factory shutdown).

In every identified content subject (news or nonnews), we coded the tone as positive (conveying the message that the national economy—or a referred sector—was thriving or improving); negative (conveying the message that the local economy—or a specific sector—was beset by a crisis); a combination of positive and negative (when the text con-notes the message that the economy was fluctuating); or unknown/neutral (when the eco-nomic condition could not be inferred, or when a message was conveyed that the economy was neither thriving nor beset by a crisis).

Our coding scheme is based on Soroka (2006), who used a similar framework to iden-tify the tone of economic items in a daily newspaper over a period of 5 years and reported coder reliability of .8 or higher per annum. In our study, the coding was performed by two students, who separately coded the tone of all the content subjects without being privy to




Hetsroni et al. 189

the goals of the research. Intercoder reliability was computed for the entire sample and was measured using Krippendorff’s alpha as follows: news items (α = .820); nonnews dia-logues and talk excerpts (α = .802). Cases of disagreement were decided by the first author.

Content analysis results. No significant differences in the distribution of content tone between the two networks were identified (χ2

(3) = 2.1, ns). Therefore, we report the tone

combined figures for each content type (news/nonnews) on Table 1. In both content types a programming bias toward a negative presentation of the economy is apparent. Negative subjects outnumber positive ones in a 1.5:1 ratio in the news and in a 2:1 ratio in nonnews programs. While the distributions of tone in the two content types are sig-nificantly different (χ2

(3) = 17.6, p < .001), this statistical difference stems mostly from

dissimilarity in the frequency of neutral and combined tone content subjects. When we compare only the frequencies of positive and negative content subjects—the differences between content types are no longer significant (χ2

(1) = 1.2, ns). In terms of topic—the

leading categories in the news were stock exchange (27.5%), real estate (18%), and marketing (15.5%). In the nonnews section—the leading categories were real estate (24%), marketing (18% of the dialogues and talk excerpts), and welfare and unemploy-ment (13.5%). To sum up, the programming contains a moderate bias toward negative coverage of the economy.

SurveyThe survey was administered door to door in Israel’s central district (“Dan”) where about 40% of the population (3 million people) resides. Towns and neighborhoods were picked according to their socioeconomic status (SES), as published by Israel’s Central Bureau of Statistics that classifies each township (and each district) into 1 of 10 clusters (Central Bureau of Statistics, 2010). We selected towns that proportionally represent the makeup of the district in terms of SES. In February 2009, research assistants surveyed these towns and were instructed to identify respondents of both sexes and proportionally reflect differ-ent age groups. Response rate (following three attempts to contact respondents on different days) was 53%. The questionnaire took 20 minutes to fill in. Research assistants wrote down the answers elicited by the respondents after the latter gave their consent to partake

Table 1. Tone of Items About the Economy in TV News and in Nonnews Primetime Programming.

Tone News (N = 222) Nonnews (N = 184)

Positive 23.3% 20.6%Negative 37.6% 41.8%Combination of positive and negative 3.2% 11.0%Neutral 35.9% 26.6%

Note: Items coded as unknown (2.3% of the news items and 3.5% of the nonnews items) are omitted.





in an academic study about the economy. Of the sample’s 356 respondents, 53% were women, and 58% were married. The respondents’ age ranged from 20 to 81. The sample’s median age (Mdn = 41) is almost identical to that of the Israeli adult population (Central Bureau of Statistics, 2010). Ethnicity wise , 94% of the respondents were Jews and the rest were Arabs. Incomewise, 42% of the respondents described their income as slightly lower or much lower than the national average (US$2,200 in 2009), while 24% described their income as more or less equal to the national average, and 34% described their income as slightly higher or much higher than the national average.

The questionnaire consisted of four parts. Part I: demographic information (age, gen-der, income,1 and marital status); Part II: items addressing future economic expectations and perceived economic conditions at present; Part III: items measuring trust in various institutions (government, media); Part IV: daily time allotted to media consumption (including TV viewing). This sequence aimed at preventing a spurious effect stemming from thinking about the media while specifying views on nonmedia topics (Shanahan & Morgan, 1999). We reversed the wording of items (where possible) in order to prevent a response set bias.

MeasuresMedia consumption. The following items were asked open-endedly: “on an average week

day, how long do you watch TV?” and “on an average weekend day, how long do you watch TV?” Items addressing different parts of the week were so weighted as to generate a measure of weekly TV viewing (cf. Morgan, 1984). Additionally, we asked about viewing of genres that comprise the nonnews part of primetime programming (dramatic series, real-ity shows, documentary films, consumer affairs programs, game shows, talent competi-tions, and skit programs (Eurodata TV, 2010) and about watching TV news because these shows often cover the economy. Likewise, we included items aimed at assessing the time devoted to consuming non-TV economy-related media: daily newspapers, business news-papers, and Internet browsing.

Trust in institutions. These items are based on the GSS (American Attitudes, 2005, pp. 16-28). Specifically, we asked about confidence in the government and confidence in the media. The statements were “how much do you trust the following . . .” Answers were ticked on a 5-point Likert-type scale ranging from no trust at all to a great deal of trust.

Economic expectations and perceived economic conditions. The items that measure future economic expectations (pessimism/optimism) at the personal and national levels and assess perceived economic conditions at these levels at present are statements predicated on Goi-del et al. (2010) and Posovac (1998). Four scales were included in the questionnaire according to the following specification:

Perceived economic situation at present at the national level. This index consisted of the following statements: “Over the past year, the economic situation in Israel has improved”; “Over the past year, the economic condition of most people has improved”; “Over the past year, our country has endorsed positive economic policies.” Evidence of unidimensionality was gained from a factor analysis (maximum likelihood), which provided a single factor




Hetsroni et al. 191

solution accounting for 71% of the model’s variance. The value of Cronbach’s alpha (α = .897) indicates high reliability.

Perceived economic situation at present at the personal level. This index contained the fol-lowing statements: “Over the past year, my economic situation has improved”; “Over the past year my salary increased”; “Over the past year, I was able to save money”; “Over the past year, I was promoted at work.” A factor analysis yielded a single factor solution that accounts for 70% of the model’s variance and points at unidimensionality. The value of Cronbach’s alpha (α = .837) indicates high reliability.

Economic expectations at the national level. This index was composed of the following statements: “In the coming year, the stock market will be on the upswing”; “In the coming year, unemployment will decrease”; “In the coming year, the economic situation will improve.” A factor analysis where a single factor accounts for 56% of the model’s variance was evidence that this index encapsulates a single concept. The scale’s Cronbach’s alpha value (α = .736) is evident of its reliability.

Economic expectations at the personal level. This index was composed of the following statements: “In the coming year, my salary will be raised”; “In the coming year, my eco-nomic situation will improve”; “In the coming year, I have a good chance to be promoted at work”; “In the coming year, my standard of living will rise”; “In the coming year, my savings will rise significantly.” Factor analysis resulted in a single factor solution that accounts for 74% of the model’s variance and indicates that the scale is unidimensional. The value of Cronbach’s alpha (α = .895) proves that it is also reliable.

Optimistic bias. Prior literature has defined optimistic bias as a systematic error in the perception of an individual’s future standing relative to group averages. Negative events and negative future outcome are seen as less likely to occur to the individual and more likely to occur to the group, whereas positive events and positive future outcome are seen as less likely to occur to the group and more likely to occur to the individual (Weinstein, 1980, 1989; Weinstein & Klein, 1996). In line with this rationale, we measured optimistic bias by deducting the mean of economic expectations at the national level (where higher values denote optimism and lower values denote pessimism) from the mean of economic expectations at the personal level.

ResultsDescriptive Statistics for Key Variables

For each level of economic expectations (personal and national) we computed the arith-metic mean, which—like the statements it was based on—ranged from 1 (pessimism) to 5 (optimism). Economic expectations appear to be somewhat pessimistic at both levels (M = 2.46, SD = 1.05 at the national level; M = 2.62, SD = .87 at the personal level). The differ-ence between expectations at the personal and the national levels (optimistic bias) is sig-nificant (t

(355) = 3.0, p = .003, d = .166). Perceptions of economic conditions at present

attest also to a pessimistic tendency with higher pessimism noted at the national level (M =





2.43, SD = 1.01) than at the personal level (M = 2.51, SD = 1.12). However, when it comes to present evaluations, the difference between the personal and the national levels is not significant (t

(355) = 1.2, ns).

The average time allotted to daily television viewing is slightly less than three and a half hours. Approximately 40% of this time is devoted to news programming and some 60% to watching entertainment (nonnews) shows. This distribution and the overall amount of time devoted to television viewing in our sample are similar to the national average that stands at 3 hours and 45 minutes daily per person (Eurodata TV, 2010).

The respondents’ level of trust in the government (M = 3.02, SD = 0.92) and in the media (M = 2.93, SD = .98) is best described as moderate, as the number (3) marks the scale’s midpoint between no trust at all (1) and a great deal of trust (5).

Hypothesis 1To test H1, according to which heavy television viewing is positively associated with higher economic pessimism at the national level, we used a hierarchical linear regression model. Economic expectations at the national level served as the dependent variable. The predictors were age, gender, family status and income (Block I—demographics), perceived economic condition at present at the personal level and perceived economic condition at present at the national level (Block II—current economic condition perception), trust in the government, trust in the media, and interaction between trust in the media and TV viewing (Block III—trust in institutions), and TV viewing, newspaper reading, business newspaper reading, Internet browsing (Block IV—media consumption). To prevent multicolinearity when includ-ing different measures of TV viewing in the same model, we ran a regression model with total viewing time as a predictor, and another model with news programming viewing and enter-tainment (nonnews) programming viewing as predictors. The results of the regression appear on Table 2 (total viewing) and Table 3 (news and nonnews programming viewing).2

H1 is corroborated by our data. Under multiple controls, total viewing and news view-ing remain significantly and positively associated with economic pessimism at the national level (β = –.108, p = .02 for total viewing; β = –.110, p = .02 for news viewing). However, entertainment programming viewing is not associated significantly with economic expec-tations at the national level (β = –.055, ns). Other statistically significant predictors are perceived current economic condition at the national level (economic pessimism at present is positively associated with pessimistic expectations for the future) and trust in the govern-ment (lower trust is related to greater pessimism). The rest of the variables including demo-graphics (age, gender, family status, ethnicity, income), trust in the media, interaction between trust in the media and TV viewing and consumption of media other than television (newspapers, business newspapers, Internet) prove to be statistically not significant in pre-dicting economic expectations at the national level. We examined interactions between TV viewing and the demographic variables. None of these interactions proved to be a signifi-cant predictor (data not shown on Table 2 and Table 3 due space limitations but are avail-able from the authors on request).




Hetsroni et al. 193

Hypothesis 2

To test H2, according to which heavy television viewing is positively associated with higher economic pessimism at the personal level, we regressed the predictors linearly and hierarchically much like we did in testing H1. However, in this model we employed eco-nomic expectations at the personal level as our dependent variable. Again, in order to

Table 2. Hierarchical Linear Regression for Economic Expectations at the National Level—Using Total Viewing as a Predictor.

Blocks βPartial

Correlation t

Demographics Age .054 .059 1.042 Gender (0 = male; 1 = female) –.015 –.018 –0.331 Ethnicity (0 = Arab; 1 = Jew) –.016 –.019 –0.337 Income –.011 –.013 –0.235 Marital status (0 = unmarried;

1 = married)–.006 –.007 –0.117

Incremental adjusted R2 = .030Perceived current economic condition Perceived economic

condition—national level.371 .327 6.089****

Perceived economic condition—personal level

.023 .024 0.668

Incremental adjusted R2 = .280****Trust in institutions Trust in government –.271 –.240 –4.351**** Trust in the media .011 .012 0.218 Trust in the media × TV

viewing interaction.042 .050 0.873

Incremental adjusted R2 = .043****Media consumption TV viewing—total –.108 –.132 –2.497* Internet browsing –.090 –.099 –1.727 Newspaper reading .068 .077 1.369 Business newspaper reading .061 .071 1.197Incremental adjusted R2 = .021*

Adjusted model R2 = .374****

Note: N = 356.*p < .05. **p < .01. ***p < .005. ****p < .001.





avoid multicolinearity, separate regressions were run for total viewing and for viewing of news and nonnews programming. The regression’s results appear on Tables 4 and 5.

H2 is supported by our data. Under multiple controls, the total time devoted to TV viewing and the time devoted to watching the news remain significantly and positively associated with economic pessimism at the personal level (β = –.185, p = .03 for total viewing; β = –.102, p = .04 for watching the news). However, entertainment programming

Table 3. Hierarchical Linear Regression for Economic Expectations at the National Level—Using News and Nonnews Viewing as Predictors.

Blocks βPartial

Correlation t

Demographics Age .052 .057 0.999 Gender (0 = male; 1 = female) –.023 –.026 –0.459 Ethnicity (0 = Arab; 1 = Jew) –.001 –.001 –0.026 Income –.006 –.008 –0.135 Marital status (0 = unmarried; 1 = married) –.016 –.018 –0.311Incremental adjusted R2 = .030Perceived current economic condition Perceived economic condition—national

level.345 .302 5.566****


.023 .025 0.432

Incremental adjusted R2 = .280****Trust in institutions Trust in government –.287 –.256 –4.645**** Trust in the media .000 .000 0.000 Trust in the media × TV viewing

interaction.069 .084 1.353

Incremental adjusted R2 = .037****Media consumption TV viewing—nonnews –.055 –.065 –1.149 TV viewing—news –.110 –.125 –2.175* Internet browsing –.090 –.104 –1.766 Newspaper reading .064 .072 1.278 Business newspaper reading .062 .071 1.239Incremental adjusted R2 = .028*


Note: N = 356.*p < .05. **p < .01. ***p < .005. ****p < .001.




Hetsroni et al. 195

viewing is not significantly associated with economic expectations at the national level (β = –.014, ns). Other statistically significant predictors are age (older people are more opti-mistic), gender (women are more optimistic at the personal level), marital status (married people are more optimistic at the personal level), and perceived current economic condi-tion at the personal and national levels (pessimism at present is associated with pessimism about the future). Other demographic variables (ethnicity, income), trust in institutions (trust in government, trust in the media, interaction between trust in the media and TV viewing), and consumption of media other than TV (newspapers, business newspapers,

Table 4. Hierarchical Linear Regression for Economic Expectations at the Personal Level—Using Total Viewing as a Predictor.

Blocks βPartial

Correlation t

Demographics Age .133 .144 2.148* Gender (0 = male; 1 = female) .136 .153 2.001* Ethnicity (0 = Arab; 1 = Jew) .061 .071 1.358 Income –.029 –.034 –0.436 Marital status (0 = unmarried; 1 = married) .168 .181 2.354*Incremental adjusted R2 = .076***Perceived current economic condition Perceived economic condition—national

level.150 .139 2.916***


.438 .422 5.951****

Incremental adjusted R2 = .278****Trust in institutions Trust in government .025 .023 0.767 Trust in the media .023 .027 0.735 Trust in the media × TV viewing interaction .072 .084 1.107Incremental adjusted R2 = .002Media consumption TV viewing—total –.185 –.186 –2.422* Internet browsing .002 .002 0.028 Newspaper reading –.068 –.078 –1.003 Business newspaper reading –.030 –.035 –0.659Incremental adjusted R2 = .026**


Note: N = 356.*p < .05. **p < .01. ***p < .005. ****p < .001.





Table 5. Hierarchical Linear Regression for Economic Expectations at the Personal Level—Using News and Nonnews Viewing as Predictors.

Blocks βPartial

Correlation t

Demographics Age .134 .143 2.541* Gender (0 = male; 1 = female) .167 .185 3.310*** Ethnicity (0 = Arab; 1 = Jew) .071 .085 1.505 Income –.046 –.055 –0.965 Marital status (0 = unmarried; 1 = married) .108 .116 2.058*Incremental adjusted R2 = .070***Perceived current economic condition Perceived economic condition—national

level.171 .153 2.724***


.440 .419 8.102****

Incremental adjusted R2 = .280****Trust in institutions Trust in government .051 .047 0.817 Trust in the media .004 .005 0.090 Trust in the media × TV viewing


Incremental adjusted R2 = .002Media consumption TV viewing—entertainment –.014 –.016 –0.300 TV viewing—news .102 .114 2.021* Internet browsing .049 .053 0.939 Newspaper reading –.031 –.034 –0.604 Business newspaper reading –.053 –.060 –1.052Incremental adjusted R2 = .012


Note: N = 356.*p < .05. **p < .01. ***p < .005. ****p < .001.

Internet) are not significant in predicting economic expectations at the personal level. We examined the interaction between TV viewing and trust in the media and several interac-tions between TV viewing and demographics, but none of these interactions proved to be statistically significant (data concerning interactions between TV viewing and demo-graphics not shown on Tables 4 and 5 owing to lack of space but are available from the authors on request).




Hetsroni et al. 197

Hypothesis 3

To test H3, according to which heavy television viewing is positively associated with greater optimistic bias in economic expectations, namely a larger gap between expecta-tions at the personal level and expectations at the national level, we used hierarchical linear regression model where optimistic bias was the dependent variable. The predictors were the same variables used in testing H1 and H2. Again, to avoid multicolinearity separate regressions were run for total viewing and for viewing of news and nonnews program-ming. The results of the procedure appear on Table 6 (total viewing) and Table 7 (nonnews and news viewing).

In this model, too, the total amount of time devoted to TV viewing emerges as a signifi-cant predictor (β = .211, p < .001), as does viewing of news programming (β = .189, p = .003); hence, H3 is corroborated by the data, although viewing of nonnews programming falls far short of the significance level in predicting optimistic bias (β = .034, ns). Other predicting variables are gender (men have larger optimistic bias), marital status (married people are more optimistically biased), perception of the present economic condition at the national level (the more pessimistic it is—the larger the optimistic bias is), perception of the present economic condition at the personal level (the more optimistic it is—the larger the optimistic bias is), and trust in the government (lower trust is related to larger optimistic bias).

DiscussionEconomic Expectations

The findings corroborate a second-order cultivation effect by linking the total amount time devoted to TV viewing and the scope of news viewing with economic pessimism at the national level and the personal level. This means that excessive attention to TV news and heavy viewing of the programming in general induce people to expect a nega-tive turn in the economy that encourages a decrease in consumption and an upsurge in conservative financial savings (Galí, 1990). While neither the effect of the general pro-gramming nor that of news is large (ranging between 1% and 3% of the explained vari-ance), their size does fall within the range reported by previous cultivation studies (Shanahan & Morgan, 1999), and their direction is consonant with the content of the relevant media.

However, viewing of nonnews entertainment programming fails to significantly corre-late with economic pessimism (although the correlation conforms to the hypothesized direction), and attending to other media (e.g., business newspapers) does not consistently correlate with any expectation about the economy. These findings merit an explanation, as nonnews TV programming features a moderately negative economic tone (noted in our content analysis), and because newspapers typically do tend to portray the economy in gray colors (Sanders & Gavin, 2004; Soroka, 2006). Our explanation consists of a number of arguments. First, regarding entertainment TV programming, the relative rarity of economic messages in this edifice (less than two messages per hour compared with more than five





economic items per hour in the news—according to our content analysis) may limit the potency of nonnews programming to shape viewers’ economic views. As for the lack of effect of non-TV media—this finding comes to terms with a popular view among cultiva-tion researchers that the apparently relaxed approach of TV makes this medium more influ-ential in the long run than more “serious” media (Gerbner et al., 2002; Shanahan & Morgan, 1999). People are more likely to develop resistance to influence of the most serious media sources, namely, newspapers and specialty magazines, in our case—business newspapers, because—to a considerable extent—people attend to these media to gain essential eco-nomic knowledge. This kind of instrumental media consumption may induce people to be

Table 6. Hierarchical Linear Regression for Optimistic Bias in Economic Expectations—Using Total Viewing as a Predictor.

Blocks βPartial

Correlation t

Demographics Age –.075 –.074 –1.207 Gender (0 = male; 1 = female) –.149 –.152 –2.702*** Ethnicity (0 = Arab; 1 = Jew) –.059 –.064 –1.126 Income .036 .039 0.689 Marital status (0 = unmarried; 1 = married) .145 .144 2.550*Incremental adjusted R2 = .051**Perceived current economic condition Perceived economic condition—national

level–.185 –.154 –2.740**


.436 .385 7.340****

Incremental adjusted R2 = .143****Trust in institutions Trust in government –.184 –.151 –2.677** Trust in the media .003 .004 0.064 Trust in the media × TV viewing interaction .095 .102 1.694Incremental adjusted R2 = .024*Media consumption TV viewing—total .211 .183 3.743*** Internet browsing –.083 –0.75 –1.442 Newspaper reading .088 .088 1.536 Business newspaper reading .024 .025 0.439Incremental adjusted R2 = .017


Note: N = 356.*p < .05. **p < .01. ***p < .005. ****p < .001.




Hetsroni et al. 199

more alert to influence attempts. Such close attention contradicts what was termed by Rubin (1984) as ritualized media consumption, which is habitual mainly in order to fill in time. The last viewing style is more common among heavy viewers (Gerbner et al., 2002), whose economic expectations are—as our data indicate—more strongly affected by their level of TV viewing than the economic expectations of heavy Internet users are affected by the length of time they spend on the Internet, or the expectations of heavy newspaper read-ers are affected by reading time.

Table 7. Hierarchical Linear Regression for Optimistic Bias in Economic Expectations—Using News and Nonnews Viewing as Predictors.

Blocks βPartial

Correlation t

Demographics Age –.083 –.083 –1.459 Gender (0 = male; 1 = female) –.181 –.187 –3.327*** Ethnicity (0 = Arab; 1 = Jew) –.068 –.077 –1.348 Income .040 .044 0.781 Marital status (0 = unmarried; 1 = married) .115 .115 2.040*Incremental adjusted R2 = .051***Perceived current economic condition Perceived economic condition—national

level–.127 –.110 –2.107*


.437 .392 7.477****

Incremental adjusted R2 = .143****Trust in institutions Trust in government –.182 –.152 –2.704** Trust in the media .011 .012 0.207 Trust in the media × TV viewing


Incremental adjusted R2 = .024*Media consumption TV viewing—nonnews .034 .038 0.662 TV viewing—news .189 .194 3.476*** Internet browsing –.085 –0.75 –1.479 Newspaper reading .084 .088 1.536 Business newspaper reading .002 .002 0.018Incremental adjusted R2 = .047***


Note: N = 356.*p < .05. **p < .01. ***p < .005. ****p < .001.





Trust in the media has no conspicuous independent or interactive effect on economic expectations. This may be part of an increasingly important pattern in cultivation theory that applies to the lack of impact of trust in the media on the cultivation effect. A similar finding was reported in a study in which fear of crime and political attitudes were employed as second-order cultivation indicators (Tsfati, 2002). The reason may be that an uncon-scious media effect like cultivation is unlikely to interact with conscious thoughts about the media. Trust in the government, on the other hand, does predict economic optimism at the national level, probably because people believe that a trustworthy and consistent govern-ment policy is a key to economic prosperity (Slemrod, 1995).

Interaction between TV viewing and any of our demographic variables proved to be not significant in predicting economic expectations. In terms of data, this finding makes sense in light of the fact that some of the demographics (ethnicity, income) lack a significant indepen-dent effect on economic expectations and the others have only a marginal effect that probably does not leave enough variance for significant interactions. The lack of significant interac-tions between TV viewing and other parameters also highlights the robustness and purity of the cultivation effect that cuts across demographic characteristics (Gerbner et al., 2002).

However, evaluation of present economic circumstances is stronger than TV viewing in predicting economic expectations. In fact, it comes out as the most potent factor in all the regression models. This is a reasonable finding in light of the strong linkage between peo-ple’s future expectations and their appraisal of present conditions (Davidson, 1982). Interestingly, while the evaluation of the personal economic situation at present is signifi-cantly associated with economic expectations at the personal level (but not at the national level), an evaluation of the national economic situation at present significantly affects expectations at both levels (although to a lesser extent at the personal level). This means that people are aware of the fact that macroeconomic conditions may affect their own eco-nomic status but not vice versa. Last but not least, the much larger effect that the evaluation of the current economic circumstances has on future economic expectations compared with the marginal and not always significant effect of demographic variables shows that eco-nomic expectations are shaped by what we think about the economy more than by our social status.

Optimistic Bias and Content FindingsWhile the content analysis was not a major part of the project and was performed practi-cally to predict what type of economic expectations TV programs would likely cultivate, the TV content findings have their own value. They adjoin an increasing edifice of research which shows that messages conveyed by different TV genres (not only news) are actually quite depressive (cf. Fouts & Burggraf, 1999; Hetsroni, 2009). Further research would be in place to identify reasons for this trend, which might be (among others): wea-riness on the part of the audience from overly optimistic trite dramatic formulas, a need on the part of the old networks to compete with more daring cable channels in fictional programming, and the higher value of negative information in news programming (Dierkens, 1991). However, accepting as a fact that contentwise popular programming is




Hetsroni et al. 201

no longer the “happyland” it used to be in previous decades (see also Jacobs, 2003, for a discussion of such change in medical drama) is not the only feasible interpretation of the content analysis findings that pertains to nonnews programming. In analyzing these pro-grams, the unit of analysis was a dialogue/talk excerpt in which the economy was a major theme. Thus, a dramatic episode, for example, could have received a number of negative codes for separate negative excerpts early in the program and one positive code for the happy ending culminating in a predominantly negative image. This image might be differ-ent from audience interpretation of the content, if viewers see the final positive message as victorious.

As for the optimistic bias, whereas previous studies of economic expectations report its presence during economic prosperity (cf. Mutz, 1998, p. 122), we were able to identify this bias in data collected during recession. While arithmetically this bias is determined by the difference between personal and national expectations, theoretically it is an independent construct that expresses the tendency to be more optimistic about ourselves than about oth-ers (Gouveia & Clarke, 2001), even when—as in our case—expectations at both levels tend to be rather pessimistic. There is evidence that optimistic bias derives from motiva-tional causes (Weinstein & Klein, 1996). In our case, the need to “protect” their economic self-esteem may induce people to be optimistically biased. Such bias becomes stronger when people experience fear, a natural feeling during recession. Watching television may enhance this tendency because the programming (which is more often negative than posi-tive in its economic tone) shows people who do badly in life and brings viewers to con-clude that they probably cannot do worse than that. Since the magnitude of the optimistic bias effect in our case is quite small (d = .166), it is not out of the question that it is more beneficial than harmful (Weinstein & Klein, 1996), and may contribute toward maintaining a higher level of self-efficacy in economic matters.

Regarding the impact of nonmedia variables—positive evaluations of the national econ-omy at present and low trust in the government also enhance optimistic bias, probably because they decrease future economic expectations at the national level (and do not decrease these expectations at the personal level). In contrast, negative evaluations of the personal economic situation predict lower optimistic bias, apparently because negative evaluators have a weaker basis in the present on which to establish future optimism con-cerning themselves. Subsequently, the gap between their personal expectations and their expectations about the public at large is narrower. Finally, being married is related to being more optimistically biased, possibly because married people are more optimistic at the personal level (Mastekaasa, 1994).

Conclusions, Limitations, and Suggestions for Future ResearchOur study is the first scholarly endeavor to establish a statistically significant relationship between TV viewing and economic expectations during recession. It is also the first work to associate exposure to different TV content types (news and nonnews) with our evaluation of the economic future. An important conclusion is that since economic optimism is negatively affected by TV viewing, heavy viewing may induce people to anticipate a further downturn





in an already recessed economy. Potential outcomes of such expectations include—at the microeconomic level—further decrease in consumption and an upsurge in conservative financial savings (Galí, 1990). When the most sensible economic decisions are reached by moderate optimists, pessimists remarkably avoid investment opportunities (Puri & Robinson, 2007). At the macro level, among other effects, pessimistic economic expectations may increase the price of financial hedging tools and insurance premiums and decrease invest-ment in high-risk stocks (Puri & Robinson, 2007) and the demand for consumer goods (Gowrisankaran & Rysman, 2009). Such market tendencies are liable to deepen recessionary cycles (Navarro, Bromiley, & Sottile, 2010). At the political realm, economic pessimism is not likely to encourage voting for incumbent candidates (Mutz, 1998).

What makes general TV viewing a predictor of economic pessimism? Predicated on the results of the content analysis the answer would be that a moderate bias toward negative coverage of the economy yields negative expectations in response to viewing. This effect is likely boosted by the tendency of negative information to influence evaluations more strongly than positive information does (Ito, Larsen, Smith, & Cacioppo, 1998). As the negative coverage of the economy appears to be a universal norm (Ju, 2008; Sanders & Gavin, 2004; Soroka, 2006), the cultivation of negative economic expectations may also be an overarching trend (cf. Tims et al., 1989 for evidence of the 1980s).

There is, however, another way to examine the positive relationship between TV view-ing and economic pessimism by considering it as a demonstrative example of the more general relationship between TV viewing and low level of satisfaction from life (Bruni & Stanca, 2008, p. 508; Morgan, 1984, p. 504). This interpretation is consonant with mood management theory, which postulates that TV viewing is a preferred choice of time spend-ing among people who feel distressed or unsatisfied (Knobloch, 2006). Pessimistic eco-nomic expectations, predominantly at the personal level, may elicit an escape to the small screen rather than vice versa. Thus, an inevitable recommendation for further research is to examine the possibly mediating effect of depressive psychological tendencies on the rela-tionship between TV viewing and pessimistic economic expectations. From the economic literature, factors that can be considered in future research are investment preferences and risk-taking propensities because risk averting investment policy is indicative of economic pessimism (Mola & Guidolin, 2009). Cross-cultural investigations are also recommended in order to ascertain that similar results would be obtained in different economies and under entirely different economic circumstances.

A conspicuous if rather inevitable limitation of our study has to do with the cross- sectional design. This precludes a definite determination of causality. The significant rela-tionship between TV viewing and economic pessimism led us to assume that watching television cultivates economic pessimism. However, it is also possible that being con-cerned about the economy attracts people to the screen, and we may not rule out that a simultaneous pattern of effects is noted here. This would mean that watching television induces economic pessimism, whereas economic pessimism encourages people to devote more time to watching TV. Of course, from an applied perspective the established associa-tion between TV viewing and economic pessimism is at least as significant as its source. A practical advice to hands-on policy makers is to invest in educational programs that would




Hetsroni et al. 203

extend media literacy concerning the effect of TV viewing on economic expectations among the public at large.

Acknowledgment

The authors thank Professor James Shanahan (Boston University) and Professor Michael Morgan (University of Massachusetts–Amherst) and two anonymous reviewers for their insightful comments that helped in shaping the article’s final version.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Notes

1. Respondents were asked whether their monthly income was much higher, slightly higher, more or less

around, slightly lower or much lower than the national average, which was presented as a rounded

baseline.

2. Computation of the interaction term in the regression used transformed Z scores of the variables. The

coefficients that appear in the tables represent the final model. Figures denoting the coefficients of fac-

tors other than TV viewing change insignificantly, when a different TV viewing measure is included in

the model.

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Author Biographies

Amir Hetsroni is associate professor in the school of communication at Ariel University Center of Samaria, Israel. His research interests include applications of cultivation theory and analyzing the appearance of objectionable content in popular programming and advertising. He is also an op-ed writer on media policy matters and served as consultant for reality TV productions.

Zachary Sheaffer is a senior lecturer in the Management and Economics Department at Ariel University Center of Samaria, Israel. eHe received his PhD in management from the University of Waikato, Hamilton New-Zealand. His research interests include organizational crisis, crisis management and business failure. Dr Sheaffer has coauthored two books and has published in such journals as Journal of Organizational Behavior, Journal of Management Studies, Journal of Applied Behavioral Science, Entrepreneurship: Theory & Practice.

Uri Ben Zion is professor and chair of the department of economics at Western Galilee College, Israel. His research interests revolve around the intersection of behavioral science and finance.

Mosi Rosenboim is an economist and a faculty member in the Guilford Glazer Faculty of Business and Management, Ben-Gurion University of the Negev and in the College of Management - both in Israel. His PhD thesis, entitled “Optimal Incentive Policy for Attracting Foreign Direct Investment”, combines tools from finance theory, behavioral finance theory, and auction theory to determine the government’s optimal economic policy for attracting FDI. His current research interests are finance, behavioral finance, and regional economic policy. He has published papers in major journals and has experience in economic and financial consulting.




Economic Expectations, Optimistic Bias, and Television Viewing During Economic Recession: A Cultivation Study

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