Begging for Change: A Comparative Analysis of How the Media Frames Domestic and International Poverty

By Lauren M. Krizay
2011, Vol. 3 No. 09 | pg. 3/3 |

Conclusion

The function of this research project was to explore the similarities and differences in how both domestic and international are framed by the most circulated news magazine in the U.S, and to recognize how this framing may impact public opinion and policy. As a whole, coverage of poverty is rare. As well, coverage of international poverty is three times more common than coverage of domestic poverty. This frames poverty as a much more prevalent problem outside of our country’s borders.

Much of United States media today has been consolidated, and approximately 90% is owned by the ‘Big Six’. These Big Six corporations include General Electric, Walt Disney, News Corp., Time Warner, Viacom and CBS (Ownership Chart). theory argues that differentials in capitalist societies, such as the United States, result in the dominant elite pushing their views and goals onto the general public for financial gain and status maintenance (Ashley and Ornstein 2005: 195-199). Applying this to the media, the six elite corporations are able to control what and how issues, including poverty, are covered and framed. In doing this, they are able to manage the media to best serve the needs of the upper class, elite corporations and individuals.

This elitist framing can be tied in with agenda setting theory, a communications theory that explains that the media has the power to strongly influence which issues are important to the public. The extent to which the media covers an issue determines whether the public views the issue as something that concerns them or not. Further, the concerns and opinions of the public shape policy and political agenda (Mortensen 2010: 357). As an overwhelming majority of United States media is controlled by a small number of elite corporations, these corporations are able to dictate public opinion, hence, directly sway and control . Frequently and more accurately covering poverty may draw negative attention to those in power, and urge others to help those in poverty, which would not directly benefit the United States’ upper class and corporations. So, the media instead covers more timely matters and current events, paired with pervasive problems that may have a more positive benefit for the elite. Through this misframing of reality and events, the public is led to hold misperceptions on poverty’s presence in the U.S., and therefore, public policy and action will be based off of wrong premises (Johansson 2007: 277).

Just as citizens within our own country have access to foreign media, such as BBC and Al Jazeera, foreign citizens have access to ours. Acknowledging this, impression management theory and social desirability bias come into play when explaining why the media is framed the way it is. In his book, The Presentation of Self in Everyday Life, theorist Erving Goffman argued that through impression management, people deliberately and strategically portray themselves in such a manner that creates and manages other’s impressions of themselves in a desirable way (Johansson 2007: 276). An individual will act in a calculated manner, displaying himself in such a way with the purpose of exuding an impression that he aims to obtain (Goffman1959: 6).

Though it may seem that Goffman’s theory strictly applies to communication and interaction between individuals, it has immediate implications for larger organizations and institutions, such as the media, as well (Johansson 2007: 278). As a country viewed as a global superpower, the United States media has the power to act in a calculated manner with the purpose of exuding an impression of economic success to not only its own citizens, but also to others worldwide. Knowing that other key players in the world’s political and economic climate have access to U.S. media, the media is able to solidify the United State’s desirable position and perception of economic power through the use of media framing and impression management. Media consumers across the globe are frequently unsuspicious, and blindly accept the realities portrayed through impression management. This gives the media the opportunity to easily reaffirm their global status, and allows them to gain much by controlling it (Goffman 1959: 8). Though the problems of poverty are far reaching within the United States, just as they are internationally, by rarely covering or depicting domestic poverty, a reality of class stability and equality within the U.S. is conveyed and widely accepted.

Following North America, whose results were skewed because all articles on domestic poverty were on North America, as opposed to the articles on international poverty that were split between regions, the region with the most coverage of poverty was Asia, particularly . Though no one would argue that poverty is a notable problem in this region, poverty is also a large problem in many other regions, particularly Africa. Interestingly, India and , countries that both fall within the ‘Asia’ region, are labeled as rising potential superpowers in today’s global society. Just as impression management serves the purpose of framing ones self in a particular light, framing another country in a negative light has the potential to negate that country’s potential for success and power, and may be in place to attempt to undermine their threat to the United State’s current position of power. By avoiding coverage of United States economic downfalls, and more commonly addressing the downfalls of other countries, the media attempts to frame the United States as superior, falsely conveying its success and equality. This rising threat to power may explain why U.S. media most commonly addressed international poverty within the context of Asia.

Considering the implications of agenda setting theory, the under and misrepresentation of domestic poverty, paired with the overall sense of dehumanization, will lead to apathy about the issue within the United States. Consequently, inadequate and insufficient political policy geared towards addressing the problem will emerge. Comparatively however, international poverty was more frequently reported on, and the images accompanying it more commonly depicted the impoverished themselves. This difference lends itself to the more common acceptance and acknowledgement of international poverty, and therefore, an increase in foreign aid and policy.

Due to the nature of this research, time constraints did not allow for a more longitudinal method of data collection. In future research, this study could be improved by coding articles over a longer period of time, perhaps ten years as opposed to one. This would likely eliminate some misrepresentations as a result of over-reporting within certain regions due to significant current events such as historical political uprisings or natural disasters. Noting the potential for the presence of political leanings associated with the reporters of a particular news organization, coding articles from other news magazines, such as Newsweek, rather than solely Time, would help eliminate these biases.


References

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Bunton, Kristie. 2006. “Considerations of Class and Media: A Review Essay.” Journal of Mass Media Ethics 21(4): 359-371.

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Clawson, Rosalee and Rakuya Trice. 2000. “Poverty as We Know It: Media Portrayals of the Poor.” The Public Opinion Quarterly 64(1): 53-64.

Cody, Sacha. 2011. “The aesthetics of the human face in .” Consumers, media, advertising, theory and . Retrieved May 15, 2011 (http://sachacody.blogspot.com/2011/01/aesthetics-of-human-face-in-advertising.html)

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Goffman, Erving. 1959. Presentation of Self in Everyday Life. Carden City, NY: Double Day Anchor Books.

Johansson, Catrin. 2007. “Goffman’s sociology: An inspiring resource for developing public relations theory.” Public Relations Review 33: 275-280.

Kim, Sei-Hill, John P. Carvalho and Andrew G. Davis. 2010. “Talking About Poverty: News Framing of Who is Responsible for Causing and Fixing the Problem.” Journalism and Mass Communication Quarterly 87(3/4): 563-579.

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Mortensen, Peter. 2010. “Poliltical Attention and Public Policy: A Study of How Agenda Setting Matters.” Scandinavian Political Studies 33(4): 356-378.

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Appendix

Location_R Frequency Table (Table 1)

Location_R Location Recoded
  Frequency Percent Valid Percent Cumulative Percent
Valid 0 Domestic 21 26.9 26.9 26.9
1 International 57 73.1 73.1 100.0
Total 78 100.0 100.0  


Region_R Frequency Table (Table 2)

Region_R Region Recoded
  Frequency Percent Valid Percent Cumulative Percent
Valid 0 North America 22 28.2 28.6 28.6
1 Asia 20 25.6 26.0 54.5
2 Africa 11 14.1 14.3 68.8
3 Middle East 11 14.1 14.3 83.1
4 Caribbean 3 3.8 3.9 87.0
5 South America 2 2.6 2.6 89.6
6 Europe 8 10.3 10.4 100.0
Total 77 98.7 100.0  
Missing System 1 1.3    
Total 78 100.0    


ImagePresent Frequency Table (Table 3)

ImagePresent CaptionYN_R
  Frequency Percent Valid Percent Cumulative Percent
Valid 0 Image Present 76 97.4 98.7 98.7
1 No Image Present 1 1.3 1.3 100.0
Total 77 98.7 100.0  
Missing System 1 1.3    
Total 78 100.0    


Setting_R Frequency Table (Table 4)

Setting_R Setting Recoded
  Frequency Percent Valid Percent Cumulative Percent
Valid 0 Urban 28 35.9 35.9 35.9
1 Rural 20 25.6 25.6 61.5
2 Wealthier Indoor 17 21.8 21.8 83.3
3 Poorer Indoor 3 3.8 3.8 87.2
4 Other 10 12.8 12.8 100.0
Total 78 100.0 100.0  


PeopleFocus_R Frequency Table (Table 5)

PeopleFocus_R People Focus Recoding
  Frequency Percent Valid Percent Cumulative Percent
Valid 0 Yes 62 79.5 80.5 80.5
1 No 15 19.2 19.5 100.0
Total 77 98.7 100.0  
Missing System 1 1.3    
Total 78 100.0    


Ages_R Frequency Table (Table 6)

Ages_R Ages Recoded
  Frequency Percent Valid Percent Cumulative Percent
Valid 0 <18 7 9.0 11.5 11.5
1 18-64 41 52.6 67.2 78.7
2 >64 2 2.6 3.3 82.0
3 Multiple Age Groups 11 14.1 18.0 100.0
Total 61 78.2 100.0  
Missing System 17 21.8    
Total 78 100.0    


Races_R Frequency Table (Table 7)

Races_R Race Recoded
  Frequency Percent Valid Percent Cumulative Percent
Valid 0 White 11 14.1 17.7 17.7
1 Black 16 20.5 25.8 43.5
2 Asian 21 26.9 33.9 77.4
3 Middle Eastern 6 7.7 9.7 87.1
4 Hispanic 3 3.8 4.8 91.9
5 Multiple Races Depicted 5 6.4 8.1 100.0
Total 62 79.5 100.0  
Missing System 16 20.5    
Total 78 100.0    


Gender_ R Frequency Table (Table 8)

Gender_R Gender Recoded
  Frequency Percent Valid Percent Cumulative Percent
Valid 0 Male 30 38.5 46.9 46.9
1 Female 12 15.4 18.8 65.6
2 Both Male and Female 19 24.4 29.7 95.3
3 Undeterminable 3 3.8 4.7 100.0
Total 64 82.1 100.0  
Missing System 14 17.9    
Total 78 100.0    


TypePerson_R Frequency Table (Table 9)

TypePerson_R Type Person Recoded
  Frequency Percent Valid Percent Cumulative Percent
Valid 0 Impoverished 36 46.2 56.3 56.3
1 Politician 14 17.9 21.9 78.1
2 Aid/Helper 3 3.8 4.7 82.8
3 Other 11 14.1 17.2 100.0
Total 64 82.1 100.0  
Missing System 14 17.9    
Total 78 100.0    


CaptionYN_R Frequency Table (Table 10)

CaptionYN_R CaptionYN Recoded
  Frequency Percent Valid Percent Cumulative Percent
Valid 0 Yes 65 83.3 85.5 85.5
1 No 11 14.1 14.5 100.0
Total 76 97.4 100.0  
Missing System 2 2.6    
Total 78 100.0    


SpinCaption_R Frequency Table (Table 11)

SpinCaption_R Spin Caption Recoded
  Frequency Percent Valid Percent Cumulative Percent
Valid 0 Positive Spin 6 7.7 9.1 9.1
1 Negative Spin 11 14.1 16.7 25.8
2 No Spin, Neutral 49 62.8 74.2 100.0
Total 66 84.6 100.0  
Missing System 12 15.4    
Total 78 100.0    


MainFocusPoverty_R Frequency Table (Table 12)

MainFocusPoverty_R Main Focus Poverty Recoded
  Frequency Percent Valid Percent Cumulative Percent
Valid 0 Yes 10 12.8 12.8 12.8
1 No 68 87.2 87.2 100.0
Total 78 100.0 100.0  


CauseListed_R Frequency Table (Table 13)

CauseListed_R Cause Listed Recoded
  Frequency Percent Valid Percent Cumulative Percent
Valid 0 Yes 24 30.8 31.6 31.6
1 No 52 66.7 68.4 100.0
Total 76 97.4 100.0  
Missing System 2 2.6    
Total 78 100.0    


SolutionGiven_R Frequency Table (Table 14)

SolutionGiven_R Solution Given Recoded
  Frequency Percent Valid Percent Cumulative Percent
Valid 0 Yes 21 26.9 26.9 26.9
1 No 57 73.1 73.1 100.0
Total 78 100.0 100.0  


EconGovt_R Frequency Table (Table 15)

EconGovt_R EconGovt Recoded
  Frequency Percent Valid Percent Cumulative Percent
Valid 0 Yes 12 15.4 15.4 15.4
1 No 66 84.6 84.6 100.0
Total 78 100.0 100.0  


Crosstab between EconGovt_R and Location _R (Table 16)

EconGovt_R EconGovt Recoded * Location_R Location Recoded Crosstabulation
  Location_R Location Recoded Total
0 Domestic 1 International
EconGovt_R EconGovt Recoded 0 Yes Count 8 4 12
% within Location_R Location Recoded 38.1% 7.0% 15.4%
1 No Count 13 53 66
% within Location_R Location Recoded 61.9% 93.0% 84.6%
Total Count 21 57 78
% within Location_R Location Recoded 100.0% 100.0% 100.0%


EconGovt_R and Location _R Chi Square Test (Table 17)

Chi-Square Tests
  Value df Asymp. Sig. (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided)
Pearson Chi-Square 11.386a 1 .001    
Continuity Correctionb 9.124 1 .003    
Likelihood Ratio 10.098 1 .001    
Fisher's Exact Test       .002 .002
Linear-by-Linear Association 11.240 1 .001    
N of Valid Cases 78        
a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is 3.23.
b. Computed only for a 2x2 table


Crosstab between PeopleFocus_R and Location_R (Table 18)

PeopleFocus_R People Focus Recoding * Location_R Location Recoded Crosstabulation
  Location_R Location Recoded Total
0 Domestic 1 International
PeopleFocus_R People Focus Recoding 0 Yes Count 13 49 62
% within Location_R Location Recoded 65.0% 86.0% 80.5%
1 No Count 7 8 15
% within Location_R Location Recoded 35.0% 14.0% 19.5%
Total Count 20 57 77
% within Location_R Location Recoded 100.0% 100.0% 100.0%


PeopleFocus_R and Location_R Chi Square Test (Table 19)

Chi-Square Tests
  Value df Asymp. Sig. (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided)
Pearson Chi-Square 4.149a 1 .042    
Continuity Correctionb 2.920 1 .088    
Likelihood Ratio 3.804 1 .051    
Fisher's Exact Test       .054 .048
Linear-by-Linear Association 4.095 1 .043    
N of Valid Cases 77        
a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is 3.90.
b. Computed only for a 2x2 table


Crosstab between Setting_R and Location_R (Table 20)

Setting_R Setting Recoded * Location_R Location Recoded Crosstabulation
  Location_R Location Recoded Total
0 Domestic 1 International
Setting_R Setting Recoded 0 Urban Count 6 22 28
% within Location_R Location Recoded 28.6% 38.6% 35.9%
1 Rural Count 1 19 20
% within Location_R Location Recoded 4.8% 33.3% 25.6%
2 Wealthier Indoor Count 6 11 17
% within Location_R Location Recoded 28.6% 19.3% 21.8%
3 Poorer Indoor Count 0 3 3
% within Location_R Location Recoded .0% 5.3% 3.8%
4 Other Count 8 2 10
% within Location_R Location Recoded 38.1% 3.5% 12.8%
Total Count 21 57 78
% within Location_R Location Recoded 100.0% 100.0% 100.0%


Setting_R and Location_R Chi Square Test (Table 21)

Chi-Square Tests
  Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 21.345a 4 .000
Likelihood Ratio 21.749 4 .000
Linear-by-Linear Association 10.766 1 .001
N of Valid Cases 78    
a. 4 cells (40.0%) have expected count less than 5. The minimum expected count is .81.


Crosstab between SolutionGiven_R and Location_R (Table 22)

SolutionGiven_R Solution Given Recoded * Location_R Location Recoded Crosstabulation
  Location_R Location Recoded Total
0 Domestic 1 International
SolutionGiven_R Solution Given Recoded 0 Yes Count 6 15 21
% within Location_R Location Recoded 28.6% 26.3% 26.9%
1 No Count 15 42 57
% within Location_R Location Recoded 71.4% 73.7% 73.1%
Total Count 21 57 78
% within Location_R Location Recoded 100.0% 100.0% 100.0%


Crosstab Between TypePerson_R and Location_R (Table 23)

TypePerson_R Type Person Recoded * Location_R Location Recoded Crosstabulation
  Location_R Location Recoded Total
0 Domestic 1 International
TypePerson_R Type Person Recoded 0 Impoverished Count 4 32 36
% within Location_R Location Recoded 28.6% 64.0% 56.3%
1 Politician Count 4 10 14
% within Location_R Location Recoded 28.6% 20.0% 21.9%
2 Aid/Helper Count 1 2 3
% within Location_R Location Recoded 7.1% 4.0% 4.7%
3 Other Count 5 6 11
% within Location_R Location Recoded 35.7% 12.0% 17.2%
Total Count 14 50 64
% within Location_R Location Recoded 100.0% 100.0% 100.0%


TypePerson_R and Location_R Chi Square Test (Table 24)

Chi-Square Tests
  Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 6.617a 3 .085
Likelihood Ratio 6.396 3 .094
Linear-by-Linear Association 6.283 1 .012
N of Valid Cases 64    
a. 4 cells (50.0%) have expected count less than 5. The minimum expected count is .66.


Crosstab between Class_R and Location_R (Table 25)

Class_R Class Recoded * Location_R Location Recoded Crosstabulation
  Location_R Location Recoded Total
0 Domestic 1 International
Class_R Class Recoded 0 Lower Class Count 3 30 33
% within Location_R Location Recoded 23.1% 60.0% 52.4%
1 Upper Class Count 7 14 21
% within Location_R Location Recoded 53.8% 28.0% 33.3%
2 Undeterminable Count 3 6 9
% within Location_R Location Recoded 23.1% 12.0% 14.3%
Total Count 13 50 63
% within Location_R Location Recoded 100.0% 100.0% 100.0%


Class_R and Location_R Chi Square Test (Table 26)

Chi-Square Tests
  Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 5.639a 2 .060
Likelihood Ratio 5.847 2 .054
Linear-by-Linear Association 4.486 1 .034
N of Valid Cases 63    
a. 2 cells (33.3%) have expected count less than 5. The minimum expected count is 1.86.


Race_R, Location_R and TypePerson_R Layered Crosstab (Table 27)

Race_R Race Recoded * Location_R Location Recoded * TypePerson_R Type Person Recoded Crosstabulation
TypePerson_R Type Person Recoded Location_R Location Recoded Total
0 Domestic 1 International
0 Impoverished Race_R Race Recoded 1 Black Count 3 8 11
% within Location_R Location Recoded 75.0% 28.6% 34.4%
2 Asian Count 0 18 18
% within Location_R Location Recoded .0% 64.3% 56.3%
4 Hispanic Count 0 1 1
% within Location_R Location Recoded .0% 3.6% 3.1%
5 Multiple Races Represented Count 1 1 2
% within Location_R Location Recoded 25.0% 3.6% 6.3%
Total Count 4 28 32
% within Location_R Location Recoded 100.0% 100.0% 100.0%
1 Politician Race_R Race Recoded 0 White Count 3 3 6
% within Location_R Location Recoded 75.0% 30.0% 42.9%
1 Black Count 1 4 5
% within Location_R Location Recoded 25.0% 40.0% 35.7%
4 Hispanic Count 0 2 2
% within Location_R Location Recoded .0% 20.0% 14.3%
5 Multiple Races Represented Count 0 1 1
% within Location_R Location Recoded .0% 10.0% 7.1%
Total Count 4 10 14
% within Location_R Location Recoded 100.0% 100.0% 100.0%
2 Aid/Helper Race_R Race Recoded 0 White Count 1 1 2
% within Location_R Location Recoded 100.0% 50.0% 66.7%
2 Asian Count 0 1 1
% within Location_R Location Recoded .0% 50.0% 33.3%
Total Count 1 2 3
% within Location_R Location Recoded 100.0% 100.0% 100.0%
3 Other Race_R Race Recoded 0 White Count 1 2 3
% within Location_R Location Recoded 20.0% 50.0% 33.3%
2 Asian Count 0 2 2
% within Location_R Location Recoded .0% 50.0% 22.2%
5 Multiple Races Represented Count 2 0 2
% within Location_R Location Recoded 40.0% .0% 22.2%
6 Other Count 2 0 2
% within Location_R Location Recoded 40.0% .0% 22.2%
Total Count 5 4 9
% within Location_R Location Recoded 100.0% 100.0% 100.0%

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