Overview and FAQ |  Introduction and Methods | Sample  | Results: Who is giving?  |  Acknowledgments and References

Introduction

A brief look at giving in the United States

Despite 2008 seeing the largest annual drop^[1] since these statistics have been tracked, over $300 billion was still donated to charitable organizations—three-quarters of which came from individual donors.¹ It is no wonder then that such a large amount of academic study has been invested in the subject. A myriad of studies have sought to investigate the motivations behind the phenomenon of charitable giving. Everything from an individual’s mood,^2-4 gender,^{3, 5-8} perception of a charity’s efficiency,^{9, 10} donor’s age,^{5, 7, 11-14} altruistic characteristics,^{3, 15} income,^{7, 11, 13, 16-20} religiosity,^{5, 12} previous volunteer experience,^{3, 12, 21} perceived generosity,^{5, 9, 22-25} financial security,^{5, 12} and a person’s educational attainment,^{6, 7, 12-14, 16, 18, 24} has been used to predict charitable giving. The results from these numerous studies has led to a shift in the way nonprofits organize fundraising campaigns. Marketing literature has had an extensive history of promoting the utilization of capitalistic marketing strategies by nonprofit organizations—dating as far back as the 1960s.²⁶ With ever-decreasing government contributions and an inundation of new nonprofits (and with it new competition), fundraising within the nonprofit sector has shifted from a discombobulated exercise of necessity^[2] to a fundamental business operation similar to the advertising and branding campaigns of for-profit companies.^23[3]

The COCOA Experiment: So why do another study?

While previous studies focused on an individual’s motivation for (and the characteristics of) giving to charities in general, predicting the amount donated, or pro-social characteristics related to donations and volunteerism, this study proposes focusing on the effects of negative (or apathetic) attitudes and beliefs toward specific causes. The cause under investigation in this case is HIV—a multifaceted and highly stigmatized disease affecting over 30 million people worldwide—approximately half a million living in the United States alone.²⁷ Attitudes toward HIV in the US are nuanced; however, many consider it to be a disease of the “other”—a “gay disease”, “injection drug users’ disease”, or a disease for the poor. In any case, blame is often ascribed to those who are HIV-positive and none of these generalizations accurately portray the full scope of the epidemic. Though many other layers of perception warrant future research, they are outside the scope of this study. Instead, I sought to determine the influence that attitudes about homosexuality and perception of HIV as a problem (in relation to other domestic issues) have on choice of charity. A few objectives included determining if choice of charity is associated with:

1. attitudes toward homosexuality,
2. public perception of HIV/AIDS as an issue in relation to other domestic concerns,
3. or social proximity to members of the LGBTQ community.

Of course, this study is not meant to provide a definitive answer to any of these questions, but rather to test the feasibility of conducting these studies and provide insight into unexplored avenues as well as add to existing research.

Methods

The COCOA Experiment was a quantitative, cross-sectional study. A total of 251 valid surveys were collected during October of 2009. Participants were directed to an online questionnaire hosted by SurveyMonkey. While IP addressew were collected to insure the removal of duplicate entries, the final data set was anonymized with no identifying information. Upon completion of the survey, participants were asked to select from a list of five charities and a donation of $5 would be given on their behalf. Recruitment occurred through posting in online forums (e.g., social networking sites, chat room, and message boards) as well as local posting areas (e.g., coffee shops and billboards around New York University). This process was approved by the University Committee on Activities Involving Human Subjects (HS# 7383).

Relevant Measures

Basic demographics: Participants were asked to self-report their month and year of birth, ethnicity, race, education status and attainment, religious and political affiliations, zip code, and yearly income.

Previous charitable giving and volunteer experience: Participants were asked to self-report number of charities and total amount of donation to charities within the last 12 months. Participants also reported approximate number of hours volunteered. Giving and experience was divided into religious and nonreligious organizations.

HIV/AIDS as a domestic issue: Participants were asked to report how large a problem they believed HIV/AIDS to be in America. Several other domestic issues were listed alongside HIV/AIDS. The scale was adapted from a Pew Research Center survey.

Social proximity to HIV: Participants were asked if they knew of a friend, colleague, or family member who died from HIV, was living with HIV, or openly gay. This scale was adapted from a Pew Research Center survey.

Attitudes toward homosexuality: Attitudes toward homosexuality was assessed through a 49-item, 5-point Likert developed by LaMar & Kite (1998).

Eligibility

To be eligible to take the survey, participants had to a) be between the ages of 25 and 55, b) earn an annual income between $20,000 and $150,000, c) given to a charity or volunteered at least one time in the past 12 months, d) live in the United States, and e) have an internet connection.

1. 5.6% decrease from 2007 in inflation-adjusted dollars
2. Think Santas in front of department stores.
3. Think the (RED) Campaign.

The COCOA Experiment has been over for a while now and, though I’ve run some of the statistical analyses, I have yet to write anything up.

It’s mostly the fault of life in general–after all, there’s only so much time you can devote to a pet project when you’ve got four jobs and you’re finishing up your masters.

Regardless, I’m sorry.

It’s been tough scheduling times with my advisor to help me run the more advanced tests, so instead of keeping the interested people waiting, I’ve decided to just release the results piecemeal. This is the first post in a series. Consider it as a sort of electronic table of contents.

While not optimal,^[1] this style of publishing does have some benefits. First, you get to see all the results as soon as I’m finished with them. Second, and more importantly, you get to suggests the direction of future results. After all, I’m interested to know what questions other people want to know using the data I collected, so if you have a question, ask it below.

However, you should really read the Methods section first so you know what data I have in order to base your questions. Lastly, I’ll get to answer your questions immediately. Use the comments below.

1. Overview [You're here.]
2. Introduction and Methods
3. Sample
4. Results 1: Who is giving?
5. Acknowledgments and References

FAQ

1) What were the eligibility requirements? Between 18-55 years of age. Make between $20,000 and $150,000 last year. Give to a charity or volunteered at an organization at least once in the last 12 months. Be a United States citizen. Have an internet connection. That’s it.

1. and certainly not of academic journal quality so be prepared for an onslaught of typos

Acknowledgments

I’d be nowhere without the invaluable help of my mentor and project advisor, Professor Perry Halkitis; Daniel Siconolfi, life and writing coach; the always helpful folks at the Center for Health, Identity, Behavior, and Prevention Studies; and everybody who forwarded, retweeted, posted, or stapled a recruitment card for me.

References

1. Foundation Center. How much did Americans give to charity in the latest year of record? 2009; http://foundationcenter.org/getstarted/faqs/html/givingstats.html. Accessed January 23, 2010.

2. Krebs DL. Altruism: An examination of the concept and a review of the literature. PSYCHOLOGICAL BULLETIN. 1970;73(4):258-302.

3. Piliavin JA, Charng HW. Altruism: A review of recent theory and research. Annual Review of Sociology. 1990;16(1):27-65.

4. Simmons RG. Presidential address on altruism and sociology. Sociological Quarterly. 1991:1-22.

5. Halfpenny P. Charity Household Survey 1988/89: Charities Aid Foundation, Tonbridge; 1990.

6. Jones A, Posnett J. Charitable donations by UK households: evidence from the Family Expenditure Survey. Applied Economics. 1991;23(2):343-351.

7. Burnett JJ. Psychographic and demographic characteristics of blood donors. Journal of Consumer Research. 1981:62-66.

8. Cermak DSP, File KM, Prince RA. A benefit segmentation of the major donor market. Journal of Business Research. 1994;29(2):121-130.

9. Harvey JW, McCrohan KF. Fundraising Costs Societal Implications for Philanthropies and Their Supporters. Business & Society. 1988.

10. Posnett J. Determinants of household giving to charity in the UK. Sources of Charity Finance, CAF, Tonbridge. 1989.

11. Danko WD, Stanley TJ. Identifying and reaching the donation prone individual: A Nationwide Assessment. Journal of Professional Services Marketing. 1986;2(1/2):117-122.

12. Edmundson B. Who gives to charity? American Demographics. 1986;8:44-49.

13. Smith SM, Beik LL. Market segmentation for fund raisers. Journal of the Academy of Marketing Science. 1982;10(3):208-216.

14. Dawson S. Four motivations for charitable giving: implications for marketing strategy to attract monetary donations for medical research. Journal of Health Care Marketing. 1988;8(2):31.

15. Guy BS, Patton WE. The marketing of altruistic causes: understanding why people help. Journal of Services Marketing. 1989;2(1):5-16.

16. Jencks C. Who gives to what? The nonprofit sector: A research handbook. 1987:321–339.

17. Kitchen H, Dalton R. Determinants of charitable donations by families in Canada: a regional analysis. Applied Economics. 1990;22(3):285-299.

18. Harvey JW. Benefit segmentation for fund raisers. Journal of the Academy of Marketing Science. 1990;18(1):77-86.

19. Amos OM. Empirical analysis of motives underlying individual contributions to charity. Atlantic Economic Journal. 1982;10(4):45-52.

20. Morgan JN, Dye RF, Hybels JH. Results from two national surveys of philanthropic activity: Univ of Michigan Survey Research; 1979.

21. Carman JM. Is involvement the key to giving? Journal (National Association for Hospital Development (US)). 1982:53.

22. Diamantopoulos A, Schlegelmilch B, Love A. Giving to charity: determinants of cash donations through prompted giving. Marketing Theory and Application. 1993;4:133-142.

23. Schlegelmilch BB, Ltd M. Targeting of Fund-raising Appeals—How to Identify Donors. European Journal of Marketing. 1988;22.

24. Schlegelmilch BB, Tynan AC. The scope for market segmentation within the charity market: an empirical analysis. Managerial and Decision Economics. 1989;10(2).

25. Yavas U, Riecken G, Parameswaran R. Using psychographics to profile potential donors. Business Atlanta. 1980;30(5):41-45.

26. Kotler P, Levy SJ. Broadening the concept of marketing. The Journal of Marketing. 1969:10-15.

27. Global Health Reporting.org. HIV/AIDS: How many people have HIV/AIDS? 2009; http://www.globalhealthreporting.org/diseaseinfo.asp?id=265.

I’m basically interested in seeing how various attitudes in regards to charities in general, perceptions of HIV as a problem in America, and attitudes about HIV vulnerable populations affect choice of HIV (or non-HIV) charity.

For more info, check out COCOAExperiment.com. Next up, the perpetual headache that is participant recruitment…

UPDATE: Results are slowly coming in. Check out the Overview for more information.

1. 5 months, 2 weeks, and change to be more specific

I’m sorry I’ve neglected you. So much awesome public health news has broken in the last month, and I just haven’t had the time to post it. Between three jobs, full time school, and moving into the city, I’ve barely had time to breathe. I promise I’ll be back though. Come October, you’ll be back in full force.

With all this crazy news about ObamaCare, a lot of facts (and “facts”) are being tossed around. Often, people will use big fancy health care words like “life expectancy” and while we all kind-of-sorta know what life expectancy is, I’m going to really get into it for you. Why? Because life expectancy is one of those things that seems to get twisted and used in all sorts of ways it was never intended.

So, we’ll start off with the basics. What is life expectancy? Life expectancy^[1] is defined as the the average (expected) number of years of life remaining at any given age.

When people say “life expectancy in _________” they are referring to the life expectancy of that country at birth, but you can define life expectancy in other ways as well. For example, life expectancy at age 1 (or at age 5) is often used in countries with very high levels of infant mortality. Why? Well, I have to get all mathy for this so I’m going to turn it into a footnote. Hover over it if you’re really interested.^[2]

So, we’ve defined it as an average. As with any average, this creates helpful comparisons between countries/cities/regions since it is independent of population size. Also with any average, it doesn’t really define the population. The life expectancy in the US just went up to it’s highest point in history at^[3] 77.9 years. That doesn’t mean you’re going to die exactly .1 years before your 78th birthday. In fact, it doesn’t even mean most people die right before their 78th birthday. It means some people died above 77.9, some below 77.9 and maybe a few at 77.9, but when you added them up and divided by the sample size, you get 77.9^[4]

So here comes the fun part. The United States ranked horribly when it comes to this. Horribly. We are ranked 35th–this puts us at slightly higher than Costa Rica and Cuba^[5] and well, well below Japan (82) or Australia (81.6) and even *gasp* Canada (81.2)! Essentially, this puts us below any industrialized country.^[6]

And that is often what gets touted. We have the lowest life expectancy of any industrialized^[7] country in the world.

This is, of course, true; however, it is also slightly misleading. You see, life expectancy is not the most robust measure of a country’s health care system. Cultural, environmental, geographical, social, structural, or legal differences can all affect this measure and health systems have a hard time countering that. It’s a great crude measure, but horrible for direct comparisons.

It’s this exact weakness that has conservatives pouncing all over the Obama plan. Specifically, there is a presentation^[8][9]–too often (mis-)cited by others–which claims that if we removed all violent crime and automobile accident deaths, we would actually be number 1 in the world in terms of life expectancy.

This is simply not correct. If any of those blogs that cited this presentation actually bothered to read the article, they would find that the regression the authors used removed automobile accidents and violent crimes and the US moved up to 17th. Then they “standardized” the US with the rest of the world by factoring in GDP. Given that the US a huge GDP compared to any other country in the world, this bumped us up to number 1 on the list.^[10]

So the idea is if we removed areas where America has a uniquely higher rate–driving accidents and murder–we’re not as bad as the rest of the world and this is a better reflection of the health care system. Now, I’ve got issues with that already, but my first question is why? Why are we suddenly developing this new metric? We already have a metric designed to reflect a good health care system–it’s called amenable mortality. Amenable mortality is the mortality rate of a country that could have been (theoretically) avoided with timely and proper medical attention.^[11]

So there you have it. Life expectancy is the tool being used in this war of ideologies and it’s about the dullest one in the box. Don’t be tricked by this “if you remove violent crime and automobile accidents” business. Complete garbage. Look for articles that cite something useful like amenable mortality, infant mortality^[12], maternal mortality, measures involving access to care, etc.

1. in the context of human health–since it also applies to animals or manufactured goods or whatever
2. This is because life expectancy is the mean of a population and as with any mean, it is very sensitive to extreme ranges–the further away from the expected value you go, the more leverage a single point will have. That is, if your mean is 50 in a population of 10, a single person dying at 1/2 years or living to 150 will largely impact your mean. Because humans more often die at young ages than live to extremely old ages, life expectancy is especially sensitive to infant mortality. Hey, that actually wasn’t very mathy at all. Good job, me.
3. a relatively unimpressive for a developed country
4. If you still don’t get this and need a mental exercise, imagine a coin and value heads as 1 and tails as 2. If you flipped that coin a million times and then averaged out the values, you would get 1.5. Yet, you never flipped 1.5, did you?
5. Granted, Cuba is a very special case in terms of public health, GDP, and life expectancy.
6. I’m sure I could find more recent numbers, but this isn’t a term paper–it’s a blog. Either use my 2006 UN data or do your own research.
7. read: has socialized health care
8. See it here (Warning: PDF).
9. based on this article–again, PDF
10. Read the “note” here for a more articulate response.
11. On that scale, the US is still ranked pretty low–17th or 19th if I remember correctly. I don’t have my textbook with me.
12. There are some issues with this measure as well, but it’s one of the better ones in our limited toolbox.

I also took a month-long course at the University of Cape Town and got to witness a health care system within the context of a truly transitional society. I learned it’s possible to provide free primary care to your entire population. I also learned doing so is difficult. I learned the battle between private and public health providers knows no geographical, cultural, or social boundary. I learned about the difficulties of expansive poverty in the wake of heated violence and racism. I learned that an amazing and beautifully crafted constitution^[4] is meaningless without political and logistical backing.

Well, suffice it to say I learned a lot. It solidified my desire to get a doctorate and go into research.^[5] I’ll keep you updated on any articles that come out of the trip.

1. and another week gallivanting through UAE and Europe
2. 4.7 minutes per survey for raw data plus an additional 30 minutes per 50 surveys for coding
3. 15.7 pounds
4. And South Africa’s is definitely one of the best constitutions I’ve ever read.
5. Though, I am not sure if I’d want to be based in another country for extended periods of time.

Here, I’m working with an organization called Sonke Gender Justice Network working on a project that will (hopefully) provide quantifiable data and put the lives of young South African males in better social context. I don’t want to dive too much into the project until it is up and running, but I’m excited about being back in Africa and running a project as the PI. There’s huge potential for this project to fine tune the unique programs Sonke is currently implementing to the very specific needs of young men.

The entire staff at Sonke has been unbelievably supportive and the mentorship of Dean Peacock^[1] has been invaluable.

That’s my blurb for now. There will be a lack of proper updates until the summer is over. Partly because I’m busy, partly because the internet here is unreliable, but mostly because I need a break.

[Photo: mine.]

1. 2007 Men’s Health Man of the Year, btw. I cannot help but poke a little fun at him about this. On his long list of wonderful accomplishments and awards, this one is undoubtedly the sexiest.

So, first, I couldn’t move all the comments over. Sorry. If you left a comment and it is gone now, it has nothing to do with me censoring you. I’m also missing a few posts that I just couldn’t bring back. The posts also didn’t come with their tags so I’m slowly filling those in. Lastly, I’ve still got to tweak this theme–I like the overall presentation, but some of it is just weird.

I was way behind schedule on this one, so I apologize to my two readers. Two jobs, full time school, midterms, finals, papers, presentations, and life–what else can I say?

Your regular blogging will continue.