Understanding the types of people who volunteer is an important part of understanding why people volunteer and the indications of what might motivate others to volunteer. First, understanding the types of volunteers, specifically board members, provides the necessary background and overall understanding of volunteering. We both found that individuals are more likely to volunteer if their characteristics are associated with high values of time.
I find that individuals are more likely to volunteer if their characteristics are related to high values of time.
Introduction
Second, I use a subset of respondents who self-identified as on-board volunteers and apply Freeman's methodology to this subset to examine whether on-board volunteers differ from off-board volunteers. This is a special case of the supply function that Freeman and I use to interpret the regression results. In Section 4, I have included a table to illustrate the common characteristics of the current population and to explain the dependent and independent variables.
Literature Review
Overview
Meier and Stulzer focus primarily on the differences between self-interested and self-giving people. In particular, their empirical analysis finds that people who volunteer more were more likely to report greater life satisfaction than non-volunteers. Meier and Stulzer find that people who place more emphasis on external than internal life goals benefit less from voluntary work.
Volunteering: The Contribution of Nonprofit and Public Sector Workers to the Volunteer Workforce.” Rotolo and Wilson use the 2002 CPS supplemental file to conduct their research.
Freeman’s Study
The first is what Freeman refers to as the "charitable production function." The amount of charity given is a function of volunteer time Tv and donations D, written formally as C(Tv, D). The third constraint is the individual's time constraint: Total time spent on work, volunteering, and leisure cannot exceed total available time. Freeman refers to Tv* as the "derived demand for volunteer time," but it also represents the individual's supply of volunteer labor.
Freeman claims that "the substitution effect in response to a change in W is b - cTw" in equation (5).
Cobb-Douglas Example
These are called the 'first order conditions'. This method produces five equations and the five unknowns G, L, D, Tv and λ, so that a unique solution can be found. The voluntary supply function (20) and the individual's donation function can be placed into the charitable production function (2′) to obtain the individual supply function for charity goods C* = C (W, Y).
Research Methods
Descriptive Statistics
The vast majority of working-age respondents are white, ranging from 80 to 90%, and the percentage is slightly higher (6-7%) for volunteers than for non-volunteers. A similar pattern applies to non-volunteers (44% vs. 53%). This change may be due to the aging of the baby boomer generation. However, in both surveys, the percentage between 35 and 54 is about 8% higher for volunteers than for non-volunteers.
The percentage employed is almost the same in both surveys at around 70-80%, and in both the percentage is a bit higher for volunteers. This could be due to a change in the population, but it is more likely due to different methods used to calculate the percentages. The average years of schooling for the 2014 volunteers increased slightly compared to 1989, and remained the same for non-volunteers.
The average for volunteers is about two years after high school, while the average for non-volunteers is about six months after high school. This may be due to a difference in how I calculate average family size (discussed below) or the changing demographics toward smaller families in the United States. This could be due to growing income inequality in the US, or a difference in the way I calculate average income.
I repeated the process for non-volunteers and interpreted the mean of 10.2 as 12.4 years of schooling, since a response of 10 corresponds to "some education but no degree". Regarding family size, the 2014 CPS includes a question on the number of children in the household (PRNMCHLD).
Dependent Variables
I'm not sure how Freeman calculated his family size since the 2014 CPS did not include a specific question about family size. Respondents could indicate their family income by one of 16 answers, where one answer was a range of incomes. I interpreted an average of 12.5 as the midpoint of this range, so I arrived at an average family income of about $55,000.
The CPS also asks how many different organizations an individual volunteered for and then asks how many hours the individual has volunteered for each organization annually. After adding the values for PTS7A-PTS7G to calculate the annual volunteer hours for each.
Independent Variables
CPS labels the question as 'highest level of school completed or diploma obtained'. The data file codes the answers as 31-46, so I subtract 30 from all the answers so that the recoded data is 1-16. For example, if the respondent is coded 02, he or she has indicated that his or her race is “all black.” Respondents have six response options: married spouse present, married spouse absent, widow, divorced, divorced, and never married.
The number of children in the respondent's household is also asked in the 2014 CPS (PNRMCHLD). The question is "number of children under 18". In the regressions, I refer to this variable as No. The 2014 CPS asked respondents to indicate whether or not they lived in a metropolitan area (GTMETSTA).
Freeman also included dummy variables to control for regional effects that I did not include when estimating the regressions. The omission of this dummy variable does not appear to have much effect on the results.
Volunteer Regressions
Although Freeman did not include whether his data were statistically significant in his tables, I used his coefficients and standard errors to calculate t-values and significance levels. For the family income variable, the coefficient is statistically significant and positive in each of the four columns. In column 2, Employed is statistically significant and positive, while columns 1, 3 and 4 all have negative coefficients, but none are statistically significant.
The Completed figure is statistically significant and positive (and about the same size) in all four columns, indicating that when individuals have higher levels of education, these individuals are more likely to volunteer. Number of children also has positive coefficients and statistically significant results in all four columns. In Table 2, the characteristics positively associated with the likelihood of volunteering are also associated with high time values. Characteristics such as income, more years of education, marriage and more children are more likely to volunteer.
These results are similar to those found in Table 1. Overall, the regressions imply that individuals with high opportunity costs are more likely to volunteer than those with lower opportunity costs. For the men in 2014 (column 1), the variables are not statistically significant for income, employed, grade completed, age, age^2*100, married, number of children and INCMSA. For the 2014 women (column 3), employment, grade completed, age, age^2*100, white, married, and INCSMA are not statistically significant variables.
Although Freeman did not include which sections of his data were statistically significant in his tables, I used his coefficients and standard errors to calculate t values and significance levels. Freeman concludes that while people with higher earning capacity are more likely to volunteer, they will volunteer fewer hours.
Board Volunteer Regressions
If a coefficient is negative, volunteers are more likely to be board volunteers than regular volunteers. Male volunteers have positive and statistically significant coefficients for Employed, Degree Completed, White, and Married. Thus, they are more likely to volunteer as a board member than as a regular volunteer if they have a higher income, have a job, have completed a higher grade, are white, or are married.
It is interesting that the number of children has a negative impact on orchard volunteering compared to other volunteering. The variable INCSMA is negative and statistically significant, as it is for men in Table 2. For women, the variables Ln (family income), Work, Degree Completed and Married all have positive and statistically significant coefficients.
If women have higher incomes, are employed, have completed a higher grade level, or are married, then they are more likely to engage on a board versus regular volunteering. For both, being employed, completing a higher grade, and being married are associated with a higher likelihood of a volunteer being a board volunteer than a regular volunteer. For male volunteers, being white makes volunteers more likely to be board volunteers, while
While there are some differences between men and women, the variables that influence the likelihood that individuals who volunteer serve as board volunteers versus regular volunteering tend to be characteristics associated with a high time value. It appears that higher value of time characteristics of volunteers are more likely to be on board.
Conclusion
None of these variables seem to affect whether female volunteers are board volunteers. This confirms once again that something else is needed to explain how and why people volunteer. I found that if individuals exhibit characteristics associated with high time values except family size, individuals are more likely to board volunteers.
This thesis has shown the importance and effects of volunteering within the United States, showing that individuals with higher opportunity costs of time volunteer more than others. This statement also applies to board volunteers who are an extreme case of the high value of time. I originally wanted to combine a qualitative analysis of board volunteers by conducting interviews alongside my quantitative analysis, but I was unable to complete the interviews.
There may also be other explanations for why the age changed, or how general volunteering and board volunteering have changed over the past 25 years. Although Tables 2 and 4 yielded significant coefficients for most of the volunteer and board volunteer variables, it is difficult to provide definitive conclusions for annual volunteer hours. According to the results, if individuals have a high value of time including earnings, employment, grade completed, age, married and number of children, they are more likely to volunteer.
The only difference between regular volunteers and committee volunteers is the negative effect of the number of children. According to Table 4, the more children there are, the less likely they are to board voluntarily.
