VOLUNTEERS MENTORING YOUTH:

Implications for Closing the Mentoring Gap 
Authors

John Foster-Bey, Senior Advisor to the Director, Office of Research and Policy Development, Corporation for

National and Community Service

Nathan Dietz, Research Associate and Statistician, Office of Research and Policy Development, Corporation for

National and Community Service

Robert Grimm, Jr., Director, Office of Research and Policy Development, Corporation for National and

Community Service

Acknowledgements

There were several individuals who generously provided their time and input on this project. Their comments and

thoughts contributed mightily to improving the quality of the final report. Dr. Jean Rhodes, professor of

psychology at University of Massachusetts-Boston and one of the nation's leading experts on youth mentoring,

reviewed and provided comments on our research design and approach and early findings. Dr. Rhodes also read

and commented on the final draft. Her comments were crucial in helping us shape the final direction of the

research. Dr. Jay A. Winsten, Associate Dean of the Harvard School of Public Health, and Tonya Wiley and Cindy

Sturdivant of MENTOR also read the final draft and provided very helpful comments and insights.

The Corporation for National and Community Service provides opportunities for

Americans of all ages and backgrounds to serve their communities and country through

three programs: Senior Corps, AmeriCorps, and Learn and Serve America. Together with

USA Freedom Corps, the Corporation is working to foster a culture of citizenship,

service, and responsibility in America.

Upon request this material will be made available in alternative formats for people with disabilities. Copyright May, 2006. The Corporation for National and Community

Service. All rights reserved.
TABLE OF CONTENTS

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

How Many Adults Actually Volunteer as Mentors? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

What Differences Exist Between Volunteers That Engage in 

Mentoring Youth and Volunteers Who Do Not Mentor? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

Age and Mentoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8

The Influence of Gender, Race, and 

Ethnicity on Mentoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8

Socio-economic Characteristics and Mentoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11

Volunteering Characteristics and Mentoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11

Who Is Most Likely to Become a Mentor? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13

Characteristics That Only Influence Volunteering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18

Characteristics That Only Influence Volunteers to 

Engage in Mentoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

Conclusion and Policy Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20

Endnotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22

Appendix 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23

Appendix 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25
2

EXECUTIVE SUMMARY

BACKGROUND

To develop a greater understanding of the characteristics

and traits that distinguish individuals whose

volunteering includes mentoring youth from volunteers

who do not mentor, the Corporation for National and

Community Service conducted a close analysis of the

2005 Volunteer Supplement of the Current Population

Survey (CPS).1 Corporation researchers examined

whether demographic, socioeconomic, or other

observable factors could be used to distinguish between

volunteers who mentor and the general population of

volunteers who do not mentor, as well as to determine

which of the above factors are most influential in

predicting who is most likely to be engaged in mentoring

activities. The expectation is that the information

gleaned will help mentoring programs better identify

and recruit the types of individuals who are most likely

to be favorably disposed toward mentoring, and thereby

help to reduce the “mentoring gap.” 2

KEY FINDINGS: 

Mentoring Is a Common Part of American

Volunteering

n18% of all volunteers—or 11.5 million of America’s 65.4

million volunteers—engage in some youth mentoring

activities each year through an organization.

The Propensity to Be a Mentor Declines with Age

nVolunteers 16 to 24 years old are the most likely

group to be mentors.

nVolunteers 65 years and older are the least likely

group to mentor. 

nStill, 41% of volunteers who engage in mentoring youth

are baby boomers—i.e., between the ages of 41 and 59.3

Black Volunteers Are More Likely Than White

Volunteers to Be Mentors 

n25% of black volunteers are engaged in mentoring,

compared to 17% of white volunteers.

nHowever, minorities do volunteer at lower rates—22%

for blacks versus 30% for whites.

nHispanic volunteers are slightly less likely to be

engaged in mentoring compared to non-Hispanics.

However, when all other factors are considered, there

are no real differences between Hispanic and nonHispanic volunteers in their probability of being

engaged in mentoring.

Male and Female Volunteers Engage in Mentoring

at Similar Rates

n18% of male volunteers and 18% of female

volunteers are engaged in mentoring.

nHowever, males do volunteer at lower rates—25% for

men versus 32% for women.

Substantial Mentoring Takes Place Through

Religious Organizations

nAlmost 43% of all volunteers engaged in mentoring

volunteer in or through religious organizations (the

next most prevalent site for mentoring is educational

organizations, at 31%).

Mentors Are Often Drawn from the Ranks of

Current Volunteers

n87% of volunteers involved in mentoring perform at

least one other volunteer activity for their main

organization in addition to mentoring, while only

40% of volunteers not involved in mentoring perform

two or more volunteer activities. 

nVolunteers who are engaged in mentoring serve a

median of 80 hours annually, while volunteers who

do not mentor serve a median of 40 hours annually.

Mentors Regularly Work Full-Time 

n59% of all volunteers who engage in mentoring work

full-time—a higher percentage than volunteers who

do not engage in mentoring (53%). 

nDespite having less discretionary time, these adult

volunteers are as inclined to mentor youth as

volunteers working part-time, and more likely than

non-working volunteers.
3

CONCLUSION AND POLICY IMPLICATIONS

The analysis shows that one of the most important

determinants of which volunteers are also involved in

mentoring activities is age. The probability of being a

mentor declines dramatically after age 24; indeed,

volunteers 65 years and older are the least likely to be

engaged in mentoring activities compared to younger

cohorts. This suggests that college-age adults and even

high schoolers are prime candidates to be recruited as

mentors. While the data also suggest that enlisting more

baby boomers and other older adults as youth mentors is

more challenging, it is important to note that a large

number of baby boomers and other older adult volunteers

currently are involved in mentoring. 

Another promising mentor recruitment strategy is

focusing on encouraging more current volunteers to

become mentors, particularly those volunteers who have

already made a substantial commitment to their main

volunteer organization. Moreover, the finding that

mentoring takes place predominantly through religious

organizations suggests that religious institutions are an

excellent area to focus recruitment efforts.

This type of strategy may pose real opportunities for

existing community-based mentoring programs. One

approach is for traditional mentoring programs to do

more partnering with other types of volunteer

organizations, such as religious institutions. This

approach is already being tried in several places. Another

approach may be for mentoring programs to recruit

volunteers to do other needed tasks. Once they’ve

formed some attachment to the organization and its

mission, these volunteers may be more amenable to

becoming mentors.

The importance of providing mentoring opportunities at

workplaces is another important strategy to reach more

youth with mentors. Our research found that volunteers

employed full-time—despite having less discretionary

time—are actually as likely to mentor as people working

part-time, and a new poll by the nonprofit group

MENTOR4 revealed that mentoring at or near the

workplace, as well as release time during work hours,

substantially increased people’s willingness to seriously

consider becoming a mentor. Together, these reports

suggest that providing convenient mentor opportunities

at work are important for recruiting more mentors.

Race and sex also have an effect on who becomes a

mentor. Although males and blacks volunteer in general

at lower rates than do females and whites, it turns out

that blacks are more likely—and males are as likely—as

their counterparts to engage in mentoring as one of their

volunteer activities. This is an interesting finding since

the general consensus in the field is that there is a

shortage of male and minority mentors. It appears that

one constraint may be the lower overall volunteer rates

of men and minorities. Assuming that the relationship

between volunteering and volunteering as a mentor

remains constant, the number of male and minority

mentors would increase if their overall volunteer rate

rose. Thus, a promising approach to developing more

black and male mentors is to invite and engage them in

volunteering in general. If, for example, the African

American volunteer rate increased by 2 percentage points

(22.1% to 24.1%), there would be another 533,000 black

volunteers, and an additional 125,000 black mentors. 

Without more knowledge about the demographics of

mentor waitlists, we do not know for certain the gender

and racial characteristics of youths waiting for a mentor,

though anecdotal evidence suggests that a

disproportionate number are minorities and males. If this

is the case, it is possible that doubling or even tripling

the numbers of male and minority mentors may not be

enough to meet the effective demand for such mentors.

Instead it may require that mentoring programs not only

increase the overall supply of mentors but employ more

mentoring models that reach more kids with one adult

mentor and other innovative mentoring approaches in

order to close the mentoring gap. 

EXECUTIVE SUMMARY
4

VOLUNTEERS MENTORING YOUTH

1 The CPS is a comprehensive and scientifically rigorous survey of 60,000 American households that is conducted each month by the U.S.

Census Bureau for the Bureau of Labor Statistics. With the Corporation’s support, the Census Bureau has administered a volunteer

supplement each September since 2002. The September 2005 survey is the first time respondents to the CPS were asked whether one of

their volunteer activities is mentoring youth. Unfortunately, the 2005 CPS volunteer supplement does not identify whether mentoring is

the primary volunteer activity for those volunteers who say they mentor—a limitation that should be corrected in the next CPS survey.

2 Other studies, including the National Mentoring Poll conducted by MENTOR, show that 14.6 million youth currently are in need of a mentor. 

3 Baby boomers are defined as those born between the years 1946 and 1964.

4 For more information on the report Mentoring in America 2005, go to www.mentoring.org.
VOLUNTEERS MENTORING YOUTH:

Implications for Closing the Mentoring Gap 
6

VOLUNTEERS MENTORING YOUTH

One of the constraints to growing the

mentoring field is the inadequate supply of

appropriate volunteers willing to become

mentors. This supply problem is probably

the primary reason that many mentoring

programs operate with long waiting lists of

unmatched young people. Because

research in the field has understandably

focused on outcomes for youth, less is

known about the volunteers who choose to

become mentors. The goal of this research

brief is to develop a greater understanding

of the characteristics and traits that

distinguish individuals whose volunteering

includes mentoring youth from volunteers who do not

mentor in order to help mentoring programs better

identify and recruit the types of individuals that are

most likely to be favorably disposed toward mentoring,

and thereby help to reduce the waiting lists of

unmatched young people. 

This brief will explore four questions:

nHow many adult volunteers are involved in some

form of youth mentoring?

nWhat, if any, differences exist between adult

volunteers who engage in mentoring as one of their

formal volunteer activities and those adult

volunteers who do not mentor as part of their

volunteer activity?

nCan we predict who is most likely to include

mentoring as part of their volunteer activities?

nCan we learn lessons about approaches that might

be most effective in increasing the supply of adults

whose volunteering includes youth mentoring? 

To answer these questions, an analysis was done of the

2005 annual Volunteer Supplement of the Current

Population Survey (CPS). The CPS is a national survey

administered monthly to more than 60,000 households

in America. It is the primary source of information on

changes in employment and income and is used by the

Bureau of Labor Statistics to estimate the monthly and

annual changes in employment. The Corporation for

National and Community Service annually sponsors the

Volunteer Supplement questions, and the U.S. Census

Bureau has administered the CPS Volunteer

Supplement annually in September since 2002. The

2005 Volunteer Supplement is the first time

respondents were asked whether one of their volunteer

activities was mentoring youth.1

The interpretation of what constitutes mentoring is left

to the respondent. While respondents might be

engaged in activities that are generally considered to

be mentoring such as being a Boy Scout/Girl Scout

Leader, or a Big Brother/Big Sister, they might also

interpret other activities such as coaching, tutoring, or

counseling to be mentoring. However, respondents are

allowed to indicate more than one volunteer activity.

Thus, many respondents who indicated that they

mentored also indicated that they may have coached or

tutored in addition to their mentoring activities. As this

brief will indicate, there are definite differences

between volunteers engaged in mentoring and

volunteers not involved in mentoring activities. 

The major limitation in using the 2005 CPS volunteer

supplement is that it does not identify whether

mentoring is the primary volunteer activity for those
7

VOLUNTEERS MENTORING YOUTH

volunteers who say they

mentor.2 This means

that for volunteers that

perform more than one

volunteer activity there

is no conclusive way of

ascertaining how much

of their volunteer time

is committed solely to

mentoring. It also does

not differentiate

between volunteers who mentor one-on-one from those

who mentor groups. Nonetheless, it provides an

extremely robust picture of adults whose volunteer

activities include mentoring youth. 

HOW MANY ADULTS ACTUALLY VOLUNTEER 

AS MENTORS?

nFinding: 11.5 million adults engage in volunteering

activities that include mentoring youth. 

nFinding: 1/6 of all adult volunteers engage in some

mentoring of youth. 

In September 2005, there were about 65.4 million

adults aged 16 and older who performed some type of

volunteer activity for a nonprofit organization. These

volunteers represented approximately 29% of the US

adult population.3 Among these 65.4 million

volunteers, 11.5 million said that at least one of their

formal volunteer activities was mentoring a young

person.4 These 11.5 million mentors constituted 17.6%

of all formal volunteers (see Table 1 in Appendix 1

and Figure 1, above). 

As the data indicate, volunteers who mentor are a

small but significant minority of all volunteers. Are

there distinct characteristics that tend to increase

the odds that certain adult volunteers will be

mentors? The remainder of this brief will compare

volunteers who mentor to volunteers who do not

mentor. The goal is to determine if there are real

differences between the two types of volunteers that

can be used to predict who is most likely to engage

in mentoring. 

WHAT DIFFERENCES EXIST BETWEEN VOLUNTEERS

THAT ENGAGE IN MENTORING YOUTH AND

VOLUNTEERS WHO DO NOT MENTOR? 

In order to better understand why some volunteers

choose to mentor and some do not, it is useful to

know more about how the members of each volunteer

group differ from each other. In this section, we

attempt to discover if differences exist in certain key

demographic, socioeconomic, and volunteering

characteristics between volunteers who mentor and

those who do not. We look at this in two ways: 1) for

selected characteristics, we examine the percentage

of volunteers who are either mentors or non-mentors.

For example, for age we examine what percentage of

20-24 year old volunteers are either mentors or nonmentors; 2) we examine the differences in the overall

population distributions for mentors and nonmentors for the same selected characteristics. For

instance, we explore what percentage or share of all

mentors are 20-24 years old. In general, we use

graphs to illustrate the first approach; and the data

in Table 1, which can be found in Appendix 1, as the

source of our findings for the second approach.

3.8

11.5

10

20

30

40

50

60

Non-mentors Total Mentors

Volunteers In Millions

Figure 1. Number of Volunteers by Mentors vs. Non-Mentors
8

VOLUNTEERS MENTORING YOUTH

AGE AND MENTORING 

nFinding: Volunteers ages 20-

24 are the most likely to

engage in mentoring youth.

nFinding: The propensity to be

a mentor declines with age.

However, a substantial

number of mentors are baby

boomers and other older

volunteers. 

As Figure 2 shows, compared to

other age groups, 20-24 year old

volunteers are the most likely to

engage in mentoring youth. In

fact, 24% of the volunteers in

this age group engage in mentoring. After age 24 the

percentage of volunteers in each age cohort who

volunteered as mentors consistently declined. For

volunteers 65 and over, only 7% engage in mentoring

as a volunteer activity. 

Despite the fact that the likelihood of a volunteer

being a mentor appears to decline with age, older

volunteers make a substantial contribution to the

overall number of mentors. For example, as Table 1

shows, baby boomers5 represent 41% of all volunteers

that mentor. Moreover, 38% of all volunteers who

mentor are over 45 years old. In short, despite the fact

that most volunteer mentors are under age 45, baby

boomers and other older volunteers are an important

source of mentors for youth (see Table 1). 

THE INFLUENCE OF GENDER, RACE, AND ETHNICITY

ON MENTORING

nFindings: Overall, males and female volunteers

appear to mentor at roughly the same rates

when they volunteer. However, more mentors are

female because females volunteer at a higher

rate than males.

nFindings: Blacks are more likely than whites or

other racial or ethnic minorities to be mentors

when they volunteer. Hispanics are slightly less

likely than non-Hispanics to be mentors when

they volunteer.

Mentoring is a programmatic approach to help connect

young people to positive adult relationships. The need

for this type of connection is thought to be particularly

acute for disadvantaged young men and for minorities.

Many mentoring programs prefer to make same gender,

same race matches where possible. Anecdotal evidence6

suggests that many programs experience a shortage of

male and minority mentors relative to the demand for

such male-to-male and minority-to-minority mentoring

matches. This gap in the supply and demand for

mentors is exemplified in the size of an organization’s

waiting list of unmatched young people.

Despite the fact that females are much more likely to

volunteer than males – for example, in 2005, 32.4% of

females and 25% of males volunteered – male and

female volunteers had roughly the same propensity to

engage in mentoring. In 2005, as Figure 3 shows,

Figure 2. Among Volunteers Percentage of Mentors by Age Cohort 

79% 75% 79% 79% 82% 86%

93%

21% 25% 21% 21% 18% 14%

7%

0.%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

16 -19 20-24 25-34 35-44 45-54 55-64 65 and

over

Age Cohort

Non-mentors Mentors
9

VOLUNTEERS MENTORING YOUTH

approximately 18% of all male volunteers and 18% of

female volunteers engaged in mentoring. However, just

as with volunteering in general, more mentors are

females than males because of the gender gap in

volunteering. Thus, as seen in Table 1, 57% of all youth

mentors were females in 2005. 

Because blacks and to a lesser extent Hispanics are

more likely than whites and Asians to grow-up in

households with only one parent present7, they may

have a higher demand for developing structured oneto-one positive relationships with adult role models.

Compared to other racial and ethnic groups, blacks

actually had the highest percentage of volunteers who

were also engaging in mentoring. In 2005, as Figure 4

demonstrates, 25% of black volunteers were engaged in

mentoring compared to only 17% of whites and 17% of

Figure 3. Among Volunteers Percentage of Mentors by Gender 

82% 82%

18% 18%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

MALE FEMALE

Gender

Non-mentors Mentors Percent

Figure 4. Among Volunteers Percentage of Mentors by Race and Ethnicity

83%

75%

83% 85% 82%

17%

25%

17% 15% 18%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

White Black Other Hispanic Non-Hispanic

Race and Ethnicity

Non-mentors Mentors
10

VOLUNTEERS MENTORING YOUTH

Asians and other racial minorities.8 While blacks

made up slightly less than 9% of all volunteers, they

were almost 12% of all mentors, as Table 1 shows.

This suggests that black volunteers are heavily

involved in mentoring. If there is a gap between the

number of African American youngsters in formal

mentoring programs requesting a mentor and the

supply of black volunteers who are willing to be

youth mentors, the problem may be a demand rather

than a supply issue. 

While black volunteers have the highest probability

of being a mentor, Hispanic volunteers are somewhat

less likely to be mentors than non-Hispanic

volunteers (see Figure 4). Hispanics were 6.9% of all

volunteers, but, as Table 1 shows, they were only

5.7% of all mentors. This tends to support the

concern that there may be a potential shortage of

Hispanic mentors. 

While our findings do indicate that Hispanic

volunteers may be less inclined to be mentors

compared to non-Hispanics, there appears to be no

evidence that male and African American volunteers

are reticent to serve as mentors. Indeed, African

American volunteers are more likely than whites or

other racial/ethnic groups to be mentors, and male

volunteers appear to be as likely as female volunteers

to be mentors. 

If there is a perceived shortage of male, black and

Hispanic mentors, it may be more a function of low

volunteer rates rather than their propensity to be

mentors. For example, if the African American

volunteer rate increased by two percentage points

(22.1% to 24.1%), holding the mentoring rates

constant, there would be another 533,000 black

volunteers, and an additional 125,000 black mentors.

Similarly, if the Hispanic volunteer rate increased 2

percentage points, there would be another 617,000

Hispanic volunteers, and another almost 254,000

Hispanic mentors. Finally, if the male volunteer rate

went up 2 percentage points, holding the mentoring

rates constant, there would be an additional 1.8 million

male volunteers, and another 400,000 male mentors. 

Figure 5. Among Volunteers Percentage of Mentors by Education Attainment 

92%

86%

19%

81%

82%

9% 14% 18%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

< High school High school only Some college College graduates

Education Attainment

Non-mentors Mentors
11

SOCIO-ECONOMIC CHARACTERISTICS 

AND MENTORING

nFinding: The chance of a volunteer being involved

in mentoring increases as a volunteer’s education

level rises. 

nFinding: Most volunteers that engage in mentoring

are employed full-time. 

In general volunteers as a class appear to be better

educated and more likely to be employed than nonvolunteers. The question we asked is whether

mentors have even higher education and greater

employment levels than the average volunteer.

Indeed, mentors appear to have higher education

levels than volunteers that do not mentor. The

chance that a volunteer will also be a mentor

appears to rise with educational attainment. Figure 5

illustrates that compared to volunteers with a high

school education or less, volunteers with a college

education are much more likely to be mentors. As

Table 1 shows, there are similar results when

differences in educational attainment are examined

within each group. Relative to non-mentors, more

mentors are college-educated and fewer have only a

high school education or less.

While education is a key indicator of socioeconomic

status, employment status is also an important

measure. Table 1 shows that a larger percentage of

volunteer mentors are employed full-time compared

to non-mentor volunteers. While on average all

volunteers are more highly connected to the labor

market than non-volunteers, mentors appear to have

higher labor market attachment than other types 

of volunteers.9

Together the education and employment findings

indicate that mentors are well-educated,

economically successful individuals. This seems

consistent with the goal of exposing at-risk young

people to successful adult role models. However, it

raises real challenges for recruitment, since efforts

aimed at trying to encourage even more of these

highly successful individuals to be youth mentors

may require new approaches.

VOLUNTEERING CHARACTERISTICS AND MENTORING

nFinding: Almost 43% of volunteers who engaged

in mentoring did so through religious

organizations, and another 31% engaged in

mentoring through or in educational institutions.

nFinding: 67% of volunteers who engage in some

mentoring volunteer 12 or more weeks per year

compared to only 49% of volunteers who are not

involved in mentoring. 

nFinding: Volunteers engaged in mentoring serve a

median of 80 hours per year, while the median

hours for volunteers not involved in mentoring is

40 hours per year.

While there appear to be differences between

volunteers who engage in mentoring versus other

volunteers that do not mentor on certain demographic

and socioeconomic indicators, mentors also may be

different in volunteer characteristics. For example, do

mentors come to volunteering in different ways than

non-mentors? Do they volunteer in different types of

organizations? Are they more active as volunteers? Are

they more likely to volunteer on a regular basis? 

Mentors and non-mentors appear to have very similar

experiences in how they became volunteers. Table 1

shows that the percentage of both groups that

approached the organization on their own is almost

identical. Similarly, there is only a slight difference in the

percentage of mentors and non-mentors who became

volunteers because someone asked them to volunteer. 

Compared to the overall volunteer population, there are

more marked differences in where these mentors do

their volunteer work. While American volunteers are

most commonly found in religious or educational

VOLUNTEERS MENTORING YOUTH
12

VOLUNTEERS MENTORING YOUTH

organizations, volunteers who mentor are even more

likely to be found in religious organizations or

educational institutions (see Table 1 and Figure 6a).

We found that 43% of volunteers who engaged in

mentoring did that activity through religious

organizations, and another 31% engaged in

mentoring through or in educational institutions

(see Table 1 and Figures 6a, 6b).

Mentors and non-mentors also seemed to have

different levels of engagement with their main

volunteer organization. Two factors illustrate this

point. First, Table 1 shows that mentors are much

more likely than non-mentors to perform multiple

volunteer activities for their main volunteer

organization. Eighty-seven percent of all

volunteers engaged in mentoring perform two or

more volunteer activities for their main volunteer

organization, compared to only 40% of volunteers

not involved in mentoring. In many ways, this is

surprising because the mentoring relationship is

often very demanding of volunteer time and

involvement. One might expect that volunteers

who mentor would be engaged in fewer rather

than more activities than non-mentors. This may

imply that volunteers become mentors because

they are already highly engaged volunteers with

their main organization. If this is the case, it

suggests that a prime source of new mentors for

an organization is among the cadre of individuals

who are already actively volunteering.

Second, while volunteering is generally seen as a

net social good, there may be important differences

between volunteering once or twice a year for a

single event and volunteering on a regular monthly

or even weekly basis. This may be particularly

important for mentors. There is evidence that the

effectiveness of mentoring is a function of how

much time mentors spend with their young charges.

Short-term mentoring matches have been shown to

do more harm than no mentoring at all.10 Given this,

it is important to examine whether there are any

differences in the amount of time mentors and nonmentors devote to their volunteer activities. 

An individual volunteering 12 or more weeks is on

average volunteering at least once per month. In our

Youth Helping America series report on volunteering

trends among teenagers, this 12-week minimum was

used to determine if an individual is a regular

43%

31%

6%

2%

10%

1% 5% 2%

Figure 6a. Percent of Volunteers Who Mentor by Type of 

Volunteer Organization

Religious org.

Educational 

Youth Services

Sports or hobby

Social and community

service/ Civic

Political, advocacy, Union,

Business

Health-oriented

Other

34%

23% 1%

19%

2%

10%

2%

9%

Figure 6b. Percent of Volunteers Who Do Not Mentor by Type of 

Volunteer Organization

Religious org.

Educational 

Youth Services

Sports or hobby

Social and community

service/ civic

Political, advocacy, union,

business

Health-oriented

Other
13

volunteer, providing volunteer

service throughout the year.11

Based on this 12-week minimum

indicator, the likelihood of a

volunteer engaging in mentoring

increases as volunteers move

from being what we called

episodic volunteers (serving a

couple weeks a year) to being

regular volunteers. To put it

another way, 67% of volunteers

engaged in mentoring are also

regular volunteers—i.e., they

volunteer 12 or more weeks per

year—compared to only 49% of

volunteers not involved in mentoring (see Table 1 

and Figure 7). These findings indicate that mentors

are much more likely than non-mentors to be

providing volunteer services on a regular basis

throughout the year.

Finally, the differences between volunteers engaged in

mentoring and volunteers not involved in mentoring in

the number of weeks volunteered annually is also

mirrored in the number of hours each group volunteers.

Table 1 illustrates that the median hours volunteered

annually for volunteers engaged in mentoring and

volunteers not involved in mentoring are 80 hours and

40 hours respectively.

SUMMARY

It appears that there are some distinct differences

between volunteers whose activities include mentoring

youth and volunteers who do not do such activities.

The chances that a volunteer will be involved in

mentoring are greatest for younger volunteers and for

African American volunteers. The probability that a

volunteer will be involved in mentoring rises as

educational attainment increases and is also higher for

employed volunteers.

Finally, mentors demonstrate some differences in their

volunteer characteristics. Mentors are more likely than

non-mentors to perform multiple volunteer activities

for their main volunteer organization. They are found

primarily in religious and educational institutions, and

they are much more likely than non-mentors to

volunteer 12 or more weeks per year. 

WHO IS MOST LIKELY TO BECOME A MENTOR?

The previous section explored differences in a variety

of demographic, socioeconomic, and volunteering

characteristics between volunteers that engage in

some mentoring activities and volunteers that are not

involved in mentoring. The strong tendency of

volunteers involved in mentoring to engage in

multiple volunteer activities and the large percentage

of such volunteers that volunteer 12 or more weeks

per year suggests that many volunteers engaged in

mentoring activities are being recruited from existing

volunteers rather than from non-volunteers. Given

this, this section will discuss which characteristics

make the most contributions to predicting which

adults will be volunteers; and which characteristics

are most useful in predicting which volunteers will

become engaged in mentoring. 

6% 25%

49%

12%

21%

67%

0 %

10%

20%

30%

40%

50%

60%

70%

80%

Episodic volunteers (0-2 weeks per year) Occasional volunteers (3-11 weeks per

year)

Regular volunteers (12 or more weeks

per year)

Number of Weeks Volunteered Annually

Figure 7. Percent Distribution of Mentors and Non-Mentors by Number of Weeks Volunteered Annually 

Non-mentors Mentors

VOLUNTEERS MENTORING YOUTH
14

VOLUNTEERS MENTORING YOUTH

Using a statistical technique

known as multivariate

analysis, we can examine the

impact that different

variables have on the

probability that an individual

will volunteer and whether

those who volunteer will also

be mentors. For instance,

according to the 2005 CPS,

the percentage of Hispanic

volunteers appears to be

lower than non-Hispanic

volunteers. In turn, the

percentage of volunteers that

also are mentors appears to

be lower for Hispanics than

for non-Hispanics. However,

Hispanics and non-Hispanics

differ on several important

demographic and

socioeconomic indicators.

Hispanics, for instance, have

lower education levels on

average than non-Hispanics.

In order to determine if

Hispanics and non-Hispanics

are truly different in their

volunteering and mentoring

behavior, we need to control

for group variations in

educational achievement, as

well as other important

factors. That is, we need to

treat both groups as if they

have the same educational

profile. In the charts that illustrate the results of our

re able to compare

anics by holding education

stant at their samplemultivariate analysis, we a

Hispanics versus non-Hisp

and all other variables con

average levels. 

Multivariate analysis allows us to determine if

observed differences in volunteering and mentoring

between Hispanics and non-Hispanics are real, and not

just the influence of variations in educational

achievement and other existing differences. This allows

Figure 8a. Probability of an Adult Being a Volunteer by Age 

40.7%

18.5% 19.1%

24.9% 26.4% 27.2% 26.6%

27.1% 28.6% 29.9% 30.2%

21.7% 21.3%

45.7%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

16 to 19 20 to 24 25 to 34 35 to 44 45 to 54 55 to 64 65 and

over

Age Cohort

Low Predicted Probability High

Figure 8b. Probability of an Adult Volunteer Being a Mentor by Age

 (with plus or minus margin of error)

23.6% 26.3%

15.5%

13.1%

10.9% 9.4%

6.6% 15.7%

18.6%

11.9%

10.1% 8.4% 6.7%

0% 4.0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

16 to 19 20 to 24 25 to 34 35 to 44 45 to 54 55 to 64 65 and over

Age Cohort

HIGH Predicted Probability of Mentoring LOW
15

VOLUNTEERS MENTORING YOUTH

us to mitigate the potential confounding effects of

education, as well as a variety of other factors, such as

age, race, gender, employment status, and family

structure. This type of analysis often results in finding

out that many differences between groups disappear,

or become stronger, once confounding factors have

been taken into account. Thus, it allows a researcher

or analyst to discover which factors provide a real

impact on a particular outcome of interest.

The capacity to predict who is likely to be a

volunteer and in turn who will be a mentor can

provide useful policy and program planning insights.

In particular, knowing who is most likely to be a

mentor can help shape and refine volunteer

recruitment strategies. It can help target efforts to

those individuals that are most likely to respond

positively. On the other hand, it might also allow you

to identify unlikely, but promising prospects. 

The type of multivariate analysis used to undertake

this assessment is called probit analysis. Simply

speaking, probit analysis estimates the probability

that a certain event will–or won’t–occur,

conditioned on the impact of different

characteristics. In this case, there are two events:

The probability that an adult will be a volunteer

and the probability that a volunteer will then

include mentoring youth as part of their

activities.12 The remainder of this section discusses

the impact of different characteristics or predictors

on the probability of each of these events. The

findings are presented in Table 2 and in a series of

graphs for each characteristic that present the

probabilities with their margins of error13 for two

probabilities: that an adult will be a volunteer, and

that a volunteer will engage in mentoring

activities. In these charts, when the margins of

error overlap for two groups, the probabilities are

roughly the same on average; when they do not

overlap, the average group differences seen in 

Table 2 (Appendix I) are statistically real,14

controlling for all other factors.

The characteristics or predictors are classified into one

of three groups:

nCommon characteristics that predict both being a

volunteer and a volunteer engaging in mentoring; 

nCharacteristics that only predict volunteering; and,

nCharacteristics that only predict which volunteers will

become mentors.

COMMON CHARACTERISTICS

There are three general categories of predictors that

provide useful information in predicting who will be a

volunteer and which volunteers will be mentors:

demographic predictors; socioeconomic predictors; and

regional or place predictors. The common demographic

predicators are age, gender and race/ethnicity. The

common socioeconomic predictors are education and

employment status. 

nFinding: All else being equal, of all age groups, 16-19

year olds have the highest probability of being

volunteers.

nFinding: All else being equal, the probability of a

volunteer engaging in mentoring declines with age.

Volunteers are most likely to engage in mentoring when

they are 16-24 years old.

All else being equal, age is important in determining both

who will volunteer and which volunteers will be mentors.

For volunteers, predicted average volunteering rates are

highest for 16-19 year olds and lowest for adults 20-34

years old. As Figure 8a shows, the rate of volunteering

increases some after age 34, but remains well below the

rate for 16 –19 year olds. However, as seen in Figure 8b,

the pattern is slightly different for mentors. All else being

equal, volunteers are most likely to engage in mentoring

when they are 16-24 years old. As volunteers age after

24 years old, mentoring rates decline on average.
16

VOLUNTEERS MENTORING YOUTH

It is not entirely clear why older

volunteers are less likely than younger

volunteers to serve as mentors.

However, it does appear that as

individuals age, mentoring a young

person becomes less appealing, possibl

as a result of changes in health, family

status, or general interest. These

findings tend to suggest that

mentoring recruitment efforts need to

target younger volunteers, particularly

those younger than 35 years old.

However, while these findings suggest

that policymakers and program

planners should be a little cautious

when they look toward baby boomers

and other older adults as a potential

source of new adult mentors, the

results indicate that baby boomers

and other older adult volunteers

currently provide a substantial supply

of volunteer mentors. 

y

nFinding: All else being equal,

females, whites, mixed race

individuals, and non-Hispanics

have a higher probability of

volunteering than males, African

Americans, and Hispanics.

nFinding: 

male and African American volunteers have a

higher probability of being involved in mentoring

as a volunteer activity than females and whites.

All else being equal, as Figures 9a, 9b, 10a, and 10b

illustrate, females, whites and mixed race individuals, and

non-Hispanics are most likely to be volunteers, on average.

However, the results are a bit different for predicting which

volunteers will engage in mentoring. Blacks are

considerably more likely to be mentors than any other

racial/ethnic group including whites. Males are actually

All else being equal, more likely to be mentors than females, on average, and

there are no real differences between the average

propensity of Hispanics and that of non-Hispanics to be

mentors. 

The findings here suggest that after controlling for

differences in other key factors, males, African Americans,

and Hispanics may be volunteering less than one would

expect given their demographic and socioeconomic

characteristics. On the other hand, these same groups are

actually performing better than expected given their

demographic and socioeconomic characteristics in

29.3%

22.3%

23.6%

30.6%

0%

5%

10%

15%

20%

25%

30%

35%

Female Male

Gender

Figure 9a. Probability of an Adult Being a Volunteer by Gender 

(with plus or minus margin of error)

Low Predicted Probability High

12.9%

10.9% 10.8%

9.2%

0%

5%

10%

15%

20%

25%

30%

35%

Female Male

Figure 9b. Probability of an Adult Volunteer Being a Mentor by Gender

(with plus or minus margin of error)

GENDER

HIGH Predicted Probability of Mentoring LOW
17

choosing to be mentors. Given this, if males’ and African

Americans’ overall volunteer rates could be increased, the

result would be a rise in the number of mentors. 

nFinding: All else being equal, part-time workers

have a higher probability of being a volunteer than

full-time workers or adults who are not working.

nFinding: All else being equal, volunteers who are

employed have a higher probability of being

involved in mentoring activities than nonworking

volunteers.

Employment and volunteering appear

to be associated. After controlling for

other relevant factors, adults who are

employed part-time and those not in

the labor force, except those that are

disabled, have higher average

volunteer rates than adults who are

employed full-time. These findings,

depicted in Figures 11a and 11b, are

somewhat different for volunteers

who choose to engage in mentoring.

Volunteers who are employed fulltime are more likely than volunteers

who are not in the labor market to

include mentoring in their volunteer

activities. The higher predicted

volunteer rates for part-time

employees and those adults not in

the labor force suggest that

volunteering seems to be associated

with having more discretionary time

available. However, this does not

appear to be the case for mentors.

Compared to volunteers who are

either non-working, or employed

part-time, being employed full-time,

and presumably having less

discretionary time, is associated with

higher predicted rates of being

involved in mentoring.

nFinding: All else being equal, the probability of

being a volunteer rises with educational

attainment.

nFinding: All else being equal, the probability of a

volunteer being involved with mentoring activities

also rises with educational attainment.

Education also appears, from Figures 11a and 11b, to be

an important independent predictor of whether an

adult becomes a volunteer, and whether a volunteer

VOLUNTEERS MENTORING YOUTH

7.3%

21.9%

12.1%

18.6%

28.1%

15.9%

27.5%

28.3% 24.9%

15.5%

26.2%

38.2%

18.4%

28.5%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Race: White or

Haw/Pac

Race: Black Race: Asian Race:

American

Indian, Alaskan

Native

More than one

race category

Latino Non-Latino

Race/Ethnicity

igure 10a. Probability of an Adult Being a Volunteer by Race and Ethnicity 

Low Predicted Probability High

13.3%

5.1%

2.8%

7.3% 7.1%

10.8% 10.6%

22.6%

11.6%

9.8% 10.2%

11.2%

18.9%

11.6%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

White Black Asian American

Indian, Alaskan

Native

More than one

race category

Latino Non-Latino

Race/Ethnicity

Figure 10b. Probability of an Adult Volunteer Being a Mentor by Race and 

Ethnicity (with plus of minus margin of error)

Low Predicted Probability of Mentoring High
18

VOLUNTEERS MENTORING YOUTH

will engage in mentoring. All else being equal,

volunteer rates rise with education. College-educated

adults are substantially more likely to be volunteers

than individuals with less education. There is a similar

effect for volunteers who mentor. The probability of a

volunteer also being a mentor rises with education.

However, the effects are not as dramatic. There is a 27-

percentage point difference in the probability of

volunteering between college-educated adults and

those with less than a high school education. For

mentors, the differences between college-educated

volunteers and volunteers without a high school

education in the probability of engaging in mentoring

youth is only about 4 percentage points. 

Given the high rates of volunteering

among the well–educated, it is

difficult to imagine that you could

increase substantially the number of

mentors by raising the overall

volunteer rates for this group.

However, it might be possible to

increase the number of mentors by

encouraging more college-educated

volunteers to include mentoring as

one of their volunteer activities. 

CHARACTERISTICS THAT ONLY

INFLUENCE VOLUNTEERING

nFinding: Volunteering rates rise 

as family income increases.

nFinding: Married adults have a 

higher probability of 

volunteering than unmarried 

adults. 

nFinding: Adults that reside with 

their own children under 18 

years of age have a higher 

probability of volunteering 

compared to adults that do not 

reside with their minor children.

here are several characteristics that

 

T

only appear to be important in predicting volunteering

and not in predicting which volunteers will choose to

engage in mentoring youth. Income was found to have

no independent influence on predicting which

volunteers are also mentors.15 However, family income is

a predictor of which adults will be volunteers. Figure 12

shows that, holding education, gender, race, and other

key characteristics constant, as family income rises, so

do average volunteering rates. The relatively high,

predicted probabilities of volunteering for adults with

high family incomes suggest that maybe increasing

volunteer rates by focusing on high-income adults may

only lead to marginal improvements in volunteer rates.

19.5%

14.0%

29.6%

41.1%

25.0%

33.0%

27.0%

12.8%

20.9%

15.8%

31.5%

43.2%

26.4%

35.9%

16.3%

29.5%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Educ: Less

than HS

Diploma

Educ: HS Grad Educ: Some

college

Educ: College

grad +

Employed, fulltimeEmployed,

part-time

Not in labor

force: Disabled

NILF(other) or

unemployed

Education/ Employment

igure 11a robabilit of an Adult Being a Volunteer by Education and Employment

(with plus or minus margin of error)

Low Predicted Probability High

6.3% 8.2% 10.0% 11.0% 10.8% 9.1% 10.5% 10.6% 12.4% 13.2% 12.8% 12.1%

8.2%

10.4%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Educ: <HS Educ: HS

Only

Educ: Some

college

Educ: College

grad +

Employed,

full-time

Employed,

part-time

NILF (not in

labor force) or

unemployed

Education/ Employment

Figure 11b. Probability of an Adult Volunteer Being a Mentor by 

Education and Employment (with plus or minus margin of error)

Low Predicted Probability of Mentoring High
19

VOLUNTEERS MENTORING YOUTH

The presence of minor children is a factor that does

not appear to predict which volunteer will be

mentors, but that does appear to contribute to the

ability to predict who will volunteer. As Table 2 and

Figure 12 show, adults residing with their own minor

children under 18 years old have a higher predicted

probability of volunteering than adults that do not

reside with their own minor children. For volunteers

who mentor, there appear to be no differences in the

probability of being a mentor between volunteers that

reside with their own minor children and volunteers

that do not reside with their own

minor children. 

Finally, the results in Table 2 and Figure

12 also illustrate that married adults

have a higher predicted probability of

being a volunteer than unmarried

adults. Marital status appears to have

no influence in predicting which

volunteers will be mentors. 

CHARACTERISTICS THAT ONLY

INFLUENCE VOLUNTEERS TO ENGAGE

IN MENTORING

nFinding: Mentors are primarily 

drawn from the ranks of already 

committed and engaged volunteers. 

Holding all other factors constant, 

volunteers engaged in four or more 

volunteer activities with their main 

volunteer organization have a 

higher probability of being engaged 

in mentoring activities than 

volunteers who engage in 1 to 3 

volunteer activities.

nFinding: All else being equal, 

volunteers who volunteer in or 

through a religious or 

educational/youth service 

organization have a higher

probability of being engaged in mentoring than

volunteers who volunteer in other types of

organizations. 

There are a number of characteristics in Table 2 that

can only be used to predict the probability of volunteers

incorporating mentoring into their service activities. It

appears, as seen in Figure 13, that volunteers who

perform more than one volunteer activity for their main

organization are more likely to be mentors than

volunteers who perform only one activity. In fact, all

else being equal, the more volunteer activities a

2.4%

25.7%

28.5%

31.1%

33.1%

23.0%

27.6%

23.2%

24.0%

28.2%

30.7%

33.2%

35.3%

24.1%

29.0%

25.4%

0%

5%

10%

5%

20%

25%

30%

5%

40%

Income: Less

than $35,000

Income: 

Between $35-

$50,000

Income: 

Between $50-

$75,000

Income: 

$75,000 and

over

Own children

under 18

No own children

under 18

Married Not Married

Socioeconomic Categories

Figure 12. Probability of an Adult Being a Volunteer by Family Income, Presence of Children, Marital 

Status (with plus or minus margin of error)

ow Predicted Probability High

1.7%

20.9%

47.4%

3.0%

3.9%

14.5%

25.6%

51.8%

-5%

5%

15%

25%

35%

45%

55%

One activity Two activities Three activities Four or more activities

olunteering Attributes

igure 13. Probability of an Adult Volunteer Being a Mentor by Number of Volunteer Activities (with plus or 

minus margin of error)

Low Predicted Probability of Mentoring High
20

VOLUNTEERS MENTORING YOUTH

volunteer performs the more likely the volunteer is to

engage in mentoring youth. 

Interpreting this finding is somewhat challenging.

However, one likely explanation is that it reflects the

fact that mentors are primarily drawn from the ranks

of already highly committed and engaged volunteers.

That is, these are men and women who have already

made a substantial commitment to volunteering for

their main volunteer organization and who are

encouraged to devote some of their volunteer time 

to mentoring.

The probability that a volunteer will be a mentor also

seems to be highly related to the types of organizations

where a volunteer does his or her volunteer work.

Mentors are primarily found in youth-focused

educational or youth-serving organizations and in

religious organizations. Figure 14 shows that the

average probability of a volunteer engaging in

mentoring is considerably higher for volunteers found

in these types of organizations than volunteers in

civic, professional, labor, or other types of

organizations. The findings indicate that targeting

volunteers in religious institutions and in schools and

youth service organizations may be an effective

strategy for bringing more volunteers to mentoring.

However, it should be noted that

mentoring programs that recruit

through the workplace often

already target schools and youth

organizations. As such, at least

some percentage of volunteers

in school and youth

organizations may actually have

come to their volunteer work

through employee volunteer

programs.16

CONCLUSION AND POLICY

IMPLICATIONS

The analysis shows that one of

the most important determinants of which volunteers

are also involved in mentoring activities is age. The

probability of being a mentor declines dramatically

after age 24; indeed, volunteers 65 years and older

are the least likely to be engaged in mentoring

activities compared to younger cohorts. This suggests

that college age adults and even high school

students are prime candidates to be recruited as

mentors. While the data also suggest that enlisting

more baby boomers and other older adults as youth

mentors is more challenging, it is important to note

that a large number of baby boomers and other older

adult volunteers currently are involved in mentoring. 

Another promising mentor recruitment strategy is

focusing on encouraging more current volunteers to

become mentors, particularly those volunteers who

have already made a substantial commitment to their

main volunteer organization. Moreover, the finding that

mentoring takes place predominantly through religious

organizations suggests that religious institutions are an

excellent area in which to focus recruitment efforts.

This type of strategy may pose real challenges for

existing community-based mentoring programs. One

approach is for traditional mentoring programs to do

more partnering with other types of volunteer

6.0%

11.6%

6.0% 4.4%

19.2%

13.8%

8.8% 6.5%

-5%

5%

15%

25%

35%

45%

55%

Main org: Educ, youth service Main org: Religious Main org: Social / comm

service

Main org: All other types

olunteering Attributes

Figure 14. Probability of an Adult Volunteer Being a Mentor by Type of Volunteer Organization (with plus or 

minus margin of error)

Low Predicted Probability of Mentoring High
21

organizations, such as religious institutions. This

approach is already being tried in several places.

Another approach may be for mentoring programs to

recruit volunteers to do other needed tasks. Once

they’ve formed some attachment to the organization

and its mission, these volunteers may be more

amenable to becoming mentors.

The importance of providing mentoring opportunities

at workplaces is another important strategy to reach

more youth with mentors. Our research found that

volunteers employed full-time—despite having less

discretionary time—are actually as likely to mentor as

people working part-time, and a new poll by the

nonprofit group MENTOR17 revealed that mentoring at

or near the workplace, as well as release time during

work hours, substantially increased people’s

willingness to seriously consider becoming a mentor.

Together, these reports suggest that providing

convenient mentor opportunities at work are

important for recruiting more mentors. 

Race and sex also have an effect on who becomes a

mentor. Although males and blacks volunteer in general

at lower rates than do females and whites, it turns out

that blacks are more likely—and males are as likely—as

their counterparts to engage in mentoring as one of

their volunteer activities. This is an interesting finding

since the general consensus in the field is that there is

a shortage of male and minority mentors. It appears

that one constraint may be the lower overall volunteer

rates of men and minorities. Assuming that the

relationship between volunteering, and volunteering as

a mentor, remains constant, the number of male and

minority mentors would increase if their overall

volunteer rate rose. Thus, a promising approach to

developing more black and male mentors is to invite

and engage them in volunteering in general. If, for

example, the African American volunteer rate increased

by 2 percentage points (22.1% to 24.1%), there would

be another 533,000 black volunteers, and an additional

125,000 black mentors. 

Without more knowledge about the demographics of

mentor waitlists, we do not know for certain the gender

and racial characteristics of youths waiting for a mentor,

though anecdotal evidence suggests that a

disproportionate number are minorities and males. If this

is the case, it is possible that doubling or even tripling

the numbers of male and minority mentors may not be

enough to meet the effective demand for such mentors.

Instead it may require that mentoring programs not only

increase the overall supply of mentors but employ more

mentoring models that reach more kids with one adult

mentor and other innovative mentoring approaches in

order to close the mentoring gap. 

VOLUNTEERS MENTORING YOUTH
22

ENDNOTES

1 “Mentoring in America in 2005” based on a National Mentoring

Poll of 2000 respondents conducted for MENTOR in 2002 and

again in 2005 also asks questions about who mentors. However,

because the CPS uses a very large sample, with a very high

response rate it is possible to do a much more extensive set of

analyses. Moreover, the CPS allows for an analysis of all types of

volunteers, not just those who mentor. 

2 This problem should be resolved in the 2006 CPS volunteer

suppement. In the 2006 supplement, respondents will be asked to

identify whether mentoring is their primary volunteer activity. 

3 The CPS considers individuals 16 and older adults.

4 There are also individuals that mentor young people informally—i.e.

not in the context of a formal organization. The CPS does not ask

questions on informal volunteer activities and so cannot provide

an estimate about the size of the informal mentor group. The

“Mentoring in America in 2005” did ask about informal mentoring

and therefore can provide some insights into the size and

characteristics of the informal mentor group.

5 Baby boomers are the generation born from 1946-1964. In 2005,

baby boomers would have been between 41 and 59 years old.

6 In 2005 MENTOR conducted several focus groups across the

country of mentoring program operators. One consistent theme

was the perceived shortage of males and minority mentors. During

these focus group sessions practitioners also indicated that

waiting lists of unmatched youth were a general problem. 

7 The 2000 US Census indicates that 57% of non-Hispanic black and

29% of Hispanic children grow-up in single parent families

compared to 21% of non-Hispanic whites. Females head the vast

majority of these families.

8 Because their numbers in the sample were relatively small, Asians,

Mixed-race, American Indians, and Pacific Islanders were combined

and reported as one category. 

9 See “Volunteering in the United States, 2005,” released by the

Bureau of Labor Statistics, December 9, 2005 (www.bls.gov/cps).

10 See “Promoting Successful Youth Mentoring Relationships: A

Preliminary Screening Questionnaire” by Jean Rhodes, Ranjinni

Reddy, Jennifer Roffman, and Jean B. Grossman. 

11 See Corporation for National and Community Service. Building

Active Citizens: The Role of Social Institutions in Teen

Volunteering. Brief 1 in the Youth Helping America series.

Washington, DC. November 2005

(http://www.cns.gov/pdf/05_1130_LSA_YHA_study.pdf). In this

brief, we divided individuals into episodic volunteers (1-2 weeks of

volunteering a year), occasional volunteers (3-11 weeks of

volunteering a year), and regular volunteers (12+ weeks of

volunteering a year)

12 The complete results for both the volunteer model and the

mentoring model can be found in Appendix 2.

13 The graphs show the predicted probabilities and the plus or minus

margin of error. For example, for age, holding all other factors

constant, Figure 8b shows that 16-19 year olds have a 43%

probability, on average, of being a volunteer. However, there is a

plus or minus error that means the real volunteering rate for this

group is between 41% and 45%. Where bars overlap on a graph it

means that there is no difference in the probability between

different characteristics. Again, looking at age, in figure 8a, the

margins of error for volunteering probabilities overlap for adults

20-24 years old and 25-34 years old.

14 The margins of error are based on the estimates of the standard

errors in Tables 3 and 4 of Appendix 2. These standard errors are

too small; due to privacy limitations, the Census Bureau does not

release information about the detailed sampling design of the CPS.

To counteract this limitation, we calculate margins of error using

99 percent confidence intervals, rather than the traditional 95

percent confidence intervals. Approximations used in other

published work suggest that these 99% confidence intervals

should rarely underestimate, and often overestimate, the true size

of the margins of error.

15 As with the other variables discussed in this section — presence of

own children and marital status — family income was originally

included as a predictor in the mentoring model. These variables

were dropped because they were not statistically significant, with

no loss of overall explanatory power and no change in the

magnitude of the other results.

16 It is useful to note, however, that the 2005 CPS indicates that few

volunteers that were engaged in mentoring appeared to have

responded to appeals at work or by an employer. When volunteers

were asked who asked them to volunteer, 3.4% of non-mentors

said it was their boss or employer. However, for volunteers that

mentored only 1.7% said they responded to an appeal from their

boss or employer. We did not report this number in the study

because the sample size was very small (only 53 observations).

17 For more information on the report Mentoring in America 2005,

go to www.mentoring.org.
23

APPENDIX 1 TABLE 1

NonMentorsVolunteers 

who Mentor

Number of Volunteers 65,357 53,854 ,503 17.6%

AGE

16 -19 4,848 3,841 1,007 9%

0-24 3,828 2,855 973 8%

25-34 9,761 7,739 2,022 18%

35-44 14,610 1,5 3,099 27%

45-54 13,600 1,109 2,492 22%

55-64 8,999 7,725 1,274 %

65 and over 8,583 7,948 635 6%

Non-Boomers 38,867 32,100 6,767 59%

Boomers 25,363 20,627 4,736 41%

OTAL 64,230 52,727 1,503 100%

GENDER

MALE 26,894 21,982 4,912 43%

FEMALE 37,337 30,746 6,591 57%

OTAL 64,230 52,727 1,503 100%

RACE/ ETHNICITY

White 55,297 45,751 9,547 83%

Black 5,705 4,282 1,423 12%

Asian 1,992 1,714 278 2%

Other 433 349 84 1%

Multi-racial 802 632 170 1%

OTAL 64,230 52,727 1,503 100%

Hispanic 4,406 3,745 660 6%

Non-Hispanic 59,824 48,982 10,842 94%

OTAL 64,230 52,727 1,503 100%

EDUCATION LEVELS (25 YEARS AND 

OVER)

Less than a high-school diploma 2,797 2,549 247 3%

High school graduates, no college 12,413 10,614 1,799 19%

Less than a bachelor's degree 16,234 13,262 2,973 31%

College graduates 24, 10 19,606 4,504 47%

OTAL 55,554 46,032 9,522 100%

PRESENCE OF CHILDREN

Without own children <18 40,193 34,086 6,107 53%

With own children <18 24,037 18,641 5,396 47%

OTAL 64,230 52,727 1,503 100%

EMPLOYMEN

Full-time 34,623 27,848 6,775 59%

Part-time 9,517 7,598 1,919 17%

Not Working 20,090 17,281 2,809 24%

OTAL 64,230 52,727 1,503 100%

VOLUNTEER ACTIVITIES

One activity 32,980 31,458 1,522 13%

wo or more act vities 31,250 21,269 9,981 87%

OTAL 64,230 52,727 1,503 100%

ORGANIZATION TYPE

Religious org. 22,548 17,696 4,851 42%

Educational 15,686 12,086 3,600 31%

Youth Services 1,376 639 737 6%

Sports or hobby 1,120 941 179 2%

Social and community service/ Civic 1,272 10,083 1,189 10%

Political, advocac , Union, Business 1,229 1,168 61 1%

Health-oriented 4,951 4,703 248 2%

Other 5,600 5,032 569 5%

OTAL 63,781 52,347 1,434 100%

ROUTE TO VOLUNTEERING

Approached the organization 26,152 21,478 4,674 41%

Was asked 27,907 23,217 4,690 41%

Other 9,392 7,373 2,018 18%

OTAL 63,451 52,068 1,383 100%

VOLUNTEER FREQUENCY

Episodic volunteers (0-2 weeks per 

year) 14,201 12,917 1,283 12%

Occasional volunteers (3- weeks per 

year) 14,434 12,143 2,291 21%

Regular volunteers (12 or more weeks 

per year) 31,634 24,270 7,364 67%

OTAL 60,269 49,330 10,939 100%

Annual Median Volunteer Hours 52 40 80 N/A

Note: Because of non-responses totals for each category will diffe

able 1. Volunteers (in 1000s) who mentor and who don't mentor, by selected characteristics, for 200

VOLUNTEER CHARACTERISTICS

otal 

Volunteers

Percent distribution of mentors by 

selected characteristics
24

Variables and Categories Predicted Prob. Low High Predicted Prob. Low High

AGE

Age: 16 to 19 43.2% 40.7% 45.7% 19.6% 15.7% 23.6%

Age: 20 to 24 20.1% 18.5% 21.7% 22.4% 18.6% 26.3%

Age: 25 to 34 20.2% 19.1% 21.3% 13.7% .9% 15.5%

Age: 35 to 44 26.0% 24.9% 27.1% .6% 10.1% 13.1%

Age: 45 to 54 27.5% 26.4% 28.6% 9.7% 8.4% 10.9%

Age: 55 to 64 28.5% 27.2% 29.9% 8.0% 6.7% 9.4%

Age: 65 and over 28.4% 26.6% 30.2% 5.3% 4.0% 6.6%

GENDER

Female 29.9% 29.3% 30.6% 10.1% 9.2% 10.9%

Male 23.0% 22.3% 23.6% .9% 10.8% 12.9%

RACE/ ETHNICITY

White or Haw/Pac 27.8% 27.3% 28.3% 10.5% 9.8% .2%

Black 23.4% 21.9% 24.9% 16.1% 13.3% 18.9%

Asian 13.8% 12.1% 15.5% 8.0% 5.1% 10.8%

American Indian, Alaskan Native 22.4% 18.6% 26.2% 6.7% 2.8% 10.6%

More than one race category 33.1% 28.1% 38.2% 14.9% 7.3% 22.6%

Latino 17.1% 15.9% 18.4% 9.3% 7.1% .6%

Non-Latino 28.0% 27.5% 28.5% 10.9% 10.2% .6%

EDUCATION

Less than HS Diploma 14.9% 14.0% 15.8% 8.4% 6.3% 10.5%

HS Grad 20.2% 19.5% 20.9% 9.4% 8.2% 10.6%

Some college 30.5% 29.6% 31.5% .2% 10.0% 12.4%

College grad + 42.1% 41.1% 43.2% 12.1% .0% 13.2%

INCOME

Income: Missing 21.4% 20.3% 22.5% N/A N/A N/A

Income: Less than $35,000 23.2% 22.4% 24.0% N/A N/A N/A

Income: Between $35-$50,000 26.9% 25.7% 28.2% N/A N/A N/A

Income: Between $50-$75,000 29.6% 28.5% 30.7% N/A N/A N/A

Income: $75,000 and over 32.2% 31.1% 33.2% N/A N/A N/A

PRESENCE OF CHILDREN

Own children under 18 34.2% 33.1% 35.3% .7% 10.5% 13.0%

No own children under 18 23.6% 23.0% 24.1% 10.3% 9.4% .2%

MARITA S ATUS

Married 28.3% 27.6% 29.0% N/A N/A N/A

Divorced 24.1% 22.7% 25.5% N/A N/A N/A

Widowed 24.9% 23.0% 26.9% N/A N/A N/A

Separated or never been married 24.3% 23.2% 25.4% N/A N/A N/A

EMPLOYMENT TATUS

Employed, full-time 25.7% 25.0% 26.4% .8% 10.8% 12.8%

Employed, part-time 34.4% 33.0% 35.9% 10.6% 9.1% 12.1%

Not in labor force: Retired 26.2% 24.6% 27.9% N/A N/A N/A

Not in labor force: Disabled 14.5% 12.8% 16.3% N/A N/A N/A

Not in labor force (other or 

unemployed)

28.3% 27.0% 29.5% N/A N/A N/A

Not in labor force for any reason N/A N/A N/A 9.3% 8.2% 10.4 %

REGION

East 22.2% 21.2% 23.1% 10.0% 8.5% .5%

Midwest 29.6% 28.6% 30.5% 10.2% 9.1% .3%

South 26.1% 25.4% 26.9% 10.4% 9.3% .5%

West 27.7% 26.7% 28.6% 12.8% .4% 14.1%

NUMBER OF VOLUNTEER 

ACTIVITIES

One activity N/A N/A N/A 3.5% 3.0% 3.9%

wo activities N/A N/A N/A 13.1% 1.7% 14.5%

Three activities N/A N/A N/A 23.2% 20.9% 25.6%

Four or more activities N/A N/A N/A 49.6% 47.4% 51.8%

TYPE OF ORGANIZATION

Main org: Civic, pol, prof, int'l N/A N/A N/A 6.3% 4.5% 8.1%

Main org: Education, youth service N/A N/A N/A 17.6% 16.0% 19.2%

Main org: Religious N/A N/A N/A 12.7% .6% 13.8%

Main org: Social / community service N/A N/A N/A 7.4% 6.0% 8.8%

Main org: All other types N/A N/A N/A 5.4% 4.4% 6.5%

HOW RESPONDENT BECAME 

INVOLVED WITH MAIN 

ORGANIZATION

How R got inv: Approached org N/A N/A N/A .3% 10.3% 12.3%

How R got inv: Other way N/A N/A N/A 12.2% 10.5% 13.8%

ABLE 2. PREDICTED PROBABILITIES FOR VOLUNTEERS AND MENTORS BY SELECTED CHARACTERISTIC

FROM PROBIT MODELS

volunteer MODEL mentor MODEL

How R got inv: Someone asked R 9.9% 9.0% 10.8%

“Not included” indicates that the variable was dropped from the final version of the mentoring model because of statistical 

insignificance. “N/A” indicates the variable was not included in the volunteering model, because non-volunteers were not 

asked to respond to the question. "Low" and "high" entries are based on 99% confidence intervals constructed usin th

delta method. See notes 13 and 14.

APPENDIX 1 TABLE 2
25

APPENDIX 2 TABLE 3

Category Coe ficient Standard Error Z-score P-value

16 to 19

20 to 24 0.097 0.07 1.33 0.182

25 to 34 -0.239 0.07 -3.57 < 0.001

35 to 44 -0.342 0.07 -5.13 < 0.001

to 54 -0.447 0.06 -6.9 < 0.001

55 to 64 -0.547 0.07 -8.41 < 0.001

65 and over -0.766 0.07 1.31 < 0.001

Female -0.095 0.03 -3.69 < 0.001

Male

Race: White or Haw/Pac

Race: Black 0.264 0.05 5.75 < 0.001

Race: Asian -0.151 0.08 -2 0.045

Race: American Indian, Alaskan 

Native -0.243 0.12 -2.08 0.037

More than one race category 0.215 0.13 1.69 0.09

Latino -0.09 0.05 -1.68 0.094

Non-Latino

Education: Less than HS Diploma

Education: HS Grad 0.059 0.06 1.04 0.3

Education: Some college 0.163 0.06 2.84 0.005

Education: College grad + 0.208 0.06 3.58 < 0.001

Own children under 18 0.076 0.03 2.35 0.019

No own children under 18

Employed, full-time 0.14 0.03 4.19 < 0.001

Employed, part-time 0.075 0.04 1.93 0.054

Not in labor force or unemployed

One activity -1.808 0.03 -56.48 < 0.001

Two activities -1.1 0.03 -33.52 < 0.001

Three activities -0.722 0.04 -19.83 < 0.001

Four or more activities

Main org: Civic, pol, prof, int'l 0.073 0.07 1.1 0.266

Main org: Education, youth service 0.674 0.04 15.88 < 0.001

Main org: Religious 0.463 0.04 .48 < 0.001

Main org: Social / community service 0.157 0.05 3.05 0.002

Main org: All other types

How R got inv: Approached org 0.074 0.03 2.81 0.005

How R got inv: Other way 0. 18 0.04 3.36 0.001

How R got inv: Someone asked R

Region: East

Region: Midwest 0.012 0.04 0.32 0.749

Region: South 0.022 0.04 0.56 0.578

Region: West 0.143 0.04 3.58 < 0.001

Constant -0.299 0.07 -4.18 < 0.001

OBSERVATIONS 27564

WALD CHI-SQUARED 4009.64

Prob.> CHI-SQUARE p < 0.001

PSEUDO R2 0.2698

LOG PSEUDO LIKELIHOOD -9299.3753

% PREDICTED CORRECTLY

At Baseline 82.3%

By Model 85.0%

Proportional Reduction in Error 15.0%

(PRE)

Reference Category

Reference Category

Reference Category

Reference Category

Reference Category

Reference Category

Reference Category

Reference Category

Table 3: Probit Model to Predict which Volunteers will be Mentors

Reference Category

Reference Category

Reference Category
26

APPENDIX 2 TABLE 4

Category Coe ficient Standard Error Z-score P-value

Age: 16 to 19

Age: 20 to 24 -0.666 0.03 -22 < 0.001

Age: 25 to 34 -0.663 0.029 -23.09 < 0.001

Age: 35 to 44 -0.472 0.029 -16.17 < 0.001

Age: 45 to 54 -0.426 0.029 -14.61 < 0.001

Age: 55 to 64 -0.395 0.031 -12.79 < 0.001

Age: 65 and over -0.399 0.035 1.4 < 0.001

Female 0.214 0.0 19.37 < 0.001

Male

Race: White or Haw/Pac

Race: Black -0.136 0.02 -6.91 < 0.001

Race: Asian -0.5 0.03 -16.86 < 0.001

Race: American Indian, Alaskan Native -0.169 0.049 -3.43 0.001

More than one race category 0.154 0.053 2.89 0.004

Latino -0.367 0.02 -18.7 < 0.001

Non-Latino

Education: Less than HS Diploma

Education: HS Grad 0.206 0.018 .21 < 0.001

Education: Some college 0.532 0.019 28 < 0.001

Education: College grad + 0.842 0.02 42.3 < 0.001

Income: Missing -0.06 0.018 -3.38 0.001

Income: Less than $35,000

Income: Between $35-$50,000 0. 17 0.018 6.55 < 0.001

Income: Between $50-$75,000 0.196 0.017 .74 < 0.001

Income: $75,000 and over 0.27 0.016 16.56 < 0.001

Own children under 18 0.313 0.015 21.39 < 0.001

No own children under 18

Married 0.123 0.017 7.13 < 0.001

Divorced -0.007 0.023 -0.31 0.756

Widowed 0.02 0.029 0.69 0.489

Separated or never been married

Employed, full-time -0.079 0.017 -4.72 < 0.001

Employed, part-time 0.174 0.02 8.59 < 0.001

Not in labor force: Retired -0.061 0.026 -2.36 0.018

Not in labor force: Disabled -0.481 0.034 -14.1 < 0.001

Unemployed or Not in labor force for 

other reasons

Region: East

Region: Midwest 0.23 0.016 14.14 < 0.001

Region: South 0.128 0.016 8. < 0.001

Region: West 0.173 0.017 10.37 < 0.001

Constant -0.948 0.028 -34.45 < 0.001

OBSERVATIONS 91467

WALD CHI-SQUARED 7907.2

Prob.> CHI-SQUARE < 0.001

PSEUDO R2 0.1051

LOG PSEUDO LIKELIHOOD -49162.532

% PREDICTED CORRECTLY

At Baseline 69.2%

By Model 71.6%

Proportional Reduction in Error 7.9%

(PRE)

Reference Category

Reference Category

Reference Category

Reference Category

Reference Category

Reference Category

Reference Category

Table 4: Probit Model to Predict which Adults will be Volunteers 

Reference Category

Reference Category

Reference Category

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