Volunteering as a Pathway to Employment:

Does Volunteering Increase Odds of Finding a Job for the Out of Work? 

Christopher Spera, Ph.D.

Robin Ghertner, M.P.P.

Anthony Nerino, M.A.

Adrienne DiTommaso, M.P.A.

June 2013

Office of Research & Evaluation

Corporation for National and Community Service
Volunteering as a Pathway to Employment:

Does Volunteering Increase Odds of Finding a Job for the Out of Work?

Corporation for National and Community Service, Office of Research and Evaluation

Washington, DC, June 2013

Authors

Christopher Spera, Ph.D.

Robin Ghertner, M.P.P.

Anthony Nerino, M.A.

Adrienne DiTommaso, M.P.A.

This report represents the work and perspectives of the authors and is the product of professional research. It does not represent the position or 

opinions of CNCS, the federal government, or the reviewers. 

The Corporation for National and Community Service

The mission of the Corporation for National and Community Service (CNCS) is to improve lives, strengthen communities, and foster civic engagement 

through service and volunteering. CNCS, a federal agency, engages more than five million Americans in service through AmeriCorps, Senior Corps, 

Social Innovation Fund, Volunteer Generation Fund, and other programs, and leads the President’s national call to service initiative, United We Serve. 

For more information, visit www.NationalService.gov 

Acknowledgments

The authors wish to acknowledge the many reviewers within and outside government for their time and thoughtful comments reviewing this study:

John Bridgeland, J.D., President and CEO of Civic Enterprises

Kristen Cambell, Chief Program Officer, National Conference on Citizenship

Lattie Coor, Ph.D., Chairman and CEO, Center for the Future of Arizona

William Galston, Ph.D., Senior Fellow, Brookings Institute, Professor of Civic Engagement, University of Maryland

Jonathan Greenblatt, MBA, Special Assistant to the President, Director of the Office of Social Innovation and Civic Participation

in the Domestic Policy Council

Brian Harris-Kojetin, Ph.D., Senior Statistician, Office of Management & Budget

Katherine Hartman, Office of Management and Budget

Lloyd Johnston, Ph.D., Distinguished Senior Research Scientist, University of Michigan

Chinhui Juhn, Ph.D, Senior Economist, Council of Economic Advisers; Professor of Economics, University of Houston

Kei Kawashima-Ginsberg, Ph.D., Lead Researcher, CIRCLE at Tufts University

Peter Levine, Ph.D., Director, CIRCLE at Tufts University

Chaeyoon Lim, Ph.D., Assistant Professor of Sociology, University of Wisconsin

Tim Marshall, Ph.D., Survey Statistician, US Census Bureau

Demetra Nightingale, Ph.D., Chief Evaluation Officer, United States Department of Labor

Dan Rosenbaum, Ph.D, Senior Economist in the Economic Policy Division, Office of Management and Budget

Kristi Tate, Director of Community Strategies, National Conference on Citizenship

Ilir Zherka, J.D., Executive Director, National Conference on Citizenship

Citation

Spera, C.; Ghertner, R., Nerino, A., DiTommaso, A. (2013). Volunteering as a Pathway to Employment: Does Volunteering Increase Odds of Finding a 

Job for the Out of Work? Corporation for National and Community Service, Office of Research and Evaluation: Washington, DC, 2013

This report is in the public domain. Authorization to reproduce it in whole or in part is granted. Upon request, this material will be made available in 

alternative formats for people with disabilities. 
Executive Summary............................................................................................................................................................... 1

I. Introduction ..................................................................................................................................................................... 3

 Volunteering as a Pathway to Employment.............................................................................................................................................................. 4

 Explanatory Mechanisms for Volunteering as a Pathway to Employment................................................................................................................. 5

 Figure 1: Social and Human Capital as Mechanisms of Volunteering as a Pathway to Employment................................................................... 5

Challenges to Modeling Volunteering and Employment........................................................................................................................................... 7

II. Methodology..................................................................................................................................................................... 9

Description of the Analytical Data Set..................................................................................................................................................................... 9

 Figure 2: Measurement of Volunteering and Employment in CPS ...................................................................................................................... 10

Descriptive Summary............................................................................................................................................................................................. 12

III. Results.............................................................................................................................................................................13

Model Fit and Results............................................................................................................................................................................................ 13

Table 1: Regression Fit Statistics...................................................................................................................................................................... 13

Table 2: Regression Estimates of Relationship between Volunteering and Employment ................................................................................... 15

 Figure 3: Percent Change in Odds of Employment Associated with Volunteering .............................................................................................. 16

 Figure 4: Change in Probability of Employment Associated with Volunteering, by Subgroup............................................................................. 17

 Figure 5: Change in Probability of Employment Associated with Volunteering, by Year..................................................................................... 18

Model Robustness................................................................................................................................................................................................. 19

 Figure 6: Predicted Probability of Employment by Pregibon’s Influence............................................................................................................. 19

Table 3: Odds Ratios for Employment, All Volunteers, New Volunteers, and Existing Volunteers ....................................................................... 20

IV. Discussion ...................................................................................................................................................................... 23

References........................................................................................................................................................................... 25

Appendices........................................................................................................................................................................... 27

Table of Contents
D Volunteering as a Pathway to Employment

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The Corporation for National and Community Service 1

EXECUTIVE SUMMARY

EXECUTIVE SUMMARY

While economists have studied numerous strategies for unemployed persons to increase their chances 

of finding employment through activities such as acquiring additional training or education, there is 

little empirical literature to date on the extent to which volunteering can serve to maximize one’s chances 

of finding employment. This paper aims to fill this critical gap by using statistical techniques to test 

the hypothesis that volunteering is associated with an increased likelihood of finding employment for 

individuals out of work. 

Methodology: This study uses data from the 2002-2012 Current 

Population Survey (CPS) September Volunteer Supplement. We identified 

those individuals in a two-year cohort and combined them to create a 

dataset containing all ten cohorts from 2002-2003 through 2011-2012. 

Because our analysis was focused on the effect of volunteering on those 

without work, we restricted the sample to include respondents in their 

first survey year that were classified as either a) unemployed, or b) not 

in the labor force but interested in working. We further restricted our 

sample to individuals 16 years old and older, resulting in a final sample 

size of 70,535. The outcome variable used in this study was whether 

the respondent was employed or not at the end of Year 2. The primary 

independent variable of interest was whether or not the individual 

volunteered at all during Year 2. We modeled the employment status of 

respondents at the end of year two as a binary outcome, using a logit 

model with survey weights, controlling for a number of demographic and 

community-level factors. We tested the fit of our model using different 

fit statistics, assessed parameter stability across subgroups, and 

conducted robustness checks, including testing for alternate volunteer 

periods as well as random effects at the metropolitan level.

Results: After controlling for demographic variables, we found that 

volunteering was associated with a 27% higher odds of employment, 

statistically significant at the 99.9% confidence level. The association 

between volunteering and employment had the strongest effect on 

individuals without a high school diploma or equivalent (51% increase 

in odds) and individuals who live in rural areas (55% increase in odds). 

We found that the relationship between volunteering and employment 

is stable across gender, race, and ethnic categories, age, time, 

Metropolitan Statistical Area, and unemployment rate. 

Discussion: The results of this study suggest a statistically significant 

and stable association between volunteering and employment. The 

overall association remains consistent across each year of the study 

period and different unemployment rates, suggesting that irrespective 

of economic conditions volunteering may add an advantage to the out 

of work seeking employment. The relationship between volunteering and 

employment is stronger for individuals without high school diplomas 

The study found that volunteering is 

associated with a 27% higher odds 

of employment.
2 Volunteering as a Pathway to Employment

and individuals living in rural areas. Volunteering may assist in 

“leveling the playing field” for these individuals who typically have a 

more difficult time finding employment, especially during a recession. 

These findings suggest it is critical for organizations that recruit 

volunteers to reach out to those who are out of work. 

This research provides useful insights into the relationship between 

volunteering and employment in the United States. For example, we 

hypothesize that the mechanisms by which volunteering could lead 

to an increase in the likelihood of finding employment for those out of 

work include an increase in social capital and human capital. These 

increases could make individuals more marketable to, or productive 

for, employers and increase their odds of finding work. Alternatively, 

some workers may see volunteering as a possible entry route into an 

organization where they would like to work. 

There are two primary limitations to the study. First, it does not 

establish a causal link between volunteering and employment. Second, 

there is the risk that volunteers differ from non-volunteers on certain 

factors that we could not measure, such as self-motivation, and 

that these factors can lead to employment but they are not derived 

from the volunteer experience. Future research should examine the 

causal mechanisms between volunteering and employment to further 

strengthen the findings presented in this report.
The Corporation for National and Community Service 3

I. INTRODUCTION

I. INTRODUCTION

The economic downturn that has plagued the United States economy over the last half-decade has 

increased the need for pathways to employment for millions of Americans struggling to find work. From 

January 2008 through October 2009, the national unemployment rate climbed from 5% to a peak of 10%, 

and although it has steadily declined since then it has remained stubbornly above historic levels. In 2012, 

some metropolitan areas still had unemployment rates exceeding 15%. Nationally, for those without a 

high school degree, unemployment was still over 10% as of April 2013. While economists have studied 

numerous strategies for out of work persons to increase their chances of finding employment, such as 

acquiring additional training or education, there is little empirical literature to date on the extent to which 

volunteering can serve to maximize one’s chances of finding employment. 

Research has shown that volunteering can provide social capital 

(Wollbaeck & Selle, 2002) and human capital (Schram & Dunsing, 

1981), two factors that have been shown to be related to employment 

outcomes (Franzen & Hangartner, 2006; and Becker, 1993). Furthermore, 

a recent report released by the National Conference on Citizenship 

in conjunction with Civic Enterprises, CIRCLE (of Tufts University), 

the Knight Foundation, and the Saguaro Seminar reported that civic 

engagement is related to a community’s ability to recover from an 

economic recession (NCoC, 2012). 

Despite the lack of quantitative data on volunteering as a potential 

pathway for economic opportunity for the out of work, volunteering has 

been promoted in the media as a way to increase one’s employment 

prospects (ABC video, 2009; CBS News, 2011; US News, 2012; or 

Smart Money, 2011). Furthermore, experts in the nonprofit sector 

and advocates of civic engagement present convincing anecdotal 

information that volunteering serves as a powerful technique for jobseekers (Institute for Employment Studies, 2011). 

Volunteering as a pathway to employment has also been promoted by

the federal government. In April 2012, the Department of Labor issued 

policy guidance in the form of an Unemployment Insurance Program 

Letter (UIPL) recognizing that volunteering can help expand opportunity 

for unemployed individuals by enabling them to develop and maintain 

skills, expand their network of contacts, and enhance their resumes, 

“ Volunteerism can be a way to help 

unemployed workers expand their 

network of contacts, improve their 

résumés, and make a positive impression 

in a competitive job market.”

Secr etar y of Labor Hilda L. Solis
4 Volunteering as a Pathway to Employment

all while making a positive impact on their communities. The guidance 

states that volunteerism can be a viable and successful strategy 

that supports reemployment and does not need to interfere with 

unemployment compensation recipients’ responsibilities to be able and 

available for work and actively seeking work. The guidance encourages 

state workforce agencies to promote volunteering to individuals 

receiving unemployment compensation (Oates, 2012). 

In issuing this policy guidance, Secretary of Labor Hilda L. Solis 

suggested that volunteering can help put citizens back to work, 

especially for the segment of Americans that have been unemployed 

for long periods of time. Specifically, Secretary Solis (2012) said: 

...volunteerism can be a way to help unemployed workers 

expand their network of contacts, improve their résumés, 

and make a positive impression in a competitive job market. 

So at the Department of Labor, we’re promoting volunteerism 

as one more strategy to help our long-term unemployed. In 

a complex 21st century economy that demands new skills of 

American workers, volunteerism is not a substitute for job 

training. But it can be an important complement. And it can 

be a way to give a leg up to job-seekers who’ve decided that 

enrolling in a training program is not the right choice for 

them at this time... The truth is – volunteering may actually 

expose job seekers to new job opportunities.

Given the importance of identifying strategies to help unemployed 

persons find work it is necessary to sufficiently validate volunteering 

as a pathway to employment using empirical evidence. The lack of 

rigorous quantitative evidence of this relationship limits the ability of 

policymakers to develop evidence-based solutions to unemployment 

and underemployment that involve volunteering as part of the solution. 

This paper aims to fill this critical gap by using statistical evidence 

to test the hypothesis that volunteering can improve the odds of 

finding employment for the out of work. With this goal in mind 

the primary research questions for the current study are: 

1) is volunteering associated with an increased likelihood 

of employment for the out of work? and 

2) how, if at all, does the relationship between volunteering and 

employment vary by demographic characteristics, labor market 

conditions, and community-level factors? 

Volunteering as a Pathway to Employment

Volunteering has long been anecdotally associated as a pathway to

employment, but empirically testing this theory has received little 

attention from social scientists and economists. To date, exploration of 

the relationship between volunteering and employment has been mainly 

descriptive in nature, and has mostly been examined as a sub-topic in 

studies on volunteer motivation (Jones, 2000; Gillespie & King, 1985; 

Hodgkinson & Weitzman, 1996; McDonald & Coffield, 1996) or as a subtopic in studies on employment in the nonprofit sector (see Gay, 1998). 

A few qualitative studies have been conducted over the years, but in 

addition to these studies being mostly descriptive in nature, they lack 

generalizability due to small and non-representative samples (Janey, 

Tuckwiller, & Lonnquist, 1991; Baines & Hardill, 2008; Erel & Tomlinson, 

2005; Tomlinson, 2010; Archer, 2005; Corden & Sainsbury, 2005). 

A handful of studies have examined volunteering and employment by 

gathering quantitative data, although most of these studies are limited 

as they focus on the volunteers’ perceptions of the benefit rather than a 

more objective outcome measure. For example, in a study of a statesponsored reemployment program in the United Kingdom, Hirst looked 

at volunteers’ perceptions of how their volunteer activity was related to 

their employment outcomes. Over half of participants surveyed directly 

attributed their success in finding work to their volunteering activities. 

Among other results, he found that unemployed volunteers who had 

spent more than six months out of work were more likely to report that 

volunteering helped them find employment than unemployed volunteers 

who had spent less than six months out of work (Hirst, 2001). Despite 

the perceptions of volunteers, Hirst’s subsequent regression analysis 

did not find evidence that voluntary participation was a significant 

factor in predicting positive labor market outcomes. He concluded 

that the characteristics of the volunteer experience such as teamwork, 

work experience, and supervisory role affect a volunteer’s chances of 

finding work. 
The Corporation for National and Community Service 5

i. iNTRODuCTiON

A study of members of formal volunteer organizations in italy showed 

more modest results, with only 24% of unemployed participants 

attributing success in fi nding employment to their volunteer experience 

(Antoni, 2009). however, a subsequent regression analysis showed that 

participation in the associations’ informal activities and participation 

in group-work with other volunteers had a positive, signifi cant effect 

on the probability of fi nding a job. in another study (Statham & Rhoton, 

1986), researchers measured labor outcomes available in the Mature 

Women’s Module of the National longitudinal Survey and explored 

the relationship between volunteering and employment with a focus 

on women’s paid and volunteer work. using data collected between 

1974 and 1981, they found that in the long run women who reported 

volunteering had increased occupational prestige relative to their 

former positions. A subsequent re-analysis of the data by Wilson and 

Musick (2000) corroborated these fi ndings. While Statham and Rhoton 

suggested that volunteering enhances human capital for working 

women in the long run, despite short run costs, their study lacks 

generalizability to the broader u.S. population. 

Explanatory Mechanisms for Volunteering as a Pathway 

to Employment

Based on the literature, volunteering can serve as a pathway to 

employment through increases in one’s social capital and human capital 

(see figure 1). Although our dataset does not allow us to test these 

hypotheses, understanding these mechanisms provides the theoretical 

support for how volunteering can lead to employment. establishing 

an empirical association between volunteering and employment is 

necessary before these mechanisms can be fully explored.

figure 1. Social and Human Capital as Mechanisms of Volunteering as a Pathway to Employment

Volunteering Employment

• Professional contacts

• Durable networks

• Employment leads

• Social relationships

• Knowledge

• Skills, abilities

• Leadership opportunities

• Work experience

Increased Human 

Capital

• Professional contacts

• Durable networks

• Employment leads

• Social relationships

Increased Social 

Capital
6 Volunteering as a Pathway to Employment

Recent literature provides empirical evidence to support the fact that 

volunteering leads to the formation of social capital (Wollbaeck & Selle, 

2002). Social capital, as defined by Bourdieu (1985), is “the aggregate 

of the actual or potential resources which are linked to possession of a 

durable network of more or less institutionalized relationships of mutual 

acquaintance or recognition.” He states that “through social capital, 

actors can gain direct access to economic resources (e.g., subsidized 

loans, investment tips, protected markets)” (Portes, 1998). Robust 

social networks are “individual resources that facilitate goal attainment 

– the goal being of either a personal or societal character” (Wollebaeck 

& Selle, 2002). Returning to work after a period of unemployment is 

one instance in which a social network can be deployed to obtain a 

goal, and extensive academic literature affirms this (Granovetter, 

1974; Montgomery, 1991; Calvò-Armengol & Jackson, 2004; Franzen & 

Hangartner, 2006), although Mouw (2003; 2006) has pointed out that 

social networks may not be the mechanism by which employment is 

attained in these situations. 

Based on Bourdieu’s definition, social capital is made up of two parts: 

individual access to resources possessed by associates, and the 

amount and quality of those resources. What supports getting a job 

isn’t just social relationships, but the resources that are attached to 

those relationships. Out of work individuals may belong to a social 

network, but that network may not be able to confer employment. 

As Portes (1998) explains in his seminal article on social capital, 

“social networks are not a natural given and must be constructed 

through investment strategies oriented to the institutionalization of 

group relations, usable as a reliable source of other benefits.” That 

means that for individuals whose network cannot provide employment 

opportunities, they can actively increase their opportunities by 

investing in additional social capital. Volunteering is one investment

strategy they can pursue.

Volunteering could also lead to employment via accumulation of human

capital, which, broadly defined by Schultz (1961) is the acquisition 

of “of all useful skills and knowledge...that is part of deliberate 

investment.” More specifically, as pointed out by Becker (1962; 1993) 

and Mincer (1958), human capital is comprised of investments in 

education, skills, and training that have a measurable economic payoff or return on investment. That return typically accrues in the form of 

increased earnings, but can be generally understood to lead to positive 

labor market outcomes. Among the many important studies linking 

human capital accumulation to volunteering (Schram & Dunsing, 

1981; Clotfelter, 1985; Day & Devlin, 1998; Mueller, 1975), Menchik 

and Weisbrod (1987) note that volunteer work “raises one’s future 

earning power by providing work experience and providing potentially 

valuable contacts.” If volunteers are gaining or updating skills that 

are needed in the workplace through their volunteer activities, those 

skills may make them more attractive to and productive for employers 

and increase their chances of becoming employed. Alternatively, 

volunteering may send a signal to employers that an individual is 

motivated and productive, providing a competitive advantage to the 

job-seeker.

Volunteering may not affect all individuals equally or in the same

ways. Individuals lacking the type of social capital that can lead 

to employment opportunities may receive a greater “boost” to their 

employment prospects due to volunteering relative to individuals with 

strong social capital who have little to gain from increased access 

to the same networks. Individuals with low levels of human capital, 

such as lower levels of education and less work experience, may 

If volunteers are gaining or updating skills 

that are needed in the workplace through 

their volunteer activities, those skills may 

make them more attractive to and productive 

for employers and increase their chances of 

becoming employed.
The Corporation for National and Community Service 7

I. INTRODUCTION

benefit more from the increased skills than those with higher levels 

of human capital. A further complication is that the groups that are 

more likely to benefit from volunteering (those with low levels of social 

and human capital) may not be the same as those that are more likely 

to volunteer. Research shows that volunteers are more likely to have 

higher socioeconomic status (including higher income, education, and 

occupational status), have extensive social networks, be married, and 

have children – all characteristics which are also highly correlated with 

positive labor market outcomes (Wilson, 2000; Wilson & Musick, 1997).

Given the impact of social and human capital on employment outcomes, 

and the role of volunteering in the accumulation of capital we expect 

to see a positive relationship between volunteering and unemployment 

for those individuals with lower levels of such capital. This includes 

individuals with lower education and lower socioeconomic status.

Challenges to Modeling Volunteering and Employment

There are three methodological challenges in modeling volunteering and 

employment. First, as stated above, volunteering is highly correlated 

with demographic characteristics that are also highly correlated with 

employment outcomes. Many of these characteristics can be controlled 

for given the appropriate methods and a robust dataset, and the dataset 

we use in this study – the 2002-2012 CPS – allows us to control for 

these. There are also likely to be other harder-to-observe aspects of 

volunteers that are unassociated with their volunteer experience, which 

help them find employment. Volunteers are more likely to be extroverts

(Wilson, 2000), and may be more motivated, two factors that are 

attractive to employers. As we cannot control for these factors in our 

models there is the risk that any relationship found could be spurious. 

A second challenge to identifying the causal pathway from volunteering 

to employment is that the relationship between the two can be 

bidirectional: volunteering can lead to employment, and employment 

can lead to volunteering. Stubbings and Humble (1984) found that 

the employed were more likely to volunteer than the unemployed. 

Preliminary analysis of our dataset from the 2002-2012 CPS found 

that the employed had a 35% higher odds of volunteering than those 

not employed. To address this risk, we focus our study only on the 

individuals not employed (and the individuals not in the work force). 

A third challenge is that the Volunteer Supplement does not identify an 

individual’s reasons or motivations for volunteering. An individual may 

volunteer for a number of reasons, and finding employment may not be 

one of them. Individuals that volunteer with employment in mind may be 

more likely to find work because they are actively developing social and 

human capital, while individuals that are not explicitly looking for work 

through their volunteer experience may be more passively developing 

that capital. 

One method to isolate the relationship between volunteering and 

employment would be to separate the individuals who are long term 

volunteers, and those that began volunteering after being out of 

work. The former group may be less likely than the latter group to be 

volunteering to find employment. While the CPS data does not track 

respondents for more than 2 years we can identify who volunteered 

only after being out of work. If volunteering helps individuals find 

work, rather than being a manifestation of unobserved factors, these 

individuals should see a benefit.
8 Volunteering as a Pathway to Employment

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The Corporation for National and Community Service 9

II. METHODOLOGY

II. METHODOLOGY

This study used nationally-representative data spanning 2002 through 2012, from the Current Population 

Survey. We used statistical techniques to estimate whether individuals out of work in one year that 

volunteer are more likely to find a job in the following year, independent of a number of demographic and 

community-level characteristics. We modeled this relationship with a logit regression model and included 

survey weights to ensure that our results are generalizable. Finally, we conducted a number of tests on our 

models to assess whether they meet statistical requirements for fit and robustness.

Description of the Analytical Data Set

This study uses data from the 2002-2012 Current Population 

Survey (CPS) September Volunteer Supplement. The CPS is a survey 

administered by the U.S. Census Bureau and the Bureau of Labor 

Statistics (BLS) monthly to a generalizable sample of the civilian 

non-institutional population, with the primary purpose to collect labor 

information including detailed demographic and employment data. 

Since 2002, the Corporation for National and Community Service (CNCS) 

has partnered with the U.S. Census Bureau and BLS to produce the 

September Volunteer Supplement, which asks respondents specific 

questions regarding the volunteer activities they have done for the prior 

twelve months. The survey questions that are the focus of this analysis 

have not changed over that time period.

Population of Interest and Sample Size. In the CPS, a sampled 

housing unit is interviewed for four consecutive months, dropped out 

of the sample for the next eight months, and interviewed again in the 

following four months, which enables comparisons across years. To 

construct our dataset we identified those individuals in each two-year 

cohort and stacked them to create a dataset containing all ten cohorts 

from 2002-2003 through 2011-2012. We further restricted our sample 

to individuals 16 years old and older. Our analysis focuses primarily on 

the employment status of the respondent at the time of the interview. 

Employment status can be employed, unemployed, and not in the labor 

force. Unemployed individuals are not working and are actively seeking 

employment. Respondents not in the labor force are not working and 

are not actively seeking employment. Because our analysis is focused 

on the effect of volunteering on those without work, we further restrict 

our sample to only include respondents classified as either unemployed, 

or not in the labor force and wanting a full or part time job in their first 

survey year. For the purposes of this study, we call either of these groups 

“out of work.” The total sample size used for preliminary analysis was 

71,671, and for our primary models it was reduced to 70,535 due to 

missing data for some variables.

Previous research has identified challenges in matching records in 

the CPS, particularly related to ensuring records are correctly matched 

(Madrian & Lefgren, 2000). Various methods have been proposed to 

match records by estimating the probability that two records correctly 

match. We thoroughly checked our data and had discussions with the 

U.S. Census Bureau on the risk of mismatches and concluded that our 

dataset had limited, if any, erroneous matches. We therefore did not take 

any additional steps to prepare the dataset.

Independent and Dependent Variables. The outcome variable used in 

this study is whether the respondent was employed or not at Year 2. The 
 10 Volunteering as a Pathway to Employment

primary independent variable of interest is whether or not the individual 

has volunteered at all during year 1. This variable was derived from 

two questions on the questionnaire: “Since September 1st of last year, 

have you done any volunteer activities through or for an organization?” 

and “Sometimes people don’t think of activities they do infrequently 

or activities for children’s schools or youth organizations as volunteer 

activities. Since September 1st of last year, have you done any of these 

types of volunteer activities?” Respondents answering yes to either of 

these questions were coded as having volunteered. 

The measure of volunteering has two limitations. first, it does not 

differentiate individuals that volunteer from individuals that perform 

unpaid labor, such as interns, or individuals participating in specifi c 

government-sponsored programs that require service. it is not clear 

whether either of these groups would classify themselves as volunteers. 

These groups may be more or less likely than volunteers to secure 

employment through their service, which may confound our ability to 

identify a relationship between volunteering and employment.

The timing of the dependent and independent variable presents a 

unique challenge to our research. A second limitation to the measure 

of volunteering is that its timing is different than the measure of 

employment. figure 2 illustrates the timing of activities in question.

We chose to model volunteering during year 2, as measured at the 

end of that year, for two key reasons. first, we can be sure that the 

respondent’s volunteering occurred after being out of work at the end of 

year 1. Second, if volunteering helps individuals fi nd work, volunteers 

during year 1 who were out of work at the start of year 1 should be more 

likely to have found work by the end of that year, thereby removing them 

from our sample. There are two risks associated with this, however. 

first, we cannot be certain whether employment at the end of year 2 

occurs before or after volunteering. Second, measuring both volunteering 

and employment at the same point introduces some endogeneity and 

subjectivity to the measure in that response to one question may 

bias response to the other. We tested the robustness of our fi ndings 

by measuring volunteering during year 1 and year 2, and the results 

showed no substantive difference.

Other variables used to isolate the independent relationship between 

volunteering and employment include various demographic variables 

from the CPS, such as gender, race, ethnicity, and parental status. We 

measure most independent variables at the end of year 1. Studies on 

employment outcomes often include a measure of household income, 

in part to proxy for socioeconomic status. We did not include income in 

our model for two reasons. The fi rst reason being because respondents 

under study are not employed at year 1, their household income is 

likely lower than what it would be had they been employed. Therefore, 

Start of Year 1 

(not measured) End of Year 1 End of Year 2

Volunteer? Volunteer?

Out of Work Employed?

figure 2. Measurement of Volunteering and Employment in CPS
The Corporation for National and Community Service 11

II. METHODOLOGY

household income in this case is an imperfect measure of socioeconomic 

status. Second, household income is not available for all observations 

in our sample – 10,709 records were missing this variable, representing 

15% of our total sample. For these reasons, we chose not to include 

income in our final models. We did, however, test if income mediates 

the relationship between volunteering and employment, and found no 

statistically significant effect.

Apart from these individual-level variables, we also collected data 

associated with the Metropolitan Statistical Areas (MSAs) where each 

respondent resided at the time of the survey. We collected the official 

September unemployment rate as estimated by BLS, and included 

both the unemployment rate and the year-on-year percent change in 

unemployment rate in the modeling. We also collected data on the 

industry sectors in each MSA from the County Business Patterns survey 

administered by the U.S. Census Bureau, which were not found to have 

substantive effect and were not included in the final models. Finally, 

we aggregated the individual-level variables up to the MSA level to 

calculate the proportion of the total population that volunteered, the 

proportion of each education level, and the makeup of occupation types 

within each MSA. Only education was found to make a substantive 

impact on the model; and we excluded the others for parsimony. One 

limitation to our dataset is that the CPS does not report a respondent’s 

MSA if the population in the MSA is under 100,000, for confidentiality 

purposes. Including MSA-level variables reduced the sample by nearly 

30%, from 71,671 to 43,596, excludes respondents prior to 2004 and 

in smaller MSAs and rural areas. A full list of all variables and their 

descriptive statistics can be found in Appendix A. 

Weighting. CPS is a multistage probability sample of housing units 

in the United States, producing estimates for the total U.S. civilian 

non-institutionalized population. To make the results fully generalizable, 

and adjust for non-response bias, survey probability weights are 

required. We constructed survey weights in accordance with guidelines 

from BLS, and used Taylor-series linearization to estimate variance. 

All statistics reported in this paper use weights, with the exception of 

some of the measures of model fit. Logit regression models – as used 

in this paper – are estimated using maximum likelihood estimation 

(MLE). MLE using survey weights does not generate a “true” likelihood, 

in that it is not the true distribution of the sample under study. Because 

of this, typical likelihood-based tests and statistics, such as likelihood 

ratio tests, Akaike Information Criterion (AIC), Bayesian Information 

Criterion (BIC), and Pregibon’s influence statistics, cannot be used. 

Wald tests can be conducted using survey data – they are practically 

equivalent to likelihood ratio tests – but they are only useful for nested 

models. Removing weights to use these specific tests allowed us to 

more broadly assess the quality of our models. As a check, we compared 

the parameter estimates for our models with and without weights and 

confirmed that they did not differ substantially.

Statistical Model Development and Testing. As the outcome variable 

in our model – employment status – is dichotomous, we used a logit 

model to identify the association with volunteering. A logit model 

transforms the binary outcome onto a log-odds scale and estimates the 

effect of each independent variable as a change in odds of having a 

specific outcome. This transformation allows the estimation of a linear 

relationship between outcome and independent variables. It is perhaps 

the most common approach to modeling dichotomous data, and most 

standard statistical packages easily incorporate survey weights into the 

estimation. 

Given the large number of potential covariates to include, and the 

high level of collinearity among them, our first step in model building 

was to explore the bivariate relationship between employment and 

each independent variable. This initial analysis was designed to 

identify control variables that potentially mediated and moderated 

the relationship between volunteering and employment. A variable 

that is highly correlated with both volunteering and employment 

could mediate the effect of volunteering, meaning: a) it confounds or 

masks the true effect of volunteering on employment; b) the effect of 

volunteering occurs through this control variable; or c) it reveals the 

effect of volunteering to be spurious. In addition, as we expected the 
12 Volunteering as a Pathway to Employment

effect of volunteering to be different for different subgroups, control 

variables could moderate the relationship between volunteering and 

employment. This means that the effect of volunteering changes in 

magnitude due to the presence or absence of the control variable. To 

explore these possibilities, we conducted an extensive series of simple 

models, including each control variable at a time, volunteering, and an 

interaction between volunteering and the control. 

After identifying potential mediators and moderators, we proceeded 

to build our final models using various fit statistics. Unlike linear 

regression, there is a lack of consensus on the best methods to assess 

the fit of a logit model. All methods have limitations that have been 

explored in depth, particularly when dealing with large sample sizes 

such as in this study. Our goal was to use a wide range of fit statistics 

in order to identify different weaknesses in our model, address them 

as the data allowed, and thereby triangulate the best possible model. 

We used comparative fit statistics, including Wald and likelihood ratio 

tests to compare nested models and AIC and BIC statistics to compare 

non-nested models. We conducted global fit tests, including the HosmerLemeshow test, Tukey’s test for non-additivity (Tukey, Pregibon 1980), 

McFadden’s R2, and McKelvey and Zavoina’s R2. To address inflated Chi2

values in the Hosmer-Lemeshow test and the Tukey’s test due to our 

large sample (a well-known limitation to Chi2 tests), we broke our total 

sample into randomly selected subsamples and ran the statistics on 

the subsamples, examining whether the fit statistic remained stable 

across each subsample. Although our purpose was not to predict 

positive outcomes, we analyzed the classification ability of our models 

by calculating sensitivity (percent of true positives correctly classified), 

specificity (percent of true negatives correctly classified), and the overall 

area under the curve (AUC) of the Receiver Operating Characteristic 

(ROC) curve. The AUC gives the probability that a randomly selected 

pair of respondents (one employed, one not employed) would have their 

predicted probabilities correctly ordered, meaning the employed would 

have a higher probability than the not employed. We also identified 

influential observations using Pregibon’s influence statistics. Finally, 

we tested the assumption that the outcome measure (log-odds of 

employment) was a linear combination of the independent variables 

using a using a locally weighted scatterplot smoothing (LOWESS) 

function, a key assumption of logit models. 

One additional check we conducted was to examine the parameter 

constancy or stability of our models for various subgroups. While we 

expect a heterogeneous effect of volunteering across certain subgroups, 

if in general the coefficients in our model fluctuate greatly across all 

groups, the interpretation at an aggregate level could overly mask the 

underlying instability. To do this, we ran seemingly-unrelated regressions 

(SUR) with a model for each subgroup. SUR allows the researcher to 

simultaneously run multiple models and use Wald tests to identify 

statistically significant differences in coefficients across the models.

Descriptive Summary

The sample used in our analysis consists of 70,535 respondents over 

the full sample period from 2002-2003 through 2011-2012, which 

represents 61,370,003 individuals when scaled up using the survey 

weights. Tables A1 and A2 in Appendix A provide summary statistics 

for all variables used in our study and compare the non-volunteer and 

volunteer subgroups. Thirteen percent of the total sample were employed 

at the end of Year 2; and 22% volunteered during Year 2. Among other 

points, females represented 58% of all respondents, students around 

3%, and 25% were in rural areas. Around 10% of the sample were in 

each year. Sixteen percent of the sample were unemployed, 55% retired, 

and 29% not in the labor force for other reasons. One percent of the 

sample had never worked before, and 1% were laid off, but expected to 

be called back to their prior job.

Volunteers and non-volunteers differed on a number of key demographic 

characteristics. In particular, volunteers were 3 percentage points 

more likely to have been employed at Year 2. They were also more likely 

to be white, female, a parent, and retired. Volunteers were also more 

likely to have more education. Our preliminary analysis suggested that 

some of these differences could result in mediation to the extent that 

these characteristics also predict employment outcomes. Importantly, 

volunteers and non-volunteers did not differ substantially in their MSAlevel characteristics. 
The Corporation for National and Community Service 13

III. RESULTS

III. RESULTS

We found that volunteering is associated with a 27% increase in odds of finding employment, highly 

statistically significant at the 99.9% confidence level. Volunteers without a high school degree and 

volunteers in rural areas have an even higher increase – 51% and 55%, respectively. Besides these 

groups, the relationship between volunteering and employment is relatively stable across gender, age, 

ethnicity, geographical area, and job market conditions. Our models satisfied common measures of fit 

and explanatory power, and the findings are robust to different model specifications.

Model Fit and Results 

The initial bivariate model with volunteering as the sole predictor 

of employment found that volunteers have a 22% higher odds of 

finding employment after being out of work than non-volunteers, 

statistically significant at the 99.9% confidence level. This corresponds 

to approximately a 3 percentage point higher probability of finding 

employment. Our models were intended to identify if this relationship 

may in fact be due to other factors and if the relationship differed for 

certain subgroups. 

Tables 1 and 2 report the regression output for three models; Table 1 

reports the fit statistics and Table 2 the parameter estimates. The first 

model, “Base,” includes a number of relevant demographic control 

variables. These variables were included either because of their theoretical 

relevance or because they were found to mediate and moderate the effect 

of volunteering. The next model, the Moderator model, included those same 

control variables and added in the interaction effects for two moderators: 

education and rural. The MSA model added MSA-level variables, including 

unemployment rate, percent change in unemployment rate from the prior 

year, and the educational makeup of the MSA.1

Table 1. Regression Fit Statistics

Statistic Base Model Moderator Model MSA Model

N 70,535 70,535 43,596

Weighted N 61,370,003 61,370,003 43,013,471

F (p) 228 (0.00) 216.30 (0.00) 142.58 (0.00)

McFadden’s R2 0.27 0.27 0.27

McKelvey & Zavoina’s R2 0.36 0.36 0.37

1 As noted above, we tested for whether the makeup of an MSA’s business sectors would have a mediating or moderating effect on volunteering. These data reduced our sample 

size further, and their effects were not substantial. We excluded them from the final model.
14 Volunteering as a Pathway to Employment

Statistic Base Model Moderator Model MSA Model

AIC (Base and Moderator model) 39907.04 39890.14 -

BIC (Base and Moderator model) 40236.94 40238.37 -

AIC (Base and MSA model) 25591.02 - 25541.27

BIC (Base and MSA model) 25886.23 - 25879.9

Likelihood Ratio Testa - Chi2

 (p) - 20.90 (0.00) 59.75 (0.00)

Hosmer & Lemeshow Test - F (p) 1.16 to 19.68 

(0.00 to 0.32)

0.62 to 23.15 

(0.00 to 0.78)

0.62 to 1.32 

(0.00 to 0.97)

Tukey’s Testb

 – coefficient of Hat2 (p) -0.06 to 0.03 

(0.00 to 0.97)

-0.057 to 0.00 

(0.01 to 0.95)

-0.06 to -0.01 

(0.62 to 1.32)

Sensitivityc 98% 98% 98%

Specificityc 54% 54% 53%

ROC AUC 85% 85% 85%

a The Likelihood Ratio (LR) test for the Moderator model is that it is an improvement from the Base model. The LR test for the MSA model is that it is an improvement 

from the Moderator model. To adjust for sample size differences, for the MSA model LR test, we ran the Moderator model on the smaller sample.

b The Hosmer & Lemeshow test and Tukey’s test are sensitive to large sample sizes. Statistics for these tests represent the range across four equally-sized randomly selected 

subsamples of the total sample for each model.

c For sensitivity and specificity, the cutoff value for classifying employed/not employed respondents was 12.99%, based on the percent of the entire sample that found work.

In general, the absolute goodness of fit statistics (McFadden’s R2, 

McKelvey & Zavoina’s R2, Tukey’s Test, and Hosmer & Lemeshow test) 

and the prediction statistics (sensitivity, specificity, and ROC AUC) show 

no substantial difference across the models. With a binary outcome, it 

is rare to find pseudo-R2 that are very high; that these measures are 

as high as they are indicates the model has enough explanatory power 

to merit consideration. The Hosmer & Lemeshow test and Tukey test 

were statistically significant on the full sample for each model, so we 

ran the statistics on four randomly-selected subsamples, of between 

10,000 and 17,500 records each. We report the range of the statistics 

in the table; although the results for some of the subsamples were 

still statistically significant, none of the coefficients in the Tukey test 

were substantively large; and the expected and observed counts in the 

Hosmer & Lemeshow test were not substantively different. We concluded 

that our models satisfied these fit measures. 

Turning to classification accuracy, although it is not the primary purpose 

of this research, these statistics – sensitivity, specificity, and ROC AUC 

– all indicate that the models have fairly strong prediction qualities. 

Sensitivity indicates that all models successfully classify 98% of all 

employed respondents, and around 54% of all not employed respondents. 

AUC indicates for 85% of any randomly-selected pair of respondents our 

models would predict that respondents who found employment have a 

higher probability of working than those who did not find employment. 

With respect to the relative fit statistics, including the AIC, BIC, and 

likelihood ratio tests, all favor the Moderator model over the Base 

Table 1. Regression Fit Statistics (continued)
The Corporation for National and Community Service 15

III. RESULTS

Table 2. Regression Estimates of Relationship between Volunteering and Employment

Base Model Moderator Model MSA Model

Variable Odds Ratio SE Odds Ratio SE Odds Ratio SE

Volunteer 1.27*** (0.05) 1.18*** (0.05) 1.25*** (0.06)

Interaction terms

 No HS diploma - - 1.28* (0.14) - -

Rural - - 1.31*** (0.12) - -

Standard errors in parenthesis. * indicates statistically significant at p<0.05; ** at p<0.01; and *** at p<0.001.

As estimated in the Base model, the overall, main relationship between 

volunteering and employment is a 27% increase in odds of finding 

work, statistically significant at the p<0.001 level. After including 

the interaction terms, the effect on two subgroups varies. The main 

effects in the MSA model only differ slightly from the Base model. Given 

the main effect is consistent with the base model, it indicates the 

MSA-level variables did not mediate the effect of volunteering overall. 

We tested whether these variables moderated the effect, and found no 

statistically or substantively significant moderation. Figure 3 reports 

effect of volunteering on different subgroups, based on the results from 

the Moderator model.

model, indicating an improved fit due to the addition of interaction 

terms. The MSA model is preferable over the Base model when using 

the MSA subsample, indicating for the subsample of observations with 

MSA-level variables that added information improves the fit; though 

given the smaller sample size and the exclusion of rural respondents, it 

is not possible to directly compare the fit of these two models.

The last measure of fit was to examine the assumption that the logit 

models’ dependent variable (log-odds of employment) is a linear 

combination of our independent variables, using a LOWESS plot. 

Analysis of the plot proved that the models satisfied this assumption.

Table 2 reports the parameter estimates for volunteering and the 

interaction between volunteering and moderating variables. Detailed 

regression output can be found in appendix B; it suffices to mention 

here that the general relationship between all control variables and 

employment is in large part what would be expected based on the 

general research on employment outcomes and is generally stable 

across the three models. 
 16 Volunteering as a Pathway to Employment

volunteering has the strongest effect on individuals who live in rural 

areas (55% increase in odds) and individuals without a high school 

diploma or equivalent (51% increase in odds). importantly, we found 

that the relationship between volunteering and employment for 

individuals with no high school degree was independent of age. if one 

were to assume that groups that volunteer more are more likely to 

benefi t from volunteering, this effect could be surprising: non-school 

age individuals without a high school degree are 16% points less 

likely than other education levels. however, if volunteering does indeed 

grow social and human capital as we theorize, those groups with 

the least social and human capital may have the most to gain from 

volunteering. individuals without high school degrees are less likely 

than those with more education to have social networks that can 

assist in fi nding employment.

The estimates from the logit models reported above are in odds ratios, 

which indicate the percent increase in odds relative to a baseline group. 

An alternative way of understanding the association of volunteering 

on the likelihood of employment is transform odds ratios to predicted 

probabilities. in this context, the estimates indicate the change in 

probability of employment associated with volunteering. Whereas an 

odds ratio applies to all respondents equally, estimated changes in 

probability affect each respondent differently according to their baseline 

probability of employment without volunteering. figure 4 reports the 

average change in probability of employment across all respondents 

in specifi c subgroups of interest. The change in probability is reported 

relative to the overall probability of employment for non-volunteers in 

the given subgroup.

figure 3. Percent Change in Odds of Employment Associated with Volunteering

Main Effect Effect on: No HS diploma Rural 0

10

20

30

40

50

60

70

80

90

100

Percent Change in Odds

27%

51% 55%

Note: The percent change in odds was calculated by combining log-odds coeffi cient from the main eff ect with the coeffi cient from the interaction eff ect, and then 

exponentiating the results. 
The Corporation for National and Community Service 17

iii. ReSulTS

figure 4. Change in Probability of Employment Associated with Volunteering, by Subgroup

4%

0%

10%

20%

30%

40%

50%

60%

All Out 

of Work 

(unemployed and

 not in labor force)

Unemp. Not in 

Labor 

Force

No HS HS Grad Collegea Non-Rural Rural Rural

No HS

Veterans Opp. 

Youth

3%

5%

5%

5%

4% 8% 4% 9% 12%

4%

Probability of Employment

4%

0%

10%

20%

30%

40%

50%

60%

All Out 

of Work 

(unemployed and

 not in labor force)

Probability of Employment

Note: Arrows represent the average increase in probability from the non-volunteer employment rate associated with volunteering.

a Includes individuals wth some college and college graduates.

Starting with the main effect, the 27% increase in odds associated 

with volunteering translates to a 4 percentage point average increase 

in probability for all respondents, meaning the probability increased 

likelihood from 13% to 17% on average. This increase differs by 

subgroup. for the unemployed (those actively seeking work), the 

increase is 5 percentage points, up from the 45% probability of 

employment for the unemployed non-volunteers. looking at individuals 

without a high school degree, volunteering is associated with an 8 

percentage point increase on average, double that of high school 

graduates, and 4 percentage points higher than respondents with 

some college or a college degree. Rural volunteers on average see a 9 

percentage point increase in employment over non-volunteers, and if the 

volunteer did not have a high school degree, that increase is as high as 

12 percentage point increase. for veterans, volunteering is associated 

with a 4 percentage point increase in probability of employment, and for 

opportunity youth (ages 16 through 24, not in school and not working), 

that increase is 5 percentage points.
18 Volunteering as a Pathway to Employment

In addition, we found that after controlling for the covariates in our 

model, the relationship between volunteering and employment was 

stable over each year of our sample. The national unemployment rate 

varied dramatically from a low of 4.4% to a high of 10% over this time 

period, and as can be seen in figure 5, employment outcomes for nonvolunteers fluctuated quite a bit. Despite these changes, volunteering 

was associated with a consistent 4% point increase in likelihood of 

employment for each year. The results from our modeling were nearly 

identical when considering fluctuations in MSA-level unemployment 

rates. Despite large variation in unemployment across every MSA in our 

sample, the average increase in the probability of finding a job was 

remarkably stable at between 4 and 5% points. These findings suggest 

that volunteering has a stable relationship with employment regardless 

of the general economic conditions.

Figure 5. Change in Probability of Employment Associated with Volunteering, by Year

10%

0%

15%

20%

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Probability of Employment

Non-Volunteers Average Increase for Volunteers

Importantly, no other variables were found to substantially moderate 

the relationship between volunteering and employment. We explored 

the stability and constancy of our parameter estimates by running 

the individual-level model for different subgroups in a seeminglyunrelated regression (SUR) framework. We compared the association 

of volunteering and employment across different variable categories, 

and if a significant difference was found, we introduced an interaction 

term into the model to test for moderation. The variables we included 

in this test were gender, unemployment status (unemployed vs. not 

in labor force), age, race/ethnicity, student status, and sample year. 

These variables were identified as important moderators during our 

preliminary analysis. In two cases did we find substantial differences 

in volunteering. We found that the relationship between volunteering 

and employment on unemployed individuals was lower than for those 

not in the labor force when modeling the two groups separately, but the 

difference was not statistically significant and may be due to random 

Note: Based on All Out of Work
The Corporation for National and Community Service 19

III. RESULTS

Figure 6. Predicted Probability of Employment by Pregibon’s Influence

0

0 .2 .4

Predicted Probability of Employment

Pregibon’s Inuence Statistic

.6 .8 1

.05

.1

.15

.2

0

.01

.02

.03

.04

.05

0 .2 .4 .6 .8 1

Predicted Probability of Employment

Pregibon’s Inuence Statistic

Moderator Model MSA Model

N=70,535 N=43,596

fluctuations in the data rather than substantive differences. We also 

found that the relationship of volunteering and employment was much 

significantly lower in 2004 than for other years. However, when treating 

that year as a moderating variable, the model fit did not improve (as 

measured by the AIC and BIC) nor did a likelihood ratio test show any 

added information to our model. These results indicate that the stability 

of the volunteer effect across race and ethnic categories, time, MSAs, 

age, gender, and unemployment status, however modest, is relevant for 

a wide range of individuals.

Model Robustness

As described above, to test the robustness of our model, we conducted 

a number of sensitivity checks, including a detailed analysis of 

influential observations and tests for alternative specifications. To 

identify influential observations, we used Pregibon’s influence statistics 

(Pregibon, 1981), which is an approximation of Cook’s distance to a 

logistic model. The alternative specifications of our model included 

using different measures for volunteering, and testing for random 

effects at the MSA level.

Influential Observations. An observation is considered influential 

if removing it substantially changes the estimate of the model’s 

coefficients (Sarkar et al, 2011). Influential observations may also 

be outliers – observations that deviate from their expected outcomes 

and are poorly explained by the model. Either situation can indicate 

omitted variables that could better predict their outcomes and reduce 

their influence. In our logit model, all individual-level variables are 

categorical, and therefore our concern is with influential covariate 

patterns, particularly if many observations share the same pattern. 

We used Pregibon’s (1981) influence statistic to identify these patterns, 

and in general, high levels of this statistic indicate that the specific 

covariate patterns are overly influencing the regression parameters 

given their actual weight in the dataset. Figure 6 shows two scatter 

plots of the predicted probability of employment by Pregibon’s influence 

for all observations from the Moderator and MSA models reported above. 

We analyzed influential observation for all three models. However, only 

the Moderator and MSA models are presented below, as the Base model 

is nested within the Moderator model. 
20 Volunteering as a Pathway to Employment

There were no covariate patterns in the Moderator model that had 

influence statistics over 0.17 and only 560 observations were over 0.1. 

Removing these observations resulted in no substantial impact in the 

model’s parameters, though model fit improved as measured by the AIC 

and BIC (with influential observations AIC/BIC = 42755.39 / 43103.62, 

without influential observations AIC/BIC = 42653.58 / 43001.5). For 

the MSA model, no covariate patterns had influence statistics over 0.05 

and only 437 observations over 0.01. Removing these observations 

did not result in any substantial parameter change, though model fit 

improved again (with influential observations AIC/BIC = 27284.77 

/ 27623.4, without influential observations AIC/BIC = 25709.06 / 

26038.43). Although fit improved in both models, because the coefficient 

on the volunteer measure did not change substantially, we retained 

these observations in the models and concluded that no observations 

substantively over-influenced our models.

Alternative Measurement of Volunteering. The first alternative 

specification we conducted was to measure volunteering in different 

ways. We have two observations for each respondent in a two-year 

period, and the respondent answered whether they volunteered for 

the 12 months prior to the measurement point. For our modeling we 

considered an individual as having volunteered if they volunteered 

during Year 2. This includes individuals who had a long history of 

volunteering (volunteering during Years 1 and 2, as well as any prior 

years), as well as those that volunteered for the first time during Year 2. 

Individuals in the former group may have volunteered prior to becoming 

out of work, and therefore they may be inherently different than those 

individuals who started volunteering after becoming unemployed. The 

latter group may have more motivation to use volunteering to find work. 

Therefore, testing the different effect on these groups may proxy some 

of the unobservable characteristics that put our main models at risk of 

selection bias outlined earlier.

Individuals who only volunteered during Year 2 (“new” volunteers) 

represented 7% of our sample, while individuals who volunteered during 

both Years 1 and 2 (“existing” volunteers) represented 15% of the 

sample. New volunteers had 21% higher odds of finding employment at 

the end of Year 2 (see table 3 for results of volunteer variables). Based 

on this model, new volunteers have a statistically significant 13% 

increase in odds of finding work, and existing volunteers see a 30% 

increase in odds. While the association of new volunteers is modest, 

it is statistically significant. This finding indicates that individuals 

who volunteered after being unemployed still have higher odds than 

non-volunteers. Without more information about the volunteer history 

of respondents, we could not explore this finding further. However, it 

strengthens the case that individuals who volunteer with a motivation to 

find employment do in fact find gains associated with volunteering. 

Table 3. Odds Ratios for Employment, All Volunteers, New Volunteers, and Existing Volunteers

Group Odds Ratio

All volunteers 1.27**

New volunteersa 1.13*

Existing volunteersb 1.30**

* Indicates statistically significant at p<0.05, ** at p<0.001

a

 New volunteers are individuals who volunteered during Year 2 but not during Year 1. 

b

 Existing volunteers are individuals who volunteered during both Years 1 and 2.
The Corporation for National and Community Service 21

III. RESULTS

Random MSA Effects. There may be particular aspects within an area 

that influence employment opportunities and volunteering that we 

were unable to account for with our data. One common way to address 

this is to specify the model with random effects by geographic area, 

and specify either a random intercept or a random coefficient on the 

volunteer variable. A significant random intercept would indicate that 

the average odds of finding work, independent of other covariates, 

fluctuate by geography in a manner not explained by other variables 

in the model. A significant random coefficient would indicate that the 

effect of volunteering on finding work fluctuates by geography in a 

manner not explained by other variables in the model.

The lowest geographic level we have is the MSA, and as stated above, 

this is not available for all observations. We modeled a random effects 

model with an intercept and the volunteer measure to test for a random 

intercept, and then again to test for a random intercept and a random 

coefficient. In the bivariate model, the intraclass correlation coefficient, 

which determines the amount of variation due to membership in a 

particular MSA, was statistically significant at the p<0.01 level, but 

practically small (0.007); when modeling a random coefficient with 

the covariates from our base model, the coefficient shrank to 0.003 

(Chi2=3.29), though still significant at p<0.05. Given the size of the 

coefficients and the sensitivity of Chi2 statistics to sample size, these 

results indicate that a random intercept did not add any information. 

Similarly, the standard deviation on the random coefficient for 

volunteering in a bivariate model was statistically significant at the 

p<0.05 level, but with a practically small standard deviation (0.02), 

indicating a random coefficient was not necessary. 

Combined with the fact that none of the MSA-level variables moderated 

the effect of volunteering, this finding indicates that the effect of 

volunteering on finding employment for out of work individuals is stable 

across geographic areas. It could be that the community-level aspects 

that influence volunteering are at a more local level. Our dataset did not 

allow us to explore this possibility.
22 Volunteering as a Pathway to Employment

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The Corporation for National and Community Service 23

IV. DISCUSSION

Although cause-and-effect cannot be established, the results of this 

study suggest a statistically significant and highly stable association 

between volunteering and employment. The overall relationship remains 

consistent across each year of the study period, suggesting that 

irrespective of economic conditions volunteering may add an advantage 

to those seeking employment. Importantly, the relationship between 

volunteering and employment is stronger for certain subgroups – 

individuals who have low educational attainment, and those living in 

rural areas. Volunteering may serve to “level the playing field” for these 

individuals who typically have a more difficult time finding employment, 

especially during a recession.

To our knowledge, this research provides the most rigorous empirical 

study to date in the United States relating volunteering to employment. 

The study builds on recent research that suggests that communities 

with higher levels of civic health had weathered the economic downturn 

better than communities with lower levels of civic health (NCoC, 2012). 

Specifically, our study provides a necessary foundation for researchers 

to explore other aspects of the relationship between volunteering and 

labor market outcomes. The current study treated volunteering as a 

dichotomous measure; further research should identify whether the 

degree of volunteering, in terms of time or other levels of commitment, 

influences employment outcomes. We were not able to fully examine the 

different types of volunteering and their relationship to employment. 

Identifying whether certain volunteer activities have a stronger 

relationship with employment would be valuable.

Although our data did not allow us to test this, we hypothesize that the 

mechanisms by which volunteering increase the likelihood of finding 

employment for the out of work include an increase in social capital 

and human capital. Our results suggest for example, that volunteering 

may indeed develop or strengthen an individual’s social capital, thereby 

providing volunteers access to opportunities for employment. This may 

be particularly important for individuals who have a greater social 

capital deficit, such as those lacking education. Educational deficits 

may impede the ability to develop professional ties that can lead to 

employment and preclude opportunities to demonstrate competence via 

academic internships. For individuals with low educational attainment, 

volunteering may promote the development of these professional 

associations. Similarly, for individuals residing in rural areas, 

volunteering may foster the development of stronger social networks, 

allowing greater access to employment opportunities if they exist. 

The acquisition of skills or knowledge and the performance of volunteer 

duties may also demonstrate higher levels of capacity, potentially 

making the volunteer more attractive to and productive for employers. 

Stronger effects for those with low educational attainment may indicate 

that these candidates are employable and bring new skills and new 

experience to the job market. The development of both social and human 

capital via the mechanism of volunteering may represent an important 

pathway to greater employment viability. The individual who volunteers 

may make himself or herself more marketable to employers, increasing 

the odds of finding work. If volunteering also provides added value to 

employers due to a higher skilled workforce, as we propose, there may 

also be benefits to society and the economy as a whole. 

While our findings suggest an association between volunteering and 

employment, our study has several limitations. Most importantly, our 

research did not establish a causal link between volunteering and 

employment. Our dataset did not allow the temporal identification of 

volunteering prior to employment, nor was it able to sufficiently control 

for all of the possible factors that could confound the relationship. 

Though both individuals with a history of volunteering and those 

who volunteered after being out of work had a higher odds of finding 

employment than those who did not volunteer, it is possible that by 

measuring volunteering we were actually measuring an unobserved 

IV. DISCUSSION
24 Volunteering as a Pathway to Employment

factor such as motivation. If this is the case, the relationship between 

volunteering and employment could be spurious. However, even in 

this situation, volunteering could bring added benefits to employment 

outcomes, to the extent that it can foster motivation and opportunity. In 

addition, while our study controlled for factors at the MSA level, there 

may be more factors associated with more local labor markets that 

influence the role of volunteering. Finally, we only examined employment 

as a dichotomous measure. Future research should explore whether 

volunteering has a relationship on full time versus part time status, 

occupation type, and earnings relative to prior employment, among other 

factors.

Future research should examine the causal mechanisms between 

volunteering and employment to further strengthen the findings 

presented in this report.
The Corporation for National and Community Service 25

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The Corporation for National and Community Service 27

Appendices

Appendices

Appendix A. Summary Statistics

Table A1. Descriptive Statistics for Individual-Level Variables

Variable Proportion of 

Total Sample

Proportion of 

Volunteers

Proportion of 

Non-Volunteers

Employed, end of Year 2* 0.13 0.15 0.12

Volunteer during Year 2 0.22 - -

Volunteer during Year 1 0.24 - -

Volunteer only Year 2 0.07 - -

Volunteer both Year 1 and 2 0.15 - -

White* 0.83 0.88 0.81

Black* 0.12 0.08 0.13

Asian* 0.03 0.02 0.03

American Indian* 0.01 0.01 0.01

More than one race 0.02 0.02 0.02

Hispanic* 0.08 0.04 0.09

Immigrant* 0.10 0.06 0.12

Female* 0.58 0.66 0.55

Full-time student* 0.03 0.04 0.03

Part-time student* 0.00 0.00 0.00

Rural 0.25 0.27 0.25

Never worked* 0.01 0.01 0.01

Laid Off, expecting to return to work 0.01 0.01 0.01

Age 16-19* 0.07 0.05 0.03

Age 20-24* 0.06 0.02 0.04

Age 25-34* 0.14 0.05 0.06

Age 35-44* 0.19 0.07 0.07

Age 45-54* 0.22 0.10 0.12

Age 55-64* 0.18 0.22 0.23

Age 65 and above* 0.14 0.50 0.45

Veteran* 0.15 0.15 0.15

Unemployed 0.16 0.16 0.16

Not in Labor Force, Retired* 0.55 0.65 0.52

Not in Labor Force, Other* 0.29 0.19 0.32
28 Volunteering as a Pathway to Employment

Appendix A. Summary Statistics

Table A1. Descriptive Statistics for Individual-Level Variables

Variable Proportion of 

Total Sample

Proportion of 

Volunteers

Proportion of 

Non-Volunteers

Parent* 0.10 0.12 0.10

No high school diploma or equivalent* 0.22 0.10 0.25

High school diploma or equivalent* 0.36 0.29 0.39

Some college* 0.24 0.29 0.23

College degree* 0.18 0.32 0.13

2003* 0.11 0.11 0.11

2004* 0.09 0.10 0.09

2005* 0.09 0.10 0.09

2006* 0.10 0.09 0.10

2007* 0.10 0.10 0.10

2008* 0.10 0.10 0.10

2009* 0.11 0.10 0.11

2010* 0.11 0.11 0.11

2011* 0.10 0.10 0.10

2012* 0.11 0.10 0.11

All statistics unweighted. N=71,671. Statistics for sample used in Base and Moderator models.

* indicates statistically significant difference between volunteers and non-volunteers at p<0.05.

Table A2. Descriptive Statistics for MSA-Level Variables

Variable Total Sample Volunteers Non-Volunteers

Unemployment rate 6.89 (2.70) 6.76 (2.63) 6.93 (2.71)

Percent change in unemp. rate from prior year 3.54 (2.11) 2.90 (2.09) 3.74 (2.11)

Proportion no HS degree or equivalent 0.16 (.05) 0.15 (0.05) 0.16 (0.05)

Proportion HS degree or equivalent 0.28 (0.06) 0.28 (0.06) 0.28 (0.06)

Proportion some college 0.28 (0.04) 0.28 (0.05) 0.28 (0.04)

Proportion college degree 0.29 (0.07) 0.29 (0.07) 0.28 (0.07)

All statistics unweighted. N=43,596. Statistics for sample used in MSA model.

Numbers represent means, standard deviations in parenthesis.

* indicates statistically significant difference between volunteers and non-volunteers at p<0.05.

(continued)
The Corporation for National and Community Service 29

Appendices

Appendix B. Detailed Regression Output

Base Moderator Model MSA Model

Variable Odds Ratio SE Odds Ratio SE Odds Ratio SE

Constant 1.00 (0.07) 1.02 (0.07) 1.50 (0.63)

Volunteer 1.27*** (0.05) 1.18*** (0.05) 1.25*** (0.06)

Black 0.71*** (0.03) 0.71*** (0.03) 0.70*** (0.04)

Asian 0.87 (0.08) 0.87 (0.08) 0.87 (0.09)

American Indian 1.00 (0.16) 1.01 (0.16) 1.34 (0.33)

Hispanic 1.03 (0.06) 1.03 (0.06) 1.04 (0.07)

More than one race 0.91 (0.11) 0.90 (0.11) 0.88 (0.12)

Immigrant 1.15* (0.06) 1.14* (0.06) 1.17** (0.07)

Female 0.75*** (0.03) 0.75*** (0.03) 0.76*** (0.03)

Full-time student 1.04 (0.10) 1.02 (0.10) 0.98 (0.11)

Part-time student 1.63** (0.29) 1.63** (0.29) 1.55* (0.32)

Rural 0.91* (0.04) 0.85*** (0.04) 

Never worked 0.50*** (0.05) 0.50*** (0.05) 0.48*** (0.05)

Laid Off, expecting to return to work 1.76*** (0.21) 1.75*** (0.21) 1.91*** (0.27)

Age 16-19a 1.64*** (0.17) 1.63*** (0.17) 1.64*** (0.21)

Age 20-24a 1.41*** (0.10) 1.41*** (0.10) 1.47*** (0.13)

Age 25-34a 1.21*** (0.07) 1.21*** (0.07) 1.21** (0.08)

Age 45-54a 0.77*** (0.04) 0.78*** (0.04) 0.76*** (0.05)

Age 55-64a 0.46*** (0.03) 0.46*** (0.03) 0.46*** (0.03)

Age 65 and abovea 0.22*** (0.02) 0.22*** (0.02) 0.20*** (0.02)

Veteran 0.89* (0.05) 0.89* (0.05) 0.89 (0.06)

Not in Labor Force, Retired 0.13*** (0.01) 0.13*** (0.01) 0.13*** (0.01)

Not in Labor Force, Other 0.21*** (0.01) 0.21*** (0.01) 0.22*** (0.01)

Parent 1.32*** (0.06) 1.32*** (0.06) 1.31*** (0.07)

No high school diploma or equivalentb 0.67*** (0.03) 0.66*** (0.03) 0.71*** (0.04)

Some collegeb 1.35*** (0.05) 1.35*** (0.06) 1.38*** (0.07)

College degreeb 1.90*** (0.09) 1.92*** (0.09) 1.91*** (0.10)

2004c 1.01 (0.07) 1.01 (0.07) 

2005c 1.08 (0.07) 1.08 (0.07) 1.05 (0.08)

2006c 1.02 (0.07) 1.02 (0.07) 0.95 (0.08)

2007c 1.01 (0.07) 1.01 (0.07) 0.96 (0.08)

2008c 0.91 (0.06) 0.91 (0.06) 0.92 (0.11)
30 Volunteering as a Pathway to Employment

Appendix B. Detailed Regression Output

Base Moderator Model MSA Model

Variable Odds Ratio SE Odds Ratio SE Odds Ratio SE

2009c 0.73*** (0.05) 0.73*** (0.05) 0.90 (0.13)

2010c 0.71*** (0.05) 0.71*** (0.05) 0.98 (0.09)

2011c 0.75*** (0.05) 0.75*** (0.05) 1.04 (0.09)

2012c 0.79*** (0.05) 0.79*** (0.05) 0.95 (0.08)

Unemployment rate (MSA) 0.93*** (0.01)

Percent change in unemp., 

from prior year (MSA)

 1.19 (0.25)

Proportion HS degree (MSA) 1.23 (0.67)

Proportion some college (MSA) 0.84 (0.48)

Proportion college degree (MSA) 1.02 (0.47)

Interaction terms 

No HS diploma 1.28* (0.15) 

Rural 1.31** (0.12) 

 

Unweighted N 70,535 43,596 43,596

Weighted N 61,370,003 43,013,471 43,013,471

Standard errors in parenthesis. * indicates significant at p<0.05; ** at p<0.01; and *** at p<0.001.

a The reference category for age variables is ages 35-44.

b The reference category for education variables is high school diploma or equivalent.

c For Base and Moderator, the reference category for year effects is 2003. For MSA model, the reference category is 2004.

(continued)
The Corporation for National and Community Service 31

Appendices

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