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MILLENNIALS AND VOLUNTEERING: SECTOR DIFFERENCES AND IMPLICATIONS FOR PUBLIC SERVICE MOTIVATION THEORY.

A new generation of younger workers is replacing retiring baby boomers in the public, private, and non-profit sectors. Those born after 1980, (1) referred as "Millennials," are reported to manifest distinct behaviors, values, and attitudes as compared to previous generations, and this is said to be presenting new and complex challenges in the workplace (Chester, 2002; Ng, Schweitzer, & Lyons, 2010). Generational transformation is also identified as one of the emerging factors that will shape public service in the near future (Perry & Buckwalter, 2010). Ironically, though, one of the most contested characteristics of the members of the Millennial generation is the nature of their social service orientation. While a number of experts characterize these young individuals as civically involved, socially conscious, interested in helping others and solving the problems of the world (Howe & Strauss, 1993; 2000; Greenberg & Weber, 2009), others portray them as the exact opposite, pointing to their narcissism, materialism, lower empathy, declining concern for others, and lower civic engagement (Twenge, 2006; 2013; Twenge, Campbell, & Freeman, 2012). This conflict has clearly been reflected in the "Generation We" versus "Generation Me" dichotomy, used by different scholars in their depictions of this young cohort. Such core values may certainly affect both social participation behaviors and the career choices of individuals.

Attracting talented individuals to public service careers to replace the retiring baby boomers is a current challenge for public and nonprofit agencies (Brookings, 2011). Public service careers have been depicted as a calling (Holzer, 1999), and the theory of public service motivation (PSM theory) has been developed by public administration scholars to explain what leads individuals to seek such roles (Perry & Wise, 1990). PSM theory posits that individuals with higher levels of public service motivation value intrinsic rewards as much as or even more than extrinsic rewards, and are compelled to public service careers more than others because of the service opportunities that these careers provide (Perry & Wise, 1990). Research has shown that there is a correlation between PSM and the appeal of working in the public and nonprofit sector, relative to the for-profit sector (Clerkin & Coggburn, 2012; Wright, & Christensen, 2010). PSM scholars have also shown that individuals in government and non-profit jobs are more likely to act in other pro-social ways (Brewer, 2003; Ertas, 2012; 2013; Houston, 2006; Lee, 2011).

Does this mean that the socially conscious and engaged "Generation We" would embrace careers in the government and nonprofit sector? Assuming that they are interested in helping others and solving communal problems, would they be more likely to volunteer in formal and informal ways than their older counterparts? Or, alternatively, would the self-important and materialistic "Generation Me" be less likely to be represented among the public and non-profit workforce compared to older generations? Since they are depicted as having declining concern for others and lower civic engagement, would they be less likely to volunteer than older workers? Recent studies have found that public and non-profit employees participate in social activities at a higher rate than their private-sector counterparts. Is this trend maintained for public and non-profit sector Millennial employees? Do they volunteer more than their private-sector peers? Are there differences with respect to their participation in formal versus informal settings? Are the differences more prominent in certain domains of volunteering activity?

This study examines these questions by using the 2010 Current Population Survey (CPS) volunteering supplement data. The CPS is a nationally representative sample of US citizens. Despite the increasing presence of Millennials in the workplace, there are relatively few comparative studies of participation behavior between younger and older workers across the public, for-profit private, and non-profit sectors. The purpose of this study is to examine the distribution of Millennial and older workers across sectors and compare the formal and informal social participation behaviors of the members of the current workforce. Similar to the bulk of previous research on the topic, the current study relies on PSM theory to establish a set of hypotheses and to guide the empirical work. Coursey et al. note that "by any reasonable definition, volunteering is associated and accounted for by similar theories and values as public work" (2011, p. 50). The next section provides a selective review of the existing literature. The data and methods section describes the 2010 Current Population Survey (CPS) volunteering supplement, the dependent and independent variables, and the analytical strategy. The analyses compare Millennials in different sectors, Millennials and older workers, and finally the domain or organization where formal volunteering took place, in order to examine whether the differences are more prominent in certain domains and whether Millennials have different preferences regarding organizational domain compared to older respondents. The penultimate section describes the findings from the analyses of the survey data; and the concluding section discusses the results and their implications for policy.

PSM THEORY, FORMAL PARTICIPATION, INFORMAL PARTICIPATION

Public service is often portrayed as "a calling" for individuals who respond to pro-social values and who desire to solve social problems, serve others, and improve public welfare (Holzer, 1999). After Perry and Wise (1990) developed a theoretical frame to describe how public service motives affect behavior, and Perry (1996) developed an empirical instrument to measure PSM, subsequent scholarly work has examined the associations between PSM and sector choice, job satisfaction, performance, organizational effectiveness, and (lately) pro-social behavior. Individuals in government positions are found to attach a high value to serving others and being useful to society (Houston, 2006; Lewis & Frank, 2002; Vandenabeele, 2008).

Studies that measure both PSM and behavioral outcomes are rare. Clerkin, Paytern, and Taylor (2009) surveyed undergraduate students and showed that students with higher levels of PSM were more likely to report a preference for engaging in charitable activity. Other scholars surveyed award-winning volunteers and found a relationship between PSM and volunteer experiences, and in choice of volunteering domain (Coursey, Brudney, Littlepage, & Perry, 2011). Another line of the literature compares individuals in different sectors in terms of their participation behavior and uses PSM theory to inform their hypotheses and to interpret their results. Using data from American National Election Survey (NES), General Social Survey (GSS), Current Population Survey (CPS), Americans Changing Lives survey, and Australian Survey of Social Attitudes, scholars found that public servants are significantly more likely to participate in civic organizations than other citizens (Brewer, 2003; Ertas, 2012, 2013; Houston, 2006; Lee, 2011; Rotolo & Wilson, 2006). This line of work, similar to the current study, makes an axiomatic appeal to PSM to explain the observed differences, so it is not clear to what extent the difference is due to motivational differences or on-the-job experiences.

Volunteerism is a complicated concept with many dimensions. One way to categorize different volunteering activities is by formality (Lee & Brudney, 2012). In order to investigate differences in participation further, participation in informal as well as formal venues is explored in the current study. Lee and Brudney (2012, p. 160) defined formal volunteering "as any contribution of unpaid time to the activities of organizations or established entities," and informal volunteering as "any assistance given directly--that is, not through a formal organization--to non-household individuals, for example, helping a neighbor or friend." Wilson and Musick (1997) demonstrated that volunteering in formal organizations has an effect on informal helping behavior, but the possession of a value set conducive to informal helping does not have an effect on formal volunteering. The authors also noted that while formal volunteering is widely studied, research on informal volunteering is scarce. As a result, the distinction between formal and informal volunteering with respect to their outcomes is not fully theorized and well understood. The reason for examining both types of participation in this paper is twofold. First, both informal and formal participation activities are based on the predisposition to help others, so the results may suggest additional support for the significance of motivation. Second, in the literature informal participation is commonly studied in connection with age. Age appears as a critical factor in volunteering literature. Volunteering rates appears to be lowest among the youth and the elderly and higher among middle-aged adults, creating an inverted-U shape for the relationship between age and volunteering (Musick & Wilson, 2008). Informal social participation is seen as a mechanism to develop social connections outside of formal membership structures, and it has been suggested that new generations of young people may prefer to participate in their communities though informal rather than formal channels (Roker & Eden, 2002; Yates & Younis, 1998). Based on previous research findings, the following hypotheses are developed (by sector):

Public-sector employees will have higher levels of formal participation than private-sector employees (H1a). Public-sector employees will have higher levels of informal participation than private-sector employees (H1b).

GENERATIONAL CATEGORIES

Shared historical or social life experiences are used to distinguish a generational group or a cohort from others (Jurkiewicz & Brown, 1998). This is a very difficult task, however, because social events affect all age groups, and as individuals get older their perspectives and values change; thus, in time, the young people of today may become more like the older people of today. Nevertheless, past research has suggested that culture and era, as well as the timing of societal events, affect the worldview, values, and personality of individuals, and scholars have investigated the differences between generational groups (Twenge, Campbell, & Freeman, 2012; Gentile, Twenge, & Campbell, 2010; Markus & Kitayama, 2010; Kasser et al., 1995). Those in their formative years are affected in a specific way by social events, since they are still in the process of developing their core values. Three generational groups currently in the workforce are Baby Boomers, Generation X, and Millennials or Generation Y. (2) Baby boomers are the cohort born during the post-World War Two baby boom, and their values and experiences were shaped by a series of events including the Cuban Missile Crisis, the assassinations of JFK and Martin Luther King, Jr., the civil rights movement, the moon landing, and the Vietnam War (Schuman & Scott, 1989). They are associated with privilege, affluence, optimism, entitlement, and questioning of authority and institutions (Schuman & Scott, 1989; Jones, 1980). Generation X is the cohort born after the baby boom. Events that shape their perspective include the Cold War, the Watergate scandal and Nixon's resignation, and changes in the economic landscape such as the oil embargo or inflation. This cohort is associated with cynicism, distrust of government and institutions.

Millennials are mostly the children of Boomers, and teenage Millennials are mostly the children of Generation X. This generation is born into a social environment where communication and social networking tools are commonplace. They use smartphones, laptops, e-mail, and social-networking platforms intensively (JGA, 2012), to the point that some researchers referred to them as the "history's first 'always connected' generation" (Talor & Keeter, 2010). Social events that took place in the formative years of the Millennial generation include September 11, the Enron Scandal, and several incidents of school violence (Twenge & Campbell, 2012). They are the most ethnically and racially diverse and also the most educated generation in US history.

PARTICIPATION BEHAVIORS OF MILLENNIALS

The magnitude and nature of Millennials' service orientation is highly contested. In early work they were characterized as a "craving community," in contrast to the apathetic position taken by GenX-ers, or the confrontational position taken by the Boomers (Debard, 2004; Howe & Strauss, 1993). Greenberg and Weber (2008), coining the phrase "Generation We," argued that the members of this group "believe in the value of political engagement and are convinced that government can be a powerful force for good" (p. 17). Later studies by Twenge (2006) and Twenge, Campbell, and Freeman (2012) found this generation to be characterized by a decline in civic interest, political participation, trust in government, concern for others, and belief in the importance of having a job that is worthwhile to society. Based on careful analyses of longitudinal data, they conclude that their results "generally support the 'Generation Me' view of generational differences rather than the 'Generation We'" (2012, p. 1). Millennials generally scored lower than previous generations in concern for others, concern for community, civic engagement, and social capital. Community service and volunteering is the one exception found to the "Generation me" portrayal (Twenge, Campbell, and Freeman, 2012, p. 1058).

Similarly, in the 2010 survey conducted by the Pew research organization, Millennial generation respondents were found to lag behind in political activism, but were also found to volunteer at rates comparable to their elders (Taylor & Keeter, 2010). Given the comparable and even higher levels of volunteering reported in different sources, the following hypotheses are constructed to reflect an expectation of increased likelihood of formal volunteering and informal community participation for Millennials (hypotheses by generation):

Millennials will have higher levels of formal participation than older respondents (H2a).

Millennials will have higher levels of informal participation than older respondents (H2b).

CAREER EXPECTATIONS OF MILLENNIALS

A conflicting mix of traits was also reported in different sources regarding the career expectations of Millennials. Earlier research suggested that Millennials value "meaningful work" the most, as opposed to "freedom" by Gen X, or "money, title, and recognition" by Boomers (Debard, 2004; Howe & Strauss, 1993). Studies that underlined the civic-mindedness of this younger generation interpreted this trait to be in line with the pro-community Generation We type arguments. Their desire to have a job that holds meaning for them is coupled with their desire to create a positive impact on people (Howe & Strauss, 2000; Lancaster & Stillman, 2010; Zemke, Raines, & Filipczak, 2000; DeBard, 2004). Later research also contests these statements. Twenge and Kasser (2013) showed that they express greater material desires than Boomers, but not as much as Generation X. The authors also found a discrepancy between youth's materialism and their beliefs about working hard, in that they placed less importance on hard work. Using two large national databases, Twenge, Campbell, and Freeman (2012) demonstrated that the importance of having a job worthwhile to society is, in fact, declining over the years. Millennials were found to be less likely than GenX-ers or Boomers to want a job that is worthwhile to society or that would help others. They also reported that they would be less likely to want to work in a social service organization or become a social worker. Studies by psychologists also argue for high rates of narcissism, materialism, and inflated expectations for Millennials (Twenge & Kasser, 2013). Such traits might influence the type of jobs and careers Millennials want or choose. The debate over whether the Millennials are altruistic and civic-minded or materialistic and self-absorbed is ongoing. Other scholars pointed out that focusing on attitudes rather than behavior might be misleading. Hais and Winograd (2011) and Winograd and Hais (2008) for example, argued that valuing extrinsic job motivators such as income or working conditions do not necessarily crowd out intrinsic motivators such care for others and creating change and that the younger generation is much more interested in working for organizations that are focused on fixing societal problems.

Research from other countries is limited, but growing (Ng, Lyons, & Schweitzer, 2012). Ng, Schweitzer, and Lyons (2010) examined career expectations and priorities data from more than 20,000 Canadian Millennial undergraduate university students. Among the factors they rated as the most desirable work-related attributes, the most important were opportunities for advancement, having good people to work with and to report to, and professional growth opportunities, while traditional attributes such as pay, benefits, and security ranked in the middle, and commitment to social responsibility ranked at the bottom. Taylor (2012) examined Australian Millennials, work preferences, and PSM levels and found that those with higher PSM are more likely to work for the public sector, as opposed to private sector. While no significant difference was observed between them and older workers in terms of preference for extrinsic motivators such as income or job security, they are found to place much higher value on job advancement and having an interesting job.

A Pew report has interesting findings regarding their attitudes toward government (Taylor & Keeter, 2010). According to the survey responses, Millennial respondents were significantly less critical of government, more likely to support an active government, and less likely to agree that government is often wasteful and inefficient, compared to any other age cohort. Their attitudes towards businesses were not drastically different than other cohorts. Another survey of US adults also reported that the Millennial respondents give the government more positive performance ratings and more strongly favor a significant role for government in addressing national challenges than other respondents (Molyneux, Teixeira, & Whaley, 2010).

In sum, while the jury is still out for the Millennials in terms of the nature of their service orientation, all research seems to agree that they volunteer at higher rates. Although limited, existing surveys that contain questions regarding their attitude towards government indicate that they are not particularly cynical about government. PSM research informs us that individuals with higher service values are attracted to careers in government and the non-profit sector because of the inherent public values or presence of opportunities for public service (Perry &Wise, 1990). Past research on civic engagement of employees by sector has found evidence of more engagement by public and non-profit employees. If Millennials with higher service motivations chose careers in the public and nonprofit sectors, they may exhibit even higher propensity to volunteer. Based on what we know about Millennials' volunteering behavior and evidence of higher levels of participation by public and non-profit sector workers, the following hypotheses are framed in order to investigate various associations by sector and generation:

Millennials in public service will have higher levels of formal participation than their private-sector peers (H3a).

Millennials in public service will have higher levels of informal participation than their private-sector peers (H3b).

VOLUNTEERING DOMAIN

Another way to categorize different volunteering activities is by the domain of the volunteer work (Cnaan & Wadsworth, 1996). Different motivations may factor into the volunteering decision in different types of organizations--such as educational or religious organizations--and therefore lumping all categories together may mask certain differences (Rossi, 2001). Studies typically rely on the functional-fit theory or the exchange theory to explain volunteerism. According to the functional-fit theory, individuals choose to volunteer in organizations that would provide them with opportunities to fulfil their motivations (Clary et al., 1994). The domain of the volunteering activity may reflect these inherent motivations. For example, a volunteer motivated by sympathy for animals may be more attracted to animal shelters, while another volunteer motivated by opportunities to influence a public policy area may choose a political action group. According to social exchange theory, social behavior is the result of an exchange process. The theory argues that individuals engage in behaviors that will provide them with benefits and rewards. As predicted by social exchange theories, individuals volunteer to satisfy both intrinsic and extrinsic motivational factors (Snyder, Clary, & Stukas, 2000; Haski-Leventhal Haski-Leventhal, 2009). For example, a volunteer motivated by opportunities to increase skills and networks for career advancement in the health sector may choose to volunteer in a hospital.

Domain differences are understudied in volunteering literature, especially in studies examining volunteering by sector domains (Coursey, Brudney, Littlepage, & Perry, 2011). Rotolo and Wilson (2006) found that private-sector workers volunteer less than public-sector workers in religious organizations, youth development, social and community service organizations, culture, arts, and education organizations, sports and hobby groups, civic and public safety organizations, and work organizations. Advocacy volunteering and volunteering in connection with health organizations or campaigns were exceptions. Lee (2011) found that government employees are more likely than nonprofit and for-profit employees to volunteer in educational organizations, but that they were no different than their for-profit counterparts with respect to social and community organization volunteering. Ertas (2013) found that government employees are more likely than their counterparts to volunteer in educational and political institutions. Research on volunteering domains by age is also limited. According to the Corporation for National and Community Service's Volunteering in America 2010 report, Millennials mentored, tutored, and taught youth at higher rates than the national average and volunteered for religious organizations at lower rates than older generation ("Volunteering in America", 2010). Millennials are also less likely to attend worship services and less likely to donate to religious causes (Taylor & Keeter, 2010). However, a recent study focusing on award-winning volunteers found that volunteers for religious organizations have higher mean PSM values than volunteers in all other domains (Coursey, Brudney, Littlepage, & Perry, 2011). In sum, there is some evidence suggesting higher levels of volunteering for public-sector over private-sector employees in various domains. Evidence also suggests that while their service orientation in general is debated, the Millennial generation do volunteer at higher rates. Since the data provides information on domains and some research papers show evidence of higher participation by public-sector respondents in several domains, following hypotheses predicting higher rates of volunteering are explored further:

Public-sector employees will have higher levels of participation in all types of organizations than private-sector employees (H4a).

Millennials will have higher levels of participation in all types of organizations than older respondents (H4b).

Millennials in public service will have higher levels of volunteering in all types of organizations than their private-sector counterparts (H4c).

DATA AND METHODS

Data from the Current Population Survey (CPS) was used to examine the research questions. The CPS includes comprehensive information on personal characteristics, employment status, and industry from a nationally representative sample of US citizens. Census Bureau staff conducted the September 2010 Volunteering survey as a supplement to that month's main survey, in order to collect information about the incidence of volunteering and other expressions of civic life in the United States. The sample from the supplement includes about 55,000 people working in one of the three sectors (public, private, and non-profit sectors). First, the formal and informal participation of individuals working in different sectors were compared by simple proportion tests. Then, separate logistic regression models were run to control for the effects of additional factors. Predicted probabilities were calculated for a series of respondent profiles to assist with interpretation of the models. Finally, separate models were used to examine participation in different types of organizations, to examine whether Millennials in public service have a preference for a particular organizational domain in their participation. Both full-time and part-time employees were included in the sample. A control variable to indicate fulltime status was included in the models to control for full-time/part-time work status. The estimates were weighted and standard errors adjusted to account for the complex survey design used by the CPS. After describing the variables, the results will be discussed for each hypothesis.

Independent Variable

The primary independent variables in the analysis are self-reported employment sector and a dichotomous variable indicating whether the respondent is a Millennial or an older respondent. In the CPS surveys, the individual class of worker code can take the following values: government--federal; government--state; government--local; private--for profit; private--non-profit; self-employed; incorporated and self-employed; or unincorporated. Two separate dichotomous variables were created to indicate whether the respondent is a public-sector employee or a non-profit-sector employee. Private-sector employees are the reference group in the models. The study focuses on comparing engagement among the employees of these three sectors, so self-employed and unemployed respondents were not included in the analyses. At the time of the survey, those who were born in 1980 and after were coded as Millennials by assigning the value 1 to the dichotomous indicator variable, leaving older respondents as the reference group. (3) In the regression models, an interaction term between government employment and Millennials is added to examine whether the effect of being a government employee varies for Millennials and others.

Dependent Variables

The supplement survey contains questions that focus on formal participation through or for organizations as well as informal participation or community engagement. To measure formal volunteering, respondents were asked whether, during the previous year, they had done any volunteer activities through or for an organization. This question was followed with a reminder about activities in educational institutions, since sometimes people don't think of activities they do for children's schools as volunteer activities. So the respondent was asked if they had done any volunteering in their child's school or youth organization as well. A dichotomous indicator was coded 1 if they reported volunteering (answered yes to either question), 0 if they did not. To examine informal community engagement, the responses to the following questions were used: (a) "Since September 1 (st) of last year, have you attended any public meetings in which there was a discussion of community affairs," (b) "Since September 1 (st) of last year, have you worked with other people in your neighborhood to fix a problem or improve a condition in your community or elsewhere?" Separate binary dependent variables were created by coding 1 if the respondent reported attending a meeting and working with others to fix a problem, 0 otherwise. The operationalization of informal participation measures is rather crude. Previous papers using CPS or similar survey data to study informal volunteering used the same or similar measures. The questions are placed sequentially after the formal volunteering questions regarding domain, task, and frequency; however this alone does not necessarily mean that those answering yes to attending public meetings or working with people in the neighborhood committed these activities in an informal setting. Attending a meeting held by the local government or working with neighbors through a Home Owners Association could lead a respondent to answer these questions affirmatively, even though these groups are not informal. To the extent this happens, validity of the measure is put at risk.

The types of organization for which volunteering took place were re-categorized from the originally recorded sixteen categories to five categories, viz. (a) religious, (b) civic (political, professional, or international, social or community service, environmental or animal care, public safety), (c) educational or youth service, (d) hospital or other health-related, (e) other organizations (sports, hobby, cultural or arts, and other types).

Control Variables

Other demographic or socio-economic control variables that might influence participation behavior were included in the models. Wealth, education, and position in social networks might affect participation. A dichotomous variable indicating college degree or more was added, with less than college degree as the reference category. The CPS collects categorical income data by asking respondents which range best represents the combined income of all household members and from all sources of income (e.g., money from jobs, net income from business, farm or rent, pensions, dividends, interest, social security payments, and any other money income received by family members who are 15 years of age or older) for the past year. The midpoint from each category is used to create an approximately continuous income variable and a logarithmic version is included in the models. Blacks and Hispanics historically have lower rates of voting than Whites, which might be reflected in their other participation. The race variable consists of three groups: Black non-Hispanic, all other races, and Hispanics; with White non-Hispanic as the reference group. Some research suggests that women may be more engaged in informal social participation activities compared to men. The gender variable was coded with 1 for female and 0 for male. To control for the effects of marital and family status, two dummy variables for married respondents and respondents with children under 18 were also included. In addition, since more time working means less time which people can spend engaging in civic activities, a dummy variable for those who work 40 or more hours is included in the multivariate models.

RESULTS

Table 1 shows the distribution of all variables by sector. About 17% of the sample is employed in government, 8% in the nonprofit sector, and the remaining 75% works in the private sector. Among Millennials, a higher proportion reported working in the private sector (82.7%) and a lower proportion reported working in the government and non-profit sectors (10.8% and 6.5% respectively) than older respondents. The first two hypotheses predicted that public-sector employees will have higher levels of formal (H1a) and informal participation (H1b) than private-sector employees. As predicted, government employees report significantly more participation than their private-sector counterparts in both formal and informal venues. For example, 40% of government workers, compared with about 26% of private-sector respondents, reported engaging in volunteer activities through or for an organization. That is a significant difference of 14 percentage points between public and private-sector respondents.

The raw differences are also notable for informal participation variables. The proportion of government employees attending public meetings to discuss community affairs is more than twice that in the private sector. Between government and private-sector employees, there is a 6 percentage point difference in the proportions of those working with other people in the neighborhood to fix a problem or improve a condition in their community. Individuals in the public sector are also more likely to report attending public meetings and working with other people than their private-sector counterparts. When participation in organizations in different domains are investigated, government employees are more likely to report participation in educational organizations. Results also show that non-profit employees appear to be more similar to government workers than private-sector workers in terms of their formal and informal participation.

A series of Kruskal-Wallis one-way analysis of variance tests, which were corrected for tied ranks, were significant for overall volunteering and two measures of informal participation, suggesting real differences in formal and informal social participation among three groups. The magnitude of these estimates suggests small associations (4). When organizational domain is considered, initial differences among sectors are significant for civic, educational, and health organizations, but not for religious and other types. These simple comparisons suggest some support for first two hypotheses (H1a and H1b); however, table 1 also shows other differences in demographic and background variables between sectors. For example, government workers are more likely to be college graduates, married, female, and work longer hours than their private-sector counterparts, and non-profit workers are less likely to have children and report working fewer hours than private-sector workers. The upcoming regression models control for these characteristics that may affect an individual's likelihood of social participation.

In table 2, participation rates are presented separately for Millennials and older respondents in different sectors, except the first column. The first column entitled "total" shows the proportions of Millennials and older respondents in all sectors reporting volunteering in formal and informal venues. Next two hypotheses expected that Millennials will have higher levels of formal participation (H2a) and higher levels of informal participation (H2b) than older respondents. Overall, proportions suggest significant effects in the opposite direction, not supporting these hypotheses (Pearson chi2(1) = 405.5 Pr = 0.000).

About 23% of Millennials reported volunteering for a formal organization compared to 32% of older respondents. (5) The proportion of Millennials attending a public meeting or working with neighbors was less than half the size of the proportion of older respondents (13% and 11% compared to 5% and 4.8% consecutively). When the types of organizations are examined separately, the distributions reveal that the higher proportion of formal volunteering by older workers are primarily driven by volunteering for a religious organization. Millennials have higher levels of participation in civic, educational, health, and other organizations than older respondents, mostly confirming hypothesis 4b, predicting that they will have higher levels of participation in all types of organizations than older respondents (H4b). However, the effect size statistics suggest very small associations. The same relationships will be explored further in the upcoming regression models that allow for inclusion of additional controls.

Turning our attention to separate distributions for Millennial and older respondents in different sectors, among Millennials, about 33% in government jobs reported volunteering for an organization, compared to 20% working in the private sector, and 37% working in nonprofit sector. The Wilcoxon test to correct for sample size indicates a small effect size, but the Kruskal-Wallis test of difference is significant. This indicates support for Hypothesis H3a, which stated that Millennials in public service will have higher levels of formal participation than their private-sector peers. Among older respondents, about 41% in government jobs reported volunteering for an organization, compared to 28% working in the private sector, and 47% working in nonprofit sector. Similar tends are observed for informal participation measures. Among Millennials, about 10% in government jobs reported attending a public meeting, compared to 4% working in the private sector, and 11% working in the nonprofit sector. Although small, significant differences provide support for hypothesis 3b, which predicted that Millennials in public service will have higher levels of informal participation than their private-sector peers. Among older respondents, about 21% in government jobs, 10% working in the private sector, and 21% working in the nonprofit sector had attended a public meeting. Among Millennials, about 7% in government jobs, 4% working in the private sector, and 9% working in the nonprofit sector worked with neighbors. Among older workers, about 15% in government jobs, 9% working in the private sector, and 16% working in the nonprofit sector worked with neighbors. The bivariate differences between participation in different organizational domains are significant in educational organizations only for Millennials, and significant in educational and health organizations only for older respondents working for the government. The effect sizes are not notable. These initial bivariate comparisons provide only partial support for hypothesis 4a, which was that public-sector employees will show higher levels of participation in all types of organizations than private-sector employees.

Results from the Logistic Regression Models

Table 3 presents results from the logistic regression models. The dependent variable in the first column is formal volunteering (through or for an organization), and the dependent variables in the next two columns are the measures of informal community engagement (attending public meetings in which there was a discussion of community affairs, and working with other people in the neighborhood to fix a problem or improve a condition in the community). (6) To test whether the relationship between working in the government sector and formal or informal participation is moderated by Millennial status, an interaction term between government employment and Millennial status is included. It is tricky to interpret interaction effects in non-linear models, because interactions involve exploring differences in differences. In a logistic regression model, the coefficients are already in the form of log odds, or, if exponentiated, in the form of odds ratios. This means that "the marginal effect of a change in both interacted variables is not equal to the marginal effect of changing just the interaction term" (Norton, Wang, & Ai, 2004, p. 154). Rather than relying solely on the statistical significance in the regression output, calculation of marginal effects is recommended to examine both multiplicative and marginal effects, because they test slightly different associations and may show effects in different directions (Buis, 2010). The entries in the table 3 a are from the original model and in the form of odds ratios. The entries in table 3b show the marginal effects.

The odds ratio for government employee is 1.39, which means that holding all else constant, the odds of volunteering through or for an organization is about 1.4 times higher for older government workers compared to older private-sector workers. This difference refers only to old workers in these two sectors, because there is an interaction effect between government and Millennial variables in the model. The interaction effect indicates by how much the effect of being a government employee differs between Millennial and old employees, but does so in multiplicative terms. Using government as the focus variable, the effect of working in the government sector on formal volunteering is seen to be greater for older respondents. The government employee coefficient represents the main effect for older respondents. If an older respondent is a government employee, the odds of volunteering in an organization are about 1.4 times as large as the odds for an older private-sector employee. Using government employment as the focus variable, the effect on formal participation of being a government employee is slightly smaller for Millennials compared to older respondents. (7) The same pattern holds for informal participation models; however, the interaction term for Millennials and government employment is not significant. These models also demonstrate that nonprofit workers are more similar to government workers than they are to private-sector workers.

The next table shows the marginal predicted probabilities along with standard error with regards formal and informal volunteering for Millennial and older individuals in the public and private sector, holding other variables at their means. (8) All differences in the table are significant. For example, the odds of formal volunteering for older private-sector workers is .28, while that odds for Millennial private-sector workers is .20, a difference of .08. The marginal effect of being a Millennial for government employees is .331. The odds of formal volunteering for older government workers is .388, a difference of -.05. The marginal odds for Millennials were lower than older respondents in either sector. However, when Millennials in public service and private sector are compared, Millennials in public service clearly have higher levels of formal participation than their private-sector peers (.331 and .197 respectively), confirming hypothesis H3a. Millennials also have lower informal participation measured both ways compared to older workers--however, note that the data did not fit the first informal participation model (attending a meeting) well. Millennials in public service have higher levels of informal participation, measured at least in one way, than their private-sector peers, partly confirming Hypothesis H3b.

To ease interpretation, a series of predicted probabilities are also calculated for hypothetical individual profiles. For example, for a single white college-graduate older male respondent working in the private sector, the probability of volunteering for an organization is about 31%. If the same respondent is a Millennial, the predicted probability of volunteering decreases to 27%. If the same individual is a government employee, the predicted probability of volunteering increases to 38% for older respondents and 34% for Millennial respondents. Predicted probabilities for informal participation follow the same pattern. For example, the probability of working with others is about 12% for a single white college-graduate older male respondent working in the private sector, but drops to about 6% for a Millennial counterpart. If the respondent works for government, the probability increases to 16% for older respondents and 8% for Millennials. These findings provide partial support for hypothesis H3a (Millennials in public service will have higher levels of informal participation than their private-sector peers), but not hypothesis H2b (Millennials will have higher levels of informal participation than older respondents).

Results from the Logistic Regression Model by Organization Type

In order to examine volunteering in organizations in different domains, separate logistic models were also run. The results presented in Table 4 provide more detail about formal participation behavior across generations and sectors. Note that the question about the domain of volunteering activity is asked only to those who had volunteered last year. The results do not confirm hypothesis 4a. Public-sector employees do not have higher levels of participation in all types of organizations than private-sector employees. The models show that the higher formal participation of government employees is driven by their participation in educational organizations, after controlling for other demographic and background factors. This is in line with previous research, using the American's Changing Lives survey, that found government workers are more likely to volunteer for educational organizations (Ertas, 2012) and CPS (Lee, 2011).

The Millennial-government interaction is for religious, educational, and civic organization volunteering. If the respondent is an older government employee, the odds of participating in an educational institution are about 1.3 times as large as the odds for others in the sample. The odds ratio on the Millennial indicator is also positive and significant. The predicted probabilities suggest that Millennials in both the private and public sectors have a higher likelihood of volunteering in an educational institution than their older counterparts. Government employment increases the probability of participation for both older and Millennial respondents. Older government employees are significantly less likely to report volunteering in health organizations, but Millennials in both private- and government-sector employment are more likely to volunteer for a health organization than older respondents. Individuals tend to volunteer in those domains from which they consume services (for example, parents volunteer in schools). Does this mean that Millennials volunteer in organizations in this domain because of elderly family members? Individuals also volunteer to satisfy intrinsic and extrinsic motivations. Are there more volunteering opportunities in these types of organizations for younger individuals? Do they offer tasks and challenges that appeal to younger individuals? Could this simply be a function of higher participation rates in the various health run/walks? There is no obvious interpretation of this finding. In addition, the regression model shows that Millennials in both government and private sectors are less likely to volunteer for or through a religious organization than their older counterparts. This is in line with research that found religion is a lower priority for Millennials (Taylor & Keeter, 2010).

Marginal predicted probabilities are listed in table 4b. Millennials do not have higher levels of participation in all types of organizations than older respondents, so Hypothesis 4b is not supported. Only two differences are significant. Millennial private-sector workers are significantly more likely to volunteer in civic and health organizations compared to older private-sector workers. Millennials in public service are significantly more likely than their private-sector counterparts to volunteer only in educational organizations. Volunteering in any other type of organization is not significantly different between Millennials in government or the private sector, so hypothesis 4c is only partially supported, since it predicted higher participation in all domains. Finally compared to older respondents, Millennials in both sectors are significantly less likely to volunteer for or through a religious organization.

DISCUSSION AND CONCLUSION

This study contributes to and expands the current scholarship in two ways. First, both formal and informal forms of participation for both Millennials and older respondents was examined. Second, the realm of formal volunteering was further examined via the domain of the volunteering activity. An extensive literature has discussed the benefits and effects of volunteering for individuals, society, and the economy. Generational differences have also been a subject of interest to scholars from a variety of fields. As the "Generation We" versus "Generation Me" dichotomy indicates, one of the most contested topics concerning the Millennial generation is the nature of their social-service orientation. A growing literature in the field of public administration points to a higher commitment to public service and higher valuation of intrinsic motivations for public servants compared to their private-sector counterparts. Relying on the subsequent research in public administration, which emphasizes pro-social behaviors in non-work domains as the theoretical background, formal and informal participation activities of Millennials and older respondents working in the public, private, and non-profit sectors were examined. The results from this research showed that government employees, as well as those working in the non-profit sector, participate more in most formal and informal activities, but that the effect on formal participation of being a government employee is smaller for Millennials. Millennials in public service have higher formal participation compared to their peers in the private sector, but not as much as their older counterparts in the public sector.

The results also suggest that the domain of formal volunteering activity matters, in that, compared to older respondents, Millennials in the private sector have a higher likelihood of volunteering in an civic and health institution, and all Millennials have a lower likelihood of volunteering through or for a religious organization. This research also aimed to shed light on informal social participation behaviors of Millennials and older respondents in different sectors. If PSM is the motivation for people's sector choice and higher involvement in formal venues, those with higher PSM may act more socially in informal venues as well. The results showed that Millennials in public service have higher participation in informal venues compared to their peers in the private sector, but not as much as their older counterparts.

Before discussing implications and contributions, some limitations warrant attention. As with all research papers, this one has certain limitations which could be addressed in future studies. First, as discussed, higher participation in both formal and informal venues implies that PSM is the rationale underlying participation behavior across individuals working in different sectors; but on-the-job experiences in a sector may certainly affect values and motivations as well. Future research with proper panel data could investigate the extent to which motivations are stimulated and cultivated by on-the-job experiences. Similarly, future research with more extensive motivation data may be able to examine whether the relationship is driven by certain dimensions of PSM and whether these dimensions are different for Millennials. The current study could not investigate this, since the CPS does not contain specific questions on individual motivations. Third, as discussed earlier, some measures such as the informal participation questions are not clear as they could be. Exploring participation with alternative data sets and methodologies will improve our understanding. Finally, to simplify analyses, Millennials were compared with other workers of all age groups. Older respondents in the comparison group include a wide range of ages. In addition, the data was collected at one particular time and such cross-sectional data does not allow comparison of young public, private, and non-profit sector workers to the young workers of one or two decades ago. Some differences may be directly related to age, and not generation, and may disappear as the young workers grow older.

Findings suggest implications for both public agencies and organizations looking for volunteers to provide their services. For public agencies, recruitment and management strategies that appeal to individuals' public service motivations are recommended. The consistently higher levels of participation for public-sector workers persisted even after controlling for other factors, and not only in formal but also in informal ways. This suggests a real motivational component in observed differences. The observed effects were smaller for Millennials in general; however, the gap between Millennials and older respondents in public service was much smaller. This may indicate that these careers still appeal to those who value intrinsic rewards and service opportunities. For those already in these careers, the value of service may be highlighted and articulated as part of human resource initiatives to improve retention rates. Other researchers have suggested simple but effective strategies to stimulate public service values in individuals, such as being in contact with or having social information on the recipients of the service (Grant et al., 2007). Through careful experiments, Grant et al. (2007) demonstrated that managers may enhance employee motivation by simply creating opportunities for employees and beneficiaries to interact. Even a simple video message by the beneficiaries of an employee's work is found to effect employee motivation as well as performance (Grant & Hofmann, 2011). Previous research has found that a sizable proportion of the population (an estimated twenty-five to thirty percent) volunteers for government organizations (Brudney & Kellough, 2000). Since Millennials in public service volunteer at higher rates than those in for-profit private sector, highlighting the presence of voluntary programs and opportunities in the work settings may be another strategy to emphasize service values and venues for employee and beneficiary interaction.

For potential future public servants, public agencies may incorporate information that appeals to individuals' public-service motivations in their service calls. Since Millennials are reported to value work/life balance as well as having good people to work with and to report to, and do not seem to be particularly cynical towards government, government agencies may wish to approach younger people or students and encourage them to actively consider careers in public service. One strategy to demonstrate how such careers bring together people who share a similar public work ethos could involve targeting social service program volunteers and students in graduate programs. Many members of the Millennial generation are returning to school to consider a career change and to get degrees in fields such as public administration, law enforcement, education, and not-for-profit administration. Holzer (1999) suggested bringing "award-winning, creative, dedicated public servants from all levels into the classroom to communicate their sense of commitment and accomplishment," in order to improve the image of government and to inspire individuals to work in therein. Public service calls which focus on the character of dedicated public servants and the creative and innovative ways in which they work, which emphasize the skills and knowledge, can be disseminated in new forms of social networking and communication channels and through targeted venues such as graduate programs and volunteer projects.

Similarly, voluntary organizations can utilize the same type of cultural fit and value congruence strategies as a volunteer recruitment tool. Previous research has shown that being asked is the most common way for a person to become a volunteer (Wilson &Musick, 1999), and Millennials want to get involved with causes they care about and are open to sharing ideas and experiences about non-profits and their causes with friends and family (JGA, 2012). Charitable organizations in need of volunteers may also encourage individuals currently engaged with their activities to invite their friends and family to join in with them. They may also make targeted calls to workers in the public agencies. Many public institutions have annual pledge drives to donate to several causes. Similar initiatives can be developed to present workers with an opportunity to pledge volunteer time in place of or in addition to donations. Emerging evidence suggests that making a pledge or commitment may lead to behavior change (Cotterill, John, & Richardson, 2013).

Several questions remain to be explored in further research. It is difficult to discuss the direct relationship between voluntary outcomes and values without information on PSM. Richer data that contains measures of PSM, work sector, and volunteering behavior is needed to disentangle the relationship between service values, sector choice, and behavioral outcomes. In addition, the data used in this project were collected from participants at one point in time, so longitudinal projects would add significantly to our understanding of the factors that contribute to potentially changing public service motivation values and civic participation behaviors. It would also be important to monitor the change in values and behavior of the Millennial generation over time. The observed differences in volunteering domains also suggest additional avenues of future research. A better understanding of the factors that lead Millennials to volunteer in certain types of organizations over others may help voluntary organizations to create volunteering prospects that are more likely to engage and retain that group.

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NOTES

(1.) Different studies use beginning birth dates from the early 1980s for categorizing members of Millennial and other generations. Twenge (2006) and Howe & Strauss (2000) used the year 1980 as the cut off.

(2.) "Generation X" is typically defined as those born between early 1960s to the early 1980s based on US Census Bureau classifications (http://aallnet.org/caucus/genxy/censusgenx.pdf). "Baby Boomers" were those who were born after 1946 to early 1960s (http://www.census.gov/prod/cen2010/briefs/c2010br-09.pdf).

(3.) As previously noted, age is a critical factor in volunteering, especially in informal venues. Musick and Wilson (2008) showed that the volunteering rates appears to be lowest among the youth and the elderly and higher among middle-aged adults. I experimented with including age and age squared, rather than the Millennial employee-older employee dichotomy, in a series of models to further explore the effects of age. First, the original model with the Millennial indicator and the Millennial government employee interaction was fitted, with the addition of age and age squared. Main sector effects remained similar to original model. High VIF and tolerance values suggested existence of multicollinearity. Next, models including age, age-squared, and an age government interaction term were fitted. Interaction term was not significant. Finally, models with simple age and squared age terms were used. Slight curvilinear effects, similar to Wilson and Musick's findings, were observed in informal volunteering models, but not in the formal volunteering model. Predicted probabilities for participation in informal volunteering activities were increasing until age 60 and started to slightly decline afterwards. The increase appeared to be pretty linear up until age 35. The difference was larger for public sector respondents and the differing effects of age widened between public and private sector with age. In sum, comparison of the results indicated no major concern against using the Millennial employee-older employee comparisons.

(4.) It is important to consider the large sample sizes used in these analyses when interpreting statistical significance. The high proportion of private-sector respondents compared to respondents from other sectors may lead to statistically significant results for even very small differences. The magnitude of these estimates should be considered carefully when interpreting the results, so Cramer's V is calculated to measure the strength of the associations between sector and each outcome measure. Small associations were observed between employment sector and formal volunteering (Cramer's V=0.1490), attending meetings (Cramer's V=0.1483), and working with other people (Cramer's V=0.943). The separate tests for Millennials and older employees produced similar small associations for formal volunteering and attending meetings and weaker associations for working together with other prople.

(5.) To keep comparisons simple, Millennial workers are compared to all others in this paper. When gen X and baby boomers are examined separately, both had higher levels of formal and informal participation than Millennial workers. Their participation levels were similar to each other, especially for formal participation. 22.86% of Millennials reported volunteering through or for an organization, compared to 32.34% of gen X and 31.13% of baby boomers. Boomers were slightly more likely to engage in informal social participation than gen X. About 5.06% of millenials, 11.46% of genX and 14.78% of boomers attended a public meeting. About 4.84% of millenials, 9.99% of genX and 12.06% of boomers worked with other people in the neighborhood.

(6.) Since the models are weighted to take the survey sampling design into account, traditional Hosmer-Lemeshow goodness-of-fit tests were not appropriate to determine whether the model fit is adequate. Following Archer and Lemeshow (2006), the Stata ado-command svylogitgof was used to estimate the F-adjusted mean residual goodness-of-fit test. The test results suggested a good fit for the formal participation model (p=.242) and the second informal participation model (p= .444), but the data did not fit the first informal participation model well (attending a meeting, p=.001).

(7.) The values being exponentiated are the log odds from the logistic regression model. For example, [e.sup.(0.3356578 - 0.1980346 + 0.1666437)] = [e.sup.(0.304267)] = 1.36

(8.) Stata's margins and lincom procedures are used following the tips in Buis (2010).

NEVBAHAR ERTAS

University of Alabama at Birmingham
Table 1
Descriptive Statistics by Sector, CPS Volunteering Supplement

                     Government   Private   Non-profit   [chi] (2)
                     employees    sector    sector

                     (n=10,330)   (n=46,4   (n=4,676)
                                  81)
Percent of total     16.8%        75.6%      7.6%
sample
Percent of           10.8%        82.7%      6.5%
Millennials
Percent of older     19.0%        73.0%      8.0%
respondents
Formal
participation
Volunteered for an   39.56%       25.74%    44.84%       742.6 (***)
organization
Religious            31.86%       31.41%    33.77%         2.593
organization
Civic organization   22.23%       24.11%    26.09%         5.755 (*)
Educational          32.56%       28.63%    22.28%        38.601 (***)
organization
Health                6.39%        8.59%    10.78%         7.516 (**)
organization
Other organization    6.96%        7.26%     7.08%         0.073
Informal
Participation
Attended public      18.72%        8.10%    18.71%       336.4 (***)
meetings
Worked with other    13.62%        7.67%    14.72%       119.1 (***)
people
Control variables
Age                  44.63        40.44     43.79
College graduate     50.30%       25.80%    52.44%
Female               57.24%       45.93%    67.75%
Has children         35.44%       34.73%    32.21%
Married              62.98%       53.22%    57.70%
Works 40 or more     74.35%       68.71%    60.94%
hours
White                72.82%       70.16%    78.98%
Black                11.85%        9.01%     8.68%
Other race           15.33%       20.84%    12.34%

Note: Last column presents the results of Kruskal-Wallis test that
compares the three groups in terms of their participation.
(***) p<0.01, (**) p<0.05, (*) p<0.1

Table 2
Formal and Informal Participation of Millennials and Older Respondents
by Sector and Type of Organization, CPS Volunteering Supplement

Millennials
                           Total    Government   Private      Non-profit
                                    employees    sector       sector

                                    (n=1,779)    (n=13,567)   (n=1,067)
Formal participation
Volunteered for an         22.86%   33.35%       20.31%       36.97%
organization
Religious organization     26.46%   24.61%       26.54%       28.70%
Civic organization         26.65%   24.41%       26.63%       30.18%
Educational organization   29.01%   34.25%       28.94%       21.60%
Health organization        10.11%    9.06%       10.21%       10.95%
Other organization          7.78%    7.68%        7.68%        8.58%
Informal Participation
Attended public meetings    5.06%    9.82%        3.96%       10.76%
Worked with other people    4.84%    6.77%        4.22%        9.41%
Older Respondents
                                    Government   Private      Non-profit
                                    employees    sector       sector
                                    (n=8,551)    (n=32,914)   (n=3,609)
Formal participation
Volunteered for an         31.87%   40.73%       27.82%       46.88%
organization
Religious organization     33.15%   33.07%       32.84%       34.91%
Civic organization         23.21%   21.86%       23.37%       25.17%
Educational organization   28.72%   32.28%       28.54%       22.43%
Health organization         7.90%    5.95%        8.12%       10.75%
Other organization          7.01%    6.84%        7.13%        6.74%
Informal Participation
Attended public meetings   12.75%   20.55%        9.76%       20.95%
Worked with other people   10.79%   15.02%        9.06%       16.22%

Millennials
                           [chi] (2)

Formal participation
Volunteered for an         129.6 (***)
organization
Religious organization       1.035
Civic organization           2.026
Educational organization     9.8 (***)
Health organization          0.248
Other organization           0.073
Informal Participation
Attended public meetings   163.5 (***)
Worked with other people     8.780 (**)
Older Respondents
                           [chi] (2)

Formal participation
Volunteered for an         540.5 (***)
organization
Religious organization       1.63
Civic organization           3.44
Educational organization    29.4 (***)
Health organization          7.2 (**)
Other organization           0.094
Informal Participation
Attended public meetings   279.1 (***)
Worked with other people    94.7 (***)

Note: Last column presents the results of Kruskal-Wallis test that
compares the three groups in terms of their participation.
(***) p<0.01, (**) p<0.05, (*) p<0.1

Table 3
Predicting Formal and Informal Participation, Logistic regression
results from CPS Volunteer Supplement

VARIABLES                  Formal
                           volunteering

Government employee         1.399 (***)
                           (0.0470)
Non-profit employee         1.829 (***)
                           (0.0802)
Millennial                  0.820 (***)
                           (0.0276)
Millennial government       1.181 (**)
employee
                           (0.0969)
Female                      1.285 (***)
                           (0.0319)
College or more             2.018 (***)
                           (0.0531)
Married                     1.213 (***)
                           (0.0345)
Has child                   1.494 (***)
                           (0.0389)
Black                       0.760 (***)
                           (0.0338)
Other race                  0.499 (***)
                           (0.0175)
Log income                  1.291 (***)
                           (0.0238)
Works 40 or more            0.728 (***)
                           (0.0197)
Constant                    0.0170 (***)
                           (0.00337)
Observations           62,669
F-adjusted test             1.279
statistic
Prob > F                    0.242

                       Informal Participation
VARIABLES              Attended meeting   Worked with
                                          others

Government employee         1.970 (***)        1.508 (***)
                           (0.0860)           (0.0723)
Non-profit employee         2.011 (***)        1.724 (***)
                           (0.116)            (0.108)
Millennial                  0.500 (***)        0.526 (***)
                           (0.0309)           (0.0334)
Millennial government       1.095              0.886
employee
                           (0.137)            (0.128)
Female                      0.944              0.858 (***)
                           (0.0343)           (0.0332)
College or more             2.020 (***)        1.739 (***)
                           (0.0792)           (0.0727)
Married                     1.223 (***)        1.216 (***)
                           (0.0513)           (0.0548)
Has child                   1.006              0.975
                           (0.0382)           (0.0394)
Black                       0.912              0.972
                           (0.0594)           (0.0665)
Other race                  0.573 (***)        0.582 (***)
                           (0.0324)           (0.0352)
Log income                  1.205 (***)        1.203 (***)
                           (0.0351)           (0.0375)
Works 40 or more            0.849 (***)        0.842 (***)
                           (0.0343)           (0.0359)
Constant                    0.0107 (***)       0.0114 (***)
                           (0.00337)          (0.00384)
Observations           62,251             62,263
F-adjusted test             3.216              0.991
statistic
Prob > F                    0.001              0.444

Odds ratios are presented in the table. Standard errors in parentheses
(***) p<0.01, (**) p<0.05, (*) p<0.1

Table 3b
Predicted Average Probabilities of Formal and Informal Volunteering

                    Millennial    Older

Formal                                          Difference  z
volunteering
Government           .331           .388        -0.057       -3.66 (***)
employees
Private sector       .197           .276        -0.079      -15.54 (***)
Difference
z                   8.95 (***)    16.10 (***)
Attended meeting
Government           .10            .19         -0.09        -8.38 (***)
employees
Private sector       .04            .10         -0.05       -18.69 (**)
Difference          0.06           0.10
z                 641 (***)       17.36 (***)
Worked with
others
Government           .06            .14         -0.08        -8.99 (***)
employees
Private sector       .04            .08         -0.05       -16.55 (***)
Difference           .02            .05
z                   2.99 (**)   1049 (***)

(***) p<0.01, (**) p<0.05, (*) p<0.1

Table 4
Predicting Participation by Organization Type, Logistic regression
results from CPS Volunteer Supplement

VARIABLES            Religious         Civic             Educational

Government            0.936             0.913             1.317 (***)
employee
                     (0.0517)          (0.0580)          (0.0759)
Non-profit            1.132 (*)         1.009             0.735 (***)
employee
                     (0.0760)          (0.0762)          (0.0572)
Millennial            0.817 (***)       1.021             1.211 (***)
                     (0.0542)          (0.0702)          (0.0830)
Millennial            0.939             0.860             1.165
government
employee
                     (0.136)           (0.133)           (0.169)
Female                1.052             0.869 (***)       0.987
                     (0.0455)          (0.0420)          (0.0464)
College or more       0.882 (***)       1.176 (***)       1.007
                     (0.0393)          (0.0594)          (0.0477)
Married               2.074 (***)       0.746 (***)       0.666 (***)
                     (0.114)           (0.0424)          (0.0382)
Has child             0.676 (***)       0.522 (***)       3.512 (***)
                     (0.0306)          (0.0276)          (0.174)
Black                 1.971 (***)       0.777 (***)       0.797 (**)
                     (0.154)           (0.0731)          (0.0738)
Other race            1.290 (***)       0.693 (***)       1.102
                     (0.0833)          (0.0555)          (0.0755)
Log income            0.901 (***)       0.946             1.220 (***)
                     (0.0286)          (0.0339)          (0.0440)
Works 40 or           0.847 (***)       1.458 (***)       0.795 (***)
more
                     (0.0395)          (0.0801)          (0.0395)
Constant              1.245             0.698             0.0346 (***)
                     (0.425)           (0.271)           (0.0136)
Observations     28,289            28,289            28,289

VARIABLES            Health             Other

Government            0.704 (***)        0.981
employee
                     (0.0744)           (0.102)
Non-profit            1.266 (**)         1.046
employee
                     (0.132)            (0.132)
Millennial            1.208 (*)          0.858
                     (0.120)            (0.0993)
Millennial            1.047              1.110
government
employee
                     (0.243)            (0.278)
Female                1.516 (***)        0.750 (***)
                     (0.115)            (0.0593)
College or more       0.924              1.062
                     (0.0711)           (0.0879)
Married               0.909              0.701 (***)
                     (0.0765)           (0.0635)
Has child             0.658 (***)        0.677 (***)
                     (0.0525)           (0.0601)
Black                 0.677 (***)        0.464 (***)
                     (0.102)            (0.0868)
Other race            0.968              0.787 (*)
                     (0.109)            (0.0993)
Log income            1.024              0.922
                     (0.0586)           (0.0503)
Works 40 or           1.178 (**)         1.053
more
                     (0.0925)           (0.0932)
Constant              0.0649 (***)       0.318 (*)
                     (0.0407)           (0.187)
Observations     28,289             28,289

Odds ratios are presented in the table. Standard errors in parentheses
(***) p<0.01, (**) p<0.05, (*) p<0.1

Table 4b
Predicted Average Probabilities of Formal Volunteering by
Organizational Type

                      Millennial   Older

Religious                                        Difference   z
organization
Government             0.26         0.33         -0.07       -2.98 (***)
employees
Private sector         0.28         0.35         -0.06       -5.29 (***)
Difference            -0.02        -0.01
Z                     -0.90        -1.08
Civic organization
Government             0.23         0.21          0.02        0.71
employees
Private sector         0.26         0.22          0.04        3.33 (***)
Difference            -0.03        -0.01
Z                     -1.31        -1.10
Educational
organization
Government             0.36         0.33          0.02        0.76
employees
Private sector         0.28         0.29         -0.01       -0.55
Difference             0.08         0.05
Z                      2.67 (***)   4.17 (***)
Health organization
Government             0.08         0.06          0.02        1.46
employees
Private sector         0.11         0.08          0.03        3.24 (***)
Difference            -0.02        -0.02
Z                     -1.52        -3.29 (***)
Other organization
Government             0.08         0.06          0.01        0.89
employees
Private sector         0.07         0.07          0.01        0.92
Difference             0.01        -0.01         -0.07
Z                      0.26        -0.55

(***) p<0.01, (**) p<0.05, (*) p<0.1
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Author:Ertas, Nevbahar
Publication:Public Administration Quarterly
Date:Sep 22, 2016
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