How Social and Human Capital Predict Participation in Lifelong Learning

Abstract

Policy makers and researchers are increasingly showing interest in lifelong learning due to a rising unemployment rate in recent years. Much attention has been paid to determinants and benefits of lifelong learning but not to the impact of social capital on lifelong learning so far. In this article, we study how social and human capital can predict participation in lifelong learning in (non)formal settings. We use, in contrast with previous studies, longitudinal data to test the hypothesis that the impact of social capital on lifelong learning in (non)formal settings differs according to the educational degree one has attained during initial schooling. Although our findings indicate that human capital, labor market position, and other individual characteristics are more important predictors of lifelong learning than social capital, they also indicate that social capital can still be beneficial for those who do not have a higher educational degree.

Keywords

lifelong learning human capital social capital

Policy makers and researchers are increasingly showing interest in lifelong learning. Due to a rising unemployment rate in recent years, policy makers believe that lifelong learning is beneficial, in particular for those who are affected by unemployment, restructuring, and career transitions (European Council, 2011). However, research evidence for the returns to learning after initial schooling remains small (Field, 2011; Knipprath & De Rick, 2014; Psacharopoulos & Patrinos, 2004). Moreover, studies have shown that participants and nonparticipants of lifelong learning differ and that, in general, young, employed, and high-qualified adults are more likely to participate than older, unemployed, and low-qualified adults (e.g., Albert, García-Serrano, & Hernanz, 2010; Ananiadou, Jenkins, & Wolf, 2004; Bischop, 1996; Blundell, Dearden, & Meghir, 1996; European Commission, 2010; Gorard, 2003; Jenkins, Vignoles, Wolf, & Galindo-Rueda, 2002; OECD, 2005). Therefore, Boeren (2009) concluded that participation in lifelong learning is marked by a Matthew effect: Adults who already have acquired a high level of skills and knowledge (hereafter: human capital) will increase their human capital even further through lifelong learning (cf. Stanovich, 1986). As a result, a gap between persons with a high level of human capital and persons with a low level of human capital will enlarge ever more.

Participants and nonparticipants do not seem to differ only in terms of age, labor status, and human capital but also in terms of socio-demographic characteristics, such as gender, socio-economic and ethnic background (e.g. Albert et al. 2010; Ananiadou et al. 2004; Bischop, 1996; Blundell et al. 1996; Boeren, Nicaise, & Baert, 2010; European Commission, 2010; Gorard, 2003; Jenkins et al. 2002; OECD, 2005). Dispositional, institutional, and situational barriers such as time constraints, lack of confidence, and negative learning experiences too may discourage adults to participate (cf. Boeren et al., 2010).

Recently, researchers have been interested in the relationship between social capital and lifelong learning. Social capital has gained increasing popularity in social, economic, and political spheres since the 1990s (Balatti & Falk, 2002; Pawar, 2006). The concept of social capital is used to describe either the resources that are made available to individuals or groups by virtue of networks or the networks themselves (Balatti & Falk, 2002; cf. Field, 2005, 2009; Pawar, 2006). Often civic engagement, trust, norms, and social skills are associated with social capital too (Desjardins & Schuller, 2007; Field, 2005, 2009).

Networks and their resources can be used to “get by” or to “get ahead” (Balatti & Falk, 2002; Field, 2009; Putnam, 2000; Woolcock, 1998). Ties to get by are called bonding ties. Bonding ties refer to rather close relationships with family members, close friends, and neighbors and are good for mobilizing solidarity (Putnam, 2000). Ties to get ahead are called bridging ties. Bridging ties refer to more distant relationships, with distant friends, associates, and colleagues, and are better for linkage to external assets and information diffusion (Putnam, 2000).

Desjardins and Schuller (2007) built a theory about the reciprocal relationship between social capital and lifelong learning. According to Desjardins and Schuller, learning yields more skills, trust, and the motivation to engage in social life. In turn, trust, social skills, and civic engagement facilitate not only informal learning processes during engagement activities but also participation in more formalized learning occasions. In addition, networks can help the individual decide to participate in lifelong learning by providing support and information (Desjardins & Schuller, 2007).

The hypothesis about the impact of lifelong learning on social capital has been partly confirmed by researchers (e.g., Boström, 2011; Harris & Daley, 2008; Kilpatrick, 2002; Preston, 2003; Schuller, Preston, Hammond, Brassett-Grundy, & Bynner, 2004). However, only a very few studies have investigated the impact of social capital on participation in lifelong learning. Therefore, in this article, we study the predictive power of social capital on participation in lifelong learning in (non)formal settings by means of a longitudinal data analysis. In addition, we investigate the interaction between social and human capital. Field (2005) and Strawn (2003), who produced the most important studies on the impact of social capital on lifelong learning so far, hypothesized that social capital may have a positive effect on participation in lifelong learning. However, in the end they found that social capital may substitute human capital and hinder learning. Their studies will be introduced in the next section. After the introduction of Field’s and Strawn’s study, we discuss our research questions and research method. Next, we present the results of our analysis and discuss to what extent social and human capital can predict participation in lifelong learning. In the final section, we draw conclusions.

The Prediction of Lifelong Learning in (Non)formal Settings by Social and Human Capital

Social Capital and Lifelong Learning in Northern Ireland: The Study of Field (2005)

Field (2005) hypothesized that people who belong to the same community will share similar norms and may encourage one another to engage in lifelong learning. In addition, large networks are more likely to provide access to information about the benefits of lifelong learning and the effectiveness of different types of learning resources (Field, 2005). However, Field found little evidence of a positive relationship between social capital and lifelong learning in Northern Ireland. In one study, Field measured the correlation between different types of civic engagement (e.g., participation in neighborhood committees, membership of religious institutions, practicing sports) and the attitude toward lifelong learning but found that these associations were often weak or curvilinear. In another study, Field observed that social capital does not necessarily have a positive impact on the acquisition of human capital after initial schooling. He acknowledged not only that the influence of social capital can be negative when networks maintain a low aspiration culture with regard to learning but also that social capital can substitute human capital. Field observed that ties in Northern Ireland are strong within families and may “play the role of a brake on personal development and change, which might otherwise threaten the stability of community life” (p. 78). Information and skills acquired informally in one’s job or in the community can be far more effective in case of for instance job seeking than formal human capital investment. In other words, social capital can encourage informal learning within the network but can equally discourage participation in learning in (non)formal settings (Field, 2005).

The Impact of Social Capital on Lifelong Learning by Unqualified Adults in Portland (United States): The Study of Strawn (2003)

Strawn (2003) measured the effect of networks on participation in (non)formal and informal learning and classified unqualified adults in different types of networks according to two criteria: (a) the number of contacts within the network and (b) the extent to which the contacts of the respondent know each other (hereafter: density). Strawn hypothesized that individuals with a broad network and a low degree of density have a high probability to encounter and accept the “dominant” discourse that lifelong learning is important and beneficial. Two logistic regressions were run: one to explain participation in (non)formal learning (i.e., participation in preparatory courses to get a high school degree, vocational education) and one to explain participation in informal learning (i.e., learning new things by reading, asking an expert, etc.) during the past 12 months. Strawn concluded that social capital and other variables do not explain participation in informal and (non)formal lifelong learning activities to the same extent (cf. Field, 2005; Jenkins et al., 2002; Weststar, 2009). She observed, for instance, that isolated people participate more often in (non)formal learning than people with a dense network. In contrast, isolated people perform less often informal learning activities. In addition, unqualified adults whose network includes one or more high-qualified persons are less inclined to participate in (non)formal learning than those whose network does not include such connections. As high-qualified persons are more likely to attain a higher socioeconomic position in society, they can provide access to information and resources helping other people get ahead (cf. Field, 2005). In other words, social capital may act as a buffer for low human capital, decreasing the need of low-qualified adults to make further human capital investments (Strawn, 2003).

Research Questions

Field (2005) and Strawn (2003) hypothesized that a broad network will encourage individuals to participate in lifelong learning but found that in reality the relationship between social capital and lifelong learning is rather complex. Social capital has a differential effect on participation in lifelong learning depending on participants’ ties and learning activity. Social ties can increase informal learning opportunities, in particular when ties are strong or “dense,” but social ties may also hinder lifelong learning in (non)formal settings. Although Field (2005) and Strawn (2003) do not mention the concepts of “bonding” and “bridging” ties explicitly, the presence of strong or dense ties within families and community in their study for informal learning resembles the concept of bonding ties, whereas the use of a broad network to get ahead resembles the concept of bridging ties. By studying how social ties are being used and how they lead to different types of learning opportunities, both Field (2005) and Strawn (2003) made an important contribution to the research literature on the effect of social capital on lifelong learning. However, many issues remain unresolved. First, Strawn did not compare unqualified adults with (highly) qualified adults, whereas Field performed his research in a specific region with distinctive forms of social organization. It might be relevant to address the question whether the effect of social capital differs according to the qualification adults attained during initial schooling outside Northern Ireland. Second, it might also be relevant to ask whether we reach similar conclusions when longitudinal data are used. Both Strawn (2003) and Field (2005) needed to rely on cross-sectional data. They claimed to investigate the impact of social capital on lifelong learning, but their measurements did not clarify what comes first: lifelong learning or the acquisition of social capital. As lifelong learning and social capital were measured simultaneously, it is possible that social capital emerged as a result of lifelong learning (cf. Desjardins & Schuller, 2007). Longitudinal data provide information about time order of events and insight into the relationship between the possession of social capital at one particular point in time and participation in lifelong learning thereafter (cf. Field, 2011; Schuller et al., 2004). Therefore, this article reinvestigates to what extent social and human capital predict participation in lifelong learning in (non)formal settings with longitudinal data. We address the following questions:

Research Question 1: To what extent do social capital and human capital predict young adults’ participation in lifelong learning in (non)formal settings?

Research Question 2: Does social capital decrease the need of young adults to participate in lifelong learning in (non)formal settings when human capital is low?

In the following section we describe our research method in more detail.

Method

The Sample

We use a representative sample of adults who were born in 1976 and lived in Flanders, the Dutch-speaking part of Belgium, at the time of the survey. They participated in a longitudinal study on school-to-work transition, called SONAR, and were interviewed at the age of 23, 26, and 29.¹ The respondents answered questions about socioeconomic background, attitudes, and other individual characteristics. In addition, they answered at each wave questions retrospectively about initial schooling, other learning activities, and their labor market position. Information about learning activities and the occupational career was registered in detail per month. As a result, a “calendar” was constructed for each respondent. On the basis of the calendars, we derived variables such as participation in lifelong learning for our study. Only adults who participated at all three waves were retained: 1,657 respondents of the original sample of 3,015 respondents (55%). Attrition, typical for longitudinal research, did not affect the representativeness of the sample significantly (Knipprath & De Rick, 2014). The characteristics of the final sample are presented in Table 1 and Table 2 and discussed in the following paragraph.

Table 1.

Summary Statistics of Categorical Variables.

Variable	n	%
Social background	1,646	100.0
Low	389	23.6
Middle	840	51.0
High	417	25.3
Locus of control	1,656	100.0
Strongly disagree	27	1.6
Disagree	185	11.2
Neutral	227	13.7
Agree	848	51.2
Strongly agree	369	22.3
Children	1,657	100.0
0	1,278	77.1
1	264	15.9
≥2	115	6.9
Household chores	1,657	100.0
0	934	56.4
1-4	289	17.4
≥5	434	26.2
Labor market position	1,648	100.0
Working	1,482	89.9
Not working	85	5.2
Unemployed and job-seeking	81	4.9

Table 2.

Summary Statistics of Binary Variables.

Variable	n	M	SD
(Non)formal learning related to one’s job	1,647	.53	.50
(Non)formal learning not related to one’s job	1,657	.15	.35
Membership	1,657	.56	.50
Volunteer work	1,656	.11	.31
Partner with higher education	1,657	.38	.48
Male	1,657	.51	.50
Native	1,643	.88	.32
Higher education respondent	1,653	.45	.50
Grade retention primary school	1,653	.08	.27
Grade retention secondary school	1,646	.31	.46
Downward track changes	1,652	.26	.44
Membership × higher education	1,654	.27	.44
Volunteer work × higher education	1,657	.06	.25
Partner with higher education × higher education respondent	1,656	.24	.43

Variables

Lifelong learning in (non)formal settings was measured by two indicators: (a) (non)formal learning related to one’s job in settings and (b) (non)formal learning not related to one’s job. Both dependent variables are binary variables measuring participation between the age of 26 and 29. Job-related learning means learning done on the respondent’s initiative or encouraged by the employer to improve job-related skills. Job-related learning occurs in a (non)formal setting, either at or outside the workplace, and either during or outside working hours. Fifty-three percent of the respondents participated at least once in job-related learning (see Table 2). Learning that is not related to one’s job comprises learning in adult education centers, public employment service centers, and the formal education system. Although this learning activity is not encouraged by an employer and is taken entirely on the initiative of the respondent, learning not related to one’s job appears to be undertaken mostly to improve career prospects (cf. Vanweddingen, 2008). Fifteen percent of the respondents participated at least once in learning not related to one’s job (see Table 2).

Social capital was measured by three variables: (a) membership of an association between the age of 21 and 26 (e.g., youth clubs, sports clubs, social-political organizations), (b) volunteer work done between the age of 21 and 26, and (c) having a partner at the age of 26 who has a higher educational degree. We assume that respondents who score positively on these indicators are more likely to have a broad network and are more likely to get in contact with people that can provide access to information and resources to get ahead (i.e., bridging ties). SONAR does not provide information about the density of the network and the strength of ties with family and community to measure the impact of bonding ties. Fifty-six percent of the respondents were a member of at least one association, and 11% performed volunteer work between the age of 21 and 26. Thirty-eight percent of the respondents had a partner with a higher educational degree (see Table 2). To measure the interaction between human capital and social capital, three variables were used: an interaction variable between (a) human capital of the respondent and membership of an association, (b) human capital of the respondent and volunteer work, and (c) human capital of the respondent and having a partner with a higher educational degree.

Human capital refers to knowledge and skills acquired through schooling, training or experience (Mincer, 1989; OECD, 2001). In our study, human capital of the respondent is measured by means of the highest qualification the respondent attained during initial schooling. A distinction is made between respondents with a higher educational degree (45%) and respondents without a higher educational degree (55%; see Table 2).

Finally, we used the following control variables: (a) sex, (b) ethnicity, (c) negative learning experiences during initial schooling, (d) social background, (e) locus of control at age 26, (f) labor market position at age 26, and (g) time constraints at age 26. To measure social background, we applied factor analysis based on information of the full sample about the labor market position and the educational level of the parents during the respondents’ secondary education enrolment. Next, factor scores were used to divide the respondents into three social classes: low social class (25% of the respondents), the middle class (50% of the respondents), and the upper social class (25% of the respondents). To measure ethnicity, we took into consideration home language and the birth place of parents and grandparents. A respondent is considered to be someone with an immigration background if at least one parent or grandparent was born outside Belgium or when the home language was not an official national language (French, Dutch, and German). Twelve percent of the respondents have an immigration background. Eighty-eight percent are native (see Table 2). In addition, we measured possible barriers to participation in lifelong learning: locus of control (one item), negative learning experiences, and time constraints. Time constraints are measured by the number of children one had and the number of household chores one performed at the age of 26. Negative learning experiences were measured by grade retention and experiencing downward track changes. Only 8% of the respondents experienced grade retention at least once in primary school, whereas 31% experienced grade retention at least once in secondary schools. Moreover, 26% of the respondents experienced at least once a downward track change in secondary school (see Table 2). In Flanders, students may choose a curriculum track in line with their interests, but they are often pushed to choose a curriculum track that is less demanding when they do not perform well in school. Both downward track changes and grade retention are considered undesirable and yield negative learning experiences. Next, labor market position is important in explaining participation in lifelong learning. With regard to the labor market position of the respondent, a distinction was made between (a) paid work (working), (b) unemployed but not seeking for a job (not working), and (c) unemployed and job seeking. Ninety percent of the respondents were working at the age of 26, whereas 10% of the respondents were not (see Table 1).

Analysis Technique

Logistic regression analyses were performed, and the results are presented for each dependent variable in four steps (see Tables 3 and 4). During the first two steps we entered the indicators for social capital (Model 1) and the indicator for human capital (Model 2) separately. With the third step, we entered social capital, human capital, and control variables to investigate whether the predictive power of social and human capital on the dependent variable changes due to other variables (Model 3). With the final step, we entered three interaction effects to study the interaction between human and social capital on participation in (non)formal learning related to one’s job and (non)formal learning not related to one’s job (Model 4). The interaction effects will be discussed in a separate section.

Table 3.

Odds Ratios of Social Capital and Other Predictors of Participation in (Non)formal Lifelong Learning Related to One’s Job Between the Age of 26 and 29.

	Odds ratios
	Model 1	Model 2	Model 3	Model 4
Constant	0.81	0.83	0.32	0.30
Membership	1.34**		1.19	1.46
Volunteer work	1.21		1.19	0.87
Partner with higher education	1.55***		1.20	1.28
Higher education respondent		2.06***	1.61***	2.10***
Male			1.21	1.21
Native			1.18	1.17
Social background
Low
Middle			1.15	1.14
High			1.02	1.01
Locus of control
Strongly disagree
Disagree			2.69	2.61
Neutral			2.12	2.02
Agree			3.09*	2.96*
Strongly agree			2.86*	2.73
Grade retention primary school			0.64*	0.66*
Grade retention secondary school			0.98	0.98
Downward track changes			0.80	0.80
Children
0
1			0.86	0.88
≥2			0.86	0.91
Household chores
0
1-4			0.76	0.77
≥5			1.03	1.04
Labor market position
Working
Not working			0.49**	0.48**
Unemployed and job-seeking			0.45**	0.46**
Membership × higher education				0.63*
Volunteer work × higher education				1.81
Higher education p × higher education r				0.88
−2 Log likelihood	2182.17	2162.81	2092.45	2086.18
Bayesian information criterion	2194.99	2169.22	2162.95	2166.29
Nagelkerke’s R²	.03	.04	.10	.10

Note. N = 1,602.

p < .05. **p < .01. ***p < .001.

Table 4.

Odds Ratios of Social Capital and Other Predictors of Participation in (Non)formal Lifelong Learning Not Related to One’s Job Between the Age of 26 and 29.

	Odds ratios
	Model 1	Model 2	Model 3	Model 4
Constant	0.14	0.17	0.08	0.07
Membership	1.19		1.22	1.45
Volunteer work	1.39		1.43	1.57
Partner with higher education	1.09		1.10	1.06
Higher education respondent		0.99	0.68**	0.84
Male			0.59***	0.58***
Native			0.76	0.75
Social background
Low
Middle			1.27	1.25
High			1.35	1.36
Locus of control
Strongly disagree
Disagree			7.06	7.33
Neutral			4.86	5.03
Agree			4.21	4.32
Strongly agree			3.42	3.51
Grade retention primary school			1.26	1.26
Grade retention secondary school			0.69*	0.69*
Downward track changes			0.75	0.76
Children
0
1			0.61*	0.63*
≥2			0.34**	0.36*
Household chores
0
1-4			1.37	1.38
≥5			0.91	0.92
Labor market position
Working
Not working			1.17	1.16
Unemployed and job-seeking			2.58**	2.55**
Membership × higher education				0.70
Volunteer work × higher education				0.86
Higher education p × higher education r				1.05
−2 Log likelihood	1322.62	1328.02	1263.11	1261.42
Bayesian information criterion	1335.45	1334.44	1333.67	1341.60
Nagelkerke’s R²	.01	.00	.07	.07

Note. N = 1,611.

p < .05. **p < .01. ***p < .001.

For each step we provide information about the individual predictors as well as about the whole model. We use odds ratios to describe the predictive power of each independent variable or predictor. When the value of an odds ratio lies between 0 and 1, the effect is negative. When the value of an odds ratio is larger than 1, there is a positive effect. When the value is 1, there is no effect. An effect is considered to be statistically significant if the p value is below .05. When a negative effect is significant, we calculate the inverse of the odds ratio to discuss the result. Several measures are given to evaluate and compare the different models: the log likelihood of the model multiplied by −2, the Bayesian information criterion (BIC) based on the log likelihood measure, and Nagelkerke’s R². BIC allows comparison of (nonnested) models taking into account the number of estimated parameters and sample size. The model with the lowest BIC value is to be preferred. Nagelkerke’s R², which varies between 0 and 1, is a pseudo R². It mimics the R² in ordinary linear regression analysis and indicates the degree of reduction of error in predicting the dependent variable by the independent variables. During the analyses, listwise deletion was applied, and a constant sample size during all four steps was maintained. The overall missing rate was very low (4%), making imputation of missing values unnecessary. Last, the distribution of the residuals did not provide indication of outliers and influential observations in the models. There was also no indication of multicollinearity.

Findings

(Non)formal Learning Related to One’s Job

All three indicators of social capital in Model 1 (Table 3) show a positive effect and contribute together significantly to the explanation of the dependent variable. Nagelkerke’s R² is .03. Two of the positive effects are statistically significant. Those adults who were a member of at least one association between the age of 21 and 26 have a probability to participate in learning related to one’s job between the age of 26 and 29 that is 1.3 times higher than the probability of those who were not a member (B = 1.34). In addition, those adults who had a partner at the age of 26 with a higher educational degree have a probability to participate in learning related to one’s job that is 1.6 times higher than the probability of those who did not have a partner with a higher educational degree (B = 1.55). In Model 2, the odds ratio of human capital (B = 2.06) indicates that young adults who have a higher educational degree are 2 times more likely to participate in learning related to one’s job than young adults who do not have a higher educational degree. Nagelkerke’s R² in Model 2 is .04, indicating a higher degree of error reduction when participation in learning related to one’s job is predicted by human capital than by social capital. A lower value of BIC in Model 2 than in Model 1 confirms this observation.

In Model 3, the odds ratios of social capital become smaller and statistically nonsignificant when control variables are entered. However, the positive effect of human capital remains significant. In the third model, which is according to the value of BIC a better model than Model 1 and Model 2, human capital, locus of control, grade retention in primary school, and labor market position have a significant effect on participation in learning related to one’s job. The probability to participate is higher among those who (strongly) agree with the statement about control over one’s life than those who do not agree with the locus of control statement. Moreover, those who were not working at the age of 26 have a lower probability to participate. When we calculate the inverse of the odds ratios, we see that those who worked have 2 times more chance to participate than those who were not working (the inverse of 0.49 for not working is 2.06; the inverse of 0.45 for job seeking is 2.23). In the final model, the interaction effects are presented. There is one significant interaction effect between human and social capital (i.e., membership of at least one association): The odds ratio is 0.63. Although this interaction effect is statistically significant, the value of BIC is higher in Model 4 than in Model 3, indicating that Model 3 without interaction effects has a slightly better fit. Interaction effects will be discussed in more detail in the final paragraph of this section.

(Non)formal Learning Not Related to One’s Job

All indicators of social capital in model 1 (Table 4) show a positive effect. However, they do not contribute significantly to the explanation of participation in learning not related to one’s job. Nagelkerke’s R² of model 1 is. 01, indicating only 1% of error reduction when learning not related to one’s job is predicted by social capital. Likewise, human capital does not show a statistical significant effect in Model 2. This indicates that, in general, participants in learning not related to one’s job do not seem to differ in terms of social and human capital from nonparticipants. However, when other variables are entered in Model 3, the effect of human capital on lifelong learning not related to one’s job becomes apparent. The probability to participate is 1.5 times higher for those who have no higher educational degree (inverse of the odds ratio of higher education is 1.5) than for those who have a higher educational degree. Other characteristics too explain participation in learning not related to one’s job significantly, and Nagelkerke’s R² increases from nearly 0 to .07 (Model 3). Unemployed respondents who were seeking for a job at the age of 26 are 2.6 times more likely to engage in learning not related to one’s job. Gender, grade retention in secondary education, and the number of children have a significant negative effect on participation. Women are 1.7 times more likely to participate (inverse of the odds ratio of male is 1.70). Those who experienced grade retention in secondary education have a probability to participate that is 1.5 times higher (inverse of the odds ratio is 1.5) than the probability of those who did not repeat a grade. In addition, children seem to hinder participation. Those who have no children at the age of 26 are 1.6 times more likely to participate than those who have one child (inverse of the odds ratio of is 1.63), and 3 times more likely than those who have two or more children (inverse of the odds ratio is 2.98). In the final model, the interaction effects are presented. The interaction effects appear to be statistically nonsignificant. Nagelkerke’s R² does not increase, and the value of BIC in Model 3 is higher than in Model 2, indicating that Model 3 without the interaction effects has a better fit.

Interaction Effects

Strawn (2003) and Field (2005) came to the conclusion that social capital may substitute human capital and hinder lifelong learning in (non)formal settings. For each dependent variable, we introduced three interaction effects measuring the interplay between social and human capital. Although the models with interaction effects proved to be slightly less efficient than the models without interaction effects, one significant interaction effect was found for learning related to one’s job: the interaction between membership of an association and human capital attained by the respondent during initial schooling. The odds ratio is 0.63 (Table 3). To understand this interaction effect, we drew a graph (Figure 1). On the x-axis social capital and on the y-axis the estimates to participate in learning are given. Two lines are drawn. The bold line shows the relationship between social capital and participation in learning related to one’s job for those who are not highly educated. The dashed line shows the relationship for those who are highly educated. We see that respondents without a higher educational degree are more likely to participate when they have social capital, whereas respondents with a higher educational degree are not likely to participate more often when they have social capital. In other words, the interaction effect contradicts Strawn’s (2003) observation that low-qualified people are less likely to participate in (non)formal learning when social capital is high.

Figure 1.

Interaction between human and social capital.

Discussion

The Effect of Social and Human Capital on Participation in Lifelong Learning

We assumed that respondents who joined at least one association, performed volunteer work, or have a partner with a higher educational degree are more likely to have a broad network with access to information, resources, and support to get ahead (i.e., bridging ties). In addition, we assumed that bridging ties might hinder participation in lifelong learning when young adults are low-qualified. However, we observed that in general social capital has a positive, but nonsignificant, effect on participation in lifelong learning by young adults. Moreover, social capital appears to be highly beneficial in particular for low-qualified young adults. Social capital seems to encourage low-qualified adults to find a job with learning opportunities, which is usually available only for high-qualified adults. These findings suggest that our social capital indicators not only assume a broader network but also might be measuring inherently the attitudes of young adults and their network. It is reasonable to argue that those who are engaged in society and perform social activities are also more likely to be open to new learning opportunities (in their job). In other words, a broader network may imply a higher probability to be exposed to and to accept a dominant discourse that learning is important. This is consistent with the original hypothesis of Strawn (2003) in her study.

The Hypothesis of the Matthew Effect to Be Reconsidered

Although social capital, in particular for low-qualified adults, may be beneficial, our findings indicate that in the end human capital has a stronger impact on participation in lifelong learning than social capital. A tertiary educational degree will provide young adults better access to jobs with learning opportunities, whereas a lower educational degree will encourage young adults to upgrade their skills and career prospects through learning opportunities outside their job. In other words, our study confirms that qualifications attained during initial schooling has a positive effect on participation in lifelong learning but only on participation in learning related to one’s job. That is, the argument that participation in lifelong learning is marked by a Matthew effect and that only high-qualified adults will increasingly improve their skills (Boeren, 2009) appears to not be valid in case of learning activities not related to one’s job. Low-qualified adults are equally motivated to upgrade their skills. In this way, they might be able to avoid an enlargement of a skills gap between low-and high-qualified adults. This observation is encouraging and contradicts the argument that lifelong learning is primarily a matter of high-qualified adults (Knipprath & De Rick, 2014). Therefore, it would be valuable to reconsider the hypothesis of the Matthew effect.

Conclusion and Suggestions for Further Research

We studied the predictive power of social and human capital on participation in lifelong learning by young adults in (non)formal settings. So far, only a very few studies have been performed on the impact of social capital on lifelong learning, and these studies were cross-sectional (Field, 2005; Strawn, 2003). On the basis of longitudinal data, we were able to differentiate the time order of events. We also controlled for alternative explanations and observed that human capital, previous labor market position, and other individual background characteristics seem to be more important than social capital to predict participation in lifelong learning. Although human capital is more important than social capital, we did find evidence that social capital may be beneficial and supplementary for those who do not have a higher educational degree. Moreover, if social capital does not hinder participation in (non)formal lifelong learning, it is meaningful to continue to encourage low-qualified adults to participate. However, there are some drawbacks in our study that may help define the need for future research. Our data were gathered among young adults. Although older people seem to be less inclined to participate in lifelong learning than younger adults (Boeren et al., 2010; OECD, 2005), it is relevant to investigate the impact of social capital on participation by older adults. In addition, we did not measure social capital as elaborately as Strawn (2003) did. This might be an explanation for the fact that our findings failed to corroborate those of Strawn (2003) and Field (2005). It would be valuable to continue research on both (non)formal and informal learning activities with more refined measurement instruments for both bridging and bonding ties. As Field (2005) and Strawn (2003) suggested, social ties, in particular when they are strong, may increase informal learning opportunities. Finally, it is not possible to be fully confident about cause and effect, even when longitudinal studies controlling for many variables as possible are being conducted (Field, 2011). We need more evidence about how social capital can facilitate learning opportunities by means of qualitative in-depth studies. These studies may help us define causal patterns and get a better insight into the interplay of social and human capital.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study has been conducted within a larger research project called “Educational and School Careers.” This research project receives financial support from the Flemish Government. There is no official grant number. For more information about the project, see .

Notes

Author Biographies

Heidi Knipprath is a research expert at the Research Institute for Labor and Society (University of Leuven), specializing in research methodology, educational effectiveness, lifelong learning, and school-to-labor market transition.

Katleen De Rick is a research manager at the Research Institute for Labor and Society (University of Leuven), specializing in lifelong learning, vocational education, and school-to-labor market transition.

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