Social Capital || Measuring Social Capital: Assessing Construct Stability of Various Operationalizations of Social Capital in a Finnish Sample

Measuring Social Capital: Assessing Construct Stability of Various Operationalizations of SocialCapital in a Finnish SampleAuthor(s): Ralf LillbackaSource: Acta Sociologica, Vol. 49, No. 2, Social Capital (Jun., 2006), pp. 201-220Published by: Sage Publications, Ltd.Stable URL: http://www.jstor.org/stable/20459926 .

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ACTA SOCIOLOGICA 2006

Measuring Social Capital Assessing Construct Stability of Various Operationalizations of Social

Capital in a Finnish Sample

Ralf Lillbacka Swedish Polytechnic, Vasa, Finland

abstract: State of research: Despite immense popularity among contemporary social scientists, opinions are divided on how social capital should be defined and

measured. In fact, few inquiries have attempted to demonstrate a stable empirical correlate, e.g. through tests for internal consistency and construct stability. Theory: Social capital is constituted and sustained through processes similar to an iterated k-player coordination game; a crucial aspect is whether individuals can utilize the social context as a medium for gaining utilities. Indicators describing how indi viduals perceive various aspects of the 'game' can be used to identify and study social capital on the individual level. Four potential indicators are considered: (1) interpersonal trust, (2) the respondent's social network, (3) self-efficacy, and (4)

membership in voluntary associations. Method and data: Various factor models are tested for cross-context stability and unidimensionality in three subsamples with highly different demographic and socio-economic characteristics; construct stability is presumed to indicate a stable empirical correlate. Finnish survey data from 2001 (n=1439) are used. Results: Cross-context stability is demonstrated for a construct

based on interpersonal trust, social network and self-efficacy, which corresponds to the concept introduced by James Coleman.

keywords: construct stability * self-efficacy * social capital * social network* trust

Introduction

This article aims at demonstrating a stable empirical correlate of social capital as manifested

in the attitudes and expectations of single individuals. Regardless of whether we construe social capital as a pure group-level phenomenon or not, it is asserted that it will still have

manifestations on the individual level. Contemporary scholars have an immense interest in the concept of social capital, and for

good reasons. Several studies report remarkable and mostly positive consequences; for instance, social capital is supposedly conducive to economic growth (Fukuyama, 1995; Knack and Keefer, 1997), low levels of social problems (Rosenfeld et al., 2001), better health (Kawachi and Kennedy, 1999), and increased civic political participation (cf. the review in Glaeser et al.,

2000: 811 ff.) The concept may also advance social theory by providing a bridge between micro

and macro-level theories. However, research is to some extent hampered by a lack of consensus regarding definition

and measurement strategy. Although there is some agreement on central characteristics, social

Acta Sociologica * June 2006 * Vol 49(2): 201-220 * DOI: 10.1177/0001699306064774 Copyright C) 2006 Nordic Sociological Association and SAGE (London, Thousand Oaks, CA and New Delhi)

www. sagepu bl ications .com



Acta Sociologica 49(2)

capital is construed in a variety of ways. This is an almost inevitable consequence of social capital usually being theoretically postulated rather than empirically demonstrated. That is, research is often conducted based on assumptions of what social capital is, rather than on a demonstra tion of an actual empirical correlate.

Furthermore, there is disagreement on whether social capital constitutes a single entity, or whether there are several forms. If we consider social capital social assets utilized for various (individual or collective) purposes, it is feasible that it can take many forms. However, in such a case the concept would, from a pragmatic point of view, have merely a heuristic value, if it refers to several, perhaps quite different phenomena. These individual phenomena would possess all explanatory power, whereas the lack of a distinct empirical correlate would render the concept of social capital problematic as an analytical tool. Apart from a certain arbitrari ness and ambiguity, it would be difficult to separate it from other (potentially overlapping) phenomena such as human capital and even some personality traits; the lack of a distinct correlate will also hamper empirical studies.

Regardless of whether a priori we consider a multidimensional concept more appropriate or not, the fundamental question of validity cannot be ignored. In order to construe more informative empirical models it must be clear whether individual aspects of social capital have better explanatory value than the concept as such, in which case it may be adequate to focus more directly on these particular aspects, e.g. social relationships, trust or other phenomena presumably associated with social capital. If this is the case, the general concept of social capital will inevitably be of lesser empirical value.

Alternatively, there is a single measurable dimension of social capital. This would be particularly desirable if we wanted to find an individual-level representation of social capital. It is feasible that a group or any other social structure can possess a variety of 'social assets'.

However, the impact on how single individuals perceive their social context and possibilities may still be similar, since social capital ultimately refers to a single aspect, i.e. whether an indi

vidual can rely on the social context as a means of obtaining various material and immaterial

utilities. For analytical purposes, this would be particularly desirable, since it would facilitate traditional survey-based inquiries; determining and comparing observable group character istics is a far more difficult task. However, if the emphasis is placed on individual-level mental representations, and we wish to conform to established standards of attitude measurements and conceptual validity, demonstrating an empirical counterpart is imperative. That is, such a correlate can be identified in the same manner as, for example, personality traits are demon strated and measured.

Accordingly, this article focuses on the possibility of a unidimensional individual-level manifestation of social capital. Group characteristics are still considered essential, since the individual-level manifestation is presumed to be based on the perception of these character istics. However, an individual-level measurement may actually facilitate group-level studies; identifying relevant group characteristics that may serve as 'assets' can prove to be a formi dable task. Studying how group members perceive their possibilities of acquiring utilities through group interaction may provide a crude but adequate substitute for group-level obser vations.

Although the article emphasizes an individual-level measurement strategy, it does not fundamentally oppose a common view that social capital should primarily be considered a group-level phenomenon. Furthermore, the article should not be considered a polemic against the view that there are several, rather different types of social capital; a view that has merits

of its own. However, the possibilities of further advance in the area should be obvious, if a

stable and internally consistent empirical correlate of social capital can be demonstrated.

Consequently, there is a purely practical argument for examining the viability of an indi vidual-level approach, which is not to say that such an approach should always be adopted.

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Lillbacka: Measuring Social Capital

It may not be possible to reach a full understanding of how social capital is constituted without considering group-level phenomena, but a limited individual-level inquiry may be a first step toward better theoretical and empirical models. When considering group-level phenomena, it is more likely that we have to acknowledge

the existence of various forms of social assets that we may refer to as 'social capital'. Based on the arguments put forth below, however, it may be methodologically less difficult to identify a one-dimensional individual-level counterpart, in the sense of an identifiable effect on an individual's social conceptions.

This may facilitate the use of traditional factor analyses as a means of identifying such a potential dimension. Indeed, it was this kind of question at issue that spurred the develop ment of factor analyses in the first place, i.e. cases when a latent theoretical construct can be identified only through a set of indicators which are themselves not perfect measurements of the phenomenon in question.

Hence, the study aims at finding a more appropriate definition of social capital, one based on actual empirical tendencies. Accordingly, the approach is explorative and initially founded on a tentative and rather general concept. The aim is to refine the concept in the light of

potential empirical findings. The empirical inquiry utilizes confirmative factor analyses, which makes it possible to

determine the stability of various factor solutions, and thus the likelihood that a stable empirical property is identified. Whereas explorative factor analyses are considered somewhat 'atheoretical', and used mainly to generate an empirical basis for further theoretical develop

ment, confirmative factor analyses are first and foremost used to test theoretical constructs. However, in this case they are utilized in an explorative manner, in an attempt to determine whether an empirically stable construct can be demonstrated, using indicators of social capital presented in previous research.

It is assumed that social capital is not merely a 'social construction', but an actual empirical phenomenon with specific properties. Hence, it can be identified as a stable recurrent factor structure across various categories of respondents. If such a pattern is found, and is indicative of an empirical correlate, it may benefit further theoretical development.

Whether this perspective and approach is appropriate or not, and whether it is even possible to identify a one-dimensional individual-level correlate, has a very simple answer. If no empirical correlate is found, or if it were to be unstable when compared across different groups of respondents, the approach could turn out to be a dead end. On the other hand, should a

stable correlate be found, it may at least suggest that the approach is viable and should be

given further attention. Should it prove fruitful, it might be useful in further studies that are not necessarily limited to individual-level analyses, but which may also incorporate group level phenomena.

The definition of social capital adopted in this article is a composite of previous definitions. Social capital is considered social resources acquired from a stable pattern of social interaction based on reciprocity, trust and mutual obligations, through which members of a group can acquire various material and non-material utilities (cf. Bordieau, 1983: 248; Putnam, 1993, quoted in Glaeser et al., 1999: 2; Astone et al., 1999: 2; Paxton, 1999: 92; Social Capital Interest

Group, 2000). The term 'social resources' refers to such social contacts, e.g. friendship ties, which an individual may mobilize in order to achieve certain ends, and which possess a

certain degree of consistency and predictability. It is also implied that the individual might not otherwise have achieved these ends, or at least with greater difficulty, and hence, the network of social contacts must have an intrinsic instrumental value. However, as further explicated below, this instrumentality must be reciprocal in order to be sustainable.

If a theoretical construct has a stable empirical correlate, this implies that several indicators of a phenomenon should be intercorrelated in a stable pattern. Such an approach is also

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plausible because social capital refers to several aspects, e.g. trust and interaction. Hence, owing to both methodological requirements and the presumed nature of the phenomenon, a test designed to demonstrate unidimensionality among several indicators would seem the

most proper approach. However, among the vast array of previous alternative measurement strategies, single

indicator measurements are very common. A fundamental disadvantage of such strategies is the inability to ascertain validity and reliability of such indicators (cf. Paxton, 1999: 89-90).

This can be illustrated by the commonly used indicator 'trust', which is likely to be affected by a large number of other factors (as such, it may probably be a better measurement of certain personality traits than of social capital), and thus, its viability as a direct measurement of social capital can be questioned.

Multiple indicators are often used, but tests for internal consistency are surprisingly rare. However, such tests can indicate that a number of indicators are unidimensionally intercorre lated, but single-sample confirmative factor analyses cannot determine whether a construct is stable. Such single-sample tests are still largely based on a priori theoretical assumptions regarding the relationship between the indicators (Joreskog and Sorbom, 1993: 114-15). For further discussion and some examples, cf. Knack and Keefer (1997), Lillbacka (2002), Paxton (1999), Rosenfeld et al. (2001), Schneider et al. (1997), Stanton-Salazar and Dornbusch (1995) and Stolle and Rochon (1998).

Cross-group confirmative factor analyses are not immune to this problem, i.e. they may falsely indicate unidimensionality, but they still constitute the most viable strategy for testing internal consistency and stability. MACS models (Means And Covariance Structure) can be used to determine whether relationships between indicators are stable across contexts in a manner that suggests a single underlying dimension, which in turn would indicate a stable empirical counterpart. Given the potential benefits, such analyses are surprisingly rare (cf. Paxton, 1999).

Considering the aim of the article, MACS analyses constitute the most appropriate empirical approach. However, it must be recognized that although comparatively robust estimates can

be obtained even with poor indicators, a minimum of three indicators is required (Little et al.,

1999). Fortunately, few two-indicator factor models are of theoretical interest in the sense of being discussed in previous research. The analysis is based on a 'atheoretical' approach of

determining whether any combination of potential indicators is found stable when compared across different groups of respondents. However, because of limited space, individual combi nations and their theoretical implications will be extensively discussed only if the results suggest that they are of interest; only a few have direct counterparts in previous research.

The availability of data also imposes certain constraints in this study. Four theoretically relevant indicators (based on previous research) can be considered. (1) Trust. With few excep tions, previous research has emphasized the extent to which an individual expects others to act benevolently towards him/her. (2) Integration in a social network (henceforth: 'social network'); i.e. an individual's social ties. (3) Membership in voluntary associations (henceforth: 'voluntary associations') is occasionally used as a single indicator. (4) Self-efficacy is seldom explicitly considered, but is implied by, for example, James Coleman (1988; cf. below).

Owing to sheer volume, the presentation of previous research cannot be exhaustive. The theoretical discussion aims primarily at suggesting how predominant theoretical notions can

be combined into a model of social capital from which indicators can be derived (cf. Lillbacka,

2002). The central aim of this article is to determine whether social capital has a stable empirical

counterpart, which, as stated above, is done by testing whether any configuration of the four theoretically relevant indicators forms a stable factor structure. Construct stability is deter

mined by comparing factor models across subsamples with highly different socio-economic

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and demographic features, which are conditions likely to affect social capital. As is further explicated below, data from a Finnish survey conducted in 2001 are used.

A model of social capital

The central features of social capital can be crudely described by means of a k-player coordi nation game. The model rests on three postulates, which should be acceptable at face value. (1) Individuals possess motivation; human behaviour is ultimately guided by motives (biological and psychological needs, preferences in a more general sense, etc.), i.e. an aim to acquire utilities. (2) Individuals are interdependent. Acquisition of utilities is (at least partly) dependent on the actions of others, which compels any group to coordinate individual behaviours. (3) Indi viduals are autonomous. Although being interdependent, individuals can choose the strategy they find most appropriate (which of course does not guarantee it actually being adequate).

However, perfect rationality cannot be assumed, e.g. due to scarce information and resources; individuals may also act irrationally for a number of reasons. As is emphasized below, it is not presupposed that individuals (always) conduct some form of deliberate cost-benefit analysis, since behaviour is often modelled on culturally salient behaviour patterns.

Interdependency makes it necessary to predict the counterpart's behaviour. Since social capital is usually considered an outcome of reciprocal interaction, it is hardly surprising that it is often described by means of an iterated 'prisoners' dilemma' (cf. Brehm and Rahn (1997); some associated notions are discussed in Boyle and Bonacich (1970: 124) and in Wrightsman (1992)). A 'prisoners' dilemma' is a game theoretical model of dyadic interaction with the optional strategies of 'cooperation' and 'defection'. ('Players' are not supposed to be able to communicate or signal their intentions.) If both 'players' cooperate, a Pareto-optimal solution is reached. However, a player can maximize his utility by defecting under the precondition that the counterpart cooperates, in which case the counterpart will experience a maximal loss of utility. Since defection facilitates maximum utility, while simultaneously guarding against the worst possible outcome, both players are likely to 'defect'. However, mutual defection will cause a loss of utility for both players (although smaller than if cooperating when the coun terpart defects). The players are 'interdependent' (the counterpart's behaviour determines the utility of an action), but are also 'autonomous', since they can freely choose strategy.

According to Brehm and Rahn (1997), social capital can be construed as the outcome of an

iterated 'prisoners' dilemma'. In iterated games a player can respond to cooperation and defection in previous games by choosing strategy to reward cooperation and punish defection in subsequent games. Thus, cooperation will yield a higher long-term utility outcome than defection. This renders cooperation more likely (although not ensured) in repeated games, since players learn to expect benevolent behaviour (Boyle and Bonacich, 1970: 126, 132, 136). Even the repeated prisoners' dilemma is a questionable description of a complex social reality, but it illustrates how autonomous, yet interdependent, actors can create social capital, i.e. a

resource for acquiring utilities through coordination of behaviours. In reality, social contexts are often 'k-player games', i.e. several and/or different 'players' are encountered in each 'game'. However, factors that enable dyadic or near-dyadic cooperative interaction may be

crucial for the creation of social capital, since this renders cooperation more likely in the long

run. It is noted elsewhere that stable patterns of social interactions are presumed necessary for social capital.

Actual social interaction is rarely based on discrete and simultaneous choices of strategy; utility outcomes may be asymmetrical and/or delayed. Intentions can be demonstrated through 'cheap talk' (cf. Farrell and Rabin, 1996), and behaviour can be deliberately coordi

nated, although these possibilities are constrained, for example, by limited resources (such as time). Daily life rarely offers a discrete range of known strategies with a known yield of utility;

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rather, it is distinctly 'fuzzy' (available strategies and their expected utilities are only partly known). However, experiments have demonstrated that introducing such parameters does not fundamentally alter the logic of a 'game', although Pareto-optimal solutions will be more difficult to achieve in complex 'games' (cf. Van Huyck et al., 1990, 1991).

Accordingly, social capital would correspond to the extent by which 'players' are able to Pareto-optimally coordinate their behaviour in a k-player 'fuzzy' coordination game. Pareto optimality is not ensured since mutual expectations may reinforce suboptimal outcomes (cf.

Macy, 1991a: 818 ff.). This does not imply a fixed 'equilibrium', but shared expectations of how others will behave may nevertheless sustain comparatively stable patterns of interaction.

As stated above, these notions do not imply that individuals are perfectly rational. In fact, in such 'fuzzy' contexts, individual behaviour may be less guided by rational deliberation and

more by incentives from the social context, e.g. by imitating the behaviour of others and by

sheer conditioning (repeating successful behaviours). Imitation of predominant behaviours may sustain stable behavioural patterns; the salience of particular strategies/behaviours will mitigate uncertainty, and thus fewer resources are required for, for example, acquisition of information (Widner and Mundt, 1998; cf. Whiteley, 2000 concerning the 'coordinating function' of social capital).

This may help individuals to coordinate their activities, since 'culturally' salient behaviours may ensure a certain degree of predictability when interacting with others. (Similar notions regarding how complex social structures can be sustained are made in classic social organiz ation theory.) Some scholars claim that, apart from being a normative system with 'core values', culture can also be considered social expectations as well as roles or behavioural models for problem-solving (cf. Nelson, 1979: 1026; Swindler, 1986). Such behavioural models are largely acquired through social learning and imitation, which, in a sense, is a cheap way of

identifying adequate behaviours. However, suboptimal and counterproductive behaviour may also be learned in a similar (partly self-sustaining) manner (Russell et al., 1976; Akers

et al., 1979; Sugden, 1989; Macy, 1991b; Van Huyck et al., 1991: 897, 900, 904; Young, 1993: 58; Kapur, 1995). Since predictability is central, conformist behaviour will ensure some degree of credibility, which in turn may attract other 'players'. Hence, social contexts are likely to be

innately biased toward conformity, since individuals attempt to ensure long-term utility (cf. Crawford and Haller, 1990; Van Huyck et al., 1991: 900).

The potential impact of cultural traits, in the form of 'cultural capital', cannot be fully considered in this inquiry. When using Finnish data for cross-group comparisons, it must be noted that Finland as a socio-cultural context is markedly homogeneous. Swedish speakers constitute the largest minority with a mere 6 per cent of the population. Some studies have

suggested a higher level of social capital among Swedish speakers; a trait that cannot be

explained in terms of socio-economic characteristics. However, in this sample, the number of Swedish speakers is too small to affect the results. Of the 2500 of the original sample, only 141 were Swedish speakers. Consequently, since the group of Swedish speakers in the effective sample became even much smaller, the language variable was not even included in the data.

Other minorities, such as the Saimi and the Roma, are even much smaller, and were also not

identified in the sample. Hence, potential differences between ethnic groups induced by various socio-cultural traits cannot be accounted for in this study.

The comparative degree of socio-cultural homogeneity might of course affect how and to what extent social capital is generated. There is a theoretical precedent in the form of the theory

of social organization, which implies that homogeneous cultural context may more easily sustain various socio-cultural symbols through which collective behaviour is coordinated. This could also imply that social capital might be more easily generated in the homogeneous Finnish society. However, such an assumption can only be tested in comparison to other societies. It is also conceivable that socio-economic and similar socio-structural features of

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modern industrialized societies have a more fundamental effect on a society's socio-cultural composition than ethnic cleavages.

There has been some disagreement on whether social capital has a distinct micro-level coun terpart (cf. Stolle and Rochon, 1998; Widner and Mundt, 1998; Astone et al., 1999: 4). However, although the model presented here largely rests on a number of presumably co-occurring group-level properties, social capital is likely to have micro-level manifestations in the form of 'mental representations' (attitudes, conceptions and expectations) of these group features. A central task of cognitive psychology is to describe how such representations serve as means of orientation in a (social) context (cf. Lundh et al., 1992: 47-8, 95-116, 137, 149-52; Steck, 1980: 45-6). Hence, properties of social interaction, as referred to here, are also implied to have corre sponding mental representations (cf. Lillbacka, 2002, for a further discussion). Indicators of social capital presented in previous research usually correspond to a feature of social inter action as well as to a particular 'mental representation'.

'Interpersonal trust', the perhaps most frequent indicator in previous research, corresponds to whether other individuals are expected to act benevolently, i.e. to choose 'cooperative' strat egies (Koehn, 1996; Kawachi and Kennedy, 1997; Schneider et al., 1997; Widner and Mundt, 1998; Glaeser et al., 1999, 2000).

Previous studies have also emphasized 'social ties', 'embeddedness' and similar features, which all refer to an individual's integration in a stable social network (cf. Portes and Sensenbren ner, 1993; Stanton-Salazar and Dornbusch, 1995; Hagan et al., 1996; Schneider et al., 1997; Frank and Yasumoto, 1998; Sandefur and Laumann, 1998; Paxton, 1999: 93; Bubolz, 2001; Fernandez et al., 2000; Glaeser et al., 2000; Smith, 2000). It was previously suggested that stable dyadic relationships facilitate social capital; Pareto-optimal (cooperative) solutions are less likely if 'co-players' are frequently and/or stochastically changed (regarding the role of stability, cf. Ellison (1993: 1065) and Macy (1991a: 826, 831). Whether the structure of the social network encourages tight relationships or wider, more general and looser acquaintances, will fundamentally affect interaction and diffusion of ideas, as suggested by Fararo (1989: 264 ff.) concerning 'biased networks'.

Most scholars clearly imply 'self-efficacy' as an element of social capital, although this is rarely explicitly asserted. However, knowledge of efficient behavioural strategies (individual and collective) is likely to affect an individual's (subjectively perceived) self-efficacy (for a review on self-efficacy and similar terms, cf. Gecas, 1989). As implied by the sheer notion of social capital as 'capital', it serves as a means for achieving aims. For instance, James Coleman

(1988) considers it an asset through which (perhaps otherwise unreachable) utility can be obtained. With reference to the previous discussion, a sufficient return of utility is required if a benevolent and cooperative interaction is to be sustained (cf. Portes and Sensenbrenner, 1993: 1322-3; Stanton-Salazar and Dornbusch, 1995; Schneider et al., 1997; Frank and Yasumoto, 1998; Sandefur and Laumann, 1998; Astone et al., 1999: 3; Morrow, 1999; Paxton, 1999: 93 ff., 101 ff.; Smith, 2000: 511; Whiteley, 2000; McNeal, 2001).

These three indicators are asserted (trust and social network) or implied (self-efficacy) by James Coleman (1988), who does not make any explicit statement regarding the empirical relationships, but they are clearly implied to co-occur; however, unidimensionality serves to strengthen the viability of his claims.

The aforementioned model does not imply 'membership in voluntary associations', but previous research has given attention to this aspect (Putnam, 1993; Kawachi and Kennedy, 1997; Booth and Bayer Richard, 1998; Eastis, 1998; Kunioka and Woller, 1999). It describes a potentially crucial aspect of the social network, since participants in associations are likely to

possess larger social networks (cf. the notion of 'strong' and 'weak' ties in Granovetter, 1973). Nevertheless, it is a highly problematic indicator since it refers to behaviour that may be exten sively affected by the structural features of a context (and by age and socio-economic status).

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For instance, small communities may have a very strong social network, but have few formal associations. Although it is likely to have a connection to social capital, this does not presup pose unidimensionality (cf. Lillbacka, 2002: 61).

Expressed in these terms, social capital is the combination of a social network, where the members expect others to act benevolently, thereby offering individual members behavioural options through which they can obtain utilities (cf. the definition in the introductory remarks). The assertion that such features tend to co-occur is highly consistent with game theoretical findings, e.g. by Macy (1991a); acquisition of utility is facilitated in contexts with stable inter action, where players have learned to expect benevolent responses.

All of these potential indicators are likely to be affected by socio-economic and demographic conditions. The possibility to sustain a stable social network, participation in associations, being dependent on the benevolence of others, as well as actual and perceived self-efficacy are all highly dependent on matters such as age, income, education, vocation and civil status. It

was suggested above that a construct is likely to have a stable empirical correlate if the relationships between indicators are invariant across contexts. These properties are most likely to have a detrimental effect on construct stability since they are potentially associated with differences in the capability to acquire social capital. Hence, there is an obvious rationale for using them as a foundation for tests of cross-context construct stability.

As previously stated, only factors with at least three indicators are considered methodolog ically viable. However, the only bivariate relationship with distinct theoretical implications is that between 'trust' and 'membership in voluntary associations', since this is emphasized in research following Putnam's (1993, 2000) studies. It was also noted that theoretical impli cations will be discussed only if a particular 'combination' of indicators turns out to be of interest (i.e. if a stable factor structure is demonstrated). The word 'combination' is used to differentiate between particular configurations of indicators and the 'models' used to determine construct stability. These are: Combination A. All indicators. Combination B. Self efficacy, trust and social network; these indicators are implied by James Coleman (1988). Combination C. Self-efficacy, social network and voluntary associations. Combination D. Self efficacy, trust and voluntary associations. Combination E. Trust, social network and voluntary associations. This variant is reminiscent of Putnam's (1993, 2000) concept.

Methodological considerations and data

The data are obtained from an International Social Survey Programme (ISSP) inquiry (postal survey) conducted in Finland during 2001. The respondents (aged 15-74) constitute a random sample drawn from the Central Population Register based on municipal code and year of birth. The questionnaire was distributed to 2500 individuals. The response rate was 57.8 per cent. A brief description of the socio-economic and demographic composition of the sample is presented in Appendix 2.

It must be emphasized that the data are not optimal for this inquiry. As described below, some survey items may have poor validity, and previous research has presented far more

adequate instruments for measuring these presumed indicators of social capital. Some sugges tions are presented in the concluding remarks. However, it should be possible to create sufficiently viable indicators using these data; the consistency in results, when compared to a previous tentative study (Lillbacka, 2002) based on other data, suggests that the shortcomings of the data do not constitute a fatal problem.

As suggested above, demonstrating unidimensionality and construct stability by means of cross-sample confirmative factor analyses is the most adequate strategy. However, whether

the indicators are valid representations (of 'self-efficacy', 'trust', etc.) or not is a potential source of bias, since the degree of internal consistency may be underestimated if the indicators are

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poorly measured. In order to minimize the measurement error of the indicators, these are based on four factor analyses (maximum likelihood with mean replacement) including the items that constitute each respective indicator. Despite being skewed, some preliminary tests (categorical principal component analyses) suggested that the items could be treated as normal and continuous. (The estimates were essentially identical to those obtained through maximum likelihood estimation.) However, the subsequent confirmative factor models are more likely to be falsely rejected than falsely accepted, due to any potential remaining bias.

Previous studies (cf. Little et al., 1999) have suggested that factor analyses generate robust estimates even with poor indicators. A single poor item (with a standardized factor loading of merely 0.20) ruled out a two-level factor analysis and similar options (see Table 1), The item

was nevertheless included, since as a third (although poor) indicator of 'self-efficacy' it was presumed to improve estimation of the underlying construct (cf. Little et al., 1999).

Table 1 Thefactor structure of thefour potential indicators of social capital estimated by maximum likelihood factor analyses with mean replacement. (n = 1439)

Item Factor loading

Membership in voluntary associations (explained variance: 24.496%) I. Membershipa in (1) a political party or similar organization, (2) a professional

union or other professional association, or (3) a church or religious association. 0.459

II. Membershipa in either (1) a sports association, hobby organization or other leisure-time association, or (2) a charity group or similar organization. 0.557

III. Membershipa in either (1) an association for the local community or (2) in

any other association or group. 0.463

Self-efficacy (explained variance: 25.487%) I. 'In general, how happy or unhappy have you felt recently?' (Answers

ranged from 1 'very happy' to 4 'not at all happy'.)b 0.197 II. 'People like me have no say in what the government does'c (Answers ranged

from 1 'wholly agree' to 4 ' wholly disagree'.)b 0.596 III. 'Let us presume that you wanted the municipality to make improvements

at your place of residence. How likely would you consider you being able to do something about it?' (Answers ranged from 1 'very likely' to 4 'not at all likely'.)b 0.606

Social network (explained variance: 28.316%) I. Who the respondent would turn to for help with housekeeping, shopping,

etc., if he/she had caught the flu and would have to stay in bed for a couple a days.d 0.488

II. Who the respondent would turn to if he/she would have to borrow a large sum of money.d 0.353

III. Who the respondent would turn to if he/she felt depressed.d 0.698

Trust (explained variance: 24.918%) I. 'There are only a few people whom I can really trust.'c, e 0.332 II. 'Usually, I can be assured that others wish what is best for me.'b, c, e 0.454 III. 'If I am not careful, others will use me.'C, e 0.656

aThe options are: 'participated more than twice during the recent year' (value = 4), 'participated once or twice' (value

= 3), 'passive member' (value = 2), and 'no membership' (value = 1). bThe variable values were reversed. CThe question

was presented as a statement to which the respondent could agree or disagree. dThe original variable values (with 18

options) were re-categorized into: 'no one' (value = 1), 'persons in authority', such as social workers, banks, medical doctors, etc. (value = 2), and 'primary group', i.e. spouse, relatives and friends (value = 3). Items are mean values of the respondent's primary and secondary choice. 'The replies ranged from 1 'wholly agree' to 5 'wholly disagree'.

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The indicator 'membership in a voluntary association' is based on three variables, referring to membership in various organizations. Each variable is created by arbitrarily combining 2-3 items, where the highest variable value of these items corresponds to the new variable value (see Table 1); the result is essentially the same if traditional summed variables are used. Owing to the phrasing (see Table 1), it is questionable whether the variable values can be treated as continuous, but preliminary tests (categorical principal component analyses and other arbitrary combinations of items) did not indicate any lack of robustness. Furthermore, corre lations between various types of associations and other items did not suggest any differences between 'bridging' organizations (which link various groups of individuals together, such as church associations) and 'bonding' organizations (which serve the preferences of a specific group in competition with others, e.g. labour unions).

The indicator referring to 'self-efficacy' is based on survey items that may have poor theor etical validity. One item refers to the respondent's self-reported 'happiness', which is not an evident manifestation of self-efficacy. However, 'happiness' is presumed to reflect whether an individual is capable of acquiring utilities (cf. Lillbacka, 2002: 41, 82 ff.). Other items refer to a political self-efficacy rather than to self-efficacy in a general sense. However, similar items are included, e.g. in Schwarzer's and Jerusalem's (1995) self-efficacy scale. Furthermore, they do, in fact, reflect the respondent's sense of being able to affect his/her environment. It can also be noted that a similar factor loading as reported below was obtained with a more theo

retically valid survey item in a previous inquiry (Lillbacka, 2002: 53, 56). This suggests that a satisfactory estimate of 'self-efficacy' is obtained.

The third indicator ('social network') measures the role of the social context as a means of obtaining utilities, and is based on items describing who the respondent would ask for help in a number of situations. The original variable values were recoded in order to describe

whether the respondent turned to friends or relatives (high social capital), welfare services provided, for example, by the municipality (lower social capital), or none (very low social capital). Each item is the average value of the respondent's first and second choice.

The fourth indicator is less problematic, since the three items measuring trust should be valid and appropriate.

Figure 1 describes the confirmative factor analysis. Construct stability across groups (i.e. measurement equivalence) presupposes that all differences between subsamples can be explained by differences in the latent variable, i.e. in the latent mean (cx) and/or standard

deviation (1). A construct is considered stable if (1) factor loadings (X) can be equated across

groups and (2) all differences in indicator means (T) can be explained by differences in the

latent mean (tau-equivalence) without loss of model fit. The change in model fit (i.e. AX2) when factor loadings are freely estimated and/or tau-equivalence is not assumed, should not be significant when related to the corresponding change in degrees of freedom (i.e. Ad.f.). If a

model fails due to poor internal consistency (having a large x2) or lack of measurement equivalence, this would indicate a lack of a stable empirical correlate.

The measurement model (i.e. the factor structure as such) will not be reported unless the factor structure achieves sufficiently good model fit.

This strategy is frequent in cross-cultural research, e.g. to ensure that psychological instru ments are culturally unbiased. The emphasis is different in this case, since the aim is to

compare groups with as different socio-economic and demographic features as possible. Accordingly, the main criterion for group distinction is purely methodological. The groups

should be as diverse as possible, with regard to as many traits as possible, since this will

increase the likelihood that differences in the way that social capital is generated are enhanced. A test for stability of factor solutions across groups would be meaningless, should the groups be similar with regard to various characteristics. However, if it can be demonstrated that the

210




WJ(2,2)= o

g Yked second foo gTi cs mae order factor; Y

i(1,1) 0 at 1 in eu m

c(nslded 0(2,2a l o(k,k) a r Tol) T(2) T(k)

Note: Parameter labels follow LISREL notation: ax = latent mean, a = latent standard deviation, =latent variance, A

=factor loadings, X = intercepts, 0 = measurement error, -q = latent variable and Y = indicator/manifest variable.

o(X() is fixed at O and P(1, 2) at l in the first subsample and are freely estimated in subsequent groups. A model is

considered measurement invariant if: (l) factor loadings (A) are equal across groups, and (2) all differences in

manifest intercepts (X), i.e. means of indicators, can be accounted for through differences in the latent intercept (cx), i.e. the factor mean. Measurement equivalence is assured if the change in x2, i.e. AX2, is non-significant when either factor loadings or manifest intercepts are freely estimated.

Figure 1 The MACS model with k indicators used to test measurement equivalence

factor structure is stable even when highly diverse groups are compared, the case for a 'one dimensional' individual-level correlate will also be stronger.

The diversity of the groups will be mitigated by a methodological constraint, however. The groups should still be sufficiently large in order to provide accurate estimates of the factor structure within each group, which makes it impossible to compare very small but highly diversified groups.

The analysis utilized the demographic and socio-economic variables provided in the survey. Individuals at different stages of life, with different needs and different socio-economic and other resources to meet them, may also develop different 'mental representations' of their immediate context (cf. Lillbacka, 2002: 31). Hence, construct stability between such groups is less likely, unless social capital actually varies according to a single dimension. A homogen eity analysis, based on civil status, education, main occupation, income, gender and age, was

211




used to generate divergent groups for the comparison. (Gender was later omitted due to poor discriminatory value.) A homogeneity analysis is an explorative approach which reduces initial variables into fewer dimensions.

Solutions with a varying number of property dimensions were tested. However, a model with two dimensions turned out to be appropriate, since further dimensions had poor eigen values, and, as Figure 2 illustrates, the dispersion of cases according to dimension 1 (D1) and dimension 2 (D2) formed three distinct groups. These are: 'group 1' (D1 < 0 and D2 < 0), 'group

2' (D1 > 0) and 'group 3' (D1 < 0 and D2 > 0), which is used as a baseline in further analyses.

A value of 0 corresponds to the average of each respective dimension. Respective frequencies of each group are 26.3 per cent (n = 378), 36.8 per cent (n = 529) and 37.0 per cent (n = 532).

Although crude, the categorization is consistent and easily interpretable if the original variables are considered. All differences/features reported below are significant (p < 0.001), or cases where a particular group constitutes (nearly) 100 per cent of an original variable value.

The three groups largely correspond to different 'generations'. 'Group 1' is the oldest; 81 per cent has a 'folk school examination' (which was replaced by the comprehensive school in the early 1970s), it constitutes 99 per cent of the fifth age quintile, includes 51 out of 53

widows/widowers, 99 per cent of those retired because of old age, and 86 per cent of those retired for other reasons. 'Group 2' is the youngest, constituting 100 per cent of the first age quintile, of those currently engaged in education, and nearly all unmarried individuals. Indi viduals with no further education than a matriculation examination are also typical for group 2, as well as belonging to the first income quintile. 'Group 3' consists primarily of adult indi viduals working full time; the group is clearly predominant in the fifth income quintile. Hence, 'group 3' constitutes an almost evident choice of baseline group. These groups represent very

qualitatively different social and socio-economic conditions, which also render qualitative differences in social capital likely; hence, they are ideal for a test of construct stability.

Results All correlations between indicators reported in Table 3 turn out to be relatively weak. A finding that has obvious theoretical implications is the lack of a significant correlation between 'trust' and 'membership in voluntary associations' (cf. Lillbacka, 2002: 61), since previous research has emphasized the latter as instrumental for the creation of social capital (cf. above).

Table 2 A homogeneity analysis offive socio-economic and demographic variables. (n = 1439)

Discrimination measures

Variable Dimension 1 Dimension2

Civil statusa 0.608 0.203

Educationb 0.617 0.386 'Which of these alternatives best describes 0.718 0.760

your life at the present moment'c Income (quintiles) 0.419 0.416

Age (quintiles) 0.794 0.724 Eigenvalues 0.631 0.498

aSeven categories: unmarried, cohabiting, married, separated, divorced, widow/widower, other. bNine categories: still in education, folk school, comprehensive school, vocational school, matriculation exam, institute-level vocational training, polytechnic exam, academic degree (lower level, bachelor), academic degree (higher level, master). cEleven categories: employed >35 h/week, employed 15-34 h/week, part-time employment (<15 h/week), assists in family-owned business, unemployed, student, age retirement, retired due to other causes, doing housework, military or civil service, and on parental leave.

212



Lillbacka: Measuring Social Capital .-g :.:B : .a ...> . .z .::: .8i - ..

4

3 - E

0 0' 2 ~~~~~~~~~~0 oa0 o

0% 00

2l- - El a 0 1 2 0

0 Do LI -~~~~0mb ~

= gr0 G rp sr L LI LI _ ~~LI

I U ~~~ L U- - - LI FlL

-2 -4 -3 2 -1 0 12

Dimension 2

O=group 1, D= -group 2, A group 3.

Figure 2 A scatterplot of the three groups based on the homogeneity analysis with two dimensions

The test of the first 'combination', which includes all indicators, is reported in Table 4. The

model is rejected due to poor fit. x2 with 18 degrees of freedom is 76.87, and two alternative

indices (the incremental fit index, ICI, and the non-normed fit index NNFI) fall below the

standard cut-off level of 0.90. Furthermore, the construct does not satisfy the requirements of

equal factor loadings (p < 0.05) and tau-equivalence (p < 0.001). Although model goodness of

fit (but not measurement equivalence) would be improved by added error covariances, these cannot be theoretically justified; the need for such modifications also suggests that the indi

cators are not unidimensionally intercorrelated.

Table 3 Correlation matrixfor the indicators

Voluntary association Self-efficacy Social network Trust

Voluntary association 1.000 Self-efficacy 0.238*** 1.000 Social network 0.179*** 0.150*** 1.000 Trust 0.045 0.183*** 0.153*** 1.000

n = 1439. *p < 0.05, **p < 0.01, **p < 0.001.

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Table 4 Assessing the modelfit of 'Combination A', a social capital construct where all indicators are included. (N1 =378,N2=529,N3=532)

Model description x2 d.f. p AX2 Ad.f. p

I. X, T invariant 76.868 18 0.000 II. X free, T invariant 56.827 10 0.000 20.041 8 0.012 III. X invariant, T free 41.041 10 0.000 35.827 8 0.000

Other fit indices (model I): RMSEA = 0.083; RMR = 0.049; GFI = 0.987; NNFI = 0.684; ICI = 0.684.

The measurement model is not reported because of poor model fit. Numbers in parentheses are standard errors. Cf. Figure 1 for 'model description'.

The second combination (see Table 5) includes 'self-efficacy', 'trust' and 'social network', which corresponds to the concept introduced by James Coleman (1988). The measurement equivalent model is consistent with the data; x2 is non-significant (p > 0.05). Although the assumption of equal factor loadings is nearly rejected (p = 0.05), and there is a minor lack of

tau-equivalence (p < 0.05), the model can be accepted merely due to good model fit. The fairly negligible amount of instability originates from a single parameter, i.e. the intercept for 'trust' in the oldest group ('group 1'), which does not differ as much as 'social network' and 'self efficacy' in comparison (see Appendix 1). Since elderly people are recipients of various forms of public welfare services, the measurement of 'social network' is biased to some extent. However, this lack of stability is negligible, especially since a measurement equivalent model fits the data very well.

The latent standard deviation in 'group 2' (the youngest) can be declared partly invariant without loss of model fit; the final model (V) fits the data very well with regard to all criteria.

Accordingly, it can be stated that a model based on James Coleman's concept appears to have

Table 5 Assessing the modelfit of 'Combination B' (James Coleman); a social capital construct based on self-efficacy, social networks, and trust. (N1 = 378, N2= 529, N3 = 532)


I. X, T invariant 14.487 8 0.070 II. A free, T invariant 1.926 2 0.382 12.561 6 0.051 III. A invariant, T free 0.380 2 0.827 14.107 6 0.028 IV. A, T invariant, except T(3) in group 1 1.571 7 0.980 12.916 1 0.000

V. A, T invariant, v invariant in group 2 15.162 9 0.087 0.675 1 0.411

Other fit indices (model V): RMSEA = 0.038; RMR = 0.008; GFI = 0.999; NNFI = 0 .927; ICI = 0.927.

Measurement model; unstandardized estimates (model V)

Indicator A T Ogroupl Ogroup2 Ogroup3

' Self-effic.' 0.382 (0.039) 0.137 (0.030) 0.398 (0.041) 0.399 (0.041) 0.397 (0.039) ' Soc. netw.' 0.259 (0.031) 0.097 (0.024) 0.757 (0.058) 0.551 (0.039) 0.311 (0.025) ' Trust' 0.244 (0.030) 0.091 (0.026) 0.410 (0.033) 0.528 (0.036) 0.465 (0.032)

Latent model Group 1 Group 2 Group 3

Mean (cx) -0.975 (0.120) -0.282 (0.100) 0 St. dev. (13) 0.618 (0.157) 1 1

Numbers in parentheses are standard errors. Cf. Figure 1 for 'model description'.

214



ig"n ..0 ....... ...Lillbacka: Measuring Social Capital .E . E E .EE . . - . . Ei..... . -E. ..

a stable empirical counterpart. Although comparing social capital between groups is not a primary concern for this inquiry, it can be noted that 'group 3', which in socio-economic terms is best integrated, also has the highest level of social capital. The level is lower in the youngest group, and is markedly low in the oldest.

The variant reported in Table 6 has an extremely poor model fit; furthermore, the assump tion of equal factor loadings (p < 0.001) and tau-equivalence (p < 0.001) is rejected. Although the lack of model fit is associated with a single parameter, i.e. the intercept for 'membership in a voluntary association' in the oldest group, it is not evident that measurement equivalence

would be achieved through a different operationalization. Owing to the fundamental weakness of the model, improvements in model fit through additional latent parameter constraints are not considered. Consequently, the model is rejected.

The combination reported in Table 7 also turns out to be of little interest, since the model does not fit the data very well and lacks tau-equivalence (p < 0.01). The latter is not associated with any particular parameter. Furthermore, a negative error term of 'self-efficacy' violates model parameters, and constitutes a so-called 'Heywood case', which suggests serious incon sistencies between the data and the model. Apparently, the indicators are not aligned according to a single underlying dimension. Accordingly, the model is rejected.

The final combination (see Table 8) is loosely similar to the concept presented by Robert Putnam. The model is rejected because of poor model fit and lack of tau-equivalence (p < 0.01). The latter is caused by the intercept for 'voluntary association', which does not conform to the pattern of other indicators; participation in associations is at its lowest in the youngest group, which otherwise tends to occupy a position between groups 1 and 3 (see Appendix 1). This illustrates why this indicator is highly problematic, since the preconditions for membership in voluntary associations may differ extensively among various groups. (Furthermore, it did not as evidently fit the theoretical model as other indicators.)

Table 6 Assessing the modelfit of 'Combination C'; a social capital construct based on voluntary associations, self-efficacy and social networks. (N1 = 378, N2 = 529, N3 = 532)

Model description X2 d.f. p AX2 Ad.f. p

I. X, T invariant 51.886 8 0.000 II. X free, T invariant 21.382 2 0.000 30.504 6 0.000 III. X invariant, T free 4.358 2 0.113 47.528 6 0.000 IV. X, Tinvariant, exceptT(l) in group 1 4.864 7 0.677 47.022 1 0.000



Table 7 Assessing the modelfit of 'Combination D'; a social capital construct based on voluntary associations, self-efficacy and trust. (N1 = 378, N2 = 529, N3 = 532)


I. X, T invariant 29.587 8 0.000 II. X free, T invariant 31.878 2 0.000 2.291 6 0.891 III. X invariant, T free 9.026 2 0.011 20.561 6 0.002



215




Table 8 Assessing the modelfit of 'Combination E'; a social capital construct based on voluntary associations, social network and trust. (N1 = 378, N2 = 529, N3 = 532)


I. X, T invariant 28.486 8 0.000 II. X free, T invariant 31.881 2 0.000 3.395 6 0.758 III. X invariant, T free 8.408 2 0.015 20.078 6 0.003 IV. A, T invariant, except T(1) in group 2 11.122 7 0.133 17.364 1 0.000



Of five configurations tested, four are rejected. Only the variant similar to the concept intro duced by James Coleman satisfies the criteria for model fit and construct stability. It is also consistent with the theoretical model presented above, with the implications of game theor etical studies (cf. Macy, 1991a), as well as with the findings of a previous inquiry (Lillbacka, 2002). A factor model with standardized loadings, estimated in the total sample, is reported in Table 9. The factor loadings are comparatively weak, but exceed any commonly accepted cut-off value. They are also similar (although marginally stronger) to those reported in the previous study, based on other data (cf. Lillbacka, 2002: 56). Another similarity is the fact that the factor loadings can be equated (0.403 with the standard error of 0.022) without loss of

model fit; x2 = 1.121 (d.f. = 2); p = 0.574 (cf. Lillbacka, 2002: 56). This may imply that the

indicators describe equally important components of social capital; this was not presupposed by the theoretical model, but hardly contradicts it.

Summary and discussion

This inquiry has attempted to address a central problem in contemporary research. Far too

little attention has been paid to whether social capital has an empirical counterpart that is stable across time and space. Previous research has demonstrated how phenomena presum ably associated with social capital have behavioural consequences, but such results do not necessarily indicate the existence of some underlying dimension of social capital. Hence, notions regarding 'social capital' and its features are often theoretically postulated rather than empirically demonstrated.

Central theoretical notions from previous research were combined into a model of social capital from which indicators were derived. The model is also highly consistent with previous game theoretical studies, e.g. Macy's (1991a) experiments regarding cooperation in repeated k-player games parallels the model in several ways.

Of five alternative constructs tested, only one turned out to be stable when compared in three groups with highly different socio-economic and demographic features. Apart from

Table 9 Afactor model based on 'Combination B', i.e. James Coleman's social capital construct, when estimated in the entire sample. (n = 1439)

Indicator Factor loading Error R2

Self-efficacy 0.424 (0.056) 0.821 (0.053) 0.179 Social network 0.355 (0.049) 0.874 (0.044) 0.126 Trust 0.432 (0.057) 0.813 (0.054) 0.187

Model goodness of fit: x2 = 0 (d.f. = 0); p = 1.000. Numbers in parentheses are standard errors, all estimates are significant (p < 0.001).

216



...... ...... . .... .. 00t:00;g00ti00i@t;Ei0iStitE:; Lillbacka: M easuring Social Capital .E i . i R - LR.

. . .. .

being more consistent with the theoretical model than the other, the construct is also similar to the concept introduced by James Coleman (1988). The result iterates the tentative findings of a previous study (Lillbacka, 2002). The construct incorporates three parameters: the respon dent's social ties, his/her trust toward other individuals, as well as a sense of self-efficacy. Based on these data and this strategy for determining construct stability and viability, the construct appears to describe an actual empirical correlate of social capital. All constructs that in some way combined 'trust' and 'membership in voluntary associations' were rejected; these indicators are not significantly correlated, which contradicts some predominant theoretical assertions. 'Membership in voluntary associations' appears to be a highly problematic indicator, e.g. being highly dependent on demographic factors (cf. Lillbacka, 2002).

Apart from the empirical findings there is, as stated above, an equally important aspect that should render the result more plausible. The factor solution that fitted data, suggests that social capital can be characterized as reciprocal social connections based on trust which facili tates acquisition of (material or immaterial) utilities among its members. This corresponds firmly to how social capital was originally conceptualized. Scholars who base their research on James Coleman's concept are hardly likely to find any contradiction between these findings and their own notion of social capital. The inquiry had the aim of refining the concept of social capital, but the findings clearly suggest that Coleman's definition is still appropriate.

The availability of data imposed certain constraints, since the data were not optimal with regard to validity and statistical properties. For instance, Rotter's (1967) interpersonal trust scale, Schwarzer's and Jerusalem's (1995) self-efficacy scale, and the items used by Glass et al. (1997) to describe social integration, constitute tried and tested scales that may be more reliable than the indicators used here. Whether a particular construct is more empirically adequate than another cannot of course

be determined in a single study. However, if these results can be repeated (similar results were presented in a previous, more tentative inquiry with other data), it might be possible to determine the nature of social capital with greater precision. This would make it possible to exploit the full potential of the concept.

Appendix 1 Pearson correlations, means and standard deviations for the indicators in each subsample

'Voluntary as.' 'Self-efficacy' 'Social network' 'Trust'

Group 1 1.000 (n = 378) 0.229 1.000

0.186 0.051 1.000 0.067 0.080 0.077 1.000

Means 0.019 -0.262 -0.193 -0.083 St.dev. 0.694 0.672 0.885 0.655

Group 2 1.000

(n = 529) 0.244 1.000 0.192 0.143 1.000 0.098 0.216 0.157 1.000

Means -0.093 0.036 0.038 -0.006

St.dev. 0.724 0.740 0.786 0.776

Group 3 1.000

(n=532) 0.249 1.000 0.165 0.163 1.000

-0.038 0.176 0.197 1.000 Means 0.0790 0.150 0.100 0.064 St.dev. 0.684 0.727 0.612 0.729

217




Appendix 2 Socio-economic characteristics of the sample

Note: numbers reported in parentheses are percentages.

Gender (n = 1439): Male (43.5) and female (56.5).

Year of birth (n = 1439): The mean year of birth is 1957.2 and the standard deviation is 16.4.

Civil status (n = 1439): Unmarried (22.1), cohabiting (15.2), married (50.2), separated (0.6), divorced (7.2), widow/widower (3.7), other (1.0).

Household composition, number of individuals in the household (n = 1430). '1 individual' (22.0), '2' (38.0), '3' (16.4), '4' (15.1), '5' (6.0) and '6 or more individuals' (2.4).

Main occupation, 'Which of these alternatives best describes your life at the present moment' (n = 1335): 'employed >35 h/week' (48.9), 'employed 15-34 h/week' (5.2), 'part-time employment <15 h/week' (1.9), ' assists in family-owned business' (0.7), 'unemployed' (6.4), 'student' (11.4), 'age retirement' (13.7), 'retired due to other causes' (7.3), 'doing housework' (1.7), 'military or civil service' (0.4) and 'on parental leave' (2.3).

Education (n = 1349): 'Still in education' (7.0), 'folk school' (14.6), 'comprehensive school' (7.3), 'vocational school' (20.9), 'matriculation exam' (7.5), 'institute-level vocational training' (21.6), 'polytechnic exam' (5.1), 'academic degree (lower level, bachelor)' (4.4) and 'academic degree (higher level, master)' (11.5).

Monthly gross income (n = 1299): The mean monthly gross income (Finnish Marks) is 9515.72, and the standard deviation is 11952.47. The rate of exchange at the adoption of the euro was: 1 Euro = 5.9 Finnish Marks.

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Biographical Note: Ralf Lillbacka is a doctor of social sciences, and works as a senior lecturer at the Sector of Social Services and Health Care at the Swedish Polytechnic, Vasa, Finland.

Address: Sector of Social Services and Health Care, Swedish Polytechnic, Seriegatan 2, 65320 Vasa, Finland. [e-mail: [email protected]]

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Social Capital || Measuring Social Capital: Assessing Construct Stability of Various Operationalizations of Social Capital in a Finnish Sample