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The economic returns of immigrants’
bonding and bridging social capital:
A case study in the Netherlands
BRAM LANCEE1
European University Institute, Florence
Migration Working Group, January 16th, 2008
Abstract. The economic integration of immigrants in a host society is a major challenge for societies.
One of the approaches to explain economic success is by the use of social networks. Social capital is
particularly important for immigrants, since they are not familiar with the labour market in the host
society. Two forms of social capital are identified; bonding refers to a dense network with thick trust,
bridging to a crosscutting network with thin trust. It is examined to what extent bonding and bridging
social capital have an effect on income and occupational status for first and second-generation
Moroccans, Turks, Surinamese and Antilleans in the Netherlands (N=1340). Whereas it is often
assumed that bonding is to ‘get by’ and bridging to ‘get ahead’, in this case, findings are less clear-cut:
levels of trust (either thick or thin) cannot explain economic outcomes. Furthermore, bridging
networks affect both occupational status and income; bonding networks only affect occupational
status.
Key words: individual social capital, immigrants, bonding, bridging, the Netherlands, labour market.
1. Introduction
The position of immigrants in the host societies is one of the main topics in the current
debate on immigration. A point of particular interest is economic integration, defined as
the degree of equality between immigrant and native residents (Van Tubergen, 2004, p.
23). This equality is a matter of concern. In the Netherlands for example, more than
twenty percent of the immigrant population is unemployed, compared to nine percent of
the native population (Dagevos, 2006, p. 11). Also, even when controlled for their
human capital, first generation immigrants have a lower income and occupational class
than the native population (Dronkers & Wanner 2006; Tesser & Dronkers, 2007). A
relevant question is therefore how immigrants can succeed in building an economic
position equal to that of native residents.
One of the approaches to explain the economic integration of immigrants is to use
social capital theory. Social capital implies that people well equipped with social resources
– in the sense of their social network and the resources of others they can call upon-
succeed better in attaining their goals. Second, people will invest in relations with others
because of the expected future value of the resources made available by these relations
(Flap & Völker, 2004, p. 6). Researchers have suggested that social capital contributes to 1Address correspondence to [email protected], European University Institute, Department of Social and Political Sciences, Via dei Roccettini 9, 50014 San Domenico di Fiesole, Italy.
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economic outcomes such as access to the labour market (Aguilera, 2002), wages
(Aguilera & Massey, 2003; Aguilera, 2003; Boxman, De Graaf, & Flap, 1991) or
occupational status (Lin, 1999; Franzen & Hangartner, 2006). The question is to what
extent social capital can help explaining the labour force status of immigrants.
Recent discussions on social capital distinguish between ‘bonding’ and ‘bridging’
(Gitell & Vidal, 1998; Putnam, 2000; Woolcock & Narayan, 2000; Leonard & Onyx,
2003; Schuller, 2007; Szreter & Woolcock, 2004). Bonding refers to within-group
connections, while bridging social capital refers to between-group connections. Often it
is argued that returns depend on the different forms of social capital that people possess
(Beugelsdijk & Smulders, 2003; Putnam, 2000; Portes, 2000). Immigrants for example,
are repeatedly characterized as a group with a tight social network (Fernandeze-Kelly,
1995). Yet, while providing security, networks of immigrants are also characterized as
being closed and isolated (Portes, 1998). That is, immigrants dominantly seem to have
social capital of the bonding type. It is assumed that whereas bonding social capital is to
‘get by’, bridging social capital is to ‘get ahead’ (Narayan, 1999; Putnam, 2000). As Flap
and Völker (2004, p. 15) put it: a relevant question regarding social capital is to what
extent do ties remain within social groups, or to what extent are they also crosscutting
and connect the resource-rich with the resource-poor?
Especially when focusing on immigrants, not much is known about the different
types of relations that lead to economic success. Whereas social capital literature nowadays
agrees on a division of the concept in bonding and bridging, they have not been
conceptualized systematically yet (Patulny & Svendsen, 2007; Schuller, 2007). The
objective of this paper is to conceptualize bonding and bridging social capital and,
subsequently, to analyze its impact on the labour market outcomes for the four main
immigrant groups in the Netherlands: Turks, Moroccans, Antilleans and Surinamese.
Accordingly, the research question is: to what extent can bonding and bridging social
capital help explain the economic success of immigrants?
2. Elements of social capital
One of the main developments in the social sciences in recent decades is the insight that
‘no man is an island’ (Flap, 2002). People’s lives are embedded in the social networks that
they form and these networks affect their lives. Consequently, people use their network
to better attain their goals. A social network can be considered a social resource, which
can produce returns in order to improve the conditions of living. In other words, one’s
social network can be treated as a capital (Flap & Völker, 2004, p. 6). Lin (2001b) defines
social capital as ‘investment and use of embedded resources in social relations for
expected returns.’ Van der Gaag and Snijders (2004, p. 200) define individual social
capital as: ‘the collection of resources owned by the members of an individual’s personal
social network, which may become available to the individual as a result of the history of
these relationships’. There is no commonly accepted definition of social capital. In this
and the following section I discuss the elements that are part of one’s social capital,
taking the definition of Van der Gaag and Snijders (2004) as a starting point. In figure
one, these elements are visualized.2
2 When empirically searching for the elements within social capital, Onyx and Bullen (2000, pp. 36-37) come to similar conclusions. They construct the following factors: ‘A) refers to participation within local community organizations and events, B) refers to agency or pro-activity in a social context, C) refers to
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Individual social capital: the collection of resources owned by the members of an individual’s personal social
network, which may become available to the individual as a result of the history of these relationships
Structural: the wires in the network
Cognitive: the nodes in the network
Type of tie Embeddedness of tie in
institution
Attitudes and values
Figure 1. Different elements of individual social capital.
Social capital can be split up into a structural and a cognitive component. The structural
component refers to the ‘wires’ in the network: the extent, intensity and quantity of links
(Poortinga, 2006). As opposed to cognitive social capital, structural social capital involves
a behavioral component. It consists of (1) a collection of ties characterized by the
relation between the people connected, and (2) the possible institutional embeddedness
of these ties. The idea of the latter is that when ties are embedded in institutions, it is
more likely that resources will be exchanged (Putnam, 1993; Veenstra, 2002; Völker &
Flap, 1995; Hooghe & Stolle, 2003).
The cognitive component refers to the ‘nodes’ in a network: attitudes and values
such as perceptions of support, reciprocity and trust that contribute to the exchange of
resources (Poortinga, 2006). Trust involves confidence or faith in the reliability of people,
systems or principles (Veenstra, 2002). Often, trust and solidarity are seen as the single
component of social capital (Putnam, 1993; Fukuyama, 1995; Coleman, 1990; Gambetta,
1988; Portes & Sensenbrenner, 1993). For example, Brisson and Usher (2005; 2007)
operationalize bonding social capital as a scale of trust and social cohesion on the
neighbourhood level. According to Portes and Sensenbrenner (1993), bounded solidarity
and enforceable trust are the main components of social capital in immigrant
communities. In this paper, I label the level of solidarity and trust in the nodes of a
network cognitive social capital.
Two aspects in the taken approach should be emphasized at this point. First,
social capital is analyzed on the individual level, as opposed to the collective level
(Snijders, 1999; Kadushin, 2004). Some scholars discuss social capital as collectively
produced and benefiting the community (Coleman, 1990; Putnam, 1993). Others
(Bourdieu, 1986; Flap, 2002; Lin, 2001a) have focused on social capital as a pool of
resources, which may be helpful for the individual’s goal attainment. In this paper, I do
not analyse the social capital effects of the ‘ethnic community’ as such (see for example
Fennema & Tillie, 1999; Fennema, 2004; Li, 2004; Kloosterman & Rath, 2001; Sanders &
Nee, 1987) but I focus on the returns of individual social capital.
Second, according to the definition of Van der Gaag and Snijders (2004),
resources may (or may not) become available. The question of may or may not refers to
feelings of trust and safety. Factors D, E and F are concerned with participation and connection within a variety of contexts, within the neighbourhood (D), among family and friends (Factor E), and within the workplace (Factor H)’.
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the distinction between access to and the actual use of resources (as discussed by Lin,
2001b). I consider social capital as access to resources. The reason for this is twofold.
First, it is not only the resources you actually use that are essential in social capital, but
also the ones that are potentially available. For example, even though one would never
use this opportunity, being able to borrow money from a friend does provide benefits
and security. The second reason is practical: measurement of use of requires special
measurement techniques, for example due to its retrospective nature (see also Van der
Gaag, 2005, pp. 16-18). Also in this case, the data do not allow for this differentiation.
3. Forms of social capital
Bonding social capital
Bonding social capital implies having dense ties and thin trust. As discussed in the
previous section, this can be split up in a structural and a cognitive part. In terms of
structural social capital, the concept of bonding is based on the idea of the ‘strength of
strong ties’ (Lin, Ensel, & Vaughn, 1981; Coleman, 1990). The strength of a relationship
refers to the degree of intensity, frequency, intimacy, reciprocity or acknowledged
obligations. The stronger the relationship, the more likely the sharing and exchange of
resources (Lin, 2001b, p. 66). Coleman (1988) adds the principle of network closure: in a
network with total closure the members of the network have ties with all members.
The clearest case of a network with a high degree of closure is probably the family.
Within the family, social capital is distributed and effectively used (Coleman, 1988;
Bubolz, 2001; Nauck, 2001) for example with respect to family businesses (Alesina &
Giuliano, 2007; Sanders & Nee, 1996). According to Middelton, Murie and Groves
(2005), the strong ties connecting family members, neighbors, close friends and business
associates can be called bonding social capital. I define bonding structural capital as ‘ties
that strongly connect people and increase the degree of network closure.’ In the section
data and measurement this is operationalized as the strength of family ties.
In terms of cognitive social capital, the relations in a network are characterized by
their degree of solidarity and trust. Within trust one can differentiate between thick or
particular and thin or generalized trust. Whereas thin or generalized trust refers to
instrumental solidarity, loose ties and trust in institutions, thick or particular trust is
associated with strong ties, solidarity, frequent and primary contacts (Hughes, Bellamy, &
Black, 1999; Newton, 1997). Bonding social capital is associated with thick and particular
trust.
The advantage of thick trust as opposed to thin trust is that it is more likely to be
enforced. Coleman (1988) relates this to network closure: the combination of closure and
thick trust increases the likelihood of resources to be exchanged. These networks consist
of people who mutually support each other because they share a similar social identity.
This support is likely to be limited to insiders (Onyx & Bullen, 2000). Hence, thick trust
in a social structure contributes to the exchange of resources within this structure.
Subsequently, cognitive bonding social capital can be described as the attitudes and values
(such as trust and solidarity) that contribute to the exchange of resources among the
members of an individual’s close and dense network.
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Bridging social capital
Structural bridging social capital refers to the collection ties that form an individuals’
‘wide’ social network. A wide social network is a network that contains structural holes
(Burt, 1992, 2001). Structural holes are gaps in networks, and an opportunity to broker
the flow of information between people or groups and create an advantage for the
individual whose relationships span the holes. A bridge is a tie that spans a structural
hole (Burt, 2002). The concept originates from Granovetter’s (1973) strength of weak
ties hypothesis, which states that weak ties are more valuable since they lead to rare
information. The advantage of bridging ties is that unique information and opportunities
come into reach (Putnam, 2000, p. 22).
In most empirical studies however, no conclusive network information is available;
consequently structural holes cannot be observed directly (see also Marsden, 1990).
Hence, ties that span structural holes need to be measured with a proxy. Structural holes
are for example gaps across socio-economic variables such as class, ethnicity and age
(Portes, 1998; Narayan, 1999). Ties that cut across these socio-economic variables can be
seen as a proxy for ties that span structural holes.
Wuthnow (2002) identifies two types of crosscutting ties: identity and status
bridging. Identity bridging refers to ties that span culturally defined differences such as
ethnicity. Since their networks are characterized as being closed and isolated (Portes,
1998), especially for immigrants, building ties that cut across the ethnic divide is
important. Status bridging refers to ties that span vertical arrangements of power, wealth
and prestige3. As a way to get access to resources, status-bridging may be increasingly
important for disadvantaged people (Wuthnow, 2002; Erickson, 1996). Access to
institutions is often assumed to contribute in establishing ties that bridge across status
(Putnam, 2000; Sabatini, 2005; Grootaert, 2002). As a result, I take access to institutions
and inter-ethnic contacts as proxies for ties that span structural holes.
Leonard and Onyx (2003) find that networks are not necessarily overlapping
through weak ties, as suggested by Granovetter (1973): “A model of society with
relatively small cohesive well-bonded groups joined to each other by loose ties may not
be the most appropriate. Perhaps a more useful model is that of a chain in which each
link is well bonded but there are also strong ties to some other links.” In other words,
building structural bridging social capital is spanning structural holes; either through
strong or weak ties.
Cognitive bridging social capital is characterized by thin or particular trust: ‘it is
associated with the organic solidarity or ‘gesellschaft’ of looser, more amorphous and
secondary relations’ (Newton, 1997, p. 578). Thin trust is also associated with confidence
in institutions or in the government (Nooteboom, 2007). Often thin trust is related to
values of modern western society.
Whereas one might have difficulties in imagining what ‘organic solidarity’ in daily
life means, values of modern society are more straightforward. For example, Uunk
(2003) analyses the ‘modern’ attitudes of the four main immigrant groups in the
Netherlands. He differentiates between 1) the sex specific division of roles, 2) the role of
3 Since bridging is a horizontal metaphor, the ties between people with a different authority or social-economic status (i.e. vertical ties) are sometimes also referred to as linking social capital (Woolcock & Narayan, 2000; WorldBank, 2001, p. 128). To avoid introducing even more terminology, I will continue to refer to status bridging.
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women in society, 3) central family issues such as marriage and children, 4) authority
relations, 5) moral issues and 6) religion. Ode and Veenman (2003) also include outward
orientation in their analysis, which includes both opinions on interethnic contacts and
the use of the host society’s language. They find that both modernization and outward
orientation contribute positively to the economic integration of immigrants in the
Netherlands.
As a result, I describe bridging cognitive social capital as ‘thin trust, that is the
attitudes and values such as modernization and outward orientation that contribute to
the exchange of resources in one’s wide social network.’ In figure two, the elements of
bonding and bridging social capital are summarized
Social capital
Bonding Bridging
Structural Network closure
• Family ties Structural holes • Interethnic ties • Access to institutions
Cognitive Thick trust
• Family related values • Solidarity/reciprocity
Thin trust • Outward orientation • Modernization values
Figure 2. The elements of individual bonding and bridging social capital.
4. Social capital and labour market outcomes
It is often found that social capital has a positive effect on both income (Aguilera &
Massey, 2003; Boxman, De Graaf, & Flap, 1991) and occupational status (see for an
overview Lin, 1999). Less is known about the returns of social capital for immigrants
specifically, especially when focusing on its different forms.
Possessing bridging social capital implies having access to unique information by
connecting to other networks. These bridges create opportunities for upward mobility on
the labour market (Granovetter, 1995; De Graaf & Flap, 1988). For immigrants, in
particular interethnic ties are valuable, since they are a link out of the ethnic community
and by that create a wider network containing more valuable resources and job
opportunities (Heath & Yu, 2005; Uunk, 2002). For example, analyzing the labour
market outcomes for Puerto Rican and Mexican immigrants in the United Sates, Aguilera
(2003; 2005) finds that non-family social capital (organizational involvement and having
inter-ethnic friends) is positively related to hourly earnings. Furthermore, earlier research
shows that a tie with a higher status improves the chances of finding a better job (Lin,
Ensel, & Vaughn, 1981; De Graaf & Flap, 1988). It is therefore expected that structural
bridging social capital has a positive impact on the labour market outcomes of
immigrants, both with respect to interethnic contacts and access to institutions.
With respect to cognitive bridging social capital, Sowell (1996) argues that minority
groups that are most capable of adapting to modern Western society have the greatest
likelihood of achieving a socio-economic position equal to that of the native population.
Ode and Veenman (2003) also make use of the SPVA data and analyze the relation
between informal participation, modernization and out-group orientation and
occupational level for immigrants in the Netherlands. They only find a significant
positive effect for modernization. The above is formulated in a general hypothesis:
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H1: There is a positive relationship between the level of bridging social capital and
labour market outcomes (i.e. income and occupational status).
For bonding social capital the effects are less clear-cut. Two possible lines of
argumentation can be followed. The first I label the ‘isolation argument’. It runs counter
to the argument of bridging social capital: whereas more bridging ties create
opportunities, high closure limits this, because the same information is being circulated
within the network. This is the argument stating that ‘whereas bonding is to get by,
bridging is to get ahead’. This could be particularly true for immigrants, since the
immigrant community is relatively isolated form the native population, who is in control
of the most valuable resources.
The second argument is the ‘closure argument’ as put forward by Coleman (1988;
1990): closure in a network provides more reliable communication channels, and
protection from exploitation by the members of the network; it hence is a capital with
positive returns. One could argue that especially immigrants need reliable
communication channels and sincere network support, since they are more vulnerable
than the native population.
Indeed, Sanders, Nee and Sernau (2002), studying Asian immigrants in Los
Angeles, find that job seekers ask their better connected relatives, friends and
acquaintances to serve as intermediaries. These networks provide resources in order to
make headway on the labour market. Furthermore, Sanders and Nee (1996) find that
family social capital -operationalized as being married and the number of relatives-
increases the likelihood of immigrants in the U.S. being self-employed. However, studies
explicitly combining measures of bonding and bridging, find that bridging does and
bonding does not affect economic outcomes such as growth (Beugelsdijk & Smulders,
2003) and economic development (Sabatini, 2006). Since the two latter studies do not
focus on immigrants, I follow the closure argument and hypothesize that:
H2: There is a positive relationship between the level of bonding social capital and
labour market outcomes.
Nonetheless, the studies mentioned above do support the statement that bonding is to
‘get by’ and bridging to ‘get ahead’. For example according to Patulny and Svendsen
(2007), the two kinds of trust promote access to different kinds of resources. It is
therefore likely that the effects of bonding and bridging are different in size. Thus, I
hypothesise:
H3: The relationship between social capital and labour market outcomes is stronger for
bridging than for bonding social capital.
4. Data and measurement
The data used is the 2002 wave of the ‘Social Position and Use of Utilities Immigrants’
Survey (SPVA) (Groeneveld & Weyers-Martens, 2003). The SPVA contains detailed
information on the economic and social position of the four biggest immigrant groups in
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the Netherlands: Turks, Moroccans, Antilleans and Surinamese4. The SPVA survey is the
main data source for monitoring the disadvantage of ethnic minorities in the Netherlands
(Guiraudon, Phalet, & Ter Wal, 2005).
The sample includes heads of households only. Other members of the household
were also included in the survey, but received a different questionnaire, in which items
on for example language proficiency were omitted. The sample consists of people
currently employed. Furthermore; I select those active on the labour market5 and in the
age category 25-65 years. The lower boundary has been chosen because those older than
24 are assumed to have finished their studies and active on the labour market; 65 is the
official retirement age. In table two a description of the sample is presented. The
dependent variable is operationalized as (1) total net monthly income in Euros and (2)
the International Socio-Economic Index for Occupational Status (ISEI)6 (Ganzeboom,
De Graaf, & Treiman, 1992). Cases with a missing value on the dependent variable are
deleted. To measure the different forms of social capital, a non-parametric IRT model for
finding cumulative scales is used: the so-called ‘Mokken scaling method’ (Mokken, 1996;
Sijtsma & Molenaar, 2002). A detailed description of the construction of the social
capital scales can be found in the Appendix. The bonding scales consist of eleven items
in total that cover family ties and values (see figure three). Interethnic contacts are
measured with a Mokken scale, based on five items (see figure four). Included are
interethnic marriages (Bijl, Zorlu, Van Rijn, Jenissen, & Blom, 2005, pp. 69-74; Clark-
Ibanez & Felmlee, 2004) and friendships (Haug, 2003; Dagevos & Ode, 2003; Fong &
Isajiw, 2000). Status bridging is measured by being a member of an association. The
associations included in the SPVA are sports/hobby clubs, unions, NGO’s, political
parties, and religious organisations. Last, cognitive bridging social capital is measured
with a Mokken scale, based on the items that were used by Uunk (2003) to measure
modernization.
A number of controls are included in the analyses. The main part of the difference
in labor force status between immigrants themselves and between immigrants and the
native population in The Netherlands is due to differences in educational attainment and
language proficiency (Euwals, Dagevos, Gijsberts, & Roodenburg, 2006, 2007;
Bevelander & Veenman, 2004; Lautenbach & Otten, 2007). Furthermore, it is suggested
that country specific human capital (that is, human capital acquired after arrival in the
host society) has most impact on labour force status (Friedberg, 2000). Besides education
in the host society, one of the most important country specific skills is language
proficiency. Generally, language proficiency is found to have a positive impact on
employment (Van Tubergen, Maas, & Flap, 2004; Chiswick & Miller, 2002), occupational
status (Forrest & Johnston, 2000) or income (Dustmann & Van Soest, 2002). Therefore,
a (Mokken) scale of language proficiency is included as a control variable7.
4 The SPVA consists of random samples of the population in a number of cities, including the four biggest cities in the Netherlands. For a detailed description of survey and sampling technique see Groeneveld & Weyers-Martens (2003). 5 Following the definition of the CCS 91: more than 11 hours work per week (Groeneveld & Weyers-Martens, 2003). 6 In the SPVA, occupation is coded with the standard 1992 classification of the Dutch Central Bureau of Statistics (CBS). Bakker et. al. (1997) describe how this can be converted into an ISEI score. 7 For a description of the construction of this scale see the appendix.
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Furthermore, I control for the educational attainment of the parents, (measured as
the highest degree obtained by either the father or the mother), age, gender, marital
status (Bevelander & Veenman, 2004), rural versus urban domicile, ethnicity, first versus
second generation and -with respect to income- for the number of contracted working
hours. First generation immigrants are defined as those who are born in Turkey,
Morocco, Suriname or the Dutch Antilles. Second generation immigrants are those that
are born in the Netherlands with at least one parent born in one of the aforementioned
countries, or those that are born abroad and migrated to the Netherlands at an age
younger than six.
Last, the duration of stay in the host society may have an impact on labour market
outcomes (Li, 2004). Logically, duration of stay also affects the creation of bridging social
capital: it is likely that the time spent in a country raises the probability of building
bridges and thin trust. When included in the analyses, duration of stay will therefore
partially incorporate the effect of social capital effect. Yet, the duration of stay also
proxies other factors influencing economic outcomes, such as familiarity with the labour
market and the institutional design of the host society (Büchel & Frick, 2005).
Structural bonding Cognitive bonding (Range: 1 do not agree at all , 5 fully agree)
Received help from parent/child Trust family more than friends Helped parent/child Discuss problems rather with family Got advice from parent/child Family members should be there for each other Gave advice to parent/child You can always count on your family Saw parent/child in past 12 months In case of worries family should help Had contact with parent/child in past 12 months. Family members keep each other informed
Figure 3. The items that measure bonding social capital
Structural bridging Cognitive bridging (Range: 1 fully agree, 5 do not agree at all)
Interethnic contacts Openness about sex is wrong Partner born in The Netherlands Contact between men and women is too liberal More contact with native Dutch than own ethnic group It is best when children live at home until they marry Has native Dutch friends or acquaintances Men and women can live together unmarried Receives visits at home from native Dutch friends or neighbors (item reversed) Contact with native Dutch in private life
Status bridging
Member of an association (yes/no)
Figure 4. The items that measure bridging social capital
6. Results
In table one, the means and standard deviation of the variables are presented, split out
for men and women. A t-test is done to see whether there is a difference between men
and women, which for most variables is the case. A table with the correlation coefficients
between the independent variables can be found in the appendix (table A6). Noting that
.48 (between structural and cognitive bridging) is the highest coefficient; there is no need
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for concerns about multi-collinearity8. Most striking is that cognitive bonding correlates
negatively with the bridging scales, and (not shown in the table) with ISEI and income
(discussed below).
In table two, occupational status is predicted by social capital, including the
relevant control variables. Model one only contains the social capital scales. All scales but
cognitive bonding predict income significantly positive. As already noted above, the role
of cognitive bonding does not affect economic outcomes positively. Without any
controls, the effect of cognitive bridging is largest, followed by structural bridging,
membership of an association, and structural bonding. Model two adds gender, dummies
for the ethnic groups, generation, marital status and domicile. Remarkably, gender does
not have a significant effect on occupational status. The analyses were also run with
interaction terms for gender and social capital, none of the terms turned out to be
significant. Compared to the Surinamese, the Moroccans and the Turks have a
significantly lower ISEI score, the Antilleans significantly higher. The second generation,
as expected, has a significantly higher occupational status. Furthermore, respondents
living in an urban area, have a significantly higher ISEI score. All social capital
coefficients decrease, but remain significant at the p<.001 level (p<.01 for structural
bonding).
Table 1. Descriptive statistics for the variables used in the regression analyses
Total Men Women Men vs. women
Mean SD Mean SD Mean SD t-test
ISEI 40.17 15.45 39.76 15.45 40.88 15.45 -1.29 Income1
1483.75 698.14 1606.80 763.11 1296.24 500.27 8.42** Social capital
Cognitive bonding (min 0, max 6) 4.11 1.01 4.19 1.01 3.98 0.98 3.63** Structural bonding (min 0, max 6) 2.45 1.53 2.26 1.44 2.80 1.62 -6.46** Cognitive bridging (min 0, max 4) 1.78 0.86 1.67 0.86 1.96 0.82 -6.03** Structural bridging (min 0, max 5) 2.57 1.20 2.54 1.23 2.63 1.13 -1.35 Membership association (0 no, 1 yes) 0.32 0.47 0.34 0.47 0.29 0.45 1.87 Human capital
Educational degree, 0 (none) to 7 (academic) 3.27 2.02 3,18 2.01 3.42 1,91 -2.13* Highest educational degree parents, 0 (none) to 7 (academic)
1.75 2.02 1.47 1.94 2.24 2.07 -6.87**
Language proficiency (min 0, max 4) 2.83 1.09 2.68 1.13 3.11 0.93 -7.29** Other
Age 40 8.53 40 8.75 40 8.13 0.20 Duration of stay (in years) 20.29 9.29 20.42 9.45 20.06 8.99 0.68 Married (%) 53.8 70.8 24.2 18.99*** Domicle (% urban as opposed to rural) 66.3 64.7 69.1 -1.62 Ethnicity Moroccans 283 238 46
Turks 342 278 63
Antilleans 446 154 155 Surinamese 309 210 236
First generation 1278 811 467 Second generation 102 68 34
Total 1380 879 501
Source: SPVA 2002. Note: * p <.05, ** p< .01.
(1) The sample size for the analysis of income is 1240.
8 Furthermore, the highest VIF value is 2.61, which is much below the often-mentioned threshold of 10.
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When controlling for education, language proficiency and parental education
(model three), there is no longer a significant difference between the Moroccans, Turks
and Surinamese. Additionally, with high and significant coefficients, the human capital
variables ‘consume’ the vast amount of the social capital effect in explaining occupational
status. The coefficients further decrease; only structural bonding and being a member of
an association remain significant (p<.05). Nevertheless, even controlled for human
capital, structural bonding and being a member of an association affect occupational
status significantly positive.
To control for factors influencing occupational status that are a natural result of
time, in model four the duration of stay is added. While this is a significant and a rather
strong predictor, the coefficients for structural bonding and membership of association
remain positive and significant.
Table 2. OLS regression predicting occupational status (ISEI), standardized coefficients (N=1380). Model 1:
social capital
Model 2: 1 & age, gender, generation and ethnic groups
Model 3: 1, 2 & human capital
Model 4: 1, 2, 3 & duration of stay
Social capital Cognitive bonding -.0.041 -.030 -.016 -.010 Structural bonding .0841** .076** .056** .044* Cognitive bridging .229*** .194*** .033 .028 Structural bridging .178*** .151*** .047 .039 Membership association .129*** .125*** .050* .046* Background
Gender (0 men, 1, women) -.041 -.035 -.032 Age -.068** .010 .-.044 Marital status (1 married, 0 other) .051 .045 .046 Generation .060* .050* .016 Moroccans -.074* .005 .000 Turks -.101** .010 -.002 Antilleans .089** .077** .093*** (Surinamese ref. cat.) Domicile (0 rural, 1 urban) .068** .041 .043*
Human capital Educational degree .466*** .476*** Language proficiency .144*** .123*** Highest educational degree parents .068** .063* Duration of stay .096** Constant 26.104*** 27.632*** 12.428*** 16.217*** Adjusted R square .176 .198 .401 .407 Source: SPVA 2002. Note: * p <.05, ** p< .01, *** p<.001.
The pattern for income is slightly different (table three). In model one, only structural
bridging and being a member of an association predict income significantly positive.
Both scales for bonding do not have a significant effect. In model two, when controlling
for age, gender, generation and the ethnic groups, coefficients for social capital drop, but
also cognitive bridging appears significant now. As opposed to occupational status, the
effect of gender on income is strong: men earn significantly more than women. For this
reason, the analyses were also run with interaction terms for gender and social capital.
12
None of the terms turned out to be significant. There is no significant impact with
respect to marital status, generation, ethnicity or the place of residence. When adding
human capital (model three), it shows that whereas both education and language
proficiency have strong explanatory power predicting income, the education of the
parents does not have a significant effect. The coefficients for social capital drop, only
structural bridging remains significant. Apparently, when controlling for human capital
only interethnic contacts make a difference with respect to income.
In the last model, the number of contracted working hours and the duration of
stay are added. The coefficient for structural bridging drops slightly, but remains
significant. Language proficiency does not have a significant effect anymore. Since the
coefficient for gender is rather high, interaction terms for gender and each of the social
capital measures were included. No significant interaction terms were found.
Table 3. OLS regression predicting income, standardized coefficients (N=1240). Model 1:
social capital
Model 2: 1 & age, gender, generation and ethnic groups
Model 3: 1, 2 & human capital
Model 4: 1, 2, 3 & duration of stay and contracted hours
Social capital Cognitive bonding -.033 -.045 -.029 -.035 Structural bonding -.007 .033 .023 .011 Cognitive bridging .062 .091** .006 -.010 Structural bridging .147*** .141*** .080* .068* Membership association .104*** .088** .048 .025 Background
Gender (0 men, 1, women) -.269*** -.264*** -.156*** Age .092** .135*** .057 Martial status (1 married, 0 other) .005 .001 .014 Generation .035 .030 -.013 Moroccans -.071 -.026 -.042 Turks .008 .078 .036 Antilleans -.030 -.024 -.005 (Surinamese ref. cat.) Domicile (0 rural, 1 urban) .030 .011 .010 Human capital
Educational degree .246*** .257*** Language proficiency .108** .063 Highest educational degree parents .008 .002 Duration of stay .135*** Contracted hours .298*** Constant 1222.297*** 928.934*** 497.505* -92.860 Adjusted R square .054 .129 .184 .268 Source: SPVA 2002. Note: * p <.05, ** p< .01, *** p<.001.
7. Conclusion and discussion
In this paper, I conceptualized bonding and bridging social capital and analysed its effect
on occupational status and income for the four main immigrant groups in the
Netherlands. I hypothesized that both bonding and bridging social capital positively
affect labour market outcomes, although bridging stronger than bonding. With respect to
the empirical results, several observations can be made.
13
First, whereas initially for most of the social capital scales the effect is highly
positive, when controlling for more variables -not surprisingly- the coefficients for social
capital decrease and its significance levels rise. Second, there are differences between the
forms of social capital: the bridging scales seem to explain more than the bonding ones;
and structural more than cognitive. Additionally, cognitive bonding has no significant
effect. Third, the results differ for income and occupational status. Fourth, with respect
to the controls, the effect of social capital is not different for men and women and when
controlling for human capital there are virtually no differences between the ethnic
groups.
With respect to the hypotheses formulated, this implies the following. H1, stating
that bonding affects labour market outcomes positively is partially accepted for
occupational status (structural bonding) and fully rejected with respect to income. H2
stated that bridging affects labour market outcomes positively. This hypothesis is partially
accepted: status bridging positively affects occupational status; structural bridging
positively affects one’s income. Finally, H3 stated that bridging has a stronger effect than
bonding is also only partially accepted. For ISEI the two significant coefficients in the
last model are virtually equal, for income, the only significant coefficient is that of
structural bridging.
Some tentative explanations can be formulated with respect to these findings.
First, regarding to the differences in cognitive and structural social capital. Following
behavioural psychology, attitudes (cognitive) result in behaviour (structural) rather than
the other way around. In other words, since they also capture attitudes, it seems logical
that the ‘structural’ scales behave better in the analyses. Another explanation could be
that structural and cognitive social capital have effects on different levels. That is whereas
on the individual level it is the actual contacts that matter, the effects of cognitive social
capital are to be found on the community or macro level. In a somewhat different way
this is also found by Poortinga (2006). When explaining self-reported health, he finds
that aggregate level social trust explains more than aggregate level civic participation, and
also more than individual level trust. Further research should be carried out to investigate
this explanation.
With respect to different effects of bridging social capital on income and
occupational status, the mechanism at hand could be different. As described in section
two, access to institutions proxies status bridging. This is exactly what seems to be
supported: ‘more’ associational membership corresponds with a higher occupational
status. Hence, access to institutions bridges across status and therefore positively affects
one’s ISEI score, rather than one’s income. The positive effect of structural bridging on
income (and not on one’s ISEI-score) could be explained as follows. Interethnic contacts
better link you to the native population, so you gain more information about the labour
market, resulting in better negotiations and thus better chances for a higher income.
Regarding bonding social capital: apparently in this case, thick or family trust does
not affect economic outcomes. Linking to the theory, this implies that the ‘isolation’
argument holds, rather than Coleman’s argument of closure. Strong ties with thick trust
isolate people, rather than improve the exchange of resources in the network. Since for
income no effects of bonding social capital were found this would plead for the isolation
argument. As for occupational status, there is a significant effect of structural bonding:
Strong family ties do affect occupational status positively.
14
Last, as noted above, the strongest predictors of income and occupational status
are educational attainment and language proficiency. This suggests that human capital
should perhaps not only be included as a control; one would benefit by analysing the
causal relations between human and social capital. Possessing social capital can be an
effect of human capital, it could however also be the other way around: due to high
levels of social capital, human capital can be build, which results in economic success.
Indeed, Boxman et. al. (1991) find that (for Dutch managers) social and human capital
interact in the process of income attainment. Such an analysis could shed another light
on the effects of social capital on labour market outcomes. Also here, further research is
desired to investigate this interaction.
In conclusion, what can be said about the statement that ‘whereas bonding is to get
by, bridging is to get ahead?’ In he case of immigrants in the Netherlands, no conclusive
evidence was found that supports this claim. It seems that bonding and bridging are not
mutually exclusive in its effects, furthermore they are different for different elements of
bonding and bridging, and they differ with respect to income and occupational status.
Appendix The measurement of social capital using cumulative scaling
For the scales that measure the several elements of social capital, Item Response Theory
(IRT) was used, rather than factor analysis. First however, the suitable items were
analyzed with an exploratory factor analysis. A Varimax rotated solution results in almost
exactly in the domains as presented in the final scales, explaining 50,4% of the variance,
and a scree plot bend at the fourth domain. This implies that based on the correlational
logic of factor analysis, it makes sense to cluster the items in the way as presented in the
tables below. Also, the Cronbachs alpha for the scales is satisfactory (see tables A1-A4).
Factor analysis is based on correlation logic and assumes interval measurement. It
therefore does not take into account that items may be related in a cumulative (or
ordinal) way. This may misrepresent the actual structure of the data (see also Van der
Eijk, 2007; Van Schuur & Kiers, 2004). The logic of IRT is based on the order of the
proportion of people that gives a positive response to an item. The scale is based on the
pattern in the items regarding the number of people that gave a positive response. For
example, few of the respondents have a native Dutch partner. It therefore correlates
relatively low with the other items that measure inter-ethnic contacts (in the factor
analysis, it is the item with the lowest factor loading). However, marrying a native Dutch
may very well be the upper part of a scale that measures inter-ethnic contacts: those who
have a native Dutch partner, also score positively on the other items. In the example,
having a partner that is born in the Netherlands is the item with lowest proportion of
positive response and thus the most ‘difficult’ item on the scale: those who marry a
native Dutch also score positive on the other items, but not (necessarily) the other way
around (this is supported by the high item- H, see table A3). Hence, IRT does account
for such an ordinal structure and may therefore be more appropriate for scale
construction. Moreover, since social capital often understood in terms of ‘more’ and
15
‘less’ IRT, is especially suitable for the measurement of social capital (Van der Gaag &
Snijders, 2004).
Therefore a non-parametric IRT model for finding cumulative scales is used, the
so-called ‘Mokken scaling method’9. This resulted in four scales, which were tested. The
relevant coefficients are presented in tables A1-A4. There are several criteria that a set of
items has to meet to form an acceptable Mokken scale. The most important measure is
Loevinger’s Homogeneity coefficient (H). The following cut-off values are conventional
to judge a Mokken scale: >.30 being a useful scale, >.40 a medium strong scale, and >.50
a strong scale (Mokken, 1996; Van Schuur, 2003). For each of the scales, H >.4, for
structural bridging H is even .66. Also the item-H is above .40 for all items. Furthermore,
the test for monotone homogeneity (i.e. the positive response to each item is a function
of the positive response to easier items in the same scale) and double monotonicity (to
assess whether the degree of difficulty across items is the same for all individuals) is
positive.
The actual scale consists of the sum of the items. Before this computation,
missing values for the individual items were imputed using two-way imputation
(described in Sijtsma & Van der Ark, 2003). The imputation is done as follows (Van
Ginkel & Van der Ark, 2007, p. 2): ‘Let PMi be the average of all observed scores of
respondent i, let IMj be the average of all observed scores on item j, and let OM be the
average of all observed scores on all items and all persons. The missing value of
respondent i on item j is then based on Xij = PMi + IMj � OM’. Imputation was done
for all cases with less than 60% of the scale items missing. Those cases with more than
60% of the values missing were deleted.
The language proficiency scale was constructed according to the same principle. In
table A5 the items are presented.
Table A1. Items structural bonding social capital N=3,787
Scale coefficient H = 0.46 Mean Item H
Received help from parent/child 1.46 0.54 Gave help from parent/child 1.62 0.52 Got advice from parent/child 1.74 0.39 Gave advice to parent/child 1.81 0.42 Saw parent/child in past 12 months 4.15 0.47 Had contact with parent/child in past 12 months. 5.07 0.46
Cronbachs Alpha =0.74
9 The software used is the Mokken Scale Analysis for Polytomous Items , MSPWIN 5.0 (Molenaar & Sijtsma, 2000).
16
Table A2. Items cognitive bonding social capital N=3,774
Scale coefficient H = 0.42 Mean Item H Trust family more than friends 3.64 0.42 Discuss problems rather with family 3.68 0.42 Family members should be there for each other 3.71 0.40 You can always count on your family 3.84 0.45 In case of worries family should help 3.98 0.44 Family members keep each other informed 4.25 0.35
Cronbachs Alpha = 0.78
Table A3. Items structural bridging social capital N=3,770
Scale coefficient H = 0.66 Mean Item H Partner born in The Netherlands 1.10 0.50 More contact with native Dutch than own ethnic group 1.78 0.49 Has native Dutch friends or acquaintances 1.82 0.62 Receives visits form native Dutch friends or neighbours 2.00 0.64 Contact with native Dutch in private life 2.09 0.65
Cronbachs Alpha =0.74
Table A4. Items cognitive bridging social capital N=3,331
Scale coefficient H = 0.44 Mean Item H Openness about sex is wrong 2.29 0.45 Contact between men and women is too liberal 2.42 0.45 It is best when children live at home until they marry 2.77 0.43 Men and women can live together unmarried (item reversed) 3.16 0.43
Cronbachs Alpha =0.71
Table A5. Items language proficiency N=3325
Scale coefficient H = 0.70 Mean Item H Problems with reading Dutch 2.12 0.69 Frequency of using Dutch with partner 2.17 0.71 Frequency of using Dutch with children 2.37 0.71 Problems with speaking Dutch 2.39 0.65
Cronbachs Alpha =0.86
17
Table A6. Bivariate correlations between independent variables.
Cog
. B
ondi
ng
Str
. Bon
ding
Cog
. B
ridgi
ng
Str
. Brid
ging
Mem
ber
Ass
.
Gen
der
Age
Mar
ried
Gen
erat
ion
Mor
occa
n
Tur
k
Ant
illea
n
Sur
inam
ese
Urb
an
Edu
catio
n
Lang
uage
Par
enta
l ed
ucat
ion
Dur
atio
n of
st
ay
Con
trac
ted
hour
s
. Cog. Bonding 1.00 Str. Bonding 0.12* 1.00 Cog. Bridging -0.41* 0.06 1.00 Str. Bridging -0.29* 0.07 0.48* 1.00 Member Ass. -0.09* 0.02 0.13* 0.15* 1.00 Gender -0.09* 0.16* 0.15* 0.03 -0.05 1.00 Age -0.04 -0.03* -0.07* -0.05 -0.03 -0.02 1.00 Married 0.19* -0.08* -0.38* -0.17* -0.01 -0.43* 0.05 1.00 Generation -0.09* 0.16* 0.23* 0.26* 0.10* 0.02 -0.35* -0.09* 1.00 Moroccan 0.20* 0.02 -0.32* -0.16* -0.08* -0.20* -0.11* 0.25* 0.01 1.00 Turk 0.22* -0.12* -0.28* -0.25* -0.03 -0.21* -0.10* 0.37* -0.07* -0.27* 1.00 Antillean -0.15* -0.07* 0.30* 0.21* 0.04 0.15* 0.02 -0.31* -0.01 -0.28* -0.31* 1.00 Surinamese -0.23* 0.15* 0.24* 0.17* 0.06 0.21* 0.16* -0.25* 0.06 -0.35* -0.39* -0.40* 1.00 Urban 0.05 0.05 -0.08* -0.20* -0.08* 0.05 0.00 -0.08* -0.03* 0.12* -0.05 -0.05 -0.01* 1.00 Education -0.20* 0.05 0.39* 0.32* 0.23* 0.05 -0.15* -0.15* 0.16* -0.13* -0.15* 0.17* 0.09 -0.01 1.00 Language prof. -0.29* 0.14* 0.44* 0.46* 0.12* 0.19* -0.05 -0.37* 0.20* -0.21* -0.45* 0.14* 0.45* -0.05 0.34* 1.00 Parental Education -0.23* 0.07 0.48* 0.33* 0.09* 0.19* -0.10* -0.33* 0.23* -0.32* -0.25* 0.40* 0.15* -0.05 0.38* 0.32* 1.00 Duration of stay -0.15* 0.19* 0.16* 0.22* 0.08* -0.02 0.46* -0.02 0.29* -0.08* -0.08* -0.16* 0.28* -0.04 0.00 0.26* 0.06 1.00 Contracted hours 0.06 -0.04 -0.03 0.00 0.07* -0.35* 0.00 0.11* -0.01* 0.05 0.07* -0.04 -0.08* 0.00 0.05 -0.03 -0.04 -0.01 1.00 Note: * p<.01.
18
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