Neighbourhood social capital, participation in the local community, and social context in England and Wales

Transcription

1 Neighbourhood social capital, participation in the local community, and social context in England and Wales Lee Bentley Institute for Social Change / CCSR University of Manchester Paper prepared for presentation at the International Political Science Association, XXII World Congress. Madrid, July 2012 DRAFT PLEASE DO NOT QUOTE WITHOUT PERMISSION

2 Abstract In recent years there has been an upsurge in research focused on participation in civil society organisations in local communities. Much of the impetus for this has come from the social capital literature and its explicit stress on the importance of interpersonal ties for a wide variety of social and individual outcomes. In this paper I focus explicitly on social capital in the neighbourhood and examine how it is related to participation in the local community and aggregate level contextual characteristics of an area such as deprivation and population turnover. Using the British Household Panel Survey (BHPS) and working within a framework of multilevel structural equation modelling I examine how different neighbourhood contexts can have an impact on the likelihood of participating and developing social capital. I show that social capital within the local community is positively related to participation and find that it is negatively related to deprivation. I find no direct effect between deprivation or population turnover and participation. I do find an effect of living in a rural area when compared to a large urban area. Introduction Recent years have seen both an increased academic and policy focus on participation within local communities and what impact this has on both the individuals living in these communities as well as the community as a whole. This has been given added impetus in many countries due to specific policy agendas which seek to encourage such engagement (in the UK for example see Office for Civil Society, 2010a; 2010b), which in turn has been driven by the academic literature on social capital and the many positive benefits attributed to the concept (Coleman, 1990; Lin 2001; Lin et al, 2001; Putnam, 1993; 2000). In this paper I am interested in understanding why people participate in civil society organisations at the local, neighbourhood, level. That is, what kinds of people in what kind of places are likely to be involved in their communities? This is important for a number of reasons. In the first instance it has been shown that participation, or engagement more generally, can have many positive benefits for individuals ranging from increased civic skills to higher levels of happiness and well-being (Verba and Nie, 1972; Verba and Schlozman, 1995; Haller and Hadler, 2006; Thoits and Hewitt, 2001). Secondly communities as a whole are likely to benefit from broader social engagement. As greater numbers of people engage in community issues it will become increasingly likely that solutions to these problems will be found and the range of skills available to a community group will increase with the concomitant benefit that average costs to individuals of participation should decrease.

3 However, it is well known that there are relatively large socio-demographic differences in participants and non-participants (Verba et al, 1995). The geographic literature also suggests that there are differences between aggregate and individual levels of participation in different places (Cox, 1969; Huckfeldt, 1979). Consequently it is likely that certain areas will have higher rates of participation than others and that these places are likely to be populated by people from different socio-demographic groups. For example a more highly educated person may be more likely to participate than a less educated individual, and a more highly educated person, living in an area populated by the highly educated, will be more likely to participate than a similarly educated person living in an area populated by less educated individuals. Therefore inequalities in participation may be reinforced by legislation which seeks to enhance engagement in local communities, if support is not offered to certain communities. There has been a great deal of debate concerning the role of social context in relation to political, and particularly electoral, participation at the local level but relatively little in relation to civic and civil participation at the local level. There has been more discussion of the relationships between context and social capital, particularly in the literature on sense of community (for example see Long and Perkins, 2007). However these have generally not been linked to participation as such, but rather to the broader concept of social capital. In this paper I seek to examine these questions using UK data from the British Household Panel Survey (BHPS) which asks individuals how regularly they attend meetings for local groups and / or voluntary organisations. I develop a measure of social capital within the neighbourhood which specifically examines how individuals feel about the relationships they have with other people in their local community. Drawing on the social capital and mobilisation literatures I suggest that individuals with stronger social networks and more positive feelings to their community will be more likely to participate. However, I also argue that this in itself will be contingent on both individual and community level characteristics. In the following I discuss the theoretical concepts I make use of in the paper. I first define participation in local groups and why this conceptualisation of participation is relevant to a discussion of social context and social capital. I then highlight the theoretical reasons that we might expect individuals to participate in local groups, before relating how and why social context should have an impact both on determining participation in local groups and neighbourhood social capital. Following this discuss the data that I use and the approach that I take in the analysis, before presenting a series of models that I estimate in order to gain some insight into these questions. I first show a single and multilevel regression model with neighbourhood social capital as the outcome variable. I then show the same for participation in local groups and, in the final part of this section, I present a model in which I jointly estimate social capital and participation. Finally, I discuss these findings.

4 Theoretical background Participation in local groups as civil participation I treat participation in a civil society organization to be a form of collective action in which members of a community work together in order to identify and solve particular social problems (Olson, 1965; Verba and Nie, 1972; Parry et al, 1992). Of course social groups and other forms of engagement might be important social outcomes in themselves, and there is strong evidence to suggest that they are important precursors to other forms of participation (Kwak et al, 2004). However, I am interested in participation defined as a group of individuals who recognise that they are part of a shared community, with shared needs, and that in acting together often in a voluntary capacity they are able to improve things for the good of the community as a whole (see Adler and Goggin, 2005: 241; Berger 2009; Ekman and Amna, 2009: 15). That is, I am interested in defining participation in the local community as a problem of collective action. This allows me to differentiate this form of participation from more general forms of social engagement. This narrow definition also makes it possible to disaggregate participation from more general forms of social capital which often includes different types of engagement which might often have different social outcomes, or indeed be outcomes themselves (Putnam 2000; Li et al, 2005). This paper focuses on any form of collective action taking place within some community which has some particular social aim. I am particularly interested in thinking of a community as a geographically bounded area within which people share common needs and characteristics (McLean and McMillan, 2003: ). I therefore use the word neighbourhood to refer to the local community. I argue that a neighbourhood is a particular kind of community within which specific problems common to all individuals living within them may be located. I investigate why certain neighbourhoods are likely to generate more participation among members of the community than others. Why might people participate in neighbourhood groups? Thinking of participation as a collective action problem it is possible to be clear that we are interested in the overall aim of the group and to infer that the majority of participants will have some general positive orientation to the aim of the group with which they are involved. It is based on the assumption that groups of people with common interests should logically work together to achieve those interests. Olson (1965: 6) was interested in those groups that are expected to serve the interests of their members (italics in original) which can clearly be very broadly defined. For

5 instance a group which seeks to keep a neighborhood tidy will clearly benefit those in the group as long as they live in the neighborhood. However, using this example we also see a clear reason why we might expect individuals to free ride in certain situations. That is, an individual will benefit from the core activity of the group even when they are not a member. As long as the group exists with enough members to fulfill its goals, individuals will benefit from a tidy neighborhood even if they themselves do not participate. However, from a cost / benefit perspective the individual cost of participation may not be outweighed by the achievement of the aims of the group. There have been many attempts to address the problems raised by collective action theory 1. One approach suggests that people calculate their utility based not only on the benefits that they are likely to gain from participating, but also make an altruistic calculation based on the perceived benefits to others (Margolis, 1982; Uhlaner, 1989). More recently Edlin et al (2007) have made a similar argument noting that people have both personal and social preferences and that under certain conditions it is these social preferences, rather than personal selfish preferences, which motivate individuals to act. This is a useful way thinking about civil participation because, as has already been noted, in many instances the specific aims of a civil group cannot be viewed as being of direct benefit to group members. This also fits into the framework provided by Berger (2009) when he argues that civil participation requires a degree of moral engagement. I also argue that it is naïve to assume that people participate for purely materialistic calculations. That is, perhaps the main reason individuals participate is because they find the act of participation in and of itself valuable. As Kahan (2003: 71) notes, [i]n collective-action settings, individuals adopt not a materially calculating posture but rather a richer, more emotionally nuanced reciprocal one. When they perceive that others are behaving cooperatively, individuals are moved by honor, altruism, and like dispositions to contribute to public goods even without the inducement of material incentives (italics in original). I argue that in the particular case of participation in local groups, social capital at the neighbourhood level is a way of linking together these approaches. I conceive of this as a measure of the strength of the networks an individual has within their neighbourhood. If individuals have strong positive feelings about their neighbourhood they will be more likely to feel positively about participating, and these individuals will be more likely to express positive attitudes if they also have strong networks within the neighbourhood. That is the more positively people feel about the place they live the more likely they will be to empathise with the needs of their friends and neighbours. It should also provide them with more opportunities to become involved through mobilisation via networks within the neighbourhood (Rosenstone and Hansen (1993). I conceptualise of these neighbourhood social ties 1 For reviews of these debates see Reisman (1990) and Whiteley (1995).

6 as being one particular facet of the broader concept of social capital. I do not measure social networks directly and instead make use of measures which assess how an individual feels about the relationships they have within the neighbourhood. This makes sense as it can be argued that it is the quality rather than the breadth of interpersonal ties which is important in promoting participation. I term this concept neighbourhood social capital in order to distinguish it from the much broader concept. The subjective nature of the questions allows a relatively nuanced measure of how an individual feels about these relationships. This is particularly useful given that our focus is specifically on the neighbourhood. For example, broader questions about social relationships more generally have been shown to be relatively strong predictors of certain forms of participation (Rosenstone and Hanson, 1993; McClurg, 2003). However, it is not clear why we would expect strong networks outside of the neighbourhood to have an impact upon participation within it. I therefore expect neighbourhood social capital to be related to an increased likelihood of mobilization. Framing the argument in this way also allows us to think about the way in which socio-demographic characteristics are related to participation. Social context We have discussed above the importance of social context for this research. However, it is unclear as to how these effects might work in practice. Agnew (2007: 23-24) addresses the impact of social context on behaviour when he discusses the difference between compositional and contextual explanations of political behaviour, noting that contextual approaches emphasise the mediating role of social and political milieux. That is, when we locate a person with specific socio-demographic characteristics in a particular context this will have an impact upon their behaviour. Understood in this way involvement in local groups occurs due to individual characteristics such as education which are then influenced by contextual characteristics common to all individuals existing within a given neighbourhood. Notably this can be linked to the nature of the social networks of these individuals in the manner discussed in the previous section. This can lead people who appear similar from a socio-demographic perspective to behave differently in different places. We can hypothesise that there are likely to be certain aggregate level characteristics of a community that will help or hinder the development of norms of behaviour related to participation within the community. This can be linked to the notion that there will be an impact on the types of networks that develop within a specific community or neighbourhood. For example neighbourhoods characterised by high economic deprivation and residential instability may have fewer strong

7 neighbourhood networks than vice versa. Given this it is reasonable to assume that there will be different levels of participation in different communities and that this will be related to specific aggregate conditions at the level of the community (Campbell, 2006; Huckfeldt, 1979; Swaroop and Morenoff, 2006). In linking the nature of local social network development to specific contextual conditions, such as economic deprivation and residential instability, I argue that such neighbourhoods are likely to display weaker local level social networks than wealthier more stable neighbourhoods, other things being equal. Given that living in the latter type of neighbourhood is also likely to be correlated with higher individual levels of income and education there is likely to be a distinct disparity between the two different types of neighbourhood in terms of the overall levels of local level social networks which develop in each. I therefore argue that neighbourhood civil society participation is likely to be more prevalent in neighbourhoods characterised by strong social networks and that this will be linked to neighbourhood level contextual characteristics. I expect deprivation to be negatively related to both neighbourhood social capital and participation. This is because deprivation often indicates that an area is socially disorganised which in turn is likely to hinder the development of social networks (Kawachi et al, 1999). It is also likely that deprived neighbourhoods will have fewer local groups due to the cost requirements in terms of facilities, skills, and money that are likely to be required in order to create them (Zimmerman, 2000). Residential instability is also likely to be negatively related to participation as more unstable neighbourhoods are likely to make it less likely that strong social networks will develop (Sampson et al, 2002). Finally neighbourhood type, which measures how rural through to metropolitan a neighbourhood should have a strong relationship insofar as we would expect more rural areas to be more socially homogenous and stable than the most urban types of area (Beggs et al, 1996). Hypotheses Following this discussion a number of hypotheses can be made: H1: Neighbourhood deprivation, residential instability, and living in an urban area will lead to lower levels of engagement in local civil society groups. H2: People living in such neighbourhoods will have lower levels of neighbourhood social capital. H3: Higher levels of neighbourhood social capital will be related to higher rates of participation in the local community.

8 Data In this study I make use of the BHPS, which commenced in 1991 and has been undertaken every year since this point. I use the 1998 wave and combine this with census data for the aggregate level variables. I use data from England and Wales as there are problems in matching data at the ward level in Scotland. The data is structured into a multilevel framework utilising the electoral ward which I refer to as a neighbourhood, of which there were roughly 9,000 in 1998, with an average population size of approximately 5,000. There are obviously some questions as to what constitutes a neighbourhood in the sense referred to here and it can be argued that a smaller bespoke measure would be preferable. However, given limitations of both sample size and the availability of data, electoral ward is useful way of measuring this. Indeed, it has advantages insofar as it is a well understood geographic identifier which has some social and political meaning. Moreover, it is reasonable given our conceptualisation of neighbourhood participation as a form of collective action with particularised objectives. The sample used here comprises 5,693 individuals in 1,530 wards. This gives us an average ward size of 3.72 people although this is subject to some variation due to missing data on some variables. I report total sample sizes in the tables for each analysis. The BHPS is particularly useful for this analysis as it includes a wide range of variables related to feelings about the neighbourhood and participation and can easily be combined with our aggregate variables of interest. The relatively large sample size also allows us to analyse data at the ward level with sufficient variation. The BHPS also has many benefits in terms of content. In the first instance we have a measure of participation in local meetings and / or voluntary groups. This is measured on a 5-point likert scale which ranges from never participates to participates more than once a week. It also asks a number of questions concerning relationships with other residents in the local area. These are summarised in table 1. These variables were used in the development of the latent variable for neighbourhood social capital which I show below. Interpersonal trust is a binary variable, as is same address as last year, tenure Table 1: Full question of neighbourhood related variables used in factor modelling y1 y2 y3 y4 y5 y6 y7 y8 Variables included in the initial modelling (all 5-point likert scale) The friendships and associations I have with other people in my neighbourhood mean a lot to me I feel like I belong to this neighbourhood If I needed advice about something I could go to someone in my neighbourhood I borrow things and exchange favours with my neighbours I would be willing to work together with others on something to improve my neighbourhood I plan to remain a resident of this neighbourhood for a number of years I like to think of myself as similar to the people who live in this neighbourhood I regularly stop and talk with people in my neighbourhood

9 status and sex. Political efficacy is a 5-point likert scale, ranging from strongly disagree to strongly agree with the statement that ordinary people don't really have a chance to influence what governments do. Social class uses the 7-point Goldthorpe class schema (Goldthorpe, 1987), and are treated as dummy variables with the highest class as the reference category. Age is continuous. Deprivation is measured using the Townsend measure (Townsend et al, 1988); population turnover uses the ONS classification and is calculated as the level of in and out migration per 1000 people. Both are continuous scales. Neighbourhood type uses a reduced version of the ONS rural and urban classification for wards in which we differentiate between city, town and village. This is categorised as dummy variables with city as the reference category. Table 2 shows descriptive statistics for all Table 2: Descriptive statistics Variable Obs Mean Std. Dev. Min Max Neighbourhood social capital Belong to neighbouhood Local friends mean a lot Advice obtainable locally Can borrow things from neighbours Plan to stay in neighbourhood Similar to others in neighbourhood Talk regularly to neighbours Within Attend local meetings and / or voluntary groups Trust: 0 = Can't be too careful Same address last year 0 = No Political efficacy 1 = Strongly disagree, 5 = Strongly agree Tenure: 0 = Owner Education: 0 = none, 7 = higher degree Sex: 0 = Female Age: Higher = older Social class: Reference = Service class higher No job Svc. class lower Routine non-manual Self employed / small owner Lower technical / manual Skilled manual Non-skilled manual Between Townsend deprivation Population turnover Neighbourhood type: Reference = City Town Village

10 variables includes in the analysis. I measure community participation using a variable which asks people how regularly they attend meetings for local groups and / or voluntary organisations. This has advantages insofar as it measures participation specifically at the level the local community, which is the point of interest here, and it is non-specific in terms of the particular type of participation in question. Asking about local meetings and voluntarism should allow us to be reasonably clear that we are referring to a type of group which has some community based aim over and above that of a purely social group while being broad enough to allow individuals to decide what this means for themselves. I include a range of socio-demographic characteristics: age, gender, education, and social class. I hypothesise that older people will be more likely to participate in neighbourhood groups, women will be more likely to participate than men, and those with higher levels of education and from a higher social class will be more likely to participate. In the first instance there are clear correlations between certain types of individual from a socio-demographic perspective and participation in a wide range of participatory activities. For instance the highly educated are generally more likely to participate in a range of activities (Lieghley, 1995; Verba et al 1993), and older people may be more likely to participate in activities such as voting, but less likely to be involved in a demonstration. Neighbourhood social capital is both an individual level variable and a neighbourhood level variable. That is I argue social capital is both an individual and community level attribute. I include a number of further variables of interest: interpersonal trust, tenure status, whether an individual lived in the same address last year, and political efficacy. Interpersonal trust has generally been shown to be a strong predictor of participation and social capital and I would expect positive relationships with both (Kahan, 2003). I expect individuals with higher levels of trust to have stronger social networks within the neighbourhood and to participate more. It is worth noting that the particular direction of this effect is likely to be reciprocal insofar as we would expect participation and social capital to lead to higher levels of trust and vice versa. However, it has been argued that trust is a relatively stable character trait developed over the life course (Uslaner, 2002; Uslaner and Brown, 2005), indicating that we would expect trust to be more firmly embedded psychologically. Tenure status and living in the same address last year are included as it is expected that individuals who own their house will be more invested in their neighbourhood, and individuals who have lived in an area for a significant amount of time to have stronger networks within the community (Sampson and Groves, 1989). I also include a measure of political efficacy which is interesting insofar the literature suggests two competing hypotheses regarding the direction of this variable. The first suggests that political efficacy should predict participation in the sense that more empowered

11 individuals should be more likely to engage (Finkel, 1985; Zimmerman, 2000). The second suggests that political efficacy will be positively related to political outcomes but negatively related to participation in local communities as these individuals participate in their local communities precisely because they are disenchanted with the formal political system (Bang and Sorenson, 2001; Li and Marsh, 2008). Methods Figure 1: Two level simultaneous regression of participation Figure 1 shows the overall model and regression relationships to be tested in this analysis. Y represents participation in local meetings, represents the latent measure of neighbourhood social capital with y ij indicators, and x ij represents a set of independent exogenous variables at both the individual i and neighbourhood level j. I estimate a simultaneous equation model with both endogenous, Y, y and variables and exogenous, x, variables. Using standard structural equation

12 modelling (SEM) notation the oval indicates a latent variable and the rectangles observed variables. The indicates a random intercept and the circles represent the intercepts of the measured variables at the group level. I use the terms within and between to distinguish between the individual and neighbourhood levels in the data. The arrows indicate regression parameters. x 11..n1 point to the predictor variables, y, as the variance of these variables that is explained by the latent variable. That is the latent variable predicts the observed variables, which is why we are able to are regressed on both the latent and outcome variable Y. We can also see the arrows from the estimate an R 2 for each, as will be seen below. I estimate the latent variable on both levels one and two. In effect the level two latent variable represents the group mean of the level one latent variable. In order to establish this it is necessary to carry out tests of measurement invariance which I discuss below. All models are estimated using MPlus, version 6.1, using the means and variance adjusted weighted least squares (WLSMV) estimator which is appropriate given distributional issues such as skewed responses (Byrne, 2012: ) 2. Clearly this model is complex and in order to conduct the analysis I will proceed by estimating a number of simpler models before the full model is shown and discussed. In the first instance I will establish a measure of neighbourhood social capital on both the within and between levels. I will then show a single and multilevel analysis of the exogenous x ij variables regressed on this. Following this I will turn to the analysis of participation by regressing the single and multilevel parts of the model onto Y and including the latent variable as an endogenous predictor. Finally I will estimate the full model which has the advantage of allowing comparisons to be made between the standardized estimates on both and Y as well as to gain a better understanding of the relationship between neighbourhood social capital and local participation. Analyses and results Measuring neighbourhood social capital As was discussed above I develop a latent factor model of neighbourhood social capital, using confirmatory factor analysis (CFA). I focus here on the particular feelings that an individual has about the relationships they have in their local area and the local area more generally. As I have argued above this is appropriate given the focus on the local neighbourhood as arena in which our measure of participation is focused. It is worth noting that many measures of social capital emphasise not only the importance of networks but also participation in groups, generally finding that they are two 2 See Muthen and Muthen ( ) for an introduction to Mplus. For an introduction to CFA modelling see Brown (2006). Byrne (2012) provides an introduction to SEM. For a discussion of multilevel and more advanced SEM techniques see Heck and Thomas (2009).

13 dimensions of the broader concept (Li et al, 2005). However, as I am specifically interested in participation as an outcome I do not measure this second participatory dimension of the concept, and instead focus on how social networks are related to one particular type of participation in a regression model. Social capital is generally conceptualised as a latent variable in which the covariance structure of a set of indicator variables, y 1...n in figure 1, is examined and the hypothesised underlying latent variable is deemed to explain this common variance. A basic 1 factor model is shown in figure 2. Figure 2: One-factor single level CFA model In this figure we see a number of measured variables denoted y 1, y 2, through y n. These are the measured variables each of which are loaded onto a common unmeasured factor. The arrows below each of these variables indicates individual measurement error specific to each variable, whereas the arrows between y 1 n and indicates an item specific factor loading, commonly denoted. Initial modelling on all 8 variables shown in table 1 resulted in an unsatisfactory solution in terms of the fit statistics and individual variable parameters, yielding a CFI and TLI of.927 and.959 respectively, an RMSEA of.127, and R 2 statistics which were below generally accepted cut-points. Table 3 shows a reduced model which excludes two variables. All variables load highly onto the factor and have R 2 values of above.5. As would be expected this model also has improved fit statistics with both a CFI and TLI of.979, above the.95 threshold (Hu and Bentler, 1999). The RMSEA is still above the recommended threshold of.08 at.130. However, it has been noted that RMSEA may be inflated in models with low degrees of freedom (df), which is the case here (df=4) (see Kenny et al, 2012). I will therefore pay attention to the behaviour of this statistic as df increase with model complexity. It should also be noted that the correlated residuals for this model are below the <.10 threshold used when assessing local areas of strain. It is also important to estimate neighbourhood social capital at the level of the neighbourhood. From a technical perspective this means that a level two latent variable also has to be estimated which will allow regression parameters on the between level to be estimated. That is, when we estimate a twolevel model of participation we will regress level two variables on the within group mean, or

14 Table 3: Reduced one-factor solution (WLSMV; N=5635) Estimate StdYX P-Value R 2 NEIGHBOURHOOD SOCIAL CAPITAL y 1 Local friends mean a lot y 2 Belong to neighbourhood y 3 Advice obtainable locally y 7 Am similar to others in neighbourhood y 8 Talk regularly to neighbours intercept, at the neighbourhood level (Heck and Thomas, 2009). It is plausible that there may be a different structure on the between level to the within level and it is therefore important to examine fit indices and individual model parameters at level two. Initially this involves estimating a model based on figure two but on two levels. The initial unrestricted model, in which all parameters are freely estimated, allows us to establish a baseline against which model restrictions can be tested. For instance here we are interested in establishing whether or not the level-two variable is similar in terms of the parameter specifications to the level one variable. This model has strong model fit indices with a CFI and TLI of.958 and.965. The RMSEA is high at.99 although this may be caused by the problem noted earlier in relation to the sensitively of this statistic when a model as few degrees of freedom, which are 6 in this model. Notably the SRMR, which takes into account the average of the magnitude of residuals, provides evidence of good model fit, and is the only statistic which does so for both levels, having a within level SRMR of.033 and a between level SRMR of.042. In the restricted model we set the metric between the within and between levels parameter loadings to be equal, or invariant. This is important as specifying an equal metric on both levels allows us to specify the same latent variables on both, which in turn allows the calculation of an intraclass correlation coefficient (ICC) and to make more meaningful comparisons between levels 1 and 2. Divergent measurement models on levels 1 and 2 would indicate the two latent variables are not the same. However, here I am interested in developing a model in which they are as close as possible to one another which will make interpretation of the impact of neighbourhood social capital easier when we include effects at different levels. Initial examination of the factor loadings suggests that while the within level model is fairly similar in the restricted and unrestricted models there are problems in estimating the between level variable. This is reflected in the SRMR for the restricted model which on the within level in.033 but is.127 on the between level. Residual variances on the between level for this model suggest that it may be necessary to fix some residuals to 0. Alternatively it is also possible to allow some variables to load freely on both levels which would allow us to estimate a partially invariant model (Byrne et al, 1989).

15 After testing various restrictions on the residuals I find that no configuration of doing so leads to well fitting model on the between level. I therefore re-specify the model and freely estimate variable y 3. I also fix the residual variance of y 2 to 0. This yields the partially invariant model shown in table 4. Table 4: Partially invariant two-level CFA model of neighbourhood social capital (WLSMV; N = 5635; cluster = 1538; cluster avg = 3.664) Estimate StdyYX S.E. Est./S.E. P-Value R 2 Within Level NEIGHBOURHOOD SOCIAL CAPITAL y 1 Local friends mean a lot Y 2 Belong to neighbourhood y 3 Advice obtainable locally y 7 Am similar to others in neighbourhood y 8 Talk regularly to neighbours Between Level NEIGHBOURHOOD SOCIAL CAPITAL y 1 Local friends mean a lot y 2 Belong to neighbourhood y 3 Advice obtainable locally y 7 Am similar to others in neighbourhood y 8 Talk regularly to neighbours This model has a CFI of.975 and a TLI of.982. The RMSEA is.070 and the within and between SRMRs are.033 and.073. There is debate concerning the appropriate cut-points for these descriptive statistics in much of the literature and there has been even less research concerning the appropriateness of such measures in multilevel models. However, the fit statistics above do seem to fall within the relatively broad restrictions suggested by Hu and Bentler (1999) for acceptable fit. We can also calculate the ICC for social capital which has a between level variance of 16.9% indicating that approximately 16.9% of the variance for this variable exists at level 2 of the model. The generally recommended level at which it becomes necessary to take clustering of data into account is 5% and it is necessary to adopt a multilevel approach (Snijders and Bosker, 1999). Adding explanatory variables We can further assess the validity of this model by assessing how it behaves when it is developed into regression model. That is, if the construct behaves as hypothesised this gives it theoretical validity over and above the simple assessment of fit statistics which may indicate non-theoretically

16 driven correlations between the indicator variables. I therefore estimate a model based on figure 1 excluding the Y variable. In order to do this I regress the individual level exogenous variables, x 1 n, on. I also include the multilevel aggregate estimates at level 2. The results of this are shown in table 5. Table 5: One and two level regression model with endogenous predictors at within and between levels on neighbourhood social capital (WLSMV; N = 4919; Clusters 1374; Average cluster = 3.580) Estimate Stdzd P-Value Estimate StdyYX P-Value Within Level Trust: 0 = Can't be too careful Same address last year 0 = No Political efficacy 1 = Strongly disagree, 5 = Strongly agree Tenure: 0 = Owner Education: 0 = none, 7 = higher degree Sex: 0 = Female Age: Higher = older Social class: Reference = Service class higher No job Svc. class lower Routine non-manual Self employed / small owner Lower technical / manual Skilled manual Non-skilled manual Residual Variances Neighbourhood social capital Between Level Townsend deprivation Population turnover Neighbourhood type: Reference = City Town Village Residual Variances Neighbourhood social capital This table shows both the single and multilevel regressions of the exogenous predictors on neighbourhood social capital. It is worth noting that there are no discrepancies between the two models in terms of the signs and magnitudes of these estimates. Examining the standardized estimates we see a number of interesting relationships that we largely predicted above. In the first instance trust is positively related to neighbourhood social capital, suggesting more trusting

17 individuals are more likely to have a higher score of neighbourhood social capital. Living at the same address as last year is also a positive predictor indicating that our hypothesis regarding length of time spent living in a neighbourhood being related to the strength of networks has support. Political efficacy is not significant at p<.05 which is not surprising given our interest in examining how it is related to participation, rather than neighbourhood social capital. Tenure is strongly associated suggesting that individuals who own their house score more highly on the latent factor. Education, sex, and age all have relatively standard interpretations. Our social class variables are notable insofar as they suggest that individuals from lower groups are more likely to have higher scores on social capital. The level 2 regression results also broadly fit the hypotheses identified above. Here we see that deprivation is significantly negatively related to neighbourhood social capital indicating that individuals who live in more deprived areas are less likely to have high social capital than those living in more affluent areas. Population turnover has no relationship which is surprising given our theoretical expectations. Living in a town compared to a city is not significant at p<.05. However, living in a village compared to a city is a significant and strong predictor of higher levels of neighbourhood social capital. Neighbourhood social capital therefore fits our theoretical expectations. I will therefore examine how these variables are related to participation. Participation model Once again table 6 shows both the single level and two-level regression models on participation in local groups. This is a probit regression model which is the MPlus default when undertaking estimation with WLSMV. Here we see that our variable of interest, neighbourhood social capital, is strongly and positively related to participation with a standardized regression coefficient of.118 in the multilevel model. Consequently we can say that individuals with higher levels of neighbourhood social capital are more likely to participate in local groups. Interpersonal trust is also strongly positively related to participation. Living at the same address last year is also significant and positive. Notably in this model political efficacy is negatively related to participation lending support to the notion that the types of individuals who participate in neighbourhood groups may be different in terms of their political orientations to people who participate in politics more generally. Tenure is non-significant in this model suggesting that home-owners and renters are not different to one another in terms of participation in local groups. The more highly educated, females, and older people are more likely to participate. It is also notable that in the participation model individuals

18 Table 6: One and two level probit regression models with endogenous predictors at within and between levels on participation in local groups (WLSMV; N = 4919; Clusters 1374; Average cluster = 3.580) Estimate Stdzd P-Value Estimate StdyYX P-Value Within Level Neighbourhood social capital Trust: 0 = Can't be too careful Same address last year 0 = No Political efficacy 1 = Strongly disagree, 5 = Strongly agree Tenure: 0 = Owner Education: 0 = none, 7 = higher degree Sex: 0 = Female Age: higher = older Social class: Reference = Service class higher No job Svc. class lower Routine non-manual Self employed / small owner Lower technical / manual Skilled manual Non-skilled manual Between Level Townsend deprivation Population turnover Neighbourhood type: Reference = City Town Village Residual variance Variances Participation in local groups from higher social classes are likely to participate. In terms of the neighbourhood level characteristics we can see that neither deprivation nor population turnover are significantly related to participation in local groups. However, living in a village when compared to a city is significantly related. These two sets of models therefore present something of a paradox with respect to neighbourhood deprivation. In table 4 we observe that individuals living in more deprived areas are less likely to report higher feelings of neighbourhood social capital. In table 5 we observe that deprivation has no direct relationship with participation taking the rest of the model into account. However, we do observe a strong relationship between social capital and participation. In other words we see arelatively strong relationship between social capital and participation and between social capital

19 and deprivation. Given that living in a deprived area is likely to be related to lower levels of social capital we can therefore hypothesise an indirect relationship. That is, the effect of deprivation on participation works in by reducing levels of social capital which in turn reduces the likelihood of participating. Simultaneous regression model of neighbourhood social capital and participation Table 7: Multilevel simultaneous regression on neighbourhood social capital and participation (WLSMV; N = 5068; Clusters = 1391; Average cluster size = 3.643) Within level Neighbourhood social capital Participation in local groups Estimate StdyYX P-Value Estimate StdyYX P-Value Neighbourhood social capital Trust: 0 = Can't be too careful Same address last year 0 = No Political efficacy 1 = Strongly disagree, 5 = Strongly agree Tenure: 0 = Owner Education: 0 = none, 7 = higher degree Sex: 0 = Female Age: Higher = older Social class: Reference = Service class higher No job Svc. class lower Routine non-manual Self employed / small owner Lower technical / manual Skilled manual Non-skilled manual Between Level Neighbourhood social capital Townsend deprivation Population turnover Neighbourhood type: Reference = City Town Village Next I estimate the simultaneous regression model shown in figure 1. In the first instance this allows us to test the importance of our parameters of interest when we include other relationships in the model. In other words we can ask what the impact of neighbourhood social capital is on

20 participation in local groups when controlling for the impact of other covariates on both. Second we can ask what the impact of the neighbourhood level variables are on participation when they are also regressed on neighbourhood social capital. This model also has a regression between the latent parameter and participation on the neighbourhood level. Table 7 shows the full structural regression, shown in figure 1, with random intercepts on both participation and attachment. The CFA and TLI are.972 and.975 respectively. The RMSEA is.032 and the SRMR is.031 on the within level and.074 on the between level. These statistics indicate that the model has a good fit on both the within and between levels. The within level regression has similar results to those shown in table 6. Notably neighbourhood social capital retains a strong positive impact on both the within and between levels in these models. The between level results are also similar to those shown earlier. However, it is worth noting that the between level regressions are non-significant for the regression of neighbourhood variables on participation. Discussion In the above I have examined a number of hypotheses and examined the complicated relationships that exist between participation in local groups, neighbourhood social capital, and contextual characteristics. Table 4 shows correlates of both individual group level effects on neighbourhood social capital. This confirmed the hypothesis that deprivation would have a negative impact on neighbourhood social capital. We also see a strong effect of living in a village area when compared with a large urban area suggesting that this kind of area is more socially homogenous, and therefore individuals are happier with their local communities, as measured here by our latent factor, neighbourhood social capital. Table 5 goes on to show this model extended to measure participation in local groups. Here we see as hypothesised, that neighbourhood social capital is strongly related to this kind of participation. We can also note that in adding our neighbourhood level contextual effects we see no impact of deprivation or population turnover on participation in local groups. However, once again the type of neighbourhood does have a strong a significant impact. Finally table 6 shows the full model shown in figure 1. Once again we can note the impact of neighbourhood social capital in this model. Here we see no relationship between our social context variables and participation directly. It is notable however that we still see a relatively strong impact of deprivation and type of neighbourhood with smaller residential seeming to engender greater feelings of social capital.