Introduction

In this book, we propose a qualitative and quantitative method and a set of indicators for evaluating social capital and related governance aspects in European Local Action Groups (LAGs). The method uses indicators and a normalised set of indices (range of values [0–1]) at different levels of aggregation to evaluate and compare the performance of LAGs in terms of social capital and governance. As Chapter 10 shows, this stepwise process can represent a contribution for explaining the intervention logic needed to operationalise social capital in LEADER. Nardone et al. (2010), Lopolito et al. (2011) and Teilmann (2012) have argued that measuring social capital in LEADER could help Managing Authorities of the Rural Development Programmes as well as the LAGs to regularly monitor and evaluate these intangible resources across the European local and rural areas where they operate. Chapters 6 and 10 also emphasised the value for LAGs in using this method to monitor their own activities and self-evaluate social outcomes and impacts on their territories. The present chapter provides the results in terms of quantitative measures for social capital and related governance aspects in nine Italian LAGs, comparing their performance at different levels of aggregation, and thus, at different levels of the intervention logic.

Our method has three key objectives. Firstly, as stated, it aims to provide LAGs as well as other agencies implementing local development initiatives with a useful tool to monitor their activities and evaluate their points of strength and weakness in the field of social capital and related governance aspects. Secondly, it also seeks to show that the heterogeneity that exists both within LAGs that are located within the same region, as well as among regions, does not conform to stereotypical North-South divisions (see studies on this issue by Putnam et al., 1993; Helliwell & Putnam, 1995; Leonardi, 1995; Bigoni et al., 2016). Thirdly, it contributes to shift the evaluation culture from a short-term perspective focused on inputs, activities and outputs of development projects to a medium- and long-term perspective focused on development processes and impacts of development strategies. For example, monitoring regular outreach communication with the public through online media may be a simple activity in the method proposed. Yet, it can foster a more active role in promoting information, collaboration and trust, spur reflection on the strengths and weaknesses of the LAG, and thus, contribute to the adoption of a medium–long-term vision to territorial development (Ray, 2006; High & Nemes, 2007; Dax & Oedl-Wieser, 2016).

Chapter 10 described how the values of the indicators were first normalised, and then aggregated into composite indicators (sub-dimensions), indices (dimensions) and composite indices (forms of social capital and governance aspects). The most robust normalisation technique was chosen and adopted to convert all values to a [0–1] range to enable aggregation as well as inter-regional and intra-regional comparisons. Finally, the aggregation process was explained as unveiling the intervention logic of social capital and governance by proposing operative tools for impact evaluations, whereby indicators measure specific actions (activities indicator), sub-dimensions indicate specific outputs (output indicators), dimensions point to the specific objectives in relation to social capital and governance (outcomes indicators) and forms correspond to general objectives measures (impact indicators).

In this chapter, we start the discussion from the highest level of aggregation (forms of social capital and related governance aspects), to offer a bird’s eye view of the impacts that may be produced, by the actions of LAGs, in terms of enhanced structural and normative-cognitive social capital or improved governance across territories. We then analyse the values of dimensions and sub-dimensions – the richer substratum of the black box of social capital – to uncover outcomes, outputs and processes more specifically. Thus, Section 2 presents the values obtained by forms of social capital and governance aspects, Section 3 discusses results by dimension, while Section 4 delves into sub-dimensions. While indicators are not specifically treated in this chapter (see Part IV for a more detailed analysis and Appendix 5 for the full list of values), the different levels of analysis (Chapter 7) provide the LAG with indications on how it could address potential weaknesses. The chapter concludes with recommendations for policy-makers, evaluators and practitioners (LAG members and staff).

A Bird’s Eye View: Impact Indicators for Social Capital and Governance

In the evaluation process, the impact indicator captures the effects produced by the actions of local development organisations in terms of enhanced structural and normative-cognitive social capital and governance. The composite indices, which represent impact indicators, are summarised by three values, which are derived from the mean of the dimensions comprising the forms of social capital – structural and normative-cognitive – and related governance aspects (Table 11.1). As explained above, the values range from 0 to 1. The principal utility of composite indices lies in the possibility to quantify the forms of social capital and governance in the areas of study and compare these results within and across LAGs and regions. These values can provide a “quick” glance at (1) the overall structure of the organisation (structural social capital); (2) the ways in which the local culture, as norms, rules and values, has “translated” the structure of the LAG into a territorial organisation (normative-cognitive social capital); and (3) the governance processes that have emerged in terms of decision-making processes, efficiency and effectiveness, organisational culture and capacity, and vertical structure.

Table 11.1 Composite indices of structural social capital, normative-cognitive social capital and governance for the selected Italian LAGs, 2012–2013
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In terms of composite indices of structural social capital, Table 11.1 shows how, of the LAGs analysed in the study, the LAGs in Umbria (Centre) have higher values, followed by the LAGs in Veneto (North), Basilicata (South), Sardinia (Island) and, finally, Apulia (South), where the lowest value is found. One possible explanation for this is the existence of network structures which provide the two LAGs in Umbria with a strong impetus for implementing the local development strategy, and lend support to the view that Central Italy regions are inclusive and endowed with strong social cohesion (Picciotti et al., 2014). We highlight that these are first findings based on selected case studies, which do not represent the entire population or regions. To verify this hypothesis a widespread analysis involving a representative sample of Italian LAGs is required, as well as longitudinal studies to verify how different elements evolve over time.

In terms of composite indices of normative-cognitive social capital, the same pattern seems to occur, with LAGs in Umbria leading the way, LAGs in Apulia at the opposite extreme, and Veneto (North), Basilicata (South) and Sardinia (Island) in the middle. Intra-regional differences also emerge: while Umbria has the LAG with the highest value (0.64 for the LAG Ternano), it also has a LAG with the third lowest value (0.49 for the LAG Valle Umbra e Sibillini). Veneto has the LAG with the second highest value (0.61 for the LAG Bassa Padovana) as well as the LAG with the fourth lowest value (0.51 for the LAG Prealpi e Dolomiti). In Basilicata, both LAGs are similar in value (0.60 for the LAG Basento Camastra and 0.56 for the LAG COSVEL). While the two LAGs in Apulia in the South of Italy present the two lowest values of the study, the LAG Meridaunia (0.48) has quite higher values than the LAG Gargano (0.10), with a value that is much closer to that of the LAGs Valle Umbra e Sibillini in the Centre and Prealpi e Dolomiti in the North. Though Putnam, Leonardi and Nanetti praised the North for its relatively richer stock of social capital, in terms of group membership and civic values (Putnam et al., 1993), here we find that some areas in the Centre and in the South portray high values of structural and normative-cognitive forms of social capital. This reflects a potential for expanding participation and development in these regions.

Patterns differ for the governance dimensions analysed in the method. Contrary to commonly held perceptions about the relatively poor institutional performance in Southern Italian regions, the LAGs with the highest values in terms of governance are found in the South: Sardinia and Basilicata, followed by Umbria, Veneto and Apulia. In this regard, it is important to highlight that governance is a broad issue and, as discussed in Chapters 5 and 7, it includes elements of institutional performance, which specifically refer to the capacity of the LAG to select projects (related to the decision-making process for this specific activity), its efficiency and effectiveness, organisational culture and capacity as well as capacity to develop relations vertically and with LAGs outside the region. An overall assessment of governance, therefore, would need to account for the inclusion of other aspects, including transparency and participation, which are explored as elements of social capital. Unlike normative-cognitive social capital, governance portrays similar values within each region. Most values are also located within a 0.32–0.68 range, showing therefore a smaller spread than in the structural and normative-cognitive forms of social capital, and thus more similar perceptions on the achievements in this area across the different LAGs.

The composite indices analysed in this section allow us to have a bird’s eye view on the general impacts that may be produced by the activities of the LAG in each of the forms considered, and how these may diverge among and within a single region. In this sense, it is possible to show how, overall, the LAG Ternano in Umbria (Centre) achieved the highest values in terms of both structural and normative-cognitive social capital as well as positive ones in terms of governance. The values for the LAGs in Apulia (South) fared among the lowest, and yet the patterns were different: an average situation for the LAG Gargano in terms of structural social capital and governance, but low for normative-cognitive social capital; and the opposite for the LAG Meridaunia, with low values in terms of structural social capital, but average in terms of normative-cognitive social capital and governance. However, for the purposes of evaluation, this analysis is purely indicative of underlying patterns. For a more detailed treatment, we now turn to the analysis of dimensions of social capital and governance aspects.

From Form to Dimension: Digging Deeper into the Role of Dimensions

While composite indices enable evaluators to quickly identify regions and LAGs with a diverse endowment of social capital, they do not point to the specific outcomes achieved by the LAGs through their activities. Indices may be analysed for each of the 15 identified dimensions to specify overall trends – not only by LAG, but also by the actual dimensions of the various forms of social capital and governance considered in the present study.

Structural Social Capital by Dimension

Structural social capital includes five dimensions, A – Context, B – Network actors, C – Horizontal structure of the network, D – Transparency and accountability and E – Reputational power. Figure 11.1 shows the boxplot representing the distribution of values around the median and the mean and the different quartiles for each dimension of structural social capital (graph A) and for each LAG (graph B). That is, it portrays the dispersion, the skewness and the outliers, through the spaces between the quartiles of each distribution. Moreover, boxplot graphs allow the comparison among the distribution (LAGs in graph A, dimensions in graph B). The analysis of the dimensions (graph A) can indicate the ones where generally, the LAGs share common trends, from those where LAGs may diverge quite significantly. For example, in graph A, dimensions A – Context, B – Network actors and D – Transparency and accountability show the largest variability in our sample, with values ranging from 0.01 to 0.84. Thus, the outcomes at this level are quite different among LAGs, with values showing a large spread at the level of the single LAG (see the example of the LAG Gargano below). Conversely, dimensions C – Horizontal structure of the network and E – Reputational power have a smaller spread in values. This means that LAGs generally share common trends and are closer in values for those dimensions, with no situations showing critically low values. For example, the range for Dimension C varies between the LAGs Sulcis (0.35) and Prealpi e Dolomiti (0.84).

Fig. 11.1
figure 1

Source: Own elaboration

Structural social capital by dimension (graph A) and by LAG (graph B)

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If we look at the data from the perspective of each LAG (graph B), the LAG Ternano in Umbria (Centre) shows the highest average value in terms of structural social capital (0.69). An analysis of values by dimension shows values higher than 0.5 for all cases: D – Transparency and accountability has the highest value (0.84), followed by E – Reputational power (0.81), B – Network actors (0.68) and A – Context (0.64), while C – Horizontal structure of the network has the lowest value (0.52). This shows that in the LAG Ternano, individuals appear to give merit to the transparency and accountability of the organisation and its members, as well as to their reputation. The LAG Meridaunia in Apulia (South) shows an opposite pattern in terms of faring last, and having its maximum value equals to 0.39. The dimensions with the highest values are C – Horizontal structure of the network (0.39) and E – Reputational power (0.22), while all other dimensions are below 0.1. While the next sections show positive areas as well, the results for the LAG Meridaunia need to be analysed with care, because of the low rates of responses from beneficiaries (20%) and members (47%) (see Chapter 15 for a detailed analysis of the LAG).

As mentioned above, the LAG Gargano, in Apulia (South), shows the most striking pattern, with a large spread of values (min 0.01 and max 0.81). The LAG Gargano has the highest value for dimension A – Context (0.81), defined here as a combination of the overall motivation of members to join the LAG (proactively rather than by invitation), and the direct and indirect knowledge of the role and projects supported by the LAG. However, the lowest values belong to the dimensions B – Network actors (0.01), that is, members’ awareness of the initiatives of the LAG and its beneficiaries, and D – Transparency and accountability (0.03), that is, attention to suggestions by beneficiaries and administrative support. Despite these unfavourable conditions, values for dimensions E – Reputational power (0.68) and C – Horizontal structure of the network (0.47) paint a more positive picture, implicating potential areas of strength in the organisation, for example in attendance at meetings and reputation of the director and the members of Board of the Directors, which could be leveraged to continue building structural social capital. Generally, given the proactive motivation in joining the membership, the LAG Gargano may point to a case in which, as the proverb goes, “the spirit is willing, but the flesh is weak” and perhaps the spirit can become a powerful source for change.

Normative-Cognitive Social Capital by Dimension

Normative-cognitive social capital includes six dimensions, F – Trust and reciprocity among members, G – Institutional trust, H – Quality of the network, I – Quality of participation, L – Shared values and M – Conflict. The analysis of normative-cognitive social capital shows a seemingly “moderate” situation. This is demonstrated in Fig. 11.2 which provides values of normative-cognitive social capital by dimension (graph A) and by LAG (graph B). Similarly to structural social capital, the average for all dimensions is between 0.44 and 0.56 (Fig. 11.2, graph A) and the values are located between 0.01 and 0.92. Dimension F – Trust and reciprocity among actors, which includes internal levels of reciprocity among actors as well as beneficiaries’ level of trust in the LAG, has the highest median (0.60) and mean values (0.56). Dimension G – Institutional trust, towards local institutional actors, has the highest value (0.92).

Fig. 11.2
figure 2

Source: Own elaboration

Normative-cognitive social capital by dimension (graph A) and by LAG (graph B)

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On the one hand, Fig. 11.2 (graph B) shows how the LAG Gargano in Apulia (South) fares worse compared to other LAGs in relation to the normative-cognitive dimensions of social capital. All values, in terms of interpersonal and institutional trust, quality of the network and participation, shared values and conflict, are below 0.2. Chapter 15 describes this situation as heterogeneous, whereby results show very low rates of interpersonal trust, as shown by vote delegation, and yet overall positive levels of trust in the Assembly. The LAG Meridaunia has the highest value for dimension H – Quality of the Network (0.79). However, values for dimensions F – Interpersonal trust and reciprocity among members (0.32) and G – Institutional trust (0.22) are quite low. This may suggest that while people’s expectations and/or contributions to the network seem to be positive, more work can be done in building trust, both among members of the LAG and towards local institutions. Chapter 15 explains how this effort is under way.

On the other hand, while the LAG Ternano in Umbria (Centre) produced the highest results in terms of overall normative-cognitive social capital, a more careful analysis shows a larger spread among the different dimensions. Trust levels are very high for trust, both as dimensions F – Interpersonal trust and reciprocity among members (0.89) and G – Institutional trust (0.92). However, dimensions L – Shared values (0.39) and M – Conflict (0.41) are at the lower end. This may imply a situation currently characterised by a high degree of goodwill, but, as described in Chapter 14, possibly at risk, due to the scarce identification of shared values and a general perception that they have worsened over time, and unless more work is done towards improving the mechanisms for participation and the internal proactivity of members.

The Dimensions of Governance

Governance includes four dimensions, N – Decision-making processes, O – Efficiency and effectiveness, P – Organisational culture and capacity and Q – Vertical structure. Figure 11.3 portrays values for governance aspects related to social capital by dimension (graph A) and by LAG (graph B). The median for dimension P – Organisational culture and capacity is 0.29, while for all other dimensions, N – Decision-making processes is 0.64, O – Efficiency and Effectiveness is 0.61 and Q – Vertical structure is 0.58. This suggests that overall, communication and monitoring of the Local Development Strategy may have to be improved across all LAGs – only the LAGs Valle Umbra e Sibillini, Basento Camastra and Sulcis have values above 0.50 for dimension P. However, planning capacity, integration in the territory, efficiency and effectiveness and integration with higher levels of governance are points of strengths across the LAGs, from where to continue building collaborations within and outside the network.

Fig. 11.3
figure 3

Source: Own elaboration

Governance aspects by dimension (graph A) and by LAG (graph B)

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The analysis by LAG shows that averages and medians range from 0.30 (Gargano) to 0.68 (COSVEL). The LAG Sulcis in Sardinia (Island) has the highest value for dimension N – Decision-making processes (0.91). The LAG Gargano in Apulia (South) has for dimension P – Organisational culture and capacity a very low value (0.06), which may be indicative of the struggles it faces in terms of communicating and monitoring its actions effectively. The higher values for decision-making process (0.64), however, may be indicative of the efforts it is carrying out in consultation processes and the selection of projects. It is often argued that designating a clear set of rules and procedures for decision-making within the organisation can encourage trust-building and the creation of participatory networks and governance structures (see, for instance, Ostrom, 2003).

The LAG Ternano in Umbria, located in central Italy, has the highest value for dimension O – Efficiency and Effectiveness (0.93) and the second highest for dimension N –Decision-making processes (0.81). Yet it is less prepared in terms of dimensions P – Organisational culture and capacity (0.29) and Q – Vertical structure (0.36). Furthermore, this is the LAG that is highly endowed with interpersonal and institutional trust. The favourable conditions set by most dimensions of social capital and governance can be used by the LAG to strengthen is communication and monitoring capacity, build more effective connections to LAGs outside its territory and influence vertical structures more effectively.

From Dimension to Sub-Dimensions and Indicators: The Black Box Is a Mosaic

The highest level of aggregation enabled us to compare LAGs within and between regions in terms of their impacts on enhancing structural and normative-cognitive social capital as well as governance. The analysis by dimension helped us to describe and graphically illustrate specific outcomes of social capital and related governance aspects for each LAG in our sample. We now turn to the analysis of sub-dimensions, which enables researchers, evaluators and practitioners to assess the outputs of social capital and related governance aspects by measuring more concretely the different components and by determining which are stronger or weaker at the level of the LAG. By way of a reminder, each sub-dimension has a code that starts with the uppercase letter of the dimension, followed by a lowercase letter indicating the position of the sub-dimension. The sub-dimensions are described in detail in Chapter 9.

Structural Social Capital by Sub-Dimensions

Generally, the data across LAGs in terms of structural sub-dimensions produce a more variegated picture than at the level of the dimensions, one that resembles a mosaic and makes visible strengths and weaknesses more effectively. Dimension A – Context includes two sub-dimensions, Access to the LAG (Aa) and Knowledge of the LAG’s role (Ab). In terms of Access to the LAG (Aa = 1.0), the LAG Gargano shows that 100% of the members of the LAG were motivated and joined proactively, a result of the interest of participants in the LEADER+ initiative and the communication campaign led by the LAG (see Chapter 15). While the data for the LAG Meridaunia merits some care due to a very low rate of responses, the sub-dimension shows that none of the members joined proactively (Aa = 0.00). The LAG Valle Umbra e Sibillini has a high value in relation to Knowledge of the LAG’s role by beneficiaries (Ab = 0.99). Conversely, the LAGs Basento Camastra (0.13) and Bassa Padovana (0.18) score quite low on this sub-dimension, while the LAGs Meridaunia and COSVEL had no responses to the corresponding question in the questionnaire.

Dimension B – Network actors includes two sub-dimensions, Knowledge of the LAG’s initiatives (Ba) and Knowledge of the LAG’s beneficiaries (Bb). In Umbria, the LAG Ternano has the highest value for Knowledge of the LAG’s initiatives (Ba = 0.95), but a lower value for Knowledge of the LAG’s beneficiaries (Bb = only scores 0.41). In the LAG Valle Umbra e Sibillini, Knowledge of the LAG’s initiatives (Ba = 0.40) is lower than Knowledge of the LAG’s beneficiaries (Bb = 1.00). This may suggest that while members of the LAG Ternano had an overall good knowledge of the LAG’s initiatives, they did not develop personal relationships with the beneficiaries themselves, the exact opposite of the situation in the LAG Valle Umbra e Sibillini. Values below 0.1 or no values (meaning no responses) for both sub-dimensions, are found in the LAGs Gargano, Meridaunia and COSVEL. This can be indicative of poor network relations among both members and beneficiaries in these LAGs, an issue which may need greater attention if the core principles of LEADER are to strengthen network-building and partnerships.

The dimension C – Horizontal structure of the network includes five sub-dimensions, Internal participation (Ca), Level of openness of the LAG (Cb), Density of relations in the LAG (Cc), Public-private relations internal to the LAG (Cd) and Proactivity of the LAG (Ce). The LAG Prealpi e Dolomiti has the highest values for three of them, including Level of openness of the LAG (Cb = 1.00), Density of relations in the LAG (Cc = 1.00) and Proactivity of the LAG (Ce = 0.64). The Level of openness suggests that the LAG activated all possible communication channels and reached all possible categories of actors reached by the other LAGs. It also shows that the LAG had the highest possible density of both information and collaborative relations among members (Cc). Finally, the LAG also had the highest number of linkages between members and between beneficiaries (Ce). The values for Internal participation are higher for the LAG Basento Camastra (Ca = 0.69), indicating a good response in terms of average annual rate of attendance to the meetings of the Board and the Assembly. Public-private relations internal to the LAG for the LAG Meridaunia is the highest (Cd = 1.00), showing that the LAG had the same share of private actors in the centre of the network and in the Assembly, and collaborations always involved both private and public actors. At the same time, the LAG Meridaunia shows low values for Internal participation (Ca = 0.07), indicating a lack of regular attendance of members at formal meetings. The lowest values for Level of openness of the LAG (Cb) are found in Basilicata, for both LAGs Basento Camastra (0.07) and COSVEL (0.15). Finally, the LAG Sulcis shows the lowest values for both Density of relations in the LAG (Cc = 0.10) and Public-private relations internal to the LAG (Cd = 0.17). The lack of density and diversity of networks may need to be addressed in order to support the creation of a more inclusive and participatory organisation.

Dimension D – Transparency and Accountability has two sub-dimensions, Transparency in the network (Da) and Network accountability (Db). The LAG Ternano has the highest value for Transparency in the network (Da = 0.86), while LAGs Gargano (0.00), Meridaunia (0.08) and COSVEL (0.02) have the lowest values. This stresses the importance of utilising communication channels which may activate actors. The LAG Ternano also has the highest value for Network accountability (Db = 0.83), followed by the LAG Bassa Padovana (0.81). The LAGs Gargano (0.06) and Meridaunia (0.00) have the lowest values, both in terms of the adoption by the LAG of suggestions made by beneficiaries and administrative support to members and beneficiaries.

Dimension E – Reputation power includes only one sub-dimension, Reputational Power of the LAG (Ea). As was pointed out in the analysis of dimensions, Reputational power of the LAG (Ea) is on average 0.6 for all LAGs, with the LAG Ternano showing the highest value (0.81) and LAG Meridaunia the lowest (0.22). The LAG Gargano fares very well (0.68), with high values in terms of the equivalence of reputational power in the Assembly and in the core of the network and in terms of the comparison between an individual’s own assessment of reputational power and that of others. Together with the maximum value obtained for the members’ motivation for joining the LAG (Aa = 1.00), they show a high degree of goodwill towards the organisation.

Normative-Cognitive Social Capital by Sub-Dimensions

The sub-dimensions for normative-cognitive social capital are: Internal level of trust in the LAG (Fa); Beneficiaries’ level of trust in the LAG (Fb); Trust towards local institutional actors (Ga); Benefits received through the network (Ha); Benefits brought to the network by members (Hb); Quality of participation in the Assembly (Ia); Quality of participation in the Board of Directors (Ib); Proactivity of beneficiaries (Ic), Perception of shared values in the territory (La); Recognition of promoters of shared values in the network (Lb); Identification with the territory (Lc); Conflict among actors of the LAG (Ma); Beneficiaries’ dissatisfaction with the LAG (Mb).

The LAG Ternano shows the highest values for the three sub-dimensions of trust (Fa = 0.87, Fb = 0.92 and Ga = 0.92). The LAG Gargano shows the lowest values for all sub-dimensions save for Proactivity of beneficiaries (Ic = 0.49) and Recognition of promoters of shared values in the network (Lb = 0.23). The LAG Meridaunia has the highest values in terms of Benefits received through the network by members (Ha = 0.87) and in the Perception on a positive change of shared values in the territory of the LAG (La = 1.00), meaning that its members may be aware of the potential of the organisation. The other LAGs in Basilicata and Sardinia have values that are within average, except for the LAG Sulcis, where the value for the Recognition of promoters of shared values in the territory is the lowest (Lb = 0.00).

The LAG Bassa Padovana shows the highest values in terms of Benefits brought to the network by members (Hb = 0.85), Quality of participation in the Assembly (Ia = 1.00) and Conflict among members (Ma = 0.71). This situation seems to indicate a very active and dynamic membership, sustained participation and personal perception of one’s contributions, as well as ability to address conflicts among members. These qualities provide a good basis for addressing weaknesses in other areas. For example, the LAG Bassa Padovana has the lowest value in terms of the Proactivity of beneficiaries (Ic = 0.00), meaning that the beneficiaries never suggested a project to the LAG or a call to another beneficiary. Nonetheless, these results could point the LAG towards encouraging more active participation and awareness on behalf of beneficiaries. Finally, while the perception of shared values in the territory of the LAG Prealpi e Dolomiti is quite low (La = 0.06), the identification with the territory by members, beneficiaries and director is the highest of all LAGs (Lc = 1.00). This means that the LAG network could play a more decisive role in defining and supporting shared values through their actions. As indicated in Part I, the intangible sources of social capital, norms, values and trust, strongly influence the quality of the network itself, the tangible aspect of social capital. The sub-dimensions discussed in this section specifically point out the outputs that LAGs could improve.

Governance by Sub-Dimensions

The sub-dimensions of governance are: Planning capacity of the LAG (Na); Transparency and monitoring in the planning process of the LAG (Nb); Integration of the LAG in the territory (Oa); Coordination of the LAG (Ob); Efficiency of the LAG (Oc); Communication capacity of the LAG (Pa); Monitoring and assessment of the LAG (Pb); Openness of the LAG outside of its territory (Qa); Vertical linking (Qb). The sub-dimensions for governance allows us to more carefully analyse how LAGs have interpreted and translated EU directives into decision-making processes, efficiency and effectiveness, capacity and relations with vertical structures.

The LAG Prealpi e Dolomiti has the highest value for Planning capacity of the LAG (Na = 1.00), given by strong decision-making processes which support the completion of the Local Development Strategy and the coherent implementation of consultation processes regarding projects. However, Transparency and monitoring in the LAG’s planning process (Nb = 0.10), and Monitoring and assessment of the LAG (Pb = 0.19) are low, thus suggesting two specific areas where the LAG may wish to improve the effectiveness of its actions. Conversely, LAGs Ternano and Sulcis have the highest values for Transparency and monitoring in the planning process (Nb = 1.00). However, while the LAG Ternano is also highly effective in terms of Efficiency of the LAG (Oc = 1.00), it is quite deficient in terms of Communication capacity (Pa = 0.16), due to limitations in external communication.

The LAG Bassa Padovana has the highest value for Coordination of the LAG (Ob = 1.00), which results from efficiency in the organisation of the Assembly and internal coordinating capacity. However, it is quite low in Monitoring and assessment of the LAG (Pb = 0.20), similarly to the LAGs Prealpi e Dolomiti (0.19), Gargano (0.12) and COSVEL (0.00). Except for the LAG Gargano, all LAGs in the Centre and South of Italy are well Integrated in the territory (Oa), with the LAG Meridaunia displaying the highest value (0.95), followed by LAGs Basento Camastra (0.88) and Sulcis (0.88). Most LAGs are also relatively well Coordinated internally (Ob), given that the lowest value is for LAG Gargano (0.34), followed by the LAGs Bassa Padovana (0.39) and Prealpi e Dolomiti (0.42).

The LAGs with the highest value for Communication capacity (Pa) are Valle Umbra e Sibillini, COSVEL and Basento Camastra (Pa = 0.50 for all three LAGs). This shows that communication is an area where more work is needed across all LAGs. Furthermore, with the exceptions of the LAGs Sulcis (0.68), Basento Camastra (0.72) and Valle Umbra e Sibillini (0.60), all LAGs have values for Monitoring and assessment (Pb) below 0.5. Conversely, apart from the LAGs Ternano (0.25), Valle Umbra e Sibillini (0.00) and Gargano (0.33), most LAGs are open to building relations outside of their territories (Qa). Finally, all LAGs show values higher than 0.20 for Vertical linking (Qb = 0.24 for the LAG Gargano is the lowest value). This sub-dimension, however, comprises relations with LAGs (which would be properly analysed as horizontal relations) and with the higher regional authorities, as well as awareness by external beneficiaries of the planning process. In this case, a more rigorous reading by indicator and by LAG would be necessary to understand the quality of vertical relations, the degree of influence at higher levels of governance and the degree of awareness of the complexities of the LAG’s planning process by beneficiaries.

Overall, all LAGs have the highest value in one or more of the sub-dimensions analysed. This means that LAGs can dig deeper under the surface of social capital, identifying and measuring the specific outputs in which they excel and the specific weaknesses which they need to address. These sub-dimensions enable evaluators and practitioners to assess the outputs of social capital and related governance aspects. A detailed analysis at the level of the indicators would enable LAGs to further evaluate the activities (activities indicator) which need to be addressed in order to improve impacts in their territories. The regional chapters in Part IV provide a detailed qualitative and quantitative analysis at the level of indicators.

Conclusion

The analysis of social capital and related governance aspects in nine Italian case studies can be carried out at different levels of aggregation, to identify indicators for activities, outputs, outcomes and impacts of the intervention logic. Thus, the analysis proposed can guide the evaluation of social capital and related aspects of governance for the LEADER Approach and other neo-endogenous approaches. By identifying strengths and weaknesses in relation to the LAG’s capacity to enhance social capital and governance, the method provides LAGs as well as other local agencies with a useful tool to introduce or change activities that will improve their outputs, outcomes and enhance overall impacts in the long run. Generally, an analysis of these factors suggests that LAGs can improve the impacts of their actions if they shift from a focus on the outputs of the activities carried out, to considerations of social processes – the intangible resources which support development programming. These can include promoting opportunities for relationship-building (information and collaboration exchange) with members, beneficiaries, but also with the wider public through regular communication and other initiatives. This analysis also suggests that LAGs can adopt a deeper awareness of the principles of the LEADER initiative and the rationale behind the regulations which support territorial development, rather than remaining simple recipients of funds. The sub-dimensions investigated, such as Knowledge of the LAG’s role and initiatives (Ab and Ba), Internal participation (Ca), Proactivity of the LAG (Ce) may seem rather disparate when the LAG’s focus is on activating “tangible” interventions and actions. Yet, they serve to spur reflection on how their actions already may support collective action and foster intangible resources, such as Internal trust (Fa) and Proactivity of beneficiaries (Ic). In this regard, the approach proposed here – which is applicable to both external and internal monitoring and evaluation – supports the long-term development of the organisation and thus its relevance, and value added, to the territory in which it operates.

The results proposed in this chapter do not explain why certain values may be high or low. As we further discuss in Chapter 12 and in Part IV, interviews in each LAG also provide qualitative data that can be used to explain context specific patterns. One might argue that these suggestions can only apply to certain LAGs or regions in Italy, limiting the utility of this method to the specific spatial and historical context. However, as a European-led initiative for rural development, LAGs carry the vision of the EU at the local level and thus share key features which are common throughout Europe (Chapter 3). These features enabled the research team to develop a method which could be applicable across the EU (see Part II and Chapter 10). Furthermore, though the method was constructed for the purposes of assessing rural development in the EU, it can be applied for monitoring and evaluation in other national and regional contexts where social capital, development agencies and participatory projects are involved in rural planning and implementation (Chapters 6 and 10). Therefore, it is particularly relevant to contexts where rural development research and policy adopt the neo-endogenous approach which is endorsed by the LEADER Approach, and is based on public-private and multi-sectoral partnerships.

The method has three major characteristics, especially in Europe, which render it feasible for broader application. Firstly, the method represents a bridge between the quantitative and qualitative divide and can thus be used by researchers or evaluators, as well as by policy-makers and practitioners, to support quantitative measures that are qualitatively assessed. This is specifically relevant in a field such as social capital, where no single metric can represent the actual complexity of the social and economic systems, but can only cover various relational aspects of the system. As the chapter demonstrates, the levels of aggregation enable us to dig deeper into these complexities by moving from a simple composite indicator to a set of forms, dimensions and sub-dimensions of social capital and governance which help to uncover and understand the sources of possible strengths and weaknesses in the actions of the organisation.

Secondly, by focusing on an organisation which is context-specific and yet shares common features across all EU countries, the method allows us to identify how the organisation has interpreted, translated and implemented its actions from the perspective of social capital and related governance aspects. LAGs introduce new languages, territorial planning logics as well as requirements for accountability, which may be novel depending on the countries involved. The introduction of new practices reshapes path dependencies which may characterise territories and leads to forms of hybridisation which are not exogenously given. In the case of Italy, the analysis showed that different regions within the Northern, Central and Southern parts demonstrated strengths and weaknesses in different aspects of social capital, defying clear-cut divisions based on geography. This hybridisation means that researchers, practitioners and evaluators cannot assess the impacts of LEADER merely from the perspective of path dependency, but must embrace approaches which capture the specificities of policy implementation in place. The method also offers researchers and evaluators the means to detect and track these changes at different levels of aggregation, and thus at different levels of the intervention logic, in order to capture the dynamics at the core of development processes and the ways they influence developmental outcomes and impacts. It thus provides a privileged and step-by-step view into the impacts of the EU’s attempt at shifting consolidated institutions and systems of governance through a neo-endogenous approach to development.

Thirdly, the results presented in the book were shared and discussed with the directors of the LAGs who participated in the study, a process which can help to change the culture of monitoring and evaluation from an external to an internal process. Consulting with directors offered a way to get first-hand information regarding gaps as well as best practices in the LAGs, and collecting feedback on the method itself. The detailed analysis using indicators and indices showed a heterogeneous “landscape”, which allowed directors to discuss with the research team the opportunities and challenges that differentially affected the conditions and possibility for change within the organisation. In some cases, when the first two years of the LEADER funding were adopted as the reference period, difficulties with starting up the programme showed quite negative values for indicators, which were not necessarily reflective of the potential of the LAGs over the full programming cycle. These results were carefully considered by the LAGs so that they could be addressed in the remainder of the programming period. The regional chapters in the next part of the book (Part IV) further clarify the quantitative results, providing a case by case explanation of the context and results obtained in each LAG.

The method proposed in this book thus begins a conversation for measuring social capital in different local contexts where neo-endogenous approaches to development are implemented. While we have focused on a quantitative assessment of values at different levels of aggregation (and thus at different levels of the intervention logic), the next chapter introduces qualitative specifications of context. Attention to the peculiarities of place is needed to break through the acceptance of deterministically-defined hypotheses connected to path dependence and lock-in, such as perceptions of the North-South divide that are dominant in Italy and elsewhere.