Relation between social capital and innovation

Fundamental to making network analysis work for development projects is an understanding of how different dimensions of social capital, human capital and innovation are related to each other. Human capital and social capital are consid-ered to be core determinants and drivers of entrepreneurial action and innovation (e.g. Grebel et al. 2003). Conversely, entrepreneurship and innovative activities draw upon and also create social capital (Casson and Della Giusta 2007). Case studies such as that for Cháparra allow the exploration of several questions, such as whether the data confirms the theoretical considerations; whether external ties or the local network position are more important; and which network measures Table 5.4 Centralization and cohesion of the local technical information network

Centralization and cohesion of the network With NGO Without NGO

Degree centralization 44.18% 18.73%

Eigenvector centralization 70.04% 56.89%

Node betweenness centralization 47.24% 28.11%

Average distance (among reachable pairs) 3.181 3.507

seem to be more appropriate. To make a contribution to addressing these ques-tions, this study analysed the correlations between different dimensions of social capital (local and external ties), human capital and the Cháparra farmer’s innova-tive performance. In addition, the impact of selected control variables (age and desire to innovate) on innovative behaviour was analysed.

Social capital is a complex concept that includes a varied set of dimensions such as the agent’s kinship and professional networks, collective actions or group assistance. In this partial analysis, emphasis was put on the position of the indi-viduals within a local technical information network and their access to external technical information (related to their agricultural business activities). To measure the role and social capital of the farmers within the valley, their degree, between-ness and eigenvector centralities were calculated (see Section 5.4 for information on centrality measures). Furthermore, the participation and active roles of farm-ers in local associations, which are related to their productive activities, were considered.

Table 5.5 Impact of the NGO on the betweenness centralities of the farmers in the local technical information network^a

Nodes Positive and negative Position in the Position in (N = 44) changes in betweenness betweenness betweenness

ranking position, centrality ranking centrality ranking when the centrality when NGO is when NGO is calculated not considered is considered without the NGO

N17 +16 4 20

N2 +16 7 23

N25 +13 6 19

N10 +11 13 24

N12 +10 11 21

N19 +9 5 14

N11 +2 1 3

N21 +1 3 4

N22 0 2 2

N31 0 8 8

N32 –7 19 12

N3 –9 18 9

N15 –9 26 17

N46 –10 15 5

N34 –10 21 11

N8 –13 42 29

N44 –14 24 10

N5 –16 23 7

Notea This table illustrates the betweenness centralities in the local technical information network and how these change when the NGO is considered in the calculation. It must be noted, though, that in other types of social relations such as friendship and kinship, the centrality rankings and the effects of the NGO are different, thus implying the need for careful interpretation.

To measure the external ties and social capital of the farmers, we asked for their external kinship networks and whether they frequently spoke with relatives living outside the valley about information related to business activities. Additionally, we asked about their participation in and attendance at fairs, expositions and other professional activities in cities as well as other valleys, as a proxy indicator for their access to external technical knowledge.

To measure the innovative performance of the smallholders a simple aggregated indicator was built. This was done by summarizing the values obtained from each farmer with regards to the various dimensions of innovation considered in the questionnaire: Innovation in Products, Processes, Marketing, Organization and Prevention (thus: Innovation performance = InnoProd + InnoProcess + InnoMarket + Inno Org + InnoPrevention). The reliability of this composed factor was controlled by a high significance level (0.003) of the Kendall-Tau correlations with another proxy indicator on the technical competences of the farmers, using expert evaluations (Arata 2008). Human capital was proxied using educational data and the amount of technical training a given farmer had received. Furthermore, we controlled for the effects of age and for the psychological variable representing the farmers’

desire to innovate.

Based on these indicators, a correlation test was applied to analyse whether social capital and innovative performance are correlated with each other. In other words, to see whether farmers with more and better network relations tended to also be more innovative, as well as if more innovative farmers tend to have more social contacts and centrality in the local network. Due to the characteristics of the sample and the heterogeneity of the factors, a Kendall’s Tau-b non-parametric correlation test was applied. Kendall’s Tau-b measures the non-parametric rank correlations between paired observations (Kendall and Gibbons 1990). It provides a distribution free test of independence and a measure of the strength of depend-ence between two variables. In doing so, it calculates the number of concordances and discordances in paired observations. Concordance occurs when paired obser-vations vary together and discordance occurs when paired obserobser-vations vary dif-ferently. The Kendall’s Tau-b coefficient is defined as follows:

τb 5 C2 D

"C 1D1T_x "C 1D1T_y

where C is the number of concordant pairs, D the number of discordant pairs, Tx is the number of tied pairs of x and Ty is the number of tied pairs of y. The values of Tau-b range from −1 (= 100 per cent negative association) to +1 (= 100 per cent positive association). A value of zero indicates the absence of association. In our case, the main reasons for using Kendall’s Tau-b instead of Spearman’s Rho or the Pearson correlation coefficient are: (a) the ordinal or non-normal distribution of several of the considered variables (e.g. network centralities, education data); (b) the rather small sample size; (c) the possible identification of outliers; and (d) the reduction of the random correlation probability.

Table 5.6 summarizes how the innovative performance of the 44 farmers correlates with their local network position, their external links and their level of education. It also shows the controls for the effects of age and the desire to innovate.

With regard to the correlations between the farmers centrality in the local and external technical information networks, it seems that a farmer with a high degree of eigenvector centrality and many external weak ties tends to be more innova-tive than a farmer with a weak local network position and few external linkages.

However, the Tau values are comparatively small. While degree and eigenvector centrality appear to be highly significant in this case, betweenness centrality is not. The main reasons for this are twofold. First, an NGO dominates the net-work, interacting and connecting with a varied set of agents. It outweighs the betweenness centrality of many other agents. Second, within the close-knit local network of this case study, information can spread fairly fast to all other agents of the local system. In the case of Chaparra, the school education and the exter-nal kinship networks (which may provide access to exterexter-nal information) do not correlate significantly with innovation. One might suppose that education would lead to human capital and improve the absorptive capacities of the farmers and the Table 5.6 Correlations^a between the farmers’ social capital and their innovation performance

Nonparametric Kendall's Tau correlations (N = 44) between different social capital dimensions and the aggregated innovation performance

Dimension Indicator Kendall’s Significance

Tau level

The peasant’s local network Degree centrality 0.345 0.003**

position Betweenness centrality 0.120 0.274

Eigenvector centrality 0.297 0.006**

Active member of 0.253 0.054 local association

External ties of the peasant Technical information 0.047 0.702 exchange with relatives

from other valleys, cities, countries

Outgoing professional 0.424 0.002**

contacts and weak ties, (e.g. technical information exchange in fairs,

expositions, business trips to other valleys and cities

Human capital Educational level 0.177 0.130

Training in the use 0.321 0.014*

and processing of wine grapes

Control variables Age –0.140 0.202

Desire to innovate 0.211 0.082 Notea Correlation:** = significant at the 0.01 level; * = significant at the 0.05 level.

kinship network access to external information and other resources. But specific training and practical learning seems to be more important in the case region than codified school knowledge. In addition, the school curricula and the interests of family members in cities are often disconnected from the needs and reality of life in agricultural communities (Hartmann 2006).

Regarding the kinship networks, it was found that all farmers have close family members living in other Peruvian cities and sometimes even foreign countries.

However, during interviews it was discovered that most farmers see the activities of their family members in other regions as disconnected from their agricultural activity in the community, even when in several cases the children of the farmers studied issues such as marketing, accounting or completed internships in mechan-ics. Much more theoretical and empirical research is necessary on the causal relations between different network measures (e.g. centralities, composition, key player metrics) and the dimensions of innovation (e.g. inputs and outputs). The causal directions between social capital and innovation are unclear: it cannot be clearly determined whether social capital leads to innovation or innovation to social capital. It seems probable that there is a feedback mechanism between them. Social capital leads to better access to valuable information and innovative performance leads to a more central position and prestige (Akçomak and Weel 2009; Eagle et al. 2010). In addition, there is a need to research and study in more detail which network measures should be applied in local communities where virtually all people know each other. For instance, within the Chaparra network, there still is significant heterogeneity in the quality and type of ties and the role of the individuals.