Get 20M+ Full-Text Papers For Less Than $1.50/day. Subscribe now for You or Your Team.

Learn More →

Does Social Innovation Reduce the Economic Marginalization of Women? Insights from the Case of Italian Solidarity Purchasing Groups

Does Social Innovation Reduce the Economic Marginalization of Women? Insights from the Case of... Maestripieri L. (2017) Does Social Innovation Reduce the Economic Marginalization of Women? Insights from the Case of Italian Solidarity Purchasing Groups, Journal of Social Entrepreneurship, 8(3): 320-337. ABSTRACT (100 words) The paper explores the relationship between gender and social innovation to highlight the possible positive effects of women’s participation in social innovation in terms of protection from economic marginalisation. It focuses on Italian solidarity purchasing groups as a case of social innovation in the domain of food and agriculture. The analysis is based on logistic regression using primary data collected in 2016 for the EU funded project CrESSI. The results show that participation in social innovation does protect households from worsening economic conditions. However, it was not empirically proven that there is a significant difference between men and women in the benefit enjoyed from the participation in solidarity purchasing groups. KEY WORDS: Marginalisation, gender, social innovation, solidarity purchasing groups, alternative food networks 1. Introduction Social innovations are increasingly proposed as a solution to mitigate the worst consequences of the recent financial crises in order to foster employment and social inclusion in European Societies. Extensively researched in recent years, given the centrality of the concept in flagship initiatives of the European Commission, the idea of social innovation has been adopted across various domains of the social and economic sciences, with several concurrent definitions of the term presented by scholars, stakeholders and policy makers (Ziegler 2017a). As policy action on social innovation gains consensus and visibility, it risks becoming a buzz word that appeals to stakeholders and scholars (Moulaert et al. 2013b). The appeal of the concept lies in the problem that it addresses (Moulaert et al. 2013a): in a neoliberal era, where the role of the state has been reduced as much as possible, social innovation seeks to give a new centrality to fruitful interrelations between market and community. It raises attention on how the beneficial outcomes of innovation are distributed among citizens (Moulaert et al. 2013a). Gender represents one of the main dimensions of inequality. Equality between men and women is particularly relevant for EU strategies for economic and societal improvement (André 2013), as a more equal society can foster economic growth in the longer term and achieve better social cohesion (Hubert and Helfferich 2016). However, gender issues have, so far, been overlooked by the social innovation debate with only a few authors discussing how social innovation can foster more equality between men and women (André 2013; Lindberg et al. 2015; Lindberg 2016). Trying to fill this gap, the author will focus on how gender can be a source of economic marginalisation, mediated by the disadvantaged condition of women in the labour market, and on the extent to which social innovations can reverse this process. Being marginalised can be defined as a positional disadvantage that derives from ‘social processes through which personal, social or environmental traits are transformed into actual or potential factors of disadvantage’ (von Jacobi et al. 2017, 151). Marginalisation means occupying a position in the labour market which is characterised by reduced bargaining power with clients/employers, a lower possibility of career advancement and non- standard contracts that could affect the individual’s economic independence. The lower integration of women into the labour market leads to instability in financial resources and increases the gap between required and available resources which creates a status of economic insecurity that could result in a full- blown state of social exclusion were any negative event to occur (Kaseauru et al. 2016). Social innovation should play a role in widening the opportunity for women to be integrated into public life in an empowered position and in reducing their exposure to financial stress and resource constraints by reversing the pre- existing processes of marginalisation. The aim of this article is to explore social innovation in terms of its consequences for gender inequality and its role in reducing the economic marginalisation of women. Can it be assumed that social innovation could be one of the tools that allow the EU to reduce gaps in the social and economic participation of women? Is social innovation a tool to tackle the economic marginalisation of women in European societies? The EU funded project CrESSI provides an opportunity to test these research questions empirically. Through the project primary data has been collected on three different cases of social innovation across Europe through interviews with the beneficiaries of socially innovative actions. The cases were: autonomous water management in Germany (Ziegler, 2017b), complementary currencies in the Netherlands (van der Linden, 2017) and, finally, solidarity purchasing groups in Italy. This study will focus on the Italian case, which is considered to be an ideal case on which to base a study on the economic marginalisation of women as it is set in the agricultural sector where employment is traditionally strongly gendered in favour of men. The paper will analyse the following aspects: first, the role of solidarity purchasing groups in fostering a wider participation of women in the food production and agricultural sector; second, the extent of the positive impact of being a supplier of solidarity purchasing groups in terms of the reduction of the economic marginalisation of women working in this sector. This last analysis will be conducted via a series of logistic regression models on the primary data collected in 2016 within the framework of the CrESSI project among solidarity purchasing group suppliers, taking the economic condition of the household the respondent belongs to as a dependent variable. This allows the researcher to assess whether participating in social innovation has prevented the family from the risk of worsening economic conditions in the last three years, thus supporting the assumption made in this paper that participation of women in the labour market ensures their individual economic independence and protects the entire household from financial constraints (Curatolo and Wolleb 2010). The paper is organised as follows: the literature review provides some insights into the marginalisation of women in the labour market and attempts to define the factors that prevent full equality between the genders in this context, while highlighting the potential of social innovation in addressing such inequality. The following section on the methodology used in this study presents the CrESSI investigation and the primary data that will be used to study the relationship between gender and social innovation. This section provides a detailed analysis of the Italian context, focusing on the disadvantaged condition of women in the agricultural sector. The main sources used for this analysis are the Eurostat database, the Italian Labour Force Survey and the descriptive analysis based on CrESSI primary data. This is followed by a regression analysis on the effect of participating in social innovation with the aim of assessing the role that gender plays in this relationship. The final paragraph presents the conclusions drawn. 2. Women’s Marginalisation and the Role of Social Innovation Social innovations are ‘socially innovative actions, strategies, practices and processes’ that ‘arise whenever problems of poverty, exclusion, segregation and deprivation or opportunities for improving living conditions cannot find satisfactory solutions in the ‘institutionalized field’ of public and private action’ (Moulaert et al. 2013a: 2). They are initiatives (usually promoted through a bottom-up process) that propose grass-root solutions that rupture pre-existing socio-economic structures and that promote the involvement of the beneficiaries in the processes (von Jacobi et al. 2017; Ziegler et al. 2017). Such initiatives usually have two axes: first, they put in question the economic, social and political relations that produce marginalisation; secondly, they promote the active participation of citizens in the process of disruption (von Jacobi et al. 2017). An innovation must be social in its ends and means to be considered a social innovation (BEPA 2010): being good for society should also imply that the action of social innovation enhances the capacity of a society to act in favour of the wider inclusion of its citizens. Social inclusion – defined as the increased participation of citizens in public life - is prominent in the process (Moulaert et al. 2013b) and social change is the desired outcome of social innovation’s activities (Lindberg 2016). Social innovation is an increasingly appealing concept, both for scholars and for European policy- makers. Especially for policy-makers, the value of social innovations might be found in the assumption that they can be more effective, when compared to traditional policies relying on market or top-down public interventions, in meeting the social demands of marginalised groups as they promote a participatory approach (BEPA 2010). Starting from the 1990s, social innovation has been increasingly extended as a concept to include collective and bottom-up actions occurring not only in urban deprived neighbourhoods, but also in peripheral rural localities, which had been overlooked in previous years (Moulaert et al. 2013b). Although social innovations disregarded gender inequalities as an object of analysis in itself (Lindberg et al. 2015; Lindberg 2016), as gender is a pervasive dimension of inequality, social innovations should have an impact on gender relations – especially if they are oriented to foster a wider participation in the labour market, even when their goals are not explicitly oriented to rebalancing gender relations in society (André 2013). In fact, as they are oriented to favour participation in public life, the potential role of social innovations in reducing the marginalised condition of women is sound. The post-industrial transformation has brought three main changes to the structure of the labour force in European societies: an increased number of service jobs with a correspondent decline of employment in manufacturing and agriculture, a stronger presence of female workers and, finally, a progressive deregulation in working contracts (Cucca and Maestripieri 2015). All the cited processes have impacted women’s labour market participation and, in recent years, a wider women’s activity rate has been recorded in all western economies as one of the consequences of the de-industrialization process. Women’s increased participation was helped by the formalization of women’s work, by opening opportunities in the service sector, by the increase in female educational attainment and the wider availability of part-time employment opportunities (Hakim 2000; Thévenon 2013). The growth has been steady since the 1970s, also due to a progressive de- standardisation of contracts: part-time and temporary jobs offered the opportunity for more women to be active in the labour market without being employed full-time, thus easing women’s participation in public life even when they have duties of care. This was an advantage for all those women who wished to be active in the labour market as well as in family life (Hakim 2000). Nevertheless, due to women’s non-standard involvement in labour markets, other sources of gender inequality, such as occupational segregation, the gender pay gap and disequilibrium in the distribution of work within families, still persist (Hakim 2000; Vosko et al. 2009), exposing women to economic insecurity and financial constraints (Kaseauru et al. 2016). Additionally, it has also been demonstrated that a wider participation of women in the labour market reduces inequality between families (Grotti and Scherer 2016). The factors that lead to women’s disadvantage on the labour market can be enumerated by the dimensions that follow: the quantity of work accessible to women, the quality of the work they have, the financial resources available from their jobs and, finally, the unequal distribution of paid/unpaid work between genders that hinders the capacity of women to be as active in the labour market as men (Maestripieri 2015). Regarding the quantity of women’s work, the gap in women’s labour market participation is still persistent – albeit it is closing especially following the financial crisis that impacted men’s occupations more than women’s (Cucca and Maestripieri 2015; Maestripieri 2015). Regarding the quality of women’s work, the persistence of a strong gender bias in non-standard work, including part-time and temporary employment, has been empirically proven (Maestripieri 2015). Although the timing varied across Europe, non-standard positions started to increase in the mid-1980s, specifically interesting women, young and other marginal workers such as migrants. Women are particularly more interested in part-time jobs than members of other social groups as part-time contracts provide an ideal way to reconcile a woman’s private and public life (Thévenon 2013). The concentration of women in certain sectors is, in fact, another possible signal of their economic marginalisation. Segregation can occur in two ways: vertically and horizontally. Vertical segregation refers to the position of women in the work hierarchies, with women more likely occupying low-skilled and low- paid positions. Horizontal segregation refers to the concentration of men and women in certain sectors. Where women make up the majority of workers, the working conditions in the sector tend to be worse than in other sectors, with increased destandardisation of contracts and lower salaries (Cucca and Maestripieri 2015). Thus, the two types of segregation (vertical and horizontal) tend to intertwine with non-standard job contracts to produce niches of women’s marginalisation (signalled by low salaries and lower attachment to the labour market). One of the most unbalanced sectors in terms of women’s presence is the agricultural sector. Previous research demonstrated that a gender rebalancing in agriculture is favoured by the diffusion of alternative farming practices and by the reduced dimensions of farms (Ball 2014). This type of farming is the main target of the activity of the social innovation that project CrESSI studied in the Italian case. In fact, solidarity purchasing groups (SPGs) are small self-organised groups of citizens that collectively purchase primary goods directly from family producers (Altraeconomia 2015), with the explicit aim of enhancing their economic and social participation (Maestripieri 2018). The novelty of their activities lies in the process of consumption: SPGs allow consumers to avoid intermediation and to promote critical consumption principles by choosing suppliers that respect the principles of sustainable agriculture. Oriented by principles of political consumerism (Stolle et al. 2005; Arcidiacono 2013; Forno and Graziano 2014), they can be considered as being social innovators, as the movement promotes new processes that open new end-markets for their suppliers (usually small organic producers, local artisans or social cooperatives that employ vulnerable individuals), thus favouring their inclusion in society by offering fair prices and enhancing their proximity with final consumers. The aim of their activities is to foster practices of solidarity with family farmers: the idea is to put into question the traditional food supply chain and to avoid the intermediation of mass retailers in order to empower consumers and producers by favouring alternative systems of food production. The principle of solidarity towards producers and sustainable agricultural productions is what motivates social innovators: SPGs explicitly seek to sustain small farming activities that operate with organic and sustainable forms of production. In order to achieve their purpose, they sustain practices of small family farmers whose production is organic (Maestripieri 2016; Maestripieri 2018). A social innovation in this sector is particularly relevant in terms of gender equality: agriculture is one of the main segregated sectors in terms of women’s participation and Italian women are usually employed as family helpers in farm households in a position that is dependent on their partners (see paragraph 4). SPGs sustain farm households and organic farming. Organic farming is more popular among women entrepreneurs in agriculture, supposedly having a rebalancing effect on gender inequality in the sector as it favours niches in which women are more likely to be active as farmers (Ball 2014). In conclusion, policies of labour de-standardization promoted by European governments in the last years have achieved mixed results. The number of women in the labour market has increased in the last decades – especially in the service sectors. However, women’s marginalised position on the labour market means that there still is insecure integration and an economic dependence on a primary earner. Even for those women who are active on the labour market, the quality of the job they get should be questioned: they are usually segregated to certain services sectors, leading to limited integration especially in the manufacturing and agricultural sectors. They also suffer worse working conditions than men, also because their income is usually considered to be additional to the breadwinner’s. Nevertheless, the decisive role of gender in creating niches of marginalisation has somehow been underplayed by the current debate on social innovation (Lindberg et al. 2015; Lindberg 2016), although the main goal of social innovation should be the promotion of a process of participation in public life and improvement of the social conditions of its beneficiaries. Previous research (Ball 2014) shows that alternative farming practices in agriculture are able to rebalance gender inequality in a sector such as food production, which is normally masculinized in developed countries. This paper seeks to investigate the extent to which the processes promoted by SPGs address the structuration of women’s disadvantage in agriculture. The following analysis will try to open the black box of social innovation in regard to women’s situation on the labour market with the aim of assessing whether the experiences of social innovation in the food and agriculture sector, through SPGs’ bottom-up processes of empowerment, are able to lift women out of marginalisation. 3. Research Design and Methodology The data presented in this paper are part of a wider primary collection of data which was gathered as part of th the actions of the EU funded project CrESSI (2014/2018) funded under the 7 Framework Programme. The general scope of the project was to explore the economic underpinnings of social innovations with a focus on how policy and practices can enhance the lives of marginalized and disempowered citizens in society. The theoretical approach of CrESSI was indebted to the theories of Jens Beckert (2010), Michael Mann (1986) and the capability approach (Sen 1999, Nussbaum 2000): the so-called extended social grid model explores the structural dynamics behind processes of marginalisation and the role of social innovation in overcoming them (Nicholls and Ziegler 2018). The project carried out a comparative investigation of three cases of social innovation that were considered as being particularly relevant to study process of marginalisation. The Italian case focused on solidarity purchasing groups: in this case, the social innovation aims at reconnecting consumers and producers, shortening the supply chain in the domain of food and agriculture. It is an ideal case to use to analyse women’s economic marginalisation as it regards an economic activity in a strongly gendered sector in which women make up the minority of workers and are usually employed in subordinate positions (see paragraph 4). The empirical enquiry in CrESSI was based on a combination of qualitative and quantitative methods, in order to investigate the two sides of the social innovation relationship (von Jacobi et al. 2015): the opinion of the social innovators (SPG members) on the functioning and practices of their SPGs, that was obtained through semi-structured interviews, and the impacts of SPGs on their beneficiaries (suppliers of SPGs) that was investigated using an online survey. In the context of this paper, the data analysis will concentrate on the data collected from the beneficiaries of the social innovation. There are two reasons behind this choice: firstly, the aim of this paper is to assess the role of social innovation in empowering women as beneficiaries of SI actions and the interviews were carried out with social innovators; secondly, the structure of the interview did not envisage a specific analysis of gender issues as it focused on the organisation of SPGs and comprised an analysis of their role in the reduction of the marginalisation of beneficiaries. The data collected qualitatively from social innovators informed the questionnaire sent to the beneficiaries, as several questions were derived from the analysis of the interviews in order to propose categories which were grounded in the phenomenon of SPGs. The final distribution of respondents in the SPGs case is illustrated in Table 1. The beneficiaries included small farms, local artisans and social cooperatives that cooperate with SPGs, while the control group comprised the same type of actors that have never sold their products to SPGs. Beneficiaries are differentiated in terms of those who are currently benefitting from the social innovation and those who have benefitted from it in the past: in the case of Italy, 750 are current beneficiaries and 175 were beneficiaries in the past. This last distinction will not be used in the analysis presented here. Table 1 – Total questionnaires in the Italian social innovation case (SPG) under the EU funded project CrESSI, distinguished by gender Men Women Total Control group, of which 1,348 692 2,040 Never heard of SPGs 217 92 309 Heard of them, but never been active with SPG 1.131 600 1.731 Actual Beneficiaries 439 311 750 Past beneficiaries 104 71 175 total 1,891 1,074 2,965 Source: CrESSI survey data, 2016 The sample was not randomly selected as a list of SPG suppliers is not available given the informality that characterises this social innovation (Maestripieri 2016). As such, the analysis cannot be representative of the entire population of SPG suppliers and/or Italian small family farmers. The control group has also not been randomly selected. Selling products to SPGs is the sole criterion used to differentiate the respondents falling under the control group from the beneficiaries. The data does not specify whether the individuals who fall within the control group are not selling their product to the SPGs out of choice or because they have not been selected by SPGs: however, the data shows that knowledge about SPGs is well spread in the sample even among those who are not currently active in SPGs, confirming the hypothesis that respondents are indeed potential beneficiaries of the social innovation under study. Respondents involved in the social innovation and control group are also distinguished by gender, with more women in the first group (41.3% vs. 33.4% in control group) and by age, with those in contact with social innovation being slightly younger (mean age 47 years old) than those in the control group (mean age 49 years old). The figures are reported in Annex – table 1. Although the sample could not be wholly representative in this case, the aim was to provide one that would be as comprehensive as possible. Different methods were used to build up such a list, including asking each interviewed SPG in the qualitative phase to share the list of suppliers (500 contacts were collected); listing producers who self-declare themselves as potential suppliers for SPGs on the two main websites devoted to the social innovation (www.retegas.it and www.eventhia.com - about 2000 contacts were collected); collecting contacts from events that were considered relevant for the movement of solidarity purchasing groups (i.e. fair-trade or organic fairs) or online public databases that could potentially comprise beneficiaries, such as local databases of biological and bio-dynamic farm households, farmers’ markets, tourist itineraries that involve local products, etc. In total, about 20,000 potential respondents received a direct invitation to participate in the survey by email: however, there was certainty about the respondents’ involvement in the social innovation for the first 500 contacts; the rest were only potentially beneficiaries. To be as comprehensive as possible, questionnaires were also circulated among representatives of the general movement of solidarity purchasing groups and beneficiaries’ associations (national associations for family farmers and social cooperatives). Questionnaires and interviews were set up to give empirical ground to the theoretical model of CrESSI. The survey’s questions focused on the role of social innovation in the life of the respondents, analysing how being involved as a supplier in an SPG group could enhance the autonomy of the person, improve their economic integration and increase life satisfaction. The questionnaire aimed to investigate the role of social forces (Beckert 2010) in fostering the autonomy perception of respondents (Sen 1999; Nussbaum 2000) along six analytical dimensions derived from the theory of Mann (Heiskala 2016). In the context of this paper, the analysis will focus on the economic status, the educational level and the employment status of the person and the characteristics of the household in which the person lives, which are considered relevant for the study of the economic marginalisation of the respondent in relation to the individual involvement in social innovation. The analysis of the impact of social innovation on the economic marginalisation of women will revolve around four logistic regression models. The aim of the analysis will be to examine how participation in social innovation could secure respondents against experiencing financial constraints that are assumed as being the outcome of economic marginalisation. This phenomenon will be measured through the exposure to the risk of worsening economic conditions of the household to which the interviewee belongs, calculated against a subjective measurement pertaining to the last three years. The question posited was: “How does the income of the household compare to the previous three years’?” with the respondent being asked to choose among the following options: inferior, same and higher. The replies were dichotomised to highlight those households who are suffering from a reduction in disposable income (the dependent variable value being 1 when the person answers “inferior”). The choice of using a dummy dependent variable responds to the necessity of preserving the reliability of the analysis, as the CrESSI sample contained a limited number of cases . The models used exclude from the analysis those who are not currently active in the labour market, including pensioners or unemployed persons (59 cases unemployed/inactive, 40 cases pensioners) on the basis of the assumption that their activity in SPGs does not have a relevant impact on their welfare as the farming activity is not likely to be their main income source. The independent variables were the following. With regard to the attributes of the person, the data included age (in years) and gender. The participation of the respondent in the social innovation is the main independent variable of the model. This variable assumes four status, dividing the sample into four groups: those who declare having a personal and financial benefit from participation in SPGs (the “core- beneficiary”); those who enjoyed a personal benefit; those who declared they had not experienced a relevant change since they entered in contact with the social innovation; and, those who have not been involved in SPGs (control group). The interactive effect between gender and social innovation was further measured using the term of interaction. The interaction control of being a woman and being in the “core-beneficiary” group measures if there is a significantly different effect than being a man and part of the same group. A second relevant dimension of analysis is the type of employment: the sample is divided on the basis of different employment classes (dependent workers, social cooperative, solo self-employed workers, employed in farm households and entrepreneurs) that respect the different characteristics of labour market integration in the CrESSI sample. This structure respects the hypothesis behind the paper: a marginalised condition in the labour market between genders is the cause of insecure economic conditions; social innovation intervenes in the process, favouring the participation of women in public life and widening their employment opportunities in more secured labour market conditions. Educational level serves as a proxy for the social position of the respondents (three classes: up to lower secondary school, secondary school, tertiary educated). The model also takes into account the household composition (three classes: single, in partnership and living with other adults) as the dependent variable measures a situation of economic marginalisation at household level, via the presence of children and partner. And, finally, the extent of participation in the labour market is taken into account with a control for the numbers of working hours per week. Finally, the analysis presented in the article cannot be statistically generalized. As already stated, the results presented below are only valid for the population of the respondents to the CrESSI survey, although they can give some further insight into how research on social innovations might develop in the future. A short descriptive paragraph is given before presenting the analysis on social innovation. The data given below is based on several sources (the Eurostat database, the Italian Labour Force Survey and the CrESSI survey) to give an in-depth description of the marginalised condition of women in Italian agriculture sector and the possible role of SPGs in overcoming it. The section below also presents the variables that will subsequently be used in the statistical analysis. 4. The Employment Situation of Italian Women and the Role of Social Innovation From a comparative perspective, Italy is not a favourable context for women’s employment, in terms of both quantity and quality of work available to women. Italy shows the lowest activity rate of women, with only 55% of women working or actively looking for work. In addition, it is also the country in which the gap between genders in employment rates is the highest in the EU (18.4% while the average in EU28 is 10.5%). However, the marginalisation experienced by women does not seem to be mainly driven by family reasons: both inactivity and part-time employment are only partially due to family obligations, unlike in the United Kingdom or in Germany. The reason for their lack of participation seems to lie in the quality of the jobs offered to women and their lack of access to the labour market. Italian women are more exposed to long- term unemployment; they are in the majority involuntarily employed in non-standard positions and only to a lesser extent do they occupy managerial or professional positions. Table 2 presents the relevant data. Table 2 – Indicators on labour market integration of women 15-64 (%), by country 2016 Indicator DE ES FR IT NL PL UK EU28 Activity rate 73.6 69.2 67.6 55.2 75.0 62.0 72.2 67.3 Family duties as main reason for inactivity 15.6 16.6 14.8 15.1 12.7 16.2 29.2 10.0 Employment rate 70.8 54.3 60.9 48.1 70.1 58.1 68.8 61.3 Gender gap in employment 7.6 10.5 6.7 18.4 9.5 12.9 9.5 10.5 Unemployment rate 3.8 21.4 9.9 12.8 6.5 6.2 4.7 8.8 Long-term unemployment rate 38.5 50.5 43.9 58.6 41.6 34.0 23.0 46.6 Part-time employment rate 46.5 24.1 29.8 32.7 76.4 9.7 40.8 31.9 Family duties as main reason for part-time 29.6 13.1 24.9 20.1 36.1 10.9 40.1 27.4 employment Main reason for part-time employment – could 10.4 59.8 42.3 58.8 8.2 25.2 12.1 24.6 not find a full-time job Temporary employment rate 13.2 26.1 16.1 14.0 20.6 27.5 6.0 14.2 Main reason for temporary employment – - 91.4 61.7 72.9 48.2 62.6 - 62.0 could not find a permanent job Area of women’s non-standard work 59.7 50.2 45.9 46.7 97 37.2 46.8 46.1 Women as managers or professionals 19.7 24.6 23.7 21.2 30.1 32.0 33.7 26.0 The labour market condition of Italian women is compared with countries which are comparable in terms of population, but different in terms of welfare regime (France, Poland, United Kingdom). Spain is also included: although usually associated in the same Mediterranean cluster, they differ in terms of gender equality index (see GEI 2012: http://eige.europa.eu/gender- statistics/gender-equality-index and Annex table XXX for further information). Germany and the Netherlands are also included as they are the other two countries included in the CrESSI data collection. The activity rate measures the percentage of individuals which are active on the labour market (employed or willing to be employed) on the total population of the same age. Employment rate is calculated as the percentage of individuals which have provided at least one hour of paid work in last week on the total population of the same age. Gender gaps are calculated male employment rates minus female employment rates. The unemployment rate measures the percentage of individuals who are willing to be employed, but have not found a job position yet on the total labour force. Area of non-standard work is roughly calculated as a linear sum of part-time employment and temporary employment. It is a raw measure as it is not possible to assess from Eurostat database how much part-time and temporary employment overlaps among women. Source: Eurostat database indicators Table 3 – Percentage of women employed by sector in Italy (15-64 y.o.) 2008 2009 2010 2011 2012 2013 2014 2015 2016 Agriculture and Forestry 31.8% 30.1% 30.3% 30.1% 29.6% 29.0% 28.6% 28.0% 27.9% Manufacturing et al 27.7% 26.6% 26.2% 26.2% 26.3% 26.4% 25.8% 25.7% 25.4% Construction 5.8% 5.8% 5.7% 6.9% 6.8% 7.1% 6.6% 6.5% 6.4% Wholesale and retail 41.0% 40.8% 40.8% 41.3% 42.2% 41.5% 41.8% 41.8% 41.0% Transportation and Storage 19.6% 19.4% 19.5% 19.1% 19.6% 20.2% 20.6% 20.8% 20.5% Accommodation and food 50.6% 51.2% 50.8% 50.5% 51.7% 52.5% 51.8% 50.2% 51.2% Advanced business services 27.1% 28.2% 28.5% 28.3% 28.8% 28.3% 27.7% 28.1% 28.1% Public Administration 34.5% 34.0% 34.4% 34.1% 34.1% 34.8% 34.4% 32.7% 33.3% Education 74.9% 75.3% 76.5% 76.9% 75.0% 74.8% 75.1% 75.6% 75.7% Health and Social work 70.1% 69.9% 69.1% 69.1% 69.7% 69.5% 70.5% 70.5% 70.3% Households as employers 89.2% 91.1% 91.4% 89.0% 88.8% 88.2% 85.2% 86.1% 87.7% Other services 58.5% 57.5% 57.8% 57.3% 56.8% 57.4% 57.4% 57.4% 56.9% Total 40.5% 40.6% 40.9% 41.3% 41.9% 42.2% 42.3% 42.1% 42.2% Source: Author’s calculation on Eurostat database Horizontal segregation is another interesting feature that characterises the Italian labour market in terms of gender equality, as shown in Table 3. In fact, women are not only less frequently employed than men in all sectors (there are 4 women every 10 employed person), but their employment is unequally distributed with a limited presence in traditional male sectors such as construction, transportation and manufacturing. Agriculture is one of the sectors showing one of the highest segregations by gender (women represent only the 30% of the total number of workers). Segregation has also slightly increased in the last few years, in counter tendency to other sectors and other countries (Cucca and Maestripieri 2015). However, in comparison with other male-dominated sectors, the agricultural sector is not distinguished in Italy by the high percentage of people who are self-employed (36.3% of total employment in the sector) but by the highest presence of family workers (6.94%), see table 4. The sector is not gender neutral: while there are more men among workers and the self-employed, there are more women among family helpers in farm households, in line with the strong female segregation already evidenced by the category ‘households’ listed as employer. Being locked within the family both in their working and their private lives exposes women to the risk of being economically dependent on their partners. Table 4 – Distribution of men and women in the agriculture sector by type of occupational position (15-64 y.o.) Men Women % on the total employment in agricolture Dependent worker 71.74% 28.26% 55,98% Self-employed worker 76.71% 23.29% 36,28% Family helper 48.18% 51.82% 6,94% Source: Author’s calculation on Italian Labour Force survey, 2016 Looking at the general context, it seems that a sector such as the agricultural sector does not provide the most favourable environment for women’s integration in the labour market as access seems problematic and the quality of the jobs in this sector is in question given the subordinate position of women within farm The category also includes Mining and quarrying; Electricity, gas, steam and air conditioning supply; Water supply; sewerage, waste management and remediation activities. The category includes Information and communication; Financial and insurance activities; Real estate activities; Professional, scientific and technical activities; Administrative and support service activities. The category includes Arts, entertainment and recreation; Activities of extraterritorial organisations and bodies; other services. households. CrESSI’s data allow further investigation into the matter, particularly in terms of the potential impact that socially innovative initiatives such as SPGs can have in terms of improving gender equality in this sector (see Table 5). In terms of the characteristics of the population involved in the analysis, the unemployed and inactive respondents are almost insignificant; however, there is a quota of respondents who are pensioners (59) but are also active in agriculture. These are mainly in the male control group. The empowerment effect of social innovation emerges from the data: there are more women who can be considered to be entrepreneurs among the women in contact with the social innovation that among women in the control group. Women in the control group tend to be more likely to be solo self-employed than women in contact with SPGs. The same empowerment effect seems to apply for men and with a stronger effect (+6.5p.p. for men, while the difference SPG/CG for women is less than 3 p.p.). Women involved in social innovation tend to belong in farm households more than women in the control group, while the same does not occur for men. This trend might highlight a possible ambivalent effect of social innovation in terms of women’s empowerment: instead of favouring the integration of women in public life, the activities of SPGs might have the effect of locking women into families, as this social innovation mostly addresses small family farmers. From the CrESSI sample, it is not possible to distinguish whether the women are owners of or family helpers in the farm households involved in the social innovation. However, when asked if they define themselves as the primary earner of the family, 36 out of 45 women in farm household answered no, against 21 out of 44 men in the same situation. This indicates that it is very unlikely they are the owners of the farm households that collaborate with SPGs, confirming the risk of entrapment into families. Table 5 – Labour market participation and gender, comparison between social innovation beneficiaries and their control group– percentages and absolute values (highlighted in grey) Men in SPGs Men C.G. Women in SPGs Women C.G. Entrepreneur 43,8% 37,3% 33,2% 30,8% Working on one's own 21,9% 26,1% 22,2% 27,5% Farm household 13,3% 14% 20,9% 16,8% Social cooperative 5,34% 1,04% 4,45% 2,6% Dependent worker 14,2% 17,4% 17,3% 18,2% total in absolute values 535 1293 372 663 Unemployed/Inactive 4 14 7 16 Pensioner 4 42 0 13 total in absolute values 543 1.349 382 692 Source: Author’s calculation on CrESSI survey data, 2016 Table 6 – Economic conditions and gender, comparison between social innovation beneficiaries and their control group– percentages and absolute values (highlighted in grey) Compared to the previous three years, Men in SPGs Men C.G. Women in SPGs Women C.G. income of household this year has been? Income is inferior 33.9% 40.6% 40.6% 48.9% Income is the same 45.7% 43.7% 40.1% 38.8% Income is superior 20.4% 15.7% 19.3% 12.4% total in absolute values 295 697 192 356 Median income class 1100-1300€ 1300-1500€ 900-1100€ 700-900€ total in absolute values 543 1348 382 692 When looking at economic conditions (table 6), however, the effect of social innovation seems to be clear. Women who are involved in social innovation experienced a reduction in their household income less frequently compared to women in the control group and the beneficial effect of the social innovation seems wider compared to men. There persists an advantage for men in terms of individual income but the gap between genders is smaller among the respondents involved in the social innovation than among the men and women in the control group (see graph 2 in annex). Nevertheless, when asked about the influence of social innovation in their lives, women respondents were less positive about the role of social innovation in improving their lives (despite evidence given above), with about 40% of the female respondents involved in the innovation declaring that they had experienced no change in terms of personal benefit or improvement in financial condition. Conversely, there is a linear effect in terms of the attitude towards the social innovation: the more a person experienced a direct personal benefit from participation in the social innovation the more they trusted SPGs and the more willing they were to recommend it within their personal network. But the effect is stronger for men that have experienced personal and financial benefit from the social innovation. In general, the social group comprised in the CrESSI sample is more willing to trust SPGs than any other relevant institution in the field, including, for example, farmers’ associations, even when they did not experience a relevant change following their involvement. Table 7 – Attitudes towards the impact of social innovation by gender– percentages and average mean scores on attitude scales (score: 1-10) Would you How much do you How much do you recommend SPGs trust SPGs? trust farmers’ to family and associations? friends? Men Women Men Women Men Women Men Women Improved financial conditions 3.4% 4.1% 9.5 9.4 8.2 7.6 4.2 3.9 and enjoyed personal benefit Personal benefit but not 16.5% 18.3% 8.4 8.3 7.1 7.3 4.2 4.8 improved financial conditions No relevant change 8.9% 14.7% 5.8 6.5 5.2 5.6 4.2 4.1 Not involved in SPGs 71.2% 62.4% - - 5.6 5.9 4.5 4.7 Total in absolute values 1701 955 434 294 951 527 1.058 581 Source: Author’s calculation on CrESSI survey data, 2016 In conclusion, women in Italy are still locked in a position of marginalisation, which seems driven by their difficulty in accessing the labour market and in securing their position as workers. They tend to concentrate in certain sectors (i.e. education or health) which are traditionally perceived as being female dominated and when they are present in male-dominated sectors such as agriculture they tend to be entrapped in a subordinate position as family helpers. The analysis based on the CrESSI data seems to confirm the hypothesis that when women are in contact with social innovation there is a positive effect: they are more frequently entrepreneurial, they seem to be better protected from reduction in income and the wage gap with men is lower. However, the effect seems to be ambivalent at least in the women’s perception: quite frequently they declare that they have experienced no changes since their involvement in SPGs and the rate of trust in SPGs is lower compared to that of the men involved in the social innovation. The following section will investigate how social innovation can reduce gender inequality and empower women in public life. 5. Discussing Women’s Marginalisation in Relation to Involvement in SPGs Studying economic marginalisation of women in relation to social innovation is not an easy task. As it was previously discussed (see paragraph 2), the outcome is mediated and influenced by several factors, of which participation in social innovation is only one among them. This is why the subsequent analysis is carried out step by step, in an effort to identify the role of different groups of variables that might intersect with the previously set research questions. The analysis assumes the positive influence that participation in social innovation can have in preventing the worsening of the economic condition of the household to which the respondent belongs. These positive returns should be different for men and women. As shown in table 3 in Annex, there is no evidence of potential multicollinearity among independent and control variables. Secondly, the relation between the dependent variable and the different groups among respondents is significant, both between men and women and between beneficiaries and the control group. The results of the Chi2 test are shown in the Annex (table 4). Table 8 – Logistic model (odds ratio) analysing worsened financial conditions as dependent variable (CrESSI sample 19-64 years old). Significance threshold: *** P>|z| less than 0.01, ** P>|z| less than 0.05, * P>|z| less than 0.10. Independent variables Model 1 Model 2 Model 3 Model 4 Gender Woman 1.35*** 1.32** 1.34** 1.31** Ref. man Status in the social innovation No relevant change 0.91 0.97 0.98 0.98 Only personal benefit 0.80 0.90 0.93 0.93 Personal and financial benefit 0.58** 0.59** 0.58** 0.48** Ref. Control group Age in years 1.02*** 1.02*** 1.02*** 1.02*** Educational level Secondary school 1.02 1.03 1.03 Up to lower secondary 1.36 1.36 1.38 Ref. Tertiary educated Employment status Social cooperative 0.92 0.90 0.90 Entrepreneur 1.18 1.16 1.16 Solo self-employed 2.16*** 2.11*** 2.11*** Farm household 1.98*** 1.93*** 1.92*** Ref. Dependent worker Number of working hours per week 1.00 1.00 1.00 Presence of children At least one cohabitating child 1.13 1.13 Ref. No cohabitating children Household structure In partnership 0.76 0.77 Living with other adults 1.32 1.33 Ref. Single person Interaction term (gender*benefit) 1.52 Educational level has been measured by referring to the international standard classification ISCED, see http://ec.europa.eu/eurostat/statistics-explained/index.php/International_Standard_Classification_of_Education_(ISCED) for more reference. Pseudo R2 0.0202 0.0402 0.0429 0.433 Number of observations 1422 1422 1422 1422 Average prediction (marginal effect) 40,3% 40,3% 40,3% 40,3% Source: Author’s calculation on CrESSI survey data, 2016 Models of regressions are shown in Table 8. The first model focuses on the main relationship under investigation, that is, the effect of being involved in the social innovation on the exposure to possible financial constraints potentially experienced in the previous three years. Gender, age and the status in the social innovation are all related to the worsening of household conditions: being a woman involves an increase of the risk of experiencing worsening economic conditions with a likelihood of 7.2 p.p. However, only the situation in which the respondent enjoys financial and personal benefit from participation in SPGs is significantly impacting the protection from the risk under investigation, with a likelihood of -12.1 p.p. It is interesting to notice that the effect of participation in social innovation decreases the risk of experiencing worsened economic conditions, while being a woman increase the risk. Age seems linearly correlated as well, with a small increase of 0.7 p.p. for each year of the respondent. The second model introduces the role of educational level and the type of employment of the person in the analysis, which remain significant both for women (+6.5 p.p.) and for those who are benefitting more from the social innovation (-11.4 p.p.). The analysis indicates that there is no effect of education on the dependent variable, nor is the amount of work provided in the farming activity significant. For further explanation of the phenomenon, it is important to highlight how the type of employment impacts the risk of experiencing a downturn in household financial conditions. In particular, being a solo self-employed or part of a farm household increases the risk when compared to being an entrepreneur, a social cooperative or a dependent worker. The extent of this increase ranges from 15 to 20 percentage points. Being an entrepreneur or part of a social cooperative does not give any significant advantage/disadvantage. The third model introduces the control of the characteristics of the household. Being in a partnership is the strongest predictor for women’s economic insecurity (Kasearu et al. 2017) and having a double income is a protection against financial constraints experienced by households (Curatolo and Wolleb 2010). However, in the CrESSI sample, there is no direct effect related to the presence of children or the type of household on the risk of experiencing worsened economic conditions, except for those living with other adults that have a 12.7% risk of being more exposed to worsening financial conditions when compared to those who are in a partnership. Still, being a woman (+6.8%) and experiencing personal and financial benefits from SPGs (- 11.8%) are relevant factors in terms of explaining the worsening of household conditions in previous years, with farm households and solo self-employed being the most exposed. Once controlled for household characteristics, the effect of being a “core-beneficiary” of SPGs becomes significant when compared to those that have been in contact with the social innovation but without experiencing a financial benefit (-10%) or a relevant change (-11.3%). Despite being only slightly significant (0.08 in the first case and 0.07 in the second ), the results indicate that only when people experience a financial benefit from the social innovation does this participation protect against worsened economic conditions. The group of “core-beneficiaries” that received financial and personal benefit from SPGs do not distinguish among the types of benefit received. As shown in table 9, it is the extent of the perception of the benefit that distinguishes those who experienced only a personal benefit from those who experienced a personal and financial benefit. The respondents who more enthusiastic participants in the social innovation are also the respondents who enjoy more economic benefits from their involvement in the form of returns on income or a wider end market for their products. Table 9 – Type of benefit enjoyed by those who experienced personal benefit from the participation to SPGs – percentages of some/large extent What type of benefit did you enjoyed from the social Only personal Personal and Gap innovation? benefit financial benefit Money/Income 73.9% 96.6% +22,7 Friendship 78.9% 90.6% +11,7 Business relations 66.9% 78.2% +11,3 Knowledge 70% 85.7% +15,7 Fair price 84.3% 91.9% +7,6 End market 60.8% 81% +20,2 Source: Author’s calculation on CrESSI survey data, 2016 The fourth model introduces the control for interactions between gender and participation in the social innovation to check if the effect of being involved in the social innovation is significantly different according to whether the respondent is a woman or a man. Checking for interaction is particularly relevant as the two variables have opposite effects on the dependent variable: social innovation decreases the risk of experiencing worsened economic conditions, while being a woman increases this risk. However, the factor is not significant in predicting the risk of a worsening of the financial condition of the household, allowing the researcher to reject the hypothesis that the positive effect of participation in social innovation is significantly different for men and for women. Still women are more likely to experience a worsening in financial conditions (+6.2%). Those who declared that they benefitted both financially and personally from SPGs have a lower likelihood of experiencing a worsened economic condition, in comparison with the control group (-15.3%), with those who reported no significant change (-14.9%) and even with those who experienced a personal benefit (-13.5%). Thanks to the interaction factor, the marginal effect of being a “core-beneficiary” shows a significance of 0.012 when compared to the control group; 0.047 when compared with those who reported no relevant change; and, 0.042 with those who reported only a personal benefit. The last model further backed the hypothesis that only being a core participant in social innovation generates positive returns in terms of protection from worsened economic conditions. The type of involvement has an impact on the risk of experiencing a worsening of economic conditions: solo self-employed and farm household are the most exposed. In the first case, which pertains to all those who carried out their activities alone without having employees or without having collaborators in the family, the risk of having experienced a downturn in the previous three years is significantly higher than dependent workers (+17.3%), those involved in social cooperatives (+19.5%) and entrepreneurs (+14%). The same trend is visible for farm households, although to a lesser extent: they have a higher likelihood, when compared to dependent workers (+15.1%), those involved in social cooperatives (+17.4%) and entrepreneurs (+11.9%). Unfortunately, the CrESSI sample does not allow further investigation into the power relations within farm households: the disadvantaged effect of being in a farm household might vary on the basis of the role assumed in it (whether one is the owner or a family helper). Secondly, the more disadvantaged position of the self-employed and farm households might be caused by the dimension of economic activity rather than the specific vulnerability of the contract in itself. Neither hypotheses can be tested on the basis of the CrESSI data alone. Further investigation is required into the matter. The type of household and the presence of children does not seem to affect the risk, with the sole exception being those who are currently living with other adults. In the latter case, the majority of respondents (91.2%) comprised adults who are still currently living with their parents. Although in Italy it is not uncommon for adults to continue living in the family home, this might lead to a higher risk of exposure to economic difficulty, which is confirmed by the fact that respondents falling within this category reported a worsening of economic conditions in the last three years. In conclusion, the regression analysis performed by the author confirms the results of the descriptive analysis. Gender and participation in social innovation are associated with the risk of experiencing worsened economic conditions: being a woman is confirmed to be a disadvantage, while social innovation seems to constitute a protection against such a disadvantage. However, the role of social innovation in improving the living conditions of the beneficiaries is empirically proved only when the SPGs benefit experienced by the person has a direct effect on improving the financial situation of that person (as shown in table 9, mostly revolving around end market or income). In all the other cases, even when the respondent reported experiencing a direct personal benefit from the social innovation, the extent of the benefit is not significant in reducing the risk of experiencing worsened economic conditions. Those who work alone as self-employed or in farm households have a higher exposure to risk, raising new questions on how labour market integration might impact men and women differently as genders are unequally distributed among these occupational conditions. But, there is no evidence of a clear gender effect of the social innovation: men and women do not experience significantly different returns from participation in social innovation. 6. Conclusion The aim of this paper was to explore the assumption that social innovations, in the form of solidarity purchasing groups, can be a tool for improving gender equality in society, in the context of the phenomenon of economic marginalisation of women. The analysis is particularly promising, as SPGs are active in a sector such as agriculture in which women are the minority and their labour market integration usually occurs in subaltern positions. The primary data offered by the EU funded project CrESSI provides a unique opportunity to further explore this issue, which has so far been disregarded by the current debate on social innovation. Contextual analysis confirms that Italy is not a favourable context for women’s labour market integration, especially in the agricultural sector. Women are entrapped in non-standard contracts, mostly as family helpers. This last condition can be particularly dangerous in terms of economic independence as it locks women in families, both in their private and public lives. The descriptive analysis of CrESSI data confirms that being in contact with SPGs has a beneficial effect for women, who are thus more active in empowered positions in the labour market (i.e. entrepreneurs) and are able to benefit from a higher income compared to the women involved in the control group. However, when asked directly about the effect of their participation women seem to be more hesitant when compared to men to report that they have experienced benefits. Additionally, women in social innovation tend to concentrate more on farm households that those in the control group. Conversely, logistic models confirm the role of gender in predicting the worsening of the economic conditions of a household, which seems to be positively mediated by participation in social innovation. The positive effect of social innovation is limited to respondents who have experienced a financial return from SPGs. However, the analysis also confirms that there is no significantly different effect on men and women that derives from participation in social innovation. The type of employment and the extent of the benefit experienced are factors that have a deeper impact on the risk of experiencing worsened economic conditions. To avoid the economic marginalisation of respondents, social innovation should then have practical returns, which in the case of SPGs can be found in wider end markets or a more stable income. In conclusion, the social innovation debate has so far overlooked the effect of socially innovative activities on beneficiaries’ marginalisation. The results presented in this paper show that only when social innovation has a concrete financial benefit for beneficiaries, it can provide protection against worsened economic conditions. To achieve practical results, fostering the wider participation of marginalised citizens in socially innovative actions is a must although previous studies have demonstrated only limited evidence that this has been possible (von Jacobi et al. 2017; Ziegler 2017a; Maestripieri 2018). The risk for social innovation initiatives is that they become a niche for cultural elites that participate as social innovators (Cruz et al. 2017; Maestripieri 2018) without being able to tackle the pre-existing process of marginalisation for the beneficiary of their actions. 7. References Altreconomia. (2015). Il libro dei GAS. Gruppi di acquisto solidali: come fare la spesa giusta, dalla “A” al KM Zero, Milano: edizioni Altreconomia. André, Isabel (2013). “Gender and Social Innovation: the role of EU policies.” In The International Handbook on Social Innovation. Collective Action, Social Learning and Transdisciplinary Research edited by Moulaert F., MacCallum D., Mehmood A. and Hamdouch. 412-424. Cheltenham/Northampton: Edward Elgar. Arcidiacono Davide (2013). Consumatori Attivi. Decisioni D’acquisto e Partecipazione per Una Nuova Etica Economica. Milano: Franco Angeli, Ball Jennifer A. (2014). “She Works Hard for the Money: Women in Kansas Agriculture” Agriculture and Human Values 31: 593–605. Beckert, Jens (2010). How Do Fields Change? The Interrelations of Institutions, Networks, and Cognition in the Dynamics of Markets, Organization Studies 31(5): 605–627. BEPA (Bureau of European Policy Advisers). (2010). “Empowering people, driving change: Social innovation in the European Union.” Accessed February 22, 2017, https://goo.gl/OvVnFV. Bernardi, Fabrizio, Chakhaia, Lela, & Leopold, Liliya (2017). “Sing Me a Song With Social Significance”: The (Mis)use of Statistical Significance Testing in European Sociological Research. European Sociological Review, 33(1): 1–15. Blossfeld, Hans, & Hakim, Catherine (1997). Between Equalisation and Marginalisation. Women Working Part-Time in Europe and the United States of America. Oxford: Oxford University Press. Cucca, Roberta and Maestripieri Lara (2015). “Urban Economic Specialization and Women’s Integration into the Labour Market in 11 European Cities.” In Local Welfare Policy Making in European Cities edited by D. Kutsar and M. Kuronen. 35 – 50. Berlin: Springer. Curatolo, Salvatore and Wolleb, Guglielmo (2010). “Income Vulnerability in Europe.” In Social Vulnerability in Europe. The New Configuration of Social Risks edited by C. Ranci. 58-87. Basingstoke, UK: Palgrave-MacMillan. Cruz Helena, Martínez Moreno Ruben and Blanco Ismael (2017). “Crisis, urban segregation and social innovation in Catalonia.” PACO, 10(1): 221-245. Fagan, Catherine and Rubery, Jill (1996). “The Salience of the Part-time Divide in the European Union,” European Sociological Review. 12(3): 227–250. Forno Francesca and Graziano Paolo R. (2014). “Sustainable Community Movement Organisations.” Journal of Consumer Culture. 14(2): 139 – 157. Grotti, Raffaele, & Scherer, Stefani (2016). “Does Gender Equality Increase Economic Inequality? Evidence from Five Countries.” Research in Social Stratification and Mobility. 45: 13–26. st Hakim, C.atherine(2000). Work-lifestyle Choices in the 21 century. Preference Theory. Oxford: Oxford University Press. Heiskala, Risto 2016. “The Evolution of the Sources of Social Power, and Some Extensions.” In Global Powers: Mann’s Anatomy of the Twentieth Century and Beyond edited by Ralph Schroeder. 11–37. Cambridge: Cambridge University Press. Hubert Agnès and Helfferich Barbara (2016). “Integrating Gender into EU Economic Governance: Oxymoron or Opportunity?” Friedrich-Ebert-Stiftung EU Office. Accessed March 31, 2017 http://www.fes- europe.eu/fileadmin/public/editorfiles/events/nov2/FES_Gender_2016_Brussels_www.pdf. Von Jacobi Nadia, Chiappero Martinetti Enrica, Giroletti Toa, Maestripieri Lara and Ceravolo Flavio (2015). Toolkit (Methodology). CrESSI Working Paper 16/2015. Accessed March 28, 2017, https://goo.gl/N1zqRm. Von Jacobi Nadia, Ziegler Rafael and Edmiston Daniel (2017). “Tackling Marginalisation Through Social Innovation? Examining the EU Social Innovation Policy Agenda from a Capabilities Perspective.” Journal of Human Development and Capabilities. 18(2): 148-162. Kasearu Kairi, Maestripieri, Lara and Ranci Costanzo (2016). “The Impact of Labour-Market Participation, Family Structure and Local Welfare Systems on the Economic Insecurity of Women.” European Societies. 19(2): 202-221. Lindberg, Malin, Forsberg, Lela, & Karlberg, Helena (2015). “Gendered Social Innovation – A Theoretical Lens for Analysing Structural Transformation in Organisations and Society.” International Journal of Social Entrepreneurship and Innovation. 3(6):472–483. Lindberg, Malin (2016). “Undoing Gender in EU’s Social Innovation Policies?” International Journal of Social Entrepreneurship and Innovation. 4(1): 67–79. van der Linden, Martijn and van Beers, Cees (2017) “Are private (digital) moneys (disruptive) social innovations? An exploration of different designs”, in Journal of Social Entrepreneurship, 8(2), XX-XX. Maestripieri, Lara (2015). “Gendering Social Vulnerability. The Role of Labour Market De- standardisation and Local Welfare.” In Local Welfare Policy Making in European Cities edited by D. Kutsar and M. Kuronen. 51-67. Berlin: Springer. Maestripieri Lara (2016). Part IV. Individual case study “Solidarity Purchasing Groups.” In Report on Relevant Actors in Historic Examples and an Empirically Driven Typology on Types of Social Innovation. CRESSI Working Paper 29/2016 edited by Scheuerle Thomas, Schimpf Gudrun-Christine, Glänzel Gunnar, Mildenberger Georg, Ziegler Rafael. 335-368, https://goo.gl/YL7wrz. Accessed 23/7/2017. Maestripieri Lara (2018). “Creating Alternative Economic Spaces. The Socially Innovative Practices of Solidarity Purchasing Groups.” In Creating (Fair) Economic Space for Social Innovation edited by Nicholls Alex and Ziegler Rafael Oxford: Oxford University Press (forthcoming). Mann, Michael. 1986/2012. A History of Power from the Beginning to AD 1760. New Edition. The Sources of Social Power Vol. 1. Cambridge: Cambridge Univ. Press. Misra Joya & King Leslie (2005). “Women, Gender and State Policies”. In The Handbook of Political Sociology. States, Civil Societies and Globalization edited by T. Janoski, R. Alford, A. Hicks & M.A. Schwartz. 526 - 545. Cambridge: Cambridge University Press. Moulaert Frank, MacCallum Diana, Mehmood Abid and Hamdouch Abdelillah (2013a). “General Introduction: The Return of Social Innovation as a Scientific Concept and a Social Practice.” In The International Handbook on Social Innovation. Collective Action, Social Learning and Transdisciplinary Research edited by Moulaert F., MacCallum D., Mehmood A. and Hamdouch. 1-6. Cheltenham/Northampton: Edward Elgar. Moulaert Frank, MacCallum Diana and Hillier Jean (2013b). “Social Innovation: Intuition, Precept, Concept, Theory and Practice” In The International Handbook on Social Innovation. Collective Action, Social Learning and Transdisciplinary Research edited by Moulaert Frank, MacCallum Diana, Mehmood Abid and Hamdouch Abdelillah, 13-24. Cheltenham/Northampton: Edward Elgar. Nicholls Alex and Ziegler Rafael (2015). “The Extended Social Grid Model.” In Creating (Fair) Economic Space for Social Innovation edited by Nicholls Alex and Ziegler Rafael Oxford: Oxford University Press (forthcoming). Nussbaum, Martha. 2000. Women and Human Development: The Capabilities Approach. Cambridge: Cambridge University Press. Sen, Amartya (1999). Commodities and Capabilities (2nd ed.). Delhi New York: Oxford University Press. Stolle Dietlind, Hooghe Marc and Micheletti Michele (2005). “Politics in the Supermarket: Political Consumerism as a Form of Political Participation”, International Political Science Review/ Revue Internationale de Science Politique. 26(3): 245–269. Thévenon, Olivier (2013). “Drivers of Female Labour Force Participation in the OECD.” OECD Social, Employment and Migration Working Papers, No. 145. OECD Publishing, https://goo.gl/Js1bGM. Accessed 23/7/2017. Vosko, Leah, Macdonald, Martha & Campbell, Iain (2009). Gender and the Contours of Precarious Employment. Oxon: Routledge. Ziegler Rafael, Molnár György, Chiappero-Martinetti Enrica and von Jacobi Nadia (2017). “Creating (Economic) Space for Social Innovation”. Journal of Human Development and Capabilities. 2829(March): 1–6. Ziegler Rafael (2017a). “Social Innovation as a collaborative concept.” Innovation: The European Journal of Social Science Research, 0(0):1-18. Ziegler, Rafael (2017b) “Citizen innovation as niche restoration - a type of social innovation and its relevance for political participation and sustainability”, in Journal of Social Entrepreneurship, 8(2), XX-XX. Acknowledgements The article has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant Agreement No 747433. I also would like to thank Marco Gavazzoni for his precious comments on paragraph 5. End Notes 1. This article has been produced thanks to the support of the European Union's Seventh Framework Programme for Research, Technological Development and Demonstration (contract nr. 613261 - project CRESSI). The information and views set out in this article (or book) are those of the author(s) only and do not necessarily reflect the official opinion of the European Union. 2. Solidarity purchasing groups is the English term for gruppi di acquisto solidale, usually shortened to GAS, which is how participants usually call the groups. In this paper the acronym SPGs is used. 3. The dependent variable has 1427 valid cases (of which 576 beneficiary). The final absolute number of respondents in models is 1422 as some of the independent variables present in the models have different missing cases from the dependent variable. 4. Results of the models will be presented in tables presenting odds ratios, while marginal effects will be used while commenting results. 5. In the paper, the perspective of Bernardi et al. (2017) on the significance threshold is adopted with the aim of focusing on the substantive significance of the factors under analysis. Annex Table 1 – Participating in the social innovation, by gender and age – percentages and total in absolute values Total in absolute SPGs Control values Beneficiary group Men 58.7% 66.1% 1891 Women 41.3% 33.4% 1074 Under 30 years old 6.7% 6.8% 200 Between 30 and 40 22.5% 18.4% 584 Between 40 and 50 29.7% 27.3% 831 Between 51 and 64 34.1% 36.7% 1063 Over 65 years old 7% 10.1% 287 Total in a.v. 925 2.040 2965 Source: Author’s calculation on CrESSI survey data, 2016 Graph 2 – Distribution of individual income by gender and participation in social innovation Men in SPGs Men in Control Group 37.35 30.61 29.39 28.02 26.85 26.78 13.22 7.782 Women in SPGs Women in Control Group 54.01 49.38 36.88 28.47 13.5 11.88 4.015 1.875 max 900 max 1500 max 2600 over 2600 max 900 max 1500 max 2600 over 2600 Individual income Source: Author’s elaboration on CrESSI survey data, 2016 Table 3 – Correlation table for the subsequent models (CrESSI sample 19-64 years old). Significance threshold: *** P>|z| less than 0.01, ** P>|z| less than 0.05, * P>|z| less than 0.10. X SPGs Gender Age Edu Emp Hours Child Partner Worsening economic 1 conditions (x) Relation SPGs -0.07*** 1 Gender 0.06** 0.06*** 1 Age in years 0.13*** -0.04** -0.07*** 1 Education 0.06** -0.11*** -0.12*** 0.15*** 1 Employment 0.14*** 0.00 0.02 0.05*** 0.14*** 1 Hours worked -0.06*** 0.04* -0.16*** -0.05*** 0.10*** 0.05*** 1 Children 0.02 0.04* 0.01 0.31*** 0.12*** -0.01 0.00 1 Partner 0.00 0.00 -0.02 -0.08*** 0.05** 0.07*** 0.00 0.15*** 1 Source: CrESSI survey data, 2016 Percent 0 20 40 60 0 20 40 60 Table 4 – Chi2 test for the dependent variable (CrESSI sample 19-64 years old). Significance threshold: *** P>|z| less than 0.01, ** P>|z| less than 0.05, * P>|z| less than 0.10. Gender** Personal and financial SPGs (Pearson chi2=5.02) benefit ** (Pearson chi2=4.41) Men Women No Yes No risk 61.82 55.75 58.90 69.79 Experienced a worsening of household economic 38.18 44.25 41.10 30.21 conditions in the last 3 years 100 100 100 100 Source: CrESSI survey data, 2016 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Social Entrepreneurship Unpaywall

Does Social Innovation Reduce the Economic Marginalization of Women? Insights from the Case of Italian Solidarity Purchasing Groups

Journal of Social EntrepreneurshipAug 23, 2017

Loading next page...
 
/lp/unpaywall/does-social-innovation-reduce-the-economic-marginalization-of-women-hGVxWO9fza

References (43)

Publisher
Unpaywall
ISSN
1942-0676
DOI
10.1080/19420676.2017.1364289
Publisher site
See Article on Publisher Site

Abstract

Maestripieri L. (2017) Does Social Innovation Reduce the Economic Marginalization of Women? Insights from the Case of Italian Solidarity Purchasing Groups, Journal of Social Entrepreneurship, 8(3): 320-337. ABSTRACT (100 words) The paper explores the relationship between gender and social innovation to highlight the possible positive effects of women’s participation in social innovation in terms of protection from economic marginalisation. It focuses on Italian solidarity purchasing groups as a case of social innovation in the domain of food and agriculture. The analysis is based on logistic regression using primary data collected in 2016 for the EU funded project CrESSI. The results show that participation in social innovation does protect households from worsening economic conditions. However, it was not empirically proven that there is a significant difference between men and women in the benefit enjoyed from the participation in solidarity purchasing groups. KEY WORDS: Marginalisation, gender, social innovation, solidarity purchasing groups, alternative food networks 1. Introduction Social innovations are increasingly proposed as a solution to mitigate the worst consequences of the recent financial crises in order to foster employment and social inclusion in European Societies. Extensively researched in recent years, given the centrality of the concept in flagship initiatives of the European Commission, the idea of social innovation has been adopted across various domains of the social and economic sciences, with several concurrent definitions of the term presented by scholars, stakeholders and policy makers (Ziegler 2017a). As policy action on social innovation gains consensus and visibility, it risks becoming a buzz word that appeals to stakeholders and scholars (Moulaert et al. 2013b). The appeal of the concept lies in the problem that it addresses (Moulaert et al. 2013a): in a neoliberal era, where the role of the state has been reduced as much as possible, social innovation seeks to give a new centrality to fruitful interrelations between market and community. It raises attention on how the beneficial outcomes of innovation are distributed among citizens (Moulaert et al. 2013a). Gender represents one of the main dimensions of inequality. Equality between men and women is particularly relevant for EU strategies for economic and societal improvement (André 2013), as a more equal society can foster economic growth in the longer term and achieve better social cohesion (Hubert and Helfferich 2016). However, gender issues have, so far, been overlooked by the social innovation debate with only a few authors discussing how social innovation can foster more equality between men and women (André 2013; Lindberg et al. 2015; Lindberg 2016). Trying to fill this gap, the author will focus on how gender can be a source of economic marginalisation, mediated by the disadvantaged condition of women in the labour market, and on the extent to which social innovations can reverse this process. Being marginalised can be defined as a positional disadvantage that derives from ‘social processes through which personal, social or environmental traits are transformed into actual or potential factors of disadvantage’ (von Jacobi et al. 2017, 151). Marginalisation means occupying a position in the labour market which is characterised by reduced bargaining power with clients/employers, a lower possibility of career advancement and non- standard contracts that could affect the individual’s economic independence. The lower integration of women into the labour market leads to instability in financial resources and increases the gap between required and available resources which creates a status of economic insecurity that could result in a full- blown state of social exclusion were any negative event to occur (Kaseauru et al. 2016). Social innovation should play a role in widening the opportunity for women to be integrated into public life in an empowered position and in reducing their exposure to financial stress and resource constraints by reversing the pre- existing processes of marginalisation. The aim of this article is to explore social innovation in terms of its consequences for gender inequality and its role in reducing the economic marginalisation of women. Can it be assumed that social innovation could be one of the tools that allow the EU to reduce gaps in the social and economic participation of women? Is social innovation a tool to tackle the economic marginalisation of women in European societies? The EU funded project CrESSI provides an opportunity to test these research questions empirically. Through the project primary data has been collected on three different cases of social innovation across Europe through interviews with the beneficiaries of socially innovative actions. The cases were: autonomous water management in Germany (Ziegler, 2017b), complementary currencies in the Netherlands (van der Linden, 2017) and, finally, solidarity purchasing groups in Italy. This study will focus on the Italian case, which is considered to be an ideal case on which to base a study on the economic marginalisation of women as it is set in the agricultural sector where employment is traditionally strongly gendered in favour of men. The paper will analyse the following aspects: first, the role of solidarity purchasing groups in fostering a wider participation of women in the food production and agricultural sector; second, the extent of the positive impact of being a supplier of solidarity purchasing groups in terms of the reduction of the economic marginalisation of women working in this sector. This last analysis will be conducted via a series of logistic regression models on the primary data collected in 2016 within the framework of the CrESSI project among solidarity purchasing group suppliers, taking the economic condition of the household the respondent belongs to as a dependent variable. This allows the researcher to assess whether participating in social innovation has prevented the family from the risk of worsening economic conditions in the last three years, thus supporting the assumption made in this paper that participation of women in the labour market ensures their individual economic independence and protects the entire household from financial constraints (Curatolo and Wolleb 2010). The paper is organised as follows: the literature review provides some insights into the marginalisation of women in the labour market and attempts to define the factors that prevent full equality between the genders in this context, while highlighting the potential of social innovation in addressing such inequality. The following section on the methodology used in this study presents the CrESSI investigation and the primary data that will be used to study the relationship between gender and social innovation. This section provides a detailed analysis of the Italian context, focusing on the disadvantaged condition of women in the agricultural sector. The main sources used for this analysis are the Eurostat database, the Italian Labour Force Survey and the descriptive analysis based on CrESSI primary data. This is followed by a regression analysis on the effect of participating in social innovation with the aim of assessing the role that gender plays in this relationship. The final paragraph presents the conclusions drawn. 2. Women’s Marginalisation and the Role of Social Innovation Social innovations are ‘socially innovative actions, strategies, practices and processes’ that ‘arise whenever problems of poverty, exclusion, segregation and deprivation or opportunities for improving living conditions cannot find satisfactory solutions in the ‘institutionalized field’ of public and private action’ (Moulaert et al. 2013a: 2). They are initiatives (usually promoted through a bottom-up process) that propose grass-root solutions that rupture pre-existing socio-economic structures and that promote the involvement of the beneficiaries in the processes (von Jacobi et al. 2017; Ziegler et al. 2017). Such initiatives usually have two axes: first, they put in question the economic, social and political relations that produce marginalisation; secondly, they promote the active participation of citizens in the process of disruption (von Jacobi et al. 2017). An innovation must be social in its ends and means to be considered a social innovation (BEPA 2010): being good for society should also imply that the action of social innovation enhances the capacity of a society to act in favour of the wider inclusion of its citizens. Social inclusion – defined as the increased participation of citizens in public life - is prominent in the process (Moulaert et al. 2013b) and social change is the desired outcome of social innovation’s activities (Lindberg 2016). Social innovation is an increasingly appealing concept, both for scholars and for European policy- makers. Especially for policy-makers, the value of social innovations might be found in the assumption that they can be more effective, when compared to traditional policies relying on market or top-down public interventions, in meeting the social demands of marginalised groups as they promote a participatory approach (BEPA 2010). Starting from the 1990s, social innovation has been increasingly extended as a concept to include collective and bottom-up actions occurring not only in urban deprived neighbourhoods, but also in peripheral rural localities, which had been overlooked in previous years (Moulaert et al. 2013b). Although social innovations disregarded gender inequalities as an object of analysis in itself (Lindberg et al. 2015; Lindberg 2016), as gender is a pervasive dimension of inequality, social innovations should have an impact on gender relations – especially if they are oriented to foster a wider participation in the labour market, even when their goals are not explicitly oriented to rebalancing gender relations in society (André 2013). In fact, as they are oriented to favour participation in public life, the potential role of social innovations in reducing the marginalised condition of women is sound. The post-industrial transformation has brought three main changes to the structure of the labour force in European societies: an increased number of service jobs with a correspondent decline of employment in manufacturing and agriculture, a stronger presence of female workers and, finally, a progressive deregulation in working contracts (Cucca and Maestripieri 2015). All the cited processes have impacted women’s labour market participation and, in recent years, a wider women’s activity rate has been recorded in all western economies as one of the consequences of the de-industrialization process. Women’s increased participation was helped by the formalization of women’s work, by opening opportunities in the service sector, by the increase in female educational attainment and the wider availability of part-time employment opportunities (Hakim 2000; Thévenon 2013). The growth has been steady since the 1970s, also due to a progressive de- standardisation of contracts: part-time and temporary jobs offered the opportunity for more women to be active in the labour market without being employed full-time, thus easing women’s participation in public life even when they have duties of care. This was an advantage for all those women who wished to be active in the labour market as well as in family life (Hakim 2000). Nevertheless, due to women’s non-standard involvement in labour markets, other sources of gender inequality, such as occupational segregation, the gender pay gap and disequilibrium in the distribution of work within families, still persist (Hakim 2000; Vosko et al. 2009), exposing women to economic insecurity and financial constraints (Kaseauru et al. 2016). Additionally, it has also been demonstrated that a wider participation of women in the labour market reduces inequality between families (Grotti and Scherer 2016). The factors that lead to women’s disadvantage on the labour market can be enumerated by the dimensions that follow: the quantity of work accessible to women, the quality of the work they have, the financial resources available from their jobs and, finally, the unequal distribution of paid/unpaid work between genders that hinders the capacity of women to be as active in the labour market as men (Maestripieri 2015). Regarding the quantity of women’s work, the gap in women’s labour market participation is still persistent – albeit it is closing especially following the financial crisis that impacted men’s occupations more than women’s (Cucca and Maestripieri 2015; Maestripieri 2015). Regarding the quality of women’s work, the persistence of a strong gender bias in non-standard work, including part-time and temporary employment, has been empirically proven (Maestripieri 2015). Although the timing varied across Europe, non-standard positions started to increase in the mid-1980s, specifically interesting women, young and other marginal workers such as migrants. Women are particularly more interested in part-time jobs than members of other social groups as part-time contracts provide an ideal way to reconcile a woman’s private and public life (Thévenon 2013). The concentration of women in certain sectors is, in fact, another possible signal of their economic marginalisation. Segregation can occur in two ways: vertically and horizontally. Vertical segregation refers to the position of women in the work hierarchies, with women more likely occupying low-skilled and low- paid positions. Horizontal segregation refers to the concentration of men and women in certain sectors. Where women make up the majority of workers, the working conditions in the sector tend to be worse than in other sectors, with increased destandardisation of contracts and lower salaries (Cucca and Maestripieri 2015). Thus, the two types of segregation (vertical and horizontal) tend to intertwine with non-standard job contracts to produce niches of women’s marginalisation (signalled by low salaries and lower attachment to the labour market). One of the most unbalanced sectors in terms of women’s presence is the agricultural sector. Previous research demonstrated that a gender rebalancing in agriculture is favoured by the diffusion of alternative farming practices and by the reduced dimensions of farms (Ball 2014). This type of farming is the main target of the activity of the social innovation that project CrESSI studied in the Italian case. In fact, solidarity purchasing groups (SPGs) are small self-organised groups of citizens that collectively purchase primary goods directly from family producers (Altraeconomia 2015), with the explicit aim of enhancing their economic and social participation (Maestripieri 2018). The novelty of their activities lies in the process of consumption: SPGs allow consumers to avoid intermediation and to promote critical consumption principles by choosing suppliers that respect the principles of sustainable agriculture. Oriented by principles of political consumerism (Stolle et al. 2005; Arcidiacono 2013; Forno and Graziano 2014), they can be considered as being social innovators, as the movement promotes new processes that open new end-markets for their suppliers (usually small organic producers, local artisans or social cooperatives that employ vulnerable individuals), thus favouring their inclusion in society by offering fair prices and enhancing their proximity with final consumers. The aim of their activities is to foster practices of solidarity with family farmers: the idea is to put into question the traditional food supply chain and to avoid the intermediation of mass retailers in order to empower consumers and producers by favouring alternative systems of food production. The principle of solidarity towards producers and sustainable agricultural productions is what motivates social innovators: SPGs explicitly seek to sustain small farming activities that operate with organic and sustainable forms of production. In order to achieve their purpose, they sustain practices of small family farmers whose production is organic (Maestripieri 2016; Maestripieri 2018). A social innovation in this sector is particularly relevant in terms of gender equality: agriculture is one of the main segregated sectors in terms of women’s participation and Italian women are usually employed as family helpers in farm households in a position that is dependent on their partners (see paragraph 4). SPGs sustain farm households and organic farming. Organic farming is more popular among women entrepreneurs in agriculture, supposedly having a rebalancing effect on gender inequality in the sector as it favours niches in which women are more likely to be active as farmers (Ball 2014). In conclusion, policies of labour de-standardization promoted by European governments in the last years have achieved mixed results. The number of women in the labour market has increased in the last decades – especially in the service sectors. However, women’s marginalised position on the labour market means that there still is insecure integration and an economic dependence on a primary earner. Even for those women who are active on the labour market, the quality of the job they get should be questioned: they are usually segregated to certain services sectors, leading to limited integration especially in the manufacturing and agricultural sectors. They also suffer worse working conditions than men, also because their income is usually considered to be additional to the breadwinner’s. Nevertheless, the decisive role of gender in creating niches of marginalisation has somehow been underplayed by the current debate on social innovation (Lindberg et al. 2015; Lindberg 2016), although the main goal of social innovation should be the promotion of a process of participation in public life and improvement of the social conditions of its beneficiaries. Previous research (Ball 2014) shows that alternative farming practices in agriculture are able to rebalance gender inequality in a sector such as food production, which is normally masculinized in developed countries. This paper seeks to investigate the extent to which the processes promoted by SPGs address the structuration of women’s disadvantage in agriculture. The following analysis will try to open the black box of social innovation in regard to women’s situation on the labour market with the aim of assessing whether the experiences of social innovation in the food and agriculture sector, through SPGs’ bottom-up processes of empowerment, are able to lift women out of marginalisation. 3. Research Design and Methodology The data presented in this paper are part of a wider primary collection of data which was gathered as part of th the actions of the EU funded project CrESSI (2014/2018) funded under the 7 Framework Programme. The general scope of the project was to explore the economic underpinnings of social innovations with a focus on how policy and practices can enhance the lives of marginalized and disempowered citizens in society. The theoretical approach of CrESSI was indebted to the theories of Jens Beckert (2010), Michael Mann (1986) and the capability approach (Sen 1999, Nussbaum 2000): the so-called extended social grid model explores the structural dynamics behind processes of marginalisation and the role of social innovation in overcoming them (Nicholls and Ziegler 2018). The project carried out a comparative investigation of three cases of social innovation that were considered as being particularly relevant to study process of marginalisation. The Italian case focused on solidarity purchasing groups: in this case, the social innovation aims at reconnecting consumers and producers, shortening the supply chain in the domain of food and agriculture. It is an ideal case to use to analyse women’s economic marginalisation as it regards an economic activity in a strongly gendered sector in which women make up the minority of workers and are usually employed in subordinate positions (see paragraph 4). The empirical enquiry in CrESSI was based on a combination of qualitative and quantitative methods, in order to investigate the two sides of the social innovation relationship (von Jacobi et al. 2015): the opinion of the social innovators (SPG members) on the functioning and practices of their SPGs, that was obtained through semi-structured interviews, and the impacts of SPGs on their beneficiaries (suppliers of SPGs) that was investigated using an online survey. In the context of this paper, the data analysis will concentrate on the data collected from the beneficiaries of the social innovation. There are two reasons behind this choice: firstly, the aim of this paper is to assess the role of social innovation in empowering women as beneficiaries of SI actions and the interviews were carried out with social innovators; secondly, the structure of the interview did not envisage a specific analysis of gender issues as it focused on the organisation of SPGs and comprised an analysis of their role in the reduction of the marginalisation of beneficiaries. The data collected qualitatively from social innovators informed the questionnaire sent to the beneficiaries, as several questions were derived from the analysis of the interviews in order to propose categories which were grounded in the phenomenon of SPGs. The final distribution of respondents in the SPGs case is illustrated in Table 1. The beneficiaries included small farms, local artisans and social cooperatives that cooperate with SPGs, while the control group comprised the same type of actors that have never sold their products to SPGs. Beneficiaries are differentiated in terms of those who are currently benefitting from the social innovation and those who have benefitted from it in the past: in the case of Italy, 750 are current beneficiaries and 175 were beneficiaries in the past. This last distinction will not be used in the analysis presented here. Table 1 – Total questionnaires in the Italian social innovation case (SPG) under the EU funded project CrESSI, distinguished by gender Men Women Total Control group, of which 1,348 692 2,040 Never heard of SPGs 217 92 309 Heard of them, but never been active with SPG 1.131 600 1.731 Actual Beneficiaries 439 311 750 Past beneficiaries 104 71 175 total 1,891 1,074 2,965 Source: CrESSI survey data, 2016 The sample was not randomly selected as a list of SPG suppliers is not available given the informality that characterises this social innovation (Maestripieri 2016). As such, the analysis cannot be representative of the entire population of SPG suppliers and/or Italian small family farmers. The control group has also not been randomly selected. Selling products to SPGs is the sole criterion used to differentiate the respondents falling under the control group from the beneficiaries. The data does not specify whether the individuals who fall within the control group are not selling their product to the SPGs out of choice or because they have not been selected by SPGs: however, the data shows that knowledge about SPGs is well spread in the sample even among those who are not currently active in SPGs, confirming the hypothesis that respondents are indeed potential beneficiaries of the social innovation under study. Respondents involved in the social innovation and control group are also distinguished by gender, with more women in the first group (41.3% vs. 33.4% in control group) and by age, with those in contact with social innovation being slightly younger (mean age 47 years old) than those in the control group (mean age 49 years old). The figures are reported in Annex – table 1. Although the sample could not be wholly representative in this case, the aim was to provide one that would be as comprehensive as possible. Different methods were used to build up such a list, including asking each interviewed SPG in the qualitative phase to share the list of suppliers (500 contacts were collected); listing producers who self-declare themselves as potential suppliers for SPGs on the two main websites devoted to the social innovation (www.retegas.it and www.eventhia.com - about 2000 contacts were collected); collecting contacts from events that were considered relevant for the movement of solidarity purchasing groups (i.e. fair-trade or organic fairs) or online public databases that could potentially comprise beneficiaries, such as local databases of biological and bio-dynamic farm households, farmers’ markets, tourist itineraries that involve local products, etc. In total, about 20,000 potential respondents received a direct invitation to participate in the survey by email: however, there was certainty about the respondents’ involvement in the social innovation for the first 500 contacts; the rest were only potentially beneficiaries. To be as comprehensive as possible, questionnaires were also circulated among representatives of the general movement of solidarity purchasing groups and beneficiaries’ associations (national associations for family farmers and social cooperatives). Questionnaires and interviews were set up to give empirical ground to the theoretical model of CrESSI. The survey’s questions focused on the role of social innovation in the life of the respondents, analysing how being involved as a supplier in an SPG group could enhance the autonomy of the person, improve their economic integration and increase life satisfaction. The questionnaire aimed to investigate the role of social forces (Beckert 2010) in fostering the autonomy perception of respondents (Sen 1999; Nussbaum 2000) along six analytical dimensions derived from the theory of Mann (Heiskala 2016). In the context of this paper, the analysis will focus on the economic status, the educational level and the employment status of the person and the characteristics of the household in which the person lives, which are considered relevant for the study of the economic marginalisation of the respondent in relation to the individual involvement in social innovation. The analysis of the impact of social innovation on the economic marginalisation of women will revolve around four logistic regression models. The aim of the analysis will be to examine how participation in social innovation could secure respondents against experiencing financial constraints that are assumed as being the outcome of economic marginalisation. This phenomenon will be measured through the exposure to the risk of worsening economic conditions of the household to which the interviewee belongs, calculated against a subjective measurement pertaining to the last three years. The question posited was: “How does the income of the household compare to the previous three years’?” with the respondent being asked to choose among the following options: inferior, same and higher. The replies were dichotomised to highlight those households who are suffering from a reduction in disposable income (the dependent variable value being 1 when the person answers “inferior”). The choice of using a dummy dependent variable responds to the necessity of preserving the reliability of the analysis, as the CrESSI sample contained a limited number of cases . The models used exclude from the analysis those who are not currently active in the labour market, including pensioners or unemployed persons (59 cases unemployed/inactive, 40 cases pensioners) on the basis of the assumption that their activity in SPGs does not have a relevant impact on their welfare as the farming activity is not likely to be their main income source. The independent variables were the following. With regard to the attributes of the person, the data included age (in years) and gender. The participation of the respondent in the social innovation is the main independent variable of the model. This variable assumes four status, dividing the sample into four groups: those who declare having a personal and financial benefit from participation in SPGs (the “core- beneficiary”); those who enjoyed a personal benefit; those who declared they had not experienced a relevant change since they entered in contact with the social innovation; and, those who have not been involved in SPGs (control group). The interactive effect between gender and social innovation was further measured using the term of interaction. The interaction control of being a woman and being in the “core-beneficiary” group measures if there is a significantly different effect than being a man and part of the same group. A second relevant dimension of analysis is the type of employment: the sample is divided on the basis of different employment classes (dependent workers, social cooperative, solo self-employed workers, employed in farm households and entrepreneurs) that respect the different characteristics of labour market integration in the CrESSI sample. This structure respects the hypothesis behind the paper: a marginalised condition in the labour market between genders is the cause of insecure economic conditions; social innovation intervenes in the process, favouring the participation of women in public life and widening their employment opportunities in more secured labour market conditions. Educational level serves as a proxy for the social position of the respondents (three classes: up to lower secondary school, secondary school, tertiary educated). The model also takes into account the household composition (three classes: single, in partnership and living with other adults) as the dependent variable measures a situation of economic marginalisation at household level, via the presence of children and partner. And, finally, the extent of participation in the labour market is taken into account with a control for the numbers of working hours per week. Finally, the analysis presented in the article cannot be statistically generalized. As already stated, the results presented below are only valid for the population of the respondents to the CrESSI survey, although they can give some further insight into how research on social innovations might develop in the future. A short descriptive paragraph is given before presenting the analysis on social innovation. The data given below is based on several sources (the Eurostat database, the Italian Labour Force Survey and the CrESSI survey) to give an in-depth description of the marginalised condition of women in Italian agriculture sector and the possible role of SPGs in overcoming it. The section below also presents the variables that will subsequently be used in the statistical analysis. 4. The Employment Situation of Italian Women and the Role of Social Innovation From a comparative perspective, Italy is not a favourable context for women’s employment, in terms of both quantity and quality of work available to women. Italy shows the lowest activity rate of women, with only 55% of women working or actively looking for work. In addition, it is also the country in which the gap between genders in employment rates is the highest in the EU (18.4% while the average in EU28 is 10.5%). However, the marginalisation experienced by women does not seem to be mainly driven by family reasons: both inactivity and part-time employment are only partially due to family obligations, unlike in the United Kingdom or in Germany. The reason for their lack of participation seems to lie in the quality of the jobs offered to women and their lack of access to the labour market. Italian women are more exposed to long- term unemployment; they are in the majority involuntarily employed in non-standard positions and only to a lesser extent do they occupy managerial or professional positions. Table 2 presents the relevant data. Table 2 – Indicators on labour market integration of women 15-64 (%), by country 2016 Indicator DE ES FR IT NL PL UK EU28 Activity rate 73.6 69.2 67.6 55.2 75.0 62.0 72.2 67.3 Family duties as main reason for inactivity 15.6 16.6 14.8 15.1 12.7 16.2 29.2 10.0 Employment rate 70.8 54.3 60.9 48.1 70.1 58.1 68.8 61.3 Gender gap in employment 7.6 10.5 6.7 18.4 9.5 12.9 9.5 10.5 Unemployment rate 3.8 21.4 9.9 12.8 6.5 6.2 4.7 8.8 Long-term unemployment rate 38.5 50.5 43.9 58.6 41.6 34.0 23.0 46.6 Part-time employment rate 46.5 24.1 29.8 32.7 76.4 9.7 40.8 31.9 Family duties as main reason for part-time 29.6 13.1 24.9 20.1 36.1 10.9 40.1 27.4 employment Main reason for part-time employment – could 10.4 59.8 42.3 58.8 8.2 25.2 12.1 24.6 not find a full-time job Temporary employment rate 13.2 26.1 16.1 14.0 20.6 27.5 6.0 14.2 Main reason for temporary employment – - 91.4 61.7 72.9 48.2 62.6 - 62.0 could not find a permanent job Area of women’s non-standard work 59.7 50.2 45.9 46.7 97 37.2 46.8 46.1 Women as managers or professionals 19.7 24.6 23.7 21.2 30.1 32.0 33.7 26.0 The labour market condition of Italian women is compared with countries which are comparable in terms of population, but different in terms of welfare regime (France, Poland, United Kingdom). Spain is also included: although usually associated in the same Mediterranean cluster, they differ in terms of gender equality index (see GEI 2012: http://eige.europa.eu/gender- statistics/gender-equality-index and Annex table XXX for further information). Germany and the Netherlands are also included as they are the other two countries included in the CrESSI data collection. The activity rate measures the percentage of individuals which are active on the labour market (employed or willing to be employed) on the total population of the same age. Employment rate is calculated as the percentage of individuals which have provided at least one hour of paid work in last week on the total population of the same age. Gender gaps are calculated male employment rates minus female employment rates. The unemployment rate measures the percentage of individuals who are willing to be employed, but have not found a job position yet on the total labour force. Area of non-standard work is roughly calculated as a linear sum of part-time employment and temporary employment. It is a raw measure as it is not possible to assess from Eurostat database how much part-time and temporary employment overlaps among women. Source: Eurostat database indicators Table 3 – Percentage of women employed by sector in Italy (15-64 y.o.) 2008 2009 2010 2011 2012 2013 2014 2015 2016 Agriculture and Forestry 31.8% 30.1% 30.3% 30.1% 29.6% 29.0% 28.6% 28.0% 27.9% Manufacturing et al 27.7% 26.6% 26.2% 26.2% 26.3% 26.4% 25.8% 25.7% 25.4% Construction 5.8% 5.8% 5.7% 6.9% 6.8% 7.1% 6.6% 6.5% 6.4% Wholesale and retail 41.0% 40.8% 40.8% 41.3% 42.2% 41.5% 41.8% 41.8% 41.0% Transportation and Storage 19.6% 19.4% 19.5% 19.1% 19.6% 20.2% 20.6% 20.8% 20.5% Accommodation and food 50.6% 51.2% 50.8% 50.5% 51.7% 52.5% 51.8% 50.2% 51.2% Advanced business services 27.1% 28.2% 28.5% 28.3% 28.8% 28.3% 27.7% 28.1% 28.1% Public Administration 34.5% 34.0% 34.4% 34.1% 34.1% 34.8% 34.4% 32.7% 33.3% Education 74.9% 75.3% 76.5% 76.9% 75.0% 74.8% 75.1% 75.6% 75.7% Health and Social work 70.1% 69.9% 69.1% 69.1% 69.7% 69.5% 70.5% 70.5% 70.3% Households as employers 89.2% 91.1% 91.4% 89.0% 88.8% 88.2% 85.2% 86.1% 87.7% Other services 58.5% 57.5% 57.8% 57.3% 56.8% 57.4% 57.4% 57.4% 56.9% Total 40.5% 40.6% 40.9% 41.3% 41.9% 42.2% 42.3% 42.1% 42.2% Source: Author’s calculation on Eurostat database Horizontal segregation is another interesting feature that characterises the Italian labour market in terms of gender equality, as shown in Table 3. In fact, women are not only less frequently employed than men in all sectors (there are 4 women every 10 employed person), but their employment is unequally distributed with a limited presence in traditional male sectors such as construction, transportation and manufacturing. Agriculture is one of the sectors showing one of the highest segregations by gender (women represent only the 30% of the total number of workers). Segregation has also slightly increased in the last few years, in counter tendency to other sectors and other countries (Cucca and Maestripieri 2015). However, in comparison with other male-dominated sectors, the agricultural sector is not distinguished in Italy by the high percentage of people who are self-employed (36.3% of total employment in the sector) but by the highest presence of family workers (6.94%), see table 4. The sector is not gender neutral: while there are more men among workers and the self-employed, there are more women among family helpers in farm households, in line with the strong female segregation already evidenced by the category ‘households’ listed as employer. Being locked within the family both in their working and their private lives exposes women to the risk of being economically dependent on their partners. Table 4 – Distribution of men and women in the agriculture sector by type of occupational position (15-64 y.o.) Men Women % on the total employment in agricolture Dependent worker 71.74% 28.26% 55,98% Self-employed worker 76.71% 23.29% 36,28% Family helper 48.18% 51.82% 6,94% Source: Author’s calculation on Italian Labour Force survey, 2016 Looking at the general context, it seems that a sector such as the agricultural sector does not provide the most favourable environment for women’s integration in the labour market as access seems problematic and the quality of the jobs in this sector is in question given the subordinate position of women within farm The category also includes Mining and quarrying; Electricity, gas, steam and air conditioning supply; Water supply; sewerage, waste management and remediation activities. The category includes Information and communication; Financial and insurance activities; Real estate activities; Professional, scientific and technical activities; Administrative and support service activities. The category includes Arts, entertainment and recreation; Activities of extraterritorial organisations and bodies; other services. households. CrESSI’s data allow further investigation into the matter, particularly in terms of the potential impact that socially innovative initiatives such as SPGs can have in terms of improving gender equality in this sector (see Table 5). In terms of the characteristics of the population involved in the analysis, the unemployed and inactive respondents are almost insignificant; however, there is a quota of respondents who are pensioners (59) but are also active in agriculture. These are mainly in the male control group. The empowerment effect of social innovation emerges from the data: there are more women who can be considered to be entrepreneurs among the women in contact with the social innovation that among women in the control group. Women in the control group tend to be more likely to be solo self-employed than women in contact with SPGs. The same empowerment effect seems to apply for men and with a stronger effect (+6.5p.p. for men, while the difference SPG/CG for women is less than 3 p.p.). Women involved in social innovation tend to belong in farm households more than women in the control group, while the same does not occur for men. This trend might highlight a possible ambivalent effect of social innovation in terms of women’s empowerment: instead of favouring the integration of women in public life, the activities of SPGs might have the effect of locking women into families, as this social innovation mostly addresses small family farmers. From the CrESSI sample, it is not possible to distinguish whether the women are owners of or family helpers in the farm households involved in the social innovation. However, when asked if they define themselves as the primary earner of the family, 36 out of 45 women in farm household answered no, against 21 out of 44 men in the same situation. This indicates that it is very unlikely they are the owners of the farm households that collaborate with SPGs, confirming the risk of entrapment into families. Table 5 – Labour market participation and gender, comparison between social innovation beneficiaries and their control group– percentages and absolute values (highlighted in grey) Men in SPGs Men C.G. Women in SPGs Women C.G. Entrepreneur 43,8% 37,3% 33,2% 30,8% Working on one's own 21,9% 26,1% 22,2% 27,5% Farm household 13,3% 14% 20,9% 16,8% Social cooperative 5,34% 1,04% 4,45% 2,6% Dependent worker 14,2% 17,4% 17,3% 18,2% total in absolute values 535 1293 372 663 Unemployed/Inactive 4 14 7 16 Pensioner 4 42 0 13 total in absolute values 543 1.349 382 692 Source: Author’s calculation on CrESSI survey data, 2016 Table 6 – Economic conditions and gender, comparison between social innovation beneficiaries and their control group– percentages and absolute values (highlighted in grey) Compared to the previous three years, Men in SPGs Men C.G. Women in SPGs Women C.G. income of household this year has been? Income is inferior 33.9% 40.6% 40.6% 48.9% Income is the same 45.7% 43.7% 40.1% 38.8% Income is superior 20.4% 15.7% 19.3% 12.4% total in absolute values 295 697 192 356 Median income class 1100-1300€ 1300-1500€ 900-1100€ 700-900€ total in absolute values 543 1348 382 692 When looking at economic conditions (table 6), however, the effect of social innovation seems to be clear. Women who are involved in social innovation experienced a reduction in their household income less frequently compared to women in the control group and the beneficial effect of the social innovation seems wider compared to men. There persists an advantage for men in terms of individual income but the gap between genders is smaller among the respondents involved in the social innovation than among the men and women in the control group (see graph 2 in annex). Nevertheless, when asked about the influence of social innovation in their lives, women respondents were less positive about the role of social innovation in improving their lives (despite evidence given above), with about 40% of the female respondents involved in the innovation declaring that they had experienced no change in terms of personal benefit or improvement in financial condition. Conversely, there is a linear effect in terms of the attitude towards the social innovation: the more a person experienced a direct personal benefit from participation in the social innovation the more they trusted SPGs and the more willing they were to recommend it within their personal network. But the effect is stronger for men that have experienced personal and financial benefit from the social innovation. In general, the social group comprised in the CrESSI sample is more willing to trust SPGs than any other relevant institution in the field, including, for example, farmers’ associations, even when they did not experience a relevant change following their involvement. Table 7 – Attitudes towards the impact of social innovation by gender– percentages and average mean scores on attitude scales (score: 1-10) Would you How much do you How much do you recommend SPGs trust SPGs? trust farmers’ to family and associations? friends? Men Women Men Women Men Women Men Women Improved financial conditions 3.4% 4.1% 9.5 9.4 8.2 7.6 4.2 3.9 and enjoyed personal benefit Personal benefit but not 16.5% 18.3% 8.4 8.3 7.1 7.3 4.2 4.8 improved financial conditions No relevant change 8.9% 14.7% 5.8 6.5 5.2 5.6 4.2 4.1 Not involved in SPGs 71.2% 62.4% - - 5.6 5.9 4.5 4.7 Total in absolute values 1701 955 434 294 951 527 1.058 581 Source: Author’s calculation on CrESSI survey data, 2016 In conclusion, women in Italy are still locked in a position of marginalisation, which seems driven by their difficulty in accessing the labour market and in securing their position as workers. They tend to concentrate in certain sectors (i.e. education or health) which are traditionally perceived as being female dominated and when they are present in male-dominated sectors such as agriculture they tend to be entrapped in a subordinate position as family helpers. The analysis based on the CrESSI data seems to confirm the hypothesis that when women are in contact with social innovation there is a positive effect: they are more frequently entrepreneurial, they seem to be better protected from reduction in income and the wage gap with men is lower. However, the effect seems to be ambivalent at least in the women’s perception: quite frequently they declare that they have experienced no changes since their involvement in SPGs and the rate of trust in SPGs is lower compared to that of the men involved in the social innovation. The following section will investigate how social innovation can reduce gender inequality and empower women in public life. 5. Discussing Women’s Marginalisation in Relation to Involvement in SPGs Studying economic marginalisation of women in relation to social innovation is not an easy task. As it was previously discussed (see paragraph 2), the outcome is mediated and influenced by several factors, of which participation in social innovation is only one among them. This is why the subsequent analysis is carried out step by step, in an effort to identify the role of different groups of variables that might intersect with the previously set research questions. The analysis assumes the positive influence that participation in social innovation can have in preventing the worsening of the economic condition of the household to which the respondent belongs. These positive returns should be different for men and women. As shown in table 3 in Annex, there is no evidence of potential multicollinearity among independent and control variables. Secondly, the relation between the dependent variable and the different groups among respondents is significant, both between men and women and between beneficiaries and the control group. The results of the Chi2 test are shown in the Annex (table 4). Table 8 – Logistic model (odds ratio) analysing worsened financial conditions as dependent variable (CrESSI sample 19-64 years old). Significance threshold: *** P>|z| less than 0.01, ** P>|z| less than 0.05, * P>|z| less than 0.10. Independent variables Model 1 Model 2 Model 3 Model 4 Gender Woman 1.35*** 1.32** 1.34** 1.31** Ref. man Status in the social innovation No relevant change 0.91 0.97 0.98 0.98 Only personal benefit 0.80 0.90 0.93 0.93 Personal and financial benefit 0.58** 0.59** 0.58** 0.48** Ref. Control group Age in years 1.02*** 1.02*** 1.02*** 1.02*** Educational level Secondary school 1.02 1.03 1.03 Up to lower secondary 1.36 1.36 1.38 Ref. Tertiary educated Employment status Social cooperative 0.92 0.90 0.90 Entrepreneur 1.18 1.16 1.16 Solo self-employed 2.16*** 2.11*** 2.11*** Farm household 1.98*** 1.93*** 1.92*** Ref. Dependent worker Number of working hours per week 1.00 1.00 1.00 Presence of children At least one cohabitating child 1.13 1.13 Ref. No cohabitating children Household structure In partnership 0.76 0.77 Living with other adults 1.32 1.33 Ref. Single person Interaction term (gender*benefit) 1.52 Educational level has been measured by referring to the international standard classification ISCED, see http://ec.europa.eu/eurostat/statistics-explained/index.php/International_Standard_Classification_of_Education_(ISCED) for more reference. Pseudo R2 0.0202 0.0402 0.0429 0.433 Number of observations 1422 1422 1422 1422 Average prediction (marginal effect) 40,3% 40,3% 40,3% 40,3% Source: Author’s calculation on CrESSI survey data, 2016 Models of regressions are shown in Table 8. The first model focuses on the main relationship under investigation, that is, the effect of being involved in the social innovation on the exposure to possible financial constraints potentially experienced in the previous three years. Gender, age and the status in the social innovation are all related to the worsening of household conditions: being a woman involves an increase of the risk of experiencing worsening economic conditions with a likelihood of 7.2 p.p. However, only the situation in which the respondent enjoys financial and personal benefit from participation in SPGs is significantly impacting the protection from the risk under investigation, with a likelihood of -12.1 p.p. It is interesting to notice that the effect of participation in social innovation decreases the risk of experiencing worsened economic conditions, while being a woman increase the risk. Age seems linearly correlated as well, with a small increase of 0.7 p.p. for each year of the respondent. The second model introduces the role of educational level and the type of employment of the person in the analysis, which remain significant both for women (+6.5 p.p.) and for those who are benefitting more from the social innovation (-11.4 p.p.). The analysis indicates that there is no effect of education on the dependent variable, nor is the amount of work provided in the farming activity significant. For further explanation of the phenomenon, it is important to highlight how the type of employment impacts the risk of experiencing a downturn in household financial conditions. In particular, being a solo self-employed or part of a farm household increases the risk when compared to being an entrepreneur, a social cooperative or a dependent worker. The extent of this increase ranges from 15 to 20 percentage points. Being an entrepreneur or part of a social cooperative does not give any significant advantage/disadvantage. The third model introduces the control of the characteristics of the household. Being in a partnership is the strongest predictor for women’s economic insecurity (Kasearu et al. 2017) and having a double income is a protection against financial constraints experienced by households (Curatolo and Wolleb 2010). However, in the CrESSI sample, there is no direct effect related to the presence of children or the type of household on the risk of experiencing worsened economic conditions, except for those living with other adults that have a 12.7% risk of being more exposed to worsening financial conditions when compared to those who are in a partnership. Still, being a woman (+6.8%) and experiencing personal and financial benefits from SPGs (- 11.8%) are relevant factors in terms of explaining the worsening of household conditions in previous years, with farm households and solo self-employed being the most exposed. Once controlled for household characteristics, the effect of being a “core-beneficiary” of SPGs becomes significant when compared to those that have been in contact with the social innovation but without experiencing a financial benefit (-10%) or a relevant change (-11.3%). Despite being only slightly significant (0.08 in the first case and 0.07 in the second ), the results indicate that only when people experience a financial benefit from the social innovation does this participation protect against worsened economic conditions. The group of “core-beneficiaries” that received financial and personal benefit from SPGs do not distinguish among the types of benefit received. As shown in table 9, it is the extent of the perception of the benefit that distinguishes those who experienced only a personal benefit from those who experienced a personal and financial benefit. The respondents who more enthusiastic participants in the social innovation are also the respondents who enjoy more economic benefits from their involvement in the form of returns on income or a wider end market for their products. Table 9 – Type of benefit enjoyed by those who experienced personal benefit from the participation to SPGs – percentages of some/large extent What type of benefit did you enjoyed from the social Only personal Personal and Gap innovation? benefit financial benefit Money/Income 73.9% 96.6% +22,7 Friendship 78.9% 90.6% +11,7 Business relations 66.9% 78.2% +11,3 Knowledge 70% 85.7% +15,7 Fair price 84.3% 91.9% +7,6 End market 60.8% 81% +20,2 Source: Author’s calculation on CrESSI survey data, 2016 The fourth model introduces the control for interactions between gender and participation in the social innovation to check if the effect of being involved in the social innovation is significantly different according to whether the respondent is a woman or a man. Checking for interaction is particularly relevant as the two variables have opposite effects on the dependent variable: social innovation decreases the risk of experiencing worsened economic conditions, while being a woman increases this risk. However, the factor is not significant in predicting the risk of a worsening of the financial condition of the household, allowing the researcher to reject the hypothesis that the positive effect of participation in social innovation is significantly different for men and for women. Still women are more likely to experience a worsening in financial conditions (+6.2%). Those who declared that they benefitted both financially and personally from SPGs have a lower likelihood of experiencing a worsened economic condition, in comparison with the control group (-15.3%), with those who reported no significant change (-14.9%) and even with those who experienced a personal benefit (-13.5%). Thanks to the interaction factor, the marginal effect of being a “core-beneficiary” shows a significance of 0.012 when compared to the control group; 0.047 when compared with those who reported no relevant change; and, 0.042 with those who reported only a personal benefit. The last model further backed the hypothesis that only being a core participant in social innovation generates positive returns in terms of protection from worsened economic conditions. The type of involvement has an impact on the risk of experiencing a worsening of economic conditions: solo self-employed and farm household are the most exposed. In the first case, which pertains to all those who carried out their activities alone without having employees or without having collaborators in the family, the risk of having experienced a downturn in the previous three years is significantly higher than dependent workers (+17.3%), those involved in social cooperatives (+19.5%) and entrepreneurs (+14%). The same trend is visible for farm households, although to a lesser extent: they have a higher likelihood, when compared to dependent workers (+15.1%), those involved in social cooperatives (+17.4%) and entrepreneurs (+11.9%). Unfortunately, the CrESSI sample does not allow further investigation into the power relations within farm households: the disadvantaged effect of being in a farm household might vary on the basis of the role assumed in it (whether one is the owner or a family helper). Secondly, the more disadvantaged position of the self-employed and farm households might be caused by the dimension of economic activity rather than the specific vulnerability of the contract in itself. Neither hypotheses can be tested on the basis of the CrESSI data alone. Further investigation is required into the matter. The type of household and the presence of children does not seem to affect the risk, with the sole exception being those who are currently living with other adults. In the latter case, the majority of respondents (91.2%) comprised adults who are still currently living with their parents. Although in Italy it is not uncommon for adults to continue living in the family home, this might lead to a higher risk of exposure to economic difficulty, which is confirmed by the fact that respondents falling within this category reported a worsening of economic conditions in the last three years. In conclusion, the regression analysis performed by the author confirms the results of the descriptive analysis. Gender and participation in social innovation are associated with the risk of experiencing worsened economic conditions: being a woman is confirmed to be a disadvantage, while social innovation seems to constitute a protection against such a disadvantage. However, the role of social innovation in improving the living conditions of the beneficiaries is empirically proved only when the SPGs benefit experienced by the person has a direct effect on improving the financial situation of that person (as shown in table 9, mostly revolving around end market or income). In all the other cases, even when the respondent reported experiencing a direct personal benefit from the social innovation, the extent of the benefit is not significant in reducing the risk of experiencing worsened economic conditions. Those who work alone as self-employed or in farm households have a higher exposure to risk, raising new questions on how labour market integration might impact men and women differently as genders are unequally distributed among these occupational conditions. But, there is no evidence of a clear gender effect of the social innovation: men and women do not experience significantly different returns from participation in social innovation. 6. Conclusion The aim of this paper was to explore the assumption that social innovations, in the form of solidarity purchasing groups, can be a tool for improving gender equality in society, in the context of the phenomenon of economic marginalisation of women. The analysis is particularly promising, as SPGs are active in a sector such as agriculture in which women are the minority and their labour market integration usually occurs in subaltern positions. The primary data offered by the EU funded project CrESSI provides a unique opportunity to further explore this issue, which has so far been disregarded by the current debate on social innovation. Contextual analysis confirms that Italy is not a favourable context for women’s labour market integration, especially in the agricultural sector. Women are entrapped in non-standard contracts, mostly as family helpers. This last condition can be particularly dangerous in terms of economic independence as it locks women in families, both in their private and public lives. The descriptive analysis of CrESSI data confirms that being in contact with SPGs has a beneficial effect for women, who are thus more active in empowered positions in the labour market (i.e. entrepreneurs) and are able to benefit from a higher income compared to the women involved in the control group. However, when asked directly about the effect of their participation women seem to be more hesitant when compared to men to report that they have experienced benefits. Additionally, women in social innovation tend to concentrate more on farm households that those in the control group. Conversely, logistic models confirm the role of gender in predicting the worsening of the economic conditions of a household, which seems to be positively mediated by participation in social innovation. The positive effect of social innovation is limited to respondents who have experienced a financial return from SPGs. However, the analysis also confirms that there is no significantly different effect on men and women that derives from participation in social innovation. The type of employment and the extent of the benefit experienced are factors that have a deeper impact on the risk of experiencing worsened economic conditions. To avoid the economic marginalisation of respondents, social innovation should then have practical returns, which in the case of SPGs can be found in wider end markets or a more stable income. In conclusion, the social innovation debate has so far overlooked the effect of socially innovative activities on beneficiaries’ marginalisation. The results presented in this paper show that only when social innovation has a concrete financial benefit for beneficiaries, it can provide protection against worsened economic conditions. To achieve practical results, fostering the wider participation of marginalised citizens in socially innovative actions is a must although previous studies have demonstrated only limited evidence that this has been possible (von Jacobi et al. 2017; Ziegler 2017a; Maestripieri 2018). The risk for social innovation initiatives is that they become a niche for cultural elites that participate as social innovators (Cruz et al. 2017; Maestripieri 2018) without being able to tackle the pre-existing process of marginalisation for the beneficiary of their actions. 7. References Altreconomia. (2015). Il libro dei GAS. Gruppi di acquisto solidali: come fare la spesa giusta, dalla “A” al KM Zero, Milano: edizioni Altreconomia. André, Isabel (2013). “Gender and Social Innovation: the role of EU policies.” In The International Handbook on Social Innovation. Collective Action, Social Learning and Transdisciplinary Research edited by Moulaert F., MacCallum D., Mehmood A. and Hamdouch. 412-424. Cheltenham/Northampton: Edward Elgar. Arcidiacono Davide (2013). Consumatori Attivi. Decisioni D’acquisto e Partecipazione per Una Nuova Etica Economica. Milano: Franco Angeli, Ball Jennifer A. (2014). “She Works Hard for the Money: Women in Kansas Agriculture” Agriculture and Human Values 31: 593–605. Beckert, Jens (2010). How Do Fields Change? The Interrelations of Institutions, Networks, and Cognition in the Dynamics of Markets, Organization Studies 31(5): 605–627. BEPA (Bureau of European Policy Advisers). (2010). “Empowering people, driving change: Social innovation in the European Union.” Accessed February 22, 2017, https://goo.gl/OvVnFV. Bernardi, Fabrizio, Chakhaia, Lela, & Leopold, Liliya (2017). “Sing Me a Song With Social Significance”: The (Mis)use of Statistical Significance Testing in European Sociological Research. European Sociological Review, 33(1): 1–15. Blossfeld, Hans, & Hakim, Catherine (1997). Between Equalisation and Marginalisation. Women Working Part-Time in Europe and the United States of America. Oxford: Oxford University Press. Cucca, Roberta and Maestripieri Lara (2015). “Urban Economic Specialization and Women’s Integration into the Labour Market in 11 European Cities.” In Local Welfare Policy Making in European Cities edited by D. Kutsar and M. Kuronen. 35 – 50. Berlin: Springer. Curatolo, Salvatore and Wolleb, Guglielmo (2010). “Income Vulnerability in Europe.” In Social Vulnerability in Europe. The New Configuration of Social Risks edited by C. Ranci. 58-87. Basingstoke, UK: Palgrave-MacMillan. Cruz Helena, Martínez Moreno Ruben and Blanco Ismael (2017). “Crisis, urban segregation and social innovation in Catalonia.” PACO, 10(1): 221-245. Fagan, Catherine and Rubery, Jill (1996). “The Salience of the Part-time Divide in the European Union,” European Sociological Review. 12(3): 227–250. Forno Francesca and Graziano Paolo R. (2014). “Sustainable Community Movement Organisations.” Journal of Consumer Culture. 14(2): 139 – 157. Grotti, Raffaele, & Scherer, Stefani (2016). “Does Gender Equality Increase Economic Inequality? Evidence from Five Countries.” Research in Social Stratification and Mobility. 45: 13–26. st Hakim, C.atherine(2000). Work-lifestyle Choices in the 21 century. Preference Theory. Oxford: Oxford University Press. Heiskala, Risto 2016. “The Evolution of the Sources of Social Power, and Some Extensions.” In Global Powers: Mann’s Anatomy of the Twentieth Century and Beyond edited by Ralph Schroeder. 11–37. Cambridge: Cambridge University Press. Hubert Agnès and Helfferich Barbara (2016). “Integrating Gender into EU Economic Governance: Oxymoron or Opportunity?” Friedrich-Ebert-Stiftung EU Office. Accessed March 31, 2017 http://www.fes- europe.eu/fileadmin/public/editorfiles/events/nov2/FES_Gender_2016_Brussels_www.pdf. Von Jacobi Nadia, Chiappero Martinetti Enrica, Giroletti Toa, Maestripieri Lara and Ceravolo Flavio (2015). Toolkit (Methodology). CrESSI Working Paper 16/2015. Accessed March 28, 2017, https://goo.gl/N1zqRm. Von Jacobi Nadia, Ziegler Rafael and Edmiston Daniel (2017). “Tackling Marginalisation Through Social Innovation? Examining the EU Social Innovation Policy Agenda from a Capabilities Perspective.” Journal of Human Development and Capabilities. 18(2): 148-162. Kasearu Kairi, Maestripieri, Lara and Ranci Costanzo (2016). “The Impact of Labour-Market Participation, Family Structure and Local Welfare Systems on the Economic Insecurity of Women.” European Societies. 19(2): 202-221. Lindberg, Malin, Forsberg, Lela, & Karlberg, Helena (2015). “Gendered Social Innovation – A Theoretical Lens for Analysing Structural Transformation in Organisations and Society.” International Journal of Social Entrepreneurship and Innovation. 3(6):472–483. Lindberg, Malin (2016). “Undoing Gender in EU’s Social Innovation Policies?” International Journal of Social Entrepreneurship and Innovation. 4(1): 67–79. van der Linden, Martijn and van Beers, Cees (2017) “Are private (digital) moneys (disruptive) social innovations? An exploration of different designs”, in Journal of Social Entrepreneurship, 8(2), XX-XX. Maestripieri, Lara (2015). “Gendering Social Vulnerability. The Role of Labour Market De- standardisation and Local Welfare.” In Local Welfare Policy Making in European Cities edited by D. Kutsar and M. Kuronen. 51-67. Berlin: Springer. Maestripieri Lara (2016). Part IV. Individual case study “Solidarity Purchasing Groups.” In Report on Relevant Actors in Historic Examples and an Empirically Driven Typology on Types of Social Innovation. CRESSI Working Paper 29/2016 edited by Scheuerle Thomas, Schimpf Gudrun-Christine, Glänzel Gunnar, Mildenberger Georg, Ziegler Rafael. 335-368, https://goo.gl/YL7wrz. Accessed 23/7/2017. Maestripieri Lara (2018). “Creating Alternative Economic Spaces. The Socially Innovative Practices of Solidarity Purchasing Groups.” In Creating (Fair) Economic Space for Social Innovation edited by Nicholls Alex and Ziegler Rafael Oxford: Oxford University Press (forthcoming). Mann, Michael. 1986/2012. A History of Power from the Beginning to AD 1760. New Edition. The Sources of Social Power Vol. 1. Cambridge: Cambridge Univ. Press. Misra Joya & King Leslie (2005). “Women, Gender and State Policies”. In The Handbook of Political Sociology. States, Civil Societies and Globalization edited by T. Janoski, R. Alford, A. Hicks & M.A. Schwartz. 526 - 545. Cambridge: Cambridge University Press. Moulaert Frank, MacCallum Diana, Mehmood Abid and Hamdouch Abdelillah (2013a). “General Introduction: The Return of Social Innovation as a Scientific Concept and a Social Practice.” In The International Handbook on Social Innovation. Collective Action, Social Learning and Transdisciplinary Research edited by Moulaert F., MacCallum D., Mehmood A. and Hamdouch. 1-6. Cheltenham/Northampton: Edward Elgar. Moulaert Frank, MacCallum Diana and Hillier Jean (2013b). “Social Innovation: Intuition, Precept, Concept, Theory and Practice” In The International Handbook on Social Innovation. Collective Action, Social Learning and Transdisciplinary Research edited by Moulaert Frank, MacCallum Diana, Mehmood Abid and Hamdouch Abdelillah, 13-24. Cheltenham/Northampton: Edward Elgar. Nicholls Alex and Ziegler Rafael (2015). “The Extended Social Grid Model.” In Creating (Fair) Economic Space for Social Innovation edited by Nicholls Alex and Ziegler Rafael Oxford: Oxford University Press (forthcoming). Nussbaum, Martha. 2000. Women and Human Development: The Capabilities Approach. Cambridge: Cambridge University Press. Sen, Amartya (1999). Commodities and Capabilities (2nd ed.). Delhi New York: Oxford University Press. Stolle Dietlind, Hooghe Marc and Micheletti Michele (2005). “Politics in the Supermarket: Political Consumerism as a Form of Political Participation”, International Political Science Review/ Revue Internationale de Science Politique. 26(3): 245–269. Thévenon, Olivier (2013). “Drivers of Female Labour Force Participation in the OECD.” OECD Social, Employment and Migration Working Papers, No. 145. OECD Publishing, https://goo.gl/Js1bGM. Accessed 23/7/2017. Vosko, Leah, Macdonald, Martha & Campbell, Iain (2009). Gender and the Contours of Precarious Employment. Oxon: Routledge. Ziegler Rafael, Molnár György, Chiappero-Martinetti Enrica and von Jacobi Nadia (2017). “Creating (Economic) Space for Social Innovation”. Journal of Human Development and Capabilities. 2829(March): 1–6. Ziegler Rafael (2017a). “Social Innovation as a collaborative concept.” Innovation: The European Journal of Social Science Research, 0(0):1-18. Ziegler, Rafael (2017b) “Citizen innovation as niche restoration - a type of social innovation and its relevance for political participation and sustainability”, in Journal of Social Entrepreneurship, 8(2), XX-XX. Acknowledgements The article has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant Agreement No 747433. I also would like to thank Marco Gavazzoni for his precious comments on paragraph 5. End Notes 1. This article has been produced thanks to the support of the European Union's Seventh Framework Programme for Research, Technological Development and Demonstration (contract nr. 613261 - project CRESSI). The information and views set out in this article (or book) are those of the author(s) only and do not necessarily reflect the official opinion of the European Union. 2. Solidarity purchasing groups is the English term for gruppi di acquisto solidale, usually shortened to GAS, which is how participants usually call the groups. In this paper the acronym SPGs is used. 3. The dependent variable has 1427 valid cases (of which 576 beneficiary). The final absolute number of respondents in models is 1422 as some of the independent variables present in the models have different missing cases from the dependent variable. 4. Results of the models will be presented in tables presenting odds ratios, while marginal effects will be used while commenting results. 5. In the paper, the perspective of Bernardi et al. (2017) on the significance threshold is adopted with the aim of focusing on the substantive significance of the factors under analysis. Annex Table 1 – Participating in the social innovation, by gender and age – percentages and total in absolute values Total in absolute SPGs Control values Beneficiary group Men 58.7% 66.1% 1891 Women 41.3% 33.4% 1074 Under 30 years old 6.7% 6.8% 200 Between 30 and 40 22.5% 18.4% 584 Between 40 and 50 29.7% 27.3% 831 Between 51 and 64 34.1% 36.7% 1063 Over 65 years old 7% 10.1% 287 Total in a.v. 925 2.040 2965 Source: Author’s calculation on CrESSI survey data, 2016 Graph 2 – Distribution of individual income by gender and participation in social innovation Men in SPGs Men in Control Group 37.35 30.61 29.39 28.02 26.85 26.78 13.22 7.782 Women in SPGs Women in Control Group 54.01 49.38 36.88 28.47 13.5 11.88 4.015 1.875 max 900 max 1500 max 2600 over 2600 max 900 max 1500 max 2600 over 2600 Individual income Source: Author’s elaboration on CrESSI survey data, 2016 Table 3 – Correlation table for the subsequent models (CrESSI sample 19-64 years old). Significance threshold: *** P>|z| less than 0.01, ** P>|z| less than 0.05, * P>|z| less than 0.10. X SPGs Gender Age Edu Emp Hours Child Partner Worsening economic 1 conditions (x) Relation SPGs -0.07*** 1 Gender 0.06** 0.06*** 1 Age in years 0.13*** -0.04** -0.07*** 1 Education 0.06** -0.11*** -0.12*** 0.15*** 1 Employment 0.14*** 0.00 0.02 0.05*** 0.14*** 1 Hours worked -0.06*** 0.04* -0.16*** -0.05*** 0.10*** 0.05*** 1 Children 0.02 0.04* 0.01 0.31*** 0.12*** -0.01 0.00 1 Partner 0.00 0.00 -0.02 -0.08*** 0.05** 0.07*** 0.00 0.15*** 1 Source: CrESSI survey data, 2016 Percent 0 20 40 60 0 20 40 60 Table 4 – Chi2 test for the dependent variable (CrESSI sample 19-64 years old). Significance threshold: *** P>|z| less than 0.01, ** P>|z| less than 0.05, * P>|z| less than 0.10. Gender** Personal and financial SPGs (Pearson chi2=5.02) benefit ** (Pearson chi2=4.41) Men Women No Yes No risk 61.82 55.75 58.90 69.79 Experienced a worsening of household economic 38.18 44.25 41.10 30.21 conditions in the last 3 years 100 100 100 100 Source: CrESSI survey data, 2016

Journal

Journal of Social EntrepreneurshipUnpaywall

Published: Aug 23, 2017

There are no references for this article.