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Family Ties, Social Capital and Small Businesses’ Efficiency. Evidence from the Italian Food Sector

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Abstract

We investigate whether technical efficiency is affected by family involvement in management, considering a sample of small and medium sized firms (SMEs) operating in the traditional manufacturing sector. A positive and significant relationship between family management and efficiency is found when adopting one-step procedures based on Stochastic Frontier Analysis (SFA). Furthermore, according to our results, local social capital seems to moderate the influence of the family involvement on the performance of firms, corroborating the idea that trust and reputational assets associated with family ties can compensate for the lack of cooperative behaviour and social networks characterizing some communities where firms operate.

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Notes

  1. Eddleston et al. (2010) consider trust as a “bridging concept to reconcile and enhance our understanding of family firms as a unique organizational form” (pp. 1043). Theoretically, the concept of trust “provides a middle ground where agency theory and others-oriented theories can meet” (Cruz et al., 2010, p. 71).

  2. According to Putnam et al. (1994), social capital refers to interpersonal trust, civic engagement, norms and features of social organisation that facilitate coordination and cooperation for mutual benefit. Social capital has been claimed to reduce free-riding problems and foster the settle of informal enforcement mechanisms, thus boosting compliance with contractual agreements and mitigating adverse selection and moral hazard problems (Coleman, 1990; Guiso et al., 2006).

  3. In our sample, about 96% of firms controlled by families are also family-managed.

  4. The Jacob index is a proxy of "agglomeration economies", which can arise if firms belonging to different sectors and located close to each other can share resources and knowledge, generating external economies of scale across the entire group.

  5. The Xth Capitalia-Unicredit survey covers the span 2004-2006, whilst the Efige survey, conducted in 2010, refers to the years 2007-2009. Both sources consider representative samples of manufacturing firms (employing more than 10 employees). However, the Capitalia survey containing information on 4,126 firms focuses on Italy, while the EFIGE database containing survey information on 14,759 firms extends its coverage to seven European economies (Austria, France, Germany, Hungary, Italy, Spain and the United Kingdom). Further details are provided in Appendix B.

  6. The IQI, inspired by the World Governance Indicator (WGI) proposed by Kaufmann et al. (2011), is based on 24 elementary indices combined in 5 groups representing the main dimensions of institutional quality. Further details are provided in Appendix B.

  7. We observe that 99% of family managed firms in 2009 has maintained its status to date, finding evidence of family ownership and management persistency (Colli, 2013). Based on this evidence, when, in a few cases (7%), we could not find the relevant information, we imputed the 2009 family/non-family managed status to the years ahead. As a robustness check, we estimate our model for the time span 2004–2009, for which the information about the family management is available in the surveys.

  8. We assess the SIZE marginal effects and the relative confidence intervals using an appropriate graph, analogous to Fig. 3 below (and available upon request). According to this graph, the marginal effect of SIZE on INEFF is not statistically significant when it is positive (at lower level of SIZE), while it becomes negative and statistically significant at higher levels of SIZE.

  9. Such a disaggregation level allows distinguishing 9 categories of business within the food manufacturing: processing and preserving of meat and production of meat products (10.1); processing and preserving of fish, crustaceans and molluscs (10.2); processing and preserving of fruit and vegetables (10.3); manufacture of vegetable and animal oils and fats (10.4); manufacture of dairy products (10.5); manufacture of grain mill products, starches and starch products (10.6); manufacture of bakery and farinaceous products (10.7); manufacture of other food products (10.8); manufacture of prepared animal feeds (10.9).

  10. This index is obtained by squaring the market share of each firm competing in a sector, within a region, and then summing the resulting numbers. Formally, in this study the HHI is calculated as follows: \(\sum _{i=1}^{n}{({ms}_{isr})}^{2}\) where \({ms}_{isr}=\frac{{TA}_{isr}}{{\sum }_{i}{TA}_{isr}}\) is the share of total assets (TA) of firm i, operating in sector s and in region r.

  11. Food, as well as consumer staple goods and pharmaceutical industries are deemed as non-cyclical sectors, as people tend to maintain stable consumption of these products even during an economic downturn (Castaner, 2009). As a matter of fact, the production of the Italian food and beverage industry remained almost stationary in 2008 (−0.1%), against a significant decline in the industrial sector (−3.2%). In 2009, the production of the entire sector, although in slight contraction (−1.5%), showed a partial stability, considering the very strong decline (−17.4%) that occurred in the industrial sector as a whole (INEA, 2009 and 2010). It is worth recalling that our analysis excludes services such as restaurants, which tend to be responsible for the decrease in household food expenditures during recessions.

  12. We also run an estimation over the period 2007–2009, substituting our key variable FAMILY with a dummy (available only in the Efige survey) coded 1 if the percentage of family members in the top management team is higher than the national average. The relative output confirms the results mentioned above, and is made available upon request.

  13. Interaction terms are frequently insignificant as their inclusion can generate a multicollinearity problem. However, as Brambor et al., (2006, p. 70) highlight: “Even if there is high multicollinearity and this leads to large standard errors on the model parameters, it is important to remember that these standard errors are never in any sense “too” large, they are always the “correct” standard errors”.

  14. When inserting the interaction term, the SOCIALCAP effect becomes contingent on FAMILY. When the latter is zero, the estimated effect of SOCIALCAP is shown by the relative coefficient, which is negative and significant. When the dummy FAMILY is one, computing the marginal effect of SOCIALCAP and the relative standard error, we find that the marginal effect is still negative and significant, but lower in absolute value. Thus, the impact of social capital appears more beneficial for SMEs, which are not managed by families, and thus are less likely to exploit “relational” or “affective” trust (Rousseau et al., 1998).

  15. Figures B2 and B3 in Appendix B show that our results are confirmed also when we consider the alternative measures SOCIALCAP2 and SOCIALCAP3.

  16. While the firm-specific component is a fixed effect in the TFE framework, it is an iid random component \(\left( {\alpha _{i} \sim N\left( {0,\sigma _{\alpha }^{2} } \right)} \right)\), uncorrelated with the explanatory variables, in the TRE model. The TFE model suffers from an incidental parameters problem (Belotti et al., 2013) when the length of the panel is short, as in our analysis and other studies selecting a TRE estimator (e.g. Castiglione et al., 2017; Agostino et al., 2018). In the TRE framework, we parameterise the variance of the inefficiency (as Castiglione et al., 2017, and Agostino et al., 2018). By doing so, we both allow the inefficiency term to be heteroskedastic and investigate the determinants of inefficiency. Indeed, for all commonly adopted distributions of the inefficiency term, the mean of the inefficiency term \({u}_{i}\) is proportional to its standard deviation. Thus, a factor influencing the variance of \({u}_{i}\) will impact on its mean in the same direction.

  17. A complete list can be found at https://sites.google.com/site/institutionalqualityindex/empirics.

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No funding was received for conducting this study.

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Authors and Affiliations

  1. Department of Economics, Statistics and Finance “Giovanni Anania”, University of Calabria, Ponte Pietro Bucci, Cubo 1/C, 87036, Arcavacata Di Rende (Cosenza), Italy

    Mariarosaria Agostino

  2. Department of Human and Social Sciences, University of Naples L’Orientale, Naples, Italy

    Sabrina Ruberto

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  1. Mariarosaria Agostino
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  2. Sabrina Ruberto
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Correspondence to Sabrina Ruberto.

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Appendices

Appendix A

Stochastic Frontier Analysis for Panel Data

Formally, a general stochastic production frontier for panel data can be specified as follows:

$${y}_{it}=\mathrm{exp}({x}_{it}\beta +{v}_{it}-u)$$
(A1)

where \({y}_{it}\) is the production at time t (t = 1,2,…,T) for the i-th firm (i = 1,2,…N); \({x}_{it}\) is a (1xk) vector of inputs of production; \(\beta\) is a (kx1) vector of unknown parameters to be estimated; \({v}_{it}\) s are assumed to be iid N(0, \({\sigma }_{V}^{2}\)) random errors, independently distributed of the \({u}_{it}\) s, which are non-negative random variables, gauging technical inefficiency modelled as follows:

$${u}_{it}={z}_{it}\delta +{w}_{it}$$
(A2)

where \({z}_{it}\) is a vector (1xm) of explanatory variables, and \(\delta\) a vector (mx1) of parameters to be estimated. The error term \({w}_{it}\) follows a truncated distribution with zero mean and variance, \({\sigma }^{2}\), thus, the time-varying inefficiency terms are independently but not identically distributed. The technical efficiency of production, assuming values between zero and one, is defined as:

$${TE}_{it}=\mathrm{exp}\left(-{u}_{it}\right)=\mathrm{exp}(-{z}_{it}\delta -{w}_{it})$$
(A3)

The parameters of the stochastic frontier function (A1) and the technical inefficiency model (A2) can be simultaneously estimated following Battese and Coelli (1995). As this procedure is commonly adopted, it is employed in the present paper for comparison purposes. However, since the Battese and Coelli (1995) model does not capture firm-specific latent heterogeneity, we prefer the adoption of a Greene (2005) estimator as our benchmark model, which has the merit of disentangling individual effects from inefficiency and has been adopted by more recent empirical research (e.g., Agostino et al., 2018; Castiglione et al., 2017; Filippini & Hunt, 2016). Indeed, Greene (2005) extends the stochastic frontier for panel data (A1) with "true" fixed effects (TFE) or "true" random effects (TRE), distinguishing time-varying technical inefficiency from time-invariant unobserved heterogeneity.Footnote 16 In this paper, equation (A4)s is a Translog production function specified as follows:

$$ln{Y}_{it}={\beta }_{0}+{\beta }_{k}ln{K}_{it}+{\beta }_{L}ln{L}_{it}+0.5{\beta }_{KK}ln{K}_{it}^{2} +0.5{\beta }_{LL}ln{L}_{it}^{2}+{\beta }_{KL}ln{K}_{it}\cdot ln{L}_{it}+\theta trend+\left({\alpha }_{i}+{v}_{it}-{u}_{it}\right)$$
(A4)

Where Y is the value added of the firm i-th at time t (\(i=1,\dots ,N\);\(t=1,\dots ,T\)) and the two production inputs are capital (K) and workers (L). Trend is a time trend accounting for technological change; \(\beta\) s are parameters to be estimated; \(\left({\alpha }_{i}+{v}_{it}-{u}_{it}\right)\) is the composite error including the firm-specific effect \({\alpha }_{i}\), the idiosyncratic term \({v}_{it}\) and the inefficiency component\({u}_{it}\).

Appendix B

The EU-EFIGE/Bruegel-UniCredit Dataset

The Xth UniCredit-Capitalia survey (2008) is based on a questionnaire sent to a sample of Italian manufacturing firms, with a number of employees from 11 to 500, and observed over the 2004–2006 period. The aforementioned sample is selected by applying three stratifications: geographical area, Pavitt sector and firms’ size. The survey provides qualitative data such as the year of establishment; group membership; size; sector; legal form; financial structure; innovation and internationalisation activities, and bank-firm relationships.

The EU-EFIGE/Bruegel-UniCredit dataset is collected within the EFIGE project (European Firms in a Global Economy: internal policies for external competitiveness) supported by the Directorate General Research of the European Commission through its seventh Framework Programme, and coordinated by Bruegel. The EFIGE questionnaire (available at https://www.bruegel.org/publications/datasets/efige/) includes about 150 items covering different areas (family ownership; structure of the firms; workforce; investment, technological innovation, research and development; internationalisation; market structure and competition; financial structure and bank-firm relationships). In order to ensure standard statistical representativeness of the collected data, the EU-EFIGE/Bruegel-UniCredit dataset fulfils three criteria: the availability of a sufficiently large sample of target companies (initially set at around 3000 firms for large countries and 500 for smaller ones); a minimum response rate for each question area; a stratification of the sample based on three dimensions (industry, region and size). To achieve representativeness targets and to ensure a proper randomization of each cell in the stratification, 135.000 firms have been contacted for all countries considered. For further details on the EU-EFIGE/Bruegel-UniCredit dataset we refer to Altomonte and Aquilante (2012).

The Institutional Quality Index (IQI)

The Institutional Quality Index (IQI) proposed by Nifo and Vecchione (2014) follows the structure of the World Governance Indicator proposed by Kaufmann et al. (2011) adopting a hierarchy configuration. More precisely, as Figure B1 illustrates, twenty-four elementary indexes are aggregated to derive five dimensions representing important characteristics of a governance system at province (NUTS3) and regional level (NUTS2): Rule of Law; Regulatory Quality; Government Effectiveness; Voice and Accountability; Corruption.

Each dimension is built following three main phases: normalization, attribution of weights, aggregation of the elementary indexes. Full technical details on these aspects are given in Nifo and Vecchione (2014), who have made available their database through the following web page: https://sites.google.com/site/institutionalqualityindex/dataset. Several studies have employed/quoted the IQI, or some dimensions of it,Footnote 17 because of two main features: compared to other indicators, such as the World Government Index and the European Quality of Government Index, the IQI it is based more on objective data than on the perceptions of citizens. Indeed, data are collected from institutional sources, research institutes and professional registers. Furthermore, it is computed not only at the regional but also at the provincial level. As the Introduction of our work highlights this aspect is particularly appropriate for our analysis, which focuses on small businesses, strongly rooted in their territory. Therefore, we employ “Voice and accountability” as a measure of local social capital. Indeed, by summarizing participation of citizens in public life (turnout in elections, participation in associations, number of social cooperatives) and their level of culture (number of published and purchased books), this dimension captures important features of social capital as defined by Putnam et al. (1994): interpersonal trust, civic engagement, norms and features of social organisation that facilitate coordination and cooperation for mutual benefit

Fig. B1
figure 4

Structure of the Institutional Quality Index (IQI)

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Fig. B2
figure 5

Estimated differences in INEFF between family and non-family firms as SOCIALCAP2 changes (–- 95% confidence interval)

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Fig. B3
figure 6

Estimated differences in INEFF between family and non-family firms as SOCIALCAP3 changes (– 95% confidence interval)

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Agostino, M., Ruberto, S. Family Ties, Social Capital and Small Businesses’ Efficiency. Evidence from the Italian Food Sector. J Fam Econ Iss 44, 935–955 (2023). https://doi.org/10.1007/s10834-022-09882-9

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