Firm ownership and productivity: a study of family and non-family SMEs
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- Barbera, F. & Moores, K. Small Bus Econ (2013) 40: 953. doi:10.1007/s11187-011-9405-9
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Motivated by a lack of consensus in the current literature, the objective of this paper is to reveal whether family firms are more or less productive than non-family firms. As a first step, this paper links family business research to the theoretical notion that family involvement has an effect on the factors of production from a productivity standpoint. Second, by using a Cobb–Douglas framework, we provide empirical evidence that family labour and capital indeed yield diverse output contributions compared with their non-family counterparts. In particular, family labour output contributions are significantly higher, and family capital output contributions significantly lower. Interestingly, differences in total factor productivity between family and non-family firms disappear when we allow for heterogeneous output contributions of family production inputs. These findings imply that the assumption of homogeneous labour and capital between family and non-family firms is inappropriate when estimating the production function.
KeywordsHeterogeneous input elasticityFamily firmCobb–Douglas production functionTotal factor productivity
The impact of ownership structure on firm performance has gained relevance since the seminal work of Berle and Means (1932). For example, see Jensen and Meckling (1976), Demsetz (1983), Fama and Jensen (1983), and Morck et al. (1988). Recently, Palia and Lichtenberg (1999) operationalized this impact by specifically focussing on the effect of managerial ownership on “productivity” and found that managerial ownership changes do drive changes in productivity. What is less established, however, is the particular impact of family ownership or, more broadly, family involvement on the productivity of the firm. Measuring this impact would in turn necessitate a comparison of family and non-family firms.
Despite more contemporary investigations into financial performance differences between family and non-family firms (Anderson and Reeb 2003; Lee 2006; Miller et al. 2007; Sciascia and Mazzola 2008), to date only a modest amount of analysis has been devoted to determining the specific effects of family involvement on the fundamental drivers of these performance differences, for example productivity. A review of previous studies reveals that while a consistent and significant relationship between family involvement and a firm’s productivity has been found, there is no consensus about the direction of this relationship. Granted, different definitions of a family firm, time periods, measures of productivity, methodologies, and data sets cause these results to vary; however, the inconsistencies beg the following fundamental question: does family involvement have a positive or negative impact on firm productivity?
to link the family business research to the theoretical notion that family involvement has an effect on the factors of production from a productivity standpoint; and
to quantify these productivity differences by use of an estimation technique which enables the economist to interpret, in a traditional manner, producer behaviour and the productivity of inputs which they use.
As a starting point, the section “The nature of family firms” reviews the family business literature which substantiates potential differences in the capital and labour inputs of family firms, and how such differences could affect specific factor output contributions and the overall total factor productivity of the firm. Based on this review, we formulate testable hypotheses. In the next section we outline our “Methodology”. The data and analytical findings are then presented in the sections “Data: the business longitudinal survey” and “Empirical results”, respectively. Finally our conclusions are discussed in the last section.
2 The nature of family firms
Considering that family run businesses are the prevalent form of business among OECD economies,1 we are very interested in understanding their production process; however, despite family businesses being the focus of study for many years, the persisting challenge facing researchers is defining what exactly a family business is. In an attempt to clarify this issue, two different conceptual approaches have been established in the literature.
Following the work of Berle and Means (1932), the first approach focuses on a structure-based classification. For example, family firms have been defined as those which are either owned, controlled, and/or managed by a family unit. Such a definition allows for a wide range of “family firms”, because the extent of family ownership, control and management can differ among individual firms, and studies have shown that different amounts of family involvement do matter empirically (Villalonga and Amit 2006; Miller et al. 2007; Sciascia and Mazzola 2008).
In fact, some researchers have come to realize that the components of family involvement do not necessarily determine whether a firm is a family firm, because the structure-based approach does not account for the possibility of intraorganisational aspirations within the firm to either increase or reduce the degree of family-based relatedness (Litz 1995). Thus, when attempting to narrow the definition of a family firm, an intention, or essence-based, approach can be useful (Chua et al. 1999). For example, the intangible desire of the family unit to transfer ownership, through succession, within the family is considered to be a unique characteristic of family firms.2
Previous investigations of the effects of family involvement on firm productivity
Study time period(s)
Data source and sample size
Measure of productivity and methodology
Kirchhoff and Kirchhoff (1987)
“University of Minnesota Data Base” data on 702 small businesses located in Minnesota, Ohio, Oregon, and Washington
Partial measure of productivity comparing sales per employee between family firms which use paid and unpaid family labour
Positive and significant correlation between productivity and the use of family labour, both paid and unpaid
McConaughy et al. (1998)
COMPUSTAT data on 219 publicly traded firms
Partial measures of productivity such as sales per employee and total asset turnover. Matched-pairs method to compare family and non-family firms
Founding family (and descendant)-controlled firms are more efficient than non-family firms. Younger founder-controlled firms are more efficient than older ones
Firm-level survey data on 506 privately held companies in Western New York
Cobb–Douglas production function using industry as a proxy for capital intensity and including an intercept dummy variable for family business. Factor elasticities are assumed to be equal for both family and non-family firms
From a “macro” perspective, family firms contribute less per firm to the examined regional economy than non-family firms. This is based on a lower level of sales generated by family firms
Bosworth and Loundes (2002)
Australian Bureau of Statistics’ “Business Longitudinal Survey” of 4354 small to medium-sized Australian firms
Cobb–Douglas production function controlling for technology, human resources and organizational characteristics, including family ownership. Factor elasticities are assumed to be equal for both family and non-family firms
Focussing on the interaction of “discretionary” investments, innovation, productivity, and profitability, family firms are incidentally found to be significantly less productive than non-family firms
Barth et al. (2005)
Firm-level survey data among 438 firms associated with the “Confederation of Norwegian Business and Industry”
Cobb–Douglas production function including intercept dummy variables for family owned and family managed firms. Factor elasticities are assumed to be equal for both family and non-family firms
Family-owned firms are less productive than non-family firms. This productivity gap can be explained by a management regime in that family owned and managed firms are significantly less productive
Martikainen et al. (2009)
S&P 500 firm data on 159 manufacturing firms. Source list originally compiled and classified by Anderson and Reeb (2003)
Cobb–Douglas production function including an intercept dummy variable for family business. Factor elasticities are tested for invariance and found to be equal for both family and non-family firms
Production technologies between family and non-family firms are found to be the same; however, on the basis of a positive and significant intercept dummy variable, family firms are found to be more efficient in their production than comparable non-family firms
Understanding family concerns and preferences are crucial for understanding family business behaviour (Ward 1988; Harris et al. 1994; Nelly and Rodríguez 2008); thus we acknowledge that different motives may drive differences in behaviour between family and non-family firms. As a result, long-term objectives akin to the continuity of the business, preservation of financial strength, and maintenance of family control may have greater precedence than immediate profits or other short term objectives. Such objectives may in turn manifest themselves in the production process. More specifically, to meet production demands, family firms may utilise labour and capital differently from non-family firms.
Further to the question of utilisation, the output contribution of both family firm labour and capital, when considered as heterogeneous production factors, may also be different from those of non-family firms. In other words, the notion that family firm labour and capital inputs yield exactly the same contribution toward output as non-family firm labour and capital may be flawed, and thus treating them as equal theoretically and empirically could be a mistake. On the basis of the literature to date, it is possible that family involvement may have both positive and negative effects on production inputs.
For example, family firms tend to avoid external debt and prefer to use internal financial resources instead. Manifestations of this behaviour have consistently been observed in that family firms have been found to have significantly lower leverage than non-family firms (Dreux 1990; Gallo and Vilaseca 1996; Anderson et al. 2003; Villalonga and Amit 2006). The rationale for such behaviour lies in the fact that inside equity holders of family firms typically have undiversified portfolios and intend to pass the firm on to their descendants, and are thus less willing to subject the firm to future cash-flow risks that result from financing via debt (McMahon and Stanger 1995).
It is for these reasons that Anderson and Reeb (2003) contend that inside family business equity holders are a unique class of shareholders. More specifically, there is strong identification by inside owners between the family and the business (Gallo and Vilaseca 1996), and family business owners, unlike owners of other companies, have to satisfy the current and future needs of family members in addition to the needs of the business (Dreux 1990). Because physical capital investments can represent large upfront costs and, once acquired, may yield irregular and uncertain returns over time, they represent a risk to the firm; and with the family’s wealth so closely tied to the firm’s future, it might become difficult for inside family owners to support such risk-taking activities (Agrawal and Nagarajan 1990; Zahra 2005; Gómez-Mejía et al. 2007). Ward (1988) outlines how such family considerations can limit the strategic aggressiveness of family firms; and Morck et al. (2000) also recognise the distaste for risk displayed by family-owned firms, arguing that they may be excessively risk averse, even to the point where they forego profitable expansion strategies, for example investment in physical capital.
Differences in the capital intensity of family-firm production alone would not necessarily deem the capital owned by family firms more or less productive; however, a reluctance to invest in physical capital may also extend to investments in capital-improving innovations. Further to this notion, Morck and Yeung (2003) consider situations in which family firms may actively suppress capital-improving innovation to protect their already established wealth. Suppression of capital-enhancing innovation may in turn reduce the output contribution of capital during family-firm production.
Other studies, however, portray family firms as more likely to invest in capital enhancing innovations. For example, Zellweger (2007) finds that family firms have a longer time horizon than non-family firms, because they have a longer CEO tenure and strive for long-term independence and succession within the family. Because of these extended horizons and the long-term presence of family owners, family-owned firms may be more likely to invest in longer-term value-maximizing projects. The lengthy presence of family owners may also result in superior knowledge of the firm’s technology, which could induce improvements in productivity (Martikainen et al. 2009); thus there is the question of whether family-firm capital contributes more or less to output than non-family-firm capital. To shed further light on this issue, it is not necessarily sufficient to simply determine whether or not family firms enhance their capital via innovation. Because it is the efficient use of capital that ultimately will affect output contribution, perhaps more central to the question is the issue of understanding how existing capital, enhanced or otherwise, is actually being utilised by family firms.
Family owners differ from non family shareholders in that the latter obtain only monetary benefits of control whereas family owners also obtain non-pecuniary benefits, for example the satisfaction of transferring the firm to descendants and, more importantly, use of capital and consumption of amenities by the family at the expense of firm profits (Demsetz and Lehn 1985). More specifically, Demsetz (1983) argues that combining ownership and control leads to such owners choosing non-pecuniary consumption, thereby drawing scarce resources away from profitable projects.
Especially when managerial ownership is large, as in many family firms, there is ample evidence of managers pursuing private benefits when their control of the firm becomes “entrenched” (Stulz 1988; Shleifer and Vishny 1997; Claessens et al. 2002). Such benefits can take the form of advantageous dividend treatment (DeAngelo and DeAngelo 2000), the preservation of “socioemotional” wealth5 (Gómez-Mejía et al. 2007), or shirking or other on-the-job consumption (Hoopes and Miller 2006); thus the capital of family firms may be used for both productive and non-productive purposes. Such self serving behaviour may reduce the share of capital in the family firm’s production process.
Despite the potential differences, the literature to date has largely assumed that capital output contributions between family and non-family firms are the same. To verify this assumption, we test the following null hypothesis.
There is no difference between the output contribution of capital in family and non-family firms.
Based on the discussion thus far, we acknowledge that any differences in physical capital intensity and production share between family and non-family firms may have implications for other production factor intensities and shares also, for example labour. In fact, there is theoretical and empirical evidence demonstrating that family firms utilise labour in the production process differently from non-family firms, and that such labour yields unique contributions to output.
A reduction in capital intensity may compel family firms to increase the utilisation of labour to meet production demand. Alternatively, family firms may prefer labour in the production process, not because they are deprived of capital, but because the use of labour may be particularly advantageous for family firms. Evidence for such an advantage is consistently found throughout the family business literature.
By assessing the human and organizational resources related to employees, Habbershon and Williams (1999) suggest that family firms manage and socialise their employees better than non-family firms, potentially leading to a competitive advantage and improved performance. Such an advantage may be based on findings that family firms have a unique working environment that promotes employee dedication and commitment (Ward 1988). Other cultural attributes pertaining to employees of family firms include a shared sense of identity, better communication with greater privacy, and emotional involvement among co-workers (Tagiuri and Davis 1996). Greater employee loyalty and trust would serve to provide the family firm with access to employees who would potentially contribute more to output than otherwise (Rutherford et al. 2005; Bertrand and Schoar 2006).
In turn, more trusting and loyal employees can be an advantage in economizing transaction costs (Williamson 1996). More specifically, the expense of auditing employee performance and employee/employer contractual protection costs, for example union representation, may be substantially less for family firms (Ouchi 1980).
Additional costs associated with labour may also be less for family firms than for their non-family counterparts. For example, although we recognise that agency costs for family firms are greater than zero,6 based on recent evidence provided by Chrisman et al. (2004), family involvement has been found to reduce overall agency problems. Empirically, lower agency costs in family firms have been shown by Moskowitz and Levering (1993) who find that family firms have lower recruitment costs, lower human resource costs, and are said to be more effective than other companies in labour-intensive businesses. Further labour cost savings can be realised because family members employed in the firm usually contribute more unpaid hours than paid employees (Benedict 1968; Rosenblatt 1985) and can potentially be paid lower efficiency wages based on a less binding non-shirking condition (De Paola and Scoppa 2009).
In addition to less costly labour, family firms may enjoy more flexible labour arrangements. Because family firms encourage informal, adaptive, and flexible work practices for their employees (Goffee and Scase 1985), those employees in turn may provide greater versatility in the production process. More specifically, family firm employees have greater occupational mobility within the firm (Becker 1974) and are, often, trained in all aspects of the business, spanning various departments and roles (Aronoff and Ward 1995; Fiegener et al. 1996). This attention to training develops through a lifetime of learning experiences inside the business leading to greater firm-specific employee knowledge, skills, and capabilities; a phenomenon which may not occur in non-family firms. Bhattacharya and Ravikumar (2001) refer to such tacit knowledge as the firm’s “special business skill” and a distinguishing characteristic of family firms. Such labour resources may be recruited to, or diverted from, specific production tasks with greater ease, further enhancing the flexibility of labour in the production process.
Despite the potential advantages, there are also some limitations on family firm labour. For example, family firms which hire employees who are also members of the family unit face a restricted labour pool from which to obtain qualified and capable talent (Anderson et al. 2003). In addition, it is recognised that there are further limits to family firm labour if family members are hired on the basis of nepotism, birth order, or gender rather than merit (Dyer 1988).
As is the case with capital, and despite the abovementioned differences, the assumption that labour output contributions are the same for family and non-family firms is prevalent in the literature. To test this assumption, our second hypothesis is as follows:
There is no difference between the output contribution of labour in family and non-family firms.
The stylised facts presented thus far lead us to an interesting question: how does family involvement affect the total productivity of the firm? That is, considering the potential bearing that family involvement may have on input factors such as labour and capital, are there any differences in total factor productivity for family firms? Previous studies have suggested that there are (Wall 1998; Bosworth and Loundes 2002; Barth et al. 2005; Martikainen et al. 2009); however, those studies have assumed fixed labour and capital output contributions for family and non-family firms. Allowing for input heterogeneity, we formulate the following third and final hypothesis:
Accounting for different input shares, there is no difference between total factor productivity of family and non-family firms.
Referring back to Table 1, previous studies have yet to address H3 in its specific form. If it is rejected, we are also interested in the direction of the difference in productivity between family and non-family firms; thus, secondary to H3 we wish to know if family firms are more or less productive than non-family firms. As in previous studies, we employ a Cobb–Douglas production function to test these hypotheses, but with the additional specification of varying production factor shares between family and non-family firms. This model and the data used are discussed in the next section(s) of this paper.
A production function expresses the maximum product obtainable from the input combination at the current state of technical knowledge (Carlson 1909); thus, in order to test this paper’s proposed hypotheses, we respecify the standard Cobb–Douglas production function.
Our specification extends beyond what has already been analysed in previous research, because we allow for varying factor elasticities, or in other words different output contributions of labour and capital inputs, among family and non-family firms. Because the Cobb–Douglas framework measures output as a function of production inputs, we can observe the effect of a family firm on the production process, namely on the output contribution of labour and capital inputs and the total factor productivity of the firm.
While such an assumption may be appealing when comparing total factor productivity for one firm with that for another, it does not shed any light on which qualitative forces are accountable or which inputs, labour or capital, are affected. Further, assuming equal factor elasticities may be particularly problematic, because the log transformation form of the Cobb–Douglas model implies that the factor elasticities for both labour and capital represent the percentage change in output with regard to labour (and holding capital constant) or capital (and holding labour constant), or in other words, the output contribution of labour and capital. The consequences of this assumption being violated when using a Cobb–Douglas framework are that actual differences in labour and capital output contribution, if present, are captured and aggregated in the total factor productivity constant8; thus the chance of finding a statistically significant difference in total productivity between family and non-family firms is enhanced when fixing the factor elasticities for both groups (Zellner et al. 1966).
Estimating the parameters specified in Eq. 2 enables us to test our proposed hypotheses. For example, H1 and H2 can be tested by observing whether there are any significant differences between family and non-family firms for estimated values of α and β, respectively. H3 can be tested by observing whether there are any significant differences between family and non-family firms for the estimated value of A. The direction of any found differences is also interesting, as a positive (negative) value of A for family firms would indicate an additive (subtractive) difference in total factor productivity specifically for family firms relative to their non-family firm counterparts, considering different capital and labour output contributions. To perform such tests, we must first have a reliable data source, which is discussed next.
4 Data: the Business Longitudinal Survey
The Australian Bureau of Statistics” “Business Longitudinal Survey” (BLS) was designed to provide information on the growth and performance of privately held Australian small and medium-sized enterprises (SME), i.e. fewer than 200 employees. The BLS is the longitudinal component of several waves of the “Business Growth and Performance Survey”. As such, the structure of the data includes not only a cross-sectional component, but also a longitudinal aspect for the years 1994–1995 to 1997–1998 inclusive.10 The BLS contains potential information for many areas of research, including industrial relations, business, finance, and economics (Hawke 2000); however, for the purpose of our analysis, and considering the neo-classical theory of production, some narrowing of the data was required.
BLS data sub-sample
BLS defined industry
Firms in 1994–1995 sub-sample
Firms in 1995–1996 sub-sample
Firms in 1996–1997 sub-sample
Firms in 1997–1998 sub-sample
Average proportions by industry from 1995–1998 (%)
Family firms in sub-sample
Average proportion of family firms from 1995–1998 (%)
Accommodation, cafes, and restaurants
Transport and storage
Finance and insurance
Property and business services
Cultural and recreational services
Personal and other services
Although the same firms are analysed in each year, their industry classification may change from one year to the next. This results in a slight variation in the total number of firms in each industry over time. Despite this, on average it can be said that manufacturing firms represent approximately 40% of all firms sampled, followed by nearly 17% representation of wholesale trade firms and 13% representation of property and business service firms; whereas less than 1% of our sample is represented by mining firms, which is understandable considering we are focussing on small to medium sized enterprises, i.e. firms with less than 200 employees.
Most importantly, for the purpose of this study, the BLS includes information on the extent of family involvement in each of the firms included in our sample, which, with other relevant variables, is discussed further in the next section.
4.1 Variable treatment
Referring to Eq. 2, where Y is a measure of total physical output, and considering that the BLS does not offer data on “output” per se, an index number for value added (VA) is used as a proxy for total output. Such an index follows Kenneth Arrow’s (1974) generally accepted “real value added” measure and is constructed by taking sales plus the change in inventories less purchases of intermediate inputs and other operating expenses. Although from a purely theoretical standpoint we would rather use actual output, in terms of number of units produced, the value-added index enables us to analyse those firms which do not necessarily have a tangible output, for example services rendered. Furthermore, the value-added index has been found to accurately measure the dependent variable in the production function that explains value added in terms of tangible and intangible primary factors, for example labour and capital, and as such the function is independent of non-primary inputs (Sato 1976).
Similar to our treatment of the dependent variable, the independent variables specified in Eq. 2 deviate slightly from the theoretical notion of primary production inputs in the sense that they are derived from the BLS data. Here we briefly discuss our proxies and operationalization of these variables.
4.1.1 Labour input
Rather than the number of labour hours worked as a measure of labour input, the number of full-time equivalent (FTE) workers employed in the firm is used. This figure is found via the sum of full-time workers and full-time equivalent part-time workers. Full-time equivalent part-time workers are found via the product of the number of part-time employees for each individual firm and a full-time equivalent ratio. The equivalent ratio is the Australian Bureau of Statistics’ estimate of average hours worked by part-time non-managerial employees per week in time t compared with full-time employees for all firms.11 Because our sampled firms utilise both part-time and full-time labour, the transformation of number of workers to FTE workers is essential to obtain a standardised, comparable measure of labour.
4.1.2 Capital input
The difficulty in measuring capital and then applying such a measurement in a production function framework has been the cause of much controversy over the years. Joan Robinson’s (1953) now famous critique shed light on these problems, sparking the so-called Cambridge–Cambridge controversies. We attempt to alleviate some of these problems by viewing a firm’s stock of capital as the specific list of all the goods in existence at any given moment. As far the BLS data are concerned, we measure capital as the value of all assets in the firm’s possession. Our measure of capital is analysed in a cross-sectional framework, so changing capital stock over time does not come into consideration because, in the short-run, the supply of concrete capital goods is assumed not to alter. Furthermore, the BLS contains data on the net value of total assets, so depreciation is factored into the measurement. Finally, we consider the notion that capital is heavily user-dependent and heterogeneous by controlling, in our analysis, for industry and for the age of the firm.
4.1.3 Family involvement
Do you consider the business to be a family business? Yes/No.
- 2.If yes, why do you consider this a family business? Family member are:
Working directors or proprietors. Yes/No.
Employed in the business. Yes/No.
Not working, but contribute to decisions. Yes/No.
Business acquired from parents. Yes/No.
Close working relationship between management and staff. Yes/No.
BLS proxies for dependent and independent variables
VAit = Salesit + closing inventoryit − opening inventoryit − purchasesit
Full-time equivalent employees
FTEit = Full-time employeesit + part-time employeesit × equivalent ratiot
Capitalit = Total liabilitiesit + total equityit
Structure and essence based definition
Do you consider the business to be a family business?
Yes = 1; No = 0
Capital to Labour ratio
K/Lit = Capital inputit/labour inputit
Type of labour
PTit = Part-time employeesit/full-time employeesit
WRit = Total wage expenseit/total expensesit
Table 3 also reports additional variables, namely the capital to labour, part-time, and wage ratios, which offer greater insight into the production of family firms, but are not directly included in Eqs. 1 and 2. Despite this, the wage ratio is used as an alternative to labour input in the section “Robustness checks”.
4.1.4 Control variables
As specified in Eq. 2, we control for heterogeneity across sectors by including both slope and intercept industry dummies. This is important, because the distribution of family firms across sectors, outlined in Table 2, is uneven. Also seen in Table 2, eleven Australian and New Zealand Standard Industrial Classifications (ANZSIC) were incorporated, leading to twenty additional estimated parameters.13 Further heterogeneity across the life cycles of different firms is also controlled for by including an age variable in our specification.
4.2 Summary statistics
Value added (95)
Value added (96)
Value added (97)
Value added (98)
Value added (95)
Value added (96)
Value added (97)
Value added (98)
Summary statistics for BLS sub-sample (1995–1998)
Panel A: full sample (N = 3,364)
Panel B: family firms (N = 1,730)
Panel C: non-family firms (N = 1,634)
Total labour (95)
Total labour (96)
Total labour (97)
Total labour (98)
Total capital (95)
Total capital (96)
Total capital (97)
Total capital (98)
Capital to labour ratio (95)
Capital to labour ratio (96)
Capital to labour ratio (97)
Capital to labour ratio (98)
Part-time employee ratio (95)
Part-time employee ratio (96)
Part-time employee ratio (97)
Part-time employee ratio (98)
Wage to total expense ratio (95)
Wage to total expense ratio (96)
Wage to total expense ratio (97)
Wage to total expense ratio (98)
Industry comparison of summary statistics between family and non-family firms (average for 95–98)
BLS defined industry
Mean difference in family capital
Mean difference in family labour
Mean difference in family PT ratio
Mean difference in family KL ratio
Mean difference in family wage ratio
Accommodation, cafes, and restaurants
Transport and storage
Finance and insurance
Property and business services
Cultural and recreational services
Personal and other services
The capital to labour ratio is defined as capital input over our standardised labour input and measures the capital intensity of any given firm. Table 5, Panel B indicates that family firms are less capital-intensive than non-family firms. This result is consistent throughout the entire sample period and, when broken down by industry, is most prevalent in the manufacturing, wholesale trade, and retail trade industries.
Lower capital intensity in theory would diminish labour productivity, because the use of tools and machinery can make labour more effective.15 Despite this, the capital to labour ratio alone only offers some insight into productivity and is more a measure of how production is undertaken, i.e. allocation of labour and capital inputs, rather than how effective said inputs contribute to output. Nevertheless, we report the capital to labour ratio here to obtain a better understanding of our specified production function in the “Methodology” section.
A “part-time ratio” is defined as the proportion of part-time workers utilised, and has been calculated as the total number of part-time workers over the number of all workers employed in any given firm.16 From this we can observe what type of labour, part or full-time, family firms prefer. Table 5, Panel B indicates that, across all years, family firms utilise more part-time workers than non-family firms. This result is significant in nearly half of all industries included in the BLS sample. One explanation could be that family firms initially associate offspring with the firm through family discussions or part-time employment (Stavrou 1999). These results may also underpin the flexible labour arrangements enjoyed by family firms, as discussed in the section “The nature of family firms”.
Given a significant preference for part-time labour and hiring, on average, fewer employees, family firms do not necessarily enjoy cheaper labour in terms of wage costs as a proportion of total expenses. Although the results vary across industry and are for the most part insignificant, we do observe some significant differences in the manufacturing and mining industries, where the wage ratio is respectively 2 and 18% higher. The wage ratio as an indicator of labour cost is a very crude measure as it accounts for total labour expenses across all levels of employment, and thus may be subject to bias; however, following Barth et al. (2005), such a measure may be useful as an alternative measure of labour input as far as the production function is concerned.
On the basis of these summary statistics, it is sufficient to say that there are observable differences between family-firm production factors relative to their non-family counterparts; however, it is still unknown whether these differences translate into unique output contributions of family labour and capital. To address this issue, using the variables outlined in the section “Data: The Business Longitudinal Survey”, we proceed to estimate our model specified in the section “Methodology”, the results of which are discussed next.
5 Empirical results
It can be seen from Eq. 2 that we allow for heterogeneous inputs for both family and non-family firms, which is the main difference between our analysis and previous research (Table 1). As mentioned, we also control for firm industry and age. In this section, the empirical results for each of our stated hypotheses are presented.
Family-firm production function with homogeneous input
Equation 1 (OLS)
Equation 1 (2SLS)
Consistent with Barth et al. (2005), Bosworth and Loundes (2002) and Wall (1998), we find that family firms are significantly less productive, from a total factor productivity standpoint, than non-family firms. This productivity gap is identified as the Family firm intercept in Table 7 and ranges from as little as 11% in 1997 to nearly 14% in 1998. Also consistent with previous findings, this gap is enlarged when endogeneity problems are addressed using the two-staged least-squares technique (right column).
Family-firm production function with heterogeneous inputs
Equation 2 (OLS)
Equation 2 (2SLS)
ln Family labour
ln Family capital
On the basis of the results listed in Table 8, we find that differences in the output contribution of family-firm capital (denoted ln Family capital) are significant relative to their non-family counterparts. As a semi-log, the negative coefficient indicates that family capital contributes less to output than non-family-firm capital by approximately 2–8%, depending on time period and estimation technique. This estimate can be interpreted as for all capital utilised, family-firm capital contributes less to total output than the benchmark non-family-firm capital (denoted ln Capital). This finding refutes H1. Furthermore, this relationship is consistent throughout time with the exception of 1997 in the two-staged least-squares technique. Despite this, the coefficient is still negative and it is worth noting that the ordinary least-squares technique estimates a significant and negative coefficient for family-firm capital in 1997.
Also apparent in Table 8 is the positive and consistent interaction between family firms and labour employed in the production process. Depending on the time period and estimation technique, we find differences in the output contribution of family firm labour (denoted ln Family labour) are significant in that it is greater than the output contribution of non-family firm labour; this difference ranges from as little as 6% to as large as 15%. This estimate can be interpreted as for all labour employed, family firm labour contributes more to total output than the benchmark non-family firm labour (denoted ln Labour). This result refutes H2 and is consistent across all time periods and remains after correcting for potential simultaneous equation bias.
Moving on to H3, interestingly, the intercept representing family firm total factor productivity differences (denoted Family firm) has become statistically insignificant when heterogeneous inputs are specified in the Cobb–Douglas production function. That is, family firm labour and capital are found to yield significantly different output contributions and, when these differences are accounted for, total factor productivity differences disappear. In this sense, H3 is confirmed in that there are no unquantifiable productivity differences between family and non-family firms when heterogeneous inputs have been considered. This leads us to believe that previous investigations suffer from omitted variable bias in that they do not consider the family differences in output elasticity of labour and capital.
Family firm and total factor productivity with heterogeneous inputs (system estimates)
Simultaneous estimate (2SLS)
H0: βi,95 = βi,96 = βi,97 = βi,98
Chi-squared value, Prob.
ln Family labour
ln Family capital
As seen in Table 9, the simultaneous coefficient estimates yield slightly different values compared with Table 8, because the system method takes into account the cross-equation error correlations when estimating parameters. This makes the model more sensitive to specification error. In fact, the estimates for Family-firm capital in 1996 and 1997 are now insignificant; however, it is worth reporting here the p-values of 0.123 and 0.272 respectively. Despite this the sign and magnitude are in accordance with our single-equation estimates.
To check the stability of these results over time, we employ a Wald coefficient test by imposing the following restriction: βi,95 = βi,96 = βi,97 = βi,98, where i = the family firm intercept and interaction effects. The chi-squared values and probabilities presented in the extreme right column of Table 9 indicate that we do not reject the possibility that these values are equal across time. In other words, we do not find any significant time effect. Based on the analysis performed in this section, we have refuted our hypotheses proposed in the section “The nature of family firms” with the interesting exception of H3. Next we test whether these results are valid when using alternative specifications to Eq. 2.
5.1 Robustness checks
Output contribution effects for family firms under alternative specifications
N = 3364
Family firm intercept
Proportion of family firms (%)
Alternative measure of output
Dependent variable: ln (Sales) 95
Dependent variable: ln (Sales) 96
Dependent variable: ln (Sales) 97
Dependent variable: ln (Sales) 98
Alternative measures of labour input
Wage ratio (95)
Wage ratio (96)
Wage ratio (97)
Wage ratio (98)
10 employees or fewer (95)
10 employees or fewer (96)
10 employees or fewer (97)
10 employees or fewer (98)
Between 10 and 100 employees (95)
Between 10 and 100 employees (96)
Between 10 and 100 employees (97)
Between 10 and 100 employees (98)
More than 100 employees (95)
More than 100 employees (96)
More than 100 employees (97)
More than 100 employees (98)
13 employees or fewer (95)
13 employees or fewer (96)
13 employees or fewer (97)
13 employees or fewer (98)
More than 13 employees (95)
More than 13 employees (96)
More than 13 employees (97)
More than 13 employees (98)
First we regress Eq. 2 using sales as the dependent variable. This procedure is consistent with Palia and Lichtenberg (1999) and Barth et al. (2005), who believe total sales to be an acceptable gauge of production output. As seen in Table 10, the relationships found in Table 8 are not affected by this alternative treatment. That is, the output contributions of family labour (capital) are still found to be greater (less) than their non-family counterparts. Furthermore, the family firm total factor productivity intercept remains insignificant once output contribution heterogeneity is accounted for. However, because the value of intermediate goods is omitted, we prefer the analysis presented in Table 8.
Next we address the issue of potential differences in labour quality by using the wage ratio, as defined in Table 3, as an alternative measure of labour input. The validity of such a measure depends heavily on the assumption that wage rates equate to the marginal productivity of labour. In Table 10 we can see that although no statistical significance is found, the direction of the family labour and capital relationships are maintained. More importantly, the family firm total factor productivity intercept remains insignificant. We still prefer the measure of labour input specified in Eq. 2, because labour quality differences between ownership structure are, in fact, already accounted for when we allow for labour output contribution heterogeneity. Furthermore, as Bath et al. (2005) report, if wages are related to productivity, they will be correlated with the error term and thus introduce a bias to the estimators of the production function.
To address the potential for our results being driven by underlying heterogeneity rather than differences in ownership structure, we narrow our sample to three sub-samples depending on the number of workers employed. Doing so would indicate whether the effects presented in Table 8 are occurring across all firms, irrespective of size. Arbitrarily, we classify firms with up to 10, between 10 and 100, and more than 100 employees. Not surprisingly, we find the family effects are strongest in firms with up to 10 employees, where nearly 60% of all family firms in our sample are concentrated. As family firm concentration falls, as is the case for firms which employ between 10 and 100 workers, differences in production factor output contribution become insignificant, yet the relationships are maintained. In firms with more than 100 employees, a sub-sample consisting of only 25% family firms, the relationships presented in Table 8 break down.
To check if this result is because of an insufficient number of family firm observations as firm size increases, we break the sample in half and report the results of Eq. 2. On the basis of the BLS data, approximately half of all firms have 13 employees or fewer. Utilising this classification, we can see that the family firm effect is significant in the smaller half of the sample across nearly all years. In the larger half of the sample the family effect is more prevalent in diminished capital output contribution, but less so in enhanced labour output contribution. This result suggests there may be a size effect in that larger family firms do not necessarily enjoy greater output contributions from their labour inputs as smaller family firms do. Despite this, and consistent with our results, when input heterogeneity is accounted for, there is no difference in total factor productivity between family and non-family firms in either classification or across any alternative specification.
As a final measure of robustness, we estimate Eq. 2 in a panel framework. Not only will such a framework effectively control for any unobserved heterogeneity, but it utilizes all the available data in our BLS sample. In the context of our analysis, we estimate a random-effects, rather than fixed-effects, model because:
the family ownership constant we are concerned with in this analysis cannot be directly estimated by use of a fixed-effect model approach, because only the within variance, variance over time, is considered and the between variance, variance across firms, is disregarded;
as is the case in our BLS sample, we assume that the firm-specific intercept values are randomly drawn from a larger population of firms.
the unobserved random disturbance, ui, for the ith firm and constant through time; and
the combined time series and cross-section disturbance, εit.
Panel estimation of the family-firm production function with heterogeneous inputs
Equation 3 (OLS)
Equation 3 (2SLS)
ln Family labour
ln Family capital
BPLM test p
As seen in Table 11, our OLS estimates reported in Table 8 are valid. That is, there is a positive (negative) interaction with family ownership and the output contribution of labour (capital). Differences in the output contribution of family-firm capital (denoted ln Family capital) in both panels is significant and show a negative difference in output contribution of 5–6% depending on the estimation procedure. Differences in family labour output contributions (denoted ln Family labour) are also significant in both panels and show a positive difference in output contribution of 6–7% depending on the estimation procedure. As with our other alternative specifications, after allowing for input heterogeneity in a panel framework, no significant total factor productivity differences between family or non-family firms are found.
Despite numerous investigations into the effect of firm ownership on performance, very little analysis has focussed specifically on the productivity of firms; even less research has been dedicated to studying the effect of family involvement on productivity. Those few studies which have tackled the issue fail, unfortunately, to reach a consensus on the direction of the relationship. Different time periods and estimation techniques contribute to the dilemma; however, the curious assumption of homogeneous output contribution of production inputs for both family and non-family firms has consistently been made in examinations which estimate total factor productivity via a Cobb–Douglas production function. This assumption, as argued in this paper, is perhaps missing family firm differences in the efficient use of labour and capital and may lead to omission bias.
Although a review of the family business literature reveals that family involvement does affect the output contribution of labour and capital, the evidence is primarily anecdotal. In this paper, we have linked this evidence to the theoretical notion of heterogeneous production factors for family firms and show empirically that family labour and capital are indeed diverse in that they yield significantly different output contributions to the firm; As far as we are aware, this is the first study which separates the labour and capital components of the family production function in this manner.
In particular, we have found that family firm labour contributes significantly more, and family capital significantly less, to output than for comparable non-family firms. We have also found that these effects, in terms of labour contribution, weaken as the firm gets larger. Contrary to previous studies, this study has shown that when we account for heterogeneous production inputs all previously found unquantifiable differences in total factor productivity disappear. This result leads us to believe that previously found qualitative productivity differences between family and non-family firms can be better explained by attributing those differences to the output contributions embodied in the firm’s heterogeneous production factors.
Despite offering a theoretical explanation for different output contributions between family and non-family firms on the basis of relatively contemporary family business research, it is important to note that the production function technique merely estimates the presence of the phenomenon and does not offer any insight into the definitive cause(s) of the relationships found. Future research should preferably explore these causes in greater depth. This study has investigated the effect of family involvement on Australian SMEs from 1995 to 1998, so we cannot claim to have definitively established whether family firms are more or less productive than non-family firms; however, we can claim that the assumption of homogeneous labour and capital shares between family and non-family firms is inappropriate. Moreover, on the basis of the unique characteristics of family firms, heterogeneous production inputs do matter empirically. Therefore, if we account for the role for family involvement and allow for unequal factor elasticities, perhaps we could better understand the differences in production strategy, planning, and other important productivity drivers between family and non-family firms. These results hopefully shed further light on the unique attributes of family firms and bring us closer to understanding the specific economic impact family involvement may have on a firm level.
Burns and Whitehouse (1996) report that 85% of businesses in the European Union and 90% of businesses in the United States are family controlled. It is also generally recognized that family businesses are critical to entrepreneurship and socioeconomic development and industrialization in unstable, low income, or transitional economies.
Handler (1994) describes the issue of succession as the most important issue that all family firms face; Chua et al. (2003) found that succession is the number one concern of family firms; and Ward (1987) goes so far as to define all family firms specifically as those that will be “passed on for the family’s next generation to manage and control”.
Some studies listed in Table 1 have concentrated on the partial productivity of family firms in that they focus on the ratio of output to a single input factor, usually labour; however partial analysis only provides a general indication of total factor productivity, because it fails to consider tradeoffs between other input factors.
One notable exception is Martikainen et al. (2009) who tested whether factor elasticities (namely the coefficient estimates for both labour and capital) are invariant across both family and non-family firms. They found that, for their sample of 159 manufacturing firms, there is no such variance in elasticity, and proceeded to test differences in productivity using fixed factor elasticities for both family and non-family firms.
According to Gómez-Mejía et al.(2007), “the socioemotional wealth of family firms comes in a variety of related forms, including the ability to exercise authority… the perpetuation of family values through the business… the preservation of the family dynasty… the conservation of the family firm's social capital… the fulfilment of family obligations based on blood ties rather than on strict criteria of competence… and the opportunity to be altruistic to family members. Losing this socioemotional wealth implies lost intimacy, reduced status, and failure to meet the family's expectations”.
If both principal and agent have the same interests, there is no conflict of interest and no “agency problem” (Berle and Means 1932; Ross 1973); thus by virtue of their intra-familial altruistic element, family firms should be exempt from agency problems (Becker 1974; Jensen and Meckling 1976; Parsons et al. 1986; Eisenhardt 1989; Daily and Dollinger 1992). However, more recent investigations have looked into other types of agency problem that may be specific to family firms (Morck and Yeung 2003; Chrisman et al. 2004).
In their analysis, Cobb and Douglas (1928) investigate production in manufacturing firms and, as a result, land is excluded as a factor of production.
In the log transformed Cobb–Douglas production function, the value of the constant coefficient is independent of labour and capital. This assumption has been made to ignore the qualitative effects of any force for which there is no quantitative data. The coefficient is thus made a “catch-all” for the effects of such forces (Cobb and Douglas 1928).
An important consideration is the simultaneous equation bias that may arise when specifying management variables in the production function (Hoch 1958). In the case of the added family firm variable, we may find that productivity depends on whether the firm is a family firm and whether the firm is a family firm depends on productivity. For example, whether a firm remains in the control, management, and ownership of the family may be endogenously determined by the performance of the firm. Poorly performing family firms may resort to outside management as a potential remedy and, on the other hand, families may be less inclined to relinquish ownership, management, or control of a highly performing firm (Demsetz and Lehn 1985; Demsetz and Villalonga 2001). If correlations between the error term and independent variables exist, coefficient estimates of Eqs. 1 and 2 may end up being biassed and therefore inconsistent, because it is assumed that independent variables are in fact independent or exogenous.
The BLS samples were drawn from the ABS Business Register, with 8745 business units being selected for inclusion in the 1994–1995 survey. For the 1995–1996 survey, 4,948 of the original selections for the 1994–1995 survey were selected, and this was supplemented by 572 new business units added to the ABS Business Register during 1995–1996. The sample for the 1996–1997 survey included 4,541 businesses which were previously sampled, and an additional sample of 529 new businesses from the 1995–1996 interrogation of the Business Register, and 551 new businesses from the 1996–1997 interrogation of the Business Register.
The equivalent ratio is simply calculated as average part-time hours per week divided by average full-time hours per week for all non-managerial employees. This information is from the Australian Bureau of Statistics’ “Employee Earnings and Hours, Australia” report as of 1998 and from the previously known “Earnings and Hours of Employees, Distribution and Composition, Australia” report.
Of all family firms responding to question 2, 34.91% selected i only; 27.45% selected both i and ii; 11.79% selected i, ii and v; 4.39% selected i and v; 3.18% selected i, ii, iv and v; and 3.18% selected i, ii and iv. On this basis, and out of 64 possible permutations, nearly 95% of all family firms at least selected i, which is understandable, because we would expect small to medium sized family firms to have a more operational classification; however, not excluding these, approximately 37% also selected iv and v, which is associated with the essence-based classification of a family firm.
“Personal and other services” was excluded and used as the benchmark industry.
Notable exceptions are the construction, accommodation, and personal services industries.
For a discussion on the relationship between capital intensity and labour productivity, see Wolff (1991).
Note that the measure of total workers, as the denominator in the part-time ratio, has not been converted to FTE workers and is simply reported as the total number of all employees in any given firm.
Considering that our sub-sample of the BLS is relatively small (i.e. the number of cross-sectional subjects, N = 3364, is greater than the number of time periods, T = 4), the family ownership dummy specified in Eq. 2 is, in fact, constant for each family firm across the entire period under analysis.
An important assumption of the random effects model is that the unobserved random disturbance, ui, is uncorrelated with the individual regressors in Eq. 3. As is the case with many panels, the Hausman test has revealed that the coefficients estimated by the efficient random effects estimator are not the same as those estimated by the consistent fixed effects estimator. In such cases a fixed effect model would be preferred; for reasons already stated, however, and to directly estimate the family firm intercept, we require a random effects approach. To overcome this issue, the Hausman–Taylor random effects procedure, with instruments, can be used to control for endogeneity (Hausman and Taylor 1981).
The authors wish to thank the Grand Valley State University’s Family Owned Business Institute for their generous funding of this study. Further acknowledgements are extended to Dr. Gulasekaran for his econometric expertise and Dr. Khalid and Dr. Craig for their support in developing this paper.