Factors Mediating Gender and Firm Performance in Lao Micro, Small, And Medium Sized Enterprises

Asia Pacific Management Review 17(2) (2012) 145-175

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Factors Mediating Gender and Firm Performance in Lao Micro, Small, and

Medium Sized Enterprises

Sengaloun Inmyxai a*, Yoshi Takahashia

a Graduate School for International Development and Cooperation, Hiroshima University, Japan

Received 13 August 2010; Received in revised form 18 November 2010; Accepted 10 April 2011

Abstract

This study investigated firm resources, networks, and operation factors that mediate the relationship between the gender of entrepreneurs and firm performance in Lao micro, small, and medium sized enterprises (MSMEs). The sample consisted of 1,534 companies, made up of 896 male-headed firms and 638 female-headed firms, with 1 to 99 employees. By the use of ordered probit, binary logistic, and multiple linear regression models, the study examined whether male-headed firms outperformed those led by females through consideration of firm resources, networks, and operation factors. The findings showed that some firm resources and networks mediate the relationship between gender and firm performance and that male-headed firms outperformed female-headed ones. However, operation factors did not show any impact on the performance of male- and female-headed firms and there was no evidence of superior performance. This paper suggests policy implications for both policy implementers and policymakers that firm resources (human and tangible resources) and networks (network participation and Information Communication Technology adoption) should be emphasized because of their contribution to firm success. The paper also recommends the reduction and/or elimination of the gap between firms operated by male and female entrepreneurs.

Keywords: Gender, mediate, male-headed firms, female-headed firms, firm performance

1. Introduction*

If there are no satisfactory job opportunities available in both government and non-government organizations (NGO), many people in Lao People’s Democratic Republic (PDR) seek to create their own businesses and be their own bosses. This leads to an increase in the number of self-employed entrepreneurs in micro, small and medium sized enterprises (MSMEs). Female entrepreneurs particularly play significant roles in these MSMEs. The Lao Department of Statistics (2009) reported that in Lao PDR 29 percent of all businesses with more than five employees are owned or headed by females. For MSMEs, about 64 percent of Lao businesses are owned and/or headed by females (MIH and GTZ, 1996). However, many Lao female entrepreneurs have limited education, work and business experience, and access to resources (ILO, 2008).

This paper applied three theories with feminist theory (liberal feminist theory and social feminist theory) as the base and the resource-based view (RBV) and the network theories as sub-theories. It hypothesized in a way that was consistent with social and liberal feminist

* Corresponding author. E-mail: [email protected]

www.apmr.management.ncku.edu.tw

S. Inmyxai, Y. Takahashi / Asia Pacific Management Review 17(2) (2012) 145-175

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theories as significant impacts of controlling resource endowments were expected to be influencing factors in the case of Lao MSMEs. These three theories were applied in a complementary way.

In the management field, RBV theory is a well-known concept in the investigation of the relationship between firm resources and firm success. In this context, if male and female entrepreneurs have equal opportunities to control similar firm resources, then similar firm performances can be expected. Furthermore, according to the network theory the process that links networks to firm performances consists of three components: soft infrastructure, hard infrastructure, and knowledge and information flow through networks. Applying these to male and female entrepreneurs, if they have similar networks, they may perform similarly. Thirdly, the suggestions of liberal feminist theory and social feminist theory may be observed when male and female entrepreneurs adopt similar operation approaches, leading to the performances of male and female entrepreneurs being expected to be similar.

Research has been conducted in the area but it is insufficient to check the effects of the relationship between the gender of entrepreneurs and firm performance. Differences in firm performances based on gender have been indirectly observed through, for instance, controlling/seeking key resources, having better networks, and adopting different operation approaches. RBV is a very popular theory to explain the condition of firm resources particularly in least developed countries as resources are not considered to be equally distributed among male- and female-headed firms. Business networks should be emphasized because the flow of information and knowledge is not evenly available to males and females. Lastly, the other important factor to be considered is the differences in operation between male- and female-headed firms. Moreover, operation factors are expected to have an impact on firm performances. Therefore, it is interesting to empirically test the differences in firm performances according to the gender of the entrepreneurs in terms of firm resources, networks, and operations.

The objective of this paper is to investigate firm resources, networks, and operation factors that mediate the relationship between the gender of entrepreneurs (or top managers) and firm performances in Lao MSMEs. The paper is divided into seven sections. Section one is the introduction. Section two briefly presents the conceptual framework. Section three covers the literature review and the development of the three hypotheses. Section four describes the research methodology. Section five presents the data analysis and discussion. Section six provides the conclusion and policy implications from the findings. Section seven acknowledges the limitations as well as makes suggestions for further research.

2. Conceptual framework

The conceptual framework provides a snapshot of the objective of this paper. The resource model is based on the concept of RBV and Grant (2002), the network model is derived from the network theory, and the operation model is drawn from liberal feminist theory and social feminist theory. As illustrated in Figure 1, this paper seeks to examine the mediating effects of


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firm resources, networks and operation on the relationship between the gender of entrepreneurs and firm performance.

Figure 1. Firm resources, networks, and operation as factors mediating the relationship between gender and firm performance

Firm resources, networks and operation factors mediate the relationship between gender and firm performance. This means that the gender of the entrepreneurs indirectly affects firm performance through firm resources, networks and operation factors or those differences in firm performance of male and female entrepreneurs can be observed through the firms having different levels of: firm resources, networks and operation factors. It is assumed that male and female entrepreneurs with similar resource endowments would perform similarly. This is in line with the social and liberal feminist theories outlined in the next Section.

3. Literature review and development of hypotheses

This paper draws on both liberal feminist theory and social feminist theory, which are expounded by Black (1989), Jagger (1983) and Fischer et al. (1993) to provide an insight into factors (firm resources, networks, and operation factors) that affect the performances of firms involving male and female entrepreneurs, an area not addressed by existing literature.

Liberal feminism (LF) is based on the assumption that females and males are equally capable of rational, human behavior (Fischer et al., 1993). The theoretical explanation for observed differences in the achievements of males and females is that females have less frequently realized their full capabilities only because they have been deprived of essential opportunities such as education. Observed psychological differences are assumed not to be innate, but rather rooted in the ways that females’ socialization discourages them from developing their full capacities. Physical differences between males and females are not relevant, as rationality is seen as having no physical basis, and females and males are assumed to be equal in their rational capacity. Other empirical studies that investigated the psychological characteristics of entrepreneurs through the exploration of cognitive and personality aspects were completed by Catley and Hamilton (1998) and Sexton and Bowman-Upton (1990). These found that there are no significant differences between male and female entrepreneurs regarding psychological characteristics.

Firm Resources

Networks

Operation

Firm Performance

Gender


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LF believes that if females gain access to equal opportunities, females and males will actualize their potential rationality more equally resulting in the diminution and disappearance of observed psychological gender differences (Fischer et al., 1993). An implicit assumption of LF is that females will become more like males because of the removal of the basis for existing differences that is the result of females’ relative deprivation. A prerequisite for this is the identification and eradication of legal and traditional forms of discrimination. These include the tendencies for females to be encouraged to take less “practical” types of education and to enter jobs that require fewer technical skills, actions that reduce their opportunities to acquire experience to establish and run larger, more profitable firms.

Social feminism (SF) suggests that there are differences between males’ and females’ experiences from the earliest moments of life that result in fundamentally different ways of seeing the world. Female experiences are an equally valid basis for developing knowledge and organizing society (Calas and Smircich, 1989). In contrast to liberal feminist thought, males and females are not considered essentially the same; among males and among females, shared experiences are assumed to help define a group-based rationality or mode of knowing (Fischer et al., 1993).

SF has somewhat more diverse theoretical origins, ranging from social learning theory to psychoanalysis (Fischer et al., 1993). Differences between males’ and females’ experiences stem from the environment and culture surrounding males and females, which can influence their decision making, strategic choices and business approaches. SF asserts that female entrepreneurs differ from male entrepreneurs in terms of values and ways of thinking, due to variations in early and ongoing socialization processes (Black, 1989). Males tend to have high levels of self-assertion, self-expansion and the urge to be a master, while females are expected to possess higher communal qualities such as selflessness, a concern for others and interpersonal sensitivity (Eagly and Wood, 1991).

The foundations of this paper are based on consensus of LF and SF (Black, 1989; Jagger 1983; Fischer et al., 1993), as applied to MSME practices. In this regard, LF is concerned with different levels of control over resource endowments, while SF is not only involved with different levels of resource endowments but is also concerned with different motivation in terms of using these endowments in order to achieve better performance. In particular, the two theories provide a significant foundation for a comparative study on firm performance and its antecedents. In this regard, “LF” is based on the assumption that if male and female entrepreneurs have the same levels of firm endowments, they expect to achieve similar firm performance, whereas “SF” makes the argument that even if male and female entrepreneurs control similar or same levels of endowments, they will not achieve similar firm performance. This is based on the notion that decisions and strategies, in terms of implementation of these endowments, are influenced by culture, by socialization and the environment within which these entrepreneurs have grown up, and so these differences between male-headed firms and female-headed firms may be reflected in differences in firm performance.

3.1 Firm resources as a factor mediating the relationship between gender and firm performance

Firm resources include anything that a firm possesses, such as assets, liabilities, capital, education, and experience. The gender of entrepreneurs is related to firm resources. Different levels of firm resources in male-headed firms (MHFs) and female-headed firms (FHFs) can result in differences in firm performance as suggested by RBV.

In business practice, male entrepreneurs tend to have more resources compared to female entrepreneurs because of the stereotyping of female entrepreneurs as conservative and risk-averse compared to males who are seen as more likely to take risks (Meier and Masters, 1988).


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Females tend to have fewer tools, assets, and chances compared to males in small business (Teoh and Chong, 2008). This implies that FHFs may have fewer resources, such as physical technology and business finance, and MHFs tend to be in a better position in terms of firm resources.

This may be the case in regard to access to business finance. Several studies have shown that female entrepreneurs are discriminated against by banks through higher interest rates and requirements for high levels of collateral and co-signers on loans and lines of credit (Stevenson, 1986). A gender bias in Canadian banking practices was found in terms of interest rates on lines of credits and loans, requirements for loan collateral, rates of loan approvals, and co-signature requirements from spouses (Riding and Swift, 1990). This may be explained by the fact that loan officers may not be familiar with applications from female entrepreneurs and therefore seek security for their lending decisions over and above tangible securities of equity and fixed assets (Hoffman, 1972).

Differences in other firm resources, such as levels of education and experience of male and female entrepreneurs, can lead to differences in firm performance. Education levels, expected to increase the possibility of entrepreneurial activity, productivity and relative success, of micro entrepreneurs in Jamaican firms were investigated to evaluate the validity of the human capital theory (Honig, 1998). Robinson and Sexton (1994) also observed that the general education and experience of entrepreneurs have a strong positive impact on firm success.

It appears that there is connection between business finance and entrepreneurs’ education and work experience in terms of access to formal finance. Fay and Williams (1993) observed that females face gender discrimination when seeking start-up capital but suggested that such a behavior by loan officers may not be intentional. This is partly because applications from borrowers with low personal equity and limited education and experience in the nominated fields are often made by female entrepreneurs (Bowen and Hisrich, 1986; Hisrich and Brush, 1986; Humphreys and McClung, 1981). This can lead to rejection of loans. Education levels of female entrepreneurs seem to be an additional intangible requirement in the form of loan security as one of the criteria to obtain loans (Jankowicz and Hisrich, 1978). Insufficient access to business finance can be seen as barriers for FHFs and can be considered as contributing to their underperformance in comparison to MHFs.

As a result of the findings of the literature review, this paper proposes hypothesis 1.

Hypothesis 1: Firm resources mediate the relationship between the gender of entrepreneurs and firm performance.

3.2 Networks as a factor mediating the relationship between gender and firm performance

The level of participation in networks by male- and female-headed firms is important because different conditions produce different performances. Networks can be useful links for entrepreneurs in MSMEs to boost the sales and supplies through personal contact, leading to improved performance. Both MHFs and FHFs can improve their performance through networks with important external parties such as suppliers, customers, and financial institutions.

Differences exist in networks based on gender, one study showing that females differ in terms of priorities in establishing networks (Teoh and Chong, 2008). These authors stated that males’ motivations regarding networks are normally in seeking personal gain whereas females emphasize affective considerations in social relationships. Also, differences in management styles according to gender may be part of the reason why females are often excluded from male networks. Therefore, different levels of network participation can contribute differences in performances of male- and female-headed firms.

Differences in perception or interests in terms of network participation between MHFs and


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FHFs can be observed that can result in varying potential to enjoy the benefits. For example, in Malaysia, female entrepreneurs and industrial associations have been established to serve as a platform for female entrepreneurs to create a network for exchanging information and experiences as well as providing different means to support, such as training programs, seminars, and workshops on motivation, leadership, and entrepreneurial development (Teoh and Chong, 2007). However, female entrepreneurs still lack formal networks because they are not interested in participation. The lack of networks among female entrepreneurs may impede the acquisition of informal advice and group financing that are important for the survival and growth of firms. The challenge faced by female entrepreneurs is to participate in networks so that they can enjoy the benefits and improve their firm performance.

Different levels of participation in informal networks based on gender can be observed. As a result different performances of MHFs and FHFs can be expected. Male entrepreneurs seem to establish better networks that can contribute to better performance compared to female entrepreneurs. Furthermore, male entrepreneurs tend to have stronger network ties that have traditionally been viewed as a way of obtaining power, a factor critical to a manager’s success (Bacharach and Laurer, 1988; Kanter, 1988). However, females are often excluded from social or informal networks such as male-only clubs, ‘old boys’ networks,’ and business lunches (Brush, 1990; Smeltzer and Fann, 1989), by lack of time (Belcourt et al., 1991), and due to their domestic responsibilities at home. Such networks can link firms with key external partners and help them to achieve superior performance in business practices.

Firm performance may also be affected by another gender-influenced factor, the adoption of Information Communication Technology (ICT). ICT has a positive effect on firm performance not only in terms of productivity, profitability, market value, and market share, but also on intermediate performance indicators such as process efficiency, service quality, cost saving, organizational and process flexibility, and customer satisfaction (Bartelsman and Doms 2000; Brynjolfsson and Yang 1996; Dedrick et al., 2003; Kohli and Devaraj 2003; Melville et al., 2004). The adoption of ICT is crucial in firm performance and the conservatism of female entrepreneurs, compared to males, to adopt it may lead to an underperformance of FHFs.

The receiving business development services (BDS) leads to a general improvement in firm performance through the acquisition of knowledge and information, especially desirable for firms seeking finance for expansion. One of the studies is that receiving advice from accountants including useful advice associated with strategic decision has been positively associated with performance (O’ Neill and Duker, 1986). Moreover, development services from outside firms were found to be related to revenue performance for Israeli female entrepreneurs (Lerner et al., 1997). FHFs may not be active in the acquisition of knowledge and information from BDS and, as a result, different performances by MHFs and FHFs can be expected.

These factors regarding the differences for male and female entrepreneurs in the existence and use of formal and informal networks, ICT adoption, and the use of BDS are recognized as having an effect on the relationship between gender and firm performance that is manifested by more positive returns for MHFs and FHFs. As a result, this paper proposes the following hypothesis:

Hypothesis 2: Networks mediate the relationship between the gender of entrepreneurs and firm performance


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3.3 Operation as a factor mediating the relationship between gender and firm performance

A number of reasons can be observed why females and males perform differently in

businesses. Different levels of operation by male- and female-headed firms may be one of the

reasons and it can be treated as a factor mediating the relationship between the gender of

entrepreneurs and firm performance as firms achieve better performance through the

implementation of better approaches.

The gender of entrepreneurs influences operation that can contribute to different

performance. Operation includes premises for business, operation months, and presence of

competitiveness. The first of these involves the matter of selection of places to carry out the

business, that is, whether the business is home-based or outside home-based (Collins-Dodd et

al., 2004; Fasci and Valdez, 1998; Kalleberg and Leicht, 1991; Loscocco et al., 1991;

Lustgarten, 1995). Operation months indicate the number of hours spent by the entrepreneurs in

their business activities (Fasci and Valdez, 1998; Lustgarten, 1995). The presence of

competition is the perception of threats and opportunities by entrepreneurs and males and

females may take different approaches to cope with this. Therefore different performances by

MHFs and FHFs can be expected due to the differences in operation.

The gender of entrepreneurs is associated with operation approaches as MHFs and FHFs

exhibit differences in selection of places for business, allocation of time for business, and risk-

taking in regard to the presence of competition, resulting in different outcomes. Therefore, the

paper proposes hypothesis 3.

Hypothesis 3: Operation factors mediate the relationship between the gender of entrepreneurs and firm performance.

3.4 Firm performance

Financial data is the preferable indicator of firm performance but firms are often unwilling

to disclose confidential financial data unless the laws require them to disclose it to the public.

Public disclosure, however, is more likely required for listed companies than for MSMEs.

Hence, this paper uses a subjective measure of financial performance based on annual sales

turnover. The subjective performance measures have been widely used in strategy-related

research and organizational research (Dess, 1987; Dess and Robinson, 1984; Lawrence and

Lorsch, 1967; Powell, 1992; Powell and Dent-Micallef, 1997; Robinson and Pearce, 1988;

Spanos and Lioukas, 2001; Venkatraman and Ramanujam, 1986) although financial data

remain popular. However, financial data is criticized for being unreliable and subject to


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inconsistent accounting practices by firms or even to managerial manipulation for different

reasons such as avoiding payment of high corporate income taxes or personal income taxes

(Dess and Robinson, 1984; Powell and Dent-Micallef, 1997; Sapienza et al., 1988). The annual

sales turnover is based on a questionnaire, a method used previously (Anna et al., 1999; Du

Rietz and Henrekson, 2000; Inmyxai and Takahashi, 2009a, 2009b, 2010a, 2010b, 2010c; Rosa

et al., 1996).

It should be noted that even though studies of the similarities and differences in the

performance of male- and female-headed firms increase, it is not easy to compare results across

studies because performance in entrepreneurial businesses was measured differently depending

on data availability on a case by case basis.

3.5 Control variables

Control variables are used to justify factors other than theoretical variables that could

explain the variance in dependent variables. In this paper, firm size, firm age, and industry

sectors are used as control variables.

Firm size: Firm size can reflect the past success and may influence the current firm

performance (Aldrich, 2000; Aldrich and Auster, 1986; Ravichandran and Lertwongsatien,

2005). Firm size can also be an important determinant of firm performance and survival

(Mukhtar, 2002). Bigger firms can provide economies of scale compared to smaller firms (Dass,

2000) and are able to produce larger quantities of output by spreading their fixed costs. Bigger

firms also benefit from the improved capacity to access critical resources such as business

finance (Penrose, 1995), particularly low cost capital (Goerzen, 2007). Hence, big firms can

gain a competitive advantage and a better performance. Ghemawat (1986) suggested that larger

size firms gain advantages of accessing resources or customers, and/or restricting rivals’

options. However, Chandy and Tellis (2000) and Kanter (1988) argued that bigger firms are

less adaptive and flexible and less able to change their resource base. As this paper uses the

firm’s sales turnover as a performance indicator, it needs to control the firm size to avoid bias

in the model.

Firm age: Firm age can be seen as an indication of external legitimacy of the existence of

inter-firm relationships, staying power, and pervasiveness of internal routines (Fichman and

Kemerer, 1993; Kalyanaram and Wittink, 1994). All these may impact on current firm

performance. Young firms may face the responsibilities of newness that can confound their

performance (Aldrich and Auster, 1986; Hannan and Freeman, 1984; Ravichandran and


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Lertwongsatien, 2005). Young firms seem to have lower sales and therefore lower profits

(Watson, 2002), whereas older firms seem to be larger in terms of sales turnover, number of

employees, and capital assets (Rosa et al., 1996). Furthermore, the older firms tend to establish

good networks and reputation in the selected markets as well as have an established

relationship with business partners, suppliers, financial institutions, communities, government,

and customers. Therefore, firm age can represent the power and experience of the firm in the

chosen industry, an influential factor for firm success.

Industry sectors: It is also important to control industry sectors to remove the bias in the

findings due to the variation in industry sectors (Boden and Nucci, 2000; Carter et al., 1997;

Chell and Baines, 1998; Du Riet and Henrekson, 2000; Fischer at al., 1993; Mukhtar, 2002;

Robinson and Sexton, 1994; Singh et al., 2001). FHFs are dominant in retail sales and the

personal and educational service industry, a so-called ‘female ghetto’ (Kalleberg and Leicht,

1991). Firms in the services and trade industries normally expect low growth rates and less

success quantified by earnings or returns on investment compared with firms in other industries

because these industries are labor intensive and highly competitive in terms of their product

markets (Humphreys and McClung, 1981). The different industrial sectors can influence

organizational context, for example, implication for firm cultures, managerial style, and control

systems (Lapierre and Denier, 2005). This paper groups industry into three sectors,

manufacturing, trading, and service. Since the nature of each sector is different, influencing

factors can be diversified across industry sectors. The behavior, strategy choices, and business

approaches can be observed differently among firms and consequently can have different

impacts on performance.

4. Research methodology

4.1 Sample and data collection

This paper used unbalanced panel data that was collected in 2005, 2007 and 2009 by the

Enterprises Baseline Survey (EBS) from the German Agency for Technical Cooperation (GTZ).

The GTZ conducts the EBS every two years. Only enterprises that were formally registered

were selected. The survey in 2005 included 370 companies in four Lao provinces, Vientiane

capital, Champasack, Luang Prabang, and Luang Namtha. The first three provinces belonged to

the economically dynamic provinces and the fourth was a rural province. For the 2007 survey,

the sample size was 470 Lao MSMEs from five Lao provinces, Vientiane capital, Champasack,

Luang Prabang, Luang Namtha, and Savanakhet. For the 2009 survey, the sample size was 694


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Lao MSMEs from the same five Lao provinces. The sample involved 1,534 companies, 896

were male-headed firms and 638 were female-headed firms, with 1 to 99 employees.

4.2 Measurement

Table 1 shows the measurements and descriptions of variables directly from the

questionnaires developed from existing literature, as cited for most variables

Table 1. Measurements of variables

Variables Measurements/descriptions

Control variables Firm size, firm age and industry sectors

Firm Size This is measured by the total number of current full-time employees. According to Prime Ministerial Decree No.42 (2004), the Lao PDR defines a micro firm as consisting of 1 to 2 employees, a small firm as 3 to 19 employees, a medium firm as 20 to 99 employees, and a large firm has 100 employees or more.

Firm age This is the number of years the MSMEs have been established/incorporated (Ravichandran and Lertwongsatien, 2005), which is taken to represent industry experience for the firm

Industry sectors These were recorded as three industry dummy variables by controlling manufacturing, trading and service (Robinson and Sexton, 1994).

Dependent Variable Performance Performance This is measured by ordinal numbers from 1 to 5 corresponding to

the level of annual sales turnover (as reported to the national tax office). From the lowest to the highest level these are: less than 200 Million Kip; 200-400 Million Kip; 401-700 Million Kip; 701-1,000 Million Kip; and more than 1,000 Million Kip (in late 2010, 1 US dollar equals approximately 8,041 Lao Kip).

Independent Variables Gender Male entrepreneur is represented by 1 while female entrepreneur

is 0. Firm Resources Firm resources are based on the concept of RBV and Grant (2002)

that classified firm resources into three categories, human, intangible, and tangible resources. Each category has a sub-category or variable in the following.

Human Resource Variables

Education of entrepreneurs, training of entrepreneurs, training for employees and working experience of entrepreneurs

Education of entrepreneurs

This is measured by ordinal numbers from 1 to 11, corresponding to the level of education of owners/managers. From the lowest to the highest level these are: no schooling, some primary school, completed primary school, some lower secondary school, completed lower secondary school, some upper secondary school, completed upper secondary school, vocational, technical, higher


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(undergraduate) and post-graduate. Training of entrepreneurs

This is whether or not any training was received since the business started. If the respondent chose ‘yes’, then the next question asked to the respondent to describe the kind of management training, including: health and safety, cost calculations, business management, accounting, marketing, law and regulations, quality management, business finance and others. This variable is measured as a dummy variable.

Training of employees This question is whether or not the employees received any training. If the respondent chose ‘yes’, then the next question asked was to describe the kind of management training they have had, whether it was on: customer services, accounting, record booking, operation of machinery and tools, computer, documentation and filing and others. Thus, this variable is measured as a dummy variable.

Work experience This is measured by the age of owners/managers, after subtracting the total years spent in education (Robinson and Sexton, 1994). Experience of entrepreneurs has a close relationship with their education and thus work experience is defined as the number of years an individual has been able to work after completing his or her education (Ibid). Because of a limitation of the data set, a more comprehensive measure of experience cannot be specified.

Intangible resource variable

Reputation is used marketing and advertising, as proxies, to build reputation (Inmyxai and Takahashi, 2009a; Inmyxai and Takahashi, 2010d).

Reputation The question is whether the firm had some investment in marketing and advertising for the last year or not. In this research, this variable is measured as a dummy variable.

Tangible resource variables

Physical technology and business finance

Physical technology This is measured by ordinal numbers from 1 to 5 corresponding to the level of technology in the business from the lowest through the highest level: hand tools/utensils; portable power tools and electric appliances; small fixed motorized equipment; large machinery; and motorized vehicles.

Business Finance The question is whether the firm received loans or not. Consequently, this variable is measured as a dummy variable.

Network Variables Network variables include three main indicators: network participation (as soft infrastructure); ICT (as hard infrastructure) and business development services (as information and knowledge flow through the networks).

Network participation The question asked whether the firm is a member of any specified organization or not. These organizations are: the Lao National Chamber of Commerce and Industry (LNCCI), business associations, Lao Young Entrepreneurs Associations, Associations of women entrepreneurs, the Vientiane and Business Women Association, business associations, business groups,


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business clubs, women’s business groups, provincial chamber of commerce and industry. This way of measuring network membership is consistent with Teoh and Chong (2007, 2008). Thus, being a member in any of the mentioned organizations is a proxy for networks. This variable is measured as a dummy variable.

Information communication technology (ICT)

The question is whether the firm uses some type of equipment for communication. If the respondent chose ‘yes’, the next question asked what types of equipment for communication did respondents have including, for example: telephone, fax, telephone/fax, internet, and others. In this research, ICT measurement is consistent with Erffmeyer and Johnson (2001), using aggregated types of communication.

Business development services (BDS)

This question is whether or not the owners/managers of a firm received any advice for the development of his/her business. This variable is measured as a dummy variable.

Operation Factor Variables

Operation factors include three main indicators: premises for businesses, operation months and presence of competitiveness (used as a proxy for a degree of taking a risk for operation).

Premises for businesses

This question is whether the place of business is home-based or in outside premises. If the business uses places outside the home as an office, it is given 1. If the business uses the home as the office, it is given 0. This variable is measured as a dummy variable.

Operation months This question indicates the amount of time that the entrepreneurs have put into the business (part-time/full-time).

Presence of competitiveness

This question is whether or not the owner/managers have any problems with competitiveness. This variable is measured as a dummy variable. Presence of competitiveness is used as a proxy for a degree of taking a risk for operation.

5. Analysis and Discussion

5.1 Ordered probit, binary logistic and multiple linear regression models

The models in this paper were ordered probit, binary logistic and multiple linear regression

depending on the dependent variables of each model. If the dependent variables were

continuous, the multiple linear regression model was used, if the dependent variables were

discrete/category (yes or no), the binary logistic regression model was used, and if the

dependent variables were both category and measured by using ordinal measures from 1 to 5

(Davidson and MacKinnon, 1993; Godfrey, 1988; Long, 1997), the ordered probit regression

model was adopted.


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5.2 Results of analysis and discussion

Differences both in characteristics of male and female entrepreneurs and in business

characteristics are displayed in Table 2. Male entrepreneurs were 60.18 percent of the sample

and female entrepreneurs were 39.82 percent.

Education levels of male and female entrepreneurs revealed significant differences. Male

entrepreneurs tended to have a higher education level relative to female entrepreneurs. For

example, 9.04 percent of male entrepreneurs obtained post-graduate degrees whereas about

2.45 percent of female entrepreneurs fell into this group. The number of male entrepreneurs

who completed undergraduate degrees was also higher than female counterparts, while female

entrepreneurs whose highest education level was completion of primary school up to vocational

school was a relatively higher proportion than male entrepreneurs.

MHFs seem to be older than FHFs in terms of business practices. The majority (72.68

percent) of FHFs were between 1 to 10 years, while 65.70 percent of MHFs fell in the same

group. Twenty-nine point two percent of MHFs had stayed in business between 11 to 20 years

compared to 24.17 percent of the firms owned by females. In general, differences in age of

businesses existed in male- and female-headed firms but in both groups, the businesses tended

to be young.

The differences in firm size can have some impact on gender-based performance. The

majority of firms in both gender categories were small-size (64.45 percent of the FHFs

compared to 60.14 percent of the MHFs). In micro-size firms, FHFs showed a slightly higher

proportion than MHFs. However, in medium-size MHFs were represented by a percentage that

was twice as high as that of FHFs.

For the industry sectors, the number of FHFs was more in trade (48.18 percent), then

service (37.13 percent), and manufacturing (14.36 percent). In contrast, MHFs were more in

service (49.83 percent) followed by trade (29.43 percent) and manufacturing (20.74 percent).

Table 3 displays the means, standard deviations and Pearson correlation matrix of the

research variables. This table does not show high correlations but multicollinearity was double-

checked by using variance inflation factor (VIF). VIF showed no serious multicollinearity

problems because the VIF of all research variables was between 1.049 to 1.531, far below the

VIF of 10 that Kennedy suggested as a warning of ‘harmful collinearity’ (Kennedy, 1992, p.

183). This suggested that there was no problematic multicollinearity that existed in the models,

meaning that the VIF statistics for each explanatory variable were at only above 1.0 (Neter et

al., 1985). This showed that no variable caused a harmful influence on the results because of

multicolinearity (Sharfman and Fernando, 2008).


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Table 2. Respondents’ demographic information for MHFs and FHFs

MHFs FHFs

Frequency Percent Frequency Percent

Gender

Male 863 60.18

Female 571 39.82

Education

No schooling 13 1.51 18 3.15

Some primary school 24 2.78 35 6.13

Completed primary school 97 11.24 102 17.86

Lower secondary school 122 14.14 102 17.86

Upper secondary school 193 22.36 133 23.29

Vocational 71 8.23 59 10.33

Technical 36 4.17 12 2.10

Higher (undergraduate) 229 26.54 96 16.81

Post-graduate 78 9.04 14 2.45

Firm Age

0-10 years old 567 65.70 415 72.68

11-20 years old 252 29.20 138 24.17

21-30 years old 35 4.06 13 2.28

31-40 years old 6 0.70 3 0.53

41-60 years old 3 0.35 2 0.35

Firm Size (No. Employees)

Micro: 1-2 163 18.89 145 25.39

Small: 3-19 519 60.14 368 64.45

Medium: 20-99 181 20.97 58 10.16

Industry Sectors

Manufacturing 179 20.74 82 14.36

Trade 254 29.43 277 48.51

Service 430 49.83 212 37.13

Table 3. Means, standard deviations and correlation matrix

Mean S.D 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

PER 1.94 1.46 1 GD 0.60 0.49 .19** 1 EDU 5.45 2.13 .30** .21** 1 TRENT 0.42 0.47 .26** .10** .21** 1 TREMP 0.67 0.47 .24** 0.02 .29** .25** 1 WEXP 33.10 11.86 .07** .11** .36** 0.03 -0.04 1

REP 0.05 0.22 0.03 0.03 .11** 0.02 0.05 -.06* 1

TEC 2.46 1.16 .17** .13** .06* -0.04 .11** .07* 0.00 1 BF 0.40 0.49 .20** .12** .07* .13** .08** -0.01 0.05 -0.01 1 NW 0.37 0.48 .20** .15** .16** .26** .18** .06* 0.00 .14** .08** 1

ICT 2.01 0.77 .25** .13** .28** .10** .19** -0.01 .14** .13** 0.02 .20** 1

BDS 0.82 0.38 0.00 -0.04 .11** .12** .07** 0.01 .09** .10** .11** .12** -.05* 1 PB 0.50 0.50 .16** 0.02 .10** .08** 0.05 .09** 0.01 .10** 0.03 .09** .09** -0.03 1

OPM 11.70 1.30 .07** .10** .08** -0.05 -0.05 0.04 .07** -.06* .09** .07** 0.00 0.00 .05* 1 PCT 0.64 0.48 0.02 .07* .08** -0.02 .08** 0.03 0.01 .18** -0.03 .09** .11** .08** 0.02 -0.02 1

FS 11.64 15.37 .52** .16** .30** .22** .24** 0.03 .15** .17** .15** .17** .24** 0.01 .18** .08** 0.04 1 FA 8.70 6.69 .12** .07** 0.01 -0.02 -.05* .35** -0.01 .08** .10** -0.02 .06* -0.01 .09** -0.01 0.02 .15** 1 Manu 0.18 0.39 0.04 .08** 0.02 0.03 -.06* .06* .23** 0.00 0.00 -0.03 .10** 0.00 -0.05 .08** 0.02 .16** 0.05 1 Trading 0.37 0.48 -0.02 .19** .14** .18** .12** -.06* -.07* -.06* -0.03 .20** .15** -0.01 -0.03 .14** 0.00 .18** .07** .36** 1 Service 0.45 0.50 -0.01 .13** .12** .15** .16** 0.01 .11** .06* 0.03 .21** .07** 0.00 .07* .08** -0.02 0.05 .11** .43** -.69** Variance inflation factor (VIF) ranges from 1.049 to 1.531 *Correlation is significant at the 0.05 level (2-tailed); **Correlation is significant at the 0.01 level (2-tailed); PER=Performance; GD =Gender; EDU = Education; TRENT = Training of entrepreneurs; TREMP = Training for employees; WEXP = Working experience; REP =Reputation; PTEC=Physical technology; BF=Business finance; NWP = Network participation; ICT = Information communication technology; BDS = Business development services; PB = premises for business; OPM = Operation months; PC=Presence of competitiveness; FS=firm size; FA=firm age; Manu = manufacturing.

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Hypothesis 1(H1): Firm resources mediate the relationship between the gender of entrepreneurs and firm performance.

Four conditions must be met to support H1 (Baron and Kenny, 1986; Newbert, 2008; Tuan and Takahashi 2010). These are: (a) MHFs (gender) must be positively related to firm resources. (b) Firm resources must be positively related to firm performance. (c) MHFs (gender) m ust be positiv ely related to f irm performance by excluding firm

resources. (d) The effects of MHFs (gender) on firm performance must be reduced or elim inated by

including firm resources. The results of the testing of H1 are displayed in Tables 4 and 5 and summarized in Table 6.

The findings showed that the majority of firm resources mediate/affect the relationship between gender and firm performance, indicating that MHFs outperform FHFs in Lao MSMEs. This was partly through firm resources (hum an resources and tangible resource s but not intangible resources) as they m et the four conditions. For example, human resources (HR), such as education, training, and experience of the entr epreneur, affected the relationship between gender and firm performance but training of employees did not. T hese findings can be explained by the fact that the gender of the entrepreneurs indirectly affected firm performance through HR. For exam ple, gender dif ferences controlled HR with dif ferent capabilities and competencies through entrepreneurs receiv ing high education, having suf ficient training, and accumulating longer working experience. This, in turn, had an im pact on the perform ance of MHFs and FHFs. For tangible resources, physical technology and business finance af fected the relationship between gender and firm performance, indicating that having high technology helped firms to be more efficient and/or effective. The acquisition of business finance allowed firms to engage in strategic business, for example, the introduction of new products and services, thus m aintaining good perform ance. This implies that MHFs perform better than FHFs through the acquisition of m ore credit to finance strategic business activities and finance physical technology (Sm eltzer and Fann, 1989) as males are risk-takers while fem ale counterparts are risk-averse (Meier and Masters, 1988). Females also experience discrimination in their dealings with banks. However, the gender variable is not related to intangible resources (reputation) and thus fails to m eet the fi rst condition. R eputation through investm ents in marketing and advertising did not af fect the relationship between gender and firm performance because intangible reso urces met only the third a nd fourth condition s. It is surp rising that reputation through m arketing and advertising is negative statis tically significant to the performance for both MHFs and FHFs, failing to meet the second condition. A reason for this may be that their m arketing and advertising ai med at the wrong consumer or customer targets and therefore worsened their firm performance. Another reason could be that particular MSMEs in developing countries may not be accustom ed to modern ways of introducing and communicating their goods and services to customers through m arketing and advertising orientation. Investment in m odern marketing approach is expect ed to have a high potential contribution to firm performance compared to the traditional marketing approach that operates by word of m outh. In this cas e these en trepreneurs failed to u tilize modern marketing approaches to boost their sales.

Some of the findings related to hum an and tang ible resources are con sistent with liberal feminist theory and social fem inist theory be cause male entrepreneurs m ay control dif ferent levels of these resources and therefore outperform females by the use of them. Specifically, five of the seven firm resource indicators had mediating effects on gender and firm performance.

Thus, H1 is partly supported.

Table 4. Effects of firm resources (Condition 1)

Firm Resources

EDU TRENT TREMP WEXP REP PTEC BF

Coef. Coef. Coef. Coef. Coef. Coef. Coef. Coef. Coef. Coef. Coef. Coef. Coef. Coef.

(Constant) 4.80*** 4.52*** -0.42*** -0.59*** -0.27 -0.17 26.46*** 25.73*** -1.92*** -1.98*** -0.97*** -1.21***

Firm size 0.04*** 0.04*** 0.02*** 0.02*** 0.07*** 0.07*** -0.030 -0.04 0.02*** 0.02*** 0.011*** 0.01*** 0.02*** 0.02***

Firm age -0.01 -0.01 -0.02 -0.02** -0.03*** -0.03*** 0.64*** 0.64*** -0.02 -0.03 0.010** 0.01** 0.03*** 0.02***

Manufacturing 0.10 -0.02 - - - - 2.61*** 2.29*** - - - - -

Trading - - -0.42*** -0.38** 0.40** 0.37** - - -1.49*** -1.48*** -0.020 0.02 0.06 0.13

Service 0.49*** 0.37*** 0.87*** 0.87*** 0.92*** 0.93*** 2.06*** 1.73* -1.99*** -1.99*** 0.096 0.10 0.18 0.17

Gender 0.69*** 0.29** -0.18 1.82*** 0.10 0.22*** 0.41***

Pseudo R2 0.09 0.09 0.09 0.09 0.13 0.13 0.13 0.02 0.02 0.03

LR Statistics 168.09*** 173.97*** 164.94*** 167.02*** 72.89*** 73.03*** 49.62*** 62.71*** 41.39*** 53.98***

Log likelihood -909.19 -906.25 -830.29 -829.25 -252.04 -251.97 -1850.69 -1844.15 -941.67 -935.37

R2 0.10 0.13 0.13 0.14

Adjusted R2 0.10 0.12 0.13 0.13

F-Statistics 41.56*** 41.9*** 54.44*** 45.55***

N 1434 1434 1434 1434 1434 1434 1434 1434 1434 1434 1434 1434 1434 1434

*** Significant at 1% ≤; **5% ≤; EDU=Education; TRENT = Training of entrepreneurs; TREMP = Training for employees; WEXP = Work experience;

REP = Reputation; PTEC = Physical technology; BF = Business finance.

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Table 5. Effects of firm resources and firm performance

Firm Performance

Model 1 (Condition 2)Model 2

(Condition 3)Model 3

(Condition 4) Model 4

Firm size 0.045*** 0.036*** 0.044*** 0.035***

Firm age 0.006 -0.005 0.004 -0.005

Manufacturing - -0.237** - -0.263

Trading 0.207** - 0.263 -

Service 0.043 -0.395*** 0.036 -0.419***

Gender - 0.355*** 0.208***

Firms Resources

Human Resources

Education 0.135*** 0.123***

Training of entrepreneurs 0.362*** 0.363***

Training for employees 0.418*** 0.431***

Work experience 0.017*** 0.015***

Intangible Resource

Reputation -0.459*** -0.458***

Tangible Resources

Physical technology 0.120*** 0.115***

Business finance 0.316*** 0.300***

Pseudo R2 0.122 0.178 0.1298 0.1801

LR Statistics 399.23*** 581.07*** 424.13*** 588.61***

Log likelihood -1434.111 -1343.193 -1421.66 -1339.42

N 1434 1434 1434 1434

*** Significant at 1%≤; **5%


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Table 6. Summary of results to support H1 No. Four

conditions must be met:

Firm Resources

Human Resources Intangible Resource

Tangible Resources

EDU TRENT TREMP WEXP REP PTEC BF

1

MHFs (gender) must be positively related to firm resources in Table 4

0.69*** Supported

0.29** Supported

-0.18 Not Supported

1.82*** Supported

0.10 Not Supported

0.22** Supported

0.41*** Supported

2 Firm resources must be positively related to firm performance in Table 5 (Model 2)

0135** Supported

0.362*** Supported

0.418*** Supported

0.017*** Supported

-0.459*** Not Supported

0.12*** Supported

0.316*** Supported

3 MHFs (gender) must be positively related to firm performance by excluding firm resources in Table 5 (Model 3). The gender variable was positively statistically significant (0.355***), indicating that MHFs outperformed FHFs. Hence, it was supported.

4 The effects of MHFs (gender) on firm performance must be reduced or eliminated by including firm resources in the Model 4 in Table 5. [By comparing the size of coefficient of gender variable in condition 3 and gender variable in condition 4, the size of coefficient for gender variable in condition 4 must be either reduced or insignificant]. The finding showed that the size of the coefficient of gender variable in Model 3 reduced from 0.355*** to 0.208*** (see Table 5). Therefore, it was supported.

Conclusion of four conditions:

Supported Supported Not Supported

Supported Not Supported

Supported Supported

Five of seven resource variables met the four conditions and therefore H1 was partly supported

*** Significant at 1% ≤; **5% ≤; EDU = Education; TRENT =Training of entrepreneurs; TREMP =Training for employees; WEXP = Work experience; REP = Reputation; PTEC = Physical technology; BF = Business finance.

Hypothesis 2 (H2): Networks mediate the relationship between the gender of entrepreneurs and firm performance.

Four conditions must be met to support H2 (Baron and Kenny, 1986; Newbert, 2008; Tuan and Takahashi, 2010). These are: (a) MHFs (gender) must be positively related to networks (b) Networks must be positively related to firm performance (c) MHFs (gender) must be positively related to firm performance by excluding networks (d) The effects of MHFs (gender) on firm performance must be reduced or eliminated by

including networks. The results of the testing of H2 are shown in Tables 7 and 8 and summarized in Table 9.


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The findings showed that networks (network participation and ICT) mediate/affect the relationship between gender and firm performance because the factors met the required four conditions. These findings imply that MHFs perform better than FHFs through network participation and ICT adoption. Male entrepreneurs had more advantages in terms of networking with external partners (Smeltzer and Fann, 1989) while female entrepreneurs may not have participated in sufficient networks to enjoy their benefits. In addition, FHFs did not acquire sufficient ICT to support business operations and improve the efficiency of connecting with external business partners, thus explaining their poor performance compared to their male counterparts. For both male- and female-headed firms, participation in networks is a powerful link with external parties such as suppliers, customers, business partners and government, adoption of ICT helps to increase the efficiency of communication. In turn, network participation and ICT adoption had positive impacts on firm performance resulting in better performance by MHFs.

However, gender was negatively related to BDS which failed to meet the first condition. It also failed to achieve the second condition as the acquisition of BDS by both MHFs and FHFs was found to reduce the level of their performance. An explanation for this is that both MHFs and FHFs may have received irrelevant advice for their businesses. Hence, BDS did not affect the relationship between gender and firm performance as it met only the third and fourth conditions, suggesting that MHFs and FHFs cannot improve their performance through BDS.

In general, this finding was in line with liberal feminist theory and social feminist theory because male and female entrepreneurs do not hold similar networks in terms of participation and ICT and consequently perform differently. In particular, it can be concluded that only some networks have an impact on gender and firm performance because two of the three network indicators are supported. Therefore, H2 is partly supported.

Table 7. Effects of networks (condition 1)

NWP ICT BDS Coef. Coef. Coef. Coef. Coef. Coef.

(Constant) -0.999*** -1.282*** 1.639*** 1.576*** 1.540*** 1.694***

Firm size 0.021*** 0.020*** 0.015*** 0.014*** 0.002 0.003

Firm age -0.006 -0.008 0.006 0.005 -0.003 -0.002

Manufacturing - - 0.303*** 0.276*** - -

Trading -0.271 -0.196 - - -0.014 -0.059

Service 0.700*** 0.704*** 0.246*** 0.217*** 0.005 0.007

Gender 0.468*** 0.157*** -0.258**

Pseudo R2 0.056 0.064 0.000 0.003

LR Statistics 104.92*** 119.92*** 0.300 3.38

Log likelihood -889.465 -881.96 -671.075 -669.532R2 0.070 0.075 Adjusted R2 0.068 0.072

F-Statistics 27.04*** 23.19***

N 1434 1434 1434 1434 1434 1434

*** Significant at 1% ≤; **5% ≤; NWP = Network participation; ICT = Information communication technology; BDS = Business development services.


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Table 8. Effects of networks and firm performance

Firm Performance

Model 1 (Condition 2) Model 2



Firm size 0.045*** 0.042*** 0.044*** 0.041***

Firm age 0.006 0.005 0.004 0.003

Manufacturing - -0.286*** - -0.330***

Trading 0.207** - 0.263 -

Service 0.043 -0.284*** 0.036 -0.333***

Gender - 0.355*** 0.306***

Networks

Network participation 0.355*** 0.331***

ICT adoption 0.174*** 0.169***

Business development services -0.055 -0.039

Pseudo R2 0.1222 0.142 0.1298 0.1476

LR Statistics 399.23*** 464.54*** 424.13*** 482.41***

Log likelihood -1434.1105 -1401.455 -1421.66 -1392.52

N 1434 1434 1434 1434

*** Significant at 1% ≤; **5% ≤

Table 9. Summary of results to support H2

No. Four conditions must be met: Networks

NWP ICT BDS

1

MHFs (gender) must be positively related to network in Table 7

0.468*** Supported

0.157*** Supported


2 Network must be positively related to firm performance in Table 8 (Model 2)

0.355*** Supported

0.174*** Supported

-0.055 Not Supported

3 MHFs (gender) must be positively related to firm performance by excluding networks in Table 8 (Model 3). The gender variable was positively statistically significant (0.355***), indicating that MHFs outperformed FHFs. Therefore, it was supported.

4 The effects of MHFs (gender) on firm performance must be reduced or eliminated by including networks in the Model 4 in Table 8. [By comparing the size of coefficient of gender variable in condition 3 and gender variable in condition 4, the size of the coefficient for gender variable in condition 4 must be either reduced or insignificant]. The finding showed that the size of the coefficient of the gender variable in Model 3 reduced from 0.355*** to 0.306*** (see Table 8). Hence, it was supported.

Conclusion of four conditions: Supported Supported Not Supported

Network participation and ICT adoption met four conditions but not BDS and thus H2 was partly supported.

*** Significant at 1% ≤; NWP = Network participation; ICT = Information communication technology;

BDS = Business development services.


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Hypothesis 3 (H3): Operation factors mediate the relationship between the gender of entrepreneurs and firm performance.

Four conditions must be met to support H3 (Baron and Kenny, 1986; Newbert, 2008; Tuan and Takahashi, 2010). These are:

(a) MHFs (gender) must be positively related to operation factors. (b) Operation factors must be positively related to firm performance. (c) MHFs (gender) must be positively related to firm performance by excluding operation

factors. (d) The effects of MHFs (gender) on firm performance must be reduced or eliminated by

including operation factors. The results of the testing of H3 are shown in Tables 10 and 11 and summarized in Table 12.

It was found that all three operation factors, premises for business, operation months, and presence of competitiveness did not mediate/affect the relationship between gender and firm performance because they did not meet all conditions. Premises for business failed to meet the first condition but it met the second, third, and fourth conditions indicating that MHFs and FHFs had better performance through the use of outside-based business. Unlike male entrepreneurs, females used home-based businesses that provide flexibility that allow the fulfillment of domestic and business roles. However, home-based businesses may not be suitable and practical locations for access by customers and suppliers. Operation months failed to meet all four conditions. For the first condition, gender failed to be positively related to operation months. For the second, spending a longer time was less efficient in the improvement of the performance of both MHFs and FHFs. This may be explained by the fact that it is matter of time management as well as allocating time in inefficient and ineffective ways. Therefore, operation months failed to improve the performance of MHFs and FHFs.

The presence of competitiveness met the first, third, and fourth conditions. For the first condition, MHFs were positively related to the presence of competitiveness, meaning that MHFs were more likely to take risks because the male entrepreneurs perceived this as both a threat and an opportunity. The males may have also taken necessary business approaches to cope with the presence of competitiveness. For the second condition, the presence of competitiveness had nothing to do with firm performance of MHFs and FHFs, indicating these conditions were neither threats nor opportunities for them.

Generally, operation factors failed to confirm liberal feminist theory and social feminist theory because male and female entrepreneurs had different premises for business, operation months, and presence of competitiveness, and these did not have a significant impact on firm performance. Thus, H3 is not supported.


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Table 10. Effects of operation factors (condition 1)

PB OPM PC

Coef. Coef. Coef. Coef. Coef. Coef.

(Constant) -0.400** 0.384** 11.993*** 12.063*** 0.576*** 0.419**

Firm size 0.033*** 0.033*** -0.004 -0.003 0.006 0.005

Firm age -0.038*** -0.038*** -0.003 -0.002 0.002 0.001

Manufacturing - - -0.424*** -0.394*** - -

Trading 0.413*** 0.408** - - -0.05 -0.001

Service 0.458*** 0.459*** -0.334*** -0.303*** -0.116 -0.118

Gender

-0.029 -0.173**

0.276**

Pseudo R2 0.042 0.042 0.002 0.005

LR Statistics 82.56*** 82.63*** 3.43 9.13

Log likelihood -952.69 -952.66 -932.23 -929.381

R2

0.024 0.028

Adjusted R2

0.209 0.024

F-Statistics

8.65*** 8.12***

N 1434 1434 1434 1434 1434 1434

*** Significant at 1% ≤; **5% ≤; PB = Premises for business; OPM = Operation months; PC = Presence of competitiveness.

Table 11. Effects of operation factors and firm performance

Firm performance

Model 1 (Condition 2) Model 2



Firm size 0.045*** 0.044*** 0.044*** 0.043***

Firm age 0.006 0.008 0.004 0.006

Manufacturing - -0.216** - -0.267***

Trading 0.207** - 0.263 -

Service 0.043 -0.190** 0.036 -0.249***

Gender - 0.355*** 0.344*** Operation Factors

Premises for businesses (PB) 0.224*** 0.218*** Operation months (OPM) -0.048 -0.041

Presence of competitiveness (PC) 0.033 0.021 Pseudo R2 0.1222 0.1265 0.1298 0.1336

LR Statistics 399.23*** 413.28*** 424.13*** 436.43***

Log likelihood -1434.11 -1427.09 -1421.66 -1415.51 N 1434 1434 1434 1434

*** Significant at 1% ≤; **5% ≤


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Table 12. Summary of results to support H3

No. Four conditions must be met Operation Factors PB OPM PC

1

MHFs (gender) must be positively related to operation factors in Table 10.



0.276*** Supported

2

Operation factors must be positively related to firm performance in Table 11 (Model 2).

0.224*** Supported


0.033 Not Supported

3 MHFs (gender) must be related to firm performance by excluding operation factors in Table 11 (Model 3). The gender variable was statistically significant (0.355***), meaning that MHFs outperformed FHFs. Thus, it was supported.

4 The effects of MHFs (gender) on firm performance must be reduced or eliminated by including operation factors in the Model 4 in Table 11. [By comparing the size of the coefficient of gender variable in condition 3 and gender variable in condition 4, the size of the coefficient for gender variable in condition 4 must be either reduced or insignificant]. The finding showed that the size of the coefficient of the gender variable in Model 3 reduced from 0.355*** to 0.344*** (see Table 11). Therefore, it was supported.

Conclusion of four conditions: Not Supported

Not Supported

Not Supported

Premises for businesses, operation months and presence of competitiveness did not meet the four conditions and therefore H3 was not supported.

*** Significant at 1% ≤; **5% ≤; PB = Premises for business; OPM = Operation months; PC = Presence of competitiveness.

6. Conclusion and policy implications

6.1 Findings and conclusion

The objective of this paper was to investigate firm resources, networks, and operation factors that mediate the relationship between the gender of entrepreneurs and firm performance in Lao MSMEs. Three hypotheses were empirically tested in a sample of 1,534 observations of Lao MSMEs from different industries. As presented in Section 5.2, the results partly supported hypotheses 1 and 2 but hypothesis 3 was not supported. The findings are mainly in line with liberal and social feminist theories that different genders do not have similar firm resources and networks thus resulting in different performances. However, the study does not provide evidence that operation factors are line with liberal feminist theory in this case.

For H1, two out of the three main firm resource indicators (some human resources and all tangible resources but not intangible resources) mediate/affect the relationship between the gender of entrepreneurs and firm performance, meaning that MHFs outperform FHFs through accessing and controlling these firm resources. The findings confirm that for firm resources, all tangible resource indicators affect the relationship between gender and firm performance whereas only some of human resource indicators affect the relationship. Intangible resources do not affect the relationship. For H2, two out of the three network indicators (network participation and ICT adoption but not BDS) mediate/affect the relationship between the gender of entrepreneurs and firm performance. Specifically, the findings confirm that MHFs outperform FHFs through the impacts of better network participation and advanced ICT adoption but not BDS. For H3, operation factors do not mediate/affect the relationship between the gender of entrepreneurs and firm performance through premises for business, operation


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months, and presence of competitiveness. In this case, there is no evidence that MHFs outperform FHFs through operation factors.

6.2 Policy implications

It is anticipated that this paper will provide useful information on the performance of firms headed by males and females for implementers of policy and policymakers. Appropriate use of this information should assist in the reduction and/or elimination of the gap between MHFs and FHFs.

6.2.1 Implementers of policy

Implementers of policy need to offer some practical implications that improve firm performance based on the findings of this paper. This paper showed that there are unequal opportunities in Lao MSMEs based on the gender of the entrepreneurs in the areas of firm resources (human resources, tangible resources) and networks (network participation and ICT adoption) that lead to performance gaps.

To reduce and/or eliminate the performance gaps between MHFs and FHFs, it is required that policy implementers introduce measures to overcome FHFs’ limited access to productive and economic resources such as land, credit and loans, equipment and tools, and technical know-how. To achieve this reduction and/or elimination, FHFs need to improve important firm resources and participation in networks. The area of firm resources involves not only such things as human resources and tangible resources but also human resource development (HRD) in their strategic plans. This means that FHFs should enhance their firm performance by continuing investment in HRD by updating and upgrading important capabilities and competencies for female entrepreneurs through training to accumulate know-how, knowledge, and skills in business operations. It is most important that FHFs be exposed to practical, useful, and relevant training appropriate to their needs to maximize the outcomes of the investments in HRD. For example, business training can be in the areas of business management, marketing, business planning, negotiation, and making contracts. Specific training courses may be in product improvement, bookkeeping, and the preparation of loan applications. FHFs should also enhance their performance through the acquisition and/utilization of credit to finance strategic business activities, such as financing advanced technology to improve productivity, quality, and product innovation. In this way, FHFs can maintain a competitive advantage and sustain superior performance. In the area of network participation, FHFs should be involved in membership of various related business associations such as Lao Young Entrepreneurs Associations, Associations of women entrepreneurs, and the Vientiane and Business Women Association. FHFs also must adopt advanced ICT tools to be competitive and to fully exploit their potential benefits.

6.2.2 Policymakers

Policymakers should encourage a good business climate for the Lao MSMEs business sector. This should be achieved by the provision of support for both MHFs and FHFs, but special assistance is required for FHFs because of their significant contribution to the Lao economy as owners/managers. FHFs need support to be competitive with MHFs.

Under the current conditions, the government can reduce the gender gap in terms of economic performance by providing incentives and good conditions for FHFs to access firm resources (human resources and tangible resources) and networks. This should result enhanced competitiveness. With regards to human resources, the government needs to improve formal education and integrate vocational education and related training systems with a focus on the needs of the labor market, in particular the needs of MSMEs. More specifically, they can


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subsidize HRD for FHFs and provide practical, useful, and relevant business training, advice, and counseling to enable female entrepreneurs to understand financing mechanisms and procedures. There should be assistance in the preparation of feasibility studies and business plans that are acceptable to lending institutions. Furthermore, the governments should disseminate information about existing sources of business training and credit availability to female entrepreneurs. The government should not only support the establishment of bank branches in all provinces, but also help to channel funds to female MSMEs via banks, micro financial, and rural financial institutions. Moreover, they should provide technical assistance such as sound advice in the maintenance of financial records and preparation of loan applications. They should, too, subsidize FHFs in the acquisition of advanced technology via suitable tax policies. This policy measure can encourage FHFs to introduce new products and services as well as improve productivity and the quality of goods and services. Furthermore, the government should stimulate ‘soft infrastructure’ by encouraging FHFs to become members of relevant business associations and networks as listed earlier. The government can additionally encourage FHFs to adopt advanced ICT through means such as tax subsidies and tax exemptions, so as to for lower the costs of ICT.

7. Limitations and further research

Due to limitations regarding secondary data, this paper could only measure the firm performance by the annual sales turnover. Further research should include comprehensive performance indicators such as return on assets (ROA), return on sales (ROS), and sale growth. In addition, this paper only included reputation as a proxy for intangible resource variables. Further research should include different intangible resources variables. This paper did not include personal life issues of MHFs and FHFs such as personal and family factors. Further study should cover these factors. Lastly, this paper did not consider non-economic performance indicators. The inclusion of these may provide more meaningful empirical studies and future research may adopt the mentioned performance indicators.

Acknowledgements

The authors express sincere gratitude to the editors and to several anonymous reviewers for their helpful suggestions and their valuable comments to improve this paper. Special thanks to the German Agency for Technical Cooperation (GTZ) that provided some data from their surveys in 2005, 2007, and 2009. Any errors that appear in the present paper are entirely the authors’ responsibility. References

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Factors Mediating Gender and Firm Performance in Lao Micro, Small, And Medium Sized Enterprises

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