Article Entrepreneurial Ability and Development of Micro Enterprise Arthaniti: Journal of Economic Theory and Practice 1–19 © 2020 Department of Economics, University of Calcutta Reprints and permissions: in.sagepub.com/journals-permissions-india DOI: 10.1177/0976747920946405 journals.sagepub.com/home/ath Susmita Chatterjee1 and Debabrata Datta2 Abstract Self-help groups (SHGs) are set up by the National Bank for Agriculture and Rural Development (NABARD) with the objective of supporting poor people of India to set up micro enterprises. However, it is not at all easy to become an entrepreneur. The empirical observation shows that while some members of SHGs succeed in becoming entrepreneurs, other continue to remain just members. This article tries to identify the factors that help this entrepreneurship. It first develops a theoretical model and then carries out an empirical exercise with the help of ground-level surveys of SHGs in several districts of West Bengal. This empirical exercise with the help of logistic regression finds out that state help, financial access and existence of marketing opportunity are necessary for entrepreneurial success. Contrary to the general belief, more years of education does not help entrepreneurship. More revealing is the finding that protective social support deters entrepreneurship. Keywords Self-help group, entrepreneurship, micro enterprise, logistic regression, matching JEL: 015, 035 I. Introduction National Bank for Agriculture and Rural Development (NABARD) set up selfhelp groups or SHGs with a view to assisting the poor people of India to set up micro enterprises. The SHG members are supposed to use the institutional support to meet their needs and try to set up micro enterprises. About 10–20 members who 1 2 Maharaja Manindra Chandra College, University of Calcutta, Kolkata, West Bengal, India. Institute of Management Technology, Nagpur, Maharashtra, India. Corresponding author: Susmita Chatterjee, Maharaja Manindra Chandra College, University of Calcutta, College Square, Kolkata, West Bengal 700073, India. E-mail: susmita.cb@gmail.com 2 Arthaniti: Journal of Economic Theory and Practice reside in the same area and have mutual trust form the group with either all women members or mixed composition of male and female members. The corpus fund is mobilised from small contributions from each member. Cash credit to the tune of six times the savings is granted to the affiliates after six–eight months of creation. The government decides to scale up the project after the success of the pilot project. SHGs were also formed under Swarnajayanti Gram Swarozgar Yojana (SGSY). SHGs are set up in various states of India as a major tool of poverty alleviation and inclusive economic development. Micro enterprises emerging from SHGs are labour intensive and directed towards generation of income for the underprivileged section. SHG–Bank Linkage Programme (BLP), started by NABARD and NGOs, has been designed to unify women into SHGs. The Ministry of Rural Development launched the National Rural Livelihood Mission (NRLM) in place of the SGSY scheme on 1 April 2013. NRLM happens to be a major programme of the Government of India for the eradication of poverty. The primary unit of NRLM is a block that works in the formation of women SHGs on the basis of mutual affinity among members. NRLM takes a saturation approach and tries to ensure that at least one woman from a poor family is motivated to join the SHG. SHGs are now part of entrepreneurial dynamics among low-income people in less developed areas. Most of the micro entrepreneurs emerging from SHGs have a social face as they create employment opportunities and generate wealth for people, who are at the base of the pyramid. These micro enterprises need to be sustainable. There are many factors which enable a member of an SHG to develop a successful micro enterprise. The study here makes an attempt to identify the factors, which contribute to the development of entrepreneurship among SHG members. The article is structured as follows. Section II presents a literature survey on SHGs and entrepreneurship. Section III presents a theoretical model and analyses the factors that determine the successful entrepreneurship of an SHG member. Section IV makes the analysis with empirical studies and statistical results. The penultimate Section V makes some general observations about factors on entrepreneurship development. The final section presents the conclusion. II. Literature on SHGs and Entrepreneurship Evidences about the role of SHGs in fostering entrepreneurship and economic development are found in literature. Anand (2002) examined the role of SHGs in women empowerment and found out that the effect of being part of an SHG was positive. Many women of SHGs are taking a stand against evils of the society such as alcoholism, illiteracy and dowry. Sidhu and Kaur (2006) studied development of entrepreneurship among rural women. They argued that these women own indigenous resources to take up an enterprise with easy availability of farm-based and livestock-based raw material and can effectively undertake both productionand processing-oriented enterprises. Another study by Swain and Wallentin (2008) Chatterjee and Datta 3 has found that there is a positive impact of SHGs on women empowerment. The study has found that there has been significant improvement in the empowerment level of SHG members, compared to the control group. Das et al. (2013, 2015) in a study on entrepreneurship and small business with reference to women SHGs in selected areas of West Bengal found mixed response on women empowerment. Other studies are also done on impact of credit on rural poor. The experiences of Bangladesh Grameen Bank exhibited that collateral-free loan had noteworthy impact on the poor (Yunus, 2004). A study by Pitt and Khanderkar (1998) examined the impact of Grameen Bank and two other microcredit organisations of Bangladesh on labour supply, schooling, household expenditures and assets. They found that in Bangladesh, credit programme has a larger consequence on the performance of poor households, when women are programme participants. Soni (2015) did a study on the role of SHGs in the district of Jalandhar district, Punjab. It should also be mentioned that there are studies which have not found out much impact of SHGs on women empowerment, poverty eradication and sustainable economic development (Banerjee et al., 2013). Thus, we have evidences from literature which point to positive as well as negligible impact of SHGs on economic development. The issue is therefore open, and there is need for further study in this regard. Our study concentrates on micro entrepreneurs, emerging from SHGs, and makes attempt to verify the role of various factors in the success of an SHG member in developing a successful micro enterprise. India has registered high economic growth in last two decades, but poverty still persists in India. Economic growth has not percolated to all the sections of the society. This is reflected in low HDI rank for India. Eradication of poverty remains the biggest economic challenge for India. India, therefore, always had to continue with pro-poor growth policies (Datt et al., 2016). One plank of this pro-poor growth policies is an attempt to eradicate poverty by creating opportunities for low income people through micro enterprises, developed by entrepreneurship among the poor section of society. Micro enterprises can create employment opportunities for a large number of people (Anand, 2007). It can also act as a mechanism for socio-economic development, reduction in poverty and employment generation (Audretsch, 2012; Austin et al., 2006; Baumol & Storm, 2007; Galindo & Méndez-Picazo, 2013).Thus micro enterprises evolving from SHGs can be an effective instrument in eradication of poverty and sustainable economic development. Economic development may, however, be more broadbased, if micro enterprises are run by women. Women entrepreneurship results in empowerment of women and there is bi-directional relationship between economic development and women empowerment (Duflo, 2011). Parity between men and women results in faster growth of the economy. As per the report of United Nations Industrial Development Organization (UNIDO), raising female-to-male employment ratio can increase GDP growth rate by as much as 34 per cent in case of some countries (UNIDO, 2007). This is because women spend a major part of their earnings on education of children resulting in development of human capital (Doepke & Terlilt, 2011). Okah-Efogo and Timba (2015) show that female 4 Arthaniti: Journal of Economic Theory and Practice entrepreneurship contributes towards economic growth by reducing unemployment particularly for women, generating revenues for government and enhancing human capital skills. There have been discussions on entrepreneurship and business development at the base of the pyramid at the business journals as well. London and Hart (2011) explained business strategies for the base of the pyramid with the objective of fortune creating. The role of small, local entrepreneurs, who are not ‘wellconnected’ with the formal capitalist economy, has been emphasised for income generation at the bottom. Mair and Marti (2006) discusses social entrepreneurship and argues that highly embedded (attachment of entrepreneur with structure, society) social entrepreneurs can easily access resources and get legitimacy. Hall et al. (2012) show in the context of Brazil that support for entrepreneurial initiative at the base of the pyramid without addressing social perspectives may lead to negative outcome. Hence, both economic and social perspectives are necessary to foster more productive entrepreneurial outcomes. Blair and Gereffi (2001) highlight the benefit of integration between local cluster in the less developed economy and global chain for sustainable productive business. Social and cultural factors like social support highly influence sustainability and entrepreneurship. Within the context of ‘culture’, it is important to consider as a basic condition, the way in which social structures and culture influence enterprise and entrepreneurship and, in particular, the existence of a ‘spirit of enterprise’. The influence of social and cultural influences on entrepreneurship and the formation of new enterprises is well established (ILO, 2004). Selfemployed workers and owners of many informal enterprises in lower-middle income countries are often forced to participate in the informal economy due to lack of other options. A lack of success in obtaining formal employment has pushed many poor women and men into informal economy. Many informal enterprise owners see their enterprise activities as a stopgap––a survival strategy and not a long-term prospect. Hence, complying with difficult laws and regulations, investing in enterprise growth and in equipment and technology, and learning business management skills are not considered appropriate in a short-term transition enterprise. However, the enterprise owner who sees herself in business for the long-term is more likely to invest time and other resources in the growth and development of the enterprise. Customer and market also affect the sustainability and success of a business (William et al., 2005). The filling of ‘unmet need’ of the customers and satisfying them with customer-focused approach, compatible with dynamic market structure are indispensable for success of the business. Ghosh (2017) suggests that stricter credit market regulation reduces entrepreneurial activity. Thus, based on literature, access to financial services and marketing opportunities have been considered as factors affecting sustainability and success of the business. III. Theoretical Model Here is a model that endeavours to capture the role of skills and entrepreneurial abilities for stimulating entrepreneurship and fostering development of micro Chatterjee and Datta 5 enterprises of the members of an SHG. The objective of this formal model is not much ambitious. It attempts only to present a simple framework in which an SHG member takes decision on entrepreneurship, so that the empirical study gets a perspective. It is needless to add that the real-life scenario is much more complex and not captured fully by this model. The model considers a population, comprising SHG members. The number of SHG members is taken to be given. We can normalise it to one, when we express the number of memberships of micro enterprise members in percentage terms. At the beginning, the persons mostly from less developed sections of the rural areas join an SHG by self-selection. They aspire to set up their micro enterprises and become successful entrepreneurs. Thus, every promoter of micro enterprises is an aspiring agent, but they differ in terms of education, family support, availability of financial access and marketing opportunity etc. The entrepreneurial ability ultimately depends on these factors. If a member of an SHG wants to develop into a successful entrepreneur, she has to establish a good marketing channel, find out prospective customers and secure credit from the formal sector with a favourable rate of interest. All these pursuits need search cost (S). Thus, search cost of a particular member depends on cost of finding out suitable marketing partner and supporting bank, idiosyncratic entrepreneurial ability, education level and quantum of availability of opportunities. This costly search results in some probability (q) of setting up a successful micro enterprise. We make two assumptions about search cost (S). S falls with better entrepreneurial ability, which depends on idiosyncratic favourable factors. Secondly, as more and more members join entrepreneurship, search cost rises. This is because opportunities become more competitive with more SHG members joining entrepreneurship. The level of entrepreneurial ability is distributed uniformly over the population. We assume that the entrepreneurial ability level goes on falling as more and more members join entrepreneurship. Since S rises as entrepreneurial ability falls, from this side also, S rises as more and more members of SHG join entrepreneurship. The choice of the member of SHG is, therefore, to choose whether to join entrepreneurship through micro enterprise or to go for wage employment and part time self-employment activity. There is a fixed income (w) from wage employment and part time self-employment activity. The member decides to go for entrepreneurship, if net earnings from entrepreneurship exceed W plus the reservation profit. Thus, the first equation is derived as follows. E =− R S {a ( n )} − cb ( i ) − C (1) where E is net return from entrepreneurship, R is revenue from business, S is the search cost, a is entrepreneurial ability, i is rate of interest, n is number of entrepreneurship aspirants and C is start up cost, borne by the entrepreneur and cb is the total cost of borrowing for the borrowed part of project cost. 6 Arthaniti: Journal of Economic Theory and Practice The model assumes fixed revenue. This is of course a stringent assumption. However, the model assumes that the SHG entrepreneur at the very beginning determines the optimum capacity, based on the set-up cost C and availability of banking and marketing facility. Since the entrepreneur here is a person with low family income and limited resources, her choice about capacity is also limited. The entrepreneur who plans to produce the maximum output, corresponding to this small capacity and being in the perfect competitive market with large number of firms, has no other decision variable like price or selling cost at her command. In this scenario, the revenue of the entrepreneur is assumed to be fixed. It is of course possible, following Stiglitz and Weiss (1981), to assume that banks appraise a loan application and derive a probability distribution of success of the project (μ) on the basis of applicant’s skill level and viability of the project. Then we get a subjective distribution of revenue R = F (R, μ) from the bank’s perspective. The bank also assumes that given μ1 > μ2, μ1 has first order stochastic dominance over μ2, implying higher expected revenue for better skilled applicant. In this case banks interpret R as expected revenue. The member decides to join entrepreneurship, when the following condition is satisfied. E ≥ w+r (2) E= w + r (3) In equilibrium where E is the earning from entrepreneurship and w is the market wage rate in the informal sector, competitively determined from the informal rural market and r is the reservation rent, expected by the entrepreneur. Once the member decides to venture for entrepreneurship, she has to search for credit. We assume that the budding entrepreneur in the SHG wants to rely on formal credit of commercial banks. Given that SHGs are sponsored by government agencies, it is natural for these members with entrepreneurial aspiration to bank on formal sector loans, although once the business is established, they may seek occasional support from informal credit market in times of exigencies. We also assume that there is excess demand for loan and there is credit rationing. Therefore, every applicant may not be granted loan. Interest rate is not considered here on just face value. It is rather a catch all variable that includes not only interest rate but other aspects of cost of borrowing like stringency of getting the loan sanctioned, delay of formal credit and possibility of bribery (Chaudhuri & Gupta, 1996, 1997). The sanctioning process of a bank loan is determined by the bank policy, which is subject to change. If the banks make the condition of loan sanction more stringent or slow the process of loan sanction (Chaudhuri & Gupta, 1996, 1997), demand for loan falls. Thus here, i is assumed to represent the cost of borrowing, increase of which reduces demand for loan. Chaudhuri and Gupta (1996) shows that the effective interest rate in the formal sector (inclusive of bribe) is equal to interest rate of informal sector. Chatterjee and Datta 7 The costly search for right marketing partner and good credit facility for successful entrepreneurship needs a matching with the right marketing partner as well as good banker and x is the probability of success in this search. As shown in Pissarides (2000) and Fonseca et al. (2001), the time-consuming search leads to a matching function, which is a single valued function of tightness of credit market that arises from excess demand for good credit. This tightness of market determines the nature of the search cost function. As shown in Stiglitz and Weiss (1981), the credit market can be characterised by credit rationing and the borrower may have to pass through a number of screening devices. This involves costly search on the part of the borrower for the right banker and successful matching. After all these searches and negotiations, demand for loan (Ld) as a function of interest rate (i) is determined. This demand for loan also rises with rise in number of entrepreneurship aspirant. Thus, we get a demand for borrowing as follows. Ld = Ld ( i, n ) (4) Demand for loan falls with increase in stringency of banks’ loan policies, implying of increase in i. The supply of loans for these micro enterprises (Ls) is taken to be fixed, given the credit rationing scenario and overall bank policy. Ls = L (5) The equilibrium condition for the loan market is Ld = Ls (6) From equations (3) and (6), we can get the following two equilibrium conditions that will determine the equilibrium value of n* and i*. R − S {a ( n )} − cb ( i ) − C = w + r (1′) Ld ( i, n ) = L (2′) From equations (1’) and (2’), we determine n* and i*. Equation (1’) shows that there are two variables n and i and so we cannot solve n and i uniquely. Similarly, equation (2’) shows that there are two variables n and i and so we cannot solve n and i uniquely from this equation. Taking both these equations together, unique solution is possible. In (1’), increase in both n and i reduces left hand side. Since right hand side is constant, it requires decrease of one variable, as other variable increases, for (1’) to hold. This produces a negatively sloped entrepreneurship choice (EC) curve. On the other hand, left hand side can be kept unchanged to match with given right-hand side by increasing both n and i for equation (2’). This produces an upward rising curve for credit market (CM). The equilibrium is established at the intersection point of CM and EC curve. If CM curve remains above the EC curve even at zero entrepreneurship, no one will be an entrepreneur. Policy should aim at raising EC curve and shifting CM curve downwards in order to increase number of successful entrepreneurs. 8 Arthaniti: Journal of Economic Theory and Practice i C M i* E O C n* n Figure 1. Equilibrium in Credit Market Source: The authors. Equilibrium for this model also needs the equality between the number of members of SHG and the sum of micro enterprise entrepreneurs and wage and part-time employed. We assume the number of SHG members is given at N. So, the equilibrium values of n*, as determined from equation (1’) and (2’) determine the allocation of SHG members between entrepreneurs and wage earners. If there is an improvement in factors, favourable to entrepreneurship across the SHG members, the EC (Figure 1) curve shifts upward, implying that at given n, members can accept higher cost of borrowing (i). As a result, both interest rate and number of entrepreneurs rise. Similarly, if credit availability increases, CM curve shifts downward, implying increase in number of entrepreneurs and decrease in cost of borrowing (i). A government subsidy to the entrepreneurs shifts the EC curve upward, implying rise in n and i. We can introduce state help (H) as an exogenous variable in equation (1) as follows. E =− R S {a ( n, H )} − cb ( i ) − C (1'') From equation (1''), we derive the following proposition. 1. State help in the form of subsidy or training increases number of entrepreneurs. For increasing the number of successful entrepreneurs, subsidy on set-up cost is to be supplemented by quantity intervention in the form of increase in supply of loan. Proof is given in the Appendix A in terms of comparative static effect. Chatterjee and Datta 9 Similarly, it can be shown that theoretically, increase in support to budding entrepreneurs and enhancement of skills, increase in access to finance and improvement in marketing opportunities should increase micro entrepreneurship. We have made an attempt to verify the above theoretical model in terms of an empirical analysis, where we introduce several factors, which are likely to influence entrepreneurship. We have collected data on two types of members of SHGs. One group member have successfully set up micro enterprises and the other group members have failed to do so. We have collected information on family support, market opportunity, access to finance and education, as per our study. The next part presents the empirical results. IV. Empirical Studies Data Data was collected during March 2017 to December 2018. For the quantitative survey, the questionnaire was prepared in English and then translated to Bengali, the local language. Pre-testing of the questionnaire was conducted in areas other than the study areas. Questionnaire was then finalised and administered among participants in the study. Questions were asked on the entrepreneurial activities as well as on general demographic and socio-economic characteristics. The survey used a quasi-experimental design, with a pre-coded questionnaire to collect data. In this study, we have interviewed 1,122 SHG members of different districts of West Bengal. In this process, we have found 633 women with small businesses. Verbal informed consent was obtained from each study participant and all collected individual data was kept confidential. Additionally, in order to supplement information obtained from quantitative survey, Focus Group Discussions (FGDs) and open-ended in-depth interviews (IDIs), were conducted on issues relating to their views about their business sustainability, about their present status and also about collaborations. All FGDs and IDIs were recorded and transcribed verbatim. Based on the interview conducted through questionnaire, information was collected on education (number of years of formal study), market opportunities in an area, financial facilities and state help. Apart from that, information about family (social) support was also collected individually. The basic settings for entrepreneurial success are complex, but here the approach is to consider the SHG members, who have succeeded in setting up micro enterprises and who have not and to identify the role of a few a priori determined factors in ensuring the entrepreneurial success. This is attempted in terms of a logistic regression analysis of the data. Hypotheses of the Study Social Support It is generally believed that social support and family support in particular helps entrepreneurship. Social support is viewed as availability—or perceived 10 Arthaniti: Journal of Economic Theory and Practice availability—of support from partners, peers, subordinates, friends, family (Kariv, 2008) for a particular person in not just in enterprise activity but in ordinary dayto-day life. It is interpersonal coping resource. However, the social support can also be viewed as some sort of protection for the women members. Going by the conventional belief of social support helping entrepreneurship development, we introduce the following hypothesis 1. We shall see whether this hypothesis holds in the context of poor and low income women as well. So, we take the following hypothesis 1. H1. Social support positively influences entrepreneurial ability. State Help It is often argued that entrepreneurship needs incubation. Low income women cannot be expected to develop into successful small entrepreneurs on their own. State is the only agency in the context of rural areas, which can play some role in providing support to entrepreneurship development, particularly of low income people. ILO’s Global Employment Agenda (GEA), emphasises role of state in skill development. The Human Resources Development Recommendation, 2004 (No. 195) also provides guidance for effective skills and employment policies that assist governments, employers and workers. This guidance emphasises education, training and lifelong learning policies and programmes, suitable in the context of twenty-first century scenario. The education and training should include the use of new information and communication technology. The role of the state in this area cannot be overemphasised here and we have in this context hypothesis 2, as follows. H2. State support (State help) positively influences entrepreneurial ability. Access to Financial Services Access to financial services helps the business to grow. Enterprises require access to financial and business development services if they are to grow and become more sustainable. Although formal financial organisations often refuse loans to micro enterprises and neglect entrepreneurship talent of the micro enterprises; their support can help to foster entrepreneurship mat bottom of the pyramid. The government also develops programme, involving financial institutions, for backward population. We, therefore, ask in our survey the following question: Do you agree that credit facilities from formal institutions increased after joining SHG and setting up micro enterprises? The answers to this question are given in 5-point Likert scale with five options as follows: 1—strongly agree; 2—somewhat agree; 3—agree; 4—somewhat disagree; 5—strongly disagree. On the basis of this answer, we test the following hypothesis 3 in this regard. H3. Access to financial services (Financial access) positively influences entrepreneurial ability. The access to market is important for success of any enterprise. Micro enterprises find it very difficult to establish market network. The ILO’s work in this field adopts a systemic view of markets and institutions (i.e., government, business and Chatterjee and Datta 11 workers, as well as business membership organisations and cooperatives) that shape the opportunities for enterprise upgrading. This involves analysing value chains and developing business service markets in order to enhance the access of businesswomen and men to business growth opportunities along these chains. So, is it really the case that marketing opportunity helps entrepreneurial development? This issue is tried to be addressed by the following two questions: (a) Do you agree that local resources are available for your business? (b) Do you agree that marketing facilities are sufficient for your business? The answers to these questions are given in 5-point Likert scale with five options as follows: 1— strongly agree; 2—somewhat agree; 3—agree; 4—somewhat disagree; 5— strongly disagree. On the basis of these answers, our logistic regression seeks to verify the following hypothesis H4 about the role of market opportunities. H4. Marketing opportunities (Localmamar) positively influence entrepreneurial ability. Education Education should enable someone to take better decision on various aspects of business. Education should lead to improvement in skills. Compared to basic education, education for longer years should be a helpful factor for entrepreneurship. We measure education by the formal years of study by the entrepreneurs. We consider hypothesis 5 in this regard. H5. Education (Edu) positively influences entrepreneurial ability. Empirical Analysis The logistic regression model tests all these above-mentioned hypotheses with the help of primary survey data. Since the response variable was a binary variable (micro enterprise entrepreneur = 1 and only SHG member = 0), multivariate binary logit regression model is used. The prediction equation is for this logistic model is (p/1-p) = eb0 + b1*Socialsupport + b2*Statehelp + b3*FinancialAccess+b4*localmamar +b5*edu p = Probability of being entrepreneur To determine the possibility of micro enterprise entrepreneurship, we have selected variables according to our hypotheses. The independent variables are based on questionnaire in 5-point Likert scale. The following questions are asked with regard to family support and their V score is calculated: 1. 2. 3. Do you agree that your parents support your decision of doing business? (Vscore 0.69) Do you agree that your husband and in-laws support your decision of doing business? (Vscore 0.71) Do you agree that your neighbours support your decision of doing business (Vscore 0.71)? 12 Arthaniti: Journal of Economic Theory and Practice These three questions form the constructs of social support. The answers to these questions in 5-point Likert scale (categorical) are averaged for use in the logistic regression. Similarly, for State Help data, we use the following three questions: 1. 2. 3. Do you get local government’s (Panchayat, BDO) help for business development (idea and skills development training)? (VScore-0.79) Do you believe that laws and rules of local government are helpful? (V Score- 0.76) Do you believe that government motivational campaigns and motivators are useful? (Vscore-0.79) Like the case of Social Support, these three questions form the constructs State Support and State Help. The answers in 5-point Likert scale (categorical) are averaged for use in the logistic regression. To measure support of formal financial institutions, our question was: Do you agree that formal credit facilities increased after joining SHG? The answers in the 5-point Likert scale are considered for data analysis. Similarly, for marketing facility two questions are asked: (a) Do you agree that local resources are available for your business? (b) Do you agree that marketing facilities are sufficient? The answers to these questions in 5-point Likert scale are averaged for data analysis. Education level is measured by the years of formal schooling. In the application of logistic regression, the first step is to determine whether the model reasonably approximates the behaviour of the data. Here, the statistic which shows the validity of the model is chi-square (χ 2), which can be reported from the omnibus tests of model coefficients. According to the significance value of chi-square (Sig. χ 2), the model that evaluates the entrepreneurship from the perspective of SHG members is valid (Table 1). After testing the validity of the model by the chi-square statistic, the second step in logistic regression is to evaluate the performance of the model by the rate of correct classification, obtained from the classification table. So, the model explains the perspective with a 59 per cent explanation rate (Table 1). The last step in logistic regression is the formulation of equations. The effect of each predictor can be gauged by coefficients, significance of the coefficients (p value) and the odd ratio. We accept or reject all the hypotheses as per the significances of the test through logistic regression analysis. V. Results The results in this analysis show the role of different factors in development of entrepreneurship through improving entrepreneurial ability. The hypothesis that social support plays a role in entrepreneurial ability is acceptable only at 10 per cent significance level but the point to note is that this role is negative. This is counter-intuitive. This shows that in the Indian rural scenario, the women who get protection in the form of family and social support perhaps do not feel the need for joining SHG and entrepreneurship. But those who do not have this support, finds in SHG as something to rely upon. Chatterjee and Datta 13 Table 1 Number of obs. 633 LR Chi 2 (5) 37.37 Prob > chi2 0.0000 Pseudo R2 0.0427 Log likelihood = –418.99 Parameters Estimated Coeff. Std Err. z P>z 95% Conf. Interval Odds Ratio 0.853972 Social support –0.157857 0.079581 –1.69 0.09 0.711411 1.0251 State help 0.342778 0.1399 3.45 0.001 1.159691 1.711557 1.408856 Financial access 0.193384 0.109468 2.14 0.032 1.016695 1.44804 Localmamar 0.260483 0.112016 3.02 0.003 1.213349 1.09558 1.536772 1.297558 Edu –0.006587 0.093057 –0.07 0.944 0.826809 1.193639 0.993434 _cons –2.12224 –4.22 0 0.044732 0.321284 0.119882 0.060298 Source: Authors’ calculation. Note: Correct classification percentage is 58.77%. Hypothesis 2 is accepted and state help appears to play the most important role in entrepreneurship, as revealed from results in Table 1. The odd ratio shows, state help improves the probability of success in entrepreneurship by 40 per cent. This matches with our theoretical result. The access to financial facility also characterises entrepreneurial ability, as shown by the acceptance of hypothesis 3 at 5 per cent level. This access improves the probability of successful entrepreneurship by 21 per cent. The acceptance of this hypothesis also validates our theoretical model. Marketing facility and physical infrastructure help entrepreneurial ability and hypothesis 4 are accepted at less than 5 per cent level. Our theoretical model also highlights this factor and here it gets support from empirical analysis. Hypothesis 5 cannot be accepted as no significant role of education is found out in entrepreneurship development in the statistical result. This is apparently surprising but the revelation in this result is that in the present education system, a few years of more study in the school does not add value to entrepreneurship development. At the policy level, this should be a lesson for the policymakers. Perspective and some observations on different influencing factors on entrepreneurship development We have found evidence to indicate the importance of a favourable economic, political and social environment for all respondents to become a successful promoter of micro enterprises. These factors generate opportunities for women to live a decent life with economic freedom. Absence of marketing opportunities and constraints on financial support are likely to deter investment and may discourage women to get into businesses. State support may be of immense help in these cases. 14 Arthaniti: Journal of Economic Theory and Practice Small enterprises have particular difficulty in gaining access to finance from formal institutions on account of banks’ avoidance of risks, complicated bank procedures and lack of suitable collaterals. There are evidences of inadequacy of supply of institutional credit in various studies (Chatterjee, 2007). It makes the transaction costs of the bank finance very high (Chaudhuri & Dastidar, 2014). These general constraints on raising investment resources and working capital restrict investment. The lack of suitable training facilities and other constraints on improving productivity and uptake of new technologies constrain foray into new markets. Public policies to encourage the market development of business services and to target the needs of smaller enterprises, particularly those run by women entrepreneurs can go a long way in helping the growth of micro enterprises (Chaudhuri & Poddar, 2016). Lessons from ILO programmes in East Africa and Southeast Asia on support of women’s entrepreneurship includes the following elements: developing a local knowledge base on women entrepreneurs; supporting voice and representation in local organisations and associations; helping business service providers at the community level to develop support services for women entrepreneurs; developing local and external partnerships to boost marketing; providing women with disabilities with space and opportunities to become successful entrepreneurs (ILO, 2006). Entrepreneurship is complex enough to define and measure. But it is vital for economic growth as it creates employment opportunities, stimulates innovation and brings in prosperity (Chaudhuri et al., 2019). There are many policies, which can be developed for women driven sustainable businesses, ranging from financial support and investment policies to area-based initiatives to inspire and facilitate entrepreneurial culture. Similar to the results of this study, Roy and Mukhopadhyay (2019) finds that innovation enhancing and female-friendly capacity-building programmes for skill development are needed to encourage entrepreneurship. Das and Dash (2016) argues that flow of gains of innovations and inventions towards entrepreneurs helps entrepreneurship. Policies targeted at capacity building through skills upgrading are likely to be important features of all such policies. However, there are no perfect blueprints, and policy choices will vary from area to area, person to person. VI. Conclusion This article has explored the important factors of nurturing entrepreneurship. Some important factors are identified but there are some counter-intuitive results as well. The major finding is that some extra years of study in high school do not help in enhancing business skills. Another finding is that too much family protection may deter entrepreneurship. Our study however has several limitations. It should be more broad-based and based on more samples from other areas, preferably from other states. Repeated study on the same samples can focus on sustainability of enterprises. This may be future research agenda. Looking at the specific gender issue in entrepreneurship is a research agenda. Chatterjee and Datta 15 Acknowledgements We convey our sincere thanks to the anonymous referee who pointed out several deficiencies in the earlier version of the article. Revision of the paper in the light of the referee’s comments helped immensely in the improvement of the quality of the article. The authors are, however, solely responsible for the remaining shortcomings. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article. Funding The author(s) received no financial support for the research, authorship and/or publication of this article. Appendix A Proof of Proposition a) Comparative static effect of state help. R − S {a ( n, H )} − cb ( i ) − C = w + r Ld ( i, n ) = L (1'') ( 2 '') − S a an dn −cbi di= S a aH dH Ldn dn + Ldi di = 0 From here we get S a aH Ldi dn = >0 dH − S a an Ldi + cbi Ldn − S a aH Ldn di = >0 dH − S a an Ldi + cbi Ldn (A1) (A2) S a < 0 (Search cost falls as entrepreneurial ability rises) aH > 0 (Entrepreneurial ability rises as government help is provided) Ldi < 0 (Demand for loan falls as interest rate rises) Numerator is positive. an < 0 (Entrepreneurial ability falls as more members join entrepreneurship) cbi > 0 (Cost of borrowing rises as i rises) Ldn > 0 (Demand for loan rises as number of entrepreneurs rises) Denominator is positive. S a aH Ldi − S a aH Ldn dn di and 0 > = > 0. This proves proposition a). = dH − S a an Ldi + cbi Ldn dH − S a an Ldi + cbi Ldn 16 Arthaniti: Journal of Economic Theory and Practice Proof of Proposition b) Consider introduction of subsidy z in the form of set up support. The equilibrium conditions are as follows after this introduction of subsidy. The demand for loan rises after the subsidy. R − S {a ( n, H )} − cb ( i ) − ( C − z ) = w + r Ld ( i, n, z ) = L (1''') ( 2 ''') − S a an dn −cbi di= dz Ldn dn + Ldi di = − Ldz dz − Ldi − cbi Ldz dn = >0 dz − S a an Ldi + cbi Ldn (A3) only when ( −L − cbi Ldz ) > 0 d i The above condition implies fall in cost on account of subsidy must exceed rise in cost on account of increase in interest rate following increase in demand for loan, as shown below. S a an Ldz Ldn di = .>0 dz − S a an Ldi + cbi Ldn (A4) As subsidy on set up cost is introduced, interest rate increases and this creates uncertainty regarding effect of subsidy on number of successful entrepreneurs. This is particularly the case when supply of loan is given, as in our model. Suppose the policy provides also for increase in supply of loan, equal to the increase in demand for loan, such that Ldz = Lsz Revised equilibrium condition in (2’’’) is Ld ( i, n, z ) = Ls ( z ) Then, − Ldi dn − Ldi − cbi ( Ldz − Lsz ) = = >0 d d dz − S a an Li + cbi Ln − S a an Ldi + cbi Ldn (A5) This shows that for increasing the number of successful entrepreneurs, not only price intervention in loan market, but also quantity intervention is needed. Otherwise, with given amount of loan, there will be excessive increase in interest rate that may not fulfil the target of any increase in number of successful entrepreneurs. Appendix B The sample composition of this study is reported in Table B1. Chatterjee and Datta 17 Table B1. Sample Composition Socio-economic Conditions Per capita income overall (median) INR % with land in own name % with gold in own name % with primary education Financial inclusion % with bank account (nationalised bank) other than SHG bank linkage % with bank account (private bank) % self-decision about spending income Focus Group 10,000 20 40 70 20 5 70 Source: Authors’ calculation. The existing and potential business opportunities are summarised in Table B2. Table B2. Potential Business Opportunities Business Activities Agro based Food based Forest based Chemical based Jute based Brass bell metal/dokra Pottery Cane & Bamboo Sheetal pati/mat Jari/embroidery kantha stitch, etc. Wood carving Coir Doll making Small shop Ornaments Number of Women 70 20 10 20 20 20 10 26 20 30 10 23 25 9 12 Source: Authors’ calculation. Table B3. Resource Availability Financial Institution Commercial Banks 15% Infrastructure i) Road a) Metal Road Cooperative Bank 60% b) Morram Road 80% Source: Authors’ calculation. 20% c) Kachcha Rasta 10% ii) Electricity a) Electrified or not 95% Others 30% Availability of transformer 60% 18 Arthaniti: Journal of Economic Theory and Practice Variables Tested for Significance of Association with Entrepreneurship in Multivariate Binary Logit Regression Model Internal conditions External and structural conditions Education (Categorical) Social Support (categorical) Entre (binary) Financial access (categorical with scoring)—Access to credit and investment State help (categorical)—A conducive business-enabling environment; established property rights and freedom of association; clear, stable, predictable rules; regulatory and legal environment; presence of local government support Localmamar (categorical with scoring)— Access of indigenous resources and local marketing of finished products. 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