The innovation necessity: Evidence from ... - Wiley Online Library

Journal of International Development J. Int. Dev. 24, S112–S121 (2012) Published online 15 December 2010 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/jid.1761

THE INNOVATION NECESSITY: EVIDENCE FROM MICROCREDIT IN THE DOMINICAN REPUBLIC STEVEN W. BRADLEY*, KENDALL ARTZ and JIMMY HULETT Baylor University, Waco, USA

Abstract: This study examines the role of business innovation as an important, but understudied intervening relationship between microcredit loans and income level. Using archival and survey data of microcredit clients from the Dominican Republic, the study finds that loan size has a positive effect on income level, but only in the presence of business innovation. We find that greater business training in relation to competitors is a significant indicator of business innovation. The findings also suggest that personal characteristics and competitive environment play a joint, but opposite role with innovation in predicting income level. Copyright # 2010 John Wiley & Sons, Ltd. Keywords: microcredit; innovation; entrepreneurship JEL classification: O1

1

INTRODUCTION

International organisations and developed countries have given considerable attention to alleviating global poverty over the last 40 years with a specific goal to halve the number of people living in absolute poverty by the year 2015 (World Bank, 2000). Economic growth in countries like India and China in recent years has lead to significant poverty reduction and improved world living standards (Barro and Sala-i-Martin, 2004). Yet, in many developing economies, this growth is uneven and not inclusive of the poorest in society (Ianchovichina and Lundstrom, 2009; Wagle, in press). Microcredit is a means to address this imbalance by providing small loans to the poor – the majority who are living on less than $2 USD per day – for the purposes of starting or expanding a business. Mohammad Yunus and Grameen Bank popularised these efforts by providing loans to the poor, but relying on social capital through group lending practices rather than physical assets as *Correspondence to: Steven W. Bradley, Hankamer Business School, Baylor University, One Bear Place #98006, Waco, TX 76798-8006, USA. E-mail: [email protected]

Copyright # 2010 John Wiley & Sons, Ltd.

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collateral. Public awareness increased further from the United Nations declaration of 2005 as the Year of Microcredit and the Nobel Peace Prize awarded to Mohammed Yunus in 2006 for establishing microcredit banks around the world. Subsequently, the number of microcredit lending institutions has risen rapidly providing credit to over 90 million clients around the world by some estimates (Fairbourne et al., 2008). There has been some evidence supporting this enthusiasm for microcredit. Early studies indicated loans improve participant income levels, increase the accumulation of assets and increase per capita household consumption (Hossain, 1988; Hashemi et al., 1996; Pitt and Khandker, 1998). On the other hand, scholars have questioned some of the methods in earlier studies and find mixed or little indication of either business growth or associated reduction in poverty from providing credit (Morduch, 1999; Banerjee et al., 2009). These later findings have led to increasing scrutiny of the microcredit industry and the empirical evidence purporting its benefits (Dichter and Harper, 2007; Karnani, 2007; Bateman and Chang, 2008). So what might explain these discrepancies in findings? Attention to this question has been directed towards institutions, clients and the economic environment (see Dichter and Harper, 2007 for an overview), but little attention has been directed towards the business opportunities pursued by clients (Karlan and Valdivia, 2010). Recent studies (e.g. Basher, 2009) provide evidence that experienced borrowers move from protective to productive activities increasing consumption over time suggesting that the type of business pursued plays a role in loan effectiveness. However, we have little detail from loan or consumption data used in most microcredit studies that would detail the nature of these productive activities. In this study, we draw from the economics and entrepreneurship literature to demonstrate that innovation in economic activity may be a necessary mediator to the conversion of microcredit loans into increased income for borrowers. By innovation, we refer to either the novel combination of existing ideas and routines (Schumpeter, 1934) or more incremental discovery of opportunities in the marketplace (Kirzner, 1997) to offer products and services. Often, microcredit businesses are replicative of others that have previously generated income (Bateman and Chang, 2008). As loan capital becomes more abundant through lending programs, well-known opportunities are more likely to be exploited. This suggests diminishing socio-economic returns on future funds invested by poor entrepreneurs unless the next wave of entrepreneurs discovers or creates opportunities that do not already exist in the market. This study examines the direct and indirect effects of innovative activity as an important link in generating higher income for microcredit clients. In doing so, this study makes at least two contributions to the literature. First, we show that the effectiveness of loan capital may be contingent on the innovativeness of the ideas to which the loans are applied. Second, we provide industry, business and individual level indicators that explain a client’s business innovation and corresponding income. The paper proceeds as follows. In Section 2, we detail the mediated model to be tested. Section 3 provides the data and the empirical analysis. In Section 4 we provide the results and offer some conclusions from the study in Section 5.

2

THE MODEL

The availability of financial capital is a central concern not only to development scholars, but across the fields of economics (Holtz-Eakin et al., 1994; Audretsch and Mahmood, 1995), strategic management (Shane, 1996; Baum et al., 2000) and entrepreneurship Copyright # 2010 John Wiley & Sons, Ltd.

J. Int. Dev. 24, S112–S121 (2012) DOI: 10.1002/jid

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(Cooper et al., 1988; Bates, 1995). Financial resources allow time for new businesses to develop products/services, learn business processes and find a niche in the market. However, there is no guarantee that resources will be converted to value-added benefits to the resource holder. The process of income generation is also dependent on individual characteristics of the owner and their ability to locate and execute on opportunities in the environment in which the business operates. This study examines several of these factors illustrated in Figure 1 and described as follows. Innovations are radical or incremental improvements in offering products or services that provide some distinct advantage over competitors. In developed economies, both positive incentives for income growth and negative incentives related to business survival drive companies to take risk through innovation (Baumol, 2002). In developing economies, the barriers to innovation are greater due to deficiencies in education, the institutional environment, technical infrastructure, social networks as well as financial resources (Aubert, 2010). For those able to overcome these obstacles and innovate, we expect there will be opportunities to generate higher income due to similar constraints on competitors.

Level of Analysis

Industry

Competitive Intensity

Business

Loan Size

Individual

General Education

Innovation

Income Level

Business Training

Locus of Control

Self Efficacy

Figure 1.

Model of antecedents and intervening variables predicting income level for microcredit businesses




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When opportunities are found in the marketplace, the act of exploitation will influence the likelihood that others will attempt to exploit those opportunities as well. As the density of competitors increases, more of the resources needed to build and sustain a business have been claimed by others leading to greater challenges in the ability for a business to survive, grow and generate income (Carroll and Hannan, 2000). Therefore, competition is expected to cause greater search for innovative business ideas while simultaneously making it more challenging to generate higher income. Evidence has been presented that microcredit loans improved participants generation of higher income, accumulation of assets and increased per capita household consumption (Hossain, 1988; Hashemi et al., 1996; Pitt and Khandker, 1998). However, we believe that the benefits of microcredit loans will develop particularly in the presence of business innovation. Schumpeter (1939) noted that financial speculation often occurs when new industries or geographic markets open up and potential gains are misjudged attracting excessive capital to new ventures. Some suggest this may be the case with microfinance (Narayana, 2010). This easy credit distorts pricing through false perceptions of demand which increases the scarcity and costs of inputs attracting imitators and dividing the entrepreneurial profit among more actors (Schumpeter, 1934). Therefore, those who have innovative ideas that are difficult to imitate are more likely to convert loan capital into greater personal income. While it would seem that the opportunity costs are low for the poor to pursue income generating opportunities, not all do so. This can be explained, in part, by individual differences that are found across segments of society. Individuals must have both the means and motive to pursue an opportunity (McMullen and Shepherd, 2006). For a given person, there are both psychological and non-psychological factors that determine the perception of opportunities and the ability to act on them. General education increases the stock of information and skills and the analytical ability to manage the different facets of starting a business (Casson, 1995; Shane, 2003). Even more so, specific business training can be applied to recognising opportunities to innovate in the market and the tools for generating income from the business. Individuals must also believe they are capable of working through the uncertainty and risk associated with a given market. Individuals higher in selfefficacy (the belief in one’s own ability to perform a given task) and locus of control (the belief in one’s ability to influence the current environment) would be expected to pursue more novel ideas and generate greater income (Rotter, 1966; Bandura, 1997; Shane, 2003). While not exhaustive, these factors together with demographic factors (age, number of dependents, business age) should provide greater evidence of microcredit loans effects on income levels.

3

EMPIRICAL ANALYSIS

The fieldwork for this study was conducted with a microcredit agency in the Dominican Republic during the summer of 2009. A survey was developed and piloted with a nonstratified sample from three townships (two rural, one urban) near Santo Domingo that are generally representative of the microcredit agency field operations. Members of the client groups meeting on site during the survey dates were interviewed with four groups from different client officers sampled. The survey respondents were primarily vendors (75 per cent), with the rest offering services (15 per cent) or manufacturing goods (10 per cent). The surveys from the microcredit clients were then combined with archival data for Copyright # 2010 John Wiley & Sons, Ltd.


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analysis. Variables were measured on a categorical Likert scale (1–5) unless otherwise indicated. The dependent variable was income level and was chosen over other available poverty indicators (savings, home construction, etc.) because of its generalisability across clients. Innovation was assessed by three items as a change in products, processes or markets. For example, ‘The way I am distributing product/services to the market is quite different than my competitors’. Competitive intensity was the number of firms competing in the same business. Loan size was current total loans outstanding and education was the natural logarithm of highest grade of schooling attained. Business training was whether the client reported receiving training in business operations measured as a dummy variable (Y ¼ 1, N ¼ 0). Locus of control, or the extent to which clients believe they control events as opposed to external forces, was measured by the item (Rotter, 1966), ‘When I get what I want, it is usually because I worked hard for it’. Self-efficacy, the belief in the ability to solve problems and achieve goals, was measured by two items (Bandura, 1997). For example, ‘If I am in trouble, I can usually think of a solution’. Owner age, owner age2, business age and number of dependents were included as control variables. Ordered-response models recognise the indexed nature of various response variables – in this case innovation and income. We used Stata’s oprobit function with maximum likelihood estimation to analyse Zavoina and McElvey’s (1975) ordered probit model.1

4

RESULTS AND DISCUSSION

Table 1 provides descriptive statistics of the data. In Table 2 we present the results of the ordered probit models predicting innovation and income level. Model 2.1 is a baseline control model. Of the controls, owner age predicts higher income in the fully specified model. Model 2.2 shows the direct effect of innovation on income level was significant. Opportunities in the marketplace may be available, but not all entrepreneurs are able to take advantage of those opportunities equally. Those that innovate by making, distributing or selling their product differently realised higher income levels. Model 2.3 and 2.4 test the significance of the environment, business and individual level indicators on innovation and income level. Competitive intensity was positive though not significant on innovation but was a significant and negative predictor of income level. As microcredit institutions continue to expand into developing areas and more people gain access to credit for starting businesses, new entrepreneurs find it increasingly difficult to start novel businesses, develop businesses with scale or generate higher income. Our observation of the lending institution studied matches prior reports of loans used to create replicative businesses within the same community (Fairbourne et al., 2008). There are several potential explanations for this phenomenon. First, credit institutions are often reluctant to recommend business opportunities with the potential that a business failure will be attributed to their poor advice. Second, microcredit lenders often do not have the resources to adequately train their clients in product and service differentiation or spend sufficient time in counselling borrowers to develop businesses that match unmet needs in the 1 Business training is possibly an endogenous variable – the same characteristics (unobservable) that make borrowers choose to participate in business training sessions are more likely to increase innovation and income level. We performed a Durbin–Wu–Hausman test on endogeneity of business training. Potential instrumental variables of gender and marital status were not significant ( p > 0.10). However, the microcredit group assigned was significant ( p < 0.05). Given that certain microcredit groups might be more likely to obtain/report business training, we control for this by clustering our analysis which adjusts the standard errors for group effects.



The Innovation Necessity Table 1. Variable

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Means, standard deviations and correlations of key variables Mean SD

1

2

3

4

1 Income level 3.90 1.26 2 Owner age 38.35 10.81 0.08 3 Business age 2.25 1.12 0.15 0.15 4 No. of dependents 2.27 1.40 0.04 0.25 0.30 5 Competitive intensity 5.19 6.50 0.28 0.00 0.01 0.06 6 Education level (Ln) 0.80 0.56 0.41 0.37 0.03 0.32 7 Business training 0.35 0.48 0.52 0.23 0.18 0.13 8 Locus of control 3.63 0.63 0.24 0.03 0.08 0.00 9 Self-efficacy 6.61 1.34 0.22 0.03 0.10 0.04 10 Loan size/1000 0.89 0.27 0.04 0.08 0.21 0.04 11 Innovation 8.06 2.34 0.31 0.14 0.08 0.07

5

6

0.02 0.16 0.13 0.16 0.16 0.00

0.44 0.13 0.28 0.06 0.25

7

8

9

10

0.03 0.13 0.48 0.22 0.14 0.05 0.33 0.14 0.19 0.03

N ¼ 51. Correlations above 0. 27 are significant at p < 0.05.

community. Third, risk adversity among the poor creates inertia towards businesses which, while providing less income, appear more certain in outcome. Business training was positive and marginally significant predictor of innovation and also had a positive and significant relationship with income level. General education level did not predict innovation, but did have a positive and significant relationship with income level. Specialised business training even more than general education helped clients locate more innovative business ideas and execute on those ideas to generate income. Locus of control, somewhat surprisingly, was negative and a significant predictor of innovation and positively related to income. While the majority of research has confirmed a positive association between locus of control (higher internal control) and innovation strategies (e.g. Miller and Toulouse, 1986; Mueller and Thomas, 2001), there is also evidence that this relationship is strongly influenced by the level of uncertainty in the environment. Entrepreneurs with a high internal locus of control are much more likely to pursue innovation in stable environments than in dynamic ones (Wijbenga and Van Witteloostuijn, 2007). Entrepreneurs, particularly those with a strong internal locus of control, become less likely to devote effort to developing innovative and complex businesses, as the ultimate future of that venture is perceived as largely unrelated to the effort. Rather, they will increasingly concentrate on refining and making more efficient the relatively simple and predictable businesses with which they are already familiar – such as those that emphasise providing simple products or services at a low cost. Self-efficacy was not significantly related to innovation or income. Finally, the fully specified model including innovation is tested in Model 2.5. The results show that loan capital becomes a significant predictor of income level in the presence of innovation. Thus, loan size was 1.13 times more likely to increase income level {odds ratio ¼ exp [0.122] ¼ 1.13}, but only when innovations were taken into account. Innovation in business ideas increased the likelihood of higher income by 1.18 times. Working together, business innovations and loan capital provide a greater likelihood that microfinance clients can increase their earnings. This study has several limitations that present future opportunities for research. First, the sample was relatively small potentially masking the effect size of the predictors in the study. Additional work along the lines of this study with a larger set of clients and other contexts would establish the generalisability of these results. Second, we examined a limited number of antecedents to innovation and income. Future studies might examine Copyright # 2010 John Wiley & Sons, Ltd.



(0.092) (0.001) (0.174) (0.128)

4

69.83 0.03 3.32

0.087 0.001 0.232 0.122

Income level

(2.1)

(0.084) (0.001) (0.187) (0.123)

5

65.78 0.07 9.64y 9.19

0.189 (0.068)

0.099 0.001 0.277 0.117

Income level

(2.2)

(0.028) (0.303) (0.390)y (0.227) (0.096) (0.053)

0.011 0.130 0.648 0.513 0.243 0.012

10

99.75 0.05 63.22 n.a.

(0.078) (0.001) (0.112) (0.078)

0.034 0.0004 0.064 0.027

Innovation

(2.3)

Models (2.4)

10

(0.020) (0.478) (0.674) (0.298) (0.125)y (0.063)y (0.071)

(0.084) (0.001) (0.206) (0.185)

11

49.04 0.30 93 97 30.88

0.058 1.016 1.632 0.738 0.213 0.122 0.162

(0.020) (0.478) (0.682) (0.276)y (0.100) (0.070) 0.050 1.014 1.844 0.539 0.128 0.110

51.00 0.28 85.71 20.82

0.144 0.001 0.195 0.004

(0.090) (0.001) (0.207) (0.189)

Income level

(2.5)

0.136 0.001 0.123 0.021

Income level

Ordered probit models predicting innovation and family income

Cases reported n ¼ 51. Unstandardised coefficient estimates are reported rather than marginal effect sizes. Reported standard errors are robust and adjusted by clustering according to microcredit group. Significant changes in Chi-square are compared to the basline Model in 2.1. p < 0.05. p < 0.01. p < 0.001. y p < 0.10.

Dependent variable Controls Owner age Owner age2 Business age No. of dependents Covariates Competitive intensity Education level (Ln) Business training Locus of control Self-efficacy Loan size/1000 Innovation Goodness-of-fit Log likelihood Pseudo R2 Wald Chi-square Significance of change in Chi-square n parameters

Variable

Table 2.

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other important predictors like social networks (Ruef, 2002) and lending group dynamics (Anthony, 2005). The ability to recognise and act on business opportunities is certainly influenced by the social connections and interactions with others in the lending group and community. The form of business innovation that matches the individual and context might be further delineated. For example, the market disequilibrium forms of innovation described by Schumpeter (1934) might be less appropriate for the poor with fewer skills and training than the market equilibrating forms of innovation described by Kirzner (1997). Finally, the level and type of business training offered by microlending agencies is worth further study to understand its role in a client’s development. 5

CONCLUSIONS

This study provides evidence that helps reconcile current debates about the effectiveness of microcredit. It illustrates that capital availability alone does not determine the likelihood that borrowers will be able to generate greater income. While prior work has presented demographic factors that influence the effectiveness of microcredit, this study looked at several unexamined environmental, business and individual determinants that play a role in the entrepreneurial process. Environmental pressures and an individual’s cognitive disposition towards the environment play a role in the ability to generate business ideas and income. Most importantly, we also show that business innovation, or ideas that differentiate one’s business from competitors, works in concert with microloan capital to improve chances for increased income. Our finding of business innovation as a significant link with client business income has policy implications for microcredit institutions. We offer the following possibilities. First, consider providing entrepreneurial training, not only in basic business skills, but also for opportunity identification and product positioning in the market (Karlan and Valdivia, 2010). Alternatively, provide clients with tested, but low-cost, franchise businesses that are innovative to the local marketplace (Fairbourne et al., 2008). Last, and perhaps the most difficult, consider increased screening of clients and provide greater resources to those that have attributes, business training or business ideas that are more likely to generate scale and create jobs in the community (Bateman and Chang, 2008). Loans made to clients for businesses with little or no potential for increasing income are detrimental to the person, the microcredit agency and the industry in the long run. ACKNOWLEDGEMENTS We would like to thank Esperanza and Hope International for their support of the project. We would also like to thank Mark Russell, Peter Crabb, Monty Lynn, Ervin Starr and Brande Davis for their efforts in data collection. REFERENCES Anthony D. 2005. Cooperation in microcredit borrowing groups: identity, sanctions, and reciprocity in the production of collective goods. American Sociological Review 70(3): 496–515. Aubert JE. 2010. Innovation policy for the developing world: success stories and promising approaches. Development Outreach 12(1): 7. Copyright # 2010 John Wiley & Sons, Ltd.


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