Week One

weeklyreading.docx

Home >Business & Finance homework help >Marketing homework help >Week One

https://search-proquest-com.contentproxy.phoenix.edu/docview/1285244089/fulltext?accountid=35812

Micro Finance Institution Lending and Female Entrepreneurs

Rogers, Tammy ; Lawton, Linda . American Journal of Entrepreneurship ; Columbus Vol. 5, Iss. 2, (Dec 2012): 36-50.

1. Full text

Abstract

Female entrepreneurs represent an important but underutilized resource in the effort to foster entrepreneurial activity in many nations. We examine the factors at the national level that may influence women entrepreneurs trying to obtain funding from microflnance institutions (MFI). Specifically we want to examine whether MFI specific factors or economic environment including national government policy has a bigger influence on women borrowers. Our results show that while the MFI specific characteristics are definitely important, the social and economic factors cannot be discounted. These factors are a major influence on female participation in MFI lending patterns. In developing market economies, the MFI has developed as a significant potential lender for the entrepreneur. However, the question remains of how likely are these MFIs to make loans to female entrepreneurs. Our study changes the focus of these previous studies to examine what increases an MFFs propensity to make loans to female entrepreneurs.

Full Text

· TranslateFull text

Headnote

ABSTRACT

We examine whether the microfinance institutions' lending patterns to women are solely a function of institution choice or whether those decisions are influenced by the economic and social environment in which the institution operates. We find that while the institution specific factors are important the social and economic environment influences the lending patterns as well. In particular we find that female education rates, consumer spending and price levels, investment price levels and government imposed market frictions as measured by the Frasier Economic Freedom of the World Index affect microfinance lending patterns to women.

INTRODUCTION AND LITERATUR REVIEW

Previous research examines what traits, characteristics, and motives lead an individual to engage in entrepreneurial activity and in turn what governmental policies will help foster that activity because according to work by Reynolds et al. (1999) and Zacharakis et al. (2000) between one-half and one-third of the differences in national economic growth rates can be attributed to the variation in entrepreneurship rates. Other research has focused on the perceived barriers that entrepreneurs and female entrepreneurs in particular may face. Funding for entrepreneurs is an often cited perceived barrier in small business creation. Roper and Scott (2009) examine the ability of UK women-owned firms to obtain external financing and how the perception of the difficulty of obtaining financing influences the decision to start a business. They find that women are around 7.4% more likely to perceive financial barriers to business start-up than men and those stronger perceptions of financial barriers among women are having a disproportionate effect on women's start-up decisions. Whether these barriers actually exist or not, the perception of funding availability influences female entrepreneurs' decision-making.

In developing market economies, the MFI has developed as a significant potential lender for the entrepreneur. However, the question remains of how likely are these MFIs to make loans to female entrepreneurs. Many MFIs accept funds from donors / investors that come with mandates to make loans to selected minority or demographic groups. At the same time, most MFIs strive toward achieving economic sustainability. These mandates and economic sustainability may or may not be compatible over the long-term. The MFI must operate within the country where they are doing business and they will be subject to the governmental regulations, capital market constraints, economic constraints, and social environment of that country. In this study we seek to determine what characteristics of MFIs and the countries in which they operate influence a MFI' s propensity to make loans to women borrowers. In other words, what factors make a MFI more likely to make loans to female entrepreneurs and small business owners?

In approaching this question, we draw upon several streams of previous research. We consider the literature dealing with female entrepreneurs specifically, the more general entrepreneurship literature dealing with governmental and social factors which affect entrepreneurship rates, and the entrepreneurship funding literature.

A significant amount of entrepreneurship research has focused on the female entrepreneur. Women are perceived as being an underutilized resource in many developing economies. Singh and Belwal (2008) posit that women entrepreneurs are vital to the survival of Ethiopia's economy. Gupta (2008) hails India's female population as "cultural entrepreneurs," citing examples of an inherent cultural sensitivity that has allowed companies to exploit new opportunities. Whether we subscribe to the "social learning" or "cognitive development" approaches to gender differences (see Barrett and Moores (2009)) or simply deny that essential differences exist, we cannot ignore the preponderance of material devoted to the motives and characteristics of women entrepreneurs. Wasilczuk and Zieba (2008) and Mayo et al. (1998) both found desire for achievement to be a driving force behind the women entrepreneurs and aspiring entrepreneurs in their respective studies in Poland and the United States. Note that these studies were completed in countries with a literate female population engaged in the labor market of a developed economy. In these conditions it is natural to find a higher prevalence of positive ("pull") factors drawing women into careers as entrepreneurs - i.e. desire for achievement, self-expression, and locus of control.

Members of ethnic minority groups or women in developing economies face socio-cultural issues which become more of a barrier to these women entering the workforce. Barriers include lack of confidence, lack of self-esteem, fear of failure, social and cultural pressures to remain at home and produce and take care of children, lack of education, etc. Although there are instances of negative ("push") factors, such as desire to avoid a "glass ceiling" and job creation in the face of high unemployment that can influence women to become entrepreneurs in well-developed market economies, we expect to see a higher rate of "push" factors (such as seeking a better life for themselves and their children) in the developing economies. Hossain et. al. (2009) find support for this difference in their study of women university students in Bangladesh. Female students at the private universities in Bangladesh find fewer barriers to involvement in entrepreneurial activity and tend to engage in it for positive factors. In contrast, the female students at the public university tend to engage in business activity as a means to elevate prior status - i.e. in response to "push" factors - propelled to involvement in an effort to get themselves out of a dire situation than the more affluent, better connected private university students.

Regardless of the initial motivation, whether the entrepreneur has engaged in business activity to better her station in life, to create a job in a high unemployment scenario, or to give her flexibility in terms of family and work, the common thread to barriers to entering the workforce as an entrepreneur is lack of capital. Obviously this is less of an issue among the more affluent or well-connected. Kwong et. al. (2009) find that among ethnic minorities in England that the Black African women were more likely to be engaged as entrepreneurs than their male counterparts as opposed to the Black Caribbean, Pakistani, and Indian minority women. Kwong found that the Black African women had significant social networks that gave them not only support in terms of examples of entrepreneurial success (and thus lessened the fear of failure that was a significant deterrent to the other ethnic minorities) but also financial support. Roper and Scott (2009) examine the ability of UK women-owned firms to obtain external financing and how the perception of the difficulty of obtaining financing influences the decision to start a business. They find that women are around 7.4% more likely to perceive financial barriers to business start-up than men and those stronger perceptions of financial barriers among women are having a disproportionate effect on women's start-up decisions. However, being female also has an additional negative effect on the start-up decision, not linked to financial barriers.

One source of funding for entrepreneurs in developing countries that is gaining interest in the literature is microfinance institutions (MFIs). Several studies have focused on the sustainability of MFIs (Ayayi and Sene, 2010, Hasan, Hassan, and Uddin, 2009, Crabb, 2008, Hartarska, and Nadolnyak, 2007, Morduch, 1999). Specifically, what factors at the micro and macro level influence the continued survival of MFIs and the eventual ability of an MFI to become self-supporting without the need for additional capital from aid programs and charitable contributions which can become uncertain in difficult economic times? Crabb (2008) and Campbell and Rogers (2011) look at the impact of economic freedom within a country on the sustainability of MFIs. Economic freedom in this context refers to the absence of economic institutions that interfere with the free market process. It is usually measured as an index that measures strength of property rights, the overall size of government, access to credit markets and sound monetary policy, freedom to trade internationally, and the presence of regulation of business and labor markets. Researchers have established that a higher degree of economic freedom fosters economic growth on a national and subnational level. This research has been summarized by deHaan & Sturm (2000), de Haan (2005), and Doucouliagos & Ulubasoglu (2006). Work by Nystrom (2008) indicates that this is also true on the individual business level; i.e. economic freedom fosters entrepreneurial activity. When examining the sustainability of MFIs, according to Crabb (2008), MFIs tend to operate in countries with a relatively low degree of economic freedom - but conversely a higher degree of economic freedom results in more sustainable MFIs. In order foster successful MFIs for the long-term, economic policies should promote economic freedom.

When considering MFIs and female entrepreneurs, Ayayi and Sene (2010) and Parida and Sinha (2010) both examine the relationship from the MFI point of view. Do an increased number of loans to females increase the sustainability of MFIs? Ayayi & Sene (2010) found that in models of MFI financial revenue less total expenses, women borrowers are insignificant in predicting financial sustainability. Parida & Sinha (2010) found evidence to support the popular belief that women are less risky borrowers, offering MFIs a greater portfolio yield.

Our study changes the focus of these previous studies to examine what increases an MFFs propensity to make loans to female entrepreneurs. We examine which institutional specific and environmental factors make an MFI more likely to make loans to women entrepreneurs as measured by the percentage of the loan portfolio that is represented by loans to women. One of the primary questions is whether the social, political, legal, and economic environment within the country will influence the lending decisions of MFIs or whether the MFI is influenced solely by MFI specific characteristics such as size of the MFI, loan portfolio at risk, and loan portfolio yields. This question is of concern for agencies that provide funds for MFIs. If the socio-economic environment decreases the likelihood of an MFI to lend to women, then many of the MFIs may not be able to meet the economic development goals of their donors and supporters who want to provide a better life for families in developing countries by making loans to women.

In considering an MFI' s propensity to loan to women, we include MFI specific variables of portfolio yield, the value of a MFFs portfolio at risk for more than 30 days, average loan balance per borrower, and the borrowers to staff ratio. These variables will provide information about a specific MFFs risk characteristics and efficiency of operations. Both of these factors may increase or decrease an MFFs propensity to lend to women.

The environmental variables in our study focus on the economic environment factors (price level of consumption, price level of investment, per capita GDP from consumption spending), the capital market in the country (external capital flows into the country, loan interest rate levels, non-performing loans at banks, domestic credit provided by banks), and social environment factors (adult literacy, primary education completion rates, secondary education rates, government spending on education and female labor market participation rates). Finally, we also include the Frasier Economic Freedom of the World Index. This index will capture the institutional factors or barriers within the economy that interfere with or increase the level entrepreneurial activity in the economy as has been shown in previous studies. In our sample, we examine if this impact remains significant for women entrepreneurs as well.

DATA AND METHODOLOGY

We draw our data from MIX, the Perm World Table, the World Bank Global Development Finance Database, the World Bank Genderstats Database, the World Bank Edstats Database, and the Fraser Institute's Economic Freedom of the World Index. Our data is a single cross-section taken in 2007. It covers 528 MFIs operating in 76 countries. We estimate our empirical model using OLS regression. The results from a BreuschPagan / Cook- Weisberg test for heteroskedasticity indicate there is no need for error correction within our model.

Table 1 provides a description of the variables. Table 2 contains the correlation matrix for the variables in the regression. The high correlation of EFW with many of the variables indicates the possibility of multicollinearity in the regression. To correct for this issue, we use a correction pioneered by Gwartney, Lawson, and Holcombe (2004, 2006). Taking cues from Friedman (1962), EFW researchers argue that political and governmental institutions drive the observed economic outcomes; that is, economic freedom leads to GDP growth, rather than GDP growth leading to economic freedom. Therefore, we regress our GDP related measures on EFW and retained the residuals. This process will "purge" GDP of the effects of EFW. The residuals and EFW can appear as regressors in the same model without creating collinearity. We applied this correction to the international capital markets flows variable, the percentage of real per capita GDP from consumer spending, and the female education variables of primary completion and progression to secondary education. All of these variables are related to economic freedom. The coefficients on these regressors in our model should be interpreted as the impact of these variables on the dependent variable after accounting for differences due to economic freedom.

The dependent variable in our model is the percent of MFI loans made to females (Womenpct). This variable is reported by the MFIs as part of the outreach data provided by the MFI to MIX Market. We are trying to predict which variables will increase an MFI' s tendency to lend to females.

The MFI specific variables of portfolio yield (PortYield), the value of a MFI's portfolio at risk for more than 30 days (PortRiskg30), average loan balance per borrower (AvgLoanBalBorr), and the borrowers to staff ratio (BorrtoStaff) are drawn from the MIX market database. To be included in the sample, the MFI must have values for each of these four variables as well as a reported return on assets and return on equity value. The MFIs in our sample have MIX diamond ratings (a measure of the quality of the financial data provided) of three or higher indicating that they have provided general information, outreach data and financial data for at least two years. The specific measures of MFI performance including the portfolio yield and the portfolio at risk for the next thirty days are measures of MFI risk and return characteristics. The portfolio yield is important because a financially strong MFI which has stronger returns on its portfolio may have more ability to make loans to women entrepreneurs. Also, as seen in other studies, women have a higher repayment rate. Therefore, less risky lenders may be more likely to seek out female entrepreneurs for loans. Other MFI specific variables in the model include the average loan balance per borrower and the ratio of borrowers to staff. The myth of the MFI typical borrower is the person in Africa who borrows the money to buy a cow and then sells milk, butter, and cheese to their neighbors as their business. In reality, many of the MFIs make small business loans that are significantly larger in size and these MFIs may actually compete within the traditional banking market for deposits and loans. As the MFIs increase their scope of operations, efficiency and costs will influence lending decisions. The borrower to staff ratio captures one measure of efficiency for the MFI. As the staff becomes more efficient and can process more loans per staff member, the MFI may experience lower costs which may increase credit availability for entrepreneurs.

The economic variables included in the model are the Frasier economic freedom of the world index (EFW), price level of consumption (pc), price level of investment (pi), percentage of per capita GDP from consumption spending (kc). The price level of consumption is a measure of the price of consumer goods in the economy. For the entrepreneur, a higher price level will mean increased sales revenue for products ceteris paribus. The price level of investment measures the cost of capital assets for the entrepreneur. The impact of this variable may be mixed because entrepreneurs faced with a higher initial cost for capital assets may choose to forgo starting a new business venture which would lead to lower entrepreneurship rates; however, an increased cost of capital assets may make entrepreneurs more likely to need to acquire funds in the capital market leading to higher loan balances and increased loan levels overall within the economy. For the GDP measure, we chose to isolate consumption spending because the end market for micro-entrepreneur products in developing countries will most likely be consumers. Finally, we consider population density. As the population within a country becomes more concentrated, it will be easier for the micro-entrepreneur to develop a market for their goods, which in turn should lead to higher rates of entrepreneurship.

We chose to include several capital market variables from the World Bank Global Development Finance Database in our study because many microfinance institutions actually compete head to head with traditional banks for both deposits and loans in many markets. These variables will directly influence the lending environment for MFIs which in turn may indirectly influence the propensity of existing MFIs to make loans to women. With a more developed capital market, the female entrepreneur may choose to pursue more traditional lending outlets like banks. However, the traditional lenders may not choose to make loans available to female entrepreneurs leading more of them to approach the MFI for funds instead. The first variable, external capital flows into the country (intcaplow), includes capital coming into the country through aid dollars as well as direct investment flows. The capital flow measure provides insight into the richness of the capital market. More depth in the capital market will increase funds available in the economy leading to more potential lenders for the entrepreneur. The cost of funds in a country is reflected by the loan interest rate levels (lendrate). For the entrepreneur an increased cost of capital funds may prevent starting a new business venture. The strength of the national economy is partially reflected by non-performing loans at banks (banknonploans). In a weak economy more bank customers will default on their loans due to job and income losses. However, the loss of income may be a catalyst which leads entrepreneurs to start a new venture. Finally, domestic credit provided by banks (dmcredfrbnk) provides a direct measure of the strength of the domestic capital market for small businesses and consumers in the country. A country with a more developed banking system may not be a fertile market for MFIs, leading to lower MFI loan rates.

Our social environment variables focus on the availability of education for females and the percentage of women who participate in the labor force. Measures of female primary education completion rates (fprimcomp), female progression to secondary education rates (fsecprog), and overall government spending on education (edupctgdp) come from the World Bank Genderstats Database and the World Bank Edstats Database. In general, some education is a necessary component for the entrepreneur. If female entrepreneurship is driven by a desire to better the family situation, knowledge is a necessary tool which allows the female entrepreneur to understand what is necessary to start a business and may provide the confidence for the woman to start a business. However, once a minimum education level is obtained, the female entrepreneur may look to other sources of funding beyond the MFI. Also, females with higher levels of education in developing countries may come from higher wealth families who can provide start-up capital for the female entrepreneur. The other gender related element that we include in our study is female labor market participation rates(flabmkt). A social culture which allows females to participate more actively in the work place will most likely provide more opportunities for female entrepreneurs in the market.

The overall model for our regression appears in the following equation.

Womenpct = b^sub 0^ + b^sub 1^(PortYield) + b^sub 2^(PortRiskg30) + b^sub 3^(AvgLoanBalBorr) + b^sub 4^(BorrtoStaff) + b^sub 5^(EFW) + b^sub 6^(kc) + b^sub 7^(pc) + b^sub 8^(pi) + b^sub 9^(intcapflow) + b^sub 10^(popden) + b^sub 11^(bnknonploans) + b^sub 12^(lendrate) + b^sub 13^(dmcredfrbnk) + b^sub 14^(fprimcomp) + b^sub 15^(fsecprog) + b^sub 16^(edupctgdp) + b^sub 17^(flabmkt)

This is a rather large model with several variables, but we are exploring a series of relationships that have been documented in the literature but have not necessarily been combined in this manner with this data set to examine the factors that influence female entrepreneurship.

DISCUSSION OF R ESULTS

Table 3 presents the results of our model. The most significant variables for predicting an MFI' s propensity to loan to women are the MFI specific variables included in the model. MFIs with stronger loan portfolios as reflected by the portfolio yield are making significantly more loans to women. Also, MFIs with riskier loan portfolios have significantly fewer female borrowers. The MFIs making larger loans tend to have fewer female borrowers; however, while this variable is statistically significant, the economic significance is more difficult to determine because of the small coefficient. In addition more efficient MFIs with higher borrower to staff ratios are making more loans to women.

The country level variables are also significant in determining the propensity of an MFI to loan to women. Economic freedom within the country has a strong positive impact on funds available to women entrepreneurs from MFIs. Consumers appear to be an important driving element for female entrepreneurship as well. As consumer spending becomes a larger percentage of overall GDP, the price level of consumption increases and the population becomes more densely concentrated, more loans are being made to women. However, increasing cost of investment capital appears to have a detrimental effect for the female borrower.

Surprisingly, the domestic banking market variables do not appear to be significant for the female borrower. The only significant variable in the capital market variables is international capital flows as is to be expected since many MFIs rely on international donors or capital markets to acquire funds.

The education variables provide some interesting insights as well. For MFI lending activity, the completion of primary education is a positive significant element for women borrowers. viously, some basic level of educational attainment is necessary to acquire a loan to start a business. However, as the female population becomes more educated and government expenditures on education increase, fewer women are taking out loans from MFIs. This result appears to potentially support the prior work by Hossain et. al. (2009 ) which finds that more educated and more affluent women tend to experience fewer barriers to entrepreneurship. These women are most likely funding their enterprises through other resources such as more traditional banks or family resources instead. On interpretation of this result is that the MFI appears to be a lender to women who are engaging in entrepreneurial activity due to push rather than pull factors.

Table 4 presents an adjusted model. The insignificant domestic credit market variables have been excluded from the model and the Frasier economic freedom index has been broken down into its five component areas: size of government (efwsize), property rights protection (efwproperty), money (efwmoney), international trade (efwtrade), and credit market and business regulation (efwcred). The reason for breaking the EFW down into its component pieces is that many academics argue that the overall measure is too aggregated to provide insight into the impact of economic freedom (Caudill, et al., 2000). In general there are one or two areas of the index that are influencing the outcomes and effects may be masked in the aggregated index. By breaking the index into its components, researchers can determine which government policies are actually promoting or are detrimental to economic well-being. (Note the sample size has increased for this model because there are some MFIs that did not have the domestic credit variables but had all of the other data.)

The results in table 4 are similar to those found in table 3 for the MFI specific variables and the economic variables other than economic freedom in both size and significance. The education variables are also similar except for primary completion rates. For the economic freedom variable there are two areas which are significant. Property rights protection appears to have a strong positive influence on female entrepreneurship participation as measured by MFI lending rates. However, lower levels of regulation tend to decrease female rates of borrowing from MFIs. In a more regulated environment, we see more women obtaining funds from MFIs.

CONCLUSION

This study looks at the factors that influence the propensity of MFIs to provide loans to women. Perhaps to be expected, the MFI specific characteristics appear to have the largest impact on the MFI' s decision to make loans to women. However, the economic and social environmental factors cannot be ignored either. Specifically, economic freedom, consumer contributions to GDP, international capital market flows, and female education levels have statistically and economically significant influence on the MFI' s lending policies with regards to female borrowers. This study offers a first look at the MFI as a provider of funds for female entrepreneurs. There are many directions for future research in this area. An examination of female entrepreneurship rates in developing countries relative to these factors may be an important next step. Another interesting question for future research is whether female entrepreneurs in more educated countries actually obtain funding from other sources or are they somehow being edged out of the market when there is less of a social agenda to promote female owned businesses in more developed capital markets. For supporters and investors of MFIs who have a social agenda of supporting female development, these results provide indications that the governmental policies and educational opportunities have to be favorable for promoting female entrepreneurship in developing countries before economic development can occur.

References

References

Afrin, S., Islam, ?., and Ahmed, S. (2010). Microcredit and Rural Women Entrepreneurship Development in Bangladesh: A Multivariate Model. Journal of Business and Management, 16(1) : 9 - 35.

Ayayi, ?., & Sene, M. (2010) What Drives Microfinance Institution's Financial Sustainability. The Journal of Developing Areas, 44(1), 303-324.

Barrett, M. and Moores, ?. (2009). Spotlights and shadows: Preliminary findings about the experiences of women in family business leadership roles. Journal of Management & Organization (2009) 15: 363-377.

Brau,James C, and Gary M Woller. (2004) Microfinance: A Comprehensive Review of the Existing Literature. Journal of Entrepreneurial Finance and Business Ventures. April 2004. Vol. 9, Iss. 1; 1-26

Campbell, Noel and Tammy Rogers (201 1). Microfinance Institution Return on Assets and Economic Freedom, working paper.

Caudill, Steven; Zanella, Fernando and Mixon, Franklin Jr. (2000). Is Economic Freedom One Dimensional? A Factor Analysis of Some Common Measures of Economic Freedom. Journal of Economic Development. 25 (June) : 17-39.

Crabb,P. (2008). Economic Freedom and the Success of Microfinance Institutions. Journal of Developmental Entrepreneurship, 13(2), 205-219.

Cull, Robert, Demirguc-Kunt, Asli, Morduch, Jonathan. (2007) Financial Performance and Outreach: A Global Analysis of Leading Microbanks. The Economic Journal [H.W Wilson - SSA] . Feb 2007. Vol. 1 17; p. Fl 07

Friedman, M. (1962) Capitalism and Freedom Chicago, IL: University of Chicago Press.

Gupta, V. (2008). An Inquiry into the Characteristics of Entrepreneurship in India. Journal of International Business Research, 7(1) : 53 - 69.

Gwartney, James and Robert Lawson with Herbert Grubel, Jakob de Haan, Jan-Egbert Sturm, and Eelco Zandberg (2009). Economic Freedom of the World: 2009 Annual Report. Vancouver, BC: The Fraser Institute. Data retrieved from www.freetheworld.com.

Gwartney, J. D.; Lawson, R. ?.; and Holcombe, R. G. (2004). Economic Freedom, Institutional Quality, and Cross-Country Differences in Income and Growth. Cato Journal 24: 205-33.

Gwartney, J. D.; Lawson, R. ?.; and Holcombe, R. G. (2006). Institutions and the Impact of Investment on Growth. Kyklos 59: 255-73.

Hasan, M., Hassan, M., & Uddin, M. (2009). Local Government Investment Outreach and Sustainability of Microfinance Institutions: A Case Study of BÜRO, Bangladesh. The Journal of Social, Political, and Economic Studies, 34(3), 318346.

Hartarska, V. & Nadolnyak, D. (2007). Do regulated microfinance institutions achieve better sustainability and outreach? Cross-country evidence. Applied Economics, 39(10), 1207.

Heston,A. ,Summers, R., & Aten,B. Penn World Table Version 6.3, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania, August 2009.

Hossain, ?., Naser, ?., Islam, R., & Zaman, ?. (2009). Differences between public and private university women graduates in the development of small enterprises: Evidence from an emerging economy. The Journal of Applied Business and Economics, 9(3), 1.

Kwong, C. Y., Thompson, P., Jones-Evans, D., and Brooksbank, D. (2009). Nascent entrepreneurial activity within female ethnic minority groups. International Journal of Entrepreneurial Behaviour & Research, 15(3) : 262 - 281.

Mayo, D. T., Becherer, R. C, and Helms, M. M. (1998). Women's Careers and Career Goals: An Intra-group Comparison of Managers, Entrepreneurs, and Women with Entrepreneurial Aspirations. Journal of Business and Entrepreneurship, 10(2) : 21-35.

Microbanking Bulletin (2005). Trend lines, Issue 10 (5), March.

Morduch, Jonathan (1999). The Microfinance Promise. Journal of Economic Literature, 37(4), 1569-1614.

Nystrom, K. (2008). The Institutions of Economic Freedom and Entrepreneurship: Evidence from Panel Data. Public Choice, 136(3) : 269-282.

Parida, P., & Sinha, A. (2010). Performance and Sustainability of Self-Help Groups in India: A Gender Perspective. Asian Development Review, 27(1), 80-103.

Reynolds, P.D., Hay, M., and Camp, S.M. (1999). Global entrepreneurship monitor. Kansas City: Kaufmann Center for Entrepreneurial Leadership.

Roper, S., & Scott, J. (2009). Perceived Financial Barriers and the Start-up Decision: An Econometric Analysis of Gender Differences Using GEM Data. International Small Business Journal, 27(2), 149.

Singh, G. and Belwal, R. (2008). Entrepreneurship and SMEs in Ethiopia Evaluating the role, prospects and problems faced by women in this emergent sector. Gender in Management: An International Journal. 23(2) : 120-136.

Wasilczuk, J. and Zieba, K. (2008). Female Entrepreneurship in Transitional Economies: The Case of Poland. Journal of Small Business and Entrepreneurship, 21(2) : 153-169.

Zacharakis, A. L., Bygrave, W. D., and Shepherd, D. A. (2000). Global entrepreneurship monitor: National entrepreneurship assessment: United States of America. Kansas City: Kaufmann Center for Entrepreneurial Leadership.

AuthorAffiliation

Tammy Rogers

[email protected]

University of Central Arkansas

Linda Lawton

[email protected]

Northern Michigan University

Word count: 5051

Top of Form

Search ProQuest...Search button

Bottom of Form

Download PDF

Cite

Save

Add to Selected items

· Documents with shared references (3385)