Gender-differentiated Social and Human Capital and the Use of Microcredit in Bangladeshi Female Entrepreneurship

  • Muntaha Rakib
  • Sayan Chakrabarty
  • Stephen Winn


In many developing countries, microfinance has become a critical component in implementing poverty reduction strategies, which are often addressed through social business and female entrepreneurship. Despite poor individuals being excluded from formal credit, microfinance organisations are delivering opportunities for them to obtain small amounts of credit. This chapter examines whether gender-differentiated social and human capital matters in social entrepreneurship, and how loans received by Bangladeshi men and women are being used in productive ways. It identifies barriers that might exclude the poorest women from these microfinance programs in Bangladesh. This analysis has important policy implications for donors, microfinance institutions and NGOs operating in developing countries as it focuses on employment generation and poverty alleviation, and also on impact on poor women in microcredit programs.


Bangladesh Female entrepreneurship Gender differentiation Human capital Microcredit Microfinance Social capital 



The authors thank Professor Patrick Alan Danaher, Dr. Jan Stenton and Miss Katrina Wilson for their feedback on previous versions of this chapter.


Table 9.10

Community participation and other factors associated with the productive use of credit by main adult male and female, marginal effects presented


Productive users of credit

Main adult male

Main adult female

Community participation of main adult male

0.085* (0.046)


Community participation of main adult female (leave out village mean)


0.131** (0.059)

Total plot size in square metre (leave out village mean)

0.000 (0.000)

0.000 (0.000)

Physical asset index (leave out village mean)

0.033 (0.426)

−0.041 (0.441)

Total livestock in TLU (leave out village mean)

0.114* (0.066)

0.045 (0.067)

Affected by climatic shocks

0.145*** (0.048)

0.160*** (0.048)

Affected by non-climatic negative shocks

−0.219*** (0.049)

−0.222*** (0.049)

Affected by positive shocks

0.060 (0.049)

0.055 (0.050)

Years of schooling of main adult male

0.009 (0.006)


Years of schooling of main adult female


0.014** (0.008)

Age of main adult male

−0.002 (0.002)


Age of main adult female


−0.006** (0.003)

Main adult male employed off-farm

−0.160** (0.071)


Main adult female employed off-farm


−0.099 (0.151)

Work experience of main adult male

−0.002 (0.002)


Work experience of main adult female


0.006** (0.002)

Household size

0.021 (0.014)

0.020 (0.014)

Men to women ratio

−0.037 (0.028)

−0.046* (0.029)

Dependency ratio

0.170 (0.156)

−0.004 (0.165)

Pseudo R-squared






Source: Authors’ calculations based on the survey data

Note: Robust standard errors are given in parentheses. The coefficient indicates the impact of a marginal change on the probability

***p < 0.01; **p < 0.05; *p < 0.1


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Copyright information

© The Author(s) 2018

Authors and Affiliations

  • Muntaha Rakib
    • 1
  • Sayan Chakrabarty
    • 2
  • Stephen Winn
    • 2
  1. 1.Department of EconomicsShahjalal University of Science and TechnologySylhetBangladesh
  2. 2.School of Teacher Education and Early Childhood, Faculty of Business, Education, Law and ArtsUniversity of Southern QueenslandSpringfieldAustralia

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