2013
DOI: 10.1596/1813-9450-6416
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Financial Inclusion and Legal Discrimination against Women: Evidence from Developing Countries

Abstract: The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Ba… Show more

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Cited by 259 publications
(233 citation statements)
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References 28 publications
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“…In a similar vein, Demirgüç-Kunt, Klapper, and Singer (2013) demonstrate the impact of general institutional norms and regulations on women's access to finance. Drawing on the Global Financial Inclusion (Global Findex) database, the authors show that legal discrimination against women as well as gender norms contribute to explaining some of the cross-country variations in access to finance.…”
mentioning
confidence: 88%
See 1 more Smart Citation
“…In a similar vein, Demirgüç-Kunt, Klapper, and Singer (2013) demonstrate the impact of general institutional norms and regulations on women's access to finance. Drawing on the Global Financial Inclusion (Global Findex) database, the authors show that legal discrimination against women as well as gender norms contribute to explaining some of the cross-country variations in access to finance.…”
mentioning
confidence: 88%
“…As the study sheds light on the business funding journey of women entrepreneurs in Jordan, it reminds us to pay much more attention than is currently visible in (published) literature on financing women entrepreneurs in "other" cultural and institutional contexts. We believe there is a communication gap because much of the work on financing women entrepreneurs in non-Western country contexts is conducted by international donor agencies, and published as reports, working papers or in practitioner-related outlets (e.g., Demirgüç-Kunt, Klapper, and Singer 2013;Mayoux 2002) and thus is less likely to be accepted for publication in the journals we (entrepreneurship) academics read and reference when debating and discussing women entrepreneurs and financing. Clearly this limits our understanding and potentially stymies debate and exchange between scholars, practitioners and policy-makers that can have deleterious consequences, for instance around the formulation of effective entrepreneurship policy (Arshed, Carter, and Mason 2014).…”
Section: A More Global View On Financing For Women Entrepreneursmentioning
confidence: 99%
“…10 There are gender gaps in bank account ownership and in the use of financial savings and credit products. The evidence also shows that the existence of legal discrimination or cultural norms that are biased against women explains part of the variation observable among countries with respect to women's access to finance (Demirguc-Kunt, IDB, 2014;Klapper and Singer, 2013;Pailhé, 2014). Other nonfinancial barriers include conditions in the business climate that affect women differently than men (e.g., the legal and regulatory environment or the quality of the existing infrastructure), the personal characteristics of business owners (e.g., differences in education or management training), limitations within financial institutions (e.g., scant familiarity with women clients and/or cultural barriers that hamper targeting products to this segment), and a financial infrastructure that limits incentives to reach more women clients or to do so appropriately (e.g., lack of access points close to the home or inappropriate design of credit bureaus and guarantee registers) (GPFI, 2011;IDB, 2014;Pailhé, 2014).…”
Section: B the Importance Of Including Disaggregation By Sex In The mentioning
confidence: 99%
“…Dominant among them are logit and probit models. Examples include [14] [15] [16]. Probit and logit models are binary classification models which are estimated by maximum likelihood methods and are utilized to measure the likelihood of an individual belonging to the group under study [15].…”
Section: Introductionmentioning
confidence: 99%