Are Women More Likely to be Credit Constrained? Evidence from Low-Income Urban Households in the Philippinesстатья из журнала
Аннотация: Abstract Abstract Based on survey data for 2002 and 2006, this paper investigates the determinants of credit constraints among women and men in two urban slum communities of Manila in the Philippines. The results show that women are more likely to be credit constrained than men. Rather than wealth, informal lenders seem to rely more on reputation and credit history to screen prospective borrowers, and the consequences of repayment delays or defaults are more severe for women than for men. These findings provide empirical support for women-targeted credit interventions in urban poor contexts, particularly those that enable women to build and capitalize on good credit histories. Keywords: Gendercredit constraintsintrahousehold allocationbargaining powerurban poorPhilippinesJEL Codes: D14J16I30 ACKNOWLEDGMENTS I am grateful to Maria S. Floro, Mieke Meurs, Amos Golan, Caren Grown, Hitomi Komatsu, Lina Salazar, Carole Biewener, Gil Skillman, anonymous referees, and seminar participants at the 2008 SEA, 2008 IAFFE, and 2009 joint URPE–IAFFE sessions at the ASSA meetings for their helpful comments and suggestions. Notes De Mel, McKenzie, and Woodruff (2009 de Mel, Suresh, McKenzie, David and Woodruff, Christopher. 2009. "Are Women More Credit Constrained? Experimental Evidence on Gender and Microenterprise Returns.". American Economic Journal: Applied Economics, 1(3): 1–32. [Crossref], [Web of Science ®] , [Google Scholar]) note, however, that while their data consist of a random sample of microentrepreneurs, these are not random samples of populations of men and women. Urban slum dwellers are underrepresented in standard surveys because sample households are typically selected on the basis of permanent residence. While there is increasing attention to the issue of credit access and credit rationing in rural areas, it is not clear whether these research findings and their corresponding policy implications are applicable in urban settings. Out of 197 households, eighteen are single-headed, of which seventeen are headed by women. Of these respondents, 46 percent moved, 18 percent were displaced due to squatter evictions or demolitions, 18 percent were from dissolved or separated households, and 14 percent were deceased. Although attrition is nonrandom, estimation of probit models with selection for various specifications suggests that error terms for the probability of attrition and the probability of being credit constrained are uncorrelated. This approach is similar to Diana Fletschner's (2008 Fletschner, Diana. 2008. "Women's Access to Credit: Does It Matter for Household Efficiency?". American Journal of Agricultural Economics, 90(3): 669–83. [Crossref], [Web of Science ®] , [Google Scholar], 2009 Fletschner, Diana. 2009. "Rural Women's Access to Credit: Market Imperfections and Intrahousehold Dynamics.". World Development, 37(3): 618–31. [Crossref], [Web of Science ®] , [Google Scholar]) qualitative credit module. Only sixteen out of 150 households reported at least one joint loan transaction. The three most cited reasons for not applying for a loan are: (1) fear of rejection (for example, "nobody is willing to lend"), (2) fear of default (for example, "may not be able to pay back loan"), and (3) prefers not to borrow (for example, "not used to borrowing"). Note that reason (2) implies price rationing – that is, the borrower's use for the funds may not generate enough returns to cover the cost of the loan. Note that the excess demand and rejected groups applied for loans and therefore had some idea of the going interest rates. On the other hand, the discouraged group may be either price or quantity rationed. They may have opted not to apply because the interest rates were too high, or they may have been willing to pay the going interest rate but did not apply because they expected to be rejected. To distinguish between these two possibilities, I use the open-ended responses of nonapplicants to classify whether they were price or quantity rationed. As a robustness check, I restrict the analysis to only those who attempted to borrow during the period. The results are qualitatively similar to those for the full sample. Only two respondents reported owning land. The majority of the assets that respondents mentioned include appliances, jewelry, and cell phones, all of which have active secondary markets. See Timothy Besley (1994 Besley, Timothy. 1994. "How Do Market Failures Justify Interventions in Rural Credit Markets?". The World Bank Research Observer, 9(1): 27–47. [Crossref], [Web of Science ®] , [Google Scholar]) for an overview of the key features of rural credit markets. A case study of a squatter area in Manila by Töru Nakanishi (1990 Nakanishi, Töru. 1990. "The Market in the Urban Informal Sector: A Case Study in Metro Manila, The Philippines.". The Developing Economies, 28(3): 271–301. [Crossref], [Web of Science ®] , [Google Scholar]) reveals the same dominance of informal lending – about 96 percent of the total borrowing was from informal sources, while the remainder was unpaid hospital debts. Banerjee and Duflo (2007 Banerjee, V. Abhijit and Duflo, Esther. 2007. "The Economic Lives of the Poor.". Journal of Economic Perspectives, 21(1): 141–68. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) report similar patterns for Udaipur and Hyderabad. Of the nineteen transactions that required collateral, five borrowers used jewelry, seven used durable goods, one used land, three used ATM cards, and three used other assets as collateral. Semiformal sources include microcredit institutions, credit unions, and pawnshops. Formal sources include banks, the Social Security System (SSS), and the Pag-IBIG Fund. Other informal lenders include funeral homes and appliance retailers who accept installment payments. The interest rate charged by lenders is correlated to the borrowers' characteristics and is implicitly accounted for by X. This is a reasonable assumption given the dominance of individual borrowing among couples. The marginal effects for men are the main effects for each variable. I compute the marginal effects for women by taking the sum of the main effect and the interaction effect for each variable, treating insignificant estimates as zero. Note that there is no statistically significant difference in the marginal effects between men and women where the female interaction effects are statistically insignificant. I use the term employee loosely in this context to indicate relative stability in earnings. I define an employee as a paid worker whose income is guaranteed by an employer, regardless of whether it is formal or informal employment.
Год издания: 2012
Авторы: Hazel Malapit
Издательство: Taylor & Francis
Источник: Feminist Economics
Ключевые слова: Microfinance and Financial Inclusion, Poverty, Education, and Child Welfare, Urban and Rural Development Challenges
Другие ссылки: Feminist Economics (HTML)
The World Bank Open Knowledge Repository (World Bank) (PDF)
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Том: 18
Выпуск: 3
Страницы: 81–108