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ndltd-OhioLink-oai-etd.ohiolink.edu-osu1243018351
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ndltd-OhioLink-oai-etd.ohiolink.edu-osu12430183512021-08-03T05:55:49Z Assortive matching of borrowers and lenders: evidence from rural Mexico Sanchez-Schwarz, Susana <p>There is evidence that the rural credit markets of developing countries exhibit assortive matching patterns that sort and link borrowing entrepreneurs and lenders according to classes. This dissertation provides an explanation for this empirical regularity. Assortive matching patterns result from cost complementarities between lending technologies and borrower abilities to convey information about their risk type to lenders.</p><p>Lenders use either standard or idiosyncratic information to support screening, monitoring, and enforcement activities. The value of standard information is not affected by the distance between borrowers and lenders; thus, other lenders can read this information at comparable costs. In contrast, idiosyncratic information is generated at low marginal cost by interactions among local residents, or among agents in the same market. Consequently, the cost of gathering idiosyncratic information is affected by the identity and location of the lender.</p><p>The data set comes from a survey of rural entrepreneurs and a series of case studies about non-bank and informal lending technologies in three rural areas of Mexico. Rural entrepreneurs exhibit low participation rates in credit markets and are rather monogamous debtors.</p><p>A benchmark model for a pairwise analysis of entrepreneur and lender interactions is developed to demonstrate the effects on equilibrium interest rates and loan size of: (i) fixed and proportional costs of borrowing and lending; and (ii) the information acquisition behavior of lenders.</p><p>The benchmark model is extended to derive conditions for assortive matching. It is shown that an assortive matching pattern results in which formal lenders specialize in serving rural entrepreneurs with high endowments of standard information.</p><p>Assortive matching patterns are associated with market segmentation, with degrees of market power, and with interest rate differentials between formal and informal lenders.</p><p>A multinomial logit model is fitted to test for the matching hypothesis that entrepreneurs with greater endowments of standard information are more likely to be matched with formal lenders who specialize in interpreting standard information. The dissertation found that rural entrepreneurs with formal accounting practices, with higher endowments of collateralizable real estate assets, and with a history of prior access to credit are more likely to obtain loans from formal lenders.</p> 1996 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1243018351 http://rave.ohiolink.edu/etdc/view?acc_num=osu1243018351 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
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NDLTD
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| language |
English
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NDLTD
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| author |
Sanchez-Schwarz, Susana
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| spellingShingle |
Sanchez-Schwarz, Susana
Assortive matching of borrowers and lenders: evidence from rural Mexico
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| author_facet |
Sanchez-Schwarz, Susana
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| author_sort |
Sanchez-Schwarz, Susana
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| title |
Assortive matching of borrowers and lenders: evidence from rural Mexico
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| title_short |
Assortive matching of borrowers and lenders: evidence from rural Mexico
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| title_full |
Assortive matching of borrowers and lenders: evidence from rural Mexico
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| title_fullStr |
Assortive matching of borrowers and lenders: evidence from rural Mexico
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| title_full_unstemmed |
Assortive matching of borrowers and lenders: evidence from rural Mexico
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| title_sort |
assortive matching of borrowers and lenders: evidence from rural mexico
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| publisher |
The Ohio State University / OhioLINK
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| publishDate |
1996
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| url |
http://rave.ohiolink.edu/etdc/view?acc_num=osu1243018351
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| work_keys_str_mv |
AT sanchezschwarzsusana assortivematchingofborrowersandlendersevidencefromruralmexico
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| _version_ |
1719427999733907456
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