What Predicts the Growth of Small Firms? Evidence from Tanzanian Commercial Loan Data

Mia Ellis, Cynthia Kinnan, Margaret McMillan and Sarah Shaukat

No 31620, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Not all firms have equal capacity to absorb productive credit. Identifying those with higher potential may have large consequences for productivity. We collect detailed survey data on small- and medium-sized Tanzanian firms who borrow from a large commercial bank, which in turn raises funds via international capital markets. Using machine learning methods to identify predictors of loan growth, we document, first, that we achieve high rates of predictive power. Second, “soft” information (entrepreneurs’ motivations for entrepreneurship and constraints faced) has predictive power over and above administrative data (sector, age, etc.). Third, there is a different and larger set of predictors for women than men, consistent with greater barriers to efficient capital allocation among female entrepreneurs.

JEL-codes: G14 J16 O16 (search for similar items in EconPapers)
Date: 2023-08
New Economics Papers: this item is included in nep-big, nep-ent, nep-fdg, nep-lab, nep-mfd and nep-sbm
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Published as Mia Ellis & Cynthia Kinnan & Margaret McMillan & Sarah Shaukat, 2023. "What Predicts the Growth of Small Firms? Evidence from Tanzanian Commercial Loan Data," Journal of Globalization and Development, vol 14(2), pages 253-320.

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