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Record W7028454279

Enhancing livelihood resilience in Makueni county, Kenya: the role of informal credit in smallholder farming

2014· dissertation· en· W7028454279 on OpenAlexaff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2014
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsMcGill University
Fundersnot available
KeywordsLivelihoodResilience (materials science)AgricultureScarcityPsychological resilienceSituatedWork (physics)Erosion
DOInot available

Abstract

fetched live from OpenAlex

Due to land degradation, growing population pressures, and frequent drought, smallholder farmers in Kenya’s food insecure drylands are struggling to meet production objectives while maintaining a diverse set of crops to support health/nutrition. In the Eastern Province’s semi-arid region of Ukambani more specifically, these factors are compounded with soil erosion and climatic variability, making any level of commercial farming a precarious venture. Bearing this in mind, most lending institutions in this area consider financing smallholders to be a very high-risk game. Moreover, developing sources of credit that are consonant with smallholders’ seasonally-dependent needs is also not always very profitable for the lending institutions. In the same way, the farmer’s perspective tells us that the transaction costs and added fees associated with traveling to a lending institution, submitting a loan application, and waiting for it to be processed are simply not congruent with the enormous time and money constraints that are central to any agrarian lifestyle. Still, chronic food insecurity is a particular feature of the smallholder livelihood system in Ukambani which layers urgency on top of an already pressing set of credit needs. Smallholder credit needs are unique in that they generally require smaller amounts of money (by most standards, “microloans”) more frequently albeit erratically, and lending institutions often do not or cannot meet these needs in a cost-effective way. With this in mind, this research is one response to the question of where these farmers turn for credit in a time of need, and how credit offers the smallholder some predictability amidst a very unpredictable and challenging environment. In this paper, I argue that credit enhances the resilience of the smallholder livelihood system by helping farmers absorb shocks. I describe the financial landscape of my fieldsite of Makueni County, Ukambani, typologizing the formal and informal sources of credit that are theoretically available to farmers. More specifically, I review the reasons that smallholder farmers are statistically less likely to turn to formal lending institutions and focus on the strengths of the informal group lending structure (chama in Kiswahili or mwethiya in Kikamba), which ethnographic evidence shows is an incredibly reliable, adaptive, and popular source of credit. In doing so, I demonstrate the ways in these groups allow Kamba farmers to transact using a variety of media (e.g. social capital, material goods, knowledge/skills, cash) that all bear similar value. Employing Jane Guyer’s (2004) concept of convertability, I argue that this broader interpretation of value and credit allows these groups to resist being vulnerable to fluctuations in cash flow or shortages in food, for instance. Because credit and debt are inherently linked concepts, I also focus on the metaphysical state of indebtedness and the various interpersonal obligations that define human social life. I use ethnographic evidence to show that incurring debt to another person (rather than an institution) strengthens one’s capacity to respond to risk and unpredictability. There is nothing binding a person to an institution apart from coercion, while interpersonal debt is grounded in other forms of obligation that bind people together. I use Parker Shipton’s (2003) concept of entrustment and Janet Roitman’s (2003) idea of debt as “unsanctioned wealth” to develop the idea that debt is actually a productive feature of smallholders’ financial lives and can therefore be framed as a positive economic indicator within the larger system of smallholder livelihood in Ukambani.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.208
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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