MétaCan
Menu
Back to cohort
Record W2751601944

Working Paper 243 - Selling crops early to pay for school: A large-scale natural experiment in Malawi

2016· preprint· en· W2751601944 on OpenAlexaboutno aff
Dillon Brian

Bibliographic record

VenueRePEc: Research Papers in Economics · 2016
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsNatural experimentPovertyMarket liquidityQuarter (Canadian coin)EconomicsDifference in differencesVolatility (finance)Agricultural economicsDemographic economicsGeographyEconomic growthFinance
DOInot available

Abstract

fetched live from OpenAlex

In this paper we use a natural experiment from Malawi to test the hypothesis that short - term expenditure needs force poor households to sell crops early, when output prices are well below their peak. The experiment comes from a change in the timing of the primary school calendar. In 2010, the school year began in September, three months earlier than in 2009. Although there is no primary school tuition in Malawi, households still incur substantial out -of-pocket school costs. We use difference – in - difference and triple difference specifications to show that the cumulative value of household- level crop sales made before September was significantly higher in 2010 than in 2009. This effect is limited to households with school-aged children, is increasing in the number of school-aged children, and is only present for households in poverty for whom the opportunity cost of liquidity is higher. Because crop prices rise substantially over the last quarter of the calendar year, back - of-the-envelope estimates indicate that poor households paid a per - child penalty of 366 - 1221 Malawi kwacha (2.5-8.5 USD) to finance school expenses three months earlier in 2010 than in 2009. More broadly, these findings highlight the high cost of liquidity for poor households, and provide empirical support for the concern that intra - annual price volatility exacerbates the negative impacts of liquidity constraints.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.308
Teacher spread0.265 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicMicrofinance and Financial InclusionFrench-language works237,207