Calling the Shots: Determinants of Financial Decision-making and Behavior in Domestic Migrant Households in India
Bibliographic record
Abstract
This article explores financial decision-making and behavior in migrant households. Literature on migration and financial inclusion usually focuses on either migrant workers and their financial needs or remittance flows and their effects on development, leaving the subject of household decision-making significantly underresearched. Using primary data from two sample surveys, one with migrant workers and one with their household members, we employ descriptive analysis to study the financial decision-making processes and outcomes. Our sample is mostly composed of male Indian domestic migrants from Bihar, Jharkhand, and eastern Uttar Pradesh. Our analysis considers the following migrant typology dimensions: duration of migration cycle, skills, and destination. Key household characteristics explored in our study include household size, the number of financial contributors in the household, the presence of an older male and children below the age of 18, and overall household income. Our results show that household members compete for influence over financial decisions and power balances change significantly whether the migrant is at home or at destination. These dynamics play an important role in determining household financial preferences. This suggests that financial products and interventions targeting specific financial behavior (for instance, financial literacy programs) need to take these factors into account since different households and different migrant types make these choices differently. JEL Codes: D14, O15, O16
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".