Conditional Cash Transfers, Debit Cards and Financial Inclusion: Experimental Evidence from Argentina
Bibliographic record
Abstract
Cash transfer and other social protection programs in developing countries have often been accompanied by measures to foster financial inclusion, such as the adoption and use of bank accounts and electronic means of payments. Argentina's social benefits are paid in bank accounts and accessed through debit cards. With the simultaneous objective of fostering formality among beneficiaries and stores, the use of debit cards for purchases has been incentivized by means of additional subsidies. We studied the low take-up of these extra benefits by means of a field experiment involving 400,000 beneficiaries of Argentinas largest conditional cash-transfer program (with 2.2 million beneficiaries who are the parents of four million children, 40% of the countrys 0-17-year olds). By using their debit card to spend the allowance, rather than withdrawing cash from ATMs, they can receive a rebate of 15% of their expenditures. However, they systematically fail to claim this benefit: only about 25% of beneficiaries receive this transfer. Our experiment provided information about the effectiveness of an information campaign conducted via text messages or through on-screen messages at ATM machines. The campaign increased purchases with debit cards and subsequent rebates significantly but not substantially in the short run. However, beneficiaries who increased their use of debit cards do not exhibit a higher probability of having access to credit through the financial system, nor higher levels of formal employment. The results indicate that cultural factors (a preference for cash), administrative hassle and citizen security issues are relevant issues that limit the potential of financial inclusion through increased use of digital means of payment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".