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ABSTRACT
Most low- and middle-income countries lack the infrastructure to efficiently process and deliver payments to beneficiaries of welfare programs. As a result, many poor people are financially excluded or receive only a portion of the funds intended for them. There are few empirical studies for policy reference to identify and justify potential returns of public investment in building technology-based infrastructure. This study replicates a recent experimental study that fills this empirical gap by examining the effect of biometrically authenticated payments, ‘Smartcards,’ on India’s two largest welfare programs (a work-for-payment scheme and a national pension program). We evaluate the original study’s findings and obtain comparable outcomes – that Smartcards decrease the time lag for recipients to receive funds, reduce leakages of benefits and increase enrollment rates in the two programs. We also examine the robustness of the original study to outliers, alternative model specifications, changes in estimation methods and treatment effects heterogeneity bias.
Acknowledgments
The author gratefully acknowledges the assistant of the original authors, Muralidharan K, Niehaus N and Sukhtankar S. We appreciate their valuable prompt responses to our questions during the study. Also, we acknowledge Professor W Robert Reed (University of Canterbury, New Zealand), Benjamin DK Wood, and Scott Neilitz for their useful suggestions.
Notes
1. The exchange rate used in this paper is US$1 to 66.5580 rupees (Rs). This is the prevailing rate as at 17 October 2016, sourced from https://www.oanda.com/currency/converter/.
2. As described in Muralidharan, Niehaus, and Sukhtankar (Citation2016, 2903), the Smartcard holds the beneficiary’s biometric data (all 10 fingerprints), digital photograph and bank account details. The card is used as a form of identification through matching the scanned fingerprints at the point-of-service collection with a unique biometric record in the database. The matching process is random and can be unreliable in authenticating transactions for multiple reasons, including technical issues and nonmatching of fingerprints. Other associated and evolving issues are extensively discussed in Afridi, Iversen, and Sharan (Citation2017) and Drèze and Khera (Citation2018).
3. The related goals to Muralidharan and colleagues’ study are No Poverty (Goal 1), Zero Hunger (Goal 2); Good Health and Well-being (Goal 3); Gender Equality (Goal 5); Decent Work and Economic Growth (Goal 8); Reduced Inequalities (Goal 10); Responsible Consumption and Production (Goal 12); Strong Institutions (Goal 16); and Global Partnerships for the Goals (Goal 17). The full list of the Sustainable Development Goals is available at: https://www.undp.org/content/dam/undp/library/corporate/brochure/SDGs_Booklet_Web_En.pdf.
4. These are variables measuring the use of Smartcards by participants in the treatment group, labelled as ‘b17_useSmartcard’ and ‘b26_1_swipeFingerprints’.
5. See Tables A8 and A9 in Appendix A of Atanda (Citation2018, 22–25) for a full report.
6. The randomization was stratified by districts and socioeconomic characteristics of surveyed households. See Section B of Muralidharan, Niehaus, and Sukhtankar (Citation2016, 2,907–2,909) for a full description of data collection procedures.
7. Adilabad, Ananthapur, Kadapa, Khammam, Kurnool, Nalgonda, Nellore and Vizianagaram.
8. The key difference between the treatment and control group is the system of payments for NREGS and SSP programs. In the treatment group mandals, payments were made through the ‘Bank → Technology Service Provider (TSP) → Customer Service Provider (CSP) → Worker’ Smartcard-enabled channel. The control group payment system channel is from ‘State → District → Mandal → Gram Panchayat → Worker.’
9. The lag between the deployment of Smartcards in the treatment and control groups was more than 2 years. Muralidharan and colleagues created the buffer group to avoid contamination of the control group before the mandals in the group were converted to the new payment system and to ensure they had sufficient time to conduct the endline surveys. Through the process, enrollment was allowed to take place in the buffer group without affecting the control mandals.
10. Muralidharan and colleagues extracted the official records on beneficiary lists and benefits paid from the official disbursement data to determine the official number of Smartcards rolled out and the proportion used to conduct transactions and the amounts disbursed, and to estimate leakages of funds. Leakage is estimated as the difference between the official payment disbursed and the reported actual payment received by the beneficiary during the survey.
11. The survey data are the combination of the baseline and endline household surveys of samples of enrolled beneficiaries in the treatment and control groups. The data include questions on the payment received, participation experience in the NREGS and SSP programs and general socioeconomic information such as income, employment, assets and consumption.
12. See full results in Atanda (Citation2018, 16–21).
13. Carded GPs are the villages that have moved to Smartcard-based payment.
14. Not Carded GPs are the villages that have not yet been converted to the use of Smartcards.
15. See Atanda (Citation2018, 10) for full description of each of the measure.
16. The outlier results are in Table B2 – B7 of Atanda (Citation2018, 27–30).
17. In the other cases where heterogeneity exists, the PC variable in model (3) was insignificant.
Additional information
Notes on contributors
Akinwande A. Atanda
Akinwande A. Atanda is a passionate data scientist and research consultant who helps businesses and government agencies derive values and generate actionable insights from data. He enjoys using econometric techniques, machine learning algorithms, data and technologies to provide efficient and reliable intelligence solutions and formulate evidence-driven policies. Over the years, Akinwande has worked for businesses and institutions in New Zealand, United States, Ghana, and Nigeria. He received his PhD from the University of Canterbury developing econometrics models, simulations and implementing machine learning algorithms for solving business, social and welfare issues.