Building State Capacity: Evidence from Biometric Smartcards in India

In 2006, the Government of Andhra Pradesh, in southeast India, started an initiative to shift towards using "Smartcards" to transfer government benefits to the poor. Smartcards were used to make payments for two large social welfare schemes: the Mahatma Gandhi National Rural Employment Scheme (NREGS)—which guarantees rural households 100 days of paid employment per year—and Social Security Pensions (SSP) —which makes monthly payments to elderly, widowed, and disabled individuals. In 2010, facing several logistical challenges, the government decided to restart the program in eight districts where the Smartcards had yet to be rolled out. These eight districts, which are spread throughout the state, have a combined rural population of about 19 million people.

We used a randomized evaluation to assess the impact of Smartcards on program performance, including speed and ease of access,
leakages in NREGS and SSP, and the welfare of program beneficiaries. We partnered with the Government of Andhra Pradesh to randomize the roll out of the program in the eight districts that had not yet received Smartcards in three waves over two years. The Smartcard program was introduced in 112 mandals (sub-districts) in the first wave (treatment group), 139 mandals in the second wave (buffer group), and the remaining 45 mandals in the third wave (control group). The analysis compared the first wave to receive the program with the third wave of mandals, where Smartcards were not introduced until after the final survey.

The program introduced two major changes to the existing payment system: it required beneficiaries to biometrically authenticate their identity before collecting payments, and it delivered payments through a Customer Service Provider (CSP) in each village, rather than at a more distant post office. When beneficiaries enrolled in the Smartcard program, their fingerprints and a photograph were taken, and they were issued a bank account and a Smartcard, which contained a chip storing the biometric and bank account information.

In order to collect a payment, beneficiaries visited the local CSP, who was usually a secondary school-educated woman from a traditionally disadvantaged caste who resided in the village. The CSP kept a small device which could read the beneficiary’s fingerprint and match it with the details stored in the Smartcard. If the match was successful, the CSP disbursed cash and the authentication device printed a receipt.

2010-09-01

2012-09-30

- Take-up rates
- Payment collection time
- Payment delays and variability
- Leakage (theft of government disbursements)
- Beneficiary satisfaction
- Cost effectiveness

The Smartcard project was India's first large-scale attempt to implement a biometric payments system. It was a composite intervention, introducing two complementary but conceptually distinct bundles of reforms: one set of technological changes, and one set of organizational ones. The Andhra Pradesh (AP) Smartcard project began in 2006, but took time to overcome initial implementation challenges including contracting, integration with existing systems, planning the logistics of enrollment and cash management, and developing processes for financial reporting and reconciliation. Because the government contracted with a unique bank to implement the project within each district, and because multiple banks participated, considerable heterogeneity in performance across districts emerged over time. In eight of twenty-three districts the responsible banks had made very little progress as of late 2009; in early 2010 the government decided to restart the program in these districts, and re-allocated their contracts to banks that had implemented Smartcards in other districts. This “fresh start” created an attractive setting for an experimental evaluation of Smartcards for two reasons. First, the roll-out of the intervention could be randomized in these eight districts. Second, the main implementation challenges had already been solved in other districts, yielding a “stable” implementation model prior to the evaluation.

Our evaluation was conducted in these eight districts, which have a combined rural population of around 19 million. While not randomly selected, they look similar to AP's remaining 13 non-urban districts on major socioeconomic indicators, including proportion rural, scheduled caste, literate, and agricultural laborers. They also span the state geographically, with representation in all three historically distinct socio-cultural regions: 2 in Coastal Andhra and 3 each in Rayalseema and Telangana. The study was conducted under a formal agreement between J-PAL South Asia and the Government of Andhra Pradesh (GoAP) to randomize the order in which mandals (subdistricts) were converted to the Smartcard system. We assigned a total of 296 mandals to treatment and control status by lottery as follows: 112 mandals were assigned to the treatment group, 139 to a “buffer” group, and 45 to a control group. We collected survey data only in the treatment and control groups; we created the buffer group to ensure we would have time to conduct endline surveys after Smartcards had been deployed in the treatment mandals but before they were deployed in the control mandals (during which period, enrollment could take place in the buffer group without affecting the control group). The realized lag between program rollout in treatment and control mandals was over two years. Randomization was stratified by district and by a principal component of socio-economic characteristics.

Randomization was done on computers using the R program

Mandal (sub-district)

Yes

296 mandals (sub-districts) in 8 districts

8,774 households sampled

Treatment group: 112 mandals
"Buffer" group: 139 mandals
Control group: 45 mandals

Anti-poverty programs in developing countries are often difficult to implement; in particular, many governments lack the capacity to deliver payments securely to targeted beneficiaries. We evaluate the impact of biometrically-authenticated payments infrastructure (“Smartcards”) on beneficiaries of employment (NREGS) and pension (SSP) programs in the Indian state of Andhra Pradesh, using a large-scale experiment that randomized the rollout of Smartcards over 157 sub-districts and 19 million people. We find that, while incompletely implemented, the new system delivered a faster, more predictable, and less corrupt NREGS payments process without adversely affecting program access. For each of these outcomes, treatment group distributions first-order stochastically dominated those of the control group. The investment was cost-effective, as time savings to NREGS beneficiaries alone were equal to the cost of the intervention, and there was also a significant reduction in the “leakage” of funds between the government and beneficiaries in both NREGS and SSP programs. Beneficiaries overwhelmingly preferred the new system for both programs. Overall, our results suggest that investing in secure payments infrastructure can significantly enhance “state capacity” to implement welfare programs in developing countries.

Muralidharan, Karthik, Paul Niehaus, and Sandip Sukhtankar. “Building State Capacity: Evidence from Biometric Smartcards in India.” Forthcoming (October 2016), American Economic Review.

https://www.povertyactionlab.org/sites/default/files/publications/388_490%20Smartcards%20Feb2016.pdf

Public employment programs play a major role in the anti-poverty strategy of many developing countries. Besides the direct wages provided to the poor, such programs are likely to affect their welfare by changing broader labor market outcomes including wages and private employment. These general equilibrium effects may accentuate or attenuate the direct benefits of the program, but have been difficult to estimate credibly. We estimate the general equilibrium effects of a technological reform that improved the implementation quality of India's public employment scheme on the earnings of the rural poor, using a large-scale experiment which randomized treatment across sub-districts of 60,000 people. We find that this reform had a large impact on the earnings of low-income households, and that these gains were overwhelmingly driven by higher private-sector earnings (90%) as opposed to earnings directly from the program (10%). These earnings gains reflect a 5.7% increase in market wages for rural unskilled labor, and a similar increase in reservation wages. We do not find evidence of distortions in factor allocation, including labor supply, migration, and land use. Our results highlight the importance of accounting for general equilibrium effects in evaluating programs, and also illustrate the feasibility of using large-scale experiments to study such effects.

Karthik Muralidharan, Paul Niehaus, and Sandip Sukhtankar. "General Equilibrium Effects of (Improving) Public Employment Programs: Experimental Evidence from India". Working Paper, September 2016.

http://econweb.ucsd.edu/~pniehaus/papers/GE.pdf

This companion piece describes process lessons from implementing the Smartcards program.

Mukhopadhyay, Piali, Karthik Muralidharan, Paul Niehaus, and Sandip Sukhtankar. "Implementing a Biometric Payment System: The Andhra Pradesh Experience." Policy Report, May 2013.

http://econweb.ucsd.edu/~kamurali/papers/Other%20Writing/Implementing%20a%20Biometric%20Payment%20System%20(18%20May%202013).pdf