X is a set of explanatory variables; µ are district fixed effects, and ε is the error term. We estimate the equation set separately for financial access and use and, likewise, separately for 2014 (short-run) and 2015 (long-run). This enables us to ascertain the short- and long-term impact of the JAM trinity. Each equation includes an appropriate set of control variables that enable us to clearly identify the equation, while allowing for possible interdependencies across equations.
The results are set out in Table 8. In Panel A, we find that individuals with Aadhaar cards were less likely to have a PMJDY account in the short run, although the reverse was the case for individuals with mobile phones. These findings are reinforced in column 3 where we find a positive and statistically significant coefficient on Aadhaar. In other words, in the immediate post-PMJDY phase, Aadhaar and mobile telephony were complementary, with each reinforcing the other. However, there was not much effect of Aadhaar on the ownership of PMJDY accounts; if anything, the relationship was the opposite, suggesting that individuals with Aadhaar cards were not inclined to open such accounts.
Table 8:3SLS estimation of JAM Trinity.
Note: Standard errors in brackets. ***, **, and * denote statistical significance at the 1, 5, and 10%, respectively.
These findings broadly carry over to the use of accounts, except for the fact that we find two significant differences. First, individuals with Aadhaar cards are more likely to use PMJDY account, contrary to the results obtained in case of account ownership.
On the other hand, the results for 2015 show that individuals with Aadhaar cards are more likely to own PMJDY accounts, contrary to the findings for 2014 where the coefficient had a negative sign. This finding can be explained by the fact that the passage of the Aadhaar (Targeted Delivery of Financial and Other Subsidies, Benefits and Services) Act in March 2016 provided statutory backing for targeted delivery of government subsidies by integrating them with Aadhaar numbers and, as a result, made it incentive-compatible for individuals with Aadhaar cards to use PMJDY accounts. In addition, individuals with PMJDY accounts are less likely to own a mobile phone, presumably reflecting the fact that having a mobile phone is not necessarily to having such an account.
The PMJDY approach was based on the twin strategies of a push and a pull. The former entails leveraging the banking architecture to address the ‘last mile’ challenge by imaginatively exploiting the use of Business Correspondents (BCs) as well as recruiting fresh BCs with adequate incentives. This is complemented with exploiting the telecommunications network in order to fast-forward the growth of mobile banking. The infrastructure includes the Intermediate Payment System (IMPS) for which standards and protocols are already in place.
The pull strategy comprised three elements: massive media campaign creating a buzz around the program; offer of accidental death insurance on all accounts that are opened under the scheme; offer of a potential overdraft facility; and finally, making application process and logistics simple and easy.
In order to address the challenges related to information gaps, Aadhaar has been made an essential piece of the PMJDY campaign. In effect, it has been stated that, to the extent possible, Aadhaar numbers will be used as e-KYC for opening bank accounts and when Aadhaar enrollments are lagging, the Unique Identification Authority of India (UIDAI) will coordinate with banks to ensure that such
This holistic approach towards exploiting innovations to further the cause of financial inclusion also seems beneficial. They have enabled to build up the credit profile of individuals or firms, which can subsequently be used as the basis for obtaining loans even in the absence of collateral, in turn moderating the transactions costs of gathering information about these borrowers, who have no or limited credit history. The technology is also proving to be a boon for a method of alternative credit scoring, such that information about bill payments and deposits is collected and used, possibly in the context of big data, to determine the likelihood of a person being a good credit risk. In addition, it can also provide a mechanism for easily collecting data about access to and patterns of usage of financial services.
To examine the effect of PMJDY on the access to and use of accounts by households, we use the three waves of Financial Inclusion Insights Survey (FIIS) by InterMedia, a private company focusing on mobile money and supported by the Bill and Melinda Gates Foundation. The survey is not a panel but cross-sectional data representative at the state level.2 Using this database, we extract information on the variables of interest such as whether the respondent has a bank account and whether the active is used actively (i.e. used to conduct financial transactions in the past 90 days). We also take into account other individual and household determinants, such as the gender (female vs. male), location (rural vs. urban), income profile (based on the Progress out of Poverty Index, PPI),3 work status and education status, marital status, holding of Aadhaar card, and receiving G2P payments in the account. We also take into account the district domestic product to control for the demand-side conditions and the number of bank branches per 1000 persons as a proxy for financial infrastructure.
For purposes of brevity, we report only the coefficients of interest and, more specifically, how the access to and use of accounts played out during the pre- and post-PMJDY periods. With access as the dependent variable, we use the Probit model, whereas when use is the dependent variable of interest we use the Heckman two-stage model. Accordingly, in stage 1, the dependent variable is a dummy depending on whether the household has access to bank account, else zero, and in stage II, the regression is estimated only for non-zero numbers. The results are shown in Table 9.
We find that access to finance in the rural areas has improved in the post-PMJDY period. To illustrate, in column 1, the coefficient on Rural * Pre-PMJDY equals −0.12, and the coefficient on Rural * Post-PMJDY equals 0.23. Both these coefficients are statistically significant at the 0.01 level. In addition, the p-value of the t-test shows that the differences between these coefficients are also statistically significant. In other words, access to finance in rural areas has become reliably higher in the post-PMJDY regime. We also find similar evidence in case of access to bank accounts for females and for persons below the poverty line (BPL). However, when we look at use of bank accounts, the evidence is less convincing, corroborating the prior cross-country evidence.
Table 9:All accounts with pre- and post-PMJDY effects.
Notes: Standard errors in parentheses. ***p < 0.01; **p < 0.05; *p < 0.10.
Technology and Financial Inclusion
A key element of the inclusion story is the use of technology. Three major developments have brought technology in a big way into the inclusion initiative. The first is the decision by the Reserve Bank to appoint Business Correspondents — extension agents of the banks to deal with small-ticket transactions and reach out to people in rural and remote areas. On the one hand, the Reserve Bank allowed banks to appoint agents, and on the other, mandated banks to have a point of presence in all locations with a minimum threshold population. The second initiative has been the use of biometric identification (Aadhaar). This has enabled the provision of a unique identity to each individual. The third is the mapping of Aadhaar numbers to the bank accounts, thereby becoming a base for technology-enabled
