Do incentives to save lead to borrowing? Evidence from a large-scale study

 



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A plenty of of policies aimed at increasing savings are currently in place, with many of them utilizing nudges. These policies are based on the assumption that savings are financed by lower consumption. However, when policymakers or researchers evaluate these interventions, they frequently focus on immediate savings outcomes without considering where the money comes from, despite the fact that a large proportion of households have liquid savings while also carrying credit card debt.

We investigate whether saving nudges increase credit card borrowing in a recent working paper. We combine data from a large-scale field experiment with detailed and accurate panel data on individual bank accounts and credit cards. The information came from Banorte, a Mexican bank that conducted a randomized experiment with 3,054,438 customers. The remaining 374,893 customers received no messages, while 2,679,545 received weekly or bi-weekly ATM and SMS messages encouraging them to save for 7 weeks during fall 2019.

We pay special attention to the borrowing response of individuals with the largest predicted response to the nudge. Potential unintended consequences of saving nudges are more likely for them. We use a causal forest to predict treatment effects at the individual level in order to identify them. The causal forest allows us to avoid the over-fitting issues that would inevitably arise if we manually searched for subpopulations with large treatment effects. A manual search would attribute large treatment effects to subpopulations where some observations show unusually large savings due to idiosyncratic shocks that could also affect borrowing outcomes. The causal forest, on the other hand, is based on a repeated split-sample procedure in which one sample is used to partition the covariate space and another to estimate the treatment effects. This rule outs the possibility that pre-treatment covariates predict a large treatment effect due to idiosyncratic shocks that may also influence other outcomes, such as borrowing decisions.



Figure 1 shows how we divided individuals into quartiles of predicted treatment effects based on the causal forest and calculated the actual treatment effects for each group. In essence, the forest identifies two groups of people: a large first group with no treatment effect (quartiles 1–3 of the predicted treatment effects), and a smaller second group with positive and significant treatment effects (the top quartile of the predicted treatment effects).

Figure 1. Treatment effect on checking account balances as a function of predicted individual treatment effects



Then, among those in the top quartile of predicted treatment effects, we examine the saving and borrowing behavior of people who have a credit card. Savings increased by 6.1 percent from a base of 31,702 MXN (3,392 USD) in the control group (1 MXN = 0.107 USD PPP). This represents a 1,948 MXN increase (208 USD). As illustrated in Figure 2, we rule out the possibility that these increased savings were accompanied by an increase in debt, as we find no significant effect on credit card borrowing. Figure 2 also shows a similar pattern for people who had a credit card and paid credit card interest at the start, or who had a credit card and had a large credit limit available on it.

Figure 2. Treatment effects on checking account balances, credit card balances, and credit card interest for people in the top quartile of predicted savings treatment effects, according to the causal forest




We then present two additional findings. First, in response to the savings nudge, spending decreases as measured by ATM withdrawals and debit and credit card expenses. Second, people who had credit card debt at the start do not use their new savings to pay off existing debt. The latter finding implies that the savings nudge exacerbated the holding of low-interest savings and high-interest debt at the same time. The average credit card interest rate in our sample is 35.2 percent, while checking accounts pay no interest. Despite the large price differences, 13.5 percent of people who pay credit card interest keep balances in their checking accounts that exceed 50 percent of their income (minimum balance observed over the 6 months previous to the intervention). Furthermore, nearly half of those in the co-holding group are also in the top quartile of predicted treatment effects from the nudge.

As a result, we wonder whether our main findings, that savings nudges reduce consumption rather than increase borrowing, are instructive about the leading explanations for the co-holding puzzle. We contend that variants of mental accounting models are likely to predict a null effect on debt from an exogenous increase in savings. Individuals effectively remove a certain amount of money (labeled as savings) from their consumption-borrowing problem by keeping savings in a separate mental account.

In summary, we find that nudge-induced savings are financed by reductions in consumption rather than new debt. Incentives result in net savings increases regardless of previous debt levels. For some people, this may be second best because they would be better off paying off existing debt. We contend that this is because people process saving and borrowing decisions in separate mental accounts.





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