University applicants in England will be given clearer information about student loans before they take them out, the government has said. Ministers said they would spell out that governments can change repayment rules and that different career choices can affect how much borrowers repay. They agreed to some but not all of the recommendations made by MPs after an inquiry into student loans, and did not say whether they would reverse a freeze to the repayment threshold for some graduates. The inquiry found the way loans had been presented to teenagers amounted to mis-selling, after the BBC revealed the Department for Education (DfE) had compared repayments to £30-a-month phone contracts. The recent debate has centred around Plan 2 loans, which were issued in England between September 2012 and July 2023, and are still issued in Wales.

In many settings, interventions may be more effective for some individuals than others, so that targeting interventions may be beneficial. We analyze the value of targeting in the context of a large-scale field experiment with over 53,000 college students, where the goal was to use "nudges" to encourage students to renew their financial-aid applications before a non-binding deadline. We begin with baseline approaches to targeting. First, we target based on a causal forest that estimates heterogeneous treatment effects and then assigns students to treatment according to those estimated to have the highest treatment effects. Next, we evaluate two alternative targeting policies, one targeting students with low predicted probability of renewing financial aid in the absence of the treatment, the other targeting those with high probability. The predicted baseline outcome is not the ideal criterion for targeting, nor is it a priori clear whether to prioritize low, high, or intermediate predicted probability. Nonetheless, targeting on low baseline outcomes is common in practice, for example because the relationship between individual characteristics and treatment effects is often difficult or impossible to estimate with historical data. We propose hybrid approaches that incorporate the strengths of both predictive approaches (accurate estimation) and causal approaches (correct criterion); we show that targeting intermediate baseline outcomes is most effective, while targeting based on low baseline outcomes is detrimental. In one year of the experiment, nudging all students improved early filing by an average of 6.4 percentage points over a baseline average of 37% filing, and we estimate that targeting half of the students using our preferred policy attains around 75% of this benefit.

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