According to Table 1, the 2015 transfer induced account usage among recipients which could have led to better knowledge of the terms of the account and higher trust in NBS. As a result, we let the utility that account holder _i_ derives from an account depend on the experimental group in 2015 _g_ and on the balance _m_ in the account. The experimental group in 2015 is _g_ = _C_ if account holder _i_ did not receive a transfer, _g_ = _T_ if account holder _i_ received the transfer in cash and _g_ = _D_ if the transfer received was directly deposited into the account.

In particular, we parameterize the value from the old account as _vg_ 0( _m_ ) = _αg_ 0 + _βg_ 0 _m_ , making explicit that the value of the account can be different depending on the treatment _g_ = _C, T_ or _D_ received in 2015. Similarly, the utility from opening a Pafupi account is _vg_ 1( _m_ ) = _αg_ 1 + _βg_ 1 _m_ . The term _b − ci_ is the net benefit from visiting the bank, as the account holder receives the cash incentive _b_ to visit the bank but must forgo cost _ci_ which also depends on _i_ ’s experimental group _g_ in 2015.<sup>7</sup> The higher account usage induced by the 2015 treatment could have lowered both the monetary cost of visiting the bank if participants discovered a cheaper mode of transportation or a more convenient route, and/or lowered its psychological cost if account holders that received transfers became more familiar with bank procedures or staff. We thus allow the 2015 treatment to affect the valuation of the accounts as well as the cost of going to the bank and we later test whether valuations and costs are the same across experimental arms.

In case of opening a Pafupi account, the individual must pay MK _c_ 1 = 1 _,_ 300. Finally, _δij_ = 1 if the account holder was asked to come to the bank the week following the visit, while it takes value _δij_ = _δj ∈_ (0 _,_ 1] if asked to come to the bank after the two-week delay, where we allow _δ_ to depend on choice _j_ , that is, on whether the account holder keeps the old account ( _j_ = 0), closes the account ( _j_ = _x_ ) or opens a Pafupi account ( _j_ = 1).

Intuitively, the two different treatments implemented during the home visits of the 2016 experiment help identify different parameters of the model: the show-up payment helps identify the cost of visiting the bank _ci_ (along with whether they received the transfer in 2015), while the timing of when they should visit the bank helps identify the discount parameters _δij_ . In addition, the balance data and whether they received the transfer in 2015 identify the parameters governing the value of the two accounts _vi_ 0 ( _m_ ) and _vi_ 1 ( _m_ ).

The probability of each choice can be written as:

> 7We note that _b_ refers to the cash incentive promised during the home visit. While some households were randomly selected to receive an additional amount (up to 1,500 MK) once they visited at the bank, the nested logit model cannot accommodate this surprise incentive in the second stage (choice of accounts). We estimate the model assuming that _b_ includes both the amount promised during the home visit and the additional surprise incentive and we obtain similar results.

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