Multiple social science researchers claim that online data collection, mainly via Amazon's Mechanical Turk (MTurk), has revolutionized the behavioral sciences (Gureckis et al. 2016; Litman, Robinson, and Abberbock 2017). While MTurk-based research has grown exponentially in recent years (Chandler and Shapiro 2016), reasonable concerns have been raised about online research participants' ability to proxy for traditional research participants (Chandler, Mueller, and Paolacci 2014). This paper reviews recent MTurk research and provides further guidance for recruiting samples of MTurk participants from populations of interest to behavioral accounting researchers. First, we provide guidance on the logistics of using MTurk and discuss the potential benefits offered by TurkPrime, a third-party service provider. Second, we discuss ways to overcome challenges related to targeted participant recruiting in an online environment. Finally, we offer suggestions for disclosures that authors may provide about their efforts to attract participants and analyze responses.

We investigate whether and how the structure of retirement savings incentives influences their relative attractiveness to taxpayers, independent of their effect on after-tax returns. To that end, we examine taxpayers’ preferences between defined contribution retirement plans with back-loaded (i.e., Roth) and front-loaded (i.e., traditional) tax incentives. In three experiments, we find limited evidence that individuals appropriately weight temporal tax rate changes, the primary factor differentiating after-tax returns across incentive structures, in their plan preferences. In contrast, we find consistent evidence that the incentive structure’s relation to taxpayers’ broader attitudes and preferences significantly impacts plan preferences. Overall, we find evidence that generally held attitudes and preferences lead to a systematic preference for back-loaded retirement plans even in situations in which taxpayers know that a back-loaded plan is economically dominated by a front-loaded plan. The results have implications for policymakers and others considering how best to encourage retirement savings.

The use of online workers as research participants has grown in recent years, prompting interest in how online workers compare to traditional accounting research participants. To date, no study has compared the relative intelligence of online workers to student subjects. Such a comparison may be important to behavioral accounting researchers given the homogeneity of accounting students relative to online subject pools and given prior research suggesting accounting students have relatively high analytic ability. While graduate students outperform both undergraduate and MTurk participants on common intelligence tests and across two reasonably complex tasks, we find that MTurk participants perform similarly to undergraduate accounting students. Overall, our results provide incremental assurance that MTurk participants are suitable subjects when accounting expertise is not explicitly required. We also provide evidence that screening MTurk workers on intelligence scores may benefit researchers who require participants with relatively high cognitive ability.

In an online experiment, the immediate (Roth) versus deferred taxation of retirement income affects taxpayers' investment decisions such that tax-deferred plan investors under-adjust for future tax burdens and overestimate their future wealth compared to Roth investors. When presented with a specific, after-tax monetary goal, Roth account holders invest more in higher-risk, higher-return assets than tax-deferred account holders. We investigate four aspects of this investment context that could alleviate these differences: 1) implementing a "do-your-best" goal, 2) reframing specific goals in pre-tax dollars, 3) explicitly prompting investors to estimate future tax burdens, and 4) providing performance feedback. These interventions reduce differences between Roth and tax-deferred investor behaviors, but do not entirely close the gap on their own. In combination, reframing goals and prompting future tax estimations encourage tax-deferred account holders to invest in risky assets to the same degree as Roth investors only when paired with performance feedback.

This study uses two online experiments to examine the potential consequences of a recent U.S. Senate proposal to prefill federal income tax returns on behalf of individuals. Consistent with omission theory, we find that prefilled returns lower compliance compared to selfcompleted returns when the prefilled returns do not estimate undocumented income (e.g., cash tips). However, the results show that including estimates of undocumented income increases taxpayer compliance relative to self-completed returns. We also find evidence suggesting that prefilled returns eliminate the often-replicated differences in reporting behavior between taxpayers in refund versus tax due settlement positions, suggesting that prefilled returns change individuals' reference points. These findings suggest the implementation of a prefilled return policy could have economically important effects on taxpayer decisions.