Redistribution generates equity benefits and deadweight loss. A canonical result in economic theory holds that policymakers generally cannot escape the problem of deadweight loss by redistributing through non-tax rules. Nonetheless, the Office of Management and Budget (OMB) released a revised framework for regulatory analysis in November 2023 that encouraged federal agencies to consider distributional benefits – but not the deadweight loss of redistribution – when choosing which non-tax rules to promulgate. By omitting deadweight loss from distributional analysis, OMB’s framework will lead to inaccurate estimates of the welfare effects of regulatory changes and will leave agencies vulnerable to legal attack on the ground that they have ignored an important aspect of regulatory redistribution in their decision-making. This article illustrates how agencies can incorporate deadweight loss into distributional analysis and thereby place their redistributive rules on firmer economic and legal footing. It shows how the “elasticity of taxable income” approach – widely used by tax economists – can be modified to the regulatory context so that agencies can estimate the deadweight loss of redistributive regulations while incurring relatively modest additional analytical burdens. Explicitly incorporating deadweight loss into distributional analysis will provide the public with a more accurate view of the welfare consequences of regulatory actions and will make regulations more robust to legal challenges.

The Biden administration has made equity a priority when issuing regulations, encouraging agencies to ensure that their regulations appropriately benefit and do not inappropriately burden disadvantaged groups. But scholarly examinations of agencies’ practices to date on understanding the distributional consequences of their regulations and on promoting equity have revealed significant gaps. In particular, agencies pay very little attention to the incidence of the costs of their regulations. The U.S. Environmental Protection Agency, for example, rarely considers the incidence of regulatory costs among disadvantaged groups, despite being an agency that conducts relatively complete benefit–cost analyses and explicitly analyzes environmental justice implications of its regulations. But this cost-blindness is a mistake; it presents a missed opportunity to use the current equity-focused momentum to make real improvements for disadvantaged groups that could have long-lasting effects. This essay calls for agencies to give more attention to the incidence of regulatory costs in order to identify needs and opportunities for grants and investments to disadvantaged groups. This approach could provide much-needed direction for a program like the Biden administration’s Justice40 initiative.

In this paper we introduce a computationally enriched experimental tool designed to investigate language ideology (change). In a free response experiment, 211 respondents returned three adjectives in reaction to the labels for five regional varieties, one ethnic variety and two supra-regional varieties of Belgian Dutch, as well as the standard accent of Netherlandic Dutch. Valence information (pertaining to the positive/negative character of the responses) and big data–based distributional analysis (to detect semantic similarity between the responses) were used to cluster the response adjectives into 11 positive and 11 negative evaluative dimensions. Correspondence analysis was subsequently used to compute and visualize the associations between these evaluative dimensions and the investigated language labels, in order to generate “perceptual maps” of the Belgian language repertoire. Contrary to our expectations, these maps unveiled not only the dominant value system which drives standard usage, but also the competing ideology which frames the increasingly occurring non-standard forms. In addition, they revealed a much richer stratification than the “one variety good, all other varieties bad” dichotomy we had anticipated: while VRT-Dutch remains the superior (albeit increasingly virtual) standard for Belgian Dutch, the stigmatized colloquial variety Tussentaal is gradually being accepted as a practical lingua franca, and the Ghent-accent is boosted by modern prestige (dynamism) features. Even more crucially, separate perceptual maps for the older and younger respondents lay bare generational change: there is a growing conceptual proximity between VRT-Dutch and Tussentaal in the younger perceptions.

The main fact that is currently known about the nature of masked L2-L1 noncognate translation priming effects in the lexical decision task is simply that those effects are significant in some studies but not in others. In an effort to better understand these effects, we examined the data pattern for very proficient Japanese–English bilinguals using RT distributional analyses. We also examined the impacts of prime and target frequency on the priming effect. Significant priming was present even on the fastest trials, becoming larger on slower trials. Nonetheless, priming effects were generally constant across prime and target frequency with the only exception being when very high frequency L2 primes were used. In that situation, priming and target frequency were negatively related, a result that essentially produced the observed pattern of increasing priming on slower trials. Implications of these results and potential reasons for the presence/absence of L2-L1 priming effects are discussed.