it may be optimal to work at a specific location and in _face-to-face_ (F2F) contact with the public or co-workers, suboptimal work arrangements are also feasible for some occupations, particularly during a pandemic.<sup>2</sup> Another caveat of using criteria where at least one sufficient condition has to be satisfied to categorize jobs is that it is not clear how to choose the number of conditions to consider when several alternatives are available. If only one condition needs to be satisfied to classify an occupation as not being able to be done at home, then the more conditions that the researcher adds to the list, the higher are the chances that at least one of them will be satisfied by a given job. For example, one of the data sets used in this paper includes a battery of questions to measure F2F contact. Two of these questions are “How often does your job usually involve sharing work-related information with co-workers?” and “How often does your job usually involve instructing, training or teaching people, individually or in groups?” A priori, both are valid proxy variables for F2F work, but while almost 100 percent of people respond “very often” to the first question in most countries, there is substantially more variation in the responses to the latter. More generally, the more questions we consider to measure F2F, the higher the fraction of workers that would be classified as having an F2F-intensive job. Discarding questions and data based on this empirical observation is somewhat arbitrary.

There are two studies that are exceptions to the one-sufficient-condition criteria. Mongey et al. (2020) construct WFH and physical proximity measures for the United States using O*NET data. For the WFH measure, they use the same set of task variables as Dingel & Neiman (2020a, 2020b), but instead of defining binary indicators, they allow both the WFH and physical proximity measures to vary between 0 and 1. Leibovici, Santacreu, & Famiglietti (2020) construct a contact-intensity measure for the United States using occupation-level information from O*NET and aggregating the possible scores for the question about performing tasks in close physical proximity to other people. The final measure can take a value from 0 to 100.

To measure jobs’ amenability to WFH, we exploit all the variables available in the data that describe job tasks related to home-based work. Instead of using a criterion based on satisfying at least one sufficient condition to classify occupations, we argue that the more (less) the conditions that are satisfied, the lower (higher) the amenability of a given job to be carried out at home. For example, according to our criteria, a job that satisfies three conditions would be less amenable to home-based-work than one that satisfies only one or two of those conditions. Accordingly, we also exploit categorical variables describing the intensity of different tasks, instead of transforming them into binary outcomes. While this approach still relies on the assumption that all

> 2 As a robustness check, we construct an index following a methodology more similar to that of Dingel and Neiman (2020a, 2020b) and Saltiel (2020) where only one condition needs to be satisfied in order for a job not to be amenable to WFH and find that it is highly correlated with our WFH measures (see Figure A 3).

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