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LitCovid-PD-FMA-UBERON

Id Subject Object Predicate Lexical cue fma_id
T1 1074-1080 Body_part denotes saliva http://purl.org/sig/ont/fma/fma59862
T2 2907-2912 Body_part denotes digit http://purl.org/sig/ont/fma/fma85518
T3 3934-3938 Body_part denotes foot http://purl.org/sig/ont/fma/fma9664
T4 7120-7124 Body_part denotes face http://purl.org/sig/ont/fma/fma24728
T5 8260-8264 Body_part denotes cell http://purl.org/sig/ont/fma/fma68646
T6 9518-9522 Body_part denotes face http://purl.org/sig/ont/fma/fma24728
T7 9639-9643 Body_part denotes face http://purl.org/sig/ont/fma/fma24728
T8 13478-13482 Body_part denotes hand http://purl.org/sig/ont/fma/fma9712
T9 14399-14405 Body_part denotes mental http://purl.org/sig/ont/fma/fma264279

LitCovid-PD-UBERON

Id Subject Object Predicate Lexical cue uberon_id
T1 1074-1080 Body_part denotes saliva http://purl.obolibrary.org/obo/UBERON_0001836
T2 2907-2912 Body_part denotes digit http://purl.obolibrary.org/obo/UBERON_0002544
T3 3863-3867 Body_part denotes feet http://purl.obolibrary.org/obo/UBERON_0002387
T4 3934-3938 Body_part denotes foot http://purl.obolibrary.org/obo/UBERON_0002387
T5 4048-4052 Body_part denotes feet http://purl.obolibrary.org/obo/UBERON_0002387
T6 7120-7124 Body_part denotes face http://purl.obolibrary.org/obo/UBERON_0001456
T7 9518-9522 Body_part denotes face http://purl.obolibrary.org/obo/UBERON_0001456
T8 9639-9643 Body_part denotes face http://purl.obolibrary.org/obo/UBERON_0001456
T9 10033-10038 Body_part denotes scale http://purl.obolibrary.org/obo/UBERON_0002542
T10 13478-13482 Body_part denotes hand http://purl.obolibrary.org/obo/UBERON_0002398
T11 15842-15847 Body_part denotes scale http://purl.obolibrary.org/obo/UBERON_0002542

LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T1 36-44 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T2 144-168 Disease denotes coronavirus disease 2019 http://purl.obolibrary.org/obo/MONDO_0100096
T3 170-178 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T4 992-1016 Disease denotes Coronavirus disease 2019 http://purl.obolibrary.org/obo/MONDO_0100096
T5 1018-1026 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T6 1232-1241 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550
T7 1632-1641 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T1 282-283 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T2 377-378 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T3 1176-1177 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T4 1202-1206 http://purl.obolibrary.org/obo/UBERON_0000473 denotes test
T5 1355-1356 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T6 1598-1599 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T7 1612-1613 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T8 1924-1927 http://purl.obolibrary.org/obo/CLO_0008697 denotes R<1
T9 1924-1927 http://purl.obolibrary.org/obo/CLO_0052381 denotes R<1
T10 2399-2400 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T11 2465-2466 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T12 2541-2548 http://purl.obolibrary.org/obo/OBI_0000968 denotes devices
T13 2907-2912 http://www.ebi.ac.uk/efo/EFO_0000881 denotes digit
T14 3238-3239 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T15 3428-3429 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T16 3511-3512 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T17 3671-3672 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T18 4426-4427 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T19 4579-4580 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T20 4997-4998 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T21 5570-5571 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T22 5670-5671 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T23 5966-5967 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T24 6353-6354 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T25 6473-6474 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T26 6534-6535 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T27 7120-7124 http://purl.obolibrary.org/obo/UBERON_0001456 denotes face
T28 8258-8264 http://purl.obolibrary.org/obo/CLO_0001020 denotes A cell
T29 8602-8603 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T30 8696-8697 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T31 8908-8909 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T32 8961-8962 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T33 8997-8999 http://purl.obolibrary.org/obo/CLO_0001236 denotes 2A
T34 9178-9179 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T35 9255-9256 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T36 9518-9522 http://purl.obolibrary.org/obo/UBERON_0001456 denotes face
T37 9639-9643 http://purl.obolibrary.org/obo/UBERON_0001456 denotes face
T38 9949-9956 http://purl.obolibrary.org/obo/CLO_0001236 denotes 2. (A)
T39 10079-10080 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T40 10279-10280 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T41 10436-10437 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T42 10521-10522 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T43 10739-10740 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T44 10788-10789 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T45 10890-10892 http://purl.obolibrary.org/obo/CLO_0001236 denotes 2A
T46 10938-10939 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T47 11224-11225 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T48 11289-11290 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T49 11496-11499 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T50 11565-11568 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T51 11754-11755 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T52 11853-11854 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T53 12097-12098 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T54 12290-12298 http://purl.obolibrary.org/obo/CLO_0037092 denotes tasks. 1
T55 12372-12373 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T56 12589-12590 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T57 12623-12624 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T58 12675-12677 http://purl.obolibrary.org/obo/CLO_0001382 denotes 48
T59 12907-12908 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T60 12952-12953 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T61 12969-12970 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T62 12985-12986 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T63 13566-13567 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T64 13924-13925 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T65 14518-14519 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T66 14665-14675 http://purl.obolibrary.org/obo/CLO_0001658 denotes activities
T67 14756-14769 http://purl.obolibrary.org/obo/OBI_0000245 denotes organizations
T68 15291-15292 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T69 15410-15411 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T70 15761-15762 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T71 16129-16136 http://www.ebi.ac.uk/efo/EFO_0000881 denotes Digital
T72 16403-16413 http://purl.obolibrary.org/obo/OBI_0000968 denotes instrument
T73 16608-16609 http://purl.obolibrary.org/obo/CLO_0001020 denotes a

LitCovid-PD-CHEBI

Id Subject Object Predicate Lexical cue chebi_id
T1 8268-8272 Chemical denotes gold http://purl.obolibrary.org/obo/CHEBI_29287|http://purl.obolibrary.org/obo/CHEBI_30050
T3 10854-10858 Chemical denotes gold http://purl.obolibrary.org/obo/CHEBI_29287|http://purl.obolibrary.org/obo/CHEBI_30050
T5 16107-16110 Chemical denotes MIT http://purl.obolibrary.org/obo/CHEBI_27847|http://purl.obolibrary.org/obo/CHEBI_53620

LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
1 36-44 Disease denotes COVID-19 MESH:C000657245
4 144-168 Disease denotes coronavirus disease 2019 MESH:C000657245
5 170-178 Disease denotes COVID-19 MESH:C000657245
12 1119-1125 Species denotes people Tax:9606
13 992-1016 Disease denotes Coronavirus disease 2019 MESH:C000657245
14 1018-1026 Disease denotes COVID-19 MESH:C000657245
15 1100-1108 Disease denotes infected MESH:D007239
16 1110-1118 Disease denotes Infected MESH:D007239
17 1232-1241 Disease denotes infection MESH:D007239
19 1632-1641 Disease denotes infection MESH:D007239
21 7978-7985 Species denotes tobacco Tax:4097
23 9594-9601 Species denotes tobacco Tax:4097
25 11345-11352 Species denotes tobacco Tax:4097
27 14418-14425 Disease denotes fitness MESH:D012640

LitCovid-PD-GO-BP

Id Subject Object Predicate Lexical cue
T1 1373-1384 http://purl.obolibrary.org/obo/GO_0065007 denotes regulations

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T1 0-54 Sentence denotes Rationing social contact during the COVID-19 pandemic:
T2 55-108 Sentence denotes Transmission risk and social benefits of US locations
T3 110-118 Sentence denotes Abstract
T4 119-254 Sentence denotes To prevent the spread of coronavirus disease 2019 (COVID-19), some types of public spaces have been shut down while others remain open.
T5 255-351 Sentence denotes These decisions constitute a judgment about the relative danger and benefits of those locations.
T6 352-600 Sentence denotes Using mobility data from a large sample of smartphones, nationally representative consumer preference surveys, and economic statistics, we measure the relative transmission reduction benefit and social cost of closing 26 categories of US locations.
T7 601-678 Sentence denotes Our categories include types of shops, entertainments, and service providers.
T8 679-846 Sentence denotes We rank categories by their trade-off of social benefits and transmission risk via dominance across 13 dimensions of risk and importance and through composite indexes.
T9 847-990 Sentence denotes We find that, from February to March 2020, there were larger declines in visits to locations that our measures indicate should be closed first.
T10 992-1109 Sentence denotes Coronavirus disease 2019 (COVID-19) is primarily spread by droplets of mucous and saliva from those who are infected.
T11 1110-1298 Sentence denotes Infected people are often asymptomatic (1), so, in the absence of a comprehensive system to test and trace individuals by infection status, all physical proximity is potentially dangerous.
T12 1299-1420 Sentence denotes To address this concern, policy makers have implemented a wide variety of regulations on work, locations, and gatherings.
T13 1421-1554 Sentence denotes Perhaps due to the infeasiblity of directly restricting visitor density, many of these restrictions vary by the type of the location.
T14 1555-1660 Sentence denotes We conceptualize the decision to shut down a location as a trade-off between infection risk and benefits.
T15 1661-1792 Sentence denotes In this paper, we make an empirical contribution regarding which types of locations pose the best and worst risk–reward trade-offs.
T16 1793-1952 Sentence denotes Governments should use this analysis to inform their decision making as they attempt to achieve their public health goals (such as R<1) at minimum social cost.
T17 1953-2058 Sentence denotes To do so, we combine several measures of the importance and danger of categories of stores and locations.
T18 2059-2193 Sentence denotes We consider 26 categories that correspond to North American Industry Classification System (NAICS) industries or combinations thereof.
T19 2195-2225 Sentence denotes Danger and Importance Measures
T20 2226-2257 Sentence denotes We collect data of three types.
T21 2258-2356 Sentence denotes These are data on the category’s transmission risk, economic output and costs, and consumer value.
T22 2357-2570 Sentence denotes To quantify the potential contribution of a location to disease transmission (i.e., its danger), we utilize a fine-grained dataset on mobility from approximately 47 million smartphone devices in the United States.
T23 2571-2658 Sentence denotes The data are collected by Safegraph, and record visits to 6 million points of interest.
T24 2659-2824 Sentence denotes The “visitation” data include information about the total number of visits, total number of visitors, home census tract of visitors, and timing and length of visits.
T25 2825-2944 Sentence denotes The “points of interest” data include information on location (full address), six-digit NAICS code, branding, and area.
T26 2945-3059 Sentence denotes The 26 location categories of interest account for ∼57% of all unique visits from January 2019 through March 2020.
T27 3060-3193 Sentence denotes Out of all categories, full-service restaurants (sit-down) is the most popular in terms of both number of visits and unique visitors.
T28 3194-3378 Sentence denotes Between February and March 2020, we observe a 24.9% drop in the total number of visits at all locations included in the analysis, reflecting the social distancing implemented in March.
T29 3379-3629 Sentence denotes To account for the fact that our data cover only a fraction of individuals in the United States, we upscale every observed visit as a function of the visitor’s home census tract to approximate the real number of visits and visitors for each location.
T30 3630-3691 Sentence denotes We create nine monthly level measures of a location’s danger.
T31 3692-3736 Sentence denotes Four are based directly on total visit data.
T32 3737-4131 Sentence denotes These are total visits, total unique visitors, person-hours of visits during crowding of more than one visitor per 113 square feet (reflecting the Centers for Disease Control and Prevention’s “six-foot” social distancing rule), and person-hours of visits during crowding of more than one visitor per 215 square feet (reflecting the German social distancing guideline of one customer per 20 m2).
T33 4132-4220 Sentence denotes Using information on the home census tract of visitors, we add five additional measures.
T34 4221-4518 Sentence denotes Four are analogous to the first four, but restricted to visits by individuals age 65 y and older (age estimates are based on the assumption that visits from older guests are proportional to their share of a census tract’s population; we use visits from all guests in calculating location density).
T35 4519-4590 Sentence denotes The final danger measure is the median distance traveled to a location.
T36 4591-4801 Sentence denotes To identify the cumulative danger of an entire category of locations, we sum the individual measures of all locations within the category (except for distance traveled, where we use the visit-weighted average).
T37 4802-4969 Sentence denotes While, in this analysis, we weigh all nine danger measures equally, our results are very similar to those when restricting attention to the first four danger measures.
T38 4970-5068 Sentence denotes We measure the benefits of a location as coming from both its economic and consumer contributions.
T39 5069-5166 Sentence denotes Our economic data come from the most recent edition of the US Census Statistics of US Businesses.
T40 5167-5302 Sentence denotes Across our 26 categories, there are 1,427,433 firms and 2,024,839 establishments, compared to 2,029,514 geolocation points of interest.
T41 5303-5391 Sentence denotes Our measures of economic importance consist of annual payroll, receipts, and employment.
T42 5392-5627 Sentence denotes Our 26 categories encompass 32 million employees, 1.1 trillion dollars in annual payroll, and 5.6 trillion dollars in annual receipts.* To measure consumer welfare, we conducted a nationally representative survey of 1,099 US residents.
T43 5628-5728 Sentence denotes Respondents were recruited through Lucid, a market research firm, during April 13 to April 15, 2020.
T44 5729-5863 Sentence denotes The survey was determined to be exempt by Massachusetts Institute of Technology’s (MIT’s) Institutional Review Board (Project E-2115).
T45 5864-5935 Sentence denotes The sample is representative by age, gender, ethnicity, and region (2).
T46 5936-6153 Sentence denotes Each respondent takes part in a series of single binary discrete choice experiments (3) where they choose which location, among two options, they would prefer to be open, whether or not the location is currently open.
T47 6154-6256 Sentence denotes Discrete choice experiments have been widely used to measure valuations of market and nonmarket goods.
T48 6257-6440 Sentence denotes To make responses consequential and incentivize respondents to respond truthfully, we gave them a chance to earn an additional monetary reward which was linked with their choices (4).
T49 6441-6520 Sentence denotes Each respondent participated in a series of binary discrete choice experiments.
T50 6521-6562 Sentence denotes We solicited a total of 32,970 decisions.
T51 6563-6667 Sentence denotes Our consumer importance measure ranks categories by the proportion of times it is preferred over others.
T52 6669-6676 Sentence denotes Results
T53 6677-7053 Sentence denotes We juxtapose how different locations fare along our four dimensions of importance (consumer importance, employment, payroll, and receipts) and nine dimensions of transmission risk (visits, unique visitors, person hours at moderate density, and person hours at high density; the same four measures for only individuals age 65 y and older; and average median distance traveled).
T54 7054-7145 Sentence denotes The core idea is that locations offering better trade-offs should face looser restrictions.
T55 7146-7333 Sentence denotes The most conservative way to make this comparison is to look at whether there are any locations that dominate another in all dimensions of lower transmission danger and higher importance.
T56 7334-7482 Sentence denotes This measure is conservative in the sense that any possible weighed aggregate measure of risk or importance will yield the same pairwise comparison.
T57 7483-7559 Sentence denotes Of our 26 categories, 13 do not dominate and are not dominated by any other.
T58 7560-7711 Sentence denotes Of the 13 remaining categories, 1) gyms and 2) cafes, juice bars, and dessert parlors are the two categories with the most dominated pairings (Fig. 1).
T59 7712-7870 Sentence denotes According to our measure, each of these locations should be opened only after banks, dentists, colleges, places of worship, and auto dealers and repair shops.
T60 7871-8019 Sentence denotes Within types of stores, we find electronics stores and furniture stores should be opened before liquor and tobacco stores and sporting goods stores.
T61 8020-8195 Sentence denotes The two locations that come out the best in this measure are banks and finance, with six dominant pairwise comparisons, and dentists, with three dominant pairwise comparisons.
T62 8196-8203 Sentence denotes Fig. 1.
T63 8205-8257 Sentence denotes Grid indicating dominating and dominated categories.
T64 8258-8403 Sentence denotes A cell is gold if the row category is better on all nine risk and four importance dimensions than the column category, and blue for the converse.
T65 8404-8551 Sentence denotes Another way to determine which locations offer the best trade-offs is to create overall indexes of danger and importance, and to look for outliers.
T66 8552-8641 Sentence denotes We create our danger index as the average rank of a category in the nine danger measures.
T67 8642-8777 Sentence denotes We create our importance index as the average rank of a category in our three economic importance and one consumer importance measures.
T68 8778-8898 Sentence denotes We up-weight the consumer importance measure so that it is equally weighted with the three economic importance measures.
T69 8899-9001 Sentence denotes There is a strong positive relationship between the danger of a category and its importance (Fig. 2A).
T70 9002-9036 Sentence denotes However, there are clear outliers.
T71 9037-9165 Sentence denotes Categories in the top left corner have high importance but low danger, and vice versa for categories in the bottom right corner.
T72 9166-9286 Sentence denotes We estimate a linear regression, including an intercept term, of the importance index as a function of the danger index.
T73 9287-9390 Sentence denotes Categories are colored by the value of the residual, which corresponds to the quality of the trade-off.
T74 9391-9553 Sentence denotes We find that banks, general merchandise stores (e.g., Walmart), dentists, grocery stores, and colleges and universities should face relatively loose restrictions.
T75 9554-9674 Sentence denotes Gyms, sporting goods stores, liquor and tobacco stores, bookstores, and cafes should face relatively tight restrictions.
T76 9675-9751 Sentence denotes Our methodology also allows us to do similar analyses for different regions.
T77 9752-9943 Sentence denotes Splitting our analysis by metropolitan and nonmetropolitan locations yields remarkably similar results, suggesting that the urban–rural divide is not an important dimension for policy makers.
T78 9944-10022 Sentence denotes Fig. 2. (A) Category cumulative importance index and cumulative danger index.
T79 10023-10143 Sentence denotes The color scale reflects the residuals, by category, of a linear regression of the importance index on the danger index.
T80 10144-10362 Sentence denotes Golden categories have disproportionately high importance for their risk, and blue categories have disproportionately low importance. (B) Change in location category visits versus the category importance–risk residual.
T81 10363-10426 Sentence denotes Marker sizes are proportional to total visits in February 2020.
T82 10427-10520 Sentence denotes There is a dramatic decrease in visits to all of these locations from February to March 2020.
T83 10521-10690 Sentence denotes A natural final question is whether these reductions in visits are spread evenly across locations, or whether the reductions follow the risk–reward trade-off we measure.
T84 10691-10894 Sentence denotes Fig. 2B plots the percent decrease in visits to a location type, from February to March 2020, as a function of “importance–risk trade-off favorability” (i.e., the gold to blue categorization in Fig. 2A).
T85 10895-10969 Sentence denotes Weighing by February 2020 visits, there is a strong positive relationship.
T86 10970-11112 Sentence denotes This suggests that at least some of the cost–benefit analysis we measure is being internalized by US consumers, businesses, and policy makers.
T87 11113-11197 Sentence denotes Two of the largest outliers are 1) colleges and universities and 2) hardware stores.
T88 11198-11313 Sentence denotes We find colleges to offer a relatively good trade-off, but most have shut down, leading to a 61% decline in visits.
T89 11314-11525 Sentence denotes Conversely, we find liquor and tobacco stores to be relatively poor trade-offs (due to mediocre economic importance and small busy stores), yet the number of visits to this category has declined by less than 5%.
T90 11526-11688 Sentence denotes Hardware stores are the location which has seen the largest increase in visits, as individuals scrounge for personal protective equipment and other home supplies.
T91 11689-11840 Sentence denotes It is important to note that these visitation changes are due to a mix of federal, state, and local government, business, and individual level actions.
T92 11842-11852 Sentence denotes Discussion
T93 11853-11972 Sentence denotes A potential limitation of this analysis is that visitors to some location types are more concentrated within the space.
T94 11973-12093 Sentence denotes We can partly account for this effect by measuring which locations offer services that require close physical proximity.
T95 12094-12296 Sentence denotes In a complementary analysis, we merge in Occupational Employment Statistics (OES) data on occupational employment mix by category and Occupational Information Network (O*NET) data on occupational tasks.
T96 12297-12527 Sentence denotes 1) Dentists and 2) barbershops and salons are the only two categories with a high share of workers requiring intense physical proximity (72% and 58%, respectively, of workers in these industries have proximity scores of over 90%).
T97 12528-12777 Sentence denotes Additionally, movie theaters, gyms, and amusement parks have a high share of workers requiring a moderately high level of physical proximity (57%, 48%, 42%, respectively, of workers in these industries have proximity requirement scores of over 80%).
T98 12778-12968 Sentence denotes We do not include these data in our main analysis because the need to be in close contact with visitors impacts both the risk of a category and the economic cost of shutting a category down.
T99 12969-13165 Sentence denotes A category with a high share of workers who do not require close proximity to visitors will find it easier to reengineer itself to increase social distance, as well as to allow for work from home.
T100 13166-13320 Sentence denotes Most retailers can offer curbside pickup rather than forcing customers to enter crowded stores (and, indeed, most states are encouraging curbside pickup).
T101 13321-13375 Sentence denotes Locations can also be made safer through use of masks.
T102 13376-13464 Sentence denotes This is especially important for locations like museums, with limited physical touching.
T103 13465-13565 Sentence denotes On the other hand, locations like gyms both emphasize physical contact and make mask use unpleasant.
T104 13566-13699 Sentence denotes A more important limitation of our data is that we incorporate no information about linkages or complementarities between industries.
T105 13700-14003 Sentence denotes If one industry is shut down, it could decrease the revenues, employment, consumer surplus, and visits of another (e.g., by depriving them of an important input), or increase them (e.g., by effectively “raising the cost” of a close substitute; we may be seeing this with restaurants and grocery stores).
T106 14004-14095 Sentence denotes In the current analysis, we effectively assume that all industries are perfect substitutes.
T107 14096-14223 Sentence denotes There are other limitations in our analysis in terms of factors that impact our rankings of both location importance and risks.
T108 14224-14440 Sentence denotes On the importance side, our binary choices do not yield information on the intensity of preferences, and leave out potentially important externalities from some locations (on mental or physical fitness, for example).
T109 14441-14566 Sentence denotes Moreover, our survey sample size is limited, and further research should use a major survey research firm and larger samples.
T110 14567-14733 Sentence denotes On the risk side, we fail to account for the fact that some locations encourage reckless physical activities or might disproportionately accommodate “superspreaders.”
T111 14734-14877 Sentence denotes Governments and civic organizations across the world have made different decisions about how to implement and relax social distancing measures.
T112 14878-14935 Sentence denotes As they do so, they have various tools at their disposal.
T113 14936-15022 Sentence denotes In the United States, many of these restrictions have been location category specific.
T114 15023-15159 Sentence denotes Details have varied from state to state, with gyms, places of worship, and liquor stores receiving particularly heterogeneous treatment.
T115 15160-15218 Sentence denotes Why are different states adopting such different policies?
T116 15219-15302 Sentence denotes One possibility is state-level variation in the importance or danger of a category.
T117 15303-15423 Sentence denotes This variation would have to be separate from urban–rural heterogeneity, which we find to not make much of a difference.
T118 15424-15545 Sentence denotes Another possibility is that, in the absence of empirical evidence, states are being forced to make decisions in the dark.
T119 15546-15673 Sentence denotes If so, we recommend that policy makers conduct analyses similar to the ones described in this paper, specific to their regions.
T120 15674-15782 Sentence denotes Regional mobility, credit card transaction, and other relevant data are available from a variety of sources.
T121 15783-15951 Sentence denotes This should be complemented with regularly conducted large-scale online consumer preference surveys to account for heterogeneity across regions, demographics, and time.
T122 15953-15999 Sentence denotes We thank Jonathan Wolf and Safegraph for data.
T123 16000-16080 Sentence denotes We thank Erik Brynjolfsson, Sinan Aral, and Dean Eckles for invaluable feedback.
T124 16081-16166 Sentence denotes We additionally thank the MIT Initiative on the Digital Economy for research funding.
T125 16167-16221 Sentence denotes We thank Victor Yifan Ye for help with visualizations.
T126 16222-16241 Sentence denotes We thank Maxwell H.
T127 16242-16274 Sentence denotes Levy, M.D. for medical insights.
T128 16275-16329 Sentence denotes We thank Manuela Collis for feedback on survey design.
T129 16330-16372 Sentence denotes The authors declare no competing interest.
T130 16373-16389 Sentence denotes Data deposition:
T131 16390-16516 Sentence denotes Data, survey instrument, derived statistics and code are available at GitHub (https://github.com/chrisnic12/RationingContact).
T132 16517-16715 Sentence denotes *Usually, public economic analyses of welfare exclude changes in labor costs in evaluating a policy, because the workers directly employed by the policy would have collected the same wage elsewhere.
T133 16716-16779 Sentence denotes However, during this crisis, there is dramatic underemployment.
T134 16780-16917 Sentence denotes Therefore, the work forces of these industries have very low opportunity costs, and their production should be counted in social surplus.