Governments' Responses to COVID-19 (Response2covid19)

Presence of Common Scales: country: name of the country or the territory; geodi: two-letters country code; iso: three-letters country code; d: date of the observation; cases: number of cases reported on the given day by the European Centre for Disease Prevention and Control; deaths: number of deaths reported on the given day by the European Centre for Disease Prevention and Control; school: binary variable equal to1 if schools were closed and 0 otherwise; school_local: binary flag to distinguish localized school closures from other cases. 1 denotes that school closures were implemented at the local level and 0 denotes that school closures were not implemented at the local level (either at the national level or no school closures). The data on the scale of school closures is imported from the UNESCO. The interaction of school and school_local allows researchers to create three levels of measures: no school closures (school=0 and school_local=0), localized school closures (school=1 and school_local==1) or national school closures (school=1 and school_local=0); domestic: binary variable equal to 1 if there was a domestic lockdown and 0 otherwise; domestic_local: binary variable to distinguish localized domestic lockdowns from other cases. 1 denotes that domestic lockdowns were implemented at the local level and 0 means that domestic lockdowns were not implemented at the local-level (either at the national level or not implemented). The nature of the domestic lockdown is based on our reading of the measures reported by the ACAPS. The interaction of domestic and domestic_local allows researchers to create three levels of measures: no domestic lockdown (domestic=0 and domestic_local=0), localized domestic lockdowns (domestic=1 and domestic_local=1) or national domestic lockdowns (domestic=1 and domestic_local=0); travel: binary variable equal to1 if travel restrictions were implemented and 0 otherwise; travel_partial: binary flag to differentiate partial travel restrictions from other cases. 1 denotes that travel restrictions were partial and 0 denotes that travel restrictions were not partial (either strict or not implemented). The nature of the travel restrictions is based on our reading of the measures reported by the ACAPS. The interaction of travel and travel_partial allows researchers to create three levels of measures: no travel restrictions (travel=0 and travel_partial=0), partial travel restrictions (travel=1 and travel_partial=1) or strict travel restrictions (travel=1 and travel_partial=0); travel_dom: binary variable equal to1 if travel restrictions within the country (e.g. inter-region travels) were implemented and 0 otherwise; travel_dom_partial: binary flag to differentiate partial domestic travel restrictions from other cases. 1 denotes that travel restrictions were partial and 0 denotes that travel restrictions were not partial (either strict or not implemented). The nature of the travel restrictions is based on our reading of the measures reported by the ACAPS. The interaction of travel and travel_partial allows researchers to create three levels of measures: no domestic travel restrictions (travel_dom=0 and travel_dom_partial=0), partial domestic travel restrictions (travel_dom=1 and travel_dom_partial=1) or strict domestic travel restrictions (travel_dom=1 and travel_dom_partial=0); curf: binary variable equal to1 if a curfew was implemented and 0 otherwise; curf_partial: binary flag to differentiate partial curfews from other cases. 1 denotes that the curfew was partial and 0 denotes that the curfew was not partial (either strict or not implemented). The nature of the curfew is based on our reading of the measures reported by the ACAPS. The interaction of curf and curf_partial allows researchers to create three levels of measures: no curfew (curf=0 and curf_partial=0), partial curfew (curf=1 and curf_partial=1) or strict curfew (curf=1 and curf_partial=0); mass: binary variable equal to1 if bans on mass gatherings were implemented and 0 otherwise; mass_partial: binary flag to distinguish localized bans on mass gatherings from other cases. 1 denotes that bans on mass gatherings were partial and 0 denotes that bans on mass gatherings were not partial (either strict or not implemented). The nature of the bans on mass gatherings is based on our reading of the measures reported by the ACAPS. The interaction of mass and mass_partial allows researchers to create three levels of measures: no bans on mass gatherings (mass=0 and mass_partial=0), localized or partial bans (mass=1 and mass_partial=1) or national or strict bans (mass=1 and mass_partial=0); elect: binary variable equal to1 if some elections were postponed and 0 otherwise; elect_partial: binary flag to differentiate countries which postponed only some of the elections from the others. 1 denotes that countries both maintained and postponed elections and 0 denotes that elections were either postponed, maintained or were not scheduled. IDEA lists all maintained and postponed elections since the beginning of 2020. The interaction of elect and elect_partial allows researchers to differentiate three settings: all elections were maintained despite COVID-19 (elect=0 and elect_partial=0), some elections were maintained and others were postponed (elect=1 and elect_partial=1) or all elections were postponed (elect=1 and elect_partial=0); sport: binary variable equal to1 if bans on sporting and large events were implemented and 0 otherwise; sport_partial: binary flag to distinguish partial bans and cancellations of sporting and large events. 1 denotes that bans on sporting and large events were localized, strict or with no spectators, 0 that bans on sporting and large events are not localized or partial (either national or no measures implemented). The nature of the bans on sporting and large events is based on our reading of the measures reported by the ACAPS. The interaction of sport and sport_partial allows researchers to create three levels of measures: no bans (sport=0 and sport_partial=0), partial bans (sport=1 and sport_partial=1) or national bans on mass gatherings (sport=1 and sport_partial=0); rest: binary variable equal to1 if restaurants were closed and 0 otherwise; rest_local: binary flag to distinguish localized and/or partial restaurant and bar closures from other cases. The variable is coded 1 in the three following situations: localized closures, limitations on the number of customers in bars and restaurants, and closures of either bars or restaurants. 0 indicates national closures or no closures at all. The coding is based on our reading of the measures reported by the ACAPS. The interaction of rest and rest_local allows researchers to create three levels of measures: no closures (rest=0 and rest_local=0), localized closures (rest=1 and rest_local=1) or national closures (rest=1 and rest_local=0); testing: binary variable equal to1 if there was a public testing policy and 0 otherwise; testing_narrow: binary flag to distinguish narrow testing policies from large testing policies. 1 denotes that testing policies were targeted to some individuals, 0 that testing policies were not targeted (either large or not implemented). The nature of the testing policy is based on the information reported in the measures “mass population testing” and “testing policy” in the ACAPS. When the measure was targeted, testing_narrow was coded 1. On the contrary, when the measure was not targeted, testing_narrow was coded 0. The interaction of testing and testing_narrow allows researchers to create three levels of measures: no testing policy (testing=0 and testing_narrow =0), narrow testing policy (testing=1 and testing_narrow =1) or large testing policy (testing=1 and testing_narrow =0); surveillance: binary variable equal to1 if mobile app or bracelet surveillance was implemented and 0 otherwise; surveillance_partial: binary variable equal to1 if the enhanced surveillance is optional or reserved for a category of person (e.g. certain professions or foreigners) and 0 otherwise, based on information in the ACAPS. When the measure was partial, surveillance_partial was coded 1. On the contrary, when the measure was strict (anybody suspected of having COVID-19), surveillance_partial was coded 0. The interaction of surveillance and surveillance_partial allows researchers to create three levels of measures: no surveillance (surveillance=0 and surveillance_partial =0), partial surveillance (surveillance=1 and surveillance_partial=1) or strict surveillance (surveillance=1 and surveillance_partial =0); masks : binary variable equal to1 if mandates to wear masks in public spaces were implemented and 0 otherwise; masks_partial: binary variable equal to1 if the obligation to wear masks is regional, based on information in the ACAPS. When the measure was regional, masks_partial was coded 1. On the contrary, when the measure was national, masks_partial was coded 0. The interaction of masks and masks_partial allows researchers to create three levels of measures: no obligations to wear masks (masks=0 and masks_partial =0), regional obligations to wear masks (masks=1 and masks_partial=1) or national obligations to wear masks (masks=1 and masks_partial =0); state: binary variable equal to1 if the state of emergency is declared and 0 otherwise; state_partial: binary variable equal to1 if the state of emergency is declared on a local basis and 0 otherwise, based on information in the ACAPS. When the measure was local, state_partial was coded 1. On the contrary, when the measure was not localized, state_partial was coded 0. The interaction of state and state_partial allows researchers to create three levels of measures: no state of emergency (state=0 and state_partial =0), partial state of emergency (state=1 and state_partial=1) or national state of emergency (state=1 and state_partial =0); cash: binary variable equal to1 if cash transfers are implemented and 0 otherwise; wage: binary variable equal to1 if wage support is implemented and 0 otherwise; credit: binary variable equal to1 if credit schemes are implemented and 0 otherwise; taxc: binary variable equal to1 if tax credits are implemented and 0 otherwise; taxd: binary variable equal to1 if tax delays are implemented and 0 otherwise; export: binary variable equal to1 if supports to importers or exporters are implemented and 0 otherwise; rate: binary variable equal to1 if the Central Bank lowered the interest rates and 0 otherwise; Rigidity_Public_Health: average of the ten coded public health measures. Public health measures are valued 0.5 if they are localized or partial and 1 if they are national or strict. 0 indicates no measures; Economic_Measures: average of the coded economic measures.通用字段说明：country：国家或地区名称；geodi：两位字母国家代码；iso：三位字母国家代码；d：观测日期；cases：欧洲疾病预防控制中心（European Centre for Disease Prevention and Control，简称ECDC）当日报告的确诊病例数；deaths：欧洲疾病预防控制中心（ECDC）当日报告的死亡病例数；school：二分类变量，若学校停课则取值为1，否则为0；school_local：区分局部停课与其他情形的二分类标记变量：取值为1代表仅在地方层面实施学校停课，取值为0代表未在地方层面实施停课（即要么为全国层面停课，要么未实施停课）；学校停课规模相关数据来源于联合国教科文组织（UNESCO）。通过school与school_local的交互项，研究者可构建三类防控措施层级：无停课（school=0且school_local=0）、局部停课（school=1且school_local=1）以及全国停课（school=1且school_local=0）；domestic：二分类变量，若实施国内封控则取值为1，否则为0；domestic_local：区分局部国内封控与其他情形的二分类标记变量：取值为1代表仅在地方层面实施国内封控，取值为0代表未在地方层面实施封控（即要么为全国层面封控，要么未实施封控）；国内封控措施的判定依据为研究团队对ACAPS发布的防控措施的解读。通过domestic与domestic_local的交互项，研究者可构建三类封控层级：无国内封控（domestic=0且domestic_local=0）、局部国内封控（domestic=1且domestic_local=1）以及全国国内封控（domestic=1且domestic_local=0）；travel：二分类变量，若实施旅行限制则取值为1，否则为0；travel_partial：区分部分旅行限制与其他情形的二分类标记变量：取值为1代表实施了部分旅行限制，取值为0代表未实施部分限制（即要么为严格旅行限制，要么未实施旅行限制）；旅行限制措施的判定依据为研究团队对ACAPS发布的防控措施的解读。通过travel与travel_partial的交互项，研究者可构建三类旅行限制层级：无旅行限制（travel=0且travel_partial=0）、部分旅行限制（travel=1且travel_partial=1）以及严格旅行限制（travel=1且travel_partial=0）；travel_dom：二分类变量，若实施国内旅行限制（如跨区域旅行管制）则取值为1，否则为0；travel_dom_partial：区分部分国内旅行限制与其他情形的二分类标记变量：取值为1代表实施了部分国内旅行限制，取值为0代表未实施部分限制（即要么为严格国内旅行限制，要么未实施国内旅行限制）；国内旅行限制措施的判定依据为研究团队对ACAPS发布的防控措施的解读。通过travel_dom与travel_dom_partial的交互项，研究者可构建三类国内旅行限制层级：无国内旅行限制（travel_dom=0且travel_dom_partial=0）、部分国内旅行限制（travel_dom=1且travel_dom_partial=1）以及严格国内旅行限制（travel_dom=1且travel_dom_partial=0）；curf：二分类变量，若实施宵禁则取值为1，否则为0；curf_partial：区分部分宵禁与其他情形的二分类标记变量：取值为1代表实施了部分宵禁，取值为0代表未实施部分限制（即要么为严格宵禁，要么未实施宵禁）；宵禁措施的判定依据为研究团队对ACAPS发布的防控措施的解读。通过curf与curf_partial的交互项，研究者可构建三类宵禁层级：无宵禁（curf=0且curf_partial=0）、部分宵禁（curf=1且curf_partial=1）以及严格宵禁（curf=1且curf_partial=0）；mass：二分类变量，若实施大型集会禁令则取值为1，否则为0；mass_partial：区分大型集会局部禁令与其他情形的二分类标记变量：取值为1代表实施了部分大型集会禁令，取值为0代表未实施部分限制（即要么为全国性严格禁令，要么未实施集会禁令）；大型集会禁令的判定依据为研究团队对ACAPS发布的防控措施的解读。通过mass与mass_partial的交互项，研究者可构建三类大型集会管控层级：无集会禁令（mass=0且mass_partial=0）、局部或部分禁令（mass=1且mass_partial=1）以及全国性或严格禁令（mass=1且mass_partial=0）；elect：二分类变量，若部分选举被延期则取值为1，否则为0；elect_partial：区分仅延期部分选举与其他情形的二分类标记变量：取值为1代表该国同时保留部分选举并延期其他选举，取值为0代表该国要么所有选举均保留、要么所有选举均延期、要么未安排选举；国际选举制度基金会（International IDEA，简称IDEA）记录了2020年初以来所有保留或延期的选举安排。通过elect与elect_partial的交互项，研究者可区分三类选举管控情形：新冠疫情期间所有选举均保留（elect=0且elect_partial=0）、部分选举保留部分延期（elect=1且elect_partial=1）以及所有选举均延期（elect=1且elect_partial=0）；sport：二分类变量，若实施体育及大型活动禁令则取值为1，否则为0；sport_partial：区分体育及大型活动部分禁令与其他情形的二分类标记变量：取值为1代表实施了局部、严格或无观众的体育及大型活动禁令，取值为0代表未实施局部或部分禁令（即要么为全国性禁令，要么未实施相关管控措施）；体育及大型活动禁令的判定依据为研究团队对ACAPS发布的防控措施的解读。通过sport与sport_partial的交互项，研究者可构建三类管控层级：无禁令（sport=0且sport_partial=0）、部分禁令（sport=1且sport_partial=1）以及全国性体育及大型活动禁令（sport=1且sport_partial=0）；rest：二分类变量，若餐厅停业则取值为1，否则为0；rest_local：区分餐厅及酒吧局部/部分停业与其他情形的二分类标记变量：当出现以下三种情形时取值为1：局部停业、限制酒吧及餐厅的顾客人数、仅关闭酒吧或餐厅；取值为0则代表实施全国性停业或未实施任何停业措施；该变量的编码依据为研究团队对ACAPS发布的防控措施的解读。通过rest与rest_local的交互项，研究者可构建三类餐饮管控层级：无停业措施（rest=0且rest_local=0）、局部停业（rest=1且rest_local=1）以及全国性停业（rest=1且rest_local=0）；testing：二分类变量，若实施公共核酸检测政策则取值为1，否则为0；testing_narrow：区分精准检测政策与大规模检测政策的二分类标记变量：取值为1代表检测政策仅针对特定人群，取值为0代表检测政策未限定人群（要么为大规模检测，要么未实施检测政策）；检测政策的判定依据为ACAPS发布的"全民大规模检测"及"检测政策"相关信息：若检测政策为定向靶向，则testing_narrow赋值为1；反之，若未限定检测人群，则testing_narrow赋值为0。通过testing与testing_narrow的交互项，研究者可构建三类检测政策层级：无检测政策（testing=0且testing_narrow=0）、定向检测政策（testing=1且testing_narrow=1）以及大规模检测政策（testing=1且testing_narrow=0）；surveillance：二分类变量，若实施移动应用或手环防疫监测则取值为1，否则为0；surveillance_partial：区分部分强化监测与其他情形的二分类标记变量：依据ACAPS的信息，若强化监测为自愿参与或仅针对特定人群（如特定职业人群或外籍人士）则取值为1，否则为0；若监测措施为部分限定，则surveillance_partial赋值为1；反之，若为严格监测（即针对所有新冠疑似人群），则surveillance_partial赋值为0。通过surveillance与surveillance_partial的交互项，研究者可构建三类监测管控层级：无监测措施（surveillance=0且surveillance_partial=0）、部分监测（surveillance=1且surveillance_partial=1）以及严格监测（surveillance=1且surveillance_partial=0）；masks：二分类变量，若实施公共场所佩戴口罩强制令则取值为1，否则为0；masks_partial：区分区域口罩令与其他情形的二分类标记变量：依据ACAPS的信息，若口罩佩戴义务为区域性实施则取值为1，若为全国性实施则取值为0；通过masks与masks_partial的交互项，研究者可构建三类口罩管控层级：无佩戴口罩义务（masks=0且masks_partial=0）、区域性佩戴口罩义务（masks=1且masks_partial=1）以及全国性佩戴口罩义务（masks=1且masks_partial=0）；state：二分类变量，若宣布进入紧急状态则取值为1，否则为0；state_partial：区分地方层面紧急状态与其他情形的二分类标记变量：依据ACAPS的信息，若仅在地方层面宣布进入紧急状态则取值为1，若为全国性宣布则取值为0；通过state与state_partial的交互项，研究者可构建三类紧急状态管控层级：无紧急状态宣告（state=0且state_partial=0）、地方层面紧急状态（state=1且state_partial=1）以及全国性紧急状态（state=1且state_partial=0）；cash：二分类变量，若实施现金转移支付政策则取值为1，否则为0；wage：二分类变量，若实施薪资支持政策则取值为1，否则为0；credit：二分类变量，若实施信贷支持计划则取值为1，否则为0；taxc：二分类变量，若实施税收抵免政策则取值为1，否则为0；taxd：二分类变量，若实施税收延期政策则取值为1，否则为0；export：二分类变量，若实施进出口商扶持政策则取值为1，否则为0；rate：二分类变量，若中央银行下调利率则取值为1，否则为0；Rigidity_Public_Health：10项编码公共卫生措施的平均得分。其中，局部或部分实施的公共卫生措施赋值为0.5，全国性或严格实施的措施赋值为1；无相关措施则得分为0；Economic_Measures：编码经济措施的平均得分。