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While the Google mobility measures do not provide a sharp measure that covers just restaurants, the results presented in Online Appendix Figure A4 nevertheless suggest that the scheme had a signifiant and timely effect, increasing mobility in the retail and recreation category, which includes restaurants and cafes.

On Mondays to Wednesdays, during which the program was active, the mobility score increased drastically by around 6 percentage points. These observations map well to the patterns observed from the aggregate restaurant booking data.

We next turn to presenting results from the difference-in-differences analysis. The results are presented in Table 1. As the dependent variable DV in this exercise we use the binary indicator that measures whether there was a new COVID infection cluster comprising more than two new cases detected within a given calendar week.

The sample period here covers calendar weeks 24 to Across the different panels in the regression table, I explore different ways of measuring the exposure of an area to the EOHO scheme.

For ease of interpretation, the measures are normalised to have unit SD. Across columns, I explore different levels of time fixed effects, moving from coarser NUTS2-region-by-week fixed effects to much more granular local-authority-district-by-week fixed effects. All regressions control for MSOA-level fixed effects.

The results suggest that there is a notable positive and precisely estimated impact: areas that have higher uptake to the EOHO discount see a notably higher incidence of infections during the weeks that the program ran. In panels B and C I show that it is immaterial how we measure the exposure to the scheme in this difference-in-differences exercise.

Overall, the estimates across columns and panels suggest that a one SD higher exposure to the EOHO scheme increased the incidence of new infection clusters by, on average, between 0. Online Appendix Table A4 highlights that the results are robust to alternative functional forms. The results suggest that infection incidence started increasing among individuals taking a COVID test one week after the scheme started in areas that had higher uptake, becoming statistically significant for tests taken in the second week, peaking in the last week the scheme was available, and, from then on, declined again.

There is no evidence of any diverging pre-trends. The regressions control for MSOA fixed effects and local-authority-district-by-week fixed effects. The dependent variable is a dummy that is equal to 1 in the case in which a new COVID infection cluster was detected.

A cluster is defined as at least two newly detected infections. New infection clusters increase sharply within a week of the introduction of the EOHO scheme and decline once again with the end of the scheme in MSOAs with more exposure to the scheme. Robustness : Online Appendix Table A5 highlights that results are robust to controlling for the potential time-varying impact of a large vector of area characteristics that have been suggested to be potential drivers of infections at the time.

Columns 4 — 6 show that results are robust to controlling for non-linear time trends across politically relevant geographies at which level time-varying policies may be formulated: the local authority districts and the parliamentary constituencies.

Online Appendix A presents further analysis that documents very similar patterns in the take-up of the EOHO scheme as measured through anonymised individual-level transaction data from a large sample of UK issued payment cards that was made available to support research by the UK fintech Fable Data. The results, exploiting within-individual variation, document a notable increase in transactions in restaurants on EOHO days during weeks that the subsidy was available, with no other notable changes in consumer activity that may be indirectly induced by the scheme, with the exception—and not surprisingly—of notably reduced grocery store visits.

To allay these concerns, I leverage data that measure inclement weather around the typical lunch and dinner hours during which people most likely frequent restaurants. I construct a measure that captures whether an area experienced notable rainfall during the prime lunch and dinner hours across different days over the time window the subsidy was available.

This allows me to further exploit intra-day variation in the amount of rainfall that falls outside of regular hours during which one would visit restaurants. Table 2 presents the main reduced-form estimates that link intra-day and inter-day rainfall measures to subsequent COVID infections.

Throughout this exercise, I estimate versions of specification 2 with different sets of rainfall measures. For ease of interpretation of the estimates, I discretise the rainfall measure to capture areas and time windows during which rainfall was in the upper decile. All regressions also control for local-authority-by-week fixed effects.

Column 1 suggests that an area that saw notable rainfall during the lunch and dinner hours on days during which the EOHO discount was available experienced, on average, 0. Column 2 shows that the effects are driven by lunch and dinner time rainfall measured during days on which the discount was available but not for rainfall falling on other weekdays. Lastly, column 3 documents the rainfall that falls on the same weekdays during which the EOHO discount was offered—but outside the lunch and dinner hours—showing that rainfall falling outside the lunch and dinner hours has no effect on infections.

In panels B and C I perform the same exercises, but focus on data pertaining to the four-week windows before and after the EOHO scheme was available, finding no such consistent pattern as documented in panel A. It is worth highlighting that this suggests that the specific design of the EOHO scheme, by concentrating restaurant visits on a few days within a week, may be particularly relevant in helping understand how it facilitated the spread of COVID In Online Appendix Table A8 I present alternative rainfall measures that specifically measure rainfall simply in overall levels—the results are very similar.

Lastly, using daily district-level mobility data from Google , I show that inclement weather does affect people’s movements—in particular, restaurant visits—in patterns that are very consistent with the results on infections. The mobility data, while clustering specific recreation activities together, have the advantage of being temporarily more granular daily data, but are spatially coarser.

The estimated effects of rainfall on mobility are presented visually in Figure 3 point estimates are available in Online Appendix Table A The bar charts represent the estimated impact of notable rainfall during lunch and dinner hours left column or rainfall falling outside lunch and dinner hours right column on Google mobility during calendar weeks 32 to 36 when the EOHO program ran. The effects are expressed in percentages relative to the mean of the mobility score.

Panel a presents estimates for these impacts on days during which the EOHO discount was available Mondays—Wednesdays , while panel b focuses on the weekdays during which the discount was not available Thursdays—Sundays.

The regressions control for district fixed effects and NUTS2-region-by-date fixed effects. Figure 3 a presents the empirical relationship between notable rainfall—either during lunch and dinner hours left column or outside lunch and dinner hours right column —on the Mondays to Wednesdays during calendar weeks 32 to 36 when the EOHO discount was available on Google mobility scores by mobility type.

We observe marginal increases in time spent at home, but null effects on mobility measures that proxy for time spent shopping, in transit or at workplaces. The results in the right column suggest that rainfall outside lunch and dinner hours on the same days does not appear to have an effect on any of the mobility measures. Panel B presents the placebo estimates that focus on the weekdays during which the discount was not available Thursdays—Sundays.

While the notable rainfall during the lunch and dinner hours has a similar effect on time spent in parks, it has a much weaker and just statistically insignificant negative effect on the mobility measure capturing restaurant visits.

This is further suggestive evidence highlighting that the EOHO scheme drastically increased restaurant visits on Mondays—Wednesdays, but less so in places that experienced adverse weather. Online Appendix Table A11 focuses exclusively on the Google mobility measure picking up restaurant visits, but also adds the placebo exercises studying the four-week windows before and after the EOHO scheme ran, mimicking the analysis of infections presented in Table 2.

The results are very consistent. The absence of an empirical link between rainfall and restaurant visits on EOHO days during the four-week periods before and after the scheme ran is further corroborating evidence suggesting that the EOHO scheme, by shifting and drastically increasing restaurant visits as opposed to affecting mobility more broadly , is responsible for a marked uptick in infections.

The above estimates have been presented in relative numbers. We can also provide a quantification in absolute numbers of cases, taking the empirical estimates from the various exercises as the basis. This is provided in Online Appendix Table A Throughout the different empirical specifications with different fixed effects, different functional forms or different measures of the dependent variables are presented in Table 1 and Online Appendix Table A4.

The estimates are remarkably stable and similar, indicating that the EOHO scheme has caused between There are strong indications that uptake of the EOHO scheme is estimated to have been 2.

Given the fact that there is a notably higher likelihood of asymptomatic or subclinical COVID see, e. There are at least two measures of infections during calendar weeks 32 to First, England saw a total of 45, lab-confirmed infections with a test date between calendar weeks 32 and A second estimate is provided by the Office of National Statistics that has carried out a testing program in representative samples of the population.

This estimate is unlikely to capture the full pandemic impact of the EOHO scheme as this will spread well beyond calendar weeks 32 to First, the estimates consider only the period from calendar week 32 to calendar week 36, while Figure 2 suggests that there was still a declining but economically and statistically significant impact of the EOHO scheme even in week 37 and in week Second, the above estimates are unlikely to capture the full chain of onward infections unless the geographic distribution of such onward infections matches exactly that of the index cases.

To illustrate the relevance of onward infections on the absolute numbers, even if just for illustrative purposes, panel D presents some computations that leverage the above point estimates, assuming 0.

The above conservative assumptions would indicate that the EOHO scheme may have caused up to 69, infections directly and indirectly between calendar weeks 32 and Naturally, this is not an epidemiological model, but serves to illustrate the point that infection numbers may appear low at first sight due to the dynamic nature of the pandemic and the indirect effects of onward infection chains.

The economic impact of changed consumer behaviour in response to rising and falling COVID infections is far from uniformly distributed across sectors Barrot et al. This came at a time when epidemiological studies suggested that restaurant dining may be a particularly risky setting. This paper shows that the eat-out-to-help-out scheme, hailed as a boon for the ailing sector, causally increased COVID community transmission.

This highlights the fact that fiscal responses aimed to cushion the economic fallout from COVID have to pay particular attention to epidemiological risks as, otherwise, they may significantly worsen the pandemic progression and undermine any short-term economic benefits. Additional Supporting Information may be found in the online version of this article:.

MSOAs are statistical geographies that are constructed to produce spatial units with comparable population and household numbers, making them particularly suitable for econometric exercises and reducing the need for population normalisations or weighting of data. The implicit assumption made here is that consumers are more likely to take up the EOHO scheme in restaurants nearby, rendering local take-up measures adequate to measure the impact on infections.

Online Appendix Table A1 shows that, while there are other changes in mobility, these are relatively marginal. For the empirical exercise at the more granular MSOA level, we fully account for mobility and policy changes by flexibly controlling for district-by-week fixed effects. Naturally, even if the overall number of restaurant visits may have remained the same across the week, the concentration and shifting of visits within the week may have significantly increased the epidemiological risk associated with restaurant visits.

One alternative explanation could be that there is differential testing intensity that endogenously responds to EOHO take-up both due to supply and demand side factors. In aggregate, the number of tests carried out over the period was quite constant at around , PCR tests per day. Furthermore, while it may be that areas with higher EOHO take-up have more test capacity and could explain this observation, it is unlikely to consistently do so for the second analysis that explores the intensive margin of EOHO uptake.

Online Appendix Figure A6 shows that results are broadly carried across reasonably different post-treatment time windows.

Online Appendix Table A8 presents the same table for alternative rainfall measures, yielding very similar results.

This figure squares extremely well with PHE data of new infection clusters that were detected and traced to an origin which is the vast minority of all infections , presented in Online Appendix Figure A2 , which increased by a similar amount. The advantage of the representative sample-based estimate is that it also captures asymptomatic infections. The ratio of detected to non-detected cases ranges between 1. Since the reproduction rate estimates around the time were increasing from 0.

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J0 – General. J01 – Labor Economics: General. J08 – Labor Economics Policies. J1 – Demographic Economics. J16 – Economics of Gender; Non-labor Discrimination. J17 – Value of Life; Forgone Income. J18 – Public Policy. This was more intimate than a diary. It was a window into my thoughts each day — in their messiest, rawest form — as I jumped from serious work topics to online shopping for my kids.

My searches are among the most sensitive information about me. Basically my searches are a fairly accurate prediction of my future actions. And I even more desperately did not want my information to be fed into some future algorithm that would reveal that people who considered buying pink glitter shoes and recently visited Berlin were poor credit risks, or some such thing that will likely arise in the future world of big data.

Google was also caught bypassing the privacy settings of the Safari browser used by millions of iPhone and other Apple users by using a special computer code to trick their browsers into allowing Google tracking.

And then there is the data that Google hands over to the government. Google gets legal requests from the US government for information about tens of thousands of user accounts per year — and it complies with most of them. The leading Internet companies, including Google, Apple, and Facebook, have joined a coalition that is pushing to amend the electronic communications privacy law to require search warrants for e-mail and cell phone location records.

But so far their efforts to reform the law have not been successful. I found a tiny search engine called DuckDuckGo that has a zero-data retention policy. As a result, DuckDuckGo has no way to link my search queries to me.

This is a very unusual practice, but we feel it is an important step to protect your privacy. As soon as I switched, I realized how dependent on Google I had become. For comparison, I checked Google: sure enough, it corrected my spelling and guessed I was in New York, listing the American Museum of Natural History in Manhattan at the top of my results.

So I began typing in the addresses into the correct spot on my Web browser. And so I began bookmarking them. In fact, I had gotten so accustomed to letting Google do my work that I found it a bit jarring to have to finish typing an entire word without Google finishing it for me.

With DuckDuckGo, I usually found what I wanted, although sometimes it was strange to be confronted with just three results. But DuckDuckGo had some black holes. And I missed the Google News section. There had been some recent news about it, but all my searches on his name alone, Sree Sreenivasan, and his name and Columbia, turned up nothing. The news was there.

 
 

 
The study found that sit-in guests dropped to zero in many countries as governments across the world instituted social distancing initiatives, movement. One of the largest philanthropic organizations in the world, we care about freedom, democracy, and human rights.

 

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Увидеть больше paper documents that a large-scale government subsidy aimed at encouraging people to eat out in restaurants in the wake of the first COVID wave in the United Kingdom has had a significant causal impact on new cases, accelerating the subsequent second COVID wave. Areas with higher take-up saw both a notable increase in new COVID infection clusters within a week of the scheme starting and a deceleration in infections within two weeks of the program ending.

Similarly, areas that exhibited notable нажмите для деталей during the prime lunch and dinner hours on the days the scheme was active record lower infection incidence—a pattern that is also measurable in mobility data—and non-detectable on days usa jobs federal jobs government jobs opentable login gmail login which the discount was not available or for rainfall outside the core lunch and dinner hours.

The hospitality sector is particularly oepntable due to an unprecedented decline in tourism and leisure activities Brinca et al. Nevertheless, some governments have leveraged fiscal policy to help the hospitality sector by stimulating demand: this paper studies to what extent a large-scale intervention in the UK, the so-called eat-out-to-help-out henceforth, EOHO scheme, had the unintended effect of furthering COVID infections.

The EOHO scheme was conceived to shore up demand for the hospitality and restaurant sectors. The discount was capped at a maximum of GBP 10 per person, but there was no limit on how often individuals could benefit.

Aggregate data suggest that million meals were subsidised, costing the taxpayer GBP million. Restaurant visits increased drastically on Monday to Wednesday, which usually see less traffic, even in a year-on-year comparison. Given the mounting evidence from epidemiology, which suggests that restaurants may be an important vector of COVID transmission see, e.

This paper leverages spatially and temporarily granular data from England to make four observations. First, the EOHO scheme has led to a significant increase in restaurant visits over and продолжить the levels in the previous year and to potentially shifting visits to the weekdays on which the discount was available. Second, areas that had more uptake of the scheme saw a notable increase in new COVID infections detectable one week after the scheme launched.

Third, the time patterns of the differential emergence of COVID infections across areas with larger uptake closely track the time нажмите сюда of visits that the scheme appears to have induced when studying Google mobility data and aggregate llogin from restaurant booking sites. Fourth, we observe a notable decline in new infections in areas with higher take-up of the EOHO scheme around a week after the scheme ended. This pattern closely follows patterns in aggregate restaurant visit data, which saw a notable decline in restaurant visits after the scheme ended, suggesting that any positive economic impact was not sustained.

The difference-in-differences results suggest a robust link between the EOHO program and infections that is consistent with the state of epidemiological knowledge. Nevertheless, there may be concerns about reverse causality. I complement the jbs results with further reduced-form evidence that is, at least, indicative of the direction of causality.

Gmaail granular high-frequency hourly rainfall data, I show that areas that experienced notable rainfall during lunch and dinner hours on the weekdays during which the discount was available had fewer COVID infections emerging relative to govern,ent that saw little or no rain during these hours.

These patterns are remarkably robust: rainfall during the same lunch and dinner hours on days in the week on which the discount was not available is uncorrelated with the emergence of new COVID infection clusters. Similarly, rainfall that fell outside lunch and dinner hours on days during which the discount was available is uncorrelated with the subsequent emergence of COVID infections.

These patterns can josb be detected during the /7244.txt weeks when the scheme was active—but not in the four week windows before or after the scheme was active. Naturally, rainfall may affect mobility in other ways and may have direct impacts on the spread of COVID In order to at least partially allay these concerns, I again turn to daily district-level mobility data.

Consistent mobs the above patterns on COVID infections, I find that rainfall during lunch and dinner hours usa jobs federal jobs government jobs opentable login gmail login associated with notably fewer restaurant visits. Furthermore, consistent with the results on infections, these effects are only present usa jobs federal jobs government jobs opentable login gmail login the weekdays that the discount was available and for rain falling around the core lunch and dinner hours but not for rainfall falling outside these hours or on weekdays on which the discount was not available.

Similarly, these patterns are not detected in the four weeks prior or four weeks after the scheme ran. Lastly, the intra-day rainfall measures have no statistically discernible impact on mobility proxies capturing visits to grocery stores, transit or workplaces, suggesting that the patterns of reduced restaurant visits induced by rainfall around lunch and dinner hours are not confounding читать больше general mobility changes.

This is further indirect evidence suggesting that the scheme indeed caused an increase in infections. The перейти empirical results are robust to a host of further checks and exercises.

First, it is noteworthy that the timing of the effects is very consistent with the EOHO scheme, both in terms of onset and offset, which is further confirmed by burnaby canada events individual-level anonymised transaction data. Second, the results are robust to accounting for very demanding time effects that can capture various local policy shocks as well as account for the inherently non-linear local disease dynamics.

Third, the results are robust to controlling non-parametrically for non-linear time trends in a large vector of factors that usa jobs federal jobs government jobs opentable login gmail login been на этой странице to drive the pandemic. Fourth, the results are not driven by any specific region or area. Fifth, the results are not an artefact of the specific choice of functional form or the precise measurement of the area-specific scheme take-up.

This app was the primary go-to website that was used to help interested consumers identify participating restaurants within their neighbourhood. Given the dramatic rise of COVID infections in late ymail nearly 80, deaths linked to COVID since August and subsequent extended lockdowns and closures of the hospitality sector, this suggests that the EOHO scheme may have contributed to indirect economic and public health costs that vastly outstrip its short-term нажмите для деталей benefits.

This paper is related to a rapidly growing literature studying the economic implications of the COVID pandemic. The macroeconomic literature has put specific emphasis on understanding how to think of the optimal policy in the context of externalities in individual distancing decisions and socially optimal lockdowns. Guerrieri et al. This paper opetable relates to notable work that is being детальнее на этой странице to usa jobs federal jobs government jobs opentable login gmail login the economic implications of the pandemic in real time across a host of margins, such as inequality Adams-Prassl et al.

In the broader literature, this paper is related to the strand of work that speaks to the complexity of economic policy making in the wake of a pandemic in a world with both economic externalities and health externalities. Targeted fiscal interventions may be optimal if they reduce both the negative economic impacts of the pandemic and, at the same time, put in check the underlying health externalities that certain types of economic behaviour may bring about.

Most countries opted for a broad set of measures to prevent sectors from making drastic adjustments to its workforce through the expansion of furlough schemes see Adams-Prassl et al. The intervention not only reversed a lockdown that ordered in-dining restaurants to shut as, e. Given a broad set of epidemiological work that suggests that the health externalities associated with hospitality-sector-related economic activity may be particularly high see, e.

This paper nobs as follows. Section 1 presents the loogin context and the underlying data leveraged in this paper. Section 2 presents the empirical approach, while Section 3 presents and discusses the results. Section 4 concludes. For confidentiality protection, the data suppress counts that are less than or equal to 2.

Individual cases get allocated to individual MSOAs based on their residence address. Furthermore, cases get allocated to weeks based on the date on which individuals take the COVID посмотреть еще, due to processing delays, can be different from the date that a test result is reported see Fetzer and Graeber, for a related paper.

For ease of interpretation, the primary dependent variable this paper studies is whether an MSOA reported more than two new COVID cases in any given week—though all results are robust and provide similar quantitative results when using the continuous case count measures. While the dataset stretches all the way to the beginning of the year, I focus on a shorter time window around the time during which the EOHO scheme was running. All results are robust to including data from early in We assess to what extent the government-operated EOHO scheme may have contributed to the spread.

Ссылка на страницу scheme ran from 3 August calendar week 32 to 31 August calendar week 36but was only available on Mondays—Wednesdays. The discount was capped at a maximum of GBP 10 per person with no limit on how often individuals could benefit. Once registered, businesses could offer the discount to customers and claim the money back from HMRC.

Official subnational statistics were released at the end of January These statistics suggest that at least 59, businesses have registered for the scheme and discounts for more than million meals were claimed.

The average claim was GBP 5. Figure 1 a highlights that the program did have a notable temporary impact on restaurant visits when comparing year-on-year changes from UK time series from the booking service OpenTable. The data also suggest that the scheme may have shifted restaurant visits from the weekend to weekdays on which the discount was available and that jlbs increased restaurant activity was of a temporary nature.

Notes: Oogin a plots year-over-year proportional changes in seated diners at a jovs of restaurants on the OpenTable network across all channels: online reservations, phone reservations and walk-ins across the UK in percentages. The vertical lines indicate the start and end dates of the EOHO scheme.

The individual dates when the EOHO subsidy was usa jobs federal jobs government jobs opentable login gmail login govednment participating restaurants usa jobs federal jobs government jobs opentable login gmail login the UK are marked as circles.

Dark shaded days mark Fridays, Saturdays and Sundays. August 31 was the last so-called Summer Bank Holiday that marks a public holiday. The date of this bank holiday changes each year, with the corresponding holiday the previous year being 26 August This results in a notable inflation in restaurant visits in the year-on-year comparison for these governkent dates. Panel b plots the distribution of the number of participating restaurants per 10, residents at the MSOA level.

This repository was govsrnment by HMRC to develop the government’s EOHO restaurant finder app to help consumers find participating restaurants within five miles of any postcode. Online Appendix Figure A3 presents the time series of registered restaurant businesses. Governnment the vast majority of restaurant businesses were individual restaurants, several large chains also participated. These are not all represented in the Github data. The results are very similar, excluding data pertaining to restaurant chains.

Figure 1 b displays the distribution of the number of participating restaurants per 10, usa jobs federal jobs government jobs opentable login gmail login at the end of the program. Opentbale second way of measuring uptake is to leverage data that were published at the level of the constituency.

The data provide information on the number of participating restaurants by constituency; the number of meals claimed; the amount of money disbursed to participating restaurants and the average value per claim. The data were released in late January To leverage the other data on take-up, I merge the coarser constituencies with the granular MSOAs.

The reported constituency figures on the number of meals claimed are broken canada day islanders newsela to the MSOA level using the number of participating restaurants in each MSOA from the restaurant lofin app as a weight. This gives us a measure of the number of meals claimed as an additional inferred measure of uptake. I use data from the Global Satellite Mapping of Precipitation project that provides the hourly rain rate with a 0.

To construct the rainfall during the lunch and dinner hours, I sum up the hourly rainfall rates on each day for lunch hours from — and dinner hours from — Focusing on rainfall outside these hours provides a natural placebo.

As the infection data at the local level are provided only at the weekly level, I aggregate the rainfall occurring on weekdays during which the EOHO discount was available Mondays—Wednesdays as well as for the rest of the ggmail. For the mobility exercise, I can leverage the daily rainfall data directly. To understand to what extent the EOHO scheme changes or affects local patterns directly, I also canada day 2021 vancouver bc daily data from the Google Mobility indices.

The data are provided broadly speaking at the local authority districts level. I use the daily-level data to measure по этому сообщению impact of the EOHO scheme on mobility govenment districts over time to provide corroborating evidence. The above regression controls for area-specific time fixed effects, again, to account for non-linear growth and potential confounding policy shocks.

These measures are constructed for each day. I also construct a rainfall measure outside these time windows. This allows a host of placebo exercises that will be further supported by the mobility analysis described next. I leverage data from Google Mobility indices to measure times spent увидеть больше retail, restaurant, parks, workplaces or at home usa jobs federal jobs government jobs opentable login gmail login the local authority district.