Handbook of regional and urban economics

Shelter-in-place ordinances were the first wide-spread policy measures aimed to mitigate the spread of COVID-19. Compliance with shelter-in-place directives is individually costly and requires behavioral changes across diverse sub-populations. Leveraging county-day measures on population movement derived from cellphone location data and the staggered introduction of local mandates, we find that economic factors have played an important role in determining the level of compliance with local shelter-in-place ordinances in the US. Specifically, residents of low income areas complied with shelter-in-place ordinances less than their counterparts in areas with stronger economic endowments, even after accounting for potential confounding factors including partisanship, population density, exposure to recent trade disputes, unemployment, and other factors. Novel results on the local impact of the 2020 CARES Act suggest stimulus transfers that addressed economic dislocation caused by the COVID-19 pandemic significantly increased social distancing.

Poverty and Economic Dislocation Reduce Compliance with COVID-19 Shelter-In-Place Protocols

Since the first death in China in early January 2020, the coronavirus (COVID-19) has spread across the globe and dominated the news headlines leading to fundamental changes in the health, social and political landscape, and an unprecedented negative impact on the current and future prospects of households, businesses and the macro-economy. In this paper, we examine consumer spending responses to the onset and spread of COVID-19, and subsequent government imposed lockdown in Great Britain, GB (England, Scotland, Wales). Our sample period spans January 1st 2020 to 18th June 2020. This allows us to observe consumer spending behavior from the initial incubation phase of the crisis. We partition the sample period into incubation (1st-17th January), outbreak (January 18th-February 21st), fever (February 22nd-March 22nd), lockdown (March 23rd–May 10th 2020) and stay alert (May 11th- June 18th) phases. Using a high frequency transaction level proprietary dataset comprising 101,059 consumers and 23 million transactions made available by a financial technology company, we find that discretionary spending declines during the fever period as the government imposed lockdown becomes imminent, and continues to decline throughout the lockdown period. Shortly after the May 10th ‘stay alert’ announcement by Prime Minister Johnson, a short-term decline in spending across all nations occurs. However, a week later, spending is at the same level as that observed prior to the announcement. There is a strong increase in groceries spending consistent with panic buying and stockpiling behaviour in the two weeks following the World Health Organisation (WHO) announcement describing COVID-19 as a pandemic. Variations in the level and composition of consumer spending across nations and regions (particularly during the early stages of the outbreak period), and by age, gender and income level are also observed. Our results are of particular relevance to government agencies tasked with the design, execution and monitoring economic impacts arising from the spread of the virus and the public health measures imposed to mitigate the health costs of the crisis.

Consumer Spending Responses to the COVID-19 Pandemic: An Assessment of Great Britain

Medical imaging is a rich source of invaluable information necessary for clinical judgements. However, the analysis of those exams is not a trivial assignment. In recent times, the use of deep learning (DL) techniques, supervised or unsupervised, has been empowered and it is one of the current research key areas in medical image analysis. This paper presents a survey of the use of DL architectures in computer-assisted imaging contexts, attending two different image modalities: the actively studied computed tomography and the under-studied positron emission tomography, as well as the combination of both modalities, which has been an important landmark in several decisions related to numerous diseases. In the making of this review, we analysed over 180 relevant studies, published between 2014 and 2019, that are sectioned by the purpose of the research and the imaging modality type. We conclude by addressing research issues and suggesting future directions for further improvement. To our best knowledge, there is no previous work making a review of this issue.

Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET

We assess the impact of home-sharing on residential house prices and rents. Using a dataset of Airbnb listings from the entire United States and an instrumental variables estimation strategy, we find that a 1% increase in Airbnb listings leads to a 0.018% increase in rents and a 0.026% increase in house prices at the median owner-occupancy rate zip code. The effect is moderated by the share of owner-occupiers, a result consistent with absentee landlords reallocating their homes from the long-term rental market to the short-term rental market. A simple model rationalizes these findings.

The Sharing Economy and Housing Affordability: Evidence from Airbnb

Suicides are a major concern for public health first and foremost because they are an avoidable cause of death. Moreover, they can be an indicator of self-reported emotional satisfaction and a good marker of overall well-being. In this study we examine how different economic and social aspects affected Portuguese suicide rates for more than one hundred years (1910-2013). We place this exercise in the specific historical context of the XX and early XXI century in Portugal, emphasizing the role of economic recessions and expansions. Controlling for aspects like wars, health care availability, political instability, and demographic changes, we find a strong association between a decline in the growth rate of real output and an increase in suicide rates for the whole population. In this regard, while male suicide rates are non-negligibly influenced by economic downturns, female suicide rates are in general more responsive to a more open political and economic environment. Our results are robust if we consider the mid-term cyclical relationship. Our findings advocate that, during recessions, public health responses should be seen as a crucial component of suicide prevention.

More than just numbers: Suicide rates and the economic cycle in Portugal (1910-2013).

Clinical decisions are sometimes based on a variety of patient's information such as: age, weight or information extracted from image exams, among others. Depending on the nature of the disease or anatomy, clinicians can base their decisions on different image exams like mammographies, positron emission tomography scans or magnetic resonance images. However, the analysis of those exams is far from a trivial task. Over the years, the use of image descriptors--computational algorithms that present a summarized description of image regions--became an important tool to assist the clinician in such tasks. This paper presents an overview of the use of image descriptors in healthcare contexts, attending to different image exams. In the making of this review, we analyzed over 70 studies related to the application of image descriptors of different natures--e.g., intensity, texture, shape--in medical image analysis. Four imaging modalities are featured: mammography, PET, CT and MRI. Pathologies typically covered by these modalities are addressed: breast masses and microcalcifications in mammograms, head and neck cancer and Alzheimer's disease in the case of PET images, lung nodules regarding CTs and multiple sclerosis and brain tumors in the MRI section.

Image descriptors in radiology images: a systematic review

This paper uses novel and comprehensive data on electronic payments from SIBS, the main provider of point of sale terminals and on-line payments in Portugal, to study the impact of the Great Lockdown on purchases. The data aggregates all individual transactions into monthly observations, by municipality and sector, between 2018 and 2020. We employ a difference-in-differences event study that relies on the assumption that the monthly evolution of purchases in the first four months of 2020 would be parallel to that of the two previous years. We identify a massive causal impact on overall purchases, from a baseline year-on-year monthly growth rate of 10% to a decrease of 45%. The sign and magnitude of the impact varies considerably across sectors. Purchases of essential goods such as supermarkets and groceries increase mildly, contrasting with severe contractions in sectors that were closed by government order or depend heavily on tourism, including the leisure industry and restaurants. We find suggestive evidence of initial stockpiling of goods, postponing of essential expenditures, and rapid recovery of purchases in tech and entertainment, possibly to adapt to the confinement. Transactions with foreign-owned cards cause an even greater negative contraction. We disentangle the total effect into the intensive margin of the average transaction and the extensive margin of the number of transactions. Buyers adjust their shopping strategies in rational ways to minimize public health risks: they go less often to supermarkets and buy more each time, and visit local groceries more.

What and how did people buy during the Great Lockdown? Evidence from electronic payments

https://run.unl.pt/bitstream/10362/106982/1/pre_print.pdf

Brown sugar, how come you taste so good? The impact of a soda tax on prices and consumption

https://doi.org/10.1016/j.regsciurbeco.2022.103774

João Pereira dos Santos

Papers

More than just numbers: Suicide rates and the economic cycle in Portugal (1910-2013).

Image descriptors in radiology images: a systematic review

What and how did people buy during the Great Lockdown? Evidence from electronic payments

Brown sugar, how come you taste so good? The impact of a soda tax on prices and consumption

The virus that devastated tourism: The impact of covid-19 on the housing market