Utility Analysis and the Consumption Function: An Interpretation of Cross-Section Data

Non-pharmaceutical interventions during the COVID-19 pandemic: A review.

Infectious diseases and human behavior are intertwined. On one side, our movements and interactions are the engines of transmission. On the other, the unfolding of viruses might induce changes to our daily activities. While intuitive, our understanding of such feedback loop is still limited. Before COVID-19 the literature on the subject was mainly theoretical and largely missed validation. The main issue was the lack of empirical data capturing behavioral change induced by diseases. Things have dramatically changed in 2020. Non-pharmaceutical interventions (NPIs) have been the key weapon against the SARS-CoV-2 virus and affected virtually any societal process. Travels bans, events cancellation, social distancing, curfews, and lockdowns have become unfortunately very familiar. The scale of the emergency, the ease of survey as well as crowdsourcing deployment guaranteed by the latest technology, several Data for Good programs developed by tech giants, major mobile phone providers, and other companies have allowed unprecedented access to data describing behavioral changes induced by the pandemic. Here, I aim to review some of the vast literature written on the subject of NPIs during the COVID-19 pandemic. In doing so, I analyze 347 articles written by more than 2518 of authors in the last $12$ months. While the large majority of the sample was obtained by querying PubMed, it includes also a hand-curated list. Considering the focus, and methodology I have classified the sample into seven main categories: epidemic models, surveys, comments/perspectives, papers aiming to quantify the effects of NPIs, reviews, articles using data proxies to measure NPIs, and publicly available datasets describing NPIs. I summarize the methodology, data used, findings of the articles in each category and provide an outlook highlighting future challenges as well as opportunities

Non-pharmaceutical interventions during the COVID-19 pandemic: a rapid review

This paper sets out the economic analytics of pensions. After introductory discussion, successive sections consider the effects of different pension arrangements on labour markets, on national savings and growth, and on the distribution of burdens and benefits. These areas are controversial and politically highly salient. While we are open about expressing our own views, the main purpose of the paper is to set out the analytical process by which we reach them, to enable readers to form their own conclusions.

The economics of pensions

Robotics and labour in agriculture. A context consideration

At the onset of the COVID-19 pandemic a large number of countries introduced a range of non-pharmaceutical interventions. Whereas the policies are similar across countries, country characteristics vary substantially. We examine the effectiveness of such policies using a cross-country variation in socio-economic, environmental and geographic, and health system dimensions. The effectiveness of policies that prescribe closures of schools and workplaces is declining with population density, country surface area, employment rate and proportion of elderly in the population; and increasing with GDP per capita and health expenditure. Cross-country human mobility data reinforce some of these results. We argue that the findings can be explained by behavioural response to risk perceptions and resource constraints. Voluntary practice of social distancing might be less prevalent in communities with lower perceived risk, associated with better access to health care and smaller proportion of elderly population. Higher population density, larger geographical area, and higher employment rate may require more resources to ensure compliance with lockdown policies.

/pdf/covid-19-the-impact-of-social-distancing-policies-cross-3uozkkhamy.pdf

COVID-19: The impact of social distancing policies, cross-country analysis.

Amid the extraordinary economic effects of COVID-19, some policymakers have turned to retirement accounts to support individuals in financial hardship. Given the haste, the long-term impacts and their heterogeneity have scarcely been analyzed. Using Monte Carlo simulations on the Chilean Social Protection Survey linked with administrative data, this study quantifies the effects of a 10% early release of pension funds. Each withdrawn dollar brings losses of 1.59 dollars in future retirement savings, reducing monthly pension benefits by 7.26%. This policy raises income inadequacy and inequality in retirement, increasing government expenditure by 4.33% to counteract these effects for 65-year-old retirees. We propose four policies to mitigate these effects and address the current challenges of most defined contribution pension schemes. Increasing contributions combined with an intragenerational solidarity component shows the biggest impacts. Contribution enforcement, reducing tax evasion, and delaying retirement by at least 1 year via incentives have lower but significant effects.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jori.12365

Effects of COVID-19 Early Release of Pension Funds. The Case of Chile

We estimate the effect of capital composition on the size of capital–skill complementarity and the skill wage premium. Disaggregating the capital stock into different types according to technological content, we find that: capital is more of a q‐complement to skilled labor than to unskilled labor; the higher the technological component of capital, the larger the size of the relative q‐complementarity between capital and skilled labor; and replacing non‐technological with technological capital might increase the skill wage premium by about 9 percent. Our results highlight that changes in capital composition matter for understanding changes in the skill wage premium.

Capital–Skill Complementarity: Does Capital Composition Matter?

Using Chilean manufacturing plants data, we�find: (1) the elasticity of substitution between capital and skilled labor is lower than the elasticity of substitution between capital and unskilled labor, and (2) the higher the technological component of the capital stock the larger the size of complementarity between capital and skilled labor. Our�findings show that capital, as an aggregate input, may under(over) state the complementarity between labor and the type of capital these workers actually use.

https://mpra.ub.uni-muenchen.de/61285/1/MPRA_paper_61285.pdf

Miguel Lorca

Papers

COVID-19: The impact of social distancing policies, cross-country analysis.

Effects of COVID-19 Early Release of Pension Funds. The Case of Chile

Capital–Skill Complementarity: Does Capital Composition Matter?

Capital-Skill Complementarity: Does capital disaggregation matter?

Capital-Skill Complementarity: Does capital disaggregation matter?