This study compares this yearâ€™s trend of EUIPOâ€™s trademark applications during May and the first twenty days of June to 2019. There are four important findings. First, overall trademark applications appear to be at the same level to last year. Second, there is significant heterogeneity by country. While many countries are at the same levels to last year, China is an outlier increasing its filings dramatically compared to 2019. Certain countries also experience a sharp decrease including Canada and Brazil. Third, the presence of entrants is higher in 2020 compared to the same period of 2019. Finally, there are some clear winners and losers in terms of business activity. Overall, service-related endeavors are less frequent compared to last year while certain product-related initiatives have experienced a significant increase. This study urges dissemination of trademark applications, across Offices, in bulk to facilitate empirical work. Unlike patent applications, which take eighteen months to become known, information on trademark applications is disclosed relatively quickly. Since they can approximate real-time business expectations of demand and are related to innovation and firm value, they can provide us with significant insights in the short-run until more data become available.
Drivas, K (2020), “The Short-Run Effect of COVID-19 on New Marketing Endeavors: Evidence from EUIPOâ€™s Trademark Applications”, COVID Economics N/A. https://cepr.org/node/390811
Knowing the prevalence of the COVID-19 infection in a population of interest, and how it changes over time and across space, is of fundamental importance for public health. Unfortunately, the fraction of cases who turn out to be positive in a test provides a distorted picture of the prevalence of the infection because the tested cases are not a random sample of the population. Since random testing of the population is costly and complicated to carry out, in this note we show how to use the available information, in conjunction with credible assumptions about unknown quantities, to obtain a range of plausible values for the prevalence of the infection. We discuss the difference between two alternative measures of prevalence and argue that one of the two is much harder to pin down with the data currently available. We apply our method to the Italian data.
Peracchi, F and D Terlizzese (eds) (2020), “Estimating the prevalence of the COVID-19 infection, with an application to Italy”, COVID Economics N/A. https://cepr.org/node/390806
Fragmented by policies, united by outcomes: This is the picture of the United States that emerges from our analysis of the spatial diffusion of Covid-19 and the scattered lock-down policies introduced by individual states to contain it. We first use spatial econometric techniques to document direct and indirect spillovers of new infections across county and state lines, as well as the impact of individual states' lock-down policies on infections in neighboring states. We find consistent statistical evidence that new cases diffuse across county lines, holding county level factors constant, and that the diffusion across counties was affected by the closure policies of adjacent states. Spatial impulse response functions reveal that the diffusion across counties is persistent for up to ten days after an increase in adjacent counties. We then develop a spatial version of the epidemiological SIR model where new infections arise from interactions between infected people in one state and susceptible people in the same or in neighboring states. We incorporate lock-down policies into our model and calibrate the model to match both the cumulative and the new infections across the 48 contiguous U.S. states and DC. Our results suggest that, had the states with the less restrictive social distancing measures tightened them by one level, the cumulative infections in other states would be about 5% smaller. In our spatial SIR model, the spatial containment policies such as border closures have a bigger impact on flattening the infection curve in the short-run than on the cumulative infections in the long-run.
Brady, R, M Insler and J Rothert (eds) (2020), “The Fragmented United States of America: The impact of scattered lock-down policies on country-wide infections”, COVID Economics N/A. https://cepr.org/node/390591
This paper uses administrative, survey, and online vacancy data to analyze the short-term labor market impacts of the COVID-19 lockdown in Greece. We find that flows into unemployment have not increased; in fact, separations were lower than would have been expected given trends in recent years. At the same time, employment was about 12 percent lower at the end of June than it would have been without the pandemic. The interrupted time series and difference-in-differences estimates indicate that this was due to a dramatic slowdown in hiring during months when job creation typically peaks in normal years, mostly in tourism. While we do not formally test the reasons for these patterns, our analysis suggests that the measures introduced to mitigate the effects of the crisis in Greece have played an important role. These measures prohibited layoffs in industries affected by the crisis and tied the major form of income support to the maintenance of employment relationships.
Betcherman, G, N Giannakopoulos, I Laliotis, I Pantelaiou, M Testaverde and G Tsimas (eds) (2020), “Reacting quickly and protecting jobs: The short-term impacts of the COVID-19 lockdown on the Greek labor market”, COVID Economics N/A. https://cepr.org/node/390592
Does the ranking of Covid-19 cases by municipalities follow a Zipf â€™s law (i.e. an estimated Pareto exponent of one)? This note tries to answer this question using daily data from Brazil for the Mar 30 - Aug 06 period. We used a Poisson Pseudo Maximum Likelihood (PPML) estimator for our estimates and the result is that the Pareto exponent of the ranking of Covid-19 cases is converging to the one obtained for the population ranking and seems to follow the pattern of the Zipf â€™s Law. We did the same exercise using Italian regionsâ€™ daily data which we use as a proxy to a long run equilibrium as the pandemic is apparently under control there. Contrary to Brazil, the Pareto exponent for the ranking for Covid-19 cases does not converge to the one estimated for the regular population. We try to advance some rationale for this contrasting behavior between them.
Comittit, V and C Shikida (eds) (2020), “Days of Zipf and Covid? Looking for evidence of Zipf’s Law in the infected Brazil”, COVID Economics N/A. https://cepr.org/node/390593