scholarly journals From a Recession to the COVID-19 Pandemic: Inflation–Unemployment Comparison between the UK and India

Economies ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 73
Author(s):  
Vijay Victor ◽  
Joshy Joseph Karakunnel ◽  
Swetha Loganathan ◽  
Daniel Francois Meyer
Keyword(s):  
Generalized Additive Model ◽  
Aggregate Demand ◽  
Additive Model ◽  
Supply Side ◽  
Fiscal Stimulus ◽  
Policy Initiatives ◽  
Short Run ◽  
Severe Recession ◽  
The Uk ◽  
The Right

The recession in India and the UK peaked in 2017 due to the implications of new policy initiatives. The outbreak of the COVID-19 pandemic at the beginning of 2020 intensified the crisis, causing a drastic decline in aggregate demand and output. India and the UK have resorted to monetary and fiscal stimulus packages to face the economic crisis. This study investigated the inflation–unemployment dynamics during the recession and COVID-19 times in India and the UK. Using a generalized additive model (GAM), the results of this study revealed that the recession had given way to stagflation in India. In contrast, in the UK, it has led to a more severe recession in the short-run. During the downturn, policy initiatives aggravate the recession and eventually turn to stagflation in India due to inflation caused by the weak supply side. However, in the UK, the policy initiatives during this downturn pushed the economy into a deeper recession due to reduced demand. The outbreak of the COVID-19 pandemic has had a similar recessionary impact on both economies. A time horizon based recovery plan is suggested to help the economies recover from stagflation and even deeper recession. This framework could enable policymakers to choose the right path of recovery within the shortest possible time.

How to Combat Recession

2018 ◽  
Author(s):  
Laurence Seidman
Keyword(s):  
Great Recession ◽  
Aggregate Demand ◽  
Government Debt ◽  
The United States ◽  
United States Congress ◽  
Fiscal Stimulus ◽  
The Great Recession ◽  
Goods And Services ◽  
Severe Recession ◽  
Stimulus Package

This work analyzes all aspects of a new policy to combat recession: “stimulus without debt.” Fear of deficits and debt kept Congress from enacting a large enough fiscal stimulus to overcome the Great Recession that began in 2008, and this fear is likely to restrict fiscal stimulus in the next severe recession. “Stimulus without debt” is a new policy that would increase aggregate demand for goods and services in a recession without increasing government debt. Stimulus without debt consists of a transfer (not loan) from the central bank to the national Treasury (or to national treasuries in the case of the eurozone) so that the Treasury does not have to borrow to finance fiscal stimulus enacted by the legislature. In the United States, Congress would enact a fiscal stimulus package that consists mainly of cash tax rebates to households but also other temporary expenditures and temporary tax cuts; the fiscal stimulus would raise aggregate demand. The Federal Reserve would use new money to give a large transfer (not loan) to the Treasury equal to the fiscal stimulus package so that the Treasury does not have to borrow to pay for the package. Hence, there would be no increase in government debt.

scholarly journals The Secular Stagnation of Productivity Growth

2019 ◽  
pp. 1-27 ◽  
Author(s):  
Servaas Storm
Keyword(s):  
Empirical Evidence ◽  
Productivity Growth ◽  
Aggregate Demand ◽  
Supply Side ◽  
Potential Growth ◽  
Secular Stagnation ◽  
Long Run ◽  
Structural Weakness ◽  
Short Run ◽  
The U.S

The concern that an economy could experience persistent stagnation, caused by a structural weakness of aggregate demand, goes back to Alvin Hansen’s (1939) thesis of ‘secular stagnation’. Hansen’s thesis has been revived in recent times, when it became clear that productivity and potential growth in the OECD countries have been declining for decades. However, in line with deep-rooted theoretical beliefs, that inadequate demand can only affect growth in the short run, secular stagnation (of potential growth) is treated as an exclusively supply-side problem, the root of which is a worrying steady decline in productivity growth. This paper argues that it is a mistake to dismiss secular demand stagnation as main cause of declining potential growth in the OECD. We argue that the theoretical case for demand-caused secular stagnation is strong and empirical evidence that it has affected the U.S. economy after the mid-1970s is entirely convincing. Demand is leading supply, also in the long run. Hansen had it right, after all.

scholarly journals Can the AD-AS Model Explain the Presence and Persistence of the Underground Economy? Evidence from Italy

Economies ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 170
Author(s):  
Gaetano Lisi
Keyword(s):  
Aggregate Demand ◽  
Underground Economy ◽  
Supply Side ◽  
Negative Effects ◽  
Basic Model ◽  
Macroeconomic Analysis ◽  
Short Run ◽  
Widespread Phenomenon ◽  
Positive Shock ◽  
First Time

The underground economy crucially affects growth and unemployment in both developed and developing countries. Nevertheless, this widespread phenomenon does not appear in the basic model for macroeconomic analysis, namely the Aggregate Demand-Aggregate Supply (or simply AD-AS) model. Therefore, this paper introduces–for the first time, to the best of our knowledge–the underground sector of the economy into the popular AD-AS model, with the aim of increasing its descriptive power. Indeed, the present theoretical extension of the AD-AS model shows that the underground economy –despite its negative effects on aggregate demand and growth– can trigger a supply-side positive shock that mitigates, at least in the short run, the problem of high unemployment. Empirical evidence from Italy is also provided.

The changing landscape of OSH regulation in the UK: Achieving the right balance in policy and practice

2013 ◽  
Author(s):  
David Hollis ◽  
Stavroula Leka ◽  
Aditya Jain ◽  
Nicholas Andreou
Keyword(s):  
Policy And Practice ◽  
The Uk ◽  
The Right

scholarly journals Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance

Risks ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 53
Author(s):  
Yves Staudt ◽  
Joël Wagner
Keyword(s):  
Random Forests ◽  
Goodness Of Fit ◽  
Generalized Additive Model ◽  
Mean Squared Error ◽  
Additive Model ◽  
Continuous Variables ◽  
Insurance Portfolio ◽  
Normal Transformation ◽  
Car Insurance ◽  
Log Normal

For calculating non-life insurance premiums, actuaries traditionally rely on separate severity and frequency models using covariates to explain the claims loss exposure. In this paper, we focus on the claim severity. First, we build two reference models, a generalized linear model and a generalized additive model, relying on a log-normal distribution of the severity and including the most significant factors. Thereby, we relate the continuous variables to the response in a nonlinear way. In the second step, we tune two random forest models, one for the claim severity and one for the log-transformed claim severity, where the latter requires a transformation of the predicted results. We compare the prediction performance of the different models using the relative error, the root mean squared error and the goodness-of-lift statistics in combination with goodness-of-fit statistics. In our application, we rely on a dataset of a Swiss collision insurance portfolio covering the loss exposure of the period from 2011 to 2015, and including observations from 81 309 settled claims with a total amount of CHF 184 mio. In the analysis, we use the data from 2011 to 2014 for training and from 2015 for testing. Our results indicate that the use of a log-normal transformation of the severity is not leading to performance gains with random forests. However, random forests with a log-normal transformation are the favorite choice for explaining right-skewed claims. Finally, when considering all indicators, we conclude that the generalized additive model has the best overall performance.

scholarly journals What do users think of the UK’s three COVID-19 contact-tracing apps? A comparative analysis

2021 ◽  
Vol 28 (1) ◽  
pp. e100320
Author(s):  
Vahid Garousi ◽  
David Cutting
Keyword(s):  
Mobile Apps ◽  
Analysis Data ◽  
End Users ◽  
Contact Tracing ◽  
User Reviews ◽  
The Public ◽  
Engineering Teams ◽  
Technical Issues ◽  
The Uk ◽  
The Right

ObjectivesOur goal was to gain insights into the user reviews of the three COVID-19 contact-tracing mobile apps, developed for the different regions of the UK: ‘NHS COVID-19’ for England and Wales, ‘StopCOVID NI’ for Northern Ireland and ‘Protect Scotland’ for Scotland. Our two research questions are (1) what are the users’ experience and satisfaction levels with the three apps? and (2) what are the main issues (problems) that users have reported about the apps?MethodsWe assess the popularity of the apps and end users’ perceptions based on user reviews in app stores. We conduct three types of analysis (data mining, sentiment analysis and topic modelling) to derive insights from the combined set of 25 583 user reviews of the aforementioned three apps (submitted by users until the end of 2020).ResultsResults show that end users have been generally dissatisfied with the apps under study, except the Scottish app. Some of the major issues that users have reported are high battery drainage and doubts on whether apps are really working.DiscussionTowards the end of 2020, the much-awaited COVID-19 vaccines started to be available, but still, analysing the users’ feedback and technical issues of these apps, in retrospective, is valuable to learn the right lessons to be ready for similar circumstances in future.ConclusionOur results show that more work is needed by the stakeholders behind the apps (eg, apps’ software engineering teams, public-health experts and decision makers) to improve the software quality and, as a result, the public adoption of these apps. For example, they should be designed to be as simple as possible to operate (need for usability).

scholarly journals Generalized additive model with embedded variable selection for bankruptcy prediction: Prediction versus interpretation

2019 ◽  
Vol 7 (1) ◽  
pp. 1597956
Author(s):  
Carlos Valencia ◽  
Sergio Cabrales ◽  
Laura Garcia ◽  
Juan Ramirez ◽  
Diego Calderona ◽  
...  
Keyword(s):  
Variable Selection ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Bankruptcy Prediction ◽  
Selection For
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