The Cyclical Behavior of Equilibrium Unemployment and Vacancies

2005 ◽  
Vol 95 (1) ◽  
pp. 25-49 ◽  
Author(s):  
Robert Shimer
Keyword(s):  
United States ◽  
Standard Deviation ◽  
Business Cycle ◽  
Labor Productivity ◽  
The United States ◽  
Search Model ◽  
Present Value ◽  
Cycle Frequency ◽  
Search And Matching Model ◽  
Cyclical Behavior

This paper argues that the textbook search and matching model cannot generate the observed business-cycle-frequency fluctuations in unemployment and job vacancies in response to shocks of a plausible magnitude. In the United States, the standard deviation of the vacancy-unemployment ratio is almost 20 times as large as the standard deviation of average labor productivity, while the search model predicts that the two variables should have nearly the same volatility. A shock that changes average labor productivity primarily alters the present value of wages, generating only a small movement along a downward-sloping Beveridge curve (unemploymentvacancy locus). A shock to the separation rate generates a counterfactually positive correlation between unemployment and vacancies. In both cases, the model exhibits virtually no propagation.

scholarly journals The Cyclical Behavior of Equilibrium Unemployment and Vacancies Revisited

2008 ◽  
Vol 98 (4) ◽  
pp. 1692-1706 ◽  
Author(s):  
Marcus Hagedorn ◽  
Iourii Manovskii
Keyword(s):  
Standard Model ◽  
Business Cycle ◽  
Search Model ◽  
Equilibrium Unemployment ◽  
Cycle Frequency ◽  
The Standard Model ◽  
Cyclical Behavior ◽  
Frequency Fluctuations ◽  
The Cost ◽  
Number Of Authors

Recently, a number of authors have argued that the standard search model cannot generate the observed business-cycle-frequency fluctuations in unemployment and job vacancies, given shocks of a plausible magnitude. We propose a new calibration strategy of the standard model that uses data on the cost of vacancy creation and cyclicality of wages to identify the two key parameters –- the value of nonmarket activity and the bargaining weights. Our calibration implies that the model is consistent with the data. (JEL E24, E32, J31, J63, J64)

scholarly journals Asymmetric dynamics between uncertainty and unemployment flows in the United States

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ali Ahmed ◽  
Mark Granberg ◽  
Victor Troster ◽  
Gazi Salah Uddin
Keyword(s):  
United States ◽  
Business Cycle ◽  
Negative Impact ◽  
The United States ◽  
Large Uncertainty ◽  
Monthly Data ◽  
Uncertainty Measures ◽  
Linear And Nonlinear ◽  
The Business Cycle ◽  
The U.S

AbstractThis paper examines how different uncertainty measures affect the unemployment level, inflow, and outflow in the U.S. across all states of the business cycle. We employ linear and nonlinear causality-in-quantile tests to capture a complete picture of the effect of uncertainty on U.S. unemployment. To verify whether there are any common effects across different uncertainty measures, we use monthly data on four uncertainty measures and on U.S. unemployment from January 1997 to August 2018. Our results corroborate the general predictions from a search and matching framework of how uncertainty affects unemployment and its flows. Fluctuations in uncertainty generate increases (upper-quantile changes) in the unemployment level and in the inflow. Conversely, shocks to uncertainty have a negative impact on U.S. unemployment outflow. Therefore, the effect of uncertainty is asymmetric depending on the states (quantiles) of U.S. unemployment and on the adopted unemployment measure. Our findings suggest state-contingent policies to stabilize the unemployment level when large uncertainty shocks occur.

scholarly journals 1414. #Vaccine Twitter Influencers: is it Just About Reach and Followers?

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S714-S715
Author(s):  
Jean-Etienne Poirrier ◽  
Theodore Caputi ◽  
John Ayers ◽  
Mark Dredze ◽  
Sara Poston ◽  
...  
Keyword(s):  
United States ◽  
Social Media ◽  
Standard Deviation ◽  
Small Proportion ◽  
The United States ◽  
Research Support ◽  
Policy Makers ◽  
Media Messages ◽  
Social Media Platforms ◽  
The Impact

Abstract Background A small number of powerful users (“influencers”) dominates conversations on social media platforms: less than 1% of Twitter accounts have at least 3,000 followers and even fewer have hundreds of thousands or millions of followers. Beyond simple metrics (number of tweets, retweets...) little is known about these “influencers”, particularly in relation to their role in shaping online narratives about vaccines. Our goal was to describe influential Twitter accounts that are driving conversations about vaccines and present new metrics of influence. Methods Using publicly-available data from Twitter, we selected posts from 1-Jan-2016 to 31-Dec-2018 and extracted the top 5% of accounts tweeting about vaccines with the most followers. Using automated classifiers, we determined the location of these accounts, and grouped them into those that primarily tweet pro- versus anti-vaccine content. We further characterized the demographics of these influencer accounts. Results From 25,381 vaccine-related tweets available in our sample representing 10,607 users, 530 accounts represented the top 5% by number of followers. These accounts had on average 1,608,637 followers (standard deviation=5,063,421) and 340,390 median followers. Among the accounts for which sentiment was successfully estimated by the classifier, 10.4% (n=55) posted anti-vaccine content and 33.6% (n=178) posted pro-vaccine content. Of the 55 anti-vaccine accounts, 50% (n=18) of the accounts for which location was successfully determined were from the United States. Of the 178 pro-vaccine accounts, 42.5% (n=54) were from the United States. Conclusion This study showed that only a small proportion of Twitter accounts (A) post about vaccines and (B) have a high follower count and post anti-vaccine content. Further analysis of these users may help researchers and policy makers better understand how to amplify the impact of pro-vaccine social media messages. Disclosures Jean-Etienne Poirrier, PhD, MBA, The GSK group of companies (Employee, Shareholder) Theodore Caputi, PhD, Good Analytics Inc. (Consultant) John Ayers, PhD, GSK (Grant/Research Support) Mark Dredze, PhD, Bloomberg LP (Consultant)Good Analytics (Consultant) Sara Poston, PharmD, The GlaxoSmithKline group of companies (Employee, Shareholder) Cosmina Hogea, PhD, GlaxoSmithKline (Employee, Shareholder)

Shifting Drivers of Growth: Policy Implications for ASEAN-5

2015 ◽  
Vol 14 (1) ◽  
pp. 157-173 ◽  
Author(s):  
Kong Yam Tan ◽  
Tilak Abeysinghe ◽  
Khee Giap Tan
Keyword(s):  
United States ◽  
Business Cycle ◽  
The United States ◽  
Policy Implications ◽  
Structural Vector Autoregression ◽  
Research Papers ◽  
Vector Autoregression Model ◽  
The Subject ◽  
Multiplier Effects ◽  
Export Destination

How the old saying “when the United States sneezes, the world catches a cold” holds true has been the subject of many research papers on global and country group business cycle synchronization and divergence. Instead of business cycle linkages, however, this paper examines the evolution of the dependence of ASEAN-5 and other Asian economies on their traditional and emerging growth engines (the United States, EU, Japan, China, and India). For this we use a structural vector autoregression model that yields time-varying growth multiplier effects. Although China has overtaken others as a major export destination for ASEAN-5 and despite the United States losing much of its relative economic clout in Asia, the multiplier effects show that the United States is still about 1.5 times more growth-enhancing than China for ASEAN-5. The EU has also not lost out completely to China as a growth engine. China, however, has overtaken Japan to become about 1.88 times more growth enhancing than Japan for ASEAN-5. India has yet to become a significant growth engine, although it is of increasing importance to Singapore, Malaysia, and Indonesia. These results call for new initiatives to balance the rising over-dependence on China.

MMPI-2 Scores of Puerto Rican, Mexican, and U.S. Latino College Students: A Research Note

2000 ◽  
Vol 87 (1) ◽  
pp. 266-268 ◽  
Author(s):  
Jose J. Cabiya ◽  
Denise A. Chavira ◽  
Francisco C. Gomez ◽  
Emilia Lucio ◽  
Jeanett Castellanos ◽  
...  
Keyword(s):  
United States ◽  
College Students ◽  
Puerto Rico ◽  
Standard Deviation ◽  
Puerto Rican ◽  
The United States ◽  
Clinical Scale ◽  
The Mean ◽  
The One ◽  
Scale Data

In this brief report, we present MMPI-2 basic validity and clinical scale data of Latino-descent persons from Puerto Rico ( n = 290), Mexico ( n = 1,920), and the United States ( n = 28). All were administered one of three Spanish translations of the MMPI-2. A review of the mean scores of these respective groups indicates similarities across all scales. Differences among these three groups, with the exception of the Mf scale (which is keyed to sex), were well within the one standard deviation band. More importantly, these findings are promising given the fact that three different translations of the MMPI-2 were applied.

scholarly journals Does wage reflect labor productivity? A comparison between Brazil and the United States

2018 ◽  
Vol 38 (4) ◽  
pp. 629-649 ◽  
Author(s):  
ALEXANDRE GORI MAIA ◽  
ARTHUR SAKAMOTO
Keyword(s):  
United States ◽  
Economic Development ◽  
Labor Productivity ◽  
Wage Differentials ◽  
The United States ◽  
Occupational Groups ◽  
Economic Cycles ◽  
Productivity Differentials ◽  
The Relationship ◽  
The U.S

ABSTRACT The study compares the relationship between wages and labor productivity for different categories of workers in Brazil and in the U.S. Analyses highlight to what extent the equilibrium between wages and productivity is related to the degree of economic development. Wages in the U.S. has shown to be more attached to labor productivity, while Brazil has experienced several economic cycles were average earnings grew initially much faster than labor productivity, suddenly falling down in the subsequent years. Analyses also stress how wage differentials, in fact, match productivity differentials for certain occupational groups, while for others they do not.

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