The Impact of Unemployment Insurance on Job Search: Evidence from Google Search Data

2013 ◽  
Author(s):  
Scott R. Baker ◽  
Andrey Fradkin
Keyword(s):  
Unemployment Insurance ◽  
Job Search ◽  
Search Data ◽  
Google Search ◽  
The Impact

scholarly journals The Impact of Unemployment Insurance on Job Search

1975 ◽  
Vol 1975 (1) ◽  
pp. 13 ◽  
Author(s):  
Stephen T. Marston ◽  
Robert E. Hall ◽  
Charles C. Holt ◽  
Martin S. Feldstein
Keyword(s):  
Unemployment Insurance ◽  
Job Search ◽  
The Impact

Erratum

ILR Review ◽  
1979 ◽  
Vol 32 (4) ◽  
pp. 533-533
Author(s):  
John M. Barron ◽  
Otis W. Gilley
Keyword(s):  
Unemployment Insurance ◽  
Job Search ◽  
Search Process ◽  
Unemployment Benefits ◽  
Insurance Benefits ◽  
Search Intensity ◽  
Unemployment Spell ◽  
Disincentive Effect ◽  
The Impact ◽  
Job Search Intensity

The Impact of Unemployment Insurance on the Search Process A SERIOUS coding error in the data used in our recent article published in the April 1979 Review has been pointed out by Joe Stone of the Bureau of Labor Statistics. Our paper proposed a test of Mortensen's hy-pothesis that both future expected unem- ployment insurance benefits and benefits re-ceived during a current unemployment spell affect an individual's search intensity. Regression 1, which remains unchanged, still provides no support for Mortensen's proposed effect of unemployment insurance benefits to be received during the subse- quent unemployment spell on the current job-search intensity of the unemployed. Regression 3, which remains unchanged, still indicates a distortion in the search process-in particular in the methods of search chosen-for current recipients of unem- ployment insurance benefits and the un- employed who are eligible and have applied for these benefits. The error affects the results of the estimnation of Equation 2. It occurred because unemployed individuals who were eligible and had applied for benefits were assigned zero weeks left to receive these benefits rather than the maximum allowable duration of benefits according to the individual's state of residence. As a result, the value of unem- ployment insurance benefits for these individuals was inadvertently set equal to zero. Yet these individuals, other things equal, were shown in our original study to have a measured job-search intensity 74 percent higher than individuals currently receiving unemployment insurance, a difference related to the time involved in the ap- plication process rather than to actual job- search efforts. Reestimation of Equation 2 controlling for this effect and correcting for the measure- ment error in the value of unemployment benefits results in one important change. The coefficient on the value of unemployment insurance benefits, though still negative, is riot different from zero for standard significance levels. A serious consequence is that the traditional disincentive effect of unemployment insurance on search in- tensity is not supported by our test. One explanation for this finding may be that individuals with larger values of unemployment benefits have a greater incentive to overstate search intensity since such benefits are dependent on search activity.

scholarly journals DIFFERENTIAL IMPACT OF COVID-19 IN AUSTRALIA: EVIDENCE FROM GOOGLE SEARCH DATA

2021 ◽  
Vol 04 (01) ◽  
pp. 5-21
Author(s):  
WEE CHIAN KOH
Keyword(s):  
Real Time ◽  
Service Sector ◽  
Retail Trade ◽  
Disease Spread ◽  
Face To Face ◽  
Delivery Services ◽  
Search Data ◽  
Google Search ◽  
The Impact ◽  
Spatial Impacts

Quantifying the immediate economic impact of COVID-19 is important to design proportionate relief and support policies. However, surveys of businesses and households are only typically available after considerable delay. We use near-real-time Google search data to examine the temporal and spatial impacts of COVID-19 on service sector activity in Australia. We find that the travel-related and consumer-facing sectors, such as aviation, tourism, hotels, restaurants, and retail trade, suffered steep contractions during the outbreak. By contrast, sectors that involve less physical and face-to-face interaction, such as info-communication technology (ICT) and delivery services, experienced significant gains. The magnitude of the impact is large. During the first COVID-19 wave between January and March, the demand for air travel, tourism, and hotel accommodation declined by 60–80%, while the demand for ICT and delivery services surged by more than 50%. In states and territories with low caseloads, the impact has also been severe due to government-enforced nationwide social distancing measures to contain disease spread. However, in states and territories that eased restrictions earlier and faster, there has been no significant reduction in demand for certain consumer-facing services. Our findings demonstrate the usefulness of high-frequency and near-real-time indicators in monitoring the rapidly unfolding effects of COVID-19.

scholarly journals A Narrative Review of the Risk Factors for Cancer and the Preventive Opportunities: Current Status, Future Perspectives, and Implications for India

2021 ◽  
Author(s):  
Vinod K. Ramani ◽  
Ganesha D. V. ◽  
Radheshyam Naik
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Air Pollution ◽  
Cancer Prevention ◽  
Assessment Tools ◽  
Biologically Active ◽  
Pubmed Database ◽  
Risk Of Cancer ◽  
Google Search ◽  
The Impact

Abstract Introduction Clinical cancer can arise from heterogenous pathways through various genetic mutations. Although we cannot predict the timeline by which an individual will develop cancer, certain risk assessment tools can be used among high-risk groups for focusing the preventive activities. As primary level of cancer prevention, healthy lifestyle approach is being promoted. The etiological factors for lung cancer include by-products of industrialization and air pollution. We need to factor the increase in household air pollution as well. Methods “PubMed” database and Google search engines were used for searching the relevant articles. Search terms with Boolean operators used include “Cancer prevention,” “Missed opportunities in cancer causation,” and “incidence of risk factors.” This review includes 20 studies and other relevant literature that address the opportunities for cancer prevention. Body The narrative describes the association between many of the risk factors and development of cancer. This includes tobacco, alcohol, infections, air pollution, physical inactivity, diet, obesity, screening and preventive strategies, chemoprevention, biomarkers of carcinogenesis, and factors that prolong the diagnosis of cancer. Discussion Reports from basic science research provide evidence on the potential of biologically active food components and pharmacological agents for mitigating the risk of cancer and its progression. However, some reports from observational studies and randomized trials have been inconsistent. We need to recognize the impact of sociodemographic factors such as age, sex, ethnicity, culture, and comorbid illness on preventive interventions. Spiral computed tomographic scan is a robust tool for early detection of lung cancer. Conclusion Infectious etiology for specific cancers provides opportunities for prevention and treatment. The complex interplay between man and microbial flora needs to be dissected, for understanding the pathogenesis of relevant malignancies. For reducing the morbidity of cancer, we need to focus on prevention as a priority strategy and intervene early during the carcinogenic process.

scholarly journals Tracking U.S. Pertussis Incidence: Correlation of Public Health Surveillance and Google Search Data Varies by State

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Christopher H. Arehart ◽  
Michael Z. David ◽  
Vanja Dukic
Keyword(s):  
Public Health ◽  
Linear Models ◽  
State Level ◽  
Individual State ◽  
Control And Prevention ◽  
Search Data ◽  
State Models ◽  
Uniform Manner ◽  
Google Search ◽  
The U.S

AbstractThe Morbidity and Mortality Weekly Reports of the U.S. Centers for Disease Control and Prevention document a raw proxy for counts of pertussis cases in the U.S., and the Project Tycho (PT) database provides an improved source of these weekly data. These data are limited because of reporting delays, variation in state-level surveillance practices, and changes over time in diagnosis methods. We aim to assess whether Google Trends (GT) search data track pertussis incidence relative to PT data and if sociodemographic characteristics explain some variation in the accuracy of state-level models. GT and PT data were used to construct auto-correlation corrected linear models for pertussis incidence in 2004–2011 for the entire U.S. and each individual state. The national model resulted in a moderate correlation (adjusted R2 = 0.2369, p < 0.05), and state models tracked PT data for some but not all states. Sociodemographic variables explained approximately 30% of the variation in performance of individual state-level models. The significant correlation between GT models and public health data suggests that GT is a potentially useful pertussis surveillance tool. However, the variable accuracy of this tool by state suggests GT surveillance cannot be applied in a uniform manner across geographic sub-regions.

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