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10.1016/j.jebo.2021.01.023 http://scihub22266oqcxt.onion/10.1016/j.jebo.2021.01.023 33551525!7849472!33551525     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33551525&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Econ+Behav+Organ 2021 ; 184 (ä): 199-231 Nephropedia Template TP {{tp|p=33551525|t=2021. Determinants of the community mobility during the COVID-19 epidemic: The role of government regulations and information |pdf=|usr=}}{{33551525}} gab.com Text Determinants of the community mobility during the COVID-19 epidemic: The role of government regulations and information JO: J Econ Behav Organ http://www.kidney.de/pget.php?&tw=1&pmid=33551525 #FOAvip This paper studies the dynamics of human mobility during the initial stage of the COVID-19 pandemic in countries around the world. The main goal of the analysis is to empirically separate voluntary reductions in mobility driven by the information about the location-specific pandemic trends from the effects of the government-imposed social distancing mandates. Google human mobility dataset is used to track the dynamics of mobility across a wide range of categories (e.g., workplace, retail and recreational activities, etc.), while information on country-specific counts of COVID-19 cases and deaths is used as a proxy for the information about the spread of the pandemic available to the population. A detailed index of stringency of the government-imposed social distancing policies in around 100 countries is used as a measure of government response. We find that human mobility does respond in a significant way to the information about the spread of the pandemic. This channel can explain about 15 percentage points of the overall reduction in mobility across the affected countries. At the same time, our results imply that government-imposed policies account for the majority of the reduction in the mobility observed during this period. Twit Text FOAVip Determinants of the community mobility during the COVID-19 epidemic: The role of government regulations and information ????J Econ Behav Organ http://www.kidney.de/pget.php?&tw=1&pmid=33551525 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33551525 #FOAvip English Wikipedia <ref>{{Cite pmid|33551525}}</ref> Determinants of the community mobility during the COVID-19 epidemic: The role of government regulations and information #MMPMID33551525 Mendolia S; Stavrunova O; Yerokhin O J Econ Behav Organ 2021[Apr]; 184 (ä): 199-231 PMID33551525show ga This paper studies the dynamics of human mobility during the initial stage of the COVID-19 pandemic in countries around the world. The main goal of the analysis is to empirically separate voluntary reductions in mobility driven by the information about the location-specific pandemic trends from the effects of the government-imposed social distancing mandates. Google human mobility dataset is used to track the dynamics of mobility across a wide range of categories (e.g., workplace, retail and recreational activities, etc.), while information on country-specific counts of COVID-19 cases and deaths is used as a proxy for the information about the spread of the pandemic available to the population. A detailed index of stringency of the government-imposed social distancing policies in around 100 countries is used as a measure of government response. We find that human mobility does respond in a significant way to the information about the spread of the pandemic. This channel can explain about 15 percentage points of the overall reduction in mobility across the affected countries. At the same time, our results imply that government-imposed policies account for the majority of the reduction in the mobility observed during this period. äDeterminants of the community mobility during the COVID-19 epidemic: T(epmc).pdf DeepDyve Pubget Overpricing