TY - JOUR
T1 - Perception versus Official Data
T2 - Employers’ Knowledge about the Aging Workforce
AU - Axelrad, Hila
N1 - Funding Information:
This work was supported by Alfred P. Sloan Foundation (Grant number 6-2015-14113). I am extremely grateful to Professor Miki Malul and Dr. Jacquelyn James for many discussions and for insightful comments. Thanks also to Vehadarata association, for their help and support. I also want to acknowledge the anonymous referees for their comments, which helped in refining the manuscript and improving its quality and coherence. In addition, this paper uses data from SHARE wave 5 (DOIs: 10.6103/SHARE.w5.100), see Borsch-Supan et al. (2013) for methodological details. The SHARE data collection has been primarily funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005- 028857, SHARE-LIFE: CIT4-CT-2006-028812), and FP7 (SHAREPREP: N_211909, SHARE-LEAP: N_227822, SHARE M4: N_261982). Additional funding from the German Ministry of Education and Research, the U.S. National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064), and various national funding sources is gratefully acknowledged (see www.share-project.org).
Funding Information:
I am extremely grateful to Professor Miki Malul and Dr. Jacquelyn James for many discussions and for insightful comments. Thanks also to Vehadarata association, for their help and support. I also want to acknowledge the anonymous referees for their comments, which helped in refining the manuscript and improving its quality and coherence. In addition, this paper uses data from SHARE wave 5 (DOIs: 10.6103/SHARE.w5.100), see Borsch-Supan et al. (2013) for methodological details. The SHARE data collection has been primarily funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005- 028857, SHARE-LIFE: CIT4-CT-2006-028812), and FP7 (SHAREPREP: N_211909, SHARE-LEAP: N_227822, SHARE M4: N_261982). Additional funding from the German Ministry of Education and Research, the U.S. National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064), and various national funding sources is gratefully acknowledged (see www.share-project.org ).
Publisher Copyright:
© 2020 Taylor & Francis.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - The current study compared employers’ perceptions and knowledge about older workers to official data, as well as employers’ views of the ideal situation regarding older workers, to assess potential gaps. A questionnaire answered by a sample of 373 employers was used to examine possible gaps between employers’ perceptions, views, and official statistical data regarding older workers. Statistical significance (T-Test) analyses suggested that gaps do exist, in issues like labor force participation rate, health status, and women’s retirement age, which may explain obstacles faced by older workers. Logistic regression models revealed the effect of personal and organizational characteristics on employers’ preferences regarding the ideal labor force participation rate of older workers, and the ideal retirement age according to their preference. Educational measures and policies aimed at increasing employers’ awareness to the official data regarding the aging workforce should be tailored to specific organizations, sectors, and employers’ characteristics.
AB - The current study compared employers’ perceptions and knowledge about older workers to official data, as well as employers’ views of the ideal situation regarding older workers, to assess potential gaps. A questionnaire answered by a sample of 373 employers was used to examine possible gaps between employers’ perceptions, views, and official statistical data regarding older workers. Statistical significance (T-Test) analyses suggested that gaps do exist, in issues like labor force participation rate, health status, and women’s retirement age, which may explain obstacles faced by older workers. Logistic regression models revealed the effect of personal and organizational characteristics on employers’ preferences regarding the ideal labor force participation rate of older workers, and the ideal retirement age according to their preference. Educational measures and policies aimed at increasing employers’ awareness to the official data regarding the aging workforce should be tailored to specific organizations, sectors, and employers’ characteristics.
KW - Older workers
KW - aging workforce
KW - employers
KW - perceptions
UR - http://www.scopus.com/inward/record.url?scp=85086878156&partnerID=8YFLogxK
U2 - 10.1080/08959420.2020.1769535
DO - 10.1080/08959420.2020.1769535
M3 - Article
C2 - 32490734
AN - SCOPUS:85086878156
VL - 33
SP - 177
EP - 199
JO - Journal of Aging and Social Policy
JF - Journal of Aging and Social Policy
SN - 0895-9420
IS - 2
ER -