The_AT Best_JJT Employers_NN2 for_IF Older_JJR Workers_NN2 These_DD2 companies_NN2 prize_VV0 experience_NN1 and_CC aim_VV0 to_TO be_VBI employers_NN2 of_IO choice_NN1 Before_II the_AT recession_NN1 kicked_VVN into_II high_JJ gear_NN1 ,_, officials_NN2 at_II Cornell_NP1 University_NN1 faced_VVD an_AT1 imminent_JJ staffing_NN1 problem_NN1 :_: retiring_JJ baby_NN1 boomers_NN2 ._. 
"_" We_PPIS2 started_VVD out_RP the_AT year_NNT1 expecting_VVG a_AT1 very_RG high_JJ level_NN1 of_IO turnover_NN1 ,_, "_" says_VVZ Mary_NP1 Opperman_NP1 ,_, the_AT university_NN1 's_GE vice_JJ president_NN1 for_IF human_JJ resources_NN2 ._. 
Instead_RR ,_, they_PPHS2 got_VVD an_AT1 economic_JJ tsunami_NN2 ._. 
The_AT school_NN1 saw_VVD its_APPGE endowment_NN1 plunge_NN1 27_MC percent_NNU during_II the_AT 2009_MC fiscal_JJ year_NNT1 ._. 
Officials_NN2 soon_RR had_VHN a_AT1 very_RG different_JJ problem_NN1 :_: They_PPHS2 needed_VVD to_TO cut_VVI jobs_NN2 ._. 
Through_II a_AT1 series_NN of_IO open_JJ forums_NN2 ,_, staff_NN and_CC faculty_NN1 were_VBDR afforded_VVN a_AT1 clear_JJ look_NN1 into_II Cornell_NP1 's_GE financial_JJ situation_NN1 ,_, and_CC when_CS the_AT school_NN1 rolled_VVD out_RP an_AT1 early-retirement_JJ incentive_NN1 program_NN1 ,_, 400_MC employees_NN2 volunteered_VVD ._. 
"_" We_PPIS2 were_VBDR very_RG open_JJ about_II the_AT fact_NN1 that_CST we_PPIS2 wanted_VVD our_APPGE oldest_JJT workers_NN2 ,_, who_PNQS might_VM be_VBI interested_JJ in_II moving_VVG on_RP ,_, to_TO have_VHI a_AT1 chance_NN1 to_TO choose_VVI to_TO do_VDI so_RR before_CS we_PPIS2 had_VHD to_TO look_VVI to_II layoffs_NN2 ,_, "_" Opperman_NP1 says_VVZ ._. 
"_" It_PPH1 was_VBDZ sort_RR21 of_RR22 typical_JJ of_IO the_AT Cornell_NP1 community_NN1 ._. 
We_PPIS2 had_VHD a_AT1 number_NN1 of_IO people_NN who_PNQS said_VVD ..._... 'If_NN1 I_PPIS1 go_VV0 ,_, someone_PN1 who_PNQS 's_VBZ in_II a_AT1 different_JJ place_NN1 in_II their_APPGE career_NN1 can_VM stay_VVI ._. '_" 
That_DD1 's_VBZ the_AT sort_NN1 of_IO giveback_NN1 you_PPY get_VV0 when_RRQ you_PPY recognize_VV0 the_AT value_NN1 of_IO your_APPGE workforce_NN1 ._. "_" 
Overall_RR ,_, the_AT Best_JJT Employers_NN2 list_NN1 is_VBZ a_AT1 large_JJ cross_NN1 section_NN1 of_IO fields_NN2 ._. 
Universities_NN2 ,_, insurers_NN2 ,_, pharmaceutical_JJ companies_NN2 ,_, staffing_NN1 firms_NN2 ,_, the_AT YMCA_NP1 of_IO Greater_NP1 Rochester_NP1 ,_, N.Y._NP1 ,_, Avis_NP2 Budget_NN1 Car_NN1 Rental_NN1 ,_, and_CC the_AT City_NN1 of_IO Glendale_NP1 ,_, Ariz._NP1 ,_, all_DB made_VVD the_AT top_NN1 50_MC ,_, culled_VVN from_II more_DAR than_CSN 200_MC applications_NN2 (_( in_II which_DDQ employers_NN2 answered_VVD questions_NN2 about_II their_APPGE workplace_NN1 policies_NN2 and_CC practices_NN2 )_) ._. 
Most_DAT surprising_JJ on_II this_DD1 list_NN1 may_VM be_VBI Intel_NP1 ,_, No._NN1 44_MC ,_, which_DDQ suggests_VVZ that_CST even_RR technology_NN1 companies_NN2 are_VBR not_XX just_RR seeking_VVG young_JJ talent_NN1 but_CCB also_RR embracing_VVG experience_NN1 ._. 
Cheap_JJ perks_NN2 ._. 
Much_DA1 of_IO what_DDQ the_AT AARP_NN1 considers_VVZ in_II compiling_VVG the_AT list_NN1 may_VM seem_VVI best_RRT suited_VVN for_IF a_AT1 tighter_JJR labor_NN1 market_NN1 and_CC better_JJR economy_NN1 :_: training_NN1 and_CC career_NN1 development_NN1 ;_; recruiting_VVG practices_NN2 ;_; health_NN1 and_CC retirement_NN1 benefits_NN2 ;_; and_CC work_VV0 options_NN2 ,_, such_II21 as_II22 job_NN1 sharing_VVG and_CC flexible_JJ scheduling_NN1 ._. 
But_CCB Deborah_NP1 Russell_NP1 ,_, the_AT AARP_NP1 's_GE director_NN1 of_IO workforce_NN1 issues_NN2 ,_, says_VVZ companies_NN2 still_JJ value_NN1 being_VBG seen_VVN as_II an_AT1 employer_NN1 of_IO choice_NN1 --_NN1 even_RR at_II a_AT1 time_NNT1 when_RRQ they_PPHS2 may_VM not_XX be_VBI hiring_VVG ._. 
And_CC while_CS some_DD perks_NN2 have_VH0 disappeared_VVN ,_, benefits_NN2 like_II flexible_JJ schedules_NN2 have_VH0 stuck_VVN around_RP ._. 
"_" It_PPH1 does_VDZ n't_XX cost_VVI money_NN1 to_TO offer_VVI people_NN flexible_JJ work_NN1 schedules_NN2 ,_, "_" Russell_NP1 says_VVZ ._. 
"_" Maybe_RR it_PPH1 's_VBZ cheaper_JJR to_TO have_VHI people_NN work_VV0 remotely_RR and_CC save_VV0 on_II energy_NN1 ._. "_" 
Many_DA2 of_IO the_AT employers_NN2 on_II the_AT list_NN1 seem_VV0 to_TO have_VHI created_VVN workplaces_NN2 where_RRQ older_JJR workers_NN2 feel_VV0 at_II home_NN1 ._. 
At_II the_AT National_JJ Institutes_NN2 of_IO Health_NN1 ,_, for_REX21 example_REX22 ,_, many_DA2 employees_NN2 work_NN1 years_NNT2 past_RT the_AT time_NNT1 they_PPHS2 are_VBR eligible_JJ to_TO retire_VVI ._. 
Workers_NN2 at_II the_AT federal_JJ agency_NN1 can_VM take_VVI part_NN1 in_II an_AT1 in-house_JJ philharmonic_JJ ,_, a_AT1 fencing_NN1 club_NN1 ,_, a_AT1 biking_JJ club_NN1 ,_, and_CC various_JJ lectures_NN2 and_CC symposiums_NN2 ._. 
First_MD Horizon_NN1 ,_, a_AT1 financial_JJ services_NN2 company_NN1 ,_, does_VDZ n't_XX hesitate_VVI to_TO hire_VVI older_JJR workers_NN2 who_PNQS will_VM bring_VVI a_AT1 greater_JJR breadth_NN1 of_IO experience_NN1 to_II a_AT1 position_NN1 ._. 
Ken_NP1 Bottoms_NN2 ,_, a_AT1 senior_JJ vice_JJ president_NN1 ,_, was_VBDZ hired_VVN five_MC years_NNT2 ago_RA and_CC is_VBZ now_RT just_RR shy_JJ of_IO 64_MC ._. 
He_PPHS1 can_VM point_VVI to_II other_JJ recent_JJ hires_NN2 who_PNQS may_VM have_VHI a_AT1 few_DA2 gray_JJ hairs_NN2 ._. 
"_" We_PPIS2 try_VV0 to_TO get_VVI the_AT best_JJT talent_NN1 we_PPIS2 can_VM ,_, "_" Bottoms_NN2 says_VVZ ._. 
This_DD1 year_NNT1 ,_, for_IF the_AT first_MD time_NNT1 ,_, AARP_NP1 compiled_VVD a_AT1 second_MD Best_JJT Employers_NN2 list_VV0 consisting_VVG solely_RR of_IO hospitals_NN2 and_CC healthcare_NN1 facilities_NN2 ._. 
In_II past_JJ years_NNT2 ,_, healthcare_NN1 providers_NN2 have_VH0 filled_VVN up_RP many_DA2 spots_NN2 on_II the_AT Best_JJT Employers_NN2 list_NN1 ,_, in_RR21 part_RR22 because_II21 of_II22 the_AT industry_NN1 's_GE outsize_JJ reliance_NN1 on_II older_JJR workers_NN2 in_II the_AT face_NN1 of_IO staffing_NN1 shortages_NN2 in_II many_DA2 positions_NN2 ._. 
(_( This_DD1 may_VM be_VBI less_DAR of_IO a_AT1 problem_NN1 in_II the_AT coming_JJ years_NNT2 than_CSN anticipated_JJ ._. 
While_CS the_AT recession_NN1 has_VHZ delayed_VVN the_AT retirement_NN1 of_IO many_DA2 workers_NN2 ,_, it_PPH1 may_VM also_RR have_VHI opened_VVN many_DA2 younger_JJR people_NN 's_GE eyes_NN2 to_II the_AT pragmatic_JJ benefit_NN1 of_IO joining_VVG an_AT1 industry_NN1 in_II need_NN1 ._. )_) 
&lsqb;_( See_VV0 how_RRQ the_AT long-term_JJ unemployed_JJ can_VM find_VVI jobs_NN2 ._. &rsqb;_) 
Know-how_NN1 ._. 
The_AT healthcare_NN1 industry_NN1 may_VM also_RR rely_VVI especially_RR heavily_RR on_II older_JJR workers_NN2 because_CS it_PPH1 prizes_VVZ technical_JJ and_CC institutional_JJ knowledge_NN1 and_CC experience_NN1 ._. 
At_II Atlantic_NP1 Health_NN1 of_IO Morristown_NP1 ,_, N.J._NP1 ,_, which_DDQ took_VVD the_AT top_JJ spot_NN1 among_II healthcare_NN1 providers_NN2 ,_, the_AT average_JJ tenure_NN1 of_IO employees_NN2 is_VBZ about_RG 14_MC years_NNT2 ._. 
"_" We_PPIS2 certainly_RR value_VV0 the_AT experience_NN1 level_NN1 and_CC the_AT experience_NN1 itself_PPX1 that_CST individuals_NN2 can_VM gain_VVI here_RL in_II our_APPGE system_NN1 ,_, and_CC we_PPIS2 certainly_RR do_VD0 n't_XX want_VVI to_TO lose_VVI that_CST because_II21 of_II22 an_AT1 age_NN1 factor_NN1 ,_, "_" says_VVZ Andrew_NP1 Kovach_NP1 ,_, Atlantic_NP1 's_GE vice_JJ president_NN1 of_IO human_JJ resources_NN2 ._. 
The_AT company_NN1 runs_VVZ two_MC hospitals_NN2 in_II northern_JJ New_NP1 Jersey_NP1 --_JJ Morristown_NN1 Memorial_NN1 Hospital_NN1 and_CC Overlook_VV0 Hospital_NN1 --_NN1 and_CC various_JJ other_JJ healthcare_NN1 facilities_NN2 ,_, including_II a_AT1 new_JJ emergency_NN1 room_NN1 in_II Union_NP1 ,_, N.J._NP1 The_AT healthcare_NN1 sector_NN1 has_VHZ benefited_VVN from_II an_AT1 aging_JJ baby_NN1 boomer_NN1 population_NN1 ,_, which_DDQ has_VHZ pushed_VVN up_RP demand_NN1 for_IF its_APPGE services_NN2 even_RR in_II a_AT1 recession_NN1 ._. 
The_AT healthcare_NN1 and_CC social_JJ assistance_NN1 sector_NN1 has_VHZ added_VVN more_DAR than_CSN a_AT1 half-million_NNO jobs_NN2 since_II December_NPM1 2007_MC ,_, according_II21 to_II22 Labor_NN1 Department_NN1 data_NN ._. 
Atlantic_NP1 ,_, for_REX21 instance_REX22 ,_, has_VHZ added_VVN staff_NN for_IF its_APPGE new_JJ emergency_NN1 room_NN1 and_CC its_APPGE new_JJ Gagnon_NN1 Cardiovascular_JJ Institute_NN1 ,_, which_DDQ opened_VVD earlier_RRR this_DD1 year_NNT1 ._. 
Atlantic_NP1 even_RR taps_VVZ retired_JJ workers_NN2 through_II its_APPGE "_" 1,000_MC Hour_NNT1 Club_NN1 ,_, "_" which_DDQ allows_VVZ retirees_NN2 to_TO work_VVI up_RG21 to_RG22 999_MC hours_NNT2 in_II a_AT1 year_NNT1 and_CC retain_VV0 their_APPGE retirement_NN1 benefits_NN2 ._. 
Employers_NN2 in_II other_JJ fields_NN2 have_VH0 launched_VVN similar_JJ initiatives_NN2 ._. 
The_AT federal_JJ stimulus_NN1 package_NN1 spurred_VVD a_AT1 sudden_JJ uptick_NN1 in_II staffing_NN1 needs_VVZ at_II government_NN1 agencies_NN2 ._. 
The_AT NIH_NN1 earlier_RRR this_DD1 year_NNT1 sent_VVD out_RP a_AT1 letter_NN1 asking_VVG recent_JJ retirees_NN2 if_CS they_PPHS2 'd_VM be_VBI interested_JJ in_II coming_VVG back_RP for_IF a_AT1 temporary_JJ period_NN1 ._. 
"_" We_PPIS2 needed_VVD people_NN who_PNQS were_VBDR experienced_VVN and_CC knew_VVN how_RRQ the_AT system_NN1 works_VVZ ,_, "_" says_VVZ Philip_NP1 Lenowitz_NP1 ,_, deputy_NN1 director_NN1 of_IO human_JJ resources_NN2 at_II NIH_NP1 ._. 
About_RG a_AT1 quarter_NN1 of_IO those_DD2 who_PNQS received_VVD the_AT letter_NN1 responded_VVN in_II the_AT affirmative_NN1 ._. 
And_CC back_RP at_II Cornell_NP1 ,_, school_NN1 officials_NN2 are_VBR working_VVG to_TO create_VVI an_AT1 employment_NN1 service_NN1 that_CST would_VM find_VVI retirees_NN2 paid_VVN work_NN1 at_II the_AT school_NN1 and_CC in_II the_AT community_NN1 to_TO augment_VVI their_APPGE pensions_NN2 ._. 
After_II all_DB ,_, those_DD2 400_MC workers_NN2 who_PNQS opted_VVD for_IF the_AT retirement_NN1 incentive_NN1 took_VVD 11,000_MC years_NNT2 of_IO experience_NN1 with_IW them_PPHO2 --_JJ simply_RR too_RG much_DA1 to_TO let_VVI go_VVI ._. 
