Aggregate Recruiting Intensity Alessandro Gavazza London School of - PowerPoint PPT Presentation

yl Aggregate Recruiting Intensity Alessandro Gavazza London School of Economics Simon Mongey New York University Gianluca Violante New York University Macroeconomics Lunch Princeton, November 8th 2016

Aggregate recruiting intensityy yl H t = A t V α t U 1 − α t Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.1/28 hello

Aggregate recruiting intensityy yl H t = A t V α t U 1 − α t The component of A accounted for by firms’ effort to fill vacancies Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.1/28 hello

Aggregate recruiting intensityy yl H t = A t V α t U 1 − α t The component of A accounted for by firms’ effort to fill vacancies Macro data • Large and persistent decline in A in the last recession • Q1: How much of the decline in A is accounted for by ARI? Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.1/28 hello

Aggregate recruiting intensityy yl H t = A t V α t U 1 − α t The component of A accounted for by firms’ effort to fill vacancies Macro data • Large and persistent decline in A in the last recession • Q1: How much of the decline in A is accounted for by ARI? Micro data (Davis-Faberman-Haltiwanger, 2013) • Fast growing firms fill vacancies more quickly • Q2: What is the transmission mechanism from macro shocks to ARI? Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.1/28 hello

Firm-level hiring technology yl Random-matching model h it = q t v it = + recruiting intensity h it q t e it v it • JOLTS vacancies - v it • BLS: “Specific position that exists... for start within 30-days... with active recruiting from outside the establishment” Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.2/28 hello

Firm-level hiring technology yl Random-matching model h it = q t v it = + recruiting intensity h it q t e it v it • JOLTS vacancies - v it • BLS: “Specific position that exists... for start within 30-days... with active recruiting from outside the establishment” • Recruitment intensity - e it 1. Shifts the filling rate (or yield) of an open position 2. Costly on a per vacancy basis • An outcome of expenditures on recruiting activities Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.2/28 hello

� � Recruiting cost by activity y Employment branding Professional services networking sites 2% 3% Tools Print / newspapers / 1% billboards 4% Agencies / third-party recruiters University recruiting 29% 5% Applicant tracking system Travel 5% 8% Contractors 8% Job boards 14% Employee referrals Other 9% 12% Bersin and Associates, Talent Acquisition Factbook (2011) - Average cost per hire (at 100+ employee firms): $3,500 Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.3/28 hello

From firm-level to aggregate recruiting intensity yl • Aggregation � e it v it d λ h = q t V ∗ H t = q t t t • Aggregate matching function V ∗ α U 1 − α = Φ t V t α U 1 − α H t = t t t • Aggregate recruiting intensity � V ∗ � α � � � v it � � α t d λ h = = Φ t e it t V t V t Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.4/28 hello

Transmission mechanism: two channelsy yl Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.5/28 hello

Transmission mechanism: two channelsy yl 1. Composition : macro shock → shift in hiring rate distribution    � h � v    n = ¯ q � e n • Slow-growing firms recruit less intensively • Great Recession - large decline in firm entry Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.5/28 hello

Transmission mechanism: two channelsy yl 1. Composition : macro shock → shift in hiring rate distribution    � h � v    n = ¯ q � e n • Slow-growing firms recruit less intensively • Great Recession - large decline in firm entry 2. Slackness : macro shock → slacker labor market ¯ h �  � v     n =  q � e n • Firms substitute away from costly hiring measures • Great Recession - large decline in market tightness Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.5/28 hello

Model y yl Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.6/28 hello

Model y yl Firm dynamics • Operate DRS technology • Idiosyncratic productivity shocks • Endogenous entry and exit Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.6/28 hello

Model y yl Firm dynamics • Operate DRS technology • Idiosyncratic productivity shocks • Endogenous entry and exit Financial frictions • Borrowing secured by collateral • Limits to equity issuance Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.6/28 hello

Model y yl Firm dynamics • Operate DRS technology • Idiosyncratic productivity shocks • Endogenous entry and exit Financial frictions • Borrowing secured by collateral • Limits to equity issuance Labor market frictions • Random matching with homogeneous workers • Recruiting effort e and vacancies v are costly Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.6/28 hello

Valueyfunctions yl Let V ( n , a , z ) be the present discounted value of dividends of a firm with employment n , net-worth a , and productivity z Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.7/28 hello

Valueyfunctions yl Let V ( n , a , z ) be the present discounted value of dividends of a firm with employment n , net-worth a , and productivity z • Exit exogenously or endogenously � � a , V i ( n , a , z ) V ( n , a , z ) = ζ a + ( 1 − ζ ) max • Fire or hire � � V f ( n , a , z ) , V h ( n , a , z ) V i ( n , a , z ) = max Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.7/28 hello

Value functions - Firing yl � V f ( n , a , z ) Z V ( n ′ , a ′ , z ′ ) Γ ( z , dz ′ ) = max d + β n ′ ≤ n , k , d s . t . � zn ′ ν k 1 − ν � σ + ( 1 + r ) a − ω n ′ − ( r + δ ) k − χ d + a ′ = k ≤ ϕ a d ≥ 0 Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.8/28 hello

Value functions - Firing yl � V f ( n , a , z ) Z V ( n ′ , a ′ , z ′ ) Γ ( z , dz ′ ) = max d + β n ′ ≤ n , k , d s . t . � zn ′ ν k 1 − ν � σ + ( 1 + r ) a − ω n ′ − ( r + δ ) k − χ d + a ′ = k ≤ ϕ a d ≥ 0 Define debt: b : = k − a Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.8/28 hello

Value functions - Hiring yl � Z V ( n ′ , a ′ , z ′ ) Γ ( z , dz ′ ) V h ( n , a , z ) = v > 0, e > 0, k , d d + β max s . t . � zn ′ ν k 1 − ν � σ + ( 1 + r ) a − ω n ′ − ( r + δ ) k − χ − C ( e , v , n ) d + a ′ = n ′ − n q ( θ ∗ ) ev = k ≤ ϕ a d ≥ 0 Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.9/28 hello

Reverse engineering the hiring-cost function yl 3.0 � h � Log vacancy yield - log = log ( qe ) v � v 2.5 � Log vacancy rate - log n 2.0 Slope = 0.82 1.5 1.0 0.5 0 0 0.5 1.0 1.5 2.0 2.5 3.0 � h � Log hiring rate log n Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.10/28 hello

Reverse engineering the hiring-cost function yl 3.0 � h � Log vacancy yield - log = log ( qe ) v � v 2.5 � Log vacancy rate - log n 2.0 Slope = 0.82 1.5 1.0 0.5 0 0 0.5 1.0 1.5 2.0 2.5 3.0 � h � Log hiring rate log n � κ 1 � γ 2 � � v κ 2 e γ 1 + C ( e , v , n ) = v , γ 1 ≥ 1, γ 2 ≥ 0 γ 2 + 1 γ 1 n � �� � Cost per vacancy Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.10/28 hello

Reverse engineering the hiring-cost function yl 3.0 � h � Log vacancy yield - log = log ( qe ) v � v 2.5 � Log vacancy rate - log n 2.0 Slope = 0.82 1.5 1.0 0.5 0 0 0.5 1.0 1.5 2.0 2.5 3.0 � h � Log hiring rate log n � h � γ 2 γ 2 log q ( θ ∗ )+ log e = Const. − log γ 1 + γ 2 γ 1 + γ 2 n � h � v � � γ 1 γ 1 log q ( θ ∗ )+ = Const. − log log γ 1 + γ 2 γ 1 + γ 2 n n Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.10/28 hello

Reverse engineering the hiring-cost function yl 3.0 � h � Log vacancy yield - log = log ( qe ) v � v 2.5 � Log vacancy rate - log n 2.0 Slope = 0.82 Slope = 0.82 1.5 1.0 0.5 0 0 0.5 1.0 1.5 2.0 2.5 3.0 � h � Log hiring rate log n � h � γ 2 γ 2 log q ( θ ∗ )+ log e = Const. − log γ 1 + γ 2 γ 1 + γ 2 n � h � v � � γ 1 γ 1 log q ( θ ∗ )+ = Const. − log log γ 1 + γ 2 γ 1 + γ 2 n n Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.10/28 hello

Value functions - Entry yl • Initial wealth: Household allocates a 0 to λ 0 potential entrants • Productivity: Potential entrants draw z ∼ Γ 0 ( z ) • Entry: Choice to become incumbent and pay χ 0 start-up costs � � a 0 , V i ( n 0 , a 0 − χ 0 , z ) V e ( a 0 , z ) = max Selection at entry based only on productivity z Life cycle: slow growth b/c of fin. constraints and convex hiring costs Equilib rium Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.11/28 hello