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

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

Ht = AtVα

t U1−α t

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.1/28 hello

Aggregate recruiting intensityy yl

Ht = AtVα

t U1−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

Ht = AtVα

t U1−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

Ht = AtVα

t U1−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

Random-matching model

hit = qtvit

+ recruiting intensity

hit = qteitvit

• JOLTS vacancies -

vit

• 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

Random-matching model

hit = qtvit

+ recruiting intensity

hit = qteitvit

• JOLTS vacancies -

vit

• BLS: “Specific position that exists... for start within 30-days... with active

recruiting from outside the establishment”

• Recruitment intensity -

eit

• 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

• Tools

1% Employment branding services 2% Professional networking sites 3% Print / newspapers / billboards 4% University recruiting 5% Applicant tracking system 5% Travel 8% Contractors 8% Employee referrals 9% Other 12% Job boards 14% Agencies / third-party recruiters 29%

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

Ht = qt

• eitvit dλh

t

= qtV∗

t

• Aggregate matching function

Ht = V∗

t αU1−α t

= ΦtVtαU1−α

t

• Aggregate recruiting intensity

Φt = V∗

t

Vt α =

• eit

vit Vt

• dλh

t

α

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.4/28 hello

Transmission mechanism: two channelsy

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.5/28 hello

Transmission mechanism: two channelsy

• 1. Composition: macro shock → shift in hiring rate distribution

  h n = ¯ q   e   v 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

• 1. Composition: macro shock → shift in hiring rate distribution

  h n = ¯ q   e   v n

• Slow-growing firms recruit less intensively
• Great Recession - large decline in firm entry
• 2. Slackness: macro shock → slacker labor market

¯ h n =

• 

q   e   v 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

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

Model y

Firm dynamics

• Operate DRS technology
• Idiosyncratic productivity shocks
• Endogenous entry and exit

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

Model y

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

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

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

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

V(n, a, z) = ζa + (1 − ζ) max

• a , Vi(n, a, z)
• Fire or hire

Vi(n, a, z) = max

• Vf (n, a, z) , Vh(n, a, z)
• Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity"

p.7/28 hello

Value functions - Firing

Vf (n, a, z) = max

n′≤n,k,d

d + β

• Z V(n′, a′, z′)Γ(z, dz′)

s.t. d + a′ =

• zn′νk1−νσ

+ (1 + r)a − ωn′ − (r + δ)k − χ k ≤ ϕa d ≥

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.8/28 hello

Value functions - Firing

Vf (n, a, z) = max

n′≤n,k,d

d + β

• Z V(n′, a′, z′)Γ(z, dz′)

s.t. d + a′ =

• zn′νk1−νσ

+ (1 + r)a − ωn′ − (r + δ)k − χ k ≤ ϕa d ≥ Define debt: b := k − a

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.8/28 hello

Value functions - Hiring

Vh(n, a, z) = max

v>0,e>0,k,d d + β

• Z V(n′, a′, z′)Γ(z, dz′)

s.t. d + a′ =

• zn′νk1−νσ

+ (1 + r)a − ωn′ − (r + δ)k − χ − C(e, v, n) n′ − n = q(θ∗)ev k ≤ ϕa d ≥

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.9/28 hello

Reverse engineering the hiring-cost function yl

0.5 1.0 1.5 2.0 2.5 3.0

Log hiring rate log h

n

• 0.5

1.0 1.5 2.0 2.5 3.0

Log vacancy yield - log h

v

• = log (qe)

Log vacancy rate - log v

n

• Slope = 0.82

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.10/28 hello

Reverse engineering the hiring-cost function yl

0.5 1.0 1.5 2.0 2.5 3.0

Log hiring rate log h

n

• 0.5

1.0 1.5 2.0 2.5 3.0

Log vacancy yield - log h

v

• = log (qe)

Log vacancy rate - log v

n

• Slope = 0.82

C(e, v, n) = κ1 γ1 eγ1 + κ2 γ2 + 1 v n γ2

• Cost per vacancy

v, γ1 ≥ 1, γ2 ≥ 0

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.10/28 hello

Reverse engineering the hiring-cost function yl

0.5 1.0 1.5 2.0 2.5 3.0

Log hiring rate log h

n

• 0.5

1.0 1.5 2.0 2.5 3.0

Log vacancy yield - log h

v

• = log (qe)

Log vacancy rate - log v

n

• Slope = 0.82

log e =

• Const. −

γ2 γ1 + γ2 log q (θ∗)+ γ2 γ1 + γ2 log h n

• log

v n

• =
• Const. −

γ1 γ1 + γ2 log q (θ∗)+ γ1 γ1 + γ2 log h n

• Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity"

p.10/28 hello

Reverse engineering the hiring-cost function yl

0.5 1.0 1.5 2.0 2.5 3.0

Log hiring rate log h

n

• 0.5

1.0 1.5 2.0 2.5 3.0

Log vacancy yield - log h

v

• = log (qe)

Log vacancy rate - log v

n

• Slope = 0.82

Slope = 0.82

log e =

• Const. −

γ2 γ1 + γ2 log q (θ∗)+ γ2 γ1 + γ2 log h n

• log

v n

• =
• Const. −

γ1 γ1 + γ2 log q (θ∗)+ γ1 γ1 + γ2 log h n

• Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity"

p.10/28 hello

Value functions - Entry

• Initial wealth: Household allocates a0 to λ0 potential entrants
• Productivity: Potential entrants draw z ∼ Γ0(z)
• Entry: Choice to become incumbent and pay χ0 start-up costs

Ve(a0, z) = max

• a0 , Vi (n0, a0 − χ0, z)
• Selection at entry based only on productivity z

Life cycle: slow growth b/c of fin. constraints and convex hiring costs

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Parameter values set externally

Parameter Value Target Discount factor (monthly) β 0.9967

• Ann. risk-free rate = 4%

Mass of potential entrants λ0 0.02

• Meas. of incumbents = 1

Size of labor force ¯ L 24.6 Average firm size = 23 Elasticity of matching function wrt Vt α 0.5 JOLTS

Add to the model

• Heterogeneity in DRS σ ∈ {σL, σM, σH}

Calibration strategy

• 1. Worker flows and labor share
• 2. Distribution of firm size and firm growth rates
• 3. Micro-evidence on job-filling and vacancy-posting
• 4. Entry and exit
• 5. Leverage for young firms and aggregate economy

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Parameter values estimated internally

Parameter Value Target Model Data Flow of home production ω 1.000 Monthly separ. rate 0.033 0.030 Scaling of match. funct. ¯ Φ 0.208 Monthly job finding rate 0.411 0.400

• Prod. weight on labor

ν 0.804 Labor share 0.627 0.640 Midpoint DRS in prod. σM 0.800 Employment share n: 0-49 0.294 0.306 High-Low spread in DRS ∆σ 0.094 Employment share n: 500+ 0.430 0.470 Mass - Low DRS µL 0.826 Firm share n: 0-49 0.955 0.956 Mass - High DRS µH 0.032 Firm share n: 500+ 0.004 0.004

• Std. dev of z shocks

ϑz 0.052

• Std. dev ann emp growth

0.440 0.420 Persistence of z shocks ρz 0.992 Mean Q4 emp / Mean Q1 emp 75.161 76.000 Mean z0 ∼ Exp(¯ z−1

0 )

¯ z0 0.390 ∆ log z: Young vs. Mature

• 0.246
• 0.353

Cost elasticity wrt e γ1 1.114 Elasticity of vac yield wrt g 0.814 0.820 Cost elasticity wrt v γ2 4.599 Ratio vac yield: <50/>50 1.136 1.440 Cost shifter wrt e κ1 0.101 Hiring cost (100+) / wage 0.935 0.927 Cost shifter wrt v κ2 5.000 Vacancy share n < 50 0.350 0.370 Exogenous exit probability ζ 0.006 Survive ≥ 5 years 0.497 0.500 Entry cost χ0 9.354 Annual entry rate 0.099 0.110 Operating cost χ 0.035 Fraction of JD by exit 0.210 0.340 Initial wealth a0 10.000 Start-up Debt to Output 1.361 1.280 Collateral constraint ϕ 10.210 Aggregate debt-to-Net worth 0.280 0.350

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Non-targeted moments yl

Moment Model Data Source Aggregate dividend / profits 0.411 0.400 NIPA

1Employment share: growth ∈ [−2.00, −0.20)

0.070 0.076 Davis et al. (2010) Employment share: growth ∈ (−0.20, −0.20] 0.828 0.848 Davis et al. (2010) Employment share: growth ∈ (0.20, 2.00] 0.102 0.076 Davis et al. (2010) Employment share: Age ≤ 1 0.013 0.020 BDS Employment share: Age ∈ (1, 10) 0.309 0.230 BDS Employment share: Age ≥ 10 0.678 0.750 BDS (1.) Firm growth rates are annual and are interior to [−2, 2] so do not include entering and exiting firms

• nstrained,

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Hire and vacancy shares by size class yl

1-9 10-49 50-249 250-999 1000+ 0.05 0.1 0.15 0.2 0.25 0.3 0.35

• A. Hires

1-9 10-49 50-249 250-999 1000+ 0.05 0.1 0.15 0.2 0.25 0.3 0.35

• B. Vacancies

Model, Data - JOLTS 2002-2007

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Vacancy and recruitment intensity by age yl

2 4 6 8 10

Age (years)

0.2 0.4 0.6 0.8 1

Log difference from 10 year old

• A. Cohort average growth and recruitment

Growth rate Recruiting intensity Vacancy rate 5 10 15

Age (years)

0.01 0.02 0.03 0.04

Fraction of firms by age (monthly)

• B. Age distributions

Hiring firms Recruiting intensity Vacant positions

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.16/28 hello

Vacancy and recruitment intensity by age yl

2 4 6 8 10

Age (years)

0.2 0.4 0.6 0.8 1

Log difference from 10 year old

• A. Cohort average growth and recruitment

Growth rate Recruiting intensity Vacancy rate 5 10 15

Age (years)

0.01 0.02 0.03 0.04

Fraction of firms by age (monthly)

• B. Age distributions

Hiring firms Recruiting intensity Vacant positions

Young firms exert more recruiting effort: true in Austrian micro-data

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.16/28 hello

Transition dynamics experiments

Trace transitional dynamics of the economy in response to:

• Tightening of financial constraint ↓ ϕ
• Size of shock: match max drop in output (Fernald, 2015)
• Requires 75% drop in ϕ
• Persistence of shock: match half-life of output decline of 3 years
• Monthly persistence of ϕ shock of 0.97

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.17/28 hello

Transition dynamics experiments

Trace transitional dynamics of the economy in response to:

• Tightening of financial constraint ↓ ϕ
• Size of shock: match max drop in output (Fernald, 2015)
• Requires 75% drop in ϕ
• Persistence of shock: match half-life of output decline of 3 years
• Monthly persistence of ϕ shock of 0.97

In the paper: examine also productivity shock

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.17/28 hello

Transition dynamics

1 2 3 4 5 6 Years

• 1
• 0.5

0.5 1 Log deviation from date 0

• A. US Data 2008:01 - 2014:01

1 2 3 4 5 6 Years

• 1
• 0.5

0.5 1 Log deviation from date 0

• B. Model - Finance ϕ-shock

Vacancies Vacancy yield Unemployment Job finding rate

• Agg. recruiting intensity

• utput,

• r

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.18/28 hello

Impulse response for Φ

1 2 3 4 5 6 Years

• 0.6
• 0.5
• 0.4
• 0.3
• 0.2
• 0.1

Log deviation from date 0 Aggregate recruiting intensity Φt Data - Aggregate match efficiency

Result I:

• Recruiting intensity accounts for ≈ 1/3 of decline in match efficiency
• Less persistence than empirical match efficiency

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Decomposing Φt

Recruiting effort policy e = Const. × q (θ∗)−

γ2 γ1+γ2 ×

h n

• γ2

γ1+γ2

Aggregate recruiting intensity Φ =

• e

v V

• dλh

α Decomposition ∆ log Φ = −α γ2 γ1 + γ2 ∆ log q(θ∗)

• Slackness effect

+ α∆ log h n

• γ2

γ1+γ2 v

V

• dλh
• Composition effect

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.20/28 hello

Decomposing Φt

1 2 3 4 5 6 Years

• 0.25
• 0.2
• 0.15
• 0.1
• 0.05

0.05 Log deviation from date 0 Aggregate recruiting intensity Φt Slackness effect Composition effect

Result II:

• Slackness effect is dominant

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Decomposing Φt

1 2 3 4 5 6 Years

• 0.25
• 0.2
• 0.15
• 0.1
• 0.05

0.05 Log deviation from date 0 Aggregate recruiting intensity Φt Slackness effect Composition effect

Result III:

• Composition effect is roughly zero
• Why? Constrained vs. unconstrained firms

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.21/28 hello

Understanding vacancy yields by size

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Understanding vacancy yields by size

6 12 18 24

Month

• 0.2

0.2 0.4 0.6

Log dev. from date 0

• C. Vacancy yield - Data 2008:01-2010:12

Size 1-9 Size 1000+ 6 12 18 24

Month

• 0.2

0.2 0.4 0.6

Log dev. from date 0

• D. Vacancy yield - Model

Size 1-9 Size 1000+ 6 12 18 24

• 1.5
• 1
• 0.5

0.5 1

Log dev. from date 0

• A. Recruiting intensity per vacancy

Unconstrained Constrained 6 12 18 24 0.1 0.2 0.3 0.4 0.5

Level

• B. Fraction constrained

Size 1-9 Size 1000+

Result IV

• Slackness and composition explains vacancy yields by size

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Summary

Results

• I. Recruiting intensity explains 1/3 of decline in match efficiency

• I. Dominant: Slack labor markets reduce need for costly recruiting

• I. Strong GE forces limit the role of the composition effect
• IV. Slackness and composition explains vacancy yields by size

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.23/28 hello

Summary

Results

• I. Recruiting intensity explains 1/3 of decline in match efficiency

• I. Dominant: Slack labor markets reduce need for costly recruiting

• I. Strong GE forces limit the role of the composition effect
• IV. Slackness and composition explains vacancy yields by size

Extensions

• 1. Effect of changes in sectoral composition of firms over recession
• 2. Construct an easy-to-measure index of aggregate recruiting intensity
• 3. Relationship to Kaas & Kircher (2015)

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.23/28 hello

• 1. Sectoral composition

2007 2008 2009 2010 2011 2012 0.05 0.10 0.15 0.20 0.25 Level

• A. Sector component: ˆ

φ

γ1 γ1+γ2

s vst Vt

Con Man Trade Prof serv Ed/Hth Hosp Gov 2007 2008 2009 2010 2011 2012

• 0.04
• 0.03
• 0.02
• 0.01

0.01 0.02 Log deviation from Jan 2001

• B. Sectoral composition effect

Result

• Sectoral composition effect adds 3% to decline in Φt (0.20 → 0.23)
• It adds some persistence too
• Driven by (i) Hospitality, (ii) Construction, (iii) Manufacturing

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.24/28 hello

• 2. Approximate index of aggregate recruiting intensity yl

DFH provide an easy-to-compute index of aggregate recruiting intensity log Φt = log(Ht/Vt) − log qt d log Φt d log(Ht/Nt) = d log(Ht/Vt) d log(Ht/Nt) − d log qt d log(Ht/Nt)

d log Φt d log(Ht/Nt) ≈ ξ d log ΦDFH

t

= ξ × d log(Ht/Nt)

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.25/28 hello

• 2. Approximate index of aggregate recruiting intensity yl

Return to model based decomposition log Φt = −α γ2 γ1 + γ2 log q(θ∗

t )

• slackness effect

+ α log hit nit

• γ2

γ1+γ2 vit

Vt

• dλh

t

• composition effect

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.26/28 hello

• 2. Approximate index of aggregate recruiting intensity yl

Return to model based decomposition log Φt = −α γ2 γ1 + γ2 log q(θ∗

t )

• slackness effect

+ α log hit nit

• γ2

γ1+γ2 vit

Vt

• dλh

t

• composition effect

GMV

d log ΦGMV

t

= α γ2 γ1 + γ2 × (1 − α) × d log θ∗

t

t to θt from transition dynamics

d log ΦGMV

t

= α γ2 γ1 + γ2 × (1 − α) εθ∗,θ

• ≈1.5

×d log θt

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.26/28 hello

• 2. Approximate index of aggregate recruiting intensity yl

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

• 0.6
• 0.5
• 0.4
• 0.3
• 0.2
• 0.1

0.1 0.2 Log Aggregate Recruiting Intensity Aggregate match efficiency DFH measure of recruiting intensity GMV measure of recruiting intensity GMV + Sectoral component

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.27/28 hello

• 3. Relation to Kaas Kircher (2015)

KK model ΦKK

t

=

q(θmt)

¯ q(θt) vmt Vt dm The reason why [recruiting intensity] is pro-cyclical in our model is that q is concave, and the cross-sectional dispersion in θmt is counter-cyclical

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.28/28 hello

• 3. Relation to Kaas Kircher (2015)

KK model ΦKK

t

=

q(θmt)

¯ q(θt) vmt Vt dm The reason why [recruiting intensity] is pro-cyclical in our model is that q is concave, and the cross-sectional dispersion in θmt is counter-cyclical Our model ∆ log Φt = −α γ2 γ1 + γ2 ∆ log q(θ∗

t )

• slackness effect

+ α∆ log hit nit

• γ2

γ1+γ2 vit

Vt

• dλh

t

• Composition effect

Dispersion effect is present but small

• ϕt shock delivers 45% increase in SD of growth rates, as in data

↓ Φt, since

γ2 γ1+γ2 < 1 but close to 1

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Equilibrium

• Aggregate state St = (λt, Ut, Zt, ϕt)
• 1. Measure of firms evolves via decision rules and z process
• 2. Labor market flows are equalized at θ∗

t : Uflows t+1 = Udemand t+1

Uflows

t+1

= Ut − H(θ∗

t , St) + F(θ∗ t , St) − λe,tn0

Udemand

t+1

= ¯ L −

• n′ (n, a, z, St) dλt
• Stationary equilibrium: measure is stationary, and S = (λ, U, Z, ϕ)

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Average life cycle of firms in the model

1 2 3 4 5 Age (years) 0.05 0.10 0.15 0.20 0.25

• B. Job creation and destruction

Job creation rate Job destruction rate 5 10 15 Age (years) 100 200 300 400

• A. Average size

High σH Med σM Low σL 1 2 3 4 5 Age (years) 0.2 0.4 0.6 0.8 1

• C. Fraction of firms constrained

5 10 15 20 Age (years) 5 10 15

• D. Average leverage

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Transition dynamics - Macro yl

1 2 3 4 5 6 Years

• 0.25
• 0.2
• 0.15
• 0.1
• 0.05

Log deviation from date 0

• A. Productivity Z-shock

1 2 3 4 5 6 Years

• 0.25
• 0.2
• 0.15
• 0.1
• 0.05

Log deviation from date 0

• B. Finance ϕ-shock

Debt/Output (B+

t /Yt)

Labor Productivity (Yt/Nt) Entry

Gavazza-Mongey-Violante, "Aggregate Recruiting Intensity" p.28/28 hello