March Basu, Boston NBER L. House, University · 2017. 5. 2. · 3.Wages of New Hires • Hall...
Transcript of March Basu, Boston NBER L. House, University · 2017. 5. 2. · 3.Wages of New Hires • Hall...
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Allocative and Remitted Wages: New Facts and Challenges for Keynesian Models
March 31, 2017
Susanto Basu, Boston College and NBER
Christopher L. House, University of Michigan and NBER
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Our questions
How cyclical are real wages?
Particularly in response to a nominal aggregate demand (monetary policy) shock?
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Current models & price persistence
• Following a monetary policy shock, we observe low price response, and thus persistent output response
• Pricing in current medium‐scale DSGE models:
( )1t t
t t t
p mcmc r w
m
a a
* *= +
= + -
• To keep p from changing much, need p* to be insensitive to a money shock mc can’t change much w can’t change much
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“The” WageWhat is the proper measure of marginal payments to labor inputs?
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“The” WageDifferent Wage Concepts in Empirical Literature
1.Average Hourly Earnings/Hourly Compensation• Stock and Watson (1999)• Christiano, Eichenbaum and Evans (2005)• Smets & Wouters (2007)
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“The” WageDifferent Wage Concepts in Empirical Literature
1.Average Hourly Earnings/Hourly Compensation
2.Composition‐Corrected Wages • Bils (1985)• Solon, Barsky and Parker (1994)• Elsby, Shin and Solon (2014)
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“The” WageDifferent Wage Concepts in Empirical Literature
1.Average Hourly Earnings/Hourly Compensation
2.Composition‐Corrected Wages
3.Wages of New Hires• Hall (2005)• Pissarides (2009)• Gertler and Trigari (2008)• Haefke, Sonntag and van Rens (2013)• Martins, Solon and Thomas (2012)
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“The” WageDifferent Wage Concepts in Empirical Literature
1.Average Hourly Earnings/Hourly Compensation
2.Composition Corrected Wages
3.Wages of New Hires
4.Implicit Contracts and Long‐Term Employment• Becker (1962)• Barro (1977) & Hall (1980)• Beaudry and DiNardo (1991, 1995)• Kudlyak (2014)
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“The” WageDifferent Wage Concepts in Empirical Literature
1.Average Hourly Earnings
2.Composition Bias
3.New Hire Wages
4.Implicit Contracts and Long‐Term Employment
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Model with Different Wage Concepts
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Standard DSGE framework (CEE)
•Rep. household (with habit formation)
•Variable capital utilization
•Increasing returns to scale
•Investment adjustment costs
•Sticky prices
Model with Different Wage Concepts
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Wage setting / labor supply
•Composition bias
•Potentially sticky allocative wage
•Remitted wage vs. allocative wage
Model with Different Wage Concepts
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Composition Bias
Model with Different Wage Concepts
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Composition Bias
• Extended household with continuum of agents
• Agents all enjoy leisure equally
• But some have higher productivity and wages
• household always sends highest‐productivity members to work
• marginal worker has lower productivity than average worker, as in data
Model with Different Wage Concepts
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AllocativeWages vs. RemittedWages
Model with Different Wage Concepts
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AllocativeWages vs. RemittedWages
Allocative wage, Xt, determines labor supply and marginal cost
Xmay be sticky: renegotiated with constant hazard(1 – W)
Model with Different Wage Concepts
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AllocativeWages vs. RemittedWages
Remitted wage
Model with Different Wage Concepts
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AllocativeWages vs. RemittedWages
Remitted wage
• periodically renegotiated (probability = s)
• ensures same expected nominal payment (PDV) as receiving X every period
Model with Different Wage Concepts
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AllocativeWages vs. RemittedWages
Remitted wage
( )0
1j t jW
t t t jj t
PDV E s Xl
bl
¥+
+=
é ùê úé ù= -ë ûê úë ûå
Model with Different Wage Concepts
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AllocativeWages vs. RemittedWages
Remitted wage
( )0
1j t jW
t t t jj t
PDV E s Xl
bl
¥+
+=
é ùê úé ù= -ë ûê úë ûå
( )0
1j t jNe
tj
w
tt EW s
lb
l
¥+
=
é ùê úé ù= -ë ûê úë ûå
Model with Different Wage Concepts
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AllocativeWages vs. RemittedWages
Average hourly earnings (all workers)
( )1 1 Newt t t tAHE AHE s H W-= - +
Model with Different Wage Concepts
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Measured Wages
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Measured Wages
1. Average Hourly Earnings (AHE)
2. Composition‐Adjusted Wages
3. Wages of New Hires
4. The “User Cost of Labor”
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Measured Wages
1. Average Hourly Earnings (AHE)
2. Composition‐Adjusted Wages
3. Wages of New Hires
4. The “User Cost of Labor”
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Simplifying Assumption
Assume:
•Constant discount factor (t 1)
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Becker (1962): Only the NPV Matters
( )( ), ,1
1tW
t t t t tt
PDV w E s wt
tt
b¥ -
= +
é ùê ú= + -ê úë ûå
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( )( ), ,1
1tW
t t t t tt
PDV w E s wt
tt
b¥ -
= +
é ùê ú= + -ê úë ûå
( )( )1
1tN N MRP
t t tt
MRP E s MRP PDVt
tt
b¥ -
= +
é ùê ú= + - =ê úë ûå
Becker (1962): Only the NPV Matters
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The User Cost of Labor: Kudlyak (2014)
( ) 11W Wt tPDV s PDVb +- -
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( ) 11W Wt tPDV s PDVb +- - N
tMRP=
The User Cost of Labor: Kudlyak (2014)
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( ) 11W Wt tPDV s PDVb +- -tUC º N
tMRP=
The User Cost of Labor: Kudlyak (2014)
( )( ), ,1
1tW
t t t t tt
PDV w E s wt
tt
b¥ -
= +
é ùê ú= + -ê úë ûå
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Measuring the User Cost
Procedure of Kudlyak (2014), extended to longer sample
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Measuring the User Cost
( )( )7
, , 1,1
ˆ ˆ ˆ1t t
t t t t t tt
UC w E s w wt
t tt
b+ -
+= +
é ùé ùê ú= + - -ë ûê úë û
å
DATA:
NLSY79 (1978‐2013, annual, panel).
Real wages (NFB deflator).
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Measuring the User Cost
Estimate, using data from NLSY79 to estimate worker fixed effects/control composition bias:
0 0
0 0
, , ,1
lnT T
i i i i it d d d d
d d d
w c X Dt t ta zt c e= =
= + + +Y + +åå
![Page 34: March Basu, Boston NBER L. House, University · 2017. 5. 2. · 3.Wages of New Hires • Hall (2005) • Pissarides (2009) • Gertlerand Trigari(2008) • Haefke, Sonntag and van](https://reader036.fdocuments.in/reader036/viewer/2022071419/6117eb6f51d57e774c43fbc5/html5/thumbnails/34.jpg)
Measuring the User Cost
Estimate, using data from NLSY79 to estimate worker fixed effects/control composition bias:
0 0
0 0
, , ,1
lnT T
i i i i it d d d d
d d d
w c X Dt t ta zt c e= =
= + + +Y + +åå
ct,t : wage paths for different cohorts
![Page 35: March Basu, Boston NBER L. House, University · 2017. 5. 2. · 3.Wages of New Hires • Hall (2005) • Pissarides (2009) • Gertlerand Trigari(2008) • Haefke, Sonntag and van](https://reader036.fdocuments.in/reader036/viewer/2022071419/6117eb6f51d57e774c43fbc5/html5/thumbnails/35.jpg)
Measuring the User Cost
Estimate, using data from NLSY79 to estimate worker fixed effects/control composition bias:
0 0
0 0
, , ,1
lnT T
i i i i it d d d d
d d d
w c X Dt t ta zt c e= =
= + + +Y + +åå
( ), ,ˆ ˆˆ ˆ ˆexpt tw c Xt tzt c= + +Y +
Construct
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1
1.2
1.4
1.6
1.8
2
2.2
2.4
1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006
Constructing the User Cost of Labor (UCL)
New Hire Wage
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1
1.2
1.4
1.6
1.8
2
2.2
2.4
1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006
Constructing the User Cost of Labor (UCL)
New Hire Wage
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1
1.2
1.4
1.6
1.8
2
2.2
2.4
1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006
Constructing the User Cost of Labor (UCL)
New Hire Wage
![Page 39: March Basu, Boston NBER L. House, University · 2017. 5. 2. · 3.Wages of New Hires • Hall (2005) • Pissarides (2009) • Gertlerand Trigari(2008) • Haefke, Sonntag and van](https://reader036.fdocuments.in/reader036/viewer/2022071419/6117eb6f51d57e774c43fbc5/html5/thumbnails/39.jpg)
1
1.2
1.4
1.6
1.8
2
2.2
2.4
1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006
Constructing the User Cost of Labor (UCL)
New Hire Wage
![Page 40: March Basu, Boston NBER L. House, University · 2017. 5. 2. · 3.Wages of New Hires • Hall (2005) • Pissarides (2009) • Gertlerand Trigari(2008) • Haefke, Sonntag and van](https://reader036.fdocuments.in/reader036/viewer/2022071419/6117eb6f51d57e774c43fbc5/html5/thumbnails/40.jpg)
1
1.2
1.4
1.6
1.8
2
2.2
2.4
1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006
Constructing the User Cost of Labor (UCL)
New Hire Wage
UCL
![Page 41: March Basu, Boston NBER L. House, University · 2017. 5. 2. · 3.Wages of New Hires • Hall (2005) • Pissarides (2009) • Gertlerand Trigari(2008) • Haefke, Sonntag and van](https://reader036.fdocuments.in/reader036/viewer/2022071419/6117eb6f51d57e774c43fbc5/html5/thumbnails/41.jpg)
1
1.2
1.4
1.6
1.8
2
2.2
2.4
1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006
Constructing the User Cost of Labor (UCL)
New Hire Wage
UCL
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Log wage cyclicality, 1978‐2013
AHE NLSY NLSY + Controls
NLSY+ Controls + FE
New Hire
UCL
HP‐FilteredGDP
0.311(0.353)
0.984(1.093)
0.960(1.082)
1.165(1.161)
1.325(1.287)
3.122**(1.351)
OLS standard errors in parentheses.
** p < 0.025
Annual data. All regressions include constant and trend. Dependent variables are time fixed effects from a first‐stage regression.
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Wages and Monetary Shocks
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Wages and Monetary ShocksSemi‐Structural VAR Shocks (Bernanke‐Blinder / CEE)
B is lower triangular.
4 lags as in CEE 1999, 2005
( ) 1t t tY A L Y Be-= +
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Wages and Monetary ShocksSemi‐Structural VAR Shocks (CEE)
YI includes GDP, C, I, P, AHE/P, and Y/N
YII includes Real Corp. Profit and Money Growth
I ff IIt t t tY Y r Y ¢é ù= ê úë û
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Wages and Monetary ShocksSemi‐Structural VAR Shocks (CEE)
Additional variables YIII new wage series (UCL, NH)
( ) 1t t tY A L Y Be-= +
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Wages and Monetary ShocksSemi‐Structural VAR Shocks (CEE)
Additional variables YIII new wage series (UCL, NH)
( )( ) ( )
1
1
0t t tIII III
t t t
Y A L Y BY a L b L Y e
e-
-
é ùé ù é ù é ùê úê ú ê ú ê ú= +ê úê ú ê ú ê úë û ë û ë ûë û
To include in quarterly VAR, we interpolate annual data. CEE sample starts before NLSY data. We splice an extrapolated series for 1965‐77.
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0 5 10 15 20 25 30-1.5
-1
-0.5
0
0.5
Average Hourly Earnings
0 5 10 15 20 25 30-1.5
-1
-0.5
0
0.5
New Hire Wage
Response of Wages to Monetary Shocks
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Response of Wages to Monetary Shocks
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Wage Dynamics in DSGE Models
Calibrated Model
•Christiano et al. (2005)
•Del Negro et al. (2013)
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Wage Dynamics in DSGE Models
Calibrated Model
Parameter Baseline Value
Frisch Labor Supply Elasticity 1.00
Intertemporal Elasticity of Sub. 1.00
Habit Weight 0.65
Avg. Duration of Prices (qtr) 10.00
Avg. Duration of Wages (X, qtr) 10.00
Investment Adjustment Cost 4.00
Utilization Elasticity 1.00
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Wage Dynamics in DSGE Models
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Wage Dynamics in DSGE Models
Benchmark Specification
• No composition bias : L/N = 1 and j(1)‐1 = 0
• Constant renegotiated remittances : s = 1.00
• Price and wage rigidity only
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Wage Dynamics in DSGE Models
Data (estimated)
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Wage Dynamics in DSGE Models
Sticky Prices
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Wage Dynamics in DSGE Models
Sticky Wages
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Wage Dynamics in DSGE Models
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Wage Dynamics in DSGE Models
Implicit Contracting
• No composition bias : L/N = 1 and j(1)‐1 = 0
• Remitted wage is reset at : s < 1.00
• Barattierri, Basu & Gottschalk (2014) find s = 0.21
• Price rigidity but NO wage rigidity
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Wage Dynamics in DSGE Models
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Wage Dynamics in DSGE Models
Sticky price model
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Wage Dynamics in DSGE Models
s = 0.5
s = 0.21
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Wage Dynamics in DSGE Models
Composition Bias
• No composition bias : L/N = 1 and j(1)‐1 = 0
• “Med.” composition bias : L/N = j(1)‐1 = 2
• “High” composition bias : L/N = j(1)‐1 = 4
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Wage Dynamics in DSGE Models
Data (estimated)
Sticky price model
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Wage Dynamics in DSGE Models
Med comp. bias
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Wage Dynamics in DSGE Models
High comp. bias
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Conclusions from DSGE Experiments
Model wants only modest composition bias
Model finds flexible allocative wages with implicit contracts helpful for matching all three wage series
With allocative wages flexible, model unable to match persistence of output response to monetary shock
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Persistence
• Following a monetary policy shock, wage component of marginal cost changes sharply
• But we still observe little price response, and persistent output response
• How to reconcile the two facts? A suggestion:
0
t t
t
t
tp mc
Y
m
m
* *
*
= +
¶<
¶
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Summing Up
Tension between micro wage data and business cycle mechanisms.
Shadow wage seems to be much more responsive than is conventionally assumed in standard DSGE models.
Greater emphasis on sources of price rigidity rather than wage rigidity required to match VAR evidence.
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Critique based on job ladders
• Hagedorn‐Manovskii (2013) suggest that history dependence in wages is only apparent, not real
• They present a search model where wages depend on match quality, determined by cumulative labor market tightness in an “employment cycle”
• Match quality is higher if workers have been employed while labor markets were tight, giving the impression of history dependence in wages
• We construct the H‐M measure of match quality and add it as a control to the wage regression
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Adjusting for Match Quality
0 5 10 15 20 25 30-1
-0.5
0
0.5New Hire Wage
0 5 10 15 20 25 30-1
-0.5
0
0.5User Cost of Labor
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Adjusting for Match Quality
0 5 10 15 20 25 30-1
-0.5
0
0.5New Hire Wage
0 5 10 15 20 25 30-1
-0.5
0
0.5User Cost of Labor