Embed Size (px)
Citation preview
Employment Reallocation and Unemployment Revisited: A Quantile
Regression Approach
Theodore PanagiotidisDepartment of Economics, University of Macedonia, Greece
and Rimini Centre for Economic Analysis, Italy.
Gianluigi PelloniDepartment of Economics, University of Bologna, Italy;
Department of Economics, Wilfrid Laurier University, Canada; and
Rimini Centre for Economic Analysis, Italy.
1
Introduction
• Intersectoral labour reallocation as a triggering force of aggregate unemployment fluctuations.
• An aggregate shock → firms to lay off workers temporarily → changes in aggregate employment and unemployment.
• Sector-specific shocks, affecting the allocation of demand across sectors, → intersectoral movements of workers → the time-consuming processes of searching, retraining and relocating → could also alter the levels of (un) employment (Lilien, 1982a).
2
3
Introduction• Up to 1980’s aggregate shocks seen the driving force of
unemployment cycles.
• Lilien(1982a) Sect. Shifts Hypothesis. : reallocation shocks →macroeconomic effects.
• Changes in demand composition operate as the driving force of unemployment fluctuations. Idiosyncratic shocks bring flows of job reallocation from declining sectors to expanding ones
4
Literature
• Lilien (1982a): reduced form equation with dispersion index:
σt = [j (N j,t / N t) ( Δ ln N j,t - Δln Nt)2]1/2
• Criticism: Lilien (1982b, WP); Abraham and Katz (1986).
• Literature Review: Gallipoli & Pelloni (2008)
Methodology• Most of the existing literature focuses on the
conditional mean response (LRM). The latter assumes symmetry and linearity.
• Literature approached the issues by employing nonlinear models both at univeriate and multivariate level.
• This paper is the first attempt to investigate this issue by quantile regression (QR): assymetry
5
• QR (Koenker and Basset, 1978) is a tool that allows us to model distributions.
• Starting point for QRM → conditional quantile function (CDF).
• The CDF of Yi at quantile τ given a vector of covariates Xi is given by:
)/()/( 1iyii XFXYQ
6
• Where is the distribution function of Yi at y, conditional on Xi. When τ=0.10 describes the lower decile of Yi given Xi.
• Reduced form of the estimated model:
• ut = ln(Ut/(1- Ut)) logistic transfromation
)/(1 iy XF
)/( ii XYQ
7
40 1 2 3( / ) ( ) ( ) ( ) ( ) ( )i iQ u X s D LM LE
.02
.04
.06
.08
.10
.12.000
.005
.010
.015
.020
.025
.030
50 55 60 65 70 75 80 85 90 95 00 05 10
Unemployment rate σ
8
9
0
5
10
15
20
25
30
.01 .02 .03 .04 .05 .06 .07 .08 .09 .10 .11 .12
De
nsi
ty
UNRATE
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
-4.0 -3.8 -3.6 -3.4 -3.2 -3.0 -2.8 -2.6 -2.4 -2.2 -2.0 -1.8D
en
sity
LOGISUNRATE
-3.3
-3.2
-3.1
-3.0
-2.9
-2.8
-2.7
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
C
-40
-20
0
20
40
60
80
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
SIGMA_PURGED2
-13
-12
-11
-10
-9
-8
-7
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
FEDERALDEFICIT
-6
-4
-2
0
2
4
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
DLAMBSL
-5
-4
-3
-2
-1
0
1
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
DLCPI_ENERGY
Quantile Process Estimates (95% CI)
10
-3.3
-3.2
-3.1
-3.0
-2.9
-2.8
-2.7
-2.6
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
C
-20
0
20
40
60
80
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
SIGMA_PURGED2(-6)
-11
-10
-9
-8
-7
-6
-5
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
FEDERALDEFICIT(-6)
-2
-1
0
1
2
3
4
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
DLAMBSL(-6)
-3
-2
-1
0
1
2
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
DLCPI_ENERGY(-6)
Quantile Process Estimates (95% CI)
11
-3.4
-3.2
-3.0
-2.8
-2.6
-2.4
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
C
-20
-10
0
10
20
30
40
50
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
SIGMA_PURGED2(-12)
-12
-10
-8
-6
-4
-2
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
FEDERALDEFICIT(-12)
-2
0
2
4
6
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
DLAMBSL(-12)
-1
0
1
2
3
4
5
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
DLCPI_ENERGY(-12)
Quantile Process Estimates (95% CI)
12
What happens if we replace the logistic transformation with unemployment rate?
13
.030
.035
.040
.045
.050
.055
.060
.065
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
C
-2
-1
0
1
2
3
4
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
SIGMA_PURGED2
-.8
-.7
-.6
-.5
-.4
-.3
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
FEDERALDEFICIT
-.2
-.1
.0
.1
.2
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
DLAMBSL
-.20
-.15
-.10
-.05
.00
.05
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
DLCPI_ENERGY
Quantile Process Estimates (95% CI)
14
.03
.04
.05
.06
.07
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
C
-1
0
1
2
3
4
5
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
SIGMA_PURGED2(-6)
-.60
-.55
-.50
-.45
-.40
-.35
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
FEDERALDEFICIT(-6)
-.08
-.04
.00
.04
.08
.12
.16
.20
.24
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
DLAMBSL(-6)
-.15
-.10
-.05
.00
.05
.10
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
DLCPI_ENERGY(-6)
Quantile Process Estimates (95% CI)
15
.03
.04
.05
.06
.07
.08
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
C
-1
0
1
2
3
4
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
SIGMA_PURGED2(-12)
-.6
-.5
-.4
-.3
-.2
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
FEDERALDEFICIT(-12)
.0
.1
.2
.3
.4
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
DLAMBSL(-12)
-.1
.0
.1
.2
.3
.4
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Quantile
DLCPI_ENERGY(-12)
Quantile Process Estimates (95% CI)
Conclusions
• Assymetry in the relationship revealed.• The higher the unemploeyment the more
reallocation is taking place.• Deficit: upward sloping, negative and
significant• Money: singificant at the 12 month lag only
when unemployment is high.• Energy: not singificant
16
