Are the Spanish Long-Term Unemployed Unemployable?

Are the Spanish Long-Term Unemployed Unemployable?
Samuel Bentolila
CEMFI
J. Ignacio García-Pérez
Universidad Pablo de Olavide and Fedea
Marcel Jansen
Universidad Autónoma de Madrid and Fedea
Presentation at the European University Institute, 3 April 2017
Bentolila, García-Pérez, Jansen 2017 Are the Spanish Long-Term Unemployed Unemployable?EUI, April 2017 1 / 41

Introduction
Motivation
The Great Recession raised the Spanish unemployment rate up to 27%
and created an enormous buildup of long-term unemployment (LTU):
16% of the labor force and 64% of the unemployed at their peaks
3 years into the recovery and despite strong job creation (2.3% p.a.),
the job-finding rates of the long-term unemployed remain low and LTU
still comprises 57% of the unemployed
Main risks:
I Economic and social exclusion of the most vulnerable groups
I High levels of structural unemployment (hysteresis)
We need to understand the causes of the buildup of LTU and the low
outflow rates to design effective policies

Introduction
Unprecedented levels of LTU and VLTU
Figure: Duration-speciﬁc unemployment rates in Spain (% of labor force)

Introduction
Spain stands out for its large increase (with Greece)
Figure: Long-term unemployment before and after the Great Recession in selected
EU countries (% of labor force)

Introduction
Sources of the rise in LTU
The buildup of LTU is due to the same factors raising unemployment:
Steep and persistent drop in aggregate demand (double-dip recession)
Bursting of a housing bubble — unemployment inﬂow of 1.7M
construction workers (mostly low-educated males)
Large-scale banking crisis (Bentolila, Jansen and Jiménez, 2015)
The shocks interacted with institutions:
Dual labor market (permanent-temporary)
Wage rigidity (Font, Izquierdo and Puente, 2015)
! Large destruction of temporary jobs and initially slow wage adjustment
We take these factors as given and study the mechanisms yielding high
unemployment persistence

Introduction
Related literature
LTU in the aftermath of the Great Recession in the US
I Krueger, Cramer and Cho (2014)
I Kroft, Lange, Notowidigdo and Katz (2016)
I Abraham, Sandusky, Haltiwanger and Spletzer (2016)
I Shimer, Alvarez and Brovickova (2016)
Unemployment duration and LTU in Europe
I European LTU: Layard, Nickell and Jackman (1991), Machin and
Manning (1999)
I Descriptive evidence on Europe: Bentolila and Jansen (eds.) (2016)
I Spanish unemployment duration: García-Pérez (1997), Bover et. al.
(2002), Rebollo and García-Pérez (2015), etc.

Introduction
Scope of the paper
We jointly estimate competing-risks duration models for
unemployment and employment to identify the factors associated with
the LTU inflow and outflow
Our analysis pays close attention to institutional factors:
I Dual structure of the labor market
I Unemployment insurance (UI) entitlement, assistance benefits
We further address some problems found in the literature on LTU in
the US: unobserved heterogeneity, self-selection and state dependence
A first step: generate a set of stylized facts – without identification of
causal effects

Introduction
Preview of results
The conditional probability of entering LTU is very high and
associated with low experience, mature age, and receipt of UI beneﬁts
Also with low skill and education, when focusing on exits to jobs
lasting at least 1 month
Construction workers do not face higher probability of LTU
Duration dependence is strong
Temporary contracts reduce the risk of entering LTU especially for the
least educated and experienced, and for workers without beneﬁts
Aggregate demand raises the exit rate more for the short- than for the
long-term unemployed, so growth alone will not solve the problem
quickly
Reservation wages are strongly correlated with the cycle and also –but
much less– with individual unemployment duration

Introduction
Outline
1 Unemployment stocks and ﬂows in the Great Recession
2 Covariates of the probability of entering and exiting LTU: Duration
models
3 Self-reported reservation wages in the Great Recession
4 Concluding remarks

Unemployment stocks and ﬂows in the Great Recession
Unemployment and LTU stocks
Unemployment rate LTU share
2007Q2 2016Q2 2007Q2 2016Q2
Total 8.0 20.0 27.8 59.8
Male 6.2 18.5 24.1 58.0
Female 10.4 21.9 30.8 61.5
16-24 years old 18.1 46.5 16.0 40.2
25-34 years old 7.5 22.4 24.9 56.1
35-44 years old 6.7 16.8 27.2 58.8
45-54 years old 6.1 17.3 39.1 67.9
55-64 years old 5.7 16.9 55.6 77.6
Primary education 10.6 35.2 33.0 65.2
Secondary, 1st stage ed. 9.6 26.8 26.5 62.0
Secondary, 2nd stage ed. 8.0 20.0 26.3 55.7
College education 5.3 11.7 26.3 56.7

Unemployment and LTU stocks
Larger increase in long-term unemployment for males
Larger shares in LTU for older and low-educated workers
Striking: the LTU share is close to 60% for nearly all groups, including
prime-age workers

Flows from employment to unemployment
(a) Temporary (b) Permanent
Figure: Quarterly ﬂow rates into unemployment by contract type (%)

Flows from unemployment to employment
(a) Temporary (b) Permanent
Figure: Quarterly ﬂow rates into employment by contract type (%)

Labor flows
Sharp increases of EU flows and sharp falls in UE flows
Both were much larger for temporary than for permanent workers
None is back to pre-crisis levels
The transition rates from unemployment to employment are negatively
related with duration:
I This may be due to true duration dependence but also to dynamic
selection, as the most employable workers leave first
! We need to control for worker observed and unobserved
characteristics

Flows from unemployment to employment
Figure: Unemployment duration-speciﬁc transitions to employment (%)

Flows between E, U, and N
Figure: Quarterly ﬂows to/from unemployment (persons)

Flows: unemployment and nonparticipation
Absolute ﬂows between unemployment and nonparticipation are large,
about one-third of those between unemployment and employment, and
should not be discarded in the analysis (Elsby, Hobijn and Sahin, 2015)
Flow rates between employment and nonemployment behave in a
similar way as those between employment and unemployment

Flow rates to and from employment
Figure: Quarterly ﬂow rates involving unemployment and nonemployment (%)

The probability of entering and exiting LTU Empirical approach
Hazard rates
Unemployment hazard rate:
hj
u(t) = Pr(Tu = t | Tu t,x(t),b(t),e(t),hu
)
= F(aj
0(t)+aj
1(t)x(t)+aj
2(t)b(t)+aj
3(t)e(t)+hu
)
Employment hazard rate:
hk
e (t) = Pr(Te = t | Te t,x(t),he
) = F(bk
0 (t)+bk
1 (t)x(t)+he
)
where: x(t) = personal characteristics and aggregate variables, e(t) =
remaining months of UI entitlement, b(t) = dummy for UA beneﬁts
aj
0(t) and bk
0 (t) represent the baseline hazards, but duration
dependence is also captured by the time-varying interaction coeﬃcients

The probability of entering and exiting LTU Empirical approach
Competing risks and unobserved heterogeneity
Competing risks: exit from a state speciﬁed as a multinomial logit
model with two alternative risks (and exits from temp and perm jobs
also distinguished)
hu(t) = heT
u (t)+heP
u (t), he(t) = he
e (t)+hu
e (t)
Unobserved heterogeneity: We allow a four mass-point distribution
function with two points for each state (Heckman and Singer, 1984)
I hu
1 and hu
2 for unemployment, he
1 and he
2 for employment
I 4 types may emerge: (hu
1 , he
1 ),(hu
1 , he
2 ),(hu
2 , he
1 ),(hu
2 , he
2 )
I Repeated individual spells and time-varying covariates allow
non-parametric identiﬁcation (Abbring and van den Berg, 2004; Gaure
et al., 2007)
These features allow us to distinguish between temporary and
permanent contracts, and to control for selection

The probability of entering and exiting LTU Sample and control variables
Data and sample selection
Our data set is a 20% random sample of all workers whose records appear
in the Continuous Sample of Working Lives (MCVL) in 2006-2014 (4%
sample of social security data)
Daily data – aggregated to monthly
We observe workers’ working careers from the start
Can construct workers’ entitlement to UI beneﬁts from work history
But cannot distinguish between nonparticipating and unemployed
So: nonemployment spells above 36 months censored at month 36
Vis-à-vis LFS: We observe short-term transitions often missed in the
LFS, but not information on other household members

Sample selection
“Mancession”: Sample restricted to native males aged 25-54 years old
(estimates for females in an appendix)
Exclude immigrants and people who appear for the ﬁrst time in the
sample being at least 30 years old (unrecorded work history?)
Only private employees excluding agriculture (exits to other states
treated as censored)
Recalls: two spells at same ﬁrm with intervening unemployment below
1 month considered as a single employment spell
Periods and sample size (unemployment):
I Expansion (2001-2007): 37,399 spells (1,346,016 obs.)
I Recession (2008-2014): 62,045 spells (1,641,889 obs.)

Control variables
Individual
Age (3 ten-year intervals), Education (4 dummy variables for the
highest degree), Skill (3 occupational levels: low, medium, high),
Actual experience ratio (no. of months employed to no. of months
of potential experience), UI beneﬁts (remaining months of
entitlement, computed using previous employment history),
Assistance unemployment beneﬁts (0-1), Fired from previous job
Aggregate
Employment growth rate by province, Quarterly national
unemployment rate, Region dummies (17), Industry dummies (6),
Month dummies (12), Step dummies for labor market reforms
(June 2010, February 2012)

The probability of entering and exiting LTU Descriptive statistics
Descriptive statistics of unemployment spells
Expansion Recession
A. Unemployment duration (mean no. months)
Exit to a temporary job 4.5 6.3
Share (%) 76.6 69.9
Exit to a permanent job 5.1 6.2
Share (%) 7.9 8.1
Censored 7.6 10.7
Share (%) 15.5 22.0
B. Unemployment insurance duration (mean no. months)
All 9.8 11.2
Temporary previous job 8.9 10.0
Permanent previous job 15.6 16.8
Number of spells 37,399 62,045

The probability of entering and exiting LTU Empirical results
Recession Temporary c. Permanent c. Temporary c. Permanent c.
Variable Coeff. z Coeff. z Variable Coeff. z Coeff. z
Age 35-44 y.o. -0.106 -4.69 0.232 4.36 Experien. 1.031 31.63 1.596 22.48
Age 45-54 y.o. -0.295 -5.00 0.438 3.79 Employ 4.142 14.25 2.904 3.78
Age 35-44⇥Dur -0.018 -1.55 -0.135 -4.34 E⇥Dur -0.711 -4.49 -0.002 0.00
Age 45-54⇥Dur -0.067 -2.06 -0.210 -3.07 U rate -0.017 -5.63 -0.066 -10.51
Second. ed. 1s 0.160 4.86 -0.185 -2.41 U⇥Dur 0.006 4.94 0.019 6.57
Second. ed. 2s 0.103 2.72 -0.111 -1.31 Dur -0.693 -13.77 -0.850 -7.06
College -0.024 -0.53 0.101 1.05 Dur2 0.337 11.37 -0.032 -0.39
Sec ed 1⇥Dur 0.003 0.19 0.166 3.43 Dur3 -0.104 -16.79 -0.029 -1.72
Sec ed 2⇥Dur -0.035 -1.83 0.180 3.45 U insur. -0.380 -37.52 -0.157 -7.04
College ⇥Dur -0.027 -1.16 0.193 3.33 UI2 0.035 24.83 0.014 4.55
High skill 0.273 7.38 0.015 0.18 UI3 -0.001 -20.30 0.000 -3.80
Medium skill 0.347 10.11 -0.155 -1.92 UI⇥Dur -0.024 -4.95 -0.035 -3.28
High skill⇥Dur -0.093 -5.01 0.173 3.48 UI⇥Dur2 0.009 3.95 0.009 1.85
Med. skill⇥Dur -0.071 -4.15 0.171 3.49 U. Assist -1.107 -46.11 -1.287 -18.78

Hazards: Duration dependence
Figure: Duration dependence in the baseline model and in the model without
unobserved heterogeneity (%)

Duration dependence and aggregate demand
Duration dependence
During 1st year the monthly hazard of average individual halves, and
halves again in 2nd year (Kroft-Lange-Notowidigdo, 2013, résumé
audit study, at 8 months the employer recall rate is 45% lower)
Not controling for unobserved heterogeneity biases estimated duration
dependence upwards
Aggregate demand (in recession)
Bad scenario: provincial employment growth at ﬁrst quartile (-4.8%),
national unemployment rate at third quartile (25.1%)
Good scenario: provincial employment growth at third quartile
(-0.4%), national unemployment rate at ﬁrst quartile (18.6%)
A cyclical upturn helps the long-term unemployed much less than the
short-term unemployed

Hazards: Aggregate demand
Figure: Eﬀect of the business cycle on hazard rates by duration (%)

Survival rates Expansion Recession
12 m 24 m 12 m 24 m
Overall 13.8 33.2 25.5 51.3
Age 25-34 years old 13.3 32.2 23.7 48.9
35-44 years old 16.0 38.6 27.5 53.9
45-54 years old 27.5 49.8 34.9 62.9
Education Primary or less 13.4 31.0 28.4 51.8
Secondary, 1st stage 11.2 27.7 24.2 47.1
Secondary, 2nd stage 14.7 32.0 26.6 50.8
College 19.6 35.9 27.6 50.7
Skill High 14.9 36.0 26.0 52.6
Medium 12.5 31.6 23.4 49.0
Low 14.5 31.9 30.7 54.0
Experience P75 10.7 28.3 18.5 43.4
P25 16.2 36.6 30.8 56.5
P10 20.6 42.2 40.6 64.9

Survival rates
From expansion to recession:
I Chances of entering LTU: 14% ! 26% (higher inflow)
I LTU that enter VLTU: 33% ! 51% (lower outflow)
Age > 45 y.o. and low experience associated w/ high survival
Education:
I Recession: Negative correlation
I Expansion: U-shape (similar for skill)
I Overall, surprisingly small differences
Construction:
I 12 m: Lowest survival in expansion, 2nd lowest in recession
I Why? Huge turnover: 60% of jobs last <3 months, 44% in others
Unemployment insurance: the highest differences across levels
Note: For survival at 24 m we normalize the rate at 12 m=100

Survival rates Expansion Recession
12 m 24 m 12 m 24 m
Industry Manufacturing 12.3 30.0 23.7 47.3
Construction 11.8 31.8 23.6 48.3
Non-market services 18.4 39.7 30.9 58.3
Trade 14.8 30.6 27.7 50.2
Hospitality 12.3 27.5 22.1 46.8
Other services 14.7 36.2 25.7 54.7
U. insurance No beneﬁts 5.7 19.9 12.1 36.0
6 months 13.0 19.9 20.6 36.0
12 months 35.0 19.9 41.9 36.0
18 months 45.3 30.9 52.6 46.9
24 months 53.3 56.1 60.7 65.6
U. assistance No 5.7 19.9 12.1 36.0
Yes 31.3 51.7 44.3 67.3

Survival rates: Beneﬁts (in recession)
Figure: Eﬀect of unemployment insurance on survival rate (%)

Censored model Survival rate v. baseline
12 m 24 m 12 m 24 m
Overall 42.9 65.7 17.4 14.5
Education Primary or less 47.6 67.8 19.2 16.0
Secondary, 1st st. 42.7 63.6 18.5 16.5
Secondary, 2nd stage 42.8 64.0 16.3 13.2
College 40.1 61.4 12.5 10.7
Experience P75 31.4 56.0 13.0 12.6
P25 50.7 71.6 19.9 15.1
P10 63.4 80.0 22.7 15.1
U insurance No beneﬁts 28.3 53.4 16.2 17.5
6 months 36.4 53.4 15.7 17.5
12 months 54.8 53.4 12.9 17.5
18 months 63.7 61.8 11.0 15.0
24 months 71.4 75.5 10.7 9.9

Survival rate: Censoring short spells
Censor exits to jobs that last less than one month
Survival rates increase (by construction), but significantly (17.4 pp)
Survival now clearly increases with education and differences are larger
Effect is larger for the least educated, least experienced, and workers
with lower entitlement to benefits
I Short-term contracts especially help these groups avoid entering LTU

Reservation wages in the Great Recession
Reservation wages during the crisis
Are exit rates low because labor demand is low or because the
long-term unemployed have high reservation wages?
Real wage rigidity is high in Spain. De la Roca (2014); Font et al.
(2015): hU = (-0.38,-0.48) in expansion, -0.24 in recession
However, the 2012 reform seems to have raised the elasticity of initial
wages somewhat. Izquierdo and Puente (2015): | hU|=0.13
We provide new evidence on the relationship between reservation
wages and unemployment duration:
I Average real reentry wage fell by 15.3% in 2010-2013 for the average
prime-age male worker in our sample (+5.2% in 2005-2009)
I Study self-reported reservation wages

Self-reported reservation wages
We use the Survey of Household Finances (EFF) survey question: At
what gross monthly wage would you be willing to work?
We pool four waves (2002, 2005, 2008, 2011)
Small sample size (2,816 obs. in total, 1,358 obs. for males) and small
panel dimension
Empirical speciﬁcation:
log(wit) = at +blog(Durit)+gUIit +dUAit +X0
it µ +uit
We control for personal characteristics, household composition and
ﬁnances (more than is typical in the literature), and aggregate
conditions (year dummies)
R2 ' 0.25

All Males All Males
Log duration -0.016 *** -0.020 ** Divorced 0.076 * 0.054
(0.006) (0.008) (0.048) (0.062)
UI beneﬁts 0.055 *** 0.021 Household head -0.014 ** -0.011
(0.015) (0.020) (0.006) (0.007)
UA beneﬁts 0.013 -0.022 Sec. ed., 2 st 0.031 ** 0.048 **
(0.021) (0.029) (0.014) (0.020)
Age 0.004 *** 0.005 *** College 0.070 *** 0.056 ***
(0.001) (0.001) (0.014) (0.018)
Female -0.159 *** Household size 0.224 *** 0.194 ***
(0.013) (0.022) (0.032)
Married 0.059 *** 0.070 *** Total income 0.014 ** 0.024 **
(0.018) (0.026) (0.007) (0.012)
Unmarried prt. 0.057 ** 0.107 *** Financial assets 0.013 *** 0.011 ***
(0.028) (0.041) (0.002) (0.003)
Separated 0.033 0.011 Debt 0.005 *** 0.005 **
(0.040) (0.055) (0.001) (0.002)

Reservation wages: ﬁndings
Positive correlation with UI beneﬁts, but not for males (6= Addison et
al., 2009, for Spain; ' Krueger and Miller, 2016, for NJ)
Very low negative correlation with unemployment duration (' Addison
et al., 2009, for Spain)
Strong association with aggregate conditions (6= Koenig et al., 2016,
for Germany and UK; ' Chodorow-Reich and Karabarbounis, 2016,
for US)

Reservation wages: aggregate conditions
Figure: Annual proﬁle of reservation wages in deviations from 2002 (%)

Concluding remarks
Summary of results
The conditional probability of entering LTU is very high (25%) and
associated to low experience, mature age, and receipt of UI beneﬁts
Low skill and education also raise this probability, when focusing on
exits to jobs lasting at least 1 month
Construction workers do not face higher probability of LTU
Duration dependence is a key source of persistence
Temporary contracts help reduce the risk of entering LTU conditional
on being unemployed, but they also induce huge unemployment inﬂows
The cycle raises the exit rate more for the short- than for the long-term
unemployed, so growth alone will not solve the problem quickly
Reservation wages are strongly correlated with the cycle and also –but
much less– with individual unemployment duration

Concluding remarks
Concluding remarks: ALMPs
Evidence suggests that well-designed intensive support to long-term
unemployed pays oﬀ (Csillag and Fertig, 2015)
Average observed program impacts are typically largest for long-term
unemployed (Card, Kluve and Weber, 2015, 2016)
New data on ALMPs reveals poor performance in Spain (Jansen 2016)
I Low participation rates (10%) and even lower in LTU (7.5%)
I First measures take place on average after 9 months
I Only 5% of LTU with UI and just 3.3% with UA participated in ALMPs
Our results indicate that workers in LTU are not so much helped by
higher aggregate demand, so they would beneﬁt the most from
expanded and improved ALMPs
Given strong duration dependence, the link between ALMPs and the
receipt of UI/UA is crucial to reduce the risk of LTU

Are the Spanish Long-Term Unemployed Unemployable?

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