Labor Market Matching Efficiency and Mismatch in Switzerland · PDF fileLabor Market Matching Efﬁciency and Mismatch in Switzerland Labor Market Matching Efﬁciency and Mismatch

Labor Market Matching Efficiency and Mismatch in Switzerland

Labor Market Matching Efficiency and Mismatch inSwitzerland

Debra Hevenstone, Emily Murphy, Helen Buchs

Swiss Job Market Monitor, University of Zurich

April 12, 2016

1 / 36


Research Question

Motivation: Evidence of declining labor marketefficiency

2008

2012

2007

2013

2014

2009

2011

2006

2010

0.010

0.015

0.020

0.025

0.030

0.035 0.040 0.045 0.050 0.055 0.060Unemployment rate

Vaca

ncy

rate

growth in long term unemployment?

increase in occupational mismatch?

2011 AV reform?

2 / 36


Research Question

Research Questions and Estimation

Predict matching ratesw/ diverse submarket

de�nitions

Valid de�nition of submarket?

(speci�city, �ows, de�nition)

Question Estimation

Mismatch level? (by education, experience)

Calculate mismatch indices

Opportunities' impact on unemployment duration (by age, migration status,

education)

Hazard analysis

(Aldashev, 2012; Canon, 2013; Bauer, 2013; Patterson, 2013; Petrongolo, 2013; Sahin, 2014)3 / 36


Research Question

Research Questions: Contribution and insights

SJMM Contribution

▶ Detailed vacancy data▶ Accurate measurement

of overall mismatch▶ Accurate measurement

of opportunities ( ViUi

)

Insights

▶ Identify submarkets▶ Determine mismatch

level & trend (bysubgroup)

▶ Understand howopportunities(tightness) impactunemployment

4 / 36


Data

Data: SJMM Advantage of geographic specificityLabor market regions (16) Districts (148)

SH

ZH

ZG

BS

LU

BE

TI

VD FR

NE

GE

GR

SO

BL

AGAR

AI

SZ

OW

SG

VS

JU

TG

NW

GL

UR

101

102

103104

105

106

107

108109111 112

201

202

203

204 205206

207

208

209

210

211

212

213

214

215216

217218

219

220

221

222

223

224

225

226

301

302

303

304305

400

501

502

503

504

505

506

600

700

800

900

1001

1002

1003

1004

1005

1006

1007

11011102

1103

1104

1105

11061107

1108

1109

1110

1200

1301

1302

1303

1304

1305

1501

15021503

1600

1723

1725

1722

1726

17281721

1727

1724

1821

1822

1831

1823

1824

1825

1827

1828

18301829

1826

1901

1902

1903

1904

1905

1906

1907

1908

1909

1910

191120012002

2003

2004

2005

2006

2007

2008

2101

2102

2103

2104

2105

2106

21072108

2221

2227

2223

2226

2222

2228

2224

23012302

2303

2304

2305

2306

2307

2308

2309

2310

2311

2312

2313

2401

2402

2403

24042405

2406

2500

2601

2602

2603

110

1401

14021403

1404

14051406

22252229

2230

▶ Too-small units▶ assume workers have a very limited search circumference▶ over-estimate mismatch

▶ Too-large units▶ assume distant jobs are accessible▶ under-estimate mismatch

See also Occupational specificity5 / 36


Data

Data: SJMM advantage, considering geographicspillover

15%

5%

3%

2%

▶ Plus matching happens on multiple dimensions (education,occupation, geography) (Sahin, 2014)See also Occupational spillover , Crossed submarkets , and Weighting approach

6 / 36


Data

Data: SJMM validation (2006-2014)

2006 2008 2010 2012 2014

SMM vacanciesDifficulty finding workers (BESTA)

Correlation (Pearson's r) 0.924

2

1

0

-1

-2

SJMM Besta X28Variables detailed broad detailedSmall firm bias no no yesConstant methodology yes yes noTiming March monthly monthlySample small universal big

7 / 36


Methods

Methods: Matching function

▶ Predict the match rate based on vacancies/unemployed

ln(mituit) = ϕ+ γdt + ln(ρi) + ψi ln(

vituit) + αln( vit

uit) + ϵit

i submarket, t time, mit matches, uit unemployed, v it vacancies

(From Cobb Douglas Matching: mit = ϕvαit u1−α

it )

1. Use best-fitting model to choose valid submarket definition2. Estimate submarket matching efficiency3. Estimate annual matching efficiency

8 / 36


Methods

Methods: Jackman Index, measuring mismatch

▶ Raw Index 1 −∑

i

(ui vi).5

where ui =uiu , vi =

uiu

Proportion of unemployment attributable to structuralimbalance

▶ Index ∗ Unemployment

Percentage points that unemployment could be reduced

See also Jackman details

9 / 36


Results

Validation through the matching function

Results: Validation through matching function (AIC)

specificity spillover multidimensionalgeography LMR, district (2) no, cont., discrete (3) no, exp, edu (3)occupation 1, 2, 3-digit (3) no, cont., discrete (3) no, exp, edu (3)

▶ Geographic submarket (18):Labor market region, no spillover

A 1-sd change in VU in Zurich increases the matching rate from 11.43%

to 11.78%

▶ Occupational submarket (27):Two-digit occupation, discretely weighted spillovers

A 1-sd change in VU for those in administrative jobs increases the

matching rate from 10.89% to 11.46%10 / 36


Results


Results: Validation through matching function (AIC)Specificity

0

2000

4000

6000

LMR district

AIC

geography

0

1000

2000

3000

1−digit 2−digit 3−digit

occupation

See also Multivariate definitions

11 / 36


Results


Results: Validation through matching function (AIC)Spillover

0

2000

4000

6000

none discretecontinuous

AIC

geography

0

1000

2000

3000

none discretecontinuous

occupation

See also Multivariate definitions

11 / 36


Results


Results: Heterogeneous matching efficiency bysubmarket

0.12

0.16

0.20

95%V/U (discrete weights)

perc

ent m

atch

ed

construction

health, education, culture, science

hospitality and services

technical and informatics

0.08

0.10

0.12

0.14

95%V/U

perc

ent m

atch

ed

Geneva

Luzern

St. Gallen

Zurich

12 / 36


Results


Results: V/U’s marginal effect varies by year

0.12

0.13

0.14

0.15

0.16

2006 2008 2010 2012 2014

0.2

0.4

0.6

0.8

1.0

1.2

V/U

Match Rate

13 / 36


Results

Labor Market Mismatch

Results: Mismatch

geographic mismatch occupational mismatch

0.00

0.05

0.10

0.15

2006 2007 2008 2009 2010 2011 2012 2013 2014year

Inde

x

0.00

0.05

0.10

0.15

2006 2007 2008 2009 2010 2011 2012 2013 2014year

Inde

x

▶ Geographic mismatch increased slightly, led the recession▶ Occupational mismatch was stable, peaked in recession

14 / 36


Results


Results: Occupational mismatch by subgroup

Inexperienced

Experienced

0.1

0.2

0.3

0.4

2006 2012

Jack

man

Inde

x

2006 2012

Basic education

Tertiary education

Apprenticeship

Experience Education

▶ Inexperienced: higher mismatch▶ ... and more in recessions

▶ Apprenticeships: lower mismatch▶ Tertiary: declining mismatch

15 / 36


Results


Results: Sensitivity to using spillover weights

Discrete

Continuous

Occupation Geography

Unweighted

2006 20120.00

0.05

0.10

0.15

0.20

2006 2012

Discrete

Continuous

Unweighted

▶ Geographic weights have little effect▶ Occupational weights decrease mismatch

See other Sensitivity results 16 / 36


Conclusion

Conclusion: Matching function

▶ Submarket Validation▶ Generally, low specificity and no spillover weighting▶ ...but for occupational submarkets, 2-digit/discrete weights

▶ Efficiency across occupational submarkets▶ Certain occupations (IT) match more efficiently▶ No evidence of agglomeration effects

▶ Efficiency over time▶ Matching rates decline in recessions, independent of labor

market tightness▶ Tightness’ effect does not change significantly with

recessions

17 / 36


Conclusion

Conclusion: Mismatch

▶ Overall▶ Has not increased very much 2006-2014▶ Moves with economic cycles

▶ By subgroup▶ Apprenticeships face the least mismatch, tertiary declining▶ Those without experience face more mismatch

▶ Sensitivity▶ (Inappropriate) geographic spillover weights have no effect▶ (Appropriate) occupational spillover weights decrease

measured mismatch

18 / 36


Thanks

Thank You

19 / 36


Appendix

Appendix: Occupational submarket specificity

SBN1 (9)

1 agriculture2 manufacturing3 technical/inform4 construction5 retail6 hospitality7 management8 health/edu/culture9 other

SBN2 (38)

81 media82 art83 caring84 education85 soc/nat sci86 health87 sport

SBN3 (87)

861 medicine862 therapy 863 dental864 veterinary865 nursing

SBN5 (380)

861.01 doctor861.02 medical ass.861.03 pharmacist861.04 pharmacy ass.

Go back to Geographic specificity

20 / 36


Appendix

Appendix: Occupational spillover

10% 3%1%

Back to Geographic spillover

See also Weighting approach

21 / 36


Appendix

Appendix: Crossed submarkets▶ Matching happens on multiple dimensions (education,

occupation, geography) (Sahin, 2014)

90% 7%3%

95%

3% 2%

See also Crossed submarkets limitations . Back to Main weighting

22 / 36


Appendix

Appendix: Crossed submarkets limitation▶ Problem is small cell counts, inflating mismatch▶ Weighting by spillover could exacerbate or improve

problem

Vacancies w/ tertiary education, 2014, by LMRZurich Ticino

IT 1201 36Banking & Insurance 288 0Law 228 0Teachers in Tertiary Education 95 0Social Sciences and Economics 134 0Natural Sciences 82 0Human Medicine and Pharmacy 248 40Veterinary Medicine 0 0

Go back to Crossed submarkets

23 / 36


Appendix

Appendix: Unemployment records validation

140

160

180

200

220

240

2006 2007 2008 2009 2010 2011 2012 2013 2014Year

Une

mpl

oyed

Job

−see

kers

AVAM ILOUnemployed in 1000s

▶ Administrative records exceeded ILO count until 2011revision

▶ ILO: Unemployed and available for work w/in 30 days▶ AVAM: Includes people who are eligible, not necessarily

available for work24 / 36


Appendix

Appendix: Spillover weighting

Appendix: Spillover weighting, data

▶ Occupational changes▶ SAKE (Swiss labor market survey)

▶ Location of residence and job▶ SAKE (Swiss labor market survey)

▶ Commuting time distribution▶ Swiss census structural survey

▶ Distances▶ Google maps (driving)

Go back to Main weighting slides

25 / 36


Appendix


Appendix: Spillover weighting, overview▶ Discrete weights (for vacancies)

▶ Geography: location of residence before job change vs.location of new job

▶ Occupation: occupation of job 1 vs. occupation of job 2

▶ Continuous weights (for vacancies)▶ Geography: probability taking a job in a given location (vs

residence)▶ Occupation: probability occupational change by sbn digit

change

V∗ =

(1, 1) (1, 2) (2, 1) (2, 2)

(1, 1) .7 .13 .13 .04(1, 2) . . . . . . . . . . . .(2, 1) . . . . . . . . . . . .(2, 2) . . . . . . . . . . . .

3479

=

3.89. . .. . .. . .


26 / 36


Appendix


Appendix: Spillover weighting, example

Transitions between occupations (1-digit switches)

1 2 3 4 5 6 7 8 91. agriculture .72 .03 .00 0.03 .07 .05 .06 .05 .002. manufacturing .01 .65 .07 .03 .07 .04 .08 .03 .023. tech/inform .00 .04 .74 .02 .05 .00 .12 .03 .004. construction .01 .10 .05 .71 .06 02 .03 .00 .005. retail .00 .04 .03 .01 .64 .03 .21 .05 .006. hospitality .00 .04 .01 .01 .06 .78 .04 .05 .007. management .00 .02 .04 .00 .11 .02 .76 .04 .008. health/ed/cult .00 .00 .02 .00 .03 .01 .07 .85 .009. other .00 .14 .07 .04 .11 .00 .07 .04 .54

(SAKE data)


27 / 36


Appendix


Flow (spillover) weighting, time pooling

Transition likelihood 2010 vs. 2013

SBN1 Remain 2. Manufacturing 3.Tech/Info

2.Manufacturing (2013) 0.73 - 0.122.Manufacturing (2010) 0.85 - 0.054.Construction (2013) 0.80 0.07 0.04.Construction (2010) 0.95 0.05 0.07.Banking/Insurance (2013) 0.83 0.01 0.057.Banking/Insurance (2010) 0.80 0.03 0.03


28 / 36


Appendix


Appendix: Spillover weighting, continuous geographic1. Calculate driving distances matrix between all district pairs2. Fit commute time distribution

0.00

0.01

0.02

0.03

0 50 100 150 200 250 300Minutes

Den

sity

Gamma fit commute Observed commute

gammashape rate1.603 0.053

3. T = Row-standardized matrix of P(commuting)4. Vspillover−adjusted = T ∗ Vraw

5. Re-weight for constant total vacancies


29 / 36


Appendix


Appendix: Spillover weighting, continuousoccupational

Basis of continuous occupational change weights

Probability ofstaying .581 digit change .062 digit change .023 digit change .085 digit change .26


30 / 36


Appendix


Appendix: Spillover weighting, continuous vs. discrete

An economist switching jobs

discrete continuousEconomist 0.34 0.58Sociologist 0.00 0.03Banker 0.09 0.00


31 / 36


Appendix


Appendix: Jackman Index alternatives/critique▶ Alternatives

▶ V-U correlations▶ Proportion of unemployed in the wrong sector

12

∑i

|ui − vi |

▶ Jackman w/ weights for submarket efficiency (Sahin, 2013)▶ Lilien Turbulence Index▶ Standard deviation of the rate of employment growth across

occupations (Evans 1993; Daly,2012)▶ Jackman advantages/disadvantages

▶ See Canon et. al., 2013▶ Composition invariance example

Increasing occupation 3 in canton 1 increased in the spread betweenoccupations 1 & 2’s vacancy and unemployment shares.

Go back to Main jackman slide

32 / 36


Appendix

Appendix: Additional Matching Equation Results

Results: Validation through matching function (AIC)Multivariate definitions

0

2000

4000

6000

none education experience

AIC

geography

0

1000

2000

3000

none educationexperience

occupation

33 / 36


Appendix

Appendix: Additional Mismatch Results

Appendix: Mismatch inflated with higher specificity

5-digit occupation

3-digit occupation

2-digit occupation1-digit occupation

2006 2014

.30

.25

.20

.15

.10

.05

0

See other Main sensitivity results

34 / 36


Appendix


Appendix: Occupational mismatch declinesconsidering spillovers (at low specificity)

raw

discrete weight

continuous weight

0

0.05

0.10

0.15

2006 2014

See other Main sensitivity results

35 / 36


Appendix


Appendix: But at high specificity, discrete weights caninflate mismatch

0.20

0.30

raw

discrete weight

continuous weight

2006 2014

See other Main sensitivity results36 / 36

Documents

Labor Market Matching Efficiency and Mismatch in Switzerland · PDF fileLabor Market Matching Efﬁciency and Mismatch in Switzerland Labor Market Matching Efﬁciency and Mismatch