Intersectoral Labor Mobility and the Growth of the Service ...

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Intersectoral Labor Mobility and the Growth of the Service Sector Donghoon Lee and Kenneth Wolpin 2006

Transcript of Intersectoral Labor Mobility and the Growth of the Service ...

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Intersectoral Labor Mobility and the Growth ofthe Service Sector

Donghoon Lee and Kenneth Wolpin

2006

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I Estimate an equilibrium model of dynamic occupationalchoice.

I Include macro shocks of productivity.

I Try to explain a large shift in occupation from manufacturingsector to service sector without a sizeable difference in wagebetween the two sectors.

I Explains the above shift even though the mobility cost is large.

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Technology

Production Function:

Y jt = ζ jtF

j(S jWt , S jP

t ,SjBt ,K j

t )

= ζ jt

αj1t(S

jPt )σ

j+ αj

2t(SjBt )σ

j

+(1− αj1t − α

j2t)[λjt(S

jWt )ν

j+ (1− λjt)(K j

t )νj](σj/ν j )

1/σj

j = G ,R

G: goods (manufacturing), S: service.Skill categories for skill units S :W : white collarP: pink collarB: blue collar

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K : homogenous capitalζ: productivity shock

Elasticity of Substitution

1/(1− ν): between capital and white collar skill.1/(1− σj) between capital-white-collar input and other skills.

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Productivity Dynamics

Sector specific real productivity Z j = pjζ j

AR(1) type process

logz jt+1 − logz jt = φj0 +∑

k=G ,R

φjk(logzkt − logzkt−1) + εjt+1

εjt : jointly normal.

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Choice Set

I work: GB (1) , GW (2), GP (3), RB (4), RW (5), RP (6)Sector: goods sector (G), service sector (R)Occupation: blue collar (B), white collar (W), pink collar (P)

I school (7)

I leisure (8)

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Preference

Uha =

6∑work:k=1

γkdka + γ7ahd

scha

+(γ80ah + γ81n05,a)d leisa + γ9d

leisa d leis

a−1

+γ10dscha (1− Dsch

a−1)I (Ea < 12)

+γ11dscha (1− Dsch

a−1)I (Ea ≥ 12) + u(cGa , cRa )

school and home utility coefficients: vary by unobserved type h.home utility depends on number of children n05 under age sixγ10 : cost of reentering highschool.γ11 : cost of reentering college.

γkha = γkh + εka, k = 7, 80

εka: joint normal.

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Budget Constraint

∑j=G ,R

pjtcja =

6∑k=1

wkatd

ka − [β1I (Ea ≥ 12) + β2I (Ea ≥ 16)] d sch

a

−8∑

k=1

6∑j=1

δjkdjad

ka−1

Income: labor income minus schooling cost minus switching cost.(δjk = 0 if j = k)

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Wage Equation

logw jhat = logr jt + logs jha

= logr jt + ωj0h + ωj

1hEa +

(6∑

k=1

ωjk2 X k

a

)ωj3

−ωj4I (a > 40)(a− 40) + ηja

Transition processYears of education: Ea = Ea−1 + d schl

a−1

Work experience: X ja = X j

a−1 + d ja−1

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Market Clearing

Aggregate Supply and Demand

Aggregate skill supply: Individual optimal choice:

Va(Ωat) = maxd jat

A∑τ=a

E[ρτ−aUτ |Ωat

]subject to the budget constraint.S jt , K l

t satisfyingAggregate skill demand:

∂pGt YGt (ξjt ,S

1t , S

2t ,S

3t ,K

Gt )

∂S jt

= r jt , j = 1, 2, 3

∂pRt YRt (ξjt ,S

4t ,S

5t , S

6t ,K

Rt )

∂S jt

= r jt j = 4, 5, 6

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Aggregate capital demand:

∂pGt YGt (ξjt , S

1t ,S

2t , S

3t ,K

Gt )

∂KGt

=∂pRt Y

Rt (ξjt , S

4t ,S

5t , S

6t ,K

Rt )

∂KRt

= rKt

Equilibrium

skill demand equals skill supply.

S jt =

65∑a=16

Nat∑n=1

s jnatdjnat(r

1t , ..., r

6t )

j = 1, ..., 6

capital demand equals capital supply.

KGt + KR

t = Kt

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Assume that the equilibrium skill rental rate has the following lawof motion:

logr jt+1 − logr jt = ηj0 +6∑

k=1

ηjk

[logr jt − logr jt−1

]+ ηj7

[logzGt+1 − logzGt

]+ ηj8

[logzRt+1 − logzRt

]Solve the equilibrium by estimating the parameter of the aboveequation.

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A Simple Example

Utility function of a two period lived (a = 1, a = 2) consumerchoosing occupation j = G ,R and buying goods or services:

Uat(CG ,CR) = δlnCG + (1− δ)lnCR + εj

subject to the budget constraint

pGCG + pRCR = Wj

Then, we know the optimal consumption to be

pGCG

W= δ,

pRCR

W= 1− δ

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and the per period indirect utility to be

Vat(p1t , p2t) = δlnδ + (1− δ)ln(1− δ) + lnWjt

−δlnpG − (1− δ)lnpR + εjt

Hence, we can safely assume that individuals choose occupations tomaximize the log wage plus the occupational specific utility shock.

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That is, for the occupational choice, we can say the individualchooses occupations to maximize in period 2, i.e. t = 2

V2(I , rG ,t+1, rR,t+1, vt+1, εt+1)

= Max lnWG2,t+1 + εG2,t+1, lnWR2,t+1 + εR2,t+1

where

WG2,t+1 = rG ,t+1 [aG I (St = G ) + aR I (St = R)] + vG ,t+1

WR2,t+1 = rR,t+1 [aG I (St = G ) + aR I (St = R)] + vR,t+1

V = Max lnWG1t + εG1 + βEV2(I , rG ,t+1, rR,t+1, vt+1, εt+1),

lnWR1t + εR1 + βEV2(I , rG ,t+1, rR,t+1, vt+1, εt+1)

whereWG1t = vGt ,WR1t = vRt

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Output:Assume population is 1 in each period.

YGt = zGtLαGGt , YRt = zRtL

αRRt

LGt = P(S1t = G |rt , rt+1) + P(S2t = G |rt−1, rt)

LRt = P(S1t = R|rt , rt+1) + P(S2t = R|rt−1, rt)

equilibrium price of labor:

rGt = αG zGtLαG−1Gt , rRt = αRzRtL

αR−1Rt

Assume that equilibrium price of labor follow the below linearprocess

rt+1 = Art + B1zt + B2zt+1

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Computing the Simple Model for T periods

1 Guess r1G , r1R , Draw z1, z2.

2 Individuals expect r2 to be

r2 = Ar1 + B1z1 + B2z2

and the occupational choice probability is

P(S1t = G |rt , rt+1),P(S2t = G |rt−1, rt)

P(S1t = R|rt , rt+1),P(S2t = R|rt−1, rt)

where t = 1

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3 Update the rental rate r∗1G , r∗1R satisfying

r∗1G = αG zG1LαG−1G1 , r∗1R = αRzR1L

αR−1R1

LGt = P(S1t = G |rt , rt+1)+P(S2t = G |rt−1, rt), t = 1

LRt = P(S1t = R|rt , rt+1)+P(S2t = R|rt−1, rt), t = 1

4 Set r1 = r∗1 and repeat 2,3 until convergence, i.e. r1and r∗1 are close.

5 Repeat 2,3,4 for t = 1, ...,T . Get the labor inputprice sequence r1, r2, ..., rT given s1, ..., sT

6 Run the regression to reset the coefficient A, B1, B2.That is,

rt+1 = Art + B1zt + B2zt+1 + vt

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Computing the equilibrium

0) Set the initial skill distribution and age distribution ofindividuals at year 1860.1) Set the parameters of the productivity VAR equation and theskill rental rate law of motion.

logz jt+1 − logz jt = φj0 +∑

k=G ,R

φjk(logzkt − logzkt−1) + εjt+1

logrt+1 − logrt = f (logrt − logrt−1, η)

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2) Solve for the individual dynamic choice problem.Calculate the occupational choice probability function.

Pr(dat |rt , zt ,wt ,Xt , da−1t)

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3) Get the guess of the period t = 1 rental price rt , productivity ztCalculate the skill supply for each period.Derive the idiosyncratic wage shock to derive the wage

w jhat = w(r jt , s

jha,Ea,Xa) + ηja

Simulate the choices in period t, using

Pr(dat |rt , zt ,wat ,Eat ,Xat , da−1t , h)

then aggregate them to derive the aggregate occupational laborsupply.

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4) Derive the new sets of equilibrium skill rental prices r j∗t andproductivity (ξj∗t and capital using the market clearing conditionsand production function, where output is known from the data.

rG∗t =

∂zG∗t Y G

t (S1t , S

2t ,S

3t ,K

Gt )

∂S jt

rR∗t =

∂zR∗t Y R

t (S4t , S

5t ,S

6t ,K

Rt )

∂S jt

∂zG∗t Y G

t (S1t ,S

2t ,S

3t ,K

Gt )

∂KGt

=∂zR∗

t Y Rt (S4

t , S5t ,S

6t ,K

Rt )

∂KRt

= rKt

rental price of capital rKt comes from the macro data.

pjtYjt = z jtF

j(S jWt ,S jP

t ,SjBt ,K j

t )

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5) Repeat 3), 4) until rt , zt converge, i.e. rt = r∗t , zt = z∗t . 6)Repeat 3) to 5) using t = 2. Repeat until t = T . 7) Given thesequence of rt , zt , t = 1, ...T , update the parameters of the rentalprice dynamics and productivity dynamics.

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Estimation: Simulated Method of Moments

I Use both CPS (Current Population Survey), 1968-2001 andthe NLSY (National Longitudinal Survey of Youth) 79.1979-1993 for schooling, sector-occupational choice andwages.

I Use Bureau of Economic Analysis (BEA) data for output andsectoral capital stock, rental rate of capital.

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Moments

Choose parameters to match the following moments from the dataand model simulation.

Moments from the CPS dataCPS: Repeated cross section data.

I Career Decisions: Proportions of occupational choices by year,age, sex, schooling level, whether preschool child is present.

I The mean log hourly real wage by schooling, occupationalcategory, year, sex, highest grade completed

I The mean 1 year difference in the log hourly real wage byoccupation, sex , age.

I Variance of hourly wages by education, sector-occupation,experience and sex.

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I Distribution of highest grade completed by year, age, sex.

I One period transition of occupation, school and home by year,age and sex.

Moments from the NLSY dataNLSY79: 1979-93, Panel data.

I Proportions of occupational choices by year, age, sex, initialschooling level, past occupational experience.

I The mean log hourly real wage by experience, occupation andsex.

I distribution of experience of all choices for ages 29-31, andyears 1990-93

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Service-manufacturing relative growth rate68-00 68-80 81-00

Output 2.01 1.26 2.51

Capital 0.65 -0.67 1.51

All

Employment 2.23 2.45 2.09

Hourly wage 0.23 -0.17 0.50

White collar

Employment 1.69 1.31 1.94

Hourly wage 0.19 0.26 0.14

Pink collar

Employment 2.20 2.07 2.28

Hourly wage -0.10 0.09 -0.22

Blue collar

Employment 1.95 2.13 1.83

Hourly wage 0.08 -0.07 0.17

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I High relative growth of the service sector, in terms of outputand employment. Especially rapid relative growth after 1981.

I It is not accompanied by a substantial increase in hourly wage.

I Occupational Structure:manufacturing sector is more blue collar intensive.White collar worker share has increased in both sectors.Ratio of females among workers are higher in the servicesector. In both sectors female ratio is increasing over time.