Demand-Driven Labor Market Polarization Diego Comin (Dartmouth) - - PowerPoint PPT Presentation

Demand-Driven Labor Market Polarization

Diego Comin (Dartmouth) joint work with Ana Danieli (Northwestern) Marti Mestieri (Northwestern) Dartmouth February 25, 2019

1 / 39

US Labor Market Outcomes Have Polarized since 1980

• Labor market outcomes in the US have polarized since the

1980s.

Wage Bill Wage Employment H M L H M L H M L 2016-1980 8.8 2.9 6.1 2.98 2.33 2.6 1.48 .18 .98 Relative to M 5.9 3.2 .65 .25 1.3 .8

• What drives the increase in inequality and polarization?

◮ skilled biased technical change ◮ trade ◮ de-unionization ◮ computerization and digitization of the economic activity ◮ changes in the school curriculae 2 / 39

Demand-Driven Polarization

1 Novel mechanism based on nonhomotheticity of demand:

◮ Income grows → demand shifts to high-income-elastic sectors

→ (relative intensity of high- and low-skill occupations in high-income elastic sectors)→ relative demand of high- and low- skilled workers increases → polarization.

3 / 39

Demand-Driven Polarization

1 Novel mechanism based on nonhomotheticity of demand:

◮ Income grows → demand shifts to high-income-elastic sectors

→ (relative intensity of high- and low-skill occupations in high-income elastic sectors)→ relative demand of high- and low- skilled workers increases → polarization.

2 Establish new empirical findings:

◮ High-income elastic sectors are intensive in high- and low-skill

• ccupations relative to middle-skill

◮ Initial Wage bill of high- and low-skill occupations

concentrated in high-income elastic sectors

◮ This pattern persists 3 / 39

Demand-Driven Polarization

1 Novel mechanism based on nonhomotheticity of demand:

◮ Income grows → demand shifts to high-income-elastic sectors

→ (relative intensity of high- and low-skill occupations in high-income elastic sectors)→ relative demand of high- and low- skilled workers increases → polarization.

2 Establish new empirical findings:

◮ High-income elastic sectors are intensive in high- and low-skill

• ccupations relative to middle-skill

◮ Initial Wage bill of high- and low-skill occupations

concentrated in high-income elastic sectors

◮ This pattern persists

3 Quantify the effect of the mechanism using GE model:

◮ Demand-driven mechanism accounts for significant shares of

wage bill change from 1980-2016

• 100% of increase for low-
• 50% of increase for high-
• 60% of decline for medium-

3 / 39

Correlation of Sectoral Growth with Income Elasticity

4 / 39

High-Skill Factor Shares and Income Elasticity

5 / 39

Low-Skill Factor Shares and Income Elasticity

6 / 39

Middle-Skill Factor Shares and Inc. Elasticity

7 / 39

High-Skill 1980 Wage Bill Shares and Income Elasticity

8 / 39

Low-Skill 1980 Wage Bill Shares and Income Elasticity

9 / 39

Middle-Skill 1980 Wage Bill Shares and Income Elasticity

10 / 39

Related Literature

• Traditional mechanisms to explain polarization:

◮ Routinization hypothesis: Autor, Levy and Murnane (2003),. . . ◮ Offshoring: Blinder (2007), Grossman and RH (2008),. . .

• Employment Shifts Between Sectors:

◮ Acemoglu and Autor (2011), Goos et al. (2014).

• Structural change and wage structure:

◮ Barany and Siegel (18), Lee and Shin (18), Buera et al. (15). ◮ Nonhomothetic CES: Comin, Lashkari and Mestieri (2015).

• Other related mechanisms:

◮ Trade, skill premium, structural change: Cravino Sotelo (18). ◮ Sectoral trade composition: Basco and Mestieri (2013). ◮ Consumption Spillovers: Manning (04), Mazzolari and Ragusa

(13), Clemens et al. (16).

◮ College-educated-specific demand elasticities: Leonardi (2015). 11 / 39

Outline

1 (If asked) Occupation classification and income elasticity

estimation

2 The multi-sector model: importance of compositional change

for job polarization

3 Demand system: what drives compositional change 4 Full-blown model with job assignment (to derive predictions

for quantity and price polarization)

5 Extensions:

◮ Trade. ◮ Looking back and ahead, from 1950 to 2036.

6 Conclusion

12 / 39

We Estimate Income Elasticities using HH Survey Data

• Use household expenditure data: CEX Survey, 2000-2002.
• Study urban HH with age of head between 25 and 64.

◮ Keep if responses in 4 rounds, not incomplete, 5th-95th

income, positive total and food expenditure, . . .

13 / 39

We Estimate Income Elasticities using HH Survey Data

• Use household expenditure data: CEX Survey, 2000-2002.
• Study urban HH with age of head between 25 and 64.

◮ Keep if responses in 4 rounds, not incomplete, 5th-95th

income, positive total and food expenditure, . . .

• Convert final good expenditures reported in the CEX into

value added using the BEA’s 2000 input-output tables.

13 / 39

We Estimate Income Elasticities using HH Survey Data

• Use household expenditure data: CEX Survey, 2000-2002.
• Study urban HH with age of head between 25 and 64.

◮ Keep if responses in 4 rounds, not incomplete, 5th-95th

income, positive total and food expenditure, . . .

• Convert final good expenditures reported in the CEX into

value added using the BEA’s 2000 input-output tables.

• Obtain total expenditure Eht and expenditure shares xhst of

HH h in sector s during quarter t.

• Use as HH controls Zh dummies for:

◮ Age (25-37, 38-50, 51-64), number of earners (≤ 2, 2+),

household size (≤ 2, 3-4, 5+), region of residence.

• Merge with BLS urban sectoral price series.

13 / 39

We Estimate a Nonhomothetic CES Demand System

• Each sector s has a demand income elasticity parameter, ǫs.

◮ Normalized to 1 for one sector ¯

s, ǫ¯

s = 1.

◮ Expenditure elasticity proportional to ǫs. 14 / 39

We Estimate a Nonhomothetic CES Demand System

• Each sector s has a demand income elasticity parameter, ǫs.

◮ Normalized to 1 for one sector ¯

s, ǫ¯

s = 1.

◮ Expenditure elasticity proportional to ǫs.

• There is a common price elasticity σ across sectors.
• Allow for heterogeneity in tastes: ζsht ≡ αs + ΓsXh + δr + δt.

14 / 39

We Estimate a Nonhomothetic CES Demand System

• Each sector s has a demand income elasticity parameter, ǫs.

◮ Normalized to 1 for one sector ¯

s, ǫ¯

s = 1.

◮ Expenditure elasticity proportional to ǫs.

• There is a common price elasticity σ across sectors.
• Allow for heterogeneity in tastes: ζsht ≡ αs + ΓsXh + δr + δt.
• Estimate system of equations for all sectors s(= ¯

s). ln xhst = ζhst + (1 − σ) ln phst ph¯

st

• +

(1 − σ)(ǫs − 1) ln Eht ph¯

st

• + ǫs ln xh¯

st + νhst.

◮ If ǫs = 1 → Homothetic CES. ◮ System of equations, estimate using GMM. 14 / 39

We Instrument Prices and Expenditures

• Want to isolate relative price variation coming from shifts in

the supply curve.

• Use average relative price in other regions controlling for time

and region dummies.

15 / 39

We Instrument Prices and Expenditures

• Want to isolate relative price variation coming from shifts in

the supply curve.

• Use average relative price in other regions controlling for time

and region dummies.

• Household expenditures have measurement error.
• Use HH annual income and HH income quintile as

instruments (∼ NPV).

15 / 39

Estimation Results of Nonhomothetic CES

Price Elasticity σ 0.63 (0.01) Income Elasticity Parameters {ǫs} Education and Health Care (6) 3.50 (0.18) Arts, Entertainment, Recreation and Food Services (7) 2.04 (0.08) Government (G) 1.00 Finance, Professional, Information,

• ther services (excl. gov’t) (FIRE, PROF, 51, 81)

0.98 (0.04) Manufacturing (31G) 0.57 (0.04) Retail, Wholesale Trade and Transportation (42, 44RT, 48T) 0.37 (0.04) Construction (23) 0.14 (0.06) Agriculture, Mining and Utilities (11,21,22) 0.10 (0.04)

• Std. Err. Clustered at HH level in parenthesis. Number of HH is 20,843.

16 / 39

We Classify Occupations According to Skill Intensity

• Use Acemoglu and Autor (2011) classification.
• 3 levels: H (high), M (middle) and L (low).

◮ Use average wage from 1980 CPS (5th to 95th). ◮ Ranking stable over time. ◮ Ranking occupations by years of schooling very similar.

• AA group finer occupations by their skill level:

◮ H: managerial, professional and technical occupations ◮ M: sales, clerical and administrative support occupations;

production, craft, repair and operative occupations; and

◮ L: service occupations (food/cleaning, personal care,

protective).

• Use employment shares from decennial census.

17 / 39

Illustration of mechanism

• Start with a one sector model

Yt = At

• j∈{H,M,L}

X αjt

jt ,

wjtXjt = αjtYt.

18 / 39

wjtXjt wj′tXj′t = αjt αj′t . (1)

• Variation in relative wage bill must come from variation in

factor intensity (αjt/αj′t).

• Importance of trade, skilled biased technical change and other

theories that change the effective factor intensity.

19 / 39

Multi-sector setting

Yst = Ast

• j∈{H,M,L}

X αjst

jst ,

(2) with

• j∈{H,M,L}

αjst = 1 wjtXjst = αjstPstYst ≡ αjstVAst. (3) wjtXjt =

• s∈S

αjstVAst. (4) αjstVAst = (αjs0 + △αjst)(VAs0 + △VAst) (5)

20 / 39

△ (wjtXjt) wj0Xj0 =

Term 1

• s∈S

γjs0 △VAst VAs0 +

Term 2

• s∈S

γjs0 △αjst αjs0 +

Term 3

• s∈S

γjs0 △VAst VAs0 △αjst αjs0

• where γjs0 ≡

αjs0VAs0

• s∈S

αjs0VAs0

High Mid Low H−M L−M Total Change 10.19 3.18 6.61 7.01 3.43 Term 1 7.05 4.66 7.09 2.39 2.43 Term 2 0.45

• 0.22

0.00 0.67 0.22 Term 3 2.69

• 1.26
• 0.47

3.95 0.79 Contribution ∆VAst 62% 82%

• Term 2 generates little variation alone! Multi-sector is key.

21 / 39

Preferences Drive Sectoral Reallocation of Production

• Representative household earns all wages.
• Nonhomothetic CES Preferences, implicitly defined:

I

• s=1

(ζsC εs

t )

1 σ c σ−1 σ

st

= 1,

◮ ζs > 0 constant taste parameter for i = 1, . . . , I. ◮ σ is the elasticity of substitution. ◮ εi governs nonhomotheticity of i. ◮ If εi = 1 − σ, we recover homothetic CES. ◮ Parameter restriction (Hanoch, 75): ζi > 0, σ > 0,

εi > 0 if σ ∈ (0, 1), εi < 0 if σ > 1.

◮ Preferences defined up to scaling factor in

1 nonhomotheticity: ˜

εi ≡ ˜ ξεi,

2 taste parameter: ˆ

ζi ≡ ˆ Ωζi.

22 / 39

Preferences Drive Sectoral Reallocation of Production

• Representative household earns all wages.
• Nonhomothetic CES Preferences, implicitly defined:

I

• s=1

(ζsC εs

t )

1 σ c σ−1 σ

st

= 1,

◮ ζs > 0 constant taste parameter for i = 1, . . . , I. ◮ σ is the elasticity of substitution. ◮ εi governs nonhomotheticity of i. ◮ If εi = 1 − σ, we recover homothetic CES.

S

• i=1
• ζiC 1−σ

t

1

σ c σ−1 σ

it

= 1

◮ Parameter restriction (Hanoch, 75): ζi > 0, σ > 0,

εi > 0 if σ ∈ (0, 1), εi < 0 if σ > 1.

◮ Preferences defined up to scaling factor in

1 nonhomotheticity: ˜

εi ≡ ˜ ξεi,

2 taste parameter: ˆ

ζi ≡ ˆ Ωζi.

22 / 39

Preferences Drive Sectoral Reallocation of Production

• Representative household earns all wages.
• Nonhomothetic CES Preferences, implicitly defined:

I

• s=1

(ζsC εs

t )

1 σ c σ−1 σ

st

= 1,

◮ ζs > 0 constant taste parameter for i = 1, . . . , I. ◮ σ is the elasticity of substitution. ◮ εi governs nonhomotheticity of i. ◮ If εi = 1 − σ, we recover homothetic CES. ◮ Parameter restriction (Hanoch, 75): ζi > 0, σ > 0,

εi > 0 if σ ∈ (0, 1), εi < 0 if σ > 1.

◮ Preferences defined up to scaling factor in

1 nonhomotheticity: ˜

εi ≡ ˜ ξεi,

2 taste parameter: ˆ

ζi ≡ ˆ Ωζi.

22 / 39

Sectoral Demand is Log-Linear

• HH facing prices {pst} with budget constraint

s pstcst ≤ Et.

• Demand (Hicksian)

cst = ζs pst Pt −σ C εs

t .

• In terms of observables (marshallian)

cst = ζs(pst/Pt)−σ(Et/Pt)εs and Pt =

• s∈S
• ζsp1−σ

st

χs xstE 1−σ

t

1−χs

• 1

1−σ

where xst = pstcst/Et and χs ≡ (1 − σ)/εs.

• Expenditure elasticity:

∂ ln cst ∂ ln Et = σ + (1 − σ) ǫs

• s xstǫs

.

23 / 39

Sectoral Demand is Log-Linear

• HH facing prices {pst} with budget constraint

s pstcst ≤ Et.

• Demand (Hicksian)

cst = ζs pst Pt −σ C εs

t .

• In terms of observables (marshallian)

cst = ζs(pst/Pt)−σ(Et/Pt)εs and Pt =

• s∈S
• ζsp1−σ

st

χs xstE 1−σ

t

1−χs

• 1

1−σ

where xst = pstcst/Et and χs ≡ (1 − σ)/εs.

• Expenditure elasticity:

∂ ln cst ∂ ln Et = σ + (1 − σ) ǫs

• s xstǫs

.

23 / 39

Sectoral Demand is Log-Linear

• HH facing prices {pst} with budget constraint

s pstcst ≤ Et.

• Demand (Hicksian)

cst = ζs pst Pt −σ C εs

t .

• In terms of observables (marshallian)

cst = ζs(pst/Pt)−σ(Et/Pt)εs and Pt =

• s∈S
• ζsp1−σ

st

χs xstE 1−σ

t

1−χs

• 1

1−σ

where xst = pstcst/Et and χs ≡ (1 − σ)/εs.

• Expenditure elasticity:

∂ ln cst ∂ ln Et = σ + (1 − σ) ǫs

• s xstǫs

.

23 / 39

We Close the Model Imposing Market Clearing

• The representative household spends all its income

Et =

• s
• j

wjtXjst.

• Goods consumed in each sector need to be produced ,

VAst = ζs(pst/Pt)−σ(Et/Pt)εs. (6)

• Wage Bill for occupation j is

wjtXjt =

• s∈S

αjstVAst =

• s∈S

αjstζsE σ+εs

t

p1−σ

st

P−εs

t

. (7)

24 / 39

We Close the Model Imposing Market Clearing

• The representative household spends all its income

Et =

• s
• j

wjtXjst.

• Goods consumed in each sector need to be produced ,

VAst = ζs(pst/Pt)−σ(Et/Pt)εs. (6)

• Wage Bill for occupation j is

wjtXjt =

• s∈S

αjstVAst =

• s∈S

αjstζsE σ+εs

t

p1−σ

st

P−εs

t

. (7) We use Equations (6) and (7) for quantifying bare-bones model

24 / 39

Quantification: Match 1980, then shock model to 2016

Initial Values

• Strategy: match 1980 (exactly).
• Demand parameters {ǫs, σ} from CEX (as discussed).
• Demand parameters {ζs} to match VA shares 1980 (BEA).
• {αs,1980} inferred from wage bill.

◮ Hours worked from Census, wages from CPS. 25 / 39

Quantification: Match 1980, then shock model to 2016

Initial Values

• Strategy: match 1980 (exactly).
• Demand parameters {ǫs, σ} from CEX (as discussed).
• Demand parameters {ζs} to match VA shares 1980 (BEA).
• {αs,1980} inferred from wage bill.

◮ Hours worked from Census, wages from CPS.

Shock the 1980 Economy with 2016 Values

• Uniform increase in productivity, match increase in real PCE:

◮ Compute same way as in BEAs with Fisher price indeces. ◮ Hold relative sectoral prices to 1980.

• Change prices {pst} to match change in relative prices (BEA).
• Hold {αs,1980} for now.

25 / 39

We Are Allowing only Variation in ∆VAst

Back to the Wage Bill Decomposition △ (wjtXjt) wj0Xj0 =

Term 1: Comp.

• s∈S

γjs0 △VAst VAs0 +

Term 2: Factor Int.

• s∈S

γjs0 △αjst αjs0 +

Term 3: Covariance

• s∈S

γjs0 △VAst VAs0 △αjst αjs0

• where γjs0 ≡

αjs0VAs0

• s∈S

αjs0VAs0 are wage bill shares.

26 / 39

We Are Allowing only Variation in ∆VAst

Back to the Wage Bill Decomposition △ (wjtXjt) wj0Xj0 =

Term 1: Comp.

• s∈S

γjs0 △VAst VAs0 +

Term 2: Factor Int.

• ✟✟✟✟✟✟

✟ ❍❍❍❍❍❍ ❍

• s∈S

γjs0 △αjst αjs0 +

Term 3: Covariance

• ✘✘✘✘✘✘✘✘✘✘✘

✘ ❳❳❳❳❳❳❳❳❳❳❳ ❳

• s∈S

γjs0 △VAst VAs0 △αjst αjs0

• where γjs0 ≡

αjs0VAs0

• s∈S

αjs0VAs0 are wage bill shares.

26 / 39

We Are Allowing only Variation in ∆VAst

Back to the Wage Bill Decomposition △ (wjtXjt) wj0Xj0 =

Term 1: Comp.

• s∈S

γjs0 △VAst VAs0 +

Term 2: Factor Int.

• ✟✟✟✟✟✟

✟ ❍❍❍❍❍❍ ❍

• s∈S

γjs0 △αjst αjs0 +

Term 3: Covariance

• ✘✘✘✘✘✘✘✘✘✘✘

✘ ❳❳❳❳❳❳❳❳❳❳❳ ❳

• s∈S

γjs0 △VAst VAs0 △αjst αjs0

• where γjs0 ≡

αjs0VAs0

• s∈S

αjs0VAs0 are wage bill shares.

Zoom in Term 1: How important is Nonhomotheticity?

• s∈S

γjs0 △VAst VAs0 =

Term1

• s∈S

γjs0 △VAst VAs0

• E

+

Term 2

• s∈S

γjs0 △VAst VAs0

• ps

+

• s∈S

γjs0 △VAst VAs0

• E

△VAst VAs0

• ps
• Term 3

26 / 39

Quantification of the Mechanism

Sectoral Growth Predictions

H M L H−M L−M Total Value Added growth 10.19 3.18 6.61 7.01 3.43 VA only growth (Term 1) 7.05 4.66 7.09 2.39 2.43 Predicted change of. . . Estimated Model 6.20 3.63 7.16 2.57 3.53 Increase in Et 5.64 3.91 6.24 1.73 2.33 Growth in pst 0.08

• 0.06

0.16 0.14 0.21 Interaction 0.48

• 0.22

0.76 0.70 0.99 % Accounted, ↑ in Et 95% 105% 92% 81% 80%

• If we assign half of interaction to Et, account for

◮ 81% of H–M, ◮ 80% of L–M. 27 / 39

Production Technologies

• Generalize production to

Yst = AstK 1−βst

st

 

• j∈{H,M,L}

˜ X αjst

jst

 

βst

, where ˜ Xjst denotes the number of efficiency units of labor are employed in occupation j in sector s in year t.

• Demand is now

˜ wjt ˜ Xjst = βstαjstpstYst, rtKst = (1 − βst) pstYst.

• Total wage bill in sector s is

J

• j=1

˜ wjt ˜ Xjst = βstpstYst

J

• j=1

αjst.

28 / 39

Demand Side

• Continuum of households indexed by h from (0,1).
• Each household inelastically supplies a unit of labor to one of

the three occupations.

• Household income is composed of the labor income plus the

rental income accrued from the capital it owns (Kht).

• We assume that capital is evenly distributed across households
• Every period household expenditure, Eht, equals household

income.

29 / 39

Preferences and Aggregate Demand

• Each household has nonhomothetic CES preferences as before.
• s∈S
• ζsUεs

ht

1

σ c σ−1 σ

hst

= 1.

• Aggregate demand for sectoral output is

Cst =

• ζsE σ+εs

ht

p−σ

st P−εs ht dh.

30 / 39

Occupational Choice

• Each HH draws a vector (ηL, ηM, ηH) of efficiency units in

each occupation

◮ Draws from iid log-normal.

• Price for each unit of skill: ( ˜

wL, ˜ wM, ˜ wH).

• The optimal choice of the agent is to select occupation s.t.

max

j∈{L,M,H}{ηj ˜

wj}.

31 / 39

Equilibrium and Overview of Quantification

• Study competitive equilibrium.
• Demand elasticities {ǫs, σ} estimated from HH expenditure

survey (CEX).

• Use moments in the data for 1980 to set the values of the

model parameters.

◮ Sectoral prices and sectoral value added in 1980 come from the

BEA.

◮ {ζs} is set to match sectoral consumption in 1980.

• {αst, βst} that is set to match the sectoral wage bill in each

sector in year t.

32 / 39

Quantification

Initial Values

• Strategy: match 1980.
• Demand parameters {ǫs, σ} from CEX
• Demand parameters {ζs} to match VA shares 1980.
• Variance of log-normal for M and H to match relative wages

in 1980.

33 / 39

Quantification

Initial Values

• Strategy: match 1980.
• Demand parameters {ǫs, σ} from CEX
• Demand parameters {ζs} to match VA shares 1980.
• Variance of log-normal for M and H to match relative wages

in 1980. Changes to the 1980 Economy

• Explore how different shocks bring us to 2016.
• Uniform increase in labor productivity to match increase in

personal consumption expenditure:

◮ Compute same way as in BEAs PCE with Fisher price indeces.

• {αst, βst} change by period and sector from the data.

33 / 39

Results

Table: Full Quantitative Model

Year

WL WM WH WM

Ls Ms Hs

WLL

• k Wk K

WM M

• k Wk K

WHH

• k Wk K

Exercise Data 1980 0.74 1.24 0.095 0.653 0.252 0.068 0.630 0.302 2016 0.80 1.49 0.129 0.488 0.383 0.088 0.421 0.491 Model 1980 0.74 1.24 0.095 0.653 0.252 0.068 0.630 0.302 2016 0.86 1.44 0.133 0.543 0.324 0.101 0.483 0.416 E 2016 0.77 1.41 0.095 0.582 0.323 0.066 0.524 0.411 α+β 2016 0.87 1.57 0.125 0.499 0.376 0.091 0.416 0.493 E+β + α Fraction of 2.17 1.32 0.88 0.93 0.95 1.15 1.02 1.01

• bserved

change1 Contribution 0.85 0.55 1.13 0.63 0.5 1.26 0.6 0.51

• f E

Contribution 0.15 0.45

• 0.13

0.37 0.5

• 0.26

0.4 0.49

• f α + β

(1) Fraction of the change produced by the full model, with changes in the level of expenditures, factor intensities and in the sectoral labor shares relative to total changed observed in the data. 34 / 39

Extensions

1 Introduce trade.

◮ Most action comes from services, which are non-traded. ◮ Correct total demand for sectoral net exports. Trade wedges

2 Backward exercise: 1950-1980.

Results ◮ Account for the rise of middle-class. ◮ Manufacturing was more of a luxury good in that period.

3 Other OECD countries.

◮ How much differences in levels of income account for different

polarization experiences?

35 / 39

Conclusions

• Sectoral growth between 1980-2016 is highly correlated with

the distribution of employment in high and low skill

• ccupations
• One consequence of this new empirical finding is that changes

in sectoral composition of output induced by increase in expenditures are a major driver of labor market polarization

• Our mechanism explains very significant share of changes in
• ccupational wage bills, relative wages and share of hours

worked from 1980-2016. For high- and low- skill occupations the mechanism accounts for around 50% and 100% of

• bserved increases in data. For medium-skill occupations our

mechanism represents around 60% of the observed decline in the data.

• Mechanism is robust to extensions and relevant for other time

periods and countries.

36 / 39

Trade Wedges

year Manufacturing Agriculture 1980 0.0082

• 0.1002

2016

• 0.1535
• 0.0331

Go back 37 / 39

Backward Exercise: 1950-1980

year

WL WM WH WM

Ls Ms Hs

WLL

• k WkK

WMM

• k WkK

WHH

• k WkK

Data 1980 0.74 1.24 0.095 0.653 0.252 0.068 0.630 0.302 1950 0.70 1.15 0.106 0.731 0.163 0.075 0.736 0.189 Sim. 1980 0.74 1.24 0.095 0.653 0.252 0.068 0.630 0.302 Sim. 1950 0.68 1.17 0.074 0.702 0.224 0.049 0.693 0.258 TFP 1950 0.79 1.18 0.122 0.660 0.218 0.094 0.651 0.254 α+β 1950 0.72 1.06 0.100 0.730 0.171 0.073 0.743 0.184 TFP+β + α

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Subperiods

H−M L−M 1980-2000: Overall 3.26 1.10 Incr Sect Shares 0.79 0.73 Incr alpha 0.38 0.09 Cov 2.09 0.28 2000-2016: Overall 3.75 2.33 Incr Sect Shares 1.60 1.69 Incr alpha 0.29 0.13 Cov 1.86 0.51

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