The Geography of Female Labor Force Participation and the ...pages.stern.nyu.edu/~lveldkam/pdfs/geography_slides.pdf · Geography of Participation Calibration mean log ability „a-0.88

Geography of Participation

The Geography of Female Labor ForceParticipation and the Diffusion of Information

Alessandra Fogli, Stefania Marcassa and Laura Veldkamp

Minneapolis Fed and NYU Stern

June 2007

1 Fogli and Veldkamp


Outline

1. How did female labor force participation evolve across the U.S.?

• Labor force participation in 3092 U.S. counties

• Two measures of spatial dependence

2. Why was there slow geographic diffusion?

• Women learn about the effects of employment on childrenby observing nearby working mothers.

• Information diffuses out from urban centers.

• Less uncertainty makes women more willing to work.

3. How much of the change can information diffusion explain?

• Calibrate using regional conditions in 1940.

• Compare spatial dependence in the model and the data.


.

1

Source: Inter-university Consortium for Political and SocialResearch “Historical, Demographic, Economic, and Social Data:

The United States, 1790-2000” (3092 counties).

3

2

4


Two Tests for Spatial Dependence

• Data: The highway distance between county centers (CTA)and female labor force participation rates by county.

• Control variables: Sectoral composition, occupationdistribution, race, marriage, fertility, urban, income, schooling.

• Test significance of potential labor force index (Tolnay ’95)

3091∑

i=1

LFPj

distanceij∀j = 1, ..., 3091, j 6= i

• Moran’s I tests for spatial clustering

I = N(d)∑

i

∑d zizi+d∑z2i+d



Results: Potential Labor Force Index

control variables Potential LFP coefficient (β3)

none 0.047 (0.012)

demographics 0.016 (0.008)

demographics & occupations 0.017 (0.007)

LFPit = β1 + β2controlsit + β3Potential LFP + εit

• A one std. dev. increase in index (std = 22) implies a 0.35-1point increase in the LFP rate.



Results: Moran’s I

• Spatial correlation is highly significant, declines with distance,but rises over time.

20 30 40 50 60 70 80 90 100

0.4

0.5

0.6

0.7

0.8

0.9

1

Distance in miles

Sp

atia

l co

rre

latio

n (

Mo

ran

I)

1940195019601970198019902000



Why Use a Learning Explanation?

Results raise 2 questions: What are that local externalities? Why isthere so much diversity in diffusion rates?

• Changes in economic circumstances or technologies (the pill,the dishwasher, ect.) don’t answer either question.

• Preference externalities, thick market externalities explain localcorrelation, but not diversity in diffusion rates.

• Local information diffusion generates both effects (externality+ friction).

• Learning reconciles many other facts: time-series, labor supplyelasticity, cross-sectional differences due to ethnicity, wealth,ability, marital status and motherhood.



Model

• Discrete infinite time. OLG economy. Large finite number ofagents whose location is indexed by i. Period 1: Agent isnurtured. Period 2: Agent works, has child and consumes.

• Preferences: over consumption and kids’ wage

U =c1−γit

1− γ+ β

w1−γi,t+1

1− γγ > 1

• Budget constrains consumption cit ∈ R+, labor nit ∈ {0, 1}.

cit = nitwit + ωit

• Wage depends on nature ai,t ∼ N(µa, σ2a) and nurture ni,t−1:

wi,t = exp(ai,t − ni,t−1θ).



Information and Beliefs

• Learn about θ.

• Priors inherited from parents: θi,0 ∼ N(µ0, σ20).

• Observe J signals: (wit, ni,t−1) and (wjt, nj,t−1) for jεJi.

• Signal have local information: j’s are drawn uniformly from theset: {j : |i− j| ≤ d}.

• Signal variance depends on local (t− 1) participation:σ2

i,t = σ2a/(

∑jεJi nj,t−1).

Update with Bayes’ rule: σ−2i,t+1 = σ−2

i,t + σ−2i,t ,

µi,t+1 =

(σ−2

i,t

σ−2i,t+1

)µi,t+

(1− σ−2

i,t

σ−2i,t+1

)(∑jεJi(log wj,t+1 − µa)nj,t∑

jεJi nj,t−1

).



Results

Participate if EUO < EUW :

EUOit =(ωit)1−γ

1− γ+

β

1− γexp

(µa(1− γ) +

12σ2

a(1− γ)2)

.

EUWit =(wit + ωit)1−γ

1− γ+

β

1− γexp

((µa − µi,t)(1− γ) +

12(σ2

a + σ2i,t)(1− γ)2

).

The probability that a woman will participate rises if...

1. The expected value of nurture µit falls.

2. Uncertainty about the value of nurture σit falls.



Calibration

mean log ability µa -0.88 women’s earnings distribution

std log ability σa 0.57 women’s earnings distribution

mean log urban ability µaC -0.32 urban wage premium

mean log endowment µω -0.28 average endowment = 1

std log endowment σω 0.75 men’s earnings distribution

outcomes observed J 3 Prob(ni,t = ni,t−1)1970− 2000

radius of local interaction d 0.04 Moran’s I in 1940 (40 miles)

prior mean θ µ0 0.04 unbiased beliefs

prior std θ σ0 1.38 1940 LFP

true value of nurture θ 0.04 children’s test scores (NLSY)

intertemporal substitution γ 2 commonly used

Initial signal set from 1930 participation rates.



Simulation Results

1940 1950 1960 1970

1980 1990 2000

0

0.5

1



Simulated Aggregate Participation Rate

1930 1940 1950 1960 1970 1980 1990 2000 20100

0.1

0.2

0.3

0.4

0.5

0.6

0.7



Conclusions

• Labor force participation spreads geographically. Looks like thespread of information through a network.

• Nearby counties’ participation rates matter, even aftercontrolling for economic and demographic factors.

• A model of information transmission where signals from nearbylocations have higher probability can explain these facts.

• Challenge for information externality theory: Why isinformation diffusion so slow? Might coordination motives alsoplay an important role?



Labor Force Participation

• Much of the increase comes from women with children.

• Mothers of children under 5: 6% participated in 1940, 60%today.

1940 1950 1960 1970 1980 1990 20000

20

40

60

80

100

Years

Pe

rce

nta

ge

Married with ChildrenNon−married and Married w/o ChildrenNon−married with ChildrenTotal


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The Geography of Female Labor Force Participation and the ...pages.stern.nyu.edu/~lveldkam/pdfs/geography_slides.pdf · Geography of Participation Calibration mean log ability „a-0.88