the geography of female labor force participation and the...

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


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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