In this presentation I describe the shape of labour force participation curve of married women in the US. It is hypothesized to be U-shaped, but it appears to be more S-shaped. However, more importantly it provides an effort to test the underlying mechanisms of the U-shape at the US state level.

1. Labour force participation of
married women:
The United States, 1860-2010
Richard Zijdeman (IISH)
Valencia, Spain
Aula 5, NIVEL 0
March 31, 2016
H-7 The causes and consequences of women’s empowerment

2. Introduction
Post WW II research shows major increase in
FLFP
• So, when did it start?
• How did this change occur?
– many hypotheses on change in FLFP
• Level of economic development (GDP)
• Reputation (social status)
FLFP = Female Labour Force participation

3. U-shape female labour force function
• U-shaped relation between country’s level of
development and FLFP:
– Higher at lower and higher levels of development
– Lower at mediocre levels of development

4. Left side of U-shape
• Rise in income, due to expanded markets or
introduction of new technology
– barriers preventing women (social custom,
employer preference)
• Reduction in the relative price of home
produced goods
• Decrease in the demand for women in
agriculture

5. Center of U-shape
• No explicit arguments (for U vs. V-shape)
• U-shape maybe explained by:
– regional dispersion of e.g. technology
– slow change in social behaviour

6. Right side of U-shape
• Improvement of women’s education,
particularly higher education
• Improvement of women’s wages

7. More in-depth on reputation
Formal barriers:
- e.g. marriage bars
Informal barriers:
– Employer preference
– Social norms or stigmas

8. Within-family-competition
Within-family-competition
– Disruptive rivalry between partners (Parsons ’49,
’54, also see Oppenheimer ’77)
– The higher the husband’s status, the bigger the
range of non-rivalrous jobs (lower and mediocre)
Ergo: the higher a husband’s occupational status,
the higher the probability of FLFP

9. Between-family-competition
• Competition between families, NOT within
families
– Reduce risk of economic hardship (two earners)
– Enhance socio-economic position
• But 19th century: few higher occupational
positions for women, so women more likely to
work when married to lower status husband
Ergo: the lower a husband’s occupational status, the higher
the probability of FLFP

10. What this papers adds
• Increased time period at both ends
• Test of theories at individual level…
• Taking regional (state) variation into account
• Census data: comparability of different age
groups and characteristics

11. Data
• IPUMS USA census data 1860-2000
– 1, 5 or 10 per cent samples
– 1970 excluded (for now)
• 2010 + 2013: American Community Survey
• married women whose husband is in the
household at time of the census
• N = 11,773,133
• NHGIS: for total population at state level
• GDP in GK dollars from CLIO-INFRA

12. Key variables
Micro (individual):
• Status husband (Duncan SEI)
• Family size
• # children under age 5
Macro (state by census year):
• Proportion of couples living at a farm
• Population per million
• Proportion in education (5-16)
• Proportion in education (16-20)

13. Methods
• Hierarchical generalized linear model (binomial)
– Nested observations
– Clustering of observations within states and time
• LME4 package in R

14. Descriptive results
• Regional variation in FLFP
• U-shaped curve between GDP and FLFP?

28. Summary of regional descriptives
• From ‘random’ (1860 – 1880)
• To horse shoe (1900 – 1930)
• To coasts (1940 – 1960)
• To Great Lakes (1980-2000)
• To ‘random’ (2010)?

29. U-shaped?

30. Explanatory results
Model with just time and cubic time effect:
• Non-linear effect indeed
• Bottom of U at 1820, not 1920 (Goldin 1994)

31. Explanatory results
Random effects:
Variance: 0.2815 Std.Dev.
Std.Dev: 0.5305
Number of obs: 11773133, groups: stime, 655
Estimate Std. Error z value
(Intercept) -2.293e+00 -2.293e+00 -75.3
age(center) -5.057e-02 -5.057e-02 -778.6
SEI husband (center) 1.964e-03 1.964e-03 66.9
family size -5.301e-02 -5.301e-02 -103.7
# children <5 -8.112e-01 -8.112e-01 -579.8
decades since 1800 2.374e-01 2.374e-01 28.8
(dec since 1800)^2 3.857e-03 3.857e-03 9.1
population (millions) -1.236e-02 -1.236e-02 -2.3
prop. living at farm -1.131e+00 -1.131e+00 -31.0
prop. fem.at.school (6-15) -4.032e+00 -4.032e+00 -140.1
prop.fem.at.school (16-20) 1.849e+00 1.849e+00 68.4
AIC BIC logLik deviance df.resid
11742052 11742224 -5871014 11742028 11773121

32. Conclusions
• On national level no evidence for U-shape
• Mechanisms underlying the U-shape appear
to be correct though:
– Inverse relation between FLFP and agriculture
– Increased FLFP with higher secondary education
• but: ‘white collar work’ or ‘cultural indicator’
– Inconclusive results for within or between family
status hypotheses

33. Caveats
• Different definitions of and instructions on
‘being in the labor force’ over time
– starting age
– e.g. 1910 census data
• So far rather imprecise measures:
– e.g. no sectorial information used
• No information on income -> SSHA 2016