The document explores the historical trends in female labor force participation (FLFP) among married women in the United States from 1860 to 2010, noting a U-shaped relationship between FLFP and economic development. It discusses various factors influencing FLFP, including social customs, education, and the occupational status of husbands. The study utilizes extensive census data and hierarchical modeling to analyze regional variations and suggests that while certain mechanisms behind the U-shape are valid, there is insufficient evidence for a definitive U-shaped trend on a national 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