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Labour force participation of married women, US 1860-2010


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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.

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Labour force participation of married women, US 1860-2010

  1. 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. 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. 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. 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. 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. 6. Right side of U-shape • Improvement of women’s education, particularly higher education • Improvement of women’s wages
  7. 7. More in-depth on reputation Formal barriers: - e.g. marriage bars Informal barriers: – Employer preference – Social norms or stigmas
  8. 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. 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. 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. 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. 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. 13. Methods • Hierarchical generalized linear model (binomial) – Nested observations – Clustering of observations within states and time • LME4 package in R
  14. 14. Descriptive results • Regional variation in FLFP • U-shaped curve between GDP and FLFP?
  15. 15. 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)?
  16. 16. U-shaped?
  17. 17. Explanatory results Model with just time and cubic time effect: • Non-linear effect indeed • Bottom of U at 1820, not 1920 (Goldin 1994)
  18. 18. 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. (6-15) -4.032e+00 -4.032e+00 -140.1 (16-20) 1.849e+00 1.849e+00 68.4 AIC BIC logLik deviance df.resid 11742052 11742224 -5871014 11742028 11773121
  19. 19. 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
  20. 20. 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