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The Difference a State Makes: Women’s Allocation of Unpaid Work in the 50 States

The Difference a State Makes: Women’s Allocation of Unpaid Work in the 50 States Jennifer Hook , Department of Sociology, University of Washington. Results. Research Context. Measures.

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The Difference a State Makes: Women’s Allocation of Unpaid Work in the 50 States

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  1. The Difference a State Makes: Women’s Allocation of Unpaid Work in the 50 States Jennifer Hook, Department of Sociology, University of Washington Results Research Context Measures • There is considerable variation in the amount of time women spend on unpaid work across the United States. Estimates, based on the 2003 American Time Use Survey, range from 22 hours a week in Idaho to over 35 hours in Nebraska. Some of this variation is explained by differences in demographic composition across the states, but some of this variation remains unexplained. • I model unpaid work time in minutes per day. The measure encompasses most forms of unpaid domestic work, including child care. • This figure shows predicted values of women’s unpaid work time for varying levels of child care costs - set at the minimum, mean, and maximum observed values, separately for mothers of infants, pre-schoolers, and school-aged children. All other variables are set at the mean. Table 1. Individual - level variables Table 2. State - level variables Figure 2. The effect of child care costs. Figure 1. Women’s mean unpaid work time by state • In this portion of the project, I examine the influence of state-level conditions on women’s unpaid work time. Theoretical and empirical work in cross-national contexts suggests that both levels of gender equality and specific policy configurations influence household labor. • The positive effect of having an infant, pre-school, or school-aged child on women’s unpaid work time is larger in states where the cost of child care is a larger percentage of the median household income. • The negative effect of women’s individual earnings is less negative where women have more political equality. • The positive effect of women’s work hours is less positive where women have more political equality. Results Hypotheses Table 3. HLM predicting women’s unpaid work time • I hypothesize that women will do less unpaid work where child care is less expensive and gender equality is greater (Hook 2005). • I expect the costs of child care to be relevant for mothers, particularly mothers of infants and pre-school aged children. • I further expect levels of gender equality to influence the effect of women’s earnings and work hours. Where gender equality is greater the negative effect of women’s earnings and of women’s work hours on unpaid work time should be greater (Fuwa 2004). Conclusions • Both state- level gender equality and specific policy conditions influence women’s unpaid work time across the United States. The effects, however, are not direct; they work through the effects of individual-level characteristics including parenthood, earnings and work hours. • In contrast to cross-national findings, women’s macro-level earning equality appears unrelated to their unpaid work time. • Women’s political equality is related, but not in the hypothesized way for women’s individual earnings. It appears that in politically unequal states, the negative effect of women’s earnings on their unpaid work time is actually greater, suggesting that where equality is low women are better able to use their increased earnings to reduce their unpaid workload. • Future work on this project will explore the intersection of paid and unpaid work in the lives of American women. Data and Sample • I utilize the American Time Use Survey (2003). • I restrict the sample to working age women (ages 22 to 59) and to states with more than 30 respondents in this category. I lose Alaska, Delaware, the District of Columbia, Hawaii, Montana, North Dakota, South Dakota, Vermont, and Wyoming. The state-level N ranges from 38 respondents in Rhode Island to 762 in California for a total N of 7,741 at the individual-level and 42 at the state-level. Methods • I estimate hierarchical linear models. Note: All education variables, Other race, and Hispanic are non-significant and omitted from the table.

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