1930/31 Time Diary Data from College Educated Women in the United States

IPUMS Time Use, in partnership with Dr. Teresa Harms of the Centre for Time Use Research, is proud to announce the public release of the 1930-31 USDA College Women Time Use study. These data provide researchers a unique look into the lives of married, college-educated women at the beginning of the Great Depression. The respondents were asked to complete a detailed record of their time use for seven consecutive 24-hour periods (see a sample daily diary below; borrowed with permission from Teresa Harms, CTUR). The women described activities in their own words, listing them consecutively as they occurred throughout the day, with a minimum interval of five minutes. They also recorded the time devoted to various homemaking tasks by other household members and paid help as well as demographic and work status data and information about the household. The data also include the verbatim activity reports and the occupations the women reported at the time of data collection. All data are available via the IPUMS American Heritage Time Use Study (AHTUS) extract system.

Sample of a Time Use Diary page

In the 1920s, the United States Department of Agriculture conducted one of the very first American research studies into the daily lives and time use of rural and urban women across the country. In 1944, the USDA published a paper titled “The Time Costs of Homemaking: A Study of 1500 Rural and Urban Households“, which summarizes the findings from the 1500 whole-week diary records kept by homemakers over two periods: 1924-1928 and 1930-1931. The earlier group of 808 included both farm and non-farm households in open country or in towns and villages of fewer than 2500 people. The later records (1930-31) comprised of 692 married alumnae of the Seven Sisters (Barnard, Radcliffe, Vassar, Bryn Mawr, Mount Holyoke, Smith and Wellesley) from the classes of 1886 to 1929. Only 75 complete time diary surveys from this latter group of college-educated women have been located in historical archives in Kansas and Maryland. Dr. Teresa Harms assumes that the remainder of the 692 are missing, the response rate was low, or many of the records were incomplete.

Harms matched over 95 percent of the names and addresses of the records from the 75 college women to US Federal Census microdata from 1920 to 1940. Variations in the spelling of family and given names and household relocations complicated the matching process, so additional sources were employed to resolve problems of identification (including birth, death, and marriage indexes; voting registers; social security numbers; city directories; military draft records; immigration and travel docu­ments; and other material, such as obituaries and newspaper articles). Learn more about Dr. Harms’ work digitizing these data and the research that she has done with them in this video.

2021 IPUMSI New Data Release Highlights

Map depicting where IPUMSI has dataIPUMS International has added 19 new census samples and new labor force surveys.  First-time data release countries include four new countries from four different continents—Finland, Mauritius, Myanmar, and Suriname. Other newly added samples extend pre-existing series. Another first is the addition of labor force surveys from Spain and Italy. See a summary of the full IPUMS collection on the IPUMSI samples page.

In addition to the new data, check out the usage-enhancing highlights that are part of this recent release.

  • Spatially-harmonized migration variables
  • New work variables that maximize the utility of newly-harmonized labor force surveys
  • New disability variables per The Washington Group recommendations
  • Access to harmonization tables and code for registered IPUMS data users
  • Population density variables for all samples with the requisite geography- POPDENSGEO1 and POPDENSGEO2 capture the population density in persons per square kilometer of the first and second administrative units of the household, respectively.
  • Variables AREAMOLLWGEO1 and AREAMOLLWGEO2 provided for additional convenience
  • New lower level single-sample variables for select countries, as well as regionalized variables and shapefiles at the 3rd administrative level for Senegal 2013 and 2002, South Africa 2016, 2011, and 2007, and Uganda 2014, and Myanmar 2014

Stay tuned for the future IPUMS International releases, which will include population density variables for lower-level geography, more 3rd-level geography variables for existing IPUMS International countries, and additional labor force surveys. In the meantime, be sure to share what you’re doing with IPUMS data with us on Twitter @ipumsi!

Locating Dimensions of Women’s Empowerment in Family Planning in Burkina Faso

By Tayler Nelson

Women’s “empowerment,” defined by Naila Kabeer[1] as “the expansion of people’s ability to make strategic life choices in a context where this ability was previously denied to them,” has been shown[2] to be associated with greater birth spacing, lower fertility, and lower rates of unplanned pregnancy. Yet scholars disagree[3] on how to measure women’s empowerment, and meanings of empowerment can shift across geographic and cultural contexts.

IPUMS PMA’s family planning surveys include variables that can help researchers investigate dimensions of women’s empowerment in family planning. All samples include indicators of women’s knowledge about family planning methods. Many survey rounds dig deeper, collecting data that can be used by researchers and policymakers.

The Burkina Faso 2018 Round 6 survey includes a range of variables measuring family planning attitudes, beliefs, and decision-making dynamics that relate to women’s empowerment. I used a weighted polychoric factor analysis[4] to investigate women’s empowerment in family planning in Burkina Faso. Factor analysis can help researchers reduce a large number of observed variables by identifying similar response patterns among observed variables and grouping them into a smaller set of underlying variables, or factors. Through analyzing how variables are grouped and the strength and signs of coefficients within these groups, researchers can glean insight into which sets of observed variables might be best at measuring an unobserved construct such as women’s empowerment.

After reviewing the literature on women’s empowerment in family planning, I selected nineteen PMA variables to capture dimensions of women’s empowerment in family planning in Burkina Faso. These included social context variables (URBAN, WEALTHQ, EDUCATTGEN), whether the woman is a current/recent family planning user (FPCURRECUSER), whether the woman has heard of family planning on television (FPTVHR) or radio (FPRADIOHR), and belief and attitude variables that use a Likert scale to measure how much the woman agrees with a particular statement about family planning (SAFEDISCKID, SAFEDISCFP, CONFLICTFP, DAMRELFP, NEGOTIATEKIDS, BELIEFCARRYPREG, BELIEFDAUPREG, AGREESPACE, AGREELIMIT, AGREECONTR, AGREEFP, AGREEPARTFP). I also combined two variables that measure who is the primary decision-maker in deciding to use (FPDECIDER) or not to use (DECNOFPUSE) family planning into a single ordinal variable (DECIDER) that reports how much input a woman has in the couple’s family planning decisions. I excluded not in universe (NIU) and missing cases as well as females who were not married or partnered; my analytical sample contained 1,686 women. All analyses applied appropriate sampling weights.

I retained three factors[5] and analyzed which characteristics or beliefs relate to each underlying factor by examining variable groupings and factor loadings. Table 1 displays all factor loadings above 0.3 for each variable. Looking at high factor loadings can help researchers identify underlying factors. I have marked moderate and strong factor loadings[6] in the table with an asterisk(*).

Table 1: Factor Loadings (promax rotations)

URBAN Urban/rural status .94 *
WEALTHQ Wealth score quintile .87 *
EDUCATTGEN Highest level of schooling attended .66 *
FPCURRECUSER Current or recent FP user .32
DECIDER Women’s level of say in decisions about whether to use/not use FP
SAFEDISCKID Safe to discuss when to have children w/ partner .66 *
SAFEDISCFP Safe to discuss FP w/ partner  .71 *
CONFLICTFP Conflict in relationship if used FP -.50
DAMRELFP Delaying or limiting children would deteriorate relationship w/ partner -.44
NEGOTIATEKIDS Able to negotiate w/ partner when to stop having children .65 *
BELIEFCARRYPREG Thinks a woman should not get pregnant if child still on her back .60 *
BELIEFDAUPREG Thinks a woman should not get pregnant if daughter is pregnant .48
AGREESPACE Agrees with couple that uses FP to space births .45 .44
AGREELIMIT Agrees with couple that uses FP to limit births .52
AGREECONTR Agrees with man/woman that uses contraception .34
AGREEFP Agrees with couples that use FP .59 .39
AGREEPARTFP Partner agrees with couples that use FP .69 *
FPTVHR Heard about FP on TV .66 * .38
FPRADIOHR Heard about FP on the radio .33

Factor 1 includes the majority of attitude and belief variables. I looked at similarities and differences between variables and their factor loadings[7] to interpret this factor. Variables with high factor loadings all relate to good spousal communication around family planning. For instance, SAFEDISCFP, which reports how much the respondent agrees that it is safe to discuss family planning with her partner, has the highest factor loading. Variables related to spousal conflict, such as CONFLICTFP, have weak but negative factor loadings. Together, these results indicate that Factor 1 might be a latent measure of spousal communication. Alternative interpretations are possible: for example, it might be that this factor simply represents support for family planning, and variables with high factor loadings are the most useful observed indicators of this factor.

For Factor 2, the high factor loadings for URBAN and WEALTHQ suggest that this factor indicates socioeconomic status. The variable for whether the woman has heard about family planning on TV loads moderately highly on this factor; this seems logical assuming TV ownership is a reflection of socioeconomic status.

For Factor 3, the factor loadings are relatively small except for the loading for the belief that women should not get pregnant if she is still carrying a child on her back. In line with a Burkina Faso 2014 report that found popular acceptance of family planning for spacing rather than limiting births and stigma around women who have children too closely together, this factor might indicate adherence to traditional Burkina Faso beliefs around family planning. AGREESPACE, which reports the respondent’s agreement with couples using contraception to space the birth of their children, also loads on this factor. This factor may indicate value of traditional beliefs or more explicit support for child spacing.

This factor analysis indicates several dimensions of empowerment for understanding family planning in Burkina Faso and suggests a handful of important variables (those with high factor loadings) users may be interested in including in analyses of female empowerment. It also highlights other areas to explore. For example, I was surprised that DECIDER was not strongly associated with any single factor (though iterations of this analysis that measured joint decision-making showed high factor loading alongside the other socioeconomic status variables in Factor 2). Researchers may want to use PMA data to investigate whether spousal communication, socioeconomic status, and geographically-specific traditional beliefs remain important factors in family planning variables across different countries and years, or look further into how decision-making might align with these factors.

Get the Stata code to replicate this analysis: Stata_synatax_ipums.txt

[1] Kabeer, Naila. 2001. “Reflections on the Measurement of Women’s Empowerment.” In: Discussing Women’s Empowerment: Theory and Practice. Stockholm: SIDA: Swedish International Development Cooperation Agency.

[2] Upadhyay, Ushma D. et al. 2014. “Women’s Empowerment and Fertility: A Review of the Literature,” Social Science & Medicine 115: 111-120.

[3] Prata, Ndola et al. 2017. “Women’s Empowerment and Family Planning: A Review of the Literature,” Journal of Biosocial Science 49(6): 713-743.

[4] I used Stata’s polychoric function in order to include a mix of binary and ordinal variables in my analysis.

[5] I used Eigenvalues and scree plots to determine how many factors to retain.

[6] I am defining moderate and strong factor loadings as those loadings of .60 and greater.

[7] I looked for common conceptual threads between the highest factor loadings (asking myself, ‘What latent factor might all of these variables be measuring?’). I also looked at weak and negative factor loadings to help interpret this factor.