Who will change the “baby?” Examining the power of gender in an experimental setting


We conduct an experiment designed to test the impact of a gender-loaded frame on the distribution of labor between care and market work. In an unframed treatment, one activity is labeled a “Multiplication Activity” and a second activity is labeled a “Monitoring Activity”. In a framed treatment, these same activities are labeled as an “Employment Activity” and a “Care Activity”. A difference between these treatments should come from the labeling of the activities, and not the nature of the activities. We find that men are more likely than women to fail at the monitoring/care activity in the framed treatment when both activities are done simultaneously by one individual for the first time. During paired rounds, we find that, in the framed treatment, women in mixed-gender pairs are more likely to specialize in monitoring/care and men are more likely to specialize in multiplication/employment. We do not find this in the unframed treatment. Our design controls for factors typically used to explain the gendered distribution of work, such as differences in earnings, income, or human capital.

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  1. 1.

    Specifically, intervening involves a subject holding their cursor over an on-screen box. Doing so for a period of time causes the condition bar to improve. The subject is unable to see or interact with the multiplication/employment activity during this time.

  2. 2.

    All subjects identified as either a man or a woman. We had no subjects identify as non-binary.

  3. 3.

    Given the limited time frame and limited interaction with others, attitudes about gender roles are unlikely to change significantly over the course of the experiment.

  4. 4.

    It is possible that an individual’s gender identity construction can include rejecting existing social norms and gender stereotypes.

  5. 5.

    Part of the cost of diverging gender norms in the real world is judgment or punishment from the rest of society (West and Zimmerman 1987; Deutsch 2007; West and Zimmerman 2009). In our experimental setting, behavior is only observed virtually through a computer interface by one’s partner and the two experimenters. Therefore, in our experimental setting, the social costs of diverging from gender norms are lower than in the real world.

  6. 6.

    Specialization in monitoring/care is defined as the individual’s time spent on the monitoring/care activity divided by the sum of both partners’ time spent on the monitoring/care activity, minus the individual’s score in the multiplication/employment activity divided by the sum of both partners’ scores.

  7. 7.

    Only in separate spheres models and only when bargaining fails would we expect primary earning women to still be doing more unpaid household and care labor (Lundberg and Pollak 1993; Carter and Katz 1997). While the separate spheres models consider gender norms as an exogenous factor impacting exit options, Agarwal (1997) argues that gender norms impact far more than exit options and can even be renegotiated through the bargaining process.

  8. 8.

    Additional literature (Gupta 2007; Gupta and Ash 2008; Killewald and Gough 2010) questions the gender performance explanation of this empirical finding, claiming the result is driven by absolute rather than relative earnings. The most recent empirical work controls for absolute earnings (Schneider 2011; Bertrand et al. 2015).

  9. 9.

    Akerlof and Kranton (2000) treat identity as exogenous in their model.

  10. 10.

    For an example of the impact of group identity conformity within the psychological literature, see Christensen et al. (2004).

  11. 11.

    A rich literature has since emerged examining how individuals both do and undo gender (Deutsch 2007; West and Zimmerman 2009).

  12. 12.

    Contrary to gendered stereotypes, Buser and Peter find that women are not better at multitasking, and, when given the choice, women are less likely to multitask. Babcock et al. (2017) finds that women are more likely to offer, to be asked, and to agree to do tasks within firms that, while important to the firm, do not factor highly in performance evaluations and promotion.

  13. 13.

    The “assisting” task involved a subject copying their partner’s paper-and-pencil quiz answers into a computer spreadsheet (Görges 2015).

  14. 14.

    Both tasks involved copying phone numbers, and were paid according to time spent, not performance (Cochard et al. 2018).

  15. 15.

    For a more recent example that includes chat opportunities, see Huang and Low (2017).

  16. 16.

    More detail on the average gains from partnership for different pair types is available in the appendix.

  17. 17.

    In solo rounds, one partner would monitor in the top-right quadrant; the other would monitor in the bottom-left quadrant. In paired rounds, both quadrants were active for both partners.

  18. 18.

    This activity was designed and programmed specifically for this project. Parties interested in running similar investigations can contact the authors for source code.

  19. 19.

    Given that the boxes have a maximum brightness, there is an upper bound on the amount of time a subject can, or at least should, spend on the monitoring activity that is a function of how many times the boxes have randomly turned dark. For this reason, we focus on relative rather than absolute contributions to care.

  20. 20.

    This means that partners always receive equal earnings in paired rounds.

  21. 21.

    Subjects were not given any information about their partners’ first- and second-round performance beyond what was communicated, by choice, through the chat interface. It is reasonable to assume that subjects were aware, before round four, about the performance of their partners in round three, as this information was observable during that round.

  22. 22.

    Previous work, e.g. (Carrell et al. 2010), has found that individuals may perform better in some tasks after seeing someone from within their identity group represented in a position of authority or success.

  23. 23.

    For full regression results see Appendix Table 11.

  24. 24.

    This effect persisted in later rounds but was not statistically significant.

  25. 25.

    For full regression results see Appendix Table 12.

  26. 26.

    One of the complicated aspects of care work in the real world is the emotional-engagement component (Folbre 2014).

  27. 27.

    For example, if a subject contributed 80% of his/her pair’s multiplication/employment score, while contributing 30% of the pair’s time spent on monitoring/care, then we calculate that subject’s “care specialization” as 30% − 80% = −50%.

  28. 28.

    We asked, "Which of the following most accurately describes your gender identity?" The two options were man and woman. We then gave subjects the option to provide additional information about their gender identity, providing an opportunity for them to identify as transgender or outside the gender binary. Only one subject included a note in this section, and the note did not impact how we classified their gender.

  29. 29.

    There were 12 students between the ages of 24 and 30. One subject was 34 and one was 42.

  30. 30.

    The remaining subjects identified as Native American, Pacific Islander, or multiracial.

  31. 31.

    We use t-tests to test for differences in means, and rank-sum tests to test for differences in medians. We report significance at the 10% level.

  32. 32.

    To compare rates, we use a probability test and report significance at the 10% level.

  33. 33.

    In other words, this measure is the number of foregone math problems divided by the amount of monitoring gained.

  34. 34.

    The share of monitoring/care contributions is the subject’s contribution divided by the sum of both partners’ contributions. The share of math score is the subjects math score divided by the sum of both partners’ math score.

  35. 35.

    In fact, in the unframed treatment, men, on average, contributed more in the monitoring activity, and women, on average, contributed more in the multiplication activity. However, the averages are not different from zero at statistically significant levels.

  36. 36.

    Because these statistics involve between-partner comparisons, each observation is one pair. Results are tested using one-sample t-tests on means, and sign-rank tests on medians. In comparing men vs. women in mixed-gender pairs, share results for men are compliments of results for women, and so comparisons are made to the even-split share of 50. Similarly, monitoring/care specializations are additive inverses, and so comparisons are made to the even-split specialization of 0.

  37. 37.

    The difference in medians between treatments was marginally significant at a p-value of 0.053. Significance results for both genders are identical due to the inverse relationship mentioned earlier.

  38. 38.

    Results in Table 8 exclude subjects that failed during the round of interest.

  39. 39.

    Besides using specialization in monitoring/care as the dependent variable, we also run regressions with individual math scores as the dependent variable. Here we find qualitatively similar results (women do less math in the framed treatment), however the only statistically significant result is women’s lower math contribution in round 3.

  40. 40.

    We, of course, find a parallel and opposite coefficient for low scoring men. Although we are uncomfortable making strong statements about the outcomes of hypothetical specialization choices, we perform a back of the envelope calculation in which we estimated hypothetical gains from specialization based on partners’ relative productivity drops from the first round to rounds two and five. Based on this admittedly rough estimate, we find efficiency losses of about eight math problems per pair in round three attributable to poor specialization decisions across all pairs for which an estimate was possible.

  41. 41.

    Also in an appendix, we discuss the productivity impact of specialization. We find that for mixed-gender pairs in the framed treatment, specialization was less beneficial than in other contexts. This would be consistent with a story of specialization against comparative advantage.

  42. 42.

    Because we only observe contributions in the two activities (correct math problems and improvements to task boxes), we cannot directly compare time spent on the two activities. Further, estimates of time spent generally rely on some measure of contribution/time which fluctuates over the course of the experiment due to a learning-by-doing effect.

  43. 43.

    Math specialization = (Score in 3/Score in 2) − (Partner score in 3/Partner score in 2). Intuitively, this measure compares the relative change in math scores (from solo round to paired round) between partners. A greater relative change suggests having had more time to spend on the multiplication/employment activity compared to one’s partner.

  44. 44.

    In the unframed treatment, we find that, in fact, men are more likely to specialize in the monitoring activity but this gender difference is not statistically significant at conventional levels.

  45. 45.

    According to the 2016 General Social Survey, young people (18 to 34) and adults with a college education were more likely to strongly disagree with the statement: “It is much better for everyone involved if the man is the achiever outside the home and the woman takes care of the home and family” (Smith et al. 2018).

  46. 46.

    P-value is 0.156.

  47. 47.

    Total math score is the sum of both partners’ individual math scores. We exclude failures.


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This project was made possible through Franklin and Marshall College faculty research funds. We would like to thank Timothy Flannery, Maria Floro, Michael E. Martell, and Abhilasha Srivastava for their useful comments and suggestions, as well as the University of Arizona for the use of their facilities.

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Additional robustness related tables

Table 9 Failures
Table 10 Specialization in monitoring/care: high vs. low scorer women in mixed-gender pairs
Table 11 Math scores in rounds 1 and 2
Table 12 Women’s specialization in monitoring/care: mixed-gender pairs (controlling for reader)
Table 13 Time spent on monitoring/care: mixed-gender pairs
Table 14 Women’s share of monitoring/care work: mixed-gender pairs
Table 15 Specialization in math: mixed-gender pairs

Same gender partnerships

To analyze differences between same gender partners we begin by comparing the total contributions to monitoring/care, the total math scores, and the failure rates between pairs of two women and pairs of two men, see Table 16. In the unframed treatment, pairs of two men had a greater contribution to monitoring in both rounds. In the framed treatment, men spend more total time on monitoring/care in the third round, but women spend more time on monitoring/care in round four, and when the rounds are combined. Interestingly, women’s total math scores are higher in the framed treatment, despite spending more time on monitoring/care, and men’s total math scores are lower in the framed treatment, despite spending roughly the same amount of time on monitoring/care. However, these differences are not statistically significant at conventional levels.

Table 16 Total contributions in same gender partnerships

In order to test whether the above-mentioned results are being driven by the framing or by variation in math ability, we estimate with individual math scores in the paired rounds controlling for individual math score in the first round (columns one and two of Table 17). We see that the framed treatment did not have an impact on either pairs of two women or two men, while first-round math scores were highly significant. We also examine the impact of the framed treatment on specialization in same gender pairs. We find that pairs of two women were more likely to specialize in the framed treatment, but that the frame did not appear to have an impact of the degree of specialization for pairs of two men (columns three and four of Table 17).

Table 17 Math and specialization degree: same gender partnerships
Table 18 Specialization in monitoring/care: high vs. low scorers in same gender partnerships

In Table 18 we show results from regressions dividing the sample into high and low scorers, as we did with mixed-gender pairs. For women, the coefficient on the framed treatment is not statistically significant at conventional levels but the p-value warrants attention.Footnote 46 The results for high and low scorer women confirms findings from Table 17. Partnerships of two women appeared to have a higher degree of specialization in the framed treatment, with high scorer women doing more math and low scorer women doing more care work. We do not find that the framed treatment had an impact on pairs of two men, but that the difference in math scores between partners was a key determinant in the level of specialization.

Efficiency and specialization

Independent of the way in which the gendered labels impacted the distribution of work, our experiment offers insights into whether sharing the work load and engaging in specialization leads to efficiency gains.

Fig. 4

Gains from partnership and specialization

We calculate gains from partnership as the difference between the total math scores from rounds three and four and the total math scores from rounds two and five.Footnote 47 In Fig. 4 we see graphically that expected gains from partnership seemed to increase with the degree of specialization.

In columns one and two of Table 19, we investigate, with an ordinary least squares estimation, whether the degree of specialization affects gains from partnership, as suggested by Fig. 4. We see that greater specialization is significantly associated with efficiency gains.

Table 19 Gains from partnerships
Table 20 Average gains from partnership by pair type and frame

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Roncolato, L., Roomets, A. Who will change the “baby?” Examining the power of gender in an experimental setting. Rev Econ Household (2020). https://doi.org/10.1007/s11150-020-09490-2

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  • Gender
  • Care work
  • Household specialization
  • Multitasking
  • Lab experiment

JEL classification

  • C90
  • D13
  • J16