Whos cooking Analysis of food preparation time in

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Who’s cooking? Analysis of food preparation time in the 2003 ATUS Jennifer Jabs, MS,

Who’s cooking? Analysis of food preparation time in the 2003 ATUS Jennifer Jabs, MS, RD and Carol M. Devine, Ph. D, RD Table 1: Characteristics of subjects Abstract Objective: To examine how individual, family, and employment characteristics are associated with time spent in daily food prep Design & subjects: Logistic & linear regression analysis of 2003 ATUS data of men & women 21 -64 years age (n=13, 211) Results: 65% women & 36% men reported food prep time. Women had greater odds of any time in food prep than men. Of those reporting any food prep time (all other variables constant) time spent in daily food prep: women=28. 2 min/d, men=20. 3 min/d. Having a partner increased women‘s odds and decreased men's odds of any time in food prep. Conclusions: Daily food prep time differed by parental &Background partner status, ethnicity, age, and other sociodemographic characteristics. Food prep was • ↑ household employment undertaken more by womenhours than men when controlling for and employment characteristics. • ↑individual, feelings offamily, time pressure • ↓ time spent in food preparation • ↑ eating foods prepared away from home Simplified analytic model Individual: Gender Age Raceethnicity Education Methods Family: Child present HH size Partner present Employmen HH income t: Status (yes/no) Hours Outcome: Time in food prep • Descriptive statistics & bivariate analysis of variables Women n=7349 food prep * May not add to 100 due to rounding Characterist ic Age: 31 -40 yr (relative to) 41 -50 yr • Dropped those with unknown income • Data examined for normality • 2 analysis performed: 51 -62 yr (21 -30 yr) Race: non-white (white) Ethnicity: Hispanic (non-Hispanic) • none vs. any food prep: Logistic regression • for any time in food prep: Ln( daily min. in food prep): Linear regression • Included variables in analytic model, 2 - & 3 -way interactions • Interacted all variables with gender; rejected hypothesis of equality of coefficients across gender; subsequent models run by gender • Influence diagnostics: removal of most influential cases made little difference in results, all kept for analysis Men n=5862 n %* 871 14. 9 1743 44. 6 1780 30. 4 1468 25. 0 4976 84. 9 886 15. 1 5148 87. 8 714 12. 2 3299 56. 3 2563 43. 7 2045 34. 9 3817 65. 1 1449 24. 7 Characteristic n %* Age: 21 -30 yr 1251 17. 0 31 -40 yr 2196 29. 9 41 -50 yr 2090 28. 4 51 -62 yr 1812 26. 7 Race: white 6090 82. 9 non-white 1259 17. 1 Ethnicity: non-Hispanic 6447 87. 7 Hispanic 902 12. 3 Education: <college degree 4119 56. 1 college degree 3230 44. 0 HH income: <$40, 000 3122 42. 5 ≥$40, 000 4227 57. 5 Family status: no partner & no 1485 20. 2 child no partner & ≥ 1 1168 15. 9 300 5. 1 child partner & no child 1659 22. 6 1405 24. 0 partner & ≥ 1 child 3037 41. 3 2708 46. 2 HH size: 1 -3 people 4747 64. 6 3690 63. 0 4 -13 people 2602 35. 4 2172 27. 1 Employment: not 2014 27. 4 743 12. 7 part-time 1313 17. 9 306 5. 2 full-time 4022 54. 7 4813 82. 1 Interview weekday 3584 48. 8 2884 49. 2 data: Results - Table 2: Odds of spending any vs no time in daily weekend 3765 51. 2 2978 50. 8 • Variables grouped for categorical comparisons • Limited to those 21 -62 yr age, not full-time students (n=13, 211) Table 3: Of those reporting any time in food prep: Ln(min/day) Education: college degree HH Income: ≥$40, 000 (<college degree) (<$40, 000) HH size: 4 -13 people (1 -3 people) Family status: no partner & ≥ 1 child partner & no child Womena n=7349 OR p>z 1. 3 5 1. 4 4 1. 9 2 0. 9 6 1. 1 7 0. 9 8 0. 8 5 1. 2 5 1. 7 3 1. 6 9 Menb n=5862 OR p>z 0. 00 1. 29 0. 0 1 0. 00 1. 42 0. 0 0 0. 00 1. 43 0. 0 0 0. 51 0. 80 0. 0 1 0. 06 0. 77 0. 0 1 0. 71 1. 11 0. 0 9 0. 01 0. 99 0. 9 1 0. 00 1. 05 0. 5 1 0. 00 1. 80 0. 00 0. 80 0. 0 1 Characterist ic Age: 31 -40 yr 41 -50 yr 51 -62 yr Race: non-white Ethnicity: Hispanic Education: college degree (relative to) HH income: ≥$40, 000 (<$40, 000) HH size: (1 -3 people) Family status: 4 -13 people (21 -30 yr) (white) (non-Hispanic) (<college degree) no partner & ≥ 1 child partner & no child partner & ≥ 1 child (no partner & no child) Employmen part-time t: full-time Daily time in food preparation (not-employed) Womena n=4748 Coeff p>z Menb n=2110 Coeff p>z 0. 021 0. 055 0. 034 0. 173 0. 270 0. 099 0. 003 0. 072 0. 112 0. 138 0. 108 0. 164 0. 250 0. 005 0. 62 0. 20 0. 48 0. 00 0. 13 0. 06 0. 17 0. 01 0. 00 0. 91 0. 93 0. 007 0. 89 0. 04 -0. 031 0. 61 0. 290 0. 00 0. 262 0. 00 0. 298 0. 00 0. 173 0. 01 0. 391 0. 00 0. 213 0. 00 -0. 225 0. 03 0. 163 - 0. 00 -0. 199 0. 00 0. 299 Interview weekend (weekday) 0. 182 0. 00 0. 359 0. 00 Table 4: Daily time in food prep Table 5: Of those reporting data: any time- Calculations from Women Men Constant 3. 338 0. 00 3. 011 0. 00 regression (min/d) Any % weights n used%in analysis) Adjvs. R 2 none : a=0. 078; b=0. 049 n(sampling Characteristic Men (logistic) Women No time in food 2601 35. 4 3752 64. 0 No partner & no 28. 2 20. 3 prep child Any time in food 4747 64. 6 2110 36. 0 No partner & ≥ 1 27. 6 26. 4 prep child Of those reporting Partner & no 37. 9 24. 1 any n range child Min/day in food 4748 1 -654 2110 1 -430 Conclusions Partner & ≥ 1 41. 6 25. 1 prep child • Many reported no time in food prep (35% women, 64% men) Mean (Std Dev) 56. 0(51. 2) 42. 4(43. 7) held atprep reference • Gendered nature of food prep: Women more likely to do. Other any variables & more food than men categories • Food prep time differed by parental & marital status, ethnicity, age, and other sociodemographic characteristics • Role differences • Women with partners have increased odds & men decreased odds of any time in food prep • Having children at home increase time reported in food prep by men & women • Smaller female: male differences among those reporting any daily food prep time • Day of week influences doing any (less likely on weekends) & time spent in food prep (longer Implications time on weekends) • The social framework in which food prep is performed has implications for food assistance policy • Limited time spent in food prep has nutritional & health implications. • If goal to understand food prep time then need to measure: food prep as a secondary activity & all household members’ time use in food prep Acknowledgements: John Cawley, Carole Bisogni, Elaine Wethington, Cornell University Office of Statistical Consulting,