Investigating the impact of housework on wages longitudinal

Investigating the impact of housework on wages: longitudinal evidence for Britain Mark Bryan Institute for Social and Economic Research, University of Essex Almudena Sevilla Sanz University of Oxford ESRC Research Methods Festival Oxford, 2 nd July 2008 ()

Introduction • Gender pay gap in the UK is (still) about 18% (full time workers). • Traditional explanation (based on differences in human capital and job characteristics) can only explain part of the gap. • Theory (e. g. Becker, 1985) suggests housework may lower wages (holding constant human capital and hours of work) because it restricts energy and flexibility available for labour market activity. • So women’s greater housework burden may contribute to gender pay gap. 10/3/2020 (2)

Our aims and contribution • Investigate effect of housework on wages in Britain (evidence already exists for US and Denmark). • Investigate channels by which housework effects operate. Amount, type or timing of housework? • We know (and show) that marriage involves specialisation into different types of housework tasks done at different times. So investigate housework effects for men and women, and for marrieds and singles. • See Bryan and Sevilla Sanz (2008) for full details: http: //www. iser. essex. ac. uk/pubs/workpaps/pdf/2008 -03. pdf • Focus on methodology in this presentation. 10/3/2020 (3)

Data • British Household Panel Survey (BHPS), waves 2 -14 (1992 -2004) • Full time employees (16 -59 years for women and 16 -64 years for men) with full work history (and other vars): 2574 men (observed over 7. 0 waves on average) and 2191 women (5. 5 waves). • Separate equations by gender and marital status to allow for specialisation in housework. Married = married or cohabiting • Housework question: “About how many hours do you spend on housework in an average week, such as time spent cooking, cleaning and doing the laundry? ” 10/3/2020 (4)

Wages and housework Variable Women Men Single women Single men Married women Married men Log wage 2. 040 (0. 532) 2. 207 1. 939 (0. 555) (0. 559) 1. 941 (0. 558) 2. 094 (0. 510) 2. 309 (0. 519) Housework (hours) 10. 638 (7. 856) 4. 659 7. 125 (4. 576) (6. 685) 4. 382 (4. 633) 12. 507 (7. 793) 4. 764 (4. 550) Observations 12123 18030 4209 4979 7914 13051 Individuals 2191 2574 1115 1179 1585 1903 10/3/2020 (5)

Descriptive wage equation wit = xit′β + γhit + vit (1) wit is log hourly wage xit is human capital, experience, year, region (and job characs) hit is weekly hours of housework vit is random error term 10/3/2020 (6)

Descriptive wage equation wit = xit′β + γhit + vit (1) • Estimate (1) by OLS using pooled data (all waves combined). So we treat differences in wages (etc) between people the same as changes in wages (etc) over time for same person. Advantage of panel data here is just to give us more observations. • In (1), γ tells us difference in wages associated with a onehour difference in housework, within groups of similar workers: same quals, experience, industry etc (depending on variables in x). 10/3/2020 (7)

Association of housework with wages (OLS) Basic spec Single women Single men Married women Married men -0. 0017 (-0. 82) 0. 0036* (1. 79) -0. 0108*** (-8. 81) -0. 0091*** (-5. 25) 0. 0015 (0. 85) -0. 0074*** (-7. 66) -0. 0066*** (-4. 97) Basic spec + -0. 0007 job characs (-0. 48) Basic specification includes experience (squared), education, region and year. Job characteristics are one-digit occupation and industry, public sector employment, establishment size, trade union coverage, and temporary and fixedterm employment. t-statistics in parentheses 10/3/2020 (8)

Percentage of gender wage gap explained by characteristics (OLS) Coefficients from Women’s from Men’s from Pooled Equation Excluding housework 19. 2 -15. 0 30. 5 Including housework 35. 9 12. 0 55. 7 10/3/2020 (9)

Descriptive wage equation wit = xit′β + γhit + vit (1) • Can γ tell us what would happen if housework changed (but nothing else did)? Probably not… (though depends on how well x controls for differences between workers). • Specifically, γ will not give us the “causal” effect of housework on wages if there are “unobserved” differences between workers which affect wages and are also related to housework. Example: career orientation. 10/3/2020 (10)

Wage equation with unobserved effect wit = xit′β + γhit + μi+ εit (2) • To progress further, need to think about and investigate unobservables in more depth. Panel data enable us to do this. • Panel data structure enables us to split error term into two components: § μi is unobserved individual effect (heterogeneity), e. g. unobserved skills, motivation, labour mkt resources. Assume constant over time! § εit is random error. Assume (for now) unrelated to explanatory variables. 10/3/2020 (11)

Wage equation with unobserved effect wit = xit′β + γhit + μi+ εit (2) • Estimate (2) as fixed-effect (FE) model using “within” estimation (xtreg, fe in Stata) • Only uses variation (changes) “within” individuals over time. • In FE model, γ tells us change in wages associated with a one-hour change in housework (and no other changes) for a given individual (holding constant unobserved effect). • [Intermediate solution is random effects (RE), but makes similar assumption to descriptive model: μi unrelated to x and h] 10/3/2020 (12)

Effect of housework on wages (FE) Single women Single men Married women Married men 0. 0011 (1. 11) -0. 0019 (-1. 42) -0. 0016*** (-3. 12) -0. 0014** (-2. 03) Basic spec + 0. 0012 job characs (1. 28) -0. 0019 (-1. 41) -0. 0014*** (-2. 73) -0. 0013** (-2. 04) Basic specification includes experience (squared), education, region and year. Job characteristics are one-digit occupation and industry, public sector employment, establishment size, trade union coverage, and temporary and fixedterm employment. t-statistics in parentheses 10/3/2020 (13)

Between and within variation in housework Single women Single men Married women Married men Within variance 12. 9 7. 8 20. 6 8. 9 Between variance 31. 8 13. 7 40. 2 11. 8 Overall variance 44. 7 21. 5 60. 8 20. 7 10/3/2020 (14)

Simultaneity and measurement error • • wit = xit′β + γhit + μi+ εit FE controls for permanent heterogeneity (μi), but not for correlation of εit with (measured) housework. Wage increase may reduce housework time, e. g. use bonus or pay rise to hire a cleaner. Then εit is negatively correlated with hit. If γ is negative, classical measurement error in housework (only) implies that measured housework and εit are positively correlated. Overall effect ambiguous. . . 10/3/2020 (15)

Alternative estimates and specifications • FE IV – but which instruments? Try spousal labour market behaviour and total number of employed household members. • Use panel to find instruments? § Estimate FD equation; instrument housework by 2 nd lag of housework in levels. § More generally use further lags: panel GMM. • Use lagged housework as alternative measure. No ambiguity in timing of wage vs housework changes. Less endogeneity bias(? ). Estimates are total effect of lagged housework (inc effect of current housework correlated with past housework). 10/3/2020 (16)

Effect of lagged housework on wages (FE) Single women Single men Married women Married men -0. 0003 (-0. 30) -0. 0011 (-0. 79) -0. 0015*** (-3. 19) -0. 0002 (-0. 24) Basic spec + -0. 0001 job characs (0. 09) -0. 0008 (-0. 63) -0. 0014*** (-2. 94) 0. 0002 (0. 25) Basic specification includes experience (squared), education, region and year. Job characteristics are one-digit occupation and industry, public sector employment, establishment size, trade union coverage, and temporary and fixedterm employment. t-statistics in parentheses 10/3/2020 (17)

Robustness checks • Include children variables to check estimated housework effect is not due to (omitted) childcare. • Include PT workers. Stronger effects (inc married men), but are FT and PT workers comparable? • Including age instead of experience (larger samples). V similar estimates. • Combined married-single equations with interactions (but fixed effect constrained to be same across marital status). 10/3/2020 (18)

Conclusions • Comparing people with similar human capital (and in similar jobs), we find a strong association of housework with wages among married people. This can “explain” about quarter of gender pay gap. • But cannot interpret causally, since data suggest that results may be affected by omitted factors like career orientation/path. Descriptive results are perhaps indicative of potential long-term changes, if housework reduction accompanied by changes in career orientation and prospects. • Controlling for unobserved factors, effects are much smaller and concentrated among married women (probably due to specialisation into certain housework tasks - see paper for details). If they reduced their housework to men’s levels, women’s wages would increase by about 1% (but still >5% of pay gap). 10/3/2020 (19)

Extra slides 10/3/2020 (20)

Effects of children • We do not have data on childcare. But we know children are associated with lower wages for women (“family gap”, e. g. Waldfogel, 1998). What about men? • Childcare is correlated with housework: in our data children are associated with 3 hours more housework for women and 0. 5 hour for men. • Does omitted childcare bias housework coefficients? • Test whether housework coefficients are picking up childcare effect. 10/3/2020 (21)

Effects of children (FE) Single women Housework No of own children in household Single men Married women Married men 0. 0009 -0. 0020 -0. 0012** -0. 0014** (0. 93) (-1. 50) (-2. 28) (-2. 14) 0. 0373* * 0. 0401*** 0. 0197** * (2. 12) (0. 69) (-6. 45) (4. 72) 10/3/2020 (22)

IV estimation • IV is potentially a solution to simultaneity and measurement error. • Have already controlled for μi using FE. So need instruments strongly correlated with (changes in ) hit and uncorrelated with εit FE IV estimation. • Focus on married individuals (as seemingly no effect for singles), allowing use of spousal characteristics. • Instruments: spousal labour market participation, hours of work, occupation and wage, and the total number of employed household members. • Also tried spousal attitudes towards domestic roles (limited number of waves, and low variation over time). 10/3/2020 (23)

Effect of housework on wages (FE IV) Basic spec Women Housework coeff Men Basic spec + job characs Women Men 0. 0098* 0. 0106* 0. 0253*** 0. 0230*** (1. 74) (-3. 72) (1. 90) (-3. 42) First-stage partial R 2 0. 011 0. 018 0. 011 0. 012 First-stage F-statistic [p-value] 4. 45 [0. 00] 9. 13 [0. 00] 4. 39 [0. 00] 8. 92 [0. 00] Sargan statistic χ2(12) [p-value] 38. 5 [0. 00] 16. 6 [0. 17] 32. 3 [0. 00] 16. 8 [0. 16] 10/3/2020 (24)

IV results • Test indicate that changes in women’s labour market behaviour are valid instruments for (changes in) spouses’ housework. • But changes in men’s labour market behaviour do not appear to be valid instruments. Due to differences in labour market dynamics of men vs women? • Focus on men’s results: one hour increase in housework leads to 2. 5% reduction in wages (compared to 0. 14% in FE equation). 10/3/2020 (25)

IV results contd. • FE IV estimate is larger in magnitude than FE estimate, suggesting measurement error dominates simultaneity bias. • Test endogeneity (either measurement error or simultaneity) by comparing FE and IV FE results using a Hausman test. • Test of housework coefficients only: χ2(1)=11. 74 reject exogenous housework (IV preferred). Test of all coefficients: χ2(30)=11. 72 do not reject exogeneity (FE preferred). • We take this as suggestive evidence that housework is measured with error for men and that FE could be seen as a lower bound (in magnitude) on the true effect. 10/3/2020 (26)

IV results • Instruments (spousal labour market behaviour and total number of employed household members) only test as valid in (married) men’s equation. • FE IV estimate is larger in magnitude than FE estimate (2. 5% compared to -0. 14%), suggesting measurement error dominates simultaneity bias. • But exogeneity test (comparing FE and IV FE) does not reject [χ2(30)=11. 72] FE preferred. • FE remains our preferred specification. But suggestive evidence that housework is measured with error and that FE could be seen as a lower bound (in magnitude) on the true effect. 10/3/2020 (27)

Including part-time workers • Previous results excluded PT workers to maintain homogenous sample (and data show that PT workers earn less and do more housework) • Re-estimate including PT workers and adding (endogenous? ) PT dummy. • PT = 6 -30 hours (exclude v short hours to alleviate measurement error problems) 10/3/2020 (28)

Including part-time workers (FE) Housework Part-time Single women Married Single men women -0. 0006 -0. 0020 -0. 0012*** -0. 0020*** (-0. 73) (-1. 49) (-3. 23) 0. 0202 0. 1974*** 0. 0282*** -0. 0370** (1. 27) (7. 10) (-2. 02) 10/3/2020 (29) (3. 54) Married men (-2. 95)

Dimensions of housework: amount, type and timing • Do only large amounts of housework have an effect? • Could explain lack of effect for singles and married men? • Try quadratic and spline functions (nodes at 5 and 10 hours). • Find no evidence of non-linear effects. 10/3/2020 (30)

Non-linear effects (FE) Single women Single men Married women Married men Housework 0. 0019 -0. 0002 0. 0033*** -0. 0014 (1. 23) (-2. 66) (-1. 19) Housework squared / 100 -0. 0020 -0. 0085 0. 0045 0. 0000 (-0. 68) (0. 01) (-0. 08) (-0. 75) 10/3/2020 (31) (1. 48)

Type and timing of housework • No information on type and timing in BHPS, but use complementary descriptive data from UK Time Use Survey 2000. • UK TUS is based on time diaries. • What doing in 10 min slots throughout day: primary, secondary activity; with whom; where. • But limitations in earnings data (net, some banded, last pay period only). Cross section only. 10/3/2020 (32)

Specialisation in housework activities Food Upkeep Laundry Garden Repairs Shopping Management Child care Men Married 20. 9 9. 6 1. 4 10. 1 9. 2 7. 9 1. 4 15. 1 Single 22. 2 5. 5 2. 2 6. 3 2. 0 8. 8 0. 8 1. 2 Women Married 43. 7 22. 5 14. 3 9. 9 1. 5 16. 0 15. 0 Single 30. 4 14. 6 6. 5 9. 3 2. 4 13. 0 1. 1 4. 4 Source: UKTUS. Full time workers; minutes of housework on weekday. 10/3/2020 (33)

Specialisation in housework activities Food Upkeep Laundry Garden Repairs Shopping Management Child care Men Married 20. 9 9. 6 1. 4 10. 1 9. 2 7. 9 1. 4 15. 1 Single 22. 2 5. 5 2. 2 6. 3 2. 0 8. 8 0. 8 1. 2 Women Married 43. 7 22. 5 14. 3 9. 9 1. 5 16. 0 15. 0 Single 30. 4 14. 6 6. 5 9. 3 2. 4 13. 0 1. 1 4. 4 Source: UKTUS. Full time workers; minutes of housework on weekday. 10/3/2020 (34)

Specialisation in housework activities • Married women specialize in more routine and time intensive activities, e. g. food, laundry, shopping. • Married men specialize in gardening, household management and repairs. • Consistent with other studies, e. g. Hersh and Stratton (2002) [US] and Fernandez and Sevilla-Sanz (2006) [Spain]. • Lack of threshold effects suggests effect on wages is not because married women’s housework activities are time-intensive • More likely, these activities need to be done routinely, usually during work-days, and cannot be postponed until the weekend. • Do not know whether these types of housework are more tiring but can check timing relative to market work. 10/3/2020 (35)

Timing of paid work and housework 10/3/2020 (36)

Timing of paid work and housework • Married men and women do more housework between 4 pm and 8 pm than singles. • But both married and single men spend about the same time on work (44% between 4 pm-8 pm) • Whereas married women do less work than singles over these times (32% of time vs 40% of time for single women). • Married women’s housework appears to be done at times that interfere with market work. 10/3/2020 (37)