Part-time employment and the gender gap in low pay for UK employees: what changed over the period 1996–2016?

Figures & data

Figure 1. Proportion of male and female employees who identify as part time rather than full time in each year 1996–2016.

Table 1. Descriptive statistics on working hours for male and female employees based on a pooled sample of all calendar years (1996–2016).

	Women	Men
Mean weekly working hours (standard deviation)	30.17 (11.46)	40.52 (10.34)
25th percentile, weekly working hours	21.00	37.00
50th percentile (median), weekly working hours	35.00	40.00
75th percentile, weekly working hours	38.00	45.00
Identify as part time rather than full time (%)	42.45	8.31
Observations	349,648	337,852

Figure 2. Relative odds (odds ratio) of being low paid for female relative to male employees in the UK for each year 1996–2016.

Note: unadjusted odds reflect the raw effect of being female relative to male on the probability of being low paid. The adjusted odds reflect this effect after controlling for age, education, job tenure, marital status, single parent status, part-time employment, social class, contact type, sector, industry and employer size. Upper and lower bars represent confidence intervals (95%) for the estimated odds.

Figure 3. Results (coefficients) from a Blinder–Oaxaca decomposition of the gender gap in low pay for UK employees in 1996 and 2016.

Note: Negative coefficients indicate that women are disadvantaged relative to men in relation to each characteristic, with disadvantage here indicating a higher probability of low pay. Positive coefficients indicate that men are disadvantaged compared to women.

Figure 4. Results (coefficients) from a Blinder–Oaxaca decomposition of the gender gap in low pay for UK employees, 1996–2016.

Note: Negative coefficients indicate that women are disadvantaged relative to men in relation to each characteristic, with disadvantage here indicating a higher probability of low pay. Positive coefficients indicate that men are disadvantaged compared to women.

Table 2. Shift-share decomposition of the change in low pay risk for male and female employees in the UK 1996–2016.

		Women	Men
Percentage change in low pay 1996–2016.		−5.02	1.37
Attributable to:	Change in the risk of low pay associated with working part time	−3.56	−2.11
	Change in the risk of low pay associated with working full time	−0.54	2.62
	Change in the share of part-time employment	−0.91	0.86

Table A1. Variables in the analysis.

Name	Type	Values	Definition
Low pay	Binary	1 Low paid 0 Not low paid	Gross hourly earning below two-thirds of the mean for all employees in each calendar year
Age	Nominal	1 16–24 2 25–34 3 34–44 4 45–54 5 55+
Marital status	Binary	1 Married 0 Not married	Married encompasses both legal marriage and cohabitation
Single parent	Binary	1 Yes 0 No	Not married (as above) with dependent children in the household
Education	Nominal	1 Degree 2 Higher education 3 A Level or equivalent 4 GCSE or equivalent 5 Other qualification 6 No qualification
Job tenure	Continuous		In years
Working hours	Binary	1 Part time 0 Full time	Respondent self-identified
Contract type	Binary	1 Temporary 0 Permanent
Social class	Nominal	1 Managerial and professional occupations 2 Intermediate occupations 3 Routine and manual occupations	NS-SEC coding
Sector	Binary	1 Private 0 Public
Industry	Nominal	1 Agriculture, energy and water 2 Manufacturing and construction 3 Hotels and restaurants; distribution 4 Transport and communication 5 Banking and finance 6 Education and health; public administration	SIC coding
Employer size	Nominal	1 1–10 employees 2 11–49 employees 3 50+ employees

Figure A1. Robustness checks: coefficient for part-time employment from three alternative Blinder–Oaxaca decompositions of the gender gap in low pay for UK employees 1996–2016.

Note: For comparison purposes, the first set of coefficients are the results reported in the main text. The second set of coefficients are calculated using an alternative definition of part-time employment (working fewer than 30 h a week in the main job). The third set of coefficients are calculated without imputing missing data on earnings. Negative coefficients indicate that women are disadvantaged relative to men in relation to each characteristic, with disadvantage here indicating a higher probability of low pay. Positive coefficients indicate that men are disadvantaged compared to women.