Turning post-materialism on its head: self-expression, autonomy and precarity at work in the creative industries

Figures & data

Table 1. Vignette factors and levels.

Concept	Factor	Level
Employment status	Contract type	Self-employed
		Employed with a permanent contract
		Employed with a temporary contract
Non-material values	Allows you to do what you love	Yes
		No
	Creative autonomy	High levels
		Low levels
Material values	Importance for portfolio	High
		Low
	Social security (pension, sickness benefit, unemployment benefit)	Yes
		No
	Relative earnings	Higher
		Same
		Lower

Figure 1. Vignette example.

Table 2. Descriptive statistics.

Variables	Categories	Range	Mean	SD
Dependent variable
Vignette rating		1–10	4.04	2.95
Level 1 (vignette)
Type of contract	Self-employed	0/1	0.33
	Permanent contract	0/1	0.33
	Temporary contract	0/1	0.33
Allows to do what you like	Doesn’t allow	0/1	0.50
	Allows	0/1	0.50
Autonomy	Low	0/1	0.50
	High	0/1	0.50
Portfolio	Low	0/1	0.50
	High	0/1	0.50
Benefits	Doesn’t offer	0/1	0.50
	Offers	0/1	0.50
Salary	Same	0/1	0.33
	Lower	0/1	0.33
	Higher	0/1	0.33
Level 2 explanatory (respondent)
Precarity		1–5	2.37	0.92
Level 2 control
Age		18–65	38.12	11.6
Education level	pre-graduate	0/1	0.04
	graduate	0/1	0.43
	post-graduate	0/1	0.53
Sector	Architecture	0/1	0.56
	Design	0/1	0.44
Migration background	native	0/1	0.58
	EU migrant	0/1	0.26
	non-EU migrant	0/1	0.16
Gender	male	0/1	0.58
	female	0/1	0.40
	other	0/1	0.02
Covid income decrease	No	0/1	0.71
	Yes	0/1	0.29
Current position	Permanent employment	0/1	0.38
	Temp. empl. or intern.	0/1	0.23
	Self-employment	0/1	0.39

Table 3. Hierarchical models.

Variables	Categories	Model 1	Model 2
		B (SE)	B (SE)
Cons.		2.88 (0.94)*	2.64 (1.08)*
Level 1 (vignette)
Type of contract	Self-employed	Ref
	Permanent contract	−0.06 (0.03)	−0.06 (0.03)
	Temporary contract	−0.06 (0.03)*	−0.06 (0.03)*
Doing what you love	Doesn’t allow	Ref
	Allows	0.24 (0.03)**	0.26 (0.08)**
Autonomy	Low	Ref
	High	0.22 (0.03)**	0.25 (0.09)*
Portfolio	Low	Ref
	High	0.15 (0.03)**	0.15 (0.03)**
Benefits	Doesn’t offer	Ref
	Offers	0.17 (0.03)**	0.17 (0.03)**
Salary	Same	Ref
	Lower	−0.12 (0.02)**	−0.12 (0.03)**
	Higher	0.16 (0.03)**	0.16 (0.03)**
Level 2
Precarity		0.15 (0.06)*	0.16 (0.07)*
Control variables
Age		−0.15 (0.05)*	−0.15 (0.05)*
Education level	pre-graduate	Ref
	graduate	−0.14 (0.07)	−0.12 (0.07)
	post-graduate	−0.20 (0.08)*	−0.19 (0.08)*
Sector	architecture	Ref
	Design	−0.02 (0.06)	−0.02 (0.05)
Migration background	Native	ref
	EU mig. background	0.10 (0.05)	0.09 (0.05)
	non-EU mig. background	0.01 (0.04)	0.01 (0.04)
Gender	Male	ref
	female	0.03 (0.05)	0.02 (0.04)
	other	0.00 (0.04)	0.00 (0.04)
Covid income decrease	No	ref
	Yes	0.06 (0.05)	0.05 (0.05)
Current contract	Permanent employment	ref
	Temp. empl. or intern.	0.04 (0.05)	0.04 (0.05)
	Self-employment	0.00 (0.06)	0.00 (0.06)
Interactions
	precarity x allows to do		−0.06 (0.00)
	precarity x autonomy		−0.01 (0.00)
Model specification
Random intercept		1.37*	1.44*
Random slope	doing what you love		0.06
	autonomy		1.78*
N (vignettes)		774	774
N (respondents)		129	129
Deviance		3493.22	3475.78

^a The table reports robust standard errors.

^b The effect of vignette set was non-significant (see Appendix 3).

*p < 0.05; **p < 0.001.

Table

Total Variance Explained
Component	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	2.257	25.076	25.076	2.257	25.076	25.076	2.152	23.908	23.908
2	1.633	18.148	43.223	1.633	18.148	43.223	1.520	16.894	40.802
3	1.157	12.850	56.073	1.157	12.850	56.073	1.374	15.272	56.073
4	1.059	11.771	67.845
5	.706	7.849	75.693
6	.641	7.120	82.813
7	.586	6.507	89.320
8	.523	5.807	95.127
9	.439	4.873	100.000

Extraction Method: Principal Component Analysis.

Table

Rotated component matrix^a
	Component
	1	2	3
job_security	.791
job_stable_income	.713
job_income_sufficient	.705
job_alternative	.631
not_different_profession		.758
work_not_interesting		.698
work_expression		.623
work_long_hours			.815
no_material_rewards			.663

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalisation.

^a Rotation converged in 5 iterations.

Table

		age	sector_r	precar	EUmig	NONEUmig	edu_grad	edu_postgrad	gndr_female	gndr_other	covid_decrease	job_tempint	job_se
Age	Pearson Correlation	1	−.094**	−.153**	−.310**	−.128**	−.298**	.285**	−.063	−.046	−.064	−.398**	.153**
	Sig. (2-tailed)		.009	.000	.000	.000	.000	.000	.080	.201	.076	.000	.000
	N	774	774	774	774	774	774	774	774	774	774	774	774
sector_r	Pearson Correlation	−.094**	1	.084*	.015	−.054	.449**	−.533**	−.063	.070	.213**	−.083*	.349**
	Sig. (2-tailed)	.009		.019	.677	.133	.000	.000	.080	.052	.000	.020	.000
	N	774	774	774	774	774	774	774	774	774	774	774	774
Precar	Pearson Correlation	−.153**	.084*	1	.042	.221**	.116**	−.168**	.074*	−.007	.446**	.231**	.196**
	Sig. (2-tailed)	.000	.019		.245	.000	.001	.000	.039	.849	.000	.000	.000
	N	774	774	774	774	774	774	774	774	774	774	774	774
EUmig	Pearson Correlation	−.310**	.015	.042	1	−.259**	.239**	−.228**	.279**	.027	−.028	.056	.008
	Sig. (2-tailed)	.000	.677	.245		.000	.000	.000	.000	.446	.435	.121	.832
	N	774	774	774	774	774	774	774	774	774	774	774	774
NONEUmig	Pearson Correlation	−.128**	−.054	.221**	−.259**	1	−.090*	.081*	−.020	.071*	−.009	.105**	−.092*
	Sig. (2-tailed)	.000	.133	.000	.000		.013	.024	.580	.047	.812	.003	.010
	N	774	774	774	774	774	774	774	774	774	774	774	774
edu_grad	Pearson Correlation	−.298**	.449**	.116**	.239**	−.090*	1	−.925**	−.018	.176**	.120**	.147**	.170**
	Sig. (2-tailed)	.000	.000	.001	.000	.013		.000	.611	.000	.001	.000	.000
	N	774	774	774	774	774	774	774	774	774	774	774	774
edu_postgrad	Pearson Correlation	.285**	−.533**	−.168**	−.228**	.081*	−.925**	1	.050	−.163**	−.137**	−.103**	−.234**
	Sig. (2-tailed)	.000	.000	.000	.000	.024	.000		.162	.000	.000	.004	.000
	N	774	774	774	774	774	774	774	774	774	774	774	774
gndr_female	Pearson Correlation	−.063	−.063	.074*	.279**	−.020	−.018	.050	1	−.127**	−.080*	.146**	−.135**
	Sig. (2-tailed)	.080	.080	.039	.000	.580	.611	.162		.000	.025	.000	.000
	N	774	774	774	774	774	774	774	774	774	774	774	774
gndr_other	Pearson Correlation	−.046	.070	−.007	.027	.071*	.176**	−.163**	−.127**	1	.013	.037	.088*
	Sig. (2-tailed)	.201	.052	.849	.446	.047	.000	.000	.000		.716	.306	.014
	N	774	774	774	774	774	774	774	774	774	774	774	774
covid_decrease	Pearson Correlation	−.064	.213**	.446**	−.028	−.009	.120**	−.137**	−.080*	.013	1	.007	.254**
	Sig. (2-tailed)	.076	.000	.000	.435	.812	.001	.000	.025	.716		.856	.000
	N	774	774	774	774	774	774	774	774	774	774	774	774
job_tempint	Pearson Correlation	−.398**	−.083*	.231**	.056	.105**	.147**	−.103**	.146**	.037	.007	1	−.438**
	Sig. (2-tailed)	.000	.020	.000	.121	.003	.000	.004	.000	.306	.856		.000
	N	774	774	774	774	774	774	774	774	774	774	774	774
job_se	Pearson Correlation	.153**	.349**	.196**	.008	−.092*	.170**	−.234**	−.135**	.088*	.254**	−.438**	1
	Sig. (2-tailed)	.000	.000	.000	.832	.010	.000	.000	.000	.014	.000	.000
	N	774	774	774	774	774	774	774	774	774	774	774	774

**Correlation is significant at the 0.01 level (2-tailed).

*Correlation is significant at the 0.05 level (2-tailed).

Table

Full multivel analysis table	Model 0:			Model 0.1:			Model 0.2			Model 1:			Model 2			Model 3
With HLM	random			fixed effects			Fixed effects			fixed effects			random slopes autonomy, allows to do			random slopes salary lower, salary higher, benefits
Full maximum likelihood	intercept			level 1 predictors			level 1 and 2 predictors and set effect			level 1 and 2 predictors			and interaction prec x aut, prec x allow			interactions precarity x benefits, precarity x salary higher, precarity x salary lower
FIXED	Par. Est.	SE	p-value	Par. Est.	SE	p-value				Par. Est.	SE	p-value	Par. Est.	SE	p-value	Par. Est.	SE	p-value
Mean/intercept	4.04	0.15	0.00	1.89	0.25	0.00	2.69	0.94	0.01	2.85	0.94	0.00	2.64	0.99	0.01	1.02
Vignette set							0.01	0.02	0.49
Perm contract (se ref)				–0.37	0.21	0.08	–0.37	0.21	0.08	–0.37	0.21	0.08	–0.37	0.21	0.08	–0.26	0.20	0.18
Temp contract (se ref)				–0.39	0.17	0.02	–0.39	0.17	0.02	–0.39	0.17	0.02	–0.39	0.17	0.02	–0.33	0.15	0.03
Allows to do (doesn't ref)				1.40	0.16	0.00	1.40	0.16	0.00	1.40	0.16	0.00	1.50	0.47	0.00	1.38	0.16	0.00
Autonomy (low ref)				1.32	0.19	0.00	1.32	0.19	0.00	1.32	0.19	0.00	1.45	0.54	0.01	1.27	0.18	0.00
Portfolio (low ref)				0.91	0.19	0.00	0.91	0.19	0.00	0.91	0.19	0.00	0.91	0.18	0.00	0.96	0.18	0.00
Benefits (doesn't ref)				1.02	0.16	0.00	1.02	0.16	0.00	1.02	0.16	0.00	0.99	0.16	0.00	0.41	0.43	0.34
Salary lower (same ref)				–0.75	0.15	0.00	–0.75	0.15	0.00	–0.75	0.15	0.00	–0.75	0.15	0.00	–0.58	0.37	0.12
Salary higher (same ref)				1.01	0.19	0.00	1.01	0.19	0.00	1.01	0.19	0.00	1.01	0.19	0.00	0.56	0.55	0.31
Age							–0.04	0.01	0.01	–0.04	0.01	0.01	–0.04	0.01	0.01	–0.04	0.01	0.01
Education pre grad (non ref)							–0.85	0.43	0.05	–0.84	0.43	0.06	–0.73	0.44	0.10	–0.87	0.55	0.12
Education post grad (non ref)							–1.16	0.49	0.02	–1.17	0.50	0.02	–1.10	0.50	0.03	–1.21	0.59	0.04
Sector (arch ref)							–0.15	0.33	0.65	–0.13	0.33	0.70	–0.13	0.32	0.69	–0.12	0.31	0.71
Eu mig (native ref)							0.63	0.32	0.05	0.66	0.32	0.04	0.61	0.32	0.06	0.72	0.32	0.03
Non eu mig (native ref)							0.06	0.35	0.86	0.10	0.35	0.78	0.08	0.35	0.82	0.05	0.33	0.89
Gender female (male ref)							0.17	0.27	0.55	0.16	0.27	0.57	0.12	0.27	0.65	0.15	0.26	0.57
Gender other (male ref)							–0.01	0.41	0.98	0.07	0.40	0.85	–0.06	0.36	0.87	0.21	0.46	0.65
Precarity							0.48	0.18	0.01	0.47	0.19	0.01	0.52	0.23	0.02	0.25	0.20	0.21
Covid income decrease							0.39	0.32	0.22	0.38	0.32	0.24	0.34	0.32	0.29	0.60	0.31	0.06
Temp intern (perm ref)							0.24	0.35	0.50	0.25	0.36	0.49	0.25	0.35	0.47	0.29	0.35	0.40
Self emp (perm ref)							0.01	0.35	0.98	0.00	0.35	0.99	0.03	0.35	0.93	0.01	0.32	0.97
Precar x autonomy													–0.06	0.22	0.79
Precar x allows to do													–0.01	0.18	0.98
Precar x benefits																0.25	0.17	0.14
Precar x salary higher																0.19	0.21	0.36
Precar x salary lower																–0.07	0.15	0.64

Table

RANDOM	Par. Est.	p-value	Par. Est.	p-value			Par. Est.	p-value	Par. Est.	p-value	Par. Est.	p-value
VAR(e(ij))	6.76		4.51		4.51		4.51		3.93		1.94
VAR(u(0j))***	1.92	0.00	2.29	0.00	1.30	0.00	1.31	0.00	1.44	0.00	0.93	0.03
VAR(u(aut))***									1.90	0.00
VAR(u(allow))***									0.07	0.44
VAR(u(benefits))***											0.90	0.02
VAR(u(salary_h))***											1.39	0.01
VAR(u(salary_l))***											0.20	>.500

Table

Model fit	Fit	par	Fit	par	p-value	Fit	par	p-value	Fit	par	p-value	Fit	par	p-value	Fit	par	p-value
Deviance	3804.25	3	3541.27	12		3492.69	24		3493.22	23		3475.78	30		3465.71332	35
Diff Dev*			262.98	9	0.00	311.56	21	0.00	48.04	11	0.00	17.44	7	0.01	27.51	12	0.01
Variance partitioning
ICC	0.22
Explained variance
R2 level 1			0.22			0.15			0.14
R2 level 2			0.00			0.32			0.32

*Chi-square test on difference in deviance.

**Models 0.1 and 0.2 are compared to model 0; model 1 is compared to model 0.1; models 2 and 3 are compared to model 1.

Table

Data NL	Model 1:	Model 2
With HLM	fixed effects	random slopes autonomy, allows to do
Full maximum likelihood	level 1 and 2 predictors	and interaction prec x aut, prec x allow

Table

FIXED	Par. Est.	SE	p-value	Par. Est.	SE	p-value	Std. dev.
mean/intercept	2.85	0.94	0.003	2.64	0.99	0.009	2.95
Vignette set							7.07
perm contract (se ref)	-0.06	0.03	0.079	-0.06	0.03	0.079	0.47
temp contract (se ref)	-0.06	0.03	0.022	-0.06	0.03	0.022	0.47
allows to do (doesn't ref)	0.24	0.03	0.000	0.26	0.08	0.002	0.5
autonomy (low ref)	0.22	0.03	0.000	0.25	0.09	0.009	0.5
portfolio (low ref)	0.15	0.03	0.000	0.15	0.03	0.000	0.5
benefits (doesn't ref)	0.17	0.03	0.000	0.17	0.03	0.000	0.5
salary lower (same ref)	-0.12	0.02	0.000	-0.12	0.02	0.000	0.47
salary higher (same ref)	0.16	0.03	0.000	0.16	0.03	0.000	0.47
Age	-0.15	0.05	0.006	-0.15	0.05	0.007	11.6
education pre grad (non ref)	-0.14	0.07	0.055	-0.12	0.07	0.100	0.5
education post grad (non ref)	-0.20	0.08	0.020	-0.19	0.08	0.030	0.5
sector (arch ref)	-0.02	0.06	0.696	-0.02	0.05	0.691	0.5
eu mig (native ref)	0.10	0.05	0.044	0.09	0.05	0.056	0.44
non eu mig (native ref)	0.01	0.04	0.777	0.01	0.04	0.817	0.37
gender female (male ref)	0.03	0.05	0.566	0.02	0.04	0.654	0.49
gender other (male ref)	0.00	0.02	0.852	0.00	0.02	0.872	0.15
Precarity	0.15	0.06	0.013	0.16	0.07	0.024	0.92
covid income decrease	0.06	0.05	0.235	0.05	0.05	0.293	0.46
temp intern (perm ref)	0.04	0.05	0.486	0.04	0.05	0.474	0.42
self emp (perm ref)	0.00	0.06	0.989	0.00	0.06	0.932	0.49
precar x autonomy				-0.06	0.00	0.785
precar x allows to do				-0.01	0.00	0.976
precar x benefits
precar x salary higher
precar x salary lower

Table

RANDOM	Par. Est.	p-value	Par. Est.	p-value
VAR(e(ij))	4.51		3.93
VAR(u(0j)) ***	1.31	0.00	1.44	0.00
VAR(u(aut)) ***			1.90	0.00
VAR(u(allow)) ***			0.07	0.44
VAR(u(benefits)) ***
VAR(u(salary_h)) ***
VAR(u(salary_l)) ***

Table

Model fit	Fit	par	p-value	Fit	par	p-value
Deviance	3493.22	23		3475.78	30
Diff Dev *	311.02	17	<.00001	17.44	7	0.014769
Variance partitioning
ICC
Explained variance
R2 level 1	0.143801
R2 level 2	0.32152