Italy: Work climate improves while job satisfaction declines

The third Quality of work survey report, published by Italy’s National Research Institute for Vocational Education and Training Employment and Social Policies (Isfol), provides both synthetic indicators and an accurate overview of the quality of work in Italy. It shows that while the work climate is still good, job satisfaction is still relatively low because of poor rewards and poor work prospects.

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Introduction

In March 2014, Italy’s National Research Institute for Vocational Education and Training Employment and Social Policies (Isfol) published its third Quality of work survey, carried out in autumn 2010. The report examines five dimensions of quality of work; the first four − ergonomics, complexity, autonomy and control – were devised by Gallino (1983), and in this survey are complemented by an economic dimension proposed by La Rosa (2000).

These dimensions were validated as independent by Centra et al. (2012) based on this survey. This report summarises some of the most important findings.



Methodology

The methodology of Isfol’s third Quality of work survey was the same as that used for previous waves. The reference population is the annual average of the labour force aged over 15 during year preceding the survey.

As in previous waves, the survey was carried out using CATI (computer-aided telephone interviews) with households that had landline telephones. A three-stage sampling process selected municipalities in the first stage which were then stratified by regions and households with at least one employed member in the second stage, and in the third stage interviewees were randomly selected from this sample. The sample size is larger than in previous waves, up from 2,000 to 5,000 interviews. This allows more precisely calibrated estimators in ex-post weighting procedures that take into account the geographic area, gender, age groups, qualification, family size, employment status and working time, occupation and sector as supplementary information. (For further details, see the national survey description and the annexes of the report.)



Main findings

Ergonomic dimension

Work environment

There is a widespread satisfaction about work climate (93% of respondents are satisfied with their work climate) and with relations with colleagues (94.8%), and both scores are well beyond that for overall satisfaction (87.3%).

There are, however, noticeable differences between companies of different sizes. Those employed in firms with less than 15 employees report the highest satisfaction for all selected indicators, especially for ‘relations with superiors’ (91.6%), while companies with 15−49 employees report the lowest overall satisfaction (85%) (Figure 1).

When comparing these figures with previous waves, overall job satisfaction has declined since 2006 (88%), but satisfaction with work climate has increased (89.4% in 2002, 90.8% in 2006).

Figure 1: Social relationships in the workplace by establishment size, 2010

Figure 1: Social relationships in the workplace by establishment size, 2010

Source: Third QWS, Isfol (2014)

Most discriminatory attitudes have declined significantly, suggesting an increasing acceptance among the employed of profound changes in the workforce (Figure 2). Discrimination based on gender (5.5%) and age (5.4%) are still the most commonly reported. Discrimination on the basis of ethnic background displays more persistence and this may be mainly due to unsolved problems of integration.

Figure 2: Forms of discrimination in the workplace, 2002, 2006, 2010 (%)

Figure 2: Forms of discrimination in the workplace, 2002, 2006, 2010 (%)

Source: Third QWS, Isfol (2014)

Health and safety at work

When considering health at work, 17.9% of the workforce report feeling that their health is at risk because of work, well below both the 2006 figure (29.2%) and the 2002 figure (20%) (Figure 3). The gap between men and women on this issue declined from 6.8 percentage points in 2002 (with more men than women concerned about the health risks of their work) to four percentage points in 2010. The proportion of men reporting concern dropped, while the proportion of women remained much the same.

Figure 3: Health at risk by gender, 2002, 2006, 2010 (%)

Figure 3: Health at risk by gender, 2002, 2006, 2010 (%)

Source: Third QWS, Isfol (2014)

Working time and work-life balance

Figure 4: Average weekly working hours by employment regime, 2010

Figure 4: Average weekly working hours by employment regime, 2010

Source: Third QWS, Isfol (2014)

Respondents reported an average working time of 38.5 hours per week. This figure includes full-time and part-time workers and is similar to the 2006 overall average (38.6 hours per week).

There are considerable differences by sex (42 hours for men, 33.2 hours for women), and by type of working time and employment relationship. In general, the less stable a respondent’s working relationship, the higher their weekly average working time. Among men, weekly hours range from 40.4 for permanent employees to 48.2 hours those who are self-employed in the strictest sense; among women, this range is 36.4 hours to 39.1 hours for each category respectively. The reverse is observed among part-timers; 25.7 average weekly hours among men and 22.6 among women who are permanent employees, compared to 20.8 hours for men and 22.6 hours for women who can be described as para-subordinates − workers who are freelance contractors or ‘bogus’ self-employed.

Men work fewer hours as their qualifications increase (an average 45.1 hours for those with only primary education compared to 39.9 for the highly qualified with a tertiary qualification). Women tend to work longer if they have more education (30.6 hours among those with primary education compared to 34.3 among those with secondary education and 32.5 among those with tertiary education). (Figure 5).

Figure 5: Average weekly working hours, 2010

Figure 5: Average weekly working hours, 2010

Source: Third QWS, Isfol (2014)

Unsocial working times have increased steadily over the three waves of the survey; 20% of respondents reported working shifts in 2010 at least once a month (17% in 2002, 18.9% in 2006), 12.2% reported night work (11.5% in 2006), 25.8% said they work on Sundays (23.4% in 2006), and 41.8% cannot avail of any daily working times flexibility.

While men work more at nights and on Sundays, they also tend to enjoy more working time flexibility and are less likely to work shifts. In general, unsocial working times decline as qualifications increase, with the exception of night work (hospital staff, for instance) and moving from permanent employment to self-employment (however, the self-employed are not in general prone to Sunday work and night work).

Shift-work is also on the increase among part-timers, reported almost by twice as many voluntary part-timers as involuntary part-timers. Sunday work, however, is reported by more involuntary part-timers (34.3%) than voluntary part-timers (20.3%) (Table 1). It seems that the lower daily flexibility reported by women is mainly due to both vertical and horizontal segregation; this confirms the findings from an Istat module on conciliation suggesting that flexibility is not used as a family-friendly strategy (IT1205029I).

Table 1: Unsocial working time by socio-demographic characteristics, 2010 (%)
 

Shift-work

Night work

Sunday work

No flexibility

Men

18.7

15.4

28.4

39.7

Women

22.1

7.4

22

44.3

Low-qualified

20.8

10.6

24.8

42.9

Medium-qualified

22.1

12.8

27.2

41.4

Highly-qualified

12.8

13.7

24.4

39.1

Permanent

25.7

13.8

22.7

54

Temporary

23.2

9.7

27.5

43.7

Para-subordinates

12.9

6.6

25.2

37.4

Self-employed

4.4

10.1

33

8.4

Total

20

12.2

25.8

41.8

Source: Third QWS, Isfol (2014)

As a consequence of the lack of family friendly strategies and the increase in unsocial working times, the share of those reporting either very good or very poor work-life balance has strongly declined for both genders, and the proportion of those who say they are able to reconcile work with private life ‘a little’ has increased (Figure 6). Self-employed and employees in the retail sector report the worst work-life balance for both sexes.

Figure 6: Work-life balance by sex, 2002, 2006, 2010 (%)

Figure 6: Work-life balance by sex, 2002, 2006, 2010 (%)

Note: Responses to the question: ‘In general, do you manage to reconcile your work with your private life?’

Source: Third QWS, Isfol (2014)

Complexity dimension

Career opportunities

The complexity dimension includes all those aspects that help workers to make complex decisions that contribute to career advancement such as creativity, problem solving or skill development.

There are clear-cut differences in career patterns by employment status. While 55.2% of employees report no change, this share drops to 37.2% among the self-employed. Of these, 46% reported an improvement in their career (Table 2).

However, at aggregate level the impact of the financial crisis is clearly visible in the decrease in those reporting some improvement, down from 49% in 2006 to 40.3% in 2010. On the other hand, the higher risks faced by the self-employed are illustrated by the higher share of those reporting a worsening of career prospects (16% compared to 6.2% among employees), especially among the poorly qualified (21.1%) and those working alone (18.6%).

More men report an improving career patterns than women, whether employees or para-subordinates (43.6% among men and 37.1% among women). Gender differences are negligible among the self-employed. Level of qualification has a stronger positive impact among the self-employed than among employees. Finally, employees’ career opportunities seem more likely to improve with company size (24.8% of those working alone report improved career prospects, compared to 42.1% in medium and large companies) while many more self-employed people working in companies with between 16 and 49 employees report improvement in their careers (79.7%).

Table 2: Career development in the present job (%)
 

Employees and para-subordinates

Self-employed

 

Worsened

Unchanged

Improved

Worsened

Unchanged

Improved

Gender

           

Men

5.7

50.7

43.6

17.0

36.8

46.3

Women

7.3

61.1

37.1

16.2

38.5

45.3

Qualification

           

Low-qualified

7.1

59.1

33.7

21.1

41.5

37.4

Medium-qualified

5.7

54.3

40.1

18.5

35.3

46.2

High-qualified

7.2

50.7

42.1

3.7

32.0

64.3

Company size

           

Working alone

8.8

66.4

24.8

18.6

37.9

43.5

2−15 employees

5.9

57.4

36.8

14.5

36.7

48.8

16−49 employees

6.7

55.7

37.7

7.5

12.8

79.7

50+ employees

6.8

51.0

42.1

0.0

48.5

51.5

Total

6.2

55.2

38.6

16.8

37.2

46.0

Source: Third QWS, Isfol (2014)

Motivating factors favouring both past career improvements and good career prospects include good learning opportunities, working for an organisation that motivates workers to do their best and jobs that match personal aspirations.

When disaggregating by employment status, the self- employed report better past and expected career improvements than employees and para-subordinates, although there are some interesting differences. Among the self-employed, working for an organisation has a stronger impact on their career advancement than learning opportunities and matching a job with personal aspirations.

Employees consider their career prospects are more favourable when they are working in an organisation that motivates them to do their best and when the job matches their personal aspiration, while the reverse is true when there are good learning opportunities.

Table 3: Past and expected career, 2010, (%)
 

Career has improved

Good career prospects

 

Employees

Self-employed

Employees

Self-employed

No learning opportunities

19.3

26.4

15.7

16.4

Learning opportunities

41.9

48.5

46.3

60.5

My organisation motivates me to do my best

28.7

21.9

19.6

32.6

My organisation does not motivate me to do my best

41.2

48.8

58.6

58.2

Job does not fit with my aspirations

28.3

32.2

40.1

50.3

Job fits with my aspirations

45.2

49.7

57.2

56.9

Total

38.6

46.0

46.0

55.5

Source: Third QWS, Isfol (2014)

Career advancement prospects decrease with establishment size and are lower for part-time workers. They are higher for medium skilled employees, while they increase with skill level and establishment size for the self-employed. Because of the small average size of enterprises in Italy, mobility among enterprises is commonly considered a means of achieving career improvement.

Responses to the 2010 survey show that the more often an employee has changed job, the more perceptions of career prospects and of past career in the current job decline. The self employed display a more optimistic view of both their past and prospective career as their number of job changes increase, although after five job changes these perceptions also begin to decline – although they are still more optimistic than employees. Needless to say, satisfaction with career prospects decline in line with the number of job changes, from 61.6% (no change) to 45.4% (five or more changes). Thus, the pattern highlighted in the 2006 survey still holds only for self-employed.

Motivation and aspiration

Asked whether they felt motivated to do their best at work, 81.3% of respondents said they were, while 68.1% considered their current job fitted their aspirations. The difference in these two figures reflects a long-standing debate over the inadequateness of Italy’s approach to work orientation, both in the educational system and in the employment services. Employment services play a marginal role in placement (around 3% of hirings). While differences by gender are limited, motivation declines with both age and qualification, thus reflecting over-skilling patterns, while current job fit increases with age (Figure 7).

Figure 7: Motivation by socio-demographic characteristics, 2010 (%)

Figure 7: Motivation by socio-demographic characteristics, 2010 (%)

Source: Third QWS, Isfol (2014)

As with over-skilling, the probability of being motivated to do one’s best at work is investigated through a logistic regression (Table A2, Annex).

Factors that depress motivation are: being male (-36%); under 45 years old (-37% among those aged 15−29 and -38% among those aged 30-44); over-skilled (-35%); performing stressful tasks (-40%); working in the public sector (-36%); being employees, regardless of employment stability or working time regime (from -60% among non-permanent full-time employees to -82% for non-permanent part-time work); and technical skills (-44%).

Motivating factors are: low (+75%) and medium (+42%) qualification levels; perceiving good career prospects (+225%); working at a normal pace (+70%); perceiving good learning opportunities (+306%); working in small firms (+33%); working in the construction industry (+94%); and especially being satisfied about one’s job (+806%). Motivating factors are therefore mainly individualistic in nature and are more likely to be perceived by those working in low qualified sectors and experience-based sectors.

Autonomy dimension

Work intensity

Working at high speed was reported by 27.2% of respondents (36.1% in 2006, 33.6% in 2002), while reports of a ‘normal’ pace of work returned to 2002 levels (51.5%, 10 percentage points higher than in 2006). Women report higher pace of work (29.9%) than men (25.4%) but less discontinuous work. This is largely due to the concentration of men in manufacturing where they are more exposed to an erratic work flow due to fluctuations in orders (Figure 8). An above average proportion of employees report a ‘normal’ pace of work (55.3% permanent employees and 57.2% non-permanent). A discontinuous pace of work is more prominent among the self-employed (30.1%).

Figure 8: Pace of work by gender and employment contract, 2010 (%)

Figure 8: Pace of work by gender and employment contract, 2010 (%)

Source: Third QWS, Isfol (2014)

Factors affecting the pace of work reflect the structural change in Italy’s labour market towards a service society. Asked to list the factors that have changed the pace of work, respondents said the needs of clients and users were most important (79.6%), followed by colleagues (54.1%); the speed of machines affected only 23.1% of the workforce (Figure 9).

Permanent employees were slightly more likely to cite the speed of a machine as a factor (24.5%) and slightly less likely to cite clients/users (75.7%). Non-permanent employees display a profile similar to permanent ones, with the exception of the machine speed factor (16.5%). Para-subordinates report being exposed to the pace induced by clients’ and users’ needs more than average (84.8%) and say their pace of work is particularly affected by the control of superiors (62.5%), and this figure illustrates that they are more dependent on hierarchical demands than employees. The self-employed, in contrast, report above-average exposure to clients’ and users’ demands (90.9%), yet below-average exposure to demands from colleagues and superiors (respectively 45% and 31.4%).

Figure 9: Factors affecting pace of work by employment status, 2010 (%)

Figure 9: Factors affecting pace of work by employment status, 2010 (%)

Source: Third QWS, Isfol (2014)

Degree of autonomy

Autonomy is summarised by asking respondents to consider seven questions (Figure 10).

Autonomy levels are quite low: 84% of respondents say they have to respect precise quality standards, 72% perform repetitive tasks and 76% do not supervise other employees. There are significant differences by types of employment that outline varying patterns of lack of autonomy.

Permanent employees are more likely to report a need to meet precise quality standards (86.7%). Their need to perform repetitive tasks (73.6%), meet tight deadlines, interrupt work for unforeseen tasks and undergo quality assessment are around the average. Overall, permanent employees display a significant degree of operative responsibility, although autonomy is not particularly high, but they also have fairly good control over timing. They also display the lowest figures for presenteeism.

Non-permanent employees and para-subordinates report that they have no supervisory responsibilities for others (87.3%), have to perform repetitive tasks (74.2%) and have no responsibility for quality assessment (44.2%), thus displaying the lowest level of responsibility and discretion.

The self-employed are more likely to report the need to work to tight deadlines (34.1%), working when sick (19.2%) and being required to cope with unforeseen interruptions to their work (26.8%). They report the lowest figures for repetitive tasks (64.4%).

Logistic regressions on the autonomy indicators show that employment regimes have an impact on autonomy patterns. Para-subordinates’ levels of autonomy are closer to those of employees than to those of the self-employed. This confirms findings from the Isfol-Plus survey identifying the bogus self-employed status of 15% of all workers officially classified as self-employed, while the share increases to 74% among para-subordinates (Mandrone and Marocco, 2012).

Figure 10: Aspects of work autonomy by employment status, 2010 (%)

Figure 10: Aspects of work autonomy by employment status, 2010 (%)

Source: Third QWS, Isfol (2014)

Control dimension

The control dimension is composed of the opportunities to choose and modify the assigned activities, the supervision modes, participation in teamworking and in decision-making.

Figure 11 summarises the main aspects of discretion at work by gender and employment status.

Discretion opportunities are lower than the strategic aspects of work (56.9% for women, 56% for men) than for operational aspects of work, such as choosing or varying the pace of work (73.5% for women, 74.5% for men), tasks (71.7% for women, 70.4% for men) methods of work (72.7% for women, 70.8% for men) and planning (76% for women, 70.6% for men).

Women report higher discretion then men because of their concentration in service sectors, while the self-employed report much higher levels of discretion than employees, ranging from about 85% (intervention opportunities in strategies and goals) to over 90% (sequence of tasks, planning and work methods), and self-employed men have wider scope than self-employed women.

Employees display the lowest room for manoeuvre on strategies and goals (50.6% for women, 43% men) and the highest in choosing or varying the pace of work (70.5% for women, 68.4%).

Figure 11: Discretion at work by gender and employment status, 2010 (%)

Figure 11: Discretion at work by gender and employment status, 2010 (%)

Source: Third QWS, Isfol (2014)

A binary scheme is used to build a synthetic control indicator for all the dimensions used in the survey. To identify its determinants, the resulting score is regressed over wide set of determinants over the total working population (all workers) and on employees only. (The armed forces are excluded from both groups.) (Table A3, Annex).

Control increases with qualification, especially for the highly qualified (+7% among all employed, +9.5% among employees, compared to the poorly qualified). Past work experience and tenure do not have a significant impact. Similarly, skill level has a strong impact on control. Its impact is stronger for low-skilled (-8%) than for medium-skilled (-6.8%) workers, and less strong for low-skilled and medium-skilled employees (respectively -8.1% and 8.8%).

Analysed by employment status, the control of non-permanent employees is lower (-5.9% in the first regression and -5.7% in the second) while the self-employed display a much higher level of control (+17.6%).

Working in manufacturing suggests a depressive effect on control, especially for employees (respectively -2.3% in the first regression and 4.4% in the second). Working in construction has a positive impact, but only when considering the whole workforce (+3.6%) because of the large share of self-employed in the sector.

Finally, control decreases with company size. Compared to the reference category, in small firms control decreases by about 3.8% in both regressions and in medium and large firms by almost 6% (all employed persons) and 6.3% (employees only).

Economic dimension

The economic dimension includes earnings, economic security, job security and perceived difficulties in having enough money to last until the end of the month. There is a long-running debate in Italy about increasing dissatisfaction among the Italian workforce, partly caused by high inequality (among the highest in the EU, and stable over time), poor career prospects due to poor economic performance, increasing taxation levels and reduction in both employment and welfare state protection (Boeri, Brandolini, 2005).

Asked if they expected a wage cut in the next 12 months, 22.9% of respondents said yes, and 17.7% reported that they might lose their current job. Para-subordinate workers were most pessimistic; 60.2% reported they might lose their job and 52.3% expected a wage cut. Of non-permanent employees, 44.6% expected a wage cut and 52.9% the loss of their job. For both these categories, the figures were well above those for both self-employed and permanent employees (figure 12).

Figure 12: Career prospects by employment status, 2010 (%)

Figure 12: Career prospects by employment status, 2010 (%

Source: Third QWS, Isfol (2014)



Commentary

The third ISFOL Quality of Work Survey marks an important step towards the maturity of working condition surveys in Italy. Its sample in considerably larger than previous waves and offers more precise estimates and the implementation of a clear analytical framework.

Some apparent improvements in working conditions may be due to the timing of the survey, after adjustment in the workforce had been achieved by removing non-permanent workers from the workforce. Structural problems highlighted by previous waves and other surveys remain unchanged and are investigated more in depth than in previous reports.

Hopefully, this new survey will help to shift the debate over Italy’s employment strategies. The current focus is on job creation, the legal framework and especially on numerical flexibility. This survey shows the need to focus instead on ‘better jobs’ that will play a crucial role in re-launching Italy’s languishing productivity and competitiveness.



References

Boeri, T. and Brandolini, M. (2005), ‘The age of discontent: Italian households at the beginning of the decade’, Giornale degli Economisti, Bocconi University, Vol. 63, pp. 449−487

Carrieri, M. and Damiano, C. (eds.) (2010), Come cambia il lavoro [How work changes], Ediesse, Rome.

Centra, M., Curtarelli, M. and Gualtieri, V. (2012), ‘La qualità del lavoro in Italia: evidenza empirica dalla 3. Indagine ISFOL-Qdl’ [The quality of work in Italy: empirical evidence from the third ISFOL-QWS], in Gallie, D., Gosetti, G., La Rosa, M. (eds.) ‘Qualità del lavoro e vita lavorativa. Cosa è cambiato e cosa sta cambiando’ [Quality of work and working life. What changed and what is changing], Sociologia del Lavoro No. 127, Franco Angeli Editione, Milan.

Centra, M. and Tronti L. (2011), ‘Capitale umano e mercato del lavoro. Spunti analitici e questioni aperte’ [Human capital and labour market. Analytical hints and open issues], Osservatorio Isfol, 2011, Vol. 1, No. 1, pp. 31−44.

Draghi, M. (2010), ‘Crescita, benessere e compiti dell’economia politica’ [Growth, welfare and economics tasks], lecture presented at the Giorgio Fua Memorial Conference, Ancona, 5 November 2010.

Gallino, L. (1985), Informatica e qualità del lavoro [Information technologies and quality of work], Einaudi, Turin.

ISFOL (2014), Le Dimensioni Della Qualità Del Lavoro - I Risultati Della III Indagine Isfol Sulla Qualità Del Lavoro [The dimensions of quality of work – The results from the third ISFOL Quality of Work Survey], European Social Fund Books, No. 183, ISFOL, Rome.

La Rosa, M. (2000), ‘Dalla sicurezza alla qualità del lavoro’ [From job security to job quality], Osservatorio ISFOL, Nos. 2−3.

Mandrone, E. and Marocco, M. (2012), ‘La variante italiana della flessibilità’ [The Italian variant of flexibility], ISFOL Research Paper, No. 1/2012, Isfol, Rome.

Ricci, A., (ed.) (2011), Istruzione, formazione e mercato del lavoro: i rendimenti del capitale umano in Italia [Education, training and labour market: the returns of human capital in Italy’], European Social Fund Books, No. 153, ISFOL, Rome.


Mario Giaccone, Ires



Annex

Table A1: Determinants of overskilling by qualification, OR (odds ratios) and significance
 

High qualified

Medium qualified

Geographic area(ref. value southern Italy)

   

Northwest

0.379***

0.634*

North-east

0.736

0.841

Centre

0.821

0.640*

Gender (ref. value women)

   

Men

0.767

1.557**

Occupational seniority

0.981

1.054*

Squared occupational seniority

1.001

0.999

Seniority in current job

0.998

0.983

Paid training

1.090

0.645**

On-the-job training

0.391**

0.572**

Undeclared or irregular contract

0.249

3.445**

Employment status (ref. value permanent employees)

   

Non-permanent employees

0.475*

1.167

Para-subordinates

1.267

1.579

Self-employed

0.946

1.609

Working times (ref. value Full-time)

   

Part-time

1.242

1.41

Occupational status (ref. value low skilled)

   

High skilled

0.408

1.548

Technical skills

0.344

1.545

Medium skilled

0.695

1.435

Type of employer (ref. value private)

   

Public

1.02

0.726

Sector (ref. value other services)

   

Agriculture

0.565

0.457

Manufacturing

0.992

0.377***

Construction

0.393

0.663

Commerce

0.355*

0.819

Workplace size (ref. value 50 employees or more)

   

15 employees or less

1.074

0.571**

16 to 49 employess

1.148

0.547**

Career prospects

1.425

1.384*

Motivating job

0.701

0.354***

Job fits with my aspirations

0.286***

0.415***

Notes (delete if none) * p =< 0.05; ** p =< 0.01; *** p =< 0.001

Source: Third QWS, Isfol (2014)

Table A2: Determinants of motivation at work, OR (odds ratios)

Gender (ref. value women)

 

Men

0.64***

Age classes (ref. value 55 +)

 

15−29 years

0.63*

30−44 years

0.62**

45−54 years

0.76

Qualification (ref. value high qualified)

 

Low qualified

1.75**

Medium qualified

1.42*

Geographic area (ref. value southern Italy)

 

Northwest

0.88

North-east

1.37*

Centre

0.93

Career prospects

3.25***

Skills matching (ref. value Matching)

 

Over-skilling

0.65***

Job fits with aspirations

1.21

Prevalence of repetitive tasks

1.17

Normal pace of work

1.70***

Stressful job

0.60***

Learning opportunities

4.06***

Training

1.03

Workplace size (ref. value 50 employees or more)

 

Work alone

1.00

15 employees or less

1.25

16 to 49 employees

1.33*

Type of employer (ref. value private)

 

Public

0.64**

Sector (ref. value other services)

 

Agriculture

1.12

Manufacturing

1.27

Construction

1.94**

Commerce

1.04

Seniority in current job

0.98***

Type of contract (ref. value self-employed )

 

Permanent full-time

0.35***

Permanent part-time

0.39***

Non-permanent and para-subordinates full-time

0.40***

Non-permanent and para-subordinates part-time

0.18***

Undeclared or irregular contract

3.69***

Occupational status (ref. value low skilled)

 

High skilled

0.81

Technical skills

0.56**

Medium skilled

0.88

Working time flexibility (ref. value entry or exit flexibility)

 

Entry and exit Flexibility

1.08

No flexibility

1.04

Job satisfaction

9.67***

Notes (delete if none) * p =< 0.05; ** p =< 0.01; *** p =< 0.001

Source: Third QWS, Isfol (2014)

Table A3: Determinants of work control by employment contract (multivariate regression)
 

All employed

Only employees

Women

1.017

0.36

Centre region

-0.158

-0.279

South islands

-1.056

-1.954*

Medium qualified

2.086**

2.799**

High qualified

7.051***

9.512***

Past experience

0.75

0.1

Tenure

0.07

0.07

Medium skilled

-6.785***

-8.831***

Low skilled

-8.013***

-8.110***

Part-time

-1.931

-1.662

Non-permanent employees

-5.939***

-5.720***

Self-employed

17.59**

 

Redundancies at workplace

0.94

1.119

Public sector

1.374

-0.351

Agriculture

3.451

3.864

Manufacturing

-2.363**

-4.428***

Construction

3.663**

2.353

Commerce

0.64

0.23

16-49 employees

-3.738***

-3.889***

50+ employees

-5.959***

-6.286***

Constant

61.89***

64.01***

Observations

4.768

3.690

R2

0.35

0.18

Notes: armed forces excluded; * p<0.1, ** p<0.05, *** p<0.01; reference categories: men, north, low qualified, employees, high skilled, full time, private, other services, 1-15 employees.

Source: Third Isfol QWS, Isfol (2014)

EF/14/60

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