Accident risk, gender, family status and occupational choice in the UK

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Labour Economics 15 (2008) 938 – 957 www.elsevier.com/locate/econbase

Accident risk, gender, family status and occupational choice in the UK ☆ S. Grazier a , P.J. Sloane a,b,⁎ a

WELMERC, School of Business and Economics, Swansea, University, UK b IZA, Bonn, Germany

Received 13 September 2006; received in revised form 16 July 2007; accepted 21 July 2007 Available online 1 August 2007

Abstract Many studies show that women are more risk averse than men. In this paper, following DeLeire and Levy [Deleire T. and Levy H. (2004) ‘Worker Sorting and the Risk of Death on the Job’, Journal of Labor Economics, Vol. 22, No. 4, pp. 210–217.] for the US, we use family structure as a proxy for the degree of risk aversion to test the proposition that those with strong aversion to risk will make occupational choices biased towards safer jobs. In line with DeLeire and Levy we find that women are more risk averse than men and those that are single with children are more risk averse than those without. The effect on the degree of gender segregation is somewhat smaller than for the US. © 2007 Elsevier B.V. All rights reserved. JEL classification: J0; J2; K2 Keywords: Accident risk; Gender segregation; Family status; Occupational choice

1. Introduction There is now a substantial literature suggesting that women are more risk averse than men. Thus, Datta Gupta et al. (2005) show, using experimental data, that when given a choice between the riskier options of a tournament and payment by piece rates men choose a tournament significantly more often than women. Brown and Taylor (2005) show that men are more likely than women to invest in risky assets such as shares and unit trusts. Ekeland et al. (2005), using psychometric data from a large ☆

The authors are grateful to R. Arabsheibani and two anonymous referees for very helpful comments on an earlier version of this paper. ⁎ Corresponding author. WELMERC, School of Business and Economics, Swansea University, Richard Price Building, Singleton Park, Swansea SA2 8PP, UK. E-mail address: [email protected] (P.J. Sloane). 0927-5371/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.labeco.2007.07.007

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population based cohort of Finns in 1996, show that men are less risk averse than women and this makes them significantly more likely to become self employed than women. Reed and Dahlquist (1994) find for the US that women are more likely to be employed in safer jobs than men and Dohmen et al. (2005) likewise for Germany. Using the British Social Attitudes Survey 2001,1 the Health and Safety Executive (2002) reports that British workers regard safe working conditions as an important job characteristic, with 83 per cent citing little risk of injury or damage to health as either very or fairly important to them; men however, were less likely than women to report this as an important job characteristic. Further significant differences in attitudes towards health and safety between men and women were reported, with men significantly less likely to say that not enough attention was paid to protecting workers from risk of injury in Britain. Men were also significantly less likely to wear protective clothing at work than women when required to do so. DeLeire and Levy (2004) use family structure as a proxy for the willingness to work in occupations with a relatively high accident rate to test the proposition that workers with strong risk aversion will tend to make occupational choices which sort them into safer jobs. They find that single mothers and single fathers are the most risk averse groups and that risk of death across occupations can explain no less than one quarter of occupational gender segregation in the USA. In this paper we attempt to replicate the DeLeire and Levy findings using UK data. As is the case with the DeLeire and Levy study the HSE data which we use do not distinguish family type. Thus, there is a potential aggregation problem.2 Firms and workers are heterogeneous. The former will differ in their ability to provide safety at work and workers will vary in their willingness to accept a hazardous job. If we assume that firms attempt to maximise profits and workers to maximise utility, an equilibrium outcome will result in which the most risk averse workers will be matched with firms which find it relatively easy to provide a safe working environment and the least risk averse workers will sort into riskier jobs for which they will receive a compensating wage differential. Turning specifically to gender, the persistence of gender occupational segregation is well established, but its causes disputed. In terms of labour demand men and women might possess different skill endowments which cause employers to favour one gender over the other for specific jobs, or employers may simply discriminate. On the supply side men and women may differ in their preferences for certain types of job, so that segregation is simply the result of individual choice and it is this which DeLeire and Levy focus upon. As preferences for risk cannot be measured directly they use gender and family structure as proxies for risk preferences. Family structure will not correlate so obviously with discrimination, although there is still a possibility of this. For example, employers may adopt paternalistic attitudes and prefer not to employ parents in occupations with a high accident rate. DeLeire and Levy hypothesise that single parents will be most risk averse because children are then totally dependent on a single parent, so that the presence of children will make both men and women more risk averse than childless couples. In turn married women will be more risk averse than married men, because they generally take the more significant role in child-rearing. Their results confirm that single parents are the group most averse to risk and, regardless of family structure, that men are less risk averse than women. Hence “single parents choose jobs with lower risk of death than their married or childless counterparts” (p. 940) and overall their results offer “strong empirical support for the hypothesis that workers sort into jobs on the basis of their preferences” (p.946). The question we address is whether similar outcomes are present in the UK, 1 The British Social Attitudes Survey is an annual survey carried out by the National Centre for Social Research. In 2001, the survey included a health and safety module sponsored by the Health and Safety Executive. 2 We are grateful to an anonymous referee for pointing this out and to another anonymous referee for further helpful comments.

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though it should be noted that the incidence of fatalities and injuries in the UK is amongst the lowest in Europe and substantially lower than in the US.3 2. Methodology We use the same approach as DeLeire and Levy (2004) to test empirically the impact of risk on occupational choice. Following Greene (2003), it is assumed that individuals choose an occupation consistent with a random utility model. Individual i's choice of occupation j (from J occupations) results in utility Uij, as illustrated by Eq. (1). Uij ¼ v VZij þ eij

ð1Þ

The exogenous variable Zij can be divided into variables that depend only on the individual (Wi) such as education, and those that vary across the occupation choices (Xj) such as risk of death. Hence: Zij ¼ ½Wi ; Xj 

ð2Þ

If the individual chooses occupation j, we assume that the resulting utility Uij is the maximum among the J utilities. The probability that choice j is made can therefore be illustrated by Eq. (3): ProbðUij N Uik Þ

for all k p j

ð3Þ

If the disturbances are independent and identically distributed with an extreme value distribution, then McFadden (1973) shows that the observed occupation choice of individual i (Yi) can be denoted by the conditional logit model, as shown by Eq. (4): V V

eb Zij ProbðYi ¼ jÞ ¼ PJ bz V ij j¼1 e

ð4Þ

Substituting in Wi and Xj for Zij we derive the following: V

V

e b x j e a i wi ProbðYi ¼ jÞ ¼ PJ b Vxj eaiVwi j¼1 e

ð5Þ

Terms that do not vary across alternatives (Wi) and are specific to the individual fall out of the probability and we are unable to estimate the effect of individual characteristics upon occupational choice (α), which are invariant to the choice. However, we can estimate β and therefore the effect that occupational characteristics have upon occupational choice, as in McFadden's (1973) fixed effects model. The conditional logit model assumes the error terms follow independently and identically an extreme value distribution. This independence of irrelevant alternatives (IIA) assumption should be tested, utilising a specification test (Hausman and McFadden, 1984). It is argued that if the omission of a choice does not significantly alter parameter values the assumption is acceptable and choices are independent from irrelevant alternatives. Two models must be estimated; the first, denoted by subscript f, the model with a full set of choices, and the second, denoted by subscript r, the model with restricted choice (omitting a possible occupational choice). The resultant

3

See Health and Safety Executive (2000) for a discussion of this.

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parameter estimates (β ) and covariance matrices (Ω) are recorded and the test statistic, which follows a chi-square distribution calculated as in Eq. (6) v2 ¼ ðbr  bf Þ VðXr  Xf Þ1 ðbr  bf Þ

ð6Þ

If the null hypothesis cannot be rejected, alternative choices are irrelevant and the IIA assumption is accepted. 3. Data Three data sets are used in the estimation: the Labour Force Survey (LFS) for data on employment, Health and Safety Executive (HSE) data on fatal and major injuries, and the Skills Survey for data on occupational characteristics. DeLeire and Levy restrict their analysis to full-time workers on the grounds that part-time workers will allocate some time to household production, for which risk data are not available. We prefer to include part-time workers in our analysis as we would expect family status to be an important determinant of whether an individual works full-time or part-time and to exclude parttimers would result in a disproportionate elimination of parents from the analysis. Further, individuals can change from full-time to part-time status and vice versa over time. To facilitate comparisons, however, the estimation was also carried out for full-time workers only, and though results are not reported here, they are quantitatively similar. DeLeire and Levy also restrict their analysis to workers aged from 25 to 34 on the grounds that current risk measures are inaccurate for older workers, who have accumulated occupation– specific knowledge. However, since current risk rates reflect the presence of older workers, there is a danger of estimation bias if older workers are left out of the analysis and for this reason we prefer to leave them in. Again for comparison purposes, we have also performed the estimation for 25 to 34 year olds only, and though again the results are not reported, they are similar.4 3.1. Accident and employment data Data on occupational risk of fatal and non-fatal injury are collected by the HSE under the Reporting of Injuries, Diseases and Dangerous Occurrences Regulations (RIDDOR) 1995, which requires employers to report fatalities and specific non-fatal injuries at work to the HSE. Risk rates are typically calculated by industry or occupation and then matched into another data set. Ideally, one would want to estimate risk for each occupation-by-industry cell, but most occupations have zero recorded deaths, so this is not practicable (see Sandy et al., 2001 for a discussion of this). To ensure sufficient observations, we take numbers of fatalities and major injuries from RIDDOR reports for 2002/3, 2003/4 and 2004/5 measured according to occupation (SOC 2000).5 We merge LFS September–November quarter data for the years 2002, 2003 and 2004 to provide data on the number of hours worked in each occupation.6 With a large sample size that 4

Self-employed workers are excluded from the estimation. RIDDOR does not require injuries to the self-employed that occurred at their own premises to be reported, and so their inclusion would bias the results. 5 It is not possible to split accurately the number of accidents by gender and family composition either here or with the data used by DeLeire and Levy. As DeLeire and Levy comment “there are too few fatalities and too few women in the occupations with the most fatalities to calculate reliable gender-specific risks of death” (p.942). The same applies to family structure. We are therefore overstating the risk women face and understating the risk men face. Male-specific measures were calculated for comparison, with no effect on the order of riskiness between occupations. 6 The LFS sample was restricted to rotation group 5 for 2003 and 2002 to prevent over-counting.

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includes detailed information on occupation, marital status and number of children, this enables workers to be divided according to family group. Initial observation reveals that between 2002 and 2004, men constituted 54 per cent of total employment, but were responsible for 96.2 per cent of fatal injuries and 73.6 per cent of major injuries at work during the same period. Thus, men are much more likely to have an accident at work than women. Death and major injury numbers are converted into rates according to 2 digit SOC 2000, giving a total of 25 occupations. A death rate per 100 full-time and part-time workers and a major injury rate per 100 full-time and part-time workers are calculated for each of the 25 occupations, using a similar method to DeLeire and Levy basing the denominator of the measure on hours worked. First we derive the total full time weekly hours and total part time weekly hours worked in each occupation. We then determine the average weekly hours worked by full time workers and the average part time weekly hours for each occupation. Dividing the total hours by the average hours separately for full and part time workers, enables us to transform the hours into the equivalent number of workers. Fatal and Injury rates can then be calculated per 100 equivalent full time and part time workers over the sample period 2002–2004. In addition, the fraction of hours worked by females employed in each occupation is also calculated. The appropriate death and major injury rates are then assigned to each worker in the LFS sample according to their 2 digit occupation code. Table 1 reports the rates for each occupation in order of the highest death rate. This reveals that 4 of the 25 occupations have a zero death rate compared to 2 out of 44 in the DeLeire and Levy study. The two occupations with the greatest death rate are Skilled Agricultural Trades (where 8 per cent of workers are female), and Elementary Trades, Plant and Storage Related Occupations (where 15 per cent of workers are female). The occupations with the least risk are Health Professionals (40 per cent female), and Teaching and Research Professionals (62 per cent female), Note that the UK death rate is lower than that calculated by DeLeire and Levy for the US. Their greatest death rate is calculated as 0.0872 per 100 workers compared to 0.0189 per 100 workers here. However, our UK rates include part-time workers and so we would expect the risk to be slightly lower. Also, the overall fatality rate at work is slightly greater in the US compared to the UK, as discussed in the gender segregation section. The death rate and the fraction of female workers employed in each occupation are significantly correlated with a coefficient of − 0.767. This indicates that occupations with the highest death rates are associated with fewer female employees. There is a significant positive correlation of 0.844 between the death rate and major injury rate. This confirms that occupations with a high death rate are also likely to have a high major injury rate. The fact that the correlation is strong but not perfect means the effect of both an occupations' death and major injury rate can be estimated separately. 3.2. Occupational Characteristics In order to estimate the effect risk of death and injury has upon occupational choice, other characteristics associated with each occupation must be controlled for. This is particularly important when comparing what influences occupational choice between different groups of workers, as specific groups may have particular preferences. For example, men may be more likely to work in more physically demanding occupations and women in occupations requiring more caring skills.7 In addition, employers may have different demands such as requiring workers to have a certain amount of strength and stamina in physically demanding jobs. Thus, it is necessary to control for occupational attributes in addition to the risk of death and injury rates. 7

See for example Thewlis et al. (2004).

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Table 1 Occupational Death and Major Injury Rates per 100 Full Time and Part Time Workers and Fraction of Female Workers

51 Skilled Agricultural Trades 91 Elementary Trades, Plant and Storage Related Occupations 81 Process Plant and Machine Operatives 53 Skilled Construction and Building Trades 82 Transport and Mobile Machine Drivers and Operatives 52 Skilled Metal and Electrical Trades 33 Protective Service Occupations 12 Managers and Proprietors in Agriculture and Services 21 Service and Technology Professionals 92 Elementary Admin and Service Occupations 31 Science and Technology Associate Professionals 62 Leisure and other Personal Service Occupations 11 Corporate Managers 54 Textiles, Printing and Other Skilled Trades 34 Culture, Media and Sports Occupations 61 Caring Personal Service Occupations 71 Sales Occupations 24 Business and Public Service Professionals 42 Secretarial and related Occupations 41 Administrative Occupations 35 Business and Public Service Associate Professionals 72 Customer Service Occupations 32 Health and Social Welfare Associate Professionals 23 Teaching and Research Professionals 22 Health Professionals

Death Rate per 100 Workers

Major Injury Rate per 100 Workers

Fraction Female

0.01886 0.00716

0.30009 0.74740

0.07952 0.14773

0.00636 0.00631 0.00568

0.92697 0.50353 0.60828

0.21928 0.00715 0.03602

0.00333 0.00265 0.00208

0.38961 0.70791 0.16311

0.01313 0.17359 0.37585

0.00156 0.00120 0.00088

0.17985 0.29944 0.17179

0.13367 0.48224 0.20944

0.00073

0.18632

0.61903

0.00052 0.00051 0.00027 0.00023 0.00020 0.00018 0.00008 0.00005 0.00005

0.06802 0.20847 0.70791 0.22486 0.19486 0.04831 0.05014 0.06233 0.03538

0.29954 0.30554 0.39251 0.89811 0.64019 0.34594 0.97174 0.71360 0.42006

0 0

0.13093 0.11504

0.66393 0.80230

0 0

0.11211 0.04648

0.61981 0.39934

Occupational attribute variables were created using the Skills Survey 2001, which surveyed 4470 individuals aged 20–64 in Great Britain. Workers were asked how important a particular skill or attribute was to their work. Table 2 lists the variables to be used in the estimation, and their definitions. Each variable takes a value from 0–4, with 4 defined as essential to the work, and 0 defined as not at all important. The mean of each attribute for each occupation is then calculated using 2 digit SOC 2000. The means are reported in Appendix 1. In addition, the percentage covered by union terms and conditions has been calculated for each occupation using the LFS. All skills variable means follow a priori expectations. In terms of the physical variables for instance, low means for strength, stamina, hands and tools are calculated for Corporate Managers (11), Administrative Occupations (41), and Business and Public Service Associate Professionals (35), indicating these skills are relatively unimportant for these occupations. In contrast, they are very important for working in Skilled Agricultural Trades (51), Skilled Metal and Electrical Trades (52), and Skilled Construction and Building Trades (53). Appendix 2 reports some of the correlations between the skills variables, death and major injury variables, and the fraction female in each occupation. Fatal and major injury are positively

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Table 2 Occupational Attributes Variables Variable

Occupational Attribute

STRENGTH STAMINA HANDS TOOLS WRITELG CALCA STATS CARING SPECIAL ANALYSE MYTIME USEPC SPEECH PERSUADE LISTEN MOTIVATE FUTURE PERCENTC

Physical strength Physical stamina Skill or accuracy in using hands or fingers Knowledge of how to use or operate tools Writing long documents Adding, subtracting, multiplying or dividing numbers Calculations using advanced mathematical or statistical procedures Counselling, advising or caring of customers or clients Specialist knowledge or understanding Analysing complex problems in depth Organising own time Using a PC or other computerised equipment Making speeches or presentations Using persuasion Listening to customers or clients Motivating staff Making decisions that affect the future of the company Percentage of workers covered by union terms and conditions

Variables created from the Skills Survey 2001.

and significantly associated with strength, stamina, hands and tools. As would be expected, occupations that require physical skills are associated with a higher death and major injury rate. In contrast, caring and speech are negatively associated with both death and major injury rates. Fraction female is significantly positively correlated with caring skills, indicating that women are more likely to work in occupations that require this attribute. Standardised scores (z statistics) were calculated for each of the occupational attribute variables to check the significance of their variation between the 25 occupations. These results suggest that there is no significant variation between occupations with respect to only five of these variables, the implications of which are considered in the conditional logit results section. 4. Estimation 4.1. Family Variables The finding that risk has a greater negative impact upon occupational choice for women could be due to direct discrimination by employers, which forces women to work in safer jobs regardless of their preferences, or indirect discrimination through, for instance, schools portraying safe occupations as stereotypical womens' jobs. As it is less likely, though possible, that this type of discrimination occurs according to family structure, the inclusion of family circumstances in the estimations allows us to consider whether workers sort into occupations according to preferences for risk. Separate samples of workers are created according to gender, marital status, and whether a worker has children,8 using data from the LFS. Table 3 describes the groups created, and the fraction of the whole sample to which each contributes. Those married with children make up the greatest fraction of both samples. In addition, we report the mean fatal and major injury risk for each family composition group. 8

Where a child is defined as a dependent under the age of 19.

Obs Fraction

Death Major

Obs Fraction

Death Major

All Men

Single Men no Children

Single Men with Children

Married Men no Children

Married Men with Children

SDW Men no Children

SDW Men with Children

42326 1.00

9718 0.2296

3800 0.0898

11477 0.2712

13222 0.3124

3064 0.0724

883 0.0209

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

0.0027 0.3021

0.0034 0.2620

0.0027 0.3011

0.0035 0.2599

0.0029 0.3535

0.0034 0.2519

0.0028 0.2975

0.0035 0.2605

0.0025 0.2847

0.0033 0.2628

0.0030 0.3278

0.0034 0.2688

0.0030 0.3436

0.0033 0.2765

All Women

Single Women no Children

Single Women with Children

Married Women no Children

Married Women with Children

SDW Women no Children

SDW Women with Children

38456 1.00

7344 0.1910

4385 0.1140

10270 0.2671

10537 0.2740

3406 0.0886

2374 0.0617

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

0.0007 0.1673

0.0016 0.1611

0.0006 0.1559

0.0015 0.1635

0.0007 0.2021

0.0014 0.1488

0.0007 0.1656

0.0018 0.1684

0.0006 0.1574

0.0015 0.1486

0.0007 0.1736

0.0016 0.1771

0.0007 0.1804

0.0015 0.1618

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Table 3 Descriptive Statistics by Family Composition

SDW = Separated, Divorced or Widowed.

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Men face an average risk of 0.0027 per 100 workers, compared to an average of 0.0007 per 100 workers for women. As predicted, women work in safer occupations. There is no significant difference in risk rates between family groups however, in this purely descriptive analysis. Correlations are calculated between each of the family variables and both the log of death and log of major injury, to compare the effect of occupational risk upon occupational choice between family groups. In line with results of the previous correlations between the risk variables and the fraction of female workers, a negative correlation is calculated for all female groups, and a positive correlation for all male groups. This emphasises the association for all men with relatively riskier jobs than women. Within the family groups, among women single females are the most averse to risk. However, contrary to expectations, the correlation is not stronger for single females with children. Nor for all female groups combined does having children result in a greater negative correlation. For men the results are more in line with what would be expected: having children reduces the correlation coefficient for all three groups except for single men where the correlation coefficients are very similar. 4.2. Conditional Logit Results The conditional logit model is utilised as only one choice can be made among a number of occupations. As we are interested in how the effect of the explanatory variables upon occupational choice varies between specific groups of workers, the model is estimated separately by gender and family structure (the groups listed in Table 3). For reasons of space we do not give the results for occupational attributes here, but overall, as expected, strength has a greater positive effect upon occupational choice for men than for women. Women are more likely to choose an occupation that requires hands skills, with the coefficient significantly negative for men (− 1.526) and significantly positive for women (2.533). A further significant difference in the estimated coefficients between the male and female samples concerns the variable listen, with the effect upon occupational choice significantly negative for men (− 2.311), but significantly positive for women (2.928). F Tests reveal occupational attributes are highly jointly significant in all estimations. Table 4 presents the estimated coefficients for the death and major injury variables from the fourteen estimations. Death has a significant coefficient of − 165.416 for the male sample, whilst for women a significant coefficient of − 216.274 is estimated. This indicates that risk of death has a greater negative effect upon women's occupational choice compared to men's. For both men and women, a significant positive coefficient is found for the major injury variable.9 Considering the within-gender family composition estimation results, in the male samples, having children is associated with a greater negative Death coefficient for all groups. Single men with no children are the least risk averse, with a significant coefficient of − 87.731. As predicted, single men with children are the male group most likely to work in the safest occupations. For females the presence of children increases aversion to risk at work for all groups except for single women, where the coefficients are similar in magnitude. Results are therefore in line with expectations and the findings of DeLeire and Levy.10 9 DeLeire and Levy find a positive coefficient for non fatal injury risk for all groups of men and women. They emphasise that such a result is not totally surprising given that a fatal injury variable is also included, and the two are correlated here with a significant correlation coefficient of 0.844 (see Appendix 2). 10 We also split the SDW groups to isolate widows, as we may expect widows with children to sort into safer work in the same way as do single parents. There are however, a very small number of widows with children in our sample, only 67 men and 123 women. Probably due to this, the risk variables are insignificant in the conditional logit model estimation for both widowed men and women with children.

Table 4 Conditional Logit Death rate and Major Injury rate Estimates by Family Group Single men no children

Single men with children

Married men no children

Married men with children

SDW men no children

SDW men with children

No. of obs Log Likelihood

42326 − 125645.270

9718 − 29236.078

3800 −10533.593

11477 − 33709.450

13222 − 37941.839

3064 − 9013.120

883 − 2542.427

Death Coefficient Standard error

− 165.416⁎⁎⁎ (4.567)

− 87.731⁎⁎⁎ (9.864)

−201.841⁎⁎⁎ (20.059)

− 151.900⁎⁎⁎ (8.630)

− 186.466⁎⁎⁎ (8.373)

− 163.888⁎⁎⁎ (17.193)

− 199.728⁎⁎⁎ (33.687)

Major Coefficient Standard error

2.964⁎⁎⁎ (0.083)

1.152⁎⁎⁎ (0.173)

2.235⁎⁎⁎ (0.371)

2.783⁎⁎⁎ (0.156)

4.352⁎⁎⁎ (0.144)

3.485⁎⁎⁎ (0.305)

4.503⁎⁎⁎ (0.571)

WOMEN

Single Women no children

Single Women with children

Married Women no children

Married Women with children

SDW Women no children

SDW Women with children

No. of obs Log Likelihood

38456 − 105311.010

7344 − 20647.331

4385 −10530.944

10270 − 27952.729

10537 − 28508.339

3406 − 9358.235

2374 − 6390.861

Death Coefficient Standard error

− 216.274⁎⁎⁎ (8.932)

− 242.929⁎⁎⁎ (26.218)

−235.515⁎⁎⁎ (43.221)

− 164.324⁎⁎⁎ (13.675)

− 207.882⁎⁎⁎ (16.383)

− 247.424⁎⁎⁎ (41.592)

− 279.922⁎⁎⁎ (45.792)

Major Coefficient Standard error

0.187⁎⁎⁎ (0.158)

0.990⁎⁎ (0.400)

0.640 (0.646)

− 0.161 (0.289)

− 0.165 (0.309)

1.332⁎⁎ (0.605)

1.039 (0.730)

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MEN

Data: LFS 2002–2004, Skills Survey 2001, HSE 2002/03-2004/05. Other variables included in estimation: Strength, Stamina, Hands, Tools, Writelg, Calca, Stats, Caring, Special, Analyse, Mytime, Usepc, Speech, Persuade, Listen, Motivate, Future, and Percentc.

947

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To interpret the relative magnitudes of the between group coefficients, we carry out simulations in the same way as DeLeire and Levy. As generally single parents are found to be the most averse to risky work, we calculate the reduction in risk each of the other family groups would face if their preferences were the same as that of a single parent. Predicted probabilities, Pj, for each group are calculated using the conditional logit estimates and from this we calculate the average predicted risk for each group.11 For men, we then set the coefficient on fatal risk equal to that estimated for single men with children and calculate a new set of predicted probabilities for each occupation. Using this, we can calculate the average predicted risk each group would face at work if they had the same preferences as single fathers. The process is repeated for women. The results show that single men without children would face a 37 per cent lower risk of death if preferences towards risk were the same as those of single men with children. Married men with children would work in occupations that were 6 per cent safer, and married men with no children in occupations that were 19 per cent safer. Risk would be 14 per cent lower for SDW men with no children and 1 per cent lower for those with children. Married women with no children would face a 22 per cent lower risk of death at work if their preferences were the same as single mothers. For married women with children, risk would be 9 per cent lower. As a further way of assessing whether the difference in risk coefficients between family groups is significant, a further model was tested for a pooled sample of male and female workers. Family group dummy variables were derived and multiplied by the fatal risk rate to create a set of interaction variables. Each family interaction variable was then included in the conditional logit model, excluding the single men with no children variable. In addition, a female⁎fatal risk variable was derived and included in the model. A significant coefficient of − 233.33 was estimated for the female⁎fatal risk variable, indicating women have a significant greater aversion to risk at work compared to men. Other results follow the same pattern estimated in the original model, with single and married women with children the most averse to risk compared to single men with no children, with both interaction coefficients significantly negative. DeLeire and Levy find a positive coefficient for their non-fatal injury variable in all of their regressions. Major injury estimates are also positive in our estimations. As reported earlier, the death and major injury variables are collinear with a correlation coefficient of 0.844, possibly explaining the positive sign when both variables are included in the model. Therefore, the estimation was repeated for each family group, first with just the death variable, and then with just the major injury variable. For reasons of space, results of these estimations are not reported. The effect of excluding the major injury variable has little impact upon the estimated death coefficients. For men and women, groups with children are still the most averse to risk with greater negative coefficients estimated for death compared to childless groups. When the death variable is excluded major injury coefficients remain either insignificant or positive for the male group.12 In the female estimations however, the major injury coefficient is significantly negative for all groups. Furthermore, the magnitude of the coefficient follows the expected pattern, with the presence of children increasing aversion to non-fatal risk. To enable direct comparison with DeLeire and Levy, the estimations were repeated for a sample of full-time workers aged 25–34. Again, women were more risk averse than men, with the death coefficient estimated as − 158.041 for women and − 83.229 for men. For the male family groups, having children increases the aversion to risk, as before. For women however, the death P Average predicted risk ¼ j rj pj where rj refers to the actual risk associated with each occupation. This is in line with research in the compensating wage differentials literature where there is a similar difficulty in showing a premium for non-fatal injury. 11

12

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variable is insignificant for single women with children. This is likely to reflect the small sample size of only 490 for this group, as there are few single mothers in this age bracket working fulltime. For married women, the coefficients are significantly negative for those with and without children and similar in magnitude. Following the results of the standardised scores in which the variation of the occupational attributes variables between occupations was considered, the conditional logit model is re-estimated excluding the five variables with no significant variation (writelong, stats, special, usepc and future). Excluding these five explanatory variables has no effect upon the pattern of results between groups for the death variable. For all female estimations however, and some male estimations, the major injury coefficient is significantly negative. It also follows the expected pattern with a greater negative coefficient for those groups that have children. It may therefore be that the five excluded variables were correlated with major injury and hence affected the coefficient when included. Overall, the results indicate women are more averse to risk than men in terms of their occupational choice. This could be due to women preferring safer work, or due to paternalistic attitudes on the part of the employer. Furthermore, within gender, risk has different effects upon occupational choice according to family structure in the expected way. This may partly be attributed to some paternalism by employers, although this would imply that employers distinguish between applicants on the basis of the presence of children in the family. Legislation in the UK does not allow interviewers to ask potential employees questions related to their marital or family status. This result suggests therefore, that preferences for risk do affect occupational choice in the UK. 4.3. Independence of Irrelevant Alternatives (IIA) As discussed in the methodology section, the conditional logit model assumes the error terms are independent across irrelevant alternatives. In this case, this requires the choice between two occupations to be independent from the choice between other occupations. If the IIA assumption does not hold, this could lead to inconsistent estimates. Following Hausman and McFadden (1984) the test statistic denoted by Eq. (6), which follows a chi-square distribution, is calculated. This test statistic is compared to the relevant value from the v2 distribution. If the test statistic is significantly greater, given by the probability value (p value), then the estimates are significantly different and the null hypothesis, and therefore the IIA assumption, is rejected. DeLeire and Levy conduct Hausman and McFadden tests for the validity of the IIA assumption, although they do not report the full results. They do however, note that “we reject that the coefficients are the same, which is equivalent in this case to rejecting the IIA property of the conditional logit model” (p.942). The choice of one occupation over another is therefore influenced by the existence of other occupations. DeLeire and Levy do not discuss the implications of this result for their estimates, possibly on the grounds that the failure of the model to satisfy the IIA assumption does not affect their main conclusion: that preferences for risk vary by gender and family structure and influence occupational choice. To test whether the IIA assumption holds in our model, Hausman and McFadden tests were conducted separately for men and women. In total, 25 tests were conducted, omitting each occupation in turn. Most of the tests resulted in large test statistics, which were significantly greater than the critical v2 value. In these cases (where p value is equal to 0.0000) the null hypothesis that differences in estimates are not significant is rejected, and the IIA assumption does not hold. It does hold however, for men when occupations 22 and 61 are omitted, and for women when occupations 11, 62 and 92 are omitted.

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As an alternative to the conditional logit model, a nested logit model can be used if the IIA test fails. To confirm the reliability of the estimates given that our model fails the IIA assumption, a nested logit was estimated. The 25 occupations were grouped into manual and non-manual occupations13 and the model re-estimated by family group.14 As found using the conditional logit model, women were more averse to death in occupational choice. For men, those that were single with no children were the least averse to fatal risk, with the Death variable coefficient insignificant. Single parents and those married with children were estimated to have the greatest aversion to fatal risk. This confirms the fact that failure to satisfy the IIA assumption in the conditional logit model does not affect the pattern of results. 4.4. Potential Problems and Further Tests The conditional logit estimates above suggest that workers have different preferences for risky work according to gender and family structure, and this in turn leads to a degree of occupational sorting. However, to be sure that the estimation results are confirming this hypothesis, it is necessary to consider a number of other possibilities that may be causing or biasing the results. As a persons' occupation itself may influence whether they get married and/or have children, family structure may be endogenous. For instance, as DeLeire and Levy speculate, “individuals who choose dangerous occupations may have difficulty attracting a spouse” (p.941). We may have therefore over-estimated the extent at which workers sort into occupations based on preferences determined by their family composition. Other results therefore, rely on the assumption that family structure is exogenous. A further issue relates to the occupational risk variables. As discussed in the data section, similar to DeLeire and Levy, we derive an average risk of death and injury variable, as it is not possible to calculate reliable gender-specific variables. It could be that instead of men sorting into relatively more dangerous occupations than women, men are less careful than women at work. Hence, our risk variables could be overstating the risk women face. In the same way as DeLeire and Levy, we examine non-fatal injury risk for men and women separately for the more detailed 3 digit occupations (81 occupations in total). A t test reveals the difference between the male and female major injury variable for the 81 occupations is insignificant. The fact that there is little difference between genders for within-occupation non-fatal injury risk, suggests that it is unlikely the pattern of results is due to men being less careful. 4.4.1. (i) Demographic Characteristics It is important to ensure that the observed results between family groups are not due to the correlation between family structure and other demographics. For example, education could be correlated with family group, which in turn could determine aversion to risk and consequently occupational choice. Therefore, following DeLeire and Levy, we need to ensure that the pattern of results is due to family group and not due to differences in education, race and union status. Three sets of dummy variables were created using the LFS data for education, race and union membership. We find, as DeLeire and Levy, that single men and women with children are much less likely to have a degree compared to other groups, their modal highest qualification being a GCSE. 13 In SOC 2000 codes, manual occupations are 51,52, 53, 54, 61, 62, 81, 82, 91, 92 and non-manual occupations are 11, 12, 21, 22, 23, 24, 31, 32, 33, 34, 35, 41, 42, 71, 72. 14 The nested logit model was estimated with fewer variables than the conditional logit model because of problems with collinearity, with variables having only two values, one for manual occupation and the other for non-manual occupation. Major injury was one of the variables dropped from the model.

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Three sets of models are estimated, again using the conditional logit model, separately for each of the family groups, including all variables as before except for Fatalhr. Fatalhr is interacted with the four education dummies in the first model, and these four interaction variables are added to the model. The same method is applied to a model for race and a model for union status. Table 5 reports interaction coefficients for the education model. The results show that more educated individuals place a greater negative weight on risk of death when making their occupation choice. For all family groups, having a degree results in the greatest risk aversion, whilst having no qualifications results in the least aversion. However, the pattern of aversion remains largely the same as before between family groups. For example, taking the variable degree⁎death, a greater negative coefficient is observed for women (−630.069) compared to men (−525.004). Within family groups, having children still results in a greater negative coefficient for all education interaction variables. Those that are married are the most averse, whereas the original estimation results found that single parents were the most averse to risk. It appears that this result was being driven by differences in education between the groups.15 The overall pattern of risk aversion however, remains unchanged. For race, the fatalhr⁎white variable again shows women to be the most averse to risk. For men and women, having children increases the aversion, with single parents and those that are separated, divorced or widowed remaining the most averse. The same pattern of results is shown for the union status model. Overall, estimating the model with race and union demographic interaction variables makes little difference to the pattern of results. The inclusion of education interaction variables however, shows the single parents result is driven by differences in education, with married parents shown to be the most averse to risk. This is consistent with the findings of DeLeire and Levy. Overall however, in terms of occupational choice in the UK, family structure and gender are driving the aversion to risk in the occupational decision, rather than the differences in other demographics between the groups. 4.4.2. (ii) Occupations Requiring Absences from Home DeLeire and Levy consider the possibility that risky occupations may also require absences from home, raising the possibility that certain groups of workers may avoid them for this reason, rather than risk aversion per se. That is, fatal risk could be correlated with absences from home. They re-estimate the model dropping occupations most likely to involve frequent absences from home: motor vehicle operators, other transportation, farm workers and forestry and fishing, but find no change in the pattern of results when these occupations are omitted. To repeat this analysis, we consider occupations in the UK that are most likely to require absences from home. We use data from the Skills Survey 2001, which asks respondents to state where their job requires them to work in the main: at home, at a fixed workplace, at a variety of places or on the move. This analysis reveals that as expected, the majority of occupations involve working mainly at a fixed workplace. Transport and Mobile Machine Drivers and Operatives (82) tend to work on the move, (63 per cent of respondents) with no other occupation coming close to this in terms of working on the move. There are occupations, however, that have a high percentage of respondents saying they work in a variety of places and on the move; for example, in Skilled Agricultural Trades (51) 48 per cent work in a variety of places. However, for this occupation, working in a variety of places is most likely to be defined as working within the local vicinity. Skilled Agricultural workers are unlikely to work great distances from home on a regular basis that require overnight stays. This occupation is, therefore, unlikely to be avoided because of a dislike for work-related absences from home. In Skilled Construction and Building Trades (53), 77 per 15

DeLeire and Levy also find this (p.940).

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Table 5 Conditional Logit Estimates by Family Group Single men no children

Single men with children

Married men no children

Married men with children

SDW men no children

SDW men with children

30426 − 88163.948

6982 − 20509.998

2719 − 7450.993

8348 − 23804.021

9386 −26187.361

2220 − 6398.491

640 − 1811.740

− 409.110⁎⁎⁎ (18.115) − 110.132⁎⁎⁎ (13.316) − 71.014⁎⁎⁎ (12.902) 16.338 (12.153)

− 421.756⁎⁎⁎ (36.998) − 558.318⁎⁎⁎ (18.270) −585.948⁎⁎⁎ (16.971) − 459.419⁎⁎⁎ (32.283) − 270.012⁎⁎⁎ (27.122) − 167.225⁎⁎⁎ (11.380) −194.666⁎⁎⁎ (11.187) − 173.292⁎⁎⁎ (22.971) − 233.425⁎⁎⁎ (25.880) − 223.041⁎⁎⁎ (15.204) −185.688⁎⁎⁎ (12.035) − 187.186⁎⁎⁎ (26.492) − 114.108⁎⁎⁎ (23.838) − 93.128⁎⁎⁎ (10.450) −124.049⁎⁎⁎ (10.451) − 83.476 (46.312)

WOMEN

Single Women no children

Single Women with children

Married Women no children

Married Women with children

SDW Women no children

SDW Women with children

27541 − 74884.062

5268 − 14739.282

3161 − 7619.419

7406 − 19975.898

7449 −19970.917

2428 − 6610.405

1720 − 4580.244

− 462.506⁎⁎⁎ (40.578) − 314.671⁎⁎⁎ (38.409) − 270.897⁎⁎⁎ (36.476) − 135.662⁎⁎⁎ (30.181)

− 629.755⁎⁎⁎ (92.301) − 657.343⁎⁎⁎ (45.927) −753.313⁎⁎⁎ (50.227) − 841.370⁎⁎⁎ (107.771) − 694.660⁎⁎⁎ (98.537)

Education Degree⁎Death

− 525.004⁎⁎⁎ (9.155) ALevel⁎Death − 182.811⁎⁎⁎ (6.147) GCSE⁎Death − 178.273⁎⁎⁎ (6.605) Noqual⁎Death − 98.105⁎⁎⁎ (5.585)

No. of obs Log Likelihood Education Degree⁎Death

− 630.069⁎⁎⁎ (22.018) ALevel⁎Death − 349.067⁎⁎⁎ (16.847) GCSE⁎Death − 318.647⁎⁎⁎ (14.876) Noqual⁎Death − 146.711⁎⁎⁎ (11.090)

− 479.569⁎⁎⁎ (59.835) − 271.441⁎⁎⁎ (46.559) − 244.315⁎⁎⁎ (48.098) − 167.778⁎ (40.986)

− 354.834⁎⁎⁎ (59.343) − 265.894⁎⁎⁎ (29.441) −426.095⁎⁎⁎ (38.227) − 503.113⁎⁎⁎ (92.532)

− 354.116⁎⁎⁎ (69.472)

− 288.609⁎⁎⁎ (54.191) − 291.775⁎⁎⁎ (26.520) −336.427⁎⁎⁎ (30.936) − 498.126⁎⁎⁎ (87.298)

− 349.951⁎⁎⁎ (61.017)

− 132.004⁎⁎⁎ (47.289) − 109.246⁎⁎⁎ (16.128) −152.839⁎⁎⁎ (24.162) − 264.534⁎⁎⁎ (78.013)

− 163.576⁎⁎⁎ (46.967)

Data: LFS 2002–2004, Skills Survey 2001, HSE 2002/03-2004/05. Other variables included in estimation: Strength, Stamina, Hands, Tools, Writelg, Calca, Stats, Caring, Special, Analyse, Mytime, Usepc, Speech, Persuade, Listen, Motivate, Future, Major, and Percentc.

S. Grazier, P.J. Sloane / Labour Economics 15 (2008) 938–957

No. of obs Log Likelihood

MEN

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Table 6 Conditional Logit Estimates: Families with Two or More Children Single Men

Married Men

SDW Men

No. of obs Log Likelihood

1664 −4461.846

8439 − 24043.214

452 − 1307.148

Death Coefficient Standard error

−260.406⁎⁎⁎ (31.674)

− 187.396⁎⁎⁎ (10.662)

− 186.492⁎⁎⁎ (47.407)

Major Coefficient Standard error

2.948⁎⁎⁎ (0.599)

4.648⁎⁎⁎ (0.182)

3.907⁎⁎⁎ (0.781)

Single Women

Married Women

SDW Women

No. of obs Log Likelihood

1924 −4319.771

6448 − 17251.990

1211 − 3235.752

Death Coefficient Standard error

−279.018⁎⁎⁎ (66.239)

− 220.488⁎⁎⁎ (21.063)

− 287.704⁎⁎⁎ (56.343)

Major Coefficient Standard error

−0.437 (1.062)

− 0.510 (0.409)

0.944 (0.979)

Data: LFS 2002–2004, Skills Survey 2001, HSE 2002/03-2004/05. Other variables included in estimation: Strength, Stamina, Hands, Tools, Writelg, Calc, Stat, Caring, Special, Analyse, Mytime, Usepc, Speech, Persuade, Listen, Motiv, Future and Percentc.

cent work in a variety of places and here this may deter certain family groups (especially parents) from choosing this occupation. Accordingly we re-estimated the model, omitting the Transport (82) and Construction (53) occupations that involve the most work away from home. Overall, the results show that women remain more averse to risky work than men and that, having children continues to increase aversion to risky work. Overall, there are no major differences between this and the previous estimation, indicating that the omission of an absence from home variable is not driving the observed occupational choice pattern between family groups. 4.4.3. (iii) Number of Children In order to consider if the number of children a worker has influences their aversion to occupation risk, we re-estimate the model splitting the family group variables further. For each marital status group, a further group for workers with two or more dependent children is created (see Table 6). The presence of more than one child increases aversion to fatal risk for all groups with the exception of SDW men with children (where the coefficients are quite similar). This suggests that the greater the number of dependents a workers has, the least likely they are to work in an occupation with a high risk of death. Table 7 Index of Segregation

Index with Actual Risk Index with Equal Risk Percentage fall in index

All Workers

Full Time Aged 25–34

50.4% 48.7% 3.3%

44.7% 40.4% 9.6%

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5. Occupational gender segregation Having established that gender does appear to influence the effect that risk has upon occupational choice, we now address the question of how much this contributes to occupational gender segregation. Regardless of whether the observed differences in occupation risk are due to preferences or discrimination, we can determine how much less occupational gender segregation there would be if all jobs had equal risk rates. The Duncan and Duncan (1955) Index of Dissimilarity is calculated as shown by Eq. (7):

D¼

J 1X j fj  mjj 2 j¼1

ð7Þ

where mj and fj refer to the predicted number of men and women in occupation j. Conditional logit models are estimated separately for men and women and the predicted probabilities used to calculate the Duncan Index of Segregation (Eq. (7)). To consider whether occupational gender segregation would be less if all occupations had equal risk, we counterfactually set risk equal to the same level in all occupations and calculate a new set of predicted probabilities and a new Duncan Index (see Table 7). The indices are calculated first for the estimation sample that includes full-time and part-time workers of all ages. To achieve an identical distribution of men and women across occupations, 50.4 per cent of women would have to change jobs. However, if risk were equal across occupations, 48.7 per cent of women would have to change occupations. Thus, occupational gender segregation would fall by 3.3 per cent if risk were equal across all occupations. To enable a direct comparison to be made with DeLeire and Levy, the exercise is repeated for a sample of full time workers aged 25–34. For this sample, occupational gender segregation would fall by 9.6 per cent if risk were equal across occupations. This is still smaller than the effect calculated for the US by DeLeire and Levy, who calculate a large fall in the index from 42.5 per cent to 24.2 per cent. However, there are generally higher risk rates for occupations in the US than in the UK. For instance, the most risky occupation found by DeLeire and Levy (Forestry and Fishing Occupations) has a death rate per 100 workers of 0.0872, whereas for the equivalent sample, the occupation with the highest death rate in the UK (Skilled Agricultural Trades) has a death rate of 0.0223 per 100 workers. We therefore expect risk to contribute less to occupation gender segregation in the UK. The difference in rates between the UK and US could reflect many factors. In particular, part of the difference could be attributed to differences in industrial and occupational structure. Differences in how the data are collected are also relevant. 16 However, EUROSTAT attempt to make international comparisons between accident rates, and as reported in HSE (2001), even after controlling for differences in employment structure and differences in how

16

In the US fatalities that occur at work but are unrelated to work are counted as a work fatality, whereas deaths that occur at home that are as a result of work are unreportable. Conversely, in the UK a fatality must be a direct cause of work to be reported, and any deaths that occur away from the work premises but are a direct cause of work must be reported.

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Table 8 Index of Segregation with 3 Digit Occupational Classification (81 Occupations)

Index with Actual Risk Index with Equal Risk Percentage fall in index

All Workers

Full Time Aged 25–34,

46.2% 37.6% 18.6%

42.7% 37.9% 11.2%

the rates are collected, accident rates for Great Britain are still lower than for the US.17 There are differences in work safety enforcement between the two countries. Wei et al. (2005) discuss the differences, with the US adopting a Worker Compensation System with a firms' contribution determined by their accident record. In contrast UK insurance is not used to pay compensation to workers. The UK instead relies more heavily on inspections and fines for enforcement. The fineness of the occupation classification affects the calculated risk rates and it will also affect the calculated index of segregation. DeLeire and Levy do not discuss whether their occupational classification affects their results. Their model is estimated for 44 occupations whereas the model here is estimated for 25. If there were fewer occupations, we might expect there to be less gender segregation. Dolton and Kidd (1994) highlight this issue on the grounds that “the more detailed the occupation breakdown, the greater the contribution of gender differences in occupation distribution to the overall gender wage differential” (p.461). The results using the 3 digit breakdown in Table 8 show that occupational gender segregation falls by 18.6% for all workers and by 11.2% for full time workers when risk is equal between 3 digit occupations. As predicted, the contribution of injury risk to gender segregation is more pronounced the finer the classification. This highlights the importance of considering the breakdown of occupations when calculating this index. In terms of the implications for the finding that differences in risk between occupations contributes to occupational gender segregation, if segregation explains part of the gender pay gap, it follows that differences in risk between occupations could contribute to the gender wage differential. This is beyond the scope of this paper, but is an area for further research. 6. Conclusion As did DeLeire and Levy for the US we find that the death rate of an occupation has a significant impact upon occupational gender segregation in the UK. This could be due either to differences in preferences for risk or to discrimination, or an element of both. The analysis further shows that within gender, occupational sorting occurs by family structure, making the discrimination interpretation, although possible, less likely. Workers do, it seems, have different preferences for risky work based on family composition. Furthermore, differences in fatal risk between occupations contribute to occupational gender segregation, with the degree of segregation falling substantially if there were no differences in risk rates between occupations. However, this is a smaller effect than DeLeire and Levy found for the US and is consistent with the lower accident risk across occupations in the UK, relative to the US. 17

The standardised rates of fatal injury are reported as 1.7 per 100 000 workers for Britain, and 2.2 per 100 000 workers for the United States.

956

Appendix A Occupational Characteristic Variable Means Strength

Stamina

Hands

Tools

Writelg

Calc

Stat

Caring

Spec.

Anal.

My time

Use PC

Speech

Pers.

Listen

Motiv.

Future

PercentC

11 12 21 22 23 24 31 32 33 34 35 41 42 51 52 53 54 61 62 71 72 81 82 91 92

0.97 1.78 0.92 0.77 1.19 0.49 1.13 1.80 2.22 1.52 0.67 0.86 0.62 3.54 2.51 3.03 2.69 2.30 1.70 1.96 0.55 2.36 2.28 2.92 2.42

1.32 2.15 1.09 1.57 1.92 1.08 1.42 2.08 2.57 2.02 1.11 0.98 1.05 3.22 2.54 2.93 2.79 2.46 2.28 2.04 1.00 2.54 2.54 2.81 2.51

1.36 2.33 1.82 2.91 1.49 0.96 2.50 2.40 1.58 2.62 1.20 1.76 2.32 3.18 3.49 3.58 3.29 2.07 2.81 2.09 1.57 3.09 2.18 2.76 2.06

1.80 2.31 2.63 2.84 1.80 1.41 2.92 2.62 1.94 2.62 1.82 1.91 2.53 3.45 3.61 3.55 3.13 1.96 2.71 2.10 2.02 3.29 2.78 2.88 2.24

2.43 1.61 2.46 2.66 2.94 2.95 1.87 2.26 2.63 2.12 2.48 1.84 2.10 1.86 1.34 1.18 1.04 1.51 1.74 1.09 1.29 1.07 0.88 1.08 1.00

3.04 2.96 2.86 2.29 2.73 2.67 2.53 2.41 1.42 1.81 2.81 2.81 1.89 1.59 2.48 2.50 2.17 1.68 1.90 2.60 2.67 2.24 1.90 2.11 1.20

1.74 1.37 2.36 1.83 1.76 1.65 1.92 1.08 0.85 0.97 1.82 1.50 0.99 1.00 1.61 1.28 0.82 0.95 0.94 1.07 1.03 1.26 0.64 0.96 0.67

2.88 3.02 2.13 3.67 3.33 3.10 2.14 3.78 3.02 2.21 2.90 2.36 2.87 2.11 1.74 2.26 1.66 3.43 3.13 2.93 3.55 1.34 2.01 1.38 1.84

3.39 3.06 3.66 3.93 3.74 3.59 3.46 3.64 3.57 3.54 3.45 2.81 2.51 3.18 3.32 3.14 2.94 2.91 3.10 2.71 3.07 2.72 2.47 2.33 1.87

2.70 2.47 3.17 3.37 2.59 3.16 2.68 2.56 2.43 2.53 2.66 1.93 1.28 2.04 2.56 2.03 1.74 1.72 1.77 1.49 2.05 1.83 1.36 1.38 1.10

3.51 3.36 3.15 3.30 3.66 3.61 3.19 3.46 2.92 3.27 3.33 3.00 2.98 3.10 2.81 2.97 2.81 2.72 2.81 2.46 2.63 2.33 2.69 2.51 2.43

3.20 2.10 3.54 2.80 3.00 3.15 3.45 2.21 2.49 3.05 3.28 3.56 3.55 0.93 1.94 0.90 0.94 1.19 1.31 2.36 3.55 1.66 1.04 1.33 0.82

2.08 1.34 1.65 2.16 2.84 2.41 1.30 1.60 1.92 3.25 2.22 0.82 0.48 0.90 0.86 0.58 0.44 0.93 1.06 0.80 1.25 0.72 0.41 0.61 0.41

2.96 2.76 2.38 2.83 3.00 2.97 2.26 2.53 2.71 3.50 2.99 1.82 1.50 1.57 1.84 1.70 1.65 2.11 1.92 1.90 2.29 1.73 1.24 1.48 1.33

3.31 3.16 3.21 3.40 3.48 3.33 3.36 3.53 3.29 2.33 3.30 3.15 3.24 2.93 2.88 2.63 2.74 3.28 2.93 2.96 3.37 2.88 2.74 2.66 2.57

3.46 3.42 2.98 3.39 3.48 3.32 2.93 3.36 3.25 3.04 3.34 3.05 3.03 3.33 2.88 3.12 3.11 2.97 3.54 3.17 3.07 3.26 2.84 3.07 3.23

2.5 2.56 1.73 2.47 2.36 2.13 1.41 1.54 0.85 1.87 2.01 1.15 1.07 2.00 1.39 1.72 1.24 1.33 2.04 1.44 0.86 1.24 1.05 1.44 1.16

22.3 18.9 32.27 58.81 72.32 35.35 38.79 63.13 63.94 26.4 34.12 42.97 24.15 18.57 33.64 20.39 19.52 32.91 28.2 17.92 36.13 36.76 37.57 28.37 28.84

Variable Abbreviations: Spec. = Special, Anal. = Analyse, Pers. = Persuade, Motiv. = Motivate. Created using the Skills Survey 2001.

S. Grazier, P.J. Sloane / Labour Economics 15 (2008) 938–957

SOC code

S. Grazier, P.J. Sloane / Labour Economics 15 (2008) 938–957

957

Appendix B Correlations

Strength Stamina Hands Tools Caring Speech Percentc Fract Female Ln(Major) Ln(Death)

Strength Stamina Hands

Tools

Caring

Speech Percentc Fract Female Ln(Major) Ln(Death)

1 0.969 0.670 0.630 − 0.537 − 0.530 − 0.286 − 0.452 0.799 0.765

1 − 0.580 − 0.466 − 0.265 − 0.526 0.522 0.685

1 0.463 0.435 0.625 −0.556 −0.542

1 0.445 0.054 − 0.570 − 0.330

1 0.667 0.597 − 0.433 − 0.412 − 0.169 − 0.422 0.774 0.750

1 0.943 −0.479 −0.474 −0.296 −0.352 0.440 0.577

1 0.173 − 0.084 − 0.028

1 − 0.525 − 0.767

1 0.844

1

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