Equality Monitoring in
Maritime & Coastguard Agency
Prepared by the In House Analytical Consultancy
1.Management Summary 4
1.2.Key Findings: Gender 4
1.3.Key Findings: Ethnicity 5
1.4.Key Findings: Disability 6
1.5.Other Key Findings 6
1.6.Information Recommendations 7
2.1.Equality Monitoring 8
2.2.General Approach 8
2.3.Analysis and Results 9
2.4.Data Groups and Subgroups 9
3.Staff in Post and Geographical Analysis 11
3.2.Comparison by Gender 11
3.3.Spring Place 12
3.5.Comparison by Ethnicity 12
3.6.Spring Place 13
3.8.Comparison by Disability 14
4.Staff in Post across Pay Bands 16
4.2.Distribution of Gender 16
4.3.Distribution of Ethnic minority Staff 17
4.4.Distribution of Disabled Staff 17
4.5.Staff distribution by Age 18
4.6.Work Patterns 18
5.Generalists and Specialists 20
7.Ceased Employment 24
8.Performance Assessment 25
8.5.Further Analysis 26
9.Training and Development 27
10.Grievances and Discipline 28
10.2.Grievance Cases 28
10.3.Discipline Cases 28
Annex A: Equality Monitoring Tables 2006/729
Annex B: Working-age populations used for Equality Monitoring comparisons43
B.2.Annual Population Survey43
1.1.1.This report contains an analysis of staff diversity data. The information was collected on MCA staff who had been in post between 1st April 2007 and 31st March 2008.
1.1.2.The report includes the analysis of information by ethnic origin, gender, disability and, in less detail, age, generalist/specialist (marine surveyors and coastguards) and work pattern (full-time/part-time).
1.1.3.The differences between groups identified in the report have been described in non-statistical terms. However, where differences have been found to be statistically significant, this has been highlighted within the commentary. Where results are not specifically discussed, this means that no statistically significant inequalities have been found.
1.2.Key Findings: Gender
1.2.1.On the 1st April 2008, MCA comprised 62.5% (817) male and 37.5% (416) female staff – significantly different to the GB working-age population. At the Head Quarters, Spring Place, 31.6% (390) staff were based, 55.4% (216) were male and 44.6% (174) were female – however, this difference was not significantly different to the local working-age population. Outside of Spring Place there were 843 staff, 71.3% (601) were male and 28.7% (242) were female – a difference that was significant.
1.2.2.Across MCA as a whole, there were more males than females in all pay bands. However, given that there were almost twice and many males as females in MCA as a whole, at individual pay band level, there were disproportionately higher numbers of females in lower pay bands and fewer in higher pay bands. For example, 46.0% (260) of the combined A and B pay bands were female; while 25.3% (145) of C-E were female and just 11.5% (11) of F-G were.
1.2.3.Of the 144 part-time staff, significantly more, 79.9% (115), were female. Looking at the distribution of specialist staff (marine surveyors and coastguard), females were significantly over represented in pay band A and underrepresented in pay band C and pay band E.
1.2.4.The age distribution for females was significantly different from that of males – there tended to be disproportionately more females- than males in younger age groups. In particular, below the age of 40, there were comparable numbers of females and males; from the age of 40 years onwards males out numbered females by almost 3:1.
1.2.5.There were no significant differences found between males and females in terms of performance marking.
1.2.6.Recruitment data was limited but there appears to be no significant difference in the proportion of applications by males and females and their appointment. However, most of the females appointed (20 out of 24) were in pay band B, where there was already a high proportion of female staff. Most male appointments tended to be spread across pay bands C to F – i.e. higher pay bands.
1.2.7.There were 1,262 incidents of training – 76.5% (965) for males and 23.5% (297) for females. Compared to the staff in post, males received disproportionately more training than females.
1.3.Key Findings: Ethnicity
1.3.1.MCA comprised 86.1% (1,062) white and 3.4% (42) ethnic minority staff and the ethnicity for 10.5% (129) staff was unknown.
1.3.2.The ethnicity mix and proportions varies considerably across Great Britain; therefore we have undertaken comparative analysis by location. In Spring Place, where 390 staff were based, 332 were white, 19 were from an ethnic minority and 39 staff did not declare their ethnicity. The difference was not significant when compared to the local working-age population. Outside of Spring Place, there were 843 staff, 730 were white, 23 from an ethnic minority and 90 unknowns. The proportion of staff from an ethnic minority – 3.1% - was comparable to the local working-age population.
1.3.3.At individual pay band level, it was difficult to undertake meaningful analysis of the distribution of staff due to small number of staff from and ethnic minority and the relatively high number of unknowns. There were around three times as many unknowns as there were staff from an ethnic minority.
1.3.4.Of the 144 staff that were part-time, 130 declared their ethnicity – 126 were white and 4 from an ethnic minority and comparable to the proportion of staff from an ethnic minority overall.
1.3.5.There was no significant difference in the representation of staff from ethnic minorities amongst generalists and specialists (marine surveyors and coastguards).
1.3.6.The proportion of staff applying from ethnic minorities was higher than the existing proportion of staff in post from an ethnic minority. The proportion of those subsequently being appointed was comparable with applications. However, these were still relatively small numbers – it total 19 applicants were from an ethnic minority and 4 were appointed.
1.3.7.Of the 152 staff leaving MCA, the ethnicity for 106 was known and 5 of these were from an ethnic minority. This was consistent with the proportion of staff in post that were from an ethnic minority.
220.127.116.11 staff received an Excel box marking. 114 of these declared their ethnicity and 6 of these from an ethnic minority. The proportion of staff from an ethnic minority receiving an Excel box mark was consistent with proportion of white staff receiving one.
1.3.9.There was no significant difference between the amount of training given to white staff and staff from an ethnic minority.
1.4.Key Findings: Disability
18.104.22.168,014 staff declared their disability status. Of these 10.5% (106) staff declared themselves to be disabled – significantly below 18.7%, the average in Great Britain for the working-age population – 89.5% (908) declared themselves to be non-disabled. The number of staff that had not declared their disability status was 129 – 10.5% of the total number of staff (1,233).
1.4.2.Within Spring Place, the number of disabled staff was not significantly different to the local working-age population, whereas outside of Spring Place it was significant with disproportionately fewer disabled staff.
1.4.3.The age distribution for disabled staff was not significantly different to that of non-disabled staff. The leaving rates, performance box marks and training for disabled and non-disabled were equitable.
1.4.4.The proportion of staff applying that declared themselves to be disabled was higher than the existing proportion of staff in post. The proportion of those subsequently being appointed was comparable with applications. However, these were still relatively small numbers – 30 applicants declared themselves to be disabled and 5 were appointed.
1.4.5.Of the 152 staff that left MCA, the disability status was known for 75 - 67 were non-disabled and 8 disabled. The difference in the proportions of staff leaving for disabled and non-disabled compared to staff in post was not significant.
1.4.6.There were no significant differences between disabled and non-disabled staff with regards to their performance management reports and training.
1.5.Other Key Findings
1.5.1.The analysis found that staff in higher pay bands tended to be awarded disproportionately more of the Excel box mark than staff at more junior pay bands. For pay bands A-B combined, 3.7% of staff received the Excel box mark, in C-E 10.9% received it while in F-G 25.0% received it.
1.6.1.By looking at administrative staff (generalists) as a separate group to marine surveyors and coastguards (specialists), the analysis has been able to pick up key differences. It may, however, be helpful to consider marine surveyors and coastguards separately in future.
2.1.1.In April 2008, DfT(c) Human Resources requested the In House Analytical Consultancy to undertake an analysis of staff equality within DfT and its Agencies.
2.1.2.This report contains an analysis of information collected on the Maritime & Coastguard Agency (MCA) staff in post on 31st March 2008; and cessations and recruitment from 1st April 2007 to 31st March 2008. In addition, the report considers training and development, discipline and grievances and an analysis of performance management reports (PMRs) returned to HR by XXXXX (for the 2007/08 reporting year).
2.1.3.The aim of the analysis was to determine whether the differences appearing in the data (e.g. by gender, ethnicity, disability, age, or pay band) were statistically significant.
2.1.4.Note that for the purpose of this report, the Senior Civil Service (SCS) for MCA has been included along with DfT(c)’s SCS. Separate reports produced by IHAC for each of the Department's agencies excludes SCS based in the agencies to avoid double counting.
2.2.1.Initially, the report looks at the staff in post and considers whether their diversity mix reflected the geographical region of where they were based. This was based on the working-age population of the catchment area – which is discussed in Annex B. To do this we considered Spring Place (Southampton) separately – with all other staff being put into an "Coast" category. This meant that the staff based at Spring Place were more likely to be “office” staff whereas, staff based in Coast were more likely to be marine surveyors or coastguards. One of the questions we asked here, for example, was whether the proportion of staff from an ethnic minority reflected the working population of the surrounding area?
2.2.2.The next step was to look at the diversity mix of each pay band to see whether there was over/under representation at pay band and pay band group level. For example, if there were twice as many males as females in MCA as a whole, was this evenly reflected in each pay band?
2.2.3.Subsequently, the report considers applications and appointments – and considers whether the diversity mix of applicants and appointed are consistent.
2.2.4.The report also looked at those leaving MCA, to consider whether disproportionate numbers of staff form each diversity group were leaving.
2.2.5.Staff performance management reports were analysed to see if any of the diversity groups had been marked differently, for example had non-disabled staff been given a disproportionate number of Excel box markings compared to disabled staff?
2.3.Analysis and Results
2.3.1.The analysis presented in this report is described in non-statistical terms. However, statistical tests have been carried out to check whether apparent differences were significant – in other words, whether it can be said that the difference is not due to random factors or chance. Where differences were found to be statistically significant, this has been highlighted. It should be noted that there may be some occasions where results and graphs appear to show a considerable difference between groups of people, but the difference was not necessarily significant. There were also some occasions where it was not possible to carry out the statistical test due to small numbers of individuals in certain groups, and therefore it was not possible to say whether the apparent difference was statistically significant or just due to chance.
2.3.2.The information on which the analysis is based can be found in Annex A. This contains tables of staff numbers, which provide context for the percentages presented in this report. In addition, in many cases numbers of staff have been presented throughout the report alongside percentages, for convenience.
2.4.Data Groups and Subgroups
2.4.1.Reasonably good data are kept by MCA on staff gender (male/female), age and pay band. However, staff data on disability, ethnicity (White, Mixed, Asian, Black, Chinese, Other) was voluntarily provided and, clearly, cannot be verified. Understandably, some staff may have been reluctant to provide this information and therefore these data often have significant numbers of unknowns or undeclared. Note - in this report, undeclared and unknowns (for gender, ethnicity and disability) have not been included in the statistical analysis. The pay band groups and age groups used in this report were:
2.4.2.Pay band groups
3.Staff in Post and Geographical Analysis
3.1.1.On the 1st April 2008 MCA comprised 1,233 staff based at numerous locations across the UK. The highest concentration of staff were based at the head quarters, Spring Place, Southampton (390) with the remaining staff based mainly at costal locations.
3.1.2.The section considers the diversity mix of staff in MCA as a whole. The analysis also considers whether the diversity makeup of the staff in MCA reflects the local working-age population. In order to undertake this analysis the staff in post within MCA were split by geographical location and their diversity aspects compared to that of the local working-age population.
3.1.3.To undertake a geographical analysis a sufficient number of staff at any particular location were required. We have therefore considered Spring Place independently and compared it to the surrounding area’s (Southampton and Hampshire) working population. For staff outside of Spring Place, a consolidated grouping of “Others” was used - comprising 843 staff. We have compared their diversity against the GB working-age population for coastal counties (as opposed to the whole of GB) as this seemed to more accurately reflect the distribution of staff in MCA. This choice mainly affects the relative size of the ethnic minority populations (as opposed to white) as it tends to exclude many of the areas with relatively high concentrations of ethnic minorities.
3.2.Comparison by Gender
3.2.1.The following chart shows the gender composition of MCA for 2007/8, it can be seen that there are roughly twice as many males as females.
22.214.171.124.6% (390) staff in MCA were based in Spring Place and the diversity mix of these staff was compared to the working population of Southampton and Hampshire.
3.3.2.Overall there were 55.4% (216) males compared to 44.6% (174) females – although there were more males than females the difference was not significantly different to the local working-age population.
3.3.3.This pattern, however, was not consistent within each pay band. For example, while there were significantly higher proportions of males in E and F compared to the local working-age population. In looking at F and G together, there were 56 staff – 47 males and 9 females – significantly different to the local working-age population.
3.4.1.There were 843 MCA staff based in smaller groups in coastal locations across the UK. Compared to the GB working-age population, the overall proportion of males 71.3% (601) were significantly more males than females 28.7% (242). At pay band level, there were significantly more males than females in C, D, E and F.
3.5.Comparison by Ethnicity
3.5.1.MCA comprised 1,233 staff, of which 86.1% (1,062) were white, 3.4% (42) were from an ethnic minority background and the ethnicity of 10.5% (129) staff was unknown. For those that declared their ethnicity, 96.2% were white and 3.8% of staff were from an ethnic minority. The chart below shows the number and proportion of staff by ethnic minority group for 2007/08 compared to the GB working-age population for 5 ethnic minority groupings.
3.5.2.Compared to the GB working-age population, there were lower proportions of Asian, Black, Chinese and Other EM staff working for MCA than there were living in Great Britain. There was, however, a higher proportion of Mixed staff. This probably reflects the fact that MCA is primarily based along the coast – which has a lower proportion of ethnic minority communities than the GB as a whole.
3.5.3.There was a relatively high proportion of MCA staff with unknown (undeclared) ethnicity. (There were roughly three times more staff not declaring their ethnicity than there were ethnic minority staff). Knowing the ethnicity of these individuals could potentially alter the results presented in this report.
3.6.1.Of the 390 staff based in London, there were 332 white, 19 ethnic minority and 39 staff that did not declare their ethnicity. Based on those that declared their ethnicity, there was not a significantly difference between the proportion of white staff compared to the local working-age population. 94.6% white staff compared to 5.4% ethnic minority staff. The local population had 96.0% white and 4.0% ethnic minority.
3.6.2.Analysis at pay band level found no significant differences between the proportion of staff from an ethnic minority background and the local working-age population.
3.7.1.Outside of Spring Place there were 3.1% (23) staff from an ethnic minority background compared to the 3.4% in the GB working-age population for coastal counties. The difference was not significant.
3.8.Comparison by Disability
3.8.1.In 2007/8 MCA comprised 8.6% (106) disabled staff, 73.6% (908) non-disabled staff and 17.8% (219) staff with undeclared disability status. The number of staff not declaring their disability status was roughly twice the number declaring themselves to be disabled.
3.8.2.In the working-age population within Great Britain, 18.7% declared themselves to be disabled – significantly more than in MCA. 37 of the of disabled staff were female and 69 were male – which was proportionate to the average gender split for non-disabled staff.
3.9.1.There were 76.7%, (299) non-disabled staff and 11.3%, (44) disabled staff. 12.1% (47) staff did not declare there disability status. The difference to the local working-age population was not significant.
3.10.1.There were 72.2%, (609) non-disabled staff and 7.4%, (62) disabled staff. 20.4 % (172) staff did not declare there disability status. The difference to the local working-age population (19.3%) was significant – although the high proportion of undeclared may be affecting this.
3.10.2.At pay band level, there were significantly fewer disabled staff in the pay band groups A-B 9.7% (34) and C-E 8.9% (25) than in the local working-age population 19.3%.
3.11.1.The graph below compares the age distribution of staff in MCA over the last three years with the equivalent age distribution of the GB working-age population.
3.11.2.Comparing the age distribution of the GB working-age population with that of staff in MCA it can be seen that staff in MCA there are higher proportions of staff in all the age groups from 45 to 59. This was more pronounced in age groups 50-54 and 55-59. MCA was particularly underrepresented in Under 20 and 20-24 year olds.
3.12.1.Spring Place had a significantly different age profile compared to the local population; staff in the age groups of 25-29 and 50-54 were significantly over represented, while staff in the Under 20 group were significantly under represented.
3.12.2.All pay bands reflected this difference to some extent, and while this may be expected in higher pay bands (where experience would often be required) it was also true in lower, entrée pay bands – pay band 2 and pay band 3.
3.13.1.The age profile for coastal locations was also significantly different to that of the GB working-age population’s – with fewer staff in the under 20 and 20-24 age bands and more in the 45-49, 50-54 and 55-59 age bands.
4.Staff in Post across Pay Bands
4.1.1.The previous section looked at how MCA compared to the local working-age population for Spring Place and a consolidated non-HQ locations we have referred to as “Coast”. This section looks at MCA as a whole and focuses on how minority groups – females, staff from an ethnic minority and disabled – are distributed across the pay bands.
4.1.2.For example, we know that in MCA there are roughly two males to every one female but was this true for each pay band? To do this, the analysis uses the overall proportion of the minority group as a basis for comparison and considers how each pay band compares it. Therefore, in this section, a significantly higher proportion of females than males (for example) means being significantly different to the average within MCA as a whole.
4.2.Distribution of Gender
4.2.1.Overall, there was a higher proportion of male staff compared to female staff in the higher pay bands. For example, there was proportionally more female staff than male staff in pay band B - 49.3%, (174) staff were female compared to 50.7%, (174) male staff. When compared to the average in MCA (where males out number females almost two to one). In contrast, in pay band E there were 88.1%, (155) male staff compared to 11.9%, (21) female staff; and in pay band F there were 89.0%, (73) male staff compared to 11.0%, (9) female staff.
4.2.2.The following graph shows the gender composition of MCA in 2007/8 across pay bands.
4.3.Distribution of Ethnic minority Staff
4.3.1.The following graph shows the ethnic minority and unknown composition of MCA staff by pay band – as it can be seen the proportions of unknown ethnicity and staff declaring their ethnicity by pay band varied considerably. Note - white staff are not shown.
4.3.2.The relatively high proportion of unknown ethnicity makes it difficult to come to a conclusion about the distribution of staff from an ethnic minority across the pay bands.
4.4.Distribution of Disabled Staff
4.4.1.The following graph shows the proportion of staff across pay bands that declared themselves disabled together with the proportion of undeclared. As with ethnicity, the relatively high proportion of undeclared makes analysis by pay band difficult.
4.5.Staff distribution by Age
4.5.1.The ages of all staff were known and the analysis found that the age distribution of males and females was significantly different. There were significantly fewer males/more females in age groups 20-24, 30-34 and 35-39 and more males/fewer females in the 55-59 and 60and over age group. As already discussed, MCA has more males than females (roughly in a ration of 2:1), however, below the age of 40 years, female outnumbered males (208 females / 203 males). From the age of 40 years onwards, however, males outnumbered females by almost three to one (208 females / 614 males). Two-thirds of MCA 66.7% (822) were 40 years old or older, compared to 33.3% (211) staff younger than 40.
4.6.1.In 2007/8 MCA had 11.7% (144) part-time staff and 88.3% (1,089) full-time staff. The following table shows the number and percentage of part-time staff over the last three years.
4.6.2. The following graph shows the proportions of male and female staff that are part-time staff in MCA across pay bands.
4.6.3.Of the 144 part time staff, 79.9% (115) were female and 29 were male – compared with MCA overall, where 33.7% of staff were female. The distribution of female part-time staff across the pay bands was not significantly different from the distribution of full-time female staff. The number of part-time male staff meant that analysis at pay band level was not possible.
4.6.4.Due to the low numbers of part-time staff from an ethnic minority (4) and staff that declared they were disabled (16), an analysis at pay band level was not possible.
5.Generalists and Specialists
5.1.1.The analysis looked at staff that were regarded as specialists (as opposed to generalists – e.g. administrative staff). For the purposes of this report these were staff that required particular expertise/training/qualifications. Primarily, these include marine surveyors and coastguards.
5.1.2.This analysis helps to show that MCA cannot be regarded as a homogeneous group of staff and that diversity issues vary from one group to another. In future it may be useful to look at marine surveyors and coastguards as two distinct groups.
5.2.1.There were 469 generalists and 764 specialists. Analysis found that there were significant differences between specialists and generalists in terms of the gender mix. For generalists, there were 57.1% (268) females and 42.9% (201) males, while for specialists there were 19.4% (148) females and 80.6% (616) males. The high proportion of males in the specialist group may be due to the requirements of the job (e.g. marine surveyors) which may favour males.
5.2.2.For generalists, at pay band level, there were proportionately more females in pay band B 71.2% (126) compared to males (51). In contrast there were proportionately more males in pay band E 68.8% (33) and pay band F 75.0% (24) – generally – females were over represented in lower pay bands and underrepresented in higher pay bands. In the combined top two pay bands F-G, there were 10 females compared to 28 males.
5.2.3.For specialists, at pay band level, there were proportionately more females in pay band A 38.9% (79) compared to males (124). There were also proportionately more males in pay band C 94.7% (142) and pay band E 95.3% (122). As with generalists – females were over represented in lower pay bands and underrepresented in higher pay bands. In the combined top two pay bands F-G, there was just one female compared to 57 males.
5.3.1.There were 424 generalist staff that had declared their ethnicity, 15 staff were from an ethnic minority, 409 were white (there were 30 unknowns). For specialists, there were 680 that had declared their ethnicity, 27 staff from an ethnic minority, 653 white staff and a further 84 unknowns. Overall (and excluding the unknowns), the distribution of staff from an ethnic minority between generalists and specialist was not significant.
5.3.2.Due to the relatively high proportion of unknowns analysis at pay band level was not possible; however, 20 of the 27 staff from an ethnic minority in the specialist group were in pay band E.
5.4.1.There were 411 generalist staff that had declared their disability status, 52 staff were disabled, 359 were non-disabled and a further 58 undeclared. For specialists, there were 603 that had declared their disability status, 54 staff were disabled, 549 were non-disabled with a further 161 unknowns. Overall (and excluding the undeclared), the distribution of disabled staff between generalists and specialist was not significant.
5.4.2.At pay band level the distribution of disabled staff was not significant – i.e. there was an even distribution of disabled staff across the pay bands for both specialists and generalists.
6.1.1.The section looks at the recruitment of staff. Detailed information on the external and internal applications, sifting and appointments were not available. The analysis used 251 records of applications of unknown type (external/internal) and considered the 51 staff that were appointed.
6.2.1.The gender was known for 250 applicants, 63.2% (158) were male and 38.8% (92) were female. The proportions of applications split between male/female were not significantly different to the staff in post.
126.96.36.199 applicants where gender was known were subsequently appointed – 52% (26) males and 48% (24) females. The proportions of each gender appointed were not significantly different to the gender of the applicants.
6.2.3.Pay band B received the most applications – 154, 51.9% (80) males and 48.1% (74) females. Although only 11 males and 20 females were appointed, the difference was not significant. There were no significant differences in the other pay bands.
6.2.4.It was noted that most of the females appointed (20 out of 24) were in pay band B, where there was already a high proportion of female staff. Most male appointments tended to be spread across pay bands C to F – i.e. higher pay bands.
6.3.1.The ethnicity was known for 237 applicants, 92.0% (218) were white and 8.0% (19) were from an ethnic minority. The proportions of applications split between white and applicants those from an ethnic minority was significantly different to the staff in post, with a relatively higher proportion of applications coming from ethnic minorities.
188.8.131.52.5% (43) of those appointed were white while 8.5% (4) came from an ethnic minority. These proportions were not significantly different compared to the applications. Analysis by pay band was not possible due to the small number of applicants from an ethnic minority.
6.4.1.The disability status was known for 164 applicants, 81.7%% (134) were non-disabled and 18.3% (30) were disabled. The proportions of applications split between non-disabled and disabled was significantly different to the staff in post, with a relatively higher proportion of applications from disabled staff.
184.108.40.206.5% (32) of those appointed were non-disabled, 13.5% (5) were disabled. These proportions were not significantly different compared to the applications. Analysis by pay band was not possible due to the small number of applicants from an ethnic minority.
7.1.1.This section compares the number of leavers during 2007/08 with the number of staff in post at the end of the reporting year. We would expect that the diversity of the staff that left MCA would be broadly similar to the staff in post.
7.2.1.Of the 152 staff that left MCA, 92 were male and 60 female. Compared to the overall number of staff in post, the proportions of males and females that left was not significantly different. This was also true at pay band level.
7.3.1.Of the 152 staff that left MCA, the ethnicity was known for 106 - 101 were white and 5 from an ethnic minority. Compared to the overall number of staff in post, the proportions of white staff and staff from and ethnic minority that left was not significantly different. Due to the small number of staff from an ethnic minority, analysis at pay band level was not conducted.
7.4.1.Of the 152 staff that left MCA, the disability status was known for 75 - 67 were non-disabled and 8 disabled. The difference in the proportions of staff leaving for disabled and non-disabled was not significant compared to the staff in post. Analysis at pay band level was not possible due to the small numbers of disabled staff.
8.1.1.This section looks at the Performance Management Reports (PMRs) for 1,334 staff reports returned by XXXX for the reporting year ending 31st March 2008. Some of the staff in MCA had more than one report – for example a member of staff being promoted or transferring will have been given a report as well as their annual one. For the purpose of this analysis, all reports have been included.
8.1.2.MCA has a four box marking system – Excel, High, Full and Under Performed. None of the staff received an Under Performed rating. The analysis considered whether there was a similar proportion of the three levels appearing in each pay band as there was overall – i.e. did each pay band reflect the overall average.
8.1.3.It was noted that the distribution of the box marks varied significantly by pay band group – with the higher pay bands having proportionately more Excel box marks and fewer Full box marks.
Percentage Distribution of Box Marks by Pay Band Group
PB A-BPB C-EPB F-GTotal
8.2.1.The gender of all 1,334 staff for was known. 116 staff received Excel, 641 High and 577 Full. Overall, 8.7% of staff – 9.1% (81) of males and 7.8% (35) of females - received Excel. This difference was not significant. No significant differences were found between the box marks for males and females within a pay band.
8.3.1.The ethnicity of all 1,212 staff for was known. 114 staff received Excel, 606 High and 492 Full. Overall, 9.4% of staff – 9.2% (108) of white and 15.4% (6) of staff from an ethnic minority - received Excel. This difference was not significant. The 6 staff from and ethnic minority that received an Excel were in the consolidated pay band group C-E. Due to the numbers of staff involved analysis at pay band was not possible.
8.4.1.Of the 1,334 PMRs, 1,096 were on staff that had declared their disability status. For those staff that had declared their disability status, 10.0% (111) staff received Excel, 566 High and 429 Full. Overall, 10.0% (99) non-disabled staff received an Excel, while 10.3% (12) disabled staff received one. The difference was not significant. Also, no significant differences were found between the box marks for males and females within a pay band or pay band group.
8.5.1.Significant differences were found between pay band groups in terms of the distribution of box marks – generally, higher pay bands received a higher proportion of the higher box marks. The results from the analysis described above had shown that there were no significant differences in box marks awarded that were based on gender, ethnicity or disability status.
8.5.2.This difference was investigated further and included looking at each member of staff’s age and whether they were full-time or part-time (as well as gender, ethnicity and disability status). Neither of these additional factors proved to be significant.
8.5.3.The uneven distribution of the higher box mark may indicate bias, however, there are potentially two other explanations. Firstly, staff in higher pay bands out performed their more junior colleagues. Secondly, there were other factors not being taken into account. Further investigation may be needed to determine which of these is true.
9.Training and Development
9.1.1.This section looks at the amount of training, in terms of incidents rather than days or hours, given to staff.
220.127.116.11,262 incidents of training were recorded – 965 for males and 297 for females. Compared to the number of staff in post, males received significantly more training than females.
18.104.22.168,125 incidents of training were recorded for staff that declared their ethnicity – 1,077 for white staff and 48 for ethnic minority staff. Compared to the number of staff in post, there was no significant difference between the amount of training given to white staff and staff from an ethnic minority.
22.214.171.124,020 incidents of training were recorded for staff that declared their disability status – 927 for non-disabled staff and 93 for disabled staff. Compared to the number of staff in post, there was no significant difference between the amount of training given to non-disabled and disabled staff.
10.Grievances and Discipline
10.1.1.The aim of this section was to determine if the number of grievances and discipline cases by diversity was in proportion to the number of staff in post. The numbers involved for both grievance and discipline were too small to carry out statistical testing – either overall or by pay band
10.2.1.There was only 1 grievance case brought against MCA – from a white, non-disabled female.
10.3.1.There were six disciple cases – four males and two females. four white staff and two staff with unknown ethnicity. Three staff were non-disabled, one disabled and two staff had not declared their disability status. The numbers were too small to be analysed.
Annex A: Equality Monitoring Tables 2006/7
Staff by Location:
Annex B: Working-age populations used for Equality Monitoring comparisons
B.1.1.In order to compare the diversity of staff in post with the local working-age population it was necessary to identify where staff were based (i.e. building) and then attach each building to a Reporting Location, e.g. London, Swansea, etc.. This meant that all of the staff based in London, for example, were considered as being in one location, irrespective of which part of London they were located in.
B.1.2.For each Reporting Location we identified a catchment area and – taking a fairly simplistic but robust approach – produced figures for a working-age population that covered both the county or Unitary Authority (UA) of the Reporting Location itself and those of the neighbouring counties/UAs within the defined catchment area. To report on staff based in London, we used the working-age population of all the London boroughs as well as those counties that border them. Obviously this approach only provides an approximation, as some staff would be prepared to commute from much further afield than the areas used here.
B.2.Annual Population Survey
B.12The population data at county/unitary level is from the Annual Population Survey (APS) for the one year period October 2006 - September 2007. This data was downloaded from www.nomisweb.co.uk on 16th June 2008 and a summary on the APS from this website is given below. Further information on the survey can be found at http://www.ons.gov.uk/about-statistics/user-guidance/lm-guide/sources/household/aps/index.html. It should be noted in particular that the APS covers Great Britain and therefore does not contain data on Northern Ireland.
“A residence based labour market survey encompassing population, economic activity (employment and unemployment), economic inactivity and qualifications. These are broken down where possible by gender, age, ethnicity, industry and occupation. Available at Local Authority level and above. Updated quarterly.”
B.2.2.This population data was combined with mid-year (30 June) population estimates for 2006, which were downloaded from www.nomisweb.co.uk on 17th June 2008. These were also at county/unitary level and were based upon results from the 2001 Census with allowance for under-enumeration. These figures covered the entire population, not just the working-age population, so to calculate this we took the number of males aged 15-64 years and females aged 15-59 years (only 5 year age bands were available).
B.3.1.The APS asks respondents whether they are currently DDA disabled , work-limiting disabled, both DDA disabled and work-limiting disabled, or not disabled. The population data on proportions of the working-age population that are disabled used in this report cover DDA disabled, work-limiting disabled, and both DDA and work-limiting disabled.
B.4.1.APS data available via www.nomisweb.co.uk was only available for the following ethnicity groups:
•Black/Black British; and
B.4.2.The remainder was calculated and this was used as the data for the White ethnicity group, and the results for the Indian and Pakistani/Bangladeshi groups were summed for use as the broader Asian ethnicity group.
B.4.3.It should be noted that no separate APS data was available for the Chinese ethnicity group, which was contained within the Other group. As a result, it was not possible to perform analysis against the Reporting Location population for the Chinese ethnicity group.
B.4.4.In order to represent this group in our charts, we used a set figure for the proportion of those of Chinese ethnic background in England of 0.74%. This figure is taken from Population Estimates by Ethnic Group (Experimental) 2006. More details on this data can be found at http://www.statistics.gov.uk/StatBase/Product.asp?vlnk=14238&More=Y