Supply and Demand Dynamics for Specific Occupational Fields

This section focuses on the interplay between the supply of and demand for skills. The variables under discussion are an over- and under-supply of skills in relation to demand. Two sources of data are used in order to make sense of these supply and demand issues. A further interplay is between training planned and training implemented. The question that arises is whether these two factors are in equilibrium or in disequilibrium. Other questions are is the question of the impact of career movement on skills shortages, and how the overlap between competing skills and occupations between the insurance and the broader financial services sector, affect skills shortages.

Obtaining data on supply and demand is difficult. However, this section makes use of the best available sources of data: the INSETA WSP and the INSETA ATR analyses of 2012.

Table 3-10 below analyses the INSETA ATR data for 2012 by examining the training planned compared to

the training actually implemented. The planned training is an indication of skills in demand by registered INSETA levy-paying companies, whereas the training implemented is the outcome in response to the indicated demand. The latter can be considered the supply of skills, because it culminates in a pool of newly trained individuals or up-skilling of current employees.

Table 3-10: April 2011 planned training versus 31 March 2012 actual training completed

Occupational Categories Planned Trained

Managers 10 430 15 757

Professionals 10 367 16 714

Sales workers 15 562 31 926

Clerical & admin workers 36 800 45 752

Community & personal service workers 215 189

Elementary workers 315 320

Technical workers 283 415

Total 73 972 111 073

Table 3-11 Pivotal Planned Trained Beneficiaries 2014

Occupation Total

Number

Percentage of Total 111204 – Senior Government Official 5 0.06% 112101 – Director (Enterprise/Organization) 26 0.31% 121101 – Finance Manager 8 0.09% 121104 – Internal Audit Manager 1 0.01% 121201 – Personnel/Human Resources Manager 1 0.01% 121202 – Business Training Manager 17 0.20% 121901 – Corporate General Manager 188 2.21% 121905 – Programme or Project Manager 4 0.05% 122101 - Sales and Marketing Manager 34 0.40% 122102 - Sales Manager 29 0.34% 122103 – Director of Marketing 6 0.07% 122105 – Customer Service Manager 402 4.73% 133101 – Chief Information Officer 1 0.01% 133104 – Application Development Manager 1 0.01% 134601 – Bank Manager 2 0.02% 134603 – Finance Markets Business Manager 1 0.01% 134904 – Office Manager 8 0.09% 134911 – Insurance Policy Administration Manager 3 0.04% 134915 – Operations Manager (Non-Manufacturing) 4 0.05% 143905 – Call or Contact Centre Manager 132 1.55%

212101 – Actuary 43 0.51% 212102 – Mathematician 1 0.01% 212103 – Statistician 64 0.75% 215201 – Electronics Engineer 1 0.01% 241101 – Accountant (General) 39 0.46% 241102 – Management Accountant 1 0.01% 241103 – Tax Professional 1 0.01% 241107 – Financial Accountant 26 0.31% 241201 – Investment Analyst 2 0.02% 241202 – Investment Manager 11 0.13% 241301 – Financial Investment Advisor 171 2.01% 242101 – Management Consultant 47 0.55% 0.13242102 – Organization & Methods Analyst 1 0.01% 242201 – Intelligence Officer 12 0.14% 242202 – Policy Analyst 112 1.32% 242207 – Compliance Officer 43 0.51%

SECTOR SKILLS PLAN 2014 Page 110 32201 – Assistant Midwife 7 0.08%

331101 – Securities Dealer 1 0.01% 331201 – Credit or Loans Officer 2 0.02%

331301 – Bookkeeper 18 0.21%

331302 – Accounting Technician 6 0.07% 331401 – Statistical & Mathematical Assistant 15 0.18% 331502 – Insurance Investigator 8 0.09% 331503 – Insurance Loss Adjustor 151 1.78% 332101 – Insurance Agent 463 5.45% 33102 – Insurance Broker 125 1.47% 332402 – Finance Broker 1 0.01% 333301 – Recruitment Consultant/Officer 14 0.16% 333903 – Sales Representative (Business Services) 27 0.32% 334101 – Office Supervisor 1 0.01% 334302 – Personal Assistant 7 0.08% 341110 – Associate Legal Professional 30 0.35% 351201 – ICT Communications Assistant 62 0.73% 351301 – Computer Network Technician 1 0.01% 411101 – General Clerk 731 8.60% 411102 – Back Office Process Consultant 1 0.01% 412101 – Secretary (General) 1 0.01% 413201 – Data Entry Operator 90 1.06%

421102 – Bank Worker 1 0.01%

422201 – Inbound Contact Centre Consultant 3542 41.67% 422202 – Outbound Contact Centre Consultant 341 4.01% 422206 – Call or Contact Centre Agent 43 0.51% 422501 – Enquiries Clerk 1 0.01% 431101- Accounts Clerk 16 0.19% 431201 – Insurance Administrator 63 0.74% 431204 – Insurance Claims Administrator 202 2.38% 432101 – Stock Clerk/Officer 1 0.01%

441203 – Mail Clerk 19 0.22%

441501 – Filing or Registry Clerk 16 0.19% 441601 – Human Resources Clerk 3 0.04% 441603 – Compensation & Benefits Clerk 3 0.04% 441903 – Program or Project Administrators 7 0.08% 522301 – Sales Assistant (General) 3 0.04% 522401 – Call Centre Salesperson 28 0.33% 531101 – Child Care Worker 28 0.33% 712103 – Abrasive Wheel Maker 1 0.01% 732101 – Delivery Driver 2 0.02% 811101 – Domestic Cleaner 1 0.13% 862913 – Events Assistant 1 0.01%

Grand Total 8501

WSP data 2014/2015

It can be seen from the table above that the occupations to which more planned pivotal training for employees has been allocated are the following, in descending order of importance from the highest to the lowest. The occupations, which have an allocated percentage of 2% and above, are regarded as more important in terms of pivotal training allocation, viz.

x inbound contact centre consultant, x general clerk,

x insurance agent, x customer service manager, x ICT systems analyst,

x outbound contact centre consultant, x developer programmer, x insurance claims administrator, x corporate general manager, x financial investment advisor,

Inbound contact centre consultant stands out above all other occupations in terms of the amount of pivotal training that has been allocated to it. What is also apparent is that the occupations that have the most employees occupying them are those to which the most planned pivotal training are directed. This table also shows that the direction of pivotal training is widely distributed across 96 occupations. Note: Occupations associated with percentages between 0.1 and 1.99%, are regarded as less meaningful in relation to planned

pivotal training. What this also shows is that the pivotal training is geared towards pivotal critical skills (so- called top-up skills) and not scarce skills.

Table 3-12: 1 April 2012 planned training versus 31 March 2013 actual training completed

Occupational Class Total

Planned

Total Trained

Percentage Difference Clerical & admin workers 23737 39492 40% Community & personal service workers 58 81 28%

Elementary workers 237 277 14%

Machinery operators & drivers 68 65 0%

Managers 8784 12415 29%

Professionals 7754 16086 52%

Sales workers 21984 31412 30%

Technicians & trade workers 940 1460 36%

Total 63562 101288 37%

Table 3-10 analyses skills development in terms of major occupational categories, ranging from clerical and

admin workers to managers. What is apparent is that in the majority of occupational categories with the exception of one, actual training implemented exceeds training planned. The one exception is community and personal service workers, where the training planned exceeds the training implemented by 26 persons. It is clear that the planned skills development in each of the major occupational categories is drastically lower than the training completed in each category. The question that arises then is why the training implemented is exceeding the training planned. What it suggests is that levy-paying stakeholder companies are largely underestimating their training requirements.

Another question is whether the stakeholder companies identifying the relevant types of training. The level of difficulty in recruiting for insurance jobs, ranging from actuaries to underwriting, is a point in question. Overall, the total number of planned training within the top seven occupational categories is 73 972. However, the total number of people trained is actually 111 073. This indicates that 37 101 more people received training than is planned. Table 3-10 also indicates that many people intended for training are in the clerical and admin category (36 800) and sales workers category (15 562). It is not surprising, therefore, that much of the training took place within the clerical (45 752) and sales workers (31 926) categories.

It can be seen from the

Table 3-11 Pivotal Planned Trained Beneficiaries 2014

Occupation Total

Number

Percentage of Total

SECTOR SKILLS PLAN 2014 Page 112 134911 – Insurance Policy Administration Manager 3 0.04%

134915 – Operations Manager (Non-Manufacturing) 4 0.05% 143905 – Call or Contact Centre Manager 132 1.55%

212101 – Actuary 43 0.51% 212102 – Mathematician 1 0.01% 212103 – Statistician 64 0.75% 215201 – Electronics Engineer 1 0.01% 241101 – Accountant (General) 39 0.46% 241102 – Management Accountant 1 0.01% 241103 – Tax Professional 1 0.01% 241107 – Financial Accountant 26 0.31% 241201 – Investment Analyst 2 0.02% 241202 – Investment Manager 11 0.13% 241301 – Financial Investment Advisor 171 2.01% 242101 – Management Consultant 47 0.55% 0.13242102 – Organization & Methods Analyst 1 0.01% 242201 – Intelligence Officer 12 0.14% 242202 – Policy Analyst 112 1.32% 242207 – Compliance Officer 43 0.51% 242208 – Organizational Risk Manager 2 0.02% 242210 – Business Administrator 23 0.27% 242211 – Internal Auditor 74 0.87% 242303 – Human Resource Advisor 10 0.12% 242401 – Training & Development Professional 68 0.80% 242402 – Occupational Instructor/Trainer 67 0.79% 243103 – Marketing Practitioner 40 0.47% 243201 – Communication Coordinator 61 0.72% 243202 – Marketing & Communication Analyst 1 0.01% 251101 – ICT Systems Analyst 361 4.25% 251201 – Software Developer 3 0.04% 251203 – Developer Programmer 246 2.89% 251901 – Quality Assurance Analyst (Computers) 3 0.04% 252101 – Database Designer & Administrator 4 0.05% 252201 – Systems Administrator 1 0.01% 252301 – Computer Network & Systems Engineer 3 0.04% 261102 – Administrative Lawyer 2 0.02% 32201 – Assistant Midwife 7 0.08% 331101 – Securities Dealer 1 0.01% 331201 – Credit or Loans Officer 2 0.02%

331301 – Bookkeeper 18 0.21%

331302 – Accounting Technician 6 0.07% 331401 – Statistical & Mathematical Assistant 15 0.18% 331502 – Insurance Investigator 8 0.09% 331503 – Insurance Loss Adjustor 151 1.78% 332101 – Insurance Agent 463 5.45% 33102 – Insurance Broker 125 1.47% 332402 – Finance Broker 1 0.01% 333301 – Recruitment Consultant/Officer 14 0.16% 333903 – Sales Representative (Business Services) 27 0.32% 334101 – Office Supervisor 1 0.01% 334302 – Personal Assistant 7 0.08% 341110 – Associate Legal Professional 30 0.35% 351201 – ICT Communications Assistant 62 0.73% 351301 – Computer Network Technician 1 0.01% 411101 – General Clerk 731 8.60% 411102 – Back Office Process Consultant 1 0.01% 412101 – Secretary (General) 1 0.01% 413201 – Data Entry Operator 90 1.06%

421102 – Bank Worker 1 0.01%

422201 – Inbound Contact Centre Consultant 3542 41.67% 422202 – Outbound Contact Centre Consultant 341 4.01% 422206 – Call or Contact Centre Agent 43 0.51% 422501 – Enquiries Clerk 1 0.01% 431101- Accounts Clerk 16 0.19% 431201 – Insurance Administrator 63 0.74% 431204 – Insurance Claims Administrator 202 2.38% 432101 – Stock Clerk/Officer 1 0.01%

441203 – Mail Clerk 19 0.22%

441501 – Filing or Registry Clerk 16 0.19% 441601 – Human Resources Clerk 3 0.04% 441603 – Compensation & Benefits Clerk 3 0.04% 441903 – Program or Project Administrators 7 0.08% 522301 – Sales Assistant (General) 3 0.04% 522401 – Call Centre Salesperson 28 0.33% 531101 – Child Care Worker 28 0.33% 712103 – Abrasive Wheel Maker 1 0.01% 732101 – Delivery Driver 2 0.02%

811101 – Domestic Cleaner 1 0.13% 862913 – Events Assistant 1 0.01%

Grand Total 8501

WSP data 2014/2015

It can be seen from the table above that the occupations to which more planned pivotal training for employees has been allocated are the following, in descending order of importance from the highest to the lowest. The occupations, which have an allocated percentage of 2% and above, are regarded as more important in terms of pivotal training allocation, viz.

x inbound contact centre consultant, x general clerk,

x insurance agent, x customer service manager, x ICT systems analyst,

x outbound contact centre consultant, x developer programmer, x insurance claims administrator, x corporate general manager, x financial investment advisor,

Inbound contact centre consultant stands out above all other occupations in terms of the amount of pivotal training that has been allocated to it. What is also apparent is that the occupations that have the most employees occupying them are those to which the most planned pivotal training are directed. This table also shows that the direction of pivotal training is widely distributed across 96 occupations. Note: Occupations associated with percentages between 0.1 and 1.99%, are regarded as less meaningful in relation to planned pivotal training. What this also shows is that the pivotal training is geared towards pivotal critical skills (so- called top-up skills) and not scarce skills.

Table 3-12, which analyses training for the period 1 April 2012 to 31 March 2013, and focuses on planned versus actual training, that the occupational class with the highest demand for planned training is clerical and admin workers. This is followed by sales workers, managers, professionals, technicians and trade workers, elementary workers, machinery operators and drivers, and finally, community and personal service workers. For all occupational classes, with the exception of machinery operators and drivers, the total number of employees trained exceeded the number of employees that are planned to be sent on training. The difference column reflects the percentage increase between the number of employees being contemplated for training and the actual number that are trained.

Although the training conducted far exceeds the training intended, the movement of employees from entry level to higher positions leads to a constant skills shortage. The competition for labour resources between the insurance and financial sectors means that one sector may be left with skills challenges as the workforce crosses over to another sector. The continuous growth of the insurance sector, even during the 2008

SECTOR SKILLS PLAN 2014 Page 114 Figure 3-6: Educational level by age in 2012

Another concern for the sector is the few graduates available to enter the sector. There are more White candidates with higher-level degrees and diplomas compared to Blacks in the 155 companies sampled. The sector is also taking on a high number of youths with Grade 10 to 12 certificates. According to the Green Paper on Post Schooling, South Africa is still not producing enough graduates to meet its economic development objectives.

South African universities are characterised by relatively low success rates: 74% in 2010 compared to a desired national norm of 80%. This results in a graduation rate of 15%, which is well below the national norm of 25% for students in three-year degree programmes in contact education. In contact universities, well under one-third of students complete their courses in regulation time and one in three graduates within four years. This is a distressing blow to the ambitions of tens of thousands of dropouts each year and is a iste of the resources of both parents and the state. Improvement of throughput rates must be the top strategic priority of university education. Among other things, this will allow us to increase the number of graduates disproportional to the increase in the relatively modest projected expansion of university enrolments. Although postgraduate enrolments in both masters and doctoral programmes remain low, compared with other levels (see Figure 3-6) they have been increasing in number over the last 15 years (Department of Higher Education and Training, 2012).

The proportion of Black doctoral graduates has also been increasing. In 1995, South Africa produced 679 doctoral graduates, which had grown to 967 in 2000, 1 188 in 2005 and 1 420 by 2010 (or 26 doctorates per million of the country’s total population). In 2010, 48% of the doctoral graduates are White (87% in 1995), 38% are African (6% in 1995), 7% are Indian (3% in 1995) and 6% are Coloured (4% in 1995). The number of Africans has, however, likely been boosted by the increased numbers of foreign students from other African countries. Approximately six out of 10 doctoral graduates are male, indicating a need to increase the number of females studying for doctorates (Department of Higher Education and Training, 2012).

Despite obvious progress concerning the numbers of doctoral graduates (with 26 doctorates per million of the country’s total population), South Africa lags far behind countries such as Portugal (569 per million), the United Kingdom (288 per million), Australia (264 per million), the United States of America (201 per million), Korea (187 per million) and Brazil (48 per million).

3.6 Concluding Remarks

It has been indicated in this chapter that the supply of financial advisors is limited. The insurance industry does not agree about whether there is an adequate or inadequate supply of actuaries. Reasons have been advanced why there are issues around the training of actuaries. Similarly, reasons have been put forward regarding why it is difficult to retain actuaries within the insurance industry. It is indicated that it is necessary to use multiple research methods to obtain data about skills supply and demand, as labour market data is not

0 5000 10000 15000 20000 25000 30000 35000 Grade 9 and below Matric Grade 10 to Grade 12 Level 5 Diploma Level 6 - 8 Degree, Honours, Masters Level 9 Doctorate PhD Level 10 <34 35-49 50-64 65+

robust. The above methods provide a comprehensive picture of skills supply and demand within the South African insurance sector. It can be seen that one major source for the above type of data is the WSP and ATR. A listing of scarce and critical skills against the OFO is provided in this chapter. Also scarce and critical skills are identified from the PWC study. This type of analysis is restricted to INSETA levy-paying companies. It is emphasised here that Grade 12 mathematics is an essential prerequisite to gain entry into the South African insurance industry. Passing of mathematics at matric level also has secondary value in that it allows one to be exempted from specific FAIS requirements. It has been emphasised that the attainment of various career directed degrees of diplomas allow for access into the insurance industry. A shortage of skills supply within the insurance sector is compounded by the fact that graduates who qualify for a career in insurance are equally employable within the broader financial services sector. The different foci of the WSP and ATR data in yielding a picture of skills supply and demand are highlighted.

What factors compound the supply of matriculates to the South African insurance industry? There is an ambiguity regarding the supply of matriculates to the insurance sector and the South African economy in general. Overall, the number of matriculate NSC enrolments has declined between 2009 and 2011; however, the number of part-time enrolments has increased. Part-time enrolments imply that it will take longer for the matriculates to become available to the insurance sector. In addition, there is a relative difference between those who wrote, compared to those who enrolled for matric.

There is an opportunity for learners who have not obtained a matriculation exemption to pursue a BTEC National Diploma, which is recognised by HESA.

Stakeholders have reiterated that there is an insufficient supply of labour at the entry-level market. They distinguished between the entry-level and the upper-level market.

Other problematic issues that have been identified are the high attrition rate of young people exiting the insurance market. A small percentage of matriculates are eligible to pursue university studies. Another factor that compounds the availability of matriculates to the insurance industry is the difference between the number of students enrolled versus those that passed Grade 12.

The supply of skills from tertiary institutions has been looked at from various perspectives: the fact that there are various types of institutions, universities, universities of technology, and comprehensive universities. These institutions offer different types of qualifications. Comprehensive universities are a combination of two different types of institutions. There are also administrative hubs which partner with other universities. The headcount of universities as well as the distribution of undergraduate and postgraduate students are looked at. The changing racial distribution of learners is looked at between 2009 and 2010. The number of Black and African learners has improved substantially between 1994 and 2010. Pertinent insurance-related qualifications that are supplied by tertiary institutions have been identified. Another relevant issue is that university funding has not kept up with enrolment growth. The supply of skills from tertiary institutions has been detrimentally affected by the poor living conditions prevalent at these institutions. Limitations in infrastructure also impinge on the ability of these institutions to supply skills. In sum, various factors impinge

SECTOR SKILLS PLAN 2014 Page 116 The supply of skills from the INSETA Education Training and Quality Assurance (ETQA) is reflected in terms of uploads to the NLRD. Another measure of the supply of skills is the number of learners who have completed learnerships. The breakdown of the number of learners in a learnership by province for 2012 is also provided. `

The reasons for companies not taking up learnerships ranged from the inability of the company to recruit the right learner to the fact that small companies do not have the capacity to handle the learnership. The reasons why there is an attrition rate of learners from learnerships is explained. It is indicated that in order to achieve a higher success rate with learnerships, improvements in various areas have to occur, for example ensuring that new learnerships only open once the old ones have closed. The role of the FET colleges in learnerships cannot be over-emphasised. The breakdown of learnerships by race and gender is also given for the 2011- 2012 financial year. Over 18 million rand is allocated to the internship project. An offshoot of this project is a project for intellectually disabled adults. The provincial spread of internships for 2012 is outlined.

A question addressed is what type of interventions exists for the supply of skills into the insurance sector? Another question is what is the impact of these interventions? Comments on the positive impact of training ranged from the impact on the level of production to the reduction of turnaround time on the processing of claims. Other foci of this chapter are the most popular training skills reported by medium and large companies in the insurance sector. This is measured for the years 2012 and 2013. The most popular occupational categories identified for training for 2013 are clerical workers, followed by sales workers, followed by professionals and then by managers. The most popular priority areas for training are identified. Non-credit-bearing training received more emphasis than credit-bearing training. Reasons for this are advanced.

It is shown that the demand for skills is established from the WSP data, vacancy analyses, and industry consultation. It is indicated that specific change drivers underpin the demand for skills.

The most sought after professional executive positions in the insurance sector and the highest areas of demand for skills are identified. It is shown that the sought after areas for recruitment varied between 2008 and 2010.

Scarce and critical skills are also identified in this chapter using the OFO. These areas ranged from actuary to occupational analyst. The top five scarce and critical skills are identified. There are four occupational categories that reflect occupational demand (INSETA, 2012).. These range from professionals to sales