Methods: demographic profile analysis of sectors within EIA&M.
In order to determine the demographic representivity of the EIA industry, it was first necessary to determine which sectors were involved in EIA, and to some extent this was informed by interviews,
- Civil society:
- Registration bodies for natural scientists and EAPs (including SACNASP, IAIA, EAP SA, IWM SA, ECSA)
- Educational institutes (universities, technikons)
- Training organizations (short course programs)
- NGOs involved in EIAs (including CBOs)
- Service providers:
- EAPS (small and large consulting companies)
- Provincial and national departments of environment
Due to time and resource constraints, only selected representative organizations’ profiles were analysed, according to geographical and urban/rural spread.
Employee/staff/student demographic profiles in terms of numbers of population group and gender were requested from these sectors. Several databases of memberships from various institutions and government departments involved in the EIA sector were asked for and examined in terms of demographic distribution. These included Waste, Environmental Science, Environmental Management, and Engineering.
In order to bulk up samples, a very simple survey form for demographic statistics of educational institutes, EAPs, NGOs, CBOs was sent to 638 of the delegates that attended the 2008 IAIA “Ten years of EIA in South Africa” conference. Of these 638, 93 emails were found to be not working anymore and of the remainder, only 13 responses were returned, but the time frame for response was unavoidably short (2 weeks) and it is unlikely there would have been a high return even if these surveys were sent out at the beginning of the project.
During our research, we found that many institutions were unwilling to give demographic data (“colour”) for employees and thus on occasion we had to identify race using the name and surname of employees. There is obviously some inaccuracy in this method, but when uncertain we formulated an objective rule of putting names into demographic groups at a 50/50 probability. This probably resulted in a very small overestimation of people in the so-called “Coloured” race class, as the main uncertainties were between the “White” and “Coloured” surname categories. Where possible, we asked for verification from key people who had been in the EIA&M field for a long time and had a wide acquaintance in this industry, but we could not do so in all cases due to resource constraints.
The results of the demographic distribution in each database were compared to
Methods: Reasons for demographic distribution of EIA&M
The key to improving transformation rates is to understand the reasons for the demographic imbalance. The methodology used to determine this was a qualitative one. A questionnaire was compiled that sought to understand, amongst many other issues, how “black” or
Methods: representivity of sampling
Interviewees were selected based on their context within the industry and how they could provide input into the skews and reasons we were detecting. So interviewees were selected from all sectors and career paths within EIA, including:
- Students currently studying environmental management
- Interns who have just left educational institutes and are being trained in the work place
- Junior and senior
- Junior and Senior White consultants
- CEOs of EAPs, including large, small firms and from different degrees of black ownership
- Government EAP officials (even though this was not included in the ToR but is particularly relevant to this SubTheme)
- Training institutions
- Civil society organizations, including CBOs and NGOs (to determine current EIA skills and demographic representivity within the EIA sector and representivity within the public participation process too)
- A focus group session of young black environmentalists and their perceptions on barriers and retention
This study could not be conducted as a fully quantitive survey due to time and budget constraints, but more importantly the status quo was examined statistically and the main trends were identified during a collation of interview results and followed up in more informal interviews with experienced people in each regard. Thus the survey drew on both quantitative and qualitative methodology in order to acquire the most relevant information in the most cost- and time-effective manner.
The methodology stratified the service provider sector into a number of parts:
- The one component is the large firms, often international. These firms tend to have an engineering history and have combined their engineering expertise and environmental impact management in one company. They have in house specialists in many fields and in practice, are often the lead consultant and provide the specialists, often the public facilitation as well. It was assumed that such companies would also be able to offer mentorship, educational opportunities, and attractive salaries, thereby attracting the majority of “black” consultants.
- The second component was the smaller firms, generally focused on Environmental Impact Assessments and related work, not historically engineering firms. As smaller firms, these companies would be competing against well established large firms, and the interviews aimed to determine whether BEE firms were able to outcompete the larger firms, both in getting work and in attracting young “black” professionals.
- The third component was individual professionals/consultants, either placed in government, large or small firms and academic institutions. The aim of these interviews was to interview professionals in middle or senior management positions, to discuss the challenges that they experienced in their careers and how they overcame them. These interviews were not limited to “black” professionals but interviewed a range of professionals in order to compare the usual challenges that professionals may encounter in their careers vs those that might be attributed to transformation. We thus acquired the perceptions and observations from both “black” and “white” practitioners.
- The fourth component was professionals in the NGO/CBO sector. The number of established NGOs active in the environmental management sector is not large and it was possible to interview representatives from most larger NGOs and from key community activists. The use by NGOs of white experienced personnel to review and comment on EIA case studies was investigated, particularly in consideration of this report’s development of recommendations for mentoring processes.
- The fifth component was geography.
is a diverse country and there are stark differences between the urban and rural areas, particularly when it comes to opportunities for environmental professionals. Interviews were held with a range of organisations and individuals from different areas of the country, focusing on a few of large versus small, rural versus urban institutions South Africa
- The sixth component was communities, and although there was some cross-cutting with Sub-Theme 7, the questionnaire and interviews drew out issues of transformation and empowerment of communities participating in EIAs too.
- The seventh component was training institutions, including organizations offering short environmental courses and university environmental management programs, to determine whether there is transformation occurring in the skills development side.
- The eighth component was government demographics. Although government demographics were not part of the terms of reference, it is clear that government has transformed at a faster pace than the other sectors. Interviews with government officials were therefore also held in order to add to the information base. Government demographic statistics were also researched.
Some research was carried out to determine how other sectors of the economy have transformed and whether there were specific lessons that could be applied to the environmental sector. Interviews were backed up by a limited desktop study of transformation issues in other sectors and best practice guidelines evaluated for their applicability in the EIA&M sector.
It became clear that the demographic datasets, the policies, the issues constitute a formidable body of data that would require much more time to analyse. As a consequence, this report can only provide an overview of the issues and challenges facing the sector and this report is therefore a first attempt at defining the issues and providing feedback for developing an agenda for future work on transformation. Please refer to Appendix One for a database of interviewees and organizations.
Collection of data: The short time frame of the project did not allow for adequate time to acquire all the relevant statistics from all the different sectors. Interviewees and survey respondents did not see such statistics as a priority on their limited time, necessitating a huge volume of follow-up telecommunication to encourage people to fill in the surveys or send the data. People were also reluctant to give out this data based on the fact it would be changing shortly in the new financial year. The tight schedule has not been conducive to the long process of getting this sort of data from big institutions. We were thus unable to collect all the institutional data we would have liked to.
Sectors requiring further review: There seems to be a paucity of Black/Coloured/Indian specialists, such as botanists, hydrologists, etc, and thus our study could not get an adequate representation of this sector. It is worth further research to see what the path of new graduates is and where these graduates end up in the industry.
Emotive and controversial nature of the statistics: Furthermore the nature of the statistics (racial profile) was of concern to many people, for what reason we were not told in the majority of cases, though some people said outright the new
Demographic representivity of skills development: As the skills required for EIA&M are broad-ranging and cross-cutting (see SubTheme 8 report), we could not get demographic statistics for population groups for the EIA&M field alone, we often had to resort to comparing broad environmental and engineering statistics from universities and institutions in order to understand why there was a lack of skilled PDI and female graduates in the EIA&M field.