Turbulence or Orderly Change? Teacher Supply and Demand in South Africa - Current Status, Future Needs and the Impact of HIV/AIDS (CIE, 2000, 36 p.)
 (introduction...) Multi-Site Teacher Education Research Project (MUSTER) List of Acronyms and Abbreviations 1. Overview 2. Background and Introduction 3. The Demographic Characteristics of Teachers 4. Incomes of Teachers and Non-teachers 5. Teacher Turnover - The Dynamics of Teaching Employment 6. Forecasting Basic Numbers 7. Regionality and Micro-Regionality of the HIV/AIDS Epidemic 8. Concluding Remarks Annex

Annex

Table A1 shows in summary form all of the “multivariate” relationships which we have used to double-check more rigorously, and either confirm or deny, trends and differences. This is presented without showing the actual numbers, both to save space and because the numbers individually are not very important. The symbols below should be read as follows: A + implies a positive relationship, so that, for example, being a teacher implies a higher salary. A *** implies that the relationship is statistically significant at the 1/10th of 1% level (an extremely high level of significance), ** at the 1% level, and * at the 5% level. We do not report relationships significant only at the 10% level. NS means that the relationship is “statistically not significant” (that is, more precisely, that the hypothesis that there is no relationship cannot be rejected with a reasonable level of certainty). Thus, for example, the symbols -,*** and -,*** in the columns for “Interaction of time trend and teacher dummy variable” and “Simple time trend for teachers only” and the row for “Hours worked” mean that, assuming the surveys are constructed properly, hours worked per week decreased for teachers and increased for other workers; in other words, there is only a 1 per 1000 chance that we would have measured these relationships as strongly as we did were they not true.

Table A1: “Multivariate” analysis of simple demographic trends, using sampling weights

 Equations where the dependent variable is either a quantity (e.g., salary) or the conditional probability of being in a group Dependent variable (1) Dummy variable for being a teacher (2) Simple time trend (3) Interaction of time trend and teacher dummy variable (4) Simple time trend for teachers only Salary +,*** +,*** +,*** Irrelevant Salary, controlling for hours worked +,*** +,*** +,*** Irrelevant Probability of being African +,*** +,*** -,*** NS Probability of being coloured -,*** +,*** NS NS Probability of being Indian NS NS NS NS Probability of being white -,*** -,*** +,*** NS Probability of being female +,*** +,*** NS +,*** Probability of being a union member +,*** +,* +,*** +,*** Age -,*** -,*** +,*** +,*** Hours worked -,*** +,*** -,*** -,*** Equations where the dependent variable is the conditional probability of being a teacher, as determined by a particular variable Condition or group Dummy for the condition or group Simple time trend Interaction of time trend and dummy for the condition or group Union membership +,*** -,*** +,*** Female +,*** -,*** -,* African +,*** NS -,*** Coloured -,*** -,*** NS Indian NS -,*** NS White -,*** -,*** +,***

Source: OHS 1995, 1997, and 1999. Author’s tabulation.

Notes: 1) The fourth column in the first panel above arises out of a different equation, in each case, from that in the first three columns. In the fourth column the non-teachers have been filtered from the database and a simple time trend is fit for the relevant quantity or conditional probability. It has to be done this way because the single equation implicit in the first three columns would be over-determined if the database had been filtered for non-teachers. 2) The simple time trends for salaries for teachers only (fourth column) are irrelevant because the salary data are in nominal terms. Thus, in these two rows only the comparisons between teachers and non-teachers are relevant (third column).

Table A2: Basic demographic characteristics of leavers and joiners from the teaching force, as compared to the entire teaching force 1998 to 1999

 Characteristic Leavers as a % of those with same characteristic in 1998 database Joiners in 1999 as a % of those with same characteristic in 1998 database Net leavers as a % of those in the 1998 database Leavers with the given characteristic as a % of all leavers Joiners with the given characteristic as a % of all joiners % of those with the given characteristic in the 1998 database Gender Male 5.37% 1.79% 3.58% 34.8% 32.4% 34.6% Female 5.33% 1.97% 3.36% 65.2% 67.6% 65.4% Population group African 4.2% 1.6% 2.6% 60.5% 64.4% 76.2% Coloured 4.8% 2.9% 1.9% 7.5% 12.6% 8.3% Indian 7.5% 1.4% 6.1% 4.5% 2.3% 3.2% White 12.0% 3.2% 8.7% 27.5% 20.7% 12.3% REQV 10 9.1% 1.6% 7.5% 6.5% 4.2% 3.6% 11 5.8% 0.3% 5.5% 4.6% 0.7% 4.0% 12 3.8% 0.2% 3.6% 11.8% 2.5% 15.5% 13 4.6% 1.7% 2.9% 34.8% 46.1% 38.6% 14 6.2% 1.9% 4.2% 31.3% 36.1% 25.8% 15 4.5% 0.8% 3.7% 8.1% 5.3% 9.1% 16 4.3% 0.5% 3.9% 2.5% 1.0% 3.0% 17 6.5% 0.8% 5.7% 0.4% 0.2% 0.3%

Source: calculated by the author from 1998 and 1999 PERSAL database.

Note: we have not presented standard errors in order to minimise overload on the table. However, in general, all of the implicit differences above are statistically significant. For example, the differences between leaving and joining rates for males and females are statistically significant, as is the difference between joining rates at REQV 13 and 14.

Table A3: Assumptions needed to drive a teacher demand and supply projection