76 CHAPTER 4
Table 4.3 Descriptive statistics: Mathematical test results
Age (years) Test 7 8
Addition Correct
Incorrect
Subtraction Correct
Incorrect
Multiplication Correct
Incorrect
Division Correct
Incorrect Number of observations
157 61
117 101
60 158 15
203 218
Source: University of KwaZulu-Natal (2004).
Table 4.4 reports the determinants of attrition from the 1998 to the 2004 round and from the 1993 to the 2004 round (see Fitzgerald, Gottschalk, and Moffitt 1998a, 1998b; Thomas, Frankenberg, and Smith 2001). Since our prin- cipal analysis focuses on variations among siblings, controlling for household fixed effects, attritions at the individual level are of interest. Given obser- vations in the 1998 round, our concern here is to determine whether the probability of being observed in the 2004 round depends on explanatory vari- ables used in the schooling investment and outcome equations. The sample is restricted to children from households found in 2004 who were between the ages of 1 and 5 in 1998 (0–5 in 1993) with height-for-age z-score values between –6 and 6.
In the attrition analysis, I use explanatory variables from the schooling equations: the height-for-age z-score, age indicators, and gender dummy, which are taken from either the 1993 or the 1998 round. To the extent that these predetermined variables are not correlated with attrition, we would not expect bias in estimates in the schooling equations owing to the attrition process. However, a possibility of attrition on unobservables still remains. For example, because of a correlation between shocks (unobservables) in the attrition and schooling equations, attrition on unobservables may cause addi- tional bias in the schooling equations.12
12 Fitzgerald, Gottschalk, and Moffit (1998a, 1998b) discuss the issues of attrition on observables and unobservables and examine attrition problems in the Panel Study of Income Dynamics.
186 27
157 56
98 115 44
169 213
9
189 16
166 39
131 74
77
128 205
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