distributed normally, an appropriate estimation technique is a dichotomous choice probit16.
4.3.2 Employment status equation results for a dichotomous choice model
As noted previously, the empirical results from this chapter are focussed upon females in the Australian and United States. Since this section estimates the dichotomous choice model, the results from this section refer to Australian and United States females who are working either full- or part-time only. In the following section which utilises the trichotomous choice model, female who are not working are included with the sample of females working full- and part-time.
This specification takes no account of the decision to work and is identical to that adopted by Main (1988) in his analysis of full- and part-time wages for females in the United Kingdom and Simpson (1986) fro Canada. The extension of the selection term to include the three choices of not working, working part-time and working full-time is explored in a following section.
TABLE 4.3 : Dichotomous employment status equation of females, Australia and the United States, 1986/8717
Australia United States
Variable Coeff t-ratio Coeff t-ratio
constant 1.578 8.676 * 0.436 4.291 * married -0.381 -3.527 * 0.238 4.249 * divorced 0.016 0.105 0.615 8.533 * race -0.541 -1.109 0.128 2.237 ** age -0.018 -4.918 * -0.002 -0.883 ed2 0.010 0.126 0.015 0.207 ed3 -0.079 -0.891 0.010 0.133 ed4 0.047 0.344 0.073 0.948 kidl -0.464 -5.241 * -0.245 -4.904 * kid2 -0.793 -9.019 * -0.415 -7.544 * kid3 -0.842 -6.793 * -0.599 -7.606 * kid4 -0.970 -4.023 * -0.761 -5.017 * kid5 -0.427 -0.934 -0.698 -2.949 * murban/city 0.148 1.403 0.143 2.876 * urban/msa -0.109 -0.896 0.040 0.942 yinc -0.008 -2.814 * -1.6E-05 -3.122 *
Number of obs = 1599 Number of obs = 5131
chi2(15) = 241.57 chi2(15) =221.51
Prob > chi2 = 0 . 0 0 0 0 Prob > chi2 =0 . 0 0 0 0
Log Likelihood = -975.88294 Log Likelihood = -3063.758
Pseudo R2 = 0.1101 Pseudo R2 = 0.0349
Table 4.3 represents the employment status of females who have chosen to work either full- or part-time. The constant represents a single, non-black, unqualified rural dweller who has no dependant children. From Table 4.3 it can be observed that the most important factors that affect a females decision to work full- or part-time is the presence and number of children. In an alternative specification of children for the United States (not reported) the age of the children is also shown to be an important factor affecting the probability18 of working. This specification is not conducted for Australia, as the Australian data used in this analysis does
17 18
Sample means for this specification are provided in Appendix D, Table Dl. This finding supports the results from Teal, F.,( 1990) for Australian females.
not contain information on the age o f children. However, Teal (1990) has estimated similar equation for Australian fem ales. He finds that the age o f dependent children, particularly the very young, does effect a woman's decision to work either full- or part-time.
Table 4.4 : Employment probability of females working full-time19
Australia United States
Pop. Mean (1) 0.56 0.69
Variable percentage point change (2)
married -0.15 0.08 divorced n.s. 0.19 race n.s. 0.04 ed2 n.s. n.s. ed3 n.s. n.s. ed4 n.s. n.s. kidl -0.18 -0.09 kid2 -0.31 -0.15 kid3 -0.32 -0.22 kid4 -0.36 -0.29 kid5 n.s. -0.27 murban/city n.s. 0.05 urban/msa n.s. n.s.
n.s. - not stated due to insignificant coefficients or sample size of cell too small to form reliable estimates.
(1) This represents the mean of the dependent variable in employment equation.
(2) The -0.15 figure for the married dummy variable in Australia may be interpreted as a decrease of 15 percentage points in the probability a female with the average characteristics of the groups, except she is unmarried, will work full-time after she is married.
Educational qualifications in Australia do not appear to be statistically significant to the decision to work either full- or part-time. This result is also observed for fem ales working full- or part-time in the United States. This is a surprising result particularly for the United States
19 Represents changes in the cumulative densities o f the full-time employment probability as a
result o f the dummy variable changing from zero to one, when all other variables are estimates at their respective means.
where part-time work is often categorised as being associated with poorly skilled persons. The lack of statistical significance of this and other variables are compared with the estimates from the trichotomous choice equation in the following sections.
Since it is not possible to directly interpret the coefficients of a probit equation, Table 4.4 provides some indication of the relative effect of each significant regressor on the probability to work full- time for a representative individual in each country.
For Australia, being married is estimated to lower the probability a representative individual will work in full-time work by around 15 percentage points. For the United States, being married, increases the probability of a female working full-time by 8 percentage points. Although being divorced in Australia did not effect (in a statistically significant way) the choice of females to work full-time, in the United States being divorced increased the probability of working full-time by 19 percentage points. Being non-white in the United States is associated with a 4 percentage point increase in the probability of working full-time.
Generally, having children is associated with lowering the probability a female works full-time. For Australia, the effect of having the first child on the probability to work full-time for the representative individual is estimated to be around 18 percentage points. For females with four children in Australia, the probability of working full-time is lowered by around 36 percentage points. For the United States, females with one child are estimated to decrease their probability of working full time by around 9 percentage points. Having five children is estimate to decrease the probability a female works full-time by around 27 percentage points.
Although the dichotomous choice model between full- and part-time work has been utilised in some studies of part-time work (see for example,
Simpsons (1986) analysis of Canadian full- and part-time wages), accounting only for sample selection between workers ignores the sample selection problems addressed in Heckman's original paper. That is, this procedure ignores those individuals who are not working. Hence, statistical inference on the basis of the parameters from the dichotomous model are predicted to be biased as a result of workers not representing a non-random sample of the population. In order to overcome this problem, a trichotomous approach to sample selection is suggested in the following section.
4.3.3 A trichotomous outcome : full- and part-time work and not