BINARY LOGISTIC REGRESSION

This study aimed to determine whether, or not, any of the set of push factors namely governance framework, infrastructure, legislation, economic uncertainty, future limiting and narcissism society, are statistical significant predictors of the likelihood that a person will emigrate to New Zealand rather than Australia by using binary logistic regression.

In assessing the model adequacy and fit, table 6.18 sets out the information regarding the predictors included in the model of push factors, while table 6.19 provides information regarding overall model fit. The number of respondents included in this model was 216 as a case-wise removal process was used for missing data on any of the variables.

The results are tabled on page 167.

Table 6.18: Variables in the equation of predictors included in the model of push factor

B S.E. Wald df Sig. Exp(B)

Step 1 Gov_framework .153 .236 .424 1 .515 1.166

Infrastructure -.018 .166 .012 1 .914 .982

Legislation -.288 .145 3.957 1 .047 .750

Economic_Unc .240 .220 1.185 1 .276 1.271

Fut_limiting -.069 .209 .109 1 .741 .933

Narc_Soc -.027 .195 .019 1 .889 .973

Constant -.142 .840 .029 1 .866 .868

The overall correct prediction classification of the model is 56.5%.

The results indicate that legislation is the only statistically significant predictor, at the 5%

level of significance. The odds ratios indicate further that:

 For each one point increase in the importance level of legislation as a push factor, the odds of a person emigrating to New Zealand decrease to 0.75.

Table 6.19: Variables in the equation of the overall model fit for push factor

Model summary Hosmer and Lemeshow Test

-2-Log-likelihood Cox & Snell R Square

Nagelkerte R Square

Chi-square df Sig

Step 1 291.754 .021 .028 10.334 8 .242

The Hosmer and Lemeshow test shows non-significance indicating that the data fits the model adequately (Hosmer & Lemeshow, 2000). The R² measures (Nagelkerke R^{2 ,} Cox and Snell R²), however, are low (0.028 and 0.021 respectively) and indicate that follow-up studies are needed to validate and improve the current model.

In assessing the model adequacy and fit, table 6.20 sets out the information regarding the predictors included in the model of pull factors, while table 6.21 provides information regarding overall model fit. The number of respondents included in this model was 75 as a case-wise removal process was used for missing data on any of the variables.

Table 6.20: Variables in the equation of predictors included in the model of pull factor

B S.E. Wald df Sig. Exp(B)

Step 1 Eff_gov_services .037 .382 .010 1 .922 1.038

Econ_certainty -.645 .368 3.068 1 .080 .525

Lower_costliving .684 .286 5.698 1 .017 1.982

Fam_circumstances -.409 .260 2.467 1 .116 .664

Better_Future .328 .295 1.238 1 .266 1.388

Constant .617 1.592 .150 1 .698 1.854

The Overall correct prediction classification of the model is 61.3%.

The results indicate that lower cost of living is the only statistically significant predictor, at the 5% level of significance. The odds ratios indicate further that:

 For each one point increase in the importance level of a lower cost of living as a pull factor, the odds of a person emigrating to New Zealand decrease to 1.982.

Table 6.21: Variables in the equation of the overall model fit for pull factor

Model summary Hosmer and Lemeshow Test

-2-Log-likelihood Cox & Snell R Square

Nagelkerte R Square

Chi-square df Sig

Step 1 89.399 .133 .180 10.187 7 .178

The Hosmer and Lemeshow test shows non-significance, indicating that the data fits the model adequately (Hosmer & Lemeshow, 2000). The R² measures (Nagelkerke R^{2 ,} Cox and Snell R²), however, are low (0.133 and 0.180 respectively) and do indicate that follow-up studies are needed to validate and improve the current model.

In assessing the model adequacy and fit, table 6.22 sets out the information regarding the predictors included in the model of personal needs factors, while table 6.23 provides information regarding overall model fit. The number of respondents included in this model was 162 as a case-wise removal process was used for missing data on any of the variables.

The results are tabled on page 169.

Table 6.22: Variables in the equation of predictors included in the model personal needs

B S.E. Wald df Sig. Exp(B)

Step 1 PhysNeeds .310 .209 2.192 1 .139 1.363

SafetyNeeds .000 .229 .000 1 .998 1.000

Belonging -.096 .200 .232 1 .630 .908

Esteem_Needs .137 .240 .325 1 .568 1.147

Constant -1.519 1.290 1.386 1 .239 .219

The Overall correct prediction classification of the model is 50.6%.

The results indicate that safety needs is the only statistically significant predictor, at the 5%

level of significance. The odds ratios indicate further that:

 For each one point increase in the importance level of safety needs as a push factor, the odds of a person emigrating to New Zealand decrease to 1.000.

Table 6.23: Variables in the equation of the overall model fit for personal needs

Model summary Hosmer and Lemeshow Test

-2-Log-likelihood Cox & Snell R Square

Nagelkerte R Square

Chi-square df Sig

Step 1 220.937 .022 .029 7.964 8 .437

The Hosmer and Lemeshow test shows non-significance indicating that the data fits the model adequately (Hosmer & Lemeshow, 2000). The R² measures (Nagelkerke R^{2 ,} Cox and Snell R²), however, are low (0.029 and 0.022 respectively) and do indicate that follow-up studies are needed to validate and improve the current model.

6.10 CONCLUSION

The purpose of this chapter was to determine what the main push factors and pull factors (both internal and external) are that contribute to the emigration or moving abroad of economically active South Africans. It also set out to determine whether there is a correlation between the push and pull factors for South African emigrants in Australia and New Zealand.

The chapter reviewed the demographics profile of the respondents. Secondly, the push, pull

and personal needs items were analysed through exploratory factor analysis to determine whether meaningful factors emerged. Inferential statistics explored the relationship between these factors by focussing on differences in importance of the push, pull and personal needs factors between emigrants residing in Australia and New Zealand. Binary Logistic regression showed whether there were factors that are statistically significant predictors of the odds that a South African emigrant will choose to locate to New Zealand. The results will be discussed in Chapter 7.