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ABSTRACT
The objective of this paper is to analyse the extent to which membership of KiwiSaver has been associated with greater accumulations of net wealth. The paper utilises two linked sources of data which cover the period 2002–2010: Statistics New Zealand's Survey of Family, Income and Employment and Inland Revenue Department administrative data on KiwiSaver membership. Two approaches are employed: difference-in-differences (where the outcomes of interest are changes in net wealth) and various panel regression techniques. Results appear consistent with earlier evaluations of KiwiSaver. Neither approach suggests KiwiSaver membership has been associated with any positive effect on net wealth accumulation.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1. For further details of the scheme see http://www.kiwisaver.govt.nz or http://www.ird.got.nz/kiwisaver.
2. In a number of cases respondents failed to provide valuation dates. In these cases we assumed that the distance between the respondents’ interview date and valuation date was the same as the average of that distance for those respondents that were able to provide valuation dates. This distance was between two and three years depending on the survey wave.
3. Scobie and Henderson (Citation2009) provide further discussion of the practicalities of indexing various assets and liabilities in SoFIE.
4. To construct a usable panel data set for analysis SoFIE also required manipulation and formatting, with the data originally being stored in around 20 separate files with different (often incompatible) formats.
5. This issue is unlikely to have affected the value of assets recorded in KiwiSaver, however.
6. In particular, the sum of income from all sources in waves 1 and 2, 3 and 4, 5 and 6 and 7 and 8 each had to be positive for every individual.
7. Assuming this measurement error is random its effects on regression results will be to potentially reduce the precision of coefficient estimates. However, it will not bias coefficient estimates as these outcome measures are used as dependant rather than explanatory variables in regressions.
8. The large difference between the mean and median levels of net wealth is indicative of a skewed distribution with a long ‘right-hand tail’; i.e. a small number of individuals with very high levels of net wealth. Similar findings are reported by Le, Gibson, and Stillman (Citation2010).
9. In comparing the median rates with saving rates estimated from the national accounts, it must be recalled that the rate reported here apply essentially to the working age population as distinct from the aggregate household saving rates, which logically are much lower.
10. Recall that while respondents assets and liabilities are measured in SoFIE only every second year, income is measured every year.
11. The rows and columns of each matrix of transition probabilities sum to 1.
12. Indeed, if one regresses the savings rate on its lag the estimated coefficient is negative and highly statistically significant.
13. To foreshadow results somewhat, when the dependant variable in regressions of Section 5 (changes in net wealth) was replaced with savings rates, regressions were able to explain only about a hundredth of the variation in savings rates that they were able to explain in changes in net wealth. That is the R2 for regressions where the dependant variable was changes in net wealth was typically in the order of 0.15 while the R2 for regressions with the savings rate as the dependant variable was around 0.0015.
14. More detailed explanations are available in Wooldridge (Citation2006), and in the context of evaluating firm assistance programmes in New Zealand in MED (Citation2011).
15. Hence the comparison groups are fixed over time. Another possibility for assignment to the KiwiSaver membership group would be to base this on KiwiSaver membership status at wave 6. However, at wave 6 few people had joined KiwiSaver, as at this stage the scheme was very new. In Section 5 we explore further the effect that such differences could make to our estimates of the impact of KiwiSaver on the net wealth accumulation of its members.
16. As before these are nominal changes in net wealth – inflation being common to both KiwiSaver members and non-members. In Section 5 we allow for the effects of house price inflation on net wealth accumulation.
17. As KiwiSaver was introduced in wave 5, the change in net wealth between waves 4 and 6 is not used in the calculation of the DiD estimator. It is included in for completeness and illustration only.
18. Law et al. (Citationin press) found very few variables were useful in predicting whether or not an individual was more or less likely to have joined KiwiSaver, including income or wealth. The few factors that were useful predictors of KiwiSaver membership were: being older, expecting New Zealand Super to be ones main source income in retirement, being of other ethnicity, being partnered, being self-employed and having an occupation of other.
19. Though a balanced panel is not necessarily required for the regressions in this section, one is enforced for consistency with the DiD analysis of the previous section.
20. The time dimension here is complicated. As will be explained shortly in some cases this represents the difference in a variable over time, in others the sum of that variable over time or a variables value at the start or end of a period. Precision to this degree in our notation is not necessary however for the current purpose.
21. An alternative to either RE or FE is Correlated Random Effects (CRE). This approach models the correlation between the αi and explanatory variables. However, given our model and the short time dimension of our panel, CRE is unlikely to be appropriate.
22. This is not a particular concern however as most dimensions upon which weights are based are included in our regressions, i.e. age, gender, ethnicity, etc.
23. Durbin-Wu-Houseman specification tests support FE estimation with test statistics of 8539.8 and 8545.69 for Tables 10 and 11 respectively.
24. Any such measurement error would only effect the precision of our results (make it more difficult to attain statistically significant results) but would not bias coefficient estimates.
25. Though not reported here we also estimated all regressions in this section with the inclusion of a variable that measured the respondents’ share of gross assets held in housing to better account for differences in portfolio composition. In all cases the estimated effect of KiwiSaver membership on net wealth accumulation was similar to those reported. However, these are not our preferred regression specifications as the inclusion of such a variable has the potential to capture some of the treatment effect from KiwiSaver membership.