Difference in the saving behaviour between Australian-born and foreign-born

The model (3.1) estimated using the 2SLS procedure was applied first to the sample of household heads and then to the extended dataset that included all household members. As suggested by Table B.2 in Appendix B, household income in 2005 and household wealth reported in 2002 are good instruments for endogenous household income and wealth when the dataset is limited to households, and personal income in 2005 and household wealth from the 2002 dataset are suitable when the models are applied to individuals.

The main conclusion drawn from the results in Table 3.1 is that saving rates are higher for Australian-born households. In particular, Australian-born household heads aged 15 or older save 3.02 per cent, and those aged 36 or older save 2.43 per cent more per annum than their foreign-born counterparts. Other personal parameters, except total number of household members, also play important roles in forming the saving habits of Australian households in both datasets. For example, although the number of household members is not significant, their higher accumulated income increases household saving rates. Understandably, a higher dependency ratio suggests additional expenses and lower saving rates. Likewise, married

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individuals are likely to be paying a house mortgage and higher household expenses and to have lower saving rates than their single counterparts.

Household wealth has a negative effect on household saving rates in both datasets, which can be explained by equity and house value being included in household wealth. The increase in net worth encourages households to further increase their borrowing due to new lending opportunities to maximise the benefits of consumption. In fact, as argued by Salotti (2010), the increase in housing wealth and government savings in developed countries for the period 1980–2005 caused household savings to decline.

Surprisingly, education also has a negative effect on the saving behaviour of households. In line with this, the intermediate clerical workers, production workers and labourers in the 36+ dataset save more than do the possibly more educated managers and administrators (base category). With the inclusion of younger households, this difference is prominent only between labourers and the base category. Although it is generally expected that people with a higher education earn a higher income and, accordingly, have higher savings, this, according to Morisset and Revodero (1995), might take time to be realised due to the lagged effect of education of approximately five years. Younger household heads are more likely to be still either paying for their education or freshly graduated, unless they do a job that does not require any qualifications such as unskilled labour. Another reason for the negative link between education and saving rates, as Morisset and Revodero argued, could be the reduced need for precautionary savings as educated people are less likely to be unemployed. If this group’s outlook can be called optimistic, then this is also consistent with the research by Harris, Loundes and Webster (2002), who argued that economic optimism is negatively correlated with household savings.

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Table 3.1 Factors affecting saving rates of Australian households (household heads only)

Variables Household heads 15

years or older

Household heads 36 years or older Household income 0.293*** 0.239*** (0.0253) (0.0275) Wealth -0.000462*** -0.000210** (0.0000881) (0.0000904) Dependency ratio -0.115*** -0.128*** (0.0407) (0.0472) Age -0.00138 -0.00852** (0.00176) (0.00374) Age squared 0.0000644*** 0.000116*** (0.0000178) (0.0000307) Gender (1 if male) 0.00850 0.0246** (0.0106) (0.0123) No. of children -0.00454 -0.0238* (0.0109) (0.0131) No. of persons 0.00451 0.00897 (0.00815) (0.00933)

Marital status (Married = base case):

Previously married 0.0439*** 0.0355** (0.0148) (0.0162) Never been married 0.0382** 0.0673***

(0.0176) (0.0224)

Level of highest education (No post-school qualification = base case):

Bachelor degree or higher -0.0259** -0.0280** (0.0109) (0.0124) Other post-school qualification -0.0320** -0.0332**

(0.0147) (0.0165)

Occupation (Managers and administrators = base case):

Professionals 0.00206 0.0248 (0.0169) (0.0194) Associate professionals -0.0128 0.0241 (0.0190) (0.0220) Trades persons -0.00533 0.0199 (0.0196) (0.0232) Advance clerical workers -0.0114 0.0293

(0.0337) (0.0378) Intermediate clerical workers 0.0211 0.0501**

(0.0184) (0.0222) Production workers 0.0197 0.0452* (0.0218) (0.0248) Elementary clerical workers -0.0245 0.0524

(0.0289) (0.0359) Labourers 0.0391* 0.0570** (0.0225) (0.0270) Born in Australia 0.0302*** 0.0243** (0.0111) (0.0121) Constant -3.014*** -2.238*** (0.257) (0.323) Observations 4634 3598 Root MSE 0.299 0.300 R-squared 0.263 0.237

Notes: The dependent variable is the household saving rate. In addition to the coefficients reported above, the regressions

also include MSR and location of household controls, which are not reported due to low significance. The sample is limited to household heads who have non-missing data on country of origin with the household saving rates above -1.28 and below 0.79. Standard errors are indicated in parentheses. *** indicates p<=0.01, ** indicates p<=0.05, * indicates p<=0.1.

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Although most of the variables have similar coefficients in both age groups, there are some differences. For example, age, gender, number of children and a few occupational variables lose significance when household heads aged 15 to 35 are added to the dataset. Household heads in this age range are likely to have younger children who require lower expenses than the children of their older counterparts. They are also most likely to still be studying; hence, they do not yet have a primary occupation. The effect of age on the household saving rate in the 15+ dataset is positive. However, in the 36+ sample the saving rate starts to increase once the household head reaches 37 years. Likewise, only among household heads aged 36 and older, the saving rates are lower for female household heads. This is consistent with the argument presented in a number of studies (Conley & Ryvicker 2004; Fisher 2010) that female respondents have greater expenses in proportion to their income, and hence they save lower amounts. But this is not applicable to household heads aged 15–35 as they, regardless of being male or female, probably do not bother about saving yet.

Table 3.2 presents the results of a similar household saving model applied to the extended dataset of all individuals and not just household heads to analyse which individual characteristics affect household saving rates. Similar to the accumulation of savings at the household level, personal income plays a significantly positive role and the number of resident children plays a significantly negative role on the contribution by household members to total household saving. Since using personal income is likely to already account for an individual’s gender, this variable is no longer significant. Similarly, a positive association of age with the household saving rate detected earlier is now captured by using personal income instead of household income. Only household saving rates of individuals aged 46 and older in the 36+ dataset remain positively associated with their age, with the saving rates decreasing until this turning point. At the same time, using personal income gives importance to the number of household members. For example, unless the individual is a household head or the spouse of a household head, a higher number of members in the individual’s household means a higher amount contributed to the total household income, resulting in higher aggregate savings. This saving level, in general, does not decrease even if a household member becomes unemployed, possibly due to the availability of unemployment benefit under the Australian social security system, but the saving level is lower for households with members who are not in the labour force.

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Table 3.2 Factors affecting saving rates of Australian households (all household members)

Variables Individuals 15 years or

older Individuals 36 years or older Income 0.0256*** 0.0371*** (0.00350) (0.00747) Wealth 0.0000153 0.000118** (0.0000499) (0.0000527) Age 0.00154 -0.00834*** (0.00133) (0.00317) Age squared 0.00000377 0.0000923*** (0.0000131) (0.0000264) Gender (1 if male) 0.00618 0.00872 (0.00760) (0.00952) No. of children -0.0494*** -0.0767*** (0.00574) (0.00781) No. of persons 0.0292*** 0.0388*** (0.00393) (0.00560) Head -0.204*** -0.167*** (0.0165) (0.0221) Spouse -0.152*** -0.104*** (0.0175) (0.0225)

Marital status (Married = base case):

Previously married -0.0635*** -0.0485*** (0.0125) (0.0139) Never been married -0.0906*** -0.0186

(0.0136) (0.0197)

Level of highest education (No post-school qualification = base case)

Bachelor degree or higher -0.0181** -0.0262*** (0.00850) (0.00986) Other post-school 0.0244** -0.00475 qualification (0.00980) (0.0118)

Employment status (Employed = base case):

Unemployed -0.0357 -0.0477

(0.0221) (0.0347) Not in labour force -0.0654*** -0.0969***

(0.0106) (0.0130) Born in Australia 0.0389*** 0.0353*** (0.00882) (0.00985) Constant 0.0763* 0.174 (0.0441) (0.127) Root MSE 0.320 0.320 R-squared 0.0868 0.104 Observations 8779 6016

Notes: The dependent variable is the household saving rate. In addition to the coefficients reported above, the regressions

also include MSR controls, which are not reported due to low significance. : The sample is limited to individuals who have non-missing data on country of origin with the household saving rates above -1.28 and below 0.79. Standard errors are indicated in parentheses. *** indicates p<=0.01, ** indicates p<=0.05, * indicates p<=0.1.

The lower contribution towards household savings by household heads or their spouses is attributable to their higher expenses including interest and mortgage payments. Their distinction from other household members could also explain why the negative effect of being married compared to being single loses its significance, since household heads and their spouses are responsible for the main share of family expenses. The saving rates of the

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households with married individuals are now even higher than those of the households with previously married and never been married for the 15–35 age group individuals. Married people have the ability to share their household expenses with their partners and save more. This is consistent with the findings of Osili and Paulson (2006), who argued that being married has a positive impact on savings account ownership. The biggest share of expenses of the married household heads – the highest income earners in the household – however, outweighs these benefits, as can be seen in Table 3.1.

In contrast with the household level results, increase in household wealth is associated with the higher household saving rate in the 36+ dataset, and there is no such association in the 15+ dataset. As argued earlier, the household saving rate is lower for households with high debt – one of the determinants of the household wealth. On the other hand, it is expected that mortgage and interest payments are primary responsibilities of the household head – the highest income earner in the household – and their spouse. Hence, by specifying the status of the head and their spouse it is possible to separate this negative debt effect from the otherwise positive wealth effect. This wealth effect, however, loses its significance when the dataset is extended to include younger household members aged 15-35 years. Younger individuals, on average, accumulate less wealth, so adding 2,700 extra observations with low wealth values could cause this variable to become insignificant.

As before, the level of the highest education of household members matters for the household saving rates; however, the effect of having a post-school qualification lower than, and different from, a bachelor degree changes from negative to positive for household members aged 15 and older. This can be explained by the distinction of household heads and their spouses who have not only the highest income in the household but also the highest level of expenses, including expenses for the education of their children. Hence, younger household members, who are not household heads and their spouses, do not experience the lagged effect of obtaining post-school qualifications such as trade qualifications, which are not very costly anyway. The higher level of responsibilities of household members aged 36 and older, however, makes obtaining any post-school qualification more financially difficult and challenging. Obtaining a bachelor degree or higher is a longer and more costly process than obtaining lesser post-school qualifications and, in most cases, requires a contribution by the children as well as their parents. This may be the reason for the negative association of holding a bachelor degree or higher by household members in both age groups with the household saving rate.

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Finally, households with native-born members aged 36 or older save 3.53 per cent more than those in the same age group who were born abroad. This difference is even higher when individuals who were aged 15 to 35 years at the time of interview are included in the sample. This contradicts the findings of Islam, Parasnis and Fausten (2010), who observed the tendency of immigrant households to save more than native households when they used Australian expenditure surveys. On the other hand, the immigrants’ lower propensity to save detected in this study is consistent with the findings for immigrant households in Germany who on average save 6-10 percentage points less than native-born Germans (Sinning 2007). However, once the remittances of temporary immigrants are treated as savings in their home countries, the savings gap between them and comparable German-born household heads disappears. Due to the absence of information on remittances from Australia, a similar analysis cannot be applied in this study. It is evident, however, that if immigrants’ remittances were accounted for, their savings, defined as the difference between their after- tax income and consumption, would even be lower. Accordingly, the difference between the savings of immigrant households and native-born households in this analysis could even be greater.

Despite the limitation described above, features of the data used in this research allow a more detailed analysis of the reasons for this savings gap in favour of Australian-born households. In addition to demographic characteristics, the possible influences of being born in a different country with a different institutional environment on immigrants’ saving behaviour was investigated. The following analysis limited the sample to immigrants; it started by investigating home-country institutional effects and proceeded by including other home- country variables.