RDP 2009-07: Estimating Marginal Propensities to Consume in Australia Using Micro Data 1. Introduction

The response of household consumption to changes in income – the marginal propensity to consume (MPC) – influences how the macroeconomy responds to various shocks. Under the permanent income hypothesis (PIH), the change in consumption in response to a shock to income depends on the nature of the shock. For permanent changes in income, households that are not liquidity constrained can in theory be expected to adjust their consumption by close to the full amount of the shock, and do so once the change is apparent rather than waiting for the extra income to arrive. When income shocks are temporary, households are likely to alter consumption by some fraction of the change in income, at the time it becomes apparent, as they attempt to smooth their consumption over time. Of course, if the income tax changes and the lump-sum transfers have the same present discounted value and households are not liquidity constrained, their effect on aggregate consumption could be expected to be similar even though the MPC from each will differ. In practice, however, at least some households are likely to face borrowing constraints, which by itself would tend to increase the correlation between household consumption and current income.

In this paper, we exploit the rich dataset provided by the Household, Income and Labour Dynamics in Australia (HILDA) Survey to estimate the marginal propensities to consume in Australia between 2005 and 2007 from two types of policy changes: changes in income tax rates and changes in lump-sum transfers to households. Changes in income tax rates are often viewed as persistent (if not permanent) by households, so we would expect households to have a high MPC out of income tax cuts. In contrast, lump-sum transfers to households are often one-off or temporary measures, so we would expect a somewhat lower MPC out of these payments.

Fiscal policy changes provide a good means of estimating MPCs since they are easily identifiable, and from the perspective of households can be considered to be exogenous. Also, such policies are often directly targeted to subsets of the population or differentially affect population subgroups, generating natural control groups to aid estimation.

Despite widespread interest in the United States and elsewhere in these issues, there has been little work done on estimating MPCs in Australia using micro data. This largely reflects data constraints; panel data on income were not available until the HILDA Survey began in 2001, and data on broad expenditure categories were not collected until 2005. Other (purely cross-sectional) expenditure surveys conducted by the Australian Bureau of Statistics (ABS) are available only infrequently.

We utilise the variation in the effect of the different fiscal policies across households to help identify the MPC out of these policies. In particular, we estimate the MPC out of income tax cuts by using a panel Euler equation linking consumption to estimates of income tax paid and various control variables (adapting the methods used by Souleles 2002 and Johnson, Parker and Souleles 2006). In the case of targeted lump-sum transfers, the Baby Bonus and the Carer Bonus, we estimate the MPC by comparing expenditure for those households that received the payment with expenditure for households that did not receive the payment but were otherwise similar in some key respects. For the Baby Bonus, the source of identification is a change in the value of the payment in the sample of households that qualified for the Baby Bonus. For the Carer Bonus, we do not have variation in the lump-sum transfer over time so our comparison group (non-recipient households) is chosen using propensity score matching (as in Brzozowski 2007). This is intended to ensure that those in the comparison group who do not receive the payment had similar characteristics to payment recipients.

Our results, though tentative due to data limitations, show that there is a wide range of estimated MPCs in Australia. The estimated MPC for non-durable goods and services out of income tax cuts, over 2005 to 2007, is relatively high within about three months of receipt. The estimated MPC for non-durables out of the Baby Bonus is lower but suffers from some issues with identification. We also find that the MPC estimates vary across different households. Households with low incomes have a higher MPC, consistent with the idea that these households are more likely to face liquidity constraints. We also examine how variations in financial stress, debt and perceptions of unemployment risk across households affect the estimates of the MPC.

The rest of the paper proceeds as follows. Section 2 explores how this study complements the existing literature. Section 3 describes the HILDA Survey and the fiscal policies examined. The empirical methodology is outlined in Section 4. Sections 5, 6 and 7 report the results for the basic specifications as well as those exploring heterogeneity in households' responses. Finally, Section 8 concludes.