RDP 2021-08: Job Loss, Subjective Expectations and Household Spending 5. Do Expectations Affect the Sensitivity of Household Spending to Unemployment?
August 2021
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So far we have documented 2 observations about Australian households. The first is that, as in other countries, individuals are able to foresee both job loss and job finding at least to some extent. The second observation is that at unemployment, households significantly reduce their spending. We now link these 2 findings and explore whether the sensitivity of consumption to unemployment is affected by whether the household expected to lose their job or not.
As shown in Section 4.1, standard consumption models predict that households will mostly adjust spending in response to unexpected shocks to current income. Any adjustment to expected income shocks could be taken as evidence against these theoretical models. We directly test these ideas within the following regression framework:
Equation (5) is specified in the same way as Equations (1) through (4), however here we only consider spending in the year of unemployment. We include the same controls as before but now also add a term for (lagged) job loss expectations of the household head, ${y}_{it-1}^{e}$. This measure ranges from 0 if they perceived no chance of job loss to 1 if they perceived job loss was certain. We also include an interaction term between lagged job loss expectations and a dummy for whether the household head became unemployed during the survey year.
Under this specification, the change in spending in response to unemployment is estimated by $\delta +\varphi *{y}_{it-1}^{e}$. Consider 2 extreme cases. The coefficient $\delta $ captures the effect of unemployment on spending for those working household heads that fully expect to not lose their jobs $\left({y}_{it-1}^{e}=0\right)$ and hence are completely ‘surprised’ by unemployment. The sum of the coefficients $\left(\delta +\varphi \right)$ captures the spending response to unemployment in the case in which a working household head fully expects to lose their job and hence is completely unsurprised by unemployment $\left({y}_{it-1}^{e}=1\right)$. The coefficient $\varphi $ therefore captures how the consumption sensitivity to unemployment varies with job loss expectations.
Within this framework we can also test whether household spending is sensitive to job loss expectations for households that do not lose their jobs. This is captured by the coefficient $\rho $ on the lagged job loss expectations term. This allows us to explore the links between job insecurity and household spending for all households, not just those actually affected by unemployment.
As before, we separately estimate Equation (5) for different types of spending as the dependent variable, including spending on groceries, meals eaten outside the home and all items (including durable goods).
Results for all 3 specifications are presented in Table 2. We find that the fall in spending at unemployment is large for those that are completely surprised by unemployment (as shown by the large negative coefficient, $\delta $). This is consistent with the predictions of standard workhorse models. But we also find that the fall in spending at unemployment is just as large when the household at least partly expects job loss (as shown by the negative coefficient on the sum of the coefficients, $\delta +\varphi $, and the fact that estimates of $\varphi $ are not significantly different from 0). This suggests that households adjust spending in response to a predictable change in income (unemployment), which contradicts the predictions of standard models. However, this result is consistent with prior empirical research suggesting that household spending is excessively sensitive to predictable income changes (Japelli and Pistaferri 2017).
For grocery expenditure, and spending on meals eaten outside the home, we find that lagged job loss probability does not provide any additional information about household expenditure, as shown by the estimates of $\rho $ that are not statistically significantly different from 0. This tells us that households who do not lose their jobs also do not adjust their spending on these expenditure categories when their expectations of job loss change. However, we find our estimate of $\rho $ to be significant and negative in the regression with total annual expenditure. The coefficient estimate of -0.04 means that households that change their probability of job loss from 0 (no chance) to 1 (100 per cent certainty) decrease their total spending in the following year by 4 per cent. This result provides some suggestive evidence that job security may matter for spending on some goods and services.
Dependent variable | Groceries | Meals eaten outside the home | Total expenditure |
---|---|---|---|
Unemployed $\left(\delta \right)$ | −0.05*** (0.01) |
−0.44*** (0.05) |
−0.05* (0.03) |
Unemployed * lagged job loss probability $\left(\varphi \right)$ | −0.03 (0.04) |
0.11 (0.15) |
−0.02 (0.07) |
Lagged job loss probability $\left(\rho \right)$ | 0.01 (0.01) |
−0.03 (0.02) |
−0.04** (0.02) |
Fixed effects | Yes | Yes | Yes |
R squared (overall) | 0.31 | 0.01 | 0.16 |
Observations | 96,060 | 91,461 | 30,558 |
Notes: ***, ** and * denote statistical significance at the 1, 5 and 10 per cent levels, respectively; standard errors in parentheses; coefficient estimate for year dummies and number of persons in household omitted Sources: Authors' calculations; HILDA Survey Release 19.0 |