RDP 2016-10: The Effect of Consumer Sentiment on Consumption 5. Relation to the Empirical Literature

One important implication of our work is that an individual's reported spending intentions captured by the consumer sentiment survey is a good proxy for actual changes in consumption. The difficulty in obtaining individual-level consumption data has led researchers (e.g. D'Acunto, Hoang and Weber 2015; Bachmann, Berg and Sims 2015) to use spending intentions as a proxy for consumption. Our results help validate the research relying on spending intentions data.

We find that consumer sentiment influences consumption and has predictive power for future movements in consumption. This is consistent with results from the earlier time series literature which tried to determine if sentiment contained information independent of current economic fundamentals by employing controls for macroeconomic fundamentals (such as income growth, stock prices and interest rates) in sentiment regressions (Carroll et al 1994; Matsusaka and Sbordone 1995; Bram and Ludvigson 1998; Ludvigson 2004). However, one issue that has faced the time series literature is that the information attributed to consumer sentiment could reflect fundamental drivers of consumption that have not been controlled for (Ludvigson 2004). By using cross-sectional variation, we implicitly control for the effect on sentiment of common macroeconomic shocks. Our approach also makes clear the source of variation used for identification – differences in sentiment related to partisanship.

We find that changes in sentiment at an individual level can have a noticeable effect on consumption. This contrasts with the earlier time series literature which found that changes in aggregate sentiment increases the explanatory power in consumption growth forecasting regressions only marginally (Ludvigson 2004).^[14] Consistent with this, Roberts and Simon (2001) show that a substantial proportion of the variation in sentiment indices can be explained by lagged macroeconomic indicators, indicating that much of the variation in aggregate sentiment is driven by variation in current fundamentals. One possibility is that time series averages mask specific episodes in which sentiment contains a lot of additional information, as argued by Blanchard (1993) and Hall (1993) for the 1990–91 US recession. In our case, the variation we use is masked in aggregate data because there are a similar number of ALP and Liberal/National voters.

In terms of what the variation in sentiment we identify represents, we believe that it is more likely to represent pure sentiment shocks resulting from partisanship than unbiased expectations about changes in future incomes for two reasons. First, the shift in sentiment between ALP and Liberal/National voters occurs immediately following a change of government. These movements in sentiment are sharp and of a similar magnitude to that observed during recessions. Consumers are more optimistic about both personal and national economic conditions when the political party they support holds office, suggesting that beliefs about changes in the income distribution are not the source of variation in sentiment. This interpretation is consistent with the political science literature, which finds that partisanship affects an individual's assessment of past and future macroeconomic conditions.

Second, we make use of an extensive set of controls to account for the fact that partisanship is correlated with economic variables. After controlling for these factors, we still find a positive relationship between sentiment and consumption. We believe that this provides some support for the notion that there could be exogenous movements in consumption predicted by sentiment, as advocated by Blanchard (1993) and Hall (1993).

Our paper is most similar to Gerber and Huber (2009) and Mian et al (2015), who both use cross-sectional county-level data to identify a relationship between partisanship and consumption following US elections as the party occupying the Presidency changed. Gerber and Huber find evidence that consumption increases more in counties that voted for the incoming president. In contrast, Mian et al report no statistically significant effect. These differences in results partly reflect how each set of authors measures consumption. Gerber and Huber use county-level sales tax revenue data, which is problematic because consumers may shop in one county but live in another. Mian et al use data similar to ours, including self-reported spending intentions from the Michigan Surveys of Consumers and motor vehicle registrations.

This leads to the question of why we find that changes in sentiment affect consumption while Mian et al (2015) do not. We believe that our data allow us to better measure sentiment, partisanship and consumption at a disaggregated level. In Appendix A we provide a reconciliation between our results and those from Mian et al. To summarise, Mian et al (2015) have to impute an individual's partisanship based on the county where they live. Imputing partisanship in our data based on an individual's postcode, rather than using their stated voting intention, results in no longer being able to see the effect of a change in government on a consumer's self-reported spending intentions. Secondly, Mian et al measure motor vehicle sales using registration data which includes sales to businesses and governments as well as households. Using our data, we find that the inclusion of business and government motor vehicle sales makes it more difficult to see a positive relationship between the ALP vote share and motor vehicle sales. Lastly, since voting is compulsory in Australia, we have a better measure of local area partisanship.

Footnote

Though Matsusaka and Sbordone (1995), using a VAR framework, do find that sentiment accounts for between 13–26 per cent of the innovation variance in GNP. [14]