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Introducing the Consumer Health Index

The Consumer Health Index (CHI) relies on Morning Consult’s high frequency economic data to monitor near-term trends in consumer demand. The index combines a predictive measure of consumers’ ability to afford purchases (lagged changes in unemployment) with a real-time indicator of consumers’ willingness to spend (the index of current personal financial conditions, a component of the index of consumer sentiment) to estimate consumers’ overall demand.

The Consumer Health Index for US adults is designed to approximate an outlook for annual growth rates in US government spending data. This construction underscores the direct applicability of the index to observed changes in spending patterns, while also aiding in ease of interpretation. To this latter point, index values are roughly indicative of directional changes in annual spending. A score of zero can thus be interpreted as a neutral value, positive values signal expansion in spending demand and negative values are suggestive of contraction.

How the Consumer Health Index complements existing metrics

Measures of consumer confidence, such as Morning Consult’s Index of Consumer Sentiment (ICS) have been explored as a predictive gauge of consumer spending. However, the relationship between consumer confidence and spending is not stable. The variables dominating the trajectory of sentiment vary over time, leading to fluctuations in the relative strength or weakness in how closely spending patterns are associated with shifts in sentiment. A recent example has been playing out in the years following the Covid-19 pandemic: When soaring inflation in 2022 tanked sentiment, topline consumer spending remained surprisingly resilient. The ICS is informative as a metric capturing the overall mood of consumers, but is insufficient on its own as a proxy for consumer spending. 

A key driver of spending is income, with the bulk of earnings for U.S. adults dependent on labor market outcomes. In the 2022 example, when the discrepancy between sentiment and spending widened, a strong labor market was a critical facilitator of strong spending despite high inflation. Employment conditions are not explicitly accounted for by the ICS, so in devising a more holistic measure of consumer demand it makes intuitive sense that a metric tracking labor market strength could serve as the missing piece. 

How the index was calculated

The theoretical composition of the Consumer Health Index called for combining two variables: A measure of sentiment, reflective of consumers’ desire to spend, and a measure of labor market strength serving as a proxy of consumers’ ability to spend. Morning Consult collects both the ICS and its Unemployment Index on a daily basis, enabling a real-time gauge of consumer demand. 

To optimize the relevance of the Consumer Health Index, multiple formulations of the Unemployment Index as well as the ICS and each of its underlying components were analyzed to identify the strongest relationship between these metrics and spending (defined in this case as annual growth in real personal consumption expenditures form the Bureau of Economic Analysis’ as well as the Census Bureau’s retail sales data). 

The best-performing ICS component was Current Personal Financial conditions for the contemporaneous period. This result makes intuitive sense, as a consumers’ attitude about their personal financial state on any given day can instantaneously impact spending decisions. For the unemployment index, the link with spending was more gradual and leading: The net change in 6-month average unemployment, three months ahead, showed the closest correlation with spending. Again, the delayed impact of changes in unemployment levels on spending makes logical sense: When a consumer loses his or her job, it may take several months for spending to be pared back as that worker can temporarily rely on savings or unemployment insurance to maintain his or her previous level of spending. Conversely, when a newly employed worker starts a new job, his or her spending might not immediately shift higher as the first several paychecks may be put toward replenishing savings or paying off debt incurred while the worker was still unemployed.

These two top-performing variables (the lagged change in unemployment and the index of current personal financial conditions) were then included as the independent variables in two regressions, one to estimate annual growth in real PCE and the second for real retail sales. The resulting models showed moderately strong explanatory power, with sentiment showing a positive influence on spending in each regression and the unemployment metric generating a negative coefficient in both cases. For the final consumer health index calculation, the coefficients of each regression were averaged to equally weight the importance of retail-specific spending and overall spending, and multiplied by 100 for ease of interpretation. The final index equation is shown below:

Applying to demographic groups

A defining characteristic of Morning Consult’s survey data is the ability to cut it by detailed demographic groups. To test its applicability across demographic groups, The Consumer Health Index equation was applied to demographic groups broken out by age, income and region. While the limited detail with government spending measures makes it impossible to specifically benchmark demographic-level index scores against demographic-level spending in the same way as can be done with adults overall and topline spending, the resulting trends broadcast by the Consumer Health Indexes for these demographic segments align with events that would specifically impact certain groups more than others.

For example, the West region showed a noticeable downward deviation from other regions of the country around the time when tech layoffs picked up in early 2023. Additionally, retirement-aged adults showed a trend similar to the S&P 500, reflective of sentiment and spending power aligning more with invested assets than job market outcomes (as would be expected given their reliance on retirement income more so than wages).

Case study: Brand-specific and custom audience insights

The underlying components for the Consumer Health index are fielded on the same daily survey used to collect information on topics such as brand usage, favorability and purchasing consideration–the full breadth of which can be accessed and analyzed through Morning Consult’s intelligence platform. The large sample size afforded by high-frequency collection of this data also enables cutting this data by detailed custom audiences, such as GenZ adults who use a specific brand at least once a week, or high-income adults considering an EV purchase. 

The Consumer Health Index applied to custom audiences can offer brand- or sector-specific insights. For example, the chart below compares index score trends among frequent diners at quick-service restaurants (those who visit these establishments more than once a week) against customers who visit less frequently (once a week or less).

The more frequent diners have generally registered higher CHI scores, which makes intuitive sense given their relatively higher demand for dining services compared with infrequent QSR visitors. Frequent diners’ demand appeared to strengthen through early 2024, aligning with cooling inflation boosting demand and discretionary spending among certain consumers, while low-frequency diners’ demand levels remained close to neutral, signaling minimal expansion. Over the last few months, however, demand dropped off sharply for all QSR customers; both the high and low-frequency groups’ CHI scores hit their lowest levels to date in July 2024. While the most loyal restaurant customers’ demand levels remain higher than those of less frequent visitors, the recent CHI decline was more dramatic among the group visiting more than once per week. The demand dropoff within this group specifically could have worrying implications for QSRs, as those customers who tend to be the most dominant contributors to foot traffic show a waning appetite to spend.

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