The Consumer Confidence Index, or CCI, comes out every month from The Conference Board. It is one of the most quoted U.S. economic indicators. It is also one of the most consistently misread. The index is a sentiment measure: a snapshot of what surveyed households say they feel about current conditions and what they expect in the near term. It is not a forecast of consumer spending, employment, or GDP. For readers of this blog, the distinction matters because the CCI often gets plotted next to hard economic data in ways that suggest predictive power the survey simply does not have. This article looks at what the CCI actually measures, how its components are built, what the historical record shows, and how to read CCI charts without falling into the prediction trap.
The CCI belongs to a family of U.S. sentiment indicators that includes the University of Michigan Consumer Sentiment Index, the OECD Consumer Confidence Indicator, and the daily Morning Consult index. These measures share a basic logic: ask a sample of households a short set of questions, convert the answers into diffusion indexes, and publish the result as a single number. The Conference Board version is based on a monthly survey of roughly 3,000 households, with five core questions. Two questions ask about current business and labor market conditions. Three ask about expectations six months ahead: business conditions, employment, and family income. The headline index is the average of the present situation component and the expectations component, with 1985 set equal to 100.
That construction matters. The CCI is a weighted summary of opinions, not a measure of actual transactions. No question asks how much a household spent last month, whether it applied for a loan, or whether it changed its savings rate. The index can rise while retail sales fall, and it can fall while payrolls grow. That is not a flaw in the survey. It is a category error in how the survey is often used.
What the Survey Actually Asks
The Conference Board publishes the questionnaire methodology in its technical notes. The five questions are:
- How would you rate the present general business conditions in your area? (Good, normal, bad)
- What would you say about available jobs in your area right now? (Plentiful, not so many, hard to get)
- Six months from now, do you think business conditions in your area will be better, worse, or the same?
- Six months from now, do you think there will be more, fewer, or the same number of jobs available in your area?
- Six months from now, do you think your total family income will be higher, lower, or about the same?
Each question is converted into a diffusion index: the percentage of positive responses minus the percentage of negative responses, with a constant added to avoid negative values in the published series. The present situation index averages the two current-conditions questions. The expectations index averages the three forward-looking questions. The headline CCI is the simple average of those two sub-indexes.
This is a standard diffusion-index method, similar to the one used in purchasing manager surveys. But there is a key difference. Purchasing manager surveys ask respondents about their own firms’ actual orders, output, and hiring. The CCI asks households for judgments about the economy as a whole. A respondent can report that business conditions are “bad” while their own household income is rising. The index aggregates perceptions, not balance sheets.
Sentiment vs. Hard Data: A Chart-Reading Exercise
Consider a common chart format: the CCI plotted on the left axis and real personal consumption expenditures growth on the right axis, both as monthly time series. The visual overlap can be striking. Both series dip during recessions. Both recover afterward. A casual reader may conclude that the CCI leads consumption, and therefore predicts it.
The problem is that the two series measure different things at different times. The CCI is released near the end of the reference month, based on surveys conducted earlier that month. Personal consumption expenditures are reported with a lag and are revised multiple times. When the two series are aligned by release date rather than reference period, the apparent lead-lag relationship often weakens or reverses. A chart that aligns the CCI with the first vintage of consumption data may show a lead. A chart that aligns the CCI with the final revised consumption data may show no lead at all.
This is a recurring theme in chart criticism: the choice of data vintage changes the story. The CCI is not revised after publication. Consumption data are revised for years. Comparing a never-revised survey to a heavily revised economic series is not a like-for-like comparison. A careful chart should state which vintage of the hard data is used and why.
What the Historical Record Shows
The CCI has been published since 1967. The longest available monthly series runs from 1967 to the present. Researchers have tested whether the CCI or its components improve forecasts of consumption, employment, or GDP once other indicators are included. The results are mixed, and the weight of evidence leans against strong predictive power.
One well-known finding is that the expectations component sometimes contains information about future spending that is not already in income or wealth data. But that information is small, unstable across time periods, and sensitive to model specification. The present situation component is largely a coincident indicator: it moves with current labor market conditions, not ahead of them. The headline index, which averages the two, inherits this ambiguity.
During the 2001 recession, the CCI fell sharply before the downturn was officially dated. During the 2007–2009 recession, the CCI peaked more than a year before the recession began, but it also fell sharply in mid-2008, after the recession was already underway. In 2020, the CCI collapsed in March and April as the pandemic hit, but the collapse was simultaneous with the economic shutdown, not ahead of it. In 2022, the CCI fell as inflation rose, but consumer spending remained resilient. These episodes do not show a consistent leading relationship.
The Conference Board itself describes the index as a measure of consumer attitudes and buying intentions, not as a forecasting tool. The technical notes state that the survey is designed to measure consumer confidence, defined as the degree of optimism on the state of the economy that consumers are expressing through their activities of savings and spending. That definition is about expression, not prediction.
Why the Prediction Frame Persists
If the CCI is not a reliable predictor, why do so many charts and headlines treat it as one? The answer lies partly in publication incentives and partly in the structure of economic commentary.
First, the CCI is timely. It is released before most hard data for the same month. That makes it useful for journalists and analysts who need something to say about the current month before retail sales or payrolls are available. The phrase “consumer confidence fell, signaling weaker spending ahead” is a convenient narrative bridge. But the bridge is built on a category error: a survey of opinions is not a transaction record.
Second, the CCI is easy to chart. It is a single monthly number with a long history. It can be plotted against almost anything. The visual simplicity invites causal interpretation. A line that falls before a recession looks like a warning. A line that rises before a recovery looks like a signal. But visual order is not statistical evidence. Without a formal test of lead-lag relationships, the chart is just two lines on a page.
Third, the CCI is widely available and free to use. The Conference Board publishes the headline index and components on its website. The St. Louis Fed’s FRED database carries the series. That accessibility is a good thing, but it also means the index is overused in contexts where a more specific measure would be better. A chart of the CCI next to vehicle sales, for example, tells you little about vehicle sales. A chart of the CCI next to the University of Michigan index tells you something about survey methodology, but not about the economy.
Reading CCI Charts Carefully
For readers who want to use CCI charts without falling into the prediction trap, a few rules help.
1. Check the axis and the comparison series
If the CCI is plotted against a hard economic series, ask whether the comparison is meaningful. The CCI is a diffusion index with a 1985 base. Real consumption growth is a percentage change. The two have different units, different volatilities, and different revision schedules. A chart that puts both on the same page without explaining the transformation is doing visual work, not analytical work.
2. Look for recession shading
Recession shading is useful context, but it can also create a false sense of timing. The CCI often falls during recessions because recessions are periods of rising unemployment and falling income. That is a coincident relationship, not a leading one. A chart that shades recessions and shows the CCI falling inside the shaded area is showing correlation, not prediction.
3. Ask about the sample and the questions
The CCI is based on a mail survey of about 3,000 households. The response rate is typically around 20 percent. The sample is designed to be representative, but nonresponse can shift the composition of respondents. The questions ask about perceptions, not plans. A chart that labels the CCI as “consumer spending expectations” is mislabeling the underlying data.
4. Compare the components
The present situation and expectations components often diverge. In mid-2022, for example, the present situation index remained relatively high while the expectations index fell. A chart that shows only the headline index hides that divergence. A chart that shows both components tells a more complete story. The divergence itself is a useful data point: it tells you that households are distinguishing between what is happening now and what they think will happen next.
What the CCI Is Good For
None of this means the CCI is useless. It is a well-constructed survey with a long history and a clear methodology. It is useful for several purposes.
First, the CCI is a consistent measure of how households say they feel about the economy. That is a legitimate object of study in its own right. Sentiment can influence political behavior, media coverage, and household financial decisions, even if it does not predict aggregate spending. A chart that treats sentiment as an outcome, not a predictor, is on solid ground.
Second, the CCI is useful for comparing sentiment across demographic groups. The Conference Board publishes breakdowns by age, income, and region. Those breakdowns can show how different groups experience the same macroeconomic conditions. A chart of CCI by income quintile, for example, can reveal that low-income households report much lower confidence than high-income households during inflationary periods. That is a descriptive finding, not a prediction, but it is a useful one.
Third, the CCI is useful for studying the relationship between sentiment and other variables, as long as the relationship is framed carefully. A researcher can ask whether changes in the CCI are associated with changes in spending after controlling for income and wealth. That is a legitimate empirical question. The answer may be yes, no, or sometimes. The point is to ask the question explicitly, not to assume the answer from a chart.
A Note on the University of Michigan Index
The University of Michigan Consumer Sentiment Index is often mentioned alongside the CCI. The two indexes are correlated but not identical. The Michigan survey uses a different sample, a different questionnaire, and a different index construction. The Michigan index is based on a telephone survey of about 500 households, with a longer questionnaire that includes questions about buying conditions for durable goods, vehicles, and homes. The Michigan index is released twice a month: a preliminary reading and a final reading. The Conference Board index is released once a month.
The differences matter for chart readers. The Michigan index is more sensitive to inflation expectations because it asks directly about expected price changes. The Conference Board index does not ask about prices. A chart that treats the two indexes as interchangeable is making a methodological error. A chart that plots both and explains the differences is doing useful comparative work.
Common Misreadings in the Wild
Several recurring chart patterns deserve specific criticism.
The “confidence leads spending” chart. This chart plots the CCI against retail sales or personal consumption expenditures and draws a vertical line from a CCI peak to a spending trough. The implication is that the CCI predicted the trough. But the vertical line is arbitrary. Without a formal test, the line is just a visual annotation. A careful version of this chart would show the full time series, mark the release dates, and note that the CCI is a survey of opinions while spending is a transaction record.
The “confidence collapse” chart. This chart zooms in on a short period, such as March 2020, and shows the CCI falling by a record amount. The implication is that the collapse was a signal of the recession. But the recession was already underway. The CCI fell because the economy shut down, not before. A careful version of this chart would show the CCI alongside the actual shutdown dates and note that the survey was conducted during the shutdown, not ahead of it.
The “confidence recovery” chart. This chart shows the CCI rising after a recession and implies that confidence drove the recovery. But the CCI often rises after a recession because employment and income are improving. The recovery drives confidence, not the other way around. A careful version of this chart would show the CCI alongside payroll growth and note the coincident timing.
Building a Better Chart
If you are making a chart with the CCI, here are some concrete practices that align with this blog’s approach to reproducible chart criticism.
First, state the data source and the vintage. The CCI is published by The Conference Board and is available on FRED as series CONCCONF. The components are available as CONCCUR for present situation and CONCEXP for expectations. If you are comparing the CCI to a hard data series, state which vintage of the hard data you are using and whether it has been revised.
Second, show the components. The headline index is an average of two sub-indexes that often diverge. A chart that shows only the headline hides information. A chart that shows both components is more honest about what the survey is measuring.
Third, avoid causal language. The CCI does not “signal” a recession. It falls during recessions. The CCI does not “predict” spending. It is correlated with spending under some conditions. Use descriptive language: “the CCI fell in March,” not “the CCI warned of a downturn.”
Fourth, include a note on what the index is not. A one-sentence note under the chart can prevent misreading: “The CCI is a survey of household opinions, not a measure of actual spending or employment.” That note is cheap insurance against the prediction trap.
FAQ: Consumer Confidence Index as a Sentiment Measure
Is the Consumer Confidence Index a leading indicator?
Not reliably. The CCI is a coincident indicator for current conditions and a weak, unstable leading indicator for expectations. The present situation component moves with current labor market conditions. The expectations component sometimes contains information about future spending, but the relationship is small and varies across time periods. Treating the headline CCI as a leading indicator overstates what the survey can support.
What is the difference between the Conference Board CCI and the University of Michigan index?
The Conference Board CCI is based on a monthly mail survey of about 3,000 households and asks five questions about current conditions and six-month expectations. The University of Michigan index is based on a telephone survey of about 500 households and asks a longer set of questions, including questions about buying conditions and expected price changes. The two indexes are correlated but not interchangeable. The Michigan index is more sensitive to inflation expectations because it asks about prices directly.
Why does the CCI sometimes fall while consumer spending rises?
Because the CCI measures opinions, not transactions. A household can report that business conditions are bad while still spending on necessities, services, or durable goods. In 2022, for example, the CCI fell as inflation rose, but consumer spending remained resilient. The divergence is a reminder that sentiment and behavior are different variables. A chart that plots the CCI against spending should explain that the two series measure different things.
How should I read a chart that plots the CCI against a recession?
Look for the timing. If the CCI falls inside the shaded recession period, that is a coincident relationship, not a leading one. If the CCI falls before the shaded period, ask whether the fall was large enough to be meaningful and whether other indicators also fell. A single line crossing a shaded area is not evidence of prediction. A careful chart will show the full time series, mark the release dates, and avoid causal language.
Where This Leaves the Blog
This article is the first in a planned series on sentiment indicators and their charting pitfalls. The next piece will examine the University of Michigan index in more detail, including its inflation expectations component and the recurring debate over whether that component predicts actual inflation. A third piece will look at the OECD Consumer Confidence Indicator and the problems of comparing sentiment across countries with different survey methods. Together, these pieces will build a reference set for readers who want to read sentiment charts with the same care they apply to hard data.
If you have a CCI chart you would like critiqued, send it in. The best submissions will be featured in a recurring column on chart misreadings. The goal is not to scold. It is to build a shared vocabulary for reading economic charts accurately, one indicator at a time.