Business forecasting statistics help turn uncertain economic conditions into explicit planning assumptions. The latest projections point to moderate U.S. growth, persistent but easing inflation, a relatively steady labor market, slower euro-area expansion, and meaningful differences across industries. They also show why a forecast should include downside probabilities rather than a single expected path.
Contents
- U.S. growth outlook
- Inflation and pricing assumptions
- Labor markets and wage planning
- Global and euro-area demand
- Industry output and employment
- Uncertainty and downside scenarios
U.S. growth outlook
The Philadelphia Fed’s Third Quarter 2026 Survey of Professional Forecasters describes a U.S. economy continuing to expand through the forecast horizon. These are forecasts, not measured historical results, and the quarterly figures are stated at annual rates.
| Forecast period | U.S. real GDP growth forecast |
|---|---|
| 2026 Q3 | 2.5% annual rate |
| 2026 Q4 | 2.3% annual rate |
| 2027 Q1 | 2.1% annual rate |
| 2027 Q2 | 2.1% annual rate |
| 2027 Q3 | 2.2% annual rate |
The same Philadelphia Fed survey puts annual-average real GDP growth at 2.1% in 2026, 2.2% in 2027, 2.1% in 2028, and 2.4% in 2029. For business planning, that pattern supports a base case of continued expansion rather than a rapid acceleration. Revenue plans can therefore use positive demand assumptions while keeping hiring, inventory, and capacity additions sensitive to weaker quarterly outcomes.
The distinction between an annual rate for a quarter and annual-average growth matters. A quarterly annual-rate forecast describes the pace attributed to that quarter; an annual-average figure describes the change in the average level across a calendar year. They are related, but they are not interchangeable inputs for a financial model. Labeling both the period and the measurement basis helps prevent a quarterly assumption from being treated as a full-year forecast.
Inflation and pricing assumptions
Inflation forecasts affect price increases, supplier negotiations, wage budgets, working capital, and the real value of future revenue. The Philadelphia Fed survey expects U.S. headline CPI inflation at an annual rate of 2.3% in 2026 Q3 and 2.5% in 2026 Q4. Core CPI inflation is forecast at 2.7% in 2026 Q3 and 2.8% in 2026 Q4.
For the full year, the survey expects headline CPI inflation of 3.6% on a Q4-over-Q4 basis in 2026 and 2.3% on the same basis in 2027. It expects core CPI inflation of 2.8% on a Q4-over-Q4 basis in 2026. The Q4-over-Q4 measure compares the fourth quarter with the fourth quarter of the prior year, so it should be kept separate from an annual average or a quarterly annual rate.
Longer-term assumptions are also available from the same source. Philadelphia Fed forecasters expect core PCE inflation of 2.2% on a Q4-over-Q4 basis in 2028. Over 2026–2035, they expect average U.S. headline CPI inflation of 2.30% and average U.S. headline PCE inflation of 2.20%.
These measures answer different questions. CPI is useful when a business’s costs or customer prices track consumer goods and services. PCE is another broad inflation measure and appears here as a longer-run forecast. Core measures exclude some volatile components, while headline measures include the broader basket. A planning model should identify the index behind each escalation assumption instead of using “inflation” as an unlabeled input.
Labor markets and wage planning
The Philadelphia Fed forecasts a U.S. unemployment rate averaging 4.3% in 2026, 4.3% in 2027, 4.2% in 2028, and 4.3% in 2029. It projects average monthly nonfarm payroll gains of 40,700 in 2026 and 75,400 in 2027.
Together, these figures describe a labor market with continued employment growth and little change in the projected average unemployment rate. Workforce plans should not treat a steady unemployment forecast as proof that recruiting will be easy. It is a macroeconomic average, not a guarantee for a particular occupation, region, or company. Still, it provides a quantified background for staffing scenarios: one plan can use the forecast payroll pace, while a downside plan can test slower hiring and longer vacancies without claiming a specific alternative outcome.
The ECB’s Results of the ECB Survey of Professional Forecasters for the third quarter of 2026 provides a separate wage outlook for the euro area. ECB forecasters expect euro-area wage growth of 3.2% in 2026, 3.0% in 2027, and 2.8% in 2028. Their longer-term expectation is 2.9% for 2031.
For employers operating in both regions, the U.S. unemployment and payroll projections and the euro-area wage projections should remain separate lines in the plan. They describe different geographies and different labor indicators. Combining them into one global labor assumption would hide the regional differences that matter for compensation budgets and hiring capacity.
Global and euro-area demand
The IMF’s World Economic Outlook, April 2026: Global Economy in the Shadow of War projects global real output growth of 3.1% in 2026 and 3.2% in 2027 under its limited-conflict reference forecast. Global headline inflation is projected at 4.4% in 2026 and 3.7% in 2027 under that reference forecast.
The IMF also quantifies how the forecast changes under different conditions. Under pre-conflict assumptions, its 2026 global growth forecast would be 3.4%. Under a more severe conflict scenario, global growth would be about 2% in 2026, while global headline inflation would be just above 6% by 2027. These are scenario-specific estimates, not a statement that the severe scenario will occur.
The euro-area outlook is weaker than the global reference forecast. ECB forecasters expect euro-area real GDP growth of 0.6% in 2026, 1.2% in 2027, and 1.3% in 2028. Their longer-term real GDP growth expectation is 1.2% for 2031. This contrast can inform geographic demand scenarios: a company with global exposure may use the IMF reference path for broad planning, while a business concentrated in the euro area may need a lower near-term volume assumption.
Scenario design is most useful when the assumptions are visible. A base case can identify the limited-conflict or central regional outlook being used; a downside case can use the IMF’s approximately 2% global growth scenario and just-above-6% inflation scenario as explicit stress references. The figures should not be blended into a single invented probability or presented as a prediction of a particular company’s sales.
Industry output and employment
The BLS Employment and output by industry projections put total U.S. industry output at $41,009.8 billion in chained 2017 dollars in 2025 and $50,044.2 billion in 2035. The figures cover 2025–2035 and use chained 2017 dollars, so they are not nominal current-dollar revenue forecasts.
Employment projections show substantial variation by sector. The BLS reports that, between 2025 and 2035, U.S. utilities employment is projected to grow 9.8%. Private healthcare and social assistance employment is projected to grow 9.5% and add 2,204,800 jobs. Professional, scientific, and technical services employment is projected to grow 8.6% and add 926,700 jobs.
The same BLS source projects information-industry employment growth of 5.3%, adding 151,000 jobs, and finance-and-insurance employment growth of 2.3%, adding 154,300 jobs. Retail-trade employment is projected to decline 0.2%, a loss of 27,500 jobs. These are U.S. projections for 2025–2035, not forecasts for every company within each industry.
| U.S. industry | BLS employment projection, 2025–2035 |
|---|---|
| Utilities | +9.8% |
| Private healthcare and social assistance | +9.5%; +2,204,800 jobs |
| Professional, scientific, and technical services | +8.6%; +926,700 jobs |
| Information | +5.3%; +151,000 jobs |
| Finance and insurance | +2.3%; +154,300 jobs |
| Retail trade | −0.2%; −27,500 jobs |
Output projections add a more specific view of technology-related sectors. BLS projects computing infrastructure, data processing, web hosting, and related-services output to grow at a 4.2% compound annual rate between 2025 and 2035. Software-publisher output is projected to grow at 4.0% annually, computer-systems-design and related-services output at 3.4%, and scientific research and development services output at 3.1% annually.
For sector planning, employment growth and output growth should be treated as different signals. Output can inform market-size or capacity scenarios, while employment can inform recruiting and delivery capacity. Neither measure supplies a company-specific margin, market share, or sales forecast, so those values should not be inferred from the industry projections.
Uncertainty and downside scenarios
The Philadelphia Fed survey quantifies recession-like quarterly risk alongside its growth forecast. Forecasters assign a 13.3% mean probability to a negative U.S. real GDP quarter in 2026 Q3, 20.0% in 2026 Q4, 21.5% in 2027 Q1, 22.0% in 2027 Q2, and 20.8% in 2027 Q3.
These probabilities are mean survey responses, not a guarantee that a negative quarter will happen and not a direct probability of a company’s revenue decline. They are useful as a reminder that a positive central forecast can coexist with material downside risk. A planning team can use them to decide how much attention to give cash buffers, flexible hiring, inventory commitments, and debt-service sensitivity.
The survey estimates the U.S. natural rate of unemployment at 4.25%. In the Philadelphia Fed’s 2026 Q3 survey, 50% of the 24 respondents answering that question said they use a natural-rate estimate in their forecasts. That result describes respondent practice in that survey; it does not establish that the estimate is correct or that every forecast should use it.
The IMF supplies another quantified planning reference: a typical defense-spending boom raises outlays by about 2.7 percentage points of GDP over two and a half years, based on the historical typical boom described in its April 2026 World Economic Outlook. Because this is a historical pattern used in the IMF’s discussion, it should remain labeled as such rather than being treated as a current forecast.
Taken together, the figures support a disciplined forecast structure: label each assumption by source, geography, period, and measurement basis; keep estimates and forecasts distinct from historical patterns; and pair a central case with explicit downside scenarios. That structure makes business forecasting statistics useful for decisions without turning broad economic projections into unsupported company-specific claims.