Tag: Real Gross Domestic Product

Economics-Watching: Tracking Business Sentiment in the Western United States

[from the Federal Reserve Bank of San Francisco, Economic Letters, 11 August, 2025]

by Hamza Abdelrahman, Luiz Edgard Oliveira and Aditi Poduri

Information the San Francisco Fed collects from businesses and community sources for the Beige Book provides timely insights into economic activity at both the national and regional levels. Two new indexes based on Beige Book questionnaire responses track business sentiment across the western United States. The indexes track data on economic activity and inflation, serving as early indicators of official data releases and helping improve near-term forecasting accuracy. The latest index readings suggest weakening economic growth and intensifying inflationary pressures over the coming months.

The San Francisco Fed serves the 12th District—the largest in the Federal Reserve System, representing nine western states, two territories, and a commonwealth. To better understand and analyze the regional economy, we collect information from a variety of business and community sources to create the San Francisco Fed’s report for the Beige Book. This is compiled with reports from other Districts and published by the Federal Reserve Board of Governors eight times a year. 

Views about the economy from businesses and communities play an important role in shaping economic outcomes. For example, expectations for future inflation can help spur or slow current consumer spending and business investment. Furthermore, economic forecasters rely on models that incorporate both more traditional “hard” quantitative data and “soft” qualitative information on sentiment. Adding these soft measures has been shown to improve the accuracy of economic forecasts (see Shapiro, Moritz, and Wilson 2022 and their cited literature). Among the many sentiment measures available, two popular approaches rely on survey data, as in the University of Michigan’s Surveys of Consumers, or on textual analysis, as in the SF Fed’s Daily News Sentiment Index.

This Economic Letter examines the economic information collected through the SF Fed’s Beige Book questionnaire over the past 10-plus years. We analyze this information by constructing sentiment indexes from the qualitative data and comparing them with quantitative measures of national and regional economic activity and inflation. We introduce two indexes—the SF Fed Business Sentiment Index and the SF Fed Inflation Gauge Index—which track our contacts’ views and expectations for economic growth and inflation, respectively. We find that these new indexes serve as reliable early indicators of official data releases and help improve near-term forecast accuracy. The SF Fed Business Sentiment Index has generally exhibited patterns similar to other recent business and household sentiment indexes, and the SF Fed Inflation Gauge Index has shown a strong uptick in expected inflation. To regularly monitor changes in these two indexes, the San Francisco Fed has launched a new Twelfth District Business Sentiment data page.

Constructing regional sentiment indexes

The San Francisco Fed sends out a Beige Book questionnaire to business and community contacts across the District eight times a year to gather regional information. In addition to answering questions regarding their organizations, respondents share their views on regional and national topics, including economic activity and inflationary pressures.

In two questions, respondents indicate whether they see national output growth and inflation rates increasing, decreasing, or staying stable over the coming year using a standard five-tiered scale. We use these responses since 2014 to formulate two business sentiment indexes, one on economic activity and another on inflation. We assign standard weights to the five-tiered qualitative scale that are symmetrical around zero. For example, we ask if activity is expected to “decrease significantly” = –2, “decrease” = –1, “remain unchanged” = 0, “increase” = 1, or “increase significantly” = 2. We add up the weighted shares of responses for each tier within each index. We then normalize each resulting series by its own average and standard deviation for ease of comparison with traditional economic indicators.

Tracking business sentiment

Figure 1 shows how the SF Fed Business Sentiment Index (blue line), compiled from responses to the question on national economic activity, compares with data on changes in national GDP (green line). We measure national output as the four-quarter change in inflation-adjusted, or real, GDP, normalized by its average and standard deviation so that it is centered around zero and, hence, more directly comparable to the SF Fed Business Sentiment Index. The vertical axis shows how many standard deviations away each observation is from its respective measure’s average from 2014 to mid-2025.

Figure 1
Economic growth versus business sentiment

Notes: Indicators normalized by their respective averages and standard deviations based on data from 2014 to present. Gray bar indicates NBER recession dates. Correlation coefficient is calculated between quarterly versions of both indicators.
Source: Bureau of Economic Analysis, FRBSF Beige Book questionnaire responses, and authors’ calculations.

The SF Fed Business Sentiment Index generally tracks the movements in national GDP over the past decade; a correlation coefficient of +0.63 on a scale of –1 to 1 indicates a moderately strong positive relationship between the two measures. A relatively recent exception started in 2022, when our index began showing a considerable decline relative to the national GDP measure. Respondents across the District were downbeat about economic growth and reported expectations of a sharp decline in consumer spending and overall household financial health following the depletion of pandemic-era savings (Abdelrahman and Oliveira 2023). A similar decline appeared in other measures of business and household sentiment. Nevertheless, overall economic growth continued at a solid pace. This decoupling between sentiment and hard data that began in 2022 was dubbed a “vibecession” (Daly 2024, Scanlon 2022).

Another possible reason for the divergence between national real GDP and our Business Sentiment Index is the influence of the regional economy. Although respondents are asked about their views of national GDP, their responses may be affected by regional outcomes. Thus, our index may also reflect a regional perspective from our business and community contacts.

Figure 2 supports this rationale, showing the SF Fed Business Sentiment Index alongside a measure of regional output growth (gold line). We find that the measures closely track one another, including for 2022 and 2023, with a correlation coefficient of +0.74. We define District real GDP growth as the year-over-year percent change in the total output of the District’s nine states as reported by the Bureau of Economic Analysis (BEA). We normalize the series as described before.

Figure 2
Regional economic growth and business sentiment

Notes: Indicators normalized by their respective averages and standard deviations based on data from 2014 to present. Gray bar indicates NBER recession dates. Correlation coefficient is calculated between quarterly versions of both indicators.
Source: Bureau of Economic Analysis, FRBSF Beige Book questionnaire responses, and authors’ calculations.

Our findings indicate that the SF Fed Business Sentiment Index can serve as an accurate early indicator for national and regional output growth. Since the regional Beige Book questionnaire is collected twice each quarter, it provides particularly timely insights into economic activity during the current quarter. By contrast, the first GDP data release for any given quarter usually arrives a full month after that quarter has ended, and initial data releases for state-level output growth arrive with even more delay.

Over the first half of this year, the SF Fed Business Sentiment Index turned negative, with contacts citing elevated uncertainty about trade policy and downbeat expectations for the labor market. This notable decline is also seen in other measures of household and business sentiment, including national measures, such as the University of Michigan’s Surveys of Consumers, and regional measures, such as the Cleveland Fed’s Survey of Regional Conditions and Expectations and the Dallas Fed’s Texas Business Outlook Surveys.

Gauging business views on inflationary pressures

Our Beige Book questionnaire responses also provide insights into how business and community contacts in the District see national inflation evolving. Figure 3 compares the SF Fed Inflation Gauge Index (blue line) with monthly changes in the year-over-year headline personal consumption expenditures (PCE) inflation rate published by the BEA (green line). We normalize the inflation series and index as discussed earlier.

Figure 3
SF Fed Inflation Gauge Index versus realized inflation

Notes: Green line is the percentage point change in year-over-year headline PCE inflation shown as a 6-month moving average. Indicators normalized by their respective averages and standard deviations based on data from 2014 to present. Gray bar indicates NBER recession dates. Correlation coefficient is calculated between quarterly versions of both indicators.
Source: Bureau of Economic Analysis, FRBSF Beige Book questionnaire responses, and authors’ calculations.

Similar to our business sentiment index, the inflation gauge index is an early indicator for official inflation data releases. The index generally tracks changes in headline PCE inflation over the past decade, with a correlation coefficient of +0.65.

The most recent index results suggest a strong uptick in expected inflation among SF Fed business contacts, with several responses citing trade policy adjustments and inflation being persistently above the Federal Reserve’s 2% target. The recent peak resembles the one in 2018, which followed heightened trade tensions with China. The surge tracks other business and household-based measures of short-term inflation expectations, such as the Atlanta Fed’s Business Inflation Expectations and the New York Fed’s Survey of Consumer Expectations.

Making better projections

Beyond tracking data on national and regional economic conditions, we consider whether our two indexes can help improve one-year-ahead projections of output growth and overall inflation. We run linear regressions on a 2014–2022 data sample and estimate out-of-sample projections for the period starting in the first quarter of 2023. We run this analysis for the three economic measures—national GDP, regional GDP, and inflation—once with our index included on the right-hand side of the regression equation and once without the index. For this analysis, we use versions of the SF Fed Business Sentiment Index and the SF Fed Inflation Gauge Index that have been aggregated quarterly.

Figure 4 compares the out-of-sample projection accuracy of the two iterations. Across all economic measures, incorporating the SF Fed Business Sentiment Index or the SF Fed Inflation Gauge Index in the regression noticeably reduced the forecast errors for the out-of-sample period. This general result appears to hold when we project output growth and inflation one quarter ahead, in line with other studies that incorporate soft data from the Beige Book in short-term projections (Balke and Petersen 2002). The results are also consistent when using a local projections method from Jordà (2005) for one-year-ahead projections of output growth and shorter-term projections of inflation. This further supports the usefulness of our qualitative measures as early indicators of the future economic landscape over the short term.

Figure 4
Forecast errors with and without SF Fed sentiment indexes

Notes: Root mean-squared errors of out-of-sample projections from 2023:Q1 to 2025:Q2 including and excluding the SF Fed Business Sentiment Index (for GDP) and SF Fed Inflation Gauge Index (for inflation).
Source: Bureau of Economic Analysis, FRBSF Beige Book questionnaire responses, and authors’ calculations.

Conclusion

Information collected from businesses and communities through the San Francisco Fed’s regional Beige Book questionnaire can provide valuable insights into the national and regional economies. Sentiment indexes described in this Letter use responses from Twelfth District Beige Book contacts to generally track economic activity and inflation. Our two indexes serve as reliable early indicators of official data, which could help improve near-term forecast accuracy. The SF Fed Business Sentiment Index remained negative for much of 2022 and 2023, possibly reflecting more subdued growth within the District relative to the United States. Meanwhile, the SF Fed Inflation Gauge Index spiked in recent months following adjustments to trade policy.

References

Abdelrahman, Hamza, and Luiz E. Oliveira. 2023. “The Rise and Fall of Pandemic Excess Savings.” FRBSF Economic Letter 2023-11 (May 8).

Balke, Nathan S., and D’Ann Petersen. 2002. “How Well Does the Beige Book Reflect Economic Activity? Evaluating Qualitative Information Quantitatively.” Journal of Money, Credit and Banking 34 (1), pp. 114–136.

Daly, Mary C. 2024. “Fireside Chat with Mary C. Daly at the San Diego County Economic Roundtable.” January 19.

Jordà, Òscar. 2005. “Estimation and Inference of Impulse Responses by Local Projections.” American Economic Review 95(1), pp. 161–182.

Scanlon, Kyla. 2022. “The Vibecession: The Self-Fulfilling Prophecy.” Kyla Substack (June 30).

Shapiro, Adam Hale, Moritz Sudhof, and Daniel Wilson. 2022. “Measuring News Sentiment.” Journal of Econometrics 228(2), pp. 221–243.

Economics-Watching: Second-Quarter GDP Growth Estimate Unchanged

[from the Federal Reserve Bank of Atlanta]

The growth rate of real gross domestic product (GDP) is a key indicator of economic activity, but the official estimate is released with a delay. The Federal Reserve Bank of Atlanta’s GDPNow forecasting model provides a “nowcast” of the official estimate prior to its release by estimating GDP growth using a methodology similar to the one used by the U.S. Bureau of Economic Analysis.

GDPNow is not an official forecast of the Atlanta Fed. Rather, it is best viewed as a running estimate of real GDP growth based on available economic data for the current measured quarter. There are no subjective adjustments made to GDPNow—the estimate is based solely on the mathematical results of the model.

Recent forecasts for the GDPNow model are available here [archived PDF]. More extensive numerical details—including underlying source data, forecasts, and model parameters—are available as a separate spreadsheet [archived XLSX]. You can also view an archive of recent commentaries from GDPNow estimates.

Please note that the Atlanta Fed no longer supports the GDPNow app. Download the EconomyNow app to get the latest GDP nowcast and more economic data.

Latest estimate: 2.4 percent — July 25, 2025

The GDPNow model estimate for real GDP growth (seasonally adjusted annual rate) in the second quarter of 2025 is 2.4 percent on July 25, unchanged from July 18 after rounding. The forecasts of the major GDP subcomponents were all unchanged or little changed from their July 18 values after this week’s releases from the U.S. Census Bureau and the National Association of Realtors.

The growth rate of real gross domestic product (GDP) measured by the U.S. Bureau of Economic Analysis (BEA) is a key metric of the pace of economic activity. It is one of the four variables included in the economic projections of Federal Reserve Board members and Bank presidents for every other Federal Open Market Committee (FOMC) meeting. As with many economic statistics, GDP estimates are released with a lag whose timing can be important for policymakers. In preparation for FOMC meetings, policymakers have the Fed Board staff projection of this “advance” estimate at their disposal. These projections—available through 2008 at the Philadelphia Fed’s Real Time Data Center—have generally been more accurate than forecasts from simple statistical models. As stated by economists Jon Faust and Jonathan H. Wright in a 2009 paper, “by mirroring key elements of the data construction machinery of the Bureau of Economic Analysis, the Fed staff forms a relatively precise estimate of what BEA will announce for the previous quarter’s GDP even before it is announced.”

The Atlanta Fed GDPNow model also mimics the methods used by the BEA to estimate real GDP growth. The GDPNow forecast is constructed by aggregating statistical model forecasts of 13 subcomponents that comprise GDP. Other private forecasters use similar approaches to “nowcast” GDP growth. However, these forecasts are not updated more than once a month or quarter, are not publicly available, or do not have forecasts of the subcomponents of GDP that add “color” to the top-line number. The Atlanta Fed GDPNow model fills these three voids.

The BEA’s advance estimates of the subcomponents of GDP use publicly released data from the U.S. Census Bureau, U.S. Bureau of Labor Statistics, and other sources. Much of this data is displayed in the BEA’s Key Source Data and Assumptions table that accompanies the “advance” GDP estimate. GDPNow relates these source data to their corresponding GDP subcomponents using a “bridge equation” approach similar to the one described in a Minneapolis Fed [archived PDF] study by Preston J. Miller and Daniel M. Chin. Whenever the monthly source data is not available, the missing values are forecasted using econometric techniques similar to those described in papers by James H. Stock and Mark W. Watson and Domenico Giannone, Lucrezia Reichlin, and David Small. A detailed description of the data sources and methods used in the GDPNow model is provided in an accompanying Atlanta Fed working paper [archived PDF].

As more monthly source data becomes available, the GDPNow forecast for a particular quarter evolves and generally becomes more accurate. That said, the forecasting error can still be substantial just prior to the “advance” GDP estimate release. It is important to emphasize that the Atlanta Fed GDPNow forecast is a model projection not subject to judgmental adjustments. It is not an official forecast of the Federal Reserve Bank of Atlanta, its president, the Federal Reserve System, or the FOMC.

Economics-Watching: Third-Quarter GDP Growth Estimate Increased

[from the Federal Reserve Bank of Atlanta’s GDPNow]

The growth rate of real gross domestic product (GDP) is a key indicator of economic activity, but the official estimate is released with a delay. The Federal Reserve Bank of Atlanta’s GDPNow forecasting model provides a “nowcast” of the official estimate prior to its release by estimating GDP growth using a methodology similar to the one used by the U.S. Bureau of Economic Analysis.

GDPNow is not an official forecast of the Atlanta Fed. Rather, it is best viewed as a running estimate of real GDP growth based on available economic data for the current measured quarter. There are no subjective adjustments made to GDPNow—the estimate is based solely on the mathematical results of the model. In particular, it does not capture the impact of COVID-19 and social mobility beyond their impact on GDP source data and relevant economic reports that have already been released. It does not anticipate their impact on forthcoming economic reports beyond the standard internal dynamics of the model.

The GDPNow model estimate for real GDP growth (seasonally adjusted annual rate) in the third quarter of 2023 is 4.1 percent on August 8, up from 3.9 percent on August 1. After recent releases from the U.S. Census Bureau, the Institute for Supply Management, the U.S. Bureau of Economic Analysis, and the U.S. Bureau of Labor Statistics, an increase in the nowcast of third-quarter real gross private domestic investment growth from 5.2 percent to 8.1 percent was slightly offset by decreases in the nowcasts of third-quarter real personal consumption expenditures growth and third-quarter real government spending growth from 3.5 percent and 2.9 percent, respectively, to 3.2 percent and 2.7 percent, while the nowcast of the contribution of the change in real net exports to second-quarter real GDP growth increased from 0.08 percentage points to 0.11 percentage points.

The next GDPNow update is Tuesday, August 15.

FRBSF Economic Letter: Can Monetary Policy Tame Rent Inflation?

[from the Federal Reserve Bank of San Francisco Economic Letter]

by Zheng Liu and Mollie Pepper

Rent inflation has surged since early 2021. Because the cost of housing is an important component of total U.S. consumer spending, high rent inflation has contributed to elevated levels of overall inflation. Evidence suggests that, as monetary policy tightening cools housing markets, it can also reduce rent inflation, although this tends to adjust relatively slowly. A policy tightening equivalent to a 1 percentage point increase in the federal funds rate could reduce rent inflation as much as 3.2 percentage points over 2½ years.

“We’ve had a time of red-hot housing market all over the country… Shelter inflation is going to remain high for some time. We’re looking for it to come down, but it’s not exactly clear when that will happen. Hope for the best, plan for the worst.”

Federal Reserve Chair Jerome Powell (2022)

The rapid run-up of shelter costs—both house prices and rents—during the recovery from the pandemic has raised questions about how inflation pressures might affect housing affordability. Since March 2022, the Federal Reserve has rapidly lifted its federal funds rate target from near zero to over 4%, and policymakers have signaled that they are open to keeping the monetary policy stance sufficiently restrictive to return inflation to the longer-run goal of 2% on average. The tightened financial conditions following those policy changes, especially the surge in mortgage interest rates, have helped cool house price growth. However, rent inflation remains elevated.

This Economic Letter examines the effectiveness of monetary policy tightening for reducing rent inflation. We estimate that, during the period from 1988 to 2019, a policy tightening equivalent to a 1 percentage point increase in the federal funds rate can reduce rent inflation—measured by 12-month percentage changes in the personal consumption expenditures (PCE) housing price index—by about 3.2 percentage points, but the full impact takes about 2½ years to materialize. Based on housing costs’ share in total PCE, this translates to a reduction in headline PCE inflation of about 0.5 percentage point over the same time horizon.

Rising housing costs

Following the COVID-19 recession, house prices and rents both surged in the United States. For example, the 12-month growth rate of Standard & Poor’s CoreLogic Case-Shiller Home Price Index accelerated from about 10% in December 2020 to over 20% in March 2022. After the Federal Reserve started raising the target for the federal funds rate in March, house price growth has slowed significantly, to about 9% in October 2022.

Rent inflation also accelerated during the pandemic period. Figure 1 shows that rent inflation—measured using 12-month changes in the PCE housing price index and including rents for tenant-occupied housing and imputed rents for owner-occupied housing—rose from a low point of about 2% in early 2021 to 7.7% by December 2022, the highest level since 1986. During the same period, rent inflation measured by 12-month changes in the shelter component of the consumer price index (CPI) experienced a similar increase. Thus, following the tightening of monetary policy, house price growth has slowed but rent inflation continues to rise.

Figure 1: PCE and CPI measures of rent inflation
Source: Bureau of Economic Analysis, Bureau of Labor Statistics, and Haver Analytics.
Note: Twelve-month percentage changes. Gray shading indicates NBER recession dates.

Economic theory suggests that some common forces such as changes in housing demand can drive both rents and house prices. For example, the expansion of remote work since the COVID-19 pandemic has increased demand for housing, raising both house prices and rents (Kmetz, Mondragon, and Wieland 2022). To the extent that the stream of current and future rents reflects the fundamental value of a house, house prices can be a leading indicator of future rent inflation (Lansing, Oliveira, and Shapiro 2022). Thus, monetary policy can affect both house prices and rents by cooling housing demand.

Housing demand responds to changes in financial conditions, such as increases in mortgage interest rates. However, theory suggests that house prices are more sensitive than rental prices to changes in financial conditions, because home purchases typically require longer-term mortgage financing. In addition, unlike rents, house prices can be partly driven by investor sentiments or beliefs, which explains the observed larger swings in house prices than in rents over business cycles (Dong et al. 2022). Long-term rental contracts can also contribute to slow adjustments in rent inflation.

Rent inflation is an important contributor to overall inflation because housing costs are an important component of total consumption expenditures. On average, housing expenditures represent about 15% of total PCE and 25% of the services component of PCE. In CPI, shelter costs represent an even larger share, accounting for about 30% of total consumption of all urban consumers and about 40% of core consumption expenditures excluding volatile food and energy components.

The contribution of rent inflation to overall PCE inflation has increased since early 2021. As Figure 2 shows, in the first quarter of 2021, rent inflation accounted for about 22% of the four-quarter change in the PCE services price index, excluding energy: 0.5 of the 2.3 percentage points increase in service prices was attributable to rent inflation. By the third quarter of 2022, the contribution of rent inflation had climbed to about one-third, or 1.5 of the 4.7 percentage point increase in service prices.

Figure 2: Rising contribution of rent inflation to services inflation
Source: Bureau of Economic Analysis, Haver Analytics, and authors’ calculations.
Note: Four-quarter changes in PCE services price index excluding energy.

Measuring policy effects

Given the rising contribution of rent inflation to overall inflation, it is important to assess the quantitative effects of monetary policy tightening on rent inflation.

For our analysis, we use a measure of monetary policy surprises constructed by Bauer and Swanson (2022). Their measure focuses on high-frequency changes in financial market indicators within a short period surrounding the Federal Open Market Committee (FOMC) policy announcements. If the public fully anticipates a policy change, then the financial market would not react to new policy announcements. However, if the market does react to an announcement, then the policy change must contain a surprise element. Thus, changes in financial market indicators, such as the price of Eurodollar futures, in a narrow window around an FOMC announcement can capture policy surprises. In practice, however, the data constructed this way are not complete surprises because they can be predicted by some macro and financial variables shortly before FOMC announcements. We follow the approach of Bauer and Swanson (2022) to purge the influences of those macro and financial variables from the measure of policy surprises. We use the resulting quarterly time series to measure monetary policy shocks, with a sample period from 1988 to 2019.

We then use a local projections model—a statistical tool proposed by Jordà (2005)—to project how rent inflation responds over time to a tightening of monetary policy equivalent to a 1 percentage point unanticipated increase in the federal funds rate in a given quarter. The model takes into account how monetary policy shocks interact with other macroeconomic variables, including lags of rent inflation, real GDP growth, and core PCE inflation.

In the final step, we compute the responses of rent inflation relative to its preshock level over a period up to 20 quarters after the initial increase in the federal funds rate.

Gradual impact of policy tightening on rent inflation

Figure 3 shows the response of rent inflation during the first 20 quarters after an unanticipated tightening of monetary policy (solid blue line). The shaded area shows the confidence band, indicating the statistical uncertainty in estimating the responses. Under the assumption that the model is correct, the shaded area contains the actual value of the rent inflation responses to the monetary policy shock roughly two-thirds of the time. The policy shock is normalized such that it is equivalent to a 1 percentage point unanticipated increase in the federal funds rate.

Figure 3: Response of rent inflation to monetary policy tightening
Source: Bureau of Economic Analysis, Bauer and Swanson (2022), and authors’ calculations.
Note: Response of rent inflation to a monetary policy shock equivalent to a 1 percentage point surprise increase in the federal funds rate. Shaded region shows 68% confidence band around the estimate.

The figure shows that monetary policy tightening has significant and gradual effects on rent inflation. On impact, a 1 percentage point increase in the federal funds rate reduces rent inflation about 0.6 percentage point relative to its preshock level. Over time, rent inflation declines gradually, falling about 3.2 percentage points in the 10 quarters following the impact. The slow adjustment in rent inflation partly reflects the stickiness in nominal rents due to long-term rental contracts. Since housing expenditures account for about 15% of total PCE, this estimate translates to a reduction in headline PCE inflation of about 0.5 percentage point, stemming from the decline in rent inflation over a period of 2½ years.

The rent component of PCE is measured based on average rents, including those locked in long-term rental contracts, which are slow to adjust to changes in economic and financial conditions. Rents on new leases, however, are more flexible. For example, the 12-month growth in Zillow’s observed rent index, which measures changes in asking rents on new leases, has slowed significantly since March 2022 (see Figure 4). Asking rents are typically a leading indicator of future average rents. Thus, the slowdown in asking rent growth could portend lower future rent inflation.

Figure 4: Year-over-year observed rent growth starting to slow
Source: Zillow and Haver Analytics.
Note: Twelve-month percentage changes in Zillow’s observed rent index. Gray shading indicates NBER recession dates.

Conclusion

Rents are an important component of consumer expenditures. Recent surges in rent inflation have led to concerns that overall inflation might stay persistently high despite tightening of monetary policy. We present evidence that monetary policy tightening is effective for reducing rent inflation, although the full impact takes time to materialize. A policy tightening equivalent to a 1 percentage point increase in the federal funds rate can reduce rent inflation up to 3.2 percentage points over the course of 2½ years. This translates to a maximum reduction in headline PCE inflation of about 0.5 percentage point over the same time horizon. Although average rents are slow to respond to policy changes, growth of asking rents on new leases has started to slow following recent monetary policy tightening. Our finding suggests that this tightening will gradually bring rent inflation down over time, thereby helping to reduce overall inflation.

Zheng Liu — Vice President and Director of the Center for Pacific Basin Studies, Economic Research Department, Federal Reserve Bank of San Francisco

Mollie Pepper — Research Associate, Economic Research Department, Federal Reserve Bank of San Francisco

[Archived PDF]

U.S. Bureau of Economic Analysis: Marine Economy, 2020

[from the U.S. Bureau of Economic Analysis]

The marine economy accounted for 1.7 percent, or $361.4 billion, of current-dollar U.S. gross domestic product (GDP) in 2020 and 1.7 percent, or $610.3 billion, of current-dollar gross output. Real (inflation-adjusted) GDP for the marine economy decreased 5.8 percent from 2019 to 2020, compared with a 3.4 percent decrease for the overall U.S. economy. Real gross output for the marine economy decreased 8.5 percent, while marine economy compensation decreased 1.2 percent, and employment decreased 10.8 percent.

Read the current release [Archived PDF]

Info Students Should Know

The Bureau of Economic Analysis (BEA)

BEA News:  Gross Domestic Product by State, Second Quarter 2019

The U.S. Bureau of Economic Analysis (BEA) has issued the following news release today:

Real gross domestic product (GDP) increased in all 50 states and the District of Columbia in the second quarter of 2019. The percent change in real GDP in the second quarter ranged from 4.7 percent in Texas to 0.5 percent in Hawaii.

The full text of the release [archived PDF] on BEA’s website can be found here.

The Bureau of Economic Analysis provides this service to you at no charge. Visit us on the Web at www.bea.gov.  All you will need is your e-mail address.  If you have questions or need assistance, please e-mail subscribe@bea.gov.