Tag: COVID-19 pandemic

Economics-Watching: Where Could Reshoring Manufacturers Find Workers?

[from the Federal Reserve Bank of Cleveland, 9 October, 2025]

by Stephan D. Whitaker, Senior Policy Economist

The United States has lost millions of manufacturing jobs in recent decades, but a variety of policies have been enacted to incentivize the creation of manufacturing jobs in America. This District Data Brief analyzes where manufacturers might find US workers to fill these roles.

Introduction

The announcement of new tariffs this year has reignited the discussion of whether the United States can expand its manufacturing employment by millions of workers. Reversing decades of manufacturing job losses is one explicit goal of the new higher tariffs. This District Data Brief presents measures of employment and demographics as context around the current and potential employment in US manufacturing. Raising manufacturing employment by 4 to 6 million workers would constitute a large increase relative to current levels. However, an increase of this scale would not be large relative to the global growth of manufacturing employment in recent decades, the current US labor force size, or the number of US adults not engaged in high-paying work.

With different priorities and approaches, policymakers have spent much of the past decade addressing issues related to the loss or absence of manufacturing in the United States. For example, America’s dependence on imported manufactured goods was highlighted at the beginning of the COVID-19 pandemic as supply chain disruptions led to shortages of medical equipment, pharmaceuticals, microchips, and other products. The CHIPS and Science Act and the Inflation Reduction Act featured tax breaks and subsidies to expand US manufacturing capacity for semiconductors, electric vehicles, and renewable energy equipment.

At the same time, economists have been documenting the loss of work opportunities and earning power by workers without college degrees as manufacturing employment has declined. In 2013, David Autor, David Dorn, and Gordon Hanson published a study that estimated the labor market impacts resulting from increased trade competition following China’s entrance into the World Trade Organization, an effect often referred to as the “China shock.” Dozens of studies have since used the regional variation in job and income losses caused by the China shock to measure the adverse impacts of job displacement on family structures, crime, health, and other social indicators. Some supporters of industrial subsidies and higher tariffs have expressed the hope that these dynamics can be put into reverse.

Read the full article [archived PDF].

Economics-Watching: Tracking the Economy in Real‑Time Through Regional Business Surveys

[from the Federal Reserve Bank of New York’s The Teller Window, 23 September 2025]

by Richard Deitz and Kartik Athreya

Federal Reserve policymakers need current information about economic conditions to make well-informed monetary policy decisions. But hard data, such as GDP and the unemployment rate, is released with a significant lag, making it difficult to get a precise, real-time read on the economy, especially during times of rapid change.

To help fill the gap, the New York Fed conducts two monthly regional business surveys: the Empire State Manufacturing Survey of manufacturers in New York state and the Business Leaders Survey, which covers service sector firms in New York state, northern New Jersey, and Fairfield County, Conn. These surveys provide timely soft data, available well before hard data is released.

Hard data is based on precise quantitative measurements, such as sales figures or the specific prices firms are charging. By contrast, soft data is qualitative, focusing on trends, expectations, and sentiment around economic activity. And while hard data looks backward, soft data from the regional surveys can look forward—providing important information about expectations for the future and emerging trends.

Gathering soft data quickly can be impactful—for example, the Empire State Manufacturing and Business Leaders surveys signaled a sharp downturn in economic activity in early March 2020 [archived PDF], providing a warning weeks before official statistics captured the full extent of the COVID pandemic’s economic impact.  

How the Surveys Work

The New York Fed launched the Empire State Manufacturing Survey in 2001. It was modeled after the Philadelphia Fed’s Business Outlook Survey, a long-running manufacturing survey that has historically been watched by financial markets and policymakers as an early signal about national manufacturing conditions. The Business Leaders Survey was launched later in 2004 and was among the first regional business surveys to target the service sector.

The surveys are sent to over 300 business executives and managers at firms across industries during the first week of every month. While about two-thirds of participating firms have 100 or fewer employees, some have hundreds or thousands of workers.

Leaders at the firms fill out a short questionnaire asking if business activity has increased, decreased, or stayed the same compared to the prior month. The surveys ask about indicators such as prices–yielding insights into inflationary pressures–as well as employment, orders, and capital spending. Respondents answer questions about how they expect these indicators to change over the next six months, offering a forward-looking perspective on the economy’s trajectory.

From the responses, New York Fed researchers construct diffusion indexes by calculating the difference between the percentage of firms reporting increased activity and those reporting decreased activity. Positive values indicate that more firms say activity increased than decreased, suggesting activity expanded over the month. Higher positive values indicate stronger growth, while lower negative values indicate stronger declines.

The surveys include local businesses, like restaurants and car dealerships, as well as firms with national and global reach, such as software manufacturers and shipping enterprises. As a result, the economic indicators derived from the surveys are often early predictors of national economic patterns, frequently aligning with hard data released later.

Getting Answers on Current Issues

The surveys regularly ask supplemental questions about current economic issues to get real-time answers. Over the last few years, the surveys have asked about firms’ experience with tariffs, inflation expectations, if the use of AI is leading to a reduction in employment, how often employees work from home [archived PDF], and whether supply availability was affecting their businesses.

Going Beyond the Indicators

In addition to providing data to track economic conditions, the regional surveys also provide a channel to hear directly from local business leaders. Every month, survey respondents are asked for their comments, offering the opportunity for businesses to share their thoughts, concerns, and experiences with the New York Fed. This helps researchers and policymakers understand how businesses are being affected by economic conditions.

The surveys act as one of the bridges between the New York Fed and the business community, ensuring the voices of regional businesses are considered in economic assessments and policy discussions as well as enhancing the ability of policymakers to make informed decisions to respond effectively to economic challenges.

Executives, owners, or managers of businesses in New York, northern New Jersey, or Fairfield County, Conn., interested in participating in the New York Fed’s monthly business surveys can find more information here. The next survey results will be released on Oct. 15 and 16.

Digitizing Heritage: Exploring the Transformation of Culture to Data

[from India in Transition by the Center for the Advanced Study of India at the University of Pennsylvania, 1 September 2025]

by Krupa Rajangam & Deborah Sutton

“Oh that. We just took some undergraduate history students on board as interns. They provided the content and it was done.”

The co-founder of a digital heritage initiative promoting interactive user interfaces offered these opening remarks. Speaking at a Delhi-based museum, he had been asked about the information provided to users as they moved their hands across an interactive board, revealing images and narratives relating to the Indian freedom movement. His response clarified that the physical and digital components of such installations—for example, the 3D-modeling software and hardware, scanning equipment and its resolution and the user interface—were more carefully designed and calibrated than the content they provided.

Contemporary cultural heritage (CH) is rife with digital innovation. The COVID pandemic accelerated this transformation as archivists and curators worked to develop content that would reach remote, locked-down audiences. Within significant limits, digital platforms can democratize and facilitate access to materials previously inaccessible. Instead of being physically siloed, digitized material—as data components and not just content on culture—can be reproduced, combined, and circulated infinitely to achieve a reach previously considered impossible. Accessibility and malleability remain one of the great boons of digital formats. But here, we consider the information economy of CH practice as it exists—and not its extraordinary and often hypothetical potential—in two, overlapping realms of digitized CH: for-profit business enterprises and academic side-hustles, related to more mainstream academic research.

In the former, questions of what is shared are often less significant than the appeal of the format. In the latter, innovation is often the result of short-term projects that languish, abandoned after project completion, and rarely find audiences. Our research builds on our individual experiences and the findings of a scoping exercise examining a number of India-based heritage projects conducted in 2021-22. It suggests the need for more careful consideration of the implications of transforming CH materials into forms of data; the change impacts everything from how we understand “originality” to the reliance on for-profit services to deliver heritage material to the public.

As digitized representations of CH and access to such formats become more widespread, are we, as CH practitioners and academics, giving enough thought to how digital technologies are reshaping the nature of CH and its audience? Beyond questions of wider reach, are we sufficiently acknowledging how these changes challenge a continued focus on originality and notions of academy as primary controllers of access to knowledge and its validity, both in research and practice?

Digitizing for Dissemination

In 2019, one of us—Deborah Sutton—developed a software platform, Safarnama, including an app and authored experiences around Delhi’s CH. The project subsequently extended to Karachi. Generating “original” content, such as audio-visual clips and old photos, to be hosted on the app platform, was key to its attractiveness and usefulness, but permissions proved tricky. Some collaborators who were initially keen to contribute content quietly withdrew, likely due to the unfamiliar format and unknown reach. The app format also raised other questions. Would incorporating content from non-digital but published scholarship require authorial permission or only acknowledgement?

In 2020, Krupa Rajangam held a sponsored incubation at the NSRCEL, a business incubator located at the Indian Institute of Management-Bangalore, to develop a web interface that would host geo-locationed stories of marginalized histories by drawing on both historical facts and lived experiences. Corporate mentors remained skeptical of her ability to source “original” content on an ongoing basis, i.e., content that was both authenticated and validated. They repeatedly advised her to focus on the format, user experience, and appeal for “mass markets” so her prototype would find audiences. Both projects equally raised questions over who would consume the content and what constitutes the public or audience.

In a scoping exercise undertaken for the Arts and Humanities Research Council (AHRC), UK, in 2021-22, we explored a number of India-based heritage projects funded by the AHRC in partnership with the Newton Fund and Indian Council for Historical Research, since 2015 (figure 1). We were particularly interested in the digital components, which all projects included, even if only a website.

Our exploratory surveys firmly established the divergence in interpreting both CH and digital technologies, which was not surprising. Some projects defined and treated CH as fixed pre-existing material, to be interpreted and presented to audiences through digital technologies. Others re-framed digital formats of CH as components of data, assembling, manipulating, and representing extant archival and other materials. The rest generated digitized CH, effectively altering its nature. Typically, such projects dealt with more ephemeral or less conventional forms of CH.

Fundamental Transformations

Notions of originality remain central to art, architectural and art historical training, and CH practice. Digitization transforms the access and retrieval value of “original” material in physical archives, such as old maps and letters, much lauded in traditional “analog” scholarship, to use value as data. Once the end-user (audience) accesses this data (whether historical facts or stories), it becomes nothing more than bytes occupying valuable space, to be deleted once consumed rather than stored, making it easy to overlook or disregard the source and its context.

For example, in the Safarnama project, the app contained carefully collected and authenticated narratives on “partition memories” in Delhi and Karachi. However, the bite-sized media format meant that users would only explore content once, as snippets. This realization led the team to develop the software and incorporate the ability to download content, which at least meant that users could collect, organize and store (archive) the assembled media.

Digitization also takes away the materiality of the archive, making it more ephemeral. Non-digital materials through, and into which we render CH can (in endless combinations and cycles) be lost, forgotten, sold, recovered, collected, displayed, and stored. Such capacities of digital files are obvious, but maintaining access depends on varied and dynamic software ecologies for existence and sustained end-user access. Digital files created within one software-architecture can be incompatible with, and therefore rendered obsolete, by another. The ethos of software development is constant change.

In another paper, we examined questions of quantity, quality, and reusability of data related to digitization of building-crafts knowledge alongside CARE and FAIR principles of data management. The principles were proposed and adopted by an international consortium of scholars and industry, the former focused on responsible collection, use, and dissemination of data, especially related to vulnerable people and the latter on sustainable data management.

As an example, one AHRC project experimented with methods to capture detailed 3D images of heritage sites and structures in dynamic crowded environments. They used one set of methods to capture the interiors and another for the exteriors, hoping to merge both together and develop holistic imagery for audiences. This proved impossible at first due to issues of software compatibility. Once that was partially resolved, the new software couldn’t handle the sheer volume of data captured—and it was unclear where and for how long such volumes of data would be stored.

New realms of intellectual property remain fuzzy. While the content on digital platforms is governed by licensing and proprietary legal frameworks, it is often hosted on open platforms, through web repositories such as GitHub. Prima facie, such openness appears to challenge the proprietorial nature of archives and other repositories as keepers of knowledge. However, it raises a host of questions about how to maintain a critical understanding of archives.

Digitization may, and should, transform access but should it obliterate the regimes through which the materials were generated and organized and what’s included or excluded? For example, a local coordinator of one project that engaged with artists commented that digital technologies are typically used to document technical skills as forms of intangible heritage and develop artist encyclopedias, saying that “they are hardly used to interrogate the reality that many ‘traditional’ artists hail from marginalized castes.” Similarly, the local coordinator of another project that engaged with communities living in and around a protected heritage site commented on how digital technologies often end up being used to create a record of heritage structures without any reference to their day-to-day setting.

Any and all digital enterprise in CH, we argue, needs to integrate the ambition to use digital methods to not just present but also counter and interrogate the material, its creation, and purpose. Digital platforms and web- and app-based software are now able to manipulate and re-situate information in unprecedented ways. The novelty of such formats can displace original, provocative, and timely considerations of the material. Often, we are so taken by the visual and structural attributes of these formats, that we accept it at face value and lose sight of the tone and content of heritage as a curated message about the past and the present.

Alongside this, digital augmentations and iterations of CH, including storage, have significant financial and infrastructural implications. The creation and maintenance of digital platforms requires either developing “in-house” digital specialization or, more commonly, reliance on private, for-profit platforms. Paying for external provision introduces complexities. Funders, including the AHRC, struggle to devise guidance or policy in relation to software licensing. However, a persistent challenge to projects, and partnerships between academic and non-academic partners, is devising data and software strategies that subsist beyond the life of the funded-research project. Often, the adverse effects of the paucity of longer-term planning around IP issues, sustainability, and data archiving falls disproportionately on the non-academic stakeholder.

While digitization foregrounds the potential and promise of complete openness and equity, maybe this is lost in practice. Or digitization may merely mark the displacement of one set of ethics with another. There is a need for more careful consideration of the implications, complexities, and risks of taking CH materials out of boxes and off shelves and transforming and generating it into data files, which are, in turn, dependent on digital platforms to provide end-user access. However, the question remains of whether heritage-related disciplines are adequately prepared and willing to confront such new ways of working, which have begun to dislodge some of the privileges extant in current forms of research and practice.

Krupa Rajangam is nearing the end of her tenure as a Fulbright Fellow at the Historic Preservation Department, Weitzman School of Design, University of Pennsylvania. Her permanent designation is Founder-Director, Saythu…linking people and heritage, a professional conservation collective based in Bangalore, India.

Deborah Sutton is a Professor in Modern South Asian History at Lancaster University.

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.

World-Watching: Container Shipping Financial Insight, Nov. 2023

[from Drewry Shipping Consultants]

Driven by weak 3Q23 financial results, the Drewry Container Equity Index decreased 3.7% last month (as of 22 Nov 2023). Additionally, asset prices continue to fall due to the supply-demand imbalance.

  • Container shipping companies’ 3Q23 financial results showcased a sharp dip in profits or even losses. On a group level, eleven liners (which report quarterly results) among our portfolio of 13 companies reported an average slump of 54.6% YoY in their 3Q23 topline. Operating costs declined 18.1% YoY amid falling chartering costs and lowering bunker prices. However, the cost reduction was insufficient to offset the plunge in topline; thus, EBIT contracted 94.1% YoY on average.
  • The Drewry Container Equity Index tumbled 28.1% YTD 2023 (ending 22 November), driven by lowering freight rates (WCI: -30.7% in YTD 2023), which squeezed earnings over the quarters. On the contrary, the S&P 500 posted an 18.4% growth. The Drewry Container Equity Index declined 3.4% in the month ending 22 November 2023. Talking about equity prices individually, APMM’s stock price fell 9.0% amid EBIT loss for its Ocean segment in 3Q23, staff cuts and reduced capex guidance, highlighting APMM’s efforts toward reducing costs faced with the bleak industry outlook. Hapag-Lloyd’s stock price slumped 22.2% as its EBIT margin (3Q23: 5.1%) slid below its pre-pandemic level (3Q19: 7.8%). ZIM became the first carrier to report impairment of assets worth USD 2.0bn in 3Q23, and its stock price fell 18.1%. Meanwhile, China-exposed container companies benefitted from the positive sentiment arising from the proposed fiscal stimulus by the Chinese government, possibly boosting the out-of-China and intra-Asia trades. Asian stocks in the broader index rose 2.0% to 19.4% in the month ending 22 November 2023.
  • Mainly driven by weak earnings prospects, the Drewry Container Equity Index trades at a P/B of 0.5x, a 47.5% discount to its pre-pandemic average (2013-19). We expect freight rates to fall sharply in 2024 and increasingly incur losses. Thus, we expect the multiple to remain suppressed.
  • As the fleet of container shipping companies expands, the charter market softens. For instance, 1-year TC rates declined 14.2% and 52.5% YoY in October for vessels sized 1,110 teu and 8,500 teu. Rates declined more for larger vessels as these constitute the majority of the order book and new deliveries. The YoY decline has continued since October 2022, but rates improved slightly during April-May 2023. However, this was not due to the fundamentally strong market but MSC and CMA CGM’s aggressive chartering of vessels to expand their fleets. Now that the two companies have stopped chartering in vessels, the charter market continues to decline.
  • Driven by the softening charter market, second-hand asset prices are also weakening. In October, on a YoY basis, prices for five-year-old vessels (2,700 teu and 7,200 teu) contracted 30.6% and 31.5%, and for 10-year-old ships, prices tumbled between 36.7% and 53.2%. Contrary to the sale and purchase market, newbuild prices (1,500 teu and 14,000 teu) continue to increase and rose by an average of 2.2% YoY, led by a shortage of capacity in shipyards.
  • The charter market and the S&P market have a direct impact on container shipping companies’ earnings. Costs related to chartering-in slots or vessels from other non-operating vessel owners form a significant portion of container shipping companies’ cost structure. In the 3Q23 results, this cost was reduced,
    marginally relieving downside pressure on the operating margin of container shipping companies. In line with the declining charter market, we expect this trend to continue in 4Q23. We also expect other companies to follow ZIM in reporting impairment losses as prices for older vessels continue to fall.

Read the report [archived PDF] for additional graphs.

Economics-Watching: Multivariate Core Trend Inflation

[from the Federal Reserve Bank of New York]

Overview

The Multivariate Core Trend (MCT) model measures inflation’s persistence in the seventeen core sectors of the personal consumption expenditures (PCE) price index.

Whether inflation is short-lived or persistent, concentrated in a few sectors or broad-based, is of deep relevance to policymakers. We estimate a dynamic factor model on monthly data for the major sectors of the personal consumption expenditures (PCE) price index to assess the extent of inflation persistence and its broadness. The results give a measure of trend inflation and shed light on whether inflation dynamics are dominated by a trend common across sectors or are sector-specific.

The New York Fed updates the MCT estimates and share sectoral insights at or shortly after 2 p.m. on the first Monday after the release of personal consumption expenditures (PCE) price index data from the Bureau of Economic Analysis. Data are available for download.

September 2023 Update

  • Multivariate Core Trend (MCT) inflation was 2.9 percent in September, a 0.3 percentage point increase from August (which was revised up from 2.5 percent). The 68 percent probability band is (2.4, 3.3).
  • Services ex-housing accounted for 0.54 percentage point (ppt) of the increase in the MCT estimate relative to its pre-pandemic average, while housing accounted for 0.50 ppt. Core goods had the smallest contribution, 0.03 ppt.
  • A large part of the persistence in housing and services ex-housing is explained by the sector-specific component of the trend.

Latest Release: 2:00 p.m. ET October 31, 2023

View the Multivariate Core Trend of PCE Inflation data here.

Frequently Asked Questions

What is the goal of the Multivariate Core Trend (MCT) analysis?

The New York Fed aims to provide a measure of inflation’s trend, or “persistence,” and identify where the persistence is coming from.

What data are reported?

The New York Fed’s interactive charts report monthly MCT estimates from 1960 to the present. The New York Fed also provides estimates of how much three broad sectors (core goods, core services excluding housing, and housing) are contributing to overall trend inflation over the same time span. The New York Fed further distinguishes whether the persistence owes to common or sector-specific components. Data are available for download.

What is the release schedule?

The New York Fed updates the estimate of inflation persistence and share sectoral insights following the release of PCE price data from the U.S. Bureau of Economic Analysis each month.

What is the modeling strategy?

A dynamic factor model with time-varying parameters is estimated on monthly data for the seventeen major sectors of the PCE price index. The model decomposes each sector’s inflation as the sum of a common trend, a sector-specific trend, a common transitory shock, and a sector-specific transitory shock. The trend in PCE inflation is constructed as the sum of the common and the sector-specific trends weighted by the expenditure shares.

The New York Fed uses data from all seventeen of the PCE’s sectors; however, in constructing the trend in PCE inflation, we exclude the volatile non-core sectors (that is, food and energy). The approach builds on Stock and Watson’s 2016 “Core Inflation and Trend Inflation.”

How does the MCT measure differ from the core personal consumption expenditures (PCE) inflation measure?

The core inflation measure simply removes the volatile food and energy components. The MCT model seeks to further remove the transitory variation from the core sectoral inflation rates. This has been key in understanding inflation developments in recent years because, during the pandemic, many core sectors (motor vehicles and furniture, for example) were hit by unusually large transitory shocks. An ideal measure of inflation persistence should filter those out.

PCE data are subject to revision by the Bureau of Economic Analysis (BEA). How does that affect MCT estimates?

BEA monthly revisions as well as other BEA periodic revisions to PCE price data do lead to reassessments of the estimated inflation persistence as measured by the MCT estimates. Larger revisions may lead to a more significant reassessment. A recent example of the latter case is described on Liberty Street Economics in “Inflation Persistence: Dissecting the News in January PCE Data.”

Historical estimates in our MCT data series back to 1960 are based on the latest vintage of data available and incorporate all prior revisions.

How does the MCT Inflation measure relate to other inflation measures?

The MCT model adds to the set of tools that aim at measuring the persistent component of PCE price inflation. Some approaches, such as the Cleveland Fed’s Median PCE and the Dallas Fed’s Trimmed Mean, rely on the cross-sectional distribution of price changes in each period. Other approaches, such as the New York Fed’s Underlying Inflation Gauge (UIG), rely on frequency-domain time series smoothing methods. The MCT approach shares some features with them, namely: exploiting the cross-sectional distribution of price changes and using time series smoothing techniques. But the MCT model also has some unique features that are relevant to inflation data. For example, it allows for outliers and for the noisiness of the data and for the relation with the common component to change over time.

How useful can MCT data be for policymakers?

The MCT model provides a timely measure of inflationary pressure and provides insights on how much price changes comove across sectors.

View the Multivariate Core Trend of PCE Inflation data here.

Economics-Watching: “Doing Nothing” Is Still Doing a Lot

[from the Federal Reserve Bank of Philadelphia, speech by Patrick T. Harker President and Chief Executive Officer at the National Association of Corporate Directors Webinar, Philadelphia, PA (Virtual)]

Good afternoon, everyone.

I appreciate that you’re all giving up part of the end of your workday for us to be together, if only virtually.

My thanks to my good friend, Rick Mroz, for that welcome and introduction.

I do believe we’re going to have a productive session. But just so you all know, as much as I enjoy speaking and providing my outlook, I enjoy a good conversation even more.

So, first, let’s take a few minutes so I can give you my perspective on where we are headed, and then I will be more than happy to take questions and hear what’s on your minds.

But before we get into any of that, I must begin with the standard Fed disclaimer: The views I express today are my own and do not necessarily reflect those of anyone else on the Federal Open Market Committee (FOMC) or in the Federal Reserve System.

Put simply, this is one of those times where the operative words are, “Pat said,” not “the Fed said.”

Now, to begin, I’m going to first address the two topics that I get asked about most often: interest rates and inflation. And I would guess they are the topics front and center in many of your minds as well.

After the FOMC’s last policy rate hike in July, I went on record with my view that, if economic and financial conditions evolved roughly as I expected they would, we could hold rates where they are. And I am pleased that, so far, economic and financial conditions are evolving as I expected, if not perhaps even a tad better.

Let’s look at the current dynamics. There is a steady, if slow, disinflation under way. Labor markets are coming into better balance. And, all the while, economic activity has remained resilient.

Given this, I remain today where I found myself after July’s meeting: Absent a stark turnabout in the data and in what I hear from contacts, I believe that we are at the point where we can hold rates where they are.

In barely more than a year, we increased the policy rate by more than 5 percentage points and to its highest level in more than two decades — 11 rate hikes in a span of 12 meetings prior to September. We not only did a lot, but we did it very fast.

We also turned around our balance sheet policy — and we will continue to tighten financial conditions by shrinking the balance sheet.

The workings of the economy cannot be rushed, and it will take some time for the full impact of the higher rates to be felt. In fact, I have heard a plea from countless contacts, asking to give them some time to absorb the work we have already done.

I agree with them. I am sure policy rates are restrictive, and, as long they remain so, we will steadily press down on inflation and bring markets into a better balance.

Holding rates steady will let monetary policy do its work. By doing nothing, we are still doing something. And I would argue we are doing quite a lot.

Headline PCE inflation remained elevated in August at 3.5 percent year over year, but it is down 3 percentage points from this time last year. About half of that drop is due to the volatile components of energy and food that, while basic necessities, they are typically excluded by economists in the so-called core inflation rate to give a more accurate assessment of the pace of disinflation and its likely path forward.

Well, core PCE inflation has also shown clear signs of progress, and the August monthly reading was its smallest month-over-month increase since 2020.

So, yes, a steady disinflation is under way, and I expect it to continue. My projection is that inflation will drop below 3 percent in 2024 and level out at our 2 percent target thereafter.

However, there can be challenges in assessing the trends in disinflation. For example, September’s CPI report came out modestly on the upside, driven by energy and housing.

Let me be clear about two things. First, we will not tolerate a reacceleration in prices. But second, I do not want to overreact to the normal month-to-month variability of prices. And for all the fancy techniques, the best way to separate a signal from noise remains to average data over several months. Of course, to do so, you need several months of data to start with, which, in turn, demands that, yes, we remain data-dependent but patient and cautious with the data.

Turning to the jobs picture, I do anticipate national unemployment to end the year at about 4 percent — just slightly above where we are now — and to increase slowly over the next year to peak at around 4.5 percent before heading back toward 4 percent in 2025. That is a rate in line with what economists call the natural rate of unemployment, or the theoretical level in which labor market conditions support stable inflation at 2 percent.

Now, that said, as you know, there are many factors that play into the calculation of the unemployment rate. For instance, we’ve seen recent months where, even as the economy added more jobs, the unemployment rate increased because more workers moved off the sidelines and back into the labor force. There are many other dynamics at play, too, such as technological changes or public policy issues, like child care or immigration, which directly impact employment.

And beyond the hard data, I also have to balance the soft data. For example, in my discussions with employers throughout the Third District, I hear that given how hard they’ve worked to find the workers they currently have, they are doing all they can to hold onto them.

So, to sum up the labor picture, let me say, simply, I do not expect mass layoffs.

I do expect GDP gains to continue through the end of 2023, before pulling back slightly in 2024. But even as I foresee the rate of GDP growth moderating, I do not see it contracting. And, again, to put it simply, I do not anticipate a recession.

Look, this economy has been nothing if not unpredictable. It has proven itself unwilling to stick to traditional modeling and seems determined to not only bend some rules in one place, but to make up its own in another. However, as frustratingly unpredictable as it has been, it continues to move along.

And this has led me to the following thought: What has fundamentally changed in the economy from, say, 2018 or 2019? In 2018, inflation averaged 2 percent almost to the decimal point and was actually below target in 2019. Unemployment averaged below 4 percent for both years and was as low as 3.5 percent — both nationwide and in our respective states — while policy rates peaked below 2.5 percent.

Now, I’m not saying we’re going to be able to exactly replicate the prepandemic economy, but it is hard to find fundamental differences. Surely, I cannot and will not minimize the immense impacts of the pandemic on our lives and our families, nor the fact that for so many, the new normal still does not feel normal. From the cold lens of economics, I do not see underlying fundamental changes. I could also be wrong, and, trust me, that would not be the first time this economy has made me rethink some of the classic models. We just won’t know for sure until we have more data to look at over time.

And then, of course, there are the economic uncertainties — both national and global — against which we also must contend. The ongoing auto worker strike, among other labor actions. The restart of student loan payments. The potential of a government shutdown. Fast-changing events in response to the tragic attacks against Israel. Russia’s ongoing war against Ukraine. Each and every one deserves a close watch.

These are the broad economic signals we are picking up at the Philadelphia Fed, but I would note that the regional ones we follow are also pointing us forward.

First, while in the Philadelphia Fed’s most recent business outlook surveys, which survey manufacturing and nonmanufacturing firms in the Third District, month-over-month activity declined, the six-month outlooks for each remain optimistic for growth.

And we also publish a monthly summary metric of economic activity, the State Coincident Indexes. In New Jersey, the index is up slightly year over year through August, which shows generally positive conditions. However, the three-month number from June through August was down, and while both payroll employment and average hours worked in manufacturing increased during that time, so did the unemployment rate — though a good part of that increase can be explained as more residents moved back into the labor force.

And for those of you joining us from the western side of the Delaware River, Pennsylvania’s coincident index is up more than 4 percent year over year through August and 1.7 percent since June. Payroll employment was up, and the unemployment rate was down; however, the number of average hours worked in manufacturing decreased.

There are also promising signs in both states in terms of business formation. The number of applications, specifically, for high-propensity businesses — those expected to turn into firms with payroll — are remaining elevated compared with pre-pandemic levels. Again, a promising sign.

So, it is against this full backdrop that I have concluded that now is the time at which the policy rate can remain steady. But I can hear you ask: “How long will rates need to stay high.” Well, I simply cannot say at this moment. My forecasts are based on what we know as of late 2023. As time goes by, as adjustments are completed, and as we have more data and insights on the underlying trends, I may need to adjust my forecasts, and with them my time frames.

I can tell you three things about my views on future policy. First, I expect rates will need to stay high for a while.

Second, the data and what I hear from contacts and outreach will signal to me when the time comes to adjust policy either way. I really do not expect it, but if inflation were to rebound, I know I would not hesitate to support further rate increases as our objective to return inflation to target is, simply, not negotiable.

Third, I believe that a resolute, but patient, monetary policy stance will allow us to achieve the soft landing that we all wish for our economy.

Before I conclude and turn things over to Rick to kick off our Q&A, I do want to spend a moment on a topic that he and I recently discussed, and it’s something about which I know there is generally great interest: fintech. In fact, I understand there is discussion about NACD hosting a conference on fintech.

Well, last month, we at the Philadelphia Fed hosted our Seventh Annual Fintech Conference, which brought business and thought leaders together at the Bank for two days of real in-depth discussions. And I am extraordinarily proud of the fact that the Philadelphia Fed’s conference has emerged as one of the premier conferences on fintech, anywhere. Not that it’s a competition.

I had the pleasure of opening this year’s conference, which always puts a focus on shifts in the fintech landscape. Much of this year’s conference centered around developments in digital currencies and crypto — and, believe me, some of the discussions were a little, shall we say, “spirited.” However, my overarching point to attendees was the following: Regardless of one’s views, whether in favor of or against such currencies, our reality requires us to move from thinking in terms of “what if” to thinking about “what next.”

In many ways, we’re beyond the stage of thinking about crypto and digital currency and into the stage of having them as reality — just as AI has moved from being the stuff of science fiction to the stuff of everyday life. What is needed now is critical thinking about what is next. And we at the Federal Reserve, both here in Philadelphia and System-wide, are focused on being part of this discussion.

We are also focused on providing not just thought leadership but actionable leadership. For example, the Fed rolled out our new FedNow instant payment service platform in July. With FedNow, we will have a more nimble and responsive banking system.

To be sure, FedNow is not the first instant payment system — other systems, whether operated by individual banks or through third parties, have been operational for some time. But by allowing banks to interact with each other quickly and efficiently to ensure one customer’s payment becomes another’s deposit, we are fulfilling our role in providing a fair and equitable payment system.

Another area where the Fed is assuming a mantle of leadership is in quantum computing, or QC, which has the potential to revolutionize security and problem-solving methodologies throughout the banking and financial services industry. But that upside also comes with a real downside risk, should other not-so-friendly actors co-opt QC for their own purposes.

Right now, individual institutions and other central banks globally are expanding their own research in QC. But just as these institutions look to the Fed for economic leadership, so, too, are they looking to us for technological leadership. So, I am especially proud that this System-wide effort is being led from right here at the Philadelphia Fed.

I could go on and talk about fintech for much longer. After all, I’m actually an engineer more than I am an economist. But I know that Rick is interested in starting our conversation, and I am sure that many of you are ready to participate.

But one last thought on fintech — my answers today aren’t going to be generated by ChatGPT.

On that note, Rick, thanks for allowing me the time to set up our discussion, and let’s start with the Q&A.

[archived PDF of the above speech]

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.

Zheng Yongnian (郑永年) on How to Address Western Public Opinion on China: Facts, Science and Reason

[from Pekingology at the Center for China and Globalization (CCG)]

“Be open, open, and more open,” especially to businesses, investors, media, universities, and research institutions. And tit-for-tat doesn’t work, the professor says.

by Zichen Wang, Shuyuan Han, and Li Huiyan

Professor Zheng Yongnian (郑永年), the Founding Director of the Institute for International Affairs at the Chinese University of Hong Kong, Shenzhen, on January 28 published an article on how China should address Western public opinion on China. His advice is in the last part of the article, and below is a translation.

(Emphasis by Pekingnology.)

First, we need to understand how such narratives are formed. Historically, China held a bias due to its self-isolation and limited knowledge of the West. Despite losing the two Opium Wars, Chinese intellectuals at that time still saw Westerners as uncivilized. It was not until China was defeated by Japan, a neighboring country once considered as China’s student, that they realized their ignorance and a need for reform. Before China’s Reform and Opening up, Chinese people barely knew anything about the West. They always assumed Westerners were in deep distress, repeating the same lack of understanding of the West.

Similarly, the West’s uncertainty and fear towards China’s rise stem from a lack of understanding and even fear of the country, and their ingrained ideology would lead to misconceptions.

China is the world’s second-largest economy. The externalities and influence of its economy on the West are obvious. Upon joining the WTO, some Chinese people also felt unsettled by the externalities of the West. Some said, “the wolf is coming.” Now it is the West that is experiencing such worries.

It is crucial to recognize the significant impact of the Western hypocritical narratives against China, even if they are based on ideology rather than facts. We must also acknowledge that ideology-based public opinion from the West can exert a powerful influence on their policies toward China.

Historically, the West tended to demonize others while presenting themselves as morally superior, which enabled them to apply Social Darwinism to international politics easily and thus legitimizing conflicts and even wars with other nations. Given the Soviet Union’s failure in the ideological arena during the Cold War, we should by no means ignore any ideology-based public opinion toward China from the West.

Second, to make rational responses to the Western ideology-based criticisms, we should draw lessons from the history of the world economy, such as the lessons of the Soviet Union, as well as our practices, such as the rhetorical battle with the West in the past few years. Coming up with an externally-facing public opinion based on a different ideology is not the most effective in addressing public opinion attacks based on an ideology. Empirically, tit-for-tat is ineffective and can worsen the situation. Again, the failure of the Soviet Union is a prime example, as its battle with a Western ideology failed. When faced with China-demonizing based on ideology from the West, we need to do the simplest thing, namely resorting to facts, science, and reason.

Third, and most importantly, China needs to prioritize its sustainable development, which ultimately benefits the country itself. It is important to recognize that the foundation of the government’s governance lies in its citizens, not Western praise. The support from its people is crucial for both the nation’s longevity and stability., China’s sustainable development also benefits the world economy by boosting its growth. As mentioned above, China has been the largest contributor to the growth of the world economy since it joined the WTO.

It is crucial to prioritize the building of a knowledge system based on China’s practical experiences. Regarding global soft power, we need a knowledge system based on our experiences rather than a certain ideology. While there has been a proposal for an autonomous knowledge system, continuous effort is still required.

Fourth, given the substantial externalities of our economy, we must further communicate and coordinate with other countries on economic policies, regardless of their respective sizes. Our duty is to fulfill the responsibility as a major player in the international community, which also benefits China.

After the 1997-1998 Asian financial crisis, China promised not to devalue its currency, and that commitment became an international public good in Asia. Similarly, after the global financial crisis from 2007 to 2008, China made similar contributions. As China re-opens its economy after the pandemic, it is important not only to take note of the hypocritical comments from certain quarters in the Western world but also to recognize the positive evaluations and high expectations from many international organizations.

Fifth, we must be open, open, and more open. Despite China’s efforts, there remains a persistent ideological camp in the West that views China through an ideological lens, a situation made worse by the past three years of the pandemic. The pandemic was so severe that it hindered travel across borders; as a result, some Western media and scholars tend to assess China through ideology since they couldn’t come here to see the facts with their own eyes.

The assessment of China through a uniform ideological lens appears to have strengthened the original Western ideological camp. However, the United States and the West have more than one ideology, and not all people believe in the prevailing ideology in the public opinion sphere. China’s openness provides a “seeing is believing” opportunity for different groups in the West. China should increase its openness to Western groups, including businesses, investors, media, universities, and research institutions. The changes in their understanding could render those ideological-based public opinions less effective.

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]