The Virtual Seminar on Climate Economics is an online seminar series hosted by the Federal Reserve Bank of San Francisco. The seminar is open to everyone interested in research on the economics of climate change—including topics drawn from macroeconomics, microeconomics, finance, econometrics, and environmental economics.

We convene on Zoom for a 50-minute talk and up to 25 minutes of discussion and Q&A.

Register to attend seminar series via Zoom.

Please submit your registration to receive emails with Zoom links to attend the series. You only need to register once to automatically get an email for each upcoming Virtual Seminar on Climate Economics with a link to join.

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September 1, 2020

Patricia Gonzalez started dressing the part of a white-collar worker early in her career. It was a way to create a professional identity much different from her family’s origins in migrant farm labor.

“In the late 1980s, I started working at a major bank. I’d put on a uniform of a skirt, pantyhose, and pumps, and become somebody else: the office person,” she says.

Over the course of 21 years at the San Francisco Fed, Patricia’s work identity shifted—to that of her authentic self. For this purchasing and contracts specialist, bringing her whole self to work unlocked an ability to stretch professional muscles and accomplish more than she ever knew she could.

Empowerment comes from more than being able to “dress for her day,” although it is nice to flex between jeans and business casual, depending on what her work schedule looks like.

“I know I can be candid with my manager, grab a coffee with colleagues, or get support from a...

August 6, 2020

By Laura Choi

COVID-19 is accelerating rapid changes in the ways we work, learn, and access key services. In particular, it’s clearer than ever that inclusion in the financial system is critical for households and businesses to access timely relief funds.

The $2 trillion CARES Act is a great example. The IRS distributed direct cash stimulus payments to individuals and couples, primarily through direct deposit to a bank account and in some cases through paper checks or prepaid debit cards sent by mail. The timely distribution of funds at scale, however, presented numerous challenges, including issues related to technology and communication.

Despite a sharp spike in unemployment since March 2020, aggregate wage growth has accelerated. This acceleration has been almost entirely attributable to job losses among low-wage workers. Wage growth for those who remain employed has been flat. This pattern is not unique to COVID-19 but is more profound now than in previous recessions. This means that, in the wake of the virus, evaluations of the labor market must rely on a dashboard of indicators, rather than any single measure, to paint a complete picture of the losses and the recovery.

Inflation Sensitivity to COVID-19 updates data on the contributions to core personal consumption expenditures (PCE) inflation by the degree of sensitivity to the economic disruptions caused by the pandemic. The decomposition is based on the methods described in Shapiro (2020a, b).

The PCE measure of U.S. inflation is considered particularly useful for identifying underlying inflation trends. It tracks the change in prices of a particular basket of goods and services purchased by consumers throughout the economy. The “core” measure excludes food and energy products, whose prices tend to be volatile.

The data on this page divide the categories of core PCE inflation into sensitive and insensitive components, as shown in Chart 1. COVID-sensitive components include those categories where either prices or quantities moved in a statistically significant manner at the onset of the pandemic, between February and April 2020. COVID-insensitive components include...

The Daily News Sentiment Index is a high frequency measure of economic sentiment based on lexical analysis of economics-related news articles. The index is described in Buckman, Shapiro, Sudhof, and Wilson (2020) and based on the methodology developed in Shapiro, Sudhof, and Wilson (2020).  

The study by Shapiro, Sudhof, and Wilson (2020, hereafter SSW), constructs sentiment scores for economics-related news articles from 16 major U.S. newspapers compiled by the news aggregator service LexisNexis. The newspapers cover all major regions of the country, including some with extensive national coverage such as the New York Times and the Washington Post. SSW selected articles with at least 200 words where LexisNexis identified the article’s topic as “economics” and the country subject as “United States.” Combining publicly available lexicons with a news-specific lexicon constructed by the authors, the study develops a sentiment-scoring model tailored specifically for...

Cyclical and Acyclical Core PCE Inflation updates data on the contributions to core personal consumption expenditures from cyclical and acyclical components, based on the methods described in Mahedy and Shapiro (2017).

The personal consumption expenditures price index (PCEPI) is one measure of U.S. inflation that is considered particularly useful for identifying underlying inflation trends. It tracks the change in prices of a particular basket of goods and services purchased by consumers throughout the economy. The “core” measure of PCEPI excludes food and energy products, whose prices tend to be volatile.

The data on this page divide the categories of core PCE inflation into cyclical and acyclical components. Cyclical components include those categories where prices tend to be more sensitive to overall economic conditions. Acyclical components include those categories that are more sensitive to industry-specific factors.

To determine which core PCE...

Despite a sharp spike in unemployment since March 2020, aggregate wage growth has accelerated. This acceleration has been almost entirely attributable to job losses among low-wage workers. Wage growth for those who remain employed has been flat. This pattern is not unique to COVID-19 but is more profound now than in previous recessions. This means that, in the wake of the virus, evaluations of the labor market must rely on a dashboard of indicators, rather than any single measure, to paint a complete picture of the losses and the recovery.

Applications to start new businesses tanked from mid-March through May, contracting more severely than during the 2008–2009 financial crisis. Since then, however, applications have recovered so strongly that the total number filed in 2020 should be similar to that for 2019, even if applications growth reverts to the average lows experienced during the early days of the pandemic. This should result in only a modest loss of new businesses and is not likely to cause much additional strain on overall jobs and productivity gains.

Inflation fell dramatically following the onset of the COVID-19 pandemic. Dividing the underlying price data according to spending category reveals that a majority of the drop in core personal consumption expenditures inflation comes from a large decline in consumer demand. This demand effect far outweighs upward price pressure from COVID-related supply constraints. A new monthly data page from the San Francisco Fed tracks how sensitivity to the economic disruptions of COVID-19 affects different categories of inflation over time.

August 6, 2020

By Laura Choi

COVID-19 is accelerating rapid changes in the ways we work, learn, and access key services. In particular, it’s clearer than ever that inclusion in the financial system is critical for households and businesses to access timely relief funds.

The $2 trillion CARES Act is a great example. The IRS distributed direct cash stimulus payments to individuals and couples, primarily through direct deposit to a bank account and in some cases through paper checks or prepaid debit cards sent by mail. The timely distribution of funds at scale, however, presented numerous challenges, including issues related to technology and communication.

Applications to start new businesses tanked from mid-March through May, contracting more severely than during the 2008–2009 financial crisis. Since then, however, applications have recovered so strongly that the total number filed in 2020 should be similar to that for 2019, even if applications growth reverts to the average lows experienced during the early days of the pandemic. This should result in only a modest loss of new businesses and is not likely to cause much additional strain on overall jobs and productivity gains.

August 14, 2020

Ask Rob Triano the secret to his success in assisting in the fight against money laundering and terrorist financing, and he’ll tell you that it’s not an advanced degree. It’s not a sixth sense for criminal activity. It’s not even the empathy that comes with holding nine different positions in banking, from teller to vice president/compliance program manager to the position he holds today at the San Francisco Fed: senior risk specialist.

Mostly it’s the 12 years he spent as an assistant coach of the boys’ varsity soccer team at Blue Valley High School in Stilwell, Kansas.

“Yep, that’s pretty much it,” Rob says. “As a coach, I try to guide the players, set an example, support them and encourage them. I can’t run out on the field and play. I have to let them play.”

It also helps that Rob embodies a paradox that’s common in sports but not quite as common in the working world: You can be driven and kind at the same time.

“When I...

U.S. fair lending laws require that we do not take into consideration protected classes such as race, religion, sexual orientation, and more. Our laws also require that individuals be provided with adverse action notices, basically an explanation of why they did not get a loan.

Machine learning as a tool is challenging for these two issues in particular:

1. The strength of machine learning is finding patterns that we cannot see ourselves by analyzing more and more information. Actively excluding information, like race, can impact how effective the algorithm is. Additionally, many things in our society are interconnected, and can become proxies for protected classes anyway. For example, where you went to school, or where you live, can be closely related to protected categories.

2. Also, many advanced machine learning techniques, like neural networks, do not provide a roadmap to the recommendations they give. This is where the term "black box" comes from. We put...

U.S. fair lending laws require that we do not take into consideration protected classes such as race, religion, sexual orientation, and more. Our laws also require that individuals be provided with adverse action notices, basically an explanation of why they did not get a loan.

Machine learning as a tool is challenging for these two issues in particular:

1. The strength of machine learning is finding patterns that we cannot see ourselves by analyzing more and more information. Actively excluding information, like race, can impact how effective the algorithm is. Additionally, many things in our society are interconnected, and can become proxies for protected classes anyway. For example, where you went to school, or where you live, can be closely related to protected categories.

2. Also, many advanced machine learning techniques, like neural networks, do not provide a roadmap to the recommendations they give. This is where the term "black box" comes from. We put...