Tag Archives: COVID-19 data and visualizations

Economic Data
in the Age of COVID-19

Dr. Steven N. Weisbart, CLU, Triple-I Senior Vice President and Chief Economist

COVID-19 pandemic has not only disrupted our economy – it has complicated the data we routinely use to understand economic developments. This is a bit like finding out the thermometer you use to tell if you have a fever is unreliable.

Here are two examples of why it’s hard to know what’s happening.

 What is the correct unemployment rate?

The April 2020 Bureau of Labor Statistics (BLS) employment report said the U-3 rate – just one of six unemployment measures BLS reports – was 14.75 percent. This number is derived by dividing the number of people counted as unemployed (23.078 million) by the civilian labor force (156.481 million), which is everyone who is either working or unemployed and looking for work.

But when the virus was recognized as a major public health threat in mid-March and April and many businesses and organizations were shut down, throwing many millions out of work, some who were affected decided to retire. This means they were no longer counted as part of the civilian labor force. This is most vividly seen by comparing the civilian labor force in February (164.6 million) with its count in April (156.5 million)—a drop of 8.1 million.

The large number of retirees affected the unemployment rate: if they had not retired, most would likely have been counted as unemployed. To keep the math in our example simple, let’s say 7 million of the retirees had remained in the labor force and been counted as unemployed (maybe the other 1 million would have retired then anyway—virus or no virus). The unemployment count would have been 30 million (23 million counted plus 7 million un-retirees) and the civilian labor force would have been 163.5 million (156.5 counted plus 7 million un-retirees).

The unemployment rate would have been announced as 30 million divided by 163.5 million, or 18.35 percent, instead of 14.75 percent.

So, which one is correct?

Are seasonal adjustments still correct?

Macroeconomists have long recognized that many economic data have seasonal patterns. For example, retail sales often spike in the last quarter of the year because of the holidays. Sales for some items, such as those bought for “back to school,” spike at other times. So, to see what’s really happening, economic data are often adjusted to account for the seasonal effects and reported after these adjustments are made.

To see the effect of seasonal adjustments, look at the following two graphs. The first is employment in the construction industry that is not seasonally adjusted. The second is the same industry and time; the only difference is that its data are seasonally adjusted.

Construction employment obviously dips in the cold months, and the drop shown in the first graph doesn’t represent any significant economic change, so the seasonal adjustment in the lower graph lets us see only changes beyond the seasonal adjustment, such as what happened in 2020.

The problem, from an economic analysis viewpoint, is that the amount of seasonal adjusting to apply is a judgment call, and it is often based on a historical period in which conditions were much as they are now. But what’s happening now has no satisfactory historical precedent.

So should we keep using the seasonal adjustment factors from before, or do they not apply to the current economic situation?

These are just two examples of datasets or analytical approaches whose relevance can be called into question in light of COVID-19 – further complicating the already complex and nuanced endeavor of attempting to understand and anticipate economic developments.   

Are Life Insurers
Writing Less Business
Because of COVID-19?

COVID-19 has changed many aspects of our lives, so it isn’t surprising to see life insurance markets affected. But some stories create false impressions that should be corrected.

The story that some life insurers are writing fewer policies “because of COVID-19” has gained traction in both traditional and social media. While not wrong, like other stories involving insurance and COVID-19, it requires context to keep it from wandering off into urban legend territory.

“Life insurers’ ability to keep their promises to policyholders depends on numerous factors,” explains Triple-I chief economist Dr. Steven Weisbart.  “Among them are interest rates and how responsibly insurers underwrite policies and manage their investments.”

Dr. Steven Weisbart
Triple-I Chief Economist

Interest rates exceptionally low

What do interest rates have to do with life insurance? Many products (whole and universal life and term life for 20 years or more) calculate premiums in the expectation that, during the life of the policy, the insurer will earn enough interest from its investments, net of investment expenses and taxes, to help pay life insurance benefits. Many life insurance and annuity policies – especially those issued 10 or more years ago – guarantee to credit at least 3 percent per year.

“Efforts to stave off the recession spurred by attempts to ‘flatten the curve’ of infections and deaths caused by the virus have led to historically low interest rates,” Weisbart says.

Gross long-term rates on the investment-grade corporate bonds life insurers primarily invest in had been 4 percent for most of the past decade and plunged below 3 percent in August 2019. Since the onset of the pandemic, rates have fallen even further (see chart).

“So, life insurers – who planned to profit from the ‘spread’ between the interest they earned on their investments and the interest they credited on their policies – have lately struggled as this spread disappeared and then reversed,” Weisbart says.

Options are limited

“So, that’s it!” I hear some of you say. “It’s all about rich insurance companies protecting their profits!”

Businesses must make a profit to stay alive, and U.S. insurers – one of the most heavily regulated and closely scrutinized businesses on the planet – have the additional requirement to maintain substantial policyholder surplus to ensure claims can be paid. Life insurers, in particular, are required to maintain a special account – the interest maintenance reserve (IMR).

“The IMR is drawn down when net interest earnings are too low to support claims – as is the case now,” Weisbart says. “If it’s exhausted, insurers can draw down surplus, but they can’t draw too much because they’re required to keep at least a minimum surplus to protect against adverse outcomes in all other lines of business.”

If their investments aren’t performing as well as expected, insurers have two options: write less business or charge more for the business they write.

Exercising a combination of these options is what life insurers are doing now.

“When interest rates eventually rise, the profitable spread will return,” Weisbart says, and competition among insurers will likely lead to more liberal underwriting and lower premiums. “But we can’t predict with confidence when that might happen.”

Until then, life insurers are tightening their criteria for issuing new policies and, in some cases, raising premiums so they can deliver what they’ve promised their existing policyholders.

CORONAVIRUS WRAP-UP: Data and Visualizations (4/20/2020)

The coronavirus crisis continues to generate data that can be valuable for understanding and decision making. Below are just a few resources that may be of interest to insurers and the people and businesses they serve.

COVID-19 Mortality Projections for U.S. States
Graphs from the University of Texas COVID-19 Modeling Consortium show reported and projected deaths per day across the United States and for individual states.
The Verisk COVID-19 Projection Tool
The Verisk COVID-19 Projection Tool has been made available to enhanceunderstanding of the potential number of worldwide COVID-19 infections and deaths. It provides an interactive dashboard that leverages the AIR Pandemic Model.
How State Insurance Departments Are Responding to COVID-19
This interactive map from PC360 highlights bulletins and procedures released by state insurance departments as of April 15, 2020.
Tracking U.S. Small and Medium Business Sentiment During COVID-19
Small and medium-size businesses account for roughly 44% of the U.S. economy and provide employment to about 59 million people. McKinsey is tracking their sentiment to gauge how their views on economic activity, employment, and financial behavior—as well as their expectations about financial institutions and public authorities—change as a result of ongoing public and private interventions.

Will COVID-19 Foul Up
Our Weather Forecasts?

Airlines have had to dramatically cut flight schedules due to the coronavirus pandemic, and some experts believe this has begun to hurt weather forecasting.


It turns out that forecasting models depend heavily on data collected by aircraft. The European Centre for Medium-Range Weather Forecasts (ECMWF) said this week that the number of aircraft reports received worldwide declined 42 percent from March 1 to 23. In less than a month, the number of aircraft reports over Europe received and used by the ECMWF fell 65 percent.

Weather forecasting models depend heavily on data collected by aircraft. 

A 2017  American Meteorological Society study found that using aircraft observations reduced six-hour forecast errors in wind, humidity, and temperature by 15 percent to 30 percent across the United States.

This is no small matter. The more accurately experts can predict impending weather, the better prepared individuals, communities, and businesses can be. Less accurate forecasts can lead to a lack of preparation and bad weather-related decisions.  From an insurance perspective, this can result in larger claims and losses.

So, late last night, worried about yet another negative implication of coronavirus, I fired off an e-mail to Triple-I non-resident scholar Phil Klotzbach. Dr. Klotzbach is a research scientist in the Department of Atmospheric Science at Colorado State University. He has published over two dozen articles in peer-reviewed journals and is quoted regularly by the Weather Channel, Forbes, The New York Times, USA Today, and The Wall Street Journal. He and his team also publish an annual forecast for the Atlantic hurricane season.

True to form – and thanks, in part, to the two-hour time difference – he responded almost immediately:

 “I don't think it's going to be a huge reduction in model skill, but the ECMWF estimates that removal of all aircraft can reduce prediction ability at upper levels in the atmosphere (~30000 feet) by around 10-15% for 12-hour predictions.  Subtracting aircraft-provided information from historical model forecasts increased errors by about 3% for surface pressure. The lack of aircraft data has a greater impact on shorter-term forecasts (e.g., <1 day) than it does on longer-term forecasts (e.g., 5-7 days), although some degradation of the forecasts continues even at longer-range timescales. 

Of course, some aircraft will still be flying, and some of the loss may be mitigated by other data sources, such as additional launches of weather balloons.”

In other words, the reduction in aircraft data is likely to degrade accuracy of same-day and longer-term forecasts a bit, and some of that degradation will likely be offset by other data resources the forecasting community brings to bear.

Amid everything we need to be concerned about while dealing with the impacts of COVID-19, the reliability of weather forecasting isn’t yet at the top of the list.