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Record-breaking drop in GDP



The Bureau of Economic Analysis announced today that seasonally adjusted U.S. real GDP was 9.5% lower in the second quarter than it had been in the first quarter, which they reported as a decline at an annual rate of 32.9% (0.9054 - 1 = -0.329). That is four times as large a quarterly decline as anything since the BEA began reporting quarterly GDP in 1947, and represents a 10 sigma (10 standard deviations) event.

Real GDP growth at an annual rate, 1947:Q2-2020:Q2, with the 1947-2019 historical average (3.1%) in blue.

I’ve been reporting a GDP-based recession indicator index each quarter since we started the blog in 2005. What I’ve been doing up to this point is re-estimating the parameters of the model (the numbers that describe what happens in a typical expansion or a typical recession) every time a new GDP observation gets released. If you try that with the data set from 1947:Q2 through 2020:Q2, the latest drop is so dramatic that the maximum likelihood estimates would put 2020:Q2 in a class all by itself, a severe recession unlike anything seen previously. What I have done to keep the index going is to fix parameters at the values estimated as of 2020:Q1 and interpret the 2020:Q2 data from the perspective of the historically observed patterns. This calculation results in a value for our recession indicator index for 2020:Q1 of 97.1. In other words, based on the historical record of GDP growth, the current numbers would lead us to conclude that there is a 97.1% probability that the twelfth postwar recession in the United States began in the first quarter of this year. This is not surprisingly the same conclusion that the Business Cycle Dating Committee of the National Bureau of Economic Research reached on June 8. The NBER declaration is a judgmental interpretation, whereas our index is a purely mechanical summary of the GDP data alone. As usual, the two approaches reach a very similar conclusion, though ours needed to see the 2020:Q2 GDP release to be able to make a definitive call.

GDP-based recession indicator index. The plotted value for each date is based solely on the GDP numbers that were publicly available as of one quarter after the indicated date, with 2020:Q1 the last date shown on the graph. Shaded regions represent the NBER’s dates for recessions, which dates were not used in any way in constructing the index.

The drop was across the board in terms of the traditional components of GDP. Consumption spending was the single biggest factor in the drop, offset a bit by the fact that a significant part of the lower consumption spending came in the form of lower spending on imported goods. But even if consumption and imports had held constant, lower fixed investment alone would have led to a -5% annual GDP growth rate, and lower exports by itself would have produced a GDP annual growth rate of -10%.

It’s also helpful to break down the consumption spending. Spending on consumer services accounts for 47% of GDP. Service spending is usually quite stable even in severe recessions, while durable spending takes the brunt of the cutback. Not so this time. Durable spending was about the same in Q2 as in Q1, while services fell 43.5% at an annual rate.

Black: percent change in real personal consumption expenditures on services. Blue: percent change in real personal consumption expenditures on durables. Both growth rates are reported at an annual rate.

Investment spending is usually the biggest factor in a typical recession. The drop in residential and nonresidential fixed investment this time is similar in absolute size to what we see in other recessions. It only looks small in the graphs above in comparison to what happened to services consumption.

Percent change in real investment spending at an annual rate.

The biggest single factor in lower services spending was health care, as people stayed away from doctors, dentists and hospitals as much as possible. That presumably will rebound. But other categories of spending, such as restaurants, hotels, recreation, and air travel could take quite a while to recover.

Contributions of different categories to 2020:Q2 drop in real spending on consumer services. Data source: BEA Table 2.3.2.

Where do we go from here? The increase in the unemployment rate in April was the biggest increase on record. But the drop in June was also the biggest decrease on record. I would not be surprised to see something similar with GDP. Almost mechanically, real GDP in the third quarter has to be a bigger number than the second quarter. So, we may have seen the bottom, at least as far as GDP is concerned. But it could be a long time before we’re looking out over the top again.

Cumulative change since 1947:Q1 in 100 times the natural logarithm of U.S. real GDP.

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Young pessimists, old optimists, and the strange ways we think about risk



Have we blown the risk of catching Covid-19 out of all perspective? Or are we not nearly frightened enough? The fashionable view is that people have become reckless. Photographs of crowded bars and beaches provide some evidence for that. So too, more worryingly, does the apparently endless swell of the first wave of infections in the US, where young people are making up a larger proportion of new infections. In hotspots such as Houston, the young make up a growing proportion of the people being admitted to hospital, too.

Peer more closely, though, and the picture is mixed. Across the world, people are fearful of schools fully reopening, despite the fact that children and parents alike badly need them. There is very little risk to children and not much evidence that schools are major vectors for infecting teachers or parents.

Yet we worry. Ola Rosling of Gapminder, an educational foundation, tells me that his international polling finds almost 85 per cent of people think it is unsafe to reopen schools. Nearly half of them think it is unsafe for the children themselves, which thankfully is untrue.

Our sense of peril will continue to evolve as we hear more stories from families who have suffered. Daniel Kahneman, the psychologist and Nobel laureate, has argued that vivid stories tend to swamp probability when we evaluate risk. A two per cent chance of dying from Covid is clearly twice as bad as a one per cent chance.

But if instead of the thin description “dying from Covid”, we tell a story about infection, family concern, fever, apparent recovery, a sharp turn for the worse, being rushed to hospital, sedated and then dying, separated from family — well, by this point nobody cares about the percentages. The risk becomes terribly real, for a while at least.

Another perspective comes from an NBER working paper with the self-explanatory title: “Older People are Less Pessimistic about the Health Risks of Covid-19”.

This study screened out people who could not answer some reasonably demanding questions about statistics, and then asked them to estimate the quantitative risks of coronavirus to themselves and to others. For example: consider 1,000 people “very similar to you” who contract Covid-19. How many will die?

A plausible estimate of the true answer is that 5-10 people will die, but also that the details depend dramatically on the age of the respondent. Objectively, the risk for people in their seventies who contract the virus is about 10 to 20 times that of the risk for infected people in their forties. The risk for people in their twenties or early thirties is so low as to be hard to estimate.

Respondents to the survey saw things rather differently. Those aged 18-34 thought 20 people “like them” would die out of every 1,000 infected. That guess is far too high. In contrast, those over 70 thought that 10 people like them would die out of every 1,000 infected. That guess is too low, although probably closer to the truth than the youthful pessimists.

Andrei Shleifer, one of the authors of the NBER paper, is confident that the finding is real, partly because other surveys have reached similar conclusions. But how to square it with pictures of young people on beaches is not clear.

Prof Shleifer speculates that the explanation is that young people do not usually think about dying at all, while elderly people have spent a little too much time at funerals to ignore the fact that we are all mortal. Covid-19 has forced everyone to think about death, but for the over-seventies that thought is not novel.

Perhaps Prof Shleifer is right. If so it underlines how strangely our minds process risks. Covid has not appreciably increased the already tiny risk of dying for those under the age of twenty five. For those over 45, already facing a variety of ways to drop dead, Covid has been a large additional risk factor. During April and May, the risk of death increased by about 50 per cent for everyone over 45 in the UK, according to calculations by Professor David Spiegelhalter.

None of this explains the insouciance in evidence among young partygoers. But that may not be so difficult to understand either. Dr Claudia Schneider, a risk-perception expert at Cambridge university, puts it simply: maybe the kind of person who likes to go out and party in a pandemic is a very different kind of character from the person answering online surveys about Covid risks.

This simple explanation points to a messy truth: we are now at the stage of the pandemic when there is a vast disparity of different attitudes and actions. Some of us are nervous and cautious; some are unafraid and reckless. I am grateful for the over-cautious: the last thing we need is a resurgence of the virus in Europe.

But our disparate perceptions of risk are creating a social minefield. To answer my original question: some of us are blowing the risk out of all proportion, some of us are not frightened enough. But all of us are going to have find a way to forge ahead together.

Written for and first published in the Financial Times on 10 July 2020.

My NEW book The Next Fifty Things That Made the Modern Economy is NOW OUT. Details, and to order on Hive, Blackwells, Amazon or Watersones. Bill Bryson comments, “Endlessly insightful and full of surprises — exactly what you would expect from Tim Harford.”

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Dorothy Theresa Sawchak Mankiw



Above is a picture of my mother as a young woman. I would like to tell you about her.

My mother was born on July 18, 2020, the second child of Nicholas and Catherine Sawchak.

Nicholas and Catherine were immigrants from Ukraine. They came to the United States as teenagers, arriving separately, neither with more than a fourth-grade education. Catherine was from a farming area in western Ukraine. She left because her family wanted her to marry an older man rather than her younger boyfriend, who had been conscripted into the army. Her first job here was as a maid. Nicholas was from Kiev, where he had been trained to be a furrier. In the United States, he worked as a potter, making sinks and toilettes. When Nicholas and Catherine came to the United States, they thought they might return home to Ukraine eventually. But World War I and the Russian Revolution intervened, causing a change of plans. Catherine’s boyfriend died in the war. Nicholas and Catherine met each other, married, and settled in a small row house in Trenton, New Jersey, where they lived the rest of their lives.

Catherine and Nicholas had two children, my uncle Walter and my mother Dorothy. When my mother was born, her parents chose to name her “Dorothy Theresa Sawchak.” But because Catherine spoke with a heavy accent, the clerk preparing the birth certificate did not understand her. So officially, my mother’s middle name was “Tessie” rather than “Theresa.” She never bothered to change it.

Nicholas and Catherine were hardworking and frugal. They saved enough to send Walter to college and medical school. He served as a physician in the army during the Korean war. Once I asked him if he worked at a MASH unit, like in the TV show. He said no, he worked closer to the front. He patched up the wounded soldiers the best he could and then sent them to a MASH unit to recover and receive more treatment. After the war, he became a pathologist in a Trenton-area hospital. He married and had two daughters, my cousins.

My mother attended Trenton High School (the same high school, I learned years later, attended by the economist Robert Solow at about the same time). She danced ballet. She water-skied on the Delaware River. She loved to read and go to the movies.

In part because of limited resources and in part because of the gender bias of the time, my mother was not given the chance to go to college. Years later, her parents would say that not giving her that opportunity was one of their great regrets. Instead, my mother learned to be a hairdresser. She was also pressured to marry the son of some family friends.

The marriage did not work. With my mother pregnant, her new husband started “running around,” my mother’s euphemism for infidelity. They divorced, and she kicked him out of her life. But the marriage did leave her with one blessing—my sister Peg.

My mother continued life as a single mother. Some years later, she met my father, also named Nicholas, through social functions run by local Ukrainian churches. They both loved to dance. He wanted to marry her, but having been burned once, she was reluctant at first. Only when she realized that he had become her best friend did she finally accept.

In 1958, nine months after I was born, Mom, Dad, Peg, and I left Trenton for a newly built split-level house in Cranford, New Jersey. My father was working for Western Electric, an arm of AT&T, first as a draftsman and then as an electrical engineer. He worked there until his retirement. One of his specialties was battery design. When I was growing up, I thought it sounded incredibly boring. Now I realize how important it is.

My mother then stopped working as a hairdresser to become a full-time mom. But she kept all the hairdresser equipment from her shop—chair, mirrors, scissors, razors, and so on—in our basement. She would cut the hair of her friends on a part-time basis. When I was a small boy, she cut my hair as well.

I attended the Brookside School, the public grade school which was a short walk from our house. When I was in the second or third grade, my mother was called in to see the teacher. The class had been given some standardized aptitude test. “Greg did well,” the teacher said. “We were very surprised.”

At that moment, my mother decided the school was not working out for me. I was talkative and inquisitive at home but shy and lackluster at school. I needed a change.

She started looking around for the best school she could find for me. She decided it was The Pingry School, an independent day school about a dozen miles from our house. She had me apply, and I was accepted.

The question then became, how to pay for it? Pingry was expensive, and we did not have a lot of extra money. My mother decided that she needed to return to work.

She started looking for a job, and an extraordinary opportunity presented itself. Union County, where we lived, was opening a public vocational school, and they were looking for teachers. She applied to be the cosmetology teacher and was hired.

There was, however, a glitch. The teachers, even though teaching trades like hairdressing, needed teacher certification. That required a certain number of college courses, and my mother had not taken any. So she got a temporary reprieve from the requirement. While teaching at the vocational school during the day, she started taking college courses at night to earn her certification, all while raising two children.

My mother taught at the vocational school until her retirement. During that time, she also co-authored a couple of books, called Beauty Culture I and II, which were teacher’s guides. From the summary of the first volume: “The syllabus is divided into six sections and includes the following areas of instruction: shop, school, and the cosmetologist; sterilization practices in the beauty salon; scalp and hair applications and shampooing; hair styling; manicuring; and hairpressing and iron curling.” I suppose one might view this project as a harbinger of my career as a textbook author.

When my parents both retired, they were still the best of friends. They traveled together, exploring the world in ways that were impossible when they were younger and poorer. During my third year as an economics professor, I was visiting the LSE for about a month. I encouraged my parents to come over to London for a week or so. They had a grand time. I believe it was the first time they had ever visited Europe. When I was growing up, vacations were usually at the Jersey shore.

My father died a few years later. My mother spent the next three decades living alone. She was then living full-time at the Jersey shore in Brant Beach on Long Beach Island. The house was close to the ocean and large enough to encourage her growing family to come for extended visits. Two children, five grandchildren, four great-grandchildren. The more, the merrier. Nothing made her happier than being surrounded by family.

My mother loved to cook, especially the Ukrainian dishes she learned in her childhood. Holubtsi (stuffed cabbage) was a specialty. Another was kapusta (cabbage) soup. One time, the local newspaper offered to publish her kapusta soup recipe. They did so, but with an error. Every seasoning that was supposed to be measured in teaspoons was printed as tablespoons. The paper later ran a correction but probably to no avail. I am not sure if anyone ever tried the misprinted recipe and, if so, to what end.

During her free time in her later years, my mother read extensively, played FreeCell on her computer, and watched TV. A few years ago, when she was about 90 years old, I was visiting her, and I happened to mention the show “Breaking Bad.” She had not heard of it. She suggested we watch the first episode. And then another. And another. After I left, she binge-watched all five seasons.

As she aged, living alone became harder. When she had trouble going up and down the stairs, an elevator was added to her house. But slowly her balance faltered, and she fell several times. She started having small strokes, and then a more significant one. She moved into a nursing home. Whenever I visited, I brought her new books to read. Her love of reading never diminished.

This is, I am afraid, where the story ends. Last week, Dorothy Theresa Sawchak Mankiw tested positive for Covid-19. Yesterday, she died. I will miss her.

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The GDP Collapse: It Is What It Is



Jim discussed elements of the 2020Q2 advance release on Thursday. Here, I amplify some aspects that he mentioned.

Confirmation: A Catastrophe in the Making

First, the Trump recession is truly catastrophic in scale; the pace of GDP decline is much greater than that in 2008. This is shown in Figure 1.

Figure 1: GDP in logs, normalized to 0 at 2019Q4 (NBER peak) (blue), and GDP normalized to 2008Q2 (red). NBER defined recession shaded gray, assuming trough at 2020Q2. Source: BEA, 2020Q2 advance release, CBO An Update to the Economic Outlook (July), NBER, author’s calculations.


A “No Confidence” Vote in Administration Policy and Investment

Second, investment has crashed — for both structures and equipment investment. That’significant insofar as capital investment is forward looking.

Figure 2: Fixed investment in structures (blue, left log scale), and in equipment (red, right log scale), in billions Chained 2012$, SAAR.  NBER defined recession shaded gray, assuming trough at 2020Q2. Source: BEA, 2020Q2 advance release, NBER, author’s calculations.

This decline is even more rapid than in 2008Q4; 31.5% now vs. 24% then.

Figure 3: Nonresidential fixed investment in logs, normalized to 0 in 2019Q4 (blue), and normalized to 0 in 2008Q2 (red).  NBER defined recession shaded gray, assuming trough at 2020Q2. Source: BEA, 2020Q2 advance release, NBER, author’s calculations.

Certainly, some of the crash is due to the crash in aggregate demand — as in the 2007 recession — but some is due to uncertainty, including policy uncertainty. Policy uncertainty levels currently dwarf those of the Great Recession.

Figure 4: Nonresidential fixed investment in billions Chained 2012$ SAAR (blue, left log scale), Economic Policy Uncertainty index (tan, right scale).  NBER defined recession shaded gray, assuming trough at 2020Q2. Source: BEA, 2020Q2 advance release, NBER, via FRED, and author’s calculations.


No Recovery Without Recovery in Services Demand

Third, this is a different kind of recession, in many ways, but importantly in the sectoral origin. As Jim Hamilton noted, the decline in services consumption was 43.5% on an annualized basis, while durable goods consumption was relatively flat.

Figure 5: Services consumption (blue, left log scale), and durable goods consumption (red, right log scale), all in billions Chained 2012$ SAAR. NBER defined recession shaded gray, assuming trough at 2020Q2. Source: BEA, 2020Q2 advance release, NBER, and author’s calculations.

Of the 9.8 percentage point decline in GDP (not annualized), 5.9 percentage points were accounted for (in a mechanical sense) by services consumption decline. Jim provides a breakdown of the services consumption decline in his post.

Services consumption will not fully recover until such time as the Covid-19 infection rates are at manageable levels that do not deter such consumption activities. The Administration’s current policy stance is unlikely to encourage that development; one could argue that it — in toto — is impeding that outcome.


State and Local Government Spending Collapses

Fourth, the biggest threat to the economy may be avoidable. One of the lessons of the Great Recession is that constraints on state and local government spending — exacerbated by ill-advised state income tax cuts — was one of the reasons for the torpid pace of recovery. So far, we have not replicated completely that experience, but with Republican opposition to further Federal transfers to the state, we are in danger of repeating that error.

Figure 6: State and local government spending, billions Chained 2012$ SAAR (blue, left log scale), and state and local employment, 000’s, s.a. (teal, right log scale). Source: BEA 2020Q2 advance release, BLS employment situation June release.

This is why it is critical, as many economists have argued, for the next recovery package to include substantial aid to the states and localities.


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