Usually Claudia Sahm is answering the hard questions in economics. She does it better than anyone else. But yesterday she asked a question, which automatically warrants some thinking.
Dr. Sahm pointed out the disconnect between total wage and salary income and payrolls. While it’s clear that transfer payments explain the higher levels of personal income during the COVID-19 recession, it’s less clear how total wages and salaries could have nearly returned to their pre-recession level with 10 million fewer jobs.
In November 2020, workers received wages and salaries equivalent to an annual rate of $9.6 trillion, an increase of two percent from November 2019. Yet over the same period, the total number of jobs fell by 9.2 million or 6.1 percent. This disconnect, pointed out by Dr. Sahm, may suggest that wage and salary data are painting an overly-rosy picture of the economy. While the staff of stats agencies hold a special place in my heart, wage and salary data for November 2020 are preliminary estimates from incomplete sources. As new data become available, the November 2020 estimates, produced by the Bureau of Economic Analysis (BEA), will be revised. Two years after the previous recession ended, BEA economists were still lowering their estimates of the wages and salaries received during the recession.
So Dr. Sahm is right; the disconnect between earnings data and payrolls data is concerning. The most plausible explanation for this gap comes from John Vorheis, who posits that jobs permanently lost were disproportionately low wage, while jobs that have come back or were not lost have more wage growth. To test this, I’ll try to estimate the gap.
First, let’s estimate what total wages and salaries might have been in 2020 for the people who were employed in 2019, if they had all remained employed. For an upper-bound estimate, the average wage growth for individuals was around six percent in 2019. Six percent growth in wages and salaries would result in an annual rate of $10 trillion in November 2020, around $400 billion more than the current estimate, or $43,500 for each missing job. The result produced by assuming six percent growth is plausible, but I’m not sure that six percent growth is a realistic assumption.
As an alternative estimate, in the first two months of 2020 total wage and salary growth was 4.5 percent while payroll growth was 1.5 percent, meaning individual wage and salary growth was around three percent. A more-realistic continuation of three percent growth would result in annual wages and salaries of $9.7 trillion in November 2020, around $100 billion above the current estimate, or $10,900 per missing job.
The more-realistic estimate, based on three percent individual wage and salary growth, suggests to me that the preliminary wage and salary data and the payrolls data do not match. It’s worth thinking therefore about possible reasons for revisions to the data. In 2019, the BEA increased total wage and salary figures by $111 billion to adjust for under-reporting of tips in taxes returns, and added $430 billion to adjust for employees paid while bypassing unemployment insurance (NIPA table 7.18). The adjustments for 2020 are not available yet, but I could imagine a situation where tips are far lower and more likely to be reported and fewer people are trying to bypass unemployment insurance. If this is the case, the current estimates of wages and salaries could be revised downward.
Version 0.4 of my notebooks for cleaning up and working with Current Population Survey public use microdata is available on GitHub. Several new variables were added, including the COVID-19 supplement questions, and several bugs were fixed. If you want to work with many months of CPS data in python, the code from these notebooks will likely be helpful. If you run the files locally and have trouble, please send me an email and I’ll try to help.
I’m still looking for a way to host the actual data files from this project. Each annual file is about 75mb after compression. Any suggestions are welcome.
As always, please contact me (firstname.lastname@example.org) if you find any errors or have any questions.
Unlike comparable countries, the US federal government does not require businesses to provide paid maternity or paternity leave to new parents. Luckily for some US parents, a handful of states have recently implemented state-level family leave laws that seek to bring their local jurisdiction into the developed world. This blog post looks at how families of infants rearrange their lives to provide care and shows that the handful of state laws have an effect large enough to be seen in nation-wide data.
Newborns require a lot of care and this care work is critical for the newborn and for society. Yet without federal protections for new parents, only 21 percent of workers have access to paid family leave as of March 2020. Further, there are substantial racial disparities in who has access; Black and Latino parents are less likely to have the already rare jobs that come with paid family benefits. Most new parents therefore face a dilemma: caring for their child during the critical first few months means giving up income.
As families with a new child temporarily rearrange their lives to care for the child, they go about it in a number of ways. Some parents (or other relatives) take weeks or months off from their job (paid or unpaid) with the expectation of returning, some work reduced hours, some leave their job completely with no expectation of returning, while others keep working. Next, I use survey data to approximate these groups and test the effect of having an infant on the number of hours people spend at their jobs during the reference week of the survey. Specifically, I look at two groups of adults ages 18 to 54: the first group lives with a related infant (under age 1), while the youngest related child in the second group is five years old.
Survey data confirm that care required by infants constrains how many hours their adult family members spend at work (chart 1). In 2000, 35 percent of the adult family members of infants did not work at all during the survey week, compared to 23.5 percent of adults in families where the youngest child is five years old. Comparable data for 2020 show the same overall pattern, with 36.2 percent of infants’ family not at work, compared to 28.4 for families where the youngest kid is five. The next section looks at whether those not at work expect to go back to a job.
Some infants’ family members who were not working in the survey reference week are employed but absent from their job, whether paid or not. In 2000, 3.4 percent of infants’ adult family report being on maternity or paternity leave, and an additional two percent report being absent for other reasons such as taking personal leave (chart 2). Among families where the youngest child is five years old, maternity or paternity leave (for example for adoption) is much more rare, while rates of other absences are about the same.
Importantly, however, the vast majority of those who report working zero hours during the reference week do not actually have a job. In 2000, 22.8 percent of adult family members of infants did not have a job and reported their main activity as family or home responsibilities, compared to 13.5 percent among families where the youngest child is age five. An additional 3.8 percent of family members of infants reported not being employed for another reason, such as school, a disability, or having trouble finding work, compared to 5.3 percent of families where the youngest child is five.
In the latest data, covering September to November 2020, infants’ family members are less likely to be non-employed caregivers (19.6 percent of the group, compared to 22.8 percent in 2000) and more likely to be on maternity or paternity leave (5.5 percent of the group, compared to 3.4 percent in 2000)1. This seems to be an encouraging development coming from the state paid family leave laws.
Digging into the causes for the increase in maternity/paternity leave, a few US states have enacted paid family leave laws, starting with California in 2002. In some form or another, by the middle of 2020, five states and the District of Columbia have these laws in place (the others are New Jersey, Rhode Island, New York, and Washington state). Three more states (Massachusetts, Connecticut, and Oregon) have passed laws but have not yet begun paying benefits.
We can see the early result of state paid family leave laws after separating the adult family of infants by whether or not they live in a state that has implemented paid family leave as of September 2020 (chart 3). In 2000, incidence of paid family leave was nearly equal between the two groups of states; neither group had paid family leave laws at the time. In 2020, among states that implemented laws, incidence of paid family leave is nearly five times higher than it was in 2000. Unpaid leave has fallen among states with paid leave laws, but still exists as there are gaps and limits in the various state systems.
In general, use of maternity and paternity leave has grown since 2000, but much of this change has come from various state efforts to make life easier for new parents. Critically, surveys of both employees and employers show the laws are a success. In California, the first state to implement paid family leave, surveys of employers show higher productivity, higher employee morale, and, in some cases, even cost savings for businesses. A federal law guaranteeing paid family leave is long overdue and would resolve the income dilemma faced by new parents with massive care responsibilities.
1 It’s worth noting that the 5.5 percent figure is not measuring the share of new parents taking maternity or paternity leave, but instead measures adult family members of infants under age 1 who report maternity or paternity leave as the reason they were absent from work in the reference week. In other words, if the source data captured only new parents with infants less than 12 weeks old, the share on family leave would be much higher, perhaps 3 or 4 times the 5.5 percent rate listed for all adult family members of infants.
The resources available to people ages 65 and older depend on several factors. For those working full-time, earnings from work can often cover expenses. For those with little or no earnings, income depends on private assets, pensions, Social Security, and welfare and public assistance, and is not always enough to avoid poverty. Despite gaps in the program, Social Security is by far the most important poverty reduction tool in the US. Social Security reduces the age 65 and older poverty rate by more than 30 percentage points.
The US Social Security Administration (SSA) Income of the Aged Chartbook, 2014 contains several charts and tables that aid in thinking about resources available to people ages 65 and older. In this blog post, I discuss two of my favorite charts from the SSA publication, update the charts for 2019, and create new versions that emphasize the importance of Social Security for disadvantaged groups.
Shares of aggregate income
The SSA chartbook includes a chart (page 17) showing how different income sources contribute to total income for both low- and high-income “aged units”. SSA “aged units” are married couples where at least one partner is age 65 or older and unmarried people ages 65 and older. Aged units are ranked by total income and divided into five equal groups, called quintiles. In 2014, the lowest quintile aged units have total income below $13,500, while the highest quintile aged units have total income above $72,129.
The SSA chart on shares of aggregate income, copied below, shows the importance of Social Security to low-income people ages 65 and older. In 2014, Social Security benefits are 80.7 percent of total income for those in the lowest income quintile, meaning Social Security benefits are more than four of every five dollars received by the group. Cash public assistance and welfare, including Supplemental Security Income, comprise an additional 9.5 percent of total income for the lowest quintile. Those in the highest income quintile also receive Social Security benefits, and the dollar amounts of their individual benefits are often higher than the dollar amounts received by the low-income group. However, the high-income group has high income because of access to other sources of income, such as earnings and pensions.
Using the latest data from the same source, I’ve recreated and updated the above SSA chart for 2019. The story is mostly the same in 2019 as it was in 2014, with a couple differences1. The 2019 quintile limits are higher, meaning overall income has increased; the lowest quintile now captures the fifth of aged units with total income below $15,000 a year, while the highest quintile covers the one-fifth with total income above $89,627. Also, cash public assistance represents a smaller share of income for the lowest quintile in 2019, and asset income provides a larger share of income for the highest quintile. In 2019, Social Security comprises 81.6 percent of total income for the lowest income quintile, compared to 80.7 percent in 2014.
Shares of aggregate income in disadvantaged groups
Total income received by people ages 65 and over, and the sources of this income, both depend heavily on race and ethnicity. Two additional charts showing the shares of aggregate income for Black and Hispanic aged units2 illustrate this point.
Compared to the overall US, the income of Black aged units is much lower at all points of the income distribution; one in five has total income below $10,434 in 2019, and four in five have total income below $54,670. Additionally, Black aged units in the lowest income quintile depend more on cash public assistance, a patchwork of income- and asset-tested welfare programs, including Supplemental Security Income, that fill some of the gaps in Social Security. Among the lowest income quintile of Black aged units, Social Security provides 70 percent of total income.
Additionally, relative to the overall US highest quintile group, the highest quintile of Black aged units have less total income and much less asset income, and depend more heavily on Social Security, earnings, and pensions. Less asset income among Black families is a result of the massive US racial wealth gap. The Federal Reserve reports that the typical White family above age 55 has $260,000 more wealth than the typical Black family in the same age group.
Aged units of Hispanic or Latino origin also have lower income when compared to aged units of any ethnicity. In 2019, one in five Hispanic aged units has total income below $10,128, while four in five have total income below $54,507. Hispanic aged units are relatively less likely to receive Social Security benefits (see chartbook page 11) and depend particularly heavily on income from working. Still, Social Security is nearly 70 percent of income for the lowest quintile of Hispanic or Latino aged units.
Among people ages 65 and older with low income, Social Security provides the vast majority of resources. Yet for many, including the bottom fifth of Black and Hispanic aged units, income is below the US poverty threshold and comes from a patchwork of complicated sources. One way to resolve this is to set the minimum Social Security Old Age benefit to the poverty threshold. Doing so would simplify many older peoples’ lives, increase their income, and eliminate the need for Supplemental Security Income and other public assistance programs.
Poverty among those ages 65 and older
A 2014 SSA chart shows the poverty rate for subgroups of older people (page 28 and copied below). The chart uses the official poverty measure developed by the SSA in the mid-1960s. The official poverty measure compares pre-tax cash income to a threshold that was initially set to three times a minimal food budget in 1963 and that is adjusted for inflation and by family size. In 2014, ten percent of people ages 65 and older are officially in poverty in the US. An additional 5.2 percent of the age group are considered “near poor” because their total income is between the poverty threshold and 125 percent of the poverty threshold. Poverty rates vary dramatically by group; almost one in five Black people ages 65 and older is in poverty in 2014, with an additional 8.5 percent nearly in poverty.
From 2014 to 2019, poverty fell in the US as more people found jobs and earnings increased. Recreating the above SSA chart for 2019 shows that the poverty rate has fallen among all groups but remains very high, particularly for disadvantaged groups. In 2019, 8.9 percent of people ages 65 and older are in poverty by the official poverty measure, and an additional 4.4 percent are near poverty. The poverty rate of Black people ages 65 and older is 18 percent in 2019, a decrease of only 1.2 percentage points since 2014, despite the unemployment rate for Blacks or African Americans ages 65 and older falling from 7.9 percent in 2014 to 4.6 percent in 2019. Among Hispanics or Latinos ages 65 or older, 17.1 percent are in poverty with an additional eight percent near poverty.
A different measure of poverty
The way poverty is measured in the 2014 SSA chartbook does not take into account some aspects of poverty that disproportionately affect older people in the US. The Supplemental Poverty Measure (SPM) was created in 2009 as a more complete measure of poverty. The SPM identifies whether the cash and non-cash resources that someone has available can meet a poverty threshold that adjusts for inflation and family size as well as additional characteristics such as geography. The SPM also accounts for various expenses, including medical expenses. Medical expenses increase the age 65 and older poverty rate by four percentage points.
The next chart uses the SPM poverty rate instead of the official poverty rate seen in the previous chart. Additionally, the next chart shows the share of each group that is removed from poverty by Social Security, meaning not in poverty specifically because of receiving Social Security benefits. For people ages 65 and older, the SPM poverty rate (12.8 percent) is higher than the official poverty rate (8.9 percent), primarily because the SPM accounts for medical expenses. Among those ages 65 and older in disadvantaged groups, the SPM poverty rate is particularly high. The SPM poverty rate is 21 percent for Black people ages 65 and older and 24.4 percent for people in the age group with Hispanic or Latino origin.
While old-age poverty is higher than it should be, the importance of Social Security for reducing old-age poverty cannot be overstated. In 2019, despite a booming economy, an additional 31.1 percent of people ages 65 and older would be in poverty if they did not receive Social Security benefits. That is, the SPM poverty rate would be 43.9 percent for those ages 65 and older but instead is 12.8 percent because of Social Security benefits.
Further, more than a third of women ages 65 and older are taken out of poverty by Social Security; the poverty rate for women in the age group is 14 percent instead of 47.5 percent. Perhaps most astoundingly, despite the relatively good economic conditions, more than half of Black and Hispanic people ages 65 and older would be in poverty in 2019 without Social Security benefits. Nearly a third, 32.6 percent, of Black people ages 65 and older are taken out of poverty by Social Security. The figure is slightly less, 29.8 percent, for those of Hispanic of Latino origin.
1 The 2014 and 2019 income categories are not strictly comparable due to a redesign of the survey to better capture pension income. See my calculations
2 The race and ethnicity of a married couple aged unit is defined by the 2014 SSA chartbook as the race and ethnicity of the husband. In the 2019 data, same sex married couples are included, so for these couples I use the race of the older partner or the race of the partner appearing first in the survey if both are the same age.
Despite the ongoing pandemic, the US Bureau of Labor Statistics report that a typical full-time worker earns the equivalent of more than $52,000 a year. This post discusses earnings from wages and salaries and presents two broader measures that are designed to include people with little or no earnings.
As a starting point for thinking about a person’s earnings: survey data show that wages tend to increase with age. The typical older worker has more work experience and is paid more than the typical younger worker. Among full-time wage and salary workers, earnings increase steadily from $25,000 per year at age 16 to more than $60,000 per year around age 45, then plateau and remain near their peak until the social security retirement age (see chart 1).
Measuring the earnings of full-time workers, however, does not tell us about people who are either working part-time, unemployed, or work-limited in another way. Typical earnings are much lower if we include the earnings of all people in the survey rather than just those who are currently working full-time. The median wage falls from $52,500 for current full-time workers to $15,290 for anyone in the survey aged 16 or older. Broken down by age, the broader measure shows earnings are zero for a typical 16 year-old, around $30,000 for the typical 25 year-old, and peak at around $42,000 for a typical person in their mid 40s (see chart 2). Median earnings past retirement age are zero.
By including people working part-time as well as people with zero earnings, the second chart reverses a major result from the first chart. Specifically, during the pandemic many people lost jobs or became part-time and therefore are removed from the 2020 measurement of full-time workers’ wages. Since jobs lost during the pandemic were more-often below the 2019 median wage, removing these jobs from a wage measurement makes the new median wage of the remaining jobs higher. In other words, the chart 1 result showing that the wage of the typical full-time worker is higher in 2020 than it was in 2019 is because the group of people being measured has changed and the typical worker is a different person. Measuring the same group of people in both 2019 and 2020 shows that earnings have instead fallen substantially during the pandemic. The reduction in earnings is confirmed by data on total wage and salary income.
Importantly, despite far worse economic conditions in 2020 than in 2019, chart 2 shows that several features of the relationship between earnings and age persist across both years. As one persistent feature, in both 2019 and 2020 the typical person who has reached retirement age has stopped working and has zero earnings. The US does a reasonably good job of providing welfare payments to people who reach retirement age, through Social Security, which facilitates retirement.
A second persistent feature is that children have no earnings in either year. With some exceptions, including newspaper delivery and family businesses, wage labor from children under the age 16 is uncommon and it is not measured in the survey. Also, many teenagers above the age of 15 are not working because of school.
Lastly, some portion of working-age adults are work-limited at any given point in time. The work-limited include people with an injury, disability, or illness, as well as students, and people with additional responsibilities at home such as caregivers and new parents. US rules around welfare of children and persons with disabilities are more strict and payments are meager, if available at all, relative to the help retirees receive from Social Security. One consequence of a work-focused welfare system is that many who become work-limited are expected to rely on the earnings of their family members to get by.
Living with family is the way many people, including children, receive resources. Living together allows people to pool their income, share costs, and use resources like a car or a kitchen more efficiently. Economists sometimes calculate a concept called “equivalized” family earnings by adjusting the total earnings of a family for the size of the family (see footnote of chart 3 for details). The result is a per person measure of family wage and salary earnings that accounts for the efficiency of the size of a person’s family. Typical equivalized family earnings are $21,620 per person per year in 2020.
By age, median equivalized family earnings are around $32,000 for newborns, $40,000 for 20-year olds, more than $50,000 for 50-year olds, and zero for those age 70 or older (see chart 3). The drop in family earnings starting at age 60 is from the combination of retirement and from people in these age groups being less likely to live with someone else who works.
Interestingly, the decrease in median earnings from 2019 to 2020 is less severe in the family earnings measure from chart 3 than in the personal earnings measure from chart 2. The resilience of equivalized family earnings to the recession is due in part to more people moving in with family. In 2020, a larger share of people utilize the resource efficiency of living with others, which is captured by an equivalized measure such as family earnings but not by the personal earnings measure.
Families with No Earnings
While the above sections focus on the typical person, measured as the median or middle member of the group, the effects of the COVID-19 recession have fallen more heavily on people nearer to the bottom of the wage distribution, an effect already evidenced in chart 1. One way to see how job losses hurt families is to look at the share of people whose family have no earnings. During a recession, the share without earnings is larger because there are fewer jobs. In 2020, 37.8 percent of people have no family earnings in the survey week, compared to 35.4 percent in 2019. By age, around 15 percent of newborns have no family earnings, compared to less than 10 percent of 45 year-olds and more than half of 70-year olds (see chart 4).
Chart 4 can help to illustrate the relationship between poverty, age, and welfare. By definition, families without earnings will be in poverty unless they receive sufficient income from welfare or from private assets. Because the elderly are far less likely to have family earnings from work, the age group relies heavily on other sources of income to avoid poverty. For elderly people without much private wealth, Social Security payments facilitate independent living and result in much less poverty for elderly people and their families. That is, social insurance makes up for a lack of family earnings and reduces elderly poverty to the same rate or lower as the overall population. Othercountriesgofurther than the US and apply this same technique to other groups of people who do not have earnings, such as children. Including people who do not have earnings when measuring earnings can be useful for thinking about how the US should treat someone when they are work-limited.
Recently released data from the 2019 Survey of Consumer Finances (SCF) show a highly unequal distribution of wealth in the US, both between and within racial groups. This is not new, but the numbers are always staggering. The typical white family has eight times the wealth of the typical black family. Within racial and ethnic groups, more than eighty percent of wealth is owned by the wealthiest twenty percent of families.
Having wealth is a huge advantage and not having wealth is a huge disadvantage, yet people argue, incorrectly in my view, that wealth statistics are not as important as they seem. I want to respond to some common arguments, which generally revolve around the connection between wealth and income.
Common argument #1: Wealth is not a good predictor of poverty
The basic argument here is that some people with low or negative net worth actually have very high income. Therefore, since poverty is defined based on income, people with low wealth are not necessarily poor. There’s nothing wrong with this argument; because wealth is measured as net worth, assets minus debts, these cases do exist. A recent medical school grad with lots of student debt and few assets will have negative wealth but a very high salary and is unlikely to be in poverty. But these are corner cases. All the argument is saying is that there exist some cases where the relationship between net worth and income falls apart.
Applying this argument to anything other than the specific corner cases is misleading. There is a strong correlation between having low wealth and having low income and nearly all poor people have low wealth. Likewise, few wealthy people are in poverty on an income basis.
More importantly, the argument does not explain differences between demographic groups. We know that the difference between black and white wealth is not in any way compensated for by black people having higher income. Overall, the story of the black-white wealth gap is an assets story, not a student debt story. Black families have substantially fewer assets, substantially less wealth, and substantially less income. We can see this by looking at net worth, financial assets, and pre-tax income:
Common argument #2: Wealth is not a permanent source of income
Again, this is an argument mostly based around corner cases. If someone has equity in their home, they may have some wealth but not necessarily have an income advantage over someone who rents. Stocks might temporarily skip paying dividends because of weak economic conditions. There are even cases where people lose money by having wealth comprised of assets that lose value. The horror.
But the bigger picture here is that assets generally do provide income or utility and having a positive net worth does mean more income when viewed from the aggregate data. I want to illustrate this in two ways. First, the pre-tax income of various groups by their wealth percentile shows a clear relationship between income and wealth. People with more wealth have higher income:
Additionally, if we look at aggregate personal income data for the US in 2019, the overall growth from 2018 is an impressive 6.8 percent after adjusting for inflation. The second largest source of that growth, explaining 1.5 percentage points, is larger payments to property owners in 2019 than in 2018:
I wouldn’t say that wealth is a permanent source of income but neither is labor. We can see this in the above chart from the change in the number of recipients of earnings (labor income). The chart shows that aggregate personal income went up 1.1 percentage points because more people were employed in 2019 than 2018. In other words, a (disproportionately black) portion of the population was still re-entering the labor force a decade after the great recession had ended. And as we’ve seen recently, people lose jobs all the time and sometimes these job losses last for many years, so what is a permanent source of income?
Perhaps a better question is whether wealth is consequential and reliable as a source of income. I’d argue that it is. Various forms of ownership income are equivalent to annualized income of more than $16,000 per person in August, despite the very severe ongoing recession.
Common argument #3: The concentration of wealth is a life cycle story
The following chart is useful for thinking about income and consumption over a person’s life:
The argument related to the life cycle story shown in this chart goes something like “wealth is concentrated because of age and reflects peoples’ retirement savings”. People argue that wealth reflects accrued earnings–it comes from the portion of the chart where labor income exceeds consumption. If wealth is simply accrued earnings used for consumption in retirement, then its okay that some (older) people have wealth and other (younger) people don’t (yet).
But the issue here is that large gaps still exist between families in the same age group and other large differences exist in ways that age does not explain. For example, some young people receive inheritance, in-vivo transfers, or other transfers like a car, help with college tuition, or help with a home down payment. Other young people simply do not receive these things. The gap by race is very wide when it comes to these kind of inter-generational transfers–they are much more common among white families than black families.
The accrued earnings story just doesn’t hold up to the data. From the Federal Reserve, “by some estimates bequests and transfers account for at least half of aggregate wealth (Gale and Scholz 1994), have recently averaged 3 percent of total household disposable personal income (Feiveson and Sabelhaus 2018), and account for more of the racial wealth gap than any other demographic or socioeconomic indicator (Hamilton and Darity 2010)”
Also, from 2016 SCF data, a large and growing share of people nearing retirement age (those 55-64) have no or negative wealth outside of home equity, and many also have no equity stake in their home. Social Security payments are the main source of income for a large portion of the retirement-age population and this should be expected to grow as defined benefit pensions plans become more and more rare. The end result of the current system of using private wealth to resolve income and consumption differences in the life cycle is massive gaps between people in access to goods and services at all points of the life cycle.
Should we focus on wealth?
First, if we think about wealth as assets rather than net worth (because the net worth differences between and within racial groups are largely the result of differences in assets), we can easily see the advantage of having money to cushion against an unexpected drop in income or unexpected expense. Black families are less likely to have emergency savings, and those with some savings have less than 1/5th the emergency savings of white families, on average. Black families are also substantially less likely to be able to borrow money from a friend or relative. For these reasons and many others, wealth is important, not just because it can be a source of income.
That said, income is still critically important. A policy focus on full employment, increased worker bargaining power, and compressing the wage distribution would make a huge difference in reducing poverty and reducing income inequality, and eventually, in reducing wealth inequality. But increasing the share of total income that goes to labor won’t solve one major source of inequality and poverty: the unequal distribution of workers in families. Some families contain one or more people who cannot or should not be working (children, students, elderly, persons with disabilities, unpaid caregivers, etc.), while other families do not. This situation is more common than people realize (around half of the country are non-workers during full employment) and is precisely where the welfare state shines. The US could use public ownership of wealth as a supplement to taxes in funding income payments to non-workers. As many countries already demonstrate, such payments can be designed to efficiently solve the inequality caused specifically by the uneven distribution of workers in families.
Importantly, whether the US manages to redirect a portion of its capital income to new welfare programs or not, the US government should make reparation payments to American descendants of slavery. People want to believe that wealth results from accrued earnings because it is a meritocratic story. The accrued earnings of millions of black people were stolen during slavery. Just because the victims died does not mean the wealth wasn’t stolen from their descendants. The theft of these earnings has contributed greatly to the massive racial wealth gap and this gap should be closed through both policies that close the racial income gap and through reparations and increased public ownership of assets.
The monthly jobs report shows a widely-cited spike in unemployment, starting in April 2020, as a result of the COVID-19 pandemic. It’s worth illustrating that employment effects go far beyond what is measured by unemployment, and additional analysis of household survey data give a more complete picture. The large number of people affected support a case for broad eligibility for government financial support.
The age 16 or older population can be divided into three categories: those who are employed in any amount, those who are not employed but are on temporary layoff or are looking for work (the unemployed), and those who are out of the labor force. The out of the labor force group includes students, caregivers, persons with disabilities, and the elderly, as well as those who have given up looking for work but would like a job.
Because the three categories in the chart above sum to 100% in any give month, a change in one category must be offset by a change in another category. For example, during a recession (shaded gray area) the employed share of the population falls as people become unemployed and leave the labor force.During the great recession in 2008-09 the job losses were protracted. From October 2006 to October 2007, the employed share of the age 16+ population fell by 0.6 percentage point. From October 2007 to October 2008, the employed share fell by 1.0 percentage point, and from October 2008 to October 2009 it fell by another 3.2 percentage points. In contrast, the 2020 recession started very abruptly. The March report covered the week before many places had shut down, but the April 2020 report showed a 9.3 percentage point drop in the employed share of the age 16+ population from April 2019. Each subsequent month, with the most recent available being July, showed a smaller one-year rate of change than the previous month.To get a bigger sample size and an overview of the April 2020 onward data, the following sections combine the monthly survey data for April through July 2020 and compare it to the corresponding combined survey data covering April to July 2019. The average decrease in the employed share of those 16 or older was 7.2 percentage points, with three-quarters of that shift moving into unemployment (a 5.2 percentage point one-year change) and one-quarter leaving the labor force (2.0 percentage point one-year change).
Overall Change by Age
Data show the effects in labor force status are disproportionate to young people. Among three-year age groups used for these calculations, the age 19-21 group was the hardest hit, with a decrease in the age group employment rate of 16.8 percentage points in April through July 2020 compared to the same four months the previous year. The age 19-21 group was also disproportionately likely to leave the labor force, with a 7.2 percentage point one-year change in the share of the population that is not in the labor force, compared to a 9.6 percentage point change in the share that is unemployed. The effect was similar but less pronounced for those ages 16-18, 22-24, and 25-27.Next we look at changes within each of the three categories discussed above.
Changes within types of employment
Shifts within the employment category are important. For example, some full-time workers have their hours cut but are still employed, and some people lose a second job. Additionally, the Bureau of Labor Statistics reported that some people who should probably should have been classified as unemployed were instead described as “employed, but not at work” in the data, particularly in April 2020. In these three examples the person is classified as employed and wouldn’t show up in the previous chart.Therefore, the next chart divides those who are employed into four nonoverlapping categories: multiple jobholders (people who have more than one job), those fully-employed (defined as working 35 or more hours or voluntarily working fewer) and at work during the reference week, those working fewer than 35 hours but who want more hours (part-time for economic reasons), and those who are employed but not at work. The one-year change in each category shows that the employment effect is substantially larger when considering the deterioration within employment categories.Most age groups saw a decrease in the “fully-employed, at work” category that was larger than the overall change in employed defined in the previous sections. This is because of the reduction in second and third jobs, the increase in people not at work, and the shift of millions of people to part-time status, despite their preference for full-time work. The reduction in multiple jobholding rates was highest among those age 22-24 (1.9 percentage point decrease). The age 49-51 group had the biggest increase in the share of the age group that works part-time but wants to work full-time, with a 3.4 percentage point increase.
Changes in Unemployment by Age
A bright spot in the four months of historically bad data is that most of the unemployed are on layoff. Unlike other recent recessions, the reduction in economic activity was a decision made for public health purposes. Arguably, the US did not do enough or move quickly enough in reducing employment and non-essential economic activity, causing the country to have many more cases and preventable deaths than western Europe.It is tough to know whether a job loss will actually be temporary, but the expectation during the early months of the shutdown was that people stay home and that policymakers provide enough income to keep most families financially stable without them going out and looking for work. The data show that people very disproportionately did report their unemployment as a layoff. That said, there was still an increase in the share of the age 16+ population that is actively looking for work. Those in their 20s are disproportionately likely to be looking for work rather than on temporary layoff, with the peak being for those 22-24, who have an increase of 1.8 percentage points in 2020 from the previous year rate of looking for work.
Changes in Labor Force Participation by Age
Perhaps the most interesting chart, and the reason I initially looked into the topic, shows changes in categories of labor force non-participation (those out of the labor force). The source for this data asks people about their major activities and their reasons for not having a job and not looking for one. Those 16 or older can be out of the labor force because of school, a disability or illness, to care for home or others, because of retirement, or they can be discouraged because they do not believe work is available for them.The data show a mixture of effects, with the most prominent being an increase in the share who are discouraged. Some other effects are more age-specific. Young people are increasingly reporting school enrollment as their reason for non-participation, but also are disproportionately increasingly non-participants in the discouraged and other categories. There were also people age 22-45 who left caregiving roles, ostensibly to work or look for work. Additionally, the retired share of those 55 and older increased.It’s worth noting here that age groups can partially offset each other in these data. For example, a portion of young people in college who would prefer to work are offset by people in their 20s and 30s who would go to school if they felt confident about leaving a job.Among those age 34 or older, there is actually a decrease in the share that report disability or illness as their reason for non-participation. It’s not clear what is happening here, though one possibility is that reductions in non-COVID-related medical procedures mean fewer people were sidelined temporarily by a surgery. Another possibility is that some portion of those who reported disability or illness found work or moved into other categories of non-participation or unemployment.
Summing up the categories
Far more people have labor market effects from the shutdown than those counted as unemployed. The unemployed average 19.5 million over the four month period and BLS reports 16.3 million unemployed in July. However, counting the people who are no longer fully employed plus those who are no longer multiple jobholders yields a total of 26.8 million people on average for the four month period.The data also show that those age 16 to 24 are disproportionately affected. The last table includes only those in this combined age group. About one in seven in this group, about five million people, fell out of the fully employed category or the multiple jobholder category. About half of those are unemployed and the other half are underutilized or left the labor force.
I’m curious about how jobs are changing for the roughly 5.5 million teachers in the US. Teaching doesn’t pay as well as jobs that require similar education, but teaching traditionally includes a summer break and retirement and healthcare benefits. This post looks at trends since 1997 in how teachers use their school summer break and shows that they are becoming more likely to work during the break.
School summer breaks show up in data on the teaching share of total hours worked. In the school year, teaching comprises 3-3.5 percent of total work hours, compared to around one percent in the summer months.
Panel data show that the share of teachers working a different job in the summer is pretty stable, but that teachers are more likely to be at work as teachers over the summer months (June, July, and August) than they were in the past.
Earlier start to school year
The individual monthly data show an earlier start to the school year in many places (August rather than September) shortening the summer break for teachers. In August 2019, teaching comprised about 2.5 percent of total work hours, compared to 1.2 percent in August 1997.
Mismatches between the timing in life of labor income and major expenses cause unnecessary poverty in the US. Labor income (earnings from working) is most common between the ages of 25 and 54 and tends to be highest in amount for people in their 40s and 50s. In contrast, education expenses accrue disproportionately to young adults, and healthcare expenses are higher for the elderly. As anotherexample, new parents tend to have lower wages than parents of teenagers while childcare expenses are very high in the first few years of a child’s life. These mismatches between the timing of income and expenses create financial stress and poverty.
One way to see this is to look at poverty by age:
The blue area shows the dollar amount of poverty reduced by the combined welfare programs and tax credits. The green area shows the amount of poverty remaining after taxes, welfare, and medical, work, and childcare expenses. The large blue area on the right of each graph shows that market income leaves enormous poverty for the elderly, but that this poverty is reduced to below-average rates by programs such as social security, supplemental security, and medicare. These programs are absolutely critical to poverty reduction but still leave some elderly in poverty.
Two age groups that stand out as facing higher-than-average levels of poverty by disposable income are young adults (students and new parents disproportionately) and those before social security retirement/medicare eligibility age. These groups correspond to the bump in disposable income poverty (green area) around age 20 and again around age 60. These age groups are less likely to work relative to 25 to 54 year olds and therefore have less labor income. Unlike 25 to 54 year olds, those who are younger or older have expenses higher than their income, hence the poverty caused by the mismatch in the timing of income and expenses.
Total poverty is defined as the dollar amount of resources that people are below their Census-defined “poverty threshold”. In the case of market income it is the total amount of money that would need to be given to people for their before-tax labor and capital income to equal their poverty threshold, according to the Supplemental Poverty Measure (SPM). Total poverty by disposable income corresponds with the SPM-poverty-rate-based amount of poverty.
Full employment–a version of the US economy where anyone who wants a job has one–is an important complement to welfare for poverty reduction, but ultimately not a substitute for welfare.
Why is full employment important?
Full employment means more labor income for people. A single person working full-time and full-year will have income above the poverty threshold, even if earning the minimum wage.
Typically full employment is also associated with less discrimination in hiring and with workers getting a stable share of output growth. Achieving full employment also better positions the US to provide for people who cannot or should not be working.
Are we at full employment?
The US is likely close–within a few million jobs–to full employment. Many economists had expected runaway inflation as the US unemployment rate fell below 5.5% but inflation has been well-contained. Unemployment is now around 3.5% which is near to historical lows.
What can’t full employment do?
People who live in income-sharing arrangements (for example a family) where the number of non-earners (for example: children, elderly, work-limited disabled, caregivers, students, etc) exceed the number of earners are at a major disadvantage, even under full employment, relative to those who live with more earners than non-earners. That is, a single mother of three kids will be relatively better off with full employment than without full employment, but is likely to struggle financially even with full employment.
The previous example is fairly intuitive, but what people don’t realize is just how few people actually work full-time and full-year, even in a near-to-full-employment economy.
In 2018, no part of the US had even 42% of its total population working full-time and full-year:
The map shows commuter zones–groups of counties that form local labor markets. Even with the economy near full employment in 2018, no part of the country has a majority of its population fully-employed. That is, even when the US is maximizing its number of workers, there are still large gaps in labor income over time and within any given point in time. And reaching the people not reached by labor income in full employment is precisely what welfare is for.
Data reject the argument that welfare is not needed in full employment. Non-earners comprise a large share of every country’s demographic picture, even at full employment, and non-earners are unevenly distributed within families. This combination means poverty cannot be solved through labor income alone. Existing welfare programs in the US, like unemployment insurance, provide income for earners who lose their job; separately, there are welfare programs that provide income to non-earners regardless of the state of the economy, such as low-income, low-asset elderly reached through Supplemental Security Income. Other countries show that adopting additional programs, for example a child allowance, would effectively and immediately reduce poverty from its stubbornly high rate.