bd CPS version 0.4 released

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 (brian.w.dew@gmail.com) if you find any errors or have any questions.

Maternity/Paternity Leave in the US

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.

Income of the Aged, 2019

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.

Measuring Earnings from Work

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.

Family Earnings

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. Other countries go further 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.

Who gets credit for the wage growth of low wage workers?

Last month, I pointed out 7% wage growth at the first decile (10th percentile). The CEA noted this same figure in a recent report, claiming credit for the uptick at the bottom of the wage distribution. My piece, in contrast, had assigned credit to the state, local, and company minimum wage increases. AEI has a piece that thanks both the long expansion and the minimum wage increases.

The AEI piece points to Atlanta Fed analysis showing that states without state minimum wage hikes have seen increases in the median wage of the first quartile (bottom 25%) of wage earners. This makes a strong case that state minimum wage increases only explain part of the story. But it’s worth testing the argument that if additional states had raised their minimum wage those states would have seen larger wage gains for low-wage workers. That is, by making the wrong decision the no- and low-minimum wage states fared worse, but despite making the wrong decision, they still fared reasonably well.

A test of my claim comes from comparing total hours worked in different wage groups and in two state groups: those that raised the state minimum wage from 2016 to 2019 and those that didn’t. To better capture total wage income, aggregate hours are used instead of number of workers. In the baseline data which cover 2016, slightly more than half (55.4%) of hours worked in the US were from people in the 27 states with minimum wage increases during 2016-2019. This is how the total hours worked are distributed by wage group and state group in 2016:

result2016

Notably, the share of hours that are paid the federal minimum wage or less is low; several times as many hours of work were paid between $7.26 and $12.00.

In the latest year of data, which ends November 2019, the story has changed:

resultlatest

The share of work hours paid less than $12 has fallen substantially since 2016. The effect is more pronounced in states that increased the minimum wage but also apparent in those that didn’t. As a result, states that didn’t raise the minimum wage now disproportionately have low wage work relative to the states that did.

It’s worth noting that total hours of work increased by five percent over the period. The concern that raising the minimum wage will cause job losses doesn’t show up in the data. The states that raised their minimum wage maintained a fairly constant share of total hours of work, claiming 55.2% of total hours over the past year.

Looking more closely at the data, the long economic expansion does deserve some credit for wage growth at the bottom of the distribution. Importantly, states that acted to raise minimum wages better shared the economic expansion with low wage workers.

Wage growth is in the pipeline

If you haven’t looked at the US wage distribution recently, you might be surprised to see nominal growth of 7.0 percent in 2019 Q3 and 6.7 percent in Q2 in first decile usual weekly earnings. My own calculations show an even stronger 8.7 percent year over year increase in October 2019. The increase in the first decile wage seems to be coming from state, local, and company increases to the minimum wage. It is also an indication that higher wages may soon be coming to the workers in the middle of the income distribution.

First, the data:

First decile wages and growth

The dark blue lines are the BLS quarterly data on first decile usual weekly earnings for full-time wage and salary earners. The light green lines (bd CPS) calculate the same series on a monthly basis.

The BLS and bd CPS series both show first decile wage growing at its strongest rate since the late 1990s. One conventional story here is that a tight labor market, as measured by a low unemployment rate, makes it harder for employers to find qualified replacements for employees who quit, which makes existing employees more likely to get raises. As a result, wage growth is usually strongest when the labor market is tight.

But there’s a catch. BLS uses the same sample to report nominal median wage growth of 3.6 percent in Q3 and 3.7 percent in Q2. BLS data on real average hourly earnings, which adjusts nominal earnings data for inflation, shows 1.2 percent real wage growth in October (nominal wage growth of 3.0 percent and inflation of 1.8 percent). This isn’t terrible, but it’s no seven percent. With the unemployment rate historically low, why isn’t median and average wage growth stronger?

Leaning on unemployment to fully explain wage growth is clearly not working. An alternative measure of whether a labor market is tight is the employment rate for people age 25-54. This measure is currently at it’s highest level since 2007 but is still the equivalent of more than a million jobs away from its late 1990s and early 2000s level. In other words, there’s evidence that the labor market is still over a million jobs away from full employment.

Maybe it’s better to ask instead why wages are growing so rapidly at the first decile and whether it means anything for wage growth more broadly. The data show that the number of full-time workers earning $400 per week or less has fallen to 7.5 million in the latest three months from 14.7 million in the same three months of 2014. The story here seems to be higher local and state minimum wages and higher company minimum wages at large employers. Several areas and employers moved the minimum wage to above $10 per hour ($400 for a 40-hour week) during the past five years.

Higher minimum wages could eventually translate into higher median wages. For example, the first decile and median wage tend to move together:

First decile and median wage growth

In the period of faster wage growth and tighter labor markets during the late 1990s, first decile wage growth accelerated and median wage growth accelerated shortly after. If employment trends continue, it seems reasonable to expect an increase in the median wage of five percent or more in 2020.

But the current period is not the late 1990s. Business fixed investment and labor productivity growth are both particularly weak now compared to then. Negative net capital investment and increasing payrolls suggest an aggregate situation akin to fewer tools per worker, which casts doubt on forecasts of sustained real wage growth.

Technical Note

Unlike the first two charts, I use some additional steps to smooth out the third chart comparing median and first decile wage growth. I calculate the wage in each month as the wage in the previous three months combined, so October 2019 is based on combined microdata from August, September, and October 2019. Additionally, I use the Census X13as program to seasonally adjust the results. The growth rate is based on this resultant seasonally-adjusted nominal wage.

In both sets of charts, I replicate the BLS process of taking a “binned median” to reduce how much the data reflect breaks around certain round numbers (like $400 per month).

Six southern US metro areas: part 6 – unemployment

My sixth blog post on mid-sized cities near the southern section of the Appalachian mountains looks at unemployment (people who do not have a job but are actively trying to find one).

The six areas of interest are: the Chattanooga-Cleveland-Dalton, TN-GA combined statistical area, the Greenville-Anderson-Spartanburg, SC combined statistical area, the Asheville, NC center-based statistical area, the Johnson City-Kingsport-Bristol, TN-VA combined statistical area, the Huntsville, AL center-based statistical area, and the Knoxville, TN center-based statistical area. See the first post in the series for more background.

The source for these results is 36 months of aggregated Current Population Survey microdata, covering January 2016 to December 2018.

Unemployment rate

In a previous post, I calculated the unemployed share of the population. This post looks at the unemployment rate, which is the unemployed share of the labor force. Over the three-year period from 2016 to 2018, the US unemployment rate averaged 4.4%. The unemployment rate varied in the six areas of interest from 2.9% in Asheville to 5.1% in Huntsville. The unemployment rate in this three-year period averages 3.5% in Knoxville, 3.9% in the Greenville-Anderson-Spartanburg area, 4.0% in the Johnson City-Kingsport-Bristol area, and 4.6% in the Chattanooga-Cleveland-Dalton area.

BLS publishes high frequency estimates of unemployment in the Local Area Unemployment Statistics report. I’ve used the multi-year averages instead, to allow analysis of why people are unemployed and for how long they have been looking for a job.

Reason for unemployment

Unemployment can be grouped into four categories, based on what people were doing before they became unemployed: 1) people who quit a job and are looking for a new one (job leavers), 2) people who lost a job and are looking for a new one (job losers), 3) people who are looking for their first job (new entrants), and 4) people who were previously not in the labor force (for example: disabled or ill, taking care of family, retired, or had simply given up hope of finding work) and are now looking for work again.

From 2016-18, nearly half of US unemployment was because people had lost a job. This “job loser” category can be the most painful, as it is perhaps the least voluntary. The person had a job, and presumably wanted to keep it, but could not, and now they are trying to find a replacement job. In contrast, the re-entrant category, which makes up 1.3% of the US labor force, can be a positive indication. From 2001 to 2014, the US labor market was particularly poor for many people, and, as a result, many decided to stop looking for work. But over the past few years, the labor market has improved, and new jobs are pulling people off of the sidelines and encouraging them to look for work again. These people show up as “re-entrants”. Job leavers can also be an indication of a strong labor market. When people are confident that they can find a better job, they are more likely to leave their current job.

In four of the six areas of interest, the unemployment rate is below the US average for the “right” reasons. That is, people are less likely to be job losers or unemployed new entrants. In Knoxville and the Johnson City-Kingsport-Bristol area, more than half of the unemployed are job leavers and re-entrants. Re-entrants are also disproportionately common in the Greenville-Anderson-Spartanburg area.

The job loser share of the labor force is at or below the national average in all six areas, and particularly low in Knoxville and Asheville. The new entrant share of the labor force is above average in Huntsville and Chattanooga. Job leavers are more common in Chattanooga and Johnson City.

unempreason

Duration of Unemployment

Another important determinant of whether unemployment is particularly painful is the duration of unemployment. If people are unemployed for a short time, they may be able to rely on unemployment benefits and, in some cases, their personal savings, to survive. However, a long period of unemployment can devastate savings, exceed the period where unemployment benefits are allowed, affect mental health, and even create a situation where people begin to lose their skills.

Long-term unemployed, measured as those whose unemployment has lasted 27 weeks or more, makes up one percent of the US labor force. The long-term unemployment rate is the same in Johnson City-Kingsport-Bristol and Knoxville. In contrast, it is more common in Huntsville (1.8%) and less common in the Greenville area (0.7%), Chattanooga (0.5%), and Asheville (0.3%).

Short-term unemployment (lasting a month or less) makes up 1.4% of the US labor force, 1.5% in the Greenville area, 1.1% in Knoxville, and 1.3% in Huntsville. The unemployed populations in both Huntsville and Knoxville are more likely to be long-term unemployed than the US average.

unempduration

The next blog post in the series will look at which industries and occupations employ people in the six areas. The jupyter notebook used to create the analysis above is here.

Six southern US metro areas: part 5 – reasons for labor force non-participation

Part five in the series of blog posts on six mid-sized southern US cities looks at why people are not participating in the labor force (not employed and not looking for work).

The six areas of interest are: the Chattanooga-Cleveland-Dalton, TN-GA combined statistical area, the Greenville-Anderson-Spartanburg, SC combined statistical area, the Asheville, NC center-based statistical area, the Johnson City-Kingsport-Bristol, TN-VA combined statistical area, the Huntsville, AL center-based statistical area, and the Knoxville, TN center-based statistical area. See the first post in the series for more background.

The source for these results is 24 months of aggregated Current Population Survey microdata, covering January to December in 2017 and 2018. Because I am using the CPS, it’s important to remember that all blog posts in the series look only at civilian, non-institutionalized people. The US has a large military and a very very large prison population, but neither are included in the survey results or in this analysis.

Employed or caring for house/family

As discussed in the previous post, the primary explanation for differences between employment rates of men and women is that women are more likely to do unpaid work in the home, such as caring for children or elderly relatives. The share of men and women who are primarily “working” (either employed or caring for house or family) is pretty much identical, with a few exceptions.

epopcare

Among people age 16-64 in the US as a whole, men (76.9%) are slightly more likely to be employed or in a care role. The story is the same in five of the six areas of interest, though the gap between men and women is a bit larger in each of the five, compared to the US as a whole. The biggest gap between men and women is in Huntsville, where 77.7% of men and 73.4% of women are employed or in care roles. In Asheville, women are slightly more likely than men to be employed or in care roles.

In two areas, Johnson City-Kingsport-Bristol and Chattanooga-Cleveland-Dalton, men and women are both quite a bit less likely to be employed or in care roles than the US average. In contrast, in the Knoxville area, the opposite is true; both men and women are more likely to be employed or in care roles than those in the US as a whole.

Other reasons for non-participation

Beyond care roles, there are large differences between the areas in the other reasons for non-participation in the labor force (people who are not employed or looking for work). Other reasons for non-participation include people that are in school (more likely among 16-24 year olds) and that are retired (more likely among 55-64 year olds). A substantial portion of the population is also not employed primarily because of disability or illness. Some are also discouraged, meaning they want a job but have stopped looking for work because they do not believe one is available for them.

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In the US as a whole, about six percent of men and women are not in the labor force due to disability. A smaller share of the population, about 4.5%, are in this category in Asheville. Likewise, women in Huntsville are slightly less likely to be out of the labor force due to disability or illness, compared to the US a whole. In Knoxville, the share is near the US average. In contrast, the share of men in the Chattanooga area (9.7%), women (8.9%) and men (8.3%) in the Greenville area, and men in the Johnson City-Kingsport-Bristol area (8.6%) who are not in the labor force due to disability or illness is well above the US average. The most striking instance is among women in Johnson City-Kingsport-Bristol, where 12.1% of the age 16-64 population is not in the labor force because of disability or illness.

If someone can explain why non-participation from disability is twice the national average for women in Johnson City-Kingsport-Bristol, please leave a comment. My speculation would be something like factory closures. Physically demanding jobs, like factory work, can lead to disability or illness. When factories close, people are left without job opportunities (often the factory is the main employer in the area), and with the scars of the their former job, thus are more likely to leave the labor force and more likely to be disabled.

Another major reason for non-participation is retirement. Among 16-64 year olds, women are more likely to be retired in the US as a whole and in each of the six areas. Partially this is because women make up a larger share of 55-64 year olds. In Asheville, where the population is older than the US average, the retired share of the age 16-64 population is much higher than in the US as a whole or in the other five areas of interest.

People who are not employed because they are in school make up about seven percent of the age 16-64 US population. This rate is pretty consistent across the six areas, with two exceptions. Women in Huntsville are more likely to be in school and not employed, and women in the Johnson City-Kingsport-Bristol area are less likely to be in school and not employed.

Finally, about two percent of the US age 16-64 population wants a job but has given up looking for one because they do not believe one is available for them. This could be because there is a lack of jobs for people with their skills, or because of discrimination. The discouraged worker share of the population is near or slightly less than the US average in five of the six areas, with the one exception being for women in the Chattanooga area, who are slightly more likely than women nationwide to be discouraged.

The next blog post in the series will look at reasons for unemployment, who is unemployed, and duration of unemployment. The jupyter notebook used in the analysis above can be found here.

Six southern US metro areas: part 4 – labor force status

Part four in the series looks at whether people are working, looking for working, or doing something else, in each of six mid-sized metro areas around the southern portion of the Appalachian mountains. Since economic cycles influence whether people want jobs and whether they can find them, this post also looks at how the labor force status has changed for men as the overall US economy improved from 2015 to 2018.

The six areas of interest are: the Chattanooga-Cleveland-Dalton, TN-GA combined statistical area, the Greenville-Anderson-Spartanburg, SC combined statistical area, the Asheville, NC center-based statistical area, the Johnson City-Kingsport-Bristol, TN-VA combined statistical area, the Huntsville, AL center-based statistical area, and the Knoxville, TN center-based statistical area. See the first post in the series for more background.

The source for these results is 12 months of aggregated CPS microdata, covering January to December 2018. The rate of change data compares the 2018 results with those from January to December 2015.

Employed, unemployed, or “not in the labor force”

This section compares the six areas in terms of whether men and women age 16 to 64 are employed, unemployed (meaning they don’t have a job and are actively trying to find one), or are “not in the labor force.” Not in the labor force, which I will generally refer to as “non-participation,” can be for any reason, such as discouragement (want a job but have given up hope of finding one), disability or illness, school, taking care of family, retirement, or for other, unspecified, reasons.

In 2018, the share of each area’s age 16-64 population that is employed, unemployed, and not in the labor force is similar to the US as a whole. Overall, in the US, 2.6% of women and 3.2% of men are unemployed (this is not the unemployment rate, but rather the unemployed share of the population). Among the six metro areas of interest, the unemployed share of women ranges from 0.8% in Knoxville to 4.3% in the Chattanooga area. The unemployed share of men ranges from 1.2% in the Johnson City-Kingsport-Bristol area to 3.3% in the Greenville area.

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In the US as a whole, 76.1% of 16 to 64 year-old men are employed, compared to 65.5% of women. The main reason for the gap is that women are more likely to be doing unpaid work at home, like taking care of children or elderly relatives. The employed share of men in the six areas ranges from 73.8% in Asheville to 78.1% in the Greenville area and Huntsville. The employed share of women ranges from 59.8% in Huntsville to 68.1% in Knoxville. Huntsville has the largest gap between men’s and women’s employment.

Finally, for those age 16-64 in the US as a whole, 31.8% of women and 20.8% of men are not in the labor force. Again, the patterns are similar in all of the six areas. The non-participating share of women ranges from 29.9% in Asheville to 36.9% in Huntsville. Among men, the non-participating share ranges from 18.6% in the Greenville area to 23.8% in the Johnson City-Kingsport-Bristol area.

Change in labor force status

While the labor force status in each of the six areas of interest doesn’t differ too much in 2018 from the US as a whole, there have been some pretty big changes within some of the areas since 2015, which are discussed in this section.

From 2015 to 2018 there was general improvement in the overall US economy, but seemingly even more substantial improvement in the local economies of most the six areas. This is evidenced by the change in the share of the each area’s population that was employed in 2018 compared to 2015. For the US as a whole, the age 16-64 employment share increased by 2.1 percentage points for women and 1.9 percentage points for men. In contrast, only women in the Greenville area (0.5pp) and men in Asheville (1.6pp) had an employment share increase that was smaller than the US average.

lfs_change

 

Among the six areas of interest, the largest change in employment share was for women in Knoxville. The employed share of this group increased by 7.3 percentage points, to 68.1% in 2018 from 60.8% in 2015. This is a massive increase in a relatively short period of time. Among men, the largest increase was 6.1 percentage points in the Greenville area, where the employed share of 16-64 year olds increased to 78.1% in 2018 from 72% in 2015.

In addition to men in Greenville and women in Knoxville, other groups with at least a four percentage point increase in the employed share of the population include women in Huntsville (4.8pp), men (5.5pp) and women (4.1pp) in Johnson City-Kingsport-Bristol, and men in Knoxville (4.3pp).

Perhaps most interestingly, and as pointed out recently by economists Matthew Boesler and Elise Gould, much of the recent change in employment seems to be coming from people who were previously not looking for work. That is, while unemployment has clearly fallen, so has non-participation. This is a very important development because unemployment is already low by historical standards, but non-participation is still high by historical standards.

In the US as a whole, non-participation explains 0.8 percentage points of the 1.9 percentage point increase in employment among men, and 1.3 percentage points of the 2.1 percentage point increase in employment among women. In contrast, it over-explains employment growth among women in the Chattanooga-Cleveland-Dalton area, who have higher rates of unemployment in addition to higher rates of employment.

Notably, non-participation in Knoxville fell by 5 percentage points for women and by 3.7 percentage points for men, over the four year period. In the Greenville area, non-participation fell by 4.9 percentage points for men but actually increased by 1.1 percentage points for women. Non-participation also increased slightly among women in Asheville. Non-participation fell by 3.4 percentage points for women in Johnson City-Kingsport-Bristol.

The next post in the series will look specifically at reasons for non-participation. The jupyter notebook used in this analysis is here.

EDIT: The original version of this post used person weights, instead of the composite weights. The text and graphics above are updated to use composite weights.

bd CPS version 0.3 released

Version 0.3 of my notebooks for cleaning up and working with Current Population Survey public use microdata is available on GitHub. Several new variables were added, much of the code was refactored for speed, and several bugs were fixed. The new version makes use of Census revised weights for 2000-2002 and December 2007, revised data on union membership and coverage in 2001 and 2002, and data on professional certification for 2015 and 2016. There is also a new notebook for creating extracts for 1989-93 from microdata hosted by NBER.

I’m looking into how to (for free or very cheaply) host the actual data files from this project, since they would definitely be useful to people who know python and want to work with CPS data. Each annual file is about 30mb after compression. Any suggestions are welcome.

As always, please contact me (brian.w.dew@gmail.com) if you find any errors or have any questions.