On August 12, 2019, Democratic presidential candidate Andrew Yang tweeted, “I’ve done the MATH, it’s not immigrants taking our jobs, it’s automation. Instead of blaming immigrants, let’s give our citizens the means to thrive through the fourth industrial revolution.” This, like much of Yang’s and others’ current discourse regarding automation, is focused on an exaggerated fear that automation can and soon will replace workers’ roles in production, resulting in widespread job loss. But for hundreds of years, technological progress has continually reshaped the way work is done—and yet this progress has never resulted in a long-term decline in the labor force. Focusing on overstated risks of job loss from automation distracts from efforts to advocate for higher wages, better benefits, and increased bargaining power—issues that have been, and will continue to be, essential to the well-being of workers and their families.

However, while there is no reason to believe that automation will lead to widespread, sustained decline in the overall number of jobs, there will be specific jobs, industries, and workers for whom the impact of automation will come with real costs, at least in the short term. One industry in which concerns about automation may be warranted in the near term is transportation. Ford and Volvo have both announced plans to put fully autonomous vehicles on the road as early as 2021; Honda has announced a partnership with GM to begin developing autonomous vehicles; and Nissan recently introduced “no-hands driving” on highways in its ProPilot 2.0. While consumer skepticism may slow down the industry’s timelines, many advances have already been made: Most new cars have computerized driver assistance options; Tesla’s Autosteer has logged at least one billion miles of supervised autonomous driving; and Caterpillar is already producing autonomous vehicles for hauling mining materials.Read more

On Friday, the Bureau of Labor Statistics will release September’s numbers on the state of the labor market. As usual, I’ll be paying close attention to nominal wage growth as well as the prime-age employment-to-population ratio, which are two of the best indicators of labor market health. Friday’s report will also give us a chance to examine the “teacher shortfall”—the gap between local public education employment and what is needed to keep up with growth in the student population.

Thousands of local public education jobs were lost during the recession which began in 2008, and those losses continued deep into the official economic recovery, even as more students started school each year. This has been true of public sector jobs in general—continued austerity at all levels of government has been a drag on public sector employment, which has failed to keep up with population growth.

Teacher strikes in several states over the last few years have highlighted deteriorating teacher pay as a critical issue. My colleagues Sylvia Allegretto and Larry Mishel find that average weekly wages of public school teachers have fallen over the last two decades and the teacher wage penalty continues to grow, reaching a record 21.4% in 2018. My colleagues Emma García and Elaine Weiss have further documented shortcomings and teacher shortages and recently how much teachers have to pay out of their own pockets for school supplies for their classrooms. Low pay makes it harder to attract and retain teachers who have the qualifications associated with teacher effectiveness in the classroom.

The costs of a significant teacher employment gap are high, consequences measurable: larger class sizes, fewer teacher aides, fewer extracurricular activities, and changes to curricula. Last year, the local public education job shortfall remained large. To solve this problem, state and local governments need to fund more teaching positions and raise pay to close the teacher pay gap and attract and retain the qualified teachers our children deserve. On Friday, I will compare where jobs in public education should be, using the pre-recession ratio, student population growth, and the most recent jobs numbers.

Read more

The state income data from the American Community Survey (ACS), released this morning by the Census Bureau, showed that in 2018, household incomes across the country rose—albeit more slowly, and in fewer states, than in the previous year. From 2017 to 2018, inflation-adjusted median household incomes grew in 33 states and the District of Columbia (14 of these changes were statistically significant.) This marks a decline from the broader growth seen between 2016 and 2017 when median household incomes grew in 40 states and the District of Columbia, with 24 of those changes being statistically significant.

The ACS data showed an increase of 0.2% in the inflation-adjusted median household income for the country as a whole—an increase of just $130 for a typical U.S. household and a slowdown in growth compared to the past three years: household incomes increased by 3.8% in 2015, 2.0% in 2016, and 2.5% in 2017. [i] Despite these increases, households in 23 states still had inflation-adjusted median incomes in 2018 below their 2007 pre-recession values, which makes this year’s slowdown particularly disappointing.

From 2017 to 2018, the largest percentage gains in household income occurred in Idaho, where the typical household experienced an increase of $2,085 in their annual income—an increase of 3.9%. Maryland remains the state with the highest median household income at $83,242, having experienced a slight increase (0.6%) from 2017 to 2018. The District of Columbia has the highest median household income in the country at $85,203—though comparing D.C. to states is problematic, since D.C. is a city, not a state. Read more

The 2018 American Community Survey (ACS) data released today shows that the slowdown in income growth from 2017 to 2018 reported earlier this month by the Census Bureau also indicates a slowdown in progress reducing poverty in many states. From 2017 to 2018, the poverty rate decreased in 36 states and the District of Columbia, with 14 of those states experiencing statistically significant declines. For comparison, from 2016 to 2017, poverty fell in 42 states plus the District of Columbia, with 20 states and the District of Columbia having statistically significant reductions.

The poverty rate rose in 14 states, with increases of 1.3 percentage points in Rhode Island, and 0.8 percentage points each, in Connecticut and Arkansas—although only Connecticut’s increase was statistically significant.

The continued reductions in poverty rates for most states are welcome news; however, most states have still not recovered to their 2007, pre-Great Recession poverty rates. Moreover, 38 states had higher poverty rates in 2018 than in 2000.

The national poverty rate, as measured by the ACS, fell 0.3 percentage points to 13.1 percent in 2018, making it nearly the same as the ACS poverty rate in 2007, when it was 13.0 percent. It remains 0.9 percentage points above the rate from 2000.

Between 2017 and 2018, West Virginia had the largest decline in its poverty rate (-1.3 percentage points), followed by Delaware (-1.1 percentage points), Louisiana (-1.1 percentage points), Idaho (-1.0 percentage points), and Arizona (-0.9 percentage points). Poverty increased most in in Rhode Island (1.3 percentage points), Connecticut (0.8 percentage points), Arkansas (0.8 percentage points), Maine (0.5 percentage point), Montana (0.5 percentage point), and Iowa (0.5 percentage point). Read more

Yesterday, the U.S. Department of Labor announced its final overtime rule, which will set the salary threshold under which salaried workers are automatically entitled to overtime pay to $35,568 a year. The rule leaves behind millions of workers who would have received overtime protections under the much stronger rule, published in 2016, that Trump administration chose to abandon.

For quick details on the history of this rulemaking, see this statement. The two tables below show just how many workers this administration is turning its back on with this rule, and how much money workers will lose. Using the same methodology used by the Department of Labor in their estimates of the economic impact of the rule, I estimate that 8.2 million workers who would have benefited from the 2016 rule will be left behind by the Trump administration’s rule, including 3.2 million workers who would have gotten new overtime protections under the 2016 rule and 5.0 million who would have gotten strengthened overtime protections under the 2016 rule. As the table shows, this administration is turning its back on 4.2 million women, 2.7 million parents of children under the age of 18, 2.9 million people of color, and 4.6 million workers without a college degree.

Table 1

Number of salaried workers left behind by the Trump overtime rule, by demographic group

	Workers left behind by 2019 rule			Under the 2016 rule			Under the 2019 rule
	Total workers left behind	Workers who would have gotten new protections under 2016 rule	Workers who would have gotten strengthened protections under 2016 rule	Total affected workers	Workers with new protections	Workers with strengthened protections	Total affected workers	Workers with new protections	Workers with strengthened protections	Total salaried workers
All	8,210,000	3,230,000	4,980,000	13,470,000	4,550,000	8,920,000	5,260,000	1,320,000	3,950,000	59,140,000
Gender
Male	4,000,000	1,410,000	2,590,000	6,560,000	1,970,000	4,590,000	2,560,000	560,000	1,990,000	32,570,000
Female	4,210,000	1,820,000	2,390,000	6,910,000	2,580,000	4,340,000	2,710,000	760,000	1,950,000	26,570,000
Parental Status
Not a parent	5,500,000	2,170,000	3,330,000	9,060,000	3,060,000	6,000,000	3,550,000	890,000	2,660,000	37,470,000
Father	1,330,000	450,000	870,000	2,130,000	630,000	1,510,000	810,000	180,000	630,000	12,210,000
Mother	1,380,000	600,000	770,000	2,280,000	860,000	1,420,000	900,000	250,000	650,000	9,460,000
Race/ethnicity
White	5,260,000	2,230,000	3,030,000	8,220,000	3,120,000	5,100,000	2,960,000	890,000	2,070,000	40,680,000
Black	1,000,000	340,000	650,000	1,680,000	480,000	1,200,000	680,000	140,000	540,000	5,460,000
Hispanic	1,240,000	360,000	880,000	2,410,000	530,000	1,880,000	1,170,000	170,000	1,000,000	7,230,000
Asian	560,000	240,000	320,000	930,000	340,000	580,000	370,000	100,000	260,000	4,810,000
Others	140,000	50,000	90,000	230,000	70,000	160,000	90,000	20,000	70,000	960,000
Age
16–24	500,000	200,000	290,000	1,000,000	320,000	680,000	500,000	120,000	380,000	2,800,000
25–34	2,400,000	1,040,000	1,360,000	3,840,000	1,420,000	2,420,000	1,440,000	380,000	1,060,000	13,510,000
35–44	1,830,000	710,000	1,120,000	2,930,000	980,000	1,950,000	1,100,000	270,000	830,000	14,550,000
45–54	1,800,000	670,000	1,130,000	2,880,000	940,000	1,940,000	1,080,000	260,000	810,000	14,330,000
55–64	1,330,000	470,000	860,000	2,170,000	670,000	1,500,000	840,000	200,000	650,000	10,720,000
65+	350,000	130,000	220,000	660,000	220,000	440,000	310,000	90,000	220,000	3,220,000
Education
Less than high school	310,000	40,000	270,000	800,000	60,000	740,000	500,000	30,000	470,000	1,980,000
High school	1,900,000	450,000	1,450,000	3,470,000	680,000	2,780,000	1,570,000	230,000	1,340,000	9,240,000
Some college	2,400,000	830,000	1,570,000	4,040,000	1,210,000	2,830,000	1,640,000	380,000	1,270,000	12,080,000
College degree	2,650,000	1,330,000	1,320,000	3,800,000	1,790,000	2,000,000	1,150,000	460,000	680,000	20,810,000
Advanced degree	950,000	580,000	370,000	1,360,000	800,000	570,000	410,000	220,000	190,000	15,030,000
States
All	8,210,000	3,230,000	4,980,000	13,470,000	4,550,000	8,920,000	5,260,000	1,320,000	3,950,000	59,140,000
Alabama	110,000	40,000	70,000	180,000	70,000	110,000	70,000	20,000	50,000	720,000
Alaska	10,000	–	10,000	20,000	10,000	10,000	10,000	–	10,000	100,000
Arizona	150,000	70,000	80,000	240,000	90,000	150,000	90,000	20,000	70,000	1,130,000
Arkansas	80,000	30,000	40,000	130,000	50,000	80,000	50,000	10,000	40,000	450,000
California	780,000	300,000	480,000	1,290,000	430,000	870,000	510,000	130,000	380,000	6,640,000
Colorado	170,000	60,000	110,000	280,000	90,000	190,000	110,000	30,000	80,000	1,240,000
Connecticut	70,000	30,000	40,000	120,000	40,000	70,000	40,000	10,000	30,000	720,000
Delaware	30,000	10,000	20,000	40,000	10,000	30,000	20,000	–	10,000	180,000
Washington, D.C.	20,000	10,000	10,000	30,000	10,000	20,000	10,000	–	10,000	240,000
Florida	680,000	270,000	420,000	1,160,000	380,000	780,000	480,000	110,000	370,000	3,880,000
Georgia	340,000	130,000	210,000	570,000	180,000	390,000	230,000	50,000	180,000	2,100,000
Hawaii	40,000	10,000	30,000	60,000	20,000	50,000	30,000	10,000	20,000	250,000
Idaho	40,000	20,000	20,000	70,000	20,000	40,000	30,000	10,000	20,000	250,000
Illinois	330,000	140,000	190,000	510,000	180,000	330,000	190,000	40,000	140,000	2,500,000
Indiana	160,000	70,000	90,000	270,000	100,000	170,000	110,000	30,000	80,000	1,090,000
Iowa	80,000	30,000	40,000	120,000	50,000	70,000	40,000	10,000	30,000	510,000
Kansas	60,000	30,000	40,000	110,000	40,000	70,000	40,000	10,000	30,000	480,000
Kentucky	110,000	40,000	60,000	170,000	60,000	110,000	70,000	20,000	50,000	660,000
Louisiana	120,000	40,000	80,000	200,000	60,000	140,000	80,000	20,000	60,000	730,000
Maine	30,000	10,000	20,000	50,000	20,000	30,000	20,000	–	10,000	220,000
Maryland	150,000	60,000	90,000	250,000	90,000	160,000	90,000	30,000	70,000	1,400,000
Massachusetts	180,000	80,000	110,000	300,000	110,000	190,000	120,000	30,000	80,000	1,670,000
Michigan	190,000	90,000	110,000	300,000	120,000	180,000	100,000	30,000	70,000	1,530,000
Minnesota	130,000	50,000	80,000	180,000	70,000	110,000	50,000	10,000	40,000	1,060,000
Mississippi	70,000	20,000	40,000	120,000	30,000	80,000	50,000	10,000	40,000	410,000
Missouri	160,000	80,000	90,000	260,000	100,000	160,000	100,000	20,000	70,000	1,030,000
Montana	20,000	10,000	10,000	30,000	10,000	20,000	10,000	–	10,000	130,000
Nebraska	50,000	20,000	30,000	80,000	30,000	50,000	30,000	10,000	20,000	330,000
Nevada	70,000	20,000	50,000	120,000	40,000	80,000	50,000	10,000	30,000	430,000
New Hampshire	30,000	20,000	20,000	50,000	20,000	30,000	20,000	10,000	10,000	280,000
New Jersey	280,000	100,000	180,000	450,000	140,000	320,000	170,000	40,000	130,000	2,200,000
New Mexico	40,000	10,000	20,000	70,000	20,000	50,000	30,000	10,000	20,000	280,000
New York	600,000	210,000	390,000	1,000,000	290,000	710,000	400,000	80,000	320,000	4,250,000
North Carolina	280,000	100,000	170,000	440,000	150,000	290,000	170,000	40,000	120,000	1,820,000
North Dakota	20,000	10,000	10,000	30,000	10,000	20,000	10,000	–	10,000	120,000
Ohio	230,000	100,000	120,000	370,000	150,000	220,000	150,000	50,000	100,000	1,770,000
Oklahoma	100,000	40,000	60,000	170,000	50,000	110,000	70,000	20,000	50,000	640,000
Oregon	90,000	40,000	50,000	150,000	50,000	90,000	50,000	20,000	40,000	670,000
Pennsylvania	310,000	130,000	180,000	490,000	190,000	310,000	190,000	60,000	130,000	2,220,000
Rhode Island	20,000	10,000	10,000	40,000	20,000	20,000	10,000	–	10,000	190,000
South Carolina	150,000	60,000	90,000	230,000	80,000	150,000	80,000	20,000	60,000	870,000
South Dakota	20,000	10,000	10,000	30,000	10,000	20,000	10,000	–	10,000	120,000
Tennessee	180,000	80,000	100,000	280,000	100,000	180,000	110,000	30,000	80,000	1,090,000
Texas	820,000	300,000	520,000	1,430,000	430,000	1,000,000	610,000	130,000	480,000	5,480,000
Utah	60,000	30,000	40,000	100,000	40,000	60,000	40,000	10,000	30,000	500,000
Vermont	20,000	10,000	10,000	30,000	10,000	20,000	10,000	–	10,000	110,000
Virginia	220,000	80,000	140,000	380,000	120,000	260,000	160,000	40,000	120,000	1,890,000
Washington	150,000	60,000	100,000	230,000	80,000	160,000	80,000	20,000	60,000	1,300,000
West Virginia	40,000	10,000	20,000	60,000	20,000	40,000	30,000	10,000	20,000	240,000
Wisconsin	120,000	50,000	70,000	180,000	70,000	110,000	60,000	20,000	40,000	930,000
Wyoming	10,000	–	10,000	20,000	10,000	10,000	10,000	–	10,000	80,000

Note: Subtotals may not add up to totals due to rounding. Following the methodology used by the U.S. Department of Labor, the estimates include all workers affected by the federal salary threshold increase, and do not account for higher state salary thresholds.

Source: EPI analysis of pooled Current Population Survey Outgoing Rotation Group microdata, 2016–2018, following the methodology used in the U.S. Department of Labor’s 2019 final rule, “Defining and Delimiting the Exemptions for Executive, Administrative, Professional, Outside Sales and Computer Employees,” 29 CFR Part 541 (published September 24, 2019).

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With this rule, the Trump administration is cheating workers out of billions. The annual wage gains from this rule are $1.4 billion dollars less than they would have been under the 2016 rule—and these annual earnings losses balloon over time because the Trump administration neglected to include automatic indexing in their rule. Once again, President Trump has turned his back on the working people of this country.

Table 2

The total annual wages workers will lose under the Trump overtime rule will grow to $1.8 billion in the first 10 years of implementation : Projected wages workers lose under the Trump overtime rule relative to the 2016 rule in the first 10 years of implementation of the Trump rule

	Projected standard threshold under the 2016 rule	Standard threshold under the 2019 rule	Wages lost under the 2019 rule relative to the 2016 rule	Total wage increase under the 2016 rule	Total wage increase under the 2019 rule
2020	$51,064	$35,568	$1,431,100,000	$1,787,200,000	$356,100,000
2021	$51,064	$35,568	$1,334,500,000	$1,606,000,000	$271,500,000
2022	$51,064	$35,568	$1,246,300,000	$1,477,100,000	$230,800,000
2023	$55,055	$35,568	$1,579,900,000	$1,770,700,000	$190,800,000
2024	$55,055	$35,568	$1,459,000,000	$1,632,400,000	$173,400,000
2025	$55,055	$35,568	$1,360,300,000	$1,504,200,000	$144,000,000
2026	$59,098	$35,568	$1,663,800,000	$1,798,500,000	$134,700,000
2027	$59,098	$35,568	$1,560,800,000	$1,687,000,000	$126,200,000
2028	$59,098	$35,568	$1,473,600,000	$1,595,800,000	$122,200,000
2029	$63,346	$35,568	$1,826,900,000	$1,938,300,000	$111,400,000

Notes: Subtotals may not add up to totals due to rounding. Following the methodology used by the U.S. Department of Labor, the estimates include all workers affected by the federal salary threshold increase, and do not account for higher state salary thresholds. Calculations account only for wage increases of workers with new protections (i.e., they do not account for workers with strengthened protections).

Source: EPI analysis of pooled Current Population Survey Outgoing Rotation Group microdata, 2016–2018, following the methodology used in the U.S. Department of Labor’s 2019 final rule, “Defining and Delimiting the Exemptions for Executive, Administrative, Professional, Outside Sales and Computer Employees,” 29 CFR Part 541 (published September 24, 2019).

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Table 3

The total annual wages workers will lose under the Trump overtime rule in 2020, by state

	Wages lost under the 2019 rule relative to the 2016 rule	Total wage increase under the 2016 rule	Total wage increase under the 2019 rule
US Total	$ 1,431,100,000	$ 1,787,200,000	$ 356,100,000
Alabama	$ 17,600,000	$ 23,700,000	$ 6,100,000
Alaska	$ 2,400,000	$ 3,100,000	$ 800,000
Arizona	$ 29,000,000	$ 35,900,000	$ 6,800,000
Arkansas	$ 11,200,000	$ 14,600,000	$ 3,400,000
California	$ 133,000,000	$ 167,100,000	$ 34,100,000
Colorado	$ 32,400,000	$ 44,600,000	$ 12,200,000
Connecticut	$ 12,400,000	$ 15,800,000	$ 3,400,000
Delaware	$ 3,200,000	$ 4,000,000	$ 800,000
Washington, D.C.	$ 4,700,000	$ 6,000,000	$ 1,300,000
Florida	$ 98,700,000	$ 117,500,000	$ 18,700,000
Georgia	$ 43,400,000	$ 53,200,000	$ 9,700,000
Hawaii	$ 4,400,000	$ 5,200,000	$ 900,000
Idaho	$ 6,400,000	$ 8,300,000	$ 1,900,000
Illinois	$ 68,500,000	$ 81,000,000	$ 12,400,000
Indiana	$ 33,000,000	$ 40,300,000	$ 7,300,000
Iowa	$ 20,400,000	$ 23,700,000	$ 3,300,000
Kansas	$ 15,300,000	$ 17,900,000	$ 2,600,000
Kentucky	$ 21,400,000	$ 28,800,000	$ 7,300,000
Louisiana	$ 20,300,000	$ 25,100,000	$ 4,800,000
Maine	$ 8,500,000	$ 9,900,000	$ 1,400,000
Maryland	$ 30,800,000	$ 42,100,000	$ 11,300,000
Massachusetts	$ 38,500,000	$ 51,500,000	$ 13,000,000
Michigan	$ 49,100,000	$ 64,100,000	$ 15,100,000
Minnesota	$ 28,500,000	$ 34,100,000	$ 5,600,000
Mississippi	$ 9,500,000	$ 12,400,000	$ 2,900,000
Missouri	$ 34,400,000	$ 39,700,000	$ 5,300,000
Montana	$ 4,500,000	$ 5,300,000	$ 800,000
Nebraska	$ 10,000,000	$ 12,500,000	$ 2,500,000
Nevada	$ 10,000,000	$ 12,200,000	$ 2,100,000
New Hampshire	$ 6,800,000	$ 8,800,000	$ 2,000,000
New Jersey	$ 34,800,000	$ 44,300,000	$ 9,500,000
New Mexico	$ 5,100,000	$ 6,800,000	$ 1,700,000
New York	$ 80,100,000	$ 99,300,000	$ 19,100,000
North Carolina	$ 45,700,000	$ 55,100,000	$ 9,400,000
North Dakota	$ 3,200,000	$ 3,900,000	$ 700,000
Ohio	$ 45,000,000	$ 60,900,000	$ 15,900,000
Oklahoma	$ 14,700,000	$ 19,900,000	$ 5,200,000
Oregon	$ 19,400,000	$ 26,500,000	$ 7,100,000
Pennsylvania	$ 51,500,000	$ 67,600,000	$ 16,100,000
Rhode Island	$ 4,600,000	$ 6,700,000	$ 2,100,000
South Carolina	$ 19,400,000	$ 23,700,000	$ 4,300,000
South Dakota	$ 3,300,000	$ 3,600,000	$ 400,000
Tennessee	$ 33,000,000	$ 42,000,000	$ 9,100,000
Texas	$ 141,700,000	$ 173,100,000	$ 31,400,000
Utah	$ 16,500,000	$ 19,900,000	$ 3,400,000
Vermont	$ 4,100,000	$ 4,700,000	$ 600,000
Virginia	$ 28,200,000	$ 35,300,000	$ 7,100,000
Washington	$ 40,500,000	$ 44,800,000	$ 4,300,000
West Virginia	$ 5,000,000	$ 6,400,000	$ 1,300,000
Wisconsin	$ 24,100,000	$ 31,200,000	$ 7,100,000
Wyoming	$ 2,600,000	$ 3,200,000	$ 600,000

Notes: Subtotals may not add up to totals due to rounding. Following the methodology used by the U.S. Department of Labor, the estimates include all workers affected by the federal salary threshold increase, and do not account for higher state salary thresholds. Calculations account only for wage increases of workers with new protections (i.e., they do not account for workers with strengthened protections).

Source: EPI analysis of pooled Current Population Survey Outgoing Rotation Group microdata, 2016–2018, following the methodology used in the U.S. Department of Labor’s 2019 final rule, “Defining and Delimiting the Exemptions for Executive, Administrative, Professional, Outside Sales and Computer Employees,” 29 CFR Part 541 (published September 24, 2019).

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The Trump-appointed National Labor Relations Board proposed a rule last week that would rob graduate teaching assistants and other student employees of the rights to organize and collectively bargain. This is just the most recent example of the board’s attack on working people. Last month, the board determined that misclassifying workers as independent contractors does not violate the National Labor Relations Act (NLRA). Before that, the General Counsel’s office released a deeply flawed memo that found that Uber drivers were not employees under the NLRA.

The trend with the Trump board seems to be to take a statute which broadly protects private sector workers and whittle away at its scope. At a time when worker advocates are demanding more workers have the right to a union and collective bargaining, the Trump board’s graduate teaching assistant proposal demonstrates a fundamental lack of understanding of the modern workforce.

Had the Trump board considered any data or conducted any meaningful analysis of the academic workplace in developing the proposed rule, it would have discovered that the last several decades have seen significant changes in labor conditions. Universities have increasingly relied on graduate teaching assistants and contingent faculty, with the growth in graduate assistant positions and non-tenure track positions outpacing the increase in tenured and tenure-track positions between the Fall of 2005 and Fall 2015.

These positions have dramatically lower compensation than faculty. The average salary of a graduate teaching assistant during the 2015-2016 school year was $35,810. Individuals who are working while enrolled in graduate school deserve livable wages. One way to address this issue is through collective bargaining—the very right the Trump board seeks to rob from these workers.

Further, in spite of the majority’s insistence that collective bargaining will harm “academic freedom,” there is a wealth of evidence to the contrary. Public universities have had graduate student worker unions for 50 years. In 2016, more than 64,000 graduate student employees were unionized at 28 institutions of higher education in the public sector. The colleges and universities with union represented student employees have not reported a loss of “academic freedom” as the Trump board suggests.

In reality, union-represented graduate student employees at public universities have reported that they enjoy higher levels of personal and professional support than that reported by non-union represented students. Unionized and nonunionized student employees report similar perceptions of academic freedom. However, union-represented graduate student workers did report receiving higher pay than non-union represented graduate student workers. Perhaps this is one reason why there have been so many successful organizing campaigns on campuses across the country the last few years. Student employees at several private universities have unionized and won better working conditions–better pay, better health care, better child care. The Trump board’s proposal would rob student employees of these gains.

The Trump board is committed to rolling back workers’ rights to a union and collective bargaining. They routinely advance political proposals based on flawed facts and legal reasoning. Through decisions, general counsel memos, and rulemaking the agency is making it more and more difficult for working people to have a voice in the workplace. All workers deserve the basic right to a union. The NLRB is the agency responsible for ensuring that right and we must hold them accountable for betraying their statutory duty.

Members of the public are invited to comment on the Trump board’s most recent proposal. Comments can be submitted here.

The notion that hard work is all that’s needed to achieve a prosperous or even comfortable living in the United States has come under increasing scrutiny in recent years as stagnant wages for most workers have led to talk about the demise of the American Dream.

Randy Schutt, a long-time progressive activist and researcher, has created a simple model to help illustrate just how much dumb luck, mere chance and circumstance, can play a role in who becomes wealthy and who remains poor.

The project, intended to illustrate certain nuances about economic inequality to students and researchers, is called “The Chancy Islands: A Land of Equally Capable People But With Unequal Luck.”

His imaginary archipelago includes places like Rugged Island and Mercy Island, the first unforgiving, the latter much less so, and everything in between—Flat Island, Combo Island, Parity Island, etc.

“We’re always told that if you work hard and persist through adversity that you can rise above your humble (or horrible) circumstances and become wealthy. But that isn’t true,” Schutt said. “Most people are so beaten down by our economic system that they have to be lucky just to get by. And they have to be very lucky to do well and extremely well to get super rich.”

The statistics bear our Schutt’s narrative. Economic mobility, defined as the chance that someone born in the bottom fifth of the income distribution can sweat their way to the top fifth, is extremely low in the United States (around 7.5%)—and actually much lower than other rich nations, because of a much weaker social safety net.

You can explore the models for yourself by going to the website. But Schutt comes to the following conclusion after having examined all of the different combinations and possibilities exhaustively:

“It turns out that even with absolutely no differences in talent or effort, severe inequality can still arise just from the random shocks of wealth-depleting natural events such as serious illnesses, bad accidents, and natural disasters,” said Schutt. “Some households will amass vast fortunes without having done anything to justify their windfall; others will slide into poverty and homelessness without having done anything to warrant their impoverishment.”

Schutt adds, on a hopefully note, that his model also suggests “a few simple mitigation measures can almost completely rebalance such a society, essentially eliminating any long-term inequality.”

Such policies include, perhaps unsurprisingly, taxing the wealthy in ways that are becoming increasingly popular with the American electorate.

Josh Bivens, director of research at EPI

The Federal Reserve meets this week against a backdrop of mounting evidence of a slowing economy. Since the last Federal Open Market Committee (FOMC) meeting, revised data on gross domestic product (the widest measure of the nation’s economic activity) and job growth have shown that 2018 saw much slower growth than previously reported.

Between April 2018 and March 2019, for example, the economy created 500,000 fewer jobs than had originally been reported. Only 105,000 jobs were created in August if temporary Census positions are excluded: this is roughly half the pace of growth that characterized pre-revision estimates of average job growth in 2018.

These clear signs of an economic slowdown raise the obvious question, “Why has growth faltered?”

While many pundits and economists have blamed the escalating trade conflict between the Trump administration and China, there are much more obvious sources of this slowdown: the Fed’s own premature interest rate increases between December 2015 and 2018 and the utter waste of fiscal resources that was the Tax Cuts and Jobs Act (TCJA) passed at the end of 2017.

To be clear, the Trump administration’s trade conflict is stupid and destructive, and its attempt to pin the blame for the slowdown on the Fed is self-serving. And the Trump administration’s scapegoating others for the weak economy takes real hubris given that its signature economic policy initiative—the TCJA—has been such an obvious failure in terms of spurring growth.

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Yesterday’s Census Bureau report on income, poverty, and health insurance coverage in 2018 shows that while there was a slowdown in overall median household income growth relative to 2017, income growth was uneven by race and ethnicity. Real median income increased 4.6% among Asian households (from $83,376 to $87,194), 1.8% among African American households (from $40,963 to $41,692), 1.1% among non-Hispanic white households (from $69,851 to $70,642), and only 0.1% among Hispanic households (from $51,390 to $51,450), as seen in Figure A. The only groups for which income growth was statistically significant were Asian and Hispanic households.

In 2018, the median black household earned just 59 cents for every dollar of income the median white household earned (unchanged from 2017), while the median Hispanic household earned just 73 cents (down from 74 cents).

Figure A

Real median household income by race and ethnicity, 2000–2018

Year	White	Black	Hispanic	Asian	White-imputed	Black-imputed	Hispanic-imputed	Asian-imputed	White	Black	Hispanic	Asian	White	Black	Hispanic	Asian
2000	$66,712	$43,380	$48,500		$69,069	$44,614	$46,989
2001	$65,835	$41,899	$47,721		$68,161	$43,091	$46,234
2002	$65,646	$40,839	$46,334	$73,660	$67,965	$42,001	$44,890	$79,501
2003	$65,388	$40,633	$45,160	$76,231	$67,698	$41,789	$43,753	$82,276
2004	$65,178	$40,292	$45,670	$76,631	$67,481	$41,438	$44,247	$82,708
2005	$65,458	$39,898	$46,360	$76,873	$67,771	$41,033	$43,846	$84,991
2006	$65,449	$40,116	$47,169	$78,291	$67,762	$41,257	$45,699	$86,560
2007	$66,676	$41,388	$46,958	$78,343	$69,032	$42,565	$45,495	$86,616
2008	$64,923	$40,154	$44,326	$74,913	$67,217	$41,296	$42,945	$82,824
2009	$63,895	$38,423	$44,628	$74,982	$66,153	$39,516	$43,238	$82,901
2010	$62,857	$37,114	$43,433	$72,402	$65,078	$38,170	$42,080	$80,048
2011	$62,001	$36,215	$43,217	$71,139	$64,192	$37,245	$41,870	$78,653
2012	$62,465	$36,945	$42,738	$73,415	$64,672	$37,996	$41,406	$81,169
2013	$62,915	$37,547	$44,228	$70,687	$65,138	$38,615	$42,850	$78,153	$65,138	$38,615	$42,850	$78,153
2014					$63,976	$37,854	$45,114	$78,883	$63,976	$37,854	$45,114	$78,883
2015					$66,721	$39,440	$47,852	$81,788	$66,721	$39,440	$47,852	$81,788
2016					$68,059	$41,924	$49,887	$85,210	$68,059	$41,924	$49,887	$85,210
2017					$69,806	$41,584	$51,717	$83,314	$69,806	$41,584	$51,717	$83,314	$69,851	$40,963	$51,390	$83,376
2018													$70,642	$41,692	$51,450	$87,194

 

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The data underlying the figure.

Notes: Because of a redesign in the CPS ASEC income questions in 2013, we imputed the historical series using the ratio of the old and new method in 2013. Solid lines are actual CPS ASEC data; dashed lines denote historical values imputed by applying the new methodology to past income trends. The break in the series in 2017 represents data from both the legacy CPS ASEC processing system and the updated CPS ASEC processing system. White refers to non-Hispanic whites, Black refers to Blacks alone or in combination, Asian refers to Asians alone, and Hispanic refers to Hispanics of any race. Comparable data are not available prior to 2002 for Asians. Shaded areas denote recessions.

Source: EPI analysis of Current Population Survey Annual Social and Economic Supplement Historical Poverty Tables (Tables H-5 and H-9).

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Based on EPI’s imputed historical income values (see the note under Figure A for an explanation), 11 years after the start of the Great Recession in 2007, only African American households remained below their pre-recession median income. Compared with household incomes in 2007, median household incomes in 2018 were down 2.1 percent for African American households, but up 0.7% for Asian households, 2.3% for non-Hispanic white households, and 13.1% for Hispanic households. Asian households continued to have the highest median income, despite large income losses in the wake of the recession.

The 2018 poverty rates also reflect the patterns of income growth between 2017 and 2018. As seen in Figure B, poverty rates for all groups were down slightly or unchanged, but remained highest among African Americans (20.7%, down 1.0 percentage point), followed by Hispanics (17.6%, down 0.7 percentage points), Asians (10.1%, up 0.4 percentage points), and whites (8.1%, down 0.4 percentage points). African American and Hispanic children continued to face the highest poverty rates—28.5% of African Americans and 23.7% of Hispanics under age 18 lived below the poverty level in 2018. African American children were more than three times as likely to be in poverty as white children (8.9%).Read more

**Correction: The SSI number in Figure B was corrected to 2,949,000 from 3,949,000.**

From 2017 to 2018, the official poverty rate fell by 0.5 percentage points, as household incomes rose modestly, albeit at a slower pace than the previous three years. This was the fourth year in a row that poverty declined, but the poverty rate remains half a percentage point higher than the low of 11.3% it reached in 2000.

Since 2010, the U.S. Census Bureau has also released an alternative to the official poverty measure known as the Supplemental Poverty Measure (SPM).

The SPM corrects many deficiencies in the official rate. For one, it constructs a more comprehensive threshold for incomes families need to live free of poverty, and adjusts that threshold for regional price differences. For another, it accounts for the resources available to poor families that are not included in the official rate, such as food stamps and other in-kind government benefits.

As shown in Figure A, a larger proportion of Americans are in poverty as measured by the SPM than as measured by the official measure. (Importantly, however, researchers who constructed a longer historical version of the SPM found that it shows greater long-term progress in reducing poverty than the official measure.) In 2018, the SPM increased by 0.1 percentage points to 13.1%. Under the SPM, 42.5 million Americans were in poverty last year, compared with 38.1 million Americans under the “official” poverty measure.Read more