Austerity at all levels of government has created a teacher shortfall [feedly]

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Austerity at all levels of government has created a teacher shortfall
// Economic Policy Institute

With the September employment data in hand, we can look at the number of teachers who are starting work or going back to school this year. The number of teachers and education staff fell dramatically during the Great Recession and has failed to get anywhere near its prerecession level, let alone the level that would be required to keep up with an expanding student population. In addition to losses from the Great Recession, the pursuit of austerity at all levels of government has meant that public education jobs are still 214,000 less than they were eight years ago. Over the last year, the number of teachers rose by 37,700. While this is clearly a positive sign, if we include the number of jobs that should have been created just to keep up with enrollment, we are currently experiencing a 372,000 job shortfall in public education. The costs of a significant teacher gap are measurable: larger class sizes, fewer teacher aides, fewer extracurricular activities, and changes to the curricula. Shortsighted austerity measures at all levels of government hit children in today's classrooms.

The Teacher Gap



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Corporate profits are way up, corporate taxes are way down [feedly]

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Corporate profits are way up, corporate taxes are way down
// Economic Policy Institute

Since 1952, corporate profits as a share of the economy have risen dramatically (from 5.5 percent to 8.5 percent), while corporate tax revenues as a share of the economy have plummeted (from 5.9 percent to just 1.9 percent).

This trend has worsened since the end of the Great Recession. Between 2010 and 2015, corporate profits averaged 9.2 percent of gross domestic product, while corporate income tax revenue averaged just 1.6 percent.

Economic Snapshot



The driving force behind the recent erosion of the corporate income tax base is the largest corporate loophole— deferral of taxes paid on profits booked abroad. Deferral allows corporations to stash billions of dollars offshore to avoid paying taxes on them. As outlined in a new Americans for Tax Fairness and EPI Chartbook, deferral will drain the U.S. Treasury of about $1.3 trillion in tax revenue over ten years. The crucial point is, however, that these profits have not actually been earned abroad. They are, in fact, offshore only on paper. Reed College professor of economics Kimberly Clausing estimates that seven tax havens are responsible for 50 percent of all foreign profits, but account for only 5 percent of their foreign employment.

If the next president and Congress decided to tackle corporate tax reform, perhaps the single largest priority should be closing the gaping deferral loophole, which not only causes the federal government to hemorrhage revenue, but also provides an incentive for U.S. companies to shift production and large profits offshore.

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Black workers’ wages have been harmed by both widening racial wage gaps and the widening productivity-pay gap [feedly]

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Black workers' wages have been harmed by both widening racial wage gaps and the widening productivity-pay gap
// Economic Policy Institute

Participants in the ongoing discussion about how to remedy centuries of economic inequality experienced by African Americans generally fall into one of two camps. One group calls for explicitly race-based or racially targeted solutions, while the other group supports race-neutral, or universal, progressive economic policies and programs. This brief focuses on the damage done to typical black workers' wages in recent decades and demonstrates that progress on both fronts is necessary to undo the damage. Specifically, widening black-white wage gaps and growing overall wage inequality between 1979 and 2015 imposed a dual penalty on black workers' wage growth. Therefore, a dual strategy is necessary to put black workers' wages back on a trajectory that lets them share in the fruits of overall economic growth while also closing persistent gaps with white workers.

Its main findings are:

Blacks are paid less than whites and the gap is growing, particularly among high earners. Specifically, the black-white wage gap—the percent by which wages of black workers lag wages of their white peers—has grown over time and at all levels of pay, but the gap is largest among the highest earners.All median workers are paid less than they should be given their increasing productivity. Since 1979, median wage growth overall has fallen far short of productivity growth, which is a measure of the additional output or income generated in an average hour of work. Stated another way, rising productivity has provided the potential for substantial pay increases for typical workers but this potential has not been realized, as the fastest wage growth has been concentrated among the highest earners. This is the defining feature of increased wage and income inequality.Closing the 26.2 percent racial wage gap alone would have raised hourly wages of the median black worker by $5.03 in 2015.Closing the racial wage gap and ensuring that median wages grew with productivity would have raised hourly wages of the median black worker by $12.33 in 2015 (from $14.14 to $26.47)—an increase of 87.2 percent. Addressing both class and racial inequality in this way would have also raised the hourly wages of the median white worker by $7.30 (from $19.17 to $26.47)—an increase of 38.1 percent.

The widening racial wage gap and its causes

The black-white wage gap is defined by Wilson and Rodgers (2016) as the percent by which wages of black workers lag wages of their white peers. In 2015, the gap for the median (50th percentile) worker was 26.2 percent. Wilson and Rodgers also provide a detailed analysis of trends in average hourly black-white wage gaps among men and women who are full-time workers with similar levels of education and experience, live in the same region of the country, and have the same metro status (urban or rural). We find that even after controlling for these observed worker characteristics, a significant black-white wage gap remains and this remaining difference in pay is largely the result of discrimination. In fact, African Americans who have ascended the wage ladder through increased education and better access to higher-paying jobs also earn less than similarly situated whites. Interestingly, over the last 36 years, racial wage gaps have grown most among the most highly educated (those with a bachelor's degree or more education).

We (Wilson and Rodgers 2016) further conclude that while racial discrimination was the largest single factor driving growing differences in the pay of white and black workers between 1979 and 2015, increased overall wage inequality consistently played a role as well. One of the most pronounced features of growing wage inequality over the last nearly four decades has been the increasing gap between productivity growth, which represents the potential for wage growth, and actual wage growth for the vast majority of all workers, regardless of race or gender. Because black workers tend to cluster lower in the wage distribution than white workers, and because between 1979 and 2015 hourly wages grew most in the higher parts of the wage distribution, this rise in overall wage inequality mechanically separated black and white workers' wages. The brief exception to this trend occurred between 1995 and 2000 when strong economic growth, tight labor markets, and widely shared economic prosperity produced slightly faster wage growth for African Americans than whites. As a result, racial wage disparities narrowed over this five-year period, but not nearly enough to completely close the gaps.

The widening productivity-pay gap and why it's relevant to black workers

Imagine for a second that it is 1979, and policymakers established a goal of completely eliminating racial wage gaps by 2015. A smart observer in 1979, asked to guess how much the median black worker's hourly wage would rise by 2015, might assume that overall median wages would track economy-productivity growth over the next 46 years. After all, median wages had kept pace with productivity over most of the period since World War II. So our observer might add to this baseline rate of growth additional growth of black workers' wages needed to close racial wage gaps.

Neither of these things happened. Overall median wages did not track productivity growth and racial wage gaps did not close, but instead widened. This kept wage growth for black workers much, much lower than it would have been otherwise.

Quantifying the double-penalty on black workers' wage growth

In order to more explicitly demonstrate the dual penalty on wage growth of black workers, this section presents data showing trends in median wage growth, the black-white wage gap, and productivity growth since 1979. While most of the discussion is based on trends at the median, we also compare wages and wage growth of black and white workers at the 10th and 95th percentiles to show how these trends intersect with growing inequality. Mathematically, the median wage is the hourly wage in the middle of the wage distribution, with half of workers earning less than the median and half earning more. Since the median wage is often associated with the "typical" worker wage, and thus the standard of living for the middle class, trends in racial wage gaps and wage growth at the median provide a useful benchmark for the state of racial economic inequality, mobility, and middle-class quality of life in the United States.

Table 1 shows real hourly wages of black and white workers at the 10th, 50th (median), and 95th percentiles of the wage distribution, along with the corresponding black-white wage gap at each percentile, and the ratio of the 95th percentile wage to the median wage by race. While the wage gaps reported in the table have not been adjusted for racial differences in worker characteristics, comparing relative wages of workers within the same wage percentile indirectly limits comparisons to workers who are more likely to be of similar education and skill levels. We present these data for the business cycle peak years of 1979, 1989, 2000, and 2007, as well as for 1995 (the point during the 1990s business cycle after which wages grew dramatically) and for 2015 (the last year for which data are available).

Table 1



The data in Table 1 clearly show the intersection of three trends between 1979 and 2015: 1) limited wage growth at the middle and lower end of the wage distribution, 2) growing class inequality as indicated by the increasing ratio of 95th-to-50th percentile wages, and 3) growing racial inequality demonstrated by increasing black-white wage gaps. Table 1 also shows that while strong wage growth has eluded moderate- and low-wage workers of both races over much of the last 36 years, to the extent that wages have grown at all, most of the growth happened in a single episode between roughly 1995 and 2000. In almost every economic cycle preceding and following the late 1990s boom, wage growth of black and white low- to middle-wage earners was nearly flat or negative. Between 1979 and 2015, the median black wage grew a meager 1.8 percent, while the median white wage grew 11.9 percent. Consequently, the black-white wage gap at the median increased from 17.7 percent in 1979 to 26.2 percent in 2015.

Wages trends have been even more disappointing for low-wage earners. From 1979 to 2015, wages at the 10th percentile declined 8.5 percent among black workers, and grew, but only by 0.7 percent, among whites. Although the racial wage gap is smallest among low-wage workers, that gap has more than tripled since 1979. Minimum-wage laws have a lot to do with patterns in black-white wage inequality at the lower end of the wage distribution. By setting a wage floor, the minimum wage compresses wages at the bottom so that employers have limited discretion in deciding how much to pay workers. However, since states and some cities have the authority to set their own minimum wages as long as they exceed the federal minimum wage, low-wage pay varies based on where workers live. Blacks disproportionately live in the Southern and Midwestern regions of the country where state minimum wages are generally lower than (or non-existent) and have been increased less frequently than in the Northeast or West. As a result, fewer low-wage black workers benefitted from state and local minimum-wage increases enacted over the last couple years, expanding the 10th percentile black-white wage gap from 7.4 percent in 2007 to 11.8 percent in 2015.

The highest-paid workers have seen the greatest wage growth between 1979 and 2015. Even during periods when median and 10th percentile workers lost ground, the hourly wages of those in the 95th percentile continued to increase. This feature in the data demonstrates growing class inequality—a widening gap between what workers at the top earn compared with everyone else. For example, 95th percentile workers earned a little more than twice the median worker of the same race in 1979; by 2015, 95th percentile workers earned at least 3 times the median worker of the same race. Yet, even among the highest earners, for whom wage growth has been relatively strong, racial wage gaps persist and have expanded. In fact, racial wage gaps grow larger as pay increases. In 1979, the 95th percentile black worker earned 21.1 percent less than the 95th percentile white. By 2015, this gap had expanded to 31.2 percent, compared with a gap of 26.2 percent at the median and 11.8 percent at the 10th percentile.

While growing racial wage gaps and the lack of wage growth among middle- and low-wage workers is disappointing enough, these patterns are even more troubling given the potential for better outcomes. Figure A shows that between 1979 and 2015, median hourly real wage growth fell far short of productivity growth—a measure of the potential for pay increases—for men as well as women and for black and white workers. However, there have been clear differences in wage growth trends of men and women, and of blacks relative to whites. These differences have determined the pattern of race and gender wage gaps. Median hourly wages of white and black men both fell, with black men suffering greater losses (of 5.7 percent, compared with losses of 1.0 percent for white men). On the other hand, median hourly wages of black and white women increased, but white women's wages grew much more (31.6 percent) than those of black women (15.2 percent). The distinct patterns of wage growth for men and women also contributed to improved gender equality in pay over this same period. Davis and Gould (2015) report that 40 percent of the narrowing in the gender wage gap over this period occurred because of falling men's wages.

Figure A



So what have wage stagnation and racial pay discrimination cost black workers? In order to answer this question, let's consider a couple different scenarios. Under the first scenario, if all we did was eliminate the 26.2 percent wage disadvantage of the median black worker by raising black workers' pay to that of whites, this would be equivalent to an hourly increase of $5.03—the difference between white and black wages at the median in 2015. In this example, we only address racial inequality, leaving class inequality and white wages unchanged.

Under the second scenario, we assume that the 1979 racial wage gap at the median had closed by 2015, or that all workers earned the overall median, and that the overall median ($16.15 in inflation-adjusted 2015 dollars, not shown in Table 1) had grown with productivity (63.9 percent) between 1979 and 2015. Under this scenario, the median black worker would be earning an hourly wage of $26.47 instead of $14.14—an increase of $12.33. That means the hourly wage of the median black worker would be an astounding 87.2 percent higher! But, under this scenario, the median white worker would also receive an hourly pay increase of $7.30—the difference between $26.47 and $19.17—boosting their wages by 38.1 percent. By addressing both class and racial inequality, all workers are made better off, with much larger gains for African Americans because of the dual penalties imposed by class and race.

There is no doubt that black workers and their families would be better off if paid the same as comparable white workers, but the benefits of racial equity will be limited if wage growth continues along the same lackluster path it has been on for nearly four decades. The full benefits of equity are achieved when all workers share in the economic prosperity they help to produce. This happens when all workers' wages grow with productivity.

About the author

Valerie Rawlston Wilson is director of the Economic Policy Institute's Program on Race, Ethnicity, and the Economy (PREE), a nationally recognized source for expert reports and policy analyses on the economic condition of America's people of color. Prior to joining EPI, Wilson was an economist and vice president of research at the National Urban League Washington Bureau, where she was responsible for planning and directing the bureau's research agenda. She has written extensively on various issues impacting economic inequality in the United States—including employment and training, income and wealth disparities, access to higher education, and social insurance—and has also appeared in print, television, and radio media. She has a Ph.D. in economics from the University of North Carolina at Chapel Hill.

Acknowledgments

This paper was made possible by a grant from the Peter G. Peterson Foundation. The statements made and views expressed are solely the responsibility of the author.

References

Current Population Survey Outgoing Rotation Group microdata. Various years. Survey conducted by the Bureau of the Census for the Bureau of Labor Statistics [machine-readable microdata file]. Washington, DC: U.S. Census Bureau.

Davis, Alyssa, and Elise Gould. 2015. Closing the Pay Gap and Beyond: A Comprehensive Strategy for Improving Economic Security for Women and Families. Washington, D.C.: Economic Policy Institute.

Wilson, Valerie, and William M. Rodgers, III. 2016. Black-White Wage Gaps Expand with Rising Wage Inequality. Washington, DC: Economic Policy Institute.

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The Simple Economics of Machine Intelligence [feedly]

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The Simple Economics of Machine Intelligence
// Digitopoly

[This post was co-written with Ajay Agrawal and Avi Goldfarb and appeared in HBR Blogs on 17 November 2016]

The year 1995 was heralded as the beginning of the "New Economy." Digital communication was set to upend markets and change everything. But economists by and large didn't buy into the hype. It wasn't that we didn't recognize that something changed. It was that we recognized that the old economics lens remained useful for looking at the changes taking place. The economics of the "New Economy" could be described at a high level: Digital technology would cause a reduction in the cost of search and communication. This would lead to more search, more communication, and more activities that go together with search and communication. That's essentially what happened.

Today we are seeing similar hype about machine intelligence. But once again, as economists, we believe some simple rules apply. Technological revolutions tend to involve some important activity becoming cheap, like the cost of communication or finding information. Machine intelligence is, in its essence, a prediction technology, so the economic shift will center around a drop in the cost of prediction.

The first effect of machine intelligence will be to lower the cost of goods and services that rely on prediction. This matters because prediction is an input to a host of activities including transportation, agriculture, healthcare, energy manufacturing, and retail.

When the cost of any input falls so precipitously, there are two other well-established economic implications. First, we will start using prediction to perform tasks where we previously didn't. Second, the value of other things that complement prediction will rise.

Lots of tasks will be reframed as prediction problems

As machine intelligence lowers the cost of prediction, we will begin to use it as an input for things for which we never previously did. As a historical example, consider semiconductors, an area of technological advance that caused a significant drop in the cost of a different input: arithmetic. With semiconductors we could calculate cheaply, so activities for which arithmetic was a key input, such as data analysis and accounting, became much cheaper. However, we also started using the newly cheap arithmetic to solve problems that were not historically arithmetic problems. An example is photography. We shifted from a film-oriented, chemistry-based approach to a digital-oriented, arithmetic-based approach. Other new applications for cheap arithmetic include communications, music, and drug discovery.

The same goes for machine intelligence and prediction. As the cost of prediction falls, not only will activities that were historically prediction-oriented become cheaper — like inventory management and demand forecasting — but we will also use prediction to tackle other problems for which prediction was not historically an input.

Consider navigation. Until recently, autonomous driving was limited to highly controlled environments such as warehouses and factories where programmers could anticipate the range of scenarios a vehicle may encounter, and could program if-then-else-type decision algorithms accordingly (e.g., "If an object approaches the vehicle, then slowdown"). It was inconceivable to put an autonomous vehicle on a city street because the number of possible scenarios in such an uncontrolled environment would require programming an almost infinite number of if-then-else statements.

Inconceivable, that is, until recently. Once prediction became cheap, innovators reframed driving as a prediction problem. Rather than programing endless if-then-else statements, they instead simply asked the AI to predict: "What would a human driver do?" They outfitted vehicles with a variety of sensors – cameras, lidar, radar, etc. – and then collected millions of miles of human driving data. By linking the incoming environmental data from sensors on the outside of the car to the driving decisions made by the human inside the car (steering, braking, accelerating), the AI learned to predict how humans would react to each second of incoming data about their environment. Thus, prediction is now a major component of the solution to a problem that was previously not considered a prediction problem.

Judgment will become more valuable

When the cost of a foundational input plummets, it often affects the value of other inputs. The value goes up for complements and down for substitutes. In the case of photography, the value of the hardware and software components associated with digital cameras went up as the cost of arithmetic dropped because demand increased – we wanted more of them. These components were complements to arithmetic; they were used together.  In contrast, the value of film-related chemicals fell – we wanted less of them.

All human activities can be described by five high-level components: data, prediction, judgment, action, and outcomes. For example, a visit to the doctor in response to pain leads to: 1) x-rays, blood tests, monitoring (data), 2) diagnosis of the problem, such as "if we administer treatment A, then we predict outcome X, but if we administer treatment B, then we predict outcome Y" (prediction), 3) weighing options: "given your age, lifestyle, and family status, I think you might be best with treatment A; let's discuss how you feel about the risks and side effects" (judgment); 4) administering treatment A (action), and 5) full recovery with minor side effects (outcome).

As machine intelligence improves, the value of human prediction skills will decrease because machine prediction will provide a cheaper and better substitute for human prediction, just as machines did for arithmetic. However, this does not spell doom for human jobs, as many experts suggest. That's because the value of human judgment skills will increase. Using the language of economics, judgment is a complement to prediction and therefore when the cost of prediction falls demand for judgment rises. We'll want more human judgment.

For example, when prediction is cheap, diagnosis will be more frequent and convenient, and thus we'll detect many more early-stage, treatable conditions. This will mean more decisions will be made about medical treatment, which means greater demand for the application of ethics, and for emotional support, which are provided by humans. The line between judgment and prediction isn't clear cut – some judgment tasks will even be reframed as a series of predictions. Yet, overall the value of prediction-related human skills will fall, and the value of judgment-related skills will rise.

Interpreting the rise of machine intelligence as a drop in the cost of prediction doesn't offer an answer to every specific question of how the technology will play out. But it yields two key implications: 1) an expanded role of prediction as an input to more goods and services, and 2) a change in the value of other inputs, driven by the extent to which they are complements to or substitutes for prediction. These changes are coming. The speed and extent to which managers should invest in judgment-related capabilities will depend on the how fast the changes arrive.

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Mortgage "Rates Rip to Highest Levels Since July 2015" [feedly]

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Mortgage "Rates Rip to Highest Levels Since July 2015"
// Calculated Risk

From Matthew Graham at Mortgage News Daily: Rates Rip to Highest Levels Since July 2015

It was all pain, all the time for mortgage rates today.  Since the election, the average conventional 30yr fixed rate has risen roughly 0.5%, putting  November 2016 on a short list of 4 worst months in more than a decade.  Two of those months were back to back amid the 2013 taper tantrum and the other was at the end of 2010.  Let it be known that the recent surge in rates is more than a mere post-election knee-jerk.  Financial markets are fully repricing their expectations of the future, and we can't even begin to assess how that future might actually pan out until Trump takes office.

In other words, buckle up for a higher mortgage rate environment.  Rates won't necessarily be immune from good days over the next few months, but I certainly wouldn't expect a quick, triumphant return to the promised land (rates from 2 weeks ago, and below) within the same time frame.   The most prevalent conventional 30yr fixed rate quote is now 4.125% on top tier scenarios, and more than a few lenders are already up to 4.25%.
emphasis added

CR Note: Refinance activity will decline sharply, and I expect some slowdown in housing (still thinking about this).

Here is a table from Mortgage News Daily:

Home Loan Rates

View More Refinance Rates

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Krugman: jobs program, or ripoff

http://mobile.nytimes.com/blogs/krugman/2016/11/19/infrastructure-build-or-privatization-scam/?_r=0&referer=

Re: [socialist-econ] NYTimes: When Work Loses Its Dignity

Great piece from a great senator!!!

Sent from my iPhone

On Nov 18, 2016, at 5:21 AM, John Case <jcase4218@gmail.com> wrote:

Sherrod brown gets it!                                When Work Loses Its Dignity http://nyti.ms/2eILux1

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