Roland Fryer – His Life Story, His Work on Education and on Police Use of Force, and Harvard

Marie Curie, Nobel laureate:  “Nothing in life is to be feared, it is only to be understood.  Now is the time to understand more, so that we may fear less.”

Roland Fryer grew up in difficult circumstances; rose to the top of his profession; did fundamental work on education that was not, however, always welcomed by the education establishment; found (contrary to all expectations, including his own) that there was not an anti-Black racial difference in police shootings in the data but rather, if anything, the opposite; and was soon thereafter suspended without pay for two years from his tenured position as a full professor at Harvard, with the research lab that served as the vehicle for his work permanently closed.

[Note:  Sources and references used, with links, are all gathered at the end of this post.]

A.  Roland Fryer

Roland Fryer is Black.  He was born in 1977 in Daytona Beach, Florida, but moved to Lewisville, Texas (about 20 miles northwest of Dallas) when he was very young.  Fryer’s mother left him (perhaps more left his father) when he was still an infant, and they did not meet again until he was in his 20s.  His father had been a math teacher but then became a copier salesman, was extremely abusive when he was drunk (which was often), and beat him often (especially when he was drunk).  And when Fryer was 15, his father was convicted of a rape and sent to prison for eight years.

Fryer spent his summers with his grandmother in Daytona Beach, Florida, where much of his extended family still lived.  And he remained close to his grandmother for many years (she passed away just a few years ago).  She was a school teacher (of sixth graders), and clearly a major influence.  But his family in Florida had difficulties.  In an interview, he noted (when asked for a count) that 8 of his 10 closest family members either died young or spent time in jail.  This was also true for many of his friends.  When staying with his grandmother in Florida, he would also frequently visit an aunt, who ran a profitable crack-cocaine operation from her home.  One day he went to visit his aunt, but arrived later than normal.  When he did he saw the home surrounded by police.  Nearly everyone there was arrested, as he likely would have been had he arrived a bit earlier.

Fryer was certainly not separate from this.  He made money by selling marijuana as well as selling items he systematically shoplifted from stores.  Given the nature of his clientele, as well as the all-cash transactions involved, he carried a gun (a .357 Magnum).

He did not do well in his studies at school.  But he was active in athletics, and must have been quite good as he was awarded a basketball scholarship to play at the University of Texas at Arlington – an NCAA Division 1 school.  It was not one of the top basketball programs in the country (it only had 11 winning seasons out of 48 as of 2004, a few years after he was there), but with that scholarship he got into a decent college.  And as he later related in an interview, while he had to take the SAT in order to qualify for the basketball scholarship, he was only interested in getting a minimum score of at least the 700 required to participate in NCAA athletics.  He got that (I am not sure by what margin – he said he did not do well in part as he was still drunk from the night before).  But keep in mind that a score of 700 (out of a possible 1600, and where one cannot get less than 400) should not be all that difficult.  Based on the more recent scores for 2020 (which are easier to find than earlier scores, but should be similar enough), a score of 700 will place you at the 3rd percentile.  Note this is percentile – that is, the 3% mark, not 30%.

He attended UT-Arlington but in the end never played on the basketball team there.  His basketball skills might not have been at that needed for the college-level game.  But he enrolled in an economics class and loved it.  He was also very good at it, despite what must not have been very good preparation in high school.  But the professor teaching the class supported him, provided tutoring as needed, and with the support of a dean he was switched from his athletic scholarship to an academic one.  He excelled in his studies, and graduated Magna cum Laude in just 2 1/2 years – not the normal four.

From UT-Arlington he enrolled in the economics Ph.D. program at Penn State – a solid school with a number of good faculty members, although not an economics program one would rank as among the top 20, say, in the US.  He received his Ph.D. in 2002, but already from 2001 had obtained a post-doc position in the Department of Economics at the University of Chicago – one of the very top schools in the country for economics.  Jim Heckman (a Nobel laureate) was a sponsor and a major influence, and at Chicago Fryer co-authored papers with Steven Levitt (of Freakonomics fame, although that came later).

He began to author or co-author a series of influential papers (some of which will be discussed below), moved to Harvard in 2003, and began to accumulate a series of honors and awards.  At Harvard he entered as a Junior Fellow in the prestigious Harvard Society of Fellows (2003-06), became an Assistant Professor of Economics in 2006, and received tenure as a full Professor of Economics in 2007 at the age of 30 – the youngest African-American ever to receive tenure at Harvard and one of the youngest ever of any race.

As further evidence of Fryer’s work at the top levels as an economist, and the public recognition of it:

a)  As I write this, Fryer is listed as the author or co-author of 51 papers distributed by the National Bureau of Economic Research (NBER);

b)  In the most recent c.v. I have been able to find – which was as of February 2018 – Fryer listed 69 papers either published or as working papers in progress;

c)  He has had papers published in many of the top economics journals, including the AER, QJE, JPE, REStat, and JPubEcon, as well as numerous other peer-reviewed journals;

d)  He received a MacArthur Fellowship in 2011 (more commonly known as the MacArthur “Genius Awards”);

e)  He is a fellow of the American Academy of Arts and Sciences;

f)  He received the John Bates Clark Medal in 2015, which is awarded annually (prior to 2009 biennially) by the American Economic Association “to that American economist under the age of forty who is judged to have made the most significant contribution to economic thought and knowledge” (from the AEA website on the award).  Many of the recipients of this award later went on to win a Nobel Prize in Economics.  The first recipient, in 1947, was Paul Samuelson.

There have been numerous other awards as well, in addition to very substantial financial support that Fryer has received to enable his research from a number of foundations as well as through government grants.  A vehicle Fryer set up in 2008 for this work was the Harvard Education Innovation Lab (or EdLabs), which employed Harvard students (undergraduate and graduate), post-docs, and others (many, if not most, recent students).  At one point at least (and based on a photo in a video), there were close to 20 people working at EdLabs.  It was a major operation, with office space just off-campus.

Roland Fryer is certainly a solid economist.

B.  Work on Education

A focus of Fryer’s work, from the start, has been on education.  I will summarize some of that work here, as it is of interest in itself in addition to giving a sense of how Fryer approaches issues.

Among his earliest papers were several co-authored with Steven Levitt of Chicago on the Black-White test score gap – its magnitude, how it changes as kids age, and underlying causes.  They found that there was no gap at the very youngest ages (9 months – although I have no idea how one assesses intelligence or abilities at such an age), but that a substantial gap had opened up by the time the children had reached kindergarten age.  They found that just a few measures of social background (socio-economic status, birth weight, poverty status) could account for this fully.  That is, Blacks from a given social background scored the same as Whites from a similar social background at kindergarten age.  After that, however, the Black-White gap continued to widen, and it no longer could be fully accounted for by social background.  Something was happening as the kids entered into their school years, with what was happening in the schools themselves a possible cause.

At around this time, Fryer also looked at the issue of “Acting White” – work that he later became well known for.  While many politicians and others asserted it to be a fact and that it was holding back exceptional Black students, there was very little if any evidence.  Some cited surveys of students on whether they felt their popularity suffered if they got good grades, and the kids generally said no.  If true, then there was no such issue as “Acting White”.  But such self-reporting is not a reliable guide.

Fryer had the insight that one could instead use a very large survey (of 90,000 students in 175 schools) undertaken in 1994, that asked the students who their best friends were.  They could list up to five males and five females.  The insight was not simply to rely on who a student said his or her best friends were, but rather count only those who also named you as one of the best friends.  If it was not reciprocal, it did not count.

Once pointed out, it is a pretty obvious insight.  But no one had pointed it out before.

Fryer found that the popularity index values he constructed were pretty similar across races at the low grade point average (GPA) of a D.  It then grew steadily for Whites (i.e. the students scored higher on the popularity index as their GPAs went up), with no drop-off among those with the highest GPAs.  But while it also at first went up for Blacks (although not as steeply as for Whites), it then dropped off following a GPA of 3.5 (i.e. halfway between an A and a B average).  “Acting white” did seem to matter.  Black students with exceptional GPAs were less popular among their peers than those with a lower GPA.

Going a bit further, Fryer found that such a relationship did not exist for Blacks in private schools.  Nor did he find it in schools that were almost entirely Black (80% or more).  But, disconcertingly for those of us who believe in integration (as I do, and as I believe Fryer does), he found the penalty on popularity of the high-achieving Black students was much larger in the more integrated schools.  That is a problem for society.  And Fryer did not try to bury the result.  While disconcerting, it came out of the data.

At some time around 2006, Fryer became involved with New York City schools.  Joel Klein (appointed by then Mayor Michael Bloomberg) was the school chancellor and trying many things.  Some of the first work Fryer did was to examine whether one could have an impact via financial incentives.  He was a frequent visitor to a school in the Bronx, and on his own at some point bought pizzas for a class to celebrate the achievement of some goal.  This was repeated a few times, but he then wanted to look more systematically at whether rewards of some sort could make a difference.

His work built up by steps from there, as reformers in other school districts sought to have various proposals tested.  With financial support from foundations, Fryer developed a series of experiments in several school districts across the country to see whether financial incentives would have an impact on student achievement.  In the end, randomized control trials were organized in five cities (New York, Chicago, Dallas, Houston, and Washington, DC), at a total cost of just $9.4 million, and with 36,000 students (18,000 receiving the financial awards and 18,000 in the control groups) in 250 schools.  The schools chosen were inner-city schools of predominantly Black and Hispanic students, with high concentrations of students whose low family income qualified them for free lunches.  Three of the school systems were headed at the time by nationally-prominent school reformers (Joel Klein in New York, Michelle Rhee in Washington, DC, and Arne Duncan – later Obama’s Secretary of Education – in Chicago).

The work was controversial, and the education establishment did not like it.  Some argued that paying financial incentives to students would “destroy their love of learning”.  But there was no evidence of this in the results.  It was also noted that it would not be much different from the practice among many middle-class parents of rewarding their kids financially if they got good grades, or did their homework on time, or read certain books, and so on.

Fryer’s findings are well worth reading (see sources cited at the end of this note), but a brief summary of the main result is that financial incentives to students on the side of inputs (do your homework, read books, attend school as you should and behave well when there) had positive effects, although sometimes with marginal statistical significance.  The positive effects in just one year relative to the control groups (based on regressions controlling for various factors) were equivalent to accelerating student progress by the equivalent of one to two months of school.  While that in itself would not close Black-White education gaps, the rate of return given the low cost was enormous.  They paid 2nd graders in Dallas just $2 for each book they read, and 5th graders in Houston just $2 for each math homework assignment completed.  But such small financial rewards led to gains of one to two months of schooling (as a statistical best estimate) relative to the control groups.

Note also that the effects found in months of schooling gained are averaged over all who were in the group offered the financial incentives -whether they took up the offers fully, took them up only partially, or did not take them up at all.  They are therefore estimating the compounding of two effects:  whether the students will undertake the activity in response to the incentive, and if they do, whether this activity will then lead to a gain in an educational outcome.  One wants to measure the two together as even if some activity might lead to fabulous gains in terms of student outcomes, it will not matter if no one takes it up in response to the incentive.

In contrast to the benefits seen on the input side, financial rewards on the output side (i.e. tied to grades received on their report cards, or scores on tests) did not have a positive effect.  Indeed, while not at all close to statistical significance, the estimated coefficients were slightly negative in most of the cases.

Fryer also examined a New York City program that provided financial incentives to teachers.  He found that they did not matter.  This might, however, have been a consequence of how the specific program was designed (working with the agreement of the teacher’s union).  The bonuses were paid in some amount per teacher, but then as a total sum to the schools meeting certain criteria on student gains.  The schools would then decide how to divide that pot of money internally.  Individual teachers did not receive a financial incentive based on the gains of their specific students.

Financial incentives to students could be considered the demand side of education.  There is also the supply side – the schools themselves and how they teach.  As just noted, financial incentives to teachers (at least in the program examined) did not appear to matter.  But Fryer also took a close look at what was being done at several chartered schools run as part of the Harlem Children’s Zone project.  Those schools have been highly successful.  Fryer looked at this systematically by comparing the outcomes of students who won the lottery to get into the over-subscribed schools and the otherwise similar students who lost in that lottery.

The schools were amazing.  Fryer found that students starting in middle school (6th grade), while far behind White peers, were able in three years (i.e. by 8th grade) when attending the Harlem Children’s Zone schools to fully close the gap in math test scores and close about half of the gap in reading scores.  And children enrolled in their elementary schools were able to fully close the gap in both scores by their 3rd grade.  Those who had applied to try to enter the Harlem Children’s Zone schools but had not won the lottery performed similarly to others in the city system.  That is, they did not do well.  And six years later, those who won the lottery to get into these schools compared to those who had not were more likely to have entered college, less likely (if female) to have been pregnant, and less likely to have been incarcerated.

The question then was whether the model of the schools being run by the Harlem Children’s Zone could be replicated elsewhere.  Fryer contacted a large number of school districts around the country, but none were interested in working with Fryer on trying to copy the model until the school district for Houston expressed a willingness.  The State of Texas was preparing to take over control of a number of Houston’s schools that were deemed to be failing, so as a last alternative Houston was willing to allow the group Fryer had organized to take over management of the failing schools instead.  They were a mix of twenty elementary, middle, and high schools.

Major changes were made:  The school day (for middle and high schools) was lengthened by an hour plus summer vacation was shortened by two weeks – this increased the number of instructional hours by about 20%; they replaced 19 of 20 principals and close to half of the teachers; added tutoring for students who were lagging (in just the 4th, 6th, and 9th grades due to funding limitations); used frequent tests to assess progress during the school year and to decide if certain lessons should be repeated; and inculcated a culture of high expectations.  Fryer calculated the extra financial cost per student of all these measures was about $1,800, or an extra 15%.

The results were largely similar to those found at the Harlem Children’s Zone for math, but less for reading.  Gains in math for students in the elementary schools were highly significant statistically and such that the racial gap was eliminated in three years.  The results on reading were also positive but less and not always statistically significant.  The gains in math for the students in the middle and high schools were almost as large, but there were almost no changes in the reading scores compared to the controls.

And an anecdote:  Fryer related in an interview that one time when teaching his undergraduate class at Harvard, one of the students came up to him after the class.  The student told him that he had gone to one of the high schools in Houston that Fryer had worked with, and wanted to say thanks.  Fryer had not been aware of this until that moment.  It was a failing school, that the State of Texas was getting ready to take over due to its poor record, and now a graduate from that school had been admitted to Harvard.

Despite these highly positive gains, school districts around the US have largely ignored this demonstration of effective schooling.  One suspects that the fact 19 of 20 school principals were replaced, as well as close to half the teachers, may be a factor.

Fryer’s work on education is revealing.  It shows Fryer is hands-on, wishes to work out practical approaches, follows the data, and is both careful and able to use rigorous statistical approaches to examine results of real-world significance.  He has followed the same approach in looking at the issue of police use of force.

C.  Racial Differences in Police Use of Force

With the increased attention being given to police shootings in the 2010s following a number of prominent cases, Fryer initiated a study to determine the degree to which there were differences across races in police shootings in the US that might reflect racial biases.  It was assumed there were – the question was by how much.

The problem is that there was very little data.  It was well known that databases kept by the FBI were far from adequate as reporting shootings to the FBI is voluntary and many police departments choose not to.  More fundamentally, reports that are just tallies of numbers do not tell much as they do not provide data on the circumstances of the shootings.

Some police departments do keep such records, but often they are just on paper and not in a machine readable form.  After contacting police departments around the country, Fryer was able to obtain data on incidents where officer weapons were discharged from 10 large police departments (the cities of Austin, Dallas, and Houston; Los Angeles county; and 6 large Florida counties), each for some subset of the years between 2000 and 2015.  They got 1,316 cases and could code for 65 different variables, but the data included only incidents when an officer’s weapon was discharged.  Hence that data could not be used to address whether there were similar incidents when an officer’s weapon might have been fired but was not.

For this, Fryer was able to obtain detailed data from the City of Houston.  They were provided access to the written files – which could vary between 2 pages and 100 pages for each incident.  From the files for incidents of serious crimes where lethal force might have been justified (defined as attempted murder of a police officer, aggravated assault of a police officer, and resisting, evading, or interfering in an arrest), they combined the incidents where an officer in fact fired a weapon with a random sample of other cases where they did not.  They ended up with 1,532 cases, and had their researchers read the files and code for 300 variables.  To help ensure accuracy, they had each file read by two researchers, acting independently, and then checked whether both coded the same for the given variables given their reading of the files.

Fryer’s key, and surprising, finding was:

“we find, after controlling for suspect demographics, officer demographics, encounter characteristics, suspect weapon and year fixed effects, that blacks are 27.4 percent less likely to be shot at by police relative to non-black, non-Hispanics” (emphasis in original).

Similarly, with the data from the 10 large police departments (including Houston, but also the nine others they were able to collect data from), Fryer was able to examine the impact of race on whether the officer or the suspect fired their weapon first.  Taking all factors into account, he found in his regression analysis that an officer firing first in the encounter was 46% less likely when the suspect was Black.  And 44% less likely if the suspect was Hispanic.

There were important limitations, of course.  For starters, the data was only for Houston (for the first analysis) and only for Houston and nine other major cities/counties (for the second analysis).  The results might well be different elsewhere.  They also had to rely on case records as recorded by the officers involved.  That could add a particular slant.  More fundamentally, the results are conditional on instances where there was some form of interaction with the police (within a specified type of serious cases examined).

For these reasons, further work on police use of force to confirm (or refute) Fryer’s findings is certainly warranted.  But for reasons that will become clear in the next section below, many academics who would otherwise research this further will likely, for understandable reasons, choose to stay away.

While there are these limitations, this is the only statistically rigorous study I am aware of that has examined such police use of force.  Furthermore, in a separate short paper, Fryer looked at how the data sets he constructed on officer involved shootings compared to those others have assembled.  That data is unfortunately limited, but Fryer identified four sets:  1)  Data assembled by The Washington Post on officer involved fatal shootings and the victims; 2)  Similar data on fatal shootings assembled by The Guardian; 3)  Data assembled by VICE News from 47 of the 50 largest police departments in the US, that includes both fatal and non-fatal shootings; and 4)  Data assembled by Cody Ross, then at University of California – Davis, as the sole academic analysis he could find. 

Fryer compared the measures of bias that each of these four data sets presented to what that measure would be for his own data from the 10 large police departments (the 10 cities and counties).  The results across the resulting five data sets were mostly similar.  For example, the Washington Post reported the percentage of Black civilians among the unarmed men killed by police.  This was 40% in the Post data, and varied between 34% and 53% in the others.  In the Fryer data it was 44%.  All of these are, of course, far higher than the Black share of the population (which is 12%).  And similarly for the measures used by each of the others.

Fryer’s data therefore does not appear to be out of line with what others have found.  But what is different is that he was able, with his much richer set of observations on other factors, to take into account in his statistical analysis the impact of these other factors and hence could isolate the role of race.  And when he did, he found no evidence of racial differences.  Indeed, in the data he was able to assemble the racial differences went the other way from what was expected.  As Fryer later described it (in a New York Times article on the research), it was “the most surprising result of my career”.

Fryer’s results were certainly controversial.  As he recounted later, many friends advised him to bury his findings and not publish them.  But he insisted he would, and his paper appeared in the Journal of Political Economy in June 2019 – one of the top economics journals in the US.

D.  Consequences

The first draft of his paper on the police use of force appeared in July 2016; a revised draft is dated January 2018; and it was published in June 2019.  During this time, in mid-2017, Fryer decided that his personal assistant at Harvard EdLabs would be let go as she was just not able to perform the job adequately.  A severance package was negotiated and agreed upon.  But an administrator at Harvard decided it should be reduced by $25,000 (I have never seen from what to what).  Had that administrator not insisted on this, the matter would have ended there.

The staff member was upset with this $25,000 reduction.  A short time later, she had lawyers file a case against Roland Fryer (as well as Harvard) claiming there had been sexual harassment at EdLabs.  In response, the Harvard office responsible for examining such claims launched an investigation.

The filing prepared by the lawyers for the assistant listed 38 complaints.  The Harvard office investigating the charges dismissed 6 immediately, leaving 32.  Of those 32, they then concluded following their investigation that a further 26 should be dismissed.  Several included what they were able to show were fabrications by the assistant – that is, they could demonstrate the assistant had lied.  That left 6 complaints.  They concluded that these could be valid, even though in some they took the assertion of the assistant over the denial of Fryer (despite knowing that the assistant had fabricated a number of the other complaints).

From what has come out (these are all based on leaks, as the documents have not been officially released) the findings were that some of the banter in the office was considered inappropriate due to sexual innuendo.  One example was Fryer complaining at some point about some Harvard administrator, and saying that the administrator was acting as if they “had not had sex since Blacks were slaves”.  The other instances were similar.

In no case was there an assertion that Fryer had made an improper advance on any individual.  Rather, there was banter in the office that some later said they considered to be inappropriate.  Only one complaint was filed (by the personal assistant let go), but during their investigation the Harvard investigators were able to find three other women (including one who had been let go in 2008, the year EdLabs opened) who were willing to say that some of what Roland had said when they worked there they now considered to be inappropriate.  The number of staff who had worked at EdLabs by that point were in the dozens – overwhelmingly strong supporters of Fryer and glad that they had worked with him, and mostly women.

That there had been such banter was not denied.  Indeed, it was open and not a secret, although apparently not common.  And while it should not be belittled if such talk was seen by anyone in the office as inappropriate, there had been no such complaints made in the ten years since EdLabs had opened until the personal assistant filed her complaint after being fired (and a Harvard administrator decided that the negotiated settlement package should be reduced by $25,000).  During those ten years, no one had ever told Fryer something along the lines of “it is inappropriate to say that and it makes me uncomfortable”.  Had someone said that, including by any of the four who later told the Harvard investigators that they now considered some of the remarks to be inappropriate, the Harvard investigators would presumably have highlighted it.  The investigators certainly would have asked all the EdLabs staff they interviewed whether they had ever said themselves to Fryer, or ever heard someone else say to Fryer, that they considered some remark in the banter to be inappropriate.  And had someone told the investigators that, the investigators would certainly have emphasized in their conclusions that Fryer continued with such banter even after some member of the EdLabs staff had told him that it made them uncomfortable.  But there was evidently no such finding.

The conclusion of the Harvard office investigating the issue was that Fryer should be required to take sensitivity training.  And that was it.  Nothing else.

But that was then deemed insufficient by the then Dean of the Faculty.  And in her position as dean she would be the one making the final decision on the penalty, if any.  She decided to establish a special committee to advise.  That committee was then made up of members she personally chose, and she included herself on it.

Following that review, the dean then decided not only that Fryer would be suspended without pay for two years – with no contact allowed with students or others at Harvard (so students being supervised would need to switch to someone else) – but also that EdLabs would be closed, and closed permanently.  All EdLabs staff would be dismissed and all its research programs ended.

She also recommended to the then president of Harvard – Lawrence Bacow – that Fryer’s tenure as a university professor be revoked.  Bacow, however, declined to make such a recommendation to the Harvard Corporation (the trustees of Harvard), who would have had the final say.  No professor at Harvard has had their tenure revoked in more than a century.

Needless to say, many of those involved with EdLabs and Fryer’s research were shocked.  It was not only that Fryer was being punished severely (where even the Harvard office charged with investigating such issues only recommended sensitivity training to address the complaint), but all of EdLabs was in effect being punished as well.  By her edict, the valuable and important work of the lab – with most of it still centered on the education of minority groups – was being stopped.

This was, however, extremely convenient for those who approached racial issues in a different way, with different conclusions reached.  That included the Dean of the Faculty herself, whose own research focused on racial issues (with an appointment in the Department of Government and jointly in the Department of African and African-American Studies).  If Fryer’s findings were correct, then aspects of her own work would be undermined.

She was born in New York to Haitian immigrants, but had a comfortable upbringing.  It could not be more different from Fryer’s.  Her father was an engineer, and the family spent several years of her childhood in Saudi Arabia when her father was posted there.  She attended Phillips Exeter Academy – perhaps the most elite prep school in the US – and then received her BA from Stanford and Ph.D. from Harvard.  She taught first at Stanford and then returned to Harvard as a full professor.  She was later appointed Dean of Social Science in 2015, and Dean of the Faculty of Arts and Sciences in 2018.

That Dean of the Faculty was Claudine Gay.  And on December 15, 2022, Harvard announced that Claudine Gay had been selected to be the next president of Harvard, with her appointment to start on July 1, 2023.

E.  Conclusion

Glenn Loury, a Professor of Economics at Brown University and a co-author of several papers with Fryer early in Fryer’s career, summed it up well:

To do the kind of work Roland does, you have to be more than brilliant. You have to be fearless. And I cannot help but suspect that now Roland is paying the price for pursuing the truth wherever it leads.  Several years ago, he was accused of sexual harassment by a disgruntled ex-assistant.  In my opinion and that of many others, those accusations are baseless.  But Harvard has used them as a pretext to shut down Roland’s lab, to curtail his teaching, and to marginalize him within the institution.

I’ll not mince words.  Those at Harvard responsible for this state of affairs should be utterly ashamed of themselves.  They have unnecessarily, heedlessly tarnished the career of an historically great economist.  Again, I can’t help but suspect that they have effectively buried vital research not because it was poorly done but because they found the results to be politically inconvenient. “Veritas” indeed.

Postscript

As of July 2021, Roland Fryer was allowed by Harvard to return to teaching – at both the graduate and undergraduate levels.  However, he would not be allowed to supervise students or participate in an advisory role for a further two years.

 

Sources and References

Links are provided to readily accessible sources, when possible.  Articles published in The New York Times are unfortunately only available behind a paywall.  For academic articles, the link will generally be to the version of the paper that appeared on the NBER site, as these are openly available online while subscriber access is generally required for the final version that appeared in the journals.  The articles as published are normally identical to the final NBER versions, or at least are very close.

a)  Biography and Life Story:

The first article below is a profile of Roland Fryer that appeared in The New York Times Magazine in March 2005, written by Stephen Dubner.  Dubner had heard of Fryer from Steven Levitt, with whom he co-authored Freakonomics (that appeared also in 2005).  This profile by Dubner did much to make Fryer a known figure outside of the economics profession.  Much of the article is on Fryer’s upbringing.

Stephen Dubner, “Towards a Unified Theory of Black America”, The New York Times Magazine, March 20, 2005.

Other sources on Fryer’s life and work:

Announcement of the award in 2015 to Roland Fryer of the AEA’s John Bates Clark Medal (with substantial material on Fryer’s research).

Roland G. Fryer biography on JRank.

b)  Curriculum Vitae and Publications:

This cv dated February 2018 is the most recent I have been able to find.  The academic papers on the list of Fryer’s NBER papers (51 as I write this) can be accessed in full from this page in most and possibly all cases (at least for the papers I have tried to look up thus far):

Roland Gerhard Fryer, Jr. “Curriculum Vitae”, February 2018.

National Bureau of Economic Research (NBER), Roland G. Fryer page.

c)  Work on the Black-White Test Score Gap, Co-Authored with Steven Levitt:

The first article is for a more general audience, and the following two the academic papers.

“Falling Behind” (with S. Levitt). Education Next 4 (August 2004): 64-71.

“Understanding the Black-White Test Score Gap in the First Two Years of School” (with S. Levitt). The Review of Economics and Statistics 86, no. 2 (May 2004): 447-464.

“The Black-White Test Score Gap Through Third Grade” (with S. Levitt). American Law and Economic Review 8, no. 2 (July 2006): 249-281.

d)  Work on “Acting White”:

The first article is for a more general audience, and the second the academic article.

“Acting White”. Education Next, Winter 2006.

“An Empirical Analysis of ‘Acting White’” (with P. Torelli).  Journal of Public Economics, 94, no. 5 (2010): 380-396.

e) Work on Financial Incentives for Students and for Teachers:

The first article is again for a more general audience, and the others for academics. 

“The Power and Pitfalls of Education Incentives” (with B. Allan), Discussion Paper 2011-07, The Hamilton Project, September 2011.

“Financial Incentives and Student Achievement: Evidence from Randomized Trials,” Quarterly Journal of Economics, 126, no. 4 (2011): 1755-1798.

“Teacher Incentives and Student Achievement: Evidence from New York City Public Schools,” Journal of Labor Economics, 31, no.2 (2013): 373-427.

f)  Work on the Chartered Schools Managed by the Harlem Children’s Zone:

“Learning from the Successes and Failures of Charter Schools,” Policy Brief 2012-06, The Hamilton Project, September 2012.

“Are High Quality Schools Enough to Increase Achievement Among the Poor?” Evidence from the Harlem Children’s Zone” (with W. Dobbie). American Economic Journal: Applied Economics 3, no. 3 (2011): 158-187.

“The Medium-Term Impacts of High-Achieving Charter Schools,” (with W. Dobbie),  Journal of Political Economy, vol 123, No. 5, October 2015.

g)  Results from New Management of a Group of Low-Performing Public Schools in Houston:

“Injecting Charter School Best Practices into Traditional Public Schools: Evidence from Field Experiments,” Quarterly Journal of Economics, August 2014, vol. 129, issue 3: 1355-1407.

h)  Work on Police Use of Force:

Link to the final NBER version of the paper:

“An Empirical Analysis of Racial Differences in Police Use of Force”, NBER Working Paper #22399, revised draft, January 2018.

Link to a pdf file of the paper as it appeared in the Journal of Political Economy:

“An Empirical Analysis of Racial Differences in Police Use of Force”, Journal of Political Economy, June 2019, vol 127, no. 3: 1210-1261.

The paper comparing statistics on racial differences in police shootings in the Fryer data to what they would be when defined as in four published sources:

“Reconciling Results on Racial Differences in Police Shootings”, AEA Papers and Proceedings, May 2018, vol. 108: 228-233.

i)  The Harvard Investigation:

  1)  The New York Times article reporting on the conclusions of the Harvard investigation of Roland Fryer:

Jim Tankersley and Ben Casselman, “Star Economist at Harvard Faces Sexual Harassment Complaints”, The New York Times, December 14, 2018.

  2)  Roland Fryer, letter to the New York Times in response:

Roland Fryer, “At a Harvard Lab, the Accused Responds”, The New York Times, December 20, 2018.

  3)  Article of Stuart Taylor, Jr., on the Harvard investigation and the bias in the New York Times report on it:

Stuart Taylor Jr., “Harvard, the NY Times and the #MeToo Takedown of a Black Academic Star”, Real Clear Investigations, January 29, 2019.

  4)  New York Times article reporting on Harvard’s decision to suspend Fryer and permanently close EdLabs:

Ben Casselman and Jim Tankersley, “Harvard Suspends Roland Fryer, Star Economist, After Sexual Harassment Claims”, The New York Times, July 10, 2019.

  5)  Glenn Loury’s assessment of the saga:

Glenn Loury, “The Truth about Roland Fryer”, March 13, 2022.

How Low is Unemployment in Historical Perspective? – The Impact of the Changing Composition of the Labor Force

A.  Introduction

The unemployment rate is low, which is certainly good, and many commentators have noted it is now (at 3.7% in September and October, and an average of 3.9% so far this year) at the lowest the US has seen since the 1960s.  The rate hit 3.4% in late 1968 and early 1969, and averaged about 3.5% in each of those years.

But are those rates really comparable to what they are now?  This is important, not simply for “bragging rights” (or, more seriously, for understanding what policies led to such rates), but also for understanding how much pressure such rates are creating in the labor market.  The concern is that if the unemployment rate goes “too low”, labor will be able to demand a higher nominal wage and that this will then lead to higher price inflation.  Thus the Fed monitors closely what is happening with the unemployment rate, and will start to raise interest rates to cool down the economy if it fears the unemployment rate is falling so low that there soon will be inflationary pressures.  And indeed the Fed has, since 2016, started to raise interest rates (although only modestly so far, with the target federal funds rate up only 2.0% points from the exceptionally low rates it had been reduced to in response to the 2008/09 financial and economic collapse).

A puzzle is why the unemployment rate, at just 3.9% this year, has not in fact led to greater pressures on wages and hence inflation.  It is not because the modestly higher interest rates the Fed has set have led to a marked slowing down of the economy – real GDP grew by 3.0% in the most recent quarter over what it was a year before, in line with the pace of recent years.  Nor are wages growing markedly faster now than what they did in recent years.  Indeed, in real terms (after inflation), wages have been basically flat.

What this blog post will explore is that the unemployment rate, at 3.9% this year, is not in fact directly comparable with the levels achieved some decades ago, as the composition of the labor force has changed markedly.  The share of the labor force who have been to college is now much higher than it was in the 1960s.  Also, the share of the labor force who are young is now much less than it was in the 1960s.  And unemployment rates are now, and always have been, substantially less for those who have gone to college than for those who have not.  Similarly, unemployment rates are far higher for the young, who have just entered the labor force, than they are for those of middle age.

Because of these shifts in the shares, a given overall unemployment rate decades ago would only have happened had there been significantly lower unemployment rates for each of the groups (classified by age and education) than what we have now.  The lower unemployment rates for each of the groups, in that period decades ago, would have been necessary to produce some low overall rate of unemployment, as groups who have always had a relatively higher rate of unemployment (the young and the less educated) accounted for a higher share of the labor force then.  This is important, yet I have not seen any mention of the issue in the media.

As we will see, the impact of this changing composition of the labor force on the overall unemployment has been significant.  The chart at the top of this post shows what the overall unemployment rate would have been, had the composition of the labor force remained at what it was in 1970 (in terms of education level achieved for those aged 25 and above, plus for the share of youth in the labor force aged 16 to 24).  For 2018 (through the end of the third quarter), the unemployment rate at the 1970 composition of the labor force would then have been 5.2% – substantially higher than the 3.9% with the current composition of the labor force.  We will discuss below how these figures were derived.

At 5.2%, pressures in the labor market for higher wages will be substantially less than what one might expect at 3.9%.  This may explain the lack of such pressure seen so far in 2018 (and in recent years).  Although commonly done, it is just too simplistic to compare the current unemployment rate to what it was decades ago, without taking into account the significant changes in the composition of the labor force since then.

The rest of this blog post will first review this changing composition of the labor force – changes which have been substantial.  There are some data issues, as the Bureau of Labor Statistics (the source of all the data used here) changed its categorization of the labor force by education level in 1992.  Strictly speaking, this means that compositional shares before and after 1992 are not fully comparable.  However, we will see that in practice the changes were not such as to lead to major differences in the calculation of what the overall unemployment rate would be.

We will also look at what the unemployment rates have been for each of the groups in the labor force relative to the overall average.  They have been remarkably steady and consistent, although with some interesting, but limited, trends.  Finally, putting together the changing shares and the unemployment rates for each of the groups, one can calculate the figures for the chart at the top of this post, showing what the unemployment rates would have been over time, had the labor force composition not changed.

B.  The Changing Composition of the Labor Force

The composition of the labor force has changed markedly in the US in the decades since World War II, as indeed it has around the world.  More people have been going to college, rather than ending their formal education with high school.  Furthermore, the post-war baby boom which first led (in the 1960s and 70s) to a bulge in the share of the adult labor force who were young, later led to a reduction in this share as the baby boomers aged.

The compositional shares since 1965 (for age) and 1970 (for education) are shown in this chart (where the groups classified by education are of age 25 or higher, and thus their shares plus the share of those aged 16 to 24 will sum to 100%):

The changes in labor force composition are indeed large.  The share of the labor force who have completed college (including those with an advanced degree) has more than tripled, from 11% of the labor force in 1970 to 35% in 2018.  Those with some college have more than doubled, from 9% of the labor force to 23%.  At the other end of the education range, those who have not completed high school fell from 28% of the labor force to just 6%, while those completing high school (and no more) fell from 30% of the labor force to 22%.  And the share of youth in the labor force first rose from 19% in 1965 to a peak of  24 1/2% in 1978, and then fell by close to half to 13% in 2018.

As we will see below, each of these groups has very different unemployment rates relative to each other.  Unemployment rates are far less for those who have graduated from college than they are for those who have not completed high school, or for those 25 or older as compared to those younger.  Comparisons over time of the overall unemployment rate which do not take this changing composition of the labor force into account can therefore be quite misleading.

But first some explanatory notes on the data.  (Those not interested in data issues can skip this and go directly to the next section below.)  The figures were all calculated from data collected and published by the Bureau of Labor Statistics (BLS).  The BLS asks, as part of its regular monthly survey of households, questions on who in the household is participating in the labor force, whether they are employed or unemployed, and what their formal education has been (as well as much else).  From this one can calculate, both overall and for each group identified (such as by age or education) the figures on labor force shares and unemployment rates.

A few definitions to keep in mind:  Adults are considered to be those age 16 and above; to be employed means you worked the previous week (from when you were being surveyed) for at least one hour in a paying job; and to be unemployed means you were not employed but were actively searching for a job.  The labor force would thus be the sum of those employed or unemployed, and the unemployment rate would be the number of unemployed in whatever group as a share of all those in the labor force in that group.  Note also that full-time students, who are not also working in some part-time job, are not part of the labor force.  Nor are those, of whatever age, who are not in a job nor seeking one.

The education question in the survey asks, for each household member in the labor force, what was the “highest level of school” completed, or the “highest degree” received.  However, the question has been worded this way only since 1992.  Prior to 1992, going back to 1940 when they first started to ask about education, the question was phrased as the “highest grade or year of school” completed.  The presumption was that if the person had gone to school for 12 years, that they had completed high school.  And if 13 years that they had completed high school plus had a year at a college level.

However, this presumption was not always correct.  The respondent might only have completed high school after 13 years, having required an extra year.  Thus the BLS (together with the Census Bureau, which asks similar questions in its surveys) changed the way the question was asked in 1992, to focus on the level of schooling completed rather than the number of years of formal schooling enrolled.

For this reason, while all the data here comes from the BLS, the BLS does not make it easy to find the pre-1992 data.  The data series available online all go back only to 1992.  However, for the labor force shares by education category, as shown in the chart above, I was able to find the series under the old definitions in a BLS report on women in the labor force issued in 2015 (see Table 9, with figures that go back to 1970).  But I have not been able to find a similar set of pre-1992 figures for unemployment rates for groups classified by education.  Hence the curve in the chart at the top of this post on the unemployment rate holding constant the composition of the labor force could only start in 1992.

Did the change in education definitions in 1992 make a significant difference for what we are calculating here?  They will matter only to the extent that:  1)  the shifts from one education category to another were large; and 2) the respective unemployment rates where there was a significant shift from one group to another were very different.

As can be seen in the chart above, the only significant shifts in the trends in 1992 was a downward shift (of about 3% points) in the share of the labor force who had completed high school and nothing more, and a similar upward shift (relative to trend) in the share with some college. There are no noticeable shifts in the trends for the other groups.  And as we will see below, the unemployment rates of the two groups with a shift (completed high school, vs. some college) are closer to each other than that for any other pairing of the different groups.  Thus the impact on the calculated unemployment rate of the change in categorization in 1992 should be relatively small.  And we will see below that that in fact is the case.

There was also another, but more minor (in terms of impact), change in 1992.  The BLS always reported the educational composition of the labor force only for those labor force members who were age 25 or above.  However, prior to 1992 it reported the figures only for those up to age 64, while from 1992 onwards it reported the figure at any higher age if still in the labor force, including those who at age 65 or more but not yet retired.  This was done as an increasing share over time of those in the US of age 65 or higher have remained in the labor force rather than retiring.  However, the impact of this change will be small.  First, the share of the labor force of age 65 or more is small.  And second, this will matter only to the extent that the shares by education level differ between those still in the labor force who are age 65 or more, as compared to those in the labor force of ages 25 to 64.  Those differences in education shares are probably not that large.

C.  Differences in Unemployment Rates by Age and Education 

As noted above, unemployment rates differ between groups depending on age and education.  It should not be surprising that those who are young (ages 16 to 24) who are not in school but are seeking a job will experience a high rate of unemployment relative to those who are older (25 and above).  They are just starting out, probably do not have as high an education level (they are not still in school), and lack experience.  And that is indeed what we observe.

At the other extreme we have those who have completed college and perhaps even hold an advanced degree (masters or doctorate).  They are older, have better contacts, normally have skills that have been much in demand, and may have networks that function at a national rather than just local level.  The labor market works much better for them, and one should expect their unemployment rate to be lower.

And this is what we have seen (although unfortunately, for the reasons noted above on the data, the BLS is only making available the unemployment rates by education category for the years since 1992):

The unemployment rates of each group vary substantially over time, in tune with the business cycle, but their position relative to each other is always the same.  That is, the rates move together, where when one is high it will also be high for the others.  This is as one would expect, as movements in unemployment rates are driven primarily by the macroeconomy, with all the rates moving up when aggregate demand falls to spark a recession, and moving down in a recovery.

And there is a clear pattern to these relationships, which can be seen when these unemployment rates are all expressed as a ratio to the overall unemployment rate:

The unemployment rate for those just entering the labor force (ages 16 to 24) has always been about double what the overall unemployment rate was at the time.  And it does not appear to be subject to any major trend, either up or down.  Those in the labor force (and over age 25) with less than a high school degree (the curve in blue) also have experienced a higher rate of unemployment than the overall rate at the time – 40 to 60% higher.  There might be some downward trend, but one cannot yet say whether it is significant.  We need some more years of data.

Those in the labor force with just a high school degree (the curve in green in the chart) have had an unemployment rate very close to the average, with some movement from below the average to just above it in recent years.  Those with some college (in red) have remained below the overall average unemployment rate, although less so now than in the 1990s.  And those with a college degree or more (the curve in purple) have had an unemployment of between 60% below the average in the 1990s to about half now.

There are probably a number of factors behind these trends, and it is not the purpose of this blog post to go into them.  But I would note that these trends are consistent with what a simple supply and demand analysis would suggest.  As seen in the chart in section B of this post, the share of the labor force with a college degree, for example, has risen steadily over time, to 35% of the labor force now from 22% in 1992.  With that much greater supply and share of the labor force, the advantage (in terms of a lower rate of unemployment relative to that of others) can be expected to have diminished.  And we see that.

But what I find surprising is that that impact has been as small as it has.  These ratios have been remarkably steady over the 27 years for which we have data, and those 27 years have included multiple cycles of boom and bust.  And with those ratios markedly different for the different groups, the composition of the labor force will matter a great deal for the overall unemployment rate.

D.  The Unemployment Rate at a Fixed Composition of the Labor Force

As noted above, those in the labor force who are not young, or who have achieved a higher level of formal education, have unemployment rates which are consistently below those who are young or who have less formal education.  Their labor markets differ.  A middle-aged engineer will be considered for jobs across the nation, while someone with who is just a high school graduate likely will not.

Secondly, when we say the economy is at “full employment” there will still be some degree of unemployment.  It will never be at zero, as workers may be in transition between jobs and face varying degrees of difficulty in finding a new job.  But this degree of “frictional unemployment” (as economists call it) will vary, as just noted above, depending on age (prior experience in the labor force) and education.  Hence the “full employment rate of unemployment” (which may sound like an oxymoron, but isn’t) will vary depending on the composition of the labor force.  And more broadly and generally, the interpretation given to any level of unemployment needs to take into account that compositional structure of the labor force, as certain groups will consistently experience a higher or lower rate of unemployment than others, as seen in the chart above.

Thus it is misleading simply to compare overall unemployment rates across long periods of time, as the compositional structure of the labor force has changed greatly over time.  Such simple comparisons of the overall rate may be easy to do, but to understand critical issues (such as how close are we to such a low rate of unemployment that there will be inflationary pressure in the labor market), we should control for labor force composition.

The chart at the top of this post does that, and I repeat it here for convenience (with the addition in purple, to be explained below):

The blue line shows the unemployment rate for the labor force since 1965, as conventionally presented.  The red line shows, in contrast, what the unemployment rate would have been had the unemployment rate for each identified group been whatever it was in each year, but with the labor force composition remaining at what it was in 1970.  The red line is a simple weighted average of the unemployment rates of each group, using as weights what their shares would have been had they remained at the shares of 1970.

The labor force structure of 1970 was taken for this exercise both because it is the earliest year for which I could find the necessary data, and because 1970 is close to 1968 and 1969, when the unemployment rate was at the lowest it has been in the last 60 years.  And the red curve can only start in 1992 because that is the earliest year for which I could find unemployment rates by education category.

The difference is significant.  And while perhaps difficult to tell from just looking at the chart, the difference has grown over time.  In 1992, the overall unemployment rate (with all else equal) at the 1970 compositional shares, would have been 23% higher.  By 2018, it would have grown to 33% higher.  Note also that, had we had the data going back to 1970 for the unemployment rates by education category, the blue and red curves would have met at that point and then started to diverge as the labor force composition changed.

Also, the change in 1992 in the definitions used by the BLS for classifying the labor force by education did not have a significant effect.  For 1992, we can calculate what the unemployment rate would have been using what the compositional shares were in 1991 under the old classification system.  The 1991 shares for the labor force composition would have been very close to what they would have been in 1992, had the BLS kept the old system, as labor force shares change only gradually over time.  That unemployment rate, using the former system of compositional shares but at the 1992 unemployment rates for each of the groups as defined under the then new BLS system of education categories, was almost identical to the unemployment rate in that year:  7.6% instead of 7.5%.  It made almost no difference.  The point is shown in purple on the chart, and is almost indistinguishable from the point on the blue curve.  And both are far from what the unemployment rate would have been in that year at the 1970 compositional weights (9.2%).

E.  Conclusion

The structure of the labor force has changed markedly in the post-World War II period in the US, with a far greater share of the labor force now enjoying a higher level of formal education than we had decades ago, and also a significantly lower share who are young and just starting in the labor force.  Since unemployment rates vary systematically by such groups relative to each other, one needs to take into account the changing composition of the labor force when making comparisons over time.

This is not commonly done.  The unemployment rate has come down in 2018, averaging 3.9% so far and reaching 3.7% in September and October.  It is now below the 3.8% rate it hit in 2000, and is at the lowest seen since 1969, when it hit 3.4% for several months.

But it is misleading to make such simple comparisons as the composition of the labor force has changed markedly over time.  At the 1970 labor force shares, the unemployment rate in 2018 would have been 5.2%, not 3.9%.  And at a 5.2% rate, the inflationary pressures expected with an exceptionally low unemployment rate will not be as strong.  This may, at least in part, explain why we have not seen such inflationary pressures grow this past year.

At One Time, You Could Work Your Way Through College – But Not Any More.

Earnings from Min Wage vs. University Costs, 1963-2013

 

At one time, not that long ago, a student could work at a minimum wage job over the summers and during holidays, and be able to cover the total cost (including room and board) of attending a four-year state university.  That is now far from possible.

With students now returning to school, it is perhaps a good time to look at what has happened to the affordability of college in recent decades for middle class families.  The chart above provides one indicator.  It compares what a student could earn in a summer job at the minimum wage, or in year-round work at the minimum wage while attending school (i.e. during summers, holidays, and part time during the academic term), as a ratio to what it would cost to attend a four-year state university.

The state university costs are for in-state tuition and required fees, plus the cost of on-campus room and board.  The figures are from the National Center for Education Statistics of the US Department of Education (with figures for 2013 calculated based on the 2012 to 2013 growth in the College Board estimates).  The university cost figures are for four-year, degree granting, state colleges and universities (i.e. they do not include two-year community colleges), and cover all such state schools.  The cost of attending the elite state schools (such as Berkeley, UVA, or the University of Michigan) would be more.  The years shown on the chart are for the beginning of the respective academic years (i.e. 2013 is for the 2013/14 academic year), and the minimum wage rate used is that which was in effect in July of that year.

The chart indicates that one could have covered the cost of attending a state university in the 1960s and 70s solely through minimum wage work.  Based on just a 17 week summer break, one would have earned enough to cover an average of 82% of the full cost of attending school.  An industrious student working full time over the summer and during vacation breaks (such as Christmas), plus 10 hours per week during the academic term, would have been able to cover the full cost and more – an average of 143% of the cost of school.  Hence summer work plus a bit more during vacations would have sufficed to cover the full cost of college.  In terms of dollar figures, the full cost of attending a state university in 1963/64 would have been $929, in the then current dollars.  A student could have earned $782 just from working at minimum wage over the summer, or $1,357 by working at minimum wage over the summer, during vacations, and 10 hours per week during the academic term.

These are, of course, just simple indicators.  One might have been able to earn more than the minimum wage, and/or worked a different number of hours.  But the point is that in the 1960s and 70s, when baby boomers such as myself were going to college, it was possible for the student alone, simply by working at the minimum wage, to have paid for the full cost of attending a four-year state university.

That began to change in the 1980s, as Reagan took office.  The change is indeed striking.  Affordability then began to fall, and it has fallen steadily since, as seen in the chart above.  By 1986, a student working even full time over the summer and during vacation breaks, and 10 hours a week during the academic term, no longer would have been able to cover the full cost of attending school.

The share of schooling costs that could be covered by work then continued to decline (with some bumps up when the minimum wage was sporadically changed) until the present day.  By 2013, summer work would only cover a quarter of the cost of schooling, while more comprehensive work over the entire year would only cover less than half.  In dollar terms, the average cost of attending a state university (for tuition, room, and board) was $18,037 per year in 2013.  But a student working over the summer at the minimum wage would have only been able to earn $4,930, or only a bit over a quarter of the cost of attending school.  Working full time over the summer and during vacations, plus 10 hours per week during the academic term, the student could have only earned $8,555, or less than half the cost of attending school.

As a consequence, students must now rely on their parents (when their parents can afford it), or a scarce number of scholarships (highly limited, especially for state schools), or on student loans.  Otherwise, they must give up on attending university.

The result has been an explosion in student loan debt outstanding.  As of June 30, 2014, student loan debt totaled an estimated $1,275 billion (based on Federal Reserve Board estimates), or five times the level outstanding in 2003 of $250 billion (the earliest figures I could find on a comparable basis; the amounts were so small earlier, that the Fed did not separately break them out).  Student loans have long been common in the US (I had them when I went to school in the early 1970s).  But the amounts outstanding then were relatively small, were at low interest rates, and were for most of us easily manageable.  It is different now.  Student loan debts have exploded in recent years, with a five-fold increase over just the past decade.

The declining affordability of college by this measure is of course a consequence of what has been happening to the two components of the measure.  One has been the unwillingness of Congress to allow the minimum wage to keep up with inflation.  As noted in an earlier post on this blog, the minimum wage in the US has stagnated over the last half century, and is indeed lower now (in real terms) than it was in 1950, when Harry Truman was president.  Real GDP per capita is 3.5 times higher now than it was in 1950, and real labor productivity has increased similarly.  These are not small increases.  I find it amazing (and shameful) that the real minimum wage is lower now than it was then.

For the period since 1963 (the earliest date in the chart), real GDP per capita and real labor productivity are both now 2.7 times higher than what they were then.  But the real minimum wage is close to 20% less now than it was in 1963.

The fall in the real minimum wage fall since the 1960s is half the story.  Note that the inflation measure used for determining the real minimum wage is the general consumer price index (the CPI).  This is the price index for the overall basket of goods and services a US household will purchase.  But the price index is an average over all the goods and services that households buy, and individual items can have price increases that are more than, or less than, this overall average.

In particular, the cost of attending a state university has increased by a good deal more than the overall CPI.  Based on the overall CPI, the real cost of attending a state university (for tuition, room, and board) is now 2.5 times what it was in 1963.  The cost of the tuition component alone is now 4.5 times higher.  The basic cause has been the cutbacks in state budgetary support for their colleges and universities, with tuition and other charges then increased to make up for it.

As a result, the minimum wage has fallen in real terms (based on the overall CPI) since the 1960s, at the same time that the real cost of attending school (relative to the overall CPI) has increased sharply.  The two factors together account for the steep fall in the share of state university costs that one can pay for by working at the minimum wage.  The curves in the chart at the top of this post show that path.

It is important to recognize that this declining affordability of attending state schools was not inevitable, but rather the result of policy choices.  The minimum wage has not been adjusted to reflect general inflation, even though real GDP per capita and labor productivity have both grown substantially.  And as was discussed in another post on this blog, there is no evidence that raising the minimum wage by the modest amounts now being discussed would lead to adverse effects on employment.

Government support for state colleges and universities has also been scaled back, leading to tuition and other cost increases substantially higher than that reflected in the general price index.  This has also been a policy choice.  And it is a policy choice that has prioritized the present generation (with tax cuts a prime example) over the coming generation, that is denying many of the coming generation the educational opportunities we ourselves had.