This is a followup to my previous article The Mathematics of the Ph.D. Glut. To recap, yes there is a Ph.D. glut in nearly all STEM (Science, Technology, Engineering, and Mathematics) fields in the United States. As a matter of federal government policy, many more Ph.D.’s are produced than can be employed as professors or other kinds of professional researchers. The Ph.D. glut in biology and medicine is especially bad currently as discussed in Brian Vastag’s July 7, 2012 Washington Post article U.S. pushes for more scientists, but the jobs aren’t there. There has been a Ph.D. glut in most STEM fields including specific fields such as mathematics and physics where specific shortages are often either claimed or strongly implied since about 1970.
The policies that have resulted in a perpetual Ph.D. glut since about 1970 are frequently justified by explicit or implicit claims that more Ph.D.’s (or other kinds of STEM workers) will translate into more scientific and technological progress and more “growth,” a popular political mantra. Has the Ph.D. glut in biology and medicine cured cancer? Not so far — after forty years and about $200 billion in inflation adjusted dollars. Many other specific examples of lack of scientific and technological progress may be cited. In fact, the Ph.D. glut is associated with a decline in real growth rates and a slowing of scientific and technological progress in most fields other than some areas of computers and electronics.
Disappointing Results
What does the evidence show? Remarkably, both the growth rate of the US Real Gross Domestic Product (GDP) and the growth rate of the per capita US Real Gross Domestic Product were significantly higher prior to 1970 than since. Now, the decline in the US growth rate is a worrisome long term trend. It is difficult to tie to any one event or policy. It has occurred under both Republican and Democratic Presidents and Congresses. The decline has occurred despite and perhaps because of the adoption of several policies such as the overproduction of Ph.D.’s and financial deregulation that are routinely and uncritically justified as producing increased “growth.”
I look at overall economic growth for an important reason. Research and development is risky and unpredictable. For any given research field, one can argue that the problem, e.g. cancer, has proved much more difficult than expected. That may be true. Luck unquestionably plays a big role. This is economist Paul Krugman’s explanation for the general lack of progress over the last forty years: where is my flying car?
But is it all just bad luck? By looking at the total economic growth rate we can, at least partially, average out the idiosyncracies of different fields. The Ph.D. glut is a universal problem in nearly all research fields not just high profile fields like physics, mathematics, and molecular biology.
The plots below show the United States Real GDP and Real GDP per capita since 1947. The GNU Octave script and the raw data used to make the plots is provided in the appendices. The raw data is from the St. Louis Federal Reserve/Bureau of Economic Analysis (BEA) and the U.S. Census Bureau.
United States Real GDP
United States Real GDP Growth Rate
United States Population Growth Rate
United States Real GDP Per Capita
United States Real GDP Per Capita Growth Rate
The analysis shows:
MEDIAN REAL GDP GROWTH RATE 1947-1970 ans = 0.040710 MEDIAN REAL GDP GROWTH RATE 1971-2011 ans = 0.027481 MEDIAN US REAL GDP PER CAPITA GROWTH 1947-1970 ans = 0.026985 MEDIAN US REAL GDP PER CAPITA GROWTH 1971-2011 ans = 0.017927
The median real GDP growth rate from 1947 to 1970 was 4.1 percent (rounded to two significant digits), as opposed to 2.7 percent from 1971 to 2011. The median real GDP per capita growth rate from 1947 to 1970 was 2.7 percent, as opposed to 1.8 percent from 1971 to 2011. The median is used to avoid the effects of outliers which can make the average or mean misleading. There is, for example, a probable outlier in the GDP data in about 1950, a growth rate of about 12 percent.
The data shows an accelerating downturn in growth rates over the last two decades. This coincides with the recent rise in many real energy prices such as gasoline. Of course, correlation does not prove causation. A number of things have risen sharply in the last two decades including the Internet, general computer use, cell phones, consumption of aspartame (the sweetener in Diet Coke), consumption of high fructose corn syrup, and diagnoses of autism (see the previous article The Mathematics of Autism), for example.
It is probable that the single biggest proximate cause of the disappointing growth over the last forty years has been limited and disappointing progress in power and propulsion technology. The plot below is from the United States Energy Information Administration and shows the real, inflation-adjusted price of a gallon of gasoline over the last forty or so years. These prices show a general reversal of the previous trend of declining real prices of gasoline during the early twentieth century (1900-1970).
The rising real price of gasoline presumably reflects that the production of gasoline and other competing energy sources has not kept up with rising global demand. Keep in mind that most demand comes from the so-called developed world: the United States, Europe, Japan, and a few other nations. It would require something like a four-fold increase in global energy production to raise the standard of living of the entire human race to US or European levels.
Gas Prices Inflation Adjusted DOE
Why Have More Ph.D.’s Produced Less Progress and Growth?
The policy of overproduction of Ph.D.’s is based on a number of assumptions that are rarely stated or discussed. Remarkably, it is quite possible that policy makers, business leaders, and others have never thought through what they are doing and why. It is doubtful that aging policy makers, senior scientists and others would consciously sabotage attempts to find cures or effective treatments for cancer or other diseases of old age, although that is what they may well have done with the current glut of biology and medicine Ph.D.s. Similarly, very few policy makers, senior scientists, or others, except perhaps a few energy industry moguls, benefit from the lack of progress in power and propulsion technology, especially if we run out of oil, natural gas, and other hydrocarbon fuels without finding a replacement: the Peak Oil scenario.
In general, science — and with it mathematics — public policy is based on a crude physical analogy. Scientists, at least the graduate students and post-doctoral researchers, are envisioned as essentially intellectual ditch diggers. Intelligence is envisioned as a single linear scale rather like Pearson’s general intelligence and equated to the physical strength of the ditch diggers: a simple “mental horspower.” If you want more, better results, hire more and stronger intellectual ditch diggers. Perhaps, there are a few super ditch diggers who are ten times stronger than the average ditch digger. It would be nice to hire them but you would rather not pay them ten times as much.
Somewhat related is a belief that control over the intellectual ditch diggers is a good thing. The more control, the more likely you will get better results. Thus, slave labor from India and China is expected to produce better science and technology than free labor from the United States. This latter view seems especially suspect. Even in physical labor, were the free factory and farm workers of the North who could quit their jobs in disgust if mistreated really less productive than the slave labor of the antebellum South? Not only did the North forge ahead into the industrial era leaving the South far behind, but England and France became dependent on imports of food from the North, not the South, so when the Civil War came, England and France ultimately sided with the North despite “King Cotton” and the textile industry lobbies.
The Ph.D. glut combined with the heavy importation of guest workers from India, China, and other Third World nations who often face serious economic hardship if fired and sent home — a common fate — creates a situation approaching slave labor. Can slave labor really cure cancer or invent new practical energy sources? Don’t hold your breath!
The logical implication of this implicit model of scientific research is simple: throw a lot of money and manpower under centralized bureaucratic control at a problem like cancer and it will fall. There have been many attempts to do this since World War II, inspired in part by the spectacular success of the Manhattan Project (see the previous article The Manhattan Project Considered as a Fluke). Like the War on Cancer, most of these efforts have
However, if scientific and mathematical research and development is not analogous to this concept of digging ditches, one might expect the Ph.D. glut to cause serious problems. The Ph.D. glut depresses wages and working conditions in critical research areas such as health and energy. The purported “best and brightest” in the United States go elsewhere — developing pseudo-scientific mathematical models for Wall Street with disastrous consequences or, somewhat better, developing apps to sell pet food over iPhones and similar gimmicks with limited but at least positive benefits. Increased quantity cannot make up for the loss of quality.
Most importantly, the Ph.D. glut greatly reduces the independence and creative freedom that has so often proven necessary to solve extremely hard scientific problems like cancer or new energy sources. The Ph.D. glut means that egos and politics dominate. Original thinkers can easily be eliminated and compliant yes men rewarded with the few permanent positions. Near slave labor from Third World nations will only further reduce this independence and creative freedom.
Conclusion
As the data shows, the Ph.D. glut is associated with a long term decline in growth rates in the United States. While quantitative data are limited, it is associated with a qualitative decline in the rate of scientific and technological proress in many fields, especially power and propulsion technologies. There is the notable exception of some computer and eletronic technologies, although artificial intelligence (AI) actually shows a similar disappointing rate of progress — unlike CPU clock speeds or video compression algorithms where progress clearly has been impressive and comparable to the historical pre-1970 levels in many other technical fields.
While it is difficult to
© 2012 John F. McGowan
About the Author
John F. McGowan, Ph.D. solves problems using mathematics and mathematical software, including developing video compression and speech recognition technologies. He has extensive experience developing software in C, C++, Visual Basic, Mathematica, MATLAB, and many other programming languages. He is probably best known for his AVI Overview, an Internet FAQ (Frequently Asked Questions) on the Microsoft AVI (Audio Video Interleave) file format. He has worked as a contractor at NASA Ames Research Center involved in the research and development of image and video processing algorithms and technology. He has published articles on the origin and evolution of life, the exploration of Mars (anticipating the discovery of methane on Mars), and cheap access to space. He has a Ph.D. in physics from the University of Illinois at Urbana-Champaign and a B.S. in physics from the California Institute of Technology (Caltech). He can be reached at [email protected].
Appendix I
GNU Octave script usgdp_per_capita.m to generate the plots in the article.
% script to compute annual growth rate of US REAL GDP
%
% (C) 2012 By John F. McGowan, Ph.D.
%
%data = dlmread('us_real_gdp.txt'); % federal reserve data
data = dlmread('us_real_gdp.txt'); % federal reserve data
year = data(:,1);
gdp = data(:,2);
figure(1)
h1 = plot(year, gdp);
set(h1, 'linewidth', 3);
title("US REAL GDP (CHAINED 2005 DOLLARS)");
xlabel('YEAR');
ylabel('BILLION DOLLARS');
pop1947 = dlmread('us_pop_1947_2012.txt');
len = length(year);
gdp_per_capita = gdp*1e9 ./ pop1947(1:len, 2);
figure(2)
h2 = plot(year, gdp_per_capita);
set(h2, 'linewidth', 3);
title("US REAL GDP PER CAPITA (CHAINED 2005 DOLLARS)");
xlabel('YEAR');
ylabel('DOLLARS');
delta = conv(gdp, [1 -1]);
growth = delta(1:end-1) ./ gdp(1:end);
[p, s] = polyfit(year(2:end), growth(2:end)*100, 3);
fit = polyval(p, year(2:end));
target = ones(size(fit))*6.8; % need average growth rate of 6.8% to absorb new Ph.D.'s
figure(3)
h3 = plot(year(2:end), growth(2:end)*100, '+', year(2:end), fit, '-r', year(2:end), target, '-g');
set(h3, 'linewidth', 3);
title("US GDP ANNUAL REAL GROWTH RATE");
xlabel('YEAR');
ylabel('PERCENT');
legend('DATA', 'SMOOTHED', 'PHD GROWTH RATE');
disp("MEDIAN REAL GDP GROWTH RATE 1947-1970");
median(growth(2:23))
disp("MEDIAN REAL GDP GROWTH RATE 1971-2011");
median(growth(24:end))
delta_pop = conv(pop1947(:,2), [1 -1]);
growth_pop = delta_pop(1:end-1) ./ pop1947(:,2);
[ppop, spop] = polyfit(year(2:end), growth_pop(2:end-1)*100.0, 3);
fit_pop = polyval(ppop, year(2:end));
figure(4)
h4 = plot(year(2:end), growth_pop(2:len)*100, '+', year(2:end), fit_pop, '-r');
set(h4, 'linewidth', 3);
title("US POPULATION GROWTH RATE");
xlabel('YEAR');
ylabel('PERCENT');
legend('DATA', 'SMOOTHED');
% growth rate of per capita gdp
delta_gdp = conv(gdp_per_capita, [1 -1]);
growth_gdp = delta_gdp(1:end-1) ./ gdp_per_capita;
[p_gdp, s_gdp] = polyfit(year(2:end), growth_gdp(2:len)*100.0, 3);
fit_gdp = polyval(p_gdp, year(2:end));
figure(5)
h5 = plot(year(2:end), growth_gdp(2:len)*100, '+', year(2:end), fit_gdp, '-r');
set(h5, 'linewidth', 3);
title("US REAL GDP PER CAPITA GROWTH RATE");
xlabel('YEAR');
ylabel('PERCENT');
legend('DATA', 'SMOOTHED');
disp('MEDIAN US REAL GDP PER CAPITA GROWTH 1947-1970');
median(growth_gdp(2:23))
disp('MEDIAN US REAL GDP PER CAPITA GROWTH 1971-2011');
median(growth_gdp(24:end))
% real price of gallon of gasoline at EIA Department of Energy
%
% https://www.eia.gov/forecasts/steo/realprices/
disp('ALL DONE');
Appendix II
US Real Gross Domestic Product (GDP) data (1947-2011) in billions of 2005 “chained” dollars (inflation adjusted) from the St. Louis Federal Reserve: us_real_gdp.txt.
1947 1793.3 1948 1868.2 1949 1838.7 1950 2084.4 1951 2192.2 1952 2305.3 1953 2314.6 1954 2379.1 1955 2535.5 1956 2582.1 1957 2589.1 1958 2654.3 1959 2782.8 1960 2800.2 1961 2975.3 1962 3097.9 1963 3262.2 1964 3429.0 1965 3720.8 1966 3881.2 1967 3977.6 1968 4174.7 1969 4259.6 1970 4253.0 1971 4442.5 1972 4750.5 1973 4948.8 1974 4850.2 1975 4973.3 1976 5187.1 1977 5446.1 1978 5811.3 1979 5884.5 1980 5878.4 1981 5950.0 1982 5866.0 1983 6320.2 1984 6671.6 1985 6950.0 1986 7147.3 1987 7451.7 1988 7727.4 1989 7937.9 1990 7982.0 1991 8062.2 1992 8409.8 1993 8636.4 1994 8995.5 1995 9176.4 1996 9584.3 1997 10000.3 1998 10498.6 1999 11004.8 2000 11325.0 2001 11370.0 2002 11590.6 2003 12038.6 2004 12387.2 2005 12735.6 2006 13038.4 2007 13326.0 2008 12883.5 2009 12873.1 2010 13181.2 2011 13441.0
Appendix III
US Population Data from the St.Louis Federal Reserve (1959-2012) and the US Census Bureau (1947-1958) combined: us_pop_1947_2012.txt.
1947,144126071 1948,146631302 1949,149188130 1950,152271417 1951,154877889 1952,157552740 1953,160184192 1954,163025854 1955,165931202 1956,168903031 1957,171984130 1958,174881904 1959,175818000 1960,179492000 1961,182404000 1962,185347000 1963,188113000 1964,190763000 1965,193308000 1966,195614000 1967,197814000 1968,199864000 1969,201821000 1970,203929000 1971,206567000 1972,208989000 1973,211053000 1974,213003000 1975,214998000 1976,217172000 1977,219262000 1978,221553000 1979,223973000 1980,226554000 1981,229004000 1982,231235000 1983,233398000 1984,235456000 1985,237535000 1986,239713000 1987,241857000 1988,244056000 1989,246301000 1990,248743000 1991,252012000 1992,255331000 1993,258799000 1994,262021000 1995,265157000 1996,268258000 1997,271472000 1998,274732000 1999,277891000 2000,281083000 2001,283960000 2002,286739000 2003,289412000 2004,292046000 2005,294768000 2006,297526000 2007,300398000 2008,303280000 2009,306035000 2010,308706000 2011,311019000 2012,313278000
This comment is carried over from the previous post on the same subject.
“My article deals with growth rate; the real growth rate of the GDP has declined. It does not deal with issues of equality or distribution. There are arguments that all or most of the per capita real GDP growth since about 1980 has gone to the top 1 percent or even 0.1 percent of the United States population. The four points raised have more to do with this.”
That’s perceptive — good, a meeting of the minds is possible. The skewed income distribution one sees is closely related to the diminished rate of increase in GDP. Most of the gains from increased productivity have gone to the upper 0.1 %, as you suggest. We would expect the rate of growth of GDP to decline in a consumer-driven economy, thanks to flat wages. This is the missing enthymeme. But we should look for more proximate causes of the decline. The basic observation is one of diminishing returns. The question is “why?”
“All of the factors — automation, cheaper manufacturing abroad, women entering the work force, and immigration from abroad — should increase the GDP even though they may lower wages and cause benefits to accrue to a small group at the top (maybe).”
Your graphs show that GDP has increased–only the rate of increase has slowed. You seem to attribute the declining rate of increase to a Ph.D. glut.
“Despite all these changes, which should boost GDP, the GDP growth rate has declined over the last forty years.”
There is another possibility: diminishing returns thanks to the factors I’ve mentioned. It’s quite possible that there are limits to increases in efficiency. Not only is it possible, but this is compatible with all of the evidence you have presented, and it comports with studies of other researchers.
Why should increased automation indefinitely result in a greater rate of increase in GDP? Why should optimization for gain from trade in competitive markets yield ever higher rates of increase in GDP?
Isn’t diminishing returns to computerized automation, outsourcing and expansion of the labor pool a better explanation for the decrease in the rate of growth in GDP than an oversupply of Ph.D.s?
You mention institutional forces at work in the scientific enterprise. Let me suggest another possibility: Editorial: Why are modern scientists so dull? How science selects for perseverance and sociability at the expense of intelligence and creativity. Medical Hypotheses 72 (2009) 237–243
Excerpt from the abstract: “Question: why are so many leading modern scientists so dull and lacking in scientific ambition? Answer: because the science selection process ruthlessly weeds-out interesting and imaginative people. At each level in education, training and career progression there is a tendency to exclude smart and creative people by preferring Conscientious and Agreeable people.”
You could be seeing a decline in the rate of increase in GDP because there are diminishing returns to efficiency. Example: high-frequency trading. There is more liquidity thanks to HFT, but despite the protestations of practitioners, researchers have shown that the “quality” of this liquidity is diminishing. And indeed, when the time horizons of investment houses, hedge funds and quant specialty shops collapse to the millisecond and microsecond, they have already admitted that it is very hard to get an informational advantage in the market.
The last sentence of the following already hints ar a more plausible explanation for the decline than a Ph.D. glut:
“Automation has been ongoing since at least the invention of the separate condenser steam engine in the 1770s. Actually, wind mills and water mills were used prior to the steam engine and there appears to have been significant automation in some areas even before the 1770s. Automation contributes to the GDP rise. The benefits of automation seem to have been greater prior to 1970 — actually the benefits seem to have been declining in a long term trend.”
According to a study by WIlliam Nordhaus of Yale University, entitled, “The Progress of Computing.” Nordhaus documents computer performance improvements on the order of 1 trillon to 5 trillion times “…in constant dollars or in terms of labor units since 1900.” During that period, Nordhaus notes that “…there were relatively small improvements in efficiency (perhaps a factor of ten) in the century before World War II. Around World War II, however, there was a substantial acceleration in productivity, and the growth in computer power from 1940 to 2002 has averaged close to 50 percent per year.” Those developments coincided with the advent of nuclear weapons.
Though my exact phrasing was increases in automation, I should have been more precise. I was referring to computerization in industry. Still. I believe that it is incorrect to say that the effect of automation on GDP were all but exhausted before 1970. The conclusion that we are witnessing diminishing returns I believe is correct, however.
“What does the evidence show? Remarkably, both the growth rate of the US Real Gross Domestic Product (GDP) and the growth rate of the per capita US Real Gross Domestic Product were significantly higher prior to 1970 than since. Now, the decline in the US growth rate is a worrisome long term trend. It is difficult to tie to any one event or policy. It has occurred under both Republican and Democratic Presidents and Congresses.”
Let’s go further back in US economic history: the 150 year period before 1970. This was a period of increasing wages and labor shortages.
After 1970, the four factors I mentioned were operating: significantly increased computerized automation, resulting in the elimination of entire categories of work; offshoring and outsourcing manufacturing and services; the influx of millions of women into the workforce during and after the womens’ liberation movement; and the influx of immigrants from Latin America.
This led to a rise in GDP in absolute terms, but thanks to flat wages since 1970, that rise could not be sustained in a consumer-driven economy. We have millions of people out of work, a shrinking middle class saddled with debt, a polity committed to benefiting the creditors–how is the second derivative of GDP expected to remain positive under those circumstances? Also, there are questions of diminishing returns to increased efficiency in automation. An overproduction of Ph.D.s couldn’t possibly account for a massive misallocation of capital at GDP levels.
The Author Responds
I am not sure how far apart we actually are. Perhaps we should respectfully agree that we disagree.
Some specific points.
I am of the view that the long term growth of the GDP or equivalent measures is largely a reflection of technological advances such as steam engines, internal combustion engines, airplanes, computers etc. Certainly, capital investment and the discovery of new resources play somewhat independent roles, but many of these overlap with technological advances. Our per capita GDP is much higher than one-hundred years ago because we have engines, power sources, and similar machines that make it possible for one person effectively to produce much more food, objects etc. than one-hundred years ago.
Although there have been some advances — batteries have improved in recent decades for example — progress in power and propulsion technology has been noticeably anemic since 1970. To give some idea, in 1960, most commercial air transportation was propeller driven airplanes. By 1970, most commerical air transport in the United Staes was by jet. Modern jets are only slightly larger than the jets of 1970 and no faster. The regular cruising speed is still about 400 miles per hour. Nor is this an isolated example from aviation. Nuclear power systems such as the RTG on the Curiousity Mars Rover are pretty much the same technologies as in the 1970s with marginal improvements. Bill Gates is apparently trying to fund development of small, compact nuclear reactors, but so far there has been minimal progress in this area. This stagnation corresponds to a period of large gluts in the production of Physics Ph.D.’s. Some of the largest specific Ph.D. gluts occurred in physics in 1970 and 1992/1993. Yet this specific oversupply of qualified labor clearly did not translate into almost any kind of better power or propulsion technology which one certainly would naively expect.
Without progress in power and propulsion technology, there is only so far one can go with computers and electronics. So, yes there would be diminishing returns, but why the lack of progress in other critical areas than computers and information-processing electronics?
Also, one would not necessarilly predict diminishing returns from computers. Computers are used for computer aided design, computer aided engineering. There are numerous products such as AutoCAD, NASTRAN, MATLAB, Mathematica, and others in widespread use. These are usually pitched as using computers and software to accelerate research and development, invention and discovery. Power and propulsion fields were generally early adopters of these technologies, using them on the supercomputers and mainframes of the 1960s, 1970s, and 1980s. We might have expected the use of computers to have accelerated the development of new power sources, engines, motors and so forth. Yet, progress slowed down in these fields. Yes, CAD tools are used to design today’s jets but those jets are only slightly better than the jets of 1970. There was much more progress in the days of pen and paper and slide rules! The likely interpretation of this is that invention and discovery is driven by human faculties such as conceptual reasoning that cannot be computerized.
With respect to the Medical Hypothesis editorial.
Editorial: Why are modern scientists so dull? How science selects for perseverance and sociability at the expense of intelligence and creativity. Medical Hypotheses 72 (2009) 237–243
How is this different from what I am saying? My position is that the Ph.D. gluts cause a slowdown in scientific progress because they create a situation where creative, imaginative people can be easily weeded out in favor of compliant drones who uncritically support the prevailing paradigm no matter what. In contrast, if one has an actual “shortage,” one cannot easily get rid of creative, imaginative people. In a genuine shortage, people take what they can get.
This is a matter of public policy. The government can limit the number of graduate students supported to the number of senior scientists who retire or die (or something like that) so that senior scientists have to compete for students. Secondly, the government can give training grants and fellowships to the graduate students that are not controlled by their thesis adviser as suggested in the Bridges to Independence report (for example).
The private sector, of course, is free to do this sort of thing without waiting for the government. Perhaps Bill Gates, Nathan Myhrvold, Peter Thiel, Andy Grove or other business leaders who seem to be trying to sponsor their own research programs will or are doing this; I think they will find that progress will require breaking free of the paradigms that dominate the government research programs in at least some important cases.
I would agree that the current downturn since 2008 reflects a collapse in aggregate demand due to the collapse of the housing bubble and probably rising inequality. I doubt this can account for the slow rate of growth over the period from 1970 to 2008. It probably reflects the minimal progress in power and propulsion technology which is critical to long term growth.
Sincerely,
John
You still have to formulate a sociologically substantive thesis concerning the relation between GDP, progress in power and propulsion technology and the putative glut of Ph.D.s.
Apologies about the Medical Hypothesis article–I meant to say this was supportive, though it sheds additional light on the matter. Training in scientific work imposes an opportunity cost related to establishing tenure at academic institutions. The competition isn’t over R&D employment in research laboratories–industry has largely abandoned them. If there were demand for scientists in power and propulsion technology then perhaps progress in those areas would eventually show up in GDP as developments were adopted.
The question every statistical and econometric analyst must answer is: “what is the effect size?” Is it that big an effect on GDP? Is the effect of thousands of Ph.D.s chasing non-existent tenure track positions enough to account for a decline in the rate of increase in GDP? Now you may not agree with this formulation, but at least it exhibits sufficient precision in the direction of substantive sociological hypothesis. Make a better one. Formulate it as a hypothesis.
I’m well aware of the use of mathematical software and models in scientific research, having worked on NSF funded projects to develop and use such software, and having extensive experience in research computing.
You may be correct, but an interpretation based on personal conviction, experience and intuition superimposed on Octave generated graphs is not sufficient. The Internet affords an unprecedented opportunity for non-mainstream voices to register their opinions–but this is going to take treatise worth of analysis.
It seems to me where I work that there aren’t enough Ph.D.s working on the projects we have, and the reason is insufficient funding. Virtually all of the Ph.D.s who come here want to pursue tenure track positions. The jobs aren’t there. Now it may be that the rate of Ph.D. production is exactly what is needed to sustain the current population of academics. Also we shouldn’t discount a three decade old trend toward deregulation, a total lack of an industrial policy compared with Germany and Japan, and massive cuts to public education. We don’t need to give the administrators presiding over these cuts the benefit of the doubt.
See this paper by economist Robert Gordon
Is U.S. Economic Growth Over? Faltering Innovation Confronts The Six Headwinds
https://av.r.ftdata.co.uk/files/2012/08/IS-U.S.-ECONOMIC-GROWTH-OVER-FALTERING-INNOVATION-CONFRONTS.pdf
Hat tip to Paul Krugman
https://krugman.blogs.nytimes.com/2012/08/28/is-economic-growth-going-down-the-drain/
Note: I am an optimist. If we get our act together, a big if at the moment, we can do better.
Sincerely,
John
This article has to be a joke, right? You realize less than 1% of Americans hold a Ph.D, and most of those don’t work in academia.
You really think that this is driving GDP change, as opposed to the manufacturing sector, financial sector etc. etc. etc.?
I mean, this is the crux of your argument:
“While it is difficult to prove that the Ph.D. glut has caused slowing rates of scientific progress, technological progress, and economic growth, it is both plausible and likely.”
It’s neither plausible nor likely, especially on a time scale of 20 years.
The Author Responds
1. What we now call research and development has always been conducted by a tiny fraction of the population. In fact my argument is that too many researchers may be impeding progress. Small numbers does not mean small impact.
2. See Robert Gordon’s paper
https://av.r.ftdata.co.uk/files/2012/08/IS-U.S.-ECONOMIC-GROWTH-OVER-FALTERING-INNOVATION-CONFRONTS.pdf
on the enormous impact on real per capita GDP of the invention of the practical internal combustion engine and other inventions in much less than twenty years from 1879 onwards.
3. When people figure out a better way to do things, the new knowledge can spread very rapidly and have enormous impact, result in very visible economic growth in just a few years.
4. We are seeing this in action right now following the invention of better video compression in 2003. In less than nine years we have a huge online video business and — more importantly — we have video conferencing which is already replacing air and road travel. If/when videoconferencing becomes as widely used as e-mail, this should reshape economy — done right it can boost GDP enormously, in less than 20 years.
Small numbers of people and small amounts of money does not mean small impact on real GDP growth, especially with advances in power and propulsion technologies.
Sincerely,
John
This is serious racial overtones by implying that Indians, and Chinese are stealing the very few opening academic jobs. I beg to differ that those Chinese, and Indian university faculty professors work for “slave wages” to suppress wages for normal “free, and independent” mind white folks. If you think about it, it is simply not true at all. The Chinese, Indian, and along with people of the Jewish faith out compete those white folks for admission to top tier universities( half of Stanford, UCLA, Berkeley etc class are Asians), and most of the top winners of science, and math competitions are won by East Asians, Indians, and Jews.
The PhD glut is simple economics. There have been cutbacks in positions at all major universities since the 1970’s and the supply is subsidized, as almost no one has to pay full tuition (although yes, it may seem like it). Subsidy on supply, restrictions on demand. Hmmmm?