With the prologue as discussed in Part I and Part II, we now can make a deeper analyses who may or will get favorable support from the 44 million voters.  Clearly, the three candidates with a score of 5 will.  Potentially, among these winners there will run as a running mate in the end. It is estimated that the US population in 2019 is around 329.6712 million.  Remember that those who have or in the process of paying their student loans are estimated at around 20 million.  The current students who are still taking classes, but have taken some sort of loans will need to be added into 44 million.  Realistically speaking, not all 44 million will vote for the candidate with a score of 5.  Here are the simulations and scenarios:

The analyses and numbers shown above are not exact, but estimated within a standard deviation and margin of errors.  It is assumed that everyone that has some sort of student loan debt will support the candidate.  Some may be not while others may have personal preference on certain candidates, regardless of their plan.  However, the estimated percent may or may not fall within a standard deviation of the true statistical mean, but it may well fall within the ballpark.  Some borrowers who have paid off their loans are excluded from the analyses.  However, their sentiment may lean toward supporting the bailed-out effort, for they know how the loan has financially impacted their life.

Roughly, 20% votes will go to the candidate who support the bailed-out.  Candidate that ranks #1 has a credible track record on this particular issue.  She has voiced her stand many years ago, and supporters of college affordability, accountability and transparency.  She is a strong advocate for improving the professionality and work quality of different college accreditation agencies.  Do not be surprise if some who do not make the cut (with a score of 5), will shift or switch their position.  However, this may show to the American public, the individual candidate motivation and interest in running for office.  Is the motivation for the public’s interest or others?

We encourage visitors of this site to read Part 1, before reading through Part II.  The Association has presented a table to show where the candidates for the 2020 big dance stand.  However, it will be beneficial to understand what systematic errors (SE), which is one of the culprits that have caused the ballooning of student loan debt are.  A simple example which may help to understand SE is using a bad or malfunction thermometer to measure how high the daily temperature in a room within a week time.  The result will be well predicted is that none of the measure is accurate.  Therefore, in order to correct the issue, get rid of the bad thermometer and replace it with a new, and good one.  As simple as that.

The same idea can be applies to address the student loan debt.  The skyrocketed of the debt has been caused by the systematic errors brought by all entities or players (Examples are: the loan servicing companies, the college, the lawmakers, the DOE and others) involved in the student loan system for decades.  Now, the public sees the $1.5 trillion loan debt.  But, one needs to remember that this is caused by “errors” that have been purposely induced in the system that benefit the individual entity in the expense of the students, their family and to sum them up the American public.  Therefore, to solve the current student loan debt, the whole system needs to be reset.  It needs to start from ground zero, for no one knows where to start if one needs to partially solve the issue.  For example, if one tries to detangle the water hose, she or he needs to know where to start.  Unfortunately, this is not the case for detangling the student loan debt. No one knows where to begin–not even the administrator or the lawmakers on the Hill. Therefore, the only way to fix the system, if there is the will to do so, is to wipe everything out and reset the whole thing.  This is the first step and a necessary condition to solve the problem.  Will this make every single head in the country in agreement and happy.  The answer is no.  By now the readers should know why and who will not happy.  Any one that got her or his pocket impacted will make the loud voice.  However, Uncle Sam needs to start some where.

With this in mind, the readers and visitors of this site can look at the table, and find out which candidate(s) will score high in this particular issue (please look on the list who score a 5 in that column).  BTW, the Association does not have any association with any of these candidates.  Our mission is to apply analytics and analyze the issue and share the logic to the American public, so that everyone will understand the “real” cause and the root of the problems.

Given the published data shown above, the Association has analyzed where each candidate’s stand on solving  US student loan Debt. Translating from what each candidate has said into different scores, we hope to rank them and share the information to the American public.  Before we go further let us critically think what a public policy can and cannot do to solve any particular problem.  Understanding logically on the problems will help to avoid making unnecessary discussions or debate.  It does not mean that the American public cannot discuss this big issue.  Rather, to do it efficiently and purposely.

As the Association has published many articles on this issue, then it will be easier for the American public to understand the root of the problems.  Basically there are 2 culprits that cause all this crisis: (1). Skyrocketed college cost and (2). Systematic errors purposely induced into the system.  No one can solve the US student loans issue until these two elements are understood and then eliminated.

In the applied econometrics or statistical world, the sources of standard deviation or variance can be classified into 2 groups: random and systematic.  Because the opportunities to make money are widely opened in this $1.5 trillion industry, then everyone is trying to get a piece out-of it.  From lending institutions to loan servicing organizations and others.  Because the interests of each institutions may not be the same, except to make as much money as possible, then each one of them is trying to induce or twick the system for they own benefits.  If there are 10 entities involving in the system then potentially there will be 10 sources of systematic errors.  Because these errors are unique to the institution that brought it in, then there is a small opportunity for other entities or outsider to change that.  For example, the  DOE is hardly check if the loan servicing company is operating under DOE’s guidelines.

Will continue to part II.

The answer may be “yes with a note”.  Many colleges in the past may have relied heavily on what has been known as “Best Practices” or BP to report institutional metrics.  One may notice that almost every US higher learning institution follow the same path on reporting their institutional metrics.  Focus heavily on reporting is what was in the past, which may be less relevant given the current changes and dynamics in the industry.  For example, majority factbook or factsheet are provided and make available on the institution’s website.  There is a clear justification of doing so which is partly to fulfill their accountability as public institutions or to show their responsibility to the donors.

While applying BP is a great way to manage an organization, one may ask how has such a policy helped them to cope with the dynamics and changes that have affected the environments where they are operating?  If the best practices represent the best of best strategies that ever invented, found or practice in the industry, then below are the critical questions to think about:

1. Why the second order for optimal condition on the institutional metrics function such as retention and graduation is greater than zero?  If this is the case, then an institution is in need to find a breakthrough to survive in the future.
2. Why there are so many higher ed institutions listed on the DoE’s HCM list? May be the logical answer is clear in that, they did not adopt the so-called BP. However, the troubled institutions may have adopted the BP, but fail to adapt to the new reality which may have caused the BPs are no longer relevant.

There is a clear flaw if organizations hold tight on the BP without looking at (1). the new reality and (2). the institution’s core competencies as discussed in Harvard Business Review written by C.K. Prahalad and Gary Hamel in1990.  The question is how a higher ed entity is able to recognize its core?  Will the core ever change because of new public policies and industry dynamics?  IRI will help finding the answers.

One of the reasons why the Association was established many years ago is to bring the awareness to the American public on potential challenges facing by Uncle Sam because of the national student loan debt.  In fact, the first article written and posted in the BLOG was written on March 13, 2013.  At that time, no one even believe what we have hypothesized.  However, today, six years later, everyone confirmed what AAEA has predicted.  Below are the recent examples of what the players have said:

1. Current administrators.
2. Wall Street.
3. Politicians.
4. Lawmakers.
5. Reporters.
6. Wealth Consultants.
7. State agencies.
8. Money managers.

There are many others that can be added to the above list.  It took 6 years for everyone to realize that student loan debt accumulations are not a good thing.  There are several things that one might be able to observe from the student loan debts: (1). common theme shown by the provided list above is nothing, but a reaction.  They all reacted on something that has happened.  But, we at AAEA applied data analytics can see what is coming; and (2). perhaps, the public rarely heard comments from the US higher ed institutions on loan debts, even the most respective School of Economics i n the country classified as the Fresh and Saltwater institutions seem to avoid talking about it, even though their institutions are one of the most important source of ideas and many of their faculty have won the Nobel Laureate award.  One may ask, why are they so quite?; (3). the American public will hear less from student loans servicing companies, such as NAVI and NNI.

One needs to be proactive instead of reactive.  However, bounded rationality, moral virtue and wisdom of which the Association has shared to the American public may have blinded players’ mind and soul such that a short-run maximizing model of Neo-classical paradigm is adopted, instead.

Back in 2013 the predecessor of the current administration has introduced the so-called CAR. It has been on the news recently that finally the White House is trying to cope with the US higher education related to the student loan debt.  The American public eager to know what public policies on higher ed are being cooked in the kitchen.  If the Congress votes for them, then there will be huge impacts on higher learning institutions, students, their family and the society.  Apparently, the policy has shifted and perhaps it has made a U-turn from ignoring the American public interests to something in supporting them.  This shift in Higher Ed policy occurs because the student loan debt has been a hot topic in the campaign trails.

Basically HEA is ≅ with CAR with a little, but very important twist to limit the amount of taken student loans which previously is not one of the elements in CAR.  However, this little tweak has huge impacts on some higher learning institutions in that it will affect their enrollment significantly.

Please click=======>HERE<=========

to access your institution’s rating, first click on the provided link (in red above), then click on the drop down menu to find the State where your institution is located, then scroll down to find out the score

Based on publicly available NCES data, in 2013 AAEA has calculated and shared to the American public a proxy of the college rating under CAR.  This rating will be updated with the new component in HEA policy.  The results will be shared soon to the American public through AAEA’s website.  Because of the new component (limit on student loan) will be added, then under HEA, the college rating will produce a College Stressed Test Score (CSTS) as a proxy of an institution’s survival probability under the new proposed HEA policy.  For now, the CAR still relevant, and it can be accessed by clicking this link.

The Association of American Education Analytics is persistent in its missions, prove that its research are honest, unbiased, current, trustworthy, relevant, independent, and helpful.  We, at AAEA can see what is coming, based on what has happened in the past.  We accomplish our research by applying IRI mindset, analytical tools, rigorous and totality in its research approaches on institutional data.

Herbert’s hypothesis is yet significant to understand the need of strategic information in the decision making process.  In the past five years, the data analytics and data scientists profession have bloomed and demand for these professionals across different industries are skyrocketed.  However, the speed of companies’ adoption on this new exciting development differs.  Rapid adjustments found in the industries characterized by ultra-competition, such as manufacturing, or retail.  However, others such as education satisfies with best-practices and data visualization approaches, instead of deploying data analytics.

Needless to say that bounded rationality (BR) has prevented any industry, or any company to max-out their opportunities, investments or resources for some may apply less-than-optimal strategies.  Making this honest mistake is natural because of BR.  It prevents any organization to make optimal choice because human beings are constrained to apply their rationality fully because lack of wisdom (LW) or add (gut) feeling, rumors and owned biases in the process.  Therefore, whatever the decisions that have been made in the past suffered from this natural constraint.  The example has been discussed by Ricardo’s–the Law of Diminishing Return.  The current example can be learned from Facebook $120 billion in 24 hours loss in market capitalization because the combination of LW and BR.

Supposed the optimal output/revenue/sales/enrollment or ROI is Π, an organization will never be able to reach Π, but (Π-ε), because of LW & BR.  The gap measured by ε can be reduced by the application of data analytics where strategic decisions are made and based upon.  One can call ε as the waste of resources, or the loss of chances or opportunities because the institutions fail to make important decision based on processed data.  Some decision makers realize this constraint, and welcome the application of data analytics.  However, many may still stick with the Neo-classical theory of profit maximization or simple rule dictated by the Wall Street.

Higher learning organizations are more comfortable with the concept of best-practices. Needless to say that these best practices are unwritten consensus among the institutions in the industry which are communicated to members through different seminars and Associations.  Should it be time to think about these best practices mindset?  The reason is simple for the best practices paradigm fails to recognize special or unique characteristic or core competence of an individual organization.  For example, if one strategy works for Harvard, it does not automatically work for Universities in North Carolina.  Or if it works for 4-year institution then it will work for 2-year colleges.  Or if it works for state-owned institutions, then it will automatically work for non-profit private entities.

Having discussed the role of process data and BR, one may conclude:

1. Strategic decisions need to made based on data analytics and not data visualization alone.
2. Companies or higher learning organizations that have relied more heavily on data analytics will perform better in the long-run and will be able to outsmart their competitors.
3. Demand for data scientists will keep increasing in the future.  Please check out the following current open position.  If interested, please click this link to apply.
4. Decision makers will rely more on information processed by data analytics.
5. Applying data visualization instead of data analytics will not produce the optimal results.
6. Return or profit cannot be maxed out indefinitely.  Therefore, a new paradigm need to be explored, adopted and applied in order to survive in the world of ultra competition.  This new paradigm will be discussed in the up-coming BLOG article.

Following our discussions on moral virtues, it is pretty obvious that any decision made without wisdom will not produce optimal results.  As shown below quoted from Encyclopedia Britannica on Aristotle’s hypothesis about moral virtue and wisdom.

“Wisdom, the intellectual virtue that is proper to practical reason, is inseparably linked with the moral virtues of the affective part of the soul. Only if an agent possesses moral virtue will he endorse an appropriate recipe for a good life. Only if he is gifted with intelligence will he make an accurate assessment of the circumstances in which his decision is to be made. It is impossible, Aristotle says, to be really good without wisdom or to be really wise without moral virtue. Only when correct reasoning and right desire come together does truly virtuous action result”

That having said, we are now understand more about Ricardo’s theory of diminishing return to scale in that no entity will ever experience positive revenue or sales growth forever.  Recent example from FEDEX shows the proof.  Even though, the Wall Street thinks otherwise.  There are all non-linear, and only positively slope in certain range.  The question is why many institutions trying to push higher?  Maybe, some of the companies are living in hallucination?

One can see the parallel with Ricardo’s hypothesis.  The land in the classical Ricardo example is parallel as the business environment at time t. The innovator, enter the market (fertile land) as the market leader (Apple in smart phone industry), then other players such as Samsung and Huawei came at time (t+i) and make the business environment more crowded or saturated (less fertile land).  These laggards, however, entered the market with better technology.  Therefore, their products perceived by the customers have higher value.  It can be observed that success will create more demand for leisure.  Since time is finite, i.e., there are only 24 hours available a day, the effect of increasing demand for leisure on the market leader is obvious.

The same thing occurs in higher ed.  Harvard as the innovator enters the market decades ago, then followed by others.  Now, that in the higher ed industry, the business environments are way too saturated with all kind of noises, especially when the administrators let all kind of for-profit organizations to join the crowd.  For those who already in the market, they are trying to expand their programs, instead of finding their “sweet spot”.  This strategy finally leads to disastrous outcomes. There are two reasons why this happens. (1). The industry has reached it steady-state, i.e., after the first and second order conditions for maximum were met and (2) Because of bounded rationality as it has been explained by Herbert’s.  Certainly, the captain of those institutions may not have the wisdom and perhaps they are leaving in the state of hallucination that makes them to be unwise and irrational in making choices.   In order to compensate their failures or to prove that they have made the rational decision, then they start to cheat or to cover-up?  In other words, any lack of wisdom leads institutions to make disastrous decisions, policy choice or strategies.  It surely will negatively affect the stake holders and the society as a whole.