Struggle for reputation

I am writing this chapter, and reading lyrics of Taylor Swift’s songs.
Should I start the chapter by

Big reputation, big reputation
Ooh, you and me, we got big reputations
Ah, and you heard about me
Ooh, I got some big enemies (yeah)
Big reputation, big reputation
Ooh, you and me would be a big conversation
Ah, and I heard about you (yeah)
Ooh, you like the bad ones, too

(from Endgame)


This ain’t for the best
My reputation’s never been worse, so
You must like me for me…
We can’t make
Any promises now, can we, babe?
But you can make me a drink

(from Delicate)


Ranking countries by credit rating: the objectivity-subjectivity dilemma again II

Critics on ratings based on their subjectivity

It is (not) difficult to believe that rating agencies perform consulting services, and this is an obvious source of potential bias in ratings. (Remember the story of woolf-boolf!) The credit ratings game is played under the condition that their principal source of revenue comes from the firms whose products they are rating.133 CRAs have been accused of biased evaluation and violating principles of objectivity. Generally, CRAs have denied the existence of any conflict of interest. They have stated that rating decisions are not made by individuals, but by committees, and the analysts have not received any compensation based on their ratings. Rating agencies now us mathematical models, the details of which are not fully disclosed. We already know that models are based on human assumptions. Furthermore, the results of any model can be overridden by humans (they might be called a “rating committee,” whose activity are kept secret). To make the rating procedure more transparent, the Dodd-Frank Wall Street Reform and Consumer Protection Act (2010) required CRAs to disclose their methodologies. (Well, it would be nice if they did so.) So we have the question: who should have the last word: the computer or the human?

Objective algorithms versus subjective conflict of interests

What would be the difference between a subjective credit rating and an objective credit rating? We have again the dilemma: what would be the difference between a subjective and an objective credit rating? A subjective credit rating would be one individual’s, or CRA’s, point of view. It would reflect the expertise of a particular analyst and their agency’s proprietary algorithms. An objective credit rating, on the other hand, would be something based on open databases and open source algorithms. If CRAs were really objective, then there wouldn’t be any need for more than one agency, and we wouldn’t have different rating results. CRAs would not make big revenues; anybody could simply use the only ratings agency’s publicly-available, consistent criteria to generate the (objectively existing) ratings.

Unhappy Reactions:From China to Europe

Several years ago, S&P downgraded China’s credit rating, and the finance ministry of the huge country vehemently criticized the validity of both S&P’s procedure and its result. Other developing countries, most importantly India, continuously rebel against the CRAs. India has a battle with Fitch, which has refused to upgrade India’s credit ratings each year since 2006. Since India has recently tried to attract more foreign investment, it is very painful to get a mediocre grade for creditworthiness. Europeans generally feel the Big Three CRAs show bias towards the United States. The US has managed to maintain its AAA rating despite a growing deficit and high levels of public debt. But in August of 2011, S&P downgraded the US’s credit rating to AA+ for the first time ever in history. The other two agencies still assign top credit scores to the U.S., but S&P affirmed the US’s AA+ credit score this year, reflecting the balance between positive and negative factors expected over the next two years.

Should we or should not we?

As debates over the merit of credit ratings abound,they remain a crucial facet of the international financial system. The spirit of this book is in accordance with the evaluation of Sebastian Mallaby from the Council on Foreign Relations:134 The best way to counter the monopolistic power of the Big Three, he argued, is for investors to stop giving their ratings so much weight.

“The reason why the subprime bubble could happen, or the reason why the European sovereign debt crisis can happen is, largely, that very blind investors bought bonds relying on ratings, and [didn’t do] their own homework about what the real credit risk was in the bonds.”

Ranking countries by credit rating: the objectivity-subjectivity dilemma again (Part I)

We already know that individuals get credit scores, while corporations and governments receive credit ratings. This is just the jargon. Governments of countries require ratings to borrow money. Credit ratings also reflect the quality of a country as an investment target, and a county’s credit rating depends on the economic and political state of the actual country. Why do countries need credit ratings?129

          Many countries rely on foreign investors to purchase their debt, and these investors rely heavily on the credit ratings given by the credit rating agencies. The benefits for a country of a good credit rating include being able to access funds from outside their country, and the possession of a good
rating can attract other forms of financing to a country, such as foreign direct investment. For instance, a company looking to open a factory in a particular country may first look at the country’s credit rating to assess its stability before deciding to invest.

 It is well known that the US leads the list of countries ranked according to external debt, followed by the United Kingdom. It is remarkable that Luxembourg has much larger debt per capita than any other countries (6 million per capita). Luxembourg is known as a major financial center, so presumably the country owns large deposits belonging to foreign people.

In principle, the rating process should give an objective and independent assessment. If the procedure were totally objective, it would be sufficient to have only one credit rating agency. But we have three big (Fitch, Moody’s and Standard & Poor), and many smaller, agencies, who might use different databases and (generally private) algorithms, and they therefore produce (slightly) different results,

Capsule history of the three famous credit rating agencies (CRAs)

In 1860, Henry Poor (1812􀀀1905) published History of Railroads and Canals in the United States, an attempt to collect and provide comprehensive information about the financial state of such transportation companies. Standard Statistics started to published ratings of different bonds in 1906, and they merged in 1941 to form Standard and Poor’s Corporation. Their product, the S&P 500, became a stock market index, a measure of economic activity. John Knowles Fitch (1880 􀀀 1943) founded the Fitch Publishing Company in 1913 to provide financial statistics for helping investors’ decision making. In 1924, they introduced the AAA through D rating system that has become the industry standard for bond ratings 130. John Moody (1868 􀀀 1958) and his Company first published “Moody’s Manual” in 1900. Moody’s Investors Service has provided ratings for nearly all of the government bond markets and today is a full-scale rating agency.

There is a Latin phrase that goes: “Quis custodiet ipsos custodes?” It is literally translated as “Who will guard the guards themselves?” A natural question arises: Who rates the credit rating agencies?131 In 1975 nationally (US!) recognized statistical ratings organizations (NRSRO) were created. Investors simply needed more reliable information to help their decision making to allocate their resources, and this demand has led to enormous growth, expansion, and influence of the credit ratings industry. Three decades later, the Credit Rating Agency Reform Act of 2006 allows the main regulatory agency—the Securities and Exchange Commission (SEC)—to regulate internal credit-rating processes. CRAs had a critical role in the financial crisis of 2008, and the details are far beyond the scope of this book. The lesson I learned from Michael Lewis’s bestseller 132 was: “The line between gambling and investing is artificial and thin.”

Pay more in tax and be happier

While there are almost infinitely many ways to rank countries, many readers will agree with me that one of the most important questions to answer is how happy a country is. In 2011, the UN General Assembly initiated a project that sought to measure the happiness of citizens of member countries. But how do we measure the happiness of a country? The measurement is mostly based on a simple task: in each country, a significant number of people are asked: “Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?”

A report issued by the UN in 2017 ranked Norway as the happiest country in the world. (The Reader already knows that the phenomenon of “pecking order” among chickens was discovered in Norway, and the Norewgian Magnus Carlsen has the highest Elo number. The neurobiologists among our readers will also remember that May-Britt Moser and Edvard Moser from Norway were awarded the Nobel Prize in Physiology or Medicine in 2014 for discovering certain types of neurons called grid cells, which are responsible for spatial information processing.) It was remarkable to see the reaction of the Prime Minister Erna Solber: “even if we top this statistic now we must continue to prioritize mental healthcare.” Actually there is no statistically significant difference among the happiest five countries, which each received scores around 7:5: Norway – 7:54; Denmark – 7:52; Iceland – 7:50; Switzerland -7:49 and Finland – 7:47. The Central African Republic had the lowest score at 2:69. In 2018, Finland took the lead, and the United States ranked 18th out of 156 countries surveyed–—down four spots from 2017’s report. In spite of a strong economy, the US ranks quite poorly on social measures such as life expectancy and suicide rates. Major factors possibly contributing to this drop in ranking are the worsening of the opioid crisis, the growing economic inequality, and the decrease in confidence in government. Investment in mental health care is likely to correlate to average happiness. A good proxy for investment in mental healthcare is the number of psychiatrists and psychologists working in mental health care per capita. Based on these figures, average happiness appears to be higher in countries that invest more in mental health care. Like it or not, developed mental health care implies the more frequent use of antidepressants. Increasing antidepressant utilization and decreasing national suicide rates have been reported recently from the happiest country in the world.

Countries where we live

As we all know, humankind has organized itself into geopolitical units, called countries.
Historically, people have preferred to belong to a particular country and to share a sense of national identity. Homophily is an archaic trait: we like to spend more time with people who are like ourselves. While there are people, however, who
believe that the idea of countries, as nation-states, is outdated and the source of
conflict, countries remain a primary means of controlling people, organizing society,
and managing the distribution of wealth.
Countries are ranked and rated now by an enormous number of criteria, adopted by
hundreds of different organizations, sometimes strongly connected to specific countries
(frequently to the US). In a book about the ranking of countries, 125 authors
Cooley and Snyder identify ninety-five indices that have been introduced to evaluate
and compare states. The indices are lumped into categories, like “Business and Economics,” “Country Risk,” “Democracy and Governance,” “Environment,” “Media and
Press,” “Security Issues and Conflict,” “Social Welfare,” and “Transparency.”
A ranked list of countries based on the social welfare function defined by Amartya Sen
has been prepared annually by using data from the Central Intelligence Agency, and
another version is prepared using data from the International Monetary Found and
United Nations. (Remember: the Sen social welfare function is calculated as product
of GDP per capita and the difference between 1 and the society’s inequality measure,
and it is reported in terms of dollars per person per year.) The last published list is
from 2015:
1. Qatar | 82884
2. Luxembourg | 49242
3. Norway | 47861
4. Singapore | 43518
5. Switzerland | 42335
6. Netherlands | 34853
7. Sweden | 34443 per
8. Denmark | 33907
9. Germany | 33719
10. Iceland | 33695
11. United States | 33260
Qatar has a well-developed oil exploration industry, and the petroleum industry accounts for 60% of the country’s GDP. Its low (but rapidly increasing due to an influx
of migrant workers) population contributes to a large GDP per capita. The population
explosion due to the immigration of (young) males has produced an extreme
gender imbalance (there are only about 700; 000 women in a country of 2:5 million
people). Many immigrants, mostly involved in building the infrastructure needed for
the upcoming (well, soccer) World Cup, live in labor camps. However, since the Gini
inequality index measures income inequalities but not social inequalities, Qatar still
leads the list. Please note, that the scores of the last six countries are close to each
others, and the specific ranking does not have too much significance. (It is somewhat

Nobody likes, everybody uses: university ranking II

Demand for Ranking
Transparency, accountability and comparability There is an increased demand of transparency, accountability and comparability of the higher educational institutions from the public and the politicians 121. Ranking methodology offered a simple and easily interpretable comparison. Ellen Hazelkornin his excellent book 122 published a list of typology of transparency, accountability and comparability  instruments:
Accreditation: certification, directly by government or via an agency, of a particular HEI
with authority/recognition as an HEI and to award qualifications.
Assessment, Quality Assurance (QA) and Evaluation: assesses institutional quality processes, or quality of research and/or teaching and learning.
Benchmarking: systematic comparison of practice and performance with peer institutions.
Classification and Profiling: typology or framework of higher education institutions to
denote diversity usually according to mission and type.
College Guides and Social Networking: provides information about higher education institutions for students, employers, peers and the general public.
Rankings, Ratings and Banding: enables national and global comparison of higher education performance according to particular indicators and characteristics which set a ”norm” of achievement. The different instruments partly reflects the past performance and partly helps to plan future activity.
Heterogeneity and comprehensivity Malcolm Gladwell has already explained the nuts and
bolts of college rankings in a New Yorker article titled ”The Order of Things”. What college rankings really tell us”, published in 2011). He describes the evolution of the U.S. News ranking systems, and the difficulties of being both ”comprehensive and heterogeneous”. (Gladwell’s italics). Comprehensive means that nearly all aspects of something is included. Gladwell gives an example for heterogeneity:

”…aims to compare Penn State—a very large, public, land-grant university with a low tuition and an economically diverse student body, set in a rural valley in central Pennsylvania and famous for its football team—with Yeshiva University, a small, expensive, private Jewish university whose undergraduate program is set on two campuses in Manhattan (one in midtown, for the women, and one far uptown, for the men) and is definitely not famous for its football team.

I think from the example it is clear that to compare these two institution much more difficult than apples to oranges. We saw in Chapter 2, that it even the latter is quite difficult. As concerns comprehensivity and heterogeneity, there is a trade-off between the two characteristics.