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Facts & Values: Distinction or Dichotomy?

This continues from the previous post on the Downfall of Rhetoric in 20th Century

Even though our goal is to explain how apparently objective looking statistics conceal arbitrary and subjective judgments, the path we take requires a detour through “epistemology”, or the theory of knowledge. Instead of the deep discussion provided by Putnam (2002, Collapse of the Fact/Value Dichotomy), we will take a shortcut, and look at how these philosophical debates and controversies about have shaped the way that social sciences in general, and statistics in particular, have conceived of the relationship between the numbers we analyzed and the real world that generates these numbers. The wide gap between the philosophers and other intellectuals can be seen clearly in their respective views regarding logical positivism. One of the lifetime advocates of logical positivism, A.J. Ayer, eventually came to the realization that “it was all wrong”. Another sympathizer and proponent, Bas Von Fraasen, opens his book “The Scientific Image” by saying that this philosophy had a “spectacular crash”:

“Today, however. no one can adhere to any of these philosophical positions to any large extent. Logical positivism, especially, even if one is quite charitable about what counts as a development rather than a change of position, had a rather spectacular crash. So let us forget these labels which never do more than impose a momentary order on the shifting sands of philosophical fortune. and let us see what problems are faced by an aspirant empiricist today.”

Despite clear acknowledgements of its failure, typical non-philosophers neither understand the evolution of positivism under pressures created by scientific discoveries, nor understand the reasons for its eventual abandonment. Despite general awareness that this philosophy has collapsed, a recent survey by Hands (2007) finds that economists continue to believe in the central tenets of logical positivism. As I have argued in Zaman (2012), the foundations of econometrics are solidly built and logical positivist principles. After the collapse of positivism, it became essential to re-examine these foundations, and re-build the discipline on a different set of fundamental principles. This revolution still remains to be carried out. Some aspects of the change required are discussed in Simpson’s Paradox. We plan to deal with a more elementary aspect, namely the relation between the data we use and the realities of the external world that these numbers are supposed to measure. But first, we must look at how the fact/value dichotomy is viewed by the general, non-philosophical public. It useful to note that we are expositing and providing a critique of the popular understanding of positivist philosophy, which must be differentiated from the more sophistical philosophical versions.

It is easily understood that there are “facts” : (F) the number of students who took the SAT in the USA in 2019 was 2,220,087. It is also true that there are values, like the “golden rule”: (V) do unto others as you would have them do unto you. There is indeed a sharp separation between these two statements; F is objective and can be verified by any independent observer – all would come to the same conclusion. V is subjective and different people can have different opinions about whether or not it is true or false. Furthermore, there is no way to establish  whether or not V is true; there is no method for checking values against objective empirical realities in the world around us, to see if it is true or false. The key argument that Hilary Putnam makes is that this distinction exists and is valid, but it is not a dichotomy. To be more explicit, it is not true that all statements can be classified into one of these two categories. Facts and Values both exist, but the vast majority of propositions we deal with in our lives and in our knowledge disciplines cannot be classified as being either a fact or a value. Furthermore, given a statement in which both facts and values are entangled, we cannot pry the two apart to create two statements, one of which is purely factual while the other is purely value.

Putnam argues that wrong conclusions have been drawn because a distinction has been inflated into a dichotomy. Treating the distinction between facts and values as a dichotomy leads to disastrous results. Once we show that something is not a “fact” – that is, it does not have any direct translation to an observable aspect of external reality – then we are forced to conclude that it must be a “value”, and hence not part of reliable human knowledge.  As Putnam has shown, in most of the knowledge that we use to conduct our daily lives, facts and values are “inextricably entangled”.   All our lives we are faced with major decisions like “which college should I go to?”, “which person should I marry?”, “which job should I apply for?”. For making such decisions, it would be useful to have an objective ranking over the choices that we have. However, as we will show later in this paper, objective rankings are not possible when there are multiple dimensions involved. For example, if one college has a strong math department, while the other has a strong english department, then choice among the two will have to be based on my personal preferences regarding the balance between the two skills I would like to acquire. However, this subjective decision is not PURELY a value judgment. There are many facts we take into consideration in arriving at such decisions. Contrary to the conception that economics is purely positive – based purely on facts – while values are used by policy makers, the facts presented to the policy makers are created via a mixture of facts, and subjective decisions regarding how to weight the different facts, in order to combine them into a single number.

Just as individual decisions are based on mixtures of facts and value, so collective choices by communities are based on mixtures of facts and values. Every nation has a large amount of wealth in terms of land, water, infrastructure, as well as skilled human beings capable of learning and producing objects. Each nation faces choices in terms of where to spend energies to achieve best results in the future. In making these choices about how much to invest in factories, how much in education, and so on, we must make subjective judgements. There is no way to avoid making value judgments when decisions require choosing over multidimensional characteristics. The positivist point of view, almost universally advocated by economists and econometricians, is that we can separate the objective and the subjective. The econometricians should present purely objective facts to the policy makers, while the policy makers use their subjective values to make decisions. Our goal in this paper is to show that this separation cannot be done. The “facts” we present to policy makers require us to make arbitrary choices. It is impossible to do otherwise, because reducing multidimensional characteristics to a single number always involves making subjective decisions regarding the relative weights of the different dimensions. At the same time it is impossible to directly present the complete and unadulterated purely objective data, because this would be incomprehensible in raw format. Any procedure for “reducing” masses of data to a small and manageable set of numbers to guide policy requires subjective decisions. Thus, nearly all of the numbers currently in use by statisticians and econometricians are mixtures of facts and values, and it is impossible to avoid doing this mixing.

In the remainder of the paper, we move from abstract philosophical consideration to practical illustrations, to show how numbers we routinely use and regard as objective, conceal value judgments. Those who are aware of how these values are built into the manufacture of statistics can use this knowledge to deceive people. They can bake in their own value judgments into the statistics which they manufacture, while maintaining an appearance of objectivity that is automatically created by the use of quantitative data. This may be reason why by far the most popular book on statistics, with more than 1.5 million copies sold, is the time-revered classic by Darrel Huff called “How to Lie with Statistics”.

Downfall of Rhetoric in 20th Century

In a sequence of posts ( Lies, Damned Lies, and Statistics, Subjectivity Concealed in Index Numbers, The Values of a Market Society, Cross-Country Comparisons of Wealth, and Purchasing Power Parity), I have tried to explain how the statistics we use conceal arbitrary value judgments. This is the modern form of rhetoric, which is deadly, because the values are built into numbers, hidden in the process by which the numbers are manufactured, and not open to discussion and dispute. The initial post gives a brief hint as to how this state of affairs emerged. This post elaborates some more on the history of how and why ancient forms of rhetoric were rejected in the 20th Century, and replaced by this modern form of rhetoric. This post could/should be the opening post of the sequence, since it provides necessary background information and historical context.

From Ancient Greece to the late 19th century, rhetoric played a central role in Western education in training orators, lawyers, counsellors, historians, statesmen, and poets. Aristotle named the three fundamental pillars of persuasion as logos, pathos, and ethos. The story of how the rise of the philosophy of logical positivism transformed rhetoric from a respected art to a despised form of trickery is both extremely important and largely unknown and unfamiliar. This story is too complex to be described in detail here, but we will provide a brief sketch, because it is central to our topic in this essay. Rhetoric remains just as necessary today as it was in the ancient times. However, open use of rhetoric has been prohibited by positivism, and so today concealed forms of rhetoric are in common use. One of most effective and powerful among modern forms of rhetoric is the use of statistics to conceal the ancient methods for persuasion. How this is done is the main topic of our essay, but we will start with a brief discussion the philosophy of logical positivism, and how it led to the concealment of rhetoric under the facade of numbers which appear to be objective.

A key element in rejection of rhetoric was the rise of the fact/value distinction, promoted strongly by logical positivism. Julie Reuben in “The Making of the Modern University: Intellectual Transformation and the Marginalization of Morality” writes about this change as follows:

“In the late nineteenth century intellectuals assumed that truth had spiritual, moral, and cognitive dimensions. By 1930, however, intellectuals had abandoned this broad conception of truth. They embraced, instead, a view of knowledge that drew a sharp distinction between “facts” and “values.” They associated cognitive truth with empirically verified knowledge and maintained that by this standard, moral values could not be validated as “true.” In the nomenclature of the twentieth century, only “science” constituted true knowledge.”

Once the positivist idea that knowledge consisted purely of facts and logic became dominant, persuasion became unnecessary. Anyone who knew the facts and applied logic would automatically come to the same conclusion. Ethos refers to the credibility of the speaker, but this is unnecessary when we are dealing with objective facts, universally observable and verifiable by all. Pathos refers to emotional appeal, which is unnecessary if the speaker can establish his case using cold hard facts and logical arguments. Of the three pillars of rhetoric, Logos became the only acceptable form, which the other two fell into disrepute. “Empty” rhetoric characterizes speakers who establish credibility, and appeal to emotions of audience, to persuade them of dubious propositions not supported by facts and logic. Logical positivism asserted that human knowledge consisted only of propositions which could be established using facts and logic, universal and objective truths, equally valid for all, and devoid of subjective judgments which could vary across people.

Elimination of rhetoric from the syllabus, and resultant loss of understanding of the art of persuasion, has caused a serious deterioration in the form and quality of intellectual discourse. Every seeker of knowledge believes that he/she has arrived at the unique and indisputable truth, which should instantly convince all rational people. The need to establish credibility, to appeal to emotions, to build a case using rhetorical skills, is disdained, as the “facts speak for themselves.” Huge amounts of puzzlement and anger result when what appears to be an immediately obvious fact to the writer/speaker fails to convince the ‘stubborn and mulish’ audience.  Failure to persuade is blamed on mental defects of the audience, rather than lack of rhetorical skills on part of the speaker.

The foundations of statistics were constructed on the basis of positivist philosophy in the early twentieth century. Great emphasis was put on facts – represented by the numbers. Rhetoric (and values), represented by how the numbers are to be interpreted, was de-emphasized. This led to a tremendous rise in the importance of numbers, as the only means to get to objective truths, cleansed of subjectivity, personal biases, and values. As the popular saying goes, “you can’t argue with the numbers”.

To understand the role of rhetoric in the twentieth century, we have to learn to think at two levels. One is the grand level of the philosophers, who are engaged in a deep and difficult conversation about the nature of human knowledge. One of the central concerns in this discussion has been the question of how do we learn about aspects of the world which we cannot observe – things like atoms, electrons, gravity, angels and God? This conversation casts its shadows on the world of ordinary mortals, who are affected by these grand ideas to a far greater extent then they realize. Lord Keynes expresses this insight as: “the ideas of economists and political philosophers, both when they are right and when they are wrong, are more powerful than is commonly understood. Indeed the world is ruled by little else. Practical men, who believe themselves to be quite exempt from any intellectual influences, are usually the slaves of some defunct economist.” 

The high-level conversation among the philosphers is summarized very briefly by Hilary Putnam (2002). He describes the origins of the idea that there is a distinction between facts and values, and how these two terms have been understood by philosophers, over the course of centuries starting with David Hume. To understand these ideas in depth would require several years of philosophical training. Nonetheless, the disputed and controversial conclusion of convoluted and complex philosophical discussions, that facts and values are sharply separated, has come to accepted as obvious and commonsense by the general public. The phrase “Just give me the facts” expresses approval of facts, and the disdain for opinions and emotions that characterize the positivist attitudes towards knowledge. The facts and logic (logos) of rhetoric are held in high esteem, while ethos and pathos are rejected as sources of information and knowledge. In the next section, we provide a low-brow discussion of the fact/value distinction – that is, we will discuss how the grand conversation among the philosophers has shaped the minds of the general public.

Purchasing Power Parity

In a sequence of posts starting with  Lies, Damned Lies, and Statistics,  I have argued that the attempt to reduce multiple indicators to one number always introduces subjective elements relating to choice of factors, and relative weights to be assigned. By using technical jargon to justify choice of weights, the value judgments involved in this choice are concealed under a cloak of objectivity. This creates a modern form of rhetoric, where the arguments are made using numbers, and the values that went into the manufacture of these numbers remain hidden, and therefore, are not discussed. This concealment of values resulted from the creation of an artificial dichotomy between facts and values which became widely accepted. Values are considered unscientific, personal opinions, and hence must be concealed.

In the previous post on Cross-Country Comparisons of Wealth, we discussed how values were inevitably involved in such comparisons. In this post, we continue this discussion in the context of the most popular device used to attempt to resolve this problem – purchasing power parity. Like all positivist methods, this creates an impressive illusion of objectivity, while being highly subjective and value-laden.

NOTE: Reading over these posts, it does seem like I am rubbing the point into the ground and pressing repeatedly something which is trivially obvious. However, my experience with teaching this material leads me to the conclusion that the idea that facts and values are sharply separated, that numbers represent indisputable facts, and that objectivity is attainable and desirable, are built in so deeply into the foundations of our thought, and reinforced by routine use of methods based on these foundations, that these beliefs are very hard to shake. Even after repeated demonstrations, students continue to believe in the myth of objectivity. Very few understand that there is no objective way to rank universities around the world, or even in a single country. So, with due apologies to post-positivist readers, I continue my relentless assault on the illusory fact/value distinction.

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Statisticians who are aware of the serious problems which arise in cross-country comparisons of GDP have come up with a device to reduce them. In making cross-country comparisons, it makes a lot of sense to consider how much a dollar can buy in the USA, and compare that with what a Rupee can buy in Pakistan. It seems like a more reliable method then using exchange rates or other benchmarks, the deficiencies of which were discussed in the previous section. Here the idea is firstly to determine a typical bundle of goods and price the bundle both in Pakistan Rupees and in US Dollars. Those two costs should be considered as equivalent because that shows a match between purchasing power of Pakistan Rupees and purchasing power of US Dollars.

As an artificial example, let a typical bundle of goods be the rental price of housing for an average person, price of food, price of clothing and basket of goods which consumers would buy. The first thing to be done is to calculate the costs both in Pakistan Rupees and US Dollars. Suppose that they are 20,000 Pakistan Rubees and 1,000 US Dollars respecively. This in PPP approach means that 20,000 Indian Rupees is equivalent to 1,000 US Dollars. This can be very different from official exchange rate. For example, the official exchange rate between Pakistan Rupees and US Dollars could be 100 to 1, that is, 100,000 Indian Rupees is equivalent to 1,000 USA Dollars.

On the surface, the PPP appears to be a good solution to the problem of cross country comparisons.  However, when we probe deeper, we find that many subjective decisions must be made to arrive at a practical implementation of the idea, and the outcome of comparison depends on these decisions. A central point is that there is no “typical bundle” of goods which is the same across the world. A typical bundle of goods for a consumer in India is radically different from a typical bundle of goods for a consumer in USA. Even when the goods are the same, a “house” in USA is very different from a “house” in India. However, the numbers which seems perfectly accurate, objective and precise, do not reveal these difficulties. We can bias comparisons by choosing the bundles differently.

No abstract, purely theoretical and statistical resolution can be made of this problem. One must ask the question of the PURPOSE of the comparison. One such purpose, is stated by Hicks (1940): ‘a long line of economists … have sought in the Social Income an index of economic welfare, of the wealth of nations’. Actually, this statement contains two purposes – economic welfare, and wealth of nations – which are conceptually different. Let us first consider the extent to which GDP measures economic welfare, or prosperity, of nations. Here the Easterlin Paradox shows quite clearly that “happiness” felt by people is largely unaffected by GDP growth over time. Also, there is no systematic relationship between GDP and social welfare across cross sections. Subsequent research has found that deeper explanations for this paradox lie in the fact the human welfare is based on character, attitude towards life, and social connections. These factors are ignored in the GDP, In fact the GDP is grounded in economic theories which falsely suggest that consumption of goods and services is the sole source of human happiness – a position which can be described as the “Coca-Cola Theory of Happiness”.

A second purpose for measuring wealth, explicitly considered by Adam Smith, relates to the power countries exercise in the international arena. Wealth provides capabilities for financing military expenditure, unfortunatelty an essential aspect of global power today. However, if wealth is used to compare the relative power of the two countries in the international arena, then emphasis would be placed on rather different factors, and weights would be rather different from those used for Purchasing Power. Suppose, for example, that we only look at the amount of money used to finance Army, Navy, and Air Force expenditures. We can add up all three, or use other sets of weights to assess power, but all such schemes are arbitrary. The question of which country is, objectively, the most powerful, cannot be answered. For example, if one country has a huge army while the other has a bigger navy, then one is more powerful on land and the other on the sea. Depending on circumstances, and terrain of struggle, either one could come out on top.

The purpose of making comparison affects radically choice of factors and weights, which is a surprise to those used to thinking of statistics as neutral, objective, and value-free. See Castles and Henderson (2005) for a discussion of the many controversies in the area of comparing GDP across nations, and the policy implications of the use of different kinds of weights and factors. However, note that, like all economists, they believe that value-neutral statistics can be found, and used as a basis of value-laden decisions. This idea, that we can separate the facts and values, is the fundamental misconception at the methodological foundations of modern economics, econometrics and statistics. Because it goes so deeply against the grain of positivist methodology that we have all absorbed, it is worth re-iterating:

Impossibility: It is impossible to make objective comparisons when multiple factors are under consideration. Choice of factors, weights, and signs (positive or negative), are all necessarily subjective.

As a consequence, it is impossible to make objective cross-country comparisons of GDP. This is in conflict with the positivist mindset, which leads us to believe that there is an objective truth, and that we CAN find the right collection of factors and weights which will reveal the truth. For example, Castle and Henderson (2005) argue that Environmentalists are using the “wrong” set of factors and weights, and they they aim to provide greater objectivity. They do not realize that objectivity is an impossible goal. All we can do is put forth our values as being better in comparison to other values; this is exactly the role of rhetoric and persuasion. Environmentalists use weights which emphasize the costs of climate change, so as to create political pressure to take action. Industrialists propose another set of weights which gives more emphasis to the market, in order to allow growth and profits at expense of the environment. Both sets of weights are subjective choices, and there are no objective choices available, but only the illusion of objectivity.

To articulate this more clearly, suppose I was charged with the task of making a cross country comparison which would show Pakistan to be ahead of USA. I would look for factors where Pakistan leads USA and give them greater weight. Easterlin’s Paradox has established firmly that measures of happiness across countries do not correlate with GDP. I would therefore argue that instead of comparing material goods directly, we should be measuring the social welfare, or happiness levels, produced by the consumption of these goods. Studies of happiness show that the structure of the family is one of the key sources of life-happiness. Children raised by single-parents suffer from a large range of problems, documented in numerous research studies. If we give weight to dimensions of social welfare which come from family and community, and consider statistics related to crime, suicide, alcoholism, loneliness, we could easily show that Pakistanis are “richer” than Americans, if wealth is defined appropriately to include social lives.

It is important to clarify that we are not arguing that there is no truth, and everything is subjective. Rather, truth is complex, multidimensional, and qualitative, and it cannot be reduced to one number. We cannot assign a single number to a country as a measure of its “wealth” and thereby make it possible to compare the lives of millions living in country with millions of lives in another country. When we attempt to reduce complex, multidimensional phenomena to one number, there is an enormous loss of information. Decisions as to what information is important and must included, and what can safely be ignored, are always subjective. In the past, rhetoric was used to emphasize importance of one set of values, and to criticise other values in use by other groups. Now, all this rhetoric is concealed within choices of factors and weights, allowing some groups to impose their values on others, under the cover of objectivity of numbers.

Cross-Country Comparisons of Wealth

This continues a sequence of posts on how objective looking statistics conceal hidden values, because a positivist approach prohibits open expression and discussion of value judgments. Previous posts in the sequence are: Lies, Damned Lies, and Statistics, Subjectivity Concealed in Index Numbers, and The Values of a Market Society.

Countries compete with each other on the GDP numbers, without any awareness of the values which are embodied in such competitions. Such comparisons are fraught with many difficulties. We illustrate the difficulties which arise when we try to compare GDP across nations. To being with, let us examine the GDP data measured in Local Currency Units (LCU) for the countries India, Pakistan, Malaysia, Bangladesh, China and Irland from the World Development Indicator (WDI) data set of the World Bank[1] which is presented in Table 1. Firstly, look at the column for the year 1970.  The largest GDP is the one for Malaysia which is 13.10 trillion MYR. On the other hand, Irland has the smallest GDP which is 2.26 billion IEP. On this basis, can one say that in 1970 Malaysia had the largest wealth and Irland had the smallest wealth? Well obviously not, because the numbers are not comparable since they are measured in LCU. The currency units are not comparable across countries. We must learn how to translate one local currency unit into another, in order to be able to compare countries according to GDP .

Country	LCU	1970	1980	1990	2000	2010
India	Trillion INR	0.48	1.50	5.86	21.70	78.00
Pakistan	Trillion PKR	0.05	0.24	0.86	3.83	14.80
Malaysia	Trillion MYR	13.10	54.30	119.00	356.00	795.00
Bangladesh	Trillion BNT	0.04	0.28	1.00	2.37	6.94
China	Trillian CNY	0.23	0.46	1.87	9.92	40.20
Irland	Billion IEP	2.26	13.00	36.70	106.00	156.00

Data is take from the World Bank WDI data set.

At first glance, this does not seem like a difficult problem. Why not use the exchange rate between the two currencies? Deeper thinking about this reveals great difficulties. First, the exchange rate is determined by international trade, exports, imports, balance of payments, and Central Bank policies. For these reasons, it can fluctuate substantially. These fluctuations do not relate to the domestic wealth of the countries. For example, going from 2018 to 2019, the dollar appreciated strongly against the rupee going from PKR 100/USD to PKR 150/USD. Measured in dollar terms, the GNP of Pakistan declined by a rather large amount. However, while this change made imports expensive, it boosted exports, and strengthened import substituting industries in the domestic economy. It makes no sense to consider this change as a reduction in the domestic wealth of Pakistan, because resources within Pakistan were not affected by the change in the exchange rates. Yet, unless we standardize the wealth by using common units of measurement, by converting to dollars, how can we compare the wealth of Pakistan with the wealth of any other country?

To compare wealth across countries, we cannot use the LCU – local currency units – because these are arbitrary and unrelated to each other. Yet, any common unit of measurement we use introduces arbitrary subjective biases into the picture, while giving an appearance of objectivity. Different types of benchmarks can be used to convert an LCU into a common unit for cross-country comparisons. However, we can use this arbitrariness to get any desired conclusions, since there are no objective methods to make such comparisons. To illustrate this, we consider a graph of the Chinese GDP from xxxx to xxxx based on WDI data. The top curve in Figure 1 is Chinese GDP in trillions of Yuan. The other curves are conversions to Dollars, gold and Indian Rupees. As can be seen from the Figure 1, each curve shows a different pattern of growth in Chinese GDP.

 

Which of these four curves represent the objective “true” picture of the growth of the Chinese economy? None of the numbers (not even the LCU picture, as we will show later) is objective truth. In fact, objective truth does not exist in this situation, and the subjective choices we make create the facts via manufactured numbers. If the subjectivity and arbitrariness is openly acknowledged, these numbers may be of some use for different purposes. The real-world context and goals must be specified and the statistical analysis must be adapted to suit the real world purpose. Again this shows the impossibility of separating the statistical analysis from the real-world context. The appearance of objectivity created by numbers allows experts to deceive the public. Someone who wants to portray the growth of China in a bad light could use the Dollar based curve, which shows the least growth. Someone who uses gold valuation of Yen could argue that the GDP of China has gone down in terms of its gold value in the recent past. Those who are not aware of the arbitrary choices made in creating such comparison could easily be decieved by this modern rhetoric based on apparently objective numbers.

The Values of a Market Society

Continued from previous post on Subjectivity Concealed in Index Numbers. Because modern epistemology rejects values as being just opinions, and only accepts facts as knowledge, values have be to disguised in the shape of facts. What better way to do this than by embodying them in cold hard and indisputable numbers? This post discussed how the GDP embodies the values of a market society.

From the sixteenth to the eighteenth century, the values of European societies were radically transformed by a complex combination of forces. Traditional social values, originating from Christianity, can be roughly summarized as follows:

1. Community: All members are part of a common body, striving together for common goals.
2. Social Responsibility: All members must take care of each other.
3. Duties: Duty to society takes precedence over individual rights.

The transition to a market society led to a new creed, described by Tawney (1926) as: “The Industrial Revolution was merely the beginning of a revolution as extreme and radical as ever inflamed the minds of sectarians, but the new creed was utterly materialistic and believed that all human problems could be resolved given an unlimited amount of material commodities.” As Polanyi (1944) has explained, a central characteristic of market society is the creation of three artificial commodities: money, labor, and land. Labor is the stuff of human lives, and it is widely accepted that our lives cannot be bought or sold for money. But market societies require labor markets, where human lives are bought and sold. Similarly, land is our habitat, and the natural relationship is to protect, preserve, enhance, enrich our environment, known metaphorically as “Mother Earth”. However, market societies convert land and natural resources into saleable commodities. Finally, money is a third commodity which gets value from our social consensus as a means of artificially storing market values across time. The creation of these commodities leads to a set of values which are anti-thetical to traditional values. Operation of a labor market, where lives are for sale, requires reducing or removing social responsibility, so that the threat of hunger forces people to work. When human live are bought and sold, community ties automatically weaken. Market societies create individualism and hedonism, where pleasure is derived from possession and consumption of material goods, instead of social relationships. For a brief summary of Polanyi’s arguments, see Summary of the Great Transformation by Polanyi.

GDP as a measure of wealth captures the values of a market society.  Conceptually at least, the GDP aims to measure the total market value of the final products which are purchased by consumers. Intermediate goods, purchased by firms as inputs towards production of final consumer goods, are not counted. This expresses perfectly that values embedded in a market society. Only goods and services which are traded on the marketplace are given value. Services done out of love, or social responsibility, do not have any value. This excludes the vast majority of what matters in traditional societies – the most valuable things are the ones which are not available for sale. Furthermore, environmental resources, forest, lakes, plants and animals – these are no value, until they are traded on the market. The Amazon forest, millions of the year in the making, and irreproducible at any price, is evaluated in the GDP by the price of the furniture made by cutting down the trees for timber. For more extensive discussion, see “Consumer Theory“.

Statistics are the eyes of the state. Things are measured when they matter, and what is measured comes to matter. Throughout the world, policy and political decisions are guided by the numbers produced by the statistics department. The GDP is value laden in term of the choices it makes as the factors which are included, the factors which are excluded, and the weights assigned to the factors. The factors which are included differ in radical ways from those valued by traditional societies:

1. Wasteful, ostentatious, and luxurious products are included, and valued at their sale prices. These products, such as Alligator skin briefcases for $20,000, would be regarded as being of negative value to society in a traditional society.
2. Basic needs – food, housing, health, education – for the poor, are valued very low, because the poor cannot afford to pay much for these services. All traditional societies would value provision of these needs very highly, instead of at market value.
3. Intangibles, such as community, social services and support provided by family and friends, are not sold in the market, and hence excluded from the GDP. Also, skills, experiences, knowledge, and capabilities of human beings, not for sale in the market, are evaluated at zero.
4. Environment, natural resources, plant and animal species, and all the wonders of the planet that make our lives worth living, are assigned zero weights in the GDP measure.

The damage done by the use of GDP as a measure of wealth is deadly because it is hidden. Modern rhetoric is especially effective because it uses numbers to persuade, without any mention of the the values that went into the manufacture of these numbers. The devastating effects of market values promoted by GDP are only now becoming apparent. We list some of these harmful consequences below:

1. Loss of Meaning in Lives: When the value of lives is measured in money, making money becomes the goal of life. This is an inherently meaningless activity, as money is only useful as a means to pursuit of higher goals. The Quran teaches us that human lives are infinitely precious, and cannot be evaluated in monetary terms.This message is aligned with the capabilities approach to development, which aims to enable human beings to develop their unique capabilities, and lead rich and fulfilling lives.
2. Destruction of community and societies: The fabric of human lives is woven from our social relationships. However, a market society values human lives only for what they can produce and sell on the marketplace. This leads to destruction of communities, and loss of happiness, due to the illusion that happiness can be created by material possessions.
3. Environmental Collapse: When natural resources are sold, the GDP records an increase, because the cost of depletion and destruction of environment is not taken into account. As many authors have noted, if we take these costs into account, the enormous growth recorded in the last century would be converted into an enormous loss. This is because the value of what has been produced is very small compared to the value of what has been destroyed in order to produce these goods. The strong drive for making short-term monetary gains by destroying planetary resources have led to the looming climate catastrophe.

If the market values embodied in the GDP were open, and available for discussion, most human being would disagree with the idea that these represent useful goals to strive for. But because they are concealed behind the rhetoric of objectivity, nearly all nations in the world emphasize the goal of increasing GDP growth, and governments rise and fall according to their ability to achieve growth targets. One of the reasons for this blind obsession with the wrong numbers, which embody anti-social values, is the specialization and fragmentation of knowledge that characterizes our times. Specialization leads to the separation of theory and practice. Statisticians specialize in manipulating the numbers, without knowledge of the real world origins of these numbers. It is the field specialist who understands the meaning of these numbers and uses the results from the statistical analysis. The statistician is supposed to do an objective analysis based purely on the numbers. Massively wrong analysis and policies result when everyone is doing his small piece of work, and no one has a global perspective.

Subjectivity Concealed in Index Numbers

This continues from the previous post on Lies, Damned Lies, and Statistics.

More than 1.5 million copies sold, more than all other textbooks of statistics combined. Online copy

The vast majority of our life experience is built upon knowledge which cannot be reduced to numbers and facts. Our hopes, dreams, struggles, sacrifices, what we live for, and what we are ready to die for – none of these things can be quantified. However, as we have discussed, logical positivists said that what cannot be observed by our senses cannot be part of a scientific theory. As a result of this false idea, later disproven by philosophers, the attempt was made to measure everything – numbers were assigned to intelligence, trust, integrity, corruption, preferences, etc. – even though a long-standing tradition, as well as common intuition, tells us that these things are qualitative, and not measurable. Scientific progress was deeply and dramatically influenced by what I have called Lord Kelvin’s Blunder(2019): “When you can measure what you are speaking about, and express it in numbers, you know something about it, when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind”. This mindset needs to the lead to assign numbers and attempt to measure the unmeasurable. The harms that this has caused has been discussed in  Beyond numbers & material rewards and Corruption: Measuring the Unmeasurable.

In this note, we would like to focus on just one aspect of this attempt to measure what cannot be measured. The idea that we can take multiple measures of performance and reduce them to a single number is known as the index number problem. Very few people realize that this is inherently impossible – all such attempts must inevitably involve making subjective decisions regarding how the different measures should be combined. What is most often done in practice is that subjective decisions hidden in choice of measures, and associated weights are justified as objective. Because of this illusion of objectivity created by standard practice, most people are unaware that there are no objective solutions to the index number problem.

Impossibility of Combining Indicators: There is no objective way to combine two or more measures of performance to come up with a single number which measures of overall performance.

We will explain and illustrate by a few examples. We start with a familiar case where scores from two exams must be merged to create a single score for the course grade. Suppose that instructor Orhan has three students who received the following scores on the midterm and final in the course:

Student	Midterm	Final
Anil	50%	100%
Bera	100%	50%
Javed	80%	80%
Dawood	65%	90%

 

The teacher has interacted with all four students throughout the semester and has got a good idea of their capabilities, over and above what scores on the exams show. Suppose he thinks that Anil is the best among the four, and would like the classrom grades to reflect this opinion. As long as weight given to the Final is greater than 60%, Anil will have the highest score. On the other hand, he may know that Bera is a brilliant student who just had a bad day on the Final. Weights of more than 60% for the midterm would make Bera the top student. Equal weights would make Javed come out on top. A slight increase in weights for the final (45% MT, 65% Fin) would make Dawood the best student. So depending on the subjective decision of the teacher, he can choose weights to make any one of the four the top student. Furthermore, by assigning weights and calculating the score, the subjective opinion will look like an objective and impartial decision. The teacher could give apparently objective reasons for any choice of weights, by referring to the subjective factors of the length, difficulty, and scope of coverage of the exams as reasons for his weights.

Which one of the four is the best student? What surprises most students is that there is no objective answer to this question. As a mathematical theorem, it is impossible to summarize the information contained in two numbers by one numbers. Two dimensions cannot be reduced to one. Whenever we carry out such a reduction, we lose half of the information contained in two numbers. When there are multiple indicators, we lose even more information. Because there is no objective answer, the choice must be made on subjective grounds. This subjective element was the topic of rhetoric – the persuasive tactics used to argue that the final should carry more weight, or that the midterm should carry more weight, or even that some factor not considered, like attendance, should be taken into account. However, the philosophy of positivism teaches us that subjective judgments and personal opinions are of no value, and only the ‘facts’ should be considered. As a result, the subjective process of assigning weights, and choosing factors, must be concealed under an appearance of objectivity. This is the “hidden rhetoric” of statistics. Unlike pre-positivist rhetoric, this form is deadly because the unsuspecting victim only sees the numbers, and is told that you cannot argue with the facts. He does not even get to see the subjective elements which have gone into the manufacturing of the these numbers. Before proceeding to our main topic of GDP, we give one more common example of how statistics are use to create a false impression of objectivity in the context of rankings of universities.

One implication of the impossibility of objectively combining multiple indicators is that it is impossible to objectively rank products which have multiple dimensions of performance. This point is made very clearly, accurately, and forcefully by Gladwell (2011). We consider only one of his examples to illustrate. The interested reader is strongly encouraged to read the original article. We consider popular methods for coming up with a single number to rank universities. This is done by making numerical judgments according to several criteria and then combining them using subjectively chosen weights. As a specific example, suppose that Criterion A is Financial Resources available to the university per student, indicated by money spent on faculty salaries, libraries, and other academic infrastructure. Criterion B is the percentage of admitted students who graduate. Criterion C is selectivity (the percentage of applicants who are admitted from total number of applicants). Hypothetical numbers for the three criteria are given below.

	A	B	C
Chicago	500	80%	10%
Stanford	1000	95%	5%
Penn State	100	90%	50%

 

Which of the three universities is the “best”? Malcom Gladwell (2011, The Order of Things) says that the question does not make sense, and it cannot be answered. The numbers and names used for illustration here are hypothetical, but plausible. Chicago is a private university which charges high fees and has a relatively easy admissions policy. It encourages fierce competition among students and selects the survivors, leading to a high dropout ratio. It invests substantial financial resources on faculty salaries and institutional overheads, providing high quality facilities and charging high fees. Stanford is an exclusive elitist university, where only a few students who are cream of the cream are admitted. The university is well equipped financially, and invests a huge amount on faculty and academic resources. Because all applicants are extremely good, nearly all complete their studies. Penn State is a large public sector university which aims to provide education to the masses. It has an easy enrollment policy, and helps and encourages all students to graduate, resulting in a low dropout rate. It invests less in resources to make education affordable for the masses, and has a high student/faculty ratio for this reason. Each of the universities has a different goal, and when evaluated with respect to its own goals, each of them is the best of the three. By choosing different weights for the different criteria, we can make the combined index come out to favor any one of the three universities as the best. There is no objective way to choose weights. In fact, it could be argued that each of the factors can be considered a virtue or a defect – it can receive negative or positive score – depending on our subjective point of view. The standard rankings assign a positive weight to financial resources, evaluating a university as higher ranking if it spends more. However, this factor is negatively correlated with affordability, which may be much more important to students, and would result in a reversal of the ranking by this factor. Similarly, there is an argument that we should try to carry along and educate all students so that high dropout rates are bad. However, we could also argue that rigorous competition leads to selection of the best students, and poor students are eliminated, resulting in the best graduates. Selectivity is good for the students who get in, but bad for the ones excluded by the process. How much weight to give each factor, which factors to consider, and whether the factor is considered as a plus or a minus, all of these are subjective decisions.

When evaluation is carried out in multiple dimensions, the choice of dimensions, weights attached to them, and whether they count as positive or negative factors are all subjective choices. However, because of the positivist philosophy of knowledge which is the basis of modern statistics, this subjectivity is concealed, so as to create an appearance of objectivity. In the rest of this article, we explore this subjectivity in the context of one of the most important and widespread measures of economic performance, namely the GDP per capita.

To be continued.

Lies, Damned Lies, and Statistics

From Ancient Greece to the late 19th century, rhetoric played a central role in Western education in training orators, lawyers, counsellors, historians, statesmen, and poets. However the rise of empiricist and positivist thinking marginalized the role of rhetoric in 20th Century university education. Julie Reuben in “The Making of the Modern University: Intellectual Transformation and the Marginalization of Morality” writes about this change as follows:

“In the late nineteenth century intellectuals assumed that truth had spiritual, moral, and cognitive dimensions. By 1930, however, intellectuals had abandoned this broad conception of truth. They embraced, instead, a view of knowledge that drew a sharp distinction between “facts” and “values.” They associated cognitive truth with empirically verified knowledge and maintained that by this standard, moral values could not be validated as “true.” In the nomenclature of the twentieth century, only “science” constituted true knowledge.”

Once the positivist idea that knowledge consisted purely of facts and logic became dominant, persuasion became unnecessary. Anyone who knew the facts and applied logic would automatically come to the same conclusion. “Rhetoric” or persuasion was considered to be a means of deception by positivists – we could persuade people only by misrepresenting the facts or by abuse of logic. The foundations of statistics were constructed on the basis of positivist philosophy in the early twentieth century. Great emphasis was put on facts – represented by the numbers. Rhetoric (and values), represented by how the numbers are to be interpreted, was de-emphasized. This led to a tremendous rise in the importance of numbers, and their use as tools of persuasion. The rhetoric of the 20th Century was based on statistics, and data were used to present the facts, without any apparent subjectivity. As the popular saying goes, “you can’t argue with the numbers”.

By the middle of the 20th Century, logical positivism had a spectacular collapse. The idea that the objective and the subjective can be sharply separated was proven to be wrong. For a recent discussion of this, see Hilary Putnam on “The Collapse of Fact/Value Distinction”. Unfortunately, these developments in the philosophy of science have not yet reached the domains of data analysis, which continues to be based on positivist foundations. Rejecting positivism requires re-thinking the disciplines related to data analysis from the foundations. In this paper, we consider just one of the foundational concepts of statistics. The question we will explore is: What is the relationship between the numbers we use (the data) and external reality? The standard conception promoted in statistics is that numbers are FACTS. These are objective measures of external reality, which are the same for all observers. About these numbers there can be no dispute, as all people who go out and measure would come up with the same number. In particular, there is no element of subjectivity, and there are no value judgments, which are built into the numbers we use. Our main goal in this paper is to show that this is not true. Most of the numbers we use in statistical analysis are based on hidden value judgements as well as subjective decisions about relative important of different factors. It would be better to express these judgments openly, so that there could be discussion and debate. However, the positivist philosophy prohibits the use of values so current statistical methodology HIDES these subjective elements. As a result, students of statistics get the impression that statistical methods are entirely objective and data-based. We will show that this is not true, and explain how to uncover value judgments built into apparently objective forms of data analysis.

It is useful to understand statistics as a modern and deadly form of rhetoric. When values are hidden in numbers, it is hard for the audience to extract, analyze, discuss, and dispute them. This is why it has been correctly noted that “There are lies, damned lies, and statistics”. The most popular statistics text of the 20th century has the title “How to Lie with Statistics”. In this sequence of posts, we will analyze some aspects of how values are hidden inside apparently objective looking numbers.