Welcome to the WEA Pedagogy Blog

Founded in May 2011; More than 12,000 members worldwide.
Please see About section for more details about the blog.

The  United States declared an economic war on China in early 2018. Economic warfare is a unilateral action that questions the existence of multilateralism and places the question of what regime we are about to enter after the weakening of the existing multilateral trade agencies. US trade policy opens the door for new relationships between emerging market economies and international financial institutions on issues of liberalisation but mostly it ends a period started in the 1980’s of unregulated international trade and opens a new one. The solution to the structural economic problems of the US, similar to those of Britain in the 1960’s is not tariffs and trade restrictions. The trade war is above all a war that will be won by one side at some point.

The recent outbreak of coronavirus has just added more uncertainties to the global trade war scenario

The next WEA on line conference Trade Wars after Coronavirus  calls for a deep reflection to matters related that includes, among other questions:

  • Will free trade be still strongly promoted through international financial institutions (IFI) policies?
  • Can any multilateral agency now take retaliation against any country that goes against the spirit of existing free trade agreements?
  • If the profit squeeze in the US comes at the same time as US technological leadership is at stake, how much at stake is it?
  • Can China actually replace the US as the economic leader in the world? If not, what does prevent it? If so, what is required? How can the US prevent this from happening?
  • Does the winner of the trade war keep the hegemony as its prize? Or, are we in a new situation of shared/partial hegemonies?
  • What are the lessons learnt from the US trade war against Japan?
  • What is the difference between this trade war and the Japanese one of the 1980’s?
  • What are the consequences of the trade wars after coronavirus to the world economy and the future of neoliberalism?
  • No more globalisation as we know it?

WEA on line Conference website


Conference Leader

Oscar Ugarteche, Instituto de Investigaciones Económicas, UNAM


Oscar   Ugarteche      ougarteche@gmail.com

and Maria Alejandra Madi      maria.madi2014@gmail.com

Key dates

Closing date for submissions

28th June, 2020

Discussion Forum opens

5th October, 2020

Discussion Forum closes

5th December, 2020

In this series of posts [Previous post:GDP Comparisons Across Time], we have demonstrated how apparently objective statistics conceal value judgments. When comparing GDP from one year to the next, we must adjust for inflation. But “inflation” is not a single number – multiple price changes for different goods must be combined into a single measure. This operation is always subjective, and hidden values are concealed within inflation indices, as we will now discuss in greater detail.

Suppose that we ignore both external and internal critique of the concept of GDP as wealth, and accept the idea that wealth in only measured by goods produced in the market, and value of goods can be evaluated at market prices. Even these drastic over-simplifications do not solve our problems, in terms of making comparisons of wealth across time. The problem is the same one that we have discussed earlier. There are multiple goods, and multiple price changes, and we must summarize all the thousands of changes in the quantities of production into one number, and similarly summarize all the prices changes by one number. Because the newspapers report on inflation and on real GDP growth, the public has the impression that this is objectively possible to do. The reality is that there are many different ways to summarize, and every choice among these is necessarily subjective and incorporates value judgments. Let us consider the problem of measuring inflation in greater depth. Note that once we have a measure of the increase in prices, we can automatically divide the nominal growth of GDP in LCU into two parts, one due to price increase, while the remaining growth is due to real GDP growth.

As we have already discussed, there are thousands of goods, and thousands of prices changes. We illustrate the problems which arise in merging multiple indicators into one. Table 3 present as artificial example. Four products are considered in the example. These are wheat, rice, corn and lentils. In year 2000, 300 units of wheat, 100 units of rice, 100 units of corn and 250 units of lentils have been produced. The market prices for these products in year 2000 are 10, 30, 50 and 25, respectively. In 2010, wheat production decreased from 300 units to 100 units and lentils production decreased from 250 units to 60 units. On the other hand, rice production increased from 100 units to 300 units and corn production increased from 100 units to 400 units. The prices of the products changed from 10 to 50, from 30 to 35, from 50 to 40 and from 25 to 75, respectively. Given these prices, Table 3 reports the inflation rates for each product at the last column. They are 400, 16.7, -20 and 200 for wheat, rice, corn and lentils, respectively. In fact, at this point, one could argue that prices are not the same throughout the year. They can fluctuate from day to day or from month to month. So, some sort of averaging is required but this complication is ignored to simplify the discussion.

Table 3: Artificial Example

Product Quantity


Quantity (2010) Price (2000) Price (2010) Inflation


Wheat 300 100 10 50 400
Rice 100 300 30 35 16.7
Corn 100 400 50 40 -20
Lentils 250 60 25 75 200

Now the question is whether the GDP of the country increased or decreased moving from year 2000 to year 2010. Just by looking at the quantities one cannot give the answer. Corn and rice production increased but wheat and lentils production decreased. The standard solution to this problem is to value the products at the market prices. While the value of the year 2000 production with 2000 prices is 17,250, the value of the year 2010 production with 2010 prices is 36,000. So, measured in LCU, the GDP has doubled. The problem is to separate this increase into a price component (inflation) and a quantity component (real GDP). Let us look at how we can try to do this.

We have four rates of inflation, one for each of the four goods – (W: 400%, R: 16.7%, C: -20%, L: 200%). Which of these four factors should be chosen, and what weights should we attach to each factor? This is the standard problem with reducing multiple factors into one. Here we have a very homogenous problem where are four items are food items, which makes it much easier than problems which arise when we are trying to combine an enormous range of diverse goods into one number. But even this extremely simple problem does not have a simple, objective solution, such that all impartial observers would agree on it.  It is generally agreed that the weights which are attached to the four price increases should be the quantities of the goods which were produced. But these quantities also changed from the base year 2000 to 2010. If we use the weights (W:300, R:100, C:100, L:250) from the base year, this is called the Laspayres index, and it come out to 140%. This is because high weights are given to W and L and lower weights to R and C. Since W and L have high inflation rates of 400% and 200%, the weighted average comes out quite high. On the other hand, the Paasche Index takes the weights for the current year, or 2010. The 2010 weights of (W:100, R:300, C:400, L:60) give a lot of weight to the low inflation good R and C which have low inflation rates of 16.7 and -20%. This gives us a Paasche inflation index of only 14%, which is 10 times less than the Laspayres Index of 140%.

Table 4: Analysis of Laspayres and Paasche Index Numbers

  Laspayres Paasche
Year 2000 2010 2000 2010
Index 100 240 100 114
Inflation (%) 140 14

Even this very simple example brings the question of which inflation rate should be used. 14% or 140%? There is no answer to this question. But both are “facts”. Going on with the artificial example, we saw the market value of output, which could be GDP, increased from 17,250 to 36,000. That is a 108% increase. If inflation is 14% as calculated by using Paasche index, then there is 94% growth rate. On the other hand, if we use Laspayres index for calculations, then inflation is 140% and growth is -32%. Which figure is correct? There is no answer to this question. To see how this reflects values, suppose that the majority of the public is poor, and eats only wheat and lentils, while a minority is rich and eats rice and corn. Then the Laspayres index better reflects the interests of the poor, who see an average 300% inflation in their food prices. The Paasche index better reflects the interest of the rich, who actually see a decline in their food bills. Every index reflects values which are built into the choices of factors and weights. These choices are arbitrary, and cannot be made objectively. Sensible ways to choose require understanding the goals – WHY are we trying to measure inflation? Without clear thinking about the values involved in constructing the inflation index, and deeper knowledge of the structure of the economy, we cannot find good measures of inflation. However, for most real-world purposes, we will find that multiple measures of inflation would be needed. For example, we could classify the population into quintiles by income, and then consider five different inflation rates, one for each segment of the population. Pragmatically, we cannot consider thousands of numbers at any one time, and for purposes of getting the big picture, it is essential to reduce multiple factors into a small number. However, we must be aware of the distortions which are introduced in this process, and not be deceived by the apparent objectivity of numbers.

(to be continued)

This continues a sequence of posts aiming to show how apparently objective statistics conceal large numbers of arbitrary value judgements.  (1) Lies, Damned Lies, and Statistics, (2) Subjectivity Concealed in Index Numbers, (3) The Values of a Market Society, (4) Cross-Country Comparisons of Wealth, (5) Purchasing Power Parity, (6) Downfall of Rhetoric in 20th Century, (7) Facts & Values: Distinction or Dichotomy?. This is the 8th post, which considers comparisons of GDP across time within a single country.

In comparisons across countries, we face the difficulty that the concept of “wealth” has varied across societies, and changed with time. The “average basket” of goods varies for each country, because different societies have different preferences and values. We cannot compare apples and oranges. It seems that these problems would be reduced if we considered a single society across time. The concept of wealth, and the average bundle of goods would remain relatively stable, at least across short periods of time. We will now discuss difficulties which arise when we consider growth across time, comparing GDP across the years for a single country.

Turning back to Table 1, we can see that all of the GDP values are increasing as time goes on for all of the countries in the Table. Does this mean that GDP has been growing in all of these countries? Well, may be “no” since the values in the Table are in local currency units. The increase may be due to increase in prices or it may be due to increase in quantities. Therefore, without knowing which increase is dominant, one can not be sure whether GDP really increased or not.

To see how deceptive just looking at the numbers can be, the case for Turkey for the years 1978 – 1988 is useful. The country experienced very high inflation over this period of time. Table 2 summarizes the information.

Table 2: Inflation and Growth of Turkey (1978 – 1988)

Year GDP (LCU)

(Billion TL)





1978 1.58 2 50
1979 2.78 -1 76
1980 5.23 -2 88
1981 7.9 5 51
1982 10.5 4 33
1983 13.9 5 33
1984 22 7 58
1985 35.1 4 60
1986 51.1 7 46
1987 74.7 9 46
1988 129 2 73

Second column in the Table is GDP in current LCU, that is Turkish Lira. GDP in 1978 was 1.58 billion TL and it was 129 billion TL in 1988 which is close to 100 times growth but actually the growth over the period was not that high. Most of the growth was due to inflation, as shown by the numbers in the last column. After deducting the inflation, the growth rates are actually quite low. So, it is clear that direct comparisons of GDP in current LCU are false and misleading. The table provides the “official” statistics, as recorded in the World Bank WDI Data set. It separates the growth in LCU into two parts. One part is the rise in prices, or inflation, while the other part is the growth of the “real” GDP, which measures wealth according to official statistical accounts. How objective is the official method, as a way of measuring real GDP, and thereby enabling us to compare the wealth of Turkey over time? We will examine the subjective values hidden in the way these numbers have been manufactured.

External and Internal Critiques of the GDP

From one year to the next, the GDP changes in many different ways. The quantities of the goods produced is increased, technological changes make the quality go up, the prices also increase, new products are introduced, some products become obsolete. Can we wrap up all of these changes and summarize them by ONE number? The simple answer is NO – this is impossible. Over time changes take place, and these can be characterized qualitatively. Using old fashioned rhetoric, a writer arguing that Turkey is making progress and experiencing growth would talk about how we have more and better roads, we have more educational institutions of higher quality, we are manufacturing high quality products, and exporting them, and similarly describe the many dimensions of change taking place in positive ways. An opponent who want to argue in the opposite direction might say that real wealth consists of friends, family, and social relationships. As the people of Turkey get more and more engaged in production of artificial goods which make no genuine contribution to our lives, we are losing our traditional values which enriched our family and social lives. Instead of learning to be human beings, our education is turning us into human resources, to be used just like machinery is used, as inputs to the production process. So, Turkey is becoming poorer, when wealth is properly understood in terms of what makes us genuinely happy, enriches our lives, and develops our human capabilities.

In the modern rhetoric, all of this discussion and debate about values is buried and concealed beneath the apparently objective official statistics. Which factors should be chosen as measures of wealth? This is not under discussion; it is automatically assumed that all products produced and sold as final consumer goods are the wealth of the nation. Once we recognize the value-based nature of this choice, there are two types of criticism which we can make of this choice of factors. The first is an internal criticism, which accepts the idea that wealth should be defined in terms of material resources, but says that we are missing essential aspects of this material wealth because they are not sold in the marketplace. Among these, the informal education, and character building, done by families, as well as social services provided by friends and relatives are extremely important. As more and more people start working in order to create wealth which is measured by the GDP, there is dramatic reduction in the non-market transactions which produce wealth, as well as in the informal economy. It is not clear whether there is a gain or a loss from this process. In particular, human capital adds enormously to productive capacities, so that it is an essential aspect of material wealth. In fact, according to World Bank report on the wealth of nations, this part is more important than the natural resources, which used to be far more significant in earlier times.

An external critique of the idea reject the idea that only markets produce wealth. It also rejects the idea that the market price is a good measure of the social value of the product. A lot of goods produced on the market are luxuries, wasteful, or useless products, which actually reduce wealth. Similarly, human capabilities are extraordinary and unique, and cannot be priced in the market. For the purposes of this article, it is sufficient to highlight that choosing market goods as the only factor to be counted as wealth, and choosing market prices as the measure of wealth, introduces market-values, substantially in conflict with traditional values, into the measure. At the same time, an appearance of objectivity is created by the numbers.

(to be continued)

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”.

In a sequence of posts ( Lies, Damned Lies, and Statistics, Subjectivity Concealed in Index NumbersThe Values of a Market SocietyCross-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.

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.


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.

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.