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Call for Papers

The advent of digital economy creates new challenges for businesses, workers, and policymakers. Moreover, business prospects for artificial intelligence and machine learning are evolving quickly. These technologies have transforming implications for all industries, businesses of all sizes, and societies. The digitalisation of economic activities calls for a deep reflection on the forces that will shape the future of the global economy.

Aims of the Conference

The objective of this conference, led by Prof. Maria Alejandra Madi and Dr. Małgorzata Dereniowska, is to discuss recent contributions to the understanding of digital economy and its consequences for business trends and labour challenges. The conference also focuses on bridging the gap between different economic theoretical approaches and the practical applications of artificial intelligence and machine learning. Related topics include law, ethics, safety, and governance.

Topics include (but are not limited to):

  • The gig economy and recent economic theoretical approaches: advances and challenges.
  • Internet of Things in retrospect and today.
  • Machine learning: integration of people and machine learning in online systems.
  • Consumer transactions and Big Analytics.
  • Time Series Data & Data for Prediction in Economics.
  • Business Transformations though Internet of Things and Artificial Intelligence.
  • Impact of artificial intelligence on business and society: automation of jobs and the future of job creation
  • Artificial intelligence for manufacturing: today and tomorrow.
  • Disruptive innovation and transforming industries: telecom, finance, and travel/transportation, logistics, etc.
  • Machine learning and eco-challenges.
  • E-government, e-democracy and e-justice.
  • Ethical and legal issues of artificial intelligence technology and its applications.
  • Digital economy and economic inequality.
  • The impact of the digital economy on competition and economic growth.
  • Technological change and ecological economics

Paper Submissions

Who Can Participate

We welcome submissions from scholars working in economics, law, political science, psychology, philosophy, and sociology.

We also welcome contributions from business executives responsible for AI initiatives, heads of innovation, data scientists, data analysts, staticians, AI consultants and service providers, and students.

Key Dates

Paper submissions: 20th October, 2019.
Notification of acceptance: 4th November, 2019.
Discussion Forum: 11th November – 9th December, 2019.

Contact

Maria Alejandra Madi: alejandra_madi@yahoo.com.br
Małgorzata Dereniowska: malgorzata.dereniowska@gmail.com

Against a rationalist top down approach to policy making, the evidence-informed policy and practice has rapidly evolved in the last two decades.

In this line of research, a new book What Works Now? Evidence-informed Policy and Practice has been edited by Annette Boaz, Huw Davies, Alec Fraser and Sandra Nutley.  It offers not only a synthesis of the role of evidence in policy making but also an analysis of its use in recent economic models and practices in the UK, Australia, New Zealand, Scandinavia, Canada and the United States. In addition to the diversity of policy and practice settings where evidence is sought and gets applied, the book considers policy examples related to healthcare, social care, criminal justice, education, environment and international development.. At the core of the argument regarding the actual relevance of ‘know-about’, ‘know-what works’, ‘know-how’, ‘know-who’ and ‘know-why’ is the belief that evidence matters.

Considering this policy scenario, the relevant question at stake is  what are the implications of the new policy design practices that mainly rely on the belief that evidence matters?

What is important to note is that behind the belief that evidence matters is a deep transformation of the public policy approach  towards a more experimental and empirical one.

At this respect, in the UK, the Nudge Unit leader David Halpern recently suggested the conceptualization of experimental government  in order to characterize the new approach to policy making based on evidence. The relevance of the potential outcomes of  systematic testing is clear in Halpern’s words:

Governments, public bodies and businesses regularly make changes to what they do. Sometimes these changes are very extensive, such as when welfare systems are reformed, school curricula are overhauled, or professional guidelines are changed. No doubt those behind the changes think they are for the best. But without systematic testing, this is often little more than an educated guest. To me, this preparedness to make a change affecting millions of people, without testing it is potentially far more unacceptable than the alternative of running trials that affect a small number of people before imposing the change to everyone. 

At the heart of his proposal about “what works best” in public policy is the use of evidence as a regular practice to select the measures that actually operate in a more efficient  way.  Moreover, no  ethical considerations about the efficient methods and goals in policy making are added to his explanation.

Taking into account the methodologies that support some policy practices that favour inductive reasoning and randomized control trials of impact evaluation (RCTs), there is a controversy around the utilization of these attempts to build experimental programmes or policy intervention. For instance, Deaton and Cartwright (2016) pointed out that there are misunderstandings around what the RCTs can really do. For them, the inductive techniques used in research do not guarantee that the relevant causal factors are taken into account across sample groups in any specified RCT. Therefore, the results of the inference pocess might be wrong. Indeed, the outcomes of RCTs can be challenged ex post, after examining the composition of the control group and the factors considered in the experimental setting. Moreover, Deaton and Cartwright also rejected the transportation of the outcoems of RCTs to other contexts since the relations of causality between variables is always context-dependent.

As the decision-making policy process in the real world relies on institutional factors that may be different elsewhere, the methodology based on RCTs does not provide a credible basis for policy making. In short, the outcomes of inductive investigation can never be completely transported across time and space.

Moreover, economists Steven D. Levitt and John A. List (2007) highlighted that human behaviour in RCTs can be affected by the selection of the individuals, the evaluation of their actions by others, and ethical issues. Then, the findings in a laboratory setting may overestimate or underestimate the effectiveness of policy interventions within real life interactions. In other words, if a policy intervention “works” and makes people better off in a laboratory, there is no guarantee that this intervention may actually do so in the real-world.

In fact, the methodology of RCTs runs the risk of considering worthless casual relationships as relevant causalities in the attempt to develop policy recommendations. In short, the use of the outcomes of RCTs as normative orientations for policy making should be put in question.

“What works” in the “sterile” environment of a laboratory does not necessarily work in a real-world where social interactions and the dynamics of institutions are overwhelmed by power relations. Therefore, ethical considerations should be considered in any attemp to  build policy proposals.

Indeed, the transformation of the economic policy approach has evidently been a remarkable one. It is worth recalling the words of Lars Syll about the current sad state of economics as a science,

A science that doesn’t self-reflect and asks important methodological and science-theoretical questions about the own activity, is a science in dire straits. The main reason why mainstream economics has increasingly become more and more useless as a public policy instrument is to be found in its perverted view on the value of methodology.

 

 

References
Boaz, A, Davies, H., Fraser, A and Nutley, S. (eds) What Works Now? Evidence-informed Policy and Practice.. Policy Press. 2019.,
Deaton, A. and Cartwright, N. (2016). Understanding and misunderstanding randomized controlled trials. NBER Working Paper No. 22595.
Halpern, D. (2015). Inside the Nudge Unit: How Small Changes Can Make a Big Difference. London: WH Allen.
Levitt, S. D. and List, J. A. (2007). What do laboratory experiments measuring social preferences reveal about the real world? Journal of Economic Perspectives, 21 (2): 153–174.
Madi, M.A.C (2019). The Dark side of Nudges. London: Routledge.
Sill, L. (2019=. Economics becomes more precise and rigorous — and totally useless
April 4. https://rwer.wordpress.com/2019/04/04/economics-becomes-more-precise-and-rigorous-and-totally-useless/

The following is a slightly revised excerpt of Section 1.2 from my paper on Empirical Evidence Against Utility Theory – Game theorists rule out Humans with hearts by assumption. The excerpt provides some empirical evidence (not needed by anyone except economists) that human actually do have hearts, and this actually affects their behavior! surprise, surprise!

The “Goeree-Holt Humans with Hearts” (GHHwH) Game: Conventional game theory operates under the assumption that both players (A-player labelled Aleena, and B-player labelled Babar) are heartless human beings. They have no emotions; rather, they are disembodied brains floating in vats. For more explanation and discussion, see Homo Economics: Cold, Calculating, and Callous. Below we discuss a game described in Goeree, Jacob K. and Charles A. Holt (2001). “Ten Little Treasures of Game Theory and Ten Intuitive Contradictions,” American Economic Review, vol. 91(5): 1402-1422. They do not provide a name for this game, so we will call it the GH Humans with Hearts game; it is a convenient way to prove the human beings do not behave like homo economicus. Furthermore, this assertion is not a surprise to anyone except economists, who are trained to think like economists. This means deep training in learning to model human behaviour as heartless, which blinds them to the complex realities of human behaviour.

GH MindHeart

At the initial node, Aleena has the choice to PLAY or to OPT-OUT. If she opts out she receives AOP < $10, and Babar received BOP – Aleena and Babar’s Opt-Out Payoff.  If Aleena chooses to PLAY, then Babar has a choice between High, which gives him $5 and Aleena $10, or Low which gives him some payoff  BLO < $5, while Aleena gets ALO.

Game-theoretic analysis of this game is simple and straightforward. We work by backwards induction. Given that BLO < $5, Babar will always play High. So Aleena can count on receiving $10, which she gets in this case. This is higher than her opt-out payoff of AOP (by assumption), so she will choose to play.

The standard game theoretic analysis of this game provides us with the following insights:

  1. The value of ALO does not matter, since Babar will always play High.
  2. The value of BLO does not matter, as long as it is strictly less than $5.
  3. If Aleena chooses PLAY, she can count on receiving $10
  4. The value of AOP does not matter, as long as AOP<$10

All four of these insights, which come from game theory for humans without hearts,  are wrong. Furthermore, ordinary untrained subjects who play this game behave in ways which show deeper understanding of human behavior.  Thus game theory systematically handicaps the understanding of actual observed behavior in this game.

Experimental evidence provided in Goeree and Holt (2010) reveals the following patterns of behavior:

  1. When the difference between $5 and $BLO is small, Aleena cannot rely on Babar choosing High. Suppose BLO = $4.75, which is only a bit smaller than $5. In one experiment, 15% of the B-players choose Low, which gives them a quarter less than the optimal move High.
  2. As a consequence of possible “mistakes” by Babar, Aleena will compare ALO to AOP, to decide between Opt-Out and Play. If her optout payoff is $7, while BLO is $0, then Aleena makes a large loss when Babar does not make the right choice of High. In this situation, experimental evidence shows that A-players often opt-out.
  3. As the difference between $5 and BOP increases, the chances of B-player playing High increase. In experiments, A-Player anticipates this and chooses to PLAY more often. None of these phenomena is predicted by game theory, showing the theory is blind to aspects of the game which untrained observers are aware off.
  4. A high value of BOP can create resentment in B-player . For example if BOP=$10, then PLAY move by player Aleena reduces the maximum payoff of B-player to $5. This could easily motivate B-player to take revenge for being deprived of $10. Babar can do this playing Low, accepting a lower payoff for himself, and punishing Aleena by giving her $0=ALO. Anticipating this, A-player takes the secure option of Opt-Out very often in this situation. Again this behavior shows greater wisdom than game theory.

Many questions about the theory of rational behavior are creating by these empirical observations on human behavior. Among the simplest of this is the question:  “Why does it happen that, 15% of the time, B-players choose the LOW outcome of $4.75 over the HIGH outcome of $5.00?”

There are many possible explanations, from satisficing to computational costs to near-indifference or fuzzy sets. One plausible explanation comes from the concept of Just Noticeable Difference (JND) – If two outcomes are sufficient close, then they are treated as “about the same” by mental decision-making heuristics.

An immediate consequence is that we cannot count on “maximization” of utility. Near-Maximization leads to a host of difficult problems for economic theories. Mankiw (1985) shows that small menu costs – changing prices at a restaurant requires reprinting the menu – can lead to large business cycles.  Similarly, Akerlof and Yellen (1985) show that “near rationality” — or approximate maximization – can have large effects on markets. More than 800 later publications which cite these papers show that approximate instead of exact maximization could be responsible for a wide range of phenomenon, such as sticky wages, the Phillips curve, non-neutrality of money, efficiency wages, and many others. If objective functions to be maximized are nearly flat in a neighbourhood of a unique global maximum, then approximate maxima can range over a very wide set, leaving the approximate equilibria very indeterminate. Virtually no one would seriously argue that people always exactly maximize utilities, but it is widely believed that approximate maximization would lead to approximately the same results as exact maximization. The literature cited above shows that this is not true; bounds on computational ability have serious consequences for economic theory

Some unexplored variants of the GH Humans with Hearts Game are listed below.

  1. Building on the idea of the JND, we can assess the attitude of the B-player towards the A-Player. Keeping the B-player payoff at 5 for HIGH and 4.75 for LOW, we can vary the A-Player Payoff and see the effects. For example, suppose ALO is set to $20, double of the $10 that A-player gets when B-player plays HIGH. It is our guess that most human beings would voluntary forego the extra 0.25 cents they would get by playing HIGH, in order to provide a benefit of an additional $10 to the A-player, regardless of whether or not they know who A is. This possibility cannot be contemplated by game-theorists or economists, committed to a model of infinite greed for human behaviour.
  2. A negative value of BOP can create gratitude in player A. Suppose that AOP is $15 and BOP is -$10. Aleena should play Opt-Out to get the payoff which is higher than $10 that she will get when Babar plays High. Suppose that ALO is $20, and while BLO is $3. When Babar gets the move, he might return the favour by deliberately making the Low move, choosing $3 instead of $5, in order to reward Aleena with the higher payoff of $20.
  3. Many possibilities for altruism and reciprocity along the lines above can be investigated. Would A-player forego Opt-Out in order to provide greater benefits to B? Would B return the favor by choosing the lower payoff, to provide a greater benefit to A? Again, conventional game theory, and economists, are completely blind to these possibilities, which do not arise in their models of heartless humans.

What is the harm of reducing the complexity of human motives to the simple one of greed? We have just seen that understanding human behavior in a very simple game requires taking into account resentment, gratitude, revenge, carelessness and reciprocity. Contrary to the reductionist economic views, a vast number of market transactions are based on motives other than greed. The widely recognized phenomenon of conspicuous consumption creates an externality and hence market failure which should be regulated – however economists fail to acknowledge the phenomenon because it requires motivations other than greed. An additional problem is that highlighting a single motive both legitimizes and encourages it: witness the “Greed is Good” maxim of Wall street.

End of slightly revised Excerpt of Section 1.2 from my paper on Empirical Evidence Against Utility Theory

Addendum 1 (Caring for Others): Developing the idea of reciprocity and altruism, I wonder what would happen if player B had an option to sacrifice his gains, in order to create greater gains for A. For example, if HIGH gives B $5 while LOW give B $0=BLO. But, A gets something big, like $100. My guess is that most B-player human beings with hearts would choose to play LOW, sacrificing $5 in order to provide $100 to the other player. A higher order question is – how would player A play in this situation? Suppose that when B-player plays HIGH, A-player gets $0 instead of $10. Then A-players would OPT-OUT and take $7, because they know that a “rational” B-player will go for the HIGH payoff, preferring $5 to $0, and so the A-player will end up with $0 if A-player chooses to PLAY. However, if I was the A-player, and the game was being played in a traditional society with norms of cooperation and generosity, I would be able to COUNT on B-player to sacrifice her $5 in order to allow me to gain $100. In a market society with rampant individualism, where children grow up in broken families and see everyone acting selfishly, and no examples of selfless sacrifice, I would not be so sure, but I may still be willing to take a chance on the B-player. If the game was being playing in a community of professional economists, then I would certainly opt-out, because, being brainwashed by their own theories, professional economists would almost certainly choose to take their $5 and ignore the option to sacrifice this small amount in order to allow me to win $100.

ADDENDUM 2 (Social Versus Market Frames): A very interesting finding of experimenters (see Dan Ariely – Predictably Irrational) is the people behave very differently in SOCIAL situations and in MARKET situations. For example, many people would be very happy to DONATE blood for a good cause, but would not agree to do so for money.  Economists have a REALLY difficult time understanding this — Nobel Laureates argued that adding a money payoff would only strengthen the motivation to donate blood, but experiments show otherwise. Economists also have difficulty understanding FRAMING effects — how the game is described MATTERS – people do not see through to the final payoffs, as game theory predicts they should. According to economists the framing – the words used to describe the game – should be irrelevant. The “rational” homo economicus sees through the empty words and only concerns himself with the payoffs. HOWEVER, human beings with hearts DO NOT behave like this. When playing this ARTIFICIAL game, HOW we described the game is EXTREMELY important. They will translate this decision into EITHER a social frame OR a market frame – and then they will behave very differently. So one important factor to consider in setting up an experimental game is whether the description of the game evokes a social context or a market context.

ADDENDUM 3 (Neutral Description of Games): Standard protocol for experimental game theory holds that we should try to describe the game in neutral language, which does not evoke any emotions. If subjects are used to decision making in adversarial market-contexts (my gain is your loss) and also in social contexts (with cooperation, generosity, and win-win outcomes) then neutral language leaves them clueless. As a result, they will ARBITRARILY translate the game into one of the two frames (my guess). This will introduce a random variation in outcomes. It might be better to explicitly specify one of the two frames, so as to eliminate this source of variation. This would be contrary to standard methodological practice.

ADDENDUM 4 (Poisoning the Well): This game illustrates the argument of Julie Nelson that Economic Theory damages Moral Imagination. A game-theorist B-player may feel happy to give up his measly five bucks in order to give a gift of $100 to the A-player, but training in game-theory tells him that this is  irrational behavior, and so he over-rides his natural impulses towards generously in order to behave like the greedy homo economicus.

The idea that knowledge is only of observables, and observables can be measured are both important drivers of policies and business and terribly wrong.

An Islamic WorldView

For reasons explained briefly in “The Emergence of Logical Positivism“, the Western intellectual tradition came to the disastrously wrong conclusions that (1) Only science can provide us with valid knowledge, and (2) science is based on observables, unlike religion which is based on unobservables. Furthermore, since qualitative aspects of observables are often subjective, a preference for the objectivity created by measurement was expressed by Lord Kelvin as follows:

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; it may be the beginning of knowledge, but you have scarcely, in your thoughts, advanced to the stage of science

This idea, that everything worth knowing, can be reduced to numerical measurements, has led Western intellectuals to attempt to measure everything, without concern about whether…

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Previous posts (  MMT Macro Final 1/3 , and  MMT Macro Final 2/3 ) have covered questions 1-4 and 5-8. This post covers the last 4 question of the MMT based  Advanced Macro course I taught last semester at PIDE. The central methodological difference at the heart of my course was the principle of Entanglement: Theories cannot be understood outside their historical context, and history cannot be understood without understanding theories used by human agents to understand and respond to that history. This is one of the three methodological principles that I have extracted from a study of  Methodology of Polanyi’s Great Transformation . This issue is discussed in the answer to question 11 below. Because of its central importance, I have also tried to explain it in greater detail in a separate 18 min video lecture. I recently came across a paper by Yair Kaldor on  The cultural foundations of economic categories: finance and class in the marginalist revolution which explains the birth of marginal utility theory in the historical context of emergence of finance and international trade as important influences on price, which were not compatible with traditional labor theories of value. The paper shows how strongly these emerging theories were influenced by the historical context, as well as by the point of view of the groups which created and spread these theories. This provides an illustration of the entanglement principle that history shapes theories, and also is shaped by theories.

ANSWERS to question 9-12 of MMT Macro Final Exam at PIDE in June 2019

9. Whereas it is commonly thought that Banks are financial intermediaries, collecting from depositor to lend to borrowers, the central business of banks is “Maturity Transformation”. Explain in detail, in context of modern economies.
Common belief about bank is that they act as financial intermediaries. It means that people who have extra money deposit it with banks who pays them return them on saving. Bank then lends this money to borrowers and charge interest (higher than saving rate) on lending. This higher return (difference in lending and saving rate) is their earning. Now it is possible that depositors want to withdraw their money while returns on lending starts later. For this it seeks loans from interbank market (if its own reserves are not enough for daily transactions) or from central bank (lender of the last resort). This is the wrong picture of how banking works, but this is widely taught and believed.
Banking actually works by maturity transformation. A thirty year mortgage loan is transformed into a sequence of one day loans. The simplest way to understand this is to consider a bank AA with ZERO assets, which makes a 30 year loan of $100,000 at 5% interest. The borrower withdraws the money and deposits it in another bank BB creating a liability which bank AA owes to bank BB. Millions of such transactions takes place everyday. Assume there is ZERO money in the system, in order to get clarity about how it works. Also assume that no one actually uses cash – everybody writes checks for every purchase, so all money created actually stays within some bank or the other. At the start of the day all banks made loans of varying maturities – say from one year to thirty years – by simply opening checking accounts in the name of people who borrowed. Then people wrote checks on these accounts to other banks. All the money got re-shuffled between the banks. A lot of liabilities for bank AA were generated when people wrote checks on their (empty) accounts, but also a lot of credits came in when people deposited checks from other banks into their accounts at AA. At the end of the day, bank AA will have either a net credit or a net loss. Overall, the entire banking sector will have net position of zero – no credit or loss. This is because no money has flowed into or out of the banking sector. So if some bank is down, then some other bank must be up. At the end of the day, inter-bank clearing takes place. Banks which are short of money borrow OVERNITE from those who have surplus at the Inter-Bank borrowing rate of 3%. Since the maximum they borrow is limited by the amount they have lent, they will always make profits on the differential between the overnite borrowing rate of 3% and the long run rate of 5%. This same process repeats every day for thirty years. So the bank AA finances a thirty year loan by daily borrowing everyday for the entire thirty years. This is maturity transformation – transformation of a thirty year loan into a sequence of one day loans over the period of the loan. Leakages of cash from the system only add some addition wrinkles which don’t matter much for the basic picture described above. See my post on Monetization, Maturity Transformation and Modern Monetary Theory. Note the dramatic difference between the Maturity Transformation and the Financial Intermediation picture of how banks work.

10. Explain the Job Guarantee Program, where the government becomes Employer of Last Resort. Explain why conventional economists think this will lead to inflation. Explain why a poorly designed JG can indeed lead to inflation but a well-designed program should not.
Minsky’s JG program suggests that govt. should act as the “Employer of the last resort”. It should give jobs to all those people who are looking for jobs according to their skills and area of expertise – as they are, where they are. Govt. should provide them on job training. Let us classify laborers as A, B, C, …, Z category according to their attractiveness to private firms, and therefore their employability. The goal of the job guarantee program is to tend to the bottom of the pool, to give jobs to Z-category workers first and then work up. The private sector has the opposite priorities and starts with A-category and work down. The government should provide a guaranteed minimum wage which anyone who wants a job can get. This should be low enough so as to not compete with jobs in the private sector. When the economy is doing well, the private sector will go down the rankings to lower categories and workers will shift out of the minimum wage government jobs to the better paying private sector jobs. In downturn the opposite will happen as workers laid off from private sector will go back to less well-paid government jobs. Full employment will be maintained throughout the business cycle.
Conventional Views: Mainstream economists find two problems with this scheme. One is: How will the government finance a massive job creation program? Where will it get the revenues for this? The MMT answer is that a sovereign government does not need to raise money. It creates money by fiat, and can just print as much money as is required for this purpose. After all, the USA government spent $29 Trillion to bailout the financial sector following the GFC without any obvious adverse effects. Many other examples throughout the world of Keynesian deficit financing and fiscal policy leading to good results are available. The second objection is that printing money and giving it to the workers will lead to inflation. The output produced in the economy will remain the same, but there will be a lot more money in the economy so prices will have to rise to achieve supply and demand equilibrium.
MMT Answers: If laborers are employed in non-productive jobs, so that they add nothing to the total output of the economy, then the conventional view is valid. If the Government hires millions of people who do nothing at all, then inflation would result, exactly as predicted by conventional theory. However, the key to the Job Guarantee program is to ensure that all people who are hired actually add to the output of the economy. By looking the least desirable and worst paid jobs in the economy, Minsky estimated that newly hired zero-skill and experience workers could contribute at least 5 times their salary in terms of production of new goods and services to the economy. Thus, additional money created to pay salaries would be counterbalanced by the additional output produced by the newly hired workers, so that there is no necessary inflationary pressure. More delicate inter-sectoral accounting is needed to ensure that this idea actually works in practice. If all new workers are hired in any one sector (like services), they will all generated demands for food, housing, education and other basic needs, leading to inflation in these sectors. So one part of the JG program involves balancing the job creation strategy in such a way that the additional demand generates is actually met by the additional production. For example, anticipating an increase in demand for food due to the JG program, we could allocate a sufficient proportion of jobs to the agricultural sector, so that additional food is created in sufficient quantities to meet the additional demand generated. Similarly, we can actually anticipate the additional demand which will be generated by using the detailed information from Household Income Expenditure Surveys and provide extra jobs and productive capacities in sectors which will receive the greatest additional demand. For more information, see “ Employment for All ”.

11. Explain the idea of “Entanglement” and illustrate the concept by showing how monetary policy in post-World War 1 era had opposite effects from those in the pre-World War 1 era, due to changed historical context.
The idea of entanglement suggests that theory and history are tangled with each other.Theories are based on attempts to understand and learn from a particular historical experience, and hence cannot be understood in isolation, separately from the historical context. For example, to understand Keynesian economic theory, we must understand the Great Depression. More complex is the other direction – we cannot understand history, without understanding the theories used by people to understand that history. This is because the response people made to historical events was governed by the theories they used to understand history. When the phenomena of poverty emerged and became widespread in England, and in European economies, people made an effort to understand this, in order to devise suitable policies to combat poverty. Most theories places the responsibility on external factors and hence recommended gentle and sympathetic treatment of the poor. However, Malthusian theories came to dominate the scene, and the English poor laws were designed in the light of these theories. Malthusian theories place the blame for poverty on the high birth rate of the poor, and recommend harsh treatment to control the population. Similarly, economic policy in the post-WW2 era in Western world was governed by Keynesian theory and this accounts for the widespread prosperity and full employment that was observed from 1945 to 1975 roughly.
The concept of entanglement is well illustrated by monetary policy in pre and post WW1 period. As the lecture on Global Financial Architecture Part II explains in detail, the same policies had different effects in the pre an post war periods. In the pre-war era, Central Banks were committed to stability of international exchange rates and prioritized this over the needs of the domestic economy. A temporary suspension of convertibility to gold was a stabilizing factor, where Central Banks sought time to borrow reserves to fulfill international obligations. Private actors assumed that Central Banks would seek to strengthen the currency and therefore moved to support the currency, in order to profit from the anticipated policy. In the post war period, Central Banks were more committed to restoration of war-ravaged domestic economies. In this period, a suspension of gold payments signaled a weakening of the currency and the currency was attacked in anticipation of further weakening. The same policy led to entirely different outcomes in the two periods because the historical context. This clearly illustrates how effects of policy depend on the historical context. To understand how policy shapes history, we can show that wrong policy, based on wrong theories about how money functions, was responsible for both World War 1 and World War 2, although the causes for the two wars were radically different.

12. Explain the sequence of events which shows how the Global Financial Crisis 2007 was the revenge of East Asia for the crisis created by over-investment by foreigners.
Atif Main and Amir Sufi explain the casual chain of GFC via East-Asia. In the beginning the East Asian emerging economies had high interest rates which attracted the foreign capitalists to invest here. They had strict controls over capital mobility but IMF and big financial investors persuaded them to remove these restrictions. They were offered the temptation that inflows of foreign capital would further enhance their growth, but they were unaware of the risks attached to this hot capital. A huge amount of foreign capital, seeking high returns, flowed into the East Asian economies. Inflow of foreign capital led to an asset price bubble in land and housing. The banks took loans with a promise to return in dollars. The bubble then burst, with a small disturbance in currency value leading to jittery speculators withdrawing huge amounts of foreign capital. Even though the ground realities of the economies remained solid, foreign investors refused to lend more money because of damaged expectations about the future. The East Asian banks did not have dollars to pay back, even Central Bank or govt. did not have enough dollars to pay back. This led to massive crises, so the countries had to go to IMF to borrow dollars. IMF imposed austerity policies on them which put them in deeper recession. The lesson they learnt was that to prevent future crises, Central Banks should have high reserves of dollars. Dollar reserve holdings at Central Banks throughout the world increased by trillions of dollars over the decade leading up to the Global Financial Crisis. Central Banks holding dollars reserves, and private institutions, wanted to hold dollars in safe liquid assets with highest possible returns. In U.S. in 1970, the rules were strict and only safe assets were securitized but this foreign demand put pressure on U.S. to securitize risky assets also in 1990’s. These extra savings or capital was put into U.S. mortgage and bond market which created house bubble. The certification agencies participated in fraud to make Mortgage Based Securities appear as AAA, almost as safe as US Treasury, even though these assets were actually very risky. Inflows of trillions of dollars of foreign investors created a bubble in US real estate and stock prices. Eventually the bubble burst, and foreign investors took out their capital and the financial sector collapsed, requiring a bailout amounting to $29 Trillion eventually. That’s why it is said that GFC was the revenge of East Asia.

Excerpt from:  Real Statistics (3/4) Statistics as Rhetoric 

{Preliminary material explains that conventional approach statistics separates theory and application — the job of ths statistician is to analyze numbers – without knowing where the come from. The job of the Field Expert is to use objective statistical analysis of numbers to get better understanding of the realities which generate the numbers. In “Real Statistics”, we assert that these two tasks cannot be separated. Theory must always be studied within the context of real world application. Also, real world phenomena cannot be understood without application of theory}}

So the statistician must always analyze numbers in the context of the real world phenomena which generated the numbers. See  My Journey from Theory to Reality  for more details about this argument. The Islamic approach rejects the idea that numbers are objective measures of reality. As we will see, most numbers being analyzed involve subjective judgments. We take the point of view that Statistics is a branch of rhetoric. We need to learn how to make ARGUMENTS with numbers.

The key rhetorical strategy of conventional statistics is hiding of the subjective elements of a statistical analysis. Both the data being analyzed, and methods of analysis, involve HUGE numbers of Subjective Assumptions. Conventional statistical analysis pretends that numbers, and analysis, is objective and factual, no room is left for arguments and persuasion. In this course, we will bring out the hidden value judgments, so that different perspectives can be explored, in light of different values, while having the same set of numerical measurements.

The key insight here is that most numbers are MADE UP, and involve HUGE numbers of subjective judgments. There are TWO types of Numbers – Facts and Fictions. The factual and objective numbers are about the External Reality. For example, Number of trees in forest, Number of people in Pakistan, Rupee Income of People in Pakistan, Prices of different goods in different places, the Quantity of Carpets produced for export. These can all be counted by numbers, and the numbers actually count something which is present in external reality, and therefore is objective.

However most numbers which enter statistical analysis, especially in the context of economics are number fictions, not number facts. These numbers are computed using subjective decisions which represent values, but these are hidden in the analysis. Instead the numbers are presented as if they are just like number facts, and hence objective measures of external reality, to which all observers would agree. Here are some examples of numbers which are fictional: IQ of a person, Wealth of Pakistan, Value of the Rupee in terms of purchasing power, Inflation Rate, Quality of Universities, Quality of Research produced by a faculty. Since this point is never made in conventional statistical texts, which treat all numbers alike, we will explain further why these numbers are fictional, not factual.

Why is IQ a Fictional Number? {… to read more, see:  Real Statistics (3/4) Statistics as Rhetoric  … }

See also, related post on Beyond Numbers and Material Rewards

A (free) online course on “Real Statistics” starts on Saturday 27th July 2019. This course develops a radical new approach to the subject. This course is designed for teachers. Registration form is linked in the first paragraph of the post linked below. Register to get put on the email list for the course.

An Islamic WorldView

[bit.do/azcsr] Insha-Allah, starting Saturday 27th July 2019, I will launch an online course entitled Real Statistics: An Islamic Approach (RSIA)- The POSTSCRIPT below lists seven previous posts which discuss the ideas which led to the creation of the course. This introductory material is too deep, difficult, and complex, for students of a basic statistics course. I am planning to cover the bare essentials in my online course Real Statistics which is meant for teachers of statistics, to give them both the understanding, and the course materials they need to run the course. Teachers can register for the course via: Registration: RSIA,[shortlink: bit.do/rsia0] They will be put on an email list which will provide weekly assignments of readings, together with tasks/quizzes to test understanding. Taking the course should enable teachers to teach students using a radically different approach, which provides deep conceptual understanding, as well as hands-on ability to…

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