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Who Pays for the Reskilling Revolution?

MD Staff

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The cost of reskilling the 1.4 million US workers likely to lose their jobs as a result of the Fourth Industrial Revolution and other structural changes over the next decade will largely fall on the government, with the private sector only able to profitably absorb reskilling for 25% of at-risk workers. This is the finding of a World Economic Forum report published today.

The report, “Towards a Reskilling Revolution: Industry-Led Action for the Future of Work”, finds that it will be possible to transition 95% of at-risk workers into positions that have similar skills and higher wages. The cost of this reskilling operation would be approximately $34 billion.

However, the report also finds that, of the 1.4 million workers at risk, the private-sector could only profitably reskill 25%, or about 350, 000 workers. For the rest, at current rates of reskilling time and costs and foregone productivity, it would be more cost-effective for businesses to replace them with workers with the correct skill-set.

The ability of the private sector to profitably absorb the reskilling burden could rise to 45% of at-risk workers if businesses collaborate to create economies of scale. The report also finds that the government could reskill as many as 77% of all at-risk workers, with a clear return on investment coming from increased tax returns and lower social costs such as unemployment compensation. For the remaining 18%, the costs outweigh the economic returns to government, while for 5% a similar-skills and higher-wage pathway is not available.

With 18% of all at-risk workers – 252,000 people – unable to be profitably reskilled by either business or the public sector, the report’s findings imply that governments must consider expanding welfare and social support, paying for negative-return reskilling due to its societal returns, and lowering the costs of reskilling and retraining through incentives to and collaboration with the private sector and educators, including apprenticeships and online learning.

The question of who pays for reskilling as hundreds of thousands of jobs become displaced over the next decade as a result of Fourth Industrial Revolution technologies – such as artificial intelligence and big data analytics – and other structural factors, is increasingly important. In a 2018 report entitled The Future of Jobs 2018, the World Economic Forum calculated that, while 75 million will be displaced worldwide through automation between 2018 and 2022, as many as 133 million new roles could be created. However, this assumes that it will be possible to provide workers with the skills to fulfil these new roles, highlighting the need for reskilling at-risk workers as well as ongoing upskilling for the majority of workers.

“Even with a conservative estimate, the reskilling challenge will cost $34 billion in the United States alone and only a part of it will be profitable for companies to take on by themselves, even if they were to think long term. The question of who pays for the stranded workers and for the upskilling needed across economies is becoming urgent. In our view, a combination of three investment options needs to be applied: companies working with each other to lower costs; governments and taxpayers taking on the cost as an important societal investment; and governments and business working together. This week at the Annual Meeting, we are exploring and building coalitions around all three,” said Saadia Zahidi, Managing Director, World Economic Forum, and Head, Centre for the New Economy and Society.

“Towards a Reskilling Revolution: Industry-Led Action for the Future of Work” is published in collaboration with the Boston Consulting Group and Burning Glass Technologies.

From planning to delivering skills

Separately, the Forum’s Closing the Skills Gap 2020 coalition, launched in 2017 with a target to reskill or upskill 10 million workers by 2020, has already secured pledges for training more than 17 million people globally. Of these, 6.4 million people have already been retrained. The initiative uses a virtual hub to capture measurable commitments from leading companies to train, reskill and upskill workers. The hub also serves as a repository of best practices and case studies.

Additionally, over the last quarter five industries began to explore the viability of making their reskilling and upskilling efforts more efficient and impactful. At the Annual Meeting, they are expected to launch sector-level collaborations to help prepare their workforces for the future of work.

To complement this business-led approach, the Forum is initiating and expanding national public-private collaboration task forces to prepare countries for the future of work. This approach has been rolled out in four economies, including Argentina, India, Oman and South Africa, with the goal to expand geographically to 10 economies by the end of 2020.

To accelerate the integration of more women into the labour force, particularly in the high-growth sectors of the future, the Forum’s Closing the Gender Gap national public-private collaborations are expanding their coverage to eight countries, including Argentina, Chile, Colombia, Costa Rica, Dominican Republic, France, Panama and Peru.

A long-term vision for skills

While reskilling and upskilling are critical short-term needs, the current mismatch between education and employment is the result of a system in which proxies for skills rather than skills themselves are taught, recruited, promoted and rewarded. Proxies such as the brand names of educational institutions and employers replace tangible evaluations of the quality of the skills held by employees. This system is not viable in an era of rapid labour market transformations brought on by the Fourth Industrial Revolution. It is under further pressure from social inequalities, which tend to become locked-in for those lacking the highest-value proxies for skills.

A new study, “Strategies for the New Economy: Skills as the Currency of the Labour Market”, published in collaboration with Willis Towers Watson, suggests how a skills-based system may be created through changes in the learning ecosystem, the workforce ecosystem and the broader enabling environment. It covers 10 strategies, including building, adapting and certifying foundational, advanced skills and adult workforce skills; realizing the potential of educational technology and personalized learning; mapping the skills content of jobs and developing a common taxonomy; and designing portable certifications. Such actions would have a positive impact on both labour market efficiency and socio-economic mobility.

The view from the C-suite

“Companies need to start to prepare for the future of work now, and up- and reskilling will be the biggest challenge in this endeavour. The good news is that there is a positive business case for up- and reskilling for many disrupted workers from a company and government perspective,” said Rich Lesser, Global Chief Executive Officer, Boston Consulting Group, USA.

“The shift to a skills-based economy presents individuals with the chance to compete for employment based on what they can do for a company. At the same time it gives companies a tremendous opportunity to more efficiently design work and organize resources,” said John J Haley, Chief Executive Officer, Willis Towers Watson, USA

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Economy

Would You Like a Thirty-Hour Workweek?

Meena Miriam Yust

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Authors: Meena Miriam Yust and Arshad M. Khan

In the earliest days, foraging was key.  Fruits, berries, edible plants and roots comprised a varied diet, the roots often mashed and made into meal. 

Then there were days when the men — usually layabouts for foraging — would get the urge in their bellies for meat.  That was when all the chatting and bonding paid off.  Working together they could down a large beast and share the meat with the whole group … feasting for several days. 

No nine-to-five slavery in those times, no five-day work week.  That is all of recent vintage.  And it leads to an unmistakable Monday morning feeling …  

Millions of alarm bells sound in the wee hours of the morning, as semi-comatose individuals slide their snooze buttons hoping for a moment’s rest before the inevitable rush to the office.  The weekend is over.  Fun over, work beckons.  Marching along like ants going to their own funeral, masses of people will soon swarm into the subway, vying for a seat in a stench-free area, surrounded shoulder to shoulder with others like them. 

And for what when we know first hand that wage buying power hasn’t changed in decades while US income inequality continues to grow.  Good luck to the rich who keep getting richer as the stock market booms while trends in wealth show the lower 60 percent have seen a net worth decline.  Can we ever get a real wage increase?  Yes, by working fewer hours for the same weekly salary when the over overtime on a few hours more would boost our financial health.  More money and free time makes for a happier work and life balance.  Just as raising the minimum wage, it would have an impact on pay inequality as economist Ben Zipperer made clear in his testimony before Congress last year.      

For most of us, next come the Tuesday blues, that lethargic, listless feeling of no escape.  Wednesdays mark the halfway point, Thursdays bring the hope of almost-Friday, and then Friday arrives with the joy of the weekend break.  But soon it will be Monday morning again.  The majority of our lives are spent working.   The weekend leaves barely enough time for recovery, laundry, and if we’re lucky a smidgen of fun, before returning to the tedium of the five-day work week.   It’s not that the powers that be are unaware of our circumstances.  As long ago as 1935, the Senate Judiciary Committee held thirty-hour work week hearings but the idea failed to get traction. . 

But is this weekly misery necessary?  And where did it come from?

One year marks an orbit of the Earth around the sun.  Months too are derived from astronomy. Five thousand years ago, the ancient Sumerian calendar had 12 months marked by the sighting of a new moon.  They did not have weeks.  And archaeologists have discovered a hunter gatherer calendar from Aberdeen, Scotland dating farther back from 8,000 B.C., which also appeared to mimic phases of the moon to track months.  

The history of the seven-day week leads us to Babylon 4,000 years ago.  With a lunar month they used seven days to represent each of the four phases of the moon, adding an intercalary day(s) to synchronize it to the actual lunar cycle.  All of which worked out very well because they believed there were seven planets in the solar system and deemed the number significant.  The seven-day week eventually spread to Egypt, Greece, and thence to India, China and Rome, ending up in the Gregorian calendar we use today. 

The five-day work week was first introduced in a New England mill in 1908.  Before this, Saturdays were a half day and Sundays a holiday.

It was not expected that humans would still be doing this a century later.  John Maynard Keynes in 1930 predicted that the work week would be reduced to 15 hours, within a couple of generations, due to advancements in technology.  In 2017, economist and historian Rutger Bregman put forward its feasibility by 2030 in his best seller, Utopia for Realists.  A Senate subcommittee in 1965 also predicted we would be working 14-hour weeks by the year 2,000.  

More recently, companies have started to study whether there are benefits to a four-day work week.  Microsoft Japan recently reported the results of a four-day work week study.  The company had employees work four days while receiving five-day pay.  The results were striking – a whopping 40% increase in productivity.  The firm also reported increased efficiency in several areas, including lower electricity and paper usage.

A New Zealand company Perpetual Guardian in 2018 experimented with a four-day work week with five-day pay.  It resulted in a 20 percent increase in productivity while employees experienced a 45 percent improvement in work-life balance.  The company has now made the policy permanent. 

Another example is a company called Basecamp.  Employees work 8 hours a day for four days. Jason Fried, the CEO, states in a New York Times op-ed that “Better work gets done in four days than in five.”  

Despite the jokes about civil servants they do work, and some very hard.  In a study of British civil servants, it was determined that those who worked 55 hours per week showed a comparatively greater cognitive decline some three years later than those working for 40 hours.  Imagine what happens to us when we extend this to a lifetime of 40-plus-hour weeks. 

The question is, do we need to work even 40 hours per week?  If Keynes predicted humans would only need to work 15 hours by this point in time, and there has been an explosion of technological advancements in the last 30 years unimaginable to him — from computers to robotics to the internet and advancements in every type of engineering and medical field — then why are we still 40-hour slaves, particularly when the Basecamp example has demonstrated that 32 hours per week is equally or perhaps more productive? 

Next is the question of whether even 32 hours, as at Basecamp, are necessary.  David Graeber is an anthropologist at the London School of Economics.  His 2018 book Bullshit Jobs: A Theory describes jobs that appear to have no useful purpose.  These are far more common that one might expect.  In a poll of British citizens, 37% considered their jobs meaningless.  In the Netherlands, 40% of respondents believed their job had no reason to exist.  Graeber defines bullshit jobs as “a form of paid employment that is so completely pointless, unnecessary, or pernicious that even the employee cannot justify its existence even though, as part of the conditions of employment, the employee feels obliged to pretend that this is not the case.”  

In many of these jobs, employees sit at a desk five days a week with nothing to do.  In other jobs, higher management invents tasks for subordinates to complete solely to fill their time.  Some jobs exist merely for appearances.  He splits them into categories, encompassing jobs with which we are all too familiar.  “Flunkies” serve the purpose of making others feel superior (these include doormen, assistants, etc.).  “Goons” encompass those such as the public relations professional whose job is to show the public that Oxford is a top school!  “Duct tapers” are people in an organization who have to deal with its incompetence.  For example, the person who handles lost luggage at an airport or addresses complaints on the phone. “Box tickers” are designed to look busy and push paper work forward. “Taskmasters” are split into two types – those that assign more bullshit work to subordinates “bullshit generators”, and those who supervise people who do not need supervision. 

For the 60% of people who do not have “bullshit jobs” – studies have shown that fewer work days increases productivity and efficiency, not to mention mental well being.  Companies will be more efficient, workers will work better and will be rested and refreshed, and employees will be more likely to stay in their jobs.  It’s a plus-sum game if the work week is cut to 30 hours/ 4 days forthwith.  Anything beyond 30 hours would be overtime, at time-and-a-half rates.  The proposal is still twice John Maynard Keynes’ 15-hour expectation.  

There is another very good reason for this proposal:  Real wages in the US have been stagnant since the 1960s while the GDP is up over four fold and the stock market Dow is up about ten times, also in real terms i.e. after allowing for inflation.  It means stock and asset holders have been getting much, much richer while the working sucker is getting nowhere.  Cutting the work week down is a fair way to get part way (a very small part) even.  It is 25 percent less work and a one-third real increase in wages making a minor dent in the horrendous inequality in the US, which happens to be way ahead in this dubious honor among all developed countries. 

It is a long time since the hunter gatherers of Scotland or the Babylonians.  Their week remains ingrained, and the weekend created thousands of years later expanded from the Biblical single day of rest to one-and-a-half days in England, and then finally to two in New England at the beginning of the 20th century.  A hundred years later, is it not high time we advanced to three days?  Or perhaps to diminish the chances of worse Monday morning blues, it might be better to work two days, have a day off, then two more days of work before the regular weekend.  Humans were not designed for undue stress, we were designed for leisure, to be gathering food as we need it, and occasionally hunting, as the Scots mentioned earlier, and others of our ancestors did happily for generations.  

Authors’ Note:  This article first appeared on Truthout.org in a shorter edited version.

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Economy

Banks and Artificial Intelligence

Giancarlo Elia Valori

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“Artificial Intelligence” is a terminology specifically invented in 1956 by John McCarthy and concerns the ability to make appropriate generalizations quickly, but based on an inevitably limited set of data.

 The wider the scope and the faster conclusions are drawn, and with minimal information, the smarter the machine’s behaviour can be defined.

 Intelligence is the creative adaptation to quick changes in the environment. This is the now classic definition, but in this case, with machines, the speed and the increasingly narrow base of the starting data are also evaluated.

 What if the starting data does not contain exactly the necessary information – which is possible? What if, again, the speed of the solution stems from the fact that the data collected is too homogeneous and does not contain the most interesting data?

 Konrad Lorenz, the founder of animal ethology, was always very careful to maintain that between instinctive behaviour and learned behaviour, external environmental and genetic sources can be equally “intelligent”. The fact, however, is that greater flexibility of a behaviour – always within a reasonable time, but not as quickly as possible – generates greater intelligence of the animal.

As said by a great student of Lorenz, Nikko Tinbergen, human beings are “representational magpies”, which means that much of their genetic and informative history has no practical value.

 When the collection of information becomes easy, the “adaptive” magpie has a very adaptive behaviour, but when the data collection is at the maximum, all data counts and we never know which, among this data, will really be put into action.

 In other words, machine data processing is a “competence without understanding”, unless machines are given all senses – which is currently possible.

 Human intelligence is defined when we are at the extreme of physically possible data acquisition, i.e. when individuals learn adaptive-innovative behaviour from direct imitation of abstract rules.

 Abstract rules, not random environmental signals.

 If machines could reach this level, they would need such a degree of freedom of expression that, today, no machine can reach, not least because no one knows how to reach this level; and how this behaviour is subsequently coded.

 What if it cannot be encoded in any way?

 The standardization of “if-then” operations that could mimic instincts, and of finalized operations (which could appear as an acquired Lorenz-style imprinting) is only a quantitative expansion of what we call “intelligence”, but it does not change its nature, which always comes after the particular human link between instinct, intelligence and learning by doing.

 Which always has an accidental, statistical and unpredictable basis. Which duck will be the first to call Konrad Lorenz “dad”, thus creating a conditioning for the others? No one can predict that.

If systematized, bio-imitation could be a way to produce – in the future – sentient machines that can create their own unique and unrepeatable intelligent way to react to the environment, thus creating a one and only intelligent behaviour. Will it be unique?

However, let us go back to Artificial Intelligence machines and how they work.

 In the 1980s there was the first phase of large investment in AI, with the British Alvey Program; the U.S. DARPA Program spending a billion US dollars on its Strategic Computing Initiative alone; finally the Japanese Fifth Generation Computer Project, investing a similar amount of money.

At the time there was the booming of “expert systems”, i.e. symbolic mechanisms that solved problems, but in a previously defined area.

From the beginning, expert systems were used in financial trading.

There was the hand of the expert system in the fall of the Dow Jones Industrial Average by 508 points in 1987. In 1990, however, Artificial Intelligence also began to be used in the analysis of financial frauds, with an ad hoc program used by the Financial Crimes Enforcement Network (FinCEN), especially with the possibility to automatically review 200,000 transactions per week and to identify over 400 illegal transactions.

Machine learning, the model on which the most widely used AI financial technology relies, is based on a work by McCullogh and Pitts in 1943, in which it was discovered that the human brain produces signals that are both digital and binary.

 A machine learning system is composed, in principle, by: 1) a problem; 2) a data source; 3) a model; 4) an optimization algorithm; 5) a validation and testing system.

 In 2011, deep learning (DL) added to the other “expert” systems.

It is a way in which machines use algorithms operating at various separate levels, as happens in the real human brain. Hence deep learning is a statistical method to find acceptably stable paradigms in a very large data set, by imitating our brain and its structure in layers, areas and sectors.

As explained above, it is a mechanism that “mimics” the functioning of the human brain, without processing it.

 DL could analyse for the first time non-linear events, such as market volatility, but its real problem was the verification of models: in 2004 Knight Capital lost 440 million US dollars in 45 minutes, because it put into action a DL and financial trading model that had not been tested before.

 In 2013, during a computer block of only 13 minutes, Goldman Sachs flooded the U.S. financial market with purchase requests for 800,000 equities. The same week., again for a computer error, the Chinese Everbright Securities bought 4 billion of various shares on the Shanghai market, but without a precise reason.

 Between 2012 and 2016, the United States invested 18.2 billion US dollars in Artificial Intelligence, while only 2.6 were invested by China and 850 million US dollars by the United Kingdom in the same period.

 The Japanese Government Pension Savings Investment Fund, the world’s largest pension fund manager, thinks it can soon replace “human” managers with advanced Artificial Intelligence systems.

 BlackRock has just organized an AILab.

 In 2017, however, China overtook the United States in terms of AI startups, with 15.2 billion funding.

 China now has 68% of AI startups throughout Asia, raising 1.345 billion US dollars on the markets for their take-off.

China has also overtaken the United States in terms of Artificial Intelligence patents over the last five years.

Nevertheless, considered together, the USA and China still account for over 50% of all AI patents worldwide.

 China also dominates the market of patents on AI technology vision systems, while deep learning data processing systems are now prey to the big global companies in the sector, namely Microsoft, Google and IBM. Similar Chinese networks are rapidly processing their new “intelligent” data collection systems, also favoured by the fact that the Chinese population is about twice as much as the US population and hence the mass of starting data is huge.

 The Chinese intelligence industry zone near Tianjin is already active.

In the end, however, how does Artificial Intelligence change the financial sector?

AI operates above all in the trading of securities and currencies in various fields: algorithmic trading; the composition and optimization of portfolios; validation of investment models; verification of key operations; robo-advising, namely robotic consultancy; the analysis of impact on the markets; the effectiveness of regulations and finally the standard banking evaluations and the analysis of competitors’ trading.

 Algorithmic trading is a real automatic transaction system – a Machine Learning program that learns the structure of transaction data and then tries to predict what will happen.

Nowadays computers already generate 70% of transactions in financial markets, 65% of transactions in futures markets and 52% of transactions in the public debt securities market.

The issue lies in making transactions at the best possible price, with a very low probability of making mistakes and with the possibility of checking different market conditions simultaneously, as well as avoiding psychological errors or personal inclinations.

In particular, algorithmic trading concerns hedge funds operations and the operations of the most important clients of a bank or Fund.

 There are other AI mathematical mechanisms that come into play here.

 There is, in fact, signal processing, which operates by filtering data to eliminate disturbing elements and observe the development trends of a market.

 There is also market sentiment.

The computer is left completely unaware of the operations in progress, until the specific algorithm is put to work – hence the machine immediately perceives the behaviour of supply and demand.

 There is also the news reader, a program that learns to interpret the main social and political phenomena, as well as pattern recognition, an algorithm teaching the machine to learn and react when the markets show characteristics allowing immediate gains.

Another algorithm is available, developed by a private computer company in the USA, which processes millions of “data points” to discover investment models or spontaneous market trends and operates on trillions of financial scenarios, from which it processes the scenarios deemed real.

 Here, in fact, 1,800 days of physical trading are reduced to seven minutes.

However, the algorithms developed from evidence work much better than human operators in predicting the future.

 Artificial Intelligence works as a prediction generator even in the oldest financial market, namely real estate.

 Today, for example, there is an algorithm, developed by a German company, that automatically “extracts” the most important data from the documents usually used to evaluate real estate transactions.

 In Singapore, Artificial Intelligence is used to calculate the value of real estate property, with a mix of algorithms and comparative market analysis. Man is not involved at all.

As to corporate governance, there are AI programs that select executives based on their performance, which is analysed very carefully.

What is certainly at work here is the scientist and naive myth of excluding subjectivity, always seen as negative. The program, however, is extremely analytical and full of variables.

 Artificial Intelligence is also used in the market of loans and mortgages, where algorithms can be processed from an infinity of data concerning clients (age, work, gender, recurrent diseases, lifestyles, etc.) and are linked to operations – always through an algorithm – which are ordered, without knowing it, from one’s own mobile phone or computer.

 So far we have focused on Artificial Intelligence algorithms.

 But there is also quantum computing (QC), which is currently very active already. Its speed cannot be reached by today’s “traditional” computers.

 It is a more suitable technology than the others to solve problems and make financial forecasts, because QC operates with really random variables, while the old algorithms simply simulate random variables.

 Quantum computing can process several procedures simultaneously, and these “coexistence states” are defined as qubits.

In a scenario analysis, QC can evaluate a potentially infinite set of solutions and results that have been randomly generated.

 An extremely powerful machine which, however, cannot determine exactly – as it also happens to slower machines – whether the scenario processed corresponds to human interests (but only to the initial ones known by the machine) or whether the procedure does not change during operations.

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The Reckoning: Debt, Democracy and the Future of American Power- Book Review

Junaid R. Soomro

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Authors: Junaid R.Soomro and Nadia Shaheen

The chapter is written by Michael Moran in which he discussed about the relations between the economic institutions with the other institutions of the state. A state is a combination of many institutions that work together as a single body to make the state run accordingly. Political and economic institutions are two major components of the state. Politics and economy somehow depend on each other from a very long time. The both concepts are old and influenced by each other. The major changes occurred after the industrial revolution that gave birth to new tactics and opportunities to the economy. Earliest, before the French Revolution the economy was controlled by the elites that were the political identities. This is the example that how those bourgeois controlled the economic structure of the state and how they shape or influence the economical aspect of the society. These involvements of both disciplines gave birth to a new subject that is known as the political economy of the states, that how political and economic policies influence each other because it is not possible for any institution to work separately. The economic institutions shape the economic structure of the state and it is controlled by many aspects, including the political institutions, the economic regulations, the political structure of the state that somehow effects the economic institution of the state.

Summary

The chapter tells us that how economic institution and other institutions are interconnected.

Firstly, the focus is on the political institutions. The recognitions of an economic institution as a political act. The “politics” and “market” are somehow interconnected.   It’s not because the political institutions shape the fate of economy, but the economy shapes it as well. From the start of the history these two aspects are there and depend on each other. We can see it through the examination of the history that how the political elites dominated the society because they were also superior financially. The political institutions somehow legitimize the economic institutions. According  to  “Godin”  different  preoccupations  drive  inquiry  in  different  disciplines:  for instance, choice in economy and the power in the politics.

Secondly the focus is the connection between institutionalism and the economic institutions.

The institutions are constructs of human mind, we cannot see or feel them. The regulations and the market grew up together. The current world politics is an example that how the regulations affect the economy and shape it as different stats can be taken as a model who are following the regulations. The institutions determine the opportunities of the society and in result the organizations are made in order or take benefit of those opportunities. There are several parallels that shape the behavior of the institutions that later affects the other institutions including the economic institution.

Thirdly, the connection between the economic institutions and the regulations.

The regulations are made to control the behavior of the institutions. This faced major change after the industrial revolution when many regulations were made that were supposed to control the outcomes of the institutions. We cannot run from globalization, this is the reason that the concept is not the same as it was in the past, but it came up with the new characteristics. Mainly the  evolution  in  the  middle  of  the  twentieth  century  created  a  paradigmatic  shift  in  the relationship of economic and political institutions. There are agencies with in the states that regulates the working on an institution and on the international level there are multinational corporations. This gives us two basic concepts. The first is uncertainty about the boundaries between the politics and economy, and the second is the importance of the agencies that fills the space and regulates the institutions.

Fourthly, the connection between the economic institutions and the capitalism.

Capitalism  and  the  economy are  directly  connected  with  each  other  because  the  industrial revolution triggered the economy. Industries were made after the revolution and the world faced a new era of progress and economic change.  The modern organizations are the basics that can be taken as the source of understanding the modern political economy. Industries were made after the industrial revolution that mainly works on the productivity, the more the productions are the more it will benefit. This era was a game changer for the economic aspect of the society and later it the economic institutions modified themselves.

Fifthly, the economic institutions and the democratic government.

The  connection  between  democratic  political  institutions  and  the  economic  institution  is complex. It depends that how far democratic government can try to constrain the operations of the economic institutions or how far the economic institutions can try the constrain the operation of the democratic government. the basic aspect of the relation is the relationship between the democracy and the market order. The control of the trade union and the control of the business. There  are  several  problems  such  as  the  tussle  between  the  capitalist  institution  and  the democratic institution. There are several measures that can make both sides work together. The democratic governments usually believe on large economic interests and they also shape it according to their interests. There come the institutional regulations that regulates the behavior of these institutions in the particular manner.

Personal analysis

State is made of many institutions. All the institutions work together this is the reason they depend on each other to work properly. The economic institution is the important institution of the state that makes it stand on its own. Today the examples are in front of us, those states th at has the best economic structures are now ruling the world. USA is the major power but with the passage of time new economic powers are competing with each other. The institutions regulate the behaviors but there are negative aspects when people use the institutions for their benefits. After the industrial revolutions there were merits and demerits. It depends on how one regulates the authority. If the institutions work properly the whole structure can be run perfectly but the interference that affects the institutions negatively can damage the structure. Today in the world where the concept of politics and economy is so dominant it is very important to regulate the bodies properly.

About the Author

Michael E. Moran (born May 1962 in  Kearny, New Jersey) is an American author and analyst of international affairs he is also a digital documentarian who has held senior positions at a host of media, financial services, and consulting organizations. A foreign policy journalist and former partner at the global consultancy Control Risks, he is author of The Reckoning: Debt, Democracy and the Future of American Power, published in 2012 by Palgrave Macmillan. He is co-author of ‘The Fastest Billion: The Story Behind Africa’s Economic Revolution’. Moran served as Editor – in-Chief at the investment bank Renaissance Capital and has been a collaborator of renowned economist Nouriel Roubini as well commentator for  Slate, the BBC and NBC News. He is also an adjunct professor of journalism at  Bard College, a Visiting Fellow in Peace and Security at the Carnegie Corporation of New York, and conceived of and served as executive producer of the award-winning Crisis Guides documentary series for the  Council on Foreign Relations.

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