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How about a little accountability for economists when they mess up? | Dean Baker

Dean Baker is co-director of the Centre for Economic and Policy Research.

Suppose our fire department was staffed with out-of-shape incompetents who didnt know how to handle a firehose. That would be really bad news, but it wouldnt be obvious most of the time because we dont often see major fires. The fire departments inadequacy would become apparent only when a major fire hit, and we were left with a vast amount of unnecessary death and destruction. This is essentially the story of modern economics.

The problem is not that modern economics lacks the tools needed to understand the economy. Just as with firefighting, the basics have been well known for a long time. The problem is with the behavior and the incentive structure of the practitioners. There is overwhelming pressure to produce work that supports the status quo (for example, redistributing to the rich), that doesnt question authority, and that is needlessly complex.

The result is a discipline in which much of the work is of little use, except to legitimate the existing power structure. In terms of the poor quality of work, it is easy to point to the failure to recognize the size and risks posed by the housing bubble in the last decade. This failure has been unbelievably costly to the US and the rest of the world.

If we compare the most recent estimates of the potential GDP of the US economy from the Congressional Budget Office (CBO) with the projections made in 2008, before the severity of the crash was recognized, the difference is $1.8tn. This is an annual figure; it implies a loss of $18tn over the course of the decade. This amount averages out to more than $54,000 for every person in the country. Other countries have seen even larger losses.

CBO is not God, so it could have been overly optimistic before the crash and is arguably too pessimistic at present. But even if we cut the number in half, we are still looking at a loss of $9tn over the course of a decade, or $27,000 per person. It is also worth noting that CBOs numbers are useful here not only because they are seen as authoritative, but they are in the centre of the profession by design. CBO raises or lowers its numbers if they are out of line with the consensus of economic forecasters.

The retrospective analyses of the overall crash have focused on the financial crisis specifically. These analyses have worked hard to convince people that seeing an impending financial crisis is an extremely difficult undertaking, but the reality is that the financial crisis was very much secondary. The overwhelming reason for the downturn and the weak recovery was the collapse of housing bubbles in the US and elsewhere that were driving growth.

It was not difficult to recognize these bubbles. House prices had risen at an unprecedented pace in the years from the mid-1990s until the crash, with no remotely plausible basis in the fundamentals of the housing market. This could be seen by a variety of measures, but most obviously from the fact that rents were still following the overall rate of inflation, as they ordinarily do. The record vacancy rate even before the crash might also have been a red flag, especially to people who believe in supply and demand determining prices.

Also, the fact that housing was driving the economy was clear from the record share of residential construction in GDP, as well as an unprecedented consumption boom driven by housing wealth. Of course these sources of demand would disappear when the bubble burst; what could anyone expect to replace six percentage points of GDP in annual demand (around $1.1tn in todays economy)?

When I tried to raise these issues in years prior to the crash, my arguments were largely laughed off by a wide range of economists. I didnt have the stature, and besides, the argument was far too simple. This is not the first time that I had a problem with making arguments that were too simple.

Back at the time of the debate over President George W Bushs social security privatization plan, I pointed out that his administrations assumed rates of return in the stock market were impossible given the current price-to-earnings (P/E) ratios in the market and the economic growth rates assumed by the social security trustees. This was an argument based on simple algebra.

Brad DeLong wanted to make this into a Brookings paper and enlisted Paul Krugman in the effort. Together they produced a paper (generously leaving me as lead author) that had an intertemporal optimization model with declining labor force growth as its key feature. (You dont have to know what an intertemporal optimization model is; just that it added complexity.)

This model had nothing to do with the underlying point (the stock market would yield the assumed returns if its price-to-earnings ratio was near its historic average of 15, rather than the P/E level near 25 that we were seeing at the time), but it was necessary to have something more complex than simple algebra to be taken seriously at Brookings.

It is easy to extend the list of failings in the economics profession. It is just now becoming accepted that our pattern of trade imposes substantial costs on large segments of the working population. This didnt require any new or novel innovations. This is a prediction of the completely mainstream Stolper-Samuelson theorem, first published three-quarters of a century ago.

And why is there so little research devoted to analysis of alternatives to patent financing of prescription drug research? The markups associated with patent protection in the sector are equivalent to tariffs of many thousand percent. Every competent economist knows that a gap that large between a government-protected price and a free-market price is a recipe for massive waste and corruption. The gap between patent-protected drug prices and free-market prices is now approaching $400bn a year in the US alone (more than 2% of GDP). This doesnt require new economic thinking; it requires economists who know introductory economics.

And how about a little accountability for economists when they mess up? There is much literature on the importance of being able to dismiss workers who do not perform their jobs well. We all know and expect that a dishwasher who keeps breaking the dishes or a custodian who cant clean the toilets loses his job.

I have suggested that economists who prescribe policies that turn out badly, or who can’t see multitrillion dollar housing bubbles coming whose collapse sinks the economy, ought to pay a price in terms of their careers. Invariably people think I am joking. When they realize I am serious, they think I am crazy or vindictive.

Leaving aside motives, let me just speak to the economics. If we have a profession in which people are rewarded with high pay and career advancement for saying the same thing as everyone else, and never face any consequences when the accepted wisdom proves to be wrong, then we should expect to see economists like the firefighters mentioned at the beginning of this piece. They aren’t qualified to do the job and our only hope is that we dont see any more major fires.

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Tech has become another way for men to oppress women | Lizzie O’Shea

“We act as if technology were neutral but it’s not. The challenge now is to remove the gender bias,” says human rights lawyer and writer Lizzie O’Shea.

“Most women in the Bay Area are soft and weak, cosseted and naive, despite their claims of worldliness, and generally full of shit,” wrote former Facebook product manager Antonio Garca Martnez in 2016. “They have their self-regarding entitlement feminism, and ceaselessly vaunt their independence. But the reality is, come the epidemic plague or foreign invasion, they’d become precisely the sort of useless baggage you’d trade for a box of shotgun shells or a jerry can of diesel.” This is from his insider account of Silicon Valley, Chaos Monkeys. The book was a bestseller. The New York Times called it an irresistible and indispensable 360-degree guide to the new technology establishment. Anyone who is surprised by the recent revelations of sexism spreading like wildfire through the technology industry has not been paying attention.

When Susan Fowler wrote about her experience of being sexually harassed at Uber, it prompted a chain of events that seemed unimaginable months ago, including an investigation led by former attorney general Eric Holder, and the departure of a number of key members of the company’s leadership team. Venture capitalist Justin Caldbeck faced allegations of harassing behaviour, and when he offered an unimpressive denial, companies funded by his firm banded together to condemn his tepidity. He subsequently resigned, and the future of his former firm is unclear. Since then, dozens of women have come forward to reveal the sexist culture in numerous Silicon Valley technology and venture capital firms. It is increasingly clear from these accounts that the problem for women in the tech industry is not a failure to lean in, it is a culture of harassment and discrimination that makes many of their workplaces unsafe and unpleasant.

At least this issue is being discussed in ways that open up the possibility that it will be addressed. But the problem of sexism in the tech industry goes much deeper and wider. Technological development is undermining the cause of women’s equality in other ways.

American academic Melvin Kranzberg’s first law of technology tells us that technology is neither inherently good nor bad, nor is it neutral. As a black mirror it reflects the problems that exist in society including the oppression of women. Millions of people bark orders at Alexa, every day, but rarely are we encouraged to wonder why the domestic organiser is voiced by a woman. The entry system for a women’s locker room in a gym recently refused entry to a female member because her title was Dr, and it categorised her as male.

But the issue is not only that technology products reflect a backward view of the role of women. They often also appear ignorant or indifferent to women’s lived experience. As the internet of things expands, more devices in our homes and on our bodies are collecting data about us and sending it to networks, a process over which we often have little control. This presents profound problems for vulnerable members of society, including survivors of domestic violence. Wearable technology can be hacked, cars and phones can be tracked, and data from a thermostat can reveal whether someone is at home. This potential is frightening for people who have experienced rape, violence or stalking.

Unsurprisingly, technology is used by abusers: in a survey of domestic violence services organisations, 97% reported that the survivors who use them have experienced harassment, monitoring, and threats by abusers through the misuse of technology. This often happens on phones, but 60% of those surveyed also reported that abusers have spied or eavesdropped on the survivor or children using other forms of technology, including toys and other gifts. Many shelters have resorted to banning the use of Facebook because of fears about revealing information about their location to stalkers. There are ways to make devices give control to users and limit the capacity for abuse. But there is little evidence that this has been a priority for the technology industry.

Products that are more responsive to the needs of women would be a great start. But we should also be thinking bigger: we must avoid reproducing sexism in system design. The word-embedding models used in things like conversation bots and word searches provide an instructive example. These models operate by feeding huge amounts of text into a computer so it learns how words relate to each other in space. It is based on the premise that words which appear near each other in texts share meaning. These spatial relationships are used in natural language-processing so that computers can engage with us conversationally. By reading a lot of text, a computer can learn that Paris is to France as Tokyo is to Japan. It develops a dictionary by association.

But this can create problems when the world is not exactly as it ought to be. For instance, researchers have experimented with one of these word-embedding models, Word2vec, a popular and freely available model trained on three million words from Google News. They found that it produces highly gendered analogies. For instance, when asked Man is to woman as computer programmer is to ?, the model will answer homemaker. Or for father is to mother as doctor is to ?, the answer is nurse. Of course the model reflects a certain reality: it is true that there are more male computer programmers, and nurses are more often women. But this bias, reflecting social discrimination, will now be reproduced and reinforced when we engage with computers using natural language that relies on Word2vec. It is not hard to imagine how this model could also be racially biased, or biased against other groups.

These biases can be amplified during the process of language learning. As the MIT Technology Review points out: “If the phrase computer programmer is more closely associated with men than women, then a search for the term computer programmer CVs might rank men more highly than women.” When this kind of language learning has applications across fields including medicine, education, employment, policymaking and criminal justice, it is not hard to see how much damage such biases can cause.

Removing such gender bias is a challenge, in part because the problem is inherently political: Word2vec entrenches the world as it is, rather than what it could or should be. But if we are to alter the models to reflect aspirations, how do we decide what kind of world we want to see?

Digital technology offers myriad ways to put these understandings to work. It is not bad, but we have to challenge the presumption that it is neutral. Its potential is being explored in ways that are sometimes promising, often frightening and amazing. To make the most of this moment, we need to imagine a future without the oppressions of the past. We need to allow women to reach their potential in workplaces where they feel safe and respected. But we also need to look into the black mirror of technology and find the cracks of light shining through.

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Japan and EU expected to sign trade deal on Thursday

Shinzo Abe to meet Donald Tusk and Jean-Claude Juncker in Brussels but UK exporters likely to see no gain due to Brexit.

The European Union and Japan are on course to sign a trade deal on Thursday, after talks gained impetus in the wake of Donald Trump’s threat to put up barriers to international commerce.

Cecilia Malmstrm, the European trade commissioner, announced she had reached a political agreement with the Japanese foreign minister, Fumio Kishida: “We ironed out the few remaining differences in the EU-Japan trade negotiations, she tweeted. We now recommend to leaders to confirm this at summit.”

Japanese prime minister Shinz Abe will meet Donald Tusk and Jean-Claude Juncker, presidents of the European council and commission respectively, for a one- day summit in Brussels, before the G20 gathering in Hamburg.

The timing is no coincidence, as Germany plans to make free trade one of the summit priorities.

In a sign of high hopes, Malmstrm and Kishida exchanged Daruma dolls, armless, headless round figures associated with persistence and luck. A part of Zen Buddhist culture, people typically paint one eye when they make a wish and the second when the goal has been reached. Malmstrm and Kishida posed for the cameras, as they coloured in the second eyes on two dolls emblazoned with the EU and Japanese flags.

But officials might be looking for divine intervention to overcome the final hurdles.

Despite a likely agreement on Thursday, the sensitive subject of a court system to settle investor disputes remains open. Japan has not accepted the EUs preferred alternative to the investor-state dispute settlement (ISDS), a system for resolving trade disputes that has been criticised by unions and activists for giving too much power to corporations. Under pressure from NGOs, the EU proposed a new kind of trade court, where judges would be appointed by governments rather than disputing parties. But Tokyo has not come round to this idea.

EU sources declined to speculate on how quickly the deal could come into force, but fine-tuning and translating the legal text, as well as getting it agreed by all EU member states could take many months.

It took the EU and Canada three years to sign a final text, following the agreement in principle in October 2013, which parallels the latest EU-Japan milestone. The Canada deal almost collapsed when a Belgian region threatened to veto the treaty. Now mostly in force, the EU-Canada deal still needs to clear the final hurdle of ratification by at least 38 national and regional assemblies.

The timetable means it is likely the UK will have left the EU by the time the Japan treaty comes into force.

When negotiations began with Tokyo in 2013, Britain was one of the biggest cheerleaders. The then UK trade minister described talks as an important step towards liberalising trade between two of the worlds largest economies.

Following the Brexit vote, Theresa May has vowed to leave the customs union, meaning British exporters are unlikely to see any benefits from the EU deal.

The deal means Japan will drop tariffs on many valuable European imports, including chocolate, pasta and some types of cheese.

In return for liberalisation of Japans highly protected dairy market, Europe has compromised by agreeing to lower tariffs on Japanese imported cars, although new rules will be phased in to help European carmakers deal with the change.

Services and an array of technical standards are also covered by the treaty, which negotiators say goes far further than old-style tariff-cutting agreements.

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