John Q

Joe Biden’s win in the US Presidential election is part of a run of good news for the Global Climate. Biden has promised to rejoin the Paris Agreement on his first day in office.

The US is then required to commit a Nationally Determined Contributions (NDCs) to reductions in carbon emissions. Biden has already announced that he is committed to achieving zero net emissions by 2050, and this, along with an interim 2030 target will presumably be the basis of the US NDC.
Net zero by 2050 is now the international norm.

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Just about 24 hours until results start coming in. As was said when the same two sides (with different names) faced off in Kansas more than 150 years ago, may victory go to the side which is stronger in numbers, as it is in right.

That’s the headline for my latest piece in Inside Story, looking at the implications of zero interest rates for renewable energy sources like solar and wind. Key para

Once a solar module has been installed, a zero rate of interest means that the electricity it generates is virtually free. Spread over the lifetime of the module, the cost is around 2c/kWh (assuming $1/watt cost, 2000 operating hours per year and a twenty-five-year lifetime). That cost would be indexed to the rate of inflation, but would probably never exceed 3c/kWh.

The prospect of electricity this cheap might seem counterintuitive to anyone whose model of investment analysis is based on concepts like “present value” and payback periods. But in the world of zero real interest rates that now appears to be upon us, such concepts are no longer relevant. Governments can, and should, invest in projects whenever the total benefits exceed the costs, regardless of how those benefits are spread over time.

In the process of working on my book-in-progress, The Economic Consequences of the Pandemic, I’ve been trying to integrate a number of facts about the economy of which I’ve been more or less aware for a while, along with claims I want to make, and put them together into a coherent account of the economic system prevailing (in advanced/developed economies( in the 21st century and how it differs from the industrial goods economy of the 20th century.

As a step towards this, I’ve put together a list of factual claims which I think can be established reasonably firmly, along with claims I want to make that will be more contentious. My plan is to put this together into a coherent analysis, including supporting evidence. So, I’m keen to get good supporting links for any of these points (I have quite a bit, but more would be helfpul). I also want to be sure I’m not missing contrary evidence, and to adjust the claims if necessary, so please point this out also.

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Another in my series of extracts from my book-in-progress, Economic Consequences of the Pandemic. So far I’ve looked at luck the limited relationship between returns and social value and the fact that risk-taking is mostly done (involuntarily) by the poor, not the rich. Now I’m going to consider possibilities for reform
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So far, in this series of extracts from my book-in-progress, Economic Consequences of the Pandemic, I’ve argued that the inequality of incomes in our society is largely a matter of luck rather than inherent personal ability, and that it is only distantly related to the social value of the contributions people make through their work. These conclusions undercut the idea that taxing those on high incomes will harm society by reducing incentives to work for the most able and social valuable workers. Although the evidence was already strong, the pandemic has brought these points into even brighter relief.

Now I want to consider the claim that we need inequality in order to encourage people to take risks. The simplest response is to point to the empirical fact that high income earners take (or, more accurately, are subject to) less risk than average not more[1].

Hardy and Ziliak (confirmed in general terms by many other sources) give the numbers https://onlinelibrary.wiley.com/doi/full/10.1111/ecin.12044?casa_token=2_dvANjFw2EAAAAA%3AiKxuB6Tn34GJBIOZlN9Hs55w9MxJlkgR0Ns1z-1UAPIosDi2G8Yq3WF9hjUTVy8HQb95t9DwLzCRel8vyg

in any given year since 1996 the level of volatility among the bottom 10% was 81% higher than the volatility among the top 1%, and this level nearly doubled since 1981

Here’s a graph illustrating this point.

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In a previous extract from my book-in-progress, Economic Consequences of the Pandemic, I argued that unequal incomes arise to a large extent from luck rather than merit. Unequal incomes are regularly justified by claiming that high incomes reflect a larger contribution to society. This has never been true as a general proposition.

Some high incomes, like those of skilled surgeons, reflect a contribution well above the norm. Others, like those of entertainers and sports stars, reflect services that are highly valued by our society whether or not they make it a better place.

Others on high incomes make only marginal contributions to society or cause active harm. The massive growth in the number and incomes of lawyers and finance professionals over the past forty years has not been matched by any obvious improvement in justice, financial security or the rational investment of capital.
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Here’s an extract from my contingent* book-in-progress, Economic Consequences of the Pandemic commissioned by Yale University Press. Comments and compliments appreciated, as always.

The Covid-19 pandemic has taught us several things about inequality, or rather, it has dramatically reinforced lessons we, as a society, have failed to learn. The first is the importance of luck in determining unequal outcomes.

Some of us will get Covid-19 and die or suffer lifelong health consequences. Others will lose their jobs and businesses. Many, however, will be unaffected or will even find themselves better off. Some of these differences may be traced to individual choices that are sensible or otherwise, such as deciding whether to wear a mask in public places. But mostly they are a matter of being in the wrong (or right) place at the wrong (or right time).
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One of the big questions about the shift to working remotely has been “what about new staff?”. To spell this out, the idea is that, while experienced workers can do everything they need to online, new employees will need personal contact to pick up tacit knowledge and firm culture. It’s inherent in the argument that these terms are difficult to define with any precision – if not, they could be formalised and taught.

This is part of a debate that’s been going on for a couple of centuries, between proposals for formal education in work-related skills and learning on the job, sometimes through apprenticeships and sometimes through “sitting next to Nelly”, that is, picking up the relevant skills by working with people who have already acquired them.

Before 1800, and with the partial exception of ministers of religion, on the job training was the only kind on offer. Since then, starting with lawyers and doctors, formal education has steadily expanded at the expense of on the job training, across a wide range of occupations and in many different countries with radically different labor markets. That includes some economies and industries where lifetime employment by a single firm has been the norm and others where work is largely done on a contract or ‘gig’ basis.

This process has always been contentious. Terms like “credentialism”, “overqualification” and “academic” (used pejoratively) have set the tone of much of the discussion. Nevertheless, there has been little evidence that the trend has been or will be reversed, and no one has managed to find, and sustain, a successful altern ative.

The work of hiring, ‘onboarding’, promoting and firing employees has not been exempt from the process. “Human resource management” emerged as a distinct profession in the second half of the 20th century, taking over much of this work from individual managers. HR departments have in turn begun to outsource some of these tasks to specialised firms such as headhunters and ‘separation management advisers’, though onboarding still appears to be done in-house for the most part.

The shift to remote working will provide another test of this process, at least when firms start hiring new staff on a large scale. Some of the concerns expressed about lack of in-person contact will probably prove to be well-founded (though not insuperable). Others, I think, will not. After a few in-person (and ideally one-to-one or small group) meetings to be introduced to new colleagues, most new hires will be able to learn the ropes through email and Zoom.

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One of the central features of the debate about working from home is that it leads to the loss of random, but productive, encounters with colleagues. I’ve responded with the observation that some of my best research ideas have come from largely unplanned encounters on the Internet.

It’s just struck me that there is a conflict here between the interests of workers and those of firms and managers.

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Like lots of others, I’m anxiously watching forecasts of the US election outcome. But it’s hard to figure out what’s going on, with Biden way ahead in the polls, behind in the betting markets and rated a 70 per cent chance by the model at 538.com. Inspired by this post from Andrew Gelman, who is working on the Economist model (Biden currently a bit over 80 per cent), and an informative tweet from Nate Silver, I’ve managed to improve my own understanding a bit. At least I think so.

Silver’s tweet confirms that the Electoral College system gives Trump a significant advantage relative to an election by popular vote. He syas
Chance of a Biden Electoral college win if he wins the popular vote by X points:

0-1 points: just 6%!
1-2 points: 22%
2-3 points: 46%
3-4 points: 74%
4-5 points: 89%
5-6 points: 98%
6-7 points: 99%

With that information, it’s easy enough to fit a normal distribution to the margin, and get an estimate probability of winning. By fiddling with the numbers, it’s easy to replicate the 538 probability estimate and also to get a probability distribution looking fairly similar to those displayed on te site. My best estimate is N(5,4), that is, the mean value for the margin is 5 points and the standard deviation is 4. The mean value is consistent with the description of the state level estimates on the 538 site, which (very roughly speaking) take the existing polls (which currently have Biden ahead by 7.4 nationally) and then give Trump 1 point for an incumbency advantage (reducing the margin by 2 points).

Looking at the Economist model (which doesn’t necessarily agree with 538 on the exact distribution of the Electoral College advantage) it fits pretty well with N(6,3)
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That’s the title of the book I’m working on for Yale University Press, and also the theme of two articles I published yesterday.

One, in The Conversation, looked at the potential benefits of remote work and the likely struggle over who will get those benefits. Key paras

For the most part, disputes over sharing the benefits of remote office work will be hashed out between employers, workers and unions, in the ordinary workings of the labour market.

But what about the other half of the workforce, who don’t have the option of working from home? In particular, what about the mostly low-paid service workers who depend on people coming into offices?

If the productivity gains made possible through remote work are to be shared by the entire community, substantial government action will be needed to make sure it happens.

The other article, in Inside Story, looks at the end of the goods economy and its replacement by an information and services economy, a transformation that’s been highlighted by the pandemic. An important implication is that investment demand by private firms is likely to stay low, even as greater public investment is desperately needed.

Tech firms like Microsoft, which now determine stock market values, don’t need much capital. The book value of Microsoft’s capital stock is less then 10 per cent of its market value. The rest is made up of intangibles, a polite word for monopoly-power network effects, intellectual property, and good old-fashioned predatory conduct.

Without any need for private sector investment, interest rates will remain low unless public investment picks up the slack. With the physical goods economy fading into the past, though, we don’t need more of the transport infrastructure projects governments automatically turn to at times like these. Rather, we need to invest in human services like health (mental and physical), education and childcare, and in information platforms that break the monopoly power of the tech giants.

These are the investments that will allow Australia to flourish in an economy dominated by information and services rather than industrial production.

Following on from the discussion between Chris and Kenan Malik, I thought I would take another look at this post from last year, where I used the term “default identity”. Since then, I’ve seen that this idea is more usually phrased as the “unmarked category” a term originating in linguistics[2]. An example, where both the linguistic and social senses are present is that of the distinction between “hyphenated Americans” and the unmarked category of Americans in general. This post by Paul Campos at LGM makes the point that much of the support for Trump comes from white men who were once the unmarked category and are now marked as a distinct category.

Being in the unmarked category represents more than “not being discriminated against”. For example, we might consider a situation where one religious group is subject to discrimination, but others are not. That does not, in itself, make the other groups privileged. Now think about the case when one group, say Christians, is taken as the unmarked category whenever religion is discussed – for example, by using the word “church” to cover religious meeting places in general. That group is privileged even if there is no active discrimination against others, or if some groups are given (implicitly, given by the dominant group) apparently equal status, as in formations like Judaeo-Christian[2]. Even where members of the marked categories receive equal treatment, it is always provisional. Conversely, a situation where discrimination is unthinkable (say, discrimination based on shoe size) is one where there is no unmarked category.

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The most memorable answer to this question came from science fiction writer Harlan Ellison, who said “Poughkeepsie” (on checking Wikipedia, I learn that he died a couple of years ago).

But in the context of discussions about remote work, I’m interested in the claim that random physical meetings (the archetypal example being corridor or water-cooler encounters with colleagues) are an important source of ideas, and therefore a reason for not working remotely.

This seems to be the kind of topic for which the data will consist mostly of anecdotes and introspection. A marginal improvement is too look over my own list of publications to see if I can identify any where the source arose from some particular interaction.

Looking at my 100 most-cited papers in Google Scholar, most collaborations are the result of planning rather than chance. In pre-Internet days, most of my collaborations started from seminars and conferences I spoke at or attended because the topic was of interest, or else from direct approaches by a colleague, usually in the same department. From the early 1990s onwards, direct approaches mostly came by email, and work has often been done the same way. In several cases, I have written joint papers before ever meeting my co-author(s), though in other cases in-person collaboration with one or two co-authors works better.

More interesting to me, are the cases where the idea has come from blogging. Some notable examples

• My Zombie Economics book. Starting with blog discussions, the idea for a book came from blog commenter Max Sawicky, and was picked up by Seth Ditchik at Princeton UP, who also commissioned Economics in Two Lessons and my current book-in-progress Economic Consequences of the Pandemic
• Cross-disciplinary collaborations with Henry Farrell and LA Paul both arising from my involvement with Crooked Timber
• This paper, which started with a comment on a blog post to the effect that “future generations” are in fact already alive (At least I think that’s how it happened. I could never locate the comment to acknowledge the source.)

It seems to me that that these are much more like the kind of serendipitous links that are supposed to be generated by water coolers.

Of course, academic research is a special kind of work, and I’m much more involved with the Internet than most of my colleagues (or, at least, a few years ahead of the general adoption trend). So, I’d be interested in anecdotes from others and links to actual research, if there is any.