jamesz

No shirking from home, please!

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Working from home massively increases productivity:

Over 10% of US employees now regularly work from home (WFH), but there is widespread skepticism over its impact and worries about “shirking from home”. We report the results of a WFH experiment at CTrip, a 16,000 employee NASDAQ-listed Chinese multinational. Call center employees who volunteered to WFH were randomly assigned to work from home or in the office for 9 months. Working from home led to a 13% performance increase, of which about 8.6% is from working more minutes per shift (fewer breaks and sick-days) and 4% from more calls per minute (attributed to a quieter working environment). Home workers also reported improved work satisfaction and their job attrition rate fell by 50%. After the experiment, the firm rolled the program out to all employees, letting them choose home or office working. Interestingly, only half of the treatment group decided to work at home, with the other half reallocating in favor of office working. After employees were allowed to choose where to work, the performance impact of WFH almost doubled, highlighting the benefits of choice when adopting modern management practices like home working.

jamesz

WSJ suggests abandoning economic models

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Simon Nixon has a provocative article in the WSJ where he argues that the current generation of New Keynesian models are useless because of their poor forecast performance. He proposes looking solely at the rate of debt reduction when forecasting economic performance:

[The] dismal science’s [forecasting] record suggests is that there is something profoundly wrong with the mainstream economics profession’s understanding of how modern economies work. The models on which its forecasts are built are clearly badly flawed.

It is true that forecasting performance is poor, but that is largely because forecasting is very, very difficult. The DSGE models Nixon refers to are important in modern macroeconomics but they were originally designed to estimate the impact of monetary policy, not to forecast the future. In fact, until very recently, most forecasting was not done with structural DSGE models but with statistical models that take their structure from the data. They provided better forecast performance and so were preferred by professional forecasters. These days, the best forecasts tend to be made by estimated DSGE models that outperform the best statistical models because they incorporate some of our understanding about how the economy works. No doubt they will be improved over time but it is incorrect to suggest that forecasters are too constrained by theory to forecast accurately. In fact, economists’ understanding of the economy helps them to provide better predictions about the future than simply using statistical relationships.

Nixon then discusses a paper by Claudio Borio at the BIS, which suggests building models that describe not only business cycles but also ‘financial cycles’. Borio’s paper highlights the monetary nature of the current recession and recommends that the next generation of macro models give serious consideration to the slow buildup of disequilibrium forces in financial booms, which then trigger deep recessions. Whether or not you agree with that diagnosis, the question he tackles is crucial: how do crises endogenously develop? Part of the reason forecasting is so difficult is that turning points are hard to pick because we don’t really understand all of the mechanisms that lead to recessions. Nixon uses that paper to claim that…

…[for] investors, the sensible response is surely to disregard all short-term forecasts based on out-dated models. They should focus instead on identifying those economies most likely to deliver a medium-term recovery by aggressively addressing their stock of debt. In the European context, it is the euro zone where the process of debt reduction and restructuring seems likely to proceed most rapidly, not least because the greater independence of the European Central Bank means there is less prospect of loose monetary policy being used to defer tough decisions.

I don’t think that is what Borio’s paper claims. Can Nixon really be advising you to ignore expert forecasters and instead put your money into EU countries, many of whom are currently facing the possibility of further recession in 2013? He has good company in suggesting that current economic models have problems and could be improved; however, the chances that they can be improved upon by following a simple heuristic like ‘less debt equals more growth’ are exceedingly slim. Indeed, there is considerable public debate among economists over the impact of debt on growth. Reinhart and Rogoff’s work has generated a lot of discussion, and there is a paper entitled ‘Macroeconomic Risk and Debt Overhang’ being presented at the ASSA conference. It’s hardly a matter that has been neglected by the profession!

Of course, it’s very difficult to diagnose and fix a problem with a single newspaper article: witness Paul Krugman’s repeated attacks on the current state of macroeconomics and the heated responses that they’ve generated, for instance. When even Nobel prize-winners can’t agree on whether there is a problem it is a sign that we don’t really understand what needs to be done. It’s great that Nixon is bringing interesting papers like Borio’s to public attention and airing the debate that’s going on in the profession. Unfortunately, in this case I don’t think his diagnosis or proposed solution are quite right.

jamesz

Old boys’ networks in finance

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There are plenty of interesting papers from the ASSA conference that we’ll be mentioning here. The first shows the importance of the old boys’ network for men in finance. Interestingly, women don’t seem to benefit from it and must instead demonstrate their competence in order to gain recognition.

Abstract:

Connection is associated with more accurate earnings forecasts for men, but not for women. Controlling for accuracy, connection is important in explaining men’s, but not women’s, probability of being voted by institutional investors as “star” analysts, an important measure of career success. For women, education achievements and accurate forecasts are important factors that determine voting outcomes. This asymmetry in the effect of connections between the two genders does not exist in an alternative, computerized process of evaluating analysts, and is most pronounced among young analysts. Our results suggest that men reap higher returns from connections than women, and that investors are more willing to rely on soft information such as connections to evaluate men than women.

jamesz

Free driver externalities

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Martin Weitzman has a new paper out that introduces the concept of a ‘free driver’ externality in the context of climate change responses:

Climate change is a global “free rider” problem because significant abatement of greenhouse gases is an expensive public good requiring international cooperation to apportion compliance among states. But it is also a global “free driver” problem because geoengineering the stratosphere with reflective particles to block incoming solar radiation is so cheap that it could essentially be undertaken unilaterally by one state perceiving itself to be in peril.

It’s a really interesting idea but can it really be described as an externality? The distinction is important because the way you frame the problem defines the solution.

He is saying that the actions of one state to combat global warming could affect other countries, imposing an externality upon them. The paper goes on to say that a governance mechanism to resolve conflicts is required and propose a particular solution. That’s all fine, but why does Weitzman refer to it as an externality? Read more

jamesz

Unconventional explanations for crime

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Kevin Drum has an interesting article on the possibility that lead poisoning may have generated a crime wave in the 90s. He reports Jessica Reyes’ work on the econometrics:

If childhood lead exposure really did produce criminal behavior in adults, you’d expect that in states where consumption of leaded gasoline declined slowly, crime would decline slowly too. Conversely, in states where it declined quickly, crime would decline quickly. And that’s exactly what she found.

Drum’s whole article is well worth reading, although I wonder if this debate will go the way of other economists’ unconventional explanations of crime.

Update: Tyler Cowen links to the other side of the debate.

jamesz

Fiscal multipliers are unhelpful

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John Quiggin has re-opened the fiscal multiplier debate to advocate for fiscal stimulus. Quiggin, along with others such as Krugman, Summers and DeLong, and Blanchard claim that the effect of government spending on production will be greater than the government’s initial injection. The empirical evidence they use tends to rely on cross-country regressions, although some calibrated modelling has been done by NIESR.

An important caveat on these studies is that they rely on the unusual macroeconomic circumstances of the current recession. In ‘normal’ times one would expect that a central bank would lean against fiscal policy, resulting in very small multiplier effects. Scott Sumner has discussed this point extensively. To summarise, he claims that:

It sort of implies ‘the’ multiplier is some sort of stable parameter out there, waited to be discovered. Like the cosmological constant. In fact, it is nothing more than an estimate of central bank incompetence, which will vary from one case to the next.

Obviously the illustrious authors of the multiplier studies aren’t unaware of this problem. The general theme of their arguments is that we are currently in a liquidity trap and monetary policy has little traction in these circumstances. Central banks are thus unable to counteract the effects of fiscal policy, which is only doing what the central bank would do itself if it could: boosting demand. Sumner rejects the idea of a liquidity trap, hence the disagreement.

Obviously, these estimates of fiscal multipliers are entirely contingent on the response of the monetary authority. As discussed on Vox, the characteristics of an economy and monetary policy regime can cause the multiplier to vary between zero and 2, which is basically the difference between being in favour of fiscal stimulus and considering it a complete waste of money. Without estimates for each country individually that means the average multiplier is likely to be a poor guide to the multiplier in an individual country. That doesn’t make the estimates ‘wrong’ but it does mean that cross-country estimates are unreliable as a guide to national, fiscal policy. From a policy perspective then, it’s unclear that these estimates provide much guidance on the extent of fiscal stimulus or austerity; at least not without a lot of investigation of country-specific factors.

Update: FT said much the same thing. Simon Wren-Lewis suggests that the theory on this is obvious and people just got too caught up in the empirics.