How important is the intensive margin of labor adjustment? : discussion of "Aggregate hours worked in OECD countries : new measurement and implications for business cycles" by Lee Ohanian and Andrea Raffo

Using new quarterly data for hours worked in OECD countries, Ohanian and Raffo (2011) argue that in many OECD countries, particularly in Europe, hours per worker are quantitatively important as an intensive margin of labor adjustment, possibly because labor market frictions are higher than in the US. I argue that this conclusion is not supported by the data. Using the same data on hours worked, I Önd evidence that labor market frictions are higher in Europe than in the US, like Ohanian and Raffo, but also that these frictions seem to asect the intensive margin at least as much as the extensive margin of labor adjustment

Combining predictions from linear models when training and test inputs differ

Methods for combining predictions from different models in a supervised learning setting must somehow estimate/predict the quality of a model's predictions at unknown future inputs. Many of these methods (often implicitly) make the assumption that the test inputs are identical to the training inputs, which is seldom reasonable. By failing to take into account that prediction will generally be harder for test inputs that did not occur in the training set, this leads to the selection of too complex models. Based on a novel, unbiased expression for KL divergence, we propose XAIC and its special case FAIC as versions of AIC intended for prediction that use different degrees of knowledge of the test inputs. Both methods substantially differ from and may outperform all the known versions of AIC even when the training and test inputs are iid, and are especially useful for deterministic inputs and under covariate shift. Our experiments on linear models suggest that if the test and training inputs differ substantially, then XAIC and FAIC predictively outperform AIC, BIC and several other methods including Bayesian model averaging.Comment: 12 pages, 2 figures. To appear in Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI2014). This version includes the supplementary material (regularity assumptions, proofs

Organizational interactions in global energy governance

This chapter explores inter-organizational relations in the field of global energy governance. It starts by mapping the policy field of energy governance, the existing literature, and the multilateral energy architecture. It then performs an organization-set analysis of the International Energy Agency (IEA), which is widely regarded as the most advanced multilateral energy organization. More precisely, it presents an overview of the IEA’s interactions with four other energy-related international organizations: the Organization of Petroleum-Exporting Countries, the Energy Charter Treaty, the Group of Eight/Group of Twenty, and the International Renewable Energy Agency. It finds that these dyadic relationships have evolved quite dramatically over the years and points out some of the salient factors that drive these relationships, before suggesting some avenues for future research

How important is the intensive margin of labor adjustment?

Using new quarterly data for hours worked in OECD countries, Ohanian and Raffo (2011) argue that in many OECD countries, particularly in Europe, hours per worker are quantitatively important as an intensive margin of labor adjustment, possibly because labor market frictions are higher than in the US. I argue that this conclusion is not supported by the data. Using the same data on hours worked, I find evidence that labor market frictions are higher in Europe than in the US, like Ohanian and Raffo, but also that these frictions seem to affect the intensive margin at least as much as the extensive margin of labor adjustment.hours worked, intensive margin labor adjustment

Towards a new multilateral energy architecture?

From climate change over peak oil to the geopolitical scramble for the Arctic, there are ample signs that a global energy crisis is unfolding. The sheer scale and urgency of this looming crisis calls for international coordination. Yet, even a cursory look at the existing international energy institutions leads to a sobering conclusion: the global energy governance architecture is weak, fragmented and incomplete. This policy brief discusses both the flaws in the multilateral energy architecture and some emerging ideas to strengthen it, such as the proposal for a Sustainable Energy Trade Agreement and the new American disclosure rules for the extractive sector

Is OPEC dead? Oil exporters, the Paris agreement and the transition to a post-carbon world

The Organization of the Petroleum-Exporting Countries (OPEC) faces a perfect storm. It is squeezed between the revolution in unconventionals, which has increased global supply of hydrocarbons and lowered their price, and the prospect of a global peak in oil demand, stemming from climate policies and the falling costs of alternative energy technologies. In the face of these challenges, media commentators have declared the death of OPEC as a cartel. This perspective argues that the claims about OPEC’s demise are misguided for four reasons: (1) OPEC never acted as a cartel, let alone a powerful one; (2) thanks to its cheap production costs, OPEC’s oil will remain competitive in a low-cost environment; (3) the group has always proved to be flexible; and (4) OPEC is still attractive to its member states, most notably as a source of prestige, as is illustrated by the recent re-entries of Indonesia and Gabon. That said, over the longer term OPEC will inevitably need to adapt to a changing external environment. A likely possibility would be for the club to gradually morph from an output-setting cartel into a forum for deliberation and information-sharing

Education, Growth and Income Inequality

When types of workers are imperfect substitutes, the Mincerian rate of return to human capital is negatively related to the supply of human capital. We work out a simple model for the joint evolution of output and wage dispersion. We estimate this model using cross-country panel data on GDP and Gini coefficients. The results are broadly consistent with our hypothesis of diminishing returns to education. The implied elasticity of substitution fits Katz and Murphy’s (1992) estimate. A one year increase in the stock of human capital reduces the rate of return by about 2 per cent. The combination of imperfect substitution and skill biased technological change closes the gap between the Mincer equation and GDP growth regressions almost completely.

Organizational capital and employment fluctuations

In this paper I present a model in which production requires two types of labor inputs: regular productive tasks and organizational capital, which is accumulated by workers performing organizational tasks. By allocating more workers from organizational to productive tasks, firms can temporarily increase production without hiring. The availability of this intensive margin of labor adjustment, in combination with adjustment costs along the extensive margin (search frictions, firing costs, training costs), makes it optimal to delay employment adjustments. Simulations indicate that this mechanism is quantitatively important even if only a small fraction of workers perform organizational tasks, and explains why the hiring rate is persistent and why employment is slow to recover after the end of a recession.Business cycles, labor market, organizational capital, jobless recoveries

Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It

We empirically show that Bayesian inference can be inconsistent under misspecification in simple linear regression problems, both in a model averaging/selection and in a Bayesian ridge regression setting. We use the standard linear model, which assumes homoskedasticity, whereas the data are heteroskedastic, and observe that the posterior puts its mass on ever more high-dimensional models as the sample size increases. To remedy the problem, we equip the likelihood in Bayes' theorem with an exponent called the learning rate, and we propose the Safe Bayesian method to learn the learning rate from the data. SafeBayes tends to select small learning rates as soon the standard posterior is not `cumulatively concentrated', and its results on our data are quite encouraging.Comment: 70 pages, 20 figure