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AbstractThe iron and steel industry generates around 10 % of global greenhouse gas emissions. The bulk of the emissions originates from the iron ore reduction. In this reduction, coal is used as a reagent. Steelmakers could switch to hydrogen-based direct reduction using hydrogen instead of coal as a reagent to reduce iron ore to pig iron. This would eliminate the CO2 emissions from the equivalent process in a traditional blast furnace. However, the process requires massive amounts of electricity. This paper looks at the economics of such a switch to "green steel." We assess a marginal increase in the production of a hypothetical green steelmaker. We also undertake an investment appraisal of a green plant, based on an ongoing installation in Northern Sweden, but also briefly consider a possible/planned investment in the US. This appraisal is complemented by computing the survival function for the net present value in a systematic sensitivity analysis. It seems highly unlikely that a green steel plant can be socially profitable. If the green plant displaces conventional steel produced within the European Union's cap-and-trade system for greenhouse gases, total emissions remain more or less unaffected; permits and emissions are simply reshuffled. Hence, if end-users of green steel pay a premium, they might pay for an illusion.

The identification of average causal effects of a treatment in observational studies is typically based either on the unconfoundedness assumption or on the availability of an instrument. When available, instruments may also be used to test for the unconfoundedness assumption (exogeneity of the treatment). In this paper, we define variables which we call quasi-instruments because they allow us to test for the unconfoundedness assumption although they do not necessarily yield nonparametric identification of the average causal effect. A quasi-instrument is defined as an instrument except for that its relation to the treatment is allowed to be confounded by unobservables, thereby resulting in a wider range of potential applications. We propose a test for the unconfoundedness assumption based on a quasi-instrument and give conditions under which the test has power. We perform a simulation study and apply the results to a case study where the interest lies in evaluating the effect of job practice on employment.

In this note we discuss how to estimate the social discount rate when banks have market power. Some data from Sweden are used to illustrate the approach. If other investments are crowded out, the implied social discount rate is around 7 percent, i.e. more or less equal to the one suggested by Burgess and Zerbe (2011) for the U.S. but similar to those often used in the EU (3-4 percent) if private consumption is crowded out by the considered investment.

AbstractThe increasing computational power has led to an increasing interest in Fisher's test in social science. As the Fisher and Neyman inference are based on different principles there is also an increasing interest in understanding the differential features of the two procedures. For example, Young (2018) found that the Fisher test has better size properties than the Neyman test in the situation with influential observations. Ding (2017), on the other hand, showed that the asymptotic variance of the mean-difference estimator (MDE) under Fisher inference is larger than that under Neyman inference, and that the asymptotic Fisher test is less powerful than the t-test even for the simplest case of homogeneous effect. Since MDE plays an important role for policy evaluation, these latter results are a concern for using Fisher's test as argued in Young (2018). With the aim of providing an understanding of the usefulness of the exact Fisher test for inference to the sample and to the population, this paper clarifies the results in Ding (2017). Using a novel Monte Carlo simulation following the same data generating processes as in Ding (2017), we demonstrate that the Fisher test has no worse power properties than the t-test even with heterogeneous effects.

This article analyses the effect of cognitive behavioral therapy (CBT) for individuals with mild or moderate mental illness. We study the effects on sick leave, health care consumption, and drug prescriptions. We find that CBT improved health and prevented sick leave for individuals who were not on sick leave when treatment was initiated but had no effect for individuals who were on sick leave when the treatment was initiated.