Volume 242, Issue 4

Textual data gained relevance as a novel source of information for applied economic research. When considering longer periods or international comparisons, often different text corpora have to be used and combined for the analysis. A methods pipeline is presented for identifying topics in different corpora, matching these topics across corpora and comparing the resulting time series of topic importance. The relative importance of topics over time in a text corpus is used as an additional indicator in econometric models and for forecasting as well as for identifying changing foci of economic studies. The methods pipeline is illustrated using scientific publications from Poland and Germany in English and German for the period 1984–2020. As methodological contributions, a novel tool for data based model selection, sBIC, is impelemented, and approaches for mapping of topics of different corpora (including different languages) are presented.

The paper derives an efficient allocation rule of adaptation aid from a donor to the recipient nations, which is different from the traditional development aid literature. The design of the allocation rule takes care of the optimum reaction of the nations on receipt of the aid. It suggests that the allocation rule must take care of (i) distributional weight of the recipients on the donor’s welfare, (ii) their vulnerability to climate change, (iii) their efficiency in using the aid and (iv) their mitigation response to the aid. It discusses the way these factors can be incorporated in an implementable rule by use of the available data. The score of a nation derived from the first three factors requires an upward/downward adjustment if the adaptation aid leads to an increase/decrease of the mitigation effort of the country. A nation receiving a higher score is rewarded with higher allocation of aid. The paper suggests as a rule of thumb that the aid in the form of technical assistance requires a downward adjustment and the aid in monetary form requires an upward adjustment in the score.

Presented analysis of gas price formation mechanism in Germany was prompted by changes brought about by technological advancements and the liberalization and harmonization of natural gas markets in the European Union after the year 2000. Because the data used in the study is generated by nonstationary stochastic processes, the cointegrated vector autoregressive model was applied as the most appropriate. The analysis pointed out that the price of natural gas, oil and the USD/EUR exchange rate influence each other in the long run and thus should be modelled together. Gas price in Germany is driven by both fundamental and financial factors, and so it rises with economic expansion, oil price increases, and the depreciation of the USD. It also reacts to changes in short-term interest rates and the volume of gas production in the US, which confirms that the shale revolution in this country has been consequential for gas prices in Europe, like any other supply shock would have been.

I introduce a new dataset for research on institutional and individual blockholders in US-listed firms. As of June 2021, the dataset contains 758,666 parsed Form 13D and 13G filings from November 1993 to May 2021, sourced from SEC EDGAR. Due to the semi-structured nature of these filings, a comparable dataset was not available for research before.

A large majority of citizens supports the German transition toward clean energy production (Energiewende). Yet, the Energiewende involves a large suite of aspects that determine the support. The social sustainability barometer provides a comprehensive data base on a large variety of questions related to the Energiewende that was collected in three survey waves spanning from 2017 to 2019 among more than 6000 household heads.