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This paper describes an algorithm, “DoomBot”, which selects parsimonious models to predict downturns over different quarterly horizons covering the ensuing two years for 20 OECD countries. The models are country- and horizon-specific and are automatically updated as the estimation sample period is extended, so facilitating out-of-sample evaluation of the algorithm. A limited combination of explanatory variables is chosen from a much larger pool of potential variables that include those that have been most useful in predicting downturns in previous OECD work. The most frequently selected variables are financial variables, especially those relating to credit and house prices, but also include equity prices and various measures of interest rates (such as the slope of the yield curve). Business cycle variables -- survey measure of capacity utilisation, industrial production, GDP and unemployment -- are also selected, but more frequently at very short horizons. The variables selected do not just relate to the domestic economy of the country being considered, but also international aggregates, consistent with findings from previous OECD work. The in-sample fit of the models is very good on standard performance metrics, although the out-of-sample performance is less impressive. The models do, however, provide a clear out-of-sample early warning of the Global Financial Crisis (GFC), especially when considered collectively, although they do generate ‘false alarms’ just ahead of the crisis. The models are less good at predicting the euro area crisis out-of-sample, but it is clear from the evolution of the choice of variables that the algorithm learns from this episode, for example through the more frequent selection of a variable measuring euro area sovereign bond spreads. The latest out-of-sample predictions made in mid-2023, suggest the probability of a downturn is at its greatest and most widespread since the GFC, with the largest contributions to such risks coming from house prices, interest rate developments (as measured by the slope of the yield curve and the rapidity of the change in short rates) and oil prices. On the other hand, warning signals from business cycle variables and equity prices, which are often good downturn predictors at short horizons, are conspicuously absent.

The use of nuclear power by states in the modern world requires supplements to international law through the development of national legislation on civil liability for nuclear damage and compensation. The situation in the Russian Federation is no exception. Russian law on civil liability for nuclear damage has not fully evolved, and currently, there is no specific law covering liability for nuclear damage, nor is there a law regarding the financial and insurance mechanisms for compensation. Instead, the current laws establish a state system of benefits and compensation for damage to health and property of citizens.

Divestment by multinational enterprises is an important yet understudied phenomenon. The few available estimates indicate that about a fifth of all foreign affiliates are divested every five years. This paper presents the findings from a novel cross-country firm-level dataset with financial and ownership information for over 62 000 foreign-owned affiliates from a selection of 41 OECD and G20 countries and their economic groups from 164 home countries for the period 2007-2014. The data allow an assessment of the relative importance of different determinants of divestment in a cross-country setting, including host country policies and bilateral factors, including trade, investment and tax agreements. The findings confirm that parents divested about one of every five foreign-owned affiliates between 2007-2014 and show that a number of host country policy and economic factors, including labour costs and international trade agreements, influence the divestment decision, on top of the firm considerations considered in previous studies.

This report contains an empirical analysis of the productivity and sustainability performance of different types of farms in thirteen countries. Farm productivity performance is measured through estimates of average productivity levels and through annual rates of technical change. Evidence on the environmental sustainability of farm groups is based on an index that reflects environmental pressure per hectare and the local environmental sustainability of production practices. In addition to environmental sustainability, the analysis also considers fundamental differences across farms with respect to farm structure, innovation of operations, individual characteristics as well as farm location. Productivity performance by farm classes is related to the environmental sustainability performance and to other farm characteristics in order to shed light on the factors that drive or impede farm performance. Empirically identifying the main conditions for and obstacles to performance improvement supports the development of effective and efficient policies targeting the performance of farms. This analysis contributes in particular to a better understanding of the synergies and trade-offs between productivity and environmental sustainability performance.

The transition to climate neutrality requires cost reductions in existing clean technologies to enable rapid deployment on a large scale, as well as the development of emerging technologies such as green hydrogen. This policy paper argues that science, technology, innovation, and industrial (STI&I) policies focusing on developing and deploying low-carbon technologies are crucial to achieving carbon neutrality. It notes however that the current level of innovation is insufficient to meet the net-zero challenge due to a policy emphasis on deployment rather than research and development (R&D) support. The paper explores the rationale for more ambitious STI&I policies targeted at R&D for climate neutrality and provides policy recommendations for an effective innovation policy for net-zero, including its interaction with the broader climate policy package.

This report presents a new effort to collect comprehensive metadata for DynEmp and MultiProd, two OECD distributed microdata projects that collect information to analyse employment dynamics and productivity in a harmonised way across countries. It gives an overall description of both projects, presents the methodologies used for the data collection, summarises the main features of the data by country and provides further country-specific information.

Global value chains (GVCs) in agriculture and food sectors are becoming an important part of the agro-food trading landscape, influencing both the nature of the gains from trade and the impacts of trade policies. This study explores the changes in trade in value added that are occurring within agro-food GVCs and the implications that participation in agro-food GVCs has had on the agro-food sectors. It makes use of a database on trade in value added for 22 agro-food sectors derived from the Global Trade Analysis Project (GTAP) database. The study finds that between 2004 and 2014, agro-food sectors have been increasing their participation in GVCs and that the links created within these production networks have become more “global” in nature. At the same time, agro-food GVCs have become increasingly centred around hubs in the People’s Republic of China and Germany where large amounts of value added are funnelled before reaching the end consumer. The study also finds that participation in agro-food GVCs is beneficial for sector development and growth – both in aggregate terms and in terms of domestic value added from exports. Of key importance has been the use of foreign value added and access to a wide diversity of imported inputs. However, policies that restrict trade and limit market openness reduce participation and sector growth and development – including policies that create barriers to trade in agro-food products themselves. In addition, the study finds that the use of services value added in exports is an important factor that contributes to sector growth, which highlights the importance of the broader policy environment to enhance the benefits from agro-food GVCs.

Earlier studies in the OECD project on decoupling estimated static models of crop production incorporating risk aversion, following the analytical framework in OECD (2001). In contrast, this paper studies primarily dynamic models of crop investment. The province of Manitoba is selected as an example of Canadian prairie crop production, and investment is defined as current expenditure on machinery and equipment for Manitoba crop production (the data are not crop specific). This is the first econometric study of dynamic crop investment decisions for Canada, and as far as can be ascertained this is the first econometric study for any country of dynamic crop investment under risk aversion. Econometric results are used to simulate impacts of agricultural programmes on crop investment.

The OECD Competition Committee debated Dynamic Efficiencies in Merger Analysis in June 2007. This document includes an executive summary and the documents from the meeting: a background note by the OECD Secretariat as well as written submissions from Brazil, Canada, the Czech Republic, the European Commission, France, Germany, Ireland, Japan, Korea, New Zealand, Portugal, Chinese Taipei, Türkiye, the United Kingdom, the United States and BIAC as well as an aide-memoire of the discussion.