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This paper develops a dynamic Agent Based Model to study the role of cross-border data flows for the joint uptake of artificial intelligence enabled software in manufacturing and engineering. The model features two technology-related business models: engineering as a face-to-face consultancy service, and engineering as a software licensing service. Engineering agents harvest data from their software clients in the home country and abroad and use the data for quality assurance and software updates. We compare scenarios along two dimensions: (i) harvesting data from own clients only versus from open data repositories, (ii) the strength of competition measured by the probability that a contract will be extended by another period. We find that restrictions on cross-border data flows slow down the speed of adoption considerably, particularly in small countries. The simulations generate an S-shaped technology uptake path for manufacturers and a U-shaped relationship between competition and technology uptake in engineering. Interestingly, cross-border data flows flatten the U.

This chapter describes the digital transition of the global recorded music industry with a focus on how artificial intelligence (AI) has changed the way music is distributed, consumed, and how AI serves as a tool for creating music. The transition has raised questions about what is copyrightable and what constitutes copyright infringements. This debate should shed more light on the rationale for copyright, namely incentives to innovate in competitive markets. The music industry is one of the most global industries in the world despite being excluded from most trade agreements. AI contributes to continual global markets as it helps identify and track recorded music to the benefit of rights holders all over the world. However, with growing tensions between the major players in the digital economy - over AI, privacy, cyber security, among others - music streaming may be caught in the crossfire.

This paper studies the role of trade for the joint uptake of AI-enabled automation in manufacturing and engineering. It develops an agent-based model (ABM) where the agents are heterogeneous manufacturers and engineering firms. The ABM features two technology-related business models: engineering as a face-to-face consultancy service and engineering as automated software. The software adoption rate follows an S-shaped curve for manufacturers and a boom and bust cycle for engineers. In the early phase, shortage of engineers constrains AI uptake, while engineers become abundant when AI is fully adopted. Trade affects the cut-off productivity level at which manufacturers switch technology, the shape of the adoption rate curve, and the incentives for engineers to develop software. Bulky transactions and different productivity distributions across countries are drivers of trade in their own right.

EØS-avtalen har bidratt til at EU er Norges klart viktigste handelspartner, med kraftig vekst i handelen særlig med nye medlemsland. For varer har EØS ført til et nesten sømløst felles marked for de sektorer som omfattes. Også for tjenester er EØS verdens dypeste handelsavtale, men forskjeller mellom regulering i ulike EØS-land er fortsatt en utfordring. Østutvidelsen fra 2004 var et fredsprosjekt og har ført til kraftig økonomisk og sosial utjevning i Europa. Innvandrere fra nye EU-medlemsland står nå for en fjerdedel av innvandrere i Norge, og de har høy yrkesdeltaking. Det indre marked og EØS har ført til økt velferd og en betydelig vekst i handelen. EØS vil være viktig for Norges industrielle omstilling etter oljen. Krig, geopolitisk spenning og svekket globalt samarbeid har sammen med det grønne skiftet ført til en rekke reformer i EU og en voksende gråsone i forhold til EØS-avtalen. Denne gråsonen spiller også over på Norges handelspolitikk overfor tredjeland.

The EEA agreement has contributed to the EU being Norway’s largest trade partner, with particularly strong trade growth with new member states. For goods, the EEA has created an almost seamless market for the sectors covered. EEA is also the world’s deepest trade agreement for services, but different regulations across EEA countries is still a challenge. The eastern enlargement from 2004 was a peace project and has led to strong economic and social convergence in Europe. Immigrants from new member countries now account for ¼ of immigrants in Norway, with high labor force participation. The internal market and the EEA has led to higher welfare and a considerable increase in trade. The EEA will be important for Norway’s industrial transition after the oil era. War, geopolitical tension and weaker global cooperation has, jointly with the green transition, led to a number of reforms in the EU and a growing grey area related to the EEA agreement. This also spills over to Norway’s trade policy for third countries.

Agent-based Simulation Modelling focuses on the agents' decision making in their individual context. The decision making details may substantially affect the simulation outcome, and therefore need to be carefully designed.

In this paper we contrast two decision making architectures: a process oriented approach in which agents generate expectations and a reinforcement-learning based architecture inspired by evolutionary game theory. We exemplify those architectures using a technology uptake model in which agents decide about adopting automation software. We find that the end result is the same with both decision making processes, but the path towards full adoption of software differs. Both sets of simulations are robust, explainable and credible. The paper ends with a discussion what is actually gained from replacing behaviour description by learning.

Against the backdrop of a new debate about a possible free trade agreement replacing Norway’s EEA membership, this article studies the impact of the EEA agreement on Norway’s services trade. Using the structural gravity model, it first analyses the impact of EEA on total services trade. Norway’s total services exports is estimated to be about 35 percent higher within EEA than with a hypothetical free trade agreement. The article next focuses on financial services and communications services. Compared to the hypothetical free trade agreement the EEA agreement has raised trade by about 50 percent in both sectors. Furthermore, providing the essential infrastructure for trade, the internal market for telecommunications and finance has boosted trade in all services sectors. Brexit does not change these estimates much.

Denne artikkelen ser på korleis EØS-medlemskapet har påverka norsk teneste­handel. Ved hjelp av ein gravitasjonsmodell studerer eg først verknaden av EØS for handel med tenester generelt. Deretter går artikkelen inn på sentrale tenestesektorar og studerer effekten av spesifikke handelspolitiske indikatorar. Analysen viser at EØS-avtalen er assosiert med rundt 35 prosent høgare eksport samanlikna med ein frihandelsavtale. I finansielle tenester og kommunikasjonstenester er eksporten rundt 50 prosent høgare med EØS-avtalen samanlikna med ein handelsavtale. Brexit endrar desse estimata overraskande lite. Opne, velregulerte og harmoniserte marknader for kommunikasjonstenester er spesielt viktig for tenestehandelen.

This paper analyses the proposed free trade agreement (FTA) between EU and India focusing on services trade. Based on the text published by the European Union, it uses the OECD STRI simulator to calculate the preference margins implied by the agreement and next predicts the impact on services trade flows using a general equilibrium structural gravity analysis. I find that the preference margin on the Services Trade Restrictiveness Index (STRI) for Indian exports to the EU is between four and eight basis points depending on the sector, while for EU's exports to India the preference margin is between 10 and 35 basis points. The predicted effect is more than a doubling of EU services exports to India, while India's services exports to the EU would increase by about 50%. EU's trade with the rest of the world would not change much, while India's exports to the rest of the world would contract by about 3%. Real services output would not change much in the EU or India. Lifting trade restrictions in the telecommunications sector is the most important policy area for facilitating services trade. About half of the predicted export expansion is driven by reforms to domestic regulation.

This policy brief sets out recommendations to achieve a new multilateral framework of trade rules in the digital arena, thereby facilitating continued digital transformation of services and growth in cross-border flows of data. The present moment is critical. Successful conclusion of World Trade Organization (WTO) negotiations on E-Commerce will support trade in digital services, underpinned by cross-border data flows, complementing the expected recovery in travel and tourism services to provide a robust basis for global economic recovery and sustainable and inclusive growth. If the talks stall and fail to complete in 2022, technological change threatens another serious blow to a global institution which is reeling and seemingly unable to manage the regulatory heterogeneity resulting from national policies that threaten to compartmentalize data governance and fragment the global digital economy.

New technology is of little use if it is not adopted, and surveys show that less than 10% of firms use Artificial Intelligence. This paper studies the uptake of AI-driven automation and its impact on employment, using a dynamic agent-based model (ABM). It simulates the adoption of automation software as well as job destruction and job creation in its wake. There are two types of agents: manufacturing firms and engineering services firms. The agents choose between two business models: consulting or automated software. From the engineering firms' point of view, the model exhibits static economies of scale in the software model and dynamic (learning by doing) economies of scale in the consultancy model. From the manufacturing firms' point of view, switching to the software model requires restructuring of production and there are network effects in switching. The ABM matches engineering and manufacturing agents and derives employment of engineers and the tasks they perform, i.e. consultancy, software development, software maintenance, or employment in manufacturing. We find that the uptake of software is gradual; slow in the first few years and then accelerates. Software is fully adopted after about 18 years in the base line run. Employment of engineers shifts from consultancy to software development and to new jobs in manufacturing. Spells of unemployment may occur if skilled jobs creation in manufacturing is slow. Finally, the model generates boom and bust cycles in the software sector.