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We introduce the Input Rank as a network measure of relevance of direct and indirect suppliers in Global Value Chains. We conceive an intermediate input to be more relevant for a downstream buyer if a negative shock to that input's productivity affects that buyer more. In particular, in our framework, the relevance of any input depends on: (i) the network position of the supplier relative to the buyer, (ii) the patterns of intermediate input intensities connecting the buyer and the supplier, and (iii) the competitive pressures along supply chains. After we compute the Input Rank from both the U.S. and the world input–output tables, we provide useful insights into the crucial role of services inputs as well as on the relatively higher relevance of domestic suppliers, and suppliers coming from regionally integrated partners. Finally, we test that the Input Rank is a good predictor of vertical integration choices made by 20,489 U.S. parent companies controlling 154,836 subsidiaries worldwide. [ABSTRACT FROM AUTHOR]

This Special Issue brings together 13 papers that examine a variety of central topics in the field of international economics and finance. These papers were presented at the 23rd Conference on International Economics held in Málaga (Spain) on 16th–17th June 2022. The conference was organised by the Spanish Association of International Economics and Finance (AEEFI) and the University of Málaga. The selected papers make up an interesting and revealing set of information to study the new challenges of the international economics and finance in a context especially marked by the aftermath of the 2008 financial crisis, the climate change, the challenge posed by the COVID‐19 crisis and the instability unleashed after the invasion of Ukraine in 2022. From different perspectives, the papers analyse how events that have particularly affected the evolution of the world economy have substantially altered the rules of international trade, foreign direct investment, as well as monetary, fiscal or sectoral policy. The conference included two keynote lectures by Per Krusell (Institute for International Economic Studies, Stockholm University) and Fabio Canova (Norwegian Business School and Budapest School for Central Banking Studies), as well as 97 selected contributions. [ABSTRACT FROM AUTHOR]

Economic complexity and machine learning have recently become popular approaches for analysing international trade. However, for effective use of machine learning in relation to economic complexity and policymaking, it is important to understand what are the key features for predictions. In this framework, this article addresses the issue of the interpretability of results obtained with a machine learning technique—namely, matrix completion—when applied to economic complexity, specifically in predicting revealed comparative advantages (RCAs) of countries in different product categories. Shapley values are used to measure the role each country plays in predicting the RCAs of other countries. Countries relevant for prediction may differ from countries whose RCA values are similar to those of the country of interest when a standard similarity measure such as cosine similarity is used. We demonstrate the usefulness of our approach to identifying comparable countries by focussing our analysis on export diversification into complex goods of selected European countries. [ABSTRACT FROM AUTHOR]

While their expertise and scientific excellence make academic star scientists attractive collaboration partners for firms, this study indicates that firms face difficulties in capturing value from collaborations with academic stars. Stars are time constrained, may be less committed to commercialization, and can be a source of undesired knowledge spillovers to other firms. The purpose of this study is to recognize the contingencies under which collaboration with star scientists is positively associated with a firm's ability to produce valuable patents (invention performance). We analyze a panel data set on the collaborations in basic research (publication data) and invention performance (patent output) of 60 prominent pharmaceutical firms. We find that basic research collaboration with academic stars is on average not associated with a performance premium above the overall positive influence of collaborating with academia. We only observe this premium if the star scientist abstains from simultaneous collaboration with other firms ('dedication') and extend her collaboration with the firm to involve not only basic but also applied research ('translation'). Extending prior work that has focused on corporate star scientists, we find that if the collaboration involves an internal firm star scientist, a translational contribution of the academic star is no longer a prerequisite, and may even be detrimental to inventive performance. Our findings inform the literatures on industry‐science links and firms' (scientific) absorptive capacity by revealing the crucial contingencies for firms to benefit from partnering with the best and brightest among academic scientists. [ABSTRACT FROM AUTHOR]

Pharmaceutical firms frequently engage in preclinical research collaboration with universities. Many of these collaborative preclinical research projects are conducted by firms' subsidiaries. In this setting, the challenge of transferring knowledge from university–industry collaborations (UICs) to internal research and development (R&D) increases, since knowledge has to be transferred twice: to the subsidiary and to the parent. To benefit from tacit and complex knowledge obtained through collaboration with academic partners, firms need to have a high absorptive capacity. However, existing research on absorptive capacity's effect on the efficacy of UIC has provided mixed results, and the knowledge transfer mechanisms remain unclear, particularly in the case of basic (or preclinical) research and of the complex knowledge transfer process from subsidiaries' collaborative efforts. Building on the knowledge‐based view and following a multidimensional perspective of absorptive capacity, this study investigates the role a firm's capabilities play in successful university knowledge integration in a firm's internal R&D. Therefore, this paper analyzes both parent firms' UICs and their subsidiaries' UICs in basic (preclinical) research. The results of our longitudinal analysis of a unique panel data set of 56 global pharmaceutical firms indicate that firms do successfully exploit valuable knowledge from preclinical research UIC in internal R&D, measured by the number of performed clinical trials. This holds true for the UIC involving the parent firm and its subsidiaries. As transformative learning dimensions of absorptive capacity, high diversity in therapeutic activity and high R&D intensity levels strengthen the positive relationship between parents' UIC and R&D performance. A high exploration intensity level of the firm and high diversity in therapeutic activity help to transfer the knowledge from subsidiaries' preclinical research UIC to parents' innovation projects. [ABSTRACT FROM AUTHOR]

An important issue in social network analysis refers to the development of algorithms for estimating parameters of a social network model, using data available from the network itself. This entails solving an optimization problem. In the paper, we propose a new method for parameter estimation in a specific social network model, namely, the so‐called p‐star random graph model with three parameters. The method is based on the mean‐field approximation of the moments associated with the three subgraphs defining the model, namely: the mean numbers of edges, 2‐stars, and triangles. A modified gradient ascent method is applied to maximize the log‐likelihood function of the p‐star model, in which the components of the gradient are computed using approximate values of the moments. Compared to other existing iterative methods for parameter estimation, which are computationally very expensive when the number of vertices becomes large, such as gradient ascent applied to maximum log‐likelihood and maximum log‐pseudo‐likelihood estimation, the proposed approach has the advantage of a much cheaper cost per iteration, which is practically independent of the number of vertices. [ABSTRACT FROM AUTHOR]

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The article focuses on anthropology of tourism in Mexico, exploring the connection between the conditions of tourism and the historical characteristics of this epistemic communities. Topics include tourismification as a developmental project of Mexico, tourism's political economy and social processes and illegal dispossession of land.

The article focuses on domesticating anthropology study of tourism in China. It discusses the history of social sciences in China social sciences in China and Fei Xiaotong's initiation of domestication of anthropology in China. It further discusses developments in Chinese domestication of anthropology, with examples of tourism-related research including ethnic tourism and cultural heritage.

The article presents an essay addressing the debate concerning Ibero-American anthropologies of tourism. Topics discussed include the essays focus on the language barrier faced by Ibero-American academics and debates within the Ibero-American anthropologies of tourism, and anglophone work in tourism studies having influenced Ibero-American anthropologies of tourism.

The article presents the author's comments on papers in the special section of the journal that discuss the anthropologies of tourism. Topics discussed include tourism studies, the essays seeming primarily framed by political economic issues, and the larger discussion of tourism or research on tourism.

The article presents the author's comments as an anthropologist of tourism based in Japan on three papers addressing the anthropologies of tourism including a paper on the possibility of Ibero-American anthropologies of tourism, the Mexican way of studying tourism, and a paper on the anthropology of tourism in China.

The relationship between international trade and foreign direct investment ( FDI) is one of the main features of globalisation. In this paper, we investigate the effects of FDI on trade from a network perspective, since FDI takes not only direct but also indirect channels from origin to destination countries because of firms' incentive to reduce tax burden, to minimise coordination costs and to break barriers to market entry. We use a unique data set of international corporate control as a measure of stock FDI to construct a corporate control network ( CCN), where the nodes are the countries and the edges are the corporate control relationships. Network measures, as the shortest path length and the communicability, are then computed on the CCN to capture the indirect channel of FDI. Empirically, we find that corporate control has a positive effect on trade both directly and indirectly. The result is robust with different specifications and estimation strategies. Hence, our paper provides strong empirical evidence of the indirect effects of FDI on trade. Moreover, we identify a number of interplaying factors such as regional trade agreements and the region of Asia. We also find that the indirect effects are more pronounced for the manufacturing sector than for primary sectors such as oil extraction and agriculture. [ABSTRACT FROM AUTHOR]

This paper investigates the role of real options reasoning in R& D project portfolio management and investment decisions of pharmaceutical firms. We analyse a unique dataset that integrates information on initiation and termination of clinical trials at the level of specific medical indications. Consistent with existing literature, we find a positive relationship between market size and firm entry in clinical trials. We also show that the option value of R& D investments, as proxied by the scope of R& D projects, affects the selection of target markets. Moreover, high-risk research areas attract more entry, in line with the predictions of real options theory. However, we also find that more flexibility in project duration and delayed project discontinuation attract higher rates of entry. Departures from pure real options reasoning are motivated by the presence of incremental learning in pharmaceutical R& D. [ABSTRACT FROM AUTHOR]

ABSTRACT In the literature, the distribution of city size is a controversial issue with two common contenders: the Pareto and the log-normal. While the first is most accredited when the distribution is truncated above a certain threshold, the latter is usually considered a better representation for the untruncated distribution of all cities. In this paper, we reassess the empirical evidence on the best-fitting distribution in relation to the truncation point issue. Specifically, we provide a comparison among four recently proposed approaches and alternative definitions of U.S. cities. Our results highlight the importance to look at issue of the best-fitting distribution together with the truncation issue and provide guidance with respect to the existing tests of the truncation point. [ABSTRACT FROM AUTHOR]

The article presents abstracts on complexity which include sense-making and language, unusual behavior's detection by mobile phone data, and network sparsity.

The article focuses on study of tourism as a social scientific subject of anthropology, discussing anthropological sources and literature on tourism. Topics include study of tourism by anthropologist Jafar Jafari, language as a barrier in knowledge on anthropology and tourism and issue of transnationalistic anthropologist.

ABSTRACT On the premise that knowledge creation defines contemporary metropolitan regions, we profile them by their inventive networks, as measured by a variety of complementary social network, technology, and patenting metrics that distinguish scalar and structural aspects. Using a comprehensive, multiyear database of patent applications, we investigate whether the knowledge creation network profiles are discriminating characteristics of metropolitan regions by establishing a new urban taxonomy for metropolitan areas in the United States. The four-class taxonomy is not only statistically significant, but it is also economically meaningful in terms of economic performance of metropolitan areas. We find that metropolitan areas benefit from a higher density of inventors in the population, and that there is a positive correlation between economic performance and metropolitan areas with inventor teams working in similar or complementary areas of technology. In fact, the structure of knowledge creation networks are fundamental to economic performance and extends to metropolitan growth rates in jobs and income. [ABSTRACT FROM AUTHOR]

Technological and market transitions are difficult to manage, and collaborations can be viewed as either resources or constraints in dynamic settings. In the biopharmaceutical industry, a paradigmatic shift in the relevant knowledge bases occurred in the mid-1990s, inducing a structural change in the network of R&D collaborations. Search and relational strategies oriented toward exploration versus exploitation have prevailed in different phases of the network evolution. Therefore, biotechnology firms have experienced overwhelming difficulties in reorienting their learning strategies throughout paradigmatic shifts and ambidextrous organizations have been able to attain superior performances in terms of stability and centralization in the R&D network. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses reports within the issue including the early innovation process of portfolio management, the insights of individual's promotion on creating divergent ideas, and the evaluation of innovative ideas by research and development managers.