This books presents a theory of economic development very different from the "stages of growth" hypothesis or strategies emphasizing foreign aid, trade, or regional association. Leaving these aside, the author breaks new ground by focusing on the use of domestic capital markets to stimulate economic performance. He suggests a "bootstrap" approach in which successful development would depend largely on policy choices made by national authorities in the developing countries themselves. Central to his theory is the freeing of domestic financial markets to allow interest rates to reflect the true scarcity of capital in a developing economy. His analysis leads to a critique of prevailing monetary theory and to a new view of the relation between money and physical capitala view with policy implications for governments striving to overcome the vicious circle of inflation and stagnation. Examining the performance of South Korea, Taiwan, Brazil, and other countries, the author suggests that their success or failure has depended primarily on steps taken in the monetary sector. He concludes that monetary reform should take precedence over other development measures, such as tariff and tax reform or the encouragement of foreign capital investment. In addition to challenging much of the conventional wisdom of development, the author's revision of accepted monetary theory may be relevant for mature economies that face monetary problems.
Global food trade plays a crucial role in ensuring food security and maintaining supply chain stability. However, its network structure evolves dynamically under the influence of geopolitical, economic, and environmental factors, making it challenging to model and predict future trade links. Effectively capturing temporal patterns in food trade networks is therefore essential for improving the accuracy and robustness of link prediction. This study introduces IVGAE-TAMA-BO, a novel dynamic graph neural network designed to model evolving trade structures and predict future links in global food trade networks. To the best of our knowledge, this is the first work to apply dynamic graph neural networks to this domain, significantly enhancing predictive performance. Building upon the original IVGAE framework, the proposed model incorporates a Trade-Aware Momentum Aggregator (TAMA) to capture the temporal evolution of trade networks, jointly modeling short-term fluctuations and long-term structural dependencies. A momentum-based structural memory mechanism further improves predictive stability and performance. In addition, Bayesian optimization is used to automatically tune key hyperparameters, enhancing generalization across diverse trade scenarios. Extensive experiments on five crop-specific datasets demonstrate that IVGAE-TAMA substantially outperforms the static IVGAE and other dynamic baselines by effectively modeling temporal dependencies, while Bayesian optimization further boosts performance in IVGAE-TAMA-BO. These results highlight the proposed framework as a robust and scalable solution for structural prediction in global trade networks, with strong potential for applications in food security monitoring and policy decision support.
Unsustainable trade in wildlife is a major threat to biodiversity and is now increasingly prevalent in digital marketplaces and social media. With the sheer volume of digital content, the need for automated methods to detect wildlife trade listings is growing. These methods are especially needed for the automatic identification of wildlife products, such as ivory. We developed machine learning-based object recognition models that can identify wildlife products within images and highlight them. The data consists of images of elephant, pangolin, and tiger products that were identified as being sold illegally or that were confiscated by authorities. Specifically, the wildlife products included elephant ivory and skins, pangolin scales, and claws (raw and crafted), and tiger skins and bones. We investigated various combinations of training strategies and two loss functions to identify the best model to use in the automatic detection of these wildlife products. Models were trained for each species while also developing a single model to identify products from all three species. The best model showed an overall accuracy of 84.2% with accuracies of 71.1%, 90.2% and 93.5% in detecting products derived from elephants, pangolins, and tigers, respectively. We further demonstrate that the machine learning model can be made easily available to stakeholders, such as government authorities and law enforcement agencies, by developing a smartphone-based application that had an overall accuracy of 91.3%. The application can be used in real time to click images and help identify potentially prohibited products of target species. Thus, the proposed method is not only applicable for monitoring trade on the web but can also be used e.g. in physical markets for monitoring wildlife trade.
We analyze a continuous-time optimal trade execution problem in multiple assets where the price impact and the resilience can be matrix-valued stochastic processes that incorporate cross-impact effects. In addition, we allow for stochastic terminal and running targets. Initially, we formulate the optimal trade execution task as a stochastic control problem with a finite-variation control process that acts as an integrator both in the state dynamics and in the cost functional. We then extend this problem continuously to a stochastic control problem with progressively measurable controls. By identifying this extended problem as equivalent to a certain linear-quadratic stochastic control problem, we can use established results in linear-quadratic stochastic control to solve the extended problem. This work generalizes [Ackermann, Kruse, Urusov; FinancStoch'24] from the single-asset setting to the multi-asset case. In particular, we reveal cross-hedging effects, showing that it can be optimal to trade in an asset despite having no initial position. Moreover, as a subsetting we discuss a multi-asset variant of the model in [Obizhaeva, Wang; JFinancMark'13].
Erina Fujiwara-Nagata, Tatiana Rochat, Bo-Hyung Lee
et al.
Abstract Flavobacterium psychrophilum, the causative agent of bacterial cold-water disease, is a devastating, worldwide distributed, fish pathogen causing significant economic loss in inland fish farms. Previous epidemiological studies showed that prevalent clonal complexes (CC) differ in fish species affected with disease such as rainbow trout, coho salmon and ayu, indicating significant associations between particular F. psychrophilum genotypes and host species. Yet, whether the population structure is driven by the trade of fish and eggs or by host-specific pathogenicity is uncertain. Notably, all F. psychrophilum isolates retrieved from ayu belong to Type-3 O antigen (O-Ag) whereas only very few strains retrieved from other fish species possess this O-Ag, suggesting a role in outbreaks affecting ayu. Thus, we investigated the links between genotype and pathogenicity by conducting comparative bath infection challenges in two fish hosts, ayu and rainbow trout, for a collection of isolates representing different MLST genotypes and O-Ag. Highly virulent strains in one host species exhibited low to no virulence in the other. F. psychrophilum strains associated with ayu and possessing Type-3 O-Ag demonstrated significant variability in pathogenicity in ayu, ranging from avirulent to highly virulent. Strikingly, F. psychrophilum strains retrieved from rainbow trout and possessing the Type-3 O-Ag were virulent for rainbow trout but not for ayu, indicating that Type-3 O-Ag alone is not sufficient for pathogenicity in ayu, nor does it prevent pathogenicity in rainbow trout. This study revealed that the association between a particular CC and host species partly depends on the pathogen’s adaptation to specific host species.
We study the problem of designing a two-sided market (double auction) to maximize the gains from trade (social welfare) under the constraints of (dominant-strategy) incentive compatibility and budget-balance. Our goal is to do so for an unknown distribution from which we are given a polynomial number of samples. Our first result is a general impossibility for the case of correlated distributions of values even between just one seller and two buyers, in contrast to the case of one seller and one buyer (bilateral trade) where this is possible. Our second result is an efficient learning algorithm for one seller and two buyers in the case of independent distributions which is based on a novel algorithm for computing optimal mechanisms for finitely supported and explicitly given independent distributions. Both results rely heavily on characterizations of (dominant-strategy) incentive compatible mechanisms that are strongly budget-balanced.
We study the impact of economic integration on agglomeration in a model where all consumers are inter-regionally mobile and have heterogeneous preferences regarding their residential location choices. This heterogeneity is the unique dispersion force in the model. We show that, under reasonable values for the elasticity of substitution among varieties of consumption goods, a higher trade integration always promotes more symmetric spatial patterns, reducing the spatial inequality between regions, irrespective of the functional form of the dispersion force. We also show that an increase in the degree of heterogeneity in preferences for location leads to a more even spatial distribution of economic activities and thus also reduces the spatial inequality between regions.
A combinatorial trade is a pair of sets of blocks of elements that can be exchanged while preserving relevant subset intersection constraints. The class of balanced and swap-robust minimal trades was proposed in [1] for exchanging blocks of data chunks stored on distributed storage systems in an access- and load-balanced manner. More precisely, data chunks in the trades of interest are labeled by popularity ranks and the blocks are required to have both balanced overall popularity and stability properties with respect to swaps in chunk popularities. The original construction of such trades relied on computer search and paired balanced sets obtained through iterative combining of smaller sets that have provable stability guarantees. To reduce the substantial gap between the results of prior approaches and the known theoretical lower bound, we present new analytical upper and lower bounds on the minimal disbalance of blocks introduced by limited-magnitude popularity ranking swaps. Our constructive and near-optimal approach relies on pairs of graphs whose vertices are two balanced sets with edges/arcs that capture the balance and potential balance changes induced by limited-magnitude popularity swaps. In particular, we show that if we start with carefully selected balanced trades and limit the magnitude of rank swaps to one, the new upper and lower bound on the maximum block disbalance caused by a swap only differ by a factor of $1.07$. We also extend these results for larger popularity swap magnitudes.
The aim of the paper is to present international standards and their implementation in the national legislations in European countries regarding judges’ right to association with special regard to judges’ right to unionise. Authors hypothesise that although not strictly envisaged in any of the hard law sources, there is a plethora of soft-law instruments to assert this right. Consequently, the authors conclude that there is nothing in the relevant international standards that a priori prevents judges from unionising. Additionally, they posit that judges benefit from collective workers’ rights generally linked to the trade unions through activities of judges’ association, even in cases where judges are explicitly prohibited from joining and forming trade unions. The latter assertion is supported by a comparative overview of practices in selected European countries.
Takefumi Nakazawa, Noboru Katayama, Shunsuke Utsumi
et al.
Mutualism is common in nature and is crucial for population dynamics, community structure, and ecosystem functioning. Studies have recently pointed out that life-history stage structure (e.g., juveniles and adults) is a key factor to better understand the ecological consequences of mutualism (termed stage-structured mutualism). Despite the potential importance, little is known about what kinds of stage-structured mutualism can evolve and when it is likely to occur. Here, we theoretically investigated how a mutualistic partner species should allocate efforts of mutualistic associations for different life-history stages of its host species to maximize its fitness. We assessed the partner’s optimal strategy by using a one host–one partner model with the host’s juvenile-adult stage structure. The results showed that different forms of stage-structured mutualism can evolve, such as juvenile-specialized association, adult-specialized association, and inter-stage partner sharing (i.e., the partner associates with both the juvenile and adult stages of the host) depending on the shape of association trade-off, i.e., how much association with one stage is weakened when the partner strengthens its association with the other stage. In general, stage-specialized association (either juvenile-specialized or adult-specialized association) tends to evolve when being associated with that stage is relatively beneficial. Meanwhile, when the association trade-off is weak, inter-stage partner sharing can occur if the mutualistic benefits of juvenile-specific and adult-specific associations are sufficiently large. We also found that when the association trade-off is strong, alternative stable states occur in which either juvenile-specialized or adult-specialized associations evolve depending on the initial trait value. These results suggest that pairwise interspecific mutualism is more complicated than previously thought, implying that we may under-or overestimate the strength of mutualistic interactions when looking at only certain life-history stages. This study provides a conceptual basis for better understanding the mechanisms underlying ontogenetic shifts of mutualistic partners and more complex mutualistic networks mediated by the life-history stages of organisms and their stage-structured interactions.
Martino Bernasconi, Matteo Castiglioni, Andrea Celli
et al.
Bilateral trade models the problem of intermediating between two rational agents -- a seller and a buyer -- both characterized by a private valuation for an item they want to trade. We study the online learning version of the problem, in which at each time step a new seller and buyer arrive and the learner has to set prices for them without any knowledge about their (adversarially generated) valuations. In this setting, known impossibility results rule out the existence of no-regret algorithms when budget balanced has to be enforced at each time step. In this paper, we introduce the notion of \emph{global budget balance}, which only requires the learner to fulfill budget balance over the entire time horizon. Under this natural relaxation, we provide the first no-regret algorithms for adversarial bilateral trade under various feedback models. First, we show that in the full-feedback model, the learner can guarantee $\tilde O(\sqrt{T})$ regret against the best fixed prices in hindsight, and that this bound is optimal up to poly-logarithmic terms. Second, we provide a learning algorithm guaranteeing a $\tilde O(T^{3/4})$ regret upper bound with one-bit feedback, which we complement with a $Ω(T^{5/7})$ lower bound that holds even in the two-bit feedback model. Finally, we introduce and analyze an alternative benchmark that is provably stronger than the best fixed prices in hindsight and is inspired by the literature on bandits with knapsacks.
Pesticides are a kind of agricultural input, whose use can greatly reduce yield loss, regulate plant growth, effectively liberate agricultural productivity, and improve food security. The availability of pesticides in economies all over the world is ensured by pesticide redistribution through international trade and economies play different roles in this process. In this work, we measure and rank the importance of economies using nine node metrics in an evolutionary way. It is found that the clustering coefficient is correlated negatively with the other eight node metrics, while the other eight node metrics are positively correlated with each other and can be grouped into three communities (betweenness; in-degree, PageRank, authority, and in-closeness; out-degree, hub, and out-closeness). We further investigate the structural robustness of the international pesticide trade networks proxied by the giant component size under three types of shocks to economies (node removal in descending order, randomly, and in ascending order). The results show that, except for the clustering coefficient, the international pesticide trade networks are relatively robust under shocks to economies in ascending orders and randomly, but fragile under shocks to economies in descending order. In contrast, removing nodes with the clustering coefficient in ascending and descending orders gives similar robustness curves. Moreover, the structural robustness related to the giant component size evolves over time and exhibits an inverse U-shaped pattern.
The presence of a high number of zero flow trades continues to provide a challenge in identifying gravity parameters to explain international trade using the gravity model. Linear regression with a logarithmic linear equation encounters an indefinite value on the logarithmic trade. Although several approaches to solving this problem have been proposed, the majority of them are no longer based on linear regression, making the process of finding solutions more complex. In this work, we suggest a two-step technique for determining the gravity parameters: first, perform linear regression locally to establish a dummy value to substitute trade flow zero, and then estimating the gravity parameters. Iterative techniques are used to determine the optimum parameters. Machine learning is used to test the estimated parameters by analyzing their position in the cluster. We calculated international trade figures for 2004, 2009, 2014, and 2019. We just examine the classic gravity equation and discover that the powers of GDP and distance are in the same cluster and are both worth roughly one. The strategy presented here can be used to solve other problems involving log-linear regression.
This paper discusses the historical development and current situation of the different forms of representation in Austrian public education. In addition to the political associations founded at the end of the 19th century, there is the single Public Service Union, which was founded in 1945 as a sectoral union of the Austrian Trade Union Confederation. In 1967, staff representatives were established in all departments of the education system. By showing the ideological polarisation on school issues and analysing the complex governance structure of the Austrian federal state, the article highlights the strengths, but also the limitations of the influence that teacher representations can exert.
Gravity models are the main mathematical tool for the study of international trade associations, for example, they can be used for the analysis of trade between Russia and Belarus. Russian-Belarussian trade is regulated by the rules of the Eurasian Economic Union (EEU), which means that the models should take into account the influence of all the member countries. Russia’s large area and length of the borders pose a significant challenge for designing spatial models of trade; a failure to give due consideration to this fact may result in incorrect estimates of the gravitational model. Alternatively, we can consider individual Russian regions and model their trade relationships with other countries. Such approach, however, is extremely difficult to implement since there are no statistical data on trade between the subjects of the Russian Federation. In order to overcome this difficulty, this paper proposes and theoretically justifies a modification of the well-known gravity model of Anderson and van Wincoop for situations where data are unavailable. The resulting modification was applied to evaluate the gravitational model of trade between Belarus and Russian regions within the EEU. In addition, the original methodology of Anderson and van Wincoop was adjusted to evaluate the effects of comparative statics from changes in the size of trade barriers. Thus, we were able to compare the trade barriers between Russia and Belarus with the trade barriers between Russia and other EEU member states.