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Introduction: At the beginning of the pandemic, the identification of transmission chains was biased towards more closely monitored sectors, such as healthcare and sociosanitary centers. Objective: The objective of our research is to describe the impact of the pandemic on the Spanish non-healthcare workers measured through health-related workplace absences. Methods: A descriptive study of the cases of COVID-19-related temporary disability (TD) between February 15th and September 17th, 2020, was carried out. TD quarantine/infection ratios were calculated for every economic sector of activity. Rates of COVID-19 TDs per 100,000 affiliated workers were obtained, by gender, age, economic activity of the company or occupation of the worker. Results: A total of 1,126,755 TDs were recorded, 45.4% in women. The overall TD rates were 5,465 quarantines and 1,878 illnesses per 100,000 women, and 4,883 quarantines and 1,690 illnesses per 100,000 men. The highest incidence rates of TD due to illness were observed in younger age groups, under 30. The median TD quarantine/infection ratio was 2.6 (Interquartile range [IQR] 1.5–3.9), and Postal and Courier activities had the highest value: 4.7 quarantines per case (IQR: 3.75–6.12). The TD rates were higher in female workers in most sectors of activity and occupations compared to men in the same sectors and with the same occupations. The results show the uneven impact of COVID-19 by occupation, with a higher rates in less qualified occupations (unskilled workers and laborers) versus the first categories of the table (directors, managers, technicians, and professionals). Conclusions: The results confirmed the high impact of COVID-19 on Spanish non-healthcare workers and it's inequalities. They also confirmed the potential use of TDs as an alternative source for epidemiological, public health surveillance and early warning of new emerging infections. [ABSTRACT FROM AUTHOR]

The Bioceanic Corridor is an international land route under implementation, which aims to connect the State of Mato Grosso do Sul, in Brazil, to the ports of Northern Chile. This new route could shorten the transport time between South America and Asia by approximately two weeks. This paper's purpose is to contextualize, map, identify, and analyze the effects of this new logistics network designed by the Bioceanic Route over the Local Productive Arrangements (LPA) in the State of Mato Grosso do Sul. To achieve these goals, a spatial econometric methodology was adopted to determine the State's productive concentration. The results indicate that this route will bring many development opportunities. However, for that to happen, favorable policies are essential and must be developed to facilitate an integration that allows for competitiveness in the State's economic activities. However, mere unplanned integration will potentially only lead to the reinforcement of already existing regional inequalities in the State. [ABSTRACT FROM AUTHOR]

The increasing prominence of urban scaling laws highlights the importance of a systematic understanding of the variational scaling rates for different economic activities. In this article, we utilize several datasets to provide the first systematic investigation of the urban scaling of manufacturing industries in China. Most existing literature assumes that the divergence in urban scaling can be explained by returns to agglomeration, with a few exceptions instead highlighting the role of knowledge complexity or a mixture of both. Our main purpose in this paper is to explain the inter-sector variation of urban scaling rates. In doing this, we provide a clearer approach to demonstrating the relations between urban scaling, returns to agglomeration, and knowledge complexity. Our findings are twofold. First, after uncovering the scaling rates (denoted as urban concentration) and returns to agglomeration (denoted as urban productivity) for each sub-manufacturing sector, we prove that, rather than being a positive predictor, returns to agglomeration is slightly negatively associated with urban scaling rates. This finding reveals that urban concentration of manufacturing may not simply be a natural consequence driven by the maximization of performance. We also show that this result of the manufacturing system contrasts with what would be found in other pure knowledge systems such as patents. Secondly, we measure the complexity for each sector and demonstrate that the variation of urban concentration can be largely explained by their complexity, consistent with the knowledge complexity perspective. Specifically, complex manufacturing sectors are found to concentrate more in large cities than less complex sectors in China. This result provides support for the view that the growth of complex activities hinges more on diversity than on efficiency. The findings above can greatly reduce the current level of ambiguity associated with urban scaling, returns to agglomeration and complexity, and have important policy implications for urban planners, highlighting the significance of a more balanced and diversified configuration of urban productive activities for the growth of innovation economy. [ABSTRACT FROM AUTHOR]

Urban agglomerations are sophisticated territorial systems at the mature stage of city development that are concentrated areas of production and economic activity. Therefore, the study of vulnerability from the perspective of production-living-ecological space is crucial for the sustainable development of the Yellow River Basin and global urban agglomerations. The relationship between productivity, living conditions, and ecological spatial quality is fully considered in this research. By constructing a vulnerability evaluation index system based on the perspectives of production, ecology, and living space, and adopting the entropy value method, comprehensive vulnerability index model, and obstacle factor diagnostic model, the study comprehensively assesses the vulnerability of the urban agglomerations along the Yellow River from 2001 to 2020. The results reveal that the spatial differentiation characteristics of urban agglomeration vulnerability are significant. A clear three-level gradient distribution of high, medium, and low degrees is seen in the overall vulnerability; these correspond to the lower, middle, and upper reaches of the Yellow River Basin, respectively. The percentage of cities with higher and moderate levels of vulnerability did not vary from 2001 to 2020, while the percentage of cities with high levels of vulnerability did. The four dimensions of economic development, leisure and tourism, resource availability, and ecological pressure are the primary determinants of the urban agglomeration's vulnerability along the Yellow River. And the vulnerability factors of various urban agglomerations showed a significant evolutionary trend; the obstacle degree values have declined, and the importance of tourism and leisure functions has gradually increased. Based on the above conclusions, we propose several suggestions to enhance the quality of urban development along the Yellow River urban agglomeration. Including formulating a three-level development strategy, paying attention to ecological and environmental protection, developing domestic and foreign trade, and properly planning and managing the tourism industry. [ABSTRACT FROM AUTHOR]

The aim of the paper is to propose the construction of an index that captures the economic complexity of cities over the globe, as well as to explore whether it is a good predictor for a range of city-level economic outcomes. This index aspires to mitigate data scarcity for cities and to provide policy makers with the tools for monitoring the evolving role of cities in the global economy. Analytically, we implement the economic complexity methodology on data for the ownership, location and economic activities of the world's 3,000 largest firms and their subsidiaries to propose a new indicator that quantifies the network of the largest cities worldwide and the economic activities of their globalized firms. We first show that complex cities are the highly diversified cities that host non-ubiquitous economic activities of firms with global presence. Then, in a sample of EU cities, we show that complex cities tend to be more prosperous, have higher population, and are associated with more jobs, human capital, innovation, technology and transport infrastructure. Last, using OLS methodology and accounting for several other confounders, we show that a higher ECI, at the city level, enhances the resilience of cities to negative economic shocks, i.e., their ability to bounce back after a shock. Specifically, we find that the expected increase of the ratio of employment in 2012 over 2006 is 0.01 (mean: 0.992; standard deviation: 0.081) when the ECI increases by 1 unit (mean: 0.371; standard deviation: 1.094), i.e., a satisfactory pace of recovery, in terms of employment. The ability to diversify in the presence of a shock, the reallocation of factors of production to other sectors and the ability to extract rents associated with those diversified activities, uncovers the mechanics of the ECI index. [ABSTRACT FROM AUTHOR]

Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can further be augmented by node attributes denoting the various socio-economic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of "hotspots" of different types of socio-economic activities as characterized by recently developed attribute-augmented network centrality measures within the urban OD network. The workflow of the proposed paper comprises the construction of temporal OD networks using two custom data sets on urban mobility in Rome and London, the addition of socio-economic activity attributes to the OD network nodes, the computation of network centrality measures, the identification of "hotspots" and, finally, the visualization and analysis of measures of their spatio-temporal heterogeneity. Our results show structural similarities and distinctions between the spatial patterns of different types of human activity in the two cities. Our approach produces simple indicators thus opening up opportunities for practitioners to develop tools for real-time monitoring and visualization of interactions between mobility and economic activity in cities. [ABSTRACT FROM AUTHOR]

Germany is among the largest countries in the world in terms of total GDP, owing largely to rapid industrialization and expansion of economic activities into several sectors. This paper contributes to the literature on German economic development by investigating the evolution of industry diversification in Germany; particularly focusing on the recent concepts of related (RV) and unrelated variety (UV) in West German regions. It also identifies the statistical and economic determinants of variation in variety over time. Among several industry structure measures; RV is the only measure that reveals a pronounced increasing trend. Since RV is composed of two parts: 1) entropy at five-digit within a two-digit classification, and 2) shares of two-digit sectors in total output, we examined which of the two components dominate. Our findings suggest that the entropy component within two-digit sectoral shares of the RV index is more dominant than the two-digit sectoral shares themselves. We further examined entries and exits of the firms among regions with top and bottom rankings in RV. Findings suggest that both the top and bottom regions experienced an increase in the total number of industries, however, exits were much less pronounced in the bottom regions. It suggests that an increase in variety among top regions is the result of the creative destruction type effect where new industries force inefficient old industries to leave the region. Finally, analysis shows support for the inverse u-shaped relationship between development and diversification. [ABSTRACT FROM AUTHOR]

With globalization, the cases of Chinese enterprises' cross-border mergers and acquisitions (M&A) are increasing rapidly. The institutional environment of the host country has become an important factor influencing M&A performance, which has a profound impact on the success or failure of cross-border M&A. Based on this, for our study, we selected cases of cross-border M&A of listed companies in China from 2007 to 2018 as research samples to empirically test the impact of the host country's governance capacity on the cross-border M&A performance of acquirers. It was found that the host country's governance capacity has a negative effect on the M&A performance in the short term, but in the long term, it can effectively improve the cross-border M&A performance of acquirers. At the same time, specific to the relationship between the governmental governance capacity of six different dimensions and long-term M&A performance, the government effectiveness, regulation quality, and rule of law have the most significant promotional effect on long-term M&A performance. This implies that acquirers should focus on the long-term impact of governmental governance capacity on M&A, and consciously lean toward countries with strong governance capacity in order to obtain long-term value growth when arranging overseas M&A activities. The conclusion of this paper provides a reliable basis on which for companies to achieve sustainable growth in complex economic activities. [ABSTRACT FROM AUTHOR]

This paper develops a methodology for the assessment of the short-run effects of lockdown policies on economic activity. The methodology combines labor market data with simulation of an agent-based model. We apply our methodology to the Santiago Metropolitan Region, Chile. We recover the model parameters from observed data, taking into account the recurring policy adjustments that characterized the study window. The model is used to build counterfactual scenarios. We estimate an 8 percent output loss in the first 5 months of the pandemic from the policy that was put in place, achieving a 56 percent reduction in the total number of infections. During this period, with an output loss to 10.5 percent of GDP, the infection rate would have decreased 92 percent, significantly delaying the spread of COVID and spike in infections. Our methodology applied to real data provided results that could be valuable in guiding policies in other lockdown situations in times of disaster, pandemics or social upheaval. [ABSTRACT FROM AUTHOR]

The emergence in the United States of large-scale “megaregions” centered on major metropolitan areas is a phenomenon often taken for granted in both scholarly studies and popular accounts of contemporary economic geography. This paper uses a data set of more than 4,000,000 commuter flows as the basis for an empirical approach to the identification of such megaregions. We compare a method which uses a visual heuristic for understanding areal aggregation to a method which uses a computational partitioning algorithm, and we reflect upon the strengths and limitations of both. We discuss how choices about input parameters and scale of analysis can lead to different results, and stress the importance of comparing computational results with “common sense” interpretations of geographic coherence. The results provide a new perspective on the functional economic geography of the United States from a megaregion perspective, and shed light on the old geographic problem of the division of space into areal units. [ABSTRACT FROM AUTHOR]

The study draws attention to the associations between monetary and economic elements and their potential environmental impacts. The study uses time series data from 1960 to 2022 to examine the connection between CO2 emissions, industrial growth, GNE, and inflation in China. The researchers utilized the well-known econometric technique of nonlinear autoregressive distributed lag (NARDL) to examine nonlinear correlations between these variables. The results reveal that GDP, inflation, and economic development influence long-term CO2 emissions. The strong positive correlation between gross national expenditures and economic activity increases CO2 emissions. In the short run, CO2 emissions are positively and statistically significantly affected by inflation. While inflation temporarily affects CO2 emissions, this effect dissipates with time. Industrial activity increases CO2 emissions, and China's fast industrialization has damaged the environment. The energy-intensive fertiliser manufacturing process and fossil fuels increase CO2 emissions. The research shows how government officials and academics may collaborate to create tailored measures to alleviate the environmental impacts of economic activity. [ABSTRACT FROM AUTHOR]

The relationship between oil price and investor sentiment is crucial to economic activity. Disentangling the shocks in crude oil price by structural VAR model, this paper analyzes the interaction between oil price shocks and investor sentiment by linear and nonlinear causality approach, TVP-VAR mode and NARDL model. The results reveal that changes of oil-specific demand shock not only linearly but also nonlinearly cause changes of investor sentiment while there is no significant link between other oil shocks (oil supply shock and aggregate demand shock) and investor sentiment. In addition, the study discovers that the oil-specific demand shock generally positively affects investor sentiment over time, and it has positive and asymmetric effects on investor sentiment in the short-run. In other words, it is the negative oil-specific demand shock rather than the positive component that has the significant impact on investor sentiment for short-run. This study could enrich current theories on the interaction between oil price and investor sentiment and serve as a supplement to current literature. [ABSTRACT FROM AUTHOR]

People are increasingly leaving digital traces of their daily activities through interacting with their digital environment. Among these traces, financial transactions are of paramount interest since they provide a panoramic view of human life through the lens of purchases, from food and clothes to sport and travel. Although many analyses have been done to study the individual preferences based on credit card transaction, characterizing human behavior at larger scales remains largely unexplored. This is mainly due to the lack of models that can relate individual transactions to macro-socioeconomic indicators. Building these models, not only can we obtain a nearly real-time information about socioeconomic characteristics of regions, usually available yearly or quarterly through official statistics, but also it can reveal hidden social and economic structures that cannot be captured by official indicators. In this paper, we aim to elucidate how macro-socioeconomic patterns could be understood based on individual financial decisions. To this end, we reveal the underlying interconnection of the network of spending leveraging anonymized individual credit/debit card transactions data, craft micro-socioeconomic indices that consists of various social and economic aspects of human life, and propose a machine learning framework to predict macro-socioeconomic indicators. [ABSTRACT FROM AUTHOR]

Wars increase the importance of government functions, yet they also constrain their ability to fulfill these functions. In particular, wars hinder economic activity, thereby limiting governments' capacity to raise the revenues required to maintain stability and meet the heightened needs of citizens. Effective governance is therefore severely undermined in times of war. However, empirical research on how wars affect government procurement is limited. We address this gap by exploring procurement dynamics using over one million public purchases of goods and services in Ukraine between January 2021 and October 2022, corresponding to the Russian invasion of the country. We document a large fall in the total number of purchases since the invasion and an increase in the share of successfully completed procurements. This higher success rate comes at the cost of efficiency, with the government paying more to source their goods. This can be attributed to the decline in the share of government purchases via online auctions and the reduced competition. Thus, the prioritization of the quick acquisition of goods and services forced governments to sacrifice cost-effectiveness. In summary, the war did not lower the successful purchasing of private goods and services, and transparency was not decreased. However, the trade-off of speed and transparency for greater costs may become increasingly problematic with the growing budget constraints resulting from the war. This article contributes to our understanding of the Ukrainian government choices during the early phase of the war. The results also highlight the importance of ensuring procurement efficiency and transparency when the war ends as reconstruction efforts will require substantial increases in government procurements. [ABSTRACT FROM AUTHOR]

Much research has suggested that night-time light (NTL) can be used as a proxy for a number of variables, including urbanization, density, and economic growth. As governments around the world either collect census data infrequently or are scaling back the amount of detail collected, alternate sources of population and economic information like NTL are being considered. But, just how close is the statistical relationship between NTL and economic activity at a fine-grained geographical level? This paper uses a combination of correlation analysis and geographically weighted regressions in order to examine if light can function as a proxy for economic activities at a finer level. We use a fine-grained geo-coded residential and industrial full sample micro-data set for Sweden, and match it with both radiance and saturated light emissions. We find that the correlation between NTL and economic activity is strong enough to make it a relatively good proxy for population and establishment density, but the correlation is weaker in relation to wages. In general, we find a stronger relation between light and density values, than with light and total values. We also find a closer connection between radiance light and economic activity, than with saturated light. Further, we find the link between light and economic activity, especially estimated by wages, to be slightly overestimated in large urban areas and underestimated in rural areas. [ABSTRACT FROM AUTHOR]

Urban data deficits in developing countries impede evidence-based planning and policy. Could energy data be used to overcome this challenge by serving as a local proxy for living standards or economic activity in large urban areas? To answer this question, we examine the potential of georeferenced residential electricity meter data and night-time lights (NTL) data in the megacity of Karachi, Pakistan. First, we use nationally representative survey data to establish a strong association between electricity consumption and household living standards. Second, we compare gridded radiance values from NTL data with a unique dataset containing georeferenced median monthly electricity consumption values for over 2 million individual households in the city. Finally, we develop a model to explain intra-urban variation in radiance values using proxy measures of economic activity from Open Street Map. Overall, we find that NTL data are a poor proxy for living standards but do capture spatial variation in population density and economic activity. By contrast, electricity data are an excellent proxy for living standards and could be used more widely to inform policy and support poverty research in cities in low- and middle-income countries. [ABSTRACT FROM AUTHOR]

Information consumption is a kind of economic activity that directly or indirectly takes information products and information services as consumption objects. Most of the existing studies have established the "formula" of rural information consumption in promoting economic and social development, while few efforts have been devoted to study the quantitative reciprocities between them. Therefore, this study aims to investigate the relationship between information consumption in rural areas and regional economic development with a case study of Zhejiang, China. Based on rural information consumption-related indexes, basic geographic data, and other publicly available regional economic development related data, we conduct the exploration with the spatial Durbin model (SDM). The study indicates that (1) from the perspective of spatial analysis, the economic development level of all prefecture-level cities in Zhejiang, China has a significant positive spatial correlation; (2) in rural areas, the network infrastructure improvement, information industry clustering, etc. can accelerate enhancing information consumption and promote regional economic development; and (3) facilitate the development of rural information consumption is conducive to alleviate the imbalance of regional economic development. It is hoped that our study can help understand the current situation of information consumption in rural China and provide a theoretical reference for rural economic and social development. [ABSTRACT FROM AUTHOR]

The study aims to shed new lights on the lead-lag relationships between the financial sector (RFSI) and economic growth (GDP) in the midst of global economic policy uncertainty (GEPU) shocks for BRICS economies. Hence, the bivariate, partial, and wavelet multiple correlations techniques are employed. From the bivariate analysis, we document positive bi-directional causality between the RFSI and economic growth over the sample period. The partial wavelet reveals that GEPU shocks distort the significance and directional comovements between the RFSI and GDP. Moreover, the outcome from the wavelet multiple cross correlations (WMCC) indicates that the RFSI is a first mover at most time scales for the BRICS economies. This is followed by GEPU which either leads or lags for most scales, especially for South Africa. The impact of GEPU on RFSI and GDP is worst for South Africa in about four cases in the medium-, and long-terms. This signifies that South Africa's financial markets and economic growth are vulnerable to GEPU. However, the impetus for GEPU to drive the comovements between the financial sector and economic activity was less pronounced in the pre-COVID analysis conducted with the WMCC. The study supports both the supply-leading and demand-following hypotheses. Our findings also underscore the need for policymakers, investors and academics alike to incessantly observe the dynamics between finance and growth across time and periodicity while considering adverse shocks from global economic policy uncertainty in tandem. [ABSTRACT FROM AUTHOR]

Recreational angling in the United States (US) is largely a personal hobby that scales up to a multibillion-dollar economic activity. Given dramatic changes to personal decisions and behaviors resulting from the COVID-19 pandemic, we surveyed recreational anglers across the US to understand how the pandemic may have affected their fishing motivations and subsequent activities. Nearly a quarter million anglers from 10 US states were invited to participate in the survey, and almost 18,000 responded. Anglers reported numerous effects of the pandemic, including fishing access restrictions. Despite these barriers, we found that the amount of fishing in the spring of 2020 was significantly greater—by about 0.2 trips per angler—than in non-pandemic springs. Increased fishing is likely associated with our result that most respondents considered recreational angling to be a COVID-19 safe activity. Nearly a third of anglers reported changing their motivation for fishing during the pandemic, with stress relief being more popular during the pandemic than before. Driven partly by the perceived safety of social fishtancing, recreational angling remained a popular activity for many US anglers during spring 2020. [ABSTRACT FROM AUTHOR]

We consider a number of Artificial Chemistry models for economic activity and what consequences they have for the formation of economic inequality. We are particularly interested in what tax measures are effective in dampening economic inequality. By starting from well-known kinetic exchange models, we examine different scenarios for reducing the tendency of economic activity models to form unequal wealth distribution in equilibrium. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 virus impacts human health and the world economy, causing in Peru, more than 800 thousand infected and a strong recession expressed in a drop of -12% in its economic growth rate for 2020. In this context, the objective of the study is to analyze the dynamics of the short-term behavior of economic activity, as well as to explain the causal relationships in a Pandemic context based on the basic number of spread (Re) of COVID-19 per day. Methods: An Autoregressive Distributed Lags (ARDL) model was used. Results: A negative and statistically significant impact of the COVID-19 shock was found on the level of economic activity and a long-term Cointegration relationship with an error correction model (CEM), with the expected sign and statistically significant at 1%. Conclusion: The Pandemic has behaved as a systemic shock of supply and aggregate demand at the macroeconomic level, which together have an impact on the recession or level of economic activity. The authors propose changing public health policy from an indiscriminate suppression strategy to a targeted, effective and intelligent mitigation strategy that minimizes the risk of human life costs and socioeconomic costs, in a context of uncertainty about the end of the Pandemic and complemented by economic, fiscal and monetary policies that mitigate the economic recession, considering the underlying structural characteristics of the Peruvian economy. [ABSTRACT FROM AUTHOR]

Nations struggled to decide when and how to end COVID-19 inspired lockdowns, with sharply divergent views between those arguing for a resumption of economic activity and those arguing for continuing the lockdown in some form. We examine the choice between continuing or ending a full lockdown within a simple optimal control model that encompasses both health and economic outcomes, and pays particular attention to when need for care exceeds hospital capacity. The model shows that very different strategies can perform similarly well and even both be optimal for the same relative valuation on work and life because of the presence of a so-called Skiba threshold. Qualitatively the alternate strategies correspond to trying essentially to eradicate the virus or merely to flatten the curve so fewer people urgently need healthcare when hospitals are already filled to capacity. [ABSTRACT FROM AUTHOR]

The expansion of Covid-19 has severely hit the community's health all over the world, killing hundreds of thousands of people, subjecting health systems to an enormous stress (besides derailing economic activities and altering personal and social behavior). Two elements are essential to monitor the evolution of the pandemic as well as to analyze the effectiveness of the response measures: reliable data and useful indicators. We present an indicator that helps to assess the impact of Covid-19 on the community's health, combining two different components: the extent of the pandemics (i.e. the share of the population affected) and its severity (the intensity of the disease on those affected). The severity measure derives from the application of an evaluation protocol that allows comparing population distributions based on the proportions of those affected with different health conditions. We illustrate the functioning of this indicator over a case study regarding the situation of the Italian regions on March 9 (the beginning of the confinement) and April 8, 2020, one month later. [ABSTRACT FROM AUTHOR]

Every nation prioritizes the inclusive economic growth and development of all regions. However, we observe that economic activities are clustered in space, which results in a disparity in per-capita income among different regions. A complexity-based method was proposed by Hidalgo and Hausmann [PNAS 106, 10570-10575 (2009)] to explain the large gaps in per-capita income across countries. Although there have been extensive studies on countries' economic complexity using international export data, studies on economic complexity at the regional level are relatively less studied. Here, we study the industrial sector complexity of prefectures in Japan based on the basic information of more than one million firms. We aggregate the data as a bipartite network of prefectures and industrial sectors. We decompose the bipartite network as a prefecture-prefecture network and sector-sector network, which reveals the relationships among them. Similarities among the prefectures and among the sectors are measured using a metric. From these similarity matrices, we cluster the prefectures and sectors using the minimal spanning tree technique. The computed economic complexity index from the structure of the bipartite network shows a high correlation with macroeconomic indicators, such as per-capita gross prefectural product and prefectural income per person. We argue that this index reflects the present economic performance and hidden potential of the prefectures for future growth. [ABSTRACT FROM AUTHOR]

Geography, including climatic factors, have long been considered potentially important elements in shaping socio-economic activities, alongside other determinants, such as institutions. Here we demonstrate that geography and climate variables satisfactorily explain the worldwide economic activity as measured by the per capita Gross Cell Product (GCP-PC) at a fine geographical resolution, typically much higher than country average. A 1° by 1° GCP-PC dataset has been key for establishing and testing a direct relationship between 'local' geography/climate and GCP-PC. Not only have we tested the geography and climate hypothesis using many possible explanatory variables, importantly we have also predicted and reconstructed GCP-PC worldwide by retaining the most significant predictors. While this study confirms that latitude is the most important predictor for GCP-PC when taken in isolation, the accuracy of the GCP-PC prediction is greatly improved when other factors mainly related to variations in climatic variables, rather than average climatic conditions as typically used, are considered. However, latitude diminishes in importance when only the wealthier parts of the globe are considered. This work points to specific features of the climate system which explain economic activity, such as the variability in air pressure. Implications of these findings range from an improved understanding of why socio-economically better-off societies are geographically placed where they are in the present, past and future to informing where new economic activities could be established in order to yield favourable economic outcomes based on geography and climate conditions. [ABSTRACT FROM AUTHOR]

Double cropping of wheat and maize is common on the North China Plain, but it provides limited income to rural households due to the small farm sizes in the region. Local farmers in Quzhou County have therefore innovated their production system by integration of watermelon as a companion cash crop into the system. We examine the economic performance and sustainability of this novel intercropping system using crop yield data from 2010 to 2012 and farm household survey data collected in 2012. Our results show that the gross margin of the intercropping system exceeded that of the double cropping system by more than 50% in 2012. Labor use in the intercropping system was more than three times that in double cropping. The lower returns per labor hour in intercropping, however, exceeded the average off-farm wage in the region by a significant margin. Nutrient surpluses and irrigation water use are significant larger under the intercropping system. We conclude that the novel wheat-maize/watermelon intercropping system contributes to rural poverty alleviation and household-level food security, by raising farm incomes and generating more employment, but needs further improvement to enhance its sustainability. [ABSTRACT FROM AUTHOR]

Caesalpinia platyloba was evaluated as an alternative for the retention of atmospheric carbon and as a feasible and viable economic activity in terms of income for tropical deciduous forest (TDF) peasants in the carbon markets. A total of 110 trees of C. platyloba from plantations and a TDF in the Northwest of Mexico were sampled. Growth (increase in height, diameter, and volume curves) was adjusted to assess their growth. Growth of individuals (height, diameter at breast height [DBH], age, and tree crown cover) was recorded. The Schumacher model (H = β0eβ 1 • E -1), by means of the guided curve method, was used to adjust growth models. Information analysis was made through the non-linear procedure with the multivariate secant or false position (DUD) method using the SAS software. Growth and increase models revealed acceptable adjustments (pseudo R2>0.8). C. platyloba reaches >8m of height with 12cm in diameter and 550cm3 of volume, presenting the highest increase at 11 years considered as basal age. Highest significant density of wood was in good quality sites (0.80g•cm-3), with a carbon content (average of 99.15tC•ha-1) at the highest density of 2500 trees•ha-1 (without thinning). Average incomes of US$483.33tC•ha-1 are expected. The profitability values (NPW = US$81,646.65, IRR = 472%, and B/C = 0.82) for C. platyloba make its cultivation a viable and profitable activity, considering a management scheme of the income derived from wood selling and from carbon credits. [ABSTRACT FROM AUTHOR]

In this study we use economic input-output analysis to calculate the inequality footprint of nations. An inequality footprint shows the link that each country's domestic economic activity has to income distribution elsewhere in the world. To this end we use employment and household income accounts for 187 countries and an historical time series dating back to 1990. Our results show that in 2010, most developed countries had an inequality footprint that was higher than their within-country inequality, meaning that in order to support domestic lifestyles, these countries source imports from more unequal economies. Amongst exceptions are the United States and United Kingdom, which placed them on a par with many developing countries. Russia has a high within-country inequality nevertheless it has the lowest inequality footprint in the world, which is because of its trade connections with the Commonwealth of Independent States and Europe. Our findings show that the commodities that are inequality-intensive, such as electronic components, chemicals, fertilizers, minerals, and agricultural products often originate in developing countries characterized by high levels of inequality. Consumption of these commodities may implicate within-country inequality in both developing and developed countries. [ABSTRACT FROM AUTHOR]

Marine spatial planning (MSP) is often considered as a pragmatic approach to implement an ecosystem based management in order to manage marine space in a sustainable way. This requires the involvement of multiple actors and stakeholders at various governmental and societal levels. Several factors affect how well the integrated management of marine waters will be achieved, such as different governance settings (division of power between central and local governments), economic activities (and related priorities), external drivers, spatial scales, incentives and objectives, varying approaches to legislation and political will. We compared MSP in Belgium, Norway and the US to illustrate how the integration of stakeholders and governmental levels differs among these countries along the factors mentioned above. Horizontal integration (between sectors) is successful in all three countries, achieved through the use of neutral ‘round-table’ meeting places for all actors. Vertical integration between government levels varies, with Belgium and Norway having achieved full integration while the US lacks integration of the legislature due to sharp disagreements among stakeholders and unsuccessful partisan leadership. Success factors include political will and leadership, process transparency and stakeholder participation, and should be considered in all MSP development processes. [ABSTRACT FROM AUTHOR]