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The gender income gap is a much debated subject both at an analytical and economic level. This article considers both, but emphasizes the different ways the data can be analyzed. The authors show that a hierarchical linear model is the best way to evaluate male-female wage differentials. Both interindustry and intraindustry wage disparities between men and women are measured by using a technique that assumes that observations within the same industry have correlated error terms. By simultaneously testing human capital factors and environmental factors, the analysis model serves as a link between theory and empirical analysis. The results show that the wage differences are larger in some industries than in others, so that it can be assumed that a gender income gap is not only a function of individual differences in qualification, but also differences between industries. The between-industry differences in gender income gaps contradict the hypothesis that gender income differential is largely due to female work preferences and the resulting segregation.

In classical test theory the reliability of a test can be estimated by test-retest correlation models. These models do not apply to data of the lowest or nominal measurement level. Instead, models for latent Markov chains may be used to correct for measurement error in panel data from three or more waves. In this article it is shown how to use the E-M algorithm for estimating the parameters of a latent Markov chain. Where previous algorithms performed badly on variables with more than two categories this algorithm performs better, although convergence is often slow. The method is applied to two trichotomous questions from the Dutch civil servants panel survey. Generally the assumptions of the model that is, a latent stationary Markov chain, are reasonably well met by the data. The probability of a correct answer, which can be interpreted as the reliability of a latent response category, is high in most cases (about. 8). Also transition tables are presented that are corrected for measurement error according to the model. Standard errors of model parameters are approximated by a finite difference method.

We review three decades of research linking social network methods with world systems theory. We identify four themes nested within two versions of a general social network methodology—the identification of network Roles and Position. The themes vary by the type of data and the definition of equivalence used to identify roles and positions. Second, we provide a demonstration of the general methodological approach taken in the literature, applying a recent methodological innovation to a newly compiled large global trade dataset. The results identify the expected core/periphery interaction pattern, suggesting that it is a fundamental feature of cross-national trade data, regardless of how the data are analyzed. We conclude by suggesting both methodological and substantive directions for future social network research on the world-system.

The assessment of land degradation and the quantification of its effects on land productivity have been both a scientific and political challenge. After four decades of Earth Observation (EO) applications, little agreement has been gained on the magnitude and direction of land degradation in the Sahel. The large number of EO datasets and methods associated with the complex interactions among biophysical and social drivers of ecosystem changes make it difficult to apply aggregated EO indices for these non-linear processes. Hence, while many studies stress that the Sahel is greening, others indicate no trend or browning. The different generations of sensors, the granularity of studies, the study period, the applied indices and the assumptions and/or computational methods impact these trends. Consequently, many uncertainties exist in regression models between rainfall, biomass and various indices that limit the ability of EO science to adequately assess and develop a consistent message on the magnitude of land degradation. We suggest several improvements: (1) harmonize time-series data, (2) promote knowledge networks, (3) improve data-access, (4) fill data gaps, (5) agree on scales and assumptions, (6) set up a denser network of long-term field-surveys and (7) consider local perceptions and social dynamics. To allow multiple perspectives and avoid erroneous interpretations, we underline that EO results should not be interpreted without contextual knowledge.

| openaire: EC/H2020/819202/EU//SOS.aquaterra Funding Information: The work was funded by ‐ , the Academy of Finland funded projects WATVUL (grant No. 317320) and TREFORM (grant no. 339834), and the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 819202). MH would like to acknowledge support from the Bill and Melinda Gates Foundation MERLIN grant (INV023682). Maa ja vesitekniikan tuki ry Publisher Copyright: © 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd. ; Although the role of livestock in future food systems is debated, animal proteins are unlikely to completely disappear from our diet. Grasslands are a key source of primary productivity for livestock, and feed-food competition is often limited on such land. Previous research on the potential for sustainable grazing has focused on restricted geographical areas or does not consider inter-annual changes in grazing opportunities. Here, we developed a robust method to estimate trends and interannual variability (IV) in global livestock carrying capacity (number of grazing animals a piece of land can support) over 2001–2015, as well as relative stocking density (the reported livestock distribution relative to the estimated carrying capacity [CC]) in 2010. We first estimated the aboveground biomass that is available for grazers on global grasslands based on the MODIS Net Primary Production product. This was then used to calculate livestock carrying capacities using slopes, forest cover, and animal forage requirements as restrictions. We found that globally, CC decreased on 27% of total grasslands area, mostly in Europe and southeastern Brazil, while it increased on 15% of grasslands, particularly in Sudano-Sahel and some parts of South America. In 2010, livestock forage requirements exceeded forage availability in northwestern Europe, and southern and eastern Asia. Although our findings imply some opportunities to increase grazing pressures in cold ...

Global insurance markets are vast and diverse, and may offer many opportunities for remote sensing. To date, however, few operational applications of remote sensing for insurance exist. Papers claiming potential application of remote sensing typically stress the technical possibilities, without considering its contribution to customer value for the insured or to the profitability of the insurance industry. Based on a systematic search of available literature, this review investigates the potential and actual support of remote sensing to the insurance industry. The review reveals that research on remote sensing in classical claim-based insurance described in the literature revolve around crop damage and flood and fire risk assessment. Surprisingly, the use of remote sensing in claim-based insurance appears to be instigated by government rather than the insurance industry. In contrast, insurance companies are offering various index insurance products that are based on remote sensing. For example, remotely sensed index insurance for rangelands and livestock are operational, while various applications in crop index insurance are being considered or under development. The paper discusses these differences and concludes that there is particular scope for application of remote sensing by the insurance industry in index insurance because (1) indices can be constructed that correlate well with what is insured; (2) these indices can be delivered at low cost; and (3) it opens up new markets that are not served by claim-based insurance. The paper finally suggests that limited adoption of remote sensing in insurance results from a lack of mutual understanding and calls for greater cooperation between the insurance industry and the remote sensing community.