Socioeconomic Datasets Research Articles

Testing the Proposed Municipality Resilience Index to Climate Change Shocks and Stresses in Mbale Municipality in Eastern Uganda

Since climate change shocks and stresses cannot be fully prevented, building resilient urban areas is gaining more attention in the global community. By building resilience, the negative impacts of climate change shocks and stresses can be alleviated. Several indices have been developed to measure urban resilience. Yet, most of these indices focus more on objective methods which require robust bio-physical and socio-economic data sets which are generally lacking in many developing countries. To reduce this challenge, the use of subjective methods has recently been suggested. This study proposed and tested a Municipality Resilience Index (MRI) which employed a subjective method to assess the resilience of Mbale municipality in Eastern Uganda against climate change shocks and stresses. The proposed MRI includes 46 variables describing the physical, social, economic and institutional dimensions. The MRI can be applied in any municipality in developing countries facing climate related shocks and stresses and with limited survey data. The application of this index to Mbale municipality shows that the municipality has a low resilience index of 0.2. Similarly, most variables in the four dimensions of resilience reflected very low resilience scores with other divisions being more resilient than the others. Furthermore, the social dimension has the lowest score as compared to the physical, economic and institutional dimensions. The findings indicate a spatial variability in the contribution of the resilience dimensions within this small geographic confine. Moreover, the findings show the strengths and weaknesses in the different dimensions of the proposed MRI. This can act as a guide for policy and practitioners on which sectors to target in order to enhance the resilience of Mbale municipality.

The impact of self-employment on the economic integration of immigrants: Evidence from Germany

Traditional approaches in migration studies suggest that self-employment and entrepreneurial activities enhance the perspectives of economic advancement of immigrants in host countries. Therefore, in many popular destinations in Western Europe and Northern America, policies encouraging the self-employment of immigrants have been proposed. But does the self-employment contribute to the economic integration of immigrants? Is it a universal, one-way avenue that guarantees the successful insertion of foreigners in a new socio-economic environment? Based on the German Socio-Economic Panel dataset, this study empirically investigates the effect of self-employment on the economic integration of immigrants in one of the most important host countries in Europe. Our results demonstrate that the current self-employment status in Germany is associated with higher income in absolute and relative (self-assessment) terms. The immigrants with previous self-employment experience in Germany are less integrated than the average. This result indirectly shows that the business economy of immigrants is associated with a high risk of failure and not all self-employed individuals succeed in advancing in economic terms in a host country. Consequently, we argue that host countries should be more cautious in promoting entrepreneurship as the “perfect” and “universal” strategy that improves economic integration.

Spatial assessments of soil organic carbon for stakeholder decision-making – a case study from Kenya

Abstract. Land degradation impacts the health and livelihoods of about 1.5 billion people worldwide. Given that the state of the environment and food security are strongly interlinked in tropical landscapes, the increasing need for land for food production, urbanization and other uses poses several threats to sustainability in the long term. This paper demonstrates the integration of land and soil health maps with socioeconomic datasets into an online, open-access platform called the Resilience Diagnostic and Decision Support Tool for Turkana County in Kenya, using the Stakeholder Approach to Risk Informed and Evidence Based Decision Making (SHARED) methodology. The paper highlights the utility of spatial assessments of soil organic carbon (SOC) for monitoring land degradation neutrality (LDN) compliance, understanding the drivers of SOC dynamics and inclusion of these in stakeholder decision-making. The main objectives of this paper were to (1) demonstrate the application of a systematic approach for land health assessments, including spatial mapping of soil organic carbon; (2) show an operational interdisciplinary framework for assessing ecosystem health and (3) showcase the application of evidence-based tools for stakeholder engagement using the SHARED approach. Through the approaches and tools presented, the paper addresses the increasing need for more integrated approaches when assessing and managing ecosystem health to meet the targets of the 2030 Agenda, including Sustainable Development Goal (SDG) 15.3. In addition to systematic and reliable biophysical and socioeconomic assessments, stakeholder engagement with evidence is crucial to support such integrated approaches.

Cold-season disasters on the Eurasian steppes: Climate-driven or man-made

Socio-ecological damage from climate-related disasters has increased worldwide, including a type of cold-season disaster (dzud) that is unique to the Eurasian steppes, notably Mongolia. During 2000–2014, dzuds killed approximately 30 million livestock and impacted the Mongolian socio-economy. The contributions of both natural and social processes to livestock mortality were not previously considered across Mongolia. Here, we consider the contribution of both multiple climate hazards (drought, cold temperatures and snow), and socioeconomic vulnerability (herders’ livestock and coping-capacity) to mortality risk. We performed multi-regression analyses for each province using meteorological, livestock and socioeconomic datasets. Our results show that 93.5% of mortality within Mongolia was caused by a combination of multi-hazards (47.3%) and vulnerability (46.2%), suggesting dzuds were both climate- and man-made. However, in high-mortality hotspots, mortality was primarily caused by multi-hazards (drought-induced pasture deficiency and deep-snow). Livestock overpopulation and a lack of coping capacities that caused inadequate preparedness (e.g., hay/forage) were the main vulnerability factors. Frequent and severe multi-hazards greatly increased the mortality risk, while increased vulnerability caused by socioeconomic changes in Mongolia since the 1990s tended to amplify the effects of multi-hazards. Thus, reductions in herder vulnerability within high-mortality hotspots would likely be an effective means of mitigating the risk of future dzuds.

Mapping the need for adaptation: assessing drought vulnerability using the livelihood vulnerability index approach in a mid-hill region of Nepal

ABSTRACTFor effective development and adaptation interventions in resource-poor regions to take place, it is critical to identify, at the highest spatial scale possible, regions of higher priority based on current needs and vulnerabilities. The index-based assessment of vulnerability to climate change and variability is typically used to identify administrative-level regions of high vulnerability using various socioeconomic and biophysical datasets. One method that combines both approaches at the community level consists of collecting highly resolved socio-economic data and using the livelihood vulnerability index (LVI) to assess population vulnerability to increased climate variability and shocks. We use this mixed-methods approach in mapping climate vulnerability of ten drought-prone villages in the central-east mid-hill region of Nepal. We integrate data from over 900 household surveys and national-level databases and identify spatial patterns in the different components of climate vulnerability. We assess to what extent climatic extremes or people's socioeconomic capacity contribute to vulnerability and may shape development needs at the sub-district scale. We find that the majority of our study area falls in the high vulnerability category with significant spatial variation. In some villages, there are different vulnerability classes in different wards, indicating that even within the lowest administrative units, there is a significant spatial variation in the level of vulnerability. Livelihood strategies, water availability, and topographic components played the most important role in determining overall vulnerability and we measure strong interconnections among different components. The interconnectedness nature of different vulnerability components is creating a self-reinforcing downward spiral of vulnerability that traps local communities in a state of heightened vulnerability. We conclude that adaptation strategies in highly vulnerable regions should include careful consideration of different livelihoods and environmental components, their fine-scale spatial variations, and interconnections.

An integrated coastal zone observatory at municipal level: the case of Kavala Municipality, NE Greece

The Integrated Coastal Zone Management (ICZM) Observatory of Kavala Municipality has been developed during the Mare Nostrum Project following the well-known DPSIR concept (Drivers, Pressures, States, Impacts and Responses). The Observatory aims to aid the day-to-day operations of the Municipality, collecting, analyzing and processing historic, present and forecasted environmental, socio-economic and legal-institutional ICZM datasets collected from a variety of sources. Data are presented in a series of well-structured layers imported in a Geographic Information System (GIS) web-environment, to aid decision-makers at Municipal, Regional and National level to formulate policies on ICZM matters; to provide reliable datasets to scientists and engineers; to engage active citizens and the broader public into coastal zone issues; to promote the distribution of information among coastal managers, stakeholders and the general public. Static datasets as topographic, bathymetric, geologic and other base maps, urban and rural planning maps, local municipal acts, public networks and locations of public interest were digitized and embedded in the system. Historic environmental data together with dynamic data produced daily by a series of coupled operational models (meteorological, hydrologic, hydrodynamic, wave and eutrophication) and climate change datasets were also included. The legal-institutional framework of the coastline and the interactive tools to enhance public participation represent novel elements of the herein presented ICZM Observatory. The Kavala Gulf mussel farmers, the local Port Authority and the decision-makers on siting hydroplane landing-zones are the main user groups of the ICZM Observatory.

A multi-regional input-output table mapping China's economic outputs and interdependencies in 2012

Multi-regional input-output (MRIO) models are one of the most widely used approaches to analyse the economic interdependence between different regions. We utilised the latest socioeconomic datasets to compile a Chinese MRIO table for 2012 based on the modified gravity model. The MRIO table provides inter-regional and inter-sectoral economic flows among 30 economic sectors in China’s 30 regions for 2012. This is the first MRIO table to reflect China’s economic development pattern after the 2008 global financial crisis. The Chinese MRIO table can be used to analyse the production and consumption structure of provincial economies and the inter-regional trade pattern within China, as well as function as a tool for both national and regional economic planning. The Chinese MRIO table also provides a foundation for extensive research on environmental impacts by linking industrial and regional output to energy use, carbon emissions, environmental pollutants, and satellite accounts.

Estimating missing values in China\u2019s official socioeconomic statistics using progressive spatiotemporal Bayesian hierarchical modeling

Due to a large number of missing values, both spatially and temporally, China has not published a complete official socioeconomic statistics dataset at the county level, which is the country’s basic scale of official statistics data collection. We developed a procedure to impute the missing values under the Bayesian hierarchical modeling framework. The procedure incorporates two novelties. First, it takes into account spatial autocorrelations and temporal trends for those easier-to-impute variables with small missing percentages. Second, it further uses the first-step complete variables as covariate information to improve the modeling of more-difficult-to-impute variables with large missing percentages. We applied this progressive spatiotemporal (PST) method to China’s official socioeconomic statistics during 2002–2011 and compared it with four other widely used imputation methods, including k-nearest neighbors (kNN), expectation maximum (EM), singular value decomposition (SVD) and random forest (RF). The results show that the PST method outperforms these methods, thus proving the effects of sophisticatedly incorporating the additional spatial and temporal information and progressively utilizing the covariate information. This study has an outcome that allows China to construct a complete socioeconomic dataset and establishes a methodology that can be generally useful for estimating missing values in large spatiotemporal datasets.

The socio-economics dynamics of Dam on Rural Communities: A case study of Oyan Dam, Nigeria

Abstract. Dams construction and operations have many benefits, nevertheless, they have also led to lots of negative social, health and human impacts. It is based on this that this study assesses the potential and socio-economics dynamics of Oyan dam between 1980 and 2016. The data used for this study include water level and discharge records of the dam between 2007 and 2016, Landsat imageries of 1984 and 2016 and socio-economic datasets for the period. Analysis of the dam potentials (water supply, agriculture and hydropower) and socio-economic impacts of the dam were carried out using basic statistical tools, land use change anaysis and field survey using questionnaire, structured interview with major stakeholders and personal observation. The results revealed that the water level and storage of the Oyan dam had a relative reduction of about 2 % as well as non-stationarity pattern of water abstraction and production for the period. The landuse classes show all classes decreased in extent except the cultivated landuse that acrued an increased of 19.9 % between 1984 and 2016. Furthermore, commercial water supply varied significantly between 2010 and 2016 while irrigation scheme is grossly under-utilized from the inception in 1983 to 2016. Finally, the result of socio-economic impacts revealed that majority of the selected communities' members are actually not benefiting from the dam and their livelihoods are not from the dam.

A dynamic double hurdle model for remittances: evidence from Germany

In the last thirty years, migrant remittances have become a stable source of external finance for developing countries. In this paper, we investigate whether aggregate persistence can be traced back to individual remitting behaviour, as a result of migrants’ intertemporal choices. We propose a dynamic random-effects double hurdle model based on micro data from the German Socio-Economic Panel dataset. Our results show that there is significant state dependence in remitting behaviour, but in steady-state neither the probability to remit nor the transferred amounts are particularly large, thus suggesting that long-term intertemporal planning is rather sporadic. On these grounds, the medium-long term counterbalancing effect of remittances on the brain drain appears to be weak.

The impact of global environmental change on vector-borne disease risk: a modelling study

BackgroundVector-borne diseases, such as dengue virus, Zika virus, and malaria, are highly sensitive to environmental changes, including variations in climate and land-surface characteristics. The emergence and spread of vector-borne diseases is also exacerbated by anthropogenic activities, such as deforestation, mining, urbanisation, and human mobility, which alter the natural habitats of vectors and increase vector–host interactions. Innovative epidemiological modelling tools can help to understand how environmental conditions interact with socioeconomic risk factors to predict the risk of disease transmission. In recent years, climate-health modelling has benefited from computational advances in fitting complex mathematical models; increasing availability of environmental, socioeconomic, and disease surveillance datasets; and improved ability to understand and model the climate system. Climate forecasts at seasonal time scales tend to improve in quality during El Niño-Southern Oscillation events in certain regions of the tropics. Thus, climate forecasts provide an opportunity to anticipate potential outbreaks of vector-borne diseases from several months to a year in advance. The aim of this study was to develop a framework to incorporate seasonal climate forecasts in predictive disease models to understand the future risk of vector-borne diseases, with a focus on dengue fever in Latin America. MethodsA Bayesian spatiotemporal model framework that quantifies the extent to which environmental and socioeconomic indicators can explain variations in disease risk was designed to disentangle the effects of climate from other risk factors using multi-source data and random effects, which account for unknown and unmeasured sources of spatial, seasonal, and inter-annual variation. The model was used to provide probabilistic predictions of monthly dengue incidence and the probability of exceeding outbreak thresholds, which were established in consultation with public health stakeholders. FindingsThis disease model framework, combined with seasonal climate forecasts, successfully produced real-time, probabilistic estimates of dengue outbreak risk ahead of a mass gathering in Brazil in June, 2014, for all 550 microregions. Additionally, the model predicted dengue outbreak risk after a major El Niño event in southern coastal Ecuador from January to November, 2016. Forecasts from the new model framework did better than those from benchmark models based on historical seasonal dengue averages. InterpretationThis flexible model framework can be adapted to predict any climate-sensitive disease at various spatiotemporal scales and in diverse ecological settings. Incorporating sub-seasonal and seasonal climate forecasts in disease prediction models could support public health decision makers in implementing timely disease-control and prevention strategies months in advance to mitigate the risk of imminent disease epidemics and emerging disease threats. FundingRoyal Society Dorothy Hodgkin Fellowship.

Human mobility and socioeconomic status: Analysis of Singapore and Boston

Recently, some studies have shown that human movement patterns are strongly associated with regional socioeconomic indicators such as per capita income and poverty rate. These studies, however, are limited in numbers and they have not reached a consensus on what indicators or how effectively they can possibly be used to reflect the socioeconomic characteristics of the underlying populations. In this study, we propose an analytical framework — by coupling large scale mobile phone and urban socioeconomic datasets — to better understand human mobility patterns and their relationships with travelers' socioeconomic status (SES). Six mobility indicators, which include radius of gyration, number of activity locations, activity entropy, travel diversity, k-radius of gyration, and unicity, are derived to quantify important aspects of mobile phone users' mobility characteristics. A data fusion approach is proposed to approximate, at an aggregate level, the SES of mobile phone users. Using Singapore and Boston as case studies, we compare the statistical properties of the six mobility indicators in the two cities and analyze how they vary across socioeconomic classes. The results provide a multifaceted view of the relationships between mobility and SES. Specifically, it is found that phone user groups that are generally richer tend to travel shorter in Singapore but longer in Boston. One of the potential reasons, as suggested by our analysis, is that the rich neighborhoods in the two cities are respectively central and peripheral. For three other mobility indicators that reflect the diversity of individual travel and activity patterns (i.e., number of activity locations, activity entropy, and travel diversity), we find that for both cities, phone users across different socioeconomic classes exhibit very similar characteristics. This indicates that wealth level, at least in Singapore and Boston, is not a factor that restricts how people travel around in the city. In sum, our comparative analysis suggests that the relationship between mobility and SES could vary among cities, and such relationship is influenced by the spatial arrangement of housing, employment opportunities, and human activities.

The groundwater risk index: Development and application in the Middle East and North Africa region

Overreliance on predominantly non-renewable groundwater resources and their subsequent depletion has given rise to adverse environmental, political, economic, and social effects. The high costs of groundwater depletion are exacerbated by the notable absence of tools designed to identify and diagnose areas at risk of groundwater degradation. In this study, a Groundwater Risk Index (GRI) was developed as a distributed composite index to assess and evaluate groundwater depletion risk by combining different environmental and socioeconomic datasets and models. GRI is designed to be used by end-users (e.g. governments, NGOs) as a multicriteria diagnostic tool to identify and determine the probability and severity of an entity experiencing the adverse effects of groundwater mining. Annual GRI results indicate that groundwater risk is highly dependent on governance and food security. Surprisingly, groundwater storage reserves were indeterminate of groundwater risk. Given the centrality of agricultural production in groundwater consumptive use, MENA countries are recommended to mitigate groundwater depletion by establishing reliable and secure virtual water transfers (agricultural trade) to achieve food security, as opposed to unsustainably exploiting finite water resources for short-term food sufficiency. The GRI's design choices, including adopting an equal weighting scheme and a linear additive aggregation approach, promote structural flexibility that enables the modification, application, and implementation of the index in other semi- to hyper-arid regions with a high level of dependency on groundwater resources.

A global map of travel time to cities to assess inequalities in accessibility in 2015.

The economic and man-made resources that sustain human wellbeing are not distributed evenly across the world, but are instead heavily concentrated in cities. Poor access to opportunities and services offered by urban centres (a function of distance, transport infrastructure, and the spatial distribution of cities) is a major barrier to improved livelihoods and overall development. Advancing accessibility worldwide underpins the equity agenda of 'leaving no one behind' established by the Sustainable Development Goals of the United Nations. This has renewed international efforts to accurately measure accessibility and generate a metric that can inform the design and implementation of development policies. The only previous attempt to reliably map accessibility worldwide, which was published nearly a decade ago, predated the baseline for the Sustainable Development Goals and excluded the recent expansion in infrastructure networks, particularly in lower-resource settings. In parallel, new data sources provided by Open Street Map and Google now capture transportation networks with unprecedented detail and precision. Here we develop and validate a map that quantifies travel time to cities for 2015 at a spatial resolution of approximately one by one kilometre by integrating ten global-scale surfaces that characterize factors affecting human movement rates and 13,840 high-density urban centres within an established geospatial-modelling framework. Our results highlight disparities in accessibility relative to wealth as 50.9% of individuals living in low-income settings (concentrated in sub-Saharan Africa) reside within an hour of a city compared to 90.7% of individuals in high-income settings. By further triangulating this map against socioeconomic datasets, we demonstrate how access to urban centres stratifies the economic, educational, and health status of humanity.

A Large-Scale Analysis of Facebook’s User-Base and User Engagement Growth

Understanding the evolution of the user base as well as the user engagement of online services is critical not only for the service operators but also for customers, investors, and users. While we can find research works addressing this issue in online services, such as Twitter, MySpace, or Google+, such detailed analysis is missing for Facebook, which is currently the largest online social network. This paper presents the first detailed study on the demographic and geographic composition and evolution of the user base and user engagement in Facebook over a period of three years. To this end, we have implemented a measurement methodology that leverages the marketing API of Facebook to retrieve actual information about the number of total users and the number of daily active users across 230 countries and age groups ranging between 13 and 65+. The conducted analysis reveals that Facebook is still growing and geographically expanding. Moreover, the growth pattern is heterogeneous across age groups, genders, and geographical regions. In particular, from a demography perspective, Facebook shows the lowest growth pattern among adolescents. Gender-based analysis showed that growth among men is still higher than the growth in women. Our geographical analysis reveals that while Facebook growth is slower in western countries, it has the fastest growth in the developing countries mainly located in Africa and Central Asia; analyzing the penetration of these countries also shows that these countries are at earlier stages of Facebook penetration. Leveraging external socioeconomic datasets, we also showed that this heterogeneous growth can be characterized by indicators, such as availability and access to Internet, Facebook popularity, and factors related with population growth and gender inequality.

Satellite Remote Sensing for Monitoring Agriculture Growth and Agricultural Drought Vulnerability Using Long-Term (1982–2015) Climate Variability and Socio-economic Data set

Climate variability significantly impact the agricultural growth, stress, cropping pattern, phenophase and its vulnerability. Satellite derived indices, climate and socio-economic data sets have been used to study the time series trend of agricultural NDVI and agriculture drought vulnerability for two states of India namely Andhra Pradesh and Telangana. The study uses NOAA AVHRR GIMMS NDVI.3g. v1 (1982–2015) data set. The trend analysis of climate and soil moisture was carried out to understand their impact on the agriculture growth/stress, length of the growing period (LGP) and projected agriculture NDVI for IPCC climate AR5 2050 RCP 2.6 scenario. A novel approach is applied to the integrated data sets i.e. satellite and climate variables including socio-economic to assess the agricultural drought vulnerability at the district level, and at the tehsil level of united Telangana and Andhra Pradesh states for the recent-past. We further projected the vulnerability using IPCC AR5 2050 and 2070 climate RCP 2.6 scenario. The study has revealed that climate and soil moisture have a significant impact on LGP and agriculture condition. The predicted agricultural NDVI are near like normal years (2007 and 2013) indicating climate change signatures are not expected in near future. There is a need to improve the understanding using higher resolution soil moisture data to plan appropriate adaptive and mitigation strategies for the agricultural drought conditions in changing climate scenario.

A hybrid optimization-agent-based model of REDD+ payments to households on an old deforestation frontier in the Brazilian Amazon

REDD+ was initially conceived of as a multi-level carbon-based payment for environmental services (PES). It is still often assumed to be a cost-effective climate change mitigation strategy, but this assumption is mostly based on theoretical studies and static opportunity cost calculations. We used spatial and socioeconomic datasets from an Amazonian deforestation frontier in Brazil to construct a simulation model of REDD + payments to households that can be used to assess REDD + interventions. Our SimREDD + model consists of dynamic optimization and land-use/cover change allocation submodels built into an agent-based model platform. The model assumes that households maximize profit under perfect market conditions and calculates the optimal household land-use/cover configuration at equilibrium under a given REDD + PES scenario. These scenarios include PES based on (1) forest area and (2) carbon stocks. Insights gained from simulations under different conditions can assist in the design of more effective, efficient, and equitable REDD + programs.

Chinese CO2 emission flows have reversed since the global financial crisis

This study seeks to estimate the carbon implications of recent changes in China’s economic development patterns and role in global trade in the post-financial-crisis era. We utilised the latest socioeconomic datasets to compile China’s 2012 multiregional input-output (MRIO) table. Environmentally extended input-output analysis and structural decomposition analysis (SDA) were applied to investigate the driving forces behind changes in CO2 emissions embodied in China’s domestic and foreign trade from 2007 to 2012. Here we show that emission flow patterns have changed greatly in both domestic and foreign trade since the financial crisis. Some economically less developed regions, such as Southwest China, have shifted from being a net emission exporter to being a net emission importer. In terms of foreign trade, emissions embodied in China’s exports declined from 2007 to 2012 mainly due to changes in production structure and efficiency gains, while developing countries became the major destination of China’s export emissions.

Organisational and extraorganisational determinants of volume of service delivery by English community pharmacies: a cross-sectional survey and secondary data analysis

ObjectivesThis study aimed to identify the organisational and extraorganisational factors associated with existing variation in the volume of services delivered by community pharmacies.Design and settingLinear and ordered logistic regression of...

Conservation performance of different conservation governance regimes in the Peruvian Amazon

State-controlled protected areas (PAs) have dominated conservation strategies globally, yet their performance relative to other governance regimes is rarely assessed comprehensively. Furthermore, performance indicators of forest PAs are typically restricted to deforestation, although the extent of forest degradation is greater. We address these shortfalls through an empirical impact evaluation of state PAs, Indigenous Territories (ITs), and civil society and private Conservation Concessions (CCs) on deforestation and degradation throughout the Peruvian Amazon. We integrated remote-sensing data with environmental and socio-economic datasets, and used propensity-score matching to assess: (i) how deforestation and degradation varied across governance regimes between 2006–2011; (ii) their proximate drivers; and (iii) whether state PAs, CCs and ITs avoided deforestation and degradation compared with logging and mining concessions, and the unprotected landscape. CCs, state PAs, and ITs all avoided deforestation and degradation compared to analogous areas in the unprotected landscape. CCs and ITs were on average more effective in this respect than state PAs, showing that local governance can be equally or more effective than centralized state regimes. However, there were no consistent differences between conservation governance regimes when matched to logging and mining concessions. Future impact assessments would therefore benefit from further disentangling governance regimes across unprotected land.