Economic Data Sets Research Articles

Prioritizing actions: spatial action maps for conservation.

Spatial prioritization is a critical step in conservation planning, a process designed to ensure that limited resources are applied in ways that deliver the highest possible returns for biodiversity and human wellbeing. In practice, many spatial prioritizations fall short of their potential by focusing on places rather than actions, and by using data of snapshots of assets or threats rather than estimated impacts. We introduce spatial action mapping as an approach that overcomes these shortfalls. This approach produces a spatially explicit view of where and how much a given conservation action is likely to contribute to achieving stated conservation goals. Through seven case examples, we demonstrate simple to complex versions of how this method can be applied across local to global scales to inform decisions about a wide range of conservation actions and benefits. Spatial action mapping can support major improvements in efficient use of conservation resources and will reach its full potential as the quality of environmental, social, and economic datasets converge and conservation impact evaluations improve.

Realistic Optimization of Parallelogram-Shaped Offshore Wind Farms Considering Continuously Distributed Wind Resources

Offshore wind power plants are becoming a realistic option for the renewable production of electricity. As an improvement tool to the profitability of OWFs, this work presents the first complete non-genetic (and non-binary) evolutionary algorithm to optimize the location, size and layout of a parallelogram-shaped offshore wind farm, as the arrangement that is becoming an standard for offshore wind farms. It has been tested in the HRI site. Most relevant economic data influencing the investment profitability have been taken into account. In addition, the paper introduces a new approach to offshore wind farm optimization based on a continuous behaviour of varying wind conditions, which allows a more realistic estimation of the energy produced. The proposed optimization approach has been tested based on the available information from HRI. Obtained solutions present similar values to the actual offshore wind farm in terms of investment and annual energy produced, but differs with respect to the optimal orientation and profitability. The contributions of this paper are: it details the first method to interpolate a continuous distribution of wind rose and Weibull parameters; it presents the first algorithm to obtain a realistic optimal solution to the location+sizing+micro-siting problem for regular arrangements; it is prepared to work with the most complete set of economic, bathymetric, and wind data.

Ambient particulate air pollution and daily stock market returns and volatility in 47 cities worldwide

We studied globally representative data to quantify how daily fine particulate matter (PM2.5) concentrations influence both daily stock market returns and volatility. Time-series analysis was applied on 47 city-level environmental and economic datasets and meta-analysis of the city-specific estimates was used to generate a global summary effect estimate. We found that, on average, a 10 μg/m3 increase in PM2.5 reduces same day returns by 1.2% (regression coefficient: − 0.012, 95% confidence interval: − 0.021, − 0.003) Based on a meta-regression, these associations are stronger in areas where the average PM2.5 concentrations are lower, the mean returns are higher, and where the local stock market capitalization is low. Our results suggest that a 10 μg/m3 increase in PM2.5 exposure increases stock market volatility by 0.2% (regression coefficient 0.002, 95% CI 0.000, 0.004), but the city-specific estimates were heterogeneous. Meta-regression analysis did not explain much of the between-city heterogeneity. Our results provide global evidence that short-term exposure to air pollution both reduces daily stock market returns and increases volatility.

Shoreline Solutions: Guiding Efficient Data Selection for Coastal Risk Modeling and the Design of Adaptation Interventions

The Caribbean is affected by climate change due to an increase in the variability, frequency, and intensity of extreme weather events. When coupled with sea level rise (SLR), poor urban development design, and loss of habitats, severe flooding often impacts the coastal zone. In order to protect citizens and adapt to a changing climate, national and local governments need to investigate their coastal vulnerability and climate change risks. To assess flood and inundation risk, some of the critical data are topography, bathymetry, and socio-economic. We review the datasets available for these parameters in Jamaica (and specifically Old Harbour Bay) and assess their pros and cons in terms of resolution and costs. We then examine how their use can affect the evaluation of the number of people and the value of infrastructure flooded in a typical sea level rise/flooding assessment. We find that there can be more than a three-fold difference in the estimate of people and property flooded under 3m SLR. We present an inventory of available environmental and economic datasets for modeling storm surge/SLR impacts and ecosystem-based coastal protection benefits at varying scales. We emphasize the importance of the careful selection of the appropriately scaled data for use in models that will inform climate adaptation planning, especially when considering sea level rise, in the coastal zone. Without a proper understanding of data needs and limitations, project developers and decision-makers overvalue investments in adaptation science which do not necessarily translate into effective adaptation implementation. Applying these datasets to estimate sea level rise and storm surge in an adaptation project in Jamaica, we found that less costly and lower resolution data and models provide up to three times lower coastal risk estimates than more expensive data and models, indicating that investments in better resolution digital elevation mapping (DEM) data are needed for targeted local-level decisions. However, we also identify that, with this general rule of thumb in mind, cost-effective, national data can be used by planners in the absence of high-resolution data to support adaptation action planning, possibly saving critical climate adaptation budgets for project implementation.

Explaining the effective reproduction number of COVID-19 through mobility and enterprise statistics: Evidence from the first wave in Japan.

This study uses mobility statistics combined with business census data for the eight Japanese prefectures with the highest coronavirus disease-2019 (COVID-19) infection rates to study the effect of mobility reductions on the effective reproduction number (i.e., the average number of secondary cases caused by one infected person). Mobility statistics are a relatively new data source created by compiling smartphone location data; they can be effectively used for understanding pandemics if integrated with epidemiological findings and other economic data sets. Based on data for the first wave of infections in Japan, we found that reductions targeting the hospitality industry were slightly more effective than restrictions on general business activities. Specifically, we found that to hold back the pandemic (that is, to reduce the effective reproduction number to one or less for all days), a 20%–35% reduction in weekly mobility is required, depending on the region. A lesser goal, 80% of days with one or less observed transmission, can be achieved with a 6%–30% reduction in weekly mobility. These are the results if other potential causes of spread are ignored; for a fuller picture, more careful observations, expanded data sets, and advanced statistical modeling are needed.

US Real GDP Growth and Impact of Covid-19

The global pandemic, COVID-19, has exacerbated the Gross Domestic Product (GDP) growth of the global economy since its outbreak in December 2019. One of the most affected economies, due to the global pandemic, is the US economy, currently crippled by an increased number of COVID-19 related deaths, layoffs, reduced work hours, and other related natural disasters, such as winter storms. Hence, it is imperative that the damage done to the GDP growth is evaluated meticulously to craft favorable monetary and fiscal policies to uplift economic performance. One of the key yet debated methods used by many economists is utilizing real GDP per capita as an economic performance measurement tool. Using two economic datasets and a multiple regression model, we compared real GDP per capita performance in the US economy between the second and third quarters of 2020. The study finds that the impact seems detrimental due to restrictions imposed on economic activities, such as business closures, disturbances in the supply chain, employee layoffs and reduced work hours. However, in the third quarter of 2020 COVID-19 after some of the COVID-19 imposed restrictions were lifted, the real GDP per capita significantly increased.

RETRACTED ARTICLE: Research on the application of conditional generative adversarial nets in economic time series data analysis

The processing of time series data is the key technical field of financial data analysis. With the continuous development of computing science, deep learning has a revolutionary impact on the traditional computing model. Among them, the generative adversarial nets GANs has achieved desirable results in the field of data generation. Revolving around the conditional generative adversarial nets cGANs, an effective Bi-LSTM generator, CNN discriminator and data processing method are designed in this paper. Also, the experiments on two economic datasets including the stock and commodity price are implemented. The results show that compared with the traditional model, the prediction performance of this research method witnesses a great improvement and it can be employed to better deal with the analysis task of non-stationary data, which is a significant point contributed by the research. In addition, the details associated with the generator mode and GANs model optimization are reported and discussed in combination with the actual situation of the experiment, and the existing problems are further explained and discussed.

The Problem of Clustering Countries of the World on the Geopolitical Data

This article deals with the problem of clustering countries by a specific set of geo-political features, including demographic, geographical, economic, and other data. The K-means method was chosen as the clustering algorithm. This clustering allows us to identify the specifics and important trends in the development of the current geopolitical situation.

Socio-economic urban scaling properties: Influence of regional geographic heterogeneities in Brazil.

The recent efforts dedicated to understanding important features and consequences of city growth have profited from the scaling approach to urban indicators. This kind of analysis can be conveniently used to investigate the impact of geo-economic transformations, like fast urbanization and industrial development, which occurred in continental size countries (e.g., India, China, and Brazil) during the past half-century. Profiting from high quality data, this work explores how scaling relationships among urban indicators are influenced by strong regional heterogeneities in Brazil. It is based on economic, infrastructure and violence related data sets for the time interval 2002-2016. Results indicate that regional specificities related to infrastructure, economic development, and geography have a larger influence on the absolute value of the urban indexes. Regional scaling similarities and differences among Brazilian regions were also uncovered. Interesting enough, the results indicate that the richest and poorest Brazilian regions share similar scaling behavior, despite all huge different local influences. By contrast, the results for the two richest regions, with similar average values of urban indexes and the same kind of local influences have rather different scaling properties. Thus, scaling analysis suggests that distinct political solutions might be necessary to improve life's quality, even for two regions with similar average values of urban indicators.

“Thinking and Working Politically”: The case of donor‐supported reform coalitions in the Philippines

AbstractMotivationReform coalitions represent a potential means for donors to support macro‐political reform in developing countries, enabling them to avoid the limitations of micro‐political or public sector reform where appropriate circumstances exist. Donor support for reform coalitions, however, is difficult to engineer and fraught with political risk, including the risk of partisan taint.PurposeThis article therefore explores the efforts of Australian Aid and the World Bank to promote reform coalitions and constituencies in the Philippines during the presidency of Benigno S. Aquino III (2010–2016) and to institutionalize the reform agenda of his administration.Approach and MethodsIt draws on a range of sources including programme design documents, mid‐term and end‐of‐term reviews, and contextual policy documents. It also draws on economic and governance data sets to explore relevant correlations.FindingsIt finds that second‐generation reform coalitions brought together stakeholders from government, the private sector and civil society and promoted economic and other reforms. These donor strategies arguably failed, however, with the election of Rodrigo Duterte in May 2016. He was critical of Aquino’s record in office and ostensibly committed to reverse engineering many of the Aquino administration’s achievements. The article critiques a governance‐centric explanatory chain of causation and proposes an expanded politics‐centric chain.Policy ImplicationsIn conceptual terms, planning of reform coalition activities must better account for dominant coalitions and the political settlements which they underpin, and must set out explicit theories of change, specifying distinct political pathways to change. In operational terms, donors must work to avoid traditional “transactional” relationships in favour of more innovative “transformative” ones and must balance “strategic opportunism” with strategic retreat and adaptation where necessary.

Economic impact of sudden cardiac arrest

Abstract Background Each year, there are approximately 5,000 out-of-hospital cardiac arrests (OHCAs) in the state of Victoria, Australia (population 6.4 million, state healthcare budget AUD$2.9 billion / €1.8billion). Mortality from OHCA approaches ninety percent. High mortality rates and survivors not returning to work is likely to have an adverse effect on the Victorian economy but this has not been previously investigated. Purpose To model the economic impact of OHCA mortality and survivors not returning to work. Methods Data on all OHCAs transported by Ambulance Victoria from July 2017- June 2018 in Victoria, Australia was collected, including age, gender, survival to hospital, survival to discharge, and survival to 12 months. Cases were excluded if arrest was precipitated by trauma, exsanguination, overdose, terminal illness, hanging, SIDS, electrocution, sepsis, respiratory causes, drowning, or neurological causes. Pre-arrest employment status of patients was modelled using the Australian Bureau of Statistics Economic Security dataset, which provides contemporary employment rates for gender-matched five-year cohorts for Australians aged 15–79 years. For survivors to 12 months, pre-arrest and post-arrest work status were confirmed. Economic impact was then calculated to a five year horizon utilizing a Markov model with probabilistic sensitivity analysis. Results 4,934 arrests meeting the inclusion criteria were transported by Ambulance Victoria in twelve months, of whom 4,639 were determined to be cardiac arrests without any exclusion criteria as a precipitant. 695 patients survived to hospital (15.0%), and 325 to discharge (7.0%). At 12 months, 303 patients were alive (6.5% of overall cases, 93.2% of those discharged from hospital). Economic modelling of age and gender-matched data indicated that 1516 patients (35%) would have been employed pre-cardiac arrest, but only 216 survivors (4.7%) would be employed at five years post-arrest. Using Markov modelling incorporating estimated earnings and the pre-determined value of a statistical life, the annual economic burden of cardiac arrest approximated AUD$4 billion (€2.5 billion) at a five-year horizon. Conclusion The annual economic impact of cardiac arrest in Victoria, Australia is approximately AUD$4 billion (€2.5 billion) in a five-year horizon. As the annual Victorian state budget for all healthcare is AUD$2.93 billion (€1.8 billion), our data suggests that the economic impact of cardiac arrest is under-appreciated. Therefore, research in this area and providing state-of-the-art care for all cardiac arrest patients should be a healthcare priority. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): NHMRC/NHF Postgraduate Scholarship, RACP JJ Billings Scholarship

Identifying territory-linked family business groups: a methodological proposal

PurposeThe purpose of this paper is to design a methodology to identify territory-linked family business groups (TLFBGs) in order to overcome the methodological challenges and ease studies about family business groups' (FBGs) impact on territories.Design/methodology/approachThe paper applied an algorithm to a data set of firms located in Gipuzkoa that were registered in the SABI database in 2018.FindingsThe paper defined a new construct, TLFBGs, and proposed a methodology that automatized the identification of TLFBGs by a seven-stage algorithm that was intended to be applicable to any firm-level economic and financial data set, including all registered firms and not only listed firms.Practical implicationsTLFBGs unveil the real relevance that family businesses have in the territorial development, encouraging the political support to family business. Additionally, the methodology provided allows understanding growth processes of family business.Originality/valueThe paper defines a new construct, TLFBGs, that highlights both the underexplored links existing between family and territory and between family and business groups, providing the process and criteria to capture it. The paper opens up large-scale empirical research on the social (and economic) influence of TLFBGs in territorial development.

Research on trend analysis method of multi-series economic data based on correlation enhancement of deep learning

The analysis on economic data based on time series takes an important position in the field of analysis on time-series data and is also an important task of the field of big data and artificial intelligence. Traditional time-series analysis method is of relatively weak competence in dealing with multi-series analysis. In this research, based on the problem associated with the analysis on time-series economic data, efficient handling method and model are put forward in the face of multi-series analysis task. Also, combined with the association rules, trend correlation and self-trend correlation among multiple series, a trend and correlation deep neural network model (TC-DNM) is established and then tested and verified by using three kinds of economic datasets with representativeness based on the trend analysis task handed by multi-series analysis. The results show that the model proposed in this research is effective than a number of baseline models, can be employed to achieve precision–recall balance and also possesses strong reusability. The two correlation models and joint models in this paper are of peculiarity and innovativeness.

Small-Time Clientelism

The extant literature on clientelism has tended to focus on large parties. This article asks what happens to small parties that operate in an electoral system dominated by large clientelistic parties. Our case study is the Romanian National Liberal Party (PNL). During the last two decades, it has become the second-largest party in Romania. This is unusual because the liberals in post-communist countries are politically weak. The main base of liberal support is typically urban middle classes. By contrast, PNL's electoral base is in the economically underdeveloped rural world. Using electoral statistics, economic datasets, and qualitative analysis, we demonstrate that PNL's success was due to pork-barrel spending and the development of clientelistic networks in which local mayors play a key role. PNL was able to grow by following the example of the dominant parties and creating networks of loyal mayors. In turn, this opens perspectives for research about clientelism in small communities, as well as comparative studies based on small parties.

Analysing the Economic Performance and Projecting for Future: The Case of Malaysia

After Independence, Malaysia had originally started with a focus on diversifying agricultural exports by growing and strengthening its export earnings and income. This approach was pursued as a reliable economic development strategy. However, with the resulting rapid development, the country transited from an agrarian economy to a manufacturing-centric economy.Considering the diverse trends of Malaysia’s economy due to the globalization and privatization, this study aims to analyze the past trends of the economy and mainly concentrate on the current and future scenario in this country. By following a historical research method and by considering the economic data sets for the period of 2005-2016, this paper finds that the impact of globalization, the impact of enterprise privatization on economic growth, job creation, and on FDI inflow, vary significantly over ten years under consideration.

Economic Disasters: A New Data Set

This study presents the construction and results of a global economic disaster data set. Our data set complements Barro and Ursúa's (2008, 2012) data. We extend the number of covered countries before and especially after World War II. For each disaster, we provide data on the country, starting and ending year, size, duration, time of recovery, size of population, and level of development.

Evaluation of Water Yield and Soil Erosion in the Three-River-Source Region under Different Land-Climate Scenarios

Exploring the water yield and soil conservation in the Three-River-Source region is of great significance for evaluating both the ecological stability of the Qinghai-Tibet Plateau, Yellow River basin, Yangtze River basin and Lancang River basin and the sustainable development of human society. The data sources for this study were land use/cover data from four phases (2000, 2005, 2010 and 2015), daily precipitation and temperature datasets, and the 1:1000000 Chinese soil database. These data were combined with vector data, such as data on settlements, roads, and rivers, along with population, economic raster datasets and CCSM4 common climate model prediction results. The Three-River-Source region was taken as the study area, and four land use/cover development scenarios and two climate change scenarios were designed based on the FLUS model and the downscaling correction method. The InVEST model was used to quantitatively simulate the water yield and soil erosion under different scenarios in the study area in 2030. The results showed the following: (1) Under different land use/cover development scenarios, grassland remained the dominant land use/cover type in the Three-River-Source region, and the area ratio was always greater than 67%. (2) Under the RCP4.5 climate scenario, the annual water yield and soil erosion increased by more than 7% and 3.9%, respectively. Under the RCP8.5 climate scenario, the annual water yield and soil erosion decreased by more than 3.3% and 1.3%, respectively. (3) Climate change played a leading role in the changes in water yield and soil erosion. Climate change contributed as much as 89.97%–98.00% to the change in water yield and 60.49%–95.64% to the change in the soil erosion modulus. However, the contribution of land use/cover changes to the change in regional water yield was only 2.00%–10.03%, and the contribution of the soil erosion modulus change was 4.36%–39.91%. Therefore, the land use development strategy in the Three-River-Source region should comprehensively consider issues such as regional development, the input of returning farmland to forest and grassland, and the resulting ecological benefits.

An Extensible Model for Historical Financial Data with an Application to German Company and Stock Market Data

Broad, long-term financial and economic datasets are a scarce resource, in particular in the European context. In this paper, we present an approach for an extensible, i.e. adaptable to future changes in technologies and sources, data model that may constitute a basis for digitized and structured long- term, historical datasets. The data model covers specific peculiarities of historical financial and economic data and is flexible enough to reach out for data of different types (quantitative as well as qualitative) from different historical sources, hence achieving extensibility. Furthermore, based on historical German company and stock market data, we discuss a relational implementation of this approach.

Weighing down America: 2020 Update a Community Approach against Obesity

The economic and social cost of obesity in America is at an all-time high and continues to rise. We estimate its cost to be roughly 6.76 percent of gross domestic product (GDP) in 2018 compared to 5.57 percent in 2014, or in dollar terms, $1.389 trillion in 2018 compared to $0.976 trillion in 2014. The current pandemic is likely to raise this cost even higher.Although Black and Hispanic populations, and individuals of all races aged 40-59 are most at risk, the latest statistics show noticeable growth in obesity prevalence, from 2014 to 2018, within the male population, especially white males and males of all races aged 20-59. In contrast, the data look more encouraging for women. The obesity rates among Hispanic women and all women aged 40-59 show a marked decline during the same period.Obesity impacts segments of the US population differently based on their behavioral and socioeconomic profiles. The Milken Institute COVID-19 Community Explorer sorts US counties around eight profiles of communities that share common patterns across behavioral, economic, and social factors. This report uses these communities and identifies which of the 26 factors considered are systemically correlated with high obesity rates for each community. This report identifies three groupings that matter for a large part of the US population:• Social and behavioral factors, such as unemployment, excessive drinking, smoking, post-secondary education, and single-parent households, have the strongest association with obesity prevalence across all eight communities' profiles.• Urban-rural factors, including rurality, housing concerns, population density, metropolitan area, violent crime rate, and the number of fast-food establishments per 100,000 people, have the second strongest association with obesity prevalence across four communities, representing 78 percent of the US population.• The Black population factor has the third strongest association with obesity prevalence across four communities. These communities represent 61 percent of the US population.The analysis combines health, behavioral, economic, and social data sets and suggests that some aspects of the obesity epidemic would be better addressed at the local level, while others would benefit from a federal initiative. It also identifies factors for each community to consider when coordinating national and local authorities and other partners such as health-care professionals, business and community leaders, school, and child care. Finally, our analysis shows that the data sets collected need to be properly combined, processed, and analyzed to inform policy in a meaningful and actionable manner.

A Scoring Rule for Factor and Autoregressive Models Under Misspecification

Factor models (FM) are now widely used for forecasting with large set of time series. Another class of models, which can be easily estimated and used in a large dimensional setting, is multivariate autoregressive models (MAR), where independent autoregressive processes are assumed for the series in the panel. We compare the forecasting abilities of FM and MAR models when assuming both models are misspecified and the data generating process is a vector autoregressive model. We establish which conditions need to be satisfied for a FM to overperform MAR in terms of mean square forecasting error. The condition indicates in presence of misspecification that FM is not always overperforming MAR and that the FM predictive performance depends crucially on the parameter values of the data generating process. Building on the theoretical relationship between FM and MAR predictive performances, we provide a scoring rule which can be evaluated on the data to either select the model, or combine the models in forecasting exercises. Some numerical illustrations are provided both on simulated data and on wel-known large economic datasets. The empirical results show that the frequency of the true positive signals is larger when FM and MAR forecasting performances differ substantially and it decreases as the horizon increases.