Regional Economic Data Research Articles

Human Capital and Growth in Arctic Regional Economies: Evidence, Policies and Institutional Perspectives

Abstract The Arctic is a region known for its natural resource endowments and challenging climate. However, evidence from elsewhere suggests that human capital development might also be vital to Arctic economic growth. This article explores the role human capital plays in economic development in the Arctic through analysis of Arctic regional demographic and economic data. The available data preliminarily suggests that human capital stock growth in Arctic regions – as represented by the population with higher education – correlates more closely with economic growth than growth in the general population. The analysis is less conclusive on physical capital development’s connection to economic development as it is with human capital growth. Based on these findings, the article also highlights Arctic-centered institutions and policies that seek to bolster the Arctic region’s human capital stock.

Effects of regional network economies on industrial productivity in Japan: dynamic total factor productivity function approach

AbstractImproving productivity performance is essential for achieving regional economic sustainability. With the advent of the network society, regional economic agents have benefited not only from local within-region interactions but also from external out-of-region economies. However, it remains unclear whether localized external economies or spillovers of externalities through inter-regional networks are more significant in improving productivity performance. Therefore, this study examined the relationships between productivity performance and geographical externalities from an industry perspective using regional economic data for Japan and employed a dynamic total factor productivity function approach. It also assessed the impact of high-quality transportation infrastructure improvements on productivity catch-up. The results revealed the long-run effects of inter-regional networks on the productivity performance of industries. Furthermore, it revealed that, as a geographical externality, the “borrowed size” effect contributed significantly to productivity improvements in both manufacturing and non-manufacturing industries. These findings suggest that enhancing both high-quality transportation infrastructure and inter-regional transportation networks is likely to improve industry productivity performance.

Tapestry: Collaborative Tool for Regional Data and Modeling

This paper introduces and describes the Tapestry regional economic data and modeling project. Tapestry is an open source set of data generating algorithms, harmonization bridges, data organizing procedures and data models. It is also a database that provides access to Tapestry also represents a collaborative environment where researchers can use and improve data generating algorithms and methods. Tapestry represents many types of regional economic data and models, but ultimately it is designed to generate inter-regional social accounting matrices for every county in the United States.

Multi-period urban hydrogen refueling stations site selection and capacity planning with many-objective optimization for hydrogen supply chain

The location and quantity of hydrogen refueling stations (HRSs) play a crucial role in the development and promotion of hydrogen fuel cell vehicles (HFCVs). This study proposes a multi-period urban HRSs site selection and capacity planning with many-objective optimization framework for hydrogen supply chain (HSC). Firstly, the city's multi-period hydrogen requirement is predicted based on a generalized Bass diffusion model. Using publicly available data, including gas station network data, geographic information system (GIS) data, population data, and regional economic data, a spatially aggregated demand model is established to allocate hydrogen requirement at candidate sites in the city of the geographic grid model with a 1 km resolution. On this basis, four interrelated objective functions (total investment cost, hydrogen requirement coverage, risk coefficient, and environmental factors) are developed. The third-generation non-dominated sorting genetic algorithm (NSGA-III) and the technique for order preference by similarity to an ideal solution (TOPSIS) are employed to achieve multi-period urban HRSs site selection and capacity planning with many-objective optimization for HSC. By comparing the results of single-objective optimization focusing on minimum cost and maximum hydrogen requirement coverage, it is observed that many-objective optimization achieves a better balance among the four conflicting objectives. After comprehensive analysis, the distribution of HRSs exhibits a clustered structure, influenced by the population and economic structure of the city. In the initial stages of HFCV development, most stations serve the peripheral areas around the city center; while n the later stages of the optimization period, a few high-capacity HRSs begin to concentrate in the city center.

Where does Xinjiang fit within the Belt and Road Initiative? Indications from CCP Documents and China’s Steel industry

ABSTRACT China’s Belt and Road Initiative (BRI) is an ambitious, contentious, and world-spanning project. Using Chinese Communist Party (CCP) documents, this paper examines one of the stated purposes of the BRI: to address regional inequality between China’s east and west, focusing upon Xinjiang. Through an analysis of regional economic data, the demographics of Xinjiang and Xinjiang governing bodies, and a case study of the Chinese steel industry, this paper shows that the BRI has so far failed to check increasing regional inequality in China and that job opportunities for local communities in Xinjiang are constrained by social and political factors.

Retraction Note: Rural regional economic data management based on blockchain technology

Retraction Note: Rural regional economic data management based on blockchain technology

Fiscal decentralization and citizens' satisfaction with public services: Evidence from a micro survey in China

Our study matches multiple rounds of China Family Panel Studies (CFPS) data from 2012 to 2020 with regional macro fiscal and economic data, and explores the impact of regional fiscal decentralization on citizens' satisfaction with public services from the subjective perception of individual respondents. The study found that the increase in regional fiscal decentralization has a significant positive effect on improving the satisfaction of citizens with public services. However, due to differences in individual characteristics among the surveyed individuals, this effect has obvious heterogeneity. Mechanism analysis showed that the enhancement of local government's awareness of public service supply and the increase of regional fiscal transparency have a promoting effect on the improvement of citizens' satisfaction with public services. From the perspective of the convergence in the perception of public service satisfaction among citizens within the same province, the increase of fiscal decentralization has not effectively promoted the equalization development of public services at the provincial level in China.

Incorporating short data into large mixed-frequency vector autoregressions for regional nowcasting

Abstract Interest in regional economic issues coupled with advances in administrative data is driving the creation of new regional economic data. Many of these data series could be useful for nowcasting regional economic activity, but they suffer from a short (albeit constantly expanding) time series which makes incorporating them into nowcasting models problematic. Regional nowcasting is already challenging because the release delay on regional data tends to be greater than that at the national level, and ‘short’ data imply a ‘ragged edge’ at both the beginning and the end of regional data sets, which adds a further complication. In this paper, via an application to the UK, we investigate various ways of including a wide range of short data into a regional mixed-frequency vector autoregression (MF-VAR) model. These short data include hitherto unexploited regional value-added tax turnover data. We address the problem of the two ragged edges by estimating regional factors using different missing data algorithms that we then incorporate into our MF-VAR model. We find that nowcasts of regional output growth are generally improved when we condition them on the factors, but only when the regional nowcasts are produced before the national (UK-wide) output growth data are published.

Decarbonization potential of regional combined heat and power development

Industrial decarbonization is a significant challenge to national net zero goals. Combined heat and power (CHP) technologies provide a means by which industrial facilities can achieve greater energy efficiency and decrease carbon emissions through cogenerating electric power and process heat. Regions possessing sufficient population density and manufacturing capacity have significant technical potential for industrial CHP development and can experience economic and environmental benefits. However, CHP implementation costs and the complexity of projects represent significant barriers to deployment with successful implementation often relying on government incentives to achieve economic feasibility. In this research, a framework is developed to evaluate the economic feasibility and environmental viability of various CHP technical potential under the various market penetration scenarios. A case study for a region in the United Sates is presented to highlight the efficacy of the framework. Projected CHP generating capacity of the region is calculated using commercial and industrial specific capacity factors. Comprehensive CHP engineering and cost data are integrated with regional economic transaction data. Then, comparative CHP application analysis is performed to determine which configuration could have the greatest regional economic and environmental impacts. Results indicated that investment in industrial topping cycle CHP to achieve the maximum technical potential generating capacity would have the greatest regional impacts resulting in $20 billion of economic outputs, creating 89,000 jobs, and avoiding approximately 3.2 million metric tons of carbon dioxide emissions. Results from this and similar research may be used by policymakers and administrators to assess potential investments in industrial decarbonization technologies and pathways.

Religion and Economic Growth: Evidence from U.S. Counties

This paper examines the impact of religious participation on regional economic growth. Using data on GDP growth for United States counties from the Bureau of Economic Analysis (BEA) Regional Economic Accounts and data on county-level religious participation from the Association of Religion Data Archives (ARDA), this paper estimates the impact of religiosity on growth using two-way fixed-effects Barro regressions for the period 2000 to 2020. In our preferred specification, a ten percentage-point increase in the county religious adherent share reduces the 10-year compound annual growth rate of per-capita GDP by 0.14 percentage points (a 19% reduction relative to the sample mean). A battery of sensitivity checks suggests our results are unlikely to be driven by omitted variable bias: both the Oster (2019) adjustment for selection on unobservables and Kinky Least Squares (KLS) regression estimates indicate that OLS understates the negative impact of religion on regional economic growth. We argue that the negative impact of religion on regional economic growth is consistent with previous findings of increased business survival and increased small business activity in a framework where the social capital generated from religious participation results in inefficiently low regional dynamism.

Evaluating economic impacts of protected areas in contexts with limited data; the case of three national parks in Iceland

Economic impacts of protected areas (PAs) are receiving more attention in recent years and methodology in this area is advancing. Multiple studies have illustrated that PAs are a potent land use strategy to generate multiple and direct economic benefits. These benefits are driven by tourism as the central economic activity in PAs worldwide. This study takes the case of Snæfellsjökull, Vatnajökull and þingvellir National Parks (NPs) in Iceland, characterized by limited regional economic data and multi-destination and -purpose visitor travel patterns. Its main objective is to advance understanding of the economic impacts related to PAs in the context of limited data availability. Our analysis is based on the widely used Money Generation Model (MGM2) -methodology, localized to the Icelandic context by using Icelandic labour data and national input-output (I–O) tables regionalized using the Flegg Location Quotient (FLQ). We provide a consistent approach for handling multi-destination and -purpose trips, and separating spending data between local and overall impacts. Based on 2019 visitor and economic data, the visitors (N = 2087) spent, on average, $113 per day in the parks and generated estimated total economic impacts between $30–99 MM with 347–1140 jobs generated across the study sites. For example, in Vatnajökull NP's southern region, the jobs supported locally by the park constituted 36% of all the jobs in the municipalities. Combined tax revenue to the state from the three parks was $88 MM. The localized methodology generated similar economic impacts as earlier studies but showed that employment impacts were previously overestimated by the default models. Our approach and findings can be used as a reference for others applying the MGM2 or similar methods, and they support policy development, decision-making and informed discussion between researchers, practitioners in PA and tourism management, municipalities and communities around PAs. Being able to show economic impacts is increasingly important for PAs to ensure sustained funding amid budget cuts and the transition of government bodies to business units. Limitations of the study include a lack of winter data for Vatnajökull and þingvellir NPs and broad categorization of the Icelandic economic data used in the I–O table regionalization. In further research, a comprehensive sustainability analysis is needed to complement the economic impact analysis and site-specific factors could be analysed in more detail.

RETRACTED ARTICLE: Rural regional economic data management based on blockchain technology

In the process of continuous development of rural economy, big data management plays an important role. Rural economic data mainly include rural labor force, added value of agriculture, forestry, fishery and animal husbandry, agricultural product output and per capita net income. Using big data to manage economic data can not only integrate and analyze a large number of data, build an economic management analysis database, but also predict and warn related risks in time and provide reasonable and effective economic management suggestions, thus ensuring the smooth operation of the economy. However, there are some shortcomings in data reliability, data quality and data management efficiency in rural regional economic data management, which have a great impact on the long-term stable development of the economy. This paper makes a systematic analysis of rural regional economic data, uses the blockchain consensus mechanism to quickly manage and analyze different types of economy in rural areas, and improves the speed of economic data analysis through smart contracts. This paper makes a comparative analysis of rural regional economic data management before and after adopting blockchain technology. In view of this phenomenon, according to the development demand of data management, this paper uses blockchain technology to effectively analyze and study the big data management of rural regional economy, and makes a comparative analysis through the tests of data operation efficiency, data quality, data management security and sharing degree. The experimental data show that the efficiency of data operation is as high as 1100 times per minute after the combination of blockchain technology, and the minimum manual modification ratio of node data is 0.24. This shows that the data quality of technical means is guaranteed. The data leakage rate per hour is at least 66%, and the data sharing volume is maintained at about 8000 views/hour and 7000 references/hour. The improvement of quality and efficiency can ensure the effectiveness of data management and improve the speed of economic development in rural areas, thus promoting good economic development. This paper uses blockchain technology to manage rural economic data, which has certain reference value for rural economic development.

Prediction models and testing of resilience in regions

A significant amount of research has been conducted regarding the resilience of the regions and the factors that contribute to allow them to face challenges, crises, or disasters. The rise of promising sectors like Machine learning (ML) and Artificial Intelligence (AI) can enhance this research using computing power in regional economic, social, and environmental data analysis to find patterns and create prediction models. Through Machine Learning, the following research introduces the use of models that can predict the performance of a region in disasters. A case study of the performance of USA Counties during the Covid19 first wave period of the pandemic and the related restrictions that were applied by the authorities was used in order to reveal the obvious or hidden parameters and factors that affected their resilience, in particular their economic response, and other interesting patterns between all the involved attributes. This paper aims to contribute to a methodology and to offer useful guidelines in how regional factors can be translated and processed by data and ML/AI tools and techniques. The proposed models were evaluated on their ability to predict the economic performance of each county and in particular the difference of its unemployment rate between March and June of 2020. The former is based on several economic, social, and environmental data -up to that point in time- using classifiers like neural networks and decision trees. A comparison of the different models' execution was performed, and the best models were further analyzed and presented. Further execution results that identified patterns and connections between regional data and attributes are also presented. The main results of this research are i) a methodological framework of how regional status can be translated into digital models and ii) related examples of predictive models in a real case. An effort was also made to decode the results in terms of regional science to produce useful and meaningful conclusions, thus a decision tree is also presented to demonstrate how these models can be interpreted. Finally, the connection between this work and the strong current trend of regional and urban digitalization towards sustainability is established.

Investigation on the spatial and temporal patterns of coupling sustainable development posture and economic development in World Natural Heritage Sites: A case study of Jiuzhaigou, China

To continue directing global sustainable development efforts from 2015 to 2030, the United Nations adopted 17 global development goals known as the Sustainable Development Goals (SDGs) when the Millennium Development Goals (MDGs) from 2000 to 2015 expired. Sustainable development of World Natural Heritage Sites is one of these 17 MDGs and a crucial step toward achieving global sustainability. A scientific and systematic indicator system that can measure the sustainable development of natural World Heritage Sites more objectively and fairly is urgently needed to support the establishment of SDG11.4 on a Chinese scale and to help with the subsequent promotion of the development of natural World Heritage Sites. This study proposes a comprehensive assessment indicator system for the sustainable development of natural heritage sites based on the theoretical framework of “value contribution-environmental effect” to quantify the sustainable development of natural heritage sites. The study is based on the ecological environment and regional economic and social data of Jiuzhaigou World Natural Heritage Site from 2010 to 2020. Finally, the degree of coupling and coordination between the natural environment and economic development is assessed and studied. The results show that tourism to the World Heritage Site drove rapid economic development in Jiuzhaigou County between 2010 and 2020. As the fame of the World Heritage Site Jiuzhaigou has grown, so has the per capita income of local locals, making them unduly reliant on tourists for a living. Meanwhile, both the 2017 earthquake and the COVID-19 epidemic in 2019 have had substantial detrimental effects on the local economy. Furthermore, the Jiuzhaigou sustainable development trend from 2010 to 2020 exhibits a “W-shaped” curve, and there is a high level of positive coupling between the Jiuzhaigou sustainable development trend and economic development, and the two are mutually reinforcing.

Effects of Smart Specialisation on Regional Labour Resilience

The global economy has experienced great volatility and uncertainty during the last dec-ades. Economic effects of global recession in the period 2008-2009 showed to be diverse in terms of ter-ritorial impacts. This has raised interest in the empirical investigation of the causes of such territorial differences and supported the increase in literature dealing with the resilience concept and determi-nants of regional economic resilience. This research addresses literature gaps in understanding the role of smart specialisation process in regional labour resilience, as it is one of the cornerstones of the new place-based regional development policy approach in the European Union (EU). To this end, we have de-veloped a new proxy for smart specialisation and employed the data for EU regional labour resilience for two different periods, recession (2007-2009) and recovery (2009-2014), which is determined based on re-gional economic performance data. Then, the EU regions were grouped in four categories considering re-sistance and recovery dimension of the resilience concept. We provide the extension of the existing liter-ature by separately analysing the recovery dimension of the resilience concept in the short and long run. The multinomial logistic model enabled us to examine in detail the differential effects of all relevant re-silience determinants. Research results indicate significant and positive effects of smart specialisation on regional labour resilience, especially for regions of the most resilient group. Furthermore, our study con-firmed the significance of other determinants for regional labour resilience, such as stage of regional de-velopment, regional economic structure, population and education. The findings could be used for estab-lishing the theoretical background for important socio-economic channels through which smart special-isation affects regional labour resilience and creating effective regional development policy measures.

Creative reindustrialization: Data base as a digital footprint for second-tier cities of the Urals and Siberia

The modern stage of development of research in the field of regional economics involves the application of a wide coverage of regional economic systems data. Their overall totals form a digital trace of the development of the territory. A scientific digital footprint of the key factors and patterns of creative industries in the second-tier cities of Siberia and the Urals, reflected in the web application, allows imagining, comparing and comprehending the current state and perspectives of creative industries in second-tier cities. In this article, considering the demography dynamics and diversification of creative business activity as well as data on created enterprises, the author highlights the cities that are interested in creative reindustrialization the most – oil and gas specialize cities of Khanty-Mansiysk autonomous okrug – Megion, Nefteyugansk, Nizhnevartovsk, Nyagan, and Uray. Second-tier cities that display a relatively higher level of economic activity diversification encompass Novokuznetsk, Biysk, Nyagan, Prokopyevsk, Rubtsovsk, Sysert, and Uray. At the same time large industrial centers in regions provide more favorable conditions for the development of creative enterprises. Whereas in the regional context, the Tomsk region and Altai Krai lag behind other regions in absolute terms. Thus, the first obvious strategical step in municipal development through creative reindustrialization is using the essential opportunities highlighted by demography conditions, mainly in oil and gas towns. Whereas large industrial centers in regions could use more favorable conditions for the development of creative enterprises for its further development.

The Economic Opportunity Mapping (EOM) Tool

Extension professionals increasingly understand data as integral to economic development planning and related efforts. However, regional economic data is often inaccurate, expensive, and unengaging for stakeholders. The Economic Opportunity Mapping Tool provides industry-specific free online interactive maps to engage stakeholders in the process of economic development planning, while also helping connect the determinants of business location with real local data on industry establishments.

Inversion of Regional Economic Trend from NPP-VIIRS Nighttime Light Data Based on Adaptive Clustering Algorithm

Night lighting is closely related to social and economic development. Inversion of socioeconomic parameters using nighttime light (NTL) remote sensing data is a research hot spot currently. In this paper, a calibration method based on adaptive clustering algorithm for the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data was proposed to remove background noise in the original imagery. The validity of the calibration method was evaluated through comparing the correlation between the corrected NTL data and the regional economic data. The result indicated that the NTL data obtained by this calibration method have higher correlation with the regional GDP data, and the values of R2 and the root mean square error (RMSE) were 0.8531 and 133.18, respectively. On this basis, the total nighttime light (TNL)-gross domestic product (GDP) regression model obtained from this paper was used to invert the GDP of Liaoning Province from 2012 to 2016. Using the TNL-GDP regression model established in high-quality regions to verify the fraudulent economic statistics of Liaoning Province, it can be proved that NTL data can be a reliable reference for reflecting regional economic development trends.

Impact of High-Tech Industries Over Time

Macro level research shows that the impact of technology varies. On a micro level, strategic technology priorities of businesses have changed over time, translating to different macro level impact. In this study, the objective is to investigate trends of the economic impact of high-tech industries over time, thereby addressing the variations in high-tech impact. Through an analysis of extensive time series regional economic data on U.S. metropolitan regions, the results show that high-tech industries began to influence regional economic performance at the turn of the millennium, escalating in importance before a decline. The study provides explanations and has implications for theory, policy, and strategic management.

Disasters and Investment: Assessing the Performance of the Underlying Economy Following a Large-Scale Stimulus in the Built Environment

Disasters are often followed by a large-scale stimulus supporting the economy through the built environment, which can last years. During this time, official economic indicators tend to suggest the economy is doing well, but as activity winds down, the sentiment can quickly change. In response to the damaging 2011 earthquakes in Canterbury, New Zealand, the regional economy outpaced national economic growth rates for several years during the rebuild. The repair work on the built environment created years of elevated building activity. However, after the peak of the rebuilding activity, as economic and employment growth retracts below national growth, we are left with the question of how the underlying economy performs during large scale stimulus activity in the built environment. This paper assesses the performance of the underlying economy by quantifying the usual, demand-driven level of building activity at this time. Applying an Input–Output approach and excluding the economic benefit gained from the investment stimulus reveals the performance of the underlying economy. The results reveal a strong growing underlying economy, and while convergence was expected as the stimulus slowed down, the results found that growth had already crossed over for some time. The results reveal that the investment stimulus provides an initial 1.5% to 2% growth buffer from the underlying economy before the growth rates cross over. This supports short-term economic recovery and enables the underlying economy to transition away from a significant rebuild stimulus. Once the growth crosses over, five years after the disaster, economic growth in the underlying economy remains buoyant even if official regional economic data suggest otherwise.