Spatial Panel Regression Research Articles

Seeing the forest and the trees: Holistic view of social distancing on the spread of COVID-19 in China

The human social and behavioral activities play significant roles in the spread of COVID-19. Social-distancing centered non-pharmaceutical interventions (NPIs) are the best strategies to curb the spread of COVID-19 prior to an effective pharmaceutical or vaccine solution. This study investigates various social-distancing measures’ impact on the spread of COVID-19 using advanced global and novel local geospatial techniques. Social distancing measures are acquired through website analysis, document text analysis, and other big data extraction strategies. A spatial panel regression model and a newly proposed geographically weighted panel regression model are applied to investigate the global and local relationships between the spread of COVID-19 and the various social distancing measures. Results from the combined global and local analyses confirm the effectiveness of NPI strategies to curb the spread of COVID-19. While global level strategies allow a nation to implement social distancing measures immediately at the beginning to minimize the impact of the disease, local level strategies fine tune such measures based on different times and places to provide targeted implementation to balance conflicting demands during the pandemic. The local level analysis further suggests that implementing different NPI strategies in different locations might allow us to battle unknown global pandemic more efficiently.

Local Ceasefires and De-escalation: Evidence From the Syrian Civil War

Local ceasefire agreements are prevalent in modern civil wars, but we know little about their impact. This study analyzes geo-referenced data on 145 local ceasefire agreements declared during the Syrian Civil War, 2011-2019, exploring their short- and long-term effects on the intensity of armed violence. Drawing on scholarship on gradual confidence-building processes, we theorize the conditions under which local ceasefire agreements may generate trust and reduce conflict intensity. Using interrupted time-series analysis and spatial panel regression, we examine factors relating to the design and strategic context of ceasefire agreements. We find that local ceasefire agreements can trigger both escalation and de-escalation in the short term. De-escalatory outcomes are more likely in the long-term, when ceasefire signatories share a history of previous interaction, and when ceasefires are implemented in a stepwise fashion. We also find evidence of spatial diffusion: local ceasefire agreements in one area reduce conflict intensity in neighboring areas.

Heterogeneous environmental regulation tools and green economy development: evidence from China

The implementation of environmental regulation policy by the government is usually an effective tool to reduce local pollution emissions. The super-efficient SBM model of unexpected output was used in this study to estimate green economy development levels in 30 Chinese provinces from 2010 to 2019 and constructed a panel econometric model. It empirically tested the theoretical hypothesis and mechanism of heterogeneous environmental regulation affecting green economy development by using the least squares estimation, the instrumental variable method, spatial panel regression, the mediating effect model, and further models. The relationship between command-controlled environmental regulation and green economy development was U-shaped, whereas green economy development was not significantly influenced by market-incentive environmental regulations. Command-controlled environmental regulation affected green economy development via the progress of pollution control technology and production technology. The strengthening of command-controlled environmental regulation progressed pollution control technology as regional enterprises continually improved, while production technology was initially suppressed, then promoted. Market-incentive environmental regulation mainly affected the green economy development level through pollution control technology progress, while the mediating effect of production technology progress was insignificant. This study provides some empirical support for the verification of Porter’s win-win hypothesis and the realization of green economic transformation in emerging countries such as China.

Analyzing housing price determinants in Izmir using spatial models

The vast majority of the studies on house price dynamics focus on either structural/locational/demographic variables in a cross-sectional setting (i.e., hedonic price modeling) or on the impact of macroeconomic fundamentals in a time series framework. In this work, we argue that both approaches fall short of providing adequate information as cross-sectional analyses largely ignore the macro-dynamics, whereas time series approaches fail to incorporate the cross-sectional dimension. Current work combines both dimensions in a panel framework and provides, in this way, a methodological contribution as well as more informative analyses as it captures the impact of a wide array of variables. Thus, this study examines the housing prices in Izmir/Turkey by adopting the above-mentioned dimensions with both panel and spatial panel regressions. The study area consists of 212 neighborhoods located in different districts of Izmir. The period of analysis covers 30 months between 2017 and 2019. As an outcome of the empirical analyses, both structural/demographic, and macroeconomic variables were found evidently important. Hence, it is understood that all dimensions (structural/locational/demographic, macroeconomic) should be incorporated into comprehensive modeling. A high spatial dependence and positive spatial spillover effects were also detected.

Spatial-Temporal Evolution and Risk Assessment of Land Finance: Evidence from China

Land finance is a special land financing mode in China under the nationalization of urban land since 1954. The policy authorizes local governments to collect fiscal revenue from land grant premiums and land taxes. As China is experiencing the social and economic transformation, heavily replying on land finance starts causing financial sustainable problems. Based on the spatial panel data of 30 provinces in China in the last two decades, we analyzed the spatial-temporal evolution of land finance. We found that the spatial variation of land finance declined during the period of study and decreased from east to west. The results revealed that land finance had significant positive spatial autocorrelation and robust spatial clustering characteristics. In addition, the spatial distribution of land finance was consistent with the population-based Hu Line. We also assessed land finance risks via a four-dimensional risk matrix through spatial panel regression (SPR). The spatial spillover effects suggested that there is inter-provincial imitation and collaboration but no competition. Our forecast indicates that most provinces will be at a relatively low risk level in the next decade except some southwest provinces. Based on the findings, we highlight the policy implications to mitigate risks and maintain sustainable land finance.

The role of urban transformation on inter-suburban commuting: evidence from Jakarta metropolitan area, Indonesia

ABSTRACT Post-suburbanization, shifting the focus of urban spatial development to its periphery, is becoming a global policy concern. Changing urban form affects travel behavior, where the traditional suburb-to-CBD commuting pattern has been contested by inter-suburban commuting. This empirical study investigates how employment decentralization and its associated heterogeneous suburban amenities have influenced commuting dynamics between suburbs in a developing country. Using the case of the Jakarta Metropolitan Area (JMA), Indonesia, spatial panel regression is applied to model inter-suburban commuting between 2008 and 2015. The results indicate that peripheral employment expansion due to industrial decentralization is followed by a significant increase in inter-suburban commuting. The mono-functional industrial subcenter has attracted more commuters from other suburbs. Yet, it also pushed some residents to commute away for jobs in service firms. The study has added a better understanding of urban form-travel behavior relationships that have seldom been researched from the Global South perspective.

Does improvement of environmental efficiency matter in reducing carbon emission intensity? Fresh evidence from 283 prefecture-level cities in China

While promoting environmental efficiency is widely perceived as one of the panaceas for solving environmental issues in China, little effort has been made to identify whether environmental efficiency improvement helps to reduce carbon emission intensity (CEI). Therefore, the work presented in this study remedies the inadequacies in the previous literature by investigating the linkages between environmental efficiency and CEI, by explicitly considering its spatiotemporal heterogeneous effect and dynamic measurement. We first measure the environmental efficiency (EE) and its growth (environmental total factor productivity) at prefecture level in China during 2003–2016 by incorporating a recently modified epsilon-based data envelopment analysis technique in the global Malmquist–Luenberger index. A spatial panel regression approach is then applied to reveal the impact mechanism of EE and its growth on the CEI. The empirical results confirm that promoting EE and environmental technology efficiency can help mitigate the urban CEI. However, the impact of economic scale efficiency on the CEI reduction has not been verified, indicating great room for CEI mitigation by optimizing the urban production scale and reducing production redundancy. Evidence from the spatiotemporal econometric analysis shows a spatially heterogeneous effect of EE on CEI, and the strength of influence tends to decrease over time. The findings also reveal that enhancing EE matters in reducing CEI for southern and western cities. Finally, several control variables have negative direct and indirect (spillover effect) effects on CEI reduction, including urbanization, industrial structure, and foreign direct investment. Our findings suggest that strengthening the synergistic effect between environmental pollution governance and carbon emission control by enhancing EE to promote low-carbon development in Chinese cities.

Spatial Heterogeneity and Its Influencing Factors of Syphilis in Ningxia, Northwest China, from 2004 to 2017: A Spatial Analysis.

Syphilis remains a growing and resurging infectious disease in China. However, exploring the influence of environmental factors on the spatiotemporal distribution of syphilis remains under explore. This study aims to analyze the spatiotemporal distribution characteristics of syphilis in Ningxia, Northwest China, and its potential environmental influencing factors. Based on the standardized incidence ratio of syphilis for 22 administrative areas in Ningxia from 2004 to 2017, spatiotemporal autocorrelation and scan analyses were employed to analyze the spatial and temporal distribution characteristics of syphilis incidence, while a fixed-effect spatial panel regression model identified the potential factors affecting syphilis incidence. Syphilis incidence increased from 3.78/100,000 in 2004 to 54.69/100,000 in 2017 with significant spatial clustering in 2007 and 2009–2013. The “high–high” and “low–low” clusters were mainly distributed in northern and southern Ningxia, respectively. The spatial error panel model demonstrated that the syphilis incidence may be positively correlated with the per capita GDP and tertiary industry GDP and negatively correlated with the number of health facilities and healthcare personnel. Sex ratio and meteorological factors were not significantly associated with syphilis incidence. These results show that the syphilis incidence in Ningxia is still increasing and has significant spatial distribution differences and clustering. Socio-economic and health-resource factors could affect the incidence; therefore, strengthening syphilis surveillance of migrants in the economically developed region and allocating health resources to economically underdeveloped areas may effectively help prevent and control syphilis outbreaks in high-risk cluster areas of Ningxia.

Regional disparities in SARS-CoV-2 infections by labour market indicators: a spatial panel analysis using nationwide German data on notified infections

BackgroundRegional labour markets and their properties are named as potential reasons for regional variations in levels of SARS-CoV-2 infections rates, but empirical evidence is missing.MethodsUsing nationwide data on notified laboratory-confirmed SARS-CoV-2 infections, we calculated weekly age-standardised incidence rates (ASIRs) for working-age populations at the regional level of Germany’s 400 districts. Data covered nearly 2 years (March 2020 till December 2021), including four main waves of the pandemic. For each of the pandemic waves, we investigated regional differences in weekly ASIRs according to three regional labour market indicators: (1) employment rate, (2) employment by sector, and (3) capacity to work from home. We use spatial panel regression analysis, which incorporates geospatial information and accounts for regional clustering of infections.ResultsFor all four pandemic waves under study, we found that regions with higher proportions of people in employment had higher ASIRs and a steeper increase of infections during the waves. Further, the composition of the workforce mattered: rates were higher in regions with larger secondary sectors or if opportunities of working from home were comparatively low. Associations remained consistent after adjusting for potential confounders, including a proxy measure of regional vaccination progress.ConclusionsIf further validated by studies using individual-level data, our study calls for increased intervention efforts to improve protective measures at the workplace, particularly among workers of the secondary sector with no opportunities to work from home. It also points to the necessity of strengthening work and employment as essential components of pandemic preparedness plans.

Spatiotemporal differences and influencing factors of high-quality utilization of land resources in the Yellow River Basin of China.

High-quality utilization of land resources (HULR) is critical to the security of land ecosystem and sustainable socioeconomic development. To promote HULR, we explored the spatiotemporal differences and influencing factors of HULR in the Yellow River Basin (an important ecological barrier in China) by using entropy method, spatial panel regression model, and geographically and temporally weighted regression model. We found that the HULR values were 0.22 to 0.28 from 2008 to 2019, showing an increasing trend with obvious spatiotemporal differences. The spatial connectivity, technological innovation, industrialization, industrial upgrading, and marketization are important factors influencing HULR, and different factors have different spatial effects in different regions. Therefore, the important principle of HULR is to pursue the sustainable land utilization within the ecological environment carrying capacity, taking into account the unique ecological and socioeconomic conditions of each region. We hope that our study can provide references for HULR around the world.

Inter-city competition and local government debts in China: a spatial econometric analysis

ABSTRACT Debts incurred by Chinese cities have skyrocketed. Policymakers and scholars are concerned with potential default risks and political, economic, and social impacts of a possible debt default. It has also drawn attention to drivers of the rapidly increasing municipal debts. This article examines the extent to which competition among Chinese cities affected the debts they accumulated. Drawing from the literature of local government strategic interaction and fiscal competition, we hypothesised that spillover effects might exist among Chinese cities’ decisions and behaviours to issue how much bonds. With access to a panel dataset of 285 cities over 2008–2016, we applied the spatial panel regression analysis to capture and gauge the spillover effects on debt accumulation of Chinese cities. Findings confirm the spillover effects among Chinese cities and support the role that inter-city competition has played in the rapid accumulation of municipal debts.

Spatial Convergence of Carbon Productivity: Theoretical Analysis and Chinese Experience.

Based on the neoclassical framework, we propose the convergence hypothesis of carbon productivity under sustainable growth and prove the different effects of knowledge spillover and technology diffusion on convergence. The convergence hypothesis is tested using China’s provincial spatial dynamic panel data from 1995 to 2019. The results show that China’s provincial carbon productivity has conditional convergence and club convergence characteristics. The convergence speed of dynamic panel regression estimation is greater than that of cross-sectional regression. The convergence rate of dynamic spatial panel regression estimation is faster depending on the spatial spillover difference between the two technologies. In the early stage, the provincial spatial dependence of China’s carbon productivity is mainly knowledge spillover, and the convergence rate is lower than that of the closed economy. Over the past decade, the spatial spillover, dominated by low-carbon technology diffusion, has become the dominant force. The convergence rate is significantly faster than that of a non-spatial-dependent economy. In addition, the mechanism test found that the development of energy efficiency dominates the spatial transfer of technology, so the overall convergence of carbon productivity in China mainly comes from the apparent convergence of energy efficiency in provinces and cities. Our conclusion provides a new reference for the emission reduction actions of countries worldwide because the spatial knowledge spillover carried by capital flows is not conducive to the pursuit of carbon productivity in less developed regions. On the contrary, the dissemination and diffusion of low-carbon technologies can significantly reduce carbon equivalent input in the production process, accelerating the pursuit of developing countries or regions.

Digital Divide and A Spatial Investigation of Convergence in ICT Development Across Provinces in Indonesia

In the digital era, technology plays a much bigger role all over the world including in Indonesia. In fact, Indonesia’s ICT Development Index (IDI) has improved over time. However, it is noticeable that disparities among regions in regards to ICT development are still wide resulting in digital divide. This paper aims to examine the convergence of regions with regard to ICT development and analyze the key factors which drive the convergence process using annual data from 2015 to 2019. Spatial panel regression method is used to test for absolute and conditional beta convergence. The results reveal that there is evidence for the existence of absolute and conditional convergence in regards to ICT development in Indonesia. Regarding conditional convergence, the convergence process takes a shorter time to reach half-time convergence by taking into account several control variables consisting of output in ICT sector, FDI, and government allocation on education. In addition, the significant spatial correlation among provinces shows that the growth of neighboring provinces’ ICT development also contributes to the convergence process. This finding implies that the digital divide has declined and the factors mentioned above are important instruments to help bridge the digital divide.

Spatial impacts of biomass resource endowment on provincial green development efficiency

Clarifying the role of biomass resource endowment in green development efficiency (GDE) is necessary for economic growth and environmental protection. This study constructed a unified theoretical analysis framework that incorporates biomass resource endowment and GDE. Based on the proposed theoretical framework and provincial data in China from 2007 to 2018, we then integrated super-efficiency slacks-based measure (SBM) with undesirable outputs and spatial econometric panel regression models to explore the effect of biomass resource endowment on GDE. We constructed four kinds of spatial weight matrices to verify the existence of the “resource curse” phenomenon in the field of GDE, with consideration for the spatial correlation of not only geographical distance but also economic distance. Our results showed that biomass resource endowment had a positive direct effect on regional GDE. A 1% increase in provincial biomass resource endowment, on average, directly led to a 0.092% increase in GDE. The impact of biomass technological innovation on GDE could be underestimated by 15% if the spatial spillover effects were ignored. Provincial biomass energy policy may not play its due role in promoting GDE because of the high cost of biomass energy.

Spatial panel count data: modeling and forecasting of urban crimes

The steadily growing access to high-quality spatio-temporal crime count data with a high level of spatial detail allows to uncover interesting relationships between crime types within and between small regional units. Data coherent forecasting of such counts has to take the integer and non-negative nature of the data into account. Spatial panel data models that meet the criterion of coherency are relatively sparse. This paper proposes a new spatial panel regression framework with fixed effects to overcome these shortcomings. Depending on whether time dynamic effects are included in the model specification, estimation and inference are based either on a pseudo maximum likelihood method or on quasi-differenced generalized methods of moments. The models’ usefulness is demonstrated in a forecasting exercise of monthly crime counts at census tract level from Pittsburgh, Pennsylvania.

Spatial Panel Data Approach on Environmental Quality in Indonesia

Indonesia adopted a strategic long-term development plan (2005-2025) targeting to achieve a green and everlasting Indonesia through implementing various environmental policies. One of the mandatory matters for governments is to continue environmental control by constructing Environmental Quality Indexes (EQI). This study focuses on the relationship between regional output or real Regional GDP, level of population density, and the government expenditure on environment quality on EQI in 34 provinces in Indonesia by the time period 2015 to 2019 using a spatial panel data approach. Within the context of spatial modeling, the interaction between provinces depends on their geographical location and condition. Using the geographic information system (GIS) and stata attributes, the coordinates and distances can be mapped and then defined for observation units in space via the spatial weight matrix used. From the perspective of spatial geography, this paper verifies the spatial dependence of Indonesia’s Environmental Quality Index (EQI). Pesaran's CD test indicates the spatial effect on the model and SAR with random effect can be considered a better-fitting spatial panel regression model. The results of the econometric spatial panel using SAR panel with random effect analysis show that Indonesia’s EQI in the provinces was dependent on the spatial. It was also found that regional GDP has a significant and negative effect on EQI and population density has a negative and significant effect on EQI. While fiscal policy on the environmental area on improving environmental quality did not pass a significance test. Thus, it is recommended to look for ways to promote green growth in the country.

Research on the coordination characteristics and interaction between the innovation-driven development and green development of the Yangtze River Economic Belt in China.

Both innovation-driven development and green development are important ways to achieve regional sustainable development. This study considers the innovation-driven development and green development evaluation systems of 130 cities in the Yangtze River Economic Belt. The entropy TOPSIS method is used to measure the innovation-driven development index and the green development index of 130 cities in the Yangtze River Economic Belt. Then, a coupling coordination evaluation model and a spatiotemporal heterogeneity analysis model are constructed to discuss the coupling coordination index of regional innovation-driven development and green development in the Yangtze River Economic Belt and to determine its temporal and spatial distribution characteristics. Finally, we choose a spatial panel regression model to explore the relationship between the innovation-driven development index and the green development index of the Yangtze River Economic Belt. The research results show that there is a significant difference between the innovation-driven development index and the green development index of the 130 cities in the Yangtze River Economic Belt in terms of the temporal and spatial distribution. The level of innovation-driven development lags behind the level of green development on the whole, and the two fail to form a good spatial matching degree. The coordination index of the two has an imbalanced distribution feature, and there is a significant direct or indirect relationship between the two structural indicators in a mathematical sense. This study improves the academic community's understanding of the interaction between innovation-driven development and green development, provides scientifically based support for green development, and offers guidance for the implementation of innovation capabilities.

Spatial distribution of neighborhood-level housing prices and its association with all-cause mortality in Seoul, Korea (2013–2018): A spatial panel data analysis

Housing prices are known to be a relevant indicator of the socioeconomic position of the neighborhood. In a society where the market system mainly drives housing prices, residents' spatial patterning is formulated according to their socioeconomic position. Dividing the 2013–2018 entire study period into three periods, we explored the spatial distribution of housing prices and all-cause mortality and their association in Seoul, the country's capital city. The government authorities' data and 2015 census data were used for the study. We mapped the spatial distribution of housing prices and all-cause mortality and investigated the changes in distribution. We conducted a pooled ordinary least square (OLS) and spatial panel regression analysis to estimate housing prices elasticity of all-cause mortality. We also explored the possible mediating role of housing prices on the educational composition's effect on all-cause mortality. We found the common trends of increasing spatial patterning of housing prices and all-cause mortality. The magnitude of spatial patterning was far greater in housing prices than all-cause mortality. A pooled OLS regression analysis found that a 1% increase in housing price was associated with a 0.11% reduction in all-cause mortality after controlling the explanatory variables. Attenuation in the regression coefficient's magnitude was found after adding the neighborhood's educational composition to the model. As a result of spatial panel analysis, we found a direction and scale similar to the housing price elasticity of all-cause mortality in the final pooled OLS model. The results suggested that spatial health inequality in Korea's urban space mainly stems from socioeconomic inequality.

The impact of urban land misallocation on inclusive green growth efficiency: evidence from China.

Inclusive green growth (IGG), as a new way to attain sustainable development, aims to achieve comprehensive and coordinated economic, social, and environmental development. How to define IGG and explore its driving factors is key to realizing IGG. This study takes China as an example, using panel data from 30 provinces in Mainland China from 2009 to 2018 for research. The epsilon-based measure (EBM) model and Global Malmquist–Luenberger (GML) index are used to evaluate China’s IGG, and a spatial panel regression model of the impact of urban land resource misallocation on IGG is established. The research found that (1) China’s IGG level from 2009 to 2018 displayed an upward trend, and combined with exploratory spatial data analysis (ESDA), it was found that IGG has an obvious spatial correlation; (2) the regression model shows that the misallocation of land resources hinders the improvement of IGG in China; and (3) the decomposition of spatial spillover effects demonstrates that the misallocation of land resources has negative externalities, which will also have adverse effects on neighboring areas.

Urban morphology and traffic congestion: Longitudinal evidence from US cities

Traffic congestion is an ever-increasing issue across urban environments in the US. One potential mitigation strategy is to improve our understanding of how the geographical patterns of urban land use influence congestion. Unfortunately, there is no consensus regarding if more sprawling or dense urban morphologies help mitigate congestion issues. To potentially clarify the conflicting findings of previous studies, we used a detailed spatial metric-based approach and panel regression to quantify the relationships between urban development patterns and congestion in 98 US urban areas from 2001 to 2011. We found that the abundance and spatial configuration of urban land uses were correlated with traffic congestion. Specifically, high degrees of polycentricity for both high-intensity and low-intensity urban land uses were associated with more congestion, while contiguous residential development was correlated with less congestion. Important distinctions were also observed between different congestion measures, as urban morphology exhibited a more substantial influence on overall congestion than rush-hour congestion. Our findings can potentially inform future land use planning by clarifying which urban morphologies alleviate traffic congestion issues.