Lag Regression Model Research Articles

Spatial Dependencies in Environmental Quality: Identifying Key Determinants

Introduction/Main Objectives: Environmental quality is essential to human development because it reflects the condition of our natural surroundings. Background Problems: Understanding the determinants of environmental quality is crucial for Indonesia as it helps identify the key factors influencing environmental quality. Novelty: Spatial models offer detailed, locationspecific insights but require extensive data and computational resources, while non-spatial models provide a broader overview with simpler data requirements but may miss important spatial nuances. This study seeks to identify the determinants of environmental quality in regencies and municipalities on Java Island, incorporating spatial effects into the analysis. Research Methods: The dependent variable is environmental quality index. The independent variables are GRDP in industrial sector, GRDP in agricultural sector, urban population rate, population density, and poverty rate. We applied spatially lag regression model using contiguity spatial weight matrix. Finding/Results: This study shows the spatially lag regression model outperforms the OLS model. GRDP in the industrial sector, GRDP in the agricultural sector, urban population rate, and population density have negative effects, suggesting the increases in these variables were associated with lower environmental quality. About 40%–44% of each variable's effect on environmental quality is due to spatial spillover effects.

Assessing Regional Economic Performance in Romania Through Panel ARDL and Panel Quantile Regression Models

This study aims to address the persistent regional economic disparities in Romania by evaluating economic performance through Panel Autoregressive Distributed Lag (pARDL) and panel quantile regression (PQR) models. The analysis focuses on the impact of key economic variables, including research and development expenditures (CTCRD), IT infrastructures (IT), the number of universities (FCL), and the average number of employees (NMSP), on regional gross domestic product (GDPR). Using data from the Romanian National Institute of Statistics for the period 2003–2022, this research seeks to understand how targeted investments and policy interventions can stimulate growth and reduce inequalities across regions. The findings highlight the important role of R&D, IT infrastructures, and technological advancements in driving economic growth, especially in less developed areas. The study also emphasizes the importance of region-specific strategies in fostering sustainable growth, promoting economic resilience, and bridging the gap between more and less prosperous regions.

Examining The Impact of Inflation on Financial Development in The Fragile Five Economies with Asymmetric Cointegration Tests

This study examines the relationship between inflation and financial development indicators for the Fragile Five countries between 1981 and 2021. The study analysed the relationship between symmetric and asymmetric distributed lag regression models. Inflation appears to have a negative impact on financial development in countries other than Brazil and India. The results prove that the most extended correction process was experienced in India, and the shortest was experienced in Indonesia among the Fragile Five countries. Research results emphasise that high inflation negatively affects financial development in these economies by increasing the cost of financial intermediation.

The Asymmetric Effects of Global Energy and Food Prices, Exchange Rate Dynamics, and Monetary Policy Conduct on Inflation in Indonesia

This research analyzes the asymmetric effects of global energy and food prices and monetary variables, including the exchange rate and money supply, on the consumer price index (CPI). The model is intended to differentiate the influence of increases and decreases in global energy and food prices, exchange rates, and money supply which cause inflation/deflation from changes in the CPI. The analysis uses the Nonlinear Autoregressive Distributed Lag (NARDL) and Quantile Regression models on data from January 2001 to February 2023. The study results show that the decline in global energy prices significantly reduces the CPI in the long run. Energy subsidies allow increases in international energy prices not to increase the CPI significantly. Meanwhile, the increase in global food prices causes inflation in the short run. The exchange rate has the most significant effect on the CPI. Depreciation of the rupiah significantly increases the CPI, which means it causes inflation, while appreciation of the rupiah does not have a significant effect. Finally, increases and decreases in the money supply have a considerable positive effect on the CPI, which confirms the logic of the monetarist view that inflation is a monetary phenomenon. Efforts to reduce dependence on imports of food and energy commodities are the key to reducing risks when importing energy and food due to rupiah depreciation. Efforts to consistently stabilize the exchange rate can support controlling and stabilizing import prices. Energy and food subsidy policies are vital in controlling inflation due to increased world energy and food prices.

Examining the role of social vulnerability, neighborhood characteristics, and geospatial patterns of firearm-related injuries and clinical outcomes in Milwaukee county

BackgroundPrevious work has shown socioenvironmental factors can influence firearm injury. Milwaukee County, Wisconsin is a diverse midwestern county with historic disinvestment in marginalized communities yielding stark segregation along racial and ethnic lines. It is also one of the many U.S. counties burdened by surging firearm injuries. The differences among communities within Milwaukee County provides a unique opportunity to explore the intersection of socioenvironmental factors that may affect clinical outcomes and geospatial patterns of firearm injury. MethodsThe trauma registry from the regional adult level 1 trauma center was queried for patients who sustained a firearm-related injury from 2015 to 2022 (N = 2402). The Social Vulnerability Index (SVI) ranking was derived using patient residence addresses to evaluate its association with traumatic injury clinical outcomes (i.e., in-hospital mortality, length of hospital stay, ICU or ventilator treatment, or injury severity score) and risk screening results for alcohol use disorder (AUD), posttraumatic stress disorder (PTSD), and depression. We evaluated hotspots of firearm injury density over time for patient residences and injury locations and distances between locations. A spatially lagged regression model tested the association between firearm injury density and SVI domains, alcohol outlet types, and park coverage. ResultsMost firearm injury patients were younger, male, racial or ethnic minorities from disadvantaged neighborhoods (SVI total; M = 0.86, SD = 0.15). SVI was not associated with any clinical outcomes. Of those screened, 12.9% screened positive for AUD and 44.5% screened at risk for PTSD, depression, or both. Hotspot analysis indicated consistent concentrations of firearm injury density. There were no differences in clinical outcomes between those injured inside or outside the home. Census tracts with lower socioeconomic status, greater off-premises and lower on-premises alcohol outlet density were associated with greater firearm injury density. ConclusionsIn Milwaukee County, firearm injury patients are injured in and often return to the same disadvantaged neighborhoods that may hamper recovery. Results replicate and expand previous work and implicate specific socioenvironmental factors for intervention and prevention of firearm injury.

Empirical Exploration of Economic Policy in the Middle East and North Africa (MENA) Region: An ARDL Approach

This article delves into the analysis of the effectiveness of economic policies implemented in 20 countries in the Middle East and North Africa (MENA) region. The countries considered include Algeria, Saudi Arabia, the Palestinian Authority, Bahrain, Djibouti, the United Arab Emirates, Egypt, Iraq, Iran, Jordan, Kuwait, Lebanon, Libya, Morocco, Mauritania, Oman, Qatar, Syria, Tunisia, and Yemen. The study is based on an Autoregressive Distributed Lag (ARDL) regression model, employing the Ordinary Least Squares (OLS) method. This model is suitable for non-stationary time series and those with a mixed order of integration. The analysis covers the period from 2000 to 2023.The results obtained highlight the diversity of the impacts of economic policies, strongly influenced by the specific economic and social contexts of each country. Governments, in their pursuit of efficiency and sustainability, are often faced with the delicate balance between multiple objectives. Among the measures considered are the money supply, inflation rate, government spending, and structural reforms. These measures have demonstrated their effectiveness in stimulating economic growth, promoting economic activity, encouraging investment, and creating employment opportunities. Monetary and fiscal policies are also crucial levers aimed at maintaining macroeconomic stability. This is expressed notably through controlling inflation, managing budget deficits, and ensuring the stability of the financial system. The results of econometric tests converge towards unanimous conclusions, suggesting the existence of one-directional causal relationships between economic growth and specific indicators of economic policy. This in-depth understanding of regional economic dynamics provides valuable insights for tailoring economic policies to each national context within the MENA region.

The Nexus of Financial Sector Development and Economic Development in Zimbabwe: 1980-2022.

Economic literature has shown that financial development has a positive effect on economic growth. Since 1980, the financial sector has faced numerous challenges albeit the importance of the sector towards economic development. This study examines the nexus between financial sector development and economic development. The study data covered the period 1980-2022 using an econometric analysis applying the Autoregressive Distributed Lag (ARDL) regression model to assess the relationship between finance sector development and economic development. The research findings indicate that bank deposits have a statistically negative effect on economic development. Broad money (M2) is statistically significant beneficial effect on economic development over long term. The results also reveal that in the long run government spending has a negative effect on economic development. Long term economic development was negatively correlated with private investment. In conclusion, ther study underscores the need to address strengthen the financial sector so as to enable economic development. Therefore, the paper recommends that the government of Zimbabwe should effectively enact policies that promote trade liberalisation, job creation and similar pursuits in order to stimulate development in turn strengthen the financial sector.

The foundational economy and the economic resilience of Czech and Slovak regions during four recessions

In this paper, the authors analyze the relationships between the foundational economy and the economic resilience of Czech and Slovak regions during four different waves of economic recession. The effects of specialization in the material and providential foundational economies, the automotive industry, and business services on short-term regional resistance measured by relative unemployment changes were tested during the global economic crisis (“the Great Recession”), the austerity crisis, the Covid-19 pandemic, and the Russo-Ukrainian War. The association between current and past recessions and the type of regions (old industrial, rural) and regional resilience were also examined. Spatial lag regression models showed that regional economic resilience patterns varied significantly from one economic shock to another. The effects of the foundational economy were weaker than expected and non-significant in case of the material foundational economy.

How optimal is Ghana's single-digit inflation targeting? An assessment of monetary policy effectiveness in Ghana

PurposeThe purpose of this study is twofold. First, to estimate an optimal inflation rate for Ghana and second, to investigate factors that account for the differences between observed and target inflation.Design/methodology/approachThe paper explored the questions within two econometric frameworks, the Autoregressive Distributed Lag (ARDL) and Threshold Regression Models using data spanning the period 1965–2019.FindingsThe study estimated a range of 5–7% optimal inflation for Ghana. While this confirms the single-digit inflation targeting by the Bank of Ghana, the range is lower than the central bank's band of 6–10%. The combined behaviours of the central bank, banks and external outlook influence inflation target misses.Practical implicationsThe study urges the central bank to continue pursuing its single-digit inflation targeting. However, it implies that there is still room for the Bank to further lower the current inflation band to achieve an optimal outcome on growth and welfare. Again, the Bank should commit to increased transparency and accountability to enhance its credibility in attaining the targeted inflation.Originality/valueThe study is one of the first attempts in Africa in Ghana to estimate an optimal inflation target and investigate the underlying factors for deviation from the targets.

Examining the Spatial Proximity of Vulnerable Communities to Cooling Centers across the Texas Triangle Megaregion

Abstract A cool environment is critical for protecting vulnerable populations from the adverse health effects associated with exposure to extreme heat. Although cooling centers are commonly established to provide temporary heat relief to the public, there is limited research exploring the spatial distributions and accessibility of cooling centers across cities in Texas. The intent of this study was to examine the spatial characteristics of cooling center locations throughout the Texas Triangle megaregion and evaluate the proximity of cooling centers to vulnerable populations. Specifically, spatial clustering analysis was used to quantitatively characterize the spatial distributions of cooling centers in San Antonio, Houston, and Dallas, while spatial lag regression was conducted to evaluate the relationships between indicators of socioeconomic vulnerability and proximity to cooling centers. The findings indicated that cooling centers exhibited clustering at short distances, which suggested there were potential spatial redundancies. The distributions of the cooling centers also illustrated possible accessibility issues due to the concentration of the locations in urban cores. The spatial lag regression models highlighted several problematic relationships, as elderly and disabled populations were located at significantly greater distances from cooling centers in San Antonio and Dallas, respectively. However, numerous insignificant relationships were also observed, which suggested that cooling center locations did not consistently marginalize or favor vulnerable populations. Therefore, a higher degree of intentionality that explicitly considers cooling center proximity to the vulnerable populations they aim to serve might be beneficial as planners and emergency managers determine cooling center locations in response to extreme heat.

Spatial distribution analysis and driving factors of traditional villages in Henan province: a comprehensive approach via geospatial techniques and statistical models

Traditional villages are repositories for preserving human artifacts and cultural heritage. An investigation of the spatial distribution characteristics and factors influencing traditional villages in provincial administrative regions can provide new insights regarding the protection of traditional villages and rural development. This study focused on 275 traditional villages in Henan Province. Using ArcGIS and GeoDa software, we analysed the spatial autocorrelation and heterogeneity of the nearest neighbour index, Gini coefficient, Moran’s I, and kernel density of the villages. Additionally, in conjunction with the Python sklearn library and GeoDetector, 15 indicators were selected to construct a decision tree model, spatial lag regression model, and geographic detector. Then the influence and interaction mechanisms of each indicator were analysed. The results revealed that (1) the spatial distribution of traditional villages in Henan Province was clustered and uneven, with a spatial layout comprising “3 high-density areas + 1 medium-density belt”; (2) overall, the number of traditional villages was negatively correlated with altitude, slope, rainfall, population density, proportion of the minority population, and historical-cultural intensity; and (3) the decision tree model results demonstrated that the selected 15 indicators had good predictive ability and that population density was particularly important. The spatial lag regression model results showed that the spatial distribution of traditional villages was positively correlated with distance from rivers, urbanization rate, and tourism resources, and negatively correlated with population density, per capita GRP, historical-cultural intensity, and NDVI. (4) The GeoDetector results indicated that historical-cultural intensity and population density were the two factors with the most significant explanatory power for the spatial differentiation of traditional villages in Henan Province. In terms of interactive factors, population density cap population was the strongest interactive driving force, followed by population cap historical-cultural intensity.

Health disparity in the spread of COVID-19: Evidence from social distancing, risk of interactions, and access to testing

- To identify and assess whether three major risk factors that due to differential access to flexible resources might help explain disparities in the spread of COVID-19 across communities with different socioeconomic status, including socioeconomic inequalities in social distancing, the potential risk of interpersonal interactions, and access to testing. Analysis uses ZIP code level weekly COVID-19 new cases, weekly population movement flows, weekly close-contact index, and weekly COVID-19 testing sites in Southern California from March 2020 to April 2021, merged with the U.S. census data to measure ZIP code level socioeconomic status and cofounders. This study first develops the measures for social distancing, the potential risk of interactions, and access to testing. Then we employ a spatial lag regression model to quantify the contributions of those factors to weekly COVID-19 case growth. Results identify that, during the first COVID-19 wave, new case growth of the low-income group is two times higher than that of the high-income group. The COVID-19 case disparity widens to four times in the second COVID-19 wave. We also observed significant disparities in social distancing, the potential risk of interactions, and access to testing among communities with different socioeconomic status. In addition, all of them contribute to the disparities of COVID-19 incidences. Among them, the potential risk of interactions is the most important contributor, whereas testing accessibility contributes least. We also found that close-contact is a more effective measure of social distancing than population movements in examining the spread of COVID-19. - This study answers critically unaddressed questions about health disparities in the spread of COVID-19 by assessing factors that might explain why the spread is different in different groups.

Spatial Inequity of Transit and Automobile Access Gap across America for Underserved Population

This study examines the correlates of the Modal Access Gap (MAG) between transit and automobile to employment opportunities in the 45 most populated American metropolitan areas by testing spatial lag regression models and employing the bivariate local indicator of spatial autocorrelation (BiLISA) at the census block group geographical level. Four findings are discerned. First, MAG is positive regardless of the metropolitan area and travel-time threshold and ranges from 0.22 to 0.98. This indicates transit trails automobile in offering access to employment opportunities. Second, millennials and carless households tend to reside in areas with a narrower MAG, while people with disabilities reside in areas with a wider MAG. Third, areas with a high share of carless households and relatively low transit access to employment opportunities are primarily clustered in the suburbs and exurbs of American metropolitan areas. Fourth, the MAG disproportionately affects socially vulnerable populations, including the elderly, people with disabilities, low-income households, Hispanics, and African Americans. Compared with the national average, modal access inequity is prevalent for the elderly in 27, for people with disabilities in 22, for low-income households in 17, for Hispanics in 14, and for African Americans in 12 metropolitan areas. This research is a necessary step forward for instilling social equity into transport planning strategies in parallel with governmental efforts.

Study on the driving mechanism of lagged effects based on different time scales in a karst drainage basin in South China

Compared to earthquakes and volcanoes, drought is one of the most damaging natural disasters and is mainly affected by rainfall losses, especially by the runoff regulation ability of the underlying watershed surface. Based on monthly rainfall runoff data recorded from 1980 to 2020, in this study, the distributed lag regression model is used to simulate the rainfall-runoff process in the karst distribution region of South China, and a time series of watershed lagged-flow volumes is calculated. The watershed lagged effect is analyzed by four distribution models, and the joint probability between the lagged intensity and frequency is simulated by the copula function family. The results show that (1) the watershed lagged effects simulated by the normal, log-normal, P-III and log-logistic distribution models in the karst drainage basin are particularly significant, with small mean square errors (MSEs) and significant time-scale characteristics. (2) Affected by spatiotemporal distribution differences in rainfall and the impacts of different basin media and structures, the lag response of runoff to rainfall differs significantly among different time scales. Especially at the 1-, 3- and 12-month scales, the coefficient of variation (Cv) of the watershed lagged intensity is greater than 1, while it is less than 1 at the 6- and 9-month scales. (3) The lagged frequencies simulated by the log-normal, P-III and log-logistic distribution models are relatively high (with medium, medium–high and high frequencies, respectively), while that simulated by the normal distribution is relatively low (medium–low and low frequencies). (4) There is a significant negative correlation (R < − 0.8, Sig. < 0.01) between the watershed lagged intensity and frequency. For the joint probability simulation, the fitting effect of the gumbel Copula is the best, followed by the Clayton and Frank-1 copulas, and while that of the Frank-2 copula is relatively weak. Consequently, the propagation mechanism from meteorological drought to agricultural or hydrological drought and the conversion mechanism between agricultural and hydrological drought are effectively revealed in this study, thereby providing a scientific basis for the rational utilization of water resources and drought resistance and disaster relief in karst areas.

Sap flow as a function of variables within nested scales: ordinary least squares vs. spatial regression models

Understanding scale-dependent influential drivers of sap flow variability can help managers and policymakers to allocate resources within a particular scale to improve forest health and resiliency against water-stress stimuli such as drought and insects, e.g. bark beetle infestations. We defined a daily measure of sap flow as a function of variables within nested scales of landscape, stand, and tree, using ordinary least squares (OLS), spatial lag and error regression models. Model covariates were elevation, latitude (Y-coordinate), longitude (X-coordinate), neighborhood tree density, tree diameter at breast height, and bark temperature for 40-surveyed Norway spruce (Picea abies) in the Czech Republic, Central Europe. Trees were spatially distributed within 19-established subplots across five plots, with distances ranging 2–9 km, at which variations in soil water potential and temperature were limited. The daily measure of sap flow within the regional scale allowed us to avoid the temporal and spatial variability of climate effects on sap flow. A relatively flat terrain across subplots also allowed us to control the effects of slope, aspect, and topography-related solar incidence angle on sap flow. Sap flow was strongly spatially autocorrelated, so OLS models failed to take spatial autocorrelation into account unless to some extent, depending on the spatial distribution of samples, by including latitude and/or longitude in the models. Among spatial regression models, spatial error models performed better than lag models, allowing to capture the effects of unmeasured independent variables. Sap flow variability for the most part (∼70%) was explained by the landscape-level variable of elevation followed by the stand-level variable of tree density, and the remaining part by variables related to tree characteristics; a nested down-scaling function, defined and visualized for the first time. Therefore, thinning forest stands and future plantations with optimum distances, based on the elevation gradients, may be required to counterbalance the allocation of resources, e.g. water, nutrients, and light, among trees, leading to enhance forest health and resiliency against water-stress stimuli.

The equity of public transport crowding exposure

Public transport crowding exposure is known to cause discomfort, stress and dissatisfaction. However, the distribution and equity of crowding exposure across socioeconomic groups has been largely unexplored. This paper opens a new research topic connecting crowding exposure in public transport to travelers’ socioeconomic characteristics. We present a framework for assessing the equity of in-vehicle crowding exposure based on automatic data sources. Two metrics are considered for quantifying the travelers’ in-vehicle crowding exposure: (1) the excess perceived travel time and (2) the relative excess perceived travel time. The proposed methodology computes the two metrics based on travel diaries and in-vehicle loads inferred from automated fare collection data. We implement Lorenz curves, Gini and Suits coefficients to evaluate horizontal (across the population) and vertical equity (considering income as well as mobility ability and need). The vertical equity is further discussed using clusters of socioeconomic groups and results from spatial lag regression models to assess the distribution of crowding exposure across socioeconomic characteristics. The results for the Stockholm Region case study indicate that crowding exposure varies substantially across the service area, with the highest values found in the denser urban areas close to Stockholm City. We find that the distribution across socioeconomic groups is relatively even, but travelers from areas that are wealthier, higher educated, have higher share of rental housing or lower vehicle ownership areas tend to be exposed to more crowding. The paper provides tools to support public transport planners in decision-making, showing where to intervene to reduce crowding exposure efficiently to achieve urban equity and sustainability.

Almanaseer, S. R. (2023) The Relationship Between Financial Integration and Financial Stability: an Application of ‎Panel Smooth Transition Model

Purpose: This research aims to determine the correlation between financial integration and financial stability and whether increasing financial integration leads to increased financial stability. Theoretical framework: The study investigates the effect of growing financial integrity on financial stability and the causal association between the two variables. Design/methodology/approach: The study uses an Autoregressive-Distributed Lag (ARDL) regression model with the help of least squares (OLS) regression to evaluate the relationship between financial integration and stability. This research work further employed a Panel Smooth Transition Model (PSTR) to determine the nonlinearity among two factors and investigated the entrance stage of financial boosting beyond which Total Factor Productivity (TFP) extension is possibly dampened. Findings: The results came forward with a positive impact of financial integrity on financial stability across many countries. Findings indicated that economic and financial integration is the primary channel to mediate the effect of monetary extremity in economies. Fiscal policy should seek measures to reduce the risk of crisis transfer and increase the benefits of financial integration to achieve financial stability. Research, Practical &amp; Social implications: The study recommends considering financial integration for economic development and avoiding the occurrence of risk by considering a fiscal policy. Originality/value: The results highlight the sectors where financial integration is required. Moreover, establishment of subsidiary international bank in the resident economy with the local financial centre’s loans has been emphasized.

Analysis of the Sustainable Driving Effect of Building Energy Consumption on Economic Development Based on the Sustainable Driving Force Model

The environmental problems caused by building energy consumption (BEC) are becoming increasingly prominent. Reducing building energy consumption can not only effectively curb environmental pollution, but also continue to promote economic development. However, there are few reports on reducing building energy consumption to continuously promote economic development. Sustainable driving force (SDF) has been widely mentioned in past research as a common-sense concept, but few systematic studies have been carried out. The main research objectives of this article include two aspects: On the one hand, this study takes the lead in establishing an SDF model and conducting systematic research on SDF. On the other hand, based on the SDF model, an empirical study is conducted on the sustainable driving effect of reducing building energy consumption on economic development in China. The main conclusions are as follows: (1) Research on the SDF model: Based on common examples, the objective reality of SDF has been theoretically proven. Based on the relevant theories of econometrics, this article believes that the “Granger causality test” and the “distributed lag regression model” can be used as basic tools for quantitative research of SDF models. (2) Research on the application of SDF model: From the Granger causality test, it can be seen that the current BEC intensity is the Granger cause of China’s economic development in the first and eighth cycles of the future. According to the results of the distributed lag regression model, reducing BEC intensity has a sustainable driving effect on economic development. Specifically, in the lagged period 1~8, the sustainable driving effect (reverse effect) of BEC on economic development gradually increases at first (coefficient value from 3878.52 to 5163.87), and then gradually decreases (coefficient value from 5163.87 to 783.534). To sum up, the SDF model can provide a reference for the quantitative study of SDF; studying the sustainable driving effect of BEC on economic development can provide a macro reference for the government to formulate “energy conservation and emission reduction” measures.

Comparing marriage and cohabitation dissolution risks across municipalities: Which characteristics matter?

AbstractResearch has shown considerable municipal‐level variation in divorce rates within countries. Given the large increase in cohabitation over the past decades, this study examines whether similar differences can also be observed in the union dissolution risks of cohabitants. By investigating whether six municipal‐level factors important in understanding spatial variation in divorce are also associated with spatial variation in the dissolution of cohabiting unions (financial uncertainty, gender roles, religiosity, social ties, alternative opportunities and educational attainment), this article aims to improve our understanding of municipal differences in the dissolution of cohabiting unions. This study is conducted on register data from Statistics Netherlands (2017–2018). For this study, unique union dissolution information per union type (marriage and cohabitation) is constructed for 355 Dutch municipalities. Nearly all explanatory factors are defined using publicly available municipal‐level information. We use spatial lag regression models to analyse differences in municipal union dissolution risks for different union types. We find that municipal‐level union dissolution risks of cohabiting and married couples are only moderately correlated, suggesting that the risk of union dissolution for cohabiting couples is not necessary high in municipalities with high divorce rates. Municipal‐level indicators of social ties, religiosity and alternative opportunities are linked to municipal‐level variation in union dissolution risks of married and cohabiting couples, whereas municipal‐level variation in financial uncertainty and educational attainment are only linked to municipal‐level variation in union dissolution risks of married couples.

국제 유가 변동성이 국내 휘발유 가격 비대칭성에 미치는 영향

Purpose - This study examines the effects of Dubai oil price and the volatility on the asymmetry of domestic gasoline price adjustment. Additionally, the study investigates the effects of “Altteul” gas-station and tax-cut policies on asymmetry.&#x0D; Design/methodology/approach - Firstly, the study calculates proxies for asymmetry and volatility of each window(every 3-month) by error-correction model and GARCH(1, 1) using daily domestic gas price and Dubai oil price from 2008/04/15 to 2022/12/31. Secondly, the study investigates the effects of the increasing rate of Dubai oil price, volatility, “Altteul” gas-station and tax-cut policies on asymmetry. The autoregressive distributed lag regression model is employed for estimations.&#x0D; Findings - The study finds that changes in the increasing rate of Dubai oil price and both types of volatility of Dubai oil price increase asymmetry. While “Altteul” gas-station and tax-cut policies decrease asymmetry. Additionally, the study fails to find that asymmetry in the Korean gasoline market in the estimation with total observations.&#x0D; Research implications or Originality - An increase in Dubai oil price volatility means an increase in cost uncertainty for gas-station owners. Since cost uncertainty is a kind of financial risk, the increase in volatility reinforces the asymmetry. The study provides supporting evidence for the idea.