Infrastructure Variables Research Articles

Infrastructure and economic growth in ECOWAS member states: The Westerlund co-integration approach

This research explores the impact of infrastructure on member states' economic growth in the Economic Community of West African States (ECOWAS). Utilizing panel secondary data sourced from the World Bank Development Indicators (WDI) and the African Infrastructure Development Index (AIDI) across all fifteen ECOWAS Member States over eighteen years, the study employs the panel Non-linear Autoregressive Distributed Lag (NARDL) model and the Westerlund co-integration test for analysis. The findings reveal that investments in infrastructure, improvements in the African Development Index, and enhancements in the Electricity Composite Index significantly contribute to the economic growth of ECOWAS countries. Specifically, infrastructure investment is associated with a 0.01 per cent increase in the Gross Domestic Product (GDP) of the ECOWAS countries studied. In comparison, the African Development Index and the Electricity Composite Index are linked to increases in GDP by 0.292 per cent and 0.987 per cent, respectively, in the long term. Based on these outcomes, the study recommends that ECOWAS country authorities enhance policies to optimize government spending on infrastructure quality. Furthermore, adopting quality-enhancing and efficiency-driven financing policies in infrastructure is advocated to complement ECOWAS's ongoing infrastructural development efforts. The realization of these recommendations hinges on the availability of accurate data for informing decisions and guiding policymakers. Hence, the study underscores the need for the ECOWAS Commission to bolster its capacity for collecting reliable data on infrastructure variables and other indicators. It also proposes that future research should focus on promoting sub-regional peer-review mechanisms for infrastructure indicators among member states and establishing structures to fortify infrastructure in West Africa.

Safety performance of dedicated and preferential bus lanes using multivariate negative binomial models for Bogotá, Colombia

Public transport priority systems such as Bus Rapid Transit (BRT) and Buses with High Level of Service (BHLS) are top-rated solutions to mobility in low-income and middle-income cities. There is scientific agreement that the safety performance level of these systems depends on their functional, operational, and infrastructure characteristics. However, there needs to be more evidence on how the different characteristics of bus corridors might influence safety. This paper aims to shed some light on this area by structuring a multivariate negative binomial model comparing crash risk on arterial roads, BRT, and BHLS corridors in Bogotá, Colombia. The analyzed infrastructure includes 712.1 km of arterial roads with standard bus service, 194.1 km of BRT network, and 135.6 km of BHLS network. The study considered crashes from 2015 to 2018 –fatalities, injuries, and property damage only– and 30 operational and infrastructure variables grouped into six classes –exposure, road design, infrastructure, public means of transport, and land use. A multicriteria process was applied for model selection, including the structure and predictive power based on [i] Akaike information criteria, [ii] K-fold cross-validation, and [iii] model parsimony. Relevant findings suggest that in terms of observed and expected accident rates and their relationship with the magnitude of exposure –logarithm of average annual traffic volumes at the peak hour (LOG_AAPHT) and the percentage of motorcycles, cars, buses, and trucks– the greatest risk of fatalities, injuries, and property damage occurs in the BHLS network. BRT network provides lower crash rates in less severe collisions while increasing injuries and fatalities. When comparing the BHLS network and the standard design of arterial roads, BHLS infrastructure, despite increasing mobility benefits, provides the lowest safety performance among the three analyzed networks. Individual factors of the study could also contribute to designing safer roads related to signalized intersection density and curvature. These findings support the unique characteristics and traffic dynamics present in the context of Bogotá that could inform and guide decisions of corresponding authorities in other highly dense urban areas from developing countries.

Variable selection methods for Log-Gaussian Cox processes: A case-study on accident data

In order to prevent and/or forecast road accidents, the statistical modeling of spatial dependence and potential risk factors is a major asset. The main goal of this article is to predict the number of accidents on a certain area by considering georeferenced accident locations crossed with variables characterizing the studied geographical area such as road characteristics as well as sociodemographic and global infrastructure variables. We model the accident point pattern by a spatial log-Gaussian Cox process (LGCP). To reduce the computation burden of LGCP models in this high-dimensional setting, we suggest a two-step procedure: to perform first automatic variable selection methods based on Poisson regression, Poisson aggregation and random forest and in a second step, to use the selected variables and perform LGCP model analysis. The dataset consists in road accidents occurred between 2017 and 2019 in the CAGB (urban community of Besançon), France. Based on LGCP analysis, we are able to identify the principal risk factors of road accidents and risky areas from CAGB region.

Analysis of The Impact of Transportation Infrastructure Investment on Regional Economic Growth of Lombok Barat Regency

The availability of good transportation infrastructure is very important in increasing regional affordability and accelerating regional economic progress and can increase GRDP in a region. The purpose of this study is to analyze the impact of transportation infrastructure investment, including road infrastructure and port infrastructure on regional economic growth in West Lombok Regency. The research method used is quantitative method. The results of this study indicate that road infrastructure (road length) has a probability value of 0.0343 <0.05 with a coefficient value of -15.39090. Thus it is concluded that road infrastructure (road length) partially has a negative and significant impact on regional economic growth in West Lombok Regency. Then, the port infrastructure variable (ship visits), with a probability value of 0.2356> (0.05) and a coefficient value of -1.868806 so it is concluded that partially port infrastructure (ship visits) has a negative and insignificant impact on regional economic growth in West Lombok Regency. In the overall context (simultaneously), when road length and ship visits are analyzed together, both simultaneously have no significant impact on the regional economic growth of West Lombok Regency, with a probability number of 0.078487 > (0.05).

Controlling aeroplane crashing through aviation infrastructure using multi-agent technology

Aeroplane crashing is a major concern for aviation safety. This article aim is to control aeroplane crashing through aviation infrastructure by using Multi-Agent Technology (MAT). For this 6 variables of aeroplane crashing, 20 variables of aviation infrastructure and 34 variables of MAT are extracted from the literatureto develop the information framework for the study. Fuzzy Multi-Criteria Decision-Making (FMCDM) technique is utilised to analyse prioritise, ranking, and identify interrelationship among them. The human error is evolved most significant factor of aeroplane crashing followed by others. For aviation infrastructure, runway is found to be the most important variable in global ranking. Whereas, in MAT pilot assistance agent is most prioritised variable in overall ranking. The proposed framework can assist the aviation industry to understand the behaviour of aviation infrastructure and MAT variables to control aeroplane crashing. The results of this study can be utilised by aviation professionals in improving aviation safety.

Community Satisfaction with Online Services in East Lombok Regency: (Case Study: BAKSO Application)

Purpose: This study analyzes the use of the BAKSO (Create Online Population Administration) application for users, namely employees of the East Lombok Regency Population and Civil Registration Office and the Admin of each village. The measurement uses variables consisting of infrastructure, ICT, bureaucracy, leadership, and implementation of digital government.Methods: The method used is a quantitative type, primary data in the form of a survey of 115 respondents from East Lombok Regency Population and Civil Registration Office office employees and village admins who used the BAKSO application (Create Online Population Administration). Using the Likert Scale (1: strongly disagree, 2: disagree, 3: neutral, 4: agree, and 5: strongly agree). The analysis technique for this study uses SmartPLS 3.Results: The results of this study show that infrastructure and ICT variables have a positive and significant influence on the implementation of digital government in the implementation of the BAKSO application. Meanwhile, bureaucratic and leadership variables have little impact and are substantial in implementing the BAKSO application in East Lombok Regency.Novelty: This study is unique because it examines users who are also employees who use the BAKSO application (Make Online Population Administration) with a measuring indicator, namely the online service index (OSI); most of the previous research on this theory was only oriented to assessing public satisfaction with online services. This research provides a new perspective on using the Online Service Index (OSI) on the scope of application-based online services from an employee perspective. In addition, the empirical contribution lies in the professionalism of employees in using the BAKSO application. So that later, it can provide space for the government to pay attention to human resources in running Online Service Index (OSI)-based application services.

Abstract WP114: Texas Stroke Program Assessment: Infrastructure, Turnover, and Burnout Implications

Introduction: Unlike state designation for trauma centers, there are no Texas State rules on certain infrastructure requirements for stroke designation and independent stroke center accreditation. To assess the current state of program manager impact, a Texas Stroke Program Survey was administered. Hypothesis: Lack of requirements results in overburdened programs, high turnover, poor mentorship, and burnout. Methods: Electronic survey contained program assessment with link to validated instrument (MBI-HSS), administered by Mind Garden Inc, to assess burnout, well-being, and work-related dimensions for medical personnel. Of the 181 Texas designated stroke facilities, 98% were contacted with each site having one anonymous senior program staff member complete the survey. Results: Survey responses were 105 among which 78 completed MBI-HSS (26% CSC, 1% TCS, 59.6% PSC, 17.3% ASRH). Respondents: 66.7% stroke coordinators, 23.8% stroke program managers/directors, and 9.5% identified as other. Majority (41%) were in current role for 1-2 yrs (19% <1 yr, 20% 3-5 yrs, 13.3% 6-9 yrs, 6.7% > 10 yrs) and 42.9% did not consider resigning. Over 5 yr period, 49.5% reported 2 or more turnovers of their position (none 26.7%, once 21.9%, twice 20%, > twice 29.5%, new role 1.9%). Majority (57.1%) reported serving more than one major role at facility. Reported 67.3% had dedicated stroke coordinators and 49% data collectors. Only 53.3% reported adequate resources and 15.2% felt program was priority with sufficient hospital support. Role on-boarding was reported by 16.2% and 61.9% reported no mentorship. MBI-HSS revealed 74.4% had burnout profile of ineffective, overextended, or burnout. Conclusion: Survey demonstrates variability in infrastructure, reduced support and mentorship, high rates of turnover and burnout in Texas stroke programs. Nearly all respondents (91.4%) felt requirements for infrastructure and support for stroke program development and growth were needed.

Analysis Of Factors Affecting Measles Rubella ( Mr ) Immunization Services For Children Aged 18-24 Months During The Pandemic At The Bumiaji Health Center In Batu City

As service providers, the role of officers must be supported by knowledge, infrastructure, response and empathy so that immunization services during a pandemic can continue to run optimally. The purpose of this study was to determine the factors that influence the Measles Rubella (MR) immunization service for children aged 18-24 months during the pandemic at the Bumiaji Health Center, Batu City.The research design used was observational with a cross sectional approach with linear regression statistical tests to determine the factors that influence the Measles Rubella (MR) immunization service in children aged 18-24 months during the pandemic at the Bumiaji Health Center, Batu City. The population was 32 immunization officers at the Bumiaji Health Center with a sample of 30 respondents using simple random sampling.The research design used was observational with a cross sectional approach with linear regression statistical tests to determine the factors that influence the Measles Rubella (MR) immunization service in children aged 18-24 months during the pandemic at the Bumiaji Health Center, Batu City. The population was 32 immunization officers at the Bumiaji Health Center with a sample of 30 respondents using simple random sampling.The results of statistical tests showed that the significance results for the knowledge variable were 0.735 > 0.05, the infrastructure variable was 0.000 < 0.05 and the response and empathy variables were 0.456 > 0.05. For the variables of knowledge, response and empathy, Ho is accepted and H1 is rejected, meaning that there is no influence of knowledge or response and empathy to immunization services. While in the infrastructure variable Ho is accepted and H1 is rejected, it means that there is an effect of response and empathy of officers on immunization services.From the results of the study, it was concluded that only infrastructure had an effect on immunization services. In the simultaneous analysis of all variables when analyzed simultaneously on immunization services, there is indeed a significant effect. The Health Office is expected to continue to support increasing knowledge, equipping infrastructure, motivating response and empathy for officers so that Measles Rubella (MR) immunization services for children aged 18-24 months during a pandemic can run even better.

Estimation of potential wildfire behavior characteristics to assess wildfire danger in southwest China using deep learning schemes

Accurate estimation of potential wildfire behavior characteristics (PWBC) can improve wildfire danger assessment. However, wildfire behavior has been estimated by most fire spread models with immeasurable uncertainties and difficulties in large-scale applications. In this study, a PWBC estimation model (named PWBC-QR-BiLSTM) was proposed by coupling the Bi-directional Long Short-Term Memory (BiLSTM) and quantile regression (QR) methods. Multi-source data, including fuel, weather, topography, infrastructure, and landscape variables, were input into the PWBC-QR-BiLSTM model to estimate the potential rate of spread (ROS) and fire radiative power (FRP) over western Sichuan of China, and then to estimate the probability density of ROS and FRP. Daily ROS and FRP were extracted from the Global Fire Atlas and the MOD14A1/MYD14A1 product. The optimal PWBC-QR-BiLSTM model was determined using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Results showed that the PWBC-QR-BiLSTM performed well in estimating potential ROS and FRP with high accuracy (ROS: R2 > 0.7 and MAPE<30%, FRP: R2 > 0.8 and MAPE<25%). The modal PWBC values extracted from the estimated probability density were closer to the observed values, which can be regarded as a good indicator for wildfire danger assessment. The variable importance analysis also verified that fuel and infrastructure variables played an important role in driving wildfire behavior. This study suggests the potential of utilizing artificial intelligence to estimate PWBC and its probability density to improve the guidance on wildfire management.

The Sharing Economy in the Tourism Sector: Microeconomic Factors

This research endeavours to explore the intricate dynamics of microeconomic factors that significantly influence the evolution of the sharing economy within the vibrant landscape of Lithuania’s tourism sector. By harnessing an extensive dataset comprising diverse tourism metrics and online platform activities, advanced regression analysis techniques are employed to uncover the intricate interplay between tourism employment and key determinants such as tourism output, accommodation infrastructure, internet accessibility, and other pertinent variables. The empirical insights gleaned from this analysis reveal compelling correlations, shedding light on the pivotal role played by these factors in shaping the burgeoning sharing economy within the tourism realm. Moreover, this study provides nuanced insights into the potential implications for future research endeavours. By elucidating the complex relationships between various economic variables and the sharing economy, this research lays the groundwork for further empirical exploration and theoretical refinement. The ramifications of this research extend beyond academia to encompass practical implications for industry practitioners and policymakers alike. By gaining a deeper understanding of the microeconomic drivers of the sharing economy in the tourism sector, stakeholders can devise more informed strategies to leverage its potential for sustainable growth and development. In conclusion, this research represents a significant contribution to the burgeoning field of sharing economy studies within the context of the tourism sector. By revealing the intricate web of microeconomic factors underpinning its development, this study provides valuable insights that can inform both academic discourse and practical decision-making processes. Ultimately, it is hoped that this research will catalyse further exploration and innovation in the field, driving continued growth and prosperity within Lithuania’s tourism sector.

Understanding of Maternal Health Status in Different Social Groups in India Using NFHS Data

Maternal health is a major public health concern in India. MMR of India has declined significantly but maternal health status has not much improved. The prevalence of anemia and low Body Mass Index (BMI) is more severe among the women belonging to the Scheduled Caste (SC) and Scheduled Tribe (ST) categories. In this paper, attempts have been made to examine the maternal health status and to identify the factors responsible for poor health status among SC and ST women. The study is purely based on secondary data taken from latest 3 rounds of NFHS (2005-6, 2015-16 &amp; 2019-21). Multivariate analysis have been carried out using panel regression model to understand the impact of determinants on maternal healthcare. The study found that the SC and ST women are more prone to anemia than others (GENERAL category women). Maternal health status is mostly controlled by per capita health expenditure and health infrastructure variables like no. of hospitals and nurses, irrespective of social class. Thus poor maternal health status in India, especially among socially disadvantaged groups of society, have been major concern. The government should be more focused on existing policies related to maternal healthcare.

Assessing Vulnerability to Natural Hazards: An Intra-district Analysis of Chamba, Himachal Pradesh

Introduction: This study examines the susceptibility of Chamba District, Himachal Pradesh, to natural hazards, with the goal of identifying the environmental dangers in the area and the elements that contribute to its vulnerability. The report utilizes an intra-district analysis to offer detailed insights into the specific issues encountered by various tehsils within Chamba District.&#x0D; Methodology: The assessment includes component indices and composite scores for each Tehsil, which cover social, economic, institutional, infrastructure, and ecological variables. The Chamba Tehsil exhibits a relatively lower level of vulnerability, as evidenced by higher scores in major component indices. On the other hand, Holi Tehsil is more susceptible to risks, mainly because of issues related to its infrastructure and ecological resilience.&#x0D; Results: The findings highlight the complex and varied aspects of vulnerability, emphasizing the need for a comprehensive strategy. The differences among Tehsils emphasize a range of distinct difficulties, which require specific and customized strategies. The resilience of Chamba Tehsil indicates a favorable pattern, whilst the challenges faced by Holi Tehsil highlight particular areas that require targeted assistance.&#x0D; Conclusion: To mitigate vulnerability, it is necessary to not only build physical infrastructure but also address socio-economic imbalances, strengthen institutions, and promote ecological sustainability. It is crucial to develop customized plans that take into account the distinct issues faced by each Tehsil. Community engagement, awareness-building, and capacity-building efforts are crucial in decreasing vulnerability and utilizing local knowledge and abilities to strengthen resilience against natural catastrophes.

Estimating the Spatial Spillover Effects of Transportation Infrastructure on Economic Growth in an Archipelagic Nation

Transportation infrastructure plays a crucial role in facilitating economic growth and mitigating socio-economic disparities. Countries with adequate transportation infrastructure have a comparative advantage in international and domestic trade and in reducing economic and social disparities. Numerous studies have examined the spatial relationship between transportation infrastructure and economic growth, particularly in developed nations like the US and China. However, research on developing countries is scarce and pays little attention to archipelagic nations like the Philippines. This paper estimates the spatial spillover effects of transportation infrastructures in the three major islands of the Philippines for the time period 2003–2020 using the spatial Durbin model (SDM). More specifically, it examined the spatial spillover effects of transportation infrastructure, specifically roads, bridges, airports, and seaports on economic growth. The findings indicate the presence of positive spillover effects of transportation infrastructure at both the national and island levels. However, the spillover effects of transportation infrastructure variables differ significantly across the three major islands. The road length in Luzon and Visayas exhibits a negative trend, whereas Mindanao demonstrates a significant positive trend. The bridges in Luzon and Visayas are positive. Airport passengers in Visayas and Mindanao are negative. Seaport passenger is negative in Luzon, Visayas, and Mindanao. Seaport cargo is positive across major islands. The results of this study indicate that the implementation of appropriate and targeted transportation infrastructure investments in each of the major islands has the potential to foster economic growth Moreover, the results can serve as a valuable resource and integral element of the government’s strategic plan for national and regional development, facilitating the acceleration of economic growth and enhancing productivity at the regional level.

Socioeconomic Paradigms Influencing the Allocation of Oncological Therapeutics: A Comparative Study

Cancer presents a formidable globalized public health challenge with extensive socioeconomic ramifications. This paper investigates the socioeconomic optimality of mRNA-based oncological platforms concerning cancer amelioration. Quantitative analysis discerned the socioeconomic effectiveness of mRNA candidates. Clinical immunogenicity data demonstrated efficacy variability amongst platforms. Cost-benefit assessment discerned CV9202's superior cost-effectiveness, representing socioeconomic optimality. These methodological developments provide a comparative basis to contextualize implementation gaps and accessibility disparities. Socioeconomic cognizance permits tailored therapeutic accommodation for economic constraints, improving patient outcomes through optimized distribution. Further generalization necessitates extensive efficacy data over prolonged timeframes encompassing long-term immunological durability. Societal stigmatization presents additional complications. This research delineates an analytical paradigm to discern optimal therapeutic allocation given socioeconomic circumstances. Improved patient-provider relations may arise from responsive distribution models sensitive to socioeconomic factors. Optimal distribution has advantageous implications for stakeholders. Future research could assess preventative applications, combination therapies, infrastructural variables, and extensive candidates. In conclusion, this methodology demonstrates means of optimizing distribution congruent with socioeconomic factors to improve accessibility and cancer outcomes through cost-effective therapeutic platforms like mRNA vaccination.

주거환경 인프라가 주택전세가격에 미치는 영향

This study aimed to confirm how the residential environment infrastructure affects the jeonse price of housing for the purpose of pure residence.Among various residential environment infrastructures, subway-related variables for transportation infrastructure and park-related variables for living infrastructure were reflected, focusing on the location of the house. Based on this, differences by housing type were additionally confirmed.&#x0D; As a result, it was found that the closer to the subway, the higher the housing jeonse price, and the closer to the park, the lower the price. By housing type, apartments respond most sensitively to the distance from the subway, and in the impact analysis with parks, it was found that the closer the APT, the higher the housing price, and the closer the others, the lower or not affect the housing price. Comparing with previous studies centering on housing sales price, the impact on transportation infrastructure showed a similar effect, while park infrastructure showed completely opposite results, confirming that the influence reflected on the housing sales price and the jeonse price is different.&#x0D; Through this study, it was confirmed how the supply of infrastructure for the purpose of improving the residential environment is reflected in the residential cost, and the results show how urban policies should be planned in response to rapidly changing households.

Green Infrastructure and Urban Vacancies: Land Cover and Natural Environment as Predictors of Vacant Land in Austin, Texas

Urban vacancies have been a concern for neighborhood distress and economic decline and have gained more recent attention as potential green infrastructure is known to benefit communities in diverse ways. To investigate this, this study looked into the relationship between land cover, natural environment, and urban vacancies in Austin, Texas. Additionally, we investigated the spatial patterns of green infrastructure and urban vacancies by different income groups to see if low income communities would potentially lack the benefits of green infrastructure. To measure green infrastructure, we used different land covers such as forests and shrublands, as well as natural environments such as tree canopies and vegetation richness, using remote sensing data. Urban vacancy information was retrieved from the USPS vacant addresses and parcel land uses. Through a series of multivariate analyses examining green infrastructure variables one by one, the study results indicate that green infrastructure interacts with residential and business vacancies differently. Additionally, low-income communities lack green infrastructure compared with the rest of the city and are exposed to more urban vacancies in their neighborhoods. Further study is required to understand the dynamics of vacancies in underserved communities and examine how existing vacant land can benefit the communities as ecological resources.

Abstract 15849: Studying the Effects of Race, Community Health, and Vascular Inequities on Outcomes for Peripheral Artery Disease: Principal Components Analysis of Community Health Metrics Using the Agency for Healthcare Research and Quality Social Determinants of Health Database

Introduction: Healthcare disparities associated with peripheral artery disease (PAD) are influenced by differences in healthcare quality and social determinants of health (SDOH). Hypothesis: We used principal components analysis (PCA) to summarize key themes of community-level SDOH variables. Methods: This retrospective analysis used data from a single health system quality improvement database of patients diagnosed with PAD between 2016-2019. Subjects were geocoded to census tracts and variables from the Agency for Healthcare Quality and Research SDOH database were obtained to examine small-area variability in social, economic, educational, physical infrastructure, and healthcare contexts. PCA was used to identify major themes in SDOH heterogeneity across communities. Relevant SDOH variables were normalized within a data matrix and those with a loading value &gt;0.2 were retained in a principal component (PC). PCs with an eigenvalue &gt; 1.0 were identified to parsimoniously exemplify key themes. Results: Among 7,187 subjects with PAD, the mean age was 72.2 11.1 years, 45.9% were female, 9.4% self-reported as non-Hispanic Black, and 86.6% as non-Hispanic White. PCA was applied to 29 SDOH variables relevant to vascular health. PCA retained 6 PCs to an SDOH matrix describing 73.8% of variance in census tract level data. The greatest variance in data was attributed to socioeconomic advantage (PC1) and urbanicity/migration context (PC2) with some overlap in Black neighborhood context (PC3), income inequality (PC4), multi-race cohesion (PC5), and underrepresented minority context (PC6). Conclusions: PCA transforms high-dimensional SDOH data into distinct typologies of community health while retaining complex features of the data. Future analysis will weight these results within a hierarchical model that accounts for patient-level characteristics and quality of care to inform a more complete social-ecological model of whole-person vascular care.

A count model of financial inclusion in Ghana: evidence from living standards surveys

PurposeThis paper aims to estimate the determinants of the intensity of use of financial inclusion by households in Ghana.Design/methodology/approachDue to the reality of a household using one or more financial products or services, this study uses the generalised Poisson model applied to GLSS6 and GLSS7 data collected in 2012/2013 and 2016/2017 respectively, to estimate the determinants of the intensity of use of financial inclusion. To deepen the analysis, a multinomial probit model is also applied.FindingsResults show that infrastructural variables such as roads, public transport and banks stimulate the intensity of financial inclusion. In addition, agricultural development characteristics such as markets and cooperatives are essential for the intensity of inclusion.Research limitations/implicationsThere is a need to incorporate how many services or depth of services that people use as part of the conceptualisation of financial inclusion, as this can provide more policy-relevant evidence to enhance priority setting in financial inclusion policies. Also, micro-level financial inclusion studies in agrarian economies should consider exploring agricultural development and infrastructure variables in the modelling framework. As lead to further studies, count models of financial inclusion should consider exploring cross-country analysis, the use of panel data, or other methodological approaches to provide more robust evidence.Originality/valuePrevious studies have not modelled financial inclusion based on a count model as a means of measuring intensity though conceptualisations highlight the fact that people use varied financial products or services. Following from this angle, to the best of the authors’ knowledge, this study provides the first attempt at analysing the underlying determinants of the number of financial products or services used by households.

Newspaper framing of motor vehicle crashes and road safety in Argentina

The media plays a key role in shaping the public’s perception of road safety. This study analyzes the newspaper coverage and framing of motor vehicle crashes (MVCs) and road safety in Argentina, South America. The content of 304 articles published by 15 newspapers in November 2020 was reviewed. The results show that episodically framed news stories (focused on a single event or incident) prevail over thematically framed articles. MVCs are presented primarily as ‘police’ events and tend to receive more coverage when fatalities are involved. There is limited information provided on contextual and risk factors, and road safety advice is rarely included. Speeding, infrastructure, alcohol and other human-related variables are the most cited risk factors. Very few articles mention the use of protective devices (seat-belt, helmet and child restraint system). Although motorcyclists represent 40% of RTC deaths in Argentina, only 20% of the news coverage was about them. News coverage was quite similar in national and regional newspapers. There is an opportunity for the media to help build a better road safety culture, but significant changes in news framing are required. Practical recommendations for editors, journalists and road safety practitioners are provided.

The Dynamic Impact of Economic Growth, Foreign Direct Investment and Infrastructure in Laos

This study examines the dynamic impacts of economic growth, foreign direct investment, and some infrastructure variables in Laos with the time series data from 1995-2020, which have used the Generalized Method of Moments (GMM) and Autoregressive Distributed Lag (ARDL) model approach. The unit root test to check the stationarity of all variables in this study using the Augment Dicky Fuller (ADF) test with considering both level and at first difference. The empirical results of GMM found that the FDI is a major catalyst for economic growth in Laos which has a positive impact and is statistically significant at the level. Similarly, the role of infrastructure variables such as agriculture, industry, and service is the main of the most important indicators in supporting the economic growth in Laos, while telecommunication, electricity, air transport, and trade are positive but insignificant at the level. However, this study also found some variables such as labor, population growth, and life expectancy in support of economic growth which have a strongly positive and significant impact at the level, but the human capital is insignificant at the level. We, therefore, recommend that the government of Laos should attract and sustain more foreign direct investment and increase the infrastructural development to achieve the target of sustainable economic growth in the near future.