OLS Model Research Articles

Association between Internet use and depression among older adults in China: the chain-mediating role of volunteer activity participation and friend network.

Depression is a significant burden on individuals and society, which requires our attention and action. As the aging wave collides with digitalization, further research is needed to understand how internet use relates to depression in older adults. This study aimed to investigate whether volunteer activity participation and friend networks played a chain mediating role in this relationship from the perspective of the socioemotional selectivity theory. This study's data was derived from the 2018 China Longitudinal Aging Social Survey (CLASS), comprising 5,558 samples. The study employed the OLS model for benchmark regression and multiple robust check methods, including altering variable settings and utilizing the instrumental variable model. In addition, the generalized structural equation model (GSEM) was used to analyze the mechanism. Using the internet was significantly associated with reduced depression in older adults (coefficient = -0.9321, p < 0.001). The instrumental variable model confirmed this reduction (coefficient = -0.9200, p < 0.001). Moreover, we found that there were three indirect pathways of Internet use on depression among older adults: the mediating role of volunteer activity participation (all p-values <0.001), the mediating role of friend network (all p-values <0.001), and the chain mediating role of both factors (all p-values <0.001). Based on the research findings, we suggest mobilizing tech-savvy older adults to assist others in adopting digital technology and using the internet. We also suggest that the government could assist in creating older adult-friendly communities. Social workers could collaborate with tech-savvy older adults to organize various voluntary activities, encouraging more senior citizens to participate. In addition, we recommend that the community should consider the form of activities to help older adults make friends first rather than solely focusing on voluntary activities.

Exploring the effect of built environment on spatiotemporal evolution of traffic congestion using a novel GTWR model: a case study of Hefei, China

ABSTRACT Understanding the impact of the built environment on traffic congestion can provide reliable references for alleviating traffic congestion. Previous research has explored traffic congestion evolution but often overlooks two key aspects: the frequency of congestion state updates and the spatial-temporal impact of built environment features. Based on data from Hefei, China, we propose a novel geographically and temporally weighted regression (GTWR) model that integrates temporal variables and spatial grids into the traditional GWR model. The empirical results show that public facilities most significantly impact congestion during morning peak hours, scenic spots during afternoon peaks, and motorcycle services in the evening. The study also reveals the rules for the spatiotemporal impact of the built environment on traffic congestion. Finally, the comparison of models shows that the GTWR model outperforms the OLS and GWR models. The findings can guide traffic managers in creating targeted strategies to enhance transportation system efficiency.

Fertility intentions of China’s one-child generation: a comparative analysis with the US

ABSTRACT In 1979, China introduced the one-child policy to curb rapid population growth, enforcing birth quotas with penalties. This study examines the impact of only-child status on fertility intentions using data from the 2017 Chinese General Social Survey. Findings from OLS and multinomial logistic regression models indicate that only children have significantly lower fertility desires, even after controlling for demographic and socioeconomic factors. Notably, only children have a 20% higher likelihood of wanting 0–1 children compared to those with siblings. An OLS model interacting only-child status with birth cohort reveals that individuals born during the peak enforcement period (1979–1983) exhibit particularly reduced fertility intentions, reflecting a stronger internalization of small family ideals. Although this effect weakens in later cohorts, the association between only-child status and lower fertility desires persists. A comparative analysis with data from the 2016 and 2018 US General Social Survey shows that these patterns are unique to China, underscoring the lasting influence of the one-child policy on fertility mindsets. The results suggest that China's continued low fertility may be partly driven by these deeply ingrained attitudes.

Estimation Model and Spatio-Temporal Analysis of Carbon Emissions from Energy Consumption with NPP-VIIRS-like Nighttime Light Images: A Case Study in the Pearl River Delta Urban Agglomeration of China

Urbanization is growing at a rapid pace, and this is being reflected in the rising energy consumption from fossil fuels, which is contributing significantly to greenhouse gas impacts and carbon emissions (CE). Aiming at the problems of the time delay, inconsistency, uneven spatial coverage scale, and low precision of the current regional carbon emissions from energy consumption accounting statistics, this study builds a precise model for estimating the carbon emissions from regional energy consumption and analyzes the spatio-temporal characteristics. Firstly, in order to estimate the carbon emissions resulting from energy consumption, a fixed effects model was built using data on province energy consumption and NPP-VIIRS-like nighttime lighting data. Secondly, the PRD urban agglomeration was selected as the case study area to estimate the carbon emissions from 2012 to 2020 and predict the carbon emissions from 2021 to 2023. Then, their multi-scale spatial and temporal distribution characteristics were analyzed through trends and hotspots. Lastly, the influence factors of CE from 2012 to 2020 were examined with the OLS, GWR, GTWR, and MGWR models, as well as a ridge regression to enhance the MGWR model. The findings indicate that, from 2012 to 2020, the carbon emissions in the PRD urban agglomeration were characterized by the non-equilibrium feature of “high in the middle and low at both ends”; from 2021 to 2023, the central and eastern regions saw the majority of its high carbon emission areas, the east saw the region with the highest rate of growth, the east and the periphery of the high value area were home to the area of medium values, while the southern, central, and northern regions were home to the low value areas; carbon emissions were positively impacted by population, economics, land area, and energy, and they were negatively impacted by science, technology, and environmental factors. This study could provide technical support for the long-term time-series monitoring and remote sensing inversion of the carbon emissions from energy consumption in large-scale, complex urban agglomerations.

Bibliometric articles in business and management: Factors affecting production and scholarly impact

Based on different theoretical and publishing approaches, this study reveals that the publication of bibliometric articles in business and management follows a logistic diffusion pattern. Fist, the analysis of citation concentration −using the Gini index, concludes that a minority of articles capture most scholarly interest, there is a moderate concentration of articles in some outlets, and a large and diverse group of authors, forming a long tail distribution. Second, a set of hypotheses are tested by alternative regression models (OLS, semi-logarithmic, and negative binomial models). Results support the importance of specific characteristics such as the synthesis of previous contributions, the methodological sophistication, or the cosmopolitanism of the collaboration of the authors. In addition, there is an inverse effect of bibliometric expansibility on the impact of each article. The authors built a database from a content analysis of 835 bibliometric articles in WoS (1981–2022). The results raise several implications and recommendations.

Gender Disparities in Frailty and Its Decomposition Among Chinese Older Adults.

To identify the evidence of gender disparities in frailty and explore the factors contributing to male-female differences. A longitudinal study. A total of 24,429 older adults (60+) were enrolled. Frailty was assessed by frailty index and frailty risk based on Rockwood's cumulative deficit frailty index. OLS and logistic regression models were conducted, with Oaxaca-Blinder and Fairlie decomposition methods to further analyse the factors contributing to gender disparities in frailty. The paper used four waves of the China Health and Retirement Longitudinal Study from 2011 to 2018, a nationwide survey organised by the National Development Institute of Peking University. Women had higher frailty status than men, with more pronounced increases. Among the factors, education has the most significant association with frailty gender disparities. The contribution of individual characteristics, particularly education, to gender disparities in frailty appeared to diminish over time, while the contribution of family and regional factors remained relatively stable, and the contribution of institutions was deemed inadequate. The frailty in Chinese older adults is generally on the rise with continuously expanded gender disparities. It is crucial to consider the contributing factors to frailty in older adults for effective prevention and intervention strategies. Additional gender-specific geriatric care policies are needed in order to address gender inequality in health. The findings of this study highlight the prevalence of increasing gender disparities in frailty and identify that the level of education, per capita annual household income as well as marital status are the most significant factors contributing to the gender gap. Those findings provide policy implications for healthcare nursing service from a gender-specific perspective in order to achieve health equity. This study has adhered to the STROBE guideline. No Patient or Public Contribution. WHAT DOES THIS PAPER CONTRIBUTE TO THE WIDER GLOBAL CLINICAL COMMUNITY: This study provided implications on gender-specific geriatric care nursing services. The study highlighted the importance of focusing on frailty and its gender disparities in geriatric clinical nursing.

Analysis of Interest Rate Determinants: Evidence from Lending Club

Nowadays, high interest rates hit the whole lending market, a number of people consider online lending institutions to trade. This dynamic generates a viable alternative to traditional bank services, allowing borrowers to choose their optimal lending plan. This paper focuses on online lending services, investigating the factors influencing the interest rate offered by Lending club institutions. Using OLS and machine learning models, the author analyses the influence of different dimensions of factors (debt level, FICO score, lending purpose, and normalized debt-to-income ratio) on the average interest rate. The results emphasize the results emphasize that Personal characteristics count for lending rate evaluation. People who have lower credit scores and bear a high debt ratio would be offered a higher interest rate than those who have a good individual background. Contrary to the conclusions achieved in other studies, when loans are given for longer durations, they tend to have higher interest rates. This is mainly because lenders have to consider factors like the risk of inflation and the potential earnings they might miss out on during that extended period.

Exploring the Income-Increasing Benefits of Rural E-Commerce in China: Implications for the Sustainable Development of Farmers

The slowdown in income not only hinders the sustainable development of farmers but also exacerbates ecological and environmental problems. Likewise, the digital economy model represented by e-commerce has become a key path for growing farmers’ income. This article is based on 2020 China Rural Revitalization Survey data and uses the OLS and mediation effect models to examine the impact and mechanism of e-commerce operations on farmers’ income. The research results indicate that e-commerce operations can significantly improve the income level of farmers. After robustness and endogeneity tests, this conclusion still holds. The mechanism analysis results indicate that e-commerce operations promote farmers’ income growth by enhancing information acquisition, reducing operating costs, and enhancing financial support. The results of the heterogeneity analysis indicate that the impact of e-commerce on farmers’ income is heterogeneous in terms of income structure, education level, and regional distribution. Specifically, e-commerce significantly increases farmers’ agricultural operating income, non-agricultural operating income, and transfer income but significantly reduces their wage income. At the same time, e-commerce has a more significant income-increasing effect on highly educated and central and western farmers. Therefore, in promoting the development of a rural digital economy, it is necessary to improve the supporting facilities of e-commerce in rural areas; strengthen the supply of information, technology, capital, talent, and other elements; continue to improve the inclusiveness of rural e-commerce; and promote the sustainable growth of farmers’ income. The above findings not only provide new channels and ideas for farmers to increase income through e-commerce operations, but also help relevant decision-making departments to formulate new policies to promote farmers’ income growth and the development of e-commerce in China.

ANALISIS SPATIAL AUTO REGRESSIVE (SAR) TERHADAP FAKTOR YANG MEMPENGARUHI KASUS DIARE DI KOTA MEDAN

Diarrhea disease is categorized as the top 10 most common diseases in North Sumatra, especially Medan City. The number of diarrhea cases in each sub-district in Medan City varies, indicating a relationship between regions. This is the basis for the use of spatial models to find out how much influence variables have on the incidence of diarrhea in Medan City. The Spatial Auto regressive (SAR) model is used to adjust the relationships that exist between a set of observations or locations. In this study, the results of SAR modeling of diarrhea cases in 21 sub-districts in Medan City compared to the OLS model produced the best model. The SAR model yielded 35.82%. The significant variable for diarrhea cases in Medan City is the number of people who do not have access to PAM water ( ).

Inflation and Economic Growth Tradeoff, and Its Impact on Indonesia Poverty

There was tradeoff between inflation and economic growth in Indonesian economy over the last three decades, from 1990 to 2021. When economic growth tended to increase, inflation would also increase. The increasing scale of business sectors due to the increasing of economic growth shifted price and wage setting in the same direction, then increasing labor demand, and decreasing poverty. This study used a VAR model to estimate tradeoff between inflation and economic growth, and OLS model to estimate the relationship between economic growth and the poverty, and also between inflation and poverty. A VAR model explained that there was a positive bi-direction causality between inflation and economic growth, however, there was uncertainty whether economic growth caused inflation, or whether inflation caused economic growth. Although economic growth had a positive effect on reducing poverty, but due to the tradeoff between inflation and economic growth, then economic policies had to be designed to fully controlling inflation during the persistence of economic growth to prevent an increasing of poverty. Expected inflation and long run inflation target should always be a major concern when setting monetary policy to avoid higher economic fluctuation that could stimulate increasing of inflation and poverty.

Estimating GDP by Fusing Nighttime Light and Land Cover Data

Accurate information on gross domestic product (GDP) is essential for better understanding the dynamics of regional economies and urbanization processes. Satellite based nighttime light datasets can well track GDP in urban areas, however, they are difficult to be used in suburban, rural and sparsely populated areas. Thus, this study explored the potential of GlobeLand30 and relief degree for improving the ability of VIIRS Nighttime Light data of GDP estimation. Firstly, we calculated the Moran’s Index (Moran’s I) to measure spatial auto-correlation of GDP. At provincial level, Moran’s I Index of GDP is 0.14, Z value is 2.29. While at the prefecture city level, it is 0.11 and 14.54, respectively. Then, we compared the results derived from geographically weighted regression (GWR) and OLS models (i.e., R2, root mean square error, corrected Akaike information criteria and residuals). Both models suggest that land cover information can significantly improve GDP estimation performance, and total nighttime light (TNL) is the most important economic indicator for estimating GDP. The coefficients of the GWR model for TNL at the provincial and prefecture levels are 1.75 and 1.19, respectively, which are significantly larger than the coefficients for other factors such as land cover and terrain undulation. In addition, the GWR model performed better than OLS model in GDP estimation at both provincial and prefecture levels, and prefecture-level models can better depict the spatial variation in detail. In provincial-level models, GWR could account for 93% of economic development, while OLS could only reflect 82%. Likewise, in prefecture-level models, R2 of GWR model improved almost 50% compared with that of OLS model.

Blocked Pipes: The Economic Consequence of Skilled Worker Shortages

The thinning of the supply of skilled labor is having major negative implications on the economy. This research explores the “skilled labor gap” by examining the plumbing industry and quantifying the economic value lost through inefficiencies and labor premiums. The analysis utilizes state-level panel data for the years 2010 to 2018, employing both a linear OLS model of pooled data and a random effects regression model to determine the relationship between the number of plumbers and the price of plumbing services (proxied in plumber wages). Price effects are modeled across industries to determine the effects on the overall economy.

“Technology enables the development of modern tourism” -- Study on the influence of tourism technology on tourism consumption

Recent years have witnessed the improvement of people’s pursuit of life quality and the increasing trend in the proportion of enjoyable consumption in residents’ consumption structure, a tourism boom is taking place all over the country. As technology continues to integrate into the tourism industry, new techniques, such as big data, cloud computing, the Beidou system, virtual reality, and so on, have been applied in tourism, providing abundant tourism products and services. This paper obtains data through a questionnaire survey and establishes an OLS model based on the collected sample data to study the influence of tourism technology level on consumers' tourism consumption. The result shows that the improvement of the tourism technology level can lead to a rise in the number of trips and the amount of tourism consumption expenditure, which plays a more prominent role in the natural landscape--the improvement of the tourism technology level can prompt consumers to prefer natural landscapes. This result is most pronounced among female consumers and consumers with middle and lower levels of education. The mechanism test indicates that tourism technology can promote consumers' tourism consumption by making it simpler to obtain tourism information, making travel more convenient, improving the interactive experience, and enhancing the exhibition effect. Based on the investigation and research, this paper finds out the aspects to be improved in the development of tourism technology and put forward some development suggestions, which will be helpful to better promote the development of tourism technology in the future.

Research on the Spatiotemporal Pattern and Influencing Mechanism of Coastal Urban Vitality: A Case Study of Bayuquan

Enhancing the spatial vitality of coastal cities is beneficial for the sustainable development of urban construction. However, how to fully utilize coastal resources and boost urban vitality is an important issue. This study takes the coastal city of Bayuquan in China’s cold region as an example. Firstly, we conducted field investigations and data mining in Bayuquan, utilizing Baidu heat map data to measure the spatial–temporal vitality of different areas in Bayuquan. Secondly, we used Moran’s I test to examine the spatial autocorrelation of coastal spatial vitality. Lastly, with the help of the OLS and GWR models, we explored the factors influencing spatial vitality and the urban built environment. The research findings indicate the following: (1) There are spatial–temporal differences in the vitality of different areas in Bayuquan, heavily influenced by the tourist season. (2) The OLS results show that the impact of the built environment on spatial vitality exhibits spatial heterogeneity during different tourist seasons. However, we found no spatial heterogeneity in the influencing factors in the harbor district. (3) The harbor district and the tourism-driven district re quire differentiated construction guidance. Facility functions and block morphology mainly influence the vitality of the harbor district, while the vitality of the tourism-driven district is primarily affected by its aesthetic characteristics. This study can propose differentiated regional construction guidance and specific feasible coastal urban design strategies for seasonally influenced coastal city construction. It holds significant implications for improving urban living quality and is vital to urban decision-makers, planners, and stakeholders.

Analysis of the Effect of Environmental Regulation on Eco-Efficiency of Service Sector

The green transformation of the service sector is crucial for promoting the construction of ecological civilization and boosting high-quality development. The aim of the relationship between environmental regulation and eco-efficiency is to explore the path of the green transformation of the service sector. Based on the provincial data in China from 2001~2019, this study investigated the effect of environmental regulation on the eco-efficiency of the service sector, concentrating on the influencing mechanism and threshold effect of environmental regulation on the eco-efficiency of the largest service sector in the Chinese economy. Applying the panel OLS model, mediating model, and threshold effect model. The main findings are as follows. First, environmental regulation is not beneficial for boosting the eco-efficiency of the service sector because of compliance costs. Second, environmental regulation can reduce the eco-efficiency of the service sector through service agglomeration. However, the effect of innovation compensation can hinder the negative influence of environmental regulation on the eco-efficiency of the service sector. Third, the inhibiting effect of environmental regulation disappears non-linearly with boosting economic development. Conversely, the positive influence of environmental regulation becomes negative, along with improving service agglomeration. At last, this study provided recommendations for the policymakers who hope to accelerate the green transformation of the service sector from the perspectives of strengthening technological innovation, reducing agglomeration, and establishing coordinated development mechanisms.

Trust and SME short-term financial policy. European evidence

PurposeDoes societal trust influence short-term financial management? Recent papers uncover the importance of societal trust for financial management in specific countries and large firms. Our paper aims to provide a comprehensive analysis of the impact of societal trust on short-term financial policies of SMEs, namely working capital management and cash holdings.Design/methodology/approachWe rely on a sample of 14,711 privately owned medium-sized manufacturing firms from 26 European countries with a sample period between 2014 and 2020. For estimation, we use pooled OLS and hierarchical linear models and control for several firm-specific and country-specific known determinants of short-term financial management. Moreover, our results are robust to the specific measurement of trust, financial constraints, and corruption.FindingsWe show a positive relationship between trust and working capital requirements investment and a negative relationship between trust and the level of cash holdings. Moreover, we show that trust attenuates the negative impact of being a financially constrained firm and the positive impact of national perceptions of corruption. Finally, in higher trustworthy environments, firms operate with relatively higher inventories and relatively lower trade credit granted and obtained.Research limitations/implicationsResults suggest that policies supporting societal trust may also foster business development and that when dealing with clients or suppliers from different trustworthy environments, firms may have to adapt their business models to incorporate trust differences between business environments.Originality/valueFirstly, the comprehensive analysis of the impact of trust on working capital management and cash holdings while controlling for different firm-level and country-level known determinants of short-term financial management. Secondly, it addresses a European sample of unquoted, medium-sized firms. Thirdly, it studies the combined effect of trust and financial constraints and trust and corruption.

Analysis of Income Potentials of Blocks Making Industry in Zamfara State, Nigeria

The study is an analysis of the income potentials of blocks making industry in Zamfara state. The research employs the used primary sources of data, whereby the data was obtained from the responses of 100 blocks making industry entrepreneurs in the study area, who were selected with the aid of simple random sampling method. Descriptive and inferential statistics were employed for data analysis thus, multiple regression analysis using OLS model. The findings revealed that, blocks industry has potentials to generate income in the study area in other words, blocks making industry in Zamfara state has is an important tool for in terms of income generation, the study recommends among others that, in addition to the existing policy by government in supporting SMEs to get access to finance, more windows should be open by the Central bank of Nigeria, Bank of Industry and Commercial Banks to enable SMEs have more access to finance.

Research on the effectiveness of hedging on SSE 50 stock index futures

Stock index futures is an important investment product, which gives financial market participants a new way to manage risks, especially in the case of investors holding more spot positions, the risk avoidance function of stock index futures is more prominent. Therefore, this paper to the optimal hedging ratio of stock index futures as the research focus, the above card 50 stock index futures main contract (code: IHL 8) as the research object, on this basis, using OLS and VAR model, for the SSE 50 index (code: 000016), and use the effectiveness of hedging index He to evaluate the effectiveness of the hedge, so as to get the most effective hedging model. Through the empirical research in this paper, it is found that the hedge effect obtained by using the VAR model is better than that of the traditional OLS model. By using the stock index futures, the market participants can effectively avoid the risk of the spot market.

Can Social Learning Promote Farmers’ Green Breeding Behavior? Regulatory Effect Based on Environmental Regulation

Farmers’ green breeding behavior is significant to environmental protection and sustainable development. Based on the micro-survey data of 1248 beef cattle farmers in Inner Mongolia Autonomous Region and Jilin Province, the OLS model and regulatory effect tests are used to analyze the influence of social learning on farmers’ green breeding behavior and to verify the mechanism of environmental regulation. Key findings include the following: (1) Social learning has a significant positive impact on farmers’ green breeding behavior. (2) Social learning has a significant positive impact on farmers’ willingness to undertake green breeding, farmers’ specific behavior of green breeding, and farmers’ continuous green breeding, among which the effect on specific behavior of green breeding is the most positive. (3) Environmental regulation plays a moderating role in the relationship between social learning and farmers’ green breeding behavior, among which all sub-dimensions of environmental regulation (incentive-based environmental regulation, binding environmental regulation and guided environmental regulation) have positive reinforcing effects, especially the most significant reinforcement effect of binding environmental regulation. Based on the research conclusions, this study proposes policy suggestions such as strengthening the training of farmers, strengthening the learning and communication among farmers’ neighbors, increasing the frequency of farmers’ use of mass media, improving the environmental regulation policy system, and promoting the coordinated development of environmental, economic, and social benefits.

Workplace accidents, economic determinants and underreporting: an empirical analysis in Italy

PurposeThis paper proposes an analysis of occupational accidents in Italy at the regional level. For this purpose, our panel is composed of 20 regions over the 2010–2019 time span.Design/methodology/approachWe apply different econometric estimation techniques (pooled OLS model, panel fixed and random effects models and semiparametric fixed model) using INAIL and ISTAT data. Our models investigate workplace accidents at the regional level by accounting for socioeconomic, labour market and productive system variables and controlling for possible underreporting bias.FindingsOverall results reveal the existence of a relevant under-notification phenomenon of accidents at work with respect to moderate accidents, that is higher especially for the southern regions of Italy. However, when considering as outcome variable an alternative set of more severe workplace accidents our model specification remains highly jointly statistically significant. Among our main findings, the analysis shows that worker skills (blue collar) strongly affect the regional pattern of workplace accidents, i.e. an increase of 1% of low paid employees generates about an increase of 1.8 severe workplace accidents per thousand workers. Moreover, we provide evidence that the size of the firm is inversely related to the occupational accident rates. Finally, our results highlight a nonlinear relationship between GDP and occupational accidents for the Italian regional context, confirmed by the high statistical significance of the quadratic term in all the estimated linear models and by the semi-parametric analysis.Originality/valueA first element of originality of our study consists of investigating the macro determinants of occupation accidents at a regional Italian level. Second, the empirical literature (Boone and Van Ours, 2006) highlights the possible bias of underreporting behaviours on nonfatal accidents in contrast to fatal accidents that are always reported. From this perspective, we have identified a few analyses (namely, Boone et al., 2011) considering different accident sets characterised by different severity degrees. Thus, this paper contributes to the literature considering five alternative subsets of accidents stratified by degree of severity (i.e. moderate, severe, moderate plus severe, severe plus fatal and total accident rates) to test for possible underreporting bias affecting our econometric model.