Two-stage Analysis Research Articles

The moderating effect of context of reception, labour market and education system on the migrant-native gap in university expectations

ABSTRACT This paper explores the cross-national variation in the gap in university expectations between native and second-generation immigrant students and whether it is moderated by characteristics of the country of residence of children of immigrants. Three domains have been considered at the national level: the context of reception of immigration (legal status of non-national immigrants and views about immigration of the local population); labour market segmentation and education systems (external differentiation). The study tests the general hypothesis that settings that enhance individual autonomy and widen room for choice by removing formal and informal barriers that migrants and their descendants face in everyday life and in specific domains (education and labour market) are associated with a larger gap in university expectations that is favourable to children of immigrants (a larger ‘immigrant advantage’). Applying a two-stage analysis to PISA 2015 data, results provide overall support for this hypothesis. More welcoming contexts of reception are associated with a larger immigrant advantage. External differentiation is negatively associated with it, both for students attending general and vocational tracks, especially the latter, as compared to non-tracked students attending common tracks. There is not a robust association between labour market segmentation and the university expectations’ gap.

Noise-Level Aware Compressed Analysis Framework for Robust Electrocardiography Signal Monitoring.

Compressed sensing (CS) has drawn much attention in electrocardiography (ECG) signal monitoring for its effectiveness in reducing the transmission power of wireless sensor systems. Compressed analysis (CA) is an improved methodology to further elevate the system's efficiency by directly performing classification on the compressed data at the back-end of the monitoring system. However, conventional CA lacks of considering the effect of noise, which is an essential issue in practical applications. In this work, we observe that noise causes an accuracy drop in the previous CA framework, thus discovering that different signal-to-noise ratios (SNRs) require different sizes of CA models. We propose a two-stage noise-level aware compressed analysis framework. First, we apply the singular value decomposition to estimate the noise level in the compressed domain by projecting the received signal into the null space of the compressed ECG signal. A transfer-learning-aided algorithm is proposed to reduce the long-training-time drawback. Second, we select the optimal CA model dynamically based on the estimated SNR. The CA model will use a predictive dictionary to extract features from the ECG signal, and then imposes a linear classifier for classification. A weight-sharing training mechanism is proposed to enable parameter sharing among the pre-trained models, thus significantly reducing storage overhead. Lastly, we validate our framework on the atrial fibrillation ECG signal detection on the NTUH and MIT-BIH datasets. We show improvement in the accuracy of 6.4% and 7.7% in the low SNR condition over the state-of-the-art CA framework.

Police Response Time and Injury Outcomes

Abstract The delayed response of law enforcement to calls for service has become a hot button issue when evaluating police department performance. While it is often assumed that faster response times could play an important role in quelling potentially violent incidents, there is no empirical evidence to support this claim. In this paper, we measure the effect of police response time on the likelihood that an incident will result in an injury. To overcome the endogeneity of more severe calls being assigned higher priority, which requires a faster response, we take several steps. First, we focus on the subset of calls for service categorised as ‘Major Disturbance—Violence’ that all receive the same priority level. Second, we instrument for police response time with the number of vehicles within a 2.5-mile radius of the incident at the time it is received by the call centre. When controlling for beat, month and time-of-day fixed effects, this instrumenting strategy allows us to take advantage of the geographical constraints faced by a dispatcher when assigning officers to an incident. In contrast to the ordinary least squares estimates, our two-stage least squares analysis establishes a strong causal relationship whereby increasing response time increases the likelihood that an incident results in an injury. The effect is concentrated among female victims, suggesting that faster response time could potentially play an important role in reducing injuries related to domestic violence.

The critical role of digital technology in sustainable development goals: A two-stage analysis of the spatial spillover effect of carbon intensity

In order to achieve sustainable development goals, China has further increased its goal of reducing carbon intensity and has made digitalization an important support for sustainable development. However, the impact of digitalization on carbon intensity reduction is still unclear. In this context, this paper first evaluates the digitalization level of 30 provincial regions in China and then constructs a spatial Durbin model for two stages, 2012–2015 and 2016–2019, so as to explore the spatial spillover effects of carbon intensity in different stages and the important roles of digital infrastructure and digital inputs in carbon intensity reduction. The main findings are as follows: (1) the current digitization level of each province in China is widely disparate, with the region showing a high level in the east and a low level in the west; (2) carbon intensity reduction has a significant spatial spillover effect, as shown by a 1% reduction in local carbon intensity and a 0.21% reduction in neighboring regions; and (3) digitalization has a more significant positive impact on the reduction in carbon intensity in stage 2. The research results are strong demonstration that digitalization drives sustainable development.

A taxonomy of competition-based approaches as innovation policy measures to foster external knowledge search

PurposeThis paper aims to provide a comprehensive view of the different competition-based approaches that policymakers can exploit to foster external knowledge search and their positioning among innovation policy measures. A growing number of companies have implemented initiatives to access external knowledge to increase their innovativeness, consistently with the open innovation paradigm. Competition-based approaches have received increasing attention by the private sector as a way to access external knowledge. However, despite their potential role as innovation policy measures, a limited attention has been devoted so far to investigate them from the policymakers’ perspective.Design/methodology/approachTo this aim, a two-stage empirical analysis has been carried out to develop a taxonomy of competition-based approaches. The first stage leveraged a multiple case study methodology including a sample of 20 competition-based approaches, while the second one leveraged interviews with Italian and European key informants.FindingsThis paper proposes a novel taxonomy including eight competition-based approaches, which differ among each other in terms of policy strategy, scope breadth and output required. Moreover, this paper enriches a well-established taxonomy of innovation policy instruments with the identified competition-based approaches.Originality/valueThis study contributes to the current debate on innovation policy by providing a taxonomy that includes eight competition-based approaches that can be exploited by policymakers to foster external knowledge search as well as their positioning among the innovation policy instruments. The taxonomy will hopefully support policymakers in identifying of the most suitable instruments in the light of their policy strategy and objectives.

Lovemarks and beyond: Examining the link between lovemarks and brand loyalty through customer advocacy in the automobile industry.

Despite the potential for businesses, our understanding of lovemark brands and their consequences is limited. Numerous psychological and brand-related consequences are linked to lovemarks, but the role of influential underlining mechanisms is not fully understood. Inspired by the norms of reciprocity theory, the current study investigates the underlining role of customer advocacy in the relationship between perceived lovemarks and brand loyalty of customers in the automobile industry. By adopting the survey method, a sample of 478 was drawn from Pakistani automobile customers. Structural equation modelling was used for the analysis. We conceptualised lovemarks and brand loyalty as reflective higher-order constructs that we analysed through a two-stage disjoint analysis. Our results support the conceptualization of lovemarks and brand loyalty as higher-order constructs. The influence of lovemarks and customer advocacy on brand loyalty was statistically significant when we controlled for age, gender, and income. Our findings also reveal that customer advocacy, seen as a company's positive interactions, mediates and play a key role in influencing the relationship between lovemarks and brand loyalty. This is among the first studies to examine the role of customer advocacy in the lovemarks-brand loyalty relationship. We examined these relationships in the automobile sector of Pakistan, which offers several theoretical and managerial implications for academia and practitioners. The implications are proposed and outlined in this study.

Optimizing Sparse Testing for Genomic Prediction of Plant Breeding Crops.

While sparse testing methods have been proposed by researchers to improve the efficiency of genomic selection (GS) in breeding programs, there are several factors that can hinder this. In this research, we evaluated four methods (M1-M4) for sparse testing allocation of lines to environments under multi-environmental trails for genomic prediction of unobserved lines. The sparse testing methods described in this study are applied in a two-stage analysis to build the genomic training and testing sets in a strategy that allows each location or environment to evaluate only a subset of all genotypes rather than all of them. To ensure a valid implementation, the sparse testing methods presented here require BLUEs (or BLUPs) of the lines to be computed at the first stage using an appropriate experimental design and statistical analyses in each location (or environment). The evaluation of the four cultivar allocation methods to environments of the second stage was done with four data sets (two large and two small) under a multi-trait and uni-trait framework. We found that the multi-trait model produced better genomic prediction (GP) accuracy than the uni-trait model and that methods M3 and M4 were slightly better than methods M1 and M2 for the allocation of lines to environments. Some of the most important findings, however, were that even under a scenario where we used a training-testing relation of 15-85%, the prediction accuracy of the four methods barely decreased. This indicates that genomic sparse testing methods for data sets under these scenarios can save considerable operational and financial resources with only a small loss in precision, which can be shown in our cost-benefit analysis.

Yoghurt Intake and Gastric Cancer: A Pooled Analysis of 16 Studies of the StoP Consortium.

Yoghurt can modify gastrointestinal disease risk, possibly acting on gut microbiota. Our study aimed at exploring the under-investigated association between yoghurt and gastric cancer (GC). We pooled data from 16 studies from the Stomach Cancer Pooling (StoP) Project. Total yoghurt intake was derived from food frequency questionnaires. We calculated study-specific odds ratios (ORs) of GC and the corresponding 95% confidence intervals (CIs) for increasing categories of yoghurt consumption using univariate and multivariable unconditional logistic regression models. A two-stage analysis, with a meta-analysis of the pooled adjusted data, was conducted. The analysis included 6278 GC cases and 14,181 controls, including 1179 cardia and 3463 non-cardia, 1191 diffuse and 1717 intestinal cases. The overall meta-analysis revealed no association between increasing portions of yoghurt intake (continuous) and GC (OR = 0.98, 95% CI = 0.94-1.02). When restricting to cohort studies, a borderline inverse relationship was found (OR = 0.93, 95% CI = 0.88-0.99). The adjusted and unadjusted OR were 0.92 (95% CI = 0.85-0.99) and 0.78 (95% CI = 0.73-0.84) for any vs. no yoghurt consumption and GC risk. The OR for 1 category of increase in yoghurt intake was 0.96 (95% CI = 0.91-1.02) for cardia, 1.03 (95% CI = 1.00-1.07) for non-cardia, 1.12 (95% CI = 1.07-1.19) for diffuse and 1.02 (95% CI = 0.97-1.06) for intestinal GC. No effect was seen within hospital-based and population-based studies, nor in men or women. We found no association between yoghurt and GC in the main adjusted models, despite sensitivity analyses suggesting a protective effect. Additional studies should further address this association.

Estimating future PM2.5-attributed acute myocardial infarction incident cases under climate mitigation and population change scenarios in Shandong Province, China

BackgroundThe effects of fine particulate matter (PM2.5) on acute myocardial infarction (AMI) have been widely recognized. However, no studies have comprehensively evaluated future PM2.5-attributed AMI burdens under different climate mitigation and population change scenarios. We aimed to quantify the PM2.5-AMI association and estimate the future change in PM2.5-attributed AMI incident cases under six integrated scenarios in 2030 and 2060 in Shandong Province, China. MethodsDaily AMI incident cases and air pollutant data were collected from 136 districts/counties in Shandong Province from 2017 − 2019. A two-stage analysis with a distributed lag nonlinear model was conducted to quantify the baseline PM2.5-AMI association. The future change in PM2.5-attributed AMI incident cases was estimated by combining the fitted PM2.5-AMI association with the projected daily PM2.5 concentrations under six integrated scenarios. We further analyzed the factors driving changes in PM2.5-related AMI incidence using a decomposition method. ResultsEach 10 μg/m3 increase in PM2.5 exposure at lag05 was related to an excess risk of 1.3 % (95 % confidence intervals: 0.9 %, 1.7 %) for AMI incidence from 2017 − 2019 in Shandong Province. The estimated total PM2.5-attributed AMI incident cases would increase by 10.9−125.9 % and 6.4–244.6 % under Scenarios 1 − 3 in 2030 and 2060, whereas they would decrease by 0.9–5.2 % and 33.0–46.2 % under Scenarios 5 – 6 in 2030 and 2060, respectively. Furthermore, the percentage increases in PM2.5-attributed female cases (2030: −0.3 % to 135.1 %; 2060: −33.2 % to 321.5 %) and aging cases (2030: 15.2–171.8 %; 2060: −21.5 % to 394.2 %) would wholly exceed those in male cases (2030: −1.8 % to 133.2 %; 2060: −41.1 % to 264.3 %) and non-aging cases (2030: −41.0 % to 45.7 %; 2060: −89.5 % to −17.0 %) under six scenarios in 2030 and 2060. Population aging is the main driver of increased PM2.5-related AMI incidence under Scenarios 1 − 3 in 2030 and 2060, while improved air quality can offset these negative effects of population aging under the implementation of the carbon neutrality and 1.5 °C targets. ConclusionThe combination of ambitious climate policies (i.e., 1.5 °C warming limits and carbon neutrality targets) with stringent clean air policies is necessary to reduce the health impacts of air pollution in Shandong Province, China, regardless of population aging.

Intraoperative hydromorphone decreases postoperative pain: an instrumental variable analysis

BackgroundIntraoperative administration of short-acting opioids might lead to increased postoperative pain and opioid requirements. There are few data describing the effects of intermediate-duration opioids such as hydromorphone on these outcomes. We have previously shown that a switch from a 2 mg to a 1 mg vial of hydromorphone was associated with decreased intraoperative dose administration. As presentation dose affected intraoperative hydromorphone administration and was unrelated to other policy changes, it could serve as an instrumental variable, assuming significant secular trends were not present during the study period. MethodsIn this observational cohort study of patients who received intraoperative hydromorphone (n=6750), an instrumental variable analysis was used to evaluate whether intraoperative hydromorphone administration affected postoperative pain scores and opioid administration. Before July 2017, hydromorphone was available as a 2-mg unit dose. From July 1, 2017 to November 20, 2017, hydromorphone was only available in a 1-mg unit dose. A two-stage least squares regression analysis was used to estimate causal effects. ResultsA 0.2-mg increase in intraoperative hydromorphone administration caused a decrease in admission PACU pain scores (mean difference, –0.8; 95% confidence interval, –1.2 to –0.4; P<0.001) and decreased maximum and time-weighted mean pain scores over 2 days postoperatively, without increased opioid administration. ConclusionsThis study suggests that intraoperative administration of intermediate-duration opioids does not cause the same effects as short-acting opioids with respect to postoperative pain. Instrumental variables can be used to estimate causal effects using observation data when unmeasured confounding is present.

Exploring argumentative strategies in student-teacher partnerships: patterns of deliberative communication

ABSTRACT A core element of student-teacher partnerships, a form of student participation, is their dialogical nature. However, little research attention has been paid to communication in this context. Therefore, this study aims to gain an in-depth understanding of their deliberative communication patterns. Seventeen deliberative episodes, derived from two cases where students and teachers collaboratively participated in a year-long inquiry project, were subjected to a two-stage analysis: a within-case analysis to determine the structure, function, features, and content of each unit of deliberation, and a cross-case analysis to iteratively compare both cases. The study reveals that in these deliberative discussions (1) students and teachers frequently and equally formulate substantive contributions or arguments, (2) they adopt four argumentative strategies: construction, confirmation, problematisation and regulation, (3) alternating use of the different argumentative strategies leads to more substantive and candid discussions, and (4) there is a need for moderating interventions. These findings call for reflection on the teachers’ role when partnering, more specifically on how to facilitate deliberative communication without compromising on the authenticity between teachers and students.

Analysis of environmental performance and interactivity of ports and regions

The port is the basic support for regional economic development and the global allocation of resources. With the rapid development of China's economy and growing ecological awareness, the assessment of port and regional efficiency has received unprecedented attention. In the current context of the COVID-19 pandemic, how the port and its region will be coordinated under the common goal of development has become a hot topic. In this study, the port subsystem (P-subsystem) and the regional subsystem (R-subsystem) are unified into the port–region system (PR system), and a new meta-frontier two-stage data envelopment analysis model is constructed to evaluate the P-subsystem efficiency and the environmental efficiency of the PR system. This research also measures the port–regional coordination level using the coordination index and explores the inefficiency of the PR system with the help of management improvement and technology improvement indices. Main results show that the overall efficiency of the Chinese PR system is increasing. The technological level of the PR system in coastal areas is close to the optimal level. The inefficiency of the Chinese PR system is mainly affected by management inefficiency. The coordination of regional and port development in China is also poor. Finally, on the basis of the research findings, this study provides targeted countermeasure suggestions to promote the efficiency enhancement and coordinated development of the PR system.

Comprehensive study on using hydrogen-gasoline-ethanol blends as flexible fuels in an existing variable speed SI engine

The rising human population is causing the utilization of enormous amounts of fossil fuels to fulfill energy needs. Various renewable sources are used as fossil fuels however those resources are not powerful in supplanting customary non-renewable energy sources like gasoline in vehicles. The depletion of conventional fossil fuel utilized in a vehicle contributes to an increased portion of air contamination and is a danger to human well-being. Also, to maintain the supply demand, many active types of research have been carried out in mixing a higher percentage of ethanol over gasoline and further moving towards flex-fuel vehicles. But there arises a problem of knocking and higher CO, and HC emissions from the engine. To overcome the above problem, ethanol could be mixed in a higher percentage over gasoline with the help of hydrogen assistance and can completely avoid the problem of knocking and reducing CO and HC emissions. In this research, the combustion, emission, and performance characteristics of a variable-speed gasoline engine fuelled with ethanol-blended gasoline along with hydrogen assistance are taken for investigation at variable speeds like 1800, 1600, 1400, and 1200 rpm. Hydrogen is added to blended fuel (E30) which has better combustion, emission, and performance than other blended fuels. Hydrogen addition is done at 2, 3, and 4 ms respectively. The outcomes showed that the E30 + H2 at 3 ms has better combustion, emission, and performance, still, the emission of NOx is higher in comparison with all the other blends due to complete combustion. Thus, a two-stage analysis has been done, one is making a comparison among various blends of ethanol, and the second one is the comparison among the various energy shares of hydrogen.

Port environmental efficiency assessment using the one-stage and two-stage model DEA: comparison of Koper and Dublin ports

Ports are important and central hubs for logistical activities in transportation chains that contribute to economic growth. Nevertheless, port activities are associated with undesirable impacts such as energy consumption and air pollutant emissions. Various policy instruments and measures have been developed and adopted to reduce energy consumption and emissions from ports. In an effort to manage all undesirable impacts of port operations, the search for best practices has proven to be an effective approach. This study examines one- and two-stage Data Envelopment Analysis (DEA) models for measuring port environmental efficiency. The adequacy of the models was analyzed using two case studies, i.e., the port of Koper and port of Dublin. The results of the study provided the best practices and the main differences between one-stage and two-stage models. Distance metrics were used to identify the necessary improvements of inefficient decision-making units (DMUs) to achieve the best practices.

Intra-urban risk assessment of occupational injuries and illnesses associated with current and projected climate: Evidence from three largest Australian cities

BackgroundIncreased risk of occupational injuries and illnesses (OI) is associated with ambient temperature. However, most studies have reported the average impacts within cities, states, or provinces at broader scales. MethodsWe assessed the intra-urban risk of OI associated with ambient temperature in three Australian cities at statistical area level 3 (SA3). We collected daily workers’ compensation claims data and gridded meteorological data from July 1, 2005, to June 30, 2018. Heat index was used as the primary temperature metric. We performed a two-stage time series analysis: we generated location-specific estimates using Distributed Lag Non-Linear Models (DLNM) and estimated the cumulative effects with multivariate meta-analysis. The risk was estimated at moderate heat (90th percentile) and extreme heat (99th percentile). Subgroup analyses were conducted to identify vulnerable groups of workers. Further, the OI risk in the future was estimated for two projected periods: 2016–2045 and 2036–2065. ResultsThe cumulative risk of OI was 3.4% in Greater Brisbane, 9.5% in Greater Melbourne, and 8.9% in Greater Sydney at extreme heat. The western inland regions in Greater Brisbane (17.4%) and Greater Sydney (32.3%) had higher risk of OI for younger workers, workers in outdoor and indoor industries, and workers reporting injury claims. The urbanized SA3 regions posed a higher risk (19.3%) for workers in Greater Melbourne. The regions were generally at high risk for young workers and illness-related claims. The projected risk of OI increased with time in climate change scenarios. ConclusionsThis study provides a comprehensive spatial profile of OI risk associated with hot weather conditions across three cities in Australia. Risk assessment at the intra-urban level revealed strong spatial patterns in OI risk distribution due to heat exposure. These findings provide much-needed scientific evidence for work, health, and safety regulators, industries, unions, and workers to design and implement location-specific preventative measures.

Fully efficient, two-stage analysis of multi-environment trials with directional dominance and multi-trait genomic selection

Key messageR/StageWise enables fully efficient, two-stage analysis of multi-environment, multi-trait datasets for genomic selection, including support for dominance heterosis and polyploidy.Plant breeders interested in genomic selection often face challenges to fully utilizing multi-trait, multi-environment datasets. R package StageWise was developed to go beyond the capabilities of most specialized software for genomic prediction, without requiring the programming skills needed for more general-purpose software for mixed models. As the name suggests, one of the core features is a fully efficient, two-stage analysis for multiple environments, in which the full variance–covariance matrix of the Stage 1 genotype means is used in Stage 2. Another feature is directional dominance, including for polyploids, to account for inbreeding depression in outbred crops. StageWise enables selection with multi-trait indices, including restricted indices with one or more traits constrained to have zero response. For a potato dataset with 943 genotypes evaluated over 6 years, including the Stage 1 errors in Stage 2 reduced the Akaike Information Criterion (AIC) by 29, 67, and 104 for maturity, yield, and fry color, respectively. The proportion of variation explained by heterosis was largest for yield but still only 0.03, likely because of limited variation for the genomic inbreeding coefficient. Due to the large additive genetic correlation (0.57) between yield and maturity, naïve selection on an index combining yield and fry color led to an undesirable response for later maturity. The restricted index coefficients to maximize genetic merit without delaying maturity were identified. The software and three vignettes are available at https://github.com/jendelman/StageWise.

Efficiency of Non-profit Organisations: a DEA Analysis in Support of Strategic Decision-Making

This study aims to analyse the efficiency of sports clubs belonging to the Academic Federation of University Sports and the influence the organisational structure holds over their performance standards. First, we included 92 clubs that registered points in the University Club. The analysis was carried out using the two-stage data envelopment analysis (DEA) methodology to analyse their efficiency. Second, we analysed how strategy, stakeholder relations and funding issues influence organisational efficiency, through a semi-structured interview with the dually efficient club manager. The results show the relevance of analysing the efficiency of these non-profit and public sport organisations.

Application of hybrid support vector Machine models in analysis of work zone crash injury severity

Crash severity models are often used to analyze the adverse effects of highway work zones on traffic safety. In this study we evaluated application of hybrid support vector machine (SVM) and hyperparameter optimization models for improved accuracy of crash severity prediction. Two hybrid models were evaluated: a genetic algorithm-optimized SVM (GA-SVM) and greedy-search optimized SVM (GS-SVM) models. The dataset used in model development and testing contained 12,198 work-zone crash observations in New Jersey over three years, from 2016 to 2018. The results indicate that the GA-SVM model outperformed both GS-SVM and the SVM with default parameters in predicting the severity of work zone crashes. While GA-SVM provided the best accuracy, it had the highest computation time. Among more than dozen factors considered in the models, the findings suggest that crash type and posted speed limit were the most significant for estimation or prediction of work-zone crash severity. The modeling approach and methods demonstrated in this study can improve the accuracy of crash prediction models. Also, a two-stage sensitivity analysis was conducted to see the impact of associated factors based on the probability of crash severity in work zones. The key findings revealed that early morning, nighttime, rainy environmental condition, rear-end crashes, a roadway with no median, and a higher posted speed limit increased the likelihood of injury and fatality in the work zone areas. This improvement will in turn lead to better informed decisions about planning and implementing work zone safety enhancements aimed at reducing severity of crashes.

Data mining–based stock price prediction using hybridization of technical and fundamental analysis

PurposeThe stock market generates massive databases of various financial companies that are highly volatile and complex. To forecast daily stock values of these companies, investors frequently use technical analysis or fundamental analysis. Data mining techniques coupled with fundamental and technical analysis types have the potential to give satisfactory results for stock market prediction. In the current paper, an effort is made to investigate the accuracy of stock market predictions by using the combined approach of variables from technical and fundamental analysis for the creation of a data mining predictive model.Design/methodology/approachWe chose 381 companies from the National Stock Exchange of India's CNX 500 index and conducted a two-stage data analysis. The first stage is identifying key fundamental variables and constructing a portfolio based on that study. Artificial neural network (ANN), support vector machines (SVM) and decision tree J48 were used to build the models. The second stage entails applying technical analysis to forecast price movements in the companies included in the portfolios. ANN and SVM techniques were used to create predictive models for all companies in the portfolios. We also estimated returns using trading decisions based on the model's output and then compared them to buy-and-hold returns and the return of the NIFTY 50 index, which served as a benchmark.FindingsThe results show that the returns of both the portfolios are higher than the benchmark buy-and-hold strategy return. It can be concluded that data mining techniques give better results, irrespective of the type of stock, and have the ability to make up for poor stocks. The comparison of returns of portfolios with the return of NIFTY as a benchmark also indicates that both the portfolios are generating higher returns as compared to the return generated by NIFTY.Originality/valueAs stock prices are influenced by both technical and fundamental indicators, the current paper explored the combined effect of technical analysis and fundamental analysis variables for Indian stock market prediction. Further, the results obtained by individual analysis have also been compared. The proposed method under study can also be utilized to determine whether to hold stocks for the long or short term using trend-based research.

The nexus between spatial structure and labour income: evidence from Vietnam

ABSTRACT Studies on spatial structure features and their influence are increasingly receiving great attention from scholars and policymakers as they play an essential role in any geographical area’s planning and economic development. Therefore, this study analyses the specific impact of spatial structure on labour income in a typical case in Vietnam. Using instrumental variables combined with two-stage least squares regression analysis (TSLS), the results from the study show the specific effects of spatial structure on labour income. Urbanization externalities are reflected in the size of the urban population, which positively impacts the labour income in the region. In contrast, the population dispersion in the decentralized model brings negative impacts. Besides, when the spatial structure of localities tends to be highly polycentric, it will have a negative impact on labour income; however, this effect is dominated by the size of the urban population.