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In recent decades it seems that various factors have led to a cultural background change, which although mainly characterized as incremental, in some cases can be sudden. A question therefore arises as to whether the way in which the cultural background has evolved during last decades affects the growth rate of economies. We use an unbalanced panel dataset comprised from 34 OECD countries from 1981 to 2019, and a Least Squares Dummy Variable Correction (LSDVC) method as well as a series of robustness tests including different methods of analysis, adding control variables and breaking the overall period into subperiods. We conclude that the cultural background during the overall period under consideration is characterized as post-materialistic and harms economic growth. Moreover, we highlight both theoretically and empirically the cultural backlash hypothesis since the cultural background of the countries under analysis presents a shift from traditional/materialistic (from 1981 up to 1998) to post-materialist values (from 1999 up to 2019). Doing so, we conclude on a positive effect of cultural background on economic growth when traditional / materialistic values prevail, and a negative effect when post-materialistic values prevail. These results highlight culture as a crucial factor for economic growth and indicate that economic policy makers should take it seriously into account before designing economic policy and in order to explain the effectiveness of economic policies implemented.

One of the main challenges economists must face while studying economic sanctions is to simulate them in their models faithfully. It seems extremely hard to achieve without a dedicated variable that precisely imitates coercive economic measures. Thence, this short paper aims to offer a framework for the construction of economic sanction indexes. Researchers can use our multipurpose index modelling to simulate economic punishment in their econometric models. If our work is framed within the Ukrainian crisis case and European economic sanctions against Russia, the method we provide can be used in any other case study. It is a great alternative to the standard use of dummy variables that witness sanctions' implementation and withdrawal. Additionally, our proposal allows calibrating the weight attributed to each sanction depending on its type, the sanction sender's ability to apply economic pressure, and the effect of time on sanctions' effectiveness. The first part of this paper details the methodology and the mathematical formalisation of our framework. It is used to build a sanction index that simulates sanctions against Russia during the Ukrainian crisis. The second part is an empirical study that compares our index to another one from the literature. Results reveal that our new sanction index witnesses the behaviour of sanctions more precisely, which leads to a stronger explanatory power. Moreover, it enhances the statistical significance of impulse-response functions generated by our SVAR models. Consequently, the framework for sanction index construction that is proposed in this paper successfully produced a reliable index that simulates the European Union's sanction against Russia in the Ukrainian crisis case.

Since the 1980′s, some Mexican municipalities have subcontracted waste collection services to private companies, with an eye on increasing the efficiency of this service. However, the impact of private management on the Mexican waste sector performance has not been evaluated. In this study, the efficiency of waste collection in Mexico was analyzed to test the hypothesis that private companies are more efficient at waste collection than municipal governments. A two stage double bootstrap Data Envelopment Analysis (DEA) was applied to a sample of 1,626 municipalities. In the first stage, unbiased efficiency scores were calculated, and in the second stage, these scores were regressed against a set of environmental covariates which were thought to affect efficiency, including a dummy variable to identify the municipalities where waste is collected by a private company. Results suggest that private waste collection companies are more efficient than municipal governments. Other environmental variables such as population density, average household income, and tourism were also found to affect waste collection efficiency. The analysis also indicates that curbside collection is associated with a higher efficiency, while separate collection of waste is negatively correlated to efficiency.

This study examines the effect of corporate social responsibility disclosure (CSRD) on earnings quality (EQ) in member countries of the Gulf Cooperation Council (GCC). Using 1845 firm-year observations covering the 2008–2016 period, this study performs panel data regressions to examine the association between CSRD as measured by a dummy variable and three common EQ measures, namely, value relevance (VR), accrual quality (AQ), and earnings persistence (PER). The study concludes that CSRD in three of the six GCC countries (Bahrain, Saudi Arabia, and the United Arab Emirates) has a positive and significant association with EQ in terms of VR. The use of generalized least squares (GLS) shows that CSRD has an effect on AQ in Kuwait and Bahrain whereas CSRD has an effect on PER only in Kuwait. Regulators of capital markets should use CSR information as guidelines to improve financial reporting quality and achieve better allocation of resources in capital markets.

A logarithmically transformed General Linear Model (GLM) is used to analyze secondary data from the following World Bank data bases: Sustainable Energy for All (SE4ALL), Spreadsheets for Carbon Emissions, and Country Development Profile. The hypothesis of renewable energy consumption as a determinant of inter-country differentials in carbon dioxide (CO2) emissions in Africa is tested. CO2 emissions constitute the dependent variable (DV) while renewable energy consumption is the independent variable (IV). Four control variables, including 3 interval variables: level of urbanization, Gross Domestic Product (GDP) per capita, Gross National Product (GNP) per capita; and one binary variable, namely nationality of former colonial power, are included. Energy consumption is found to be inversely linked to CO2 emissions; it explains about 80% (R2 = 0.795, significant at the 0.05 level) of the inter-country differentials in CO2 emissions. These results confirm the study’s expectations and lend credence to findings extolling renewable energy consumption as a means of curbing CO2 emissions in Europe and North America.

This paper analyzes the effects of weight-based pricing on municipal waste generation in Flanders (Belgium) by applying the bias-corrected least squares dummy variables estimation model to account for the dynamic relationship. The study also considers yearly weight-based pricing dummy variables to clarify the annual effects after its introducing and price elasticities of weight-based pricing systems from the both viewpoints of short and long terms. The results by the dynamic panel estimations showed that the continuous participation of weight-based pricing decreases the amount of residual waste significantly by approximately 10.4%. However, the remarkable decrease (approximately 21.4%) was observed only in the first year after the introduction and the reduction effect gradually decreases following its introduction and it disappears in five years. The study also showed that price elasticities of weight-based pricing were smaller than that of volume-based pricing for five years after its introduction and consequently the difference between them disappeared in five years. In addition, the study indicated that the estimation results by the non-dynamic fixed effects model overestimated the long-term effects in weight-based pricing and price effects while underestimating the short-term effect by its introduction. The study suggested that we should consider dynamic effects and remove the bias from the least squares dummy variables estimators when we examine the effects of weight-based pricing. ispartof: Waste Management vol:128 pages:132-141 ispartof: location:United States status: published

The Cell Transmission Model (CTM) has been widely used to design variable speed limit (VSL) controllers. Since the CTM is an idealization of the actual traffic flow, the issue of robustness of VSL with respect to uncertainties such as disturbances due to modeling and measurement errors is an important problem. In this paper, we modify the CTM to include a constant disturbance term that is unknown and could change over time. The stability properties of the modified open-loop CTM are analyzed and used to motivate the design of a robust VSL controller. The VSL controller is designed to reject external disturbances, which are constant over large intervals of time, while guaranteeing that the density converges to the desired equilibrium state corresponding to high flow and speed. Microscopic simulations based on the commercial software VISSIM are used to demonstrate the effectiveness of the proposed robust VSL controller and associated benefits for traffic scenarios along a large segment of I-710 in Southern California.

Traffic crashes at signalized intersections are frequently linked to driver behavior at the onset of the circular yellow (CY) indication. To better understand behavioral factors that influence a driver’s decision to stop or go at an intersection, this study analyzed the behavior of the driver of a subject vehicle at the onset of the CY indication. Driver performance data from 53 participants were collected in the Oregon State University Driving Simulator, simulating scenarios of driving through high-speed intersections under various conditions. Data included interactions where the driver stopped at the stop line (n = 644) or proceeded through the intersection (n = 628) in response to a CY indication. Data were analyzed as panel data while considering 12 indicator variables related to the driver’s stop/go decision. These indicator variables included time to stop line (TTSL), tailway time, following vehicle type, vehicle speed at the onset of the CY indication, and demographics (age, gender, driving experience, level of education, personal vehicle type, number of times driving per week, number of miles driving last year, participation in previous simulation studies. A random-parameter binary logit model was used to determine contributing factors for driver decision making at the onset of CY indication while accounting for unobserved heterogeneity. Four indicator variables were significantly related to the driver’s stop/go decision, but three factors varied across observations. Findings showed that a driver’s stop/go decision in response to a CY indication was associated with the time to the stop line (TTSL), tailway time to the following vehicle, subject vehicle speed at the onset of the CY indication, and driver’s age (20–36 years), but was not significantly associated with classification of the following vehicle. Also, the findings indicated that a shorter tailway increased a subject driver’s red-light running frequency. These findings provide insights into variables that affect driver decisions in a vehicle-following situation at the onset of the CY indication. This information can help make better decisions in smart traffic control systems such as to extend/decrease the green interval slightly to avoid decisions that are more difficult.

In order to improve the efficiency of the electrical safety operations of private electric facilities, the use of AI and IoT is expected. In this paper, we propose a leakage current alarm prediction model using a random forest and an artificial neural network. Customer information, periodic inspection history, alarm occasions on the previous day, and weather information are used as explanatory variables. A grid search was performed for hyperparameter optimization of each model, and generalization performance was evaluated using OOB verification and cross-validation. As a result of comparing the performances of the two models by the PR curve, it was found that the random forest had a larger PR curve and had better prediction performance.

Air cargo is vital to the modern supply chain because it provides efficient and timely delivery. In the past decade, the development of high-speed rail (HSR) in China has resulted in significant impacts on air cargo traffic, which has long been neglected by the existing studies. In this research, we quantify such impacts using HSR entry dummy variables and employ a panel regression approach to capture the impacts, specifically, the impacts on air cargo volumes and flight frequencies. We conduct a case study using quarterly air cargo traffic data in China, which contains 321 city pairs and spans between January 2011 and December 2017. We separately analyze the impacts on belly-hold cargo (carried by combi or passenger aircraft) traffic and freighter cargo (carried by freighters) traffic. We first capture the general impacts on belly-hold cargo traffic and freighter cargo traffic for all city pairs. Then, to quantify the distance level impacts on belly-hold cargo traffic, we categorize city pairs into multiple groups according to their distance and apply the panel regression approach in each group. Our main results include: (1) The entry of HSR services reduces the belly-hold cargo volumes and flight frequencies by 21.9% and 19.4%, respectively; (2) The strongest impacts on belly-hold cargo volumes and flight frequencies are observed in the medium-haul group within a distance of 800–1300 km; (3) After the entry of HSR services, the cargo volumes and flight frequencies of freighter cargo generally increase by 22.3% and 20.9%, respectively; (4) The whole air cargo traffic in the measurement of cargo volumes and flight frequencies decreases after the entry of HSR, and the impacts on whole air cargo volumes are relatively smaller than the belly-hold cargo volumes in the short-haul (no more than 800 km) and long-haul (more than 1300 km) groups.

The aim of this study is to examine the relationship between public ownership (public float), share warrants and the market performance of IPOs on the Indonesia Stock Exchange (IDX). The public ownership is measured as the percentage of shares held by the public after the IPO, share warrants are measured as dummy variables, and market performance is measured by both initial returns and 36 months cumulative abnormal returns. The test is conducted by OLS and Quantile Regression to find out whether the percentage of shares held by the public after the IPOs and warrants offering have relationships with both the initial and the long-term shares market performance. The sample consists of 124 IPOs companies during 2009-2014. OLS shows not only that public float and warrant offerings are related to initial returns, but also warrants mediate the relationships between public float and initial performance. The analysis with quantile regression shows that the positive relationship between public float and short-term market performance occurs in the IPO with middle-level initial returns (i.e. between 40th to 60th quantiles). The effect of warrant offerings on initial returns simply does not appear on the lower quantiles (10th through 30th quantiles). We find no relationships between public float and warrant offerings with the long-term market performance, which gives an indication that warrant offerings in Indonesia are a form of staged financing policy.

This paper investigates the impacts of high-speed rail (HSR) development on regional equity in China during 2007–2017. The equity in terms of economic output and HSR service is characterized at national, divisional (Eastern, Central, and Western), and provincial levels. The Gini indices associated with prefectures’ gross regional product (GRP) per capita, HSR connectivity and accessibility are measured to assess disparity from different aspects. Instead of measuring the effect of HSR entry by a dummy variable representing the presence of HSR service only, this paper estimates the effects of HSR by various terms regarding the HSR presence, network coverage, service quality, and equity of HSR development. In particular, the HSR network coverage is captured by the number of prefectures connected to HSR. Service quality is characterized by the HSR connectivity (frequency of HSR services) and accessibility (average travel time and economic potential). The frequency and accessibility related metrics are further decomposed into two sub-variables representing the intra- and inter-provincial metrics to differentiate the service availability and travel time between prefectures in a same or different province(s). The influence between neighboring or adjoining provinces on each other’s economic equity is identified by spatial autocorrelation effect. Our main findings include: 1) National equity is gradually improved in terms of both GRP and HSR developments in China between 2007 and 2017; the provinces or divisions with larger GRP tend to be less equitable; 2) The inauguration of HSR has positive correlation with provincial equity; however, the positive effect diminishes with the spread of HSR coverage; 3) HSR accessibility (average travel time and economic potential) has more significant effect on provincial economic equity than frequency; 4) Effects of intra-provincial accessibility and connectivity (HSR service within the province) dominate inter-provincial metrics (services to other provinces); 5) The prosperous and balanced HSR development in neighboring provinces helps promote provincial economic equity of one another.

Some studies consider variable speed limit (VSL) control as a viable option to prevent traffic breakdown at bottlenecks by limiting the mainline flow with reduced speed limits. However, few studies consider the location of the application area as a design variable of the problem. This paper explains why the location of a VSL control area is crucial to prevent the capacity drop phenomenon at lane drop bottlenecks. We first define two types of stationary states, congested and uncongested, inside a lane drop bottleneck assuming the Lighthill-Whitham Richards model with bounded acceleration. In particular, the characteristics of these stationary states and their admissible conditions are discussed thoroughly. If the speed limit imposed is low enough, the location of the VSL application area is irrelevant to ensure an uncongested stationary state inside the bottleneck. However, for a given range of speed limits, the location of the VSL application area should be designed carefully to allow for uncongested stationary states and prevent the occurrence of the capacity drop. We formulate an optimization problem and show that, contrary to the general belief, the larger the speed limit, the farther the VSL application area should be from the bottleneck. Finally, the results are extended to other types of bottlenecks, such as sag or tunnel bottlenecks. To the best of our knowledge, this is the first study to analytically identify, formulate, and solve the optimal location problem for variable speed limit application areas. It makes fundamental contributions to both traffic flow theory (by analyzing the stationary states for VSL-controlled bottlenecks) and traffic control (by determining the optimal location of a VSL application area). Moreover, the results presented are of practical relevance because they can help to establish some guidelines for practitioners to implement VSL control strategies.

Although many variable decomposition methods for cooperative co-evolution (CC) have been proposed, researches on scalable and efficient decomposition have rarely been done, particularly, for the large-scale global optimization (LSGO) problems. In this paper, we propose an efficient variable interdependency identification and decomposition method, called EVIID. Different from existing studies focusing on only limited scale efficient or accurate variable decomposition, our purpose is to develop a scalable variable decomposition method with high efficiency and accuracy even on very high-dimensional problems. EVIID utilizes three core strategies: a binary variable space search, a dynamic perturbation caching, and a pre-variable sorting. Their synergy effect enables scalable and efficient variable decomposition without sacrificing decomposition accuracy by pruning many redundant computations required to identify interdependencies among decision variables. In comprehensive experiments, EVIID showed highly scalable decomposition ability on 1000 to 10,000 dimensional benchmark problems compared against the state-of-the-art variable decomposition methods. Moreover, when EVIID was embedded into practical CC frameworks, it showed good optimization performance and also fast convergence.

This paper employs a high dimensional variable selection technique to select a subset of suicide determinants from 167 potential factors, which are then used to estimate ‘natural’ suicide rates for US states by least squares dummy variables. Over the period 2005-2017, all states are found to have a non-zero and positive natural suicide rate, below their respective actual average rate. Higher actual rates suggest deterioration in socioeconomic conditions; inaccessible and unaffordable mental health care for certain sections of the population and inadequate implementation of measures to identify and reduce suicidal mortality. Evidence-informed policies aiming for zero suicide target could draw inspiration from exemplar states, to direct resources towards states with greater relative differences between actual average and natural suicide rates.

International audience; This paper investigates the relationship between competition and innovation at the firm level. Recent papers (Aghion et al., 2005; Askenazy et al., 2013; Rafique Hashmi, 2013) advocate a non-linear relationship, conciliating the Schumpeterian and Arrowian views. In this paper, the effect of competition on innovation is studied at the firm level using a large dataset for France coming from the annual surveys on R&D, covering the period 2000-2013, where a dummy variable is available for product or process innovation. The econometric results do not confirm the inverted-U shape relationship between the Lerner index and innovation, found by Aghion et al. (2005). This may be due to the use of firm-level data rather than industry-level data. However, for most firms, there is a negative effect of competition on innovation, meaning that more competition in the industry or a small market share has a negative effect on the propensity to innovate, either in products or processes, and the effect seems to be slightly stronger for product than for process innovation.

Calculating the hedge and safe-haven properties of gold and Bitcoin via GARCH model and quantile regression with dummy variables. We find that: (1) Neither gold nor Bitcoin can serve as a strong hedge or safe-haven for economic policy uncertainty (EPU) at the average condition. (2) Bitcoin is more responsive to EPU shocks, while gold maintains stability with smaller hedge and safe-haven coefficients. (3) In most cases, both gold and Bitcoin can act as the weak hedge and weak safe-haven against EPU during the extreme bearish and bullish markets, which two can be considered for portfolio diversification during the normal market.

The main goal of this study is to develop a model of subjective feelings for frontal visibility, often called the feeling of ‘openness’, for automobile drivers. Moreover, this study proposed a method to overcome the limitations of the rating scale method, although it has been used in a great number of studies. To this end, eight potential design variables of automobiles were extracted from 25 candidate design variables. A questionnaire for evaluating the openness of an automobile was developed based on these variables. By using this questionnaire, a user experiment was conducted to investigate a relationship between the feeling of openness of automobiles and the design variables. Twenty-six participants evaluated the feeling of openness and the perceived rate of the design variables for 30 automobiles. The results showed that the variables such as the height of headlining, the height of cluster housing, the inclination angle of wind shield, and the volume of A-pillar were the critical design variables affecting the feeling of openness of the automobiles. Moreover, the optimal perceived level of each design variable was found out. The results of this study can be applied to designing automobile interior for good frontal visibility.

This research empirically analyzes the impact of various instruments of economic sanctions on official exchange rate volatility by employing data from a panel of 23 target countries covering the period 1996–2015 and using the Least Squares Dummy Variable Corrected (LSDVC) model. Our findings suggest that economic sanctions do significantly influence the target countries’ exchange rate volatility. Specifically, we are able to see different sanction present its different effects on exchange rate volatility. Furthermore, the robustness evidence of the eliminating country as Iran, eliminating variable of political ideology, intercepting time period, cross-sectional regression analysis, using real exchange rate volatility as proxy variable and a new sanctions database, are basically consistent with the previous finding. Overall, our empirical findings offer implications for those sanctioned countries about how to stabilize their exchange rate when facing sanctions.

This paper investigates the impact of international sanctions on energy efficiency by employing the panel fixed effect as well as average marginal effect from the Tobit model via data on 30 sanctioned states over the period 1996–2015 with international sanctions including unilateral, plurilateral, U.S., EU, UN, economic, and non-economic cases. Overall, we find that the imposition of unilateral sanctions leads to a 0.067% decrease in energy efficiency, but that of plurilateral sanctions positively contribute to energy efficiency in the case of the full sample of countries. Moreover, the imposition of UN sanctions has a greater decrease on energy efficiency in the target states than the 0.042% reduction of energy efficiency when the sanctionist is the U.S. For robustness, empirical results indicate that the imposition of plurilateral sanctions results in a drop of energy efficiency in Islamic countries, but an increase in non-Islamic countries, while there are also negative shocks induced by the imposition of EU sanctions on energy efficiency in Asian countries, but not for non-Asian countries. We also consider endogenous problems and dynamic specification by using indicator variables and System GMM. In summary, our empirical findings provide policy suggestions for those sanctioned countries about how to maintain energy efficiency when facing international sanctions.

A new autoregressive model with seasonal dummy variables in which coefficients of seasonal dummies vary smoothly and deterministically over time is introduced. The error variance of the model is seasonally heteroskedastic and multiplicatively decomposed as in ARCH models. This variance is also allowed to be smoothly and deterministically time-varying. Under regularity conditions, consistency and asymptotic normality of the maximum likelihood estimators of parameters of this model is proved. The purpose of the model is to find out how the average monthly temperatures in the well-known central England temperature series have been varying during the period of more than 240 years. The main result is that warming has occurred but that there are notable differences between months. In particular, no warming is found for February, April, May and June.

It has been widely recognized that the entry of high-speed rail (HSR) has significant impacts on air travel. Most, if not all, studies quantify such impacts using HSR entry dummy variables, namely, dummy variables that indicate the entry of HSR, in various econometric models. Although existing studies have attempted to explain the heterogeneity of the impacts of HSR entry, their approaches are often based on simple segmentation rules based on the distance between the origin and destination cities or whether the city pair involves a hub city. In this research, we use a systematic approach to analyze the heterogeneous impacts of HSR entry on air travel, which is new to the literature. Specifically, we take a hierarchical panel regression approach to analyze the Chinese domestic air travel data, which records, for each city pair, its monthly passenger volume and flight frequency from January 2011 to December 2017. We conduct data filtering and aggregation in the data preprocessing. At the lower level of our hierarchical model, we explore the heterogeneity of the coefficients for the HSR entry dummy variables and allow them to vary across city pairs; while at the upper level, we explain this heterogeneity using a structural equation that considers the difference of in-vehicle travel time between HSR and air travel, and the geo-economical characteristics of the origin and destination cities. Our main results include: (1) Consistent with existing literature, the entry of HSR reduces air passenger volumes and flight frequencies by 17.88% and 15.80%, respectively; (2) Strong heterogeneity exists in the impacts of HSR entry on both passenger volumes and flight frequencies; (3) For air passenger volumes, the impact of HSR entry intensifies at an accelerated rate when the difference of in-vehicle travel time between HSR and air decreases; (4) For air passenger volumes, the impact of HSR entry is stronger for city pairs that contain a hub city; (5) For air passenger volumes, the impact of HSR entry is smaller among city pairs that contain a western region city; (6) For flight frequencies, we find similar results, except that the factor of whether the city pair contains a hub city is non-significant; and (7) The determinants explain about 70% of heterogeneity of impacts of HSR entry on both the passenger volumes and flight frequencies.

This study investigates how government policies affect firm-level innovation. To empirically test this relationship, we use panel data for the Korean renewable energy technology firms. Taking into account the results of various panel framework tests and sample size, we establish a panel vector autoregressive model in the first difference, and use a bias-corrected least squares dummy variable estimator to test complex dynamic relationships between public subsidies, firm heterogeneities (size, age, and slack), industry dynamic competition, and innovation. Based on the estimations, we find that there is a positive bidirectional causal relationship between firms’ innovation and each of the following: research and development (R&D) subsidy, available organizational slack, and industry dynamic competition. Non-R&D subsidy, firm size, and age do not have significant direct effects on firm-level innovation. However, non-R&D subsidy is involved in the relationship between R&D subsidy and firms’ innovation. We discuss some implications based on the findings of this study.

This paper analyses the overall trend of key macroeconomic variables of BRICS nations impacted by the US financial crises (2008–2009) through structural break analysis for period 2000–2013. Dummy v...

Energy consumption of buildings is an important component of national energy consumption, and, thus, developing accurate models for predicting future demand in buildings is essential for facility managers, electricity providers, and policy makers in tackling the energy scarcity. In this study, multiple linear regression (MLR), time series and Grey models were developed for estimating the HVAC electricity consumption of a commercial building located in Paris, France. The data was collected between June 2015 and April 2016. Weather variables (outdoor temperature, relative humidity, global radiation) and four dummy variables, which represent the working days, were taken into account in the regression analysis. Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Squared Error (MSE) were selected for comparing the performance of the recommended models. The results show that MLR performs better with a RMSE of 18.3806 compared to time series and Grey model with RMSE of 20.5114 and 21.8478, respectively.

This study investigates the relationship between government ideology and technical innovativeness of the whole country by using multinational panel data covering 110 countries from 1995 to 2015 and by employing comprehensive indicators for technical innovation, i.e. trademark and patent application in empirical research. We utilize the panel generalized method of moment (GMM) technique to introduce dynamic variables in order to avoid the bias caused by potential endogeneity. Next, we generate three cross items to examine whether the effect of government ideology on technical innovation differs under different circumstances. We then change the measurement of government ideology to assure the reliability of our empirical results, further investigate whether the changes of political ideology influence technical innovation, and carry out a robustness test by dividing the whole sample into two sub-samples based on the level of democracy, by employing two samples which limited by 3 main coalition parties in government at most, such as re-elected government and non-alternated government in countries. Overall, the empirical results show that a leftist ruling party inhibits the progress of technical innovation, whereas a right-wing ruling party promotes the appearance of new technology. The effect of government ideology on innovation is not similar under some circumstances. More evidence is presented by using two dummy variables to represent left-wing and right-wing.

The current study discusses the linear and non-linear relationship between trade, and non-renewable and renewable energy consumption using data of 72 countries extending from 1990 to 2012. Our empirical models integrate some additional variables which affect the above-mentioned relationship such as GDP, CO2 emissions, domestic resources abundance, and some interaction terms. The innovation of the paper is not only to compare between developed and developing countries but also between industrialized countries and non-industrialized ones by integrating some dummy variables into the models. The empirical results demonstrate that trade and energy consumption (renewable or non-renewable) have a mutually reinforcing linear relationship. Moreover, the validity of non-linear relationships is accepted only in the case of the impact of non-renewable on trade. This latter follows an inverted U-shape form in the case of developed and industrialized countries while a U-Shape one in the case of developing and non-industrialized countries. In addition, the findings indicate a positive contribution of GDP, capital, and labor on trade and energy consumption. However, CO2 emissions and natural resource have positive impacts on trade and non-renewable energy while negative effect on renewable energy. It implies that policy makers should develop appropriate policies to profit from trade and energy consumption.

We propose a new estimation method of the Lorenz curve based on dummy variable regression for granting clear economic connotations to parameters in the estimation of the Lorenz curve. The dummy variable regression model of the Lorenz curve is established by introducing dummy variables to group data about income level. On this basis, it is proved that the regression function is endowed with the convexity that the Lorenz curve should have. To verify the effectiveness of the new method, empirical study is carried out taking the income data of urban and rural residents in the Chinese Household Income Project Survey (CHIP) database as examples. In the empirical study, the proposed model is compared with several existing classical parameter models. The results indicate that the slope parameter of the model represents the cumulative income–population resilience of corresponding residents, and reflects the income structure of residents to some extent. Meanwhile, compared with classical parameter models, the newly proposed method has advantages of higher fitting precision and stronger adaptability.

This paper uses a number of different panel data estimators, including fixed effects, bias-corrected least squares dummy variables (LSDVC), generalised methods of moments (GMM), feasible generalised least squares (FGLS), and random coefficients (RC) to analyse the impact of real oil price volatility on the growth in real GDP for 17 member countries of the Organisation for Economic Co-operation and Development (OECD), over a 144-year time period from 1870 to 2013. The main finding of the study is that oil price volatility has a negative and statistically significant impact on economic growth of the OECD countries in the sample. In addition, when allowing for slope heterogeneity, oil-producing countries are significantly negatively impacted by oil price uncertainty, most notably Norway and Canada.

Professional drivers are particularly exposed to drowsiness and distraction inasmuch as they drive for long periods of time and as a daily routine. Therefore, several studies have been conducted to investigate driverś behavior, supported by controlled experiments (e.g. naturalistic and driving simulator studies). However, due to emerging technologies, new study methods can be developed to complement existing studies. In this study, retrospective data gathered from a driver monitoring system (DMS), which monitored 70 professional drivers from different companies, was used to investigate the effect of journey characteristics on the number of alerts due to either distraction or drowsiness. Two separate negative binomial models were developed, including explanatory variables describing the continuous driving time (sub-journey time), the journey time (a set of sub-journeys), the number of breaks and the breaking duration time. Dummy variables were also included. Interesting results were observed such as increasing continuous driving time, the number of distraction and drowsiness alerts increase too. In contrast, the journey time has the opposite effect decreasing the number of alerts. In the case of distraction alerts, stopping the vehicle during the journey (break) was not statistically significant and the increase in the breaking duration time showed an unexpected effect as the number of alerts increased. This was not the case of drowsiness alerts in which the frequency of breaks and the breaking duration time decreases the alerts. The companies (for which the drivers work) affect the alert frequency differently. The study shows that there is potential in terms of using the data obtained by the new technologies to complement other type of studies based on controlled experiments but also to enhance the development of technologies taking into account the driver profile and the type of journey.

This paper presents a new methodology for solving multi-stage distribution substations expansion planning (MSDSEP) problem. The proposed method can determine the optimal location, capacity, associated service area and construction timing of substations with aiming to the maximum profit for supplying the forecasted demand. The objective function is of the difference between benefit and cost of expansion. The present worth of revenue from selling energy is considered as the expansion benefit while the present worth of cost from purchased energy, investment cost for installing and/or upgrading substations and the construction cost of medium voltage (MV) feeders are considered as the expansion cost. Moreover, the technical constraints consist of network radiality, voltage drop, expansion capacity and operation capacity of substations. The developed method generates the binary variables for deciding connection between load points to substations and the integer variables for deciding expansion capacity of substations. While the dynamic bacterial foraging algorithm (DBFA) process is used for searching the best binary variable and the best integer variable. The effectiveness of proposed method is demonstrated on the distribution system of Tabriz city Iran which compared with single-stage distribution substations expansion planning DSEP (SSDSEP). The results showed that MSDSEP can provide more economical solution than SSDSEP.

Designing monitoring programs to detect impending climate change effects on marine vertebrates is challenging, as data sufficient for large-scale power analysis is severely limited, yet sensitivity and response time of potential indicator variables are key uncertainties. In the absence of such data, the experience of researchers can be used to inform decision making on monitoring design to detect impacts of climate change. We used expert elicitation to identify ecological traits of seabirds and marine mammals that have or were expected to respond to climate change. We analyzed the projected biological changes for five general categories of ecological traits that have been shown elsewhere to relate to climate signals: foraging and diet, body mass, breeding phenology, breeding success, and population size. Based on analysis of 106 traits in the five categories, 29 experts rated foraging- and diet-related traits to be the most responsive to climate change, although predictions for traits in this category were also the most variable across experts. Body mass related traits were projected to change almost as frequently, but with much lower variance. The timespan over which experts expected to see change also varied between trait categories. Foraging success was expected to respond most quickly. Considering sensitivity and response rate, we predict that the duration of foraging trips will be the best climate change indicator among the 106 traits. When combined with cost estimates, our results allow managers to choose ecological indicators that deliver information on system response in the most cost-effective manner.

Hyperspectral indices have proven to be useful for quantifying various plant parameters including a number of biochemical components. However, it remains a great challenge to unveil the physical and physiological mechanisms of wavelengths used by these statistically/empirically identified indices, and this unveiling is a critical step towards developing generally applicable indices. In this study, we have introduced a dummy variable to the well-accepted leaf scale radiative transfer model (PROSPECT-4) and have conducted a series of virtual experiments by tweaking different absorption features (intensity, peak wavelength, and half-width) of the dummy variable for investigating the underlying mechanisms of those wavelengths used by efficient indices. Results clearly indicated that the informative bands used in efficient indices, instead of the target parameter’s absorption peaks, are generally concentrated among wavelengths that the targeted parameter has relatively high specific absorption coefficients compared with other biochemical components. This finding has also been validated by replacing the dummy variable with carotenoids based on PROSPECT-5B simulated dataset. Further analysis reveals that although the concentrations of chlorophyll are higher than other pigments in most leaves, the commonly recommended wavelength of 550 nm may be disturbed by other pigments and may only be applicable when anthocyanins are minor. Results obtained in this study have largely explained why specific wavelengths are used in efficient indices, and thus this should lay a basis for understanding the underlying mechanisms and help to develop robust indices for estimating vegetation biochemical parameters.

The mainstream International Relations (Neorealism and Neoliberalism) believes the world to be in a status of anarchy consisting of identical states as the unit of study. Based on their definitions, states are in a constant state of competition for survival for which they key increasing their power. This one-sided approach fails to take into account several key aspects of the current global system. States are not identical and differences in their internal structures and institutional arrangements will affect their interactions significantly. The purpose of this study is to see if there is any significant difference in Turkey’s relation with the members of the European Union between Abdullah Gul’s and Recep Tayyip Erdogan’s administration. In order to do so, several panel data estimations have been conducted. The dependent variable for assessing international relations in this study is Turkey’s trade with the members of the European Union. The data consists of 25 European states’ GDP, Exchange Rate, and Inflation Rate. The two administrations are added as dummy variables. Ahmet Necdet Sezer’s period is also added to the model as the control period. The total period under study is 2000-2020. According to the findings of the model, there is a significant difference in Turkey’s behavior in terms of trade with the EU members between the two administrations under study.

This study examines the effect of transfer pricing, leverage, and sales growth on tax avoidance. The study conducted to gain more consideration on tax avoidance issue worldwide, particularly in member of Indonesia Stock Exchange (IDX) sample of manufacturing companies in the food and beverage sub-sector of the consumer goods industry listed on the Indonesia Stock Exchange (IDX) is used. The number of samples of 30 is the number of the study population with an observation time of four years from 2016 to 2019. Multiple regression model statistics with one dummy variable representing the transfer pricing variable are performed and used for Hypothesis testing. The results indicate that there is no effect of transfer pricing and sales growth on tax avoidance, while leverage has an effect on tax avoidance.

This paper primarily studies the possible existence of the January Effect or the Turn-Of-The-Year Effect in the Vietnamese stock markets and the study proceeds on two propositions. First, if the January anomaly is ascribed to the tax-related selling, it should be clear in the month of April in the Vietnamese context. Second, if the phenomenon is due to some other reason than it should make itself visible in the month of January in Vietnamese market given its interrelationship with international markets. This study also explores the chances of other common seasonal anomalies discrediting the efficient market hypothesis in the Vietnamese market viz., Other January Effect and Beginning of the month and End of the month effect. This study has used VN 500, S&P VN Nifty, VN Nifty Junior, VN mid cap and VN small cap indices of National Stock Exchange of Vietnam (VN). Statistical techniques like dummy variable regression analysis, ARIMA modeling, parametric and non-parametric tests, etc. have been used to fulfill the objective of the study. The findings of the study exhibit a significantly pronounced April Effect in VN small cap and VN midcap indices, with relatively much lower return in March (although statistically not significant). These findings are consistent with the tax-loss-selling hypothesis. An interesting finding of the study, which is apparently unique, is the presence of statistically significant and strongly positive December effect in all the studied indices.

The article analyses the short-term effects of aggregate economic sentiment on the expected GDP growth in Russia based on the results of regular large-scale surveys of business activity of the Federal State Statistics Service of the Russian Federation for the period 1998-2020. The main purpose of the study is to substantiate the predictive value of the opinions of economic agents in expanding macroeconomic information, including in crisis periods. The authors calculate a composite economic sentiment indicator (ESI), which combines quarterly information for the analysed period on 18 indicators of surveys with a sample of about 24,000 organizations of all size in basic kinds of economic activity, and 5,000 consumers in all Russian regions. The authors prove the possibility of using a vector autoregression model (VAR) with dummy variables to measure the relationship between GDP growth and ESI time series. Scenario estimates of GDP growth until the end of 2021 are based on the expected impulses in the ESI dynamics at the end of 2020, which differ in the amplitude and duration of their impact on economic growth, primarily due to the coronavirus effect. According to the results, under all possible scenarios for the development of business trends, national economic growth can exceed the level of the end of 2019, starting from the third quarter of 2021

The nowcast revision analysis is about how a nowcast of the target variable is revised in response to data releases in real time. The content of a data release includes data revisions as well as new observations. The nowcast also changes when the model parameter is revised. The existing revision analysis is capable of breaking down the new-observations effect on the nowcast into contributions from individual indicator variables, but no such breakdown is available for the data-revisions effect and the parameter-revisions effect. This paper shows how the breakdown can be applied to all three effects. For illustration, the new method is applied to nowcasting U.S. GDP growth for 2021:Q2.

Over the last 30 years, a majority of US states adopted Right-to-Carry (RTC) laws at the same time that crime rates dramatically decreased. A large literature has examined whether RTC laws contributed to or slowed this decline in crime, with most studies concluding that they have no significant effect on crime. However, this research has been plagued by methodological challenges, many of which are exacerbated by the common approach of modeling the effect of RTC laws using a binary dummy variable to indicate a one-time change in policy. Recently, Donohue, Aneja and Weber (2019a) have employed a novel synthetic control approach which they suggest indicates that RTC laws significantly increase violent crime. However, we show that this analysis is highly sensitive to modeling choices, and Donohue et al. chose a specification that has been criticized by Kaul et al. (2017) as mistaken because it prevents covariates from exercising any influence on the development of predicted crime rates. Correcting this to properly incorporate covariates dramatically changes the estimated effect in many states; and comprehensive synthetic control analysis reveals no significant effect on crime. Given the methodological challenges inherent in binary approaches to modeling the effects of RTC laws, we gather data on the growth of carry permits in states over time, which allows us to investigate the phenomenon of interest - the actual ability to carry - in a manner that is theoretically more valid and econometrically more powerful. Employing two different methods for estimating missing data - modeling the growth of permits as a logistical growth process and imputing missing data using the Amelia II package - we find that the growth in carry permits has no effect on violent crime rates, homicide rates, firearm homicide rates, or non-firearm homicide rates. This study provides further, strong evidence that the dramatic growth in the ability to carry firearms for self-defense in recent decades has not exacerbated crime rates.

Background: We are still in the midst of a deadly pandemic caused by the SARS-CoV-2 virus causing COVID-19, and at the moment there is a shortage of licensed vaccines. Several models and methodologies have been applied in the study, analysis, prediction and modeling of COVID-19 in the world, so it is important to estimate the trend in the behavior of the epidemiological curve of the COVID-19 pandemic worldwide. Methods: In the realization of the study were used the data of the pandemic of new cases to COVID-19 in Cuba, which were taken from the press conference offered by the Ministry of Public Health (MINSAP), by Dr. Francisco Duran Garcia (National Director of Hygiene and Epidemiology of MINSAP), from Monday to Sunday, at 9.00am on national television and Cuban radio, since the pandemic began in March 2020 until July 15, 2021. The forecast was made using the methodology of Regressive Objective Regression (ROR), which has been implemented for different variables (viruses, parasites and bacteria that have circulated in Villa Clara province, Cuba) and even the SARS-CoV-2 virus causing COVID-19. A very long term forecast was made, up to November 1, 2021 with data taken up to July 15, 2021. The objective regressive modeling (ROR) is based on a combination of dummy variables with ARIMA modeling. In this methodology it is necessary to first create the dichotomous variables DS, DI and NoC (NoC: Number of cases of the base, DS represents a sawtooth function and DI this same function, but in inverted form), in such a way that the variable to be modeled is trapped between these parameters and a large amount of variance can be explained. Subsequently, the module corresponding to the Regression analysis of the statistical package SPSS version 19.0 is executed, specifically the ENTER method where the predicted variable and the ERROR are obtained. Then, the autocorrelograms of the ERROR variable will be obtained, paying attention to the maximums of the significant partial autocorrelations PACF. Finally, these regressed variables are included in the new regression in a process of successive approximations until a white noise in the regression errors is obtained. Objective: This research was undertaken to predict new COVID-19 cases, as well as the impact of vaccination in Cuba using the Regressive Objective Regression (ROR) methodology. Findings: The intensification and sustainability of hygienic and sanitary measures, as well as social distancing, and the massive, constant and sustained use of nasobuco, must be taken into account in the prognosis of new cases, since the number of new cases will be higher and may reach values higher than or close to 10,000 cases. As for the impact of vaccination in Cuba with the "Abdala" vaccine, progress is not yet as desired, since it will not be until October 2 of the current year that approximately 6.5 to 7 million people in Cuba will be vaccinated with the three doses, so that it will not be until October 22 that zero new cases could be reached, so that, from the first half of December of the current year, herd immunity could begin to appear in Cuba. Interpretation: Despite being a new disease in the world, COVID-19 can also be followed, modeled and predicted by means of ROR modeling, which allows reducing the number of deceased, severe and critical patients for a better management of the pandemic. On the other hand, the use of vaccines has shown that they can protect against COVID-19. Funding Information: None to declare. Declaration of Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

In this paper we hypothesize that not all stocks and sectors are affected equally by COVID-19 in terms of return volatility. Specifically, we hypothesize that at least some sectors (Information Technology, Consumer Discretionary, Telecom Services, Consumer Staples and Energy) must show statistically significant differences. We analyze eleven SP500 sectors and FATANG stocks, estimating an Asymmetric Power GARCH model including a dummy variable to account for the outbreak. Results reveal an exacerbation of volatility after February 2020 and validate our hypothesis with few exceptions. Based on a likelihood ratio test, the null hypothesis is rejected in most cases in favor of our APARCH(1, 1).

This study combines three distinct empirical models of stock returns into a single model: the autoregressive model, which suggests that stock returns are determined by its own past values, the (generalised) autoregressive conditional heteroscedasticity model, which suggests that stock returns volatility is determined by its past values and by returns shocks, and the day-of-the-week effect, which suggests that stock returns are higher on particular days of the week (usually Fridays). All three models represent departures from the Random Walk Hypothesis (RWH), in the sense of proposing a certain degree of predictability in stock returns. The study examines the extent to which the AR-GARCH model with day-of-the-week dummy variables for twenty major stocks from the Indian banking sector. The stock price data was collected from the National Stock Exchange (NSE). The study period selected was Apr. 1, 2018 to Mar. 31, 2019, a period of one year.

In this paper, we develop a two-stage approach to estimate the treatment effects of dummy endogenous variables using high-dimensional instrumental variables (IVs). In the first stage, instead of using a conventional linear reduced-form regression to approximate the optimal instrument, we propose a penalized logistic reduced-form model to accommodate both the binary nature of the endogenous treatment variable and the high dimensionality of the instrumental variables. In the second stage, we replace the original treatment variable with its estimated propensity score and run a least-squares regression to obtain a penalized Logistic-regression Instrumental Variables Estimator (LIVE). We show theoretically that the proposed LIVE is root-n consistent with the true treatment effect and asymptotically normal. Monte Carlo simulations demonstrate that the LIVE is more efficient than existing IV estimators for endogenous treatment effects. In applications, we use the LIVE to investigate whether the Olympic Games facilitate the host nation's economic growth and whether home visits from teachers enhance students' academic performance. In addition, the R functions for the proposed algorithms have been developed in an R package, naivereg.

Using a large dataset of 82,818 investments in start-up firms from December 1965 through August 2019, we document that the biggest group of investors are venture capital firms (about 79% of all observations). The majority of in-state investments are found clustered in California (with the San Francisco VC Hub), Massachusetts (with the Boston VC Hub) and New York. Returns generated from early round investing to late round decrease generally: early investors generate a higher return than late stage investors, and IPO exits entail the highest return. We find that geographical proximity is significantly and positively associated with cumulative returns or annualized returns, when we use actual distance (in km) between the VC investment firm HQ and portfolio firm HQ, or a cross-region indicator variable when the portfolio firm HQ is out-of-state as compared to VC investment firm HQ. The relation between investment proximity and returns continues to be robust even after controlling for selection bias, and in a matched-sample analysis where we pair each proximate investment with a matched distant investment. We find that VC Director and other outside directors as a proportion of all Directors on the boards on portfolio companies is significantly higher, on average, for in-state and shorter distance investments as compared to out-of-state or longer distance investments, indicating better involvement by VCs in their more proximate investments.

Lockdown measures to contain COVID-19 put a strain on firms’ liquidity. To support their cash flow some businesses issued dinner bonds to their clients, priced at a discount, redeemable when safe distance restrictions end. These contracts derived their value from a binary variable, in this case, being in-business post-lockdown, and bear significant associated problems. For instance, the value gained by customers with the advance purchase goes to zero if the business goes bankrupt. There are two contributions in this paper, first, a simple valuation model of dinner bonds is developed, and second, a proposal for a large-scale aid program for small businesses is set in a dinner bond based framework, with the addition of two features: (i) the support of a guarantor for businesses’ liabilities; and, (ii) the intervention of a financial regulator. The modified scheme also allows governments to leverage public policy actions on society’s goodwill.

This paper investigates the effects of SEC intervention from July 15, 2008 on the stability of “too big to fail” corporations. We show that: 1. SEC policy intervention maps to shocks in stock market variables, 2. stock market variables and a policy intervention indicator variable are informative in predicting corporate bond market variables, 3. in sharp contrast to the literature, the stabilizing effect of SEC intervention depends on the correlation between shocks to several stock variables and their transmission mechanism to the corporate bond variables, 4. for struggling institutions this policy intervention has much higher and in some cases opposite effects that last longer.

We use the gravity model to explain the spatiotemporal patterns of the spread of three infectious diseases: SARS-CoV, influenza A (H1N1), and SARS-CoV-2 (also known as COVID-19). We log-linearise the gravity model to run a negative binomial regression analysis that describes the relationship between the cumulative number of confirmed cases within a 10-day window and socioeconomic variables such as population size, gross domestic product per capita, and distance between capital cities and the city where the first confirmed case was reported (i.e., Hong Kong, Mexico City, and Wuhan). We also introduce two additional variables, the number of confirmed cases with a 1- day lag and a restriction dummy variable which is coded as 1 after a government introduces a policy to restrict the entry of foreign nationals and otherwise 0, to control for internal transmission and the emergence of multiple sources of transmission. For all three infectious diseases, population and economic variables are positively related to the cumulative number of confirmed cases within a 10-day window. The values for these variables are also statistically significant at any conventional significance level throughout the transmission period. However, the significance of geographical distance varies. In the early stage when there is a single geographical source of transmission, the further the distance from the origin, the lower the cumulative number of confirmed cases. In the intermediate stage where transmission of the disease is explosive, there are multiple geographical sources and the distance variable loses its explanatory power. In the containment stage where the spread of disease is more likely to occur within a country, not between countries, geographical distance becomes statistically significant again and is positively associated with the cumulative number of confirmed cases. The gravity model can be effectively used to explain the spatiotemporal dynamics of infectious disease transmission.

This study examines the effect of private and public sector led financial sector transparency on bank interest margins across eighty-six economies. Using a two-step dynamic system generalized method of moments, least square dummy variables, fixed effects and bootstrap quantile panel models between 2005 and 2016, the findings of the two-step GMM are reported as follows. First, results reveal that financial sector transparency whether led by private or public sector reduces interest margins. Second, while no statistical evidence was found on which of the two (private or public sector led transparency) is more effective in dealing with bank interest margins, public sector-led financial transparency is found to be more consistent in reducing bank interest margins across many more economies. Third, the study shows that the effect of financial sector transparency is visible at lower and middle levels of bank interest margins implying that economies with lower and moderately high bank interest margin level can benefit more from policies targeted at improving transparency in the financial sector. These findings imply that the sampled countries must enact policies and laws that deepen and expand financial sector transparency in order to potentially reduce bank interest margins for the good of banking market participants and society at large.

This study develops a novel generalised seasonality test that utilises sequential dummy variable regressions for seasonality periodicity equal to prime numbers. It allows both to test for existence of any seasonal patterns against the broad null hypothesis of no seasonality and to isolate most prominent seasonal cycles while using harmonic mean p-values to control for multiple testing. The proposed test has numerous applications in time series analysis. As an example, it is applied to identify seasonal patterns in 76 national stock markets to detect trading cycles, determine their length, and test the weak-form efficient market hypothesis.

Applied work often studies the effect of a binary variable (“treatment”) using linear models with additive effects. I study the interpretation of the OLS estimands in such models when treatment effects are heterogeneous. I show that the treatment coefficient is a convex combination of two parameters, which under certain conditions can be interpreted as the average treatment effects on the treated and untreated. The weights on these parameters are inversely related to the proportion of observations in each group. Reliance on these implicit weights can have serious consequences for applied work, as I illustrate with two well-known applications. I develop simple diagnostic tools that empirical researchers can use to avoid potential biases. Software for implementing these methods is available in R and Stata. In an important special case, my diagnostics require only the knowledge of the proportion of treated units.