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The article analyzes 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 (Rosstat) for the period 1998–2021. The main purpose of the study is to substantiate the predictive value of the opinions of economic agents in expanding macroeconomic information, especially during crisis periods. The authors aggregate quarterly information for the analyzed period on 18 indicators of surveys with a sample of about 24,000 organizations in basic kinds of economic activity and 5,000 consumers in all Russian regions in a composite economic sentiment indicator (ESI). Then, a statistical analysis of the time series of ESI and GDP growth is carried out, including the identifcation of the integrability order with testing for stationarity and the presence of causality between indicators. The authors prove the possibility of using a vector autoregression (VAR) model with dummy variables to measure the investigated relationship.The forecasting results reflect the interconnection of two time series with the response in the dynamics of the estimated variable (GDP growth) to the reaction of the business environment and the simulation of fluctuations in the ESI dynamics, which are set by the authors and correspond to the expected economic sentiments amid possible crisis changes. Probabilistic estimates of GDP growth until mid-2022 are based on scenario impulses in the ESI dynamics at the 3rd quarter of 2021, which differ in the amplitude and duration of their impact on economic growth, primarily due to coronavirus shocks. According to the results, under all scenarios for the development of business trends introduced by the authors, national economic growth can exceed by the middle of 2022 the pre-pandemic level of the 4th quarter of 2019 (102,9%).

We present an innovative activity that uses data about LEGO sets to help students self-discover multiple linear regressions. Students are guided to predict the price of a LEGO set posted on Amazon.com (Amazon price) using LEGO characteristics such as the number of pieces, the theme (i.e., product line), and the general size of the pieces. By starting with graphical displays and simple linear regression, students are able to develop additive multiple linear regression models as well as interaction models to accomplish the task. We provide examples of student responses to the activity and suggestions for teachers based on our experiences. Supplementary materials for this article are available online.

Although tuberculosis (TB) incidence has significantly declined in high-income, low-incidence (HILI) countries, challenges remain in managing TB in vulnerable populations who may struggle to stay on anti-TB treatment (ATT). Factors associated with non-adherence to ATT are well documented; however, adherence is often narrowly conceived as a fixed binary variable that places emphasis on individual agency and the act of taking medicines, rather than on the demands of being on treatment more broadly. Further, the mechanisms through which documented factors act upon the experience of being on treatment are poorly understood. Adopting a relational approach that emphasizes the embeddedness of individuals within dynamic social, structural, and health systems contexts, this scoping review aims to synthesize qualitative evidence on experiences of being on ATT and mechanisms through which socio-ecological factors influence adherence in HILI countries. Six electronic databases were searched for peer-reviewed literature published in English between January 1990 and May 2020. Additional studies were obtained by searching references of included studies. Narrative synthesis was used to analyze qualitative data extracted from included studies. Of 28 included studies, the majority (86%) reported on health systems factors, followed by personal characteristics (82%), structural influences (61%), social factors (57%), and treatment-related factors (50%). Included studies highlighted three points that underpin a relational approach to ATT behavior: 1) individual motivation and capacity to take ATT is dynamic and intertwined with, rather than separate from, social, health systems, and structural factors; 2) individuals’ pre-existing experiences of health-seeking influence their views on treatment and their ability to commit to long-term regular medicine-taking; and 3) social, cultural, and political contexts play an important role in mediating how specific factors work to support or hinder ATT adherence behavior in different settings. Based on our analysis, we suggest that person-centered clinical management of tuberculosis should 1) acknowledge the ways in which ATT both disrupts and is managed within the everyday lives of individuals with TB; 2) appreciate that individuals' circumstances and the support and resources they can access may change over the course of treatment; and 3) display sensitivity towards context-specific social and cultural norms affecting individual and collective experiences of being on ATT.

PURPOSE: The study aims to analyze the relationship between the pace of economic development expressed by the GDP index and the level of income inequalities measured by the Gini coefficient., DESIGN/METHODOLOGY/APPROACH: The research hypothesis assumes that the level of income inequalities influences GDP growth. I hypothesize that this relationship is negative – a lower level of income inequalities favor the economic growth. I use the GDP per capita (per adult) year-to-year index to measure the pace of economic development. GDP index is a dependent variable in all estimated models. The explanatory variables are, GINI index, net national saving, public goods spending, country's dummy variables, year dummy variables., FINDINGS: Using data from 43 countries covering the years 1990-2017, I prove that (1) higher income inequality is related to higher economic growth (but only on the level of the sample); (2) a level of savings affects the economic growth positively; (3) a higher level of spending on public goods affects GDP positively (on the level of the whole sample and in the group of more improve rished countries)., PRACTICAL IMPLICATIONS: The analysis confirms the positive relationship between income inequalities and the pace of economic development, but only at the whole sample level. Higher public spending positively affects economic growth. Savings accumulated by the citizens significantly affect economic growth, as a higher level of savings creates greater investment opportunities., ORIGINALITY/VALUE: Inequalities are an inherent part of society and the economy. It is often presumed that if the level of income inequality is too high, it negatively affects the economy by lowering the development pace. Although the previous findings are somewhat mixed, I pose the research hypothesis assuming a low level of income inequality is linked to higher GDP growth., The project is financed by the Ministry of Science and Higher Education in Poland under the programme "Regional Initiative of Excellence" 2019 - 2022 project number 015/RID/2018/19 total funding amount 10 721 040,00 PLN., peer-reviewed

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.

The aim of this paper is to investigate the determinants of the movement of non-performing loans in the banking sector of Bosnia and Herzegovina, as well as the impact of the COVID-19 pandemic on them. For this purpose, secondary data from the banking sector of BiH were used, over which a multi-regression analysis was performed. The variables non-performing loans, GDP, loan loss provision, and dummy variable COVID-19 were used in the analysis. The results of the analysis showed a significant influence of all three mentioned variables. The existence of a significant positive link between non-performing loans and the state of the country's economy has been proven. In addition to the above, it has been proven that the variable COVID-19 has a delayed effect on NPLs, due to the current application of the moratorium on loans.

This paper investigates the effects of political instability resulting from successive regime changes and the 30‐year civil war on the cost efficiency of local banks in Sri Lanka. A translog cost function was used to estimate bank cost efficiency, and a multivariate regression model was used to investigate the determinants of bank cost inefficiency. On average, the cost efficiency of banks in Sri Lanka during the study period was relatively high. Sri Lankan banks performed more efficiently during the second political regime (2001–2004), which had more liberal political and economic policies to promote the private sector compared with other regimes. During this period, the government was involved in peace talks, leading to a ceasefire agreement that temporarily stopped the violence. Furthermore, the positive coefficients for the dummy variables representing the third and fourth political regimes showed that more restrictive economic policies can increase bank cost inefficiencies. The results also indicate that immediately following the civil war, Sri Lankan banks recorded a decline in cost efficiency. Cost inefficiency was positively associated with banks' risk aversion, credit risk, market share, interest rate risk and increase in GDP and negatively associated with non‐earning assets, liquidity, bank size and concentration.

The COVID-19 pandemic exerted a strong impact on the economy, including on the industrial sector. In this context, we focused our analysis on estimating the impact of the COVID-19 shock on the EU industry developments. Therefore, we estimated two econometric models to identify the impact of the COVID-19 shock on the confidence in industry and on the industrial production, over the period January 2018 - November 2020, using Panel EGLS method. Our estimation is based on Eurostat data with monthly frequency, but also on a dummy variable we have created to catch the COVID-19 restrictions (shock). We have found a strong impact of the COVID-19 shock on both industrial indicators, which supports the need to continue adopting appropriate policies to boost this sector, since the outlooks are still exposed to uncertainty and depend on the vaccination speed. Copyright © 2021 Maria-Daniela TUDORACHE, Tamara Maria NAE and Ionut JIANU.

Customer satisfaction is the key, where the company will survive because there is customer loyalty. This study aims to evaluate the effect of service quality (SERVQUAL) in measuring customer satisfaction in Islamic banking. The respondents of this study are Islamic banking customers in Surakarta. The sampling technique using convenience random sampling was obtained as many as 121 respondents. Data analysis used multiple linear regression with additional modifications of dummy variables to see the effect of the covid-19 pandemic on customer satisfaction. The results show that tangible, reliability, and empathy have a significant effect on customer satisfaction. Meanwhile, responsiveness and assurance variables have no significant effect on customer satisfaction. Other results found that the pandemic had no significant effect, meaning that Islamic banking continued to try to provide excellent service, whether in a pandemic or not.

Flowsheet simulations of chemical processes on an industrial scale require the solution of large systems of nonlinear equations, so that solvability becomes a practical issue. Additional constraints from technical, economic, environmental, and safety considerations may further limit the feasible solution space beyond the convergence requirement. A priori, the design variable domains for which a simulation converges and fulfills the imposed constraints are usually unknown and it can become very time-consuming to distinguish feasible from infeasible design variable choices by simply running the simulation for each choice. To support the exploration of the design variable space for such scenarios, an adaptive sampling technique based on machine learning models has recently been proposed. However, that approach only considers the exploration of the convergent domain and ignores additional constraints. In this paper, we present an improvement which particularly takes the fulfillment of constraints into account. We successfully apply the proposed algorithm to a toy example in up to 20 dimensions and to an industrially relevant flowsheet simulation.

Mine planning is influenced by many sources of uncertainty. Significant sources of geological uncertainty in mine planning include uncertainty in layout of geological domains and uncertainty in metal grades. These two sources of uncertainty cannot be modeled separately because the distribution of the grade is controlled usually by geological domains. Two approaches exist for combining these two sources of uncertainty: the joint simulation approach and the cascade approach. In this paper, these two approaches were compared using a real case study. To this end, uncertainty in iron grade (quantitative variable) and ore zones (qualitative variable) was modeled using both approaches. There were some considerable differences in the results obtained by each approach, which confirm the importance of choosing the most appropriate approach with consideration of the dominate features of a deposit.

The main objective of the study is to determine the impact of migration on remittance for some different countries in SAARC region. Country wise yearly data of outflows migrations and inflows remittances from 1990 to 2017 are collected from 6 countries in SAARC region which are considered in the analysis. To complete this study, some statistical analysis like as multivariate analysis and panel analysis are computed. The highest number of average migrant is found in India with yearly average number of out migrant is 209992 and average inflow remittances is 33214.45 million US dollars which is also high among these six countries, whereas the lowest number of out migrated people are found in Maldives with a yearly average number of migrants is 437 and the amount of average inflows remittances is 3.08 million US dollars which is also lowest among in SARRC countries. India, Bangladesh and Pakistan are comparatively high out migrated countries as well as high remittance receiving countries. In MANOVA analysis, significant Hotelling test statistic indicates the population mean vector with migration and remittances for different countries in SAARC region are not equal. LM test statistic supported to run a panel regression model for analyzing the data. Furthermore significant Hausman test statistic suggests for analyzing fixed effect panel regression model. Outputs from panel regression model show that there have significant positive contributions of migration for all of these countries to the remittance. i.e. if the outflows migration of these region increases, the yearly average inflows remittance will be increased. Considering Bangladesh as a base country in dummy variable regression model in panel analysis, all others countries have significant contribution of migrations to remittances compared with that of Bangladesh. One can use simulation study for getting fruitful results. Also the researcher can apply discrimination analysis to get better results.

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.

PurposeThis study aims to examine the influence of internal and external factors on the credit risk (represented by nonperforming financing [NPF]) of Indonesian Sharīʿah rural banks (SRBs) – a type of Islamic bank that provides Islamic financial services especially to small and medium businesses in Indonesia. Internal variables comprise capital adequacy ratio (CAR), financing to deposit ratio (FDR), return on assets (ROA), operating expense ratio (OER), financing to value (FTV) and profit and loss sharing (PLS) financing ratio. External variables comprise inflation, economic growth and interest rate.Design/methodology/approachThe study uses the annual reports of SRBs in Indonesia as secondary data for the years 2010–2019. Auto regressive distributed lag (ARDL) is used as the analysis method to examine the short-run and long-run relationships between the variables.FindingsThe findings indicate that four variables experienced a lag in the short run, namely, NPF, inflation, CAR and PLS, with different results recorded for each of the variables. Furthermore, the long-run results show that CAR and ROA influence the NPF of SRBs positively, whereas inflation and PLS have a negative influence on NPF. The rest of the variables – notably economic growth, interest rate, FDR, FTV and OER – do not have an influence on NPF in SRBs.Research limitations/implicationsThe level of NPF in SRBs exceeds the provision of the Central Bank of Indonesia. The findings are expected to have implications for SRBs and the regulator to consider and to manage the factors related to NPF properly due to the important role of SRBs in small and medium businesses’ development.Originality/valueThis study measures the determinants of NPF using internal and external variables, including the addition of a dummy variable, notably FTV. This study also uses ARDL to analyze the financial policies involving data at the present time and lagged time.

Social vulnerability index (SoVI) has been widely used to measure the extent to which people or places are socially vulnerable. The SoVI is an aggregate composite index that linearly combines a few principal components resulted from the principal components analysis on a number of selected social vulnerability indicator variables, and it can quantify the relative level of overall social vulnerability but cannot inform the specific local social indicators that contribute to the vulnerability in various degrees. The specific social indicators that either attenuate or amplify local social vulnerability are of much need in policy making to reduce social vulnerability. This study explores the differential contributions of the constituent components of SoVI and investigates how the local indicator variables have evolved over time and across the Greater Houston metropolitan area in the USA using the geographically weighted principal components analysis. It found that the overall social vulnerability as measured by SoVI has exhibited persistent spatial patterns in the Greater Houston area since 1970; however, the spatial patterns of the SoVI are not equally constituted by the components of the SoVI. In particular, the high social vulnerability of suburban areas is mainly the result of one principal component that highly correlates with the percentage of mobile homes. It also found that the indicator variables of social vulnerability have exhibited great spatial heterogeneity and dependence at local scale, and they vary over time but persist on disadvantages in economic condition, mobility, and family structure.

This research explored the effect of incentives to complete online quizzes during a course. When a 1% weighting incentive per quiz was removed, student engagement dropped dramatically. There is also evidence that students who continued to complete quizzes did so with less vigour, spending less time on each quiz, starting them later in the week and having fewer attempts per quiz on average. The average mark per quiz was also lower once an incentive to complete them was removed. There was no significant difference in examination performance based on how many quizzes a student continued to attempt after incentives were removed. However, a comparison to a control group of students who sat the same invigilated assessments showed that, relative to term test performance, final exam mean score was 7% lower for the cohort who had the incentive to complete online quizzes removed. This differed from the control group, who showed no difference between term test and final examination mean scores when quiz incentives were maintained for the entire course. Building on previous research, this study demonstrates that a binary variable representing engagement in online quizzes did not capture the quality of that online engagement. Implications for practice or policy: Completion of online formative assessments by students is reduced if course leaders remove small-stakes incentives. The removal of small-stakes incentives by course leaders harms student motivation and achievement. Specifically, students who complete formative online assessment without incentives have fewer attempts, start them later in the availability window, spend less time completing them and record a lower mean score than those with incentives. Average final examination achievement is lower when incentives to complete online formative assessments are removed by course leaders.

The inconsistent distribution of dividends is a unique phenomenon and it needs to be examined. Therefore, the purpose of this study is to examine ten predictors affecting dividend policy of the inconsistent distribution of dividends. This study used the purposive sampling method to analyze the data that were obtained from a total sample of 133 observation objects collected in the 19 real estates, property, and building construction companies listed on the IDX Between 2013 - 2019. Furthermore, the method used is hypotheses testing and statistical analysis tool used is the hierarchical multiple panel data regression with the Least Squares Dummy Variables. The results obtained from panel A are firm risk, financial leverage, and investment opportunity that affect the dividend policy. Meanwhile, the panel B results are company risk, financial leverage, investment opportunity, and previous dividend, although the previous dividend had no effect due to the different direction of influence. This study proves the determinants and relevance of the parametric statistical analysis in the inconsistent distribution of dividends. Moreover, it is useful for managerial practitioners to pay attention to predictors for increasing company performances and to ensure investors obtain optimal return of their dividend.

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.

We study how banks’ special interests affect the resolution of failed banks. Using a sample of FDIC auctions between 2007 and 2016, we find that bidding banks that lobby regulators have a higher probability of winning an auction. However, the FDIC incurs larger costs in such auctions, amounting to 24.8 percent of the total resolution losses. We also show that lobbying winners match less well with acquired banks and display worse post-acquisition performance than their non-lobbying counterparts, suggesting that lobbying interferes with an efficient allocation of failed banks. Our results provide new insights into the bank resolution process and the role of special interests.

PurposeIn this paper, we aim to answer two questions. First, whether firms manipulate reported earnings via pension assumptions when facing mandatory contributions. Second, whether firms alter their earnings management behavior when the Financial Accounting Standard Board (FASB) mandates disclosure of pension asset composition and a description of investment strategy under SFAS 132R.Design/methodology/approachOur basic approach is to run linear regressions of firm-year assumed returns on the log of pension sensitivity measures, controlling for current and lagged actual returns from pension assets, fiscal year dummies and industry dummies. The larger the pension sensitivity ratios, the stronger the effects from inflated ERRs on reported earnings. We confirm the early results that the regression slopes are positive and highly significant. We construct an indicator variable DMC to capture the mandatory contributions firms face and another indicator variable D132R to capture the effect of SFAS 132R. DMC takes the value of one for fiscal years during which an acquisition takes place and zero otherwise. D132R takes the value of one for fiscal years after December 15, 2003 and zero otherwise.FindingsOur sample covers the period from June 1992 to December 2017. Our key results are as follows. The estimated coefficient (t-statistic) on DMC is 0.308 (6.87). Firms facing mandatory contributions tend to set ERRs at an average 0.308% higher. The estimated coefficient (t-statistic) on D132R is −2.190 (−13.70). The new disclosure requirement under SFAS 132R constrains all firms to set ERRs at an average 2.190% lower. The estimate (t-statistic) on the interactive term DMA×D132R is −0.237 (−3.29). When mandatory contributions happen during the post-SFAS 132R period, firms tend to set ERRs at 0.237% lower than they would do otherwise in the pre-SFAS 132R period.Originality/valueWhen firms face mandatory contributions, typically firm experience negative stock market returns. We examine whether managers manage earnings to mitigate such negative impact. We find that firms inflate assumed returns on pension assets to boost their reported earnings when facing mandatory contributions. We also find that managers alter earnings management behavior, in the case of mandatory contributions, following the introduction of new pension disclosure standards under SFAS 132R that become effective on December 15, 2003. Under the new SFAS 132R requirement, firms need to disclose asset allocation and describe investment strategies. This imposes restrictions on managers' discretion in making ERR assumptions, since now the composition of pension assets is a key determinant of the assumed expected rate of return on pension assets. Firms need to justify their ERRs with their asset allocations.

Objetivo: Investigar los principales impactos de la volatilidad cambiaria sobre las exportaciones colombianas de sus principales socios comerciales para el periodo 2001-2019, con el uso de variables de control además de la medida de volatilidad cambiaria, como el pib de los países, distancia y dummy variables de contigüidad y lenguaje común. Metodología: mco agrupado, modelos de panel de efectos fijos y aleatorios y modelo de pseudo máxima verosimilitud de Poisson. Resultados: Los resultados mostraron que la volatilidad cambiaria es perjudicial para la relación comercial entre Colombia y sus socios comerciales. Un aumento de 1% en la volatilidad cambiaria en el largo plazo puede reducir las exportaciones colombianas en 0.25-0.4%. Los resultados también sugieren que la información pasada es particularmente relevante para evaluar el impacto de la volatilidad en el comercio. Como se esperaba, los ingresos de los exportadores e importadores pueden aumentar el comercio y la distancia puede reducir el comercio. Limitaciones: Los datos sectoriales utilizados se pueden explorar mejor. Originalidad: Por primera vez se utiliza esta metodología y análisis de datos para investigar el impacto de la volatilidad cambiaria en el comercio colombiano. Conclusiones: Los resultados añaden otra evidencia empírica a la literatura sobre tipo de cambio y comercio, donde las políticas económicas que apuntan a estabilizar el tipo de cambio probablemente aumentarán el volumen de comercio para Colombia y sus socios comerciales. Recepción: 25/05/2020 Aceptación: 10/03/2021

PurposeContaminated food is a major source of illnesses around the world. This research seeks to learn how people assign responsibility for two food contamination risks and how they allocate costs to reduce these risks to four members of the food supply chain. The aims are to identify differences between countries and test options to control for cultural differences.Design/methodology/approachA random sample of online panellists from six countries (N = 6,090) was surveyed on how they assigned responsibility for controlling natural and accidental food contamination (traditional food safety) and for controlling intentional contamination (food defense) to farmers, transporters/distributors, retailer grocery stores/restaurants and consumers. They were also asked how they would allocate food safety and defense costs to the four groups. Differences between countries were tested with dummy variables and cultural measures.FindingsIn nearly every country, respondents assigned the least responsibility and allocated the smallest cost shares to consumers. In multivariate models, responsibility and cost-share results differed, suggesting that preferences varied by country and that respondents did not allocate costs the same way they assessed responsibility. The food safety and defense models also differed, implying that the respondents believed the two sources of contamination represented different risks.Originality/valueThis is the first study to examine how adults allocate the responsibility and costs for food safety and defense to farmers, transporters/distributors, retailer grocery stores/restaurants and consumers. Other research did not differentiate between these two food risks. This study also compared Hofstede's cultural measures with the recently developed Minkov's cultural measures.

Purpose of the study: The current study estimated the impact of current account gaps (CAGAP) on inflation in South Asian countries, namely, Pakistan, Bangladesh, India, Nepal, and Sri Lanka. Methodology: CAGAP is estimated through macroeconomic fundamentals by applying panel time series data methodology from 1990 to 2018. We adopted the bias-corrected least square dummy variable (LSDVC) estimation technique for the time series macro and dynamic panel to find the impact of CAGAP on inflation. Principal findings: CAGAP negatively affected consumer price inflation rate while Lag of inflation, trade openness, age dependency, and oil prices positively affected inflation rate in the selected sample countries. In LSDVC, the Blundell and Bond (BB), Arellano-Bond (AB), Anderson and Hsiao (AH) estimates are determined while system and difference GMM estimates also confirmed the results. Therefore, LSDVC-AB is selected from the three versions of LSDVC as baseline regression based on higher significance and lower standard error. Applications of the Study: CAGAP affects inflation, so it should be estimated annually in all these countries for macroeconomic stability as IMF annually estimates for developed countries in an external sector report. It is worthwhile to estimate CAN regularly and watch it for CAB evaluation and future Adjustment. Based on the results, the study recommends that tailored policies and interventions focus on the structural distortions and slow-changing factors to eradicate CAGAP. Novelty/ Originality of the Study: A few empirical studies have scrutinized the role of CAB on macroeconomic variables. No empirical study on CAGAP and its consequences are available in the selected region's existing literature to the best of our knowledge.

This study aims to analyze the factors that affect the financing supply of Islamic banking during pandemic COVID-19 outbreak in Indonesia. The variable of financial performances of Islamic banking such as Return on Assets (ROA), Capital Adequacy Ratio (CAR), Financing to Deposit Ratio (FDR) and Non-Performing Financing (NPF) are used as the independent variables. Meanwhile, the impact of COVID-19 pandemic is analyzed using dummy variable. Furthermore, each of the independent variables and the dummy variable are tested its influence to the dependent variable, namely the financing supply of Islamic banking by using a proxy of the total financing. Ordinary Least Square (OLS) with double log model used as a data analysis technique with the results of the study show that the variable of ROA has a positive but not significant effect to the financing supply. Variable CAR and FDR each shows a negative and not significant effect to the financing supply. Whereas, the variable of NPF shows a negative and significant effect to the financing supply. Meanwhile, COVID-19 pandemic contributes in giving a positive and significant difference effect to the financing supply than before the presence of COVID-19 in Indonesia. The results of this study prove that the phenomenon of credit crunch in Islamic banking relative can be addressed during pandemic COVID-19 outbreak in Indonesia.

This study examines the impact of the interactions of audit litigation and ownership structure on audit quality by Big 4 and non-Big 4 audit firms in Oman. This study uses modified audit opinion as proxies for audit quality, binary variable for audit litigation and percentage of shares owned by large shareholders and minority shareholders (consisting of Arab [non-GCC] shareholders) for ownership structure. The study uses size, risk, types of activity and ages of the firms as control variables. For the analysis and explanation of results descriptive statistics, correlation, regression techniques and T-test are used. Based on a sample of 107 listed companies on Muscat Securities Market (MSM) for 2013–2017, we find that audit litigation has a significant impact on audit quality for Big 4 audit firms, but not for non-Big 4 audit firms. Also, the results indicate that there is no difference between Big 4 and non-Big 4 audit firms as far as litigation risk is concerned.

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

PurposeThe main purpose of this study is to examine the relationship between credit risk and financial performance in non-financial firms.Design/methodology/approachIn order to test the relationship between Altman Z-score model as a credit risk proxy and the Return on Asset and Equity as indicator for financial performance with control variables leverage, liquidity and firm size. Least Square Dummy Variable regression analysis is opted. This research's sample included 69 non-financial companies from the Pakistan Stock Exchange KSE-100 Index between 2012 and 2017.FindingsThis study establishes the findings that Altman Z-score, leverage and firm size significantly impact the financial performance of the KSE-100 non-financial firms. However, liquidity is found to be insignificant in this study. Altman Z-score and firm size have shown a positive relationship to the financial performance, whereas leverage is inversely related.Practical implicationsThis study brings in a new and useful insight into the literature on the relationship between credit risk and financial performance. The results of this study provide investors, businesses and managers related to non-financial firms in the KSE-100 index with significant insight about credit risk's impact on performance.Originality/valueThe evidence of the credit risk and financial performance on samples of non-financial firms has not been studied; mainly it has been limited to the banking sector. This study helps in the evaluation of Altman Z-score's performance in the non-financial firms in KSE-100 index as well.

Developing an individual tree diameter increment (ΔDBH) model is the basis of near-natural management of mixed, uneven-aged oak forests. This analysis used remeasurement data (2009–2014) comprising...

The bioenergy industry is a proven option to replace fossil fuels in the heat supply and partly in the transport sector and generation of electricity. The study aims to investigate the empirical model of whether bioenergy use and institutional quality increase forest destruction in the European Union 28 countries covering the period 1990–2018, using the pooled ordinary least squares (pooled OLS), fixed effect, random effect, and least square dummy variable corrected. The findings indicate that bioenergy use within an institutional framework significantly contributes to a reduction in forest degradation regardless of the development status of countries in the region. This shows that efficient institutions and governments can significantly contribute to reducing forest degradation in the region especially in developed countries. Thus, policy-makers should stimulate the sustainability criteria of bioenergy application end-uses, intensify the quality of institutions, and ensure bioenergy use under effective governance.

This research aims to obtain empirical evidence of the effect of fraud diamond (pressure as proxied by financial stability, opportunity which is proxied by effective monitoring, rationalization which is proxied by total accruals, and proxied capability by change of directors) on financial statement fraud at Islamic Commercial Banks. The population in this study were banking companies that were on the list of Islamic Commercial Banks. Then the sample was taken using purposive sampling technique. The research used logistic regression because the dependent variable is a dummy variable. The results of this research indicate that the pressure variable has no effect on financial statement fraud. Opportunity variable has a negative effect on financial statement fraud. Then, the rationalization variable has a negative effect on financial statement fraud. And the capability variable has no effect on financial statement fraud. The results of this study are expected to be useful for companies, especially banking, as a material for consideration in preventing fraud on financial statements.

One of the direct economic consequences of dam failure (DF) is that water supply for irrigation is affected and incomes of the agriculture sector (AS) are reduced. The main purpose of this study is to apply a linear programming model (LPM), which, the objective function of the model was set to maximize the income function of the region AS with accessible water sources and function of crops production before and after the DF by using dummy variable (DV) regression models to optimize water supply for irrigation. The results obtained indicate that the consumption of surface water(SW) and groundwaters (GW), before the DF has not been optimized, as there are 15.5 % source loss in SW and 14.5 % in GW. After the DF, the allocation of SW in the best possible situation of access to SW sources is independent of the model input. It has a fixed value equivalent to 86 million cubic meters (MCM), which indicates a 116% decrease in comparison with the optimized value. Total accessible water sources are decreased by 36 % and using GW is 15 % more than an average long period time. A based on the finding from this research and its comparison with previous studies, this model is appropriate for water supply programming after DF and for dry weather Conditions.

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.

This article adopts the augmented versions of the Gravity Model to examine the effects of the signing of the ASEAN-China Free Trade Agreement (ACFTA) on the bilateral aggregate trades. Specifically, ACFTA dummy variables are incorporated in the basic model is to estimate the direction and magnitude of the ACFTA effects. A total of 79 trading partners of ASEAN member countries plus China were examined in this article. The study finds that the Gross Domestic Product, population, natural endowment, distance, and common language are the main determining factors of the bilateral trade for ASEAN member countries and their trading partners. Estimated results from this Augmented Gravity Model showed that ACFTA had increased the bilateral aggregate trades not only between intra-bloc member countries but also intra-bloc and extra-bloc countries. With this positive finding, ASEAN and China could consider expanding their free trade area to a broader regional perspective, enhancing economic growth and reducing regional inequality.

Analysing the determinants of wine prices has always been a field of interest in the wine economics literature. By estimating hedonic price functions, however, most papers generally remain at the country level with regions generally neglected or treated as simple dummy variables. The aim of this paper is to analyse the determinants of wine prices at the regional level by using Latent Variable Path Modelling with Partial Least Squares and Principal Component Analysis on the example of Hungarian wines. This approach is able to capture the regional specialties of wine production and provides a better insight into price determination. Results suggest that intrinsic values play a major but ambiguous role in determining regional wine prices, especially in the case of sugar content. It also becomes apparent that specific Geographical Indications (GIs) play a crucial role in price determination, instead of GI use per se. Moreover, individual brands also have an important role, as Tier1 and Tier2 wineries tend to sell their wines at higher prices and in smaller batch sizes.

The categorical data has a significant role in representing statistical binary variables, and they are analyzed by means of grouping the response variable into ordered categories. Thereby, the dependent variable becomes of type binary qualitative variable. The data related to the financial position of world countries is classified within the categorical data. This work is to study the economic effects of an individual's different factors on determining the richness or poorness levels of a selected population of countries. Moreover, a logistic regression model is to be created to estimate these levels. As a sample of research, the categorical data relevant to the financial status of 20 Arabic countries were drawn from the website of the World Bank, WB. In addition, for comparison purpose, another similar set of categorical data was generated by MATLAB too. The paper has been based on two hypotheses, first is the well-known regression models, like the ordinary least squares or maximum likelihood, are not accurate in case of binary qualitative variables. Second, is utilizing the logistic regression model as an alternative model to achieve the paper goal. The paper results, for both WB data and MATLAB data, have successfully proved the ability of the logistic regression model in manipulating the categorical data and predicting the coefficients of the corresponding regression models.

Two systems of additive equations were developed to predict aboveground stand level biomass in log products and harvest residue from routinely measured or predicted stand variables forPinus radiataplantations in New South Wales, Australia. These plantations were managed under three thinning regimes or stand types before clear-felling at rotation age by cut-to-length harvesters to produce sawlogs and pulpwood. The residue material following a clear-fell operation mainly consisted of stumps, branches and treetops, short off-cut and waste sections due to stem deformity, defects, damage and breakage. One system of equations did not include dummy variables for stand types in the model specification and was intended for more general use in plantations where stand density management regimes were not the same as the stand types in our study. The other system that incorporated dummy variables was for stand type-specific applications. Both systems of equations were estimated using 61 plot-based estimates of biomass in commercial logs and residue components that were derived from systems of equations developed in situ for predicting the product and residue biomass of individual trees. To cater for all practical applications, two sets of parameters were estimated for each system of equations for predicting component and total aboveground stand biomass in fresh and dry weight respectively. The two sets of parameters for the system of equations without dummy variables were jointly estimated to improve statistical efficiency in parameter estimation. The predictive performances of the two systems of equations were benchmarked through a leave-one-plot-out cross validation procedure. They were generally superior to the performance of an alternative two-stage approach that combined an additive system for major components with an allocative system for sub-components. As using forest harvest residue biomass for bioenergy has increasingly become an integrated part of forestry, reliable estimates of product and residue biomass will assist harvest and management planning for clear-fell operations that integrate cut-to-length log production with residue harvesting.

Prolonging non‐pharmaceutical interventions (NPIs) used in the control of pandemics can cause a devastating effect on the overall economic and social welfare levels. Therefore, policymakers are facing a difficult duty in terms of implementing economically and socially sustainable and acceptable measures. The aim of this study is to investigate the effectiveness of NPIs implemented to control the COVID‐19 pandemic. To this end, eight NPI measures were analysed, and their effects on the number of cases were investigated for France, Spain, China, and South Korea. In the study, the treatment effect of these mechanisms on the daily increase rate of the total number of cases during a certain period was analysed by using logarithmic linear regression with a dummy variables model. The findings indicate that the measures are effective against the spread of the pandemic at different levels. The findings also suggest that the most effective measure in decreasing the number of cases is workplace closure. An analysis comparing the effectiveness of countrywide measures and regional measures shows that school closing is the most effective measure to decrease the number of cases when implemented countrywide as opposed to regional implementation.

Dummy variable maximum likelihood estimation for binary response panel models struggles to estimate coefficients or substantive quantities in either short or rare event panels. The standard respons...

We investigate the change in adjustment speed of debt maturity for East Asian firms between 1990 and 2017 by including two exogenous shocks: the Asian Financial Crisis 1997-1998 (AFC) and the Global Financial Crisis 2007-2009 (GFC). We employ the least square dummy variable correction and find that East Asian firms have a slower adjustment of long-term debt over time. Besides, the decrease in adjustment speed of long-term debt after the GFC is more compared to the decrease after the AFC. Further analysis shows the optimal debt maturity differs across countries and industries. Another important implication of our results is that firms in high governance countries are more likely to close the gap between the actual and target debt maturity in time. Overall, debt holders and investors should consider financial uncertainties. © 2021 Ege Universitesi. All rights reserved.

Environmental damage and global warming has become an issue in the world. Developments in the construction project has a large influence in environmental change on the surface of the earth. Construction activities start from the constructing stage to the operating stage. Green construction is one part of the sustainable development that is required to participate in environmental sustainability. With this background, the author agreed to explain the criteria, stages of difficulty and obstacles in implementing Green Construction. Data collected by questionnaire method and data analysis using SPSS Statistics 26. The results of this study obtained the value of the Green Construction criteria that applied is waste project variables with an average value of 3,748 and the lowest is site project variables with average 3,198. The most dominant factor of difficulty in applying Green Construction is the water usage variable with an average value of 3,283 and the lowest difficulty in applying is the energy variable with an average of 2,817. The most challenging obstacle factor in implementing Green Construction is the cost factor with an average value of 4.00 and the lowest obstacle is the efficient design variable with an average of 2.96. Thus, from the results of this study is expected that the contractor can help more actively in improving the implementation of Green Construction in this construction project.

Tree mortality models play an important role in predicting tree growth and yield, but existing mortality models for Larix gmelinii subsp. principis-rupprechtii, an important species used for regeneration and afforestation in northern China, have overlooked potential regional influences on tree mortality. This study used data acquired from 102 temporary sample plots (TSPs) in natural stands of Prince Rupprecht larch in the state-owned Guandi Mountain Forest (n = 67) and state-owned Boqiang Forest (n = 35) in northern China. To model stand-level tree mortality, we compared seven model forms of county data. Three continuous (dominant height, plot mean diameter, and basal area per hectare) and one dummy variable with two levels (region) were used as fixed effects variables. Tree morality variations caused by forest blocks were accounted for using forest blocks as a random effect in selected models. Results showed that tree mortality significantly positively correlated with stand basal area and dominant height, but negatively correlated with stand mean diameter. Incorporating both the dummy variables and random effects into the tree mortality models significantly increased the fitting improvements, and Hurdle Poisson mixed-effects model showed the most attractive fit statistics (largest R2 and smallest RMSE) when employing leave-one-out cross-validation. These mixed-effects dummy variable models will be useful for accurately predicting Larix tree mortality in different regions.

The study aims to examine the CO2 emissions by considering the implication of COVID-19 under strict lockdown in India. The nonlinear (asymmetric) relationship is investigated between CO2 emission and COVID-19 with its specific determinants. The positive and negative asymmetries of COVID-19 determinants are also captured by using econometric techniques. The daily data series of CO2 emission, new confirmed cases, confirmed deaths, and lockdown as dummy variables from January 30, 2020, to December 1, 2020, for India is analyzed by employing the nonlinear autoregressive distributed lag model. This research revealed a significant nonlinear relationship between CO2 emission and COVID-19. The bound test and asymmetric coefficients are confirmed by the variables' long- and short-run relationships. The dynamic multiplier graphs present that India's strict lockdown due to the rapid increase in COVID-19 patients significantly reduces toxic gas emissions, especially CO2 emissions. This asymmetric relationship has been proficiently declared that unhealthy public routine, extra traffic, and unhygienic gases released in the air become the reason for environmental destruction. The lockdown is practically imposed for specific periods and reasons, contributing to reducing toxic emissions, but it is not a permanent solution for environmental sustainability. The government of India, policymakers, and environmentalists should make people aware of unhealthy and environmentally envying activities and policies and long-term applicable strategies should be designed to upgrade the environment's quality.

Price limits are applied to control risks in various futures mar- kets. In this research, we proposed an adapted autoregressive model for the observed futures return by introducing dummy variables that represent limit moves. We also proposed a stochastic volatility model with dummy variables. These two models are used to investigate the existence of price de- layed discovery eect and volatility spillover eect from price limits. We give an empirical study of the impact of price limits on copper and natural rubble futures in Shanghai Futures Exchange (SHFE) by using MCMC method. It is found that price limits are ecient in controlling copper futures price, but the rubber futures price is distorted signicantly. This implies that the eects of price limits are signicant for products with large uctuation and frequent limits hit.

PurposeThis research explores the effect of bioenergy use on carbon dioxide releases in 28 European Union (EU-28) affiliated members starting from 1990 to 2018.Design/methodology/approachApplying panels' fixed effect (FE) estimator and random effect (RE) estimator, the regressed findings are highly validated as they were robust by panel least square dummy variable corrected (LSDVC) and pooled ordinary least square (Pooled OLS) estimators.FindingsThe findings claimed that carbon dioxide releases decrease with an incline in bioenergy use and trade openness. On the other hand, fossil-fuel and economic growth indicators mounting carbon dioxide releases. The result implies that carbon dioxide releases in EU-28 region members can be mitigated significantly by mounting the quantity bioenergy use in generation channel. This will mostly participate in combating environmental pollution.Practical implicationsThe study suggests for EU28 region members to enhance the portion of bioenergy in their fuel access to decrease emitted carbon dioxide. Governors in EU28 members should mainly encourage bioenergy expansion to raise its security and availability. The politicians of the EU28 members must assert on efficacy and productivity of bioenergy production to achieve energy accessibility and decrease dependency on conventional energy.Originality/valueThis research applies the recently improved model, the panel data analysis approach, which considered for the first-class impacts of estimators on the dependent variable and deals with the several problems of the common Pooled OLS estimator's manner and performance. Finally, this research contributes to the previous studies on ecological sustainability by examining the presence correlation among carbon dioxide emissions, bioenergy sustainability, trade openness, fossil fuel and gross domestic product in the EU28 region. Hence, it proves our research novelty, originality and contribution to the body of knowledge.

Objectives: Exploratory Factor Analysis (EFA) is a very popular statistical technique for identifying potential latent structure underlying a set of observed indicator variables. EFA is used widely in the social sciences, business and finance, machine learning, and the health sciences, among others. Research has found that standard methods of estimating EFA model parameters do not work well when the sample size is relatively small (e.g. less than 50) and/or when the number of observed variables approaches the sample size in value. The purpose of the current study was to investigate and compare some alternative approaches to fitting EFA in the case of small samples and high dimensional data. Results of both a small simulation study, and an application of the methods to an intelligence test revealed that several alternative approaches designed to reduce the dimensionality of the observed variable covariance matrix worked very well in terms of recovering population factor structure with EFA. Implications of these results for practice are discussed.

Companies generally prefer to pay small amounts of tax and use aggressive taxation strategies. This study aims to examine the effect of family ownership on tax aggressiveness moderated by corporate governance. Family ownership is measured by dummy variable 1 or 0, corporate governance with the proportion of the composition of independent commissioners, and tax aggressiveness using the Effective Tax Rate (ETR) on consumer goods companies listed on the Indonesian Stock Exchange in 2018. Data analysis using Moderated Regression Analysis (MRA). The results of this study indicate that family ownership does not affect tax aggressiveness, corporate governance has a positive effect on tax aggressiveness, and corporate governance strengthens the relationship between family ownership and tax aggressiveness. The research implication is that it can be an input in making decisions for the government regarding taxation, for companies related to decision making regarding corporate governance, as well as for investors for investment decisions.