Dummy Variable Model Research Articles

Maintenance Cost Growth in Aging Aircraft: Analysis of a New DHS Dataset

This article finds that maintenance cost per flight hour increases by 8.0% as the average age of a fleet of aircraft increases by 1 year. Cost growth appears to decrease slightly as aircraft age, with an older group of aircraft showing maintenance cost per flight hour increasing by 7.6%. This is based on analysis of a new database from U.S. Customs and Border Protection Air and Marine Operations (CBP AMO). The article employs a Fixed Effect (FE) Least-Squares Dummy Variable (LSDV) regression model based on panel data from Fiscal Year 2015 (FY2015) to FY2020. The conclusion describes possible areas of additional research. This project’s findings may be interesting to the U.S. Coast Guard (USCG) and the Department of Defense (DoD). USCG aircraft conduct many similar missions to AMO’s aircraft (both organizations are part of the Department of Homeland Security [DHS]). DoD may find the analysis interesting for three reasons. First, while many of AMO’s approximately 200 aircraft are based on commercial designs, it includes military aircraft such as the UH-60, P-3, and MQ-9. Second, many of DHS’s acquisition and maintenance processes mirror DoD’s. Third, AMO’s data are a new dataset for examining aircraft maintenance costs already analyzed using DoD databases such as the Air Force Total Ownership Cost (AFTOC) and Naval Visibility and Management of Operating and Support Costs (VAMOSC) databases.

The Effect of Covid-19 on Bank Profitability Determinants of Developed and Developing Economies

We have learned from the Covid 19 crisis that pandemics are inevitable, and the financial systems need to be ready to withstand such global shocks. This study aims to uncover determinants that are vital in maintaining bank profitability during such a crisis by providing evidence on how Covid-19 impacted bank profitability. Additionally, we test whether the crisis moderated the relationship between typical determinants and bank profitability across developed and developing economies. The study uses generalized least squared dummy variable model estimation. Six-year quarterly bank specific data of the top 10 countries from South Asia and Europe based on the highest GDPs is used. The study finds evidence of a significant impact of the pandemic on bank return on assets and equity. Additionally, the evidence suggests that covid-19 impacted bank profitability differently across the developed and developing countries. Covid-19 caused profitability to fall in the homogeneous European banks, whereas profitability increased for South Asian banks during the Covid crisis. We find that the pandemic moderated the relationship between bank profitability and its determinants. Credit quality and bank efficiency became less important in determining bank profitability during the Covid period. In contrast, bank size and maintaining liquidity became more important determinants. Additionally, the magnitude of capital ratio as a determinant of return on assets decreased during Covid. The moderating role of covid-19 on bank profitability determinants would be of value to both academicians and practitioners.

Developing a new model for predicting the diameter distribution of oak forests using an artificial neural network

The parameters of the probability density function (PDF) may be estimated using the parameter prediction method (PPM) and the parameter recovery method (PRM). However, these methods can suffer from accuracy issues. We developed and evaluated the prediction accuracy of two PPMs (stepwise regression model and dummy variable model) and an artificial neural network (ANN) to predict diameter distribution using data collected from 188 oak forest plots. The results demonstrated that the Weibull distribution performed well in fitting the diameter distribution. Compared with the stepwise regression model, the PPM model with stand type as a dummy variable reduced the predictional errors in estimating the parameters b and c of the Weibull distribution, but the prediction accuracy of the diameter distribution showed no significant improvement. Compared with the two PPM models, the ANN model with diameter class (C), average diameter (D) and stand type (T) as input variables decreased the RRMSE by 2.9% and 4.33% in estimating diameter distribution, respectively. The satisfactory prediction accuracy and simple model structure indicated that an ANN worked well for the prediction of the diameter distribution with few requirements and high practicality.

Panel Data Modeling for Indian Food Grain Production

The present investigation was carried out to study the food grain production trends in selected states of India for the period 2001-02 to 2020-2021. In this study a panel data regression model is used to combat the complicated relations and strong auto-correlation present in the crop production time series data. Panel data modelling control the heterogeneity of cross-sectional units over time. The results reveal that between state-to-state food grain production is highly significant, the highest food grain production was registered in Uttar Pradesh, followed by Punjab and Madhya Pradesh. Very lowest was registered in Kerala and Himachal Pradesh. Levin, Lin & Chu t statistics values are found to be significant indicating that the variable under study is stationary at level and hence the variable under study is I(0).Among the three different models viz., constant coefficient model, fixed effect model and random effect model, the fixed effect model was selected as an appropriate trend model as per the Hausman test. Based on the statistical significance of the estimated coefficients and the substantial increase in the R2 value to 95%, the fixed effect model or the least-square dummy variable regression model performs better than the panel least square regression model. The Hausman test confirms the selection of fixed effect over the random effect model. The fixed effects are positive in Andhra Pradesh, Karnataka, Kerala, Tamil Nadu, Gujarat, Chhattisgrah, Bihar, Haryana and Madhya Pradesh. Negative fixed effects are observed in Himachal Pradesh, Jharkhand, Maharashtra, Odisha, Punjab, Rajasthan, Uttar Pradesh, Uttarakhand and West Bengal. Increases in food grain production have been observed

Corporate characteristics and HR disclosure: a missing link in Indian corporate sector

The present study has been conducted to know the current state of voluntary human resource (HR) disclosure practices in an emerging economy of India. This study also examines the association between corporate characteristics and the level of HR disclosure by Indian listed companies, based on a sample of 125 companies listed on National Stock Exchange (NSE-200 Index) for the time period of nine years (F.Y. 2012-2013 to 2020-2021). The outcomes revealed that there is only 45.51% HR information disclosed by the selected companies. Further, the outcomes of two-way least square dummy variable (LSDV) regression model revealed that out of selected independent variables only company age, company size, ownership concentration, debt-equity ratio, and total number of pages of an annual report have significantly associated with the level of HR disclosure. The present study provides useful insights to policymakers for constructing a human resource disclosure index (HRDI) which should be mandatory for Indian companies to disclose the items that are included in the index.

Urban Social Landscape Transitions between 1990 and 2010: Case study of Wuhan, China

Urban social development interacts with physical ecological dynamics. China’s urban social landscape study attracted attentions within recent decades, but the research mainly focused on the first tier cities. This paper aimed to reveal China’s urbanization since the 1980s from the perspectives of social dynamic and second tier cities, through the study of Wuhan one of the largest city in central China. The analysis was based on the population census data in Wuhan in 1990, 2000, and 2010. Variables referred to demography, migration, age composition, marital status, household type, education, employment, and housing status. Factorial analysis was employed to find the dominant urban social dimensions. A principal component analysis was conducted to extract the principal factors while a varimax algorithm was used for rotation. Further, the comprehensive urban social structures were mapped based on the cluster analysis of the selected factors. To conceptualize the urban social structure, dummy variable linear regression models were conducted. The results showed that Wuhan urban social dimensions diversified definitely during the recent decades, which were mainly reflected as increasing concerns of the tertiary industry employment, highly educated population unemployed, elders, incoming migrants, and housing improvement. The predominant urban social structure was conceptualized as “three central towns, two wings, and one axis”. The author concluded that urban social landscape study should be introduced in sustainable urban planning, with the concerns of challenges in an aging society, social equity between local residents and incoming migrants, as well as between rural and urban population, social impacts on physical ecological degradation, and so on.

Impact of stock health on fish prices: Evaluation and implications for food accessibility.

The continuous rise of the world’s population has made food security a major point of the global agenda, with fisheries providing a key source of nutrition, especially in developing countries. Ensuring their health is key to maintain the availability of the resource, but its effect over accessibility is yet unclear. In this paper, we discuss the relevance of stock health for ensuring the price accessibility of the resource. A Least Square Dummy Variable panel model is proposed for bluefin tuna prices, with a biological explanatory component, and dummy variables reflecting changes in fishing trends. Both have proven to be significant to explain annual price variations, with improvements in stock health achieving price reductions.

Capital Expenditure and Investment on Economic Growth in West Papua Province 2015-2019

This study was conducted to know the effect of capital expenditure and investment on economic growth in West Papua in 2015-2019. First, sampling was carried out using the saturated sampling technique with the number of samples obtained in 13 districts/cities. Panel data with Least Square Dummy Variable (SLDV) model estimation is used as an analysis method and Stata 16 functions as a data processing program. The research then found that the capital expenditure variable had a negative and insignificant effect on economic growth. Then it was found that the investment variable has a positive and significant impact on economic growth. Compared with the results of studies using only independent variables, the use of control variables (population and average length of schooling) will present the most significant coefficient of determination at the time of full regression. These results then indicate that the control variable has a strong influence on economic growth.

A Marginal Comparative Study on Cultural Consumption of Chinese Urban and Rural Residents

This paper uses the data of per capita cultural consumption and per capita disposable income of urban and rural residents from 2000 to 2020 to study the marginal tendency of cultural consumption of urban and rural residents in my country through the dummy variable model. Empirical research shows that both urban and rural residents' cultural consumption and income show a significant positive correlation. By comparison, it can be found that the rural residents in China are significantly backward than urban residents in such respects as total cultural consumption volume, proportion of cultural consumption in their incomes, and the consumption ability related to cultural marginal propensity. Therefore, when formulating corresponding policies, it is particularly necessary to formulate appropriate policies to promote the development of rural cultural undertakings in accordance with the actual conditions in rural areas of China.

Impact of Outflows Migration on Inflows Remittance of Different Countries in SAARC Region

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.

The Impact of Company Characteristics on Human Resource Disclosure Index: A Study on Service Sector Companies in India

The main purpose of the study is to know the impact of company characteristics on Human Resource Disclosure Index (HRDI) in service sector companies in India. The sample consists of 22 service sector companies listed on the NSE-500 Index. The data of dependent variable are collected from annual reports using the content analysis approach, and the data of independent variables are collected from CMIE Prowess Database and annual reports of the sample companies. The time period of the study ranges from 2012–13 to 2017–18. For analysis, descriptive statistics, Pearson’s correlation matrix and One-way Least-Square Dummy Variable (LSDV) regression model are used. The findings of the study show that the mean percentage of HRDI is 34.26. It represents the low level of HR disclosure. Further, the results of the LSDV regression model demonstrate that net fixed assets, market capitalization, return on total assets, quick ratio and pages of an annual report have significant positive and net sales, profit after tax, current ratio and type of auditor have significant negative influence on the HRDI. Overall, it can be said that the HRDI used in the present study is a first step towards evaluating the voluntary HR disclosure practices of listed companies in India from the stakeholder perspective and voluntary disclosure of HR information help to rebuild the trust of stakeholders in an accounting system.

Mengestimasi Surplus Konsumen Permintaan Daging Sapi Potong: Studi Empiris di Wilayah Perdesaan dan Perkotaan Indonesia

This research aims to analyze the consumer surplus of beef cutlets in Sokaraja and Kembaran Subdistrict in the Banyumas Regency. This research has a novelty where other researchers have never used the residential area (urban and rural). The taken sample is 100 people divided into two, in Sokaraja and Kembaran Subdistrict, in the Banyumas Regency. The used analytical tools are the classic assumption test, multiple regression analysis of dummy variable models, and statistical tests. This research shows that the average number of willingness to pay beef cutlets in Sokaraja and Kembaran is Rp. 136.900 per kilogram. This number is higher in comparison to the paid cutlet, which is Rp. 127.650. The total surplus obtained by the consumer in this research is Rp. 412.494.500. This research implies that the related governmental institution must be focused to either add the production of beef cutlet or do the import of beef cutlet in order to fulfill the demand due to the consumers' willingness to pay is still above the average of the beef cutlet's price itself.

Examining the Economic Impacts of Climate Change on Net Crop Income in the Ethiopian Nile Basin: A Ricardian Fixed Effect Approach

Climate change affects crop production by distorting the indestructible productive power of the land. The objective of this study is to examine the economic impacts of climate change on net crop income in Nile Basin Ethiopia using a Ricardian fixed effect approach employing the International Food Policy Research Institute (IFPRI) household survey data for Ethiopia in 2015 and 2016. The survey samples were obtained through a three-stage stratified sampling technique from the five regions (Amhara, Tigray, Benishangul Gumuz, Oromia, and Southern Nation Nationality and People (SNNP) along the Nile basin Ethiopia. There are only 12–14% female household heads while there are 80–86% male households in the regions under study. In the regions, more than half of (64%) the household heads are illiterate and almost only one-tenth of them (12%) had received remittance from abroad from their relatives or children. Crop variety adoption rate is minimal, adopted by the 31% of farmers. Only 30% of the surveyed farmers mentioned that they planted their crop seeds in row whereas the rest 70% had not applied this method. The regression results from the fixed effect least square dummy variable model showed that literacy, household size, remittance, asset value, and total land holdings have significant and positive impacts on the net crop income per hectare. The regional dummy variables estimate indicated that all the regions are negatively affected by climate change at varying levels. Strategies to climate change adaptation have significant and positive contributions in leveraging the damaging effects of climate change. The results also showed that increased winter and summer temperature and rainfall increase net crop income per hectare. The estimated coefficient of the interaction term of spring temperature and rainfall is significant and negative. On the other hand, while the mean annual temperature is damaging to crops, annual rainfall is beneficial. It can be deduced that, while increased temperature and rainfall in summer and winter increase the net crop income, the converse is true for winter and spring seasons. The study also proposes a specific, context-dependent, farm-level adaptation analysis of how farmers cope with the different climatic impacts of the Nile Basin and maintain the income levels that they have previously enjoyed.

Optimization in Water Resources At Dry Weather Conditions Before and After the Dam Failure By Using Dummy Variable Regression Approach

Abstract 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.

Effects of non-pharmaceutical interventions against COVID-19: A cross-country analysis.

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.

Generalized or general mixed-effect modelling of tree morality of Larix gmelinii subsp. principis-rupprechtii in Northern China

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 Effect Of Applying Digital Transformation System On The Support Of Some Indicators Support Food

the research paper is mainly aims to know the targeted current and future status of food support and the impact of the application of the electronic card system for food support. Qualitative and quantitative methods were utilized to achieve the study purpose, as well as exponential Function to estimate the growth rates, Simple & Linear Regression, Dummy Variable and forecasting model, secondary data collected from CAPMS. The main results of the study can be Summarized as follows: There is an increasing cash support trend towards food support, Ration goods and bread support about 160%, 18%, 15% respectively during the period2000-2019, but 13%, 16%, 11% in real support respectively during this period. The change rate of 1% total cash support will change the amount support of food by 0.208 unit, and change in the amount of cash support unit for the bread support will change the support food by 1.042 unit. It is expected to increase food subsidies an bread by about 22%, 50% 2025 respectively. In addition to subsidies for food, commodities and bread support has doubled about 20 times, despite the total support doubling about 67.8 times, this may necessary correcting the support path and reaching the support for the best. because decreasing percentage of food support from about 63% of the total support to about 19% of the total support during the period

Determinants of Export Demand Function for Malawi Tobacco

This paper examines the export demand function of Malawi tobacco using panel data from 1997 to 2019. It estimates the relationship of tobacco quantity exported with Malawi’s export price, competitor’s export price, Gross Domestic Product (GDP), Real effective exchange rate (REER) as well as the impact of the World Health Organization’s (WHO) Framework Convention on Tobacco Control (FCTC). A double log fixed effects model with driscoll- kraay standard errors as well as fixed effects model with Least Square Dummy Variables (LSDV) model was used for empirical estimation. The empirical results confirmed that there exists a significant relationship among Malawi tobacco export demand, trade partner’s real income and export price. Furthermore, REER was elastic, while own price and cross price elasticities were less than zero. The results indicated that Malawi should take advantage of the high incomes of the tobacco importing countries to enhance international market share.

The Nexus between corporate governance and human resource disclosure: evidence from India

The objective of the present study is to examine the nexus between corporate governance and human resource disclosure in Indian corporate sector. The study is based on 125 companies listed on NSE-200 Index for the time period of ranging from F.Y. 2012-2013 to 2019-2020. For analysis, 'descriptive statistics, Pearson's correlation matrix, two-way least square dummy variable (LSDV) regression model' are used. The outcomes of two-way LSDV regression model depicted that there is a significant positive impact of 'board size, board meeting, market capitalisation, pages of an annual report and a significant negative of board independence, promoter's holding, debt-equity ratio on human resource disclosure index of the selected Indian listed companies'. Overall, it can be said that this research suggests a direction for directors who want to improve the consistency and integrity of their human resource disclosures.

The Effect of Regional Minimum Wage and Inflation on HDI in Central Java

Since 2017 Central Java has a high Human Development Index (HDI); its rank in 2019 is still the same as in 2010. The study investigates regional minimum wage and inflation facts in Central Java and how the HDI estimation model's constants differ differregency/city in different regions of Central Java. The study utilizes the Fixed Effect Model built using panel data for 2010-2019. It employs the Least Square Dummy Variable Model to get the difference constant for each regency and city in Central Java. The FEM model implies that the effects of RMW and inflation are the same for all regencies/cities, caused by the same cultural background. The results of this study show that RMW and inflation have significant positive effects on HDI and that cities tend to have higher constants compared to regencies