An Assessment of the Impact of Industrialization on Physical Environment and Socio-economic Conditions around the Alipur Industrial Area, Bangladesh

Massive industrialization promotes economic growth and causes environmental pollution and degradation. The purpose of this research is to determine the impact of industrialization on the physical environment and socio-economic condition at Alipur industrial areas, Habiganj, Bangladesh, by measuring water, soil, air, sound quality parameters and a random sampling questionnaire survey on socio-economic conditions. Most of the measured physicochemical parameters exceeded the acceptable limit of inland surface water. The pH of the effluent water ranged from 4.83 to 8.58, which was found lower than the standard level for two points. The DO level was within the range of 1.98 to 3.32 mg/L indicating that aquatic life is in danger because of the lower level of DO. BOD, COD, TSS, and TDS ranged from 133 to 255.8 mg/L, 330 to 566 mg/L, 1960 to 2170 mg/L and 4110 to 5500 mg/L, respectively. The concentration of Nitrates (14.63 mg/l), Phosphate (10.33 mg/l), and Copper (5.49 mg/l) in the water samples found more than the inland surface, public sewer STP, and Irrigated land standard. The concentrations of CO (10.71), NO2 (90.56), and SO2 (104.34) in the air are near the acceptable level, indicating that the air was moderately polluted. The Durbin-Watson statistic is 0.495 from the model summary indicating the research model has a positive auto-correction, and the coefficient of significance is at 0.00, and Test F at 150.345 suggests that the model is suitable. Furthermore, the coefficient of the land area lost due to industrial park construction is found at 0.00, indicating household income increased when people lose land and non-agricultural sectors like building houses, investing in services, traffic systems. On the other hand, it is undeniable that few members whose land is acquired turned unemployed during industrial parks, resulting in the high number of unemployed workers being high and income declines.

Health Expenditure and Its Human Capital Determinants in Iran

Background: Human capital is an effective variable on the health condition of a society and its changing changes health expenditure as the proxy of health. This study aimed to investigate the relationship between human capital determinants and health expenditure. Methods: An empirical model was used with 7 variables included gender parity (GPI) index, literacy rate, life expectancy at birth, GDP per capita, physician per capita, and hospital’s bed as the independent variable and health expenditure as depended variable. After unit root test of data by using Zivot-Andrews method, the model was estimated by ordinary least square (OLS) method. Result: GPI had the negative and significant impact on health expenditure. Literacy had the positive and significant impact on depended variable. In addition, GDP per capita and life expectancy had positive and significant on health expenditure. Hospital bed and physician per capita did not have the significant relationship with health expenditure. The value of R-squared and Durbin-Watson statistic were 0.99 and 1.95 respectively, which showed good model fit. Conclusion: literacy rate and GPI index as the proxy of human capital had the different impact on health expenditure. The first had positive and the latter had negative. GDP per capita had the positive impact that showed health was a normal good.

Predicting the future price of a commodity using the OWMA operator: An approximation of the interest rate and inflation in the brown pastusa potato price

This main aim of this paper is to propose a methodology for the prediction of the future price of the brown pastusa potato in Colombia, taking into consideration the variables of interest rate, as measured by fixed term deposits (FTDs), and inflation rate, as measured by the consumer price index (CPI). The methodology conducts linear regression analysis and assesses the results using the significance test, the Durbin-Watson statistic, analysis of the variance inflation factor (VIF) and the coefficient of determination. After that, the forecast of the independent variables has been conducted with the ordered weighted moving average (OWMA) operator and new proposed OWA operators using probabilities that are presented in the paper. Using these new methods and the proposed econometric model, it is possible to establish future prices. The results show a greater impact of the interest rate than inflation, as well as the need to include supply and demand variables that have not been included due to the absence of systematic information.

Investigating the Role of Audit Time Pressure on Dividend Quality in Companies Listed in Stock Exchange of Tehran

The present study investigated the role of audit time pressure on dividend quality in listed companies in Stock Exchange of Tehran. Auditors are often pressured to set time budgets. Due to the increasing importance of audit firms for achieving time budgets as a measure of effectiveness, their difficulties in measuring audit effectiveness, the potential conflict between doing the right things and control costs for high quality audits have intensified. In order to study the aforementioned subject, the statistical population of the audit firms of the member of the Society of Certified Public Accountants within the city of Tehran was determined and the required data were collected from these institutes by random sampling. Durbin–Watson statistic method and correlation with Eviews software were also used to test the research hypotheses. The results indicated that audit time pressure has a significant effect on dividend quality.

Examination Of The Relationship Between Oil Price Shock And Macroeconomic Variables In Nigeria

The price of oil is one of the important macroeconomic indicators because of the extreme importance of supplying oil to different countries of the world to meet their energy needs. As Nigeria’s economy depends on oil prices, the country remains vulnerable to fluctuations in world oil prices. During periods of rising oil prices caused by macroeconomic and political conditions in the international market, the state usually has a positive trade balance, there is an increase in foreign exchange reserves and the revaluation of the national currency. The purpose of the article is to evaluate the relationship between an oil price change and Nigeria’s economic growth rate using regression analysis. The source of statistical information is data from the National Bureau of Statistics, the Nigerian National Petroleum Corporation, and the Nigerian Energy Commission. By checking the time series for steady-state using the advanced Dickie-Fuller test, a regression equation is constructed where the dependent variable is represented as the price of oil and the independent variables are key macroeconomic indicators. The econometric model constructed is adequate because the determination coefficient and the adjusted determination coefficient are 0.97 and 0.96 respectively. The Durbin-Watson statistic in the model is 1.98, meaning the model is reliable. Oil price fluctuations have been found to be related to investment, economic growth, and exchange rates, as well as to inflation. The paper argues that the use of the shock of oil prices should be supported, as it promotes economic growth and is not inflationary. Therefore, the authors believe that the government, which is the main beneficiary of cash, should also implement strategies that counterbalance the propensity for the economic downturn. Based on the analysis, a set of priority measures was proposed: enhancing financial liberalization, combating corruption, transparency of government activities, creating an open currency market, and developing non-inflationary monetary and fiscal strategies. Keywords: oil price, macroeconomic variables, energy needs, Organization of Petroleum Exporting Countries, Dickie-Fuller Extended Test, Petroleum Exporters.

P004: Effect of telephone triage (811) calls on a regional poison centre

Introduction: Telephone Triage Services (TTS) manage phone calls from the public regarding general medical problems and provide telephone advice. This telephone based care can overlap with care provided by Poison Centres. Our objective was to examine the impact of a provincial 811 TTS on the IWK Regional Poison Centre (RPC). Methods: This is a retrospective descriptive study using interrupted time series methodology. We compared monthly IWK RPC call volume in the pre-811 era (January 2007-July 2009) and the post-811 era (September 2009-December 2017). We summarized the characteristics of callers who accessed the IWK RPC in terms of client age, sex, intentionality, time of day, call disposition and outcome. Caller characteristics were compared between the pre- and post-811 eras using chi-square test for categorical variables. We used segmented regression analysis to evaluate changes in slope of call volume in the pre- and post 811 eras. The Durbin-Watson statistic was performed to test for serial correlation and the Dickey-Fuller test to investigate seasonality. Results: The dataset included 82683 calls to the IWK RPC – 27028 pre-811 and 55655 post-811. Overall, 55% of calls were for female clients and the largest age group was children aged 0-5 years (37%). Most calls originated from home (47%), followed by a health care facility (23%). Most calls were managed at home (65%). Less than 3% of calls resulted in major effect or death. The Durbin Watson statistic was not statistically significant (p = 0.94). The Dickey-Fuller test indicated series stationarity (p = 0.001). There was no statistically significant change in call volume to the IWK RPC due to the introduction of 811 (p = 0.39). There was no significant variation by time of day, day of week or month, with most calls occurring in the evening. There were significantly more calls regarding intentional ingestions in the post-811 era (23% vs. 19% pre-811, p < .001). Outcomes in the pre and post 811 eras were as follows: minor/no effect/non-toxic/minimal 80% vs. 78%; moderate 7% vs. 10%; and, major/death 1.7% vs. 2.0%. Conclusion: The introduction of a TTS did not change call volumes at our RPC. The increase in the percentage of calls about intentional ingestions may reflect an increase in call acuity as the 811-TTS likely manages calls about minor/non-toxic ingestions without consulting with the RPC. Our future research will examine the nature of poison related calls to the 811-TTS.

How real are observed trends in small correlated datasets?

The eye may perceive a significant trend in plotted time-series data, but if the model errors of nearby data points are correlated, the trend may be an illusion. We examine generalized least-squares (GLS) estimation, finding that error correlation may be underestimated in highly correlated small datasets by conventional techniques. This risks indicating a significant trend when there is none. A new correlation estimate based on the Durbin–Watson statistic is developed, leading to an improved estimate of autoregression with highly correlated data, thus reducing this risk. These techniques are generalized to randomly located data points in space, through the new concept of the nearest new neighbour path. We describe tests on the validity of the GLS schemes, allowing verification of the models employed. Examples illustrating our method include a 40-year record of atmospheric carbon dioxide, and Antarctic ice core data. While more conservative than existing techniques, our new GLS estimate finds a statistically significant increase in background carbon dioxide concentration, with an accelerating trend. We conclude with an example of a worldwide empirical climate model for radio propagation studies, to illustrate dealing with spatial correlation in unevenly distributed data points over the surface of the Earth. The method is generally applicable, not only to climate-related data, but to many other kinds of problems (e.g. biological, medical and geological data), where there are unequally (or randomly) spaced observations in temporally or spatially distributed datasets.

Models for Measuring Height-Diameter Relationships for Agarwood (AquilariamalaccensisLamk) Plantations in Bangladesh

The relationship between tree height and diameter is an important element in growth and yield models, in carbon stock estimation and timber volume models, and in the description of stand dynamics.In this paper considered18 functional models and evaluated the performance that predict total tree height from diameter at breast height of agarwood. The models were applied to A.malaccensisLamk (Agarwood) which is economically important tree species planted in some potential forest areas of Bangladesh.A total of 5,866 tree heights and corresponding diameters at breast heights were extracted from many forest areas in Sylhet, Chittagong, Cox's Bazar and Chittagong Hill Tracts (Rangamati) forest division. The model goodness of fit values were evaluated in terms of adjusted coefficient of determination (R2), root mean squared error (RMSE), Akaike’s information criterion (AIC),Durbin-Watson statistic value,homogeneity of the residuals and significance of the regression parameters. The results of the study indicated that the height-diameter relationship can best be described by non-linear models. The best three models selected for the species with ranking in terms of goodness of fit. The Gompertz ; Parabolic and Logistic with R2=0.91 were height-diameter models performed better than other models.

Research of New Criteria for Detecting First-order Residuals Autocorrelation in Regression Models

When estimating regression models using the least squares method, one of its prerequisites is the lack of autocorrelation in the regression residuals. The presence of autocorrelation in the residuals makes the least-squares regression estimates to be ineffective, and the standard errors of these estimates to be untenable. Quantitatively, autocorrelation in the residuals of the regression model has traditionally been estimated using the Durbin-Watson statistic, which is the ratio of the sum of the squares of differences of consecutive residual values to the sum of squares of the residuals. Unfortunately, such an analytical form of the Durbin-Watson statistic does not allow it to be integrated, as linear constraints, into the problem of selecting informative regressors, which is, in fact, a mathematical programming problem in the regression model. The task of selecting informative regressors is to extract from the given number of possible regressors a given number of variables based on a certain quality criterion.The aim of the paper is to develop and study new criteria for detecting first-order autocorrelation in the residuals in regression models that can later be integrated into the problem of selecting informative regressors in the form of linear constraints. To do this, the paper proposes modular autocorrelation statistic for which, using the Gretl package, the ranges of their possible values and limit values were first determined experimentally, depending on the value of the selective coefficient of auto-regression. Then the results obtained were proved by model experiments using the Monte Carlo method. The disadvantage of the proposed modular statistic of adequacy is that their dependencies on the selective coefficient of auto-regression are not even functions. For this, double modular autocorrelation criteria are proposed, which, using special methods, can be used as linear constraints in mathematical programming problems to select informative regressors in regression models.

Impact analysis of US quantitative easing policy on emerging markets

Purpose Since the 2008 financial crisis, the USA has three times implemented quantitative easing (QE) policy. The results of the policy, however, were far below all expectations. Furthermore, it flooded emerging markets (EMs) with low-priced dollars. The purpose of this paper is to investigate the overall and individual impacts of the policy on EMs. Design/methodology/approach This study uses panel data regression model together with the fixed effects model. Also, a unit root test is conducted to check stationary properties of the data, as well as Durbin-Watson statistic to check serial correlation issues in the models. In estimating empirical models, this paper employs macroeconomic data set of stock market returns, exchange rates, lending interest rates, consumer price index, monetary aggregates and foreign exchange reserves from seven diversified emerging economies. The EMs in this study include China, Indonesia, Singapore, Hong Kong, Taiwan, Russia and Brazil. The time period undertaken in this study is from 2008 to 2012. In order to measure impacts of the different stages of the policy, the authors use dummy variables to represent each stage of the policy. Findings The results of the study show that the QE policy has significant impacts on foreign exchange reserves, foreign exchange markets and stock markets of the sample economies. Domestic credit markets, however, appear to be least influenced field by the policy. Finally, the results show that only the first stage of the policy exhibits strong significant impacts, however, leverage of the policy decreases over time. Research limitations/implications Further studies may use different samples, also variables that measure foreign capital inflows such as changes in financial accounts, foreign direct investment and foreign portfolio investment. Originality/value The present study has the following contributions on assessing the impacts of QE policy. First, the overall and individual impacts of the policy are analyzed. Second, in order to establish more valid results, the sample of this study is designed to include several EMs from three continents and diverse regions.