Including Dummy Variables Research Articles

A simple approach to dealing with partial contestation

AbstractObjectiveWe propose a simple approach to dealing with partial contestation in models of multiparty elections.MethodsOur proposed approach is to add a tiny value to the vote share of parties that do not contest a district and then to include dummy variables identifying those districts in which parties do not compete. We can then estimate a single system of equations using a seemingly unrelated regression (SUR) approach and Aitchison's log‐ratio transformation. In our SUR system, we interact the dummy variable for a party that partially contested districts with other predictors in the equation that uses the share of votes of the same party in the log‐ratio outcome. Finally, we estimate robust standard errors for predictors in this equation to address heteroscedasticity.ResultsWe demonstrate the utility of our approach using simulated data and election results from the English parliamentary elections in 2017.ConclusionFrom our simulations, we find that our recommended approach performs as well as that proposed by Tom, Tucker, and Wittenberg. Our strategy is advantageous in that it is easy to estimate, uses information from all districts, and addresses partial contestation in real‐world elections with a single system of seemingly unrelated regressions.

Effects of Wage Differentials, MFA, and GSP on Exports in the Bangladesh Textile and Garment Industry

ABSTRACT In this paper, we investigate the effect of inter-country wage differentials on textile and garment (T&G) exports of Bangladesh and present an account of the important issue of why textile and garment exports are directed from developing countries to developed countries. Our empirical results imply that the wage differentials between developed and developing countries constitute one of the main drivers of T&G exports. In addition, we checked other factors such as the Multi-Fiber Arrangement (MFA) and the Generalized System of Preferences (GSP) by including dummy variables representing the MFA quota withdrawal and the GSP. We found that removal of the MFA had a significant negative effect, as expected. Our study confirms the positive correlation between GSP and exports. After phasing out the MFA and its quota system, the wage differentials not only boost the T&G exports from Bangladesh, but also increase the effectiveness of GSP. Given such findings, we discuss relevant policy implications.

Case Study of Setting Up Linear Regression Model for Turnover of a High-tech Company

This research paper wished to discover whether a linear regression model is enough to summarize the relationship between different factors and the turnover of a high-tech company. To learn it, we first read through a lot of references to find out the mysterious origin of the linear regression model. Moreover, we must pay attention to the similarities and differences between scientists discoveries and our goal. Methodology in the whole process was also impressive since the data was obtained from employees working for the company, which means the data was brand new and confidential in certain respects. Therefore, we tried our best to protect all the involvers. The following parts are the results statement and discussion. We used the multiple linear regression model in Excel to obtain tables and figures, which helped to directly understand the data and analyze the correlation between the independent variables (i.e. what we always used to estimate turnover), including dummy variables and the dependent variable (i.e. turnover). Although we could improve, we still criticize ourselves in the discussion. Finally, we made conclusions in our research with the help of official websites, references mentioned in the last part, participants offering experimental data, writing classes provided, etc. The conclusion might be one-sided, but the arduous research process we have gone through made the conclusion credible, valuable, and precious.

VAR and VECM models were used to investigate the factors that influence of Indian securities market performance, including the period of Covid 19's financial crises

Using a dummy variable, we evaluate commodities in addition to macroeconomic considerations of the Indian securities market from 2010 to 2021, which includes the era of the Covid 19 crises. We used the Bombay Stock Exchange (BSE) (Sensex) for securities market performance and developed a Vector Auto regressive models that combines the short- and long-run model of economics. On stock price indexes, we discovered that the Indian securities market reflects both macroeconomic indicators and prices of commodity. Growth in the economy, inflation, interest, rates, currency rates, crude oil prices, and gold prices are all factors to consider were all used in this study to see how they affected BSE (Sensex) prices during the Covid 19 crises. In their first difference, all series were judged to be stationary. We discovered that shocks to all eight factors had both positive and negative effects on BSE (Sensex) prices in the short and long term, including Covid 19 crises. Each securities market index’s most significant impulse is its own shock, decreasing from short to long-term. We also used the Joint Co-Integration Test to detect and confirm the lack of a long-term equilibrium link (cointegration) between all eight variables, resulting in four cointegration equations with an estimated error correction term at the 0.05 level (speed of adjustment towards equilibrium) of 0.007362. Vector Error Correction Mode (VECM), on the other hand, suggests that the BSE (Sensex) has a significant value with its lagged values of.007362 and 0.517952. We created Vector Auto regressive (VAR) models for the BSE (Sensex) using eight independent variables, including Dummy variables, but their statistics were not significant, despite the fact that the lagged value of crude oil, gold prices, the rupee, and the BSE (Sensex) lagged value were all significant. We proceed to estimate VECM. We proved the short and long-term effects of lagged BSE(Sensex) prices, crude oil prices, gold prices, and the currency on the BSE (SENSEX) using several robustness tests. Dummy factors have also been included to see how the Covid 19 crisis affected the BSE (Sensex) prices. We discovered that crude oil prices followed value, gold prices lagged value, and rupees based on the dollar had a significant impact on BSE(Sensex) pricing over the study period, including the Covid 19 crises from March 2020 to June 2021.

Influence of Self-Reported Health Impairments on German EQ-5D-5L Values

ObjectivesThe German EQ-5D-5L value set is based on the average preferences of the general population. Nevertheless, in Germany, there is an ongoing debate about the appropriateness of using general population preferences and whether patient preferences should be used instead. Thus, this research aimed to determine the robustness of the German EQ-5D-5L valuation data for the general population compared with those with health impairments. MethodsSubgroups were built on the self-reported quality of life, measured with the EQ-5D-5L. To identify which groups significantly influenced the value set, different regression models, including dummy variables for the subgroups, were tested while controlling for preference heterogeneity. Backward selection based on the Akaike information criterion led to significant subgroup dummies, which were analyzed in more detail. For each significant subgroup, the value set model was estimated separately. The models were then compared. Sociodemographics of the subgroups were considered. ResultsThree significant dummies were identified: state 11111, severity levels 5 to 7, and self-reported problems with pain/discomfort. The value sets for the 6 subgroups were compared with the national German value set, showing only marginal deviations. The mean absolute deviation ranged from 0.004 to 0.013. No different densities were identified for the decrements of the different value sets. ConclusionsPeople with self-reported health impairments do not have different EQ-5D-5L health state preferences compared with the German general population. Further research is needed to determine whether the presence of a chronic health condition has a larger influence on health state valuation than the general population.

Spillover effect of the oil palm boom on the growth of surrounding towns in the eastern Amazon

Although oil palm production has been suggested as a driver of development in some countries, this relationship has yet to be established in Brazilian territory. In Brazil, 90% of oil palm production is concentrated in the eastern Amazon, where socioenvironmental costs associated with medium and large agro industries have already been demonstrated. However, there is still a lack of understanding of the contribution of this production to the economic growth of surrounding towns and of how this activity influences nonagricultural sectors. This study investigated the spillover effects of the value of oil palm fruit bunch production in the largest Brazilian producing region, located in the northeast of the state of Pará, and analysed the impact on economic growth, demographics and the labour market from 2002 to 2017. The results of an econometric analysis of fixed effects panel including dummy variables with individual, temporal and two-way effects show a relation between the oil palm fruit bunch production value and nonagricultural sectors (e.g., service sectors). This may have contributed to the formal nonagricultural labour market, population growth and tax generation of the surrounding towns and industrial dynamization, mainly since 2010, coinciding with the period of incentive policies. However, the growth of the economy and urban demography was concentrated in one microregion (Tomé-Açu). We argue that oil palm agribusiness may play an important role in urban growth by providing economic improvement, increasing the value added to services, contributing to urbanization, and promoting intersectoral links. On the other hand, towns dependent on the production of a commodity may be susceptible to market fluctuations with an impact on their economic growth. For greater benefits, socioenvironmental externalities also need to be monitored and controlled.

Spatial Econometric Approach to Modeling Election Results in Russia: Municipal Level

In this article we assess the role of mutual influence of voters living in neighboring territories and the influence of socio-economic factors on the example of voting results for the main candidate in the 2018 elections in Russia. We claim that spatial factors (neighboring of municipalities, regions and belonging of municipalities to the same region) significantly affect the results of voting for the main candidate in each municipality. To confirm this hypothesis, we evaluated several different specifications of the Durbin model, which include dummy variables for the region and other spatial factors, and compared the results with the specifications of the model without taking into account spatial factors. We confirmed main hypothesis: the results of voting depend on the region in which the municipality is included, and, in addition, there is a positive spatial autocorrelation (the results of voting in neighboring municipalities depend on each other). The absence of consideration of spatial factors reduces the quality of regression fitting, there coefficient estimates are biased, and the qualitative picture of the results obtained is distorted. We also showed that the economic situation of the region also affects the results of the voting: economically stronger the municipality received higher share of votes for the main candidate.

The Influence of Saemangeum Reclaimed Landscape Colors on Perceived Restorativeness and Revisit Intention

Background and objective: This study was conducted to find out the effects of Saemangeum reclaimed landscape colors on perceived restorativeness and revisit intention.Methods: A survey was conducted on 120 general respondents, and the responses from 112 respondents were analyzed, except 8 inappropriate responses. The average age of all respondents was 48.60 ± 12.99.Results: Regarding psychological indicators for Saemangeum reclaimed landscape images, this study analyzed the demographic variables of the visitors by four factors of the Perceived Restorativeness Scale (PRS). The result showed that there were statistical significance in age, education, average monthly income, and occupation. The correlation analysis showed that there was a positive correlation between satisfaction and preference in the Saemangeum reclaimed landscape, four factors of PRS, and loyalty showed positive correlations and had statistical significance. As for the preference in Saemangeum reclaimed landscape colors, purple was the most preferred among the eight colors. For the question about satisfaction and loyalty to the Saemangeum reclaimed landscape, more than 87% of respondents responded 'Agree' and 'Strongly agree.' Moreover, a regression analysis including dummy variables was conducted to identify the influence of satisfaction, preference, PRS, and demographic variables (control variables) of visitors. The results showed that preference and fascination among the four factors of PRS had a positive effect on loyalty to the Saemangeum reclaimed landscape. In contrast, the age 20 - 29 (<i>β</i> = -.29, <i>p</i> < .05) had a significant negative effect on loyalty. In other words, higher attention restoration due to preference and 'fascination' and age older than 29 resulted in higher loyalty to the Saemangeum reclaimed landscape. Meanwhile, gender, education, and occupation did not affect the preference.Conclusion: In conclusion, this study identified that the Saemangeum reclaimed landscape affects positive emotions such as 'fascination' and landscape preferences of visitors, and thus would be useful for increasing the revisit intention of visitors.

매출변동 규모가 비대칭적 원가행태에 미치는 영향

ABJ focus on the current period sales-change direction and predicted asymmetric cost behavior. BBCM extended ABJ model by considering the prior period’s sales changes on asymmetric cost behavior. Recently, Ciftci and Zoubi(2019) investigated the impacts of current sales-change size on asymmetric cost behavior extending BBCM’s two-period model. The purpose of our study is to analyze the effects of current sales-change size on asymmetric cost behavior in Korea using Ciftci and Zoubi model. The results of the analysis are as follows. First, In the case of prior sales increase, current small(medium) sales change affects more significantly to the stickiness of cost than large current sales change. In addition, in the case of prior sales decrease, current small(medium) sales change lessens the stickiness of cost more significantly than large current sales change. These results are consistent with our hypotheses. However, medium-size sales change’ coefficient is large than that of small-size sales change, so the relationship between current sales change and cost stickiness is not monotonic, and the model including dummy variables’ main effect terms is somewhat unstable, and in the case of prior sales decrease, the model doesn’t show anti-stickiness. We need more additional research to find out these anomalies are due to difference of cost behavior between Korea and U.S. or defects of the model.

Difference in Emotional Evaluation according to the Preference of Coleus Cultivars

Background and objective: This study investigated the preferences and repurchase intentions (loyalty) of respondents to 8 coleus (Coleus scutellarioides) cultivars potentially useful for tropical style streetscapes.Methods: A survey was conducted on 148 general respondents, and the responses from 136 respondents were analyzed excluding 12 inappropriate copies.Results: Through demographic analysis on plant gardening experience, plant preference tendency, and plant-related event, we discovered that plant preference tendency was lowest in the 20s and highest in the 60s and above, indicating that plant preference tendency significantly increased with age. Out of 6 factors on perceived importance of plants, environmental and emotional relaxation for improved quality of life were high. As a result of examining the emotional responses felt by looking at the images of 8 coleus cultivars, there was a statistically significant difference among cultivars. Major emotional words were 'calm' and 'comfortable'. There was a positive correlation between loyalty to coleus, preference, Korean version of WHOQOL-BREF, plant gardening activity level, and the importance of plants, and a negative correlation with PSS-K (Korean Perceived Stress Scale). We conducted a multiple regression analysis including dummy variables to examine the effect of plant gardening activity level, PSS-K, and Korean version of WHOQOL-BREF on loyalty of survey respondents. The results showed that the effect was greatest in the order of plant-related event, physical health domain, age (50's), covers, environment domain, male, and age (30's).Conclusion: Our results demonstrated that coleus could mitigate the psychological stress and depression. As 'Lime Delight' with simple green color showed the highest preference among cultivars, we recommend a green cultivar from the psychological point of view rather than combination planting of multi-colored cultivars.

기업수명주기에 따른 배당정책이기업가치에 미치는 영향

This paper examines the effect of high dividend payout policy on firm value according to the corporate life cycle. For the empirical analysis, we conduct the effect of the interaction term between the corporate life cycle and the dividend payout rate measured by cash flow patterns, on Tobin's Q measured by firm value.
 According to the life cycle effect of DeAngelo et al. (2006), we expect that the high dividend payout of the maturity stage will have a more positive effect on firm value than the dividend payout of companies in other life cycles. As a result of the analysis, we confirm that the high dividend payout in the maturity stage is positively associated with firm value compared to the other corporate life cycles. In the analysis, which includes dummy variables in other corporate life cycles except for the maturity stage, the dividend payout in the introduction stage and the decline stage has a lower positive effect on firm value than the dividend payout in other corporate life cycles. On the other hand, we find that the dividend payout in the growth stage has a higher positive effect on firm value than the dividend payout in other corporate life cycles.
 As an additional analysis, we divide by market and find that the dividend payout at the maturity stage is positively associated with firm value compared to the other corporate life cycles in all markets. And the dividend payout at the introduction stage and decline stage is negatively associated with firm value. In addition, as a result of analyzing the dividend payout by dividing it by various measurement methods(dividend to sales, net income, equity), we find that the dividend payout of the maturity stage is positively associated with firm value compared to the other corporate life cycles.
 This results imply that there is a difference in the value relevance of the dividend payout policy according to the corporate life cycle. Through this, we expect that information of the dividend payout policy effects can work differently according to the corporate life cycle.

Study on Air Pollution Behavior of VOCs with Photochemical Monitoring Stations Using EGARCH Model in Southern Taiwan

This study adopted the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model to examine the 10 ozone precursors of the highest concentrations among the 54 that were assessed over a number of years at the four photochemical assessment monitoring stations (PAMSs) in the Kaohsiung–Pingtung Area in Taiwan. First, the 10 ozone precursors, which were all volatile organic compounds (VOCs), were analyzed using the factor analyses in multiple statistical analyses that had the most significant impact on the area’s ozone formation: mobile pollution factor, which included 1,2,4-Trimethylbenzene (C9H12), toluene (C7H8), and Isopropyl benzene (C9H12). Then, taking into consideration that the number sequences might be affected by standardized residuals, this study applied the vector autoregressive moving average-EGARCH (VARMA-EGARCH) model to analyze the correlation between the three VOCs under different polluting activities. The VARMA-EGARCH model in this research included dummy variables representing changing points of variance structures in the variance formula to predict the conditional variance. This process proved able to effectively estimate the relevant coefficients of the three VOCs’ dynamic conditions that changed with time. The model also helped to prevent errors from occurring when estimating the conditional variance. Based on the testing results, this study determined the VARMA(2,1)-EGARCH(1,0) as the most suitable model for exploring the correlation between the three VOCs and meteorological phenomena, as well as the interplay between them in regard to interaction and formation. With the most representative of the three, toluene (TU), as the dependent variable and 1,2,4-Trimethylbenzene (TB) and Isopropyl benzene (IB) as the independent variables, this study found it impossible to calculate the TU concentration with TB and IB concentrations in the same period; estimations of TB and IB concentrations with a period of lag time were required because TU was mainly contributed by automobiles and motorcycles in Kaohsiung. TB and IB resulted from other stationary pollution sources in the region besides cars and motorbikes. When TU was evenly distributed and stayed longer in the atmosphere, the TB and IB concentrations were lower, so distribution conditions and concentrations could not be used to effectively estimate the concentration of toluene. This study had to wait until the next period, or when stationary pollution sources started producing TB and IB of higher concentrations during the daytime, in order to estimate the TU concentrations in a better photochemical situation.

The Role of Physician Professional Networks in Physicians' Receipt of Pharmaceutical and Medical Device Industries' Payments.

Financial relationships between physicians and the pharmaceutical and medical device industries are common, but the factors associated with physicians receiving payments are unknown. The objective of this study is to evaluate the influence of physicians' professional networks' characteristics on the receipt of payments among physicians. Network analysis of cross-sectional data PARTICIPANTS: US physicians who shared Medicare patients with other physicians in 2015 (N=357,813). Proportion of a physician's professional network that received industry payments and other network characteristics including number of physician connections, how central the physician is within the network, and the tightness of the referral network in which a physician is located. Relative risk of receiving industry payments. We used modified Poisson regression to control for confounding by gender, time since graduation, practice size, and practice setting (teaching hospital vs. not). We included dummy variables for specialty and hospital referral region level. The proportion of a physician's peers in their professional network that received payments was strongly associated with receipt of pharmaceutical or device industry payments by the physician (top vs bottom quartile aRR=1.28, 95%CI=1.25-1.31). Physician's centrality within a network had a small positive effect on receiving payment (top vs bottom quartile aRR=1.02, 95%CI=1.01-1.04). Network density also had a small negative association with receipt of payment (top vs bottom quartile aRR=0.97, 95%CI=0.96-0.98). Network characteristics, particularly the receipt of payments among physicians one shares patients with, are associated with whether a physician receives payments. This finding has implications for institutional regulation of industry payments to physicians and demonstrates how institutional policy may impact not only the physicians within the institution but also physicians outside of the institution.

Effect of macroeconomic variables on Jakarta composite index before and the time of COVID19

The aim of this study is to examine the effect of macro-economic variables on the Jakarta composite index in Indonesia, both in the short and long term before Covid19 and the Covid19 period, as well as to examine whether there is a difference before Covid19 and the Covid19 period. Macroeconomic variables include exchange rate (ER), inflation (INF), interest rate (INT) and output growth (IP). This study uses data from the times series for the period 2019.1 to 2020.12 by including dummy variables. The first stage is to test the stationary of the data with the root test unit, and the result is that the data is only stationary in the first and second differences. In addition, the co-integration test between the variables and the results shows a co-integration relationship between the variables so that the model can be analyzed using the Error Correction Model (ECM) to see the short-term balance. The results of the ECM model analysis show that, in the short term, the only macroeconomic variables affecting the Jakarta composite index in Indonesia prior to Covid19 and the Covid19 period are only the exchange rate. In the long run before Covid19, the only ones affecting the Jakarta composite index were the exchange rate, while the eCovid19 period was the rate of exchange and inflation. Other results indicate that there is a difference in the average.

Additive predictions of aboveground stand biomass in commercial logs and harvest residues for rotation age Pinus radiata plantations in New South Wales, Australia

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 for Pinus radiata plantations 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.

Modeling Soil Moisture from Multisource Data by Stepwise Multilinear Regression: An Application to the Chinese Loess Plateau

This study aims to integrate multisource data to model the relative soil moisture (RSM) over the Chinese Loess Plateau in 2017 by stepwise multilinear regression (SMLR) in order to improve the spatial coverage of our previously published RSM. First, 34 candidate variables (12 quantitative and 22 dummy variables) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and topographic, soil properties, and meteorological data were preprocessed. Then, SMLR was applied to variables without multicollinearity to select statistically significant (p-value < 0.05) variables. After the accuracy assessment, monthly, seasonal, and annual spatial patterns of RSM were mapped at 500 m resolution and evaluated. The results indicate that there was a high potential of SMLR to model RSM with the desired accuracy (best fit of the model with Pearson’s r = 0.969, root mean square error = 0.761%, and mean absolute error = 0.576%) over the Chinese Loess Plateau. The variables of elevation (0–500 m and 2000–2500 m), precipitation, soil texture of loam, and nighttime land surface temperature can continuously be used in the regression models for all seasons. Including dummy variables improved the model fit both in calibration and validation. Moreover, the SMLR-modeled RSM achieved better spatial coverage than that of the reference RSM for almost all periods. This is a significant finding as the SMLR method supports the use of multisource data to complement and/or replace coarse resolution satellite imagery in the estimation of RSM.

Association of WIC Participation and Electronic Benefits Transfer Implementation

The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) is an important source of nutritional support and education for women and children living in poverty; although WIC participation confers clear health benefits, only 50% of eligible women and children currently receive WIC. In 2010, Congress mandated that states transition WIC benefits by 2020 from paper vouchers to electronic benefits transfer (EBT) cards, which are more convenient to use, are potentially less stigmatizing, and may improve WIC participation. To estimate the state-level association between transition from paper vouchers to EBT and subsequent WIC participation. This economic evaluation of state-level WIC monthly benefit summary administrative data regarding participation between October 1, 2014, and November 30, 2019, compared states that did and did not implement WIC EBT during this time period. Difference-in-differences regression modeling allowed associations to vary by time since policy implementation and included stratified analyses for key subgroups (pregnant and postpartum women, infants younger than 1 year, and children aged 1-4 years). All models included dummy variables denoting state, year, and month as covariates. Data analyses were performed between March 1 and June 15, 2020. Statewide transition from WIC paper vouchers to WIC EBT cards, specified by month and year. Monthly number of state residents enrolled in WIC. A total of 36 states implemented WIC EBT before or during the study period. EBT and non-EBT states had similar baseline rates of poverty and food insecurity. Three years after statewide WIC EBT implementation, WIC participation increased by 7.78% (95% CI, 3.58%-12.15%) in exposed states compared with unexposed states. In stratified analyses, WIC participation increased by 7.22% among pregnant and postpartum women (95% CI, 2.54%-12.12%), 4.96% among infants younger than 1 year (95% CI, 0.95%-9.12%), and 9.12% among children aged 1 to 4 years (95% CI, 3.19%-15.39%; P for interaction = .20). Results were robust to adjustment for state unemployment and poverty rates, population, and Medicaid expansion status. In this study, the transition from paper vouchers to WIC EBT was associated with a significant and sustained increase in enrollment. Interventions that simplify the process of redeeming benefits may be critical for addressing low rates of enrollment in WIC and other government benefit programs.

Nowcasting unemployment insurance claims in the time of COVID-19

Near-term forecasts, also called nowcasts, are most challenging but also most important when the economy experiences an abrupt change. In this paper, we explore the performance of models with different information sets and data structures in order to best nowcast US initial unemployment claims in spring of 2020 in the midst of the COVID-19 pandemic. We show that the best model, particularly near the structural break in claims, is a state-level panel model that includes dummy variables to capture the variation in timing of state-of-emergency declarations. Autoregressive models perform poorly at first but catch up relatively quickly. The state-level panel model, exploiting the variation in timing of state-of-emergency declarations, also performs better than models including Google Trends. Our results suggest that in times of structural change there is a bias–variance tradeoff. Early on, simple approaches to exploit relevant information in the cross sectional dimension improve forecasts, but in later periods the efficiency of autoregressive models dominates.

The Impact of Fossil-Fuels and Biomass Combustion on Outdoor PM2.5 Mass Associations with Respiratory Emergency Department Visits in Dhaka, Bangladesh

Introduction: Developed world studies report associations between particle air pollution and increased risk of emergency department (ED) visits for respiratory diseases, but few were conducted in the south Asia region, where PM2.5 composition is different. Differences are from greater biomass and residue crop-burning, which may have differing health impacts from past PM2.5 studies. Despite that fact, an “equal toxicity” assumption has been made in most global PM2.5 effect estimates. We evaluated this assumption in Dhaka.Methods: The acute effects of PM2.5 and source component’s on 38,233 respiratory ED visits during 2014-2017 were assessed using time-series modeling, adjusted for temporal trend, day of week, holidays, relative humidity, and temperature. Daily counts of respiratory ED visits were obtained from National Institute of Chest Diseases and Hospital. Analyses were conducted to test for seasonal effect modification by including dummy variables for monsoon vs. non-monsoon seasons.Results: PM2.5 yielded statistically significant association with increased respiratory ED visits [Excess Risk, ER=0.88%, 95%CI: 0.37%, 1.39%] per 10 μg/m3 increase. The effect of PM2.5 air pollution per 10 µg/m3 varies with season, with the highest during monsoon season [2.84%, 95%CI: 0.74, 4.91] vs. non-monsoon season [0.77%, 95%CI: 0.23%, 1.31%], suggesting that the effect size per unit mass is much larger when fossil-fuel combustion sources dominated exposures, with effect per unit mass declining when crop-burning and dust sources dominated exposures. A source-specific health effect analysis model using elemental source tracers provided consistent results, with fossil-fuel PM2.5 having a larger ER per 10 μg/m3 than biomass-combustion PM2.5.Conclusion: This study found that the respiratory health in Dhaka is significantly affected by particle air pollution, with greater impact by fossil-fuel PM2.5. This disparity in health impact by PM2.5 source origin should be considered in PM2.5 risk assessment, PM2.5 mitigation measure prioritization, and clinical advice.

Does the legal origin affect corporate cash holding?

PurposeThe study examines the role of a country's legal system in predicting the corporate cash holdings using a sample of 18 countries inherited with distinct legal traditions. The central point of the study is the comparative assessment of legal frameworks in shaping the corporate finance policies.Design/methodology/approachThe authors employ host of regression techniques including dummy variables, panel data regression and Fama–MacBeth regressions to establish the relationship.FindingsThe study results support the idea of “theory of law and finance” that legal tradition is a key factor determining corporate behaviour and policy. In particular, the authors observe that firms operating in civil law systems hold significantly higher cash as compared to their peers from common law systems. Moreover, the authors report that the law system affects the corporate cash holdings through the channels of economic development and shareholder's protection, yet in opposite directions. This is because the authors find that in developed countries where civil law tradition prevails, firms hold reasonably higher cash. Moreover, if the firm belongs to high investors' protection country with civil law traditions, the cash holdings get substantially reduced. Besides, the authors find that the predictability of widely held determinants of cash holdings is not invariant of law traditions, and it holds true also when analysed in conjunction with the financial crisis. Overall, the authors find support for their postulation that corporate cash management policies are likely to be different across legal traditions. The study results are robust to the controls for various firm and country-specific antecedents of cash holdings and to the alternate econometric techniques.Practical implicationsThe study findings would encourage the government and firm policymakers and regulators in strengthening the investor protection rights which would further augment the legal system and firm-specific corporate governance mechanisms. This would mitigate agency issues and managers would be forced to undertake investor-friendly financial policies especially corporate cash holdings which would be resulting into shareholder value maximization.Originality/valueThe study contributes uniquely since the existing literature is largely silent on the role that legal tradition of a country has on the cash holdings of its firms.