scholarly journals The Determinants of Profitability of the Pharmaceutical Industry of Bangladesh: A Random Effect Analysis

2019 ◽  
Vol 10 (2) ◽  
pp. 68
Author(s):  
Md. Shahidul Islam ◽  
Muhammad Saifuddin Khan
Keyword(s):  
Pharmaceutical Industry ◽  
Random Effect ◽  
Operating Cost ◽  
Effect Analysis ◽  
Return On Equity ◽  
Factors Affecting ◽  
Operating Income ◽  
Gdp Growth Rate ◽  
Effect Regression ◽  
Random Effect Analysis

The present study investigates the factors affecting the profitability of the pharmaceutical industry of Bangladesh. Using the data of 20 listed pharmaceutical companies fully functional in Bangladesh, we found both the firm specific and the macroeconomic specific variables affect the profitability of the companies. A panel dataset of 10 years starting from 2007 to 2016, we ran the random effect regression. The regression output depicts that among the firm specific variables, sales, operating income, operating cost, return on equity and total liabilities have significant effect on the profitability of the companies. We also found that the GDP growth rate and the rate of inflation among the macroeconomic variables have significant deterministic role on the profitability. The regression results followed by recommendations will be a great help to the policy makers both from inside and outside of the corporations.

scholarly journals Labor Productivity and Foreign Direct Investment in the Indonesian Manufacturing Sector

2019 ◽  
Vol 8 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Luhur Selo Baskoro ◽  
Yonsuke Hara ◽  
Yoshihiro Otsuji
Keyword(s):  
Foreign Direct Investment ◽  
Labor Productivity ◽  
Direct Investment ◽  
Social Protection ◽  
Manufacturing Sector ◽  
Random Effect ◽  
Effect Analysis ◽  
Positive Externalities ◽  
Random Effect Analysis

This paper investigates the determinants of foreign direct investment (FDI) inflow, focusing on the effect of labor productivity in the Indonesian manufacturing sector. Indonesia has the advantage of abundant labor supply in attracting FDI to bring positive externalities to its economy. Based on this background, this paper is aimed to study and to improve FDI inflow through a random effect analysis of 19 manufacturing industries from 2001 to 2014. The empirical result shows that labor productivity, wages, and export have become significant factors that attract FDI. FDI inflow in this sector tends to target non-labor industries. For the labor-intensive industries, the primary strategy is to increase labor quality through improvement in education, training, internship program, and worker certification. Improving research and development climate, and maintaining the quality of labor through health and social protection regulation can attain improvement in non-labor intensive industries.

Meta-analysis and Random Effect Analysis

2017 ◽  
pp. 51-62
Author(s):  
Ton J. Cleophas ◽  
Aeilko H. Zwinderman
Keyword(s):  
Meta Analysis ◽  
Random Effect ◽  
Effect Analysis ◽  
Random Effect Analysis

Conjunction group analysis: An alternative to mixed/random effect analysis

NeuroImage ◽  
2007 ◽  
Vol 37 (4) ◽  
pp. 1178-1185 ◽  
Author(s):  
Ruth Heller ◽  
Yulia Golland ◽  
Rafael Malach ◽  
Yoav Benjamini
Keyword(s):  
Random Effect ◽  
Group Analysis ◽  
Effect Analysis ◽  
Random Effect Analysis

scholarly journals Factors affecting the liquidity of commercial banks in India: a longitudinal analysis

2019 ◽  
Vol 14 (4) ◽  
pp. 78-88 ◽  
Author(s):  
Shyam Bhati ◽  
Anura De Zoysa ◽  
Wisuttorn Jitaree
Keyword(s):  
Significant Relationship ◽  
Random Effect ◽  
Consumer Price Index ◽  
Macroeconomic Factors ◽  
Return On Equity ◽  
Factors Affecting ◽  
Regulatory Factors ◽  
Long Term Effect ◽  
Indian Banks ◽  
Reserve Ratio

This paper examines the long-term effect of various regulatory, bank-specific and macroeconomic factors on the determination of liquidity in Indian banks. For this purpose, the study uses a random effect panel data regression model and tests it with data on Indian banks for 21 years, covering the period from 1996 to 2016. The model considers the effect of regulatory factors, cash reserve ratio, and statutory liquidity, and incorporates four different liquidity ratios specific to the Indian banking scenario. The results of the analysis show contrasting relationships between the independent variables and the dependent variables measured by four liquidity ratios.It is interesting to note that Indian banks rely more on asset-based liquidity and less on liability-based liquidity. More specifically, the most important liquidity ratio of L1 (liquid assets to total assets ratio) showed a significant relationship with macroeconomic variables of discount rates, call rates, foreign exchange reserve, exchange rate with US dollar, consumer price index and gross domestic product. L1 also showed a significant relationship with bank-specific variables of capital to total assets and bank size. However, the regulatory factors of cash reserve ratio and profitability determined by return on equity (ROE) and non-performing assets were not found to have any effect on liquidity of Indian banks.

scholarly journals Assessing factors affecting FDI in developing Asian countries

2021 ◽  
Vol 9 (2) ◽  
pp. 16-28
Author(s):  
P. Gupta
Keyword(s):  
Random Effect ◽  
Least Square ◽  
Market Size ◽  
Asian Countries ◽  
Factors Affecting ◽  
Ordinary Least Square ◽  
Independent Variables ◽  
Fixed Capital Formation ◽  
Effect Regression ◽  
Independent Variable

The paper focuses on various factors that affect the inflow of Foreign Direct Investment in developing countries. The study majorly deals with Asian countries, namely India, China, Myanmar, Nepal, Pakistan, Bangladesh and Bhutan, that are progressing from being aid-dependent to trading giants. The factors affecting FDI are majorly categorised into dependent and independent variables. Here, in this study, the dependent variable considered is FDI inflow, and independent variables are market size, the value of the currency, export, import, gross fixed capital formation, GDP deflator, cost of borrowing and economic reforms. Pooled Ordinary Least Square (OLS), fixed effect and random effect regression analysis is done to ascertain the best regression model and various tests are performed to check the intensity of effect caused by each independent variable on our dependent variable.

scholarly journals 465. Comparative Efficacy of Double vs. Single Antibiotic Regimens for the Empiric Treatment of MRSA-Induced Acute Bacterial Skin and Skin Structure Infection

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S227-S228
Author(s):  
Abdullah A Alhifany ◽  
Nisrin Bifari ◽  
Yasser Alatawi ◽  
Saad Ullah Malik ◽  
Thamer Almangour
Keyword(s):  
Clinical Cure ◽  
Meta Analysis ◽  
Random Effect ◽  
Fixed Effect ◽  
Skin Structure Infection ◽  
Effect Analysis ◽  
Gram Negative ◽  
Skin Structure ◽  
Structure Infection ◽  
Random Effect Analysis

Abstract Background The initial management of Acute bacterial skin and skin structure infection (ABSSSI) is burdensome. It requires empirical antibiotic therapy that covers both gram-positive and gram-negative bacteria. Vancomycin plus aztreonam are the most commonly used antibiotic combination, nonetheless, they have many limitations which limits their use. Hence, many new single agents with MRSA and gram-negative coverage, oral options, and/or good safety profile have been developed to be a potential alternative such as: ceftaroline, ceftobiprole, tigecycline and the recent FDA approved antibiotic (delafloxacin). In the absence of head-to-head trials comparing these agents, we decided to conduct a network meta-analysis for these therapeutic regimens. Methods A Bayesian network meta-analysis of randomized clinical trials identified in PubMed/Medline and Embase databases was conducted. We performed both fixed and random effect models for clinical cure as the primary outcome of interest. Additionally, rankograms were generated using the surface under the cumulative ranking curve (SUCRA) to obtain the treatment ranking probabilities in relation to their relative effect. Results We identified 10 eligible studies involving 4,914 patients. The indirect comparison demonstrated that delafloxacin showed no difference in terms of clinical cure compared with ceftaroline (OR, 0.82, 95% Cr.I 0.39–1.8), ceftobiprole (OR, 0.79, 95% Cr.I 0.32–1.9), SOC (OR, 1.2, 95% Cr.I 0.62–2.4) and tigecycline (OR, 1.0, 95% Cr.I 0.45–2.2) in the fixed effect analysis, nor in the random-effect analysis (OR, 0.8, 95% Cr.I 0.26–2.2; OR, 0.78, 95% Cr.I 0.2–3.0; OR, 1.2, 95% Cr.I 0.51–3.1; and OR, 0.96, 95% Cr.I 0.30–3.0), respectively. Furthermore, the ranking probabilities in the fixed-effect and random-effect analysis showed that ceftaroline was ranked the first in terms of clinical cure (SUCRA, 40.02%) followed by ceftobiprole (SUCRA, 22.80%), delafloxacin (SUCRA, 16.60%), SOC (SUCRA, 13.80%), and then tigecycline (SUCRA, 6.70%). Conclusion Ceftaroline, ceftbiprole, delafoxacin, SOC and tigecycline are similarly effective. However, delafloxacin provides better convenience. Further comparative studies regarding their safety are needed. Disclosures All authors: No reported disclosures.

scholarly journals Bank Specific Determinants of Bank Profitability in Indonesia for The Period 2008-2019

2020 ◽  
Vol 3 (2) ◽  
pp. 13-21
Author(s):  
Muhammad Luthfi Utomo ◽  
Achmad Herlanto Anggono
Keyword(s):  
Fixed Effects ◽  
Low Cost ◽  
Operating Cost ◽  
Third Party ◽  
Fixed Effects Model ◽  
Return On Assets ◽  
Bank Profitability ◽  
Return On Equity ◽  
Operating Income ◽  
Panel Data Regression

Banks have an important role in the economy and are the significant driver of economic growth for its financing to many industries in the economy. Thus, banks need to be maintained profitable to keep operating and avoid the major impact of bank failure. This study attempts to know the relationship between bank-specific variables of Liquid Assets to Total Assets (LATA), Non-performing Loans to Total Loans (NPLTL), Operating Cost to Operating Income (OCOI), Third-party funds to Total Assets (TPFTA), and Core Capital Tier 1 to Total Assets (TIER1TA) toward bank profitability by using Return on Assets (ROA) and Return on Equity (ROE) as the measure. The data used are 7 banks of BUKU 4 category for the period 2008-2019 in quarterly frequency. The research uses panel data regression of the fixed-effects model. The findings show that LATA is significant negative to ROA and ROE, NPLTL is significant positive to ROA and ROE, OCOI is significant negative to ROA and ROE, TPFTA is significant positive to ROA and ROE, and TIER1TA is significant negative toward ROA and ROE. Banks should maintain their operating expense low, increase their interest income, and getting a source of funds with low cost to get more profit.

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